{"listings":[{"id":"845e6990-5ddf-4862-9fa4-aaddb30e6239","summary":{"name":"StreamNative MCP Server","subtitle":"Enables AI agents and MCP-compatible clients to securely discover, access, and act on StreamNative Cloud resources.","setting":{"visibility":"PUBLIC"},"created_at":1772087348589,"updated_at":1773957407044,"categories":["OTHER"],"listingType":"STANDARD","provider_id":"d6ea0226-f44f-4e01-b0a9-9adcba60cbf9"},"detail":{"description":"**Overview**\nStreamNative MCP Server enables AI agents and MCP-compatible clients to securely discover, access, and act on real-time streaming resources in StreamNative Cloud. 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There are two types of relationships; A drug either \"Caused side effects\" or \"was effective against symptom.\n\n**Benefits**\n- helps to determine whether or not adverse reactions are recorded in the european summary of product characteristics (spc).\n- meddra preferred terms (pt) that are present in the dataset are used to facilitate the accuracy of adr (adverse drug reactions) descriptions.\n- meddra preferred terms (pt) are used to map terms of the spc.\n- helps to understand which drugs or chemicals cause which type of disease\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [Adverse Reaction with Preferred Term and System Organ Class Codes](https://www.johnsnowlabs.com/marketplace/adverse-reaction-with-preferred-term-and-system-organ-class-codes)\n\t- The database includes adverse drug reaction that is associated with the product and substance with MedDRA Preferred Term (PT), PT codes, System Organ Class (SOC) codes etc. This structured database can be used to know whether or not adverse reactions are recorded in the European Summary of Product Characteristics (SPC).\n- [Chemicals Contributing to Disease](https://www.johnsnowlabs.com/marketplace/chemicals-contributing-to-disease)\n\t- This dataset consists of information that helps to determine if certain chemicals contribute to a disease.\n- [Drug Relationship Database](https://www.johnsnowlabs.com/marketplace/drug-relationship-database)\n\t- This dataset is about the relationship of a drug to certain symptoms or diseases. There are two types of relationships; A drug either \"Caused side effects\" or \"was effective against symptom\".\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated\n- Data Updates\n - Data updates support replace-on-update: outdated foreign keys are deprecated, not deleted\n\n**Our data is curated and enriched by domain experts** \n\n Each dataset is manually curated by our team of doctors, pharmacists, public health & medical billing experts:\n- Field names, descriptions, and normalized values are chosen by people who actually understand their meaning\n- Healthcare & life science experts add categories, search keywords, descriptions and more to each dataset\n- Both manual and automated data enrichment supported for clinical codes, providers, drugs, and geo-locations\n- The data is always kept up to date – even when the source requires manual effort to get updates\n- Support for data subscribers is provided directly by the domain experts who curated the data sets\n- Every data source’s license is manually verified to allow for royalty-free commercial use and redistribution.\n\n**Need Help?**\nIf you have questions about our products, contact us at [info@johnsnowlabs.com](mailto:info@johnsnowlabs.com). \n 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This comprehensive approach empowers businesses to make informed decisions using validated consumer opinions and behaviors"},"popularity":1942},{"id":"1b56a8cf-de77-4ff4-9b94-6213cd948d35","summary":{"name":"Dataplatr Contact Center Analytics - Call Details Hub Accelerator","subtitle":"Call Detail Hub on Databricks with modular design, transformations using AWS Connect, Taleo, Five9 and Genesys.","share":{"name":"cc_call_details_hub","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1758264770573,"updated_at":1779856934024,"categories":["FINANCIAL","ENERGY","RETAIL","TRANSPORTATION_AND_LOGISTICS","ADVERTISING_AND_MARKETING"],"listingType":"STANDARD","provider_id":"3e1fd420-8722-4ebc-abaa-79f86ceffda0"},"detail":{"description":"Contact us at: chandra.reddy@dataplatr.com\nFor consultations or custom inquiries: https://dataplatr.com/contact-us\n\n**Dataplatr Call Detail Hub Accelerator**\nPurpose-Built for AWS Connect, Taleo, Five9 & Genesys Call Detail Pipelines\nTransforming Call Detail Analytics with Databricks Lakehouse Architecture\n\nEnterprises handling massive volumes of call records across multiple platforms often face fragmented systems, inconsistent data and limited visibility into call quality, routing efficiency and network performance. Managing call detail data effectively is critical for telecom analytics, customer experience optimization, fraud detection and operational cost management.\nThe Dataplatr Call Detail Hub (CDH) Accelerator is a ready-to-use solution that streamlines the ingestion, transformation and analysis of call detail records (CDRs) from platforms such as **Amazon Connect**, **Five9**, **Taleo** and **Genesys**. Built on the proven medallion architecture (Bronze–Silver–Gold) using Delta Lake and configuration-driven execution, the accelerator ensures audit-ready, compliance-focused outputs while providing scalability for enterprise call data use cases.\nBy leveraging advanced analytics, automation and AI, the Dataplatr CDH Accelerator enables enterprises to gain actionable insights into call performance, routing optimization, usage patterns and potential anomalies. Interactive dashboards and reports empower telecom teams, contact centers and network operations to enhance service quality, detect issues proactively and improve operational decision-making while ensuring reliable, unified data.\n\n**Business Challenge & Solution**\n**The Call Detail Analytics Challenge** – Common Pain Points\n\n- **Fragmented Call Data**: CDRs spread across platforms without a single, reliable, centralized source of truth for accurate reporting and analysis.\n- **High Call Volumes and Long Wait Times:**: Increasing call volumes can overwhelm agents, causing longer queue and wait times, leading to customer frustration and abandoned calls. Intelligent call routing and AI voice agents are necessary to manage these peaks effectively.\n- **Low First-Call Resolution (FCR) Rates:** Customers often need multiple contacts to resolve issues, impacting satisfaction and efficiency. Better agent training and real-time data access can improve FCR.\n- **Outdated Technology and System Integration**: Legacy systems slow workflows and limit insights. Modern cloud-based, integrated platforms improve scalability, agent efficiency, and customer experience.\n- **Security and Compliance Risks:**: Increased cyber threats and data privacy regulations require robust protection and compliance monitoring to secure customer data and avoid penalties.\n- **Inconsistent Service Quality:**: Variation in agent performance and communication quality across channels leads to uneven customer experience. Standardized workflows, QA monitoring, and coaching help maintain consistency.\n\n\n**The Dataplatr Call Details Hub Solution**\n Our Call Detail Hub Accelerator addresses these challenges with:\n- **Comprehensive Call Detail Analysis**: Quickly identify spikes in inbound call traffic, reasons for abandoned outbound calls, and recurring interaction patterns.\n- **Queue & Wait Time Optimization**: Pinpoint bottlenecks in call routing to reduce queue congestion, lower average wait time, and improve customer service responsiveness.\n- **Performance Metrics for Calls**: Automatically track and report on Average Handle Time (AHT), First Call Resolution (FCR), and After-Call Work (ACW) for both inbound and outbound calls.\n- **Proactive Issue Detection & Resolution**: Detect issues such as extended wait times or unresolved calls in real time, alerting supervisors for rapid intervention. Take proactive steps to prevent customer dissatisfaction or compliance risks before they escalate.\n- **Sentiment & Customer Experience Analysis**:Use feedback and interaction data to uncover pain points, personalize responses, and enhance overall caller experience.\n\n\n**Medallion Architecture Implementation**\n**Bronze Layer (Raw CDR Ingestion)**\n- **Call Detail Records**: Raw logs including caller/callee IDs, timestamps, duration, cost and routing paths from Salesforce, Five9 and Genesys.\n- **Customer Master**: Unified customer profiles linked to call activity, usage patterns, preferences, demographics and engagement history.\n- **Network Metadata**: Switch IDs, routing tables, QoS indicators, termination points, connection details and performance metrics.\n- **Billing & Cost Data**: Call charges, rate plans, billing adjustments, usage summaries, payment history and discount details.\n\n**Silver Layer (Cleansed & Standardized CDR Data)**\n- **Unified Call Repository**: Deduplicated, standardized CDRs with data quality validation, enrichment, indexing and audit tracking.\n- **Normalized Network Metrics**: Harmonized routing and performance details with consistent formatting, validation checks and comparative benchmarks.\n- **Usage Enrichment**: Geolocation, call type categorization, fraud risk scores, customer segmentation, usage trends and contextual insights.\n- **Billing Alignment**:Linked, validated billing data with reconciliation workflows, audit trails, exception handling and compliance checks.\n\n\n**Gold Layer (Business-Ready Call Analytics)**\n- **Call Volume Dashboards**: Trend analysis, peak load insights, routing performance metrics, historical comparisons, anomaly detection and capacity planning.\n- **Network Performance Reports**: Quality of service (QoS) tracking, dropped call analysis and routing optimization.\n- **Revenue & Cost Insights**: Call profitability, rate plan performance, cost-to-serve analytics, margin optimization, revenue forecasting and expense breakdowns.\n- **Predictive Intelligence**: Fraud detection alerts, network load forecasting, anomaly detection, capacity planning, churn prediction and performance optimization.\n\n\n\n**Marketplace Deliverables**\nCore CDH Accelerator Package – Databricks Implementation Assets\n- **Production-Ready Notebooks**: Complete CDR data pipeline with robust error handling, monitoring, automated testing and performance optimization.\n- **Config Templates**: Pre-configured JSON templates for Salesforce, Five9, Genesys CDR modules, customization guidelines and version control support.\n- **Delta Table Schemas**: Optimized Bronze, Silver and Gold table structures tailored to call detail analytics, query performance, scalability and data governance.\n- **CDC Framework**: Real-time, reliable change data capture for immediate CDR processing, fault tolerance, scalability and downstream synchronization.\n\n\n**Documentation & Training**\n- **Implementation Guide**: Detailed setup instructions with proven best practices for call detail analytics, performance tuning, security and maintenance.\n- **Configuration Manual**: Parameter descriptions, customization options and usage guidelines with examples, validation rules and troubleshooting tips.\n- **User Training Materials**: Role-based interactive training for telecom analysts, operations teams and fraud monitoring staff.\n\n**Why Choose Dataplatr Call Detail Hub Accelerator**\n- **Telecom & Contact Center Expertise**: Proven success with Genesys, Five9, Avaya, NICE and Salesforce ecosystems.\n- **Immediate Impact**: Deploys in weeks, not months, with prebuilt AI and BI assets for rapid value realization and insights.\n- **Scalable Architecture**: Databricks-native design scales with call volumes and supports advanced AI and automation.\n\nTransform your call detail operations from fragmented logs to predictive, insight-driven intelligence with Dataplatr’s Call Detail Hub Accelerator. Built on Databricks best practices with compliance-first design, this solution delivers faster troubleshooting, fraud prevention and revenue optimization while providing a foundation for future AI-driven capabilities.\n\nHere are some articles Dataplatr published on Medium — please check these out:\n[Overview of Contact Center Analytics: Unlock insights with Dataplatr](https://medium.com/@dataplatr/an-overview-of-contact-center-analytics-unlock-insights-with-dataplatr-part-1-5b9428719f2c) \n[Contact Center Analytics : Unlock Call Details KPIs and Dashboard for actionable insights](https://medium.com/@dataplatr/contact-center-analytics-unlock-call-details-kpis-and-dashboard-for-actionable-insights-part-2-b452e0bb0961) \n[Contact Center Analytics: Unveiling the Power of WFM with Strategies, Insights, and Impact](https://medium.com/@dataplatr/contact-center-analytics-unveiling-the-power-of-wfm-with-strategies-insights-and-impact-part-4-68878706c5f2) \n[Contact Center Analytics: A Comprehensive Guide to Quality Assurance (QA ) and Analytics](https://medium.com/@dataplatr/contact-center-analytics-a-comprehensive-guide-to-quality-assurance-qa-and-analytics-part-5-cb7f60f9ed3c) \n\nNote: By clicking '**Get Access**', you agree to our **Terms of Service**. 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The goal is to demonstrate how Verticalization/Domain Adaptation for the [Travel] sector can be easily achieved using our two-step approach to LLM Fine-Tuning. An overview of this approach can be found at: [From General-Purpose LLMs to Verticalized Enterprise Models](https://www.bitext.com/blog/general-purpose-models-verticalized-enterprise-genai/)\n\nThe dataset has the following specifications:\n\n- Use Case: Intent Detection\n- Vertical: Travel\n- 33 intents assigned to 11 categories\n- 31658 question/answer pairs, with approximately 1000 per intent\n- 72 entity/slot types\n- 10 different types of language generation tags\n\nThe categories and intents are derived from Bitext's extensive experience across various industry-specific datasets, ensuring the relevance and applicability across diverse contexts.\n\n**Dataset Token Count**\n\nThe dataset contains a total of 4.16 million tokens across 'instruction' and 'response' columns. This extensive corpus is crucial for training sophisticated LLMs that can perform a variety of functions including conversational AI, question answering, and virtual assistant tasks in the travel domain.\n\n**Fields of the Dataset**\n\nEach entry in the dataset comprises the following fields:\n\n- tags\n- instruction: a user request from the Travel domain\n- category: the high-level semantic category for the intent\n- intent: the specific intent corresponding to the user instruction\n- response: an example of an expected response from the virtual assistant\n\n**Categories and Intents**\n\nThe dataset covers a wide range of travel-related categories and intents, which are:\n\n- **BAGGAGE**: check_baggage_allowance\n- **BOARDING_PASS**: get_boarding_pass, print_boarding_pass\n- **CANCELLATION_FEE**: check_cancellation_fee\n- **CHECK_IN**: check_in\n- **CONTACT**: human_agent\n- **FLIGHT**: book_flight, cancel_flight, change_flight, check_flight_insurance_coverage, check_flight_offers, check_flight_prices, check_flight_reservation, check_flight_status, purchase_flight_insurance, search_flight, search_flight_insurance\n- **PRICES**: check_trip_prices\n- **REFUND**: get_refund\n- **SEAT**: change_seat, choose_seat\n- **TIME**: check_arrival_time, check_departure_time\n- **TRIP**: book_trip, cancel_trip, change_trip, check_trip_details, check_trip_insurance_coverage, check_trip_offers, check_trip_plan, check_trip_prices, purchase_trip_insurance, search_trip, search_trip_insurance\n\n**Entities**\n\nThe entities covered by the dataset include:\n\n- **{{WEBSITE_URL}}**, common with most intents.\n- **{{APP_NAME}}**, featured in intents like change_flight, check_arrival_time.\n- **{{CUSTOMER_SUPPORT}}**, associated with intents like check_flight_reservation, check_trip_insurance_coverage, check_trip_plan.\n- **{{ORIGIN_CITY}}**, relevant to intents such as book_flight, change_flight.\n- **{{DESTINATION_CITY}}**, featured in intents like book_flight, book_trip.\n\nThis comprehensive list of entities ensures that the dataset is well-equipped to train models that are highly adept at understanding and processing a wide range of travel-related queries and tasks.\n\n**Language Generation Tags**\n\nThe dataset includes tags indicative of various language variations and styles adapted for Travel, enhancing the robustness and versatility of models trained on this data. These tags categorize the utterances into different registers such as colloquial, formal, or containing specific travel jargon, ensuring that the trained models can understand and generate a range of conversational styles appropriate for different customer interactions in the travel sector.\n\n**Language Generation Tags**\n\nThe dataset includes tags that reflect various language variations and styles, crucial for creating adaptable and responsive conversational AI models within the travel sector. These tags help in understanding and generating appropriate responses based on the linguistic context and user interaction style.\n\n**Tags for Lexical variation**\n\n- **M - Morphological variation**: Adjusts for inflectional and derivational forms.\n - Example: \"is my account active\", \"is my account activated\"\n- **L - Semantic variations**: Handles synonyms, use of hyphens, and compounding.\n - Example: “what's my balance date\", “what's my billing date”\n\n**Tags for Syntactic structure variation**\n\n- **B - Basic syntactic structure**: Simple, direct commands or statements.\n - Example: \"activate my card\", \"I need to check my balance\"\n- **I - Interrogative structure**: Structuring sentences in the form of questions.\n - Example: “can you show my balance?”, “how do I transfer money?”\n- **C - Coordinated syntactic structure**: Complex sentences coordinating multiple ideas or tasks.\n - Example: “I want to transfer money and check my balance, what should I do?”\n- **N - Negation**: Expressing denial or contradiction.\n - Example: \"I do not wish to proceed with this transaction, how can I stop it?\"\n\n**Tags for language register variations**\n\n- **P - Politeness variation**: Polite forms often used in customer service.\n - Example: “could you please help me check my account balance?”\n- **Q - Colloquial variation**: Informal language that might be used in casual customer interactions.\n - Example: \"can u tell me my balance?\"\n- **W - Offensive language**: Handling potentially offensive language which might occasionally appear in frustrated customer interactions.\n - Example: “I’m upset with these charges, this is ridiculous!”\n\n**Tags for stylistic variations**\n\n- **K - Keyword mode**: Responses focused on keywords.\n - Example: \"balance check\", \"account status\"\n- **E - Use of abbreviations**: Common abbreviations.\n - Example: “acct for account”, “trans for transaction”\n- **Z - Errors and Typos**: Includes common misspellings or typographical errors found in customer inputs.\n - Example: “how can I chek my balance”\n\n**Other tags not in use in this Dataset**\n\n- **D - Indirect speech**: Expressing commands or requests indirectly.\n - Example: “I was wondering if you could show me my last transaction.”\n- **G - Regional variations**: Adjustments for regional language differences.\n - Example: American vs British English: \"checking account\" vs \"current account\"\n- **R - Respect structures - Language-dependent variations**: Formality levels appropriate in different languages.\n - Example: Using “vous” in French for formal addressing instead of “tu.”\n- **Y - Code switching**: Switching between languages or dialects within the same conversation.\n - Example: “Can you help me with my cuenta, please?”\n\nThese tags not only aid in training models for a wide range of customer interactions but also ensure that the models are culturally and linguistically sensitive, enhancing the customer experience in travel environments.\n\n**License**\n\nThe `Bitext-travel-llm-chatbot-training-dataset` is released under the **Community Data License Agreement (CDLA) Sharing 1.0**. This license facilitates broad sharing and collaboration while ensuring that the freedom to use, share, modify, and utilize the data remains intact for all users.\n\n**Key Aspects of CDLA-Sharing 1.0**\n\n- **Attribution and ShareAlike**: Users must attribute the dataset and continue to share derivatives under the same license.\n- **Non-Exclusivity**: The license is non-exclusive, allowing multiple users to utilize the data simultaneously.\n- **Irrevocability**: Except in cases of material non-compliance, rights under this license are irrevocable.\n- **No Warranty**: The dataset is provided without warranties regarding its accuracy, completeness, or fitness for a particular purpose.\n- **Limitation of Liability**: Both users and data providers limit their liability for damages arising from the use of the dataset.\n\n**Usage Under CDLA-Sharing 1.0**\n\nBy using the `Bitext-travel-llm-chatbot-training-dataset`, you agree to adhere to the terms set forth in the CDLA-Sharing 1.0. It is essential to ensure that any publications or distributions of the data, or derivatives thereof, maintain attribution to the original data providers and are distributed under the same or compatible terms of this agreement.\n\nFor a detailed understanding of the license, refer to the [official CDLA-Sharing 1.0 documentation](https://cdla.dev/sharing-1-0/).\n\nThis license supports the open sharing and collaborative improvement of datasets within the AI and data science community, making it particularly suited for projects aimed at developing and enhancing AI technologies in the travel sector.\n\n---\n\n(c) Bitext Innovations, 2024\n","terms_of_service":"https://www.bitext.com/api-terms-of-use/","documentation_link":"","privacy_policy_link":"https://www.bitext.com/privacy-policy/","embedded_notebook_file_infos":[{"id":"0bfafc8b-e1e0-4a96-8a15-cda5fab1adfc","marketplace_file_type":"EMBEDDED_NOTEBOOK","file_parent":{"parent_id":"b5752e30-9089-4a86-a95f-97726b73d9d7","file_parent_type":"LISTING"},"mime_type":"text/html","download_link":"https://marketplace-file-storage-prod.s3-fips.us-east-1.amazonaws.com/EMBEDDED_NOTEBOOK/LISTING_b5752e30-9089-4a86-a95f-97726b73d9d7/0bfafc8b-e1e0-4a96-8a15-cda5fab1adfc?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEGgaDnVzLWVhc3QtMS1maXBzIkYwRAIgbC8sWIoYaHR8y7Wq86nPb0pufBl4%2BM4PWpeN%2B5PW9VQCIG7BhAVBDCWTpxFDLeuKYm%2Ff1JLz4iccPMzter0YdhgWKqYECDAQABoMNDE0MzUxNzY3ODI2IgxNAJrHbXaZG20jNfMqgwQyGEb4TsoVOoNvJ%2BaA%2FZ7zeu8hGcHqCeNfblP%2FQo5g6q2aRNtpwtPRKMlhYcIE%2FUQaNT1Lqmyptu32VUQTpzjTTxRVmJrDPkheSCkV06XQ%2FpTMKjU8CC7X1b%2FqYf8CrBwZT7Cv4C5WPjEET38f2A79h0xV6bZgPy28zQWrSeIJ9w6AfZBxiE24%2FVc08zVA0ivCT6lsy7nuU0wcR%2FMg2C9nd1vaMnQ923VpD21CKfVVYcrNWL2b%2FQ%2BNAO%2BxPjPgjiN6UPQbvbvZxkSdhgrGtecIg9NgqGkMcvM5VLTDtdC1kdeZetfOC2obtA%2BT%2F0gy3tIDo6WM5hcQfw743%2FAU9oaGnRBl%2BwoxRRDCZA%2F4jnPdluICSI7NCr6C3HHOe4CSZJjRihczxPA%2FgQczAVjAPs6CrK9N9pIzt0vNJpGUTgg4AoxftTvGfciDREilSctPIcBk6GwB%2Bzt4wbtNCmEPU%2BFD46rc6CWVQmmLwbKOeHw70ipMVFlOc8NlXkEtGO2vu6H44MstSSc6daOVUYUavQSGZ6jGhEjsP2YGz8KDm9XQotB%2FtyVBqiZV01OKqDmmBJA%2FXjUpSJ8M%2FeJXhe5m%2BKcxzNObtoJRI7jci3Tpt08XZyXZ7%2FyIV8H30ya468X3cfkTXrm972uga3z7iXAdubyEHnQIua%2FuvtwOHILi6O5D1Me2MzCPvcnXBjqTAYDoMWBt%2B3AVFuqSs2dmDQNBnvEhXfMgTedKLD5PJHb3aIl15iBkLaNu36GYnVCp9ZU2lSYcTbY5QwNnNeTfJTNy9bKhwzru6Sg%2BslhN%2BTS0zkTn%2BeLMY4tHgPEtEZ2cp%2FCk6GwqswvnYVKHjwO8q%2B1Mx8lc08fVEfarE9dke1GGFqoddx%2Bq%2BZUyHbDlAVfrCmf0yw%3D%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20261110T000135Z&X-Amz-SignedHeaders=host&X-Amz-Expires=86400&X-Amz-Credential=ASIAWA6KKHEJORWBH4N3%2F20261110%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=fe9cc6c0d0cbbea6e6e01349cbad5c00f7ec3977c47b63e8b7275f0331c1ed4e","created_at":1727608700611,"updated_at":1727608700611,"display_name":"Travel_Dataset_Sample_Notebook","status":"FILE_STATUS_PUBLISHED"}],"geographical_coverage":"[\"Global\"]","cost":"FREE","data_source":"Internally generated by Bitext using proprietary NLP and NLG technologies.","assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"Bitext Innovation International","description":"We understand that one of the critical factors in achieving exceptional performance with LLMs is the quality and relevance of the training data. 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Use these insights to future-proof teams, update skill taxonomies, or target relevant upskilling content.\t\n\nSELECT\n core_skills.skill,\n COUNT(*) AS skill_count\nFROM job_role_core_and_soft_skills AS core_skills\nJOIN master_job_role AS master_roles\n ON core_skills.job_role = master_roles.job_role\nJOIN vertical_split_country_job_role AS job_roles\n ON core_skills.job_role = job_roles.job_role\nWHERE LOWER(core_skills.job_role) LIKE '%product%'\n AND job_roles.industry = 'Hospital & Healthcare'\nGROUP BY core_skills.skill\nORDER BY skill_count DESC;\"\nIdentify high-demand skills for Clinical Research & Development family in Southern Asia\tUsing Draup’s real-time demand signals, identify the most sought-after skills segmented by job family, country, or role type to align L&D programs, hiring criteria, or market-entry strategies.\t\"SELECT\n core_skills.skill,\n COUNT(*) AS skill_demand_count\nFROM job_role_core_and_soft_skills AS core_skills\nJOIN master_job_role AS master_roles\n ON core_skills.job_role = master_roles.job_role\nJOIN talent_metrics_country_job_role AS talent_roles\n ON core_skills.job_role = talent_roles.job_role\nWHERE master_roles.job_family = 'Clinical Research & Development'\n AND talent_roles.country IN (\n 'Afghanistan', 'Bangladesh', 'Bhutan', 'India',\n 'Maldives', 'Nepal', 'Pakistan', 'Sri Lanka'\n )\nGROUP BY core_skills.skill\nORDER BY skill_demand_count DESC;\n\n***Analyze emerging vs. declining skills within Product Management in the Healthcare sector.***\t\n\nDraup’s longitudinal skill tracking highlights which skills are growing or waning in across roles. Use these insights to future-proof teams, update skill taxonomies, or target relevant upskilling content.\t\n\nSELECT\n core_skills.skill,\n COUNT(*) AS skill_count\nFROM job_role_core_and_soft_skills AS core_skills\nJOIN master_job_role AS master_roles\n ON core_skills.job_role = master_roles.job_role\nJOIN vertical_split_country_job_role AS job_roles\n ON core_skills.job_role = job_roles.job_role\nWHERE LOWER(core_skills.job_role) LIKE '%product%'\n AND job_roles.industry = 'Hospital & Healthcare'\nGROUP BY core_skills.skill\nORDER BY skill_count DESC;\n\n***Top soft skills in need for Data Scientist  talent in the USA***\t\n\nExplore the most frequently requested soft skills for a job role. Draup’s job description intelligence allows filtering by country, role, and industry to support well-rounded talent assessment, hiring frameworks, or curriculum design.\t\n\nSELECT\n core_skills.skill,\n COUNT(*) AS skill_count\nFROM job_role_core_and_soft_skills AS core_skills\nJOIN master_job_role AS master_roles\n ON core_skills.job_role = master_roles.job_role\nJOIN talent_metrics_country_job_role AS talent_roles\n ON core_skills.job_role = talent_roles.job_role\nWHERE master_roles.job_role = 'Data Scientist'\n AND core_skills.is_soft_skill = TRUE\n AND talent_roles.country IN ('United States', 'United States of America')\nGROUP BY core_skills.skill\nORDER BY skill_count DESC;\n\n***What are the top career paths ahead of Database Administrators and top courses to bridge the skills gap to complete a career transition***\n\nDraup’s career path intelligence reveals the most common forward transitions for a job role; e.g. Data Engineers, Cloud DBAs, or Platform Architects for Database Administrators. Combine this with Draup’s skill gap insights to recommend targeted certifications and courses that accelerate reskilling and enable successful career progression.\t\n\nWITH forward_paths AS (\n SELECT \n start_role as start_role,\n end_role AS target_role\n FROM reskill_career_path_transition\n WHERE LOWER(start_role) = 'database administrator'\n),\nskill_gaps AS (\n SELECT \n f.start_role,\n f.target_role,\n s.skills AS skill_name,\n s.skill_category\n FROM forward_paths f\n JOIN reskill_transition_skill_gap_analysis s\n ON LOWER(s.start_role) = LOWER(f.start_role)\n AND LOWER(s.end_role) = LOWER(f.target_role)\n),\ncourses AS (\n SELECT \n t.skill_name,\n COLLECT_LIST(\n course_title || ' (' || course_provider || ', ' || level_of_difficulty || ', ' || course_duration || ')'\n ) AS recommended_courses\n FROM reskill_course_library\n LATERAL VIEW explode(skills) t AS skill_name\n GROUP BY t.skill_name\n),\ncertifications AS (\n SELECT \n t.skill_name,\n COLLECT_LIST(\n course_title || ' (' || issuing_authority || ')'\n ) AS recommended_certifications\n FROM reskill_certfication_library\n LATERAL VIEW explode(skills) t AS skill_name\n GROUP BY t.skill_name\n)\nSELECT \n sg.start_role,\n sg.target_role,\n sg.skill_name,\n sg.skill_category,\n c.recommended_courses,\n cert.recommended_certifications\nFROM skill_gaps sg\nLEFT JOIN courses c\n ON LOWER(sg.skill_name) = LOWER(c.skill_name)\nLEFT JOIN certifications cert\n ON LOWER(sg.skill_name) = LOWER(cert.skill_name)\nORDER BY sg.target_role, sg.skill_category, sg.skill_name","terms_of_service":"https://draup.com/privacy-policy#:~:text=Unless%20provided%20for%20in%20this,that%20work%20on%20our%20behalf","documentation_link":"","privacy_policy_link":"https://draup.com/privacy-policy","geographical_coverage":"[\"Global\"]","cost":"PAID","update_frequency":{"interval":1,"unit":"WEEKLY"},"collection_granularity":{"interval":1,"unit":"DAILY"},"data_source":"Public Sources","assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"Draup, Inc.","description":"Draup is an enterprise agentic intelligence platform built for HR and Sales leaders navigating transformation. 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For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [Africa Child Growth Admin 1 2000 to 2015](https://www.johnsnowlabs.com/marketplace/africa-child-growth-admin-1-2000-to-2015)\n\t- This dataset provides estimates were produced for under-5 stunting, wasting, and underweight prevalence at the 5x5 km-level in 51 countries in Africa between 2000-2015.\n- [Big Cities Behavioural Health Risk And Protection Factors](https://www.johnsnowlabs.com/marketplace/big-cities-behavioural-health-risk-and-protection-factors)\n\t- This dataset contains estimates of health risk and protection factors prevalence, shared by the Big Cities Health Coalition members represented by the largest metropolitan health departments in the United States. The estimated values of prevalence cover the 2010-2016 period and are described by gender and race/ethnicity.\n- [CDC Diabetes Statistics Total Number](https://www.johnsnowlabs.com/marketplace/cdc-diabetes-statistics-total-number)\n\t- This dataset contains information on the total proportion of adults diagnosed with diabetes, collected from the system of health-related telephone surveys, the Behavioral Risk Factor Surveillance System (BRFSS), conducted in more than 400,000 patients, from 50 states in the US, the District of Columbia and three US territories.\n- [CDC Diabetes Statistics](https://www.johnsnowlabs.com/marketplace/cdc-diabetes-statistics)\n\t- This dataset contains information on the proportion by age, total number, male and female and sex of adults of adults diagnosed with diabetes, collected from the system of health-related telephone surveys, the Behavioral Risk Factor Surveillance System (BRFSS), conducted in more than 400,000 patients, from 50 states in the US, the District of Columbia and three US territories.\n- [Global Alcohol Use 1990 to 2016](https://www.johnsnowlabs.com/marketplace/global-alcohol-use-1990-to-2016)\n\t- This dataset provides estimates of the world prevalence of current drinking, abstention, and the population average of standard alcoholic drinks daily from the Global Burden of Disease (GBD) Study 2016.\n- [Global Smoking Prevalence 1980 to 2012](https://www.johnsnowlabs.com/marketplace/global-smoking-prevalence-1980-to-2012)\n\t- This dataset provides the prevalence of daily smoking by age and sex and the number of cigarettes per smoker per day for 187 countries from 1980 to 2012.\n- [NHS Hospital Finished Admission Episodes Related to Drug Misuse](https://www.johnsnowlabs.com/marketplace/nhs-hospital-finished-admission-episodes-related-to-drug-misuse)\n\t- This dataset presents information on the number of hospital admissions for diseases, injuries and conditions that can be attributed to drug misuse and are based on the tenth revision of the International Classification of Diseases (ICD-10) Codes.\n- [UK Obesity Related Hospital Admissions By Age and Gender](https://www.johnsnowlabs.com/marketplace/uk-obesity-related-hospital-admissions-by-age-and-gender)\n\t- This dataset consists of information on hospital admissions relating to being obese based on patient's gender and age. Data is taken from the Hospital Episode Statistics (HES) databank produced by NHS Digital.\n- [UK Obesity Related Hospital Admissions By Geographical Granularity](https://www.johnsnowlabs.com/marketplace/uk-obesity-related-hospital-admissions-by-geographical-granularity)\n\t- This dataset consists of information on hospital admissions relating to being obese based on geographical distribution. Data is taken from the Hospital Episode Statistics (HES) databank produced by NHS Digital.\n- [UK Women Smoking Status At Time Of Delivery](https://www.johnsnowlabs.com/marketplace/uk-women-smoking-status-at-time-of-delivery)\n\t- This dataset contains statistics regarding the smoking status of women in England at the time of the delivery by country and Clinical Commissioning Groups (CCGs). 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Our company has enabled thousands of engineering, data science, product, and other technical teams to build compliant, innovative, software solutions using B2B data.\n- People Data Labs provides company and person data via flat files and APIs\n\n**About this Dataset**\n\n- This is our free company dataset of company profiles with reduced fields. The data in this file is updated quarterly. We also list our paid company dataset.\n- This data covers global companies with at least 1 employee in the People Data Labs dataset.\n\n**Tables Included**\n\n1. 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The territorial vintages of the worldwide area boundaries are compatible with our globally consistent and comparable [socio-demographic](https://www.mbi-geodata.com/en/sociodemographic-data/) and [economic](https://www.mbi-geodata.com/en/purchasing-power-overview/) data.\nOur area boundaries for Serbia are offered at the following levels:\n\n- **Postal Code Level** (Postcode 2-digit, 5-digit on request): This fine-grained level of detail makes the data suitable for a wide range of applications – for example, enriching customer databases, which are often organized by postal code.\n- **Administrative Level** (Opštine): Covers both larger administrative areas (e.g., regions, states, provinces) as well as finer subdivisions such as districts, counties, or municipalities.\n- **Micro Level** (Naselja, Statistički Krugovi): Subdivisions of postal code or administrative boundaries, such as census areas or street segments. This is the most granular level for local analysis. | on request\n\n**Use Cases:**\n\n- **Market Analysis**: Use MBI’s boundaries to visualize data on a map, learn about your customers, and uncover untapped market potential.\n- **360-Degree Customer View**: Link boundaries to customer data, sociodemographic, and economic data to gain a holistic understanding of customer needs, behaviors, and potential value.\n- **Location Planning**\n•\tUse MBI’s boundaries to visualize data on a map, learn about your customers\n•\tPlan the provision of services and products across different regions\n•\tDefine sales territories\n•\tVisualize catchment areas and plan actions to reach potential customers\n\n**Additional Insights:**\n[About Area Boundaries](https://www.mbi-geodata.com/en/area-boundaries/)\n\n**Data Structure:**\nThe area boundaries include the following variables:\nCTRYCODE: Two-digit country code by ISO 3166\nNAME: Name of administrative area / Name of the most populous administrative area / Name of the respective micro-area or the next higher level\nADMINCODE / POSTCODE / MICROCODE: Administrative code / Postal code / Micro-code\nWKT: Geography as Well Known Text (lat/long WGS84)\n\n*with only a few specific exceptions","terms_of_service":"https://service-mbi-geodata.com/s/HNp7Z2tws4WDqWd","documentation_link":"https://www.mbi-geodata.com/en/mbi-supplier-manufacturer-worldwide-geodata/europe/serbia-maps/","privacy_policy_link":"https://www.mbi-geodata.com/en/data-protection/","update_frequency":{"interval":1,"unit":"YEARLY"},"assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://service-mbi-geodata.com/s/H6N9Jke42Gdom4y"},"provider_summary":{"name":"Michael Bauer International GmbH – MBI Geodata","description":"For over 15 years, Michael Bauer International (MBI) has been a leading provider of globally consistent and comparable geographic data, including more than 3 million postcode areas and other detailed datasets like sociodemographics and purchasing power. 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MBI is part of the Michael Bauer Group, alongside Michael Bauer Research GmbH and Michael Bauer Micromarketing GmbH."},"popularity":1805},{"id":"dc454521-c6b2-4749-94a4-13c19e6696f1","summary":{"name":"Geolocet | Points of Interest Data | Malta - stores,bars, pubs, restaurants, hotels, cafes, and more","subtitle":"Supercharge Your Malta Retail Strategies: 7,000+ locations in 50+ categories - Shops, Pubs, Restaurants, Cafes,","setting":{"visibility":"PUBLIC"},"created_at":1710834938733,"updated_at":1752137662519,"categories":["RETAIL","TRAVEL_AND_TOURISM"],"listingType":"PERSONALIZED","provider_id":"5d3ddc17-a7e8-4794-a760-13587e1de2c6"},"detail":{"description":"# Supercharge Your Malta Retail Strategies: over 7,000 Pubs, Restaurants, Cafes, Hotels & More\n\nOur comprehensive Points of Interest dataset offers nearly 7,000+ locations of points of interest across Malta, empowering you with the data you need for your location intelligence analysis to succeed.\n\n## Key Features:\n\n- **Extensive Coverage:** Discover a vast network of stores, supermarkets, pubs, cafes, restaurants, bars, hotels, cinemas, and more – all across Malta.\n- **Detailed Data:** Gain valuable insights with POI names, precise coordinates, and POI categories.\n- **Tailored Data Access:** Choose from over 50 Points of Interest categories or define custom geographic regions to get exactly the data you need.\n\n## Category Breakdown:\n\n - **fast food:** 797 PoIs\n - **hotel:** 450 PoIs\n - **restaurant:** 389 PoIs\n - **clothing store:** 378 PoIs\n - **bar:** 341 PoIs\n - **cafe:** 339 PoIs\n - **beauty salon:** 279 PoIs\n - **hair care:** 251 PoIs\n - **tourist attraction:** 243 PoIs\n - **all others:** 4,200 PoIs\n\n## Additional Benefits:\n\n- **Enhanced Enrichment:** Explore optional add-ons to elevate our points of interest dataset and gain deeper insights, such as opening hours, ratings, and expanded contact details.\n- **Effortless Acquisition:** Access a free sample and download the full dataset or use it directly from AWS\n- **Best Price Guarantee:** Get the most competitive pricing without sacrificing data quality.\n\n## Use Cases:\n\n- Find the perfect spot for a new bar, pub, restaurant, cafe, hotel, or any other venue in Malta.\n- Get insights into your competitors' locations across Malta.\n\n## Additional Products:\n\n- **[Demographics Data for Malta](https://geolocet.com/products/malta-2021-demographics-data):** Enrich your insights with comprehensive Malta demographics.\n- **[Polygons of the regions and administrative divisions of Malta](https://geolocet.com/products/malta-all-available-administrative-boundaries):** Gain deeper context with shapefile formatted polygon files for the administrative divisions of Malta.\n\n## Call to Action:\n\nUnleash the power of location intelligence through our Hospitality Points of Interest dataset for Malta!\nChoose the option that best suits your needs:\n\n- **Download a Sample:** Evaluate the potential of our base POI dataset before you commit.\n- **Purchase the Full Dataset:** Unlock the full potential of your location analysis.\n\n\nIf you're looking for data for Malta, [look here](https://geolocet.com/pages/malta) for off-the-shelf or tailor-made datasets to suit your specific requirements.\n\nAlternatively, contact our sales team at [contact@geolocet.com](mailto:contact@geolocet.com) to discuss your specific needs and explore our customization options. We'll help you craft the perfect data solution to supercharge your business.\n","terms_of_service":"https://geolocet.com/pages/terms-of-use","documentation_link":"","privacy_policy_link":"https://geolocet.com/pages/privacy-policy","geographical_coverage":"[\"Europe\"]","data_source":"","assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://geolocet.com/pages/terms-of-use"},"provider_summary":{"name":"Geolocet LTD","description":"Geolocet provides premium geospatial data for retail analysis, GIS, and market planning. We equip businesses with accurate, ready-to-query data to drive spatial analysis, site selection, and customer segmentation.\n\nCore Offerings:\n\nDemographics: Socioeconomic data covering population, age, and employment across diverse geographies.\n\nPoints of Interest (PoI): Datasets spanning retail, healthcare, and transport for catchment analysis.\n\nAdministrative Boundaries: GIS-ready boundaries, including municipalities and postcodes, for territory management.\n\nBrand Locations: Chain location datasets to monitor store networks and market expansion.\n\nWealth Data: Granular economic indicators like purchasing power to identify high-potential markets.\n\nWe source from trusted providers, delivering highly customizable datasets with comprehensive coverage across Europe and worldwide."},"popularity":1427},{"id":"6cf064b7-1fca-4a8f-addf-03ffd8bfdfd6","summary":{"name":"CMS National Provider Directory: Release Archive","subtitle":"Every release of the federal provider directory, including the ones CMS no longer serves. Diff them in one query.","share":{"name":"ainpi-ndh-archive","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1787524212126,"updated_at":1792186456517,"categories":["HEALTH","PUBLIC_SECTOR"],"listingType":"STANDARD","provider_id":"1391b933-c642-420e-86a6-98c1283a4b57"},"detail":{"description":"CMS publishes the National Provider Directory as a bulk FHIR export and serves only the current version. When a new release lands, the previous one is gone from the source. This archive keeps them. That is the entire product, and it is free.\n\nWHAT IS IN IT. Three releases so far, 2026-05-08, 2026-08-20 and 2026-10-07, 78,924,582 rows across six tables: Practitioner, PractitionerRole, Organization, OrganizationAffiliation, Location and Endpoint. Every row carries the untouched FHIR resource JSON, plus flattened columns so ordinary questions do not require JSON parsing. Every release is extracted by the same code, so comparing them is not comparing two parsers. Each table is partitioned by release_date, which makes a cross-release comparison a WHERE clause rather than a download.\n\nORGANIZATION TO ENDPOINT. Two further tables, org_endpoint and org_endpoint_summary, map FHIR endpoint URLs to organization NPIs and to the EHR vendor behind them, where either source says so. A source column says where each row came from: the directory's own managingOrganization, or the public endpoint files of eight EHR vendors. The vendor rows are each vendor's own claim; CMS has not checked them. Where the two sources name different organizations for one URL, both rows are kept. 40,005 of the 68,888 vendor rows name an organization without an NPI.\n\nMEASURES. A further table, measures, holds the measure series: a fixed set of questions asked of every release, one row per question and release_date, with the numerator, denominator and value as AINPI published them. A comparable_to_previous column says whether two consecutive points can be read as a trend; a change of definition is marked as a break, not a move. A missing row means AINPI withheld that point, usually because a zero could not be told apart from a field that was never read. It is not a zero. Where a published value was later corrected, the row carries both the current value and the value first published.\n\nWHAT IT IS FOR. Questions that need two releases at once. One worked example, included as a notebook: between May and August CMS added about seven million PractitionerRole records, a 173% rise. The share of clinicians who have one moved 4.5 points, from 26.9% to 31.4%. The rest went to people the directory already described, taking the average covered clinician from two role records to nearly five. That is a real improvement and a different improvement from covering more people, and a headline record count cannot tell them apart. The 2026-10-07 release then removed 5.5 million PractitionerRole records, and coverage held, which is the same lesson read in reverse.\n\nBEFORE YOU WRITE A DIFF. Practitioner ids embed the NPI and are stable across releases. Organization ids are stable for provider records, which are NPI-keyed; its tax records (about half the table in August, under 4% in October) carry a random UUID. CMS regenerated Endpoint and Location ids at 2026-05-08 and again at 2026-08-20 (zero overlap with the previous release each time), so an _id join can report false churn; join Endpoint on _address for every pair. The table comments carry the same warning and the notebook demonstrates it.\n\nWHERE IT COMES FROM. Maintained by AINPI (ainpi.dev), a public-interest audit of federal provider directory data. Every measurement it publishes is pre-registered before the numbers are computed, the compute scripts are open, and corrections are published when a source changes underneath a claim.\n\nLICENCE. The underlying federal files are US government works and are not subject to copyright. AINPI claims no rights over them and grants none, because it has none to grant. The compilation and the extraction code are Apache-2.0. Attribution is requested rather than required, for a practical reason: a figure quoted without its release date cannot be checked against the release it came from.\n\nNOT A SOURCE OF TRUTH ABOUT ANY INDIVIDUAL. This is a measurement of a federal file. Do not make an enrolment, credentialing, payment or network decision about a named provider from a record here without checking the primary sources: the NPPES registry, the OIG exclusions list, and SAM.gov.\n\nCORRECTIONS WELCOME. If a number here disagrees with something you can verify, we would rather hear it: https://github.com/FHIR-IQ/AINPI/issues","terms_of_service":"https://ainpi.dev/terms","documentation_link":"https://ainpi.dev/archive","privacy_policy_link":"https://ainpi.dev/privacy","geographical_coverage":"[\"United States\"]","cost":"FREE","update_frequency":{"interval":3,"unit":"MONTHLY"},"data_source":"CMS National Provider Directory bulk FHIR export (directory.cms.gov)","assets":["ASSET_TYPE_DATA_TABLE","ASSET_TYPE_NOTEBOOK"],"license":"https://ainpi.dev/data-license"},"provider_summary":{"name":"FHIR IQ","description":"FHIR IQ works on healthcare interoperability: HL7 FHIR implementation, provider directory data quality, and federal health data standards.\n\nIt maintains AINPI (ainpi.dev), a public-interest audit of the CMS National Provider Directory. AINPI registers each measurement before computing the numbers, publishes the compute scripts, and issues a correction when a source changes underneath a claim.\n\nListings from this provider are healthcare directory data and the tools to read it. The first is an archive of every published release of the federal provider directory, including the releases CMS no longer serves. It ships as Delta tables partitioned by release, so one query can compare two versions. Later listings cover crosswalks derived from it and an MCP server that answers audit questions for an agent.\n\nThe underlying federal files are US government works and carry no copyright. The extraction code is Apache-2.0."},"popularity":1903},{"id":"4c2ed32a-327a-414e-bfb5-b894fdf63480","summary":{"name":"2025 Collectable Sneakers Price Trends DS1","subtitle":"This dataset is a curated snapshot of 4,800 rows of collectable sneakers detail. 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Full dataset available for download upon request or on the AIDC platform. Research and analytics completed Maths with Kanchana LLC. Distributed exclusively through AIDC Data LLC.\n\n**Column Descriptions**\n**Brand: The sneaker brand (e.g., Nike, Reebok, ASICS).\nTitle: Full title or listing description of the sneaker, often including model and rarity details.\nType: General category of the shoe (e.g., Athletic).\nCondition: Item condition as listed (e.g., New with box, Pre-owned).\nPrice: Listed or transacted price in U.S. dollars.\nItemLocation: City, state, and country where the item is located.\nSeller: An anonymized identifier for the seller.\nShipsTo: Regions or countries to which the seller ships.\nExcludesShipping: Regions or countries the seller does not ship to.\nLastUpdated: Timestamp of the last known listing update (if available).\n\n**Ethical Data Use Statement**\nAll data in this dataset was collected exclusively from public sources using ethical research methods. No private or sensitive information is included. 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No private or sensitive information is included. PII clear.","size":0.0022,"assets":["ASSET_TYPE_MEDIA"],"license":"https://aidc.app/sample-agreements"},"provider_summary":{"name":"AIDC, Inc.","description":"Great AI starts with great data. We have it. Ship data like software. AIDC packages external data as governed Delta tables with Unity Catalog, complete with schema, metadata, lineage, and clear license terms. Pre‑vetted contracts minimize back‑and‑forth, so squads move from browse to pipeline in days. Quality control is operated by Kaggle Grandmasters and senior data engineers at that standard, so datasets behave in production and pass audits. Coverage spans luxury item pricing (valuation, comp, elasticity studies) to retail property analytics (site selection, underwriting, performance prediction). Instrument adoption and prove lift: reduced ETL, faster training, higher dataset usability rates. Repeatable motion across teams: import → govern → build → measure → scale. Available now on Databricks Marketplace."},"popularity":1880},{"id":"d0d3a76a-b410-43af-9fa5-0ac3549fd456","summary":{"name":"OASDI Benefits","subtitle":"Number of beneficiaries or percentage of beneficiaries, amount, type of total monthly OASDI benefits by state and county","share":{"name":"oasdi_benefits","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1682356114895,"updated_at":1698965635315,"categories":["HEALTH","SCIENCE_AND_RESEARCH"],"listingType":"STANDARD","provider_id":"f88849f8-9d77-4de2-b4c7-03d55cc3768e"},"detail":{"description":"\n**Overview**\nNumber of beneficiaries or percentage of beneficiaries, amount, type of total monthly OASDI benefits by state and county\n\n**Description**\nInformation on the Social Security beneficiary population which includes the people receiving Old-Age, Survivors, and Disability Insurance (OASDI) benefits at the local level. It presents basic program data on the number and type of beneficiaries and the amount of benefits paid in each state and county. It also shows the numbers of men and women aged 65 or older receiving benefits. This report is a useful planning aid for Social Security Administration field offices and for those providing information to federal, state, and local government agencies. It presents basic program data on the number and type of beneficiaries and the amount of benefits paid in each state, Social Security Administration field office, and ZIP Code.\n\n**Benefits**\n- General information on old-age, survivors, and disability insurance (oasdi) benefits\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [Amount And Type Of OASDI Benefits By State And County 2019](https://www.johnsnowlabs.com/marketplace/amount-and-type-of-oasdi-benefits-by-state-and-county-2019)\n\t- The dataset provides information on the Social Security beneficiary population which includes the people receiving Old-Age, Survivors, and Disability Insurance (OASDI) benefits at the local level. The amount and type of OASDI benefits are shown in the in the dataset. It presents basic program data on the number and type of beneficiaries and the amount of benefits paid in each state and county. It also shows the numbers of men and women aged 65 or older receiving benefits.\n- [Amount And Type Of OASDI Benefits By State And County 2020](https://www.johnsnowlabs.com/marketplace/amount-and-type-of-oasdi-benefits-by-state-and-county-2020)\n\t- The dataset provides information on the Social Security beneficiary population which includes the people receiving Old-Age, Survivors, and Disability Insurance (OASDI) benefits at the local level. The amount and type of OASDI benefits are shown in the in the dataset. It presents basic program data on the number and type of beneficiaries and the amount of benefits paid in each state and county. It also shows the numbers of men and women aged 65 or older receiving benefits.\n- [Number Of Beneficiaries Receiving OASDI Benefits By State County 2019](https://www.johnsnowlabs.com/marketplace/number-of-beneficiaries-receiving-oasdi-benefits-by-state-county-2019)\n\t- The dataset provides information on the Social Security beneficiary population which includes the people receiving Old-Age, Survivors, and Disability Insurance (OASDI) benefits at the local level. The number of OASDI beneficiaries are shown in the in the dataset. It presents basic program data on the number and type of beneficiaries and the amount of benefits paid in each state and county.\n- [Number Of Beneficiaries Receiving OASDI Benefits By State County 2020](https://www.johnsnowlabs.com/marketplace/number-of-beneficiaries-receiving-oasdi-benefits-by-state-county-2020)\n\t- The dataset provides information on the Social Security beneficiary population which includes the people receiving Old-Age, Survivors, and Disability Insurance (OASDI) benefits at the local level. The number of OASDI beneficiaries are shown in the in the dataset. It presents basic program data on the number and type of beneficiaries and the amount of benefits paid in each state and county.\n- [OASDI Beneficiaries Total Monthly Benefits by State and ZIP Code 2014](https://www.johnsnowlabs.com/marketplace/oasdi-beneficiaries-total-monthly-benefits-by-state-and-zip-code-2014)\n\t- The dataset provides information on the Social Security beneficiary population which includes people receiving Old-Age, Survivors, and Disability Insurance (OASDI) benefits at the ZIP Code level. It presents basic program data on the number and type of beneficiaries and\nthe amount of benefits paid in each state, Social Security Administration field office, and ZIP Code.\n- [OASDI Beneficiaries Total Monthly Benefits by State and ZIP Code 2015](https://www.johnsnowlabs.com/marketplace/oasdi-beneficiaries-total-monthly-benefits-by-state-and-zip-code-2015)\n\t- The dataset provides information on the Social Security beneficiary population which includes people\nreceiving Old-Age, Survivors, and Disability Insurance (OASDI) benefits at the ZIP\nCode level.\n- [OASDI Beneficiaries Total Monthly Benefits by State and ZIP Code 2016](https://www.johnsnowlabs.com/marketplace/oasdi-beneficiaries-total-monthly-benefits-by-state-and-zip-code-2016)\n\t- The dataset provides information on the Social Security beneficiary population which includes people receiving Old-Age, Survivors, and Disability Insurance (OASDI) benefits at the ZIP\nCode level.\n- [OASDI Beneficiaries Total Monthly Benefits by State and ZIP Code 2017](https://www.johnsnowlabs.com/marketplace/oasdi-beneficiaries-total-monthly-benefits-by-state-and-zip-code-2017)\n\t- The dataset provides information on the Social Security beneficiary population which includes people receiving Old-Age, Survivors, and Disability Insurance (OASDI) benefits at the ZIP\nCode level.\n- [OASDI Beneficiaries Total Monthly Benefits by State and ZIP Code 2018](https://www.johnsnowlabs.com/marketplace/oasdi-beneficiaries-total-monthly-benefits-by-state-and-zip-code-2018)\n\t- The dataset provides information on the Social Security beneficiary population which includes people receiving Old-Age, Survivors, and Disability Insurance (OASDI) benefits at the ZIP Code level.\n- [OASDI Beneficiaries Total Monthly Benefits by State and ZIP Code 2019](https://www.johnsnowlabs.com/marketplace/oasdi-beneficiaries-total-monthly-benefits-by-state-and-zip-code-2019)\n\t- The dataset provides information on the Social Security beneficiary population which includes people receiving Old-Age, Survivors, and Disability Insurance (OASDI) benefits at the ZIP Code level.\n- [OASDI Beneficiaries Total Monthly Benefits by State and ZIP Code 2020](https://www.johnsnowlabs.com/marketplace/oasdi-beneficiaries-total-monthly-benefits-by-state-and-zip-code-2020)\n\t- The dataset provides information on the Social Security beneficiary population which includes people receiving Old-Age, Survivors, and Disability Insurance (OASDI) benefits at the ZIP Code level.\n- [Percentage Of Beneficiaries Receiving OASDI Benefits By State Age 2019](https://www.johnsnowlabs.com/marketplace/percentage-of-beneficiaries-receiving-oasdi-benefits-by-state-age-2019)\n\t- The dataset provides information on the Social Security beneficiary population which includes people receiving Old-Age, Survivors, and Disability Insurance (OASDI) benefits at the local level. State data on beneficiaries as a percentage of the total resident population and of the population aged 65 or older are shown in the dataset.\n- [Percentage Of Beneficiaries Receiving OASDI Benefits By State Age 2020](https://www.johnsnowlabs.com/marketplace/percentage-of-beneficiaries-receiving-oasdi-benefits-by-state-age-2020)\n\t- The dataset provides information on the Social Security beneficiary population which includes people receiving Old-Age, Survivors, and Disability Insurance (OASDI) benefits at the local level. State data on beneficiaries as a percentage of the total resident population and of the population aged 65 or older are shown in the dataset.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated\n- Data Updates\n - Data updates support replace-on-update: outdated foreign keys are 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We understand that different projects may have varying data needs, and we are here to tailor our data to match your specific requirements.\n\n ## **Key Features:**\n---\n* **Population Data Hungary:** Gain access to the latest and most accurate population statistics for Hungary, meticulously organized by municipality.\n\n* **Demographic Insights:** Dive deep into the Hungarian demographic landscape with data on 62 attributes, including: \n* Total population,\n* Gender distribution,\n* 5-years age bands population distribution,\n* Gender divided by age bands distribution,\n* Dwellings,\n* Size of dwellings.\n\nAll demographic attributes are available at the municipal level.\n\n* **Geodemographic Insights:** Unlock geodemographic data Hungary-wide to inform location-based decision-making, marketing strategies, and policy formulation.\n* **Seamless Data Access:** Our data is available for instant download in CSV format. No lengthy sign-up processes or NDAs required on our side – just download the data you need.\n* **Data Samples for Evaluation:** To ensure you're getting exactly what you need, we provide free downloadable samples in CSV format for data evaluation.\n* **Data accuracy:** Our datasets are created using the latest demographics data available in national statistics bureaus, guaranteeing accuracy and reliability.\n* **Best-in-Class Quality:** We guarantee best-in-class quality, backed by over two decades of experience in modeling demographics data and an outstanding quality management process.\n* **Competitive Pricing:** We maintain the lowest prices on the market, ensuring that you get the most cost-efficient demographics data acquisition process.\n---\nAdditionally, for those looking to enrich their analysis or project, we offer:\n* **[Administrative Boundaries](https://geolocet.com/products/hungary-all-available-administrative-boundaries):** If you require geographical boundaries (polygons) for the municipalities of Hungary, consider exploring our Administrative Boundaries product, which seamlessly integrates with this dataset.\n\n* **[Point of Interest (PoI) Data](https://geolocet.com/pages/aggregated-points-of-interest-poi-data):** If you're interested in Points of Interest data, please don't hesitate to contact us for customized datasets to enhance your geographical analysis.\n\n* **[Data Customization](https://geolocet.com/pages/data-services-by-geolocet):** We understand that your project may have specific requirements. 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Trust in the accuracy and reliability of our official statistical data to drive your projects forward.\nContact us at *contact@geolocet.com* today to access this invaluable resource and explore Hungary census data like never before.\n\n[Geolocet LTD](https://geolocet.com)\n","terms_of_service":"https://geolocet.com/pages/terms-of-use","documentation_link":"","privacy_policy_link":"https://geolocet.com/pages/privacy-policy","geographical_coverage":"[\"Europe\"]","cost":"PAID","data_source":"","assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://geolocet.com/pages/terms-of-use"},"provider_summary":{"name":"Geolocet LTD","description":"Geolocet provides premium geospatial data for retail analysis, GIS, and market planning. 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Our team of experts delivers unrivaled insights and leading data and technology solutions, partnering with customers to expand their perspective, operate with confidence, and make decisions with conviction."},"popularity":1964},{"id":"6c7080a9-e398-430a-9fd7-59989428bbfa","summary":{"name":"New York Sports Teams Social & Sponsorship Data","subtitle":"Historical Sponsorship and social media advertising data for New York teams","setting":{"visibility":"PUBLIC"},"created_at":1721404872802,"updated_at":1721673268901,"categories":["ADVERTISING_AND_MARKETING","MEDIA","SPORTS"],"listingType":"PERSONALIZED","provider_id":"51878089-78ed-4918-a28e-002263206245"},"detail":{"description":"**Overview**\nHistorical Sponsorship and social media advertising data for New York teams: New York Jets, New York Giants, New York Yankees, New York Mets, New York Knicks, Brooklyn Nets, New York Rangers, New York Islanders, New York Liberty, and Gotham FC.\n\nThis dataset includes engagement, impression, and valuation metrics for the teams and their sponsors.\n\nEasily benchmark all professional sports teams in the New York area, or understand social media and sponsorship trends.\n\n**Product details**\nDetails of the dataset are included below:\n\n**Field Name - *TYPE*: Description**\nauthor - ***STRING***: Author of the content\nauthor_url - ***STRING***: URL to the author's profile or page\nchannel - ***STRING***: Channel where the content is published\ndate - ***DATETIME***: Date and time of the content publication\ndescription - ***STRING***: Caption and other details\nengagement - ***INTEGER***: Total engagement metrics\nentity - ***STRING***: Main entity associated with the content\nescore - ***FLOAT***: Engagement score\nestimated_impressions - ***INTEGER***: Estimated number of times content is viewed\nestimated_views - ***INTEGER***: Estimated number of views\nid - ***STRING***: Unique identifier for the content\nliked_count - ***INTEGER***: Number of likes on the content\nmedium - ***STRING***: Media platform\nmedium_id - ***STRING***: Unique ID for the medium\nmedium_url - ***STRING***: URL to the post on the platform\noriginal_description - ***STRING***: Caption\nplays - ***INTEGER***: Number of times content is played\npromotion_status - ***STRING***: Organic, Boosted, etc.\nreplied_count - ***INTEGER***: Number of replies to the content\nreposted_count - ***INTEGER***: Number of times content is reposted\nsport - ***STRING***: Sport category related to the content\ntimestamp - ***TIMESTAMP***: Exact time of data recording\nurl - ***STRING***: URL of the content on Blinkfire's platform\nvaluation - ***FLOAT***: Estimated value of the content\nvariety - ***STRING***: Type or category of content variety\nvideo_views - ***INTEGER***: Number of views for videos\nview_count - ***INTEGER***: Total count of views\ntitle - ***STRING***: Title of the content\ncrests - ***RECORD***: Records of crest-related information\ncrest_url - ***STRING***: URL for crest images or information\ncrest_entity_id - ***STRING***: ID for crest-related entity\ncrest_entity_name - ***STRING***: Name of the crest-related entity\ncrest_occurrences - ***STRING***: Occurrences of crest in the content\ncrest_enclosure_occurrences - ***RECORD***: Records of crest enclosure occurrences\nenclosure_id - ***STRING***: ID for the content enclosure\noccurrences - ***STRING***: Occurrences of a specific item in content\nhashtags - ***STRING***: Related hashtags used in the content\nbrand_name - ***STRING***: Name of the brand featured\nbrand_detection_type - ***STRING***: Type of brand detection method used\nbrand_duration - ***FLOAT***: Duration of brand exposure in content\nbrand_duration_percentage - ***STRING***: Percentage of content duration for brand spotting\nbrand_logo_impressions - ***STRING***: Impressions of the brand logo\nbrand_screenshare - ***FLOAT***: Screen visibility percentage for brand\nbrand_valuation - ***STRING***: Valuation of the brand in the content\nbrand_viewer_time - ***STRING***: Time viewers spend on the brand\nenclosures - ***RECORD***: Records of content enclosures\ncontent_type - ***STRING***: Type of content (e.g., text, video)\ndisplay_url - ***STRING***: URL for display content\nguid - ***STRING***: Global Unique Identifier for content\nheight - ***INTEGER***: Height of the content image or video\nlength - ***INTEGER***: Length of the content video\nmedia_url - ***STRING***: URL to the media file\nthumbnail_url - ***STRING***: URL to the content thumbnail\nwidth - ***INTEGER***: Width of the content image or video\nentity_id - ***STRING***: ID for the main entity\nentity_type - ***STRING***: Type of the main entity\nentity_name - ***STRING***: Name of the main entity\nentity_explicit_name - ***STRING***: Explicit name of the main entity\n\n\n\n**Additional Insights**\nDataset provided by Blinkfire Analytics, Inc. \nhttps://www.blinkfire.com/landing","terms_of_service":"https://www.blinkfire.com/legal","documentation_link":"","privacy_policy_link":"https://www.blinkfire.com/tos","geographical_coverage":"[\"United States and Canada\"]","cost":"PAID","data_source":"https://www.blinkfire.com/landing","assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"Blinkfire Analytics, Inc.","description":"Blinkfire Analytics is an AI-first company with leading business intelligence & marketing analytics products to evaluate sports, media, and entertainment sponsorships across social media, digital, and Advanced TV. Using artificial intelligence, machine learning, and proprietary computer vision technology, Blinkfire Analytics measures media value and impact – allowing rights holders, players, influencers, agents, and brands to better engage their fans and sponsors across multiple platforms. Many of the world’s top teams and leagues rely on Blinkfire Analytics’ real-time, always-on platform for sports and entertainment sponsorship evaluation to quantify engagement and sponsorship dollars."},"popularity":1043},{"id":"6f030d33-d187-4c0f-877f-a3a3a0e94d92","summary":{"name":"Global Oil Demand","subtitle":"The Global Oil Demand dataset offers access to end-use product demand for gasoline, gasoil and other products","setting":{"visibility":"PUBLIC"},"created_at":1701971995032,"updated_at":1708949096349,"categories":["ENERGY","FINANCIAL"],"listingType":"PERSONALIZED","provider_id":"eff4b18d-fe1b-4884-9591-eab9d16d2e2a"},"detail":{"description":"**Overview**\n\nThe Global Oil Demand dataset allows users to deepen their understanding of the impact of recent global events on refined products demand around the world. The dataset provides historical data and monthly updated forecasts for individual petroleum products, including gasoline, diesel, LPG, Naphtha, and more.\n\nYou can leverage this dataset to increase your confidence in making the right business decisions by comparing demand forecasts across multiple sources. Build different scenarios and eloquent dashboards by blending this data with your own in your internal modeling systems.\n\nThis dataset includes:\n\n- Forecasts provided to the end of the next calendar year, rolled in January of each year\n- Historical forecast coverage from January 2021\n\n- Historical demand coverage from 2015\n\n- Coverage of 153 countries\n\nProducts include:\n\n- Crude Oil (end-use only)\n\n- Ethane\n\n- Gasoil/Diesel, including Biodiesel\n- Jet Fuel/Kerosene\n\n- Motor Gasoline: Finished and blending components, including oxygenates\n\n- LPG (Butane and Propane)\n\n- Naphtha\n\n- Residual Fuel Oil\n\n- Bitumen\n\n- Other Products Include: Aviation Gasoline, Refinery Gas (Still Gas), Petroleum Coke, Lubricants, Bitumen, Paraffin Waxes\n\n**Use cases**\n- Demand Forecasting - Allows you to deepen your understanding of the impact of recent global events on refined products demand around the world\n- Market Analysis - Increase confidence in making the right business decisions by comparing demand forecasts across multiple sources\n- Pricing Analysis - Based on the insights from the short-term oil demand model, you can make more informed decisions about the near-term investments, planning, and building trading strategies\n\n**Product details**\n\nSample Tables:\nA ) DEMAND\nB ) REF_DATA_COUNTRIES\n\nSample Fields:\nA ) DEMAND\nYEAR\nREGION_ID\nREGION_NAME\nPRODUCT_ID\nPRODUCT_NAME\nVALUE\nSCENARIO_ID\nUNITS\n\nB ) REF_DATA_COUNTRIES\nCOUNTRY_ID\nCOUNTRY_NAME\nREGION_ID\n\nView Descriptions:\n\n- DEMAND - The most recent demand scenario\n- DEMAND_ARCHIVE - The archived demand scenarios\n- REF_DATA_COUNTRIES - The names of the countries available in this dataset as well as the associated region\n- REF_DATA_DATA_QUALITY - Identifier for forecast vs history in each demand scenario provided\n- REF_DATA_PRODUCTS - The list of the products for which demand forecast is provided\n- REF_DATA_REGIONS - The names of the regions available in this dataset\n- REF_DATA_SCENARIO_LIST - The list of historical demand scenarios\n- GLOBALOILDEMAND_DICTIONARY -Contains descriptions and examples of each of the columns in the above views for a better understanding of the dataset","terms_of_service":"https://www.spglobal.com/en/privacy/privacy-policy-english","documentation_link":"https://www.marketplace.spglobal.com/en/datasets/global-oil-demand-(1681761729)","privacy_policy_link":"https://www.spglobal.com/en/privacy/privacy-policy-english","geographical_coverage":"[\"Global\"]","cost":"PAID","update_frequency":{"interval":1,"unit":"MONTHLY"},"collection_date_start":1609523855688,"assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"S&P Global Energy","description":"At S&P Energy, our complete view of global energy and commodities markets enables our customers to make decisions with conviction and create long-term, sustainable value. For more than 100 years, we've been a trusted connector that brings together thought leaders, market participants, governments, and regulators to co-create solutions that lead to progress. Vital to navigating Energy Transition, S&P Global Energy's coverage includes oil and gas, power, chemicals, metals, agriculture, and shipping. S&P Global Energy is a division of S&P Global (NYSE: SPGI)."},"popularity":1916},{"id":"216de717-aa5a-452c-b4f8-b690f48f263e","summary":{"name":"Draup Global Job Postings Data (Sales) - Sample","subtitle":"Comprehensive and near real-time view of hiring demand across companies worldwide","share":{"name":"global_job_postings_data(talent)-sample","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1788941880213,"updated_at":1788941880213,"categories":["ADVERTISING_AND_MARKETING","COMMERCE","ECONOMICS","EDUCATION","OTHER","SCIENCE_AND_RESEARCH"],"listingType":"STANDARD","provider_id":"9900a255-6aa9-496f-9b6a-421a3568af3b"},"detail":{"description":"**Overview**\nDraup’s Global Job Postings dataset provides a comprehensive and near real-time view of hiring demand across companies worldwide. It captures detailed information on job roles, skill requirements, educational backgrounds, certifications, pay, and benefits, while also mapping postings to business functions and workloads. This enables HR teams, workforce planners, and talent intelligence leaders to analyze emerging workforce needs, identify skill gaps, and anticipate hiring patterns across geographies and industries.\nFor talent-focused decision-makers, the dataset reveals trends in role evolution, technology adoption within jobs, and shifts in functional demand—helping organizations align workforce planning with business priorities. By offering visibility into hiring activity at both company and market levels, it equips talent acquisition leaders to design smarter sourcing strategies, benchmark against competitors, and anticipate future demand for critical skills.\n\n**Methodology & Quality**\nThe dataset is aggregated from global job boards, company career portals, and verified postings across industries. Data is continuously refreshed to ensure relevance, with postings standardized through a structured pipeline that normalizes job titles, roles, skills, and workloads. Automated translation and lemmatization processes improve comparability across geographies, while deduplication and validation routines minimize noise and bias. Updates are applied on a frequent basis to maintain timeliness, and robust standardization ensures consistency for longitudinal analysis of workforce demand.\n\n**Key Tables Include**\n•\tCompany & Job Identifiers – Standardized company and posting IDs for consistent linkage across datasets.\n•\tJob Role & Titles – Normalized job roles, standardized titles, and translated raw job titles.\n•\tJob Descriptions – Consolidated and translated job descriptions for consistent role analysis.\n•\tSkills & Tech Stack – Extracted core skills, soft skills, and associated technology stacks for each posting.\n•\tBusiness Functions & Workloads – Mapped to functional areas and workload categories to align postings with organizational structures.\n•\tEducation & Certifications – Requirements including degrees, certifications, and qualifications linked to roles.\n•\tCompensation & Benefits – Pay ranges, benefits, and perks as advertised in postings.\n•\tExperience Requirements – Minimum experience levels and role-specific experience indices.\n•\tLanguage Requirements – Language skills and multilingual expectations for specific roles.\n•\tPosting Metadata – Posting dates, update timestamps, and system load dates for time-based analysis.\n\n**Use Cases:**\n***Discover what roles, locations, and skills are your competitors hiring for.***\n\nTrack competitor hiring activity by monitoring the job roles, skills, and geographies they are targeting. This visibility helps HR teams understand competitor workforce strategies, anticipate skill shortages, and sharpen their own talent acquisition efforts.\t\n\nSELECT\n company_name,\n role AS job_role,\n location.country,\n location.region,\n location.location,\n skill AS core_skill,\n date_posted\nFROM job_postings_company \nLATERAL VIEW explode(jd.draup_job_role) role_tbl AS role\nLATERAL VIEW explode(jd.consolidated_JDs) loc_tbl AS location\nLATERAL VIEW explode(jd.core_skills) core_tbl AS skill\nWHERE jd.date_posted >= date_add(current_date, -180);\n\n***Has the demand for a specific job role increased or decreased in a location?***\t\n\nAnalyze hiring demand trends for any job role in a given geography. These insights help talent teams anticipate attrition risks—if demand is surging, existing employees are more likely to be poached—allowing HR to design retention and workforce planning strategies accordingly.\t\n\nSELECT\nrole_tbl.role AS job_role,\nloc_tbl.country,\nloc_tbl.region,\nDATE_TRUNC('month', jd.date_posted) AS posting_month,\nCOUNT(*) AS total_job_postings\nFROM job_postings_company jd\nLATERAL VIEW explode(jd.draup_job_role) role_tbl AS role\nLATERAL VIEW explode(jd.consolidated_JDs) loc_tbl AS loc\nWHERE role_tbl.role = 'Data Engineer'\nAND loc_tbl.country = 'India'\nAND jd.date_posted >= date_add(current_date, -365)\nGROUP BY role_tbl.role, loc_tbl.country, loc_tbl.region, DATE_TRUNC('month', jd.date_posted)\nORDER BY posting_month;\n\n***Find out what benefits are most commonly offered by product companies in India***\t\n\nBenchmark compensation packages beyond salary by analyzing benefits offered by companies in a target geography. These insights guide HR in refining employer value propositions and designing competitive offers to attract and retain top talent.\t\n\nSELECT\n benefit,\n COUNT(DISTINCT job_descriptions.company_name) AS companies_offering,\n COUNT(*) AS total_job_postings\nFROM job_postings_company\nJOIN company_overview\n ON job_postings_company.company_id = company_overview.company_id\nLATERAL VIEW explode(jd.benefits.categories) benefit_tbl AS benefit\nLATERAL VIEW explode(jd.consolidated_JDs) consoli_jds AS job_descriptions\nWHERE job_descriptions.country = 'India'\n AND LOWER(company_overview.company_type) = 'product' \nGROUP BY benefit\nORDER BY companies_offering DESC, total_job_postings DESC;\n\n***Find what new skills are emerging within a particular job role in a location***\n\nIdentify rising or emerging skills associated with a role in a region, based on the latest job postings. This helps organizations adjust training programs, update role definitions, and proactively build the skills needed to stay competitive in fast-changing markets.\t\n\nWITH recent_skills AS (\n SELECT DISTINCT\n role AS job_role,\n job_descriptions.country,\n skill\n FROM job_postings_company\n LATERAL VIEW explode(jd.draup_job_role) role_tbl AS role\n LATERAL VIEW explode(jd.consolidated_JDs) consolid_jds AS job_description\n LATERAL VIEW explode(jd.core_skills) skill_tbl AS skill\n WHERE role = 'Data Engineer'\n AND job_descriptions.country = 'India'\n AND job_postings_company.date_posted >= date_add(current_date, -180) \n),\npast_skills AS (\n SELECT DISTINCT\n role AS job_role,\n job_descriptions.country,\n skill\n FROM job_postings_company\n LATERAL VIEW explode(jd.draup_job_role) role_tbl AS role\n LATERAL VIEW explode(jd.consolidated_JDs) consoli_jds AS job_descriptions\n LATERAL VIEW explode(jd.core_skills) skill_tbl AS skill\n WHERE role = 'Data Engineer'\n AND job_descriptions.country = 'India'\n AND job_postings_company.date_posted BETWEEN date_add(current_date, -365) AND date_add(current_date, -181) \n)\nSELECT\n recent_skills.job_role,\n recent_skills.country,\n recent_skills.skill AS emerging_skill\nFROM recent_skills\nLEFT JOIN past_skills p\n ON recent_skills.job_role = past_skills.job_role\n AND recent_skills.country = past_skills.country\n AND recent_skills.skill = past_skills.skill\nWHERE past_skills.skill IS NULL;","terms_of_service":"https://draup.com/privacy-policy#:~:text=Unless%20provided%20for%20in%20this,that%20work%20on%20our%20behalf","documentation_link":"","privacy_policy_link":"https://draup.com/privacy-policy","geographical_coverage":"[\"Global\"]","update_frequency":{"interval":1,"unit":"MONTHLY"},"collection_granularity":{"interval":1,"unit":"DAILY"},"assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"Draup, Inc.","description":"Draup is an enterprise agentic intelligence platform built for HR and Sales leaders navigating transformation. By combining unparalleled real-time global data with agentic AI, Draup enables strategic decision-making across talent and revenue functions.\n \nThrough its Talent Intelligence solution, Draup empowers HR teams to drive workforce planning, precision hiring, predictive skill architecture, and competitive benchmarking. Its Sales Intelligence solution helps go-to-market teams uncover deep account insights, identify buying triggers, and accelerate revenue growth.\n \nTrusted by over 300 global enterprises - including five of the Fortune 10 - Draup supports companies like Microsoft, PepsiCo, PayPal, and Moderna in building future-ready workforces and growth strategies."},"popularity":1109},{"id":"9c82aa78-ac29-4a36-a179-4ad16bf72b93","summary":{"name":"Devin by Cognition","subtitle":"Cognition operates Devin, the first autonomous software engineer. Devin plans, writes, tests, and ships production code","setting":{"visibility":"PUBLIC"},"created_at":1783465447108,"updated_at":1783465619267,"categories":["OTHER"],"listingType":"PERSONALIZED","provider_id":"ba7f21a2-bdb2-417f-94f3-5d09dbdc0456"},"detail":{"description":"**Overview**\nCognition helps engineers operate more like architects: strategizing, designing systems, and focusing on problem solving, while agents handle the repetitive engineering work.\n\nCognition operates Devin, the first autonomous software engineer. Devin plans, writes, tests, and ships production code on its own, working inside your codebase and the tools your team already uses. Devin is deployed at some of the largest and most complex institutions in the world.\n\nEngineers spend less than 20% of their time writing code. The rest goes into understanding systems, planning changes, reviewing work, and testing. Most AI tools optimize a narrow slice of that workflow. Cognition is built for all of it.\n\nThe Cognition Platform delivers one unified platform with four product surfaces, each built for a different mode of work. Devin Cloud executes autonomously in the background, asynchronously and at scale. Devin Desktop is the agentic IDE for challenging, creative, human-in-the-loop work. Devin CLI fits engineers who live on the command line. Devin Review catches security vulnerabilities, logic errors, and code quality issues before engineers ever open a PR, with 70-90% remediated automatically.\n\nTeams use Devin across every part of the engineering lifecycle. For event-driven work: automatically scan and remediate vulnerabilities on every PR, automate root cause analysis and reduce MTTR during incidents, enforce test coverage by writing and applying tests for every new untested code path, and execute tickets end to end from scoping to merged PR. For longer-horizon work: language migrations, large refactors, version upgrades, and codebase restructuring; technical debt and on-prem to cloud modernization; data warehouse migrations and ETL development; CI/CD autotriage; browser-based QA testing; and documentation maintenance. The work engineers dread - handled.\n\nCognition is enterprise-ready. Deploy in your VPC. Manage agent fleets with built-in governance, analytics, and role-based access. Production-proven at Fortune 50 scale across financial services, technology, healthcare, and defense.\n\nIndustry leaders don't buy Devin. They hire Devin.\n\n**Product details**\n\n- Cognition operates Devin, the first autonomous software engineer. Devin plans, writes, tests, and ships production code on its own, working inside your codebase and the tools your team already uses. Cognition helps engineers operate more like architects, focusing on strategy and design while agents handle the repetitive engineering work.\n\n- Every use case across the full SDLC. Devin handles vulnerability remediation, incident triage and RCA, unit test coverage, code review, code migrations and refactors, version upgrades, technical debt, on-prem to cloud modernization, data warehouse migrations, ETL development, CI/CD autotriage, and documentation - all from one platform. Assign a ticket and Devin scopes, plans, and executes. The work engineers dread, handled.\n- Enterprise-Grade and production-proven at Fortune 50 scale. Deploy in your VPC, enforce role-based access, and manage agent fleets with built-in governance and analytics. Only pay when agents are doing valuable work. Subscribe through Databricks Marketplace and consolidate all AI engineering spend on your Databricks bill.","terms_of_service":"https://cognition.com/legal/enterprise-terms-of-service","documentation_link":"https://docs.devin.ai/get-started/devin-intro","privacy_policy_link":"https://cognition.com/legal/privacy-policy","assets":["ASSET_TYPE_GIT_REPO"],"license":""},"provider_summary":{"name":"Cognition","description":"Makers of Devin, the first AI software engineer. We are an applied AI lab building end-to-end software agents. We're building collaborative AI teammates that enable engineers to focus on more interesting problems and empower engineering teams to strive for more ambitious goals."},"popularity":1998},{"id":"403c7b43-42f1-49c3-94be-f21c67ac9c9a","summary":{"name":"Anonymized Vehicle Probe Data","subtitle":"Probe data, or Floating Car data is anonymized timestamped trajectory-level vehicle location data","setting":{"visibility":"PUBLIC"},"created_at":1741258372283,"updated_at":1760440631189,"categories":["GEOSPATIAL","TRANSPORTATION_AND_LOGISTICS"],"listingType":"PERSONALIZED","provider_id":"534843a5-204d-4f28-9f5f-a6331173bba6"},"detail":{"description":"**Description**\nProbe data, or Floating Car data as it is often referred to, is anonymized timestamped trajectory-level vehicle location data.\n\n**Data Source**\nThe data is sourced by millions of vehicles globally from our vetted partners including:\n\n- Automotive OEMs; vehicles with embedded connectivity from the car manufacturer\n- Fleets with after-sales in-vehicle hardware telematics devices\n\n**Use Cases**\n- **Traffic management:** Our data fuels dynamic traffic light systems, incident response management and other ITS applications (Intelligent Transport Systems)\n\n- **Transportation analytics:** Traffic consultants, road operators, municipalities and SaaS analytics platforms use our data for understanding road safety, road usage, people movement and performing targeted before & after analytics to assess infrastructure and signage changes.\n- **Site-selection:** Our data supports models that assess the best location for new retail store openings and EV charging stations rollout on the basis of footfall and cannibalization estimates.\n \n- **Event Planning:** Organizers of events like concerts or sports can analyze traffic patterns to optimize event organisation including start/end times and manage in real time such events.\n \n- **Route Planning:** Companies with large vehicle fleets (e.g., delivery, taxi, or transportation companies) use this data to optimise routes, reduce fuel consumption, and improve overall operational efficiency.\n \n- **Emission Analysis:** Stakeholders can estimate emission hotspots based on traffic information \n\n**Probe Data Schema**\n**Main attributes**\nThe following data attributes are available via the Mobito Probe Data Product:\n\n- unique_id or trip_id\n- timestamp\n- latitude\n- longitude\n- speed\n- heading\n- vehicle_type\n\n**Extended Probe data - Additional attributes**\nAdditional attributes might be available depending on the geography of interest. These attributes are offered with an additional cost.\n\n**Indicative list:**\n\n- Acceleration in X, Y, Z axis\n- Fuel level\n- Electrical consumption\n- Wiper state\n- Fog lights\n- ESP\n- Air temperature\n- Fuel type\n- Vehicle make & model\n- Vehicle weight\n- Road type and speed limit\n\n**Probe Data Specs**\n**Capturing frequency**\nCapturing frequency differs depending on the data source and the geography.\n\nIt ranges from 3 seconds up to a minute.\n\n*Note that long distance un-interrupted travelling in highways might lower the frequency*\n\n**Historicity**\nHistorical data availability as of 2021. Availability differs per country.\n\n**Geographical granularity**\nData can be delivered on the following geographical levels:\n\n- Country\n- State/ province\n- City\n- Custom polygon area\n- Specific road segment\n\n**Vehicle Types**\nThe following vehicle types area available in the datasets (availability varies per country):\n\n- Passenger car\n- Light commercial vehicle (LCV). This is a van type of vehicle. Examples: (Renault Master, Renault Trafic, Mercedes-Benz Sprinter, Peugeot Boxer, Volkswagen Crafter, Fiat Ducato, Opel Crossland, Citroen Berlingo etc.)\n- Heavy truck (more than 3.5 tones)\n- Bus\n- Other (unclassified)\n\n**Car brands and models**\nMOBITO offers multi-brand vehicle data. However, vehicle make and model details are available for only a limited portion of our total car park.\n\n**Data Record Identifier**\nEach probe data record is matched with an identifier (hashed string) so that multiple records that belong to the same vehicle can be identified. There are 2 types of IDs:\n\n- **Unique ID:** An ID for each vehicle that is persistent for a maximum of 24 hours\n- **Trip ID:** A unique ID for each single trip. A trip is defined by the trip stops that each vehicle performs. In each stop, a new trip ID is generated\n\n**Data Anonymisation**\nFurther anonymisation is applied on the data in order to prevent any possibility of tracking or identifying individual vehicles or drivers. For more details, refer to the [Mobito Anonymisation](https://support.mobito.io/en/articles/10617488-mobito-anonymisation)\n\n**Volume Coverage**\nOur data volume availability varies across regions and countries, with some areas having extensive coverage while others are more limited.\n\nThe majority of our data is available in Europe.\n\n**Indicative volume coverage for 2024 (number of vehicles):**\n- Netherlands: 2M+\n- Italy: 760k+\n- Germany: 175k+\n- United Kingdom: 130k+\n- France: 125k+\n\n\nFor more details, refer to the Mobito [Probe Data](https://support.mobito.io/en/articles/10638625-probe-data-product) page.\n","terms_of_service":"https://support.mobito.io/en/articles/10627684-data-license-terms","documentation_link":"https://support.mobito.io/en/articles/10638625-probe-data-product","privacy_policy_link":"https://www.mobito.io/privacy-policy","geographical_coverage":"[\"Global\"]","cost":"PAID","data_source":"Car manufacturers and fleet service providers","assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://support.mobito.io/en/articles/10627684-data-license-terms"},"provider_summary":{"name":"Mobito","description":"Mobito is a mobility data solutions provider that enables organizations to monetize and utilize mobility data. We offer access to anonymized vehicle data from millions of vehicles globally, sourced from vetted car manufacturers and fleet service providers. \n\nOur data products include timestamped vehicle location data (probe data), anonymized harsh driving behavior events (such as harsh braking, acceleration, cornering, crashes), standstill (stop location) events, and origin-destination insights. \n\nThese data sets are used in various use cases, including traffic management, transportation analytics, site selection (e.g. EV charging station locations), and road safety."},"popularity":1476},{"id":"d3a4ab33-a428-40f9-b6ab-9255d5cb6756","summary":{"name":"Singaporean Tamil Speech Collection","subtitle":"A collection of 40 hours of high-quality, audio recordings and verified transcriptions for Singaporean Tamil","setting":{"visibility":"PUBLIC"},"created_at":1792585491412,"updated_at":1793112402236,"categories":["SCIENCE_AND_RESEARCH"],"listingType":"PERSONALIZED","provider_id":"f6e9a544-3103-4533-8706-321f7282d7d3"},"detail":{"description":"**Get this dataset on the Mozilla Data Collective.** Licensing and download happen on MDC rather than through Databricks. [See the full details and get access on MDC](https://mozilladatacollective.com/datasets/cmrp1sdsj00zemm0822j75968?utm_source=databricks&utm_medium=referral&utm_campaign=databricks_marketplace&utm_content=listing_cta).\n\nThis dataset provides 40 hours of high-quality audio recordings and corresponding text transcriptions of the Singaporean Tamil language. The collection includes unique utterances per language to support diverse acoustic modeling. All transcriptions have undergone a manual verification process to ensure high linguistic accuracy. Recordings feature a balanced mix of genders and various age groups to minimize bias in downstream AI models. This data is specifically designed for training Automatic Speech Recognition (ASR) systems, Text-to-Speech (TTS) synthesis, and general linguistic research for Singaporean Tamil language.\n\n**At a glance**\n\n- **Language:** Tamil (Singapore)\n- **Task:** Text-to-speech (TTS)\n- **Format:** WAV, TSV\n\n**Dataset Summary**\n\nA curated collection of high-quality read speech recordings from native Singaporean Tamil speakers. The recordings were collected through controlled voice collection sessions and are paired with verified text transcripts. The dataset is intended to support the development of Text-to-Speech (TTS) systems and other low-resource language technologies.\n\nThe dataset contains approximately 40 hours of recorded speech.\n\n**Data creation**\n\nNative Singaporean Tamil speakers were recruited. Sentence prompts were prepared by linguistically competent contributors. Speech recordings were manually reviewed and paired with verified transcripts.\n\n**Collection environment**\n\nSpeech was collected during controlled recording sessions to promote consistency in recording quality.\n\n**Sources**\n\nGenerated specifically for this project. Read speech recordings paired with verified text transcripts. Original recordings are stored in WAV (.wav) format.\n\n**Technical specifications**\n\nAudio: WAV, mono, 16-bit, 44.1 kHz.\n\n**Dataset structure**\n\nThe dataset contains:\n\nAn audio/ directory containing WAV recordings. A metadata.tsv file, tab-separated, with the transcript and metadata of each recording.\n\n**Metadata**\n\nField Description\n\n* **speaker_name**: The speaker, as a number, language, gender and age, such as v001_SGTA_F_29\n* **file_name**: Audio filename, without the .wav extension\n* **language**: Recording language\n* **age**: Speaker age\n* **gender**: Speaker gender\n* **theme**: Recording theme\n* **domain**: Recording domain\n* **sentence_ID**: Unique sentence identifier\n* **sentence**: Verified transcript\n\n**Intended use**\n\nThis dataset is intended for:\n\n- Text-to-Speech (TTS) model development and evaluation\n- Speech synthesis research\n- Academic and educational research\n- Low-resource language technology development\n- Pronunciation and linguistic analysis\n\nThis dataset is not intended for:\n\n- Speaker identification\n- Voice biometric systems\n- Surveillance applications\n- Individual profiling or re-identification","terms_of_service":"https://mozilladatacollective.com/terms/consumers?utm_source=databricks&utm_medium=referral&utm_campaign=databricks_marketplace&utm_content=listing_terms","documentation_link":"https://mozilladatacollective.com/datasets/cmrp1sdsj00zemm0822j75968?utm_source=databricks&utm_medium=referral&utm_campaign=databricks_marketplace&utm_content=listing_documentation","privacy_policy_link":"https://mozilladatacollective.com/privacy?utm_source=databricks&utm_medium=referral&utm_campaign=databricks_marketplace&utm_content=listing_privacy","embedded_notebook_file_infos":[{"id":"18083f00-a5f1-4c0b-a4b2-1fbd37ebe859","marketplace_file_type":"EMBEDDED_NOTEBOOK","file_parent":{"parent_id":"d3a4ab33-a428-40f9-b6ab-9255d5cb6756","file_parent_type":"LISTING"},"mime_type":"text/html","download_link":"https://marketplace-file-storage-prod.s3-fips.us-east-1.amazonaws.com/EMBEDDED_NOTEBOOK/LISTING_d3a4ab33-a428-40f9-b6ab-9255d5cb6756/18083f00-a5f1-4c0b-a4b2-1fbd37ebe859?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEGgaDnVzLWVhc3QtMS1maXBzIkYwRAIgbC8sWIoYaHR8y7Wq86nPb0pufBl4%2BM4PWpeN%2B5PW9VQCIG7BhAVBDCWTpxFDLeuKYm%2Ff1JLz4iccPMzter0YdhgWKqYECDAQABoMNDE0MzUxNzY3ODI2IgxNAJrHbXaZG20jNfMqgwQyGEb4TsoVOoNvJ%2BaA%2FZ7zeu8hGcHqCeNfblP%2FQo5g6q2aRNtpwtPRKMlhYcIE%2FUQaNT1Lqmyptu32VUQTpzjTTxRVmJrDPkheSCkV06XQ%2FpTMKjU8CC7X1b%2FqYf8CrBwZT7Cv4C5WPjEET38f2A79h0xV6bZgPy28zQWrSeIJ9w6AfZBxiE24%2FVc08zVA0ivCT6lsy7nuU0wcR%2FMg2C9nd1vaMnQ923VpD21CKfVVYcrNWL2b%2FQ%2BNAO%2BxPjPgjiN6UPQbvbvZxkSdhgrGtecIg9NgqGkMcvM5VLTDtdC1kdeZetfOC2obtA%2BT%2F0gy3tIDo6WM5hcQfw743%2FAU9oaGnRBl%2BwoxRRDCZA%2F4jnPdluICSI7NCr6C3HHOe4CSZJjRihczxPA%2FgQczAVjAPs6CrK9N9pIzt0vNJpGUTgg4AoxftTvGfciDREilSctPIcBk6GwB%2Bzt4wbtNCmEPU%2BFD46rc6CWVQmmLwbKOeHw70ipMVFlOc8NlXkEtGO2vu6H44MstSSc6daOVUYUavQSGZ6jGhEjsP2YGz8KDm9XQotB%2FtyVBqiZV01OKqDmmBJA%2FXjUpSJ8M%2FeJXhe5m%2BKcxzNObtoJRI7jci3Tpt08XZyXZ7%2FyIV8H30ya468X3cfkTXrm972uga3z7iXAdubyEHnQIua%2FuvtwOHILi6O5D1Me2MzCPvcnXBjqTAYDoMWBt%2B3AVFuqSs2dmDQNBnvEhXfMgTedKLD5PJHb3aIl15iBkLaNu36GYnVCp9ZU2lSYcTbY5QwNnNeTfJTNy9bKhwzru6Sg%2BslhN%2BTS0zkTn%2BeLMY4tHgPEtEZ2cp%2FCk6GwqswvnYVKHjwO8q%2B1Mx8lc08fVEfarE9dke1GGFqoddx%2Bq%2BZUyHbDlAVfrCmf0yw%3D%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20261110T000135Z&X-Amz-SignedHeaders=host&X-Amz-Expires=86400&X-Amz-Credential=ASIAWA6KKHEJORWBH4N3%2F20261110%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=c272695347009ab5b62f037c78ff34f5bf5310c9ebec746afb6a1fa341140438","created_at":1793112345079,"updated_at":1793112345079,"display_name":"Singaporean Tamil speech - first look","status":"FILE_STATUS_PUBLISHED"}],"assets":["ASSET_TYPE_DATA_TABLE","ASSET_TYPE_MEDIA"],"license":"https://community.mozilladatacollective.com/mdc-data-licence-agreement-1-0?utm_source=databricks&utm_medium=referral&utm_campaign=databricks_marketplace&utm_content=listing_licence"},"provider_summary":{"name":"Mozilla Data Collective","description":"Mozilla Data Collective is building a Multilingual, Multicultural, Multimodal tech future by giving everyone the Data Platform for Human Agency and Fair Value Exchange."},"popularity":944},{"id":"3e043e20-fa59-4f4b-9a7d-ca34aa43407b","summary":{"name":"African Actions Annotated Videos for AI (Health Domain)","subtitle":"10 egocentric videos of actions in Nigeria, with 1,666 time-stamped action-object-tool segments in the health domain","setting":{"visibility":"PUBLIC"},"created_at":1792585645318,"updated_at":1793112402779,"categories":["SCIENCE_AND_RESEARCH","HEALTH"],"listingType":"PERSONALIZED","provider_id":"f6e9a544-3103-4533-8706-321f7282d7d3"},"detail":{"description":"**Get this dataset on the Mozilla Data Collective.** Licensing and download happen on MDC rather than through Databricks. [See the full details and get access on MDC](https://mozilladatacollective.com/datasets/cmrxokivf009bl4076o9i6wx0?utm_source=databricks&utm_medium=referral&utm_campaign=databricks_marketplace&utm_content=listing_cta).\n\nA multimodal, egocentric video dataset of everyday activities performed in Nigeria, built to bring African contexts into the data used to train embodied, agentic, and multimodal AI across many domains.\n\nThe \"Health domains\" subset contains 10 egocentric videos and 1,666 time-stamped action-object-tool segments in the \"health\" domain.\n\nEvery activity is broken into short segments. Each segment labels the action being performed, the object it is performed on, the tool used, and a natural-language description in English and in an indigenous Nigerian language (Igbo, Hausa, or Yoruba). In total the dataset provides 1,666 annotated segments spanning 652 distinct action–object units, making the \"how\" of African tasks in the health domain — not just the \"what\" — available to machines.\n\n**At a glance**\n\n- **Language:** English, Igbo, Hausa, Yoruba\n- **Format:** MP4, JSON\n\n**Overview**\n\nAfrican Actions is a multimodal, **egocentric (first-person) video dataset of everyday activities performed in Nigeria**. It was created to address two gaps in the data used to train modern AI: most human-activity datasets capture Western environments, and many are released under licenses that limit real-world use. African Actions brings African objects, tools, environments, languages, and ways of doing things into the data used to train embodied, agentic, and multimodal AI.\n\nIn this dataset, each video shows an action in the health domain, e.g. a healthcare procedure, recorded from the point of view of the person performing it, and is paired with fine-grained, time-stamped annotations describing what is happening.\n\n**Dataset statistics**\n\n- **Videos:** 10 egocentric MP4 recordings\n- **Annotated segments:** 1,666 time-stamped action segments\n- **Distinct action–object units:** 652 (the fundamental unit of the dataset)\n- **Distinct actions:** 114; **objects:** 160; **tools:** 37; **secondary objects:** 31\n- **Everyday activities covered:** 8 distinct tasks, all in the \"Health\" domain.\n- **Indigenous-language descriptions (per segment):** Igbo — 1,299; Hausa — 178; Yoruba — 164\n- **Natural-language coverage:** 100% of segments have an English description; 1,641 (98.5%) have an indigenous-language description (the other 25 are deliberate \"No action\" segments documenting moments of inactivity, which by design carry no translation)\n\n**What's in the archive**\n\nThe archive's naijaactions-dataset/ directory holds:\n\n- videos/: MP4 files, egocentric recordings, one per video\n- annotations/: JSON files, one per video\n\nThe videos/ folder and the annotations/ folder pair one-to-one: every video has exactly one annotation file, and every annotation file points to a real video via its video_filename. The task_id → task name + domain mapping is published in the **Tasks** section of this dataset card, not as a separate file inside the archive.\n\n**Annotation schema**\n\nEach annotation file holds a video_id, the video_filename, and a list of segments. Each segment has:\n\n- **start** and **end**: when the segment starts and ends\n- **action**, **object**, **tool** and **secondary_object**: what is done, to what, and with what\n- **nld_en**: the description in English\n- **language** and **nld_indigenous**: the indigenous language, and the description in it\n\ntool, secondary_object, language, and nld_indigenous may be empty strings when not applicable. An example file is in the [full datasheet on MDC](https://mozilladatacollective.com/datasets/cmrxokivf009bl4076o9i6wx0?utm_source=databricks&utm_medium=referral&utm_campaign=databricks_marketplace&utm_content=listing_description).\n\n**The action–object unit**\n\nThe fundamental building block of the dataset is the **action–object unit**. This reflects a core design belief: the value of activity data lies not just in *what* action is performed, but in *how* it is performed — and \"how\" is shaped by the object involved, the tool used, and the environment. For example, *cutting* changes significantly when cutting a potato versus a plantain; the object influences the motion, technique, and even the tool selected. Capturing these fine-grained units lets models learn the authentic dynamics of human activity across diverse African settings.\n\n**Video filename structure**\n\nEach MP4 file in videos/ is named from a task identifier and a recorder identifier, with a part number when a recording was split, for example NA027_NAVR10.mp4:\n\n- **NA**: three-digit task identifier (e.g. NA001). Every distinct task ID present in the dataset has exactly one row in the **Tasks** section of this dataset card, with the task's human-readable name.\n- **NAVR**: the identifier of the on-the-ground recorder who filmed the session. Multiple recorders may cover the same task; the recorder ID lets you track per-recorder coverage and stratify by recording style without revealing the recorder's identity.\n- **P**: sequential part number when a single task recording was split across multiple files (e.g. for very long sessions, or when the recorder paused between sub-activities). Parts of the same (task_id, recorder_id) are contiguous in time within the original recording session.\n\nThe corresponding annotation file in annotations/ uses the same identifier with lowercase letters (e.g. na027_navr10.json for NA027_NAVR10.mp4). The video_filename field inside each annotation file always carries the canonical uppercase MP4 filename.\n\n**Languages**\n\nNatural-language descriptions are provided in **English** plus one of three major Nigerian languages — **Igbo, Hausa, or Yoruba** — chosen to match the linguistic and cultural context of each activity.\n\n**Activity domains**\n\nThe full dataset uses 8 formal domain labels in its per-task index: Food/Kitchen, Art & Craft, Health, Construction & Fabrication, Compound & House Chores, Agriculture, Household, Fashion.\n\nThe activities themselves span everyday Nigerian life. The domain for this dataset is\n\n- **Healthcare & diagnostics** — malaria/typhoid/HIV/pregnancy testing, setting an IV drip, herbal medicine preparation, bone setting (Health).\n\n**How the data was collected**\n\n**Team.** The on-the-ground team for this release comprised **10 recorders** (identified in filenames as NAVR01–NAVR10), **17 trained annotators** (identified internally as NAAN), **28 quality reviewers** (some of whom are also annotators, wearing a second hat during the QC phase), plus a small group of native-speaker **language consultants** for Igbo, Hausa, and Yoruba who filled or verified every indigenous-language description in the curated release. A local coordinator, a welfare officer, and technical-support staff rounded out the project team.\n\nData collection followed a community-centred, human-in-the-loop methodology:\n\n- **Recording.** Trained **video facilitators** recorded **task performers** (community members with deep knowledge of an activity) as they carried out a task naturally from start to finish. Separating the recorder from the performer responds to real-world conditions (familiarity with devices, age, comfort, cultural norms) and lets performers focus entirely on the activity.\n- **Egocentric viewpoint.** Facilitators used a camera position that approximates a first-person view, capturing how the activity looks from the performer's own perspective. No scripted voice narration was required.\n- **Recording review.** Each clip was checked for egocentric framing, clarity (sharpness/lighting), continuous capture of the activity, technical standards, and the absence of unintended content (e.g. bystander faces) before moving on.\n- **Annotation.** Annotators labelled each pre-segmented video on a bespoke annotation platform across three layers: (1) action–object pair, (2) tool used, and (3) a natural-language description in English and an indigenous language.\n- **Annotation review & quality assurance.** Separate reviewers validated annotations against detailed guidelines for internal consistency, linguistic accuracy, and alignment with the annotation layers, followed by a dedicated quality-assurance pass. **Language consultants** (native speakers) filled and verified the indigenous-language fields to ensure culturally accurate terms.\n\n**Quality control**\n\nThe dataset passed through multiple independent review stages — recording review, annotation review, quality assurance, and a final language/oversight review — before curation. During curation, recordings that were rejected, duplicated, or failed quality checks were excluded, and a small number of segments and videos were removed via blacklists; the curated release in this archive contains only videos that pass these checks and have complete annotations.\n\nConcretely, of the segments in scope for QC, **98.7%** received at least one completed QC review. **1.7%** of those reviews flagged a hard error (typically a label/description mismatch or a duplicate segment); **6.8%** flagged a soft warning (style or vocabulary issues that did not block shipping). A dedicated **correction phase** subsequently revisited segments that had been flagged, applying fixes to roughly **28%** of the input population; the corrected, re-reviewed segments are the ones included in this release. On top of these reviewer checks, an automated quality-report pipeline applied additional hard-gate filters (translation-verification status, language-allowlist enforcement, vocabulary integrity, NLD content presence, segment duration sanity, and video-level dropout-ratio thresholds) before the final release was packaged.\n\n**Consent audio.** Verbal consent from each task performer was captured as a separate audio recording (one MP3 per performer/recorder pair) and is retained by the project team under access controls. These consent recordings are not bundled in this archive to protect performer privacy; they can be furnished to Mozilla or to a delegated reviewer on request for audit purposes.\n\n**Known characteristics and limitations**\n\n- **Egocentric is a spectrum.** Most videos approximate a first-person view but are not all perfectly head-mounted; some are captured close to the performer's eye level by a facilitator.\n- **Natural camera motion.** Some shakiness from the activity itself is present and expected; sustained blur was screened out during review.\n- **Audio is secondary.** Audio captures the ambient/environmental sound of each activity rather than clean speech; it is not intended for speech tasks.\n- **Short segments.** Segments are fine-grained (median ~3 seconds), reflecting the action–object granularity of the annotations.\n- **Translation coverage.** 25 segments (1.5%) do not carry an indigenous-language description; these are the deliberate \"No action\" moments which by design require no translation. Every other segment ships with both English and an indigenous (Igbo, Hausa, or Yoruba) description that has been verified by a translation consultant.\n- **Sampling bias.** The dataset reflects activities as practised by trained task performers recruited through community partners in Nigeria. It is not a randomly-sampled cross-section of Nigerian life and was not designed to be statistically representative of the population. This release covers 8 tasks, all in the Health domain, drawn from a larger collection whose domains are covered unevenly. Users building models intended to generalise beyond this dataset should evaluate carefully whether the captured distribution matches their target context.\n- **Environmental bias.** Recordings were made in real working settings (kitchens, workshops, clinics, fields), not in laboratory studios. Lighting, background, and ambient noise vary across sessions. Some recording locations recur (multiple tasks were filmed in shared facilities such as the project's Aries office and Graceville, Nkwagu), which means tasks recorded in those locations share visual context that does not generalise to other places.\n- **No predefined train/validation/test splits.** The archive is shipped as a single curated pool. Researchers are expected to construct their own splits according to their use case. Useful split keys include video_id (to avoid the same video appearing on both sides), the task ID (to evaluate task-level generalisation), and the recorder ID (to evaluate cross-recorder generalisation).\n\n**Tasks**\n\nThe dataset spans **8 distinct tasks**, identified by the NA prefix in each video filename. 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StewardOps is not designed, intended, or authorized for processing, storing, or transmitting sensitive or regulated data. This includes, but is not limited to, personally identifiable information (PII), protected health information (PHI), payment card information (PCI), or any other data that is regulated by specific data privacy or data security laws governing the processing of such data.\n\nOwnership:\nStewardOps and all related intellectual property rights remain the exclusive property of Provider and its licensors. No license or other rights are granted except the limited use of StewardOps for the purposes described herein.\n\nNo Warranty: THE DOWNLOADED VERSION OF STEWARDOPS IS PROVIDED \"AS IS\" AND \"AS AVAILABLE,\" WITHOUT WARRANTIES OF ANY KIND, WHETHER EXPRESS, IMPLIED, STATUTORY, OR OTHERWISE. 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Yet, many organizations struggle with fragmented data, inconsistent lifecycle tracking, and limited visibility into funnel performance—leading to poor forecasting and missed revenue opportunities.\n\nThe **Dataplatr Salesforce Accelerator** solves this by streamlining ingestion, transformation, and analysis of Salesforce data using the **Databricks Medallion Architecture (Bronze–Silver–Gold) with Delta Lake**. It delivers curated, audit-ready datasets that scale across sales teams, regions, and channels. 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revenue planning and sales team performance tracking.\n\n**Dataplatr Salesforce Customer Journey Solution**\n- **Unified Funnel Analytics** – Consolidated Lead, Opportunity, Contract, and Campaign data into a single Customer Journey dataset.\n- **Automated Conversion Metrics** – Tracks Lead → Opportunity, Lead → SC, and Lead → Campaign conversion percentages, cycle times, and funnel drop-offs.\n- **Campaign Effectiveness Analysis** – Provides Campaign creation trends, channel distribution, and state-level campaign performance.\n- **Channel & Referral Insights** – Harmonized Lead Source, Channel, Referral, and Partner data for accurate attribution.\n- **Revenue Impact Tracking** – Connects Contracts to Opportunities and Campaigns for end-to-end ROI analysis.\n\n**Medallion Architecture Implementation**\n**Bronze Layer – Raw Salesforce Data Ingestion**\n- **Lead (Lead ID, Source, Channel, Created Date)** – Captures all raw lead attributes directly from Salesforce with timestamps for lifecycle tracking.\n- **Opportunity (Opportunity ID, Stage Timestamps, Proposal/Utility Info)** – Stores key opportunity progress points, proposal counts, and utility data for analysis.\n- **Contract (Contract ID, Purchase Date, PTO Date, System Size)** – Records finalized contract details including purchase timelines, PTO milestones, and system metrics.\n- **Campaign (Campaign ID, Created Date, Channel, State)** – Ingests campaign metadata to track campaign origin, launch dates, and state-level performance.\n- **Account / Contact (Partner, Install Branch, Referral Attribution)**– Consolidates partner details, branch assignments, and referral information for accurate attribution.\n\n**Silver Layer – Cleansed & Standardized Data**\n- **Standardized Lead/Opportunity/SC lifecycle attributes (Source, Channel, Referral)** – Applies uniform definitions across all lifecycle attributes for consistent reporting.\n- **Deduplicated and harmonized Prospect IDs across objects** – Ensures unique identifiers, avoiding mismatched prospect data across Salesforce entities.\n- **Normalized cycle time flags (Proposal Generated, Credit Pass, Site Model Complete)** – Generates standardized event flags enabling cycle-time analytics across all records.\n- **Exception handling for missing Campaigns or mismatched IDs** – Detects, logs, and manages anomalies where campaign or ID relationships are incomplete.\n\n**Gold Layer – Business-Ready Customer Journey Analytics**\n- **Customer Journey Table (customer_journey)** – Unified view of lifecycle events enabling end-to-end customer journey reporting and analysis.\n- **Lead → Opportunity → Contract mapping** – Creates transparent relational mapping of leads through opportunities into finalized service contracts.\n- **Conversion indicators (Proposal Generated, Credit Pass, Site Model Complete)** – Defines key sales process checkpoints driving funnel conversion performance metrics.\n- **Sales Channel attribution (FSC/RSA, Build Partner, Utility Company, Division)** – Provides multi-dimensional sales channel insights across partner, utility, and division levels.\n- **Lifecycle dates (Lead Created, Opp Created, SC Created, Campaign Created)** – Consolidates lifecycle milestone dates for historical tracking and trend analytics.\n\n**Dashboard Widgets**\n**Lead–Opportunity–Contract Funnel**\n- **Monthly Leads & Conversion Rates Trend** – Displays monthly trends for total leads, conversion rates, and cross-object engagement insights.\n- **Cycle Time Analysis** – Highlights average lead-to-opportunity, lead-to-contract, and lead-to-campaign conversion durations by month.\n- **Conversion Funnel** – Visualizes funnel stages stepwise, with emphasis on drop-off rates and conversion bottlenecks.\n- **Opportunity → SC Cycle Time** – Measures average duration for converting opportunities into service contracts for efficiency tracking.\n\n**Campaign Analysis**\n- **Monthly Campaign Created Date Trend** – Shows recurring campaign creation activity and monthly growth patterns across regions.\n- **State-Wise Campaigns** – Summarizes campaign distributions grouped by U.S. states for regional sales planning insights.\n- **Channel Distribution by Campaigns** – Breaks down campaigns across channels, uncovering patterns of marketing effectiveness.\n\n**Marketplace Deliverables**\n- **Production-Ready Notebooks** – End-to-end Salesforce Customer Journey pipeline, including Lead, Opportunity, Contract, and Campaign ingestion, transformation, and analytics with error handling and detailed logging.\n- **Config Templates** – Pre-configured JSON mappings for Lead, Opportunity, Contract, Account, Contact, and Campaign objects, including field-level transformations and harmonization rules.\n- **Delta Table Schemas** – Optimized Bronze–Silver–Gold tables for Customer Journey metrics, conversion flags, cycle times, and campaign attribution.\n- **CDC Framework** – Change Data Capture for near real-time updates on Leads, Opportunities, Contracts, and Campaigns.\n- **Data Quality Framework** – Automated rules validating IDs, Lead → Opportunity → SC mapping, date fields, and campaign/channel consistency.\n\n**Documentation & Support**\n- **Implementation Guide** – Step-by-step Salesforce object setup, ingestion configuration, and Databricks deployment instructions.\n- **Configuration Manual** – Field mappings (e.g., Lead ID → Opportunity Prospect ID, Campaign Name → Campaign Attribution) and usage guidelines for pipeline execution.\n\n**Why Choose Dataplatr Salesforce Customer Journey Accelerator**\n- **CRM Domain Expertise** – Built by Salesforce analytics specialists familiar with Lead, Opportunity, Contract, and Campaign lifecycles.\n- **Rapid Time-to-Value** – Deploy end-to-end Customer Journey analytics quickly using pre-built pipelines, queries, and dashboards.\n- **Enterprise-Grade Security & Compliance** – Audit trails, data lineage, and object-level governance baked in.\n- **Cost-Effective Solution** – Leverage your Databricks Lakehouse investment without expensive third-party analytics tools.\n- **Proven Results** – Real-world improvements in lead-to-opportunity conversion, contract closure efficiency, and campaign performance tracking.\n- **Optimized for Reporting** – Curated, audit-ready outputs for BI tools, dashboards, and executive reporting.\n\nTransform your lead-to-contract analytics from siloed object reporting to intelligent customer journey insights with Dataplatr's Salesforce Customer Journey Accelerator. Built on Salesforce and Databricks best practices with a sales-and-marketing-first design, this solution delivers immediate visibility into Leads, Opportunities, Contracts, and Campaigns while enabling advanced analytics and AI-driven optimization.\n\nExplore our Medium articles for deeper insights and real-world implementations of our Databricks-based analytics solutions.\n\n[Transform financial decision-making with Databricks](https://medium.com/@dataplatr/empowering-financial-decision-making-using-databricks-and-power-bi-insights-5beb6ee7a08c) using CDC-driven pipelines and Lakehouse architecture to deliver actionable insights across GL, AP, AR, and Fixed Assets with automated, finance-ready data models.\n\n[Leverage the Databricks Lakehouse Platform to transform People Analytics](https://medium.com/@dataplatr/maximizing-people-analytics-with-the-power-of-databricks-lakehouse-platform-and-omni-analytics-523e4a9cf5e5) with CDC-driven pipelines and real-time insights across headcount, hiring, and HR metrics using a scalable, finance-grade data architecture.\n\nNote: By clicking '**Get Access**', you agree to our **Terms of Service**. You acknowledge that **Dataplatr Corp** will recognize your organization as a customer/user for promotional and reference purposes.\n\n\nContact us at: chandra.reddy@dataplatr.com\nFor consultations or custom inquiries: https://dataplatr.com/contact-us \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","terms_of_service":"https://docs.google.com/forms/d/e/1FAIpQLSdnlNmSfrFRQDa_ToOL3ydTOPcWI3m3lWor2plLbiqF1JRINg/viewform","documentation_link":"","privacy_policy_link":"https://dataplatr.com","embedded_notebook_file_infos":[{"id":"4e3bfa21-31c8-4ff3-9fd1-9dfd3b8e1ff6","marketplace_file_type":"EMBEDDED_NOTEBOOK","file_parent":{"parent_id":"5ce9300f-918b-4f0c-a95c-44d695a16520","file_parent_type":"LISTING"},"mime_type":"text/html","download_link":"https://marketplace-file-storage-prod.s3-fips.us-east-1.amazonaws.com/EMBEDDED_NOTEBOOK/LISTING_5ce9300f-918b-4f0c-a95c-44d695a16520/4e3bfa21-31c8-4ff3-9fd1-9dfd3b8e1ff6?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEGgaDnVzLWVhc3QtMS1maXBzIkYwRAIgbC8sWIoYaHR8y7Wq86nPb0pufBl4%2BM4PWpeN%2B5PW9VQCIG7BhAVBDCWTpxFDLeuKYm%2Ff1JLz4iccPMzter0YdhgWKqYECDAQABoMNDE0MzUxNzY3ODI2IgxNAJrHbXaZG20jNfMqgwQyGEb4TsoVOoNvJ%2BaA%2FZ7zeu8hGcHqCeNfblP%2FQo5g6q2aRNtpwtPRKMlhYcIE%2FUQaNT1Lqmyptu32VUQTpzjTTxRVmJrDPkheSCkV06XQ%2FpTMKjU8CC7X1b%2FqYf8CrBwZT7Cv4C5WPjEET38f2A79h0xV6bZgPy28zQWrSeIJ9w6AfZBxiE24%2FVc08zVA0ivCT6lsy7nuU0wcR%2FMg2C9nd1vaMnQ923VpD21CKfVVYcrNWL2b%2FQ%2BNAO%2BxPjPgjiN6UPQbvbvZxkSdhgrGtecIg9NgqGkMcvM5VLTDtdC1kdeZetfOC2obtA%2BT%2F0gy3tIDo6WM5hcQfw743%2FAU9oaGnRBl%2BwoxRRDCZA%2F4jnPdluICSI7NCr6C3HHOe4CSZJjRihczxPA%2FgQczAVjAPs6CrK9N9pIzt0vNJpGUTgg4AoxftTvGfciDREilSctPIcBk6GwB%2Bzt4wbtNCmEPU%2BFD46rc6CWVQmmLwbKOeHw70ipMVFlOc8NlXkEtGO2vu6H44MstSSc6daOVUYUavQSGZ6jGhEjsP2YGz8KDm9XQotB%2FtyVBqiZV01OKqDmmBJA%2FXjUpSJ8M%2FeJXhe5m%2BKcxzNObtoJRI7jci3Tpt08XZyXZ7%2FyIV8H30ya468X3cfkTXrm972uga3z7iXAdubyEHnQIua%2FuvtwOHILi6O5D1Me2MzCPvcnXBjqTAYDoMWBt%2B3AVFuqSs2dmDQNBnvEhXfMgTedKLD5PJHb3aIl15iBkLaNu36GYnVCp9ZU2lSYcTbY5QwNnNeTfJTNy9bKhwzru6Sg%2BslhN%2BTS0zkTn%2BeLMY4tHgPEtEZ2cp%2FCk6GwqswvnYVKHjwO8q%2B1Mx8lc08fVEfarE9dke1GGFqoddx%2Bq%2BZUyHbDlAVfrCmf0yw%3D%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20261110T000135Z&X-Amz-SignedHeaders=host&X-Amz-Expires=86400&X-Amz-Credential=ASIAWA6KKHEJORWBH4N3%2F20261110%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=7a6df2f284506a65e31aba29e8cd3aeafdc7e738e747f36007f5de5aaab8b567","created_at":1770728512524,"updated_at":1770728512524,"display_name":"Salesforce_Accelerator_V2","status":"FILE_STATUS_PUBLISHED"}],"assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"Dataplatr Corp","description":"Dataplatr Corp specializes in Data Analytics & AI with pre-built data models for Enterprise Applications like SAP, Oracle EBS, Workday, Salesforce to empower businesses to unlock the full potential of their data. 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Our B2B and B2C datasets are built from deterministic sources and continuously updated. \n\n**Segment Categories:** \n\n- Industry Sector – Technology, Healthcare, Construction, Retail, Manufacturing \n- Growth Signals – Revenue growth, hiring activity, expansion plans, recent funding \n- Revenue Bands – $250K to $50M \n- Headcount – 5 to 250 employees \n- Geographic Scope – Local, Regional, Emerging National Businesses \n- Operational Triggers – CRM/ERP adoption, digital campaigns, new partnerships \n- Contact Intelligence – C-level executives, operations leaders, decision-makers \n- Firmographics – Business age, structure, credit tier, industry awards \n\n**Key Features:** \n\n- Tuned for identifying SMBs with strong forward momentum \n- Verified contact information at the executive and operational level \n- Predictive growth attributes including recent expansion, hiring, and funding \n- Firm-level adoption of business tools, services, and digital marketing \n- Compliant with all B2B data and privacy regulations (GDPR, CCPA) \n- Designed for CRM, sales automation, and campaign integration \n \n\n**Use Cases:**\n- Software & SaaS Sales: Reach companies scaling operations and ready for CRM, ERP, or HR tech \n- Financial & Insurance Providers: Target growing businesses with evolving capital, coverage, and risk needs \n- Consulting & Professional Services: Identify high-growth firms needing HR, legal, IT, or operational support \n- B2B Channel & Product Expansion: Build partner pipelines and cross-sell into emerging SMB accounts \n- Go-to-Market Planning: Analyze SMB behavior across verticals to shape pricing, bundling, and positioning \n- Database Enrichment: Strengthen your first-party data with verified SMB growth indicators and firmographics \n\n**Next Steps:**\n- Request listing access \n- Data Axle team will contact you to finalize audience, data cut, and delivery method / frequency\n\n**Business Need:**\n- Emerging Account Targeting: Identify SMBs poised for scale-up with clear growth and investment signals \n- Sales Prioritization: Focus reps on SMBs with verified activity across hiring, revenue, and expansion \n- Strategic Outreach: Engage earlier in the growth lifecycle to build long-term vendor relationships \n- Channel Optimization: Feed data into sales intelligence, partner platforms, or growth targeting tools \n- B2B Campaign Accuracy: Improve conversion by aligning offerings with growth-stage operational needs ","terms_of_service":"https://www.data-axle.com/terms-and-conditions/","documentation_link":"https://www.data-axle.com/our-data/","privacy_policy_link":"https://www.data-axle.com/privacy-policy/","geographical_coverage":"[\"United States and Canada\"]","cost":"PAID","update_frequency":{"interval":1,"unit":"MONTHLY"},"collection_granularity":{"interval":1,"unit":"MONTHLY"},"data_source":"Data Axle sources consumer data from 150+ inputs (real estate, tax, census, public records) and business data from 4,000+ sources, verified via 25M+ phone calls.","assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://www.data-axle.com/search/license"},"provider_summary":{"name":"Data Axle","description":"Data Axle is a leader in data-driven marketing and business intelligence solutions for enterprise, small business, nonprofit, and political organizations. 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Survey-based averages miss the real picture, and raw job postings only show pay 30-40% of the time. Our Salary Data fixes this by combining parsed pay from postings that list it with AI-predicted ranges for the 60-70% that don't.\n\nThis Salary Data covers 800M+ deduplicated US job postings from 2022 to the present, updated weekly. Whether you are benchmarking compensation for a single role or modeling total labor costs across a portfolio, Salary Data gives you the full picture of what employers actually pay, not what surveys estimate.\n\nWhat This Salary Data Covers\n\nThis Salary Data is sourced from Indeed, LinkedIn, and 50,000+ employer career sites. Every record includes:\n\n• Job title and normalized title (from 50,000+ standardized categories)\n• Company name, location (city, state, ZIP), and industry\n• Base salary, minimum, maximum, and average salary\n• AI-predicted salary for postings without explicit pay\n• Seniority level (Entry, Mid, Senior, Lead, Executive)\n• Work type (Remote, Hybrid, Onsite) and employment type\n• Google Maps location match with ZIP and coordinates\nThis Salary Data covers the United States from 2022 to present, updated weekly.\n\nWhat You Can Do With This Salary Data\n\nCompensation Benchmarking\n\nHR and compensation teams use Salary Data to set competitive pay bands, validate internal structures, and benchmark roles against real employer postings. Salary Data includes both parsed pay from listings that show it and AI-predicted ranges for the 60-70% that don't, giving you a complete view of what employers offer across every role and market.\n\n• Benchmark salaries for specific roles, seniority levels, and geographies using Salary Data records\n• Compare base pay, total comp, and predicted ranges across companies and industries with Salary Data\n• Set competitive salary bands grounded in actual employer postings rather than survey averages using Salary Data\n• Validate pay equity across roles and seniority levels using Salary Data parsed and predicted pay fields\nHR Analytics\n\nPeople analytics teams use Salary Data to track compensation trends, power internal dashboards, and benchmark internal pay against external market rates. Salary Data updated weekly means your dashboards reflect what companies are posting today, not last quarter.\n\n• Track compensation trends over time across roles, industries, and geographies using Salary Data\n• Power People Analytics dashboards with Salary Data parsed and AI-predicted pay fields\n• Benchmark internal pay against external Salary Data records by role, region, and seniority\n• Analyze competitor compensation strategies using Salary Data job posting and pay records\nFinancial Intelligence and Investment Analysis\n\nInvestors and deal teams use Salary Data as a leading indicator of company health. Salary Data compensation trends often shift 30-60 days before changes appear in financial reports, giving PE, VC, and hedge fund teams early signals on acquisition targets and portfolio companies.\n\n• Monitor portfolio company hiring velocity and compensation levels using Salary Data signals\n• Use Salary Data hiring and pay trends as alternative data for investment thesis validation\n• Estimate labor cost exposure for target companies using Salary Data by role, seniority, and location\n• Assess sector-wide wage inflation trends through Salary Data historical records\n• Track early warning signals through sudden shifts in Salary Data pay ranges or posting velocity\nWorkforce Planning and Site Selection\n\nSite selection consultants and economic development teams use Salary Data to quantify labor cost differences across geographies and prove competitive compensation environments for RFIs. Salary Data with ZIP-level granularity gives workforce planners the inputs needed to model total labor costs for any proposed location.\n\n• Map labor cost differentials by geography using Salary Data pay records at ZIP and city level\n• Prove competitive compensation environments for site selection RFIs with Salary Data\n• Forecast headcount spend for geographic expansion using Salary Data by role and location\n• Build workforce cost benchmarks using Salary Data parsed and predicted pay fields\nPayroll Forecasting and FP&A\n\nFinance and FP&A teams use Salary Data to model compensation costs, forecast headcount spend, and build budget scenarios grounded in real market data. Salary Data by role, seniority, and location gives finance teams the inputs needed to move beyond internal assumptions.\n\n• Model payroll costs by role, seniority, and geography using Salary Data pay benchmarks\n• Forecast headcount spend scenarios using Salary Data as a real-world compensation input\n• Validate internal compensation assumptions against external Salary Data records\n• Analyze compensation-to-revenue ratios across sectors using Salary Data historical records\nHow We Build This Salary Data\n\nThe salary estimation model is the core engine behind this Salary Data. About 60-70% of job postings do not include explicit pay. The model predicts salary ranges for those records using company-level compensation history, geographic cost-of-living adjustments, role seniority, and industry norms. Trained on 50M+ observations, it achieves a Mean Absolute Percentage Error under 15%, making this Salary Data a reliable foundation for compensation benchmarking and financial modeling.\n\nThe title taxonomy model normalizes every Salary Data job title into 50,000+ standardized categories from 20M+ raw titles, enabling consistent comparison across companies, industries, and geographies.\n\nJob category models classify each Salary Data record by seniority (Entry, Mid, Senior, Lead, Executive) and work modality (Remote, Hybrid, Onsite) using LLM-based analysis of the full job description.\n\nOur annotation team validates every Salary Data model output before delivery.\n\nWhat Makes This Salary Data Different\n\n• Full pay coverage: Salary Data combines parsed pay from postings that list it with AI-predicted ranges for the 60-70% that don't\n• 800M+ records: Salary Data collapses billions of raw postings into deduplicated records that reflect true hiring intent\n• Prediction accuracy: Salary Data salary predictions achieve a MAPE under 15% trained on 50M+ observations\n• Matchable: Salary Data joins directly with job postings, company profiles, and Google Maps location data\n• Normalized titles: Salary Data job titles are standardized to 50,000+ categories for cross-company comparison\n• Weekly updates: Salary Data refreshes weekly so compensation benchmarks reflect current employer activity\nWho Uses This Salary Data\n\n• HR and Compensation Teams use Salary Data to benchmark salaries, set competitive pay bands, and validate internal pay structures\n• People Analytics Teams use Salary Data to track compensation trends and power internal pay equity dashboards\n• Finance and FP&A Teams use Salary Data to model payroll costs, forecast headcount spend, and build budget scenarios\n• Investors, PE Firms, and VC Funds use Salary Data as alternative data for due diligence, portfolio monitoring, and investment thesis validation\n• Compensation Consultants use Salary Data to advise clients on market rates, competitive pay positioning, and role benchmarking\n• Site Selection Consultants and EDOs use Salary Data to quantify labor cost differentials and build workforce cost reports for RFIs\n• HR Tech Companies and B2B Platforms use Salary Data to power compensation benchmarking tools, salary estimators, and employer intelligence products\nDelivery and Format\n\nSalary Data is delivered in CSV, JSON, or Parquet via AWS S3 or Google Cloud Storage. Custom filters by geography (ZIP, city, state), company, date range, title, industry, seniority, and salary range. Compatible with Snowflake, Databricks, Power BI, Tableau, Salesforce, and most BI platforms.","terms_of_service":"https://decanaria.com/privacy-policy","contact_email":"support@datarade.ai","privacy_policy_link":"https://decanaria.com/privacy-policy","assets":["ASSET_TYPE_DATA_TABLE"]},"provider_summary":{"name":"Canaria Inc.","description":"Canaria is a leading provider of enriched job posting and labor market data. We collect 1B+ job postings from Indeed, LinkedIn and 200,000+ employer career sites and deduplicate them to 400M+ unique jobs, with deepest coverage in the US plus postings from 243 other countries and territories since 2022.\r\n\r\nData arrives analysis-ready: SOC occupation and NAICS industry codes, normalized titles across 99,799 canonical roles, a 43,000+ entry skills and credentials taxonomy, salary estimates backed by 44,000+ benchmark cells with 95% confidence intervals, worker type (W2 vs 1099), stock tickers for listed employers, and a confidence score on every classified field.\r\n\r\nHedge funds, HR tech platforms, workforce planners and researchers choose Canaria when they want enterprise-grade enrichment without an enterprise contract. Delivery by API or as CSV and Parquet files to S3, GCS, Snowflake or SFTP. Public commercial data only, GDPR and CCPA compliant."},"popularity":1525},{"id":"fb40fc53-5ce0-4af3-9bd7-ae900401c1df","summary":{"name":"MBI Area Boundaries for France – Sample","subtitle":"MBI's precise & seamless boundaries for well-founded decisions in France. Free trial data.","share":{"name":"share_boundaries_mbi_fr_r_2025","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1785141728609,"updated_at":1785141728609,"categories":["ADVERTISING_AND_MARKETING","CLIMATE_AND_ENVIRONMENT","ENERGY","GEOSPATIAL","MEDIA","OTHER","PUBLIC_SECTOR","RETAIL","TRANSPORTATION_AND_LOGISTICS","TRAVEL_AND_TOURISM"],"listingType":"STANDARD","provider_id":"aa09abd6-ff7f-4452-803d-b8c43b4c07cb"},"detail":{"description":"MBI'S GLOBAL AREA BOUNDARIES COVER MOST COUNTRIES WORLDWIDE AND **CAN BE LICENSED FOR EACH COUNTRY INDIVIDUALLY** AND IN BUNDLES.\n[MBI](https://www.mbi-geodata.com/en/) digital boundaries are available for virtually all countries worldwide.* Created by our experts according to consistent global standards, the polygons are so precise and harmonized that they align seamlessly across borders. The territorial vintages of the worldwide area boundaries are compatible with our globally consistent and comparable [socio-demographic](https://www.mbi-geodata.com/en/sociodemographic-data/) and [economic](https://www.mbi-geodata.com/en/purchasing-power-overview/) data.\nOur area boundaries for Franceare offered at the following levels:\n\n- **Postal Code Level** (Postcode 5-digit): This fine-grained level of detail makes the data suitable for a wide range of applications – for example, enriching customer databases, which are often organized by postal code.\n- **Administrative Level** (Communes): Covers both larger administrative areas (e.g., regions, states, provinces) as well as finer subdivisions such as districts, counties, or municipalities.\n- **Micro Level** (IRIS): Subdivisions of postal code or administrative boundaries, such as census areas or street segments. This is the most granular level for local analysis. | on request\n\n**Use Cases:**\n\n- **Market Analysis**: Use MBI’s boundaries to visualize data on a map, learn about your customers, and uncover untapped market potential.\n- **360-Degree Customer View**: Link boundaries to customer data, sociodemographic, and economic data to gain a holistic understanding of customer needs, behaviors, and potential value.\n- **Location Planning**\n•\tUse MBI’s boundaries to visualize data on a map, learn about your customers\n•\tPlan the provision of services and products across different regions\n•\tDefine sales territories\n•\tVisualize catchment areas and plan actions to reach potential customers\n\n**Additional Insights:**\n[About Area Boundaries](https://www.mbi-geodata.com/en/area-boundaries/)\n\n**Data Structure:**\nThe area boundaries include the following variables:\nCTRYCODE: Two-digit country code by ISO 3166\nNAME: Name of administrative area / Name of the most populous administrative area / Name of the respective micro-area or the next higher level\nADMINCODE / POSTCODE / MICROCODE: Administrative code / Postal code / Micro-code\nWKT: Geography as Well Known Text (lat/long WGS84)\n\n*with only a few specific exceptions","terms_of_service":"https://www.mbi-geodata.com/en/data-protection/","documentation_link":"https://www.mbi-geodata.com/en/mbi-supplier-manufacturer-worldwide-geodata/europe/france-maps/","privacy_policy_link":"https://www.mbi-geodata.com/en/data-protection/","update_frequency":{"interval":1,"unit":"YEARLY"},"assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://service-mbi-geodata.com/s/RWooYMaqngsDKtF"},"provider_summary":{"name":"Michael Bauer International GmbH – MBI Geodata","description":"For over 15 years, Michael Bauer International (MBI) has been a leading provider of globally consistent and comparable geographic data, including more than 3 million postcode areas and other detailed datasets like sociodemographics and purchasing power. MBI's high-quality small-scale data spans 248 countries, supporting businesses across all industries in location planning, branch networks, and distribution strategies. Recognized as a key data source, MBI is the preferred provider for leading GIS software and technology companies worldwide as well as one of the first global distributors for HERE Technologies. MBI is part of the Michael Bauer Group, alongside Michael Bauer Research GmbH and Michael Bauer Micromarketing GmbH."},"popularity":14},{"id":"7f0a7b9e-1fd5-4a2a-b2e1-7d1e90086e10","summary":{"name":"Restaurants in Brazil (BR) - Places Data Sample","subtitle":"\uD83C\uDDE7\uD83C\uDDF7 Restaurant location & metadata table for Brazil, with 30K+ restaurant points-of-interest (PoIs).","share":{"name":"br_restaurants_marketplace","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1746423321862,"updated_at":1746426489905,"categories":["ADVERTISING_AND_MARKETING","COMMERCE","GEOSPATIAL","RETAIL"],"listingType":"STANDARD","provider_id":"bcde9d2e-7e2f-4266-8c17-45d2e7bdc27e"},"detail":{"description":"## **Overview**\n\n> Learn more about this dataset on [catalog.dotlas.com](https://catalog.dotlas.com/restaurants/unified_restaurant_establishments/)\n\n> This is a sample of our Brazil Restaurants data product (temporal). The product contains **`30K+ restaurants`** updated monthly, and additional columns catered for leads and segmentation. \n\n> Additionally, we also curate restaurant menu data including dishes, prices, price history, restaurant promotions and social insights. \n\nFor pricing and licensing details please [**contact us**](mailto:info@dotlas.com)\n\n**Use cases** \n\n\uD83D\uDD70 Analyze competitor opening hours on each day of the week to help create an optimal operating time frame.\n\uD83D\uDCC8 Identify trending cuisines in different regions by analyzing the frequency and distribution of various cuisine types in different cities.\n\uD83D\uDCB0 Use the distribution data to identify underserved areas with high potential for new restaurant openings based on the lack of certain cuisine types.\n\n### **Potential Sectors**\n* Food & Beverages (F&B)\n* GIS (Geographic Information Systems) and Geocoding Services\n* Marketing and Advertising Leads\n* Competitor Monitoring & Market Analysis\n* Financial tickering of F&B brand performance\n* Site Selection & Suitability Analysis\n* Real Estate Proximity Analysis\n\n**Key features** \n\n▶️ **Restaurant Identification**: Includes unique identifiers and names for each restaurant.\n\n▶️ **Geographical Data**: Detailed information about the `country`, `subregion`, `city`, `area`, and precise geographical coordinates (`latitude` and `longitude`) of each restaurant.\n\n▶️ **Contact Information**: Addresses, phone numbers","terms_of_service":"https://dotlas.com/terms-of-service/","documentation_link":"https://catalog.dotlas.com/restaurants/unified_restaurant_establishments/","privacy_policy_link":"https://dotlas.com/privacy-policy","geographical_coverage":"[\"Latin America and Caribbean\"]","cost":"FREE","data_source":"Dotlas collects and coalesces information from multiple online and offline sources","assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"Dotlas","description":"Dotlas provides high-quality geographical and temporal data on restaurants, real estate and hospitality sectors around the world. 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Additionally, they are included in the preview for your convenience.\n\n**Use cases**\n- Compute egress costs for a specific share.\n- Perform additional breakdowns to learn about individual customer usage patterns.\n- Learn which files are the most popular.\n\n**Additional Details**\nThe logs are joined with cloud provider IP range tables and Delta Sharing table, share, and recipient information system tables to generate egress bytes transferred, attributed by share and recipient.\n\nThese notebooks are meant to be run as a Delta Live Table (DLT) template which will automatically generate a detailed cost report.\n\n**How to Use**\n\n1. [Enable S3 Server Access Logging](https://docs.aws.amazon.com/AmazonS3/latest/userguide/ServerLogs.html) on your delta sharing bucket. \n2. Import the attached notebooks into your workspace.\n3. Set up and execute the pipeline to generate a denormalized IP address -> cloud region mapping table. use the `IpRanges` notebook.\n4. Set up and execute the pipeline to do cloud provider storage access logs analysis. Follow the instructions in the `S3AccessLogDlt` notebook. \n\n**Please note** that you must specify the output table from executing the IpRanges notebook as the input for this pipeline.\n\n**Requirements**\n - DLT enabled\n - You will be responsible for DLT pipeline costs\n - Available on: `AWS`\n - (AWS): S3 server access logging enabled on the shared bucket.\n - Metastore Admin or `CREATE EXTERNAL LOCATION` permission is required to mount the storage access logs to your Unity Catalog\n\n**Limitations**\n1. Only attributes costs for managed tables, but not other data assets such as external tables, volumes, models.\n2. 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We are a cross-cloud platform that works across AWS, Azure, and Google.\nThis unified approach simplifies your modern data stack by eliminating the data silos that traditionally separate and complicate data engineering, analytics, BI, data science and machine learning. It’s built on open source and open standards to maximize flexibility. And, its common approach to data management, security and governance helps companies operate more efficiently and innovate faster."},"popularity":2127},{"id":"e6876a14-ea72-4ef7-8b98-f420f382f29f","summary":{"name":"Restaurants in India (IN) - Places Data Sample","subtitle":"\uD83C\uDDEE\uD83C\uDDF3 Restaurant location & metadata table for India, with 250k+ restaurant points-of-interest (PoIs) updated frequently","share":{"name":"in_restaurants_marketplace","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1746423594778,"updated_at":1746426471474,"categories":["ADVERTISING_AND_MARKETING","COMMERCE","GEOSPATIAL","RETAIL","TRAVEL_AND_TOURISM"],"listingType":"STANDARD","provider_id":"bcde9d2e-7e2f-4266-8c17-45d2e7bdc27e"},"detail":{"description":"## **Overview**\n\n> Learn more about this dataset on [catalog.dotlas.com](https://catalog.dotlas.com/restaurants/unified_restaurant_establishments/)\n\n> This is a sample of our India Restaurants data product (temporal). The product contains **`250K+ restaurants`** updated frequently, and additional columns catered for leads and segmentation. Sample contains about 500 restaurants from select neighbourhoods in Mumbai, Bengaluru and Delhi.\n\n> Additionally, we also curate restaurant menu data including dishes, prices, price history, restaurant promotions and social insights. \n\nFor pricing and licensing details please [**contact us**](mailto:info@dotlas.com)\n\n**Use cases** \n\n\uD83D\uDD70 Analyze competitor opening hours on each day of the week to help create an optimal operating time frame.\n\uD83D\uDCC8 Identify trending cuisines in different regions by analyzing the frequency and distribution of various cuisine types in different cities.\n\uD83D\uDCB0 Use the distribution data to identify underserved areas with high potential for new restaurant openings based on the lack of certain cuisine types.\n\n### **Potential Sectors**\n* Food & Beverages (F&B)\n* GIS (Geographic Information Systems) and Geocoding Services\n* Marketing and Advertising Leads\n* Competitor Monitoring & Market Analysis\n* Financial tickering of F&B brand performance\n* Site Selection & Suitability Analysis\n* Real Estate Proximity Analysis\n\n**Key features** \n\n▶️ **Restaurant Identification**: Includes unique identifiers and names for each restaurant.\n\n▶️ **Geographical Data**: Detailed information about the `country`, `subregion`, `city`, `area`, and precise geographical coordinates (`latitude` and `longitude`) of each restaurant.\n\n▶️ **Contact Information**: Addresses, phone numbers\n\n▶️ **Restaurant Types and Facilities**: Flags dark kitchens, and facilities available in restaurant","terms_of_service":"https://dotlas.com/terms-of-service/","documentation_link":"https://catalog.dotlas.com/restaurants/unified_restaurant_establishments/","privacy_policy_link":"https://dotlas.com/privacy-policy","geographical_coverage":"[\"Asia-Pacific\"]","cost":"FREE","data_source":"Dotlas collects and coalesces information from multiple online and offline sources","assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"Dotlas","description":"Dotlas provides high-quality geographical and temporal data on restaurants, real estate and hospitality sectors around the world. 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Plan and execute campaigns with real behavioural and demographic signals, including CPG, e-commerce, and retail verticals.\n-For DSPs and SSPs — enrich your bidstream with audience overlays for targeting, or use ID linkage to extend cross-device graph coverage across one of the world's largest mobile markets.\n-For fintech and credit platforms — enrich thin-file profiles with behavioural and app usage signals to improve credit decisioning and risk modelling for India's large underbanked population.\n-For game publishers and gaming DSPs — access app-level usage data at scale to build and activate gaming audience segments across India's fast-growing mobile gaming market.\n\nAdditional data points and custom segment configurations are available on request — reach out to discuss your specific use case.","terms_of_service":"https://event.kindred.co/privacy-policy#:~:text=Kindred%20does%20not%20collect%20data,insights%20and%20audience%20curation%20services.","contact_email":"marketplace@datarade.ai","privacy_policy_link":"https://event.kindred.co/privacy-policy#:~:text=Kindred%20does%20not%20collect%20data,insights%20and%20audience%20curation%20services.","assets":["ASSET_TYPE_DATA_TABLE"]},"provider_summary":{"name":"Path IQ","description":"PathIQ delivers 2B+ verified audience profiles across 35 markets — purchase intent, location, telco, demographic, and behavioural signals in a single stack."},"popularity":326},{"id":"98bc6a97-2c8a-4468-b7a4-22e9f2f30ea2","summary":{"name":"Falkor Grid","subtitle":"Digital twin solution for DSOs with data contextualization and load flow visualization and analysis.","setting":{"visibility":"PUBLIC"},"created_at":1790338686997,"updated_at":1790339235303,"categories":["ENERGY"],"listingType":"PERSONALIZED","provider_id":"039595fa-0183-453c-8dc3-a01d601b90f5"},"detail":{"description":"**Overview**\n\n*As the energy system is facing an influx of intermittent, distributed and renewable energy and a sharp increase in electrification the complexity of planning and operating the energy system increases. These changes are causing challenges related to power quality and grid stability, more demanding customers with low-cost expectations, and new competitors with innovative solutions.*\n\nA digital twin solution will support the increasing complexity and enable new business models. Falkor Grid is a digital twin of the distribution power grid. It is a SaaS solution accessible from the browser or through API. It uses asset documentation from the Network Information System (NIS) and models it in a map displaying all components from lines and cables to transformers, generators, and end-users.\n\nThe simulation engine forecasts load and production down to meter level and performs a load flow analysis of the entire grid. The main output from the load flow is bottleneck detection and voltage quality. In addition, the solution can include live and planned switch status to reflect the grid topology and identify disconnected areas correctly. This provides an always-on status of the power grid with possibilities to look into the past, present, and future.\n\nThe simulation engine collects, adapts, and contextualizes data from different sources and systems to make them available for new applications and use cases.\n\n**Key benefits**\n- **Improve information management** (IT/OT data, Documents, Images, 3D models, AI Asset Co-Pilot)\n- **Reduce CENS** (ML forecast models, Congestion forecast, Voltage deviation forecast, Loss forecast, What-if scenarios, N-1 contingency analysis)\n- **Reduce MTBF and maintenance costs** (Detect condition degradation, Plan/schedule/execute maintenance & inspections)\n- **Optimize CAPEX spend** (Fast simulation of various grid expansion scenarios, Assessmentof flexibility vs newlines, Enable conditional connections)\n- **Reduce losses and improve service restoration time** (Grid optimization with recommended topology configuration to reduce losses, FLISR scenarios assessment)","terms_of_service":"https://falkor.com/trust-centre/b2c/b2c-terms-of-use","documentation_link":"https://falkor.com/","privacy_policy_link":"https://falkor.com/en/trust-centre/privacy","geographical_coverage":"[\"Global\"]","assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://creativecommons.org/licenses/by/4.0/"},"provider_summary":{"name":"Falkor AS","description":"Falkor (formerly Kongsberg Digital, a KONGSBERG company) is an industrial software and AI provider for asset-intensive industries, including oil & gas, renewables & utilities, and chemicals. On Databricks Marketplace, Falkor offers its industrial intelligence platform, the Industrial Work Surface, and complementary modules such as K-Spice and LedaFlow for dynamic process and multiphase flow simulation, SiteCom, and industrial AI agents. Together they contextualise real-time, transactional and master data into digital twins, cockpits and end-to-end workflows for operations, maintenance, asset management and risk management. Through open APIs, customers can connect Falkor solutions to their Databricks lakehouse to unify OT/IT data and scale analytics and AI."},"popularity":711},{"id":"5e19dcd1-3c82-4510-afa7-f9c549eb77f7","summary":{"name":"StarMine MarketPsych Media Sentiment Model (MMS)","subtitle":"A 1–100 stock ranking model predicting 30-day returns from media sentiment signals, orthogonal to fundamental factors.","setting":{"visibility":"PUBLIC"},"created_at":1773107429469,"updated_at":1787933219257,"categories":["FINANCIAL"],"listingType":"PERSONALIZED","provider_id":"37cd9fdb-b591-487b-9347-9731e1727ee8"},"detail":{"description":"**Overview**\nThe StarMine MarketPsych Media Sentiment (MMS) Model is a daily 1–100 percentile stock ranking system predicting next-month relative share price returns for 16,000+ global equities. Built on LSEG MarketPsych Analytics (LMA) — the global standard in financial media sentiment data — MMS distills billions of tagged news and social media signals into three actionable component scores: Equity, Business, and Management. The model has delivered an average top-bottom decile spread of 10.4% annually since 2006, with 12.3% in out-of-sample years, and maintains low correlation with traditional market factors and other StarMine models, making it a powerful complement to fundamental quant strategies.\n\n**Use cases**\n- Alpha generation: Rank global stocks by media sentiment to identify long/short opportunities with a 30-day return horizon\n- Factor model enrichment: Integrate MMS as an orthogonal sentiment factor alongside fundamental, momentum, and valuation signals\n- Portfolio monitoring: Track Equity, Business, and Management component scores to diagnose sentiment-driven risk by theme\n- Buy/sell signal generation: Use the 1–100 daily score as a standalone recommendation or screening filter across regions and sectors\n- Regime analysis: Investigate sentiment dynamics by sector, country, or market cap during distinct macro environments\n\n**Product details**\n- Datasets represented include the MMS Overall Score table, MMS Component Scores table (Equity, Business, Management), and LMA Input Signals table.\n- Sample fields include asset_id, date, mms_overall_score, mms_equity_score, mms_business_score, mms_management_score, and region.\n- Coverage spans 16,000+ active and historical global equities across 6 regional models: U.S., Developed Europe, Developed Asia ex-Japan, Japan, Canada, and Emerging Markets.\n- History available back to 1998; recommended test period from 2006 onwards.\n- Scores delivered daily as integer percentile ranks (1–100); updated each trading day.\n\nA python notebook is available upon request.\n\n**Additional Insights**\n- Whitepaper: StarMine MarketPsych Media Sentiment Model (Liu, Peterson & Luciani, 2020) — full methodology, performance statistics, and academic foundations available on request from LSEG\n- Underlying data: Built on LSEG MarketPsych Analytics (LMA), also available as a standalone listing on Databricks Marketplace\n- Academic support: MMS methodology draws on 20+ years of peer-reviewed research on media sentiment and equity price underreaction, including work from Empirical Research Partners and 500+ academic citations to the underlying LMA dataset\n- Book: Trading on Sentiment: The Power of Minds over Markets by Richard Peterson (Wiley, 2016)\n- Contact: support@marketpsych.com or your LSEG account manager for trials and licensing","terms_of_service":"https://www.marketpsych.com/terms","documentation_link":"https://www.marketpsych.com/mms/intro","privacy_policy_link":"https://www.marketpsych.com/privacy","geographical_coverage":"[\"Global\"]","cost":"PAID","update_frequency":{"interval":1,"unit":"MONTHLY"},"collection_granularity":{"interval":1,"unit":"DAILY"},"collection_date_start":1772418499246,"collection_date_end":1772418507393,"data_source":"Features derived from the LSEG MarketPsych Analytics dataset (news and social media-based)","size":20.0,"assets":["ASSET_TYPE_DATA_TABLE","ASSET_TYPE_MEDIA","ASSET_TYPE_GIT_REPO"],"license":""},"provider_summary":{"name":"MarketPsych","description":"MarketPsych provides AI-powered natural language processing (NLP) and sentiment analytics for financial markets. Exclusively partnered with LSEG since 2011, we transform global news, social media, and corporate transcripts into structured data feeds covering 130,000+ entities across all major asset classes — equities, bonds, currencies, cryptocurrencies, ETFs, countries (macroeconomic indicators and geopolitical risks), and commodities (energy, materials. and agriculture). Our data assets include social media text archives, real-time sentiment and thematic indicators, ESG analytics, earnings transcript analytics, and a predictive equity ranking model. With history back to 1998, 28 languages, near-real-time updates, and 50+ sentiment dimensions per entity, our datasets power alpha generation, risk management, ESG monitoring, and economic nowcasting at hedge funds, asset managers, brokerages, and research institutions in over 25 countries. Over 200 academic papers cite our data."},"popularity":787},{"id":"ce744edc-28ef-48af-8951-c77ccca081e2","summary":{"name":"Electronic Health Records (EHR) Data : Cardiology","subtitle":"High-quality de-identified EHR data, showing comprehensive patient journeys physician narratives in Cardiology","share":{"name":"electronichealthrecordscardiology","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1724848702162,"updated_at":1724848702162,"categories":["HEALTH","SCIENCE_AND_RESEARCH"],"listingType":"STANDARD","provider_id":"dc00cb61-5b9a-403e-8b4f-71e78dd44d6c"},"detail":{"description":"**Overview**\n\nShaip is a premier provider of data on de-identified electronic health records (EHR). The Shaip EHR data comprises de-identified EHR data that is nationally representative. This data is sourced from practices across the U.S., covering the continuum of care delivery—from independent, single-provider offices to large, integrated delivery systems, including both primary and specialty care. \n\nThis data provides a comprehensive picture of the patient’s journey and physician narrative throughout their hospital stay. With approximately **4 million + unique patient records**, the participating hospitals, offer unique insights into variations in care across healthcare network hospitals and clinics. \n\n**The samples provided and the entire datasets are Gold Standard**\n\n\n**Use cases**\n\n**For Healthcare Organizations**\n\n- Population health management: Analyze patient populations to identify trends and improve overall health.\n- Revenue cycle management: Streamline billing and claims processes. \n- Quality improvement: Track and measure performance to identify areas for improvement. \n- Regulatory compliance: Ensure adherence to healthcare regulations and standards. \n\n**For Researchers and Scientists**\n\n- Drug discovery and development: Identify potential drug targets and patient populations for clinical trials.\n- Disease research: Analyze patient data to understand disease progression and identify risk factors.\n- Public health surveillance: Monitor disease outbreaks and track public health trends.\n\n**For Payers**\n\n- Risk assessment: Evaluate the health risks of insured populations.\n- Fraud detection: Identify potential fraudulent claims.\n- Care management: Manage high-cost patients and chronic conditions.\n\n**Product details**\n\n**Metadata Information:**\n- Includes anonymized MRN\n- Admission and discharge dates\n- Length of stay, \n- Gender \n- Patient class\n- Payer \n- Financial class\n- State \n- Discharge disposition\n- Age \n- DRG\n- DRG description\n- Reimbursement amount\n- AMLOS, GMLOS\n- Risk of mortality \n- Severity of illness\n- Grouper\n- Hospital Zip code, and more.\n\n**Geographical Distribution:** Records from various US regions \n- North East (46.4%)\n- South (8.2%)\n- Midwest (3.1%)\n- West (28.1%)\n- Others (14.2%).\n\n**Patient Classes:** Covers all patient classes—\n- Inpatient\n- Outpatient (Clinical, Rehab, Recurring, Surgical Day Care)\n- Emergency\n\n**Age Groups:** Records for all age groups—\n- <10 years (7.9%)\n- 11-20 years (5.7%)\n- 21-30 years (10.9%)\n- 31-40 years (11.7%)\n- 41-50 years (10.4%)\n- 51-60 years (13.8%)\n- 61-70 years (16.1%)\n- 71-80 years (13.3%)\n- 81-90 years (7.8%)\n- 90+ years (2.4%).\n\n**Gender Ratio:** 46% male and 54% female.\n\n**Language**\n- EnglishOTS\n- French, Spanish, and Portuguese can be acquired very quickly\n- Other Languages - Custom sourcing or translation from English to target language\n\n**Additional Insights**\n\n**Privacy:** Documents have PII redacted according to Safe Harbor Guidelines and HIPAA compliance.","terms_of_service":"https://www.shaip.com/terms-of-service/","documentation_link":"","privacy_policy_link":"https://www.shaip.com/privacy-policy/","geographical_coverage":"[\"United States and Canada\"]","cost":"FREE","data_source":"","assets":["ASSET_TYPE_MEDIA"],"license":""},"provider_summary":{"name":"Shaip","description":"Shaip is a leading AI data platform and services provider specializing in the sourcing, creation, licensing, and transformation of high-quality, ethical, and domain-specific data for AI and machine learning applications. Shaip supports Healthcare AI, Computer Vision, Generative AI, and Conversational AI use cases for leading AI labs and enterprise customers worldwide. Learn more at shaip.com"},"popularity":1844},{"id":"8d3c9ab8-f45b-456e-a83d-de45aff5f957","summary":{"name":"PEI Secondaries Investor Editorial Insights & Data sample","subtitle":"Sample dataset unifying editorial insight with investor profiles, fund data, commitments, mandates, and rankings.","share":{"name":"pei_secondaries_sample","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1768933367750,"updated_at":1769445870263,"categories":["FINANCIAL"],"listingType":"STANDARD","provider_id":"c1c6fe00-2142-4377-8293-c30ca67ea773"},"detail":{"description":"This is a free sample of the PEI Secondaries Investor Editorial Insights & Data listing. The full listing delivers the most actionable dataset available for private secondaries intelligence, connecting unrivaled editorial insight with more than 17 million datapoints for a unified, trusted view across global markets.\n\nBuilt to power specialist secondaries investing workflows, the premium dataset includes investor and manager profiles, fundraising and fund performance data, LP commitments and mandates – enriched with the industry’s most trusted journalist insight and asset class defining rankings.\n\n**Leverage PEI Secondaries Investor Editorial Insight & Data to**: \n- Benchmark fund terms, strategies, and performance (IRR/TVPI/DPI/RVPI) \n- Build fundraising strategies backed by verified investor allocation, mandate and commitment data \n- Strengthen strategic planning with award-winning editorial context combined with extensive market, fundraising and deal data\n\nBuilt for decision-makers, **PEI filters out the noise to surface only the most relevant, verified intelligence your teams actually need**. Derived through extensive multi-party sourcing, this structured, high-quality data originates from:\n- 1st-party proprietary research and direct surveys\n- Regulatory filings from global jurisdictions\n- Direct submissions from fund managers and investors\n- Intelligence sourced through PEI events and networks\n- Continuous input from the industry’s most recognised editorial team\n\nEach table is professionally validated through:\n- Multi-stage programmatic and human review\n- Cross-referencing across historical datasets\n- Manual validation by infrastructure specialists\n- Ongoing updates and historical enrichment\n- Normalised schema alignment across institutions, funds, performance, and commitments\n\nThe result is **an actionable dataset built for accuracy, completeness, and reliability** to enhance smarter investing decisions.\n\n\n**What’s included?**\nIn this sample dataset you will receive 10 rows of data across the below categories. \n\n*The industry’s leading editorial analysis and archive*\nFull history of Secondaries Investor reporting — interviews, features, analyses, rankings, trend narratives, market shifts — transformed into a structured, queryable knowledge layer that contextualises fundraising, deal activity, and strategy evolution.\n\n*Investor (LP) Profiles*\nDeep intelligence on pension funds, insurance companies, sovereign wealth funds, family offices, endowments, wealth managers, and governments actively allocating to secondaries funds:\n- Mandates, sector focus and asset-class allocations\n- Ticket sizes, pacing, re-ups and geographic preferences\n- AUM history and commitment behaviour\n- LP strategy shifts surfaced early via editorial coverage\n\n*Investor commitments*\nComprehensive LP → fund commitments:\n- Amounts, dates, pacing patterns\n- Co-investment participation\n- Fundraising cycle behaviour\n- Forward-looking allocation signals\n\n*Investor mandates*\nForward-looking, actionable data and insights including:\n- Allocation targets and ranges\n- ESG and impact requirements\n- Mandate maturity and open opportunities\n- Asset class specific appetite (real estate, private equity, credit, infrastructure & more)\n\n*Fund Manager (GP) Profiles*\nStandardised, structured GP intelligence:\n- Strategy focus and geographic reach\n- Fundraising history, track record and team structure\n- AUM and strategy evolution\n- Service providers and advisory relationships\n\n\n*Fund profiles & fund terms*\nDetailed fund-level data including:\n- Strategy, sector focus and investment scope\n- Target and final close sizes\n- Fund lifecycle events and staging\n- Detailed fund terms — fees, carry, hurdles, liquidity, governance\n\n\n*Fund performance*\nA fully standardised performance dataset for private markets secondaries vehicles, including:\n- IRR\n- TVPI\n- DPI\n- RVPI\n- NAV\n- Contribution & distribution timelines\n- Quarterly performance updates\n\n*Cashflows & distributions*\n• Capital called\n• Capital distributed\n• Net cashflow calculations\n• Timing patterns for pacing and capital planning\n\n\n*Service providers*\nProfiles and involvement of:\n- Legal advisors\n- Administrators\n- Placement agents\n- Consultants\n\n\n*Ranking data*\n- SI 50 rankings\n- Investor Reports\n- Service provider league tables\n\n\n*Data structure and coverage*\n- **institution** — Investor (LP), Manager (GP) and Service Provider (SP) profiles \n- **editorial_insight** — industry-leading secondaries insight and analysis\n- **institution_focus_summary** — sector, strategy and region preferences \n- **institution_asset_class_allocation**— secondaries allocation breakdown \n- **institution_assets_under_management** — historic AUM data \n- **branch** — office & geographic metadata \n- **fund** — standardised fund profiles \n- **fund_focus_summary** — strategy, sector and region details \n- **fund_performance** — up to 10 rows in sample, full range in production \n- **fund_stage** — fundraising and lifecycle staging \n- **ranking** — latest-year secondaries-specific rankings\n\n\n*Data format and delivery*\n- Delivered as Delta tables for maximum performance on the Databricks Lakehouse \n- Fully queryable via SQL, Python, Spark, and MLflow \n- Updated daily for continuous freshness \n- Normalised schemas unlocking seamless joins across LPs, GPs, funds, commitments, performance, mandates and rankings \n- Designed for integration into enterprise analytics, AI workflows, and data product","terms_of_service":"https://www.pei.group/terms-and-conditions/","documentation_link":"","privacy_policy_link":"https://www.pei.group/privacy-policy/","data_source":"","assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"PEI Group","description":"PEI Group is the leading private markets intelligence source across private equity, real estate, infrastructure, private credit, secondaries, venture, and responsible investing.\nTrusted by 35,000+ fund managers, investors, service providers, and advisers – including 97% of the world’s largest 300 private equity managers – we combine verified market data with award-winning independent editorial coverage and event participation so you can make informed decisions, faster."},"popularity":1200},{"id":"73fac669-4c80-4097-b17d-b19eef80cdc8","summary":{"name":"USPS Zip Code Crosswalk | HUD","subtitle":"These files allow users to combine or convert data using ZIP code, census geography, Congressional district, and more.","setting":{"visibility":"PUBLIC"},"created_at":1677702516288,"updated_at":1698965635273,"categories":["GEOSPATIAL","PUBLIC_SECTOR","DEMOGRAPHICS"],"listingType":"PERSONALIZED","provider_id":"f00071b1-2db4-4f8f-a4bc-4c62c790f25a"},"detail":{"description":"**Overview**\n\nOne of the many challenges that social science researchers and practitioners face is the difficulty of relatingZIP codes to Census Bureau geographies. There are valuable data available only at the ZIP code level that, when combined with demographic data tabulated at various Census geography levels, could open up new avenues of exploration.\n\nWhile some acceptable methods of combining ZIP codes and Census geography exist, they have limitations. To provide additional avenues for merging these data, PD&R has released the HUD-USPS Crosswalk Files. These unique files are derived from data in the quarterly USPS Vacancy Data. They originate directly from the USPS; are updated quarterly, making them highly responsive to changes in ZIP code configurations; and reflect the locations of both business and residential addresses.\n\n**Product Details**\n\nIncludes the following tables:\n\n\n- **stat_geo_xwalk.geo_xwalk_[geo1]_[geo2]_quarterly_hud**\n - **Update frequency:** Quarterly\n - **Source:** HUD PD&R\n - **Description:** each file will contain crosswalk information between _geo1_ and _geo2_. The tables can be used to allocate data from ZIP codes to other geographical regions. For more information, see the [codebook](https://www.huduser.gov/portal/datasets/usps_crosswalk.html#codebook).\n\n\n\n**Data Sources**\n\n\nThe data in this product comes from **HUD PD&R**.\n\nThe U.S. Department of Housing and Urban Development's (HUD's) Office of Policy Development and Research (PD&R) supports the Department's efforts to help create cohesive, economically healthy communities.\n\nPD&R is responsible for maintaining current information on housing needs, market conditions, and existing programs, as well as conducting research on priority housing and community development issues. The Office provides reliable and objective data and analysis to help inform policy decisions. PD&R is committed to involving a greater diversity of perspectives, methods, and researchers in HUD research.\n\n\n- **Source Homepage:** [HUD User Home Page](https://www.huduser.gov/portal/home.html)\n- **Source Data:** [HUD USPS ZIP CODE CROSSWALK FILES\n](https://www.huduser.gov/portal/datasets/usps_crosswalk.html)\n- **License:** [CC BY](https://creativecommons.org/2016/02/01/u-s-department-labor-adopts-cc-licensing-policy-department-wide/)\n- **Frequency:** Quarterly\n\n\n\n\n","terms_of_service":"https://rearc-data-public-assets.s3.amazonaws.com/Rearc_Data_DSA.pdf","documentation_link":"","privacy_policy_link":"https://rearc-data-public-assets.s3.amazonaws.com/Rearc+Privacy+Policy.pdf","assets":["ASSET_TYPE_DATA_TABLE"]},"provider_summary":{"name":"Rearc","description":"Rearc provides a vast library of data products for Healthcare, Financial Services, and ESG use cases. Whether you're looking for genomes or asset prices, there is a Rearc data product to address your team's data needs. If you're looking for something that's not yet available in our library, our data curation team can help you source that data through the Databricks Marketplace as well."},"popularity":1793},{"id":"52af1ba2-d469-4804-a6e3-8cd3bea64d73","summary":{"name":"SAMPLE APAC Data Suite | 4M+ Translations | 1.6M+ Words | Natural Language Processing Data | ...","subtitle":"Note: The CSV format sample above is provided for preview only. Full datasets may differ in format depending on ...","share":{"name":"oxford_languages_9f39f787_fea1_43f9_a5ab_b78fa5db32c8_samples_share","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1789386259055,"updated_at":1789386259055,"categories":["OTHER"],"listingType":"STANDARD","provider_id":"79c6c146-3407-48ec-8737-5b9b06406932"},"detail":{"description":"APAC Data Suite offers high-quality language datasets. Ideal for NLP, AI, LLMs, translation, and education, it combines linguistic depth and regional authenticity to power scalable, multilingual language technologies.\n\nDiscover our expertly curated language datasets in the APAC Data Suite. Compiled and annotated by language and linguistic experts, this suite offers high-quality resources tailored to your needs. This suite includes: \n\n- Monolingual and Bilingual Dictionary Data \nFeaturing headwords, definitions, word senses, part-of-speech (POS) tags, and semantic metadata. \n\n- Semi-bilingual Dictionary Data \nEach entry features a headword with definitions and/or usage examples in Language 1, followed by a translation of the headword and/or definition in Language 2, enabling efficient cross-lingual mapping. \n\n- Sentence Corpora \nCurated examples of real-world usage with contextual annotations for training and evaluation. \n\n- Synonyms & Antonyms \nLexical relations to support semantic search, paraphrasing, and language understanding. \n\n- Audio Data \nNative speaker recordings for speech recognition, TTS, and pronunciation modeling. \n\n- Word Lists \nFrequency-ranked and thematically grouped lists for vocabulary training and NLP tasks. The word list data can cover one language or two, such as Tamil words with English translations. \n\n\nEach language may contain one or more types of language data. Depending on the dataset, we can provide these in formats such as XML, JSON, TXT, XLSX, CSV, WAV, MP3, and more. Delivery is currently available via email (link-based sharing) or REST API. \n\nIf you require more information about a specific dataset, please contact us Growth.OL@oup.com. \n\nBelow are the different types of datasets available for each language, along with their key features and approximate metrics. If you have any questions or require additional assistance, please don't hesitate to contact us. \n\n1. Assamese Semi-bilingual Dictionary Data: 72,200 words | 83,700 senses | 83,800 translations. \n\n2. Bengali Bilingual Dictionary Data: 161,400 translations | 71,600 senses. \n\n3. Bengali Semi-bilingual Dictionary Data: 28,300 words | 37,700 senses | 62,300 translations. \n\n4. British English Monolingual Dictionary Data: 146,000 words | 230,000 senses | 149,000 example sentences. \n\n5. British English Synonyms and Antonyms Data: 600,000 synonyms | 22,000 antonyms. \n\n6. British English Pronunciations with Audio: 250,000 transcriptions (IPA) | 180,000 audio files. \n\n7. French Monolingual Dictionary Data: 42,000 words | 56,000 senses | 43,000 example sentences. \n\n8. French Bilingual Dictionary Data: 380,000 translations | 199,000 senses | 146,000 example translations. \n\n9. Gujarati Monolingual Dictionary Data: 91,800 words | 131,500 senses. \n\n10. Gujarati Bilingual Dictionary Data: 171,800 translations | 158,200 senses. \n\n11. Hindi Monolingual Dictionary Data: 46,200 words | 112,700 senses. \n\n12. Hindi Bilingual Dictionary Data: 263,400 translations | 208,100 senses | 18,600 example translations. \n\n13. Hindi Synonyms and Antonyms Dictionary Data: 478,100 synonyms | 18,800 antonyms. \n\n14. Hindi Sentence Data: 216,000 sentences. \n\n15. Hindi Audio data: 68,000 audio files. \n\n16. Indonesian Bilingual Dictionary Data: 36,000 translations | 23,700 senses | 12,700 example translations. \n\n17. Indonesian Monolingual Dictionary Data: 120,000 words | 140,000 senses | 30,000 example sentences. \n\n 18. Korean Monolingual Dictionary Data: 596,100 words | 386,600 senses | 91,700 example sentences. \n\n19. Korean Bilingual Dictionary Data: 952,500 translations | 449,700 senses | 227,800 example translations. \n\n20. Mandarin Chinese (simplified) Monolingual Dictionary Data: 81,300 words | 162,400 senses | 80,700 example sentences. \n\n21. Mandarin Chinese (traditional) Monolingual Dictionary Data: 60,100 words | 144,700 senses | 29,900 example sentences. \n\n22. Mandarin Chinese (simplified) Bilingual Dictionary Data: 367,600 translations | 204,500 senses | 150,900 example translations. \n\n23. Mandarin Chinese (traditional) Bilingual Dictionary Data: 215,600 translations | 202,800 senses | 149,700 example translations. \n\n24. Mandarin Chinese (simplified) Synonyms and Antonyms Data: 3,800 synonyms | 3,180 antonyms. \n\n25. Malay Bilingual Dictionary Data: 106,100 translations | 53,500 senses. \n\n26. Malay Monolingual Dictionary Data: 39,800 words | 40,600 senses | 21,100 example sentences. \n\n27. Malayalam Monolingual Dictionary Data: 91,300 words | 159,200 senses. \n\n28. Malayalam Bilingual Word List Data: 76,200 translation pairs. \n\n29. Marathi Bilingual Dictionary Data: 45,400 translations | 32,800 senses | 3,600 example translations. \n\n30. Nepali Bilingual Dictionary Data: 350,000 translations | 264,200 senses | 1,300 example translations. \n\n31. New Zealand English Monolingual Dictionary Data: 100,000 words \n\n32. Odia Semi-bilingual Dictionary Data: 30,700 words | 69,300 senses | 69,200 translations. \n\n33. Punjabi Bilingual Dictionary Data: 114,500 translations | 56,400 senses. \n\n34. Punjabi Semi-bilingual Dictionary Data: 16,600 words | 23,900 senses | 46,900 translations. \n\n35. Portuguese Monolingual Dictionary Data: 143,600 words | 285,500 senses | 69,300 example sentences. \n\n36. Portuguese Bilingual Dictionary Data: 300,000 translations | 158,000 senses | 117,800 example translations. \n\n37. Tamil Monolingual Dictionary Data: 21,000 words | 29,100 senses | 37,700 example sentences. \n\n38. Tamil Semi-bilingual Dictionary Data: 48,300 words | 63,700 senses | 103,800 translations. \n\n39. Telugu Bilingual Dictionary Data: 46,400 translations | 39,200 senses | 9,300 example translations. \n\n40. Telugu Semi-bilingual Dictionary Data: 10,800 words | 13,600 senses | 13,600 translations. \n\n41. Thai Bilingual Dictionary Data: 213,100 translations | 90,400 senses. \n\n42. Urdu Bilingual Dictionary Data: 153,300 translations | 98,800 senses. \n\n43. Urdu Semi-bilingual Dictionary Data: 90,600 words | 141,200 senses | 178,100 translations. \n\n44. Vietnamese Monolingual Dictionary Data: 41,200 words | 54,800 senses | 67,400 example sentences. \n \n\nUse Cases: \n\nWe consistently work with our clients on new use cases as language technology continues to evolve. These include NLP applications, TTS, dictionary display tools, games, translation, word embedding, and word sense disambiguation (WSD). \n\nIf you have a specific use case in mind that isn't listed here, we’d be happy to explore it with you. Don’t hesitate to get in touch with us at Growth.OL@oup.com to start the conversation. \n\n\nPricing: \n\nOxford Languages offers flexible pricing based on use case and delivery format. Our datasets are licensed via term-based IP agreements and tiered pricing for API-delivered data. Whether you’re integrating into a product, training an LLM, or building custom NLP solutions, we tailor licensing to your specific needs. \n\nContact our team or email us at Growth.OL@oup.com to explore pricing options and discover how our language data can support your goals. \n\nPlease note that some datasets may have rights restrictions. Contact us for more information. \n\n\nAbout the sample: \n\nThe samples offer a brief overview of one or two language datasets (monolingual or/and bilingual dictionary data). To help you explore the structure and features of our dataset, we provide a sample in CSV format for preview purposes only. \n\nIf you need the complete original sample or more details about any dataset, please contact us (Growth.OL@oup.com) to request access or further information.","terms_of_service":"https://corp.oup.com/privacy-policy","contact_email":"support@datarade.ai","privacy_policy_link":"https://corp.oup.com/privacy-policy","assets":["ASSET_TYPE_DATA_TABLE"]},"provider_summary":{"name":"Oxford Languages","description":"Oxford Languages delivers multilingual language datasets designed to power the next generation of language technologies. Built through decades of research and curated by expert lexicographers, our data fuels diverse applications – from text-to-speech (TTS) and predictive text to language models dictionary display tools, assistive tech, chatbots, games, and more. With over 60 languages and a wide range of features, our structured datasets ensure linguistic accuracy, cultural nuance, and domain relevance – ideal for AI, NLP, and ML development."},"popularity":420},{"id":"dbddddf0-e329-47f9-a5df-f642f27ca496","summary":{"name":"Spanish Hate Speech Voice Dataset: Real-World Toxicity Vocalized for Content Moderation","subtitle":"Spanish hate speech vocalized by real, consented voices—ideal for toxic speech detection, moderation, and bias research.","setting":{"visibility":"PUBLIC"},"created_at":1756312357977,"updated_at":1756312396726,"categories":["DEMOGRAPHICS","OTHER","SCIENCE_AND_RESEARCH"],"listingType":"PERSONALIZED","provider_id":"3d28060f-1726-48a7-b699-f4a84328832e"},"detail":{"description":"**Overview**\nThis dataset features a collection of Spanish-language hate speech and toxic content sourced from established toxicity datasets (haternet, chileno, homomex, hascosva, misocorpus) and vocalized synthetically by consented speakers. It includes thousands of toxic utterances representing diverse expressions, targets, and emotional intensities. 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Spanish Audio Data (word-level) \n\nCurated word-level audio data for the Spanish language, which covers all varieties of world Spanish, providing rich dialectal diversity in the Spanish language. \n\n- Audio files: 20,900 \n- Format: XLSX (for index), MP3 and WAV (audio files) \n\n6. Spanish Word List Data \n\nThis language data contains a carefully curated and comprehensive list of 450,000 Spanish words. \n\n- Wordforms: 450,000 \n- Format: CSV and TXT formats \n- Delivery: Email (link-based file sharing) \n\n\nUse Cases: \n\nWe consistently work with our clients on new use cases as language technology continues to evolve. These include NLP applications, TTS, dictionary display tools, games, translation, word embedding, and word sense disambiguation (WSD). \n\nIf you have a specific use case in mind that isn't listed here, we’d be happy to explore it with you. Don’t hesitate to get in touch with us at Oxford.Languages@oup.com to start the conversation. \n\nPricing:\n\nOxford Languages offers flexible pricing based on use case and delivery format. Our datasets are licensed via term-based IP agreements and tiered pricing for API-delivered data. Whether you’re integrating into a product, training an LLM, or building custom NLP solutions, we tailor licensing to your specific needs. \n\nContact our team or email us at Oxford.Languages@oup.com to explore pricing options and discover how our language data can support your goals.\n\n\nAbout the sample: \n\nThe samples offer a brief overview of one or two language datasets (monolingual or/and bilingual dictionary data). 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This data package also shows the survey validated by the federal Centers for Medicare and Medicaid Services (CMS) as a standard assessment tool for all hospitals throughout the nation.\n\n**Benefits**\n- Useful for patients to check facility options and their satisfaction level.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. 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Satisfaction](https://www.johnsnowlabs.com/marketplace/health-and-hospitals-corporation-patient-satisfaction)\n\t- This dataset showcases patient-satisfaction at Health and Hospitals Corporation (HHC) hospitals which are measured by a standardized survey known as the Hospital Consumer Assessment of Healthcare Providers & Systems (HCAHPS).\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP 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royalty-free commercial use and redistribution.\n\n**Need Help?**\nIf you have questions about our products, contact us at [info@johnsnowlabs.com](mailto:info@johnsnowlabs.com). \n 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The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [Vaccine Adverse Event Reporting System 2014](https://www.johnsnowlabs.com/marketplace/vaccine-adverse-event-reporting-system-2014)\n\t- The Vaccine Adverse Event Reporting System (VAERS) 2014 was created by the Food and Drug Administration (FDA) and Centers for Disease Control and Prevention (CDC) to receive reports about adverse events that may be associated with vaccines. No prescription drug or biological product, such as a vaccine, is completely free from side effects.\n- [Vaccine Adverse Event Reporting System 2015](https://www.johnsnowlabs.com/marketplace/vaccine-adverse-event-reporting-system-2015)\n\t- The Vaccine Adverse Event Reporting System (VAERS) 2015 was created by the Food and Drug Administration (FDA) and Centers for Disease Control and Prevention (CDC) to receive reports about adverse events that may be associated with vaccines. No prescription drug or biological product, such as a vaccine, is completely free from side effects.\n- [Vaccine Adverse Event Reporting System 2016](https://www.johnsnowlabs.com/marketplace/vaccine-adverse-event-reporting-system-2016)\n\t- The Vaccine Adverse Event Reporting System (VAERS) 2016 was created by the Food and Drug Administration (FDA) and Centers for Disease Control and Prevention (CDC) to receive reports about adverse events that may be associated with vaccines. No prescription drug or biological product, such as a vaccine, is completely free from side effects.\n- [Vaccine Adverse Event Reporting System 2017](https://www.johnsnowlabs.com/marketplace/vaccine-adverse-event-reporting-system-2017)\n\t- The Vaccine Adverse Event Reporting System (VAERS) 2017 was created by the Food and Drug Administration (FDA) and Centers for Disease Control and Prevention (CDC) to receive reports about adverse events that may be associated with vaccines. No prescription drug or biological product, such as a vaccine, is completely free from side effects.\n- [Vaccine Adverse Event Reporting System 2018](https://www.johnsnowlabs.com/marketplace/vaccine-adverse-event-reporting-system-2018)\n\t- The Vaccine Adverse Event Reporting System (VAERS) 2018 was created by the Food and Drug Administration (FDA) and Centers for Disease Control and Prevention (CDC) to receive reports about adverse events that may be associated with vaccines. No prescription drug or biological product, such as a vaccine, is completely free from side effects.\n- [Vaccine Adverse Event Reporting System 2019](https://www.johnsnowlabs.com/marketplace/vaccine-adverse-event-reporting-system-2019)\n\t- The Vaccine Adverse Event Reporting System (VAERS) 2019 was created by the Food and Drug Administration (FDA) and Centers for Disease Control and Prevention (CDC) to receive reports about adverse events that may be associated with vaccines. No prescription drug or biological product, such as a vaccine, is completely free from side effects.\n- [Vaccine Adverse Event Reporting System 2020](https://www.johnsnowlabs.com/marketplace/vaccine-adverse-event-reporting-system-2020)\n\t- The Vaccine Adverse Event Reporting System (VAERS) 2020 was created by the Food and Drug Administration (FDA) and Centers for Disease Control and Prevention (CDC) to receive reports about adverse events that may be associated with vaccines. No prescription drug or biological product, such as a vaccine, is completely free from side effects.\n- [Vaccine Adverse Event Reporting System 2021](https://www.johnsnowlabs.com/marketplace/vaccine-adverse-event-reporting-system-2021)\n\t- The Vaccine Adverse Event Reporting System (VAERS) 2021 was created by the Food and Drug Administration (FDA) and Centers for Disease Control and Prevention (CDC) to receive reports about adverse events that may be associated with vaccines. No prescription drug or biological product, such as a vaccine, is completely free from side effects.\n- [Vaccine Adverse Event Reporting System 2022](https://www.johnsnowlabs.com/marketplace/vaccine-adverse-event-reporting-system-2022)\n\t- The Vaccine Adverse Event Reporting System (VAERS) 2022 was created by the Food and Drug Administration (FDA) and Centers for Disease Control and Prevention (CDC) to receive reports about adverse events that may be associated with vaccines. No prescription drug or biological product, such as a vaccine, is completely free from side effects.\n- [Vaccine Adverse Event Reporting System Symptoms 2014](https://www.johnsnowlabs.com/marketplace/vaccine-adverse-event-reporting-system-symptoms-2014)\n\t- The signs, symptoms, and diagnoses provided are assigned codes and affixed to the case for indexing purposes that are found in this dataset. Information obtained from the original VAERS (Vaccine Adverse Event Reporting System) report, follow-up inquiries, and coding activities are stored in a secure computerized database for analysis. Scanned facsimiles of the original reports are also maintained in a computerized image-base for FDA and CDC vaccine surveillance activities.\n- [Vaccine Adverse Event Reporting System Symptoms 2015](https://www.johnsnowlabs.com/marketplace/vaccine-adverse-event-reporting-system-symptoms-2015)\n\t- The signs, symptoms, and diagnoses provided are assigned codes and affixed to the case for indexing purposes that are found in this dataset. Information obtained from the original VAERS (Vaccine Adverse Event Reporting System) report, follow-up inquiries, and coding activities are stored in a secure computerized database for analysis. Scanned facsimiles of the original reports are also maintained in a computerized image-base for FDA and CDC vaccine surveillance activities.\n- [Vaccine Adverse Event Reporting System Symptoms 2016](https://www.johnsnowlabs.com/marketplace/vaccine-adverse-event-reporting-system-symptoms-2016)\n\t- The signs, symptoms, and diagnoses provided are assigned codes and affixed to the case for indexing purposes that are found in this dataset. Information obtained from the original VAERS (Vaccine Adverse Event Reporting System) report, follow-up inquiries, and coding activities are stored in a secure computerized database for analysis. Scanned facsimiles of the original reports are also maintained in a computerized image-base for FDA and CDC vaccine surveillance activities.\n- [Vaccine Adverse Event Reporting System Symptoms 2017](https://www.johnsnowlabs.com/marketplace/vaccine-adverse-event-reporting-system-symptoms-2017)\n\t- The signs, symptoms, and diagnoses provided are assigned codes and affixed to the case for indexing purposes that are found in this dataset. Information obtained from the original VAERS (Vaccine Adverse Event Reporting System) report, follow-up inquiries, and coding activities are stored in a secure computerized database for analysis. Scanned facsimiles of the original reports are also maintained in a computerized image-base for FDA and CDC vaccine surveillance activities.\n- [Vaccine Adverse Event Reporting System Symptoms 2018](https://www.johnsnowlabs.com/marketplace/vaccine-adverse-event-reporting-system-symptoms-2018)\n\t- The signs, symptoms, and diagnoses provided are assigned codes and affixed to the case for indexing purposes that are found in this dataset. Information obtained from the original VAERS (Vaccine Adverse Event Reporting System) report, follow-up inquiries, and coding activities are stored in a secure computerized database for analysis. Scanned facsimiles of the original reports are also maintained in a computerized image-base for FDA and CDC vaccine surveillance activities.\n- [Vaccine Adverse Event Reporting System Symptoms 2019](https://www.johnsnowlabs.com/marketplace/vaccine-adverse-event-reporting-system-symptoms-2019)\n\t- The signs, symptoms, and diagnoses provided are assigned codes and affixed to the case for indexing purposes that are found in this dataset. Information obtained from the original VAERS (Vaccine Adverse Event Reporting System) report, follow-up inquiries, and coding activities are stored in a secure computerized database for analysis. Scanned facsimiles of the original reports are also maintained in a computerized image-base for FDA and CDC vaccine surveillance activities.\n- [Vaccine Adverse Event Reporting System Symptoms 2020](https://www.johnsnowlabs.com/marketplace/vaccine-adverse-event-reporting-system-symptoms-2020)\n\t- The signs, symptoms, and diagnoses provided are assigned codes and affixed to the case for indexing purposes that are found in this dataset. Information obtained from the original VAERS (Vaccine Adverse Event Reporting System) report, follow-up inquiries, and coding activities are stored in a secure computerized database for analysis. Scanned facsimiles of the original reports are also maintained in a computerized image-base for FDA and CDC vaccine surveillance activities.\n- [Vaccine Adverse Event Reporting System Symptoms 2021](https://www.johnsnowlabs.com/marketplace/vaccine-adverse-event-reporting-system-symptoms-2021)\n\t- The signs, symptoms, and diagnoses provided are assigned codes and affixed to the case for indexing purposes that are found in this dataset. Information obtained from the original VAERS (Vaccine Adverse Event Reporting System) report, follow-up inquiries, and coding activities are stored in a secure computerized database for analysis. Scanned facsimiles of the original reports are also maintained in a computerized image-base for FDA and CDC vaccine surveillance activities.\n- [Vaccine Adverse Event Reporting System Symptoms 2022](https://www.johnsnowlabs.com/marketplace/vaccine-adverse-event-reporting-system-symptoms-2022)\n\t- The signs, symptoms, and diagnoses provided are assigned codes and affixed to the case for indexing purposes that are found in this dataset. Information obtained from the original VAERS (Vaccine Adverse Event Reporting System) report, follow-up inquiries, and coding activities are stored in a secure computerized database for analysis. Scanned facsimiles of the original reports are also maintained in a computerized image-base for FDA and CDC vaccine surveillance activities.\n- [Vaccine Adverse Event Reporting System Vaccine Information 2014](https://www.johnsnowlabs.com/marketplace/vaccine-adverse-event-reporting-system-vaccine-information-2014)\n\t- A report to Vaccine Adverse Event Reporting System (VAERS) generally does not prove that the identified vaccine(s) caused the adverse event described. It only confirms that the reported event occurred sometime after the vaccine was given, which is what this dataset showcases. No proof that the event was caused by the vaccine is required in order for VAERS to accept the report. VAERS accepts all reports without judging whether the event was caused by the vaccine.\n- [Vaccine Adverse Event Reporting System Vaccine Information 2015](https://www.johnsnowlabs.com/marketplace/vaccine-adverse-event-reporting-system-vaccine-information-2015)\n\t- A report to Vaccine Adverse Event Reporting System (VAERS) generally does not prove that the identified vaccine(s) caused the adverse event described. It only confirms that the reported event occurred sometime after the vaccine was given, which is what this dataset showcases. No proof that the event was caused by the vaccine is required in order for VAERS to accept the report. VAERS accepts all reports without judging whether the event was caused by the vaccine.\n- [Vaccine Adverse Event Reporting System Vaccine Information 2016](https://www.johnsnowlabs.com/marketplace/vaccine-adverse-event-reporting-system-vaccine-information-2016)\n\t- A report to Vaccine Adverse Event Reporting System (VAERS) generally does not prove that the identified vaccine(s) caused the adverse event described. It only confirms that the reported event occurred sometime after the vaccine was given, which is what this dataset showcases. No proof that the event was caused by the vaccine is required in order for VAERS to accept the report. VAERS accepts all reports without judging whether the event was caused by the vaccine.\n- [Vaccine Adverse Event Reporting System Vaccine Information 2017](https://www.johnsnowlabs.com/marketplace/vaccine-adverse-event-reporting-system-vaccine-information-2017)\n\t- A report to Vaccine Adverse Event Reporting System (VAERS) generally does not prove that the identified vaccine(s) caused the adverse event described. It only confirms that the reported event occurred sometime after the vaccine was given, which is what this dataset showcases. No proof that the event was caused by the vaccine is required in order for VAERS to accept the report. VAERS accepts all reports without judging whether the event was caused by the vaccine.\n- [Vaccine Adverse Event Reporting System Vaccine Information 2018](https://www.johnsnowlabs.com/marketplace/vaccine-adverse-event-reporting-system-vaccine-information-2018)\n\t- A report to Vaccine Adverse Event Reporting System (VAERS) generally does not prove that the identified vaccine(s) caused the adverse event described. It only confirms that the reported event occurred sometime after the vaccine was given, which is what this dataset showcases. No proof that the event was caused by the vaccine is required in order for VAERS to accept the report. VAERS accepts all reports without judging whether the event was caused by the vaccine.\n- [Vaccine Adverse Event Reporting System Vaccine Information 2019](https://www.johnsnowlabs.com/marketplace/vaccine-adverse-event-reporting-system-vaccine-information-2019)\n\t- A report to Vaccine Adverse Event Reporting System (VAERS) generally does not prove that the identified vaccine(s) caused the adverse event described. It only confirms that the reported event occurred sometime after the vaccine was given, which is what this dataset showcases. No proof that the event was caused by the vaccine is required in order for VAERS to accept the report. VAERS accepts all reports without judging whether the event was caused by the vaccine.\n- [Vaccine Adverse Event Reporting System Vaccine Information 2020](https://www.johnsnowlabs.com/marketplace/vaccine-adverse-event-reporting-system-vaccine-information-2020)\n\t- A report to Vaccine Adverse Event Reporting System (VAERS) generally does not prove that the identified vaccine(s) caused the adverse event described. It only confirms that the reported event occurred sometime after the vaccine was given, which is what this dataset showcases. No proof that the event was caused by the vaccine is required in order for VAERS to accept the report. VAERS accepts all reports without judging whether the event was caused by the vaccine.\n- [Vaccine Adverse Event Reporting System Vaccine Information 2021](https://www.johnsnowlabs.com/marketplace/vaccine-adverse-event-reporting-system-vaccine-information-2021)\n\t- A report to Vaccine Adverse Event Reporting System (VAERS) generally does not prove that the identified vaccine(s) caused the adverse event described. It only confirms that the reported event occurred sometime after the vaccine was given, which is what this dataset showcases. No proof that the event was caused by the vaccine is required in order for VAERS to accept the report. VAERS accepts all reports without judging whether the event was caused by the vaccine.\n- [Vaccine Adverse Event Reporting System Vaccine Information 2022](https://www.johnsnowlabs.com/marketplace/vaccine-adverse-event-reporting-system-vaccine-information-2022)\n\t- A report to Vaccine Adverse Event Reporting System (VAERS) generally does not prove that the identified vaccine(s) caused the adverse event described. It only confirms that the reported event occurred sometime after the vaccine was given, which is what this dataset showcases. No proof that the event was caused by the vaccine is required in order for VAERS to accept the report. VAERS accepts all reports without judging whether the event was caused by the vaccine.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated\n- Data Updates\n - Data updates support replace-on-update: outdated foreign keys are deprecated, not deleted\n\n**Our data is curated and enriched by domain experts** \n\n Each dataset is manually curated by our team of doctors, pharmacists, public health & medical billing experts:\n- Field names, descriptions, and normalized values are chosen by people who actually understand their meaning\n- Healthcare & life science experts add categories, search keywords, descriptions and more to each dataset\n- Both manual and automated data enrichment supported for clinical codes, providers, drugs, and geo-locations\n- The data is always kept up to date – even when the source requires manual effort to get updates\n- Support for data subscribers is provided directly by the domain experts who curated the data sets\n- Every data source’s license is manually verified to allow for royalty-free commercial use and redistribution.\n\n**Need Help?**\nIf you have questions about our products, contact us at [info@johnsnowlabs.com](mailto:info@johnsnowlabs.com). \n 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The dataset includes forecasted freight rates and historical model-run forecasts.\nThe Freight Rate Forecast dataset delivers forward-looking freight price forecasts, monthly market intelligence, and in-depth analysis across clean and dirty tankers supported by a global economic outlook. Navigate freight-market volatility with greater confidence.\nTurn market complexity into actionable insight with an independent, analytics-driven view of tanker freight markets. Our forecasting methodology evaluates more than 3,000 market drivers and combines S&P Global’s breadth of shipping and macroeconomic expertise with machine learning and advanced analytics.\nThe result is a clear, unbiased market outlook giving you the analytical rationale to anticipate change, assess opportunities, and make faster, better-informed decisions.\n\nThis dataset includes:\n\n- Forecasts and key market drivers across major Platts shipping routes for clean and dirty tankers, VLCC, Suezmax, Aframax, LR2, LR1, and MR.\n- 36 months of monthly freight rate forecasts, giving you the forward visibility to plan ahead and respond decisively.\n\nA complete intelligence package: the dataset brings together Freight Rate Forecast Data (structured data) and Freight Rate Forecast Insights (unstructured research reports), so you can combine decision-ready numbers with the market context behind them. The field count below applies to Freight Rate Forecast Data only.\n\n**Use cases**\n\n- Fundamental Analysis - Assess short-term freight trends for tankers(VLCC, Suezmax, Aframax, LR2, LR1 and MR) to decide where best to commit tonnage, with rate forecasts running 36 months ahead, updated monthly, for 28 covered route, built on the underlying drivers of freight demand, supply, and vessel activity.\n- Market Analysis - Understand how the freight market is evolving by accessing historical model runs, so you can see what was published at any point in time and test how the view has shifted relative to market movements.\n- Risk Analysis - Improve chartering strategies, trading positions and risk management with a monthly forecast giving a single average rate per route out to 36 months, not just the point estimate, with seasonality built in.\n\n**Product details**\nSample Tables:\nA) FREIGHT_RATE_FORECAST\nB ) FREIGHT_RATE_FORECAST_DRIVERS\n\nSample Fields:\nA ) FREIGHT_RATE_FORECAST\n\n- COMMODITY\n- UNIT_OF_MEASURE\n- CURRENCY\n- REPORT_FOR_DATE\n- SYMBOL\n- AS_OF_DATE\n- VESSEL_SIZE_CLASS\n- DESCRIPTION_OF_SYMBOL\n- VALUE\n\nB) FREIGHT_RATE_FORECAST_DRIVERS\n\n- COMMODITY\n- REPORT_FOR_DATE\n- SYMBOL\n- AS_OF_DATE\n- VESSEL_SIZE_CLASS\n- DESCRIPTION_OF_THE_DRIVER\n- IMPACT_ON_FREIGHT\n- VALUE\n\nView Descriptions:\n\n- FREIGHT_RATE_FORECAST - Provides access to current spot freight rate assessments.\n- FREIGHT_RATE_FORECAST_DRIVERS - Provides access to current spot freight rate drivers.\n\n","terms_of_service":"https://www.spglobal.com/en/privacy/privacy-policy-english","documentation_link":"","privacy_policy_link":"https://www.spglobal.com/en/privacy/privacy-policy-english","geographical_coverage":"[\"Global\"]","update_frequency":{"interval":1,"unit":"MONTHLY"},"collection_granularity":{"interval":1,"unit":"MONTHLY"},"collection_date_start":1325356200000,"assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"S&P Global Energy","description":"At S&P Energy, our complete view of global energy and commodities markets enables our customers to make decisions with conviction and create long-term, sustainable value. 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We do all the matching on your behalf.\n\nThe data set is updated daily.\n\nThe sample data shows decoded flight numbers for Swiss, Alaska Airlines, and Horizon Air.\n\nContact us to get access to ch-aviation's AWS S3 sample data bucket as well allowing you to build proof of concepts with all of our sample data. \n\nThe direct bucket URL for this data set is: https://eu-central-1.console.aws.amazon.com/s3/buckets/dataservices-standardised-samples?region=eu-central-1&bucketType=general&prefix=flight_number_decoding/&showversions=false\n\nFull Technical Data Dictionary: https://about.ch-aviation.com/flight-number-decoding/","terms_of_service":"https://www.ch-aviation.com/privacy","contact_email":"marketplace@datarade.ai","privacy_policy_link":"https://www.ch-aviation.com/privacy","assets":["ASSET_TYPE_DATA_TABLE"]},"provider_summary":{"name":"ch-aviation","description":"For more than 20 years, we have been supplying decision-makers around the globe with relevant and up-to-date aviation intelligence and insightful news. 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Operating in the education data and school analytics industry, SchoolDigger aggregates, standardizes, and enriches public school data from all 50 U.S. states and the District of Columbia.\nSchoolDigger's data assets include comprehensive school directory information, student demographics, test score results, school performance rankings, and historical trend data spanning over a decade. These datasets enable customers to analyze school quality metrics, track educational outcomes over time, and integrate authoritative K-12 data into real estate platforms, academic research, and business applications."},"popularity":726},{"id":"1f104138-f4d8-4581-833e-4dff93edee37","summary":{"name":"SAMPLE Mobile Location Data | Brazil | +100M Unique Devices | +50M Daily Users | +50B Events / Month","subtitle":"Quadrant provides privacy first global location data and foot traffic data for AI, analytics, and business ...","share":{"name":"quadrant_2debde37_9fa0_4161_bc62_91e17dd2221d_samples_share","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1790087075958,"updated_at":1790087075958,"categories":["GEOSPATIAL"],"listingType":"STANDARD","provider_id":"a8064e88-f58c-4540-97d8-309869635380"},"detail":{"description":"Quadrant provides privacy first global location data and foot traffic data that help organizations understand how people interact with places across more than 200 countries and territories. Our enterprise grade datasets enable businesses to measure visitation trends, analyze mobility patterns, support AI models, improve location intelligence, and make confident data driven decisions.\n\nBuilt for analytics and AI, Quadrant's location data helps organizations uncover meaningful geographic patterns across industries including retail, commercial real estate, transportation, urban planning, financial services, market research, tourism, and consumer insights. Whether you are evaluating site performance, measuring market activity, building predictive models, or analyzing movement trends, our data provides a trusted foundation for scalable analysis.\n\nOur location data is privacy first, quality controlled, and designed to meet the needs of modern enterprises. Every dataset undergoes rigorous validation and continuous quality monitoring to ensure consistency, accuracy, and reliability while supporting responsible data practices.\n\nQuadrant offers flexible geographic coverage that adapts to your business requirements. Unlike providers limited to predefined geographic boundaries, we support reporting and analysis at the city, county, state, regional, and national levels, making it easier to analyze Points of Interest, compare markets, evaluate trade areas, and understand mobility across locations.\n\nKey benefits include:\n\nGlobal coverage across more than 200 countries and territories\nPrivacy first and quality controlled location data\nAI ready and analytics friendly datasets\nFlexible geographic reporting at the city, county, state, regional, and national levels\nHistorical data available from 2022 onwards\nTailored datasets aligned with your business requirements\nExpert technical support from our in house data specialists\n\nQuadrant's location data and foot traffic data power a wide range of applications including market intelligence, consumer behavior analysis, site selection, competitive benchmarking, catchment analysis, mobility analytics, urban planning, location based services, and AI driven business intelligence. 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Our POI data spans 200+ countries and includes 300+ million places with 50+ verified attributes, enabling organizations to build better maps, search experiences, market intelligence, and location based products. 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By unifying executive firmographics, payor mix, and clinical performance with Definitive Healthcare’s affiliation intelligence, this curated dataset highlights the organizational hierarchies that dictate purchasing power. \n\nFor Go-To-Market and Operations teams, this data layer enables you to efficiently map integrated delivery network (IDN) decision-making chains and target high value accounts based on real-world clinical footprints.\n\n**Key Highlights**\n- Organizational Hierarchy: Parent/Child relationships across hospitals, health systems and other important clinical enterprise\n- Commercial Intelligence: Detailed payor mix, financial health and technology installations\n- Provider Profiles: Executive contacts, physical practice locations and clinical quality metrics\n\n\n\n**High Impact Use cases**\n\n- *Commercial Targeting & Segmentation*: \nIdentify and prioritize high value targets by augmenting sales data with Atlas firmographics to reveal facilities with the highest clinical volume and budget. \n- *Market Sizing & Opportunity Analysis*: \nAccurately quantify your target addressable market by pairing data with clinical performance and financial health metrics to identify high-value white space. Enabling your team to pinpoint specific care gaps that your product or service is uniquely positioned to fill.\n- *Influence & Hierarchy Mapping*:\nUncover corporate parents behind clinics to ensure your GTM teams are targeting true financial decision makers. Align your territories with Integrated Delivery Networks footprints.\n\n**Product details**\n\n*Sample Tables:*\n-Demographic information on all healthcare organizations (HCOs) in the United States\n-Demographic information on all healthcare providers (HCPs) in the United States\n-Active affiliations between HCPs and HCOs\n-8 additional tables\n\n*Fields Included:*\n-HCO NPI Number\n-HCO Name\n-HCO Address Line 1\n-HCO Address Line 2\n-HCO City\n-HCO State\n-HCO Zip Code\n-HCP NPI Number\n-HCP First Name\n-HCP Last Name\n-HCP Primary Specialty\n-Affiliation Score\n-Affiliation Rank\n-117 additional fields\n\nReady to transform your strategy?\n\nTo unlock the full power of Definitive Healthcare’s data, please engage with our team: databricks@definitivehc.com \n","terms_of_service":"https://www.definitivehc.com/terms-of-use","documentation_link":"https://www.definitivehc.com/data-products","privacy_policy_link":"https://www.definitivehc.com/about","geographical_coverage":"[\"United States and Canada\"]","update_frequency":{"interval":1,"unit":"MONTHLY"},"data_source":"Definitive Healthcare’s proprietary research","assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"Definitive Healthcare","description":"Definitive Healthcare is a data and analytics company focused on the business side of healthcare. 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New categories are released regularly. \n- New Movers & Homeowners -- Recently relocated or pre-move individuals in-market for services, furnishings, and insurance\\\n Use case: Target with broadband, security systems, local services, or homeowners' insurance. \n- Parents & Family Stage Shifts -- New parents, empty nesters, and families with children entering key schooling or adulthood phases\\\n Use case: Promote family health plans, educational savings, or lifestyle adjustments. \n- Career Changes & Job Market Transitions -- Recently employed, promoted, or seeking new roles, open to financial and lifestyle products\\\n Use case: Engage with banking products, insurance, relocation, or upskilling offers. \n- Retirement & Pre-Retirement Audiences -- Consumers nearing or entering retirement, often seeking healthcare, financial planning, or leisure\n Use case: Drive Medicare enrollment, travel offers, retirement planning services. \n- Relationship & Household Formation Changes -- Indicators of marriage, divorce, cohabitation---impacting housing, finances, and insurance needs\\\n Use case: Enable targeting for joint financial products, renter's/home insurance, or legal services. \n- Health & Wellness Journey Starters -- Consumers initiating lifestyle changes like fitness, diet, or quitting smoking; often first-time category buyers\\\n Use case: Promote wellness programs, supplements, fitness services, and healthy meal plans. \n\n***Key Features*** \n\n- AI-modeled life event and lifestyle transition scores per individual \n- Behavioral, transactional, and situational signal integration \n- Updated quarterly to reflect changing life-stage data and triggers \n- Available as prebuilt segments or custom-built upon request \n- Delivered with identity spine or hashed match keys for activation \n\n***Use Cases*** \n\n- Launch real-time campaigns aligned with consumer transitions (move, baby, retirement) \n- Reactivate lapsed users or deepen personalization with life-stage context \n- Fuel multi-channel lifecycle programs with transition-based triggers \n- Build top-of-funnel audiences for financial, health, and retail categories tied to life changes \n- Optimize upsell/cross-sell at key inflection points using lifestyle insights \n\n***Next Steps*** \n\n- Request listing access \n- Data Axle team will contact you to finalize audience, data cut, and delivery method / frequency \n- Need a Custom Audience? We deliver tailored segments within 5--10 business days \n\n***Business Needs*** \n\n- 360-Degree Customer View\\\n Enrich your customer profiles by adding verified life-stage, behavioral, and event-driven context---enabling you to target not just who they are, but what stage they're in. \n- Customer Acquisition\\\n Identify consumers entering high-opportunity life transitions where product decisions are made---such as home purchases, family changes, or career shifts. Reach them when intent is naturally high. \n- Demand Forecasting\\\n Leverage predictive AI to anticipate future demand based on life event trajectories. Enable better campaign timing for services that align with household evolution and personal milestones. \n- Identity Resolution\\\n Maintain accurate identity through life transitions---such as name changes, relocations, or household reconfiguration---ensuring continued targeting and measurement fidelity. \n- Market Segmentation\\\n Build micro-segments based on lifestyle progression. Example: \"Post-grad urban movers,\" \"First-time parents,\" or \"Recently divorced professionals\"---targeted by real-time transitional states. \n- Audience Segmentation\\\n Use AI-generated personas tied to transitional behaviors. Move beyond static data to include signals of change---allowing for adaptive, emotionally intelligent outreach. \n- Audience Activation\\\n Deploy transition-based audiences across digital, email, OTT, social, and direct mail channels. 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We improve business performance for our clients by using our Business & Consumer data solutions and omnichannel marketing services, enabling our clients to acquire, grow, and retain their customers more efficiently and effectively, and enhance their platforms, products, and customer insights."},"popularity":329},{"id":"38a67489-ef4f-4052-91f2-e3760b473392","summary":{"name":"BioContextAI MCP - Unified Structural & Systems Biology Intelligence","subtitle":"Connect AI agents to 16 curated biomedical tools like AlphaFold, STRING, and UniProt within Databricks.","setting":{"visibility":"PUBLIC"},"created_at":1780677622996,"updated_at":1780692736588,"categories":["SCIENCE_AND_RESEARCH","OPEN_SOURCE","HEALTH","EDUCATION"],"listingType":"STANDARD","provider_id":"d74c8aa3-7681-485d-8300-eb957bac7194"},"detail":{"description":"**Generate a bearer token by signing up here: https://climb.ai/labs/hls-mcp. Once you sign up, a token will be emailed to you instantly.**\n\n**MCP Overview:**\nThe BioContextAI Knowledgebase MCP Server connects your Databricks AI agents to a federated layer of 16 curated tools across structural biology, protein networks, cell type biology, proteomics, pathways, and literature. It is the companion server to the Climb native MCP suite covering databases not served by dedicated apps including AlphaFold, STRING, Human Protein Atlas, InterPro, PanglaoDB, PRIDE, Reactome, and Europe PMC. UniProt is the canonical protein identity path across the entire Climb MCP ecosystem.\n\n**Use cases:**\n\n- Retrieve AlphaFold structure predictions for any protein by gene symbol via automatic UniProt ID resolution\n- Map protein-protein interactions from STRING with configurable confidence thresholds and species filters\n- Fetch UniProt protein annotations including function, disease associations, and subcellular localization\n- Retrieve tissue and cell type expression profiles from Human Protein Atlas and PanglaoDB marker gene data\n- Query protein domain architecture from InterPro, pathway context from Reactome, and proteomics datasets from PRIDE\n- Search Europe PMC for full-text literature complementary to the dedicated PubMed MCP server\n\n**Sample prompts:**\n\nPrompt Example 1: Protein Network & Structure Analysis\n\n- \"Retrieve UniProt annotations for EGFR, then map its STRING protein-protein interactions above a confidence score of 0.7. For the top 3 interaction partners, fetch AlphaFold structure predictions and return a network summary table with interaction scores.\"\n\nPrompt Example 2: Domain Architecture & Pathway Context\n\n- \"Retrieve the full domain architecture for EGFR. Fetch its Reactome pathways and Human Protein Atlas expression and pathology profiles to build an integrated target characterization summary for EGFR.\"\n\n**These tools are available when installed:**\n\n- AlphaFold\n - get_alphafold_info_by_protein_symbol: Query AlphaFold structure predictions by protein symbol via UniProt ID conversion\n- Europe PMC\n - get_europepmc_articles: Search Europe PMC articles by query, title, abstract, or author with Boolean logic\n - get_europepmc_fulltext: Retrieve full-text XML for a PMC ID\n- Human Protein Atlas\n - get_human_protein_atlas_info: Retrieve expression, localization, and pathology data by gene ID or symbol\n- InterPro\n - get_interpro_entry: Fetch InterPro entry details including family, domain, and functional site metadata\n - get_protein_domains: Return full protein domain architecture and all InterPro matches\n - search_interpro_entries: Search InterPro by name, type, database, GO term, or species\n- PanglaoDB\n - get_panglaodb_marker_genes: Retrieve cell type marker genes with filters for species, organ, and cell type\n - get_panglaodb_options: Return available filter options for organs and cell types\n- PRIDE\n - get_pride_project: Retrieve metadata and experimental details for a specific PRIDE proteomics project\n - search_pride_projects: Search PRIDE for mass spectrometry proteomics projects by keyword\n- Reactome\n - get_reactome_info_by_identifier: Query Reactome pathway data by identifier\n- STRING\n - get_string_id: Map gene names, synonyms, or UniProt IDs to STRING database IDs\n - get_string_interactions: Retrieve protein-protein interactions above a confidence threshold with species parameter\n - get_string_similarity_scores: Retrieve protein homology similarity scores based on Smith-Waterman bit scores above 50\n- UniProt\n - get_uniprot_protein_info: Retrieve protein annotations from UniProt by protein ID, name, or gene symbol\n\n**Additional Information:**\nThis MCP Server is powered by Climb, a leading Consulting and Solutions Integrator Partner of Databricks. Learn more about Climb here: [climb.ai](https://climb.ai) \n\n![Climb logo](https://climb-static-assets.s3.us-west-2.amazonaws.com/assets/PNG/Dark/Climb+Logo+Left-Thirds.png)","terms_of_service":"https://climb.ai/terms-of-service","documentation_link":"https://biocontext.ai","privacy_policy_link":"https://climb.ai/privacy-policy","update_frequency":{"interval":1,"unit":"MONTHLY"},"assets":["ASSET_TYPE_MCP"],"license":""},"provider_summary":{"name":"BioContext AI","description":"This MCP Server is powered by Climb, a leading Consulting and Solutions Integrator Partner of Databricks. Learn more here: climb.ai | BioContextAI is an open-source initiative that bridges the gap between AI language models and specialized biomedical knowledge, building the infrastructure for reliable AI agents that can access validated scientific resources across databases spanning genes, proteins, drugs, diseases, and clinical data. Its community-driven registry catalogs biomedical data tools following FAIR principles (Findable, Accessible, Interoperable, and Reusable) so that researchers can discover and connect the right resources to their AI workflows without building custom integrations for each source. Published in Nature Biotechnology, BioContextAI is freely available at biocontext.ai and supports any compatible AI assistant or development environment. This MCP Server is powered by Climb.ai, a leading Data and Consulting Partner of Databricks."},"popularity":1775},{"id":"3f880a4c-83ca-431f-a1ed-d9615f1138be","summary":{"name":"S&P Global Text MCP Server","subtitle":"S&P Global's dedicated text agent: a deterministic retrieval layer letting LLMs query textual data in real time.","setting":{"visibility":"PUBLIC"},"created_at":1785336313814,"updated_at":1789069641971,"categories":["FINANCIAL"],"listingType":"PERSONALIZED","provider_id":"bb123ba0-f713-4a39-8c51-2daca088d1de"},"detail":{"description":"**Overview**\n\nProntoNLP Model Context Protocol (MCP) serves as the dedicated text agent for S&P Global's extensive textual datasets. 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Every call is deterministic: the same query returns the same result set, every time, making outputs reproducible for research, audit, and compliance review.\n\nThe architecture of ProntoNLP MCP is engineered to prioritize security at every stage. Acting exclusively as a retrieval agent, ProntoNLP never sees the user's prompt and it remains entirely hidden from its own system. Only the structured parameters like tickers, date ranges, or topic keywords reach our servers, so proprietary questions stay proprietary.\n\n**Use cases**\n- Streamline AI integration with financial document corpora like earnings calls, 10-Ks, and analyst transcripts, powering buy-side research coverage workflows and sell-side and IR peer-disclosure benchmarking at earnings-call speed \n- Automate real-time monitoring of earnings commentary, thematic trends, and fundamental market shifts for operational decision-making across corporate strategy, M&A, and quant and systematic desks\n- Deploy MCP in enterprise environments to standardize access to trusted intelligence across teams and applications, giving credit and risk groups a single early-warning layer for covenant, liquidity, and downgrade-adjacent language across issuers \n- Facilitate regulatory and compliance workflows by anchoring every AI output to sentence-level result IDs that link back to source documents\n- Support portfolio management and investment committee workflows by fusing structured price, estimate, and consensus data with unstructured transcript sentiment in a single conversational thread\n\n\n**Product details**\n\nDocuments available:\n\n- Earnings call transcripts (full text with speaker attribution)\n- 10-K and 10-Q filings\n- 8-K disclosures\n- Conference presentations and investor day materials\n- Sell-side and broker research summaries\n\nComing soon:\n\n- Credit rating agency commentary\n- Central Bank and macro-policy communications\n- Expert Network transcripts\n- News\n\n\n**Additional Insights**\n- Document and company intelligence for accessing business descriptions, filings, and competitor rankings\n- Trend intelligence for surfacing trending topics, charting time-series sentiment, and exploring semantic networks\n- Stock and financial data covering historical prices, percentage movements, top movers, and analyst consensus estimates\n- Context and metadata for retrieving surrounding passage context, identifying speakers, and discovering available documents\nInteractive visualizations including deep research widgets, document mind maps, and topic heatmaps generated on demand\n- Chainable pipelines that compose naturally to move from macro trend discovery to company-level due diligence\n- Deterministic, reproducible outputs, returning the same result every time for the same query\n- Zero-prompt-exposure architecture, ensuring that only tool arguments are visible to the platform","terms_of_service":"https://www.spglobal.com/en/terms-of-use","documentation_link":"","privacy_policy_link":"https://www.spglobal.com/en/privacy/privacy-policy-english","assets":["ASSET_TYPE_MCP"],"license":""},"provider_summary":{"name":"S&P Global Market Intelligence","description":"At S&P Global Market Intelligence, we understand the importance of accurate, deep and insightful information. Our team of experts delivers unrivaled insights and leading data and technology solutions, partnering with customers to expand their perspective, operate with confidence, and make decisions with conviction."},"popularity":1868},{"id":"205b09c9-f180-4f03-8de1-96c84accf2ab","summary":{"name":"American Academy of Orthopaedic Surgeons Guidelines for Hip Fractures","subtitle":"This data package consists of a summary of the recommendations of the American Academy of Orthopaedic Surgeons (AAOS) ..","share":{"name":"american_academy_of_orthopaedic_surgeons_guidelines_for_hip_fractures","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1682355757318,"updated_at":1698965635225,"categories":["HEALTH","SCIENCE_AND_RESEARCH"],"listingType":"STANDARD","provider_id":"f88849f8-9d77-4de2-b4c7-03d55cc3768e"},"detail":{"description":"\n**Overview**\nThis data package consists of a summary of the recommendations of the American Academy of Orthopaedic Surgeons (AAOS) Clinical Practice Guideline on the Management of Hip Fractures in the Elderly.\n\n**Description**\n- Arthroplasty VS Internal Fixation Mortality in Elderly Hip Fractures dataset is a study of the strong evidence that supports arthroplasty for patients with hip fractures like unstable (displaced) femoral neck instead of internal fixation.\n- Aspirin or Clopidogrel Early VS Delayed Treatment in Hip Fractures dataset showcases the limited evidence that supports not delaying hip fracture surgery for patients on aspirin and/or clopidogrel.\n- Bipolar VS Unipolar Hemiarthroplasty Function in Elderly Hip Fractures dataset shows moderate evidence that supports the outcomes of unipolar and bipolar hemiarthroplasty for hip fractures like unstable (displaced) femoral neck fractures are similar.\n- Cemented VS Uncemented Arthroplasty Mortality in Elderly Hip Fractures dataset shows moderate evidence that supports the preferential use of cemented femoral stems in patients undergoing arthroplasty for hip fractures of the femoral neck.\n- Cephallomedullary Device VS Sliding Hip Screw Fracture Complications dataset shows moderate evidence that supports using a cephalomedullary device for the treatment of patients with unstable intertrochanteric hip fractures.\n- Cephallomedullary Device VS Sliding Hip Screw Fracture Functions dataset shows moderate evidence that supports using a cephalomedullary device for the treatment of patients with unstable intertrochanteric hip fractures.\n- Posterior VS Direct Lateral Surgical Approach in Elderly Hip Fractures dataset shows moderate evidence that supports higher dislocation rates with a posterior approach in the treatment of displaced femoral neck fractures with hip arthroplasty.\n- Regional Analgesia VS Pain Control Management of Elderly Hip Fractures dataset shows strong evidence that supports regional analgesia versus control or improvement of preoperative pain in patients with hip fracture.\n- Spinal VS General Anesthesia Management for Elderly Hip Fractures dataset shows strong evidence that supports similar outcomes for general or spinal anesthesia for patients undergoing hip fracture surgery.\n- Traction VS No Traction for Pain Management of Elderly Hip Fractures dataset shows moderate evidence that does not support routine use of preoperative traction for patients with a hip fracture.\n- Treatment Studies for Advanced Imaging for Elderly Hip Fractures dataset shows strong evidence that supports the high applicability of advanced imaging for some outcomes in the management of hip fracture in the elderly.\n- Treatment Studies for Preoperative Regional Analgesia in Hip Fractures dataset shows strong evidence that supports regional analgesia to improve preoperative pain control in patients with hip fracture.\n\n**Benefits**\n- Aaos guideline consists of a systematic review of the available literature regarding the management of hip fractures in the elderly patients (defined as age 65 or older) with hip fractures. the purpose of this guideline is to improve the treatment based on the current best evidence.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [Arthroplasty VS Internal Fixation Mortality in Elderly Hip Fractures](https://www.johnsnowlabs.com/marketplace/arthroplasty-vs-internal-fixation-mortality-in-elderly-hip-fractures)\n\t- This dataset is a study of the strong evidence that supports arthroplasty for patients with hip fractures like unstable (displaced) femoral neck instead of internal fixation.\n- [Aspirin or Clopidogrel Early VS Delayed Treatment in Hip Fractures](https://www.johnsnowlabs.com/marketplace/aspirin-or-clopidogrel-early-vs-delayed-treatment-in-hip-fractures)\n\t- This dataset showcases the limited evidence that supports not delaying hip fracture surgery for patients on aspirin and/or clopidogrel.\n- [Bipolar VS Unipolar Hemiarthroplasty Function in Elderly Hip Fractures](https://www.johnsnowlabs.com/marketplace/bipolar-vs-unipolar-hemiarthroplasty-function-in-elderly-hip-fractures)\n\t- This dataset shows moderate evidence that supports the outcomes of unipolar and bipolar hemiarthroplasty for hip fractures like unstable (displaced) femoral neck fractures are similar.\n- [Cemented VS Uncemented Arthroplasty Mortality in Elderly Hip Fractures](https://www.johnsnowlabs.com/marketplace/cemented-vs-uncemented-arthroplasty-mortality-in-elderly-hip-fractures)\n\t- This dataset shows moderate evidence that supports the preferential use of cemented femoral stems in patients undergoing arthroplasty for hip fractures of the femoral neck.\n- [Cephallomedullary Device VS Sliding Hip Screw Fracture Complications](https://www.johnsnowlabs.com/marketplace/cephallomedullary-device-vs-sliding-hip-screw-fracture-complications)\n\t- This datset shows moderate evidence that supports using a cephalomedullary device for the treatment of patients with unstable intertrochanteric hip fractures.\n- [Cephallomedullary Device VS Sliding Hip Screw Fracture Functions](https://www.johnsnowlabs.com/marketplace/cephallomedullary-device-vs-sliding-hip-screw-fracture-functions)\n\t- This dataset shows moderate evidence that supports using a cephalomedullary device for the treatment of patients with unstable intertrochanteric hip fractures.\n- [Posterior VS Direct Lateral Surgical Approach in Elderly Hip Fractures](https://www.johnsnowlabs.com/marketplace/posterior-vs-direct-lateral-surgical-approach-in-elderly-hip-fractures)\n\t- This dataset shows moderate evidence that supports higher dislocation rates with a posterior approach in the treatment of displaced femoral neck fractures with hip arthroplasty.\n- [Regional Analgesia VS Pain Control Management of Elderly Hip Fractures](https://www.johnsnowlabs.com/marketplace/regional-analgesia-vs-pain-control-management-of-elderly-hip-fractures)\n\t- This dataset shows strong evidence that supports regional analgesia versus control or improvement of preoperative pain in patients with hip fracture.\n- [Spinal VS General Anesthesia Management for Elderly Hip Fractures](https://www.johnsnowlabs.com/marketplace/spinal-vs-general-anesthesia-management-for-elderly-hip-fractures)\n\t- This dataset shows strong evidence that supports similar outcomes for general or spinal anesthesia for patients undergoing hip fracture surgery.\n- [Traction VS No Traction for Pain Management of Elderly Hip Fractures](https://www.johnsnowlabs.com/marketplace/traction-vs-no-traction-for-pain-management-of-elderly-hip-fractures)\n\t- This dataset shows moderate evidence that does not support routine use of preoperative traction for patients with a hip fracture.\n- [Treatment Studies for Advanced Imaging for Elderly Hip Fractures](https://www.johnsnowlabs.com/marketplace/treatment-studies-for-advanced-imaging-for-elderly-hip-fractures)\n\t- This dataset shows strong evidence that supports the high applicability of advanced imaging for some outcomes in the management of hip fracture in the elderly.\n- [Treatment Studies for Preoperative Regional Analgesia in Hip Fractures](https://www.johnsnowlabs.com/marketplace/treatment-studies-for-preoperative-regional-analgesia-in-hip-fractures)\n\t- This dataset shows strong evidence that supports regional analgesia to improve preoperative pain control in patients with hip fracture.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated\n- Data Updates\n - Data updates support replace-on-update: outdated foreign keys are deprecated, not deleted\n\n**Our data is curated and enriched by domain experts** \n\n Each dataset is manually curated by our team of doctors, pharmacists, public health & medical billing experts:\n- Field names, descriptions, and normalized values are chosen by people who actually understand their meaning\n- Healthcare & life science experts add categories, search keywords, descriptions and more to each dataset\n- Both manual and automated data enrichment supported for clinical codes, providers, drugs, and geo-locations\n- The data is always kept up to date – even when the source requires manual effort to get updates\n- Support for data subscribers is provided directly by the domain experts who curated the data sets\n- Every data source’s license is manually verified to allow for royalty-free commercial use and redistribution.\n\n**Need Help?**\nIf you have questions about our products, contact us at [info@johnsnowlabs.com](mailto:info@johnsnowlabs.com). \n 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In addition to the benchmark they offer, the fact that the ranks are based on critical measures for population health (the health outcomes) and for the factors that influence the health (the health determinants) chosen by their impact (weight) on health, the stakeholders can easily identify those factors that need to be managed in order to improve the population health. Another advantage of this data package for the user the rank of each measure using quartiles. Because of this the quartiles along with the ranks offer benchmarks for the overall status of population health and for each category of factors that influence health (health services, behavioral factors, socio-economic factors and environmental factors) and also for the measures based on which the ranks are calculated. 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The additional measures belong to the following categories: health outcomes, health determinants (healthcare, behavioral and socioeconomic factors) and demographics.\n- [US Counties Health Ranks Data Information And Top Performers 2017-2022](https://www.johnsnowlabs.com/marketplace/us-counties-health-ranks-data-information-and-top-performers-2017-2022)\n\t- The dataset contains additional information related to the data sources and years used to calculate the US counties ranks and data for the top performers and US overall values. The dataset comes in addition to the main dataset, US Counties Ranks By Health Outcomes And Determinants.\n- [US Counties Ranks By Health Outcomes And Determinants 2010-2022](https://www.johnsnowlabs.com/marketplace/us-counties-ranks-by-health-outcomes-and-determinants-2010-2022)\n\t- The dataset contains US counties ranking data based on measures of health outcomes and health determinants. The measures used to establish counties ranks are related to length and quality of life for health outcomes and to health behavior, clinical care, socioeconomic and physical environment factors for health determinants. US counties are described along with their FIPS (Federal Information Processing Standard) code and the US state they belong.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated\n- Data Updates\n - Data updates support replace-on-update: outdated foreign keys are deprecated, not deleted\n\n**Our data is curated and enriched by domain experts** \n\n Each dataset is manually curated by our team of doctors, pharmacists, public health & medical billing experts:\n- Field names, descriptions, and normalized values are chosen by people who actually understand their meaning\n- Healthcare & life science experts add categories, search keywords, descriptions and more to each dataset\n- Both manual and automated data enrichment supported for clinical codes, providers, drugs, and geo-locations\n- The data is always kept up to date – even when the source requires manual effort to get updates\n- Support for data subscribers is provided directly by the domain experts who curated the data sets\n- Every data source’s license is manually verified to allow for royalty-free commercial use and redistribution.\n\n**Need Help?**\nIf you have questions about our products, contact us at [info@johnsnowlabs.com](mailto:info@johnsnowlabs.com). \n 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Our comprehensive data suite, sourced directly from core blockchain networks, is a treasure trove for a spectrum of users, from budding enthusiasts to seasoned institutional analysts. By capturing intricate details like balances and internal messages, we allow users to decipher patterns in assets movements, whether they originate from large-scale investors, everyday users, or potentially even nefarious actors. Logs and metrics grant insights into the operational health of the blockchain, pinpointing potential anomalies or inefficiencies. With data on token transfers and transactions, one can seamlessly monitor real-time asset movements—a boon for traders detecting market patterns and enterprises ensuring regulatory compliance. Furthermore, insights into token holder demographics and total supply offer invaluable perspectives for potential investors and market researchers, shedding light on token distribution and potential concentration risks. Lastly, our real-time mempool data provides a window into pending transactions, a pivotal tool for anticipating shifts in network congestion and gas pricing. In an ecosystem where informed decisions hinge on reliable data, our offering stands out, laying the foundation for strategic moves in the dynamic blockchain landscape.\n\nTo get access to the full datasets and all historical data, please contact sales@amberdata.io.\n\n**Use cases**\n\n- Fundamental Analysis\n- Market Analysis\n- Pricing Analysis\n- Quantitative Analysis\n- Risk Analysis\n\n**Product details**\n\nSample tables:\n- Blockchain Bitcoin Pending Transactions\n- Blockchain Ethereum Pending Transactions\n- Blockchain Litecoin Pending Transactions\n\nSample fields:\n- blockNumber\n- fee\n- from\n- gas\n- gasPrice\n- hash\n- input\n- nonce\n- value\n- to\n- (31 additional fields available)\n\nFor more details, refer to the embedded notebook.\n\n**Additional Insights**\nAdd any additional insights on the listing here. This can include relevant links pertaining to research related to the listing dataset, etc.","terms_of_service":"https://www.amberdata.io/terms","documentation_link":"https://docs.amberdata.io/docs/overview-1","privacy_policy_link":"https://www.amberdata.io/privacy","embedded_notebook_file_infos":[{"id":"fb7833ab-6504-4828-a5e2-e5b46612cf37","marketplace_file_type":"EMBEDDED_NOTEBOOK","file_parent":{"parent_id":"105dba59-3c4b-4082-85f3-17f61d2f8372","file_parent_type":"LISTING"},"mime_type":"text/html","download_link":"https://marketplace-file-storage-prod.s3-fips.us-east-1.amazonaws.com/EMBEDDED_NOTEBOOK/LISTING_105dba59-3c4b-4082-85f3-17f61d2f8372/fb7833ab-6504-4828-a5e2-e5b46612cf37?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEGgaDnVzLWVhc3QtMS1maXBzIkYwRAIgbC8sWIoYaHR8y7Wq86nPb0pufBl4%2BM4PWpeN%2B5PW9VQCIG7BhAVBDCWTpxFDLeuKYm%2Ff1JLz4iccPMzter0YdhgWKqYECDAQABoMNDE0MzUxNzY3ODI2IgxNAJrHbXaZG20jNfMqgwQyGEb4TsoVOoNvJ%2BaA%2FZ7zeu8hGcHqCeNfblP%2FQo5g6q2aRNtpwtPRKMlhYcIE%2FUQaNT1Lqmyptu32VUQTpzjTTxRVmJrDPkheSCkV06XQ%2FpTMKjU8CC7X1b%2FqYf8CrBwZT7Cv4C5WPjEET38f2A79h0xV6bZgPy28zQWrSeIJ9w6AfZBxiE24%2FVc08zVA0ivCT6lsy7nuU0wcR%2FMg2C9nd1vaMnQ923VpD21CKfVVYcrNWL2b%2FQ%2BNAO%2BxPjPgjiN6UPQbvbvZxkSdhgrGtecIg9NgqGkMcvM5VLTDtdC1kdeZetfOC2obtA%2BT%2F0gy3tIDo6WM5hcQfw743%2FAU9oaGnRBl%2BwoxRRDCZA%2F4jnPdluICSI7NCr6C3HHOe4CSZJjRihczxPA%2FgQczAVjAPs6CrK9N9pIzt0vNJpGUTgg4AoxftTvGfciDREilSctPIcBk6GwB%2Bzt4wbtNCmEPU%2BFD46rc6CWVQmmLwbKOeHw70ipMVFlOc8NlXkEtGO2vu6H44MstSSc6daOVUYUavQSGZ6jGhEjsP2YGz8KDm9XQotB%2FtyVBqiZV01OKqDmmBJA%2FXjUpSJ8M%2FeJXhe5m%2BKcxzNObtoJRI7jci3Tpt08XZyXZ7%2FyIV8H30ya468X3cfkTXrm972uga3z7iXAdubyEHnQIua%2FuvtwOHILi6O5D1Me2MzCPvcnXBjqTAYDoMWBt%2B3AVFuqSs2dmDQNBnvEhXfMgTedKLD5PJHb3aIl15iBkLaNu36GYnVCp9ZU2lSYcTbY5QwNnNeTfJTNy9bKhwzru6Sg%2BslhN%2BTS0zkTn%2BeLMY4tHgPEtEZ2cp%2FCk6GwqswvnYVKHjwO8q%2B1Mx8lc08fVEfarE9dke1GGFqoddx%2Bq%2BZUyHbDlAVfrCmf0yw%3D%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20261110T000135Z&X-Amz-SignedHeaders=host&X-Amz-Expires=86400&X-Amz-Credential=ASIAWA6KKHEJORWBH4N3%2F20261110%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=92ad939fae3263828ae61a8b85e20f947ca2b621823e8d7b292503d03d3c662a","created_at":1714198272379,"updated_at":1714198272379,"display_name":"Sample Notebook - Blockchain Data","status":"FILE_STATUS_PUBLISHED"}],"geographical_coverage":"[\"Global\"]","cost":"FREE","assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"Amberdata","description":"Amberdata is the leading provider of digital asset data. We deliver comprehensive data and insights into blockchain networks, crypto markets, and decentralized finance, empowering financial institutions with data for research, trading, risk, analytics, reporting, and compliance. Amberdata serves as a critical piece of infrastructure for financial institutions entering the asset class and participating in digital asset markets."},"popularity":1984},{"id":"a85395bc-e4e3-443c-a8e3-8c059e848b42","summary":{"name":"Geolocet | Points of Interest Data | Grocery Store Locations - Italy","subtitle":"Complete GIS-ready dataset of 142,911 Italy grocery locations (brands & independents). Optimized for Spark/Databricks.","setting":{"visibility":"PUBLIC"},"created_at":1780473601500,"updated_at":1780473614350,"categories":["GEOSPATIAL","RETAIL"],"listingType":"PERSONALIZED","provider_id":"5d3ddc17-a7e8-4794-a760-13587e1de2c6"},"detail":{"description":"# **All Supermarket Locations in Italy: Complete Geographic Dataset**\n---\n### **Premium GIS-Ready Retail Location Intelligence Data for Databricks Analytics**\n\nThis high-fidelity geospatial dataset provides a complete, exhaustive, and standardized overview of **142,911** verified grocery store and supermarket locations across Italy. Updated as of **May 28, 2026**, this comprehensive national dataset captures the entire Italian grocery landscape—encompassing both major branded retail chains and independent local markets. It offers an analysis-ready foundation for full-scale retail site selection, competitive intelligence, logistics optimization, and spatial demographics mapping.\n\nFully compatible with **Apache Spark**, **Delta Lake**, and **Databricks Unity Catalog** workflows.\n\n---\n\n## **Complete National Coverage: Branded & Independent Grocers**\nUnlike top-tier only datasets, this exhaustive collection covers maximum geographic depth across all 20 Italian regions (including highly dense areas like Campania, Lombardia, and Lazio). It includes over 130,000 independent and regional markets alongside the top 13 national supermarket chains:\n\n- **Major National Supermarkets & Hypermarkets:** \n [CONAD (3,238 locations)](https://geolocet.com/products/italy-conad-grocery), \n [Carrefour (1,227 locations)](https://geolocet.com/products/italy-carrefour-grocery), \n [Decò (651 locations)](https://geolocet.com/products/italy-deco-grocery), \n [Famila (394 locations)](https://geolocet.com/products/italy-famila-grocery), \n [Esselunga (202 locations)](https://geolocet.com/products/italy-esselunga-grocery)\n\n- **Leading Discounters (Hard & Soft Discount):** \n [Eurospin (1,323 locations)](https://geolocet.com/products/italy-eurospin-grocery), \n [MD (931 locations)](https://geolocet.com/products/italy-md-grocery), \n [Lidl (851 locations)](https://geolocet.com/products/italy-lidl-grocery), \n [Dpiù (317 locations)](https://geolocet.com/products/italy-dpiu-grocery), \n [ALDI (211 locations)](https://geolocet.com/products/italy-aldi-grocery)\n\n- **Regional, Proximity & Specialty Formats:** \n [Spar (1,484 locations)](https://geolocet.com/products/italy-spar-grocery), \n [A&O (496 locations)](https://geolocet.com/products/italy-aando-grocery), \n [Bofrost (63 locations)](https://geolocet.com/products/italy-bofrost-grocery)\n\n**Plus over 131,000 independent, regional, and unbranded grocers** fully mapped and standardized across all regions (e.g., Campania: 17,504 sites; Lombardia: 16,274 sites; Sicilia: 13,813 sites).\n\n---\n\n## **Technical Specifications & Schema Definitions**\nOptimized for zero-friction schema enforcement when reading via `spark.read.format(\"csv\")`. The structured schema below ensures flawless rendering in Databricks notebooks and high accuracy for LLM semantic indexing.\n\n* **`GUID`** (`STRING`): Unique global identifier for deterministic primary key mapping.\n* **`Title`** (`STRING`): Official store name, brand banner, or independent title of the location.\n* **`Latitude`** (`DOUBLE`): Precise WGS84 latitude coordinate for point-in-polygon & spatial joins.\n* **`Longitude`** (`DOUBLE`): Precise WGS84 longitude coordinate for spatial indexing (H3 / S2).\n* **`Street_No` / `Street`** (`STRING`): Normalized building/house number and street name elements.\n* **`City` / `Postal_Code`** (`STRING`): Core postal addresses and primary municipal settlement names.\n* **`Commune`** (`STRING`): Italian municipal administrative boundary designation (Comune).\n* **`Region`** (`STRING`): Macro-administrative boundary classification (e.g., Lombardia, Lazio, Campania).\n* **`Admin_level_1` / `_2`** (`STRING`): Additional local administrative area and provincial identifiers.\n* **`Population`** (`INTEGER`): Contextual demographic population metric attributed to the location.\n* **`Wheelchair`** (`STRING`): Accessibility flags indicating wheelchair mobility support.\n* **`Popularity_Score`** (`INTEGER`): Relative foot-traffic/rating metric graded on a standardized 1-10 scale.\n* **`Opening_Hours`** (`STRING`): Standard daily operational schedules and structured operating text.\n* **`Phone` / `Website`** (`STRING`): Direct communication data, branch-specific landing URLs, and domain vectors.\n\n---\n\n## **Databricks Workflow & Spatial Analytics Use Cases**\n\n- **Whitespace & Underserved Market Analysis:** Query the full 142,911 locations against population density tables to identify true retail deserts and expansion opportunities untouched by major national chains.\n- **H3 Geospatial Indexing:** Convert the precise WGS84 `Latitude` and `Longitude` fields into Uber H3 spatial hexagons (`h3_longlattocell`) to cross-reference with granular Italian demographic or municipal income raster files.\n- **Supply Chain & Territory Optimization:** Combine store location points with regional administrative boundary layers to optimize last-mile delivery routes, distribution warehouse placements, and franchise territory planning.\n\n---\n\n## **Data Evaluation & Custom Ingestion**\n\n### **Evaluate with a Free Sample Dataset**\nVerify the structure, column types, and coordinate precision before full implementation. A comprehensive schema sample is available for download in CSV/Parquet-friendly formats.\n\n*Want deeper market insights?* Learn more about the brand network in our comprehensive report: **[View the Italian Top Brands Grocery Report](https://geolocet.com/pages/italy-top-brands-grocery-report)**\n\n### **Enterprise Delivery & Tailored Formatting**\nWhile natively available as an analysis-ready CSV for DBFS ingestion, we accommodate enterprise-level data architecture needs. Alternative formats - including **GeoJSON, Esri Shapefiles, PostgreSQL dumps, Excel, or direct Delta sharing profiles** - can be generated. Reach out to our engineering team at **contact@geolocet.com** for integration support or custom European POI data requests.","terms_of_service":"https://geolocet.com/pages/terms-of-use","documentation_link":"","privacy_policy_link":"https://geolocet.com/pages/privacy-policy","assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://geolocet.com/pages/terms-of-use"},"provider_summary":{"name":"Geolocet LTD","description":"Geolocet provides premium geospatial data for retail analysis, GIS, and market planning. We equip businesses with accurate, ready-to-query data to drive spatial analysis, site selection, and customer segmentation.\n\nCore Offerings:\n\nDemographics: Socioeconomic data covering population, age, and employment across diverse geographies.\n\nPoints of Interest (PoI): Datasets spanning retail, healthcare, and transport for catchment analysis.\n\nAdministrative Boundaries: GIS-ready boundaries, including municipalities and postcodes, for territory management.\n\nBrand Locations: Chain location datasets to monitor store networks and market expansion.\n\nWealth Data: Granular economic indicators like purchasing power to identify high-potential markets.\n\nWe source from trusted providers, delivering highly customizable datasets with comprehensive coverage across Europe and worldwide."},"popularity":987},{"id":"96963aa8-585d-4ccb-92d1-9d83e578dea3","summary":{"name":"Business Contact Data","subtitle":"Company-level phone, mobile, email, website and web domain, recorded on the company record.","setting":{"visibility":"PUBLIC"},"created_at":1788236315169,"updated_at":1788350247996,"categories":["ADVERTISING_AND_MARKETING","COMMERCE"],"listingType":"PERSONALIZED","provider_id":"cef74d15-013f-4a83-b55c-815ec7a71d40"},"detail":{"description":"**Overview**\n\nHow to reach the company. Phone, mobile, email, website and web domain, recorded on the company record itself rather than against a named individual.\n\nThis is deliberately a company-level product. If you need to know who works there, that is a separate listing.\n\nThis listing contains sample data. The full product is delivered under a separate agreement.\n\n**Use cases**\n\n- Adding a reachable channel to an account list that has company names but no way to contact them.\n- Web domain matching, to resolve companies against your own records.\n- Coverage assessment before committing to an outbound programme in a given market.\n\n**Product details**\n\n- Dataset represented: business_contact_sample, 300 companies across 68 countries, 11 columns.\n- Sample fields include CompanyName, Country, City, Phone, Mobile, Email, Website, WebDomain and MarketabilityIndicator.\n- This product joins our other listings on RecordID.\n\nFor more details, refer to the embedded notebook.\n\n**Additional Insights**\n\nThis product contains no social profile links.\n\nA minority of email addresses follow a personal-name pattern and may identify an individual employee: about 12% of non-empty values, against about 42% that use a role prefix such as info@ or contact@. Those personal-pattern addresses have been removed from this sample. The full product contains them, and you should treat the column as capable of carrying personal data.\n\nSole-trader records are excluded from this sample. Fill rates measured on this sample run above the product, because the companies were selected for cross-product presence: Email is 21.3% here against 4.5% in the full reference sample. Ask us for coverage on your target population.\n\nUpdate cadence: static sample extracted 28 July 2026, refreshed when the listing is refreshed. 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Speech recordings were manually reviewed and paired with verified transcripts.\n\n**Collection environment**\n\nSpeech was collected during controlled recording sessions to promote consistency in recording quality.\n\n**Sources**\n\nGenerated specifically for this project. Read speech recordings paired with verified text transcripts. Original recordings are stored in WAV (.wav) format.\n\n**Technical specifications**\n\nAudio:\n\n- WAV (uncompressed PCM), 44.1 kHz, 16-bit, mono, SNR >= 35 dB, no clipping, 0.5 to 1 second silence padding.\n- Duration per file: 8 to 18 seconds (average 15 seconds).\n\n**Speakers**\n\nTwo speakers:\n\n- Male, 29 years old\n- Female, 31 years old\n\n**Dataset structure**\n\nThe dataset contains:\n\nAn audio/ directory containing WAV recordings. 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It adapts dynamically to any dataset or business context—without requiring predefined schemas or domain assumptions.\n\n**Key Highlights**\n\n- 10 + AI Functions\n- 100 % PDF Analysis\n- ∞ Data Agnostic\n- Requires only a Genie Space ID and a LLM Model Serving Endpoint\n\n**Use cases**\n\n- Conversational Analytics\nAsk questions about any structured, semi-structured, or unstructured data and receive instant summaries, visualizations, and recommendations.\n\n- Automated Insight Generation\nCombine descriptive metrics, predictive modeling, and prescriptive actions—delivered through autonomous analytic agents.\n\n- Document Intelligence\nUpload and query PDFs, text files, or reports to extract relevant information, detect patterns, and summarize findings.\n\n- Forecasting & Next-Best Action\nGenerate forecasts, classify trends, and produce intelligent “next-step” suggestions tailored to your context.\n\n- Universal Data Exploration\nWorks seamlessly across domains such as finance, manufacturing, operations, customer analytics, R&D, or compliance—without retraining or data-model reconfiguration.\n\n**Product details**\n\nThe app is data-agnostic and compatible with any dataset accessible in your Genie Space. 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Health-Related Quality of Life (HRQOL) surveillance is used to identify unmet population health needs including recognizing trends, disparities, and determinants of health in the population. Self-assessed health status is also a more powerful predictor of mortality and morbidity than many objective measures of health. HRQOL measures make it possible to demonstrate scientifically the impact of health on quality of life, going well beyond the old paradigm that was limited to what can be seen under a microscope.\n\nThe healthy aging data by Centers for Disease Control and Prevention (CDC) comprises of data on a range of key indicators of health and well-being, screenings and vaccinations, and mental health among older adults at the national and state levels. These indicators provide a snapshot of currently available surveillance information, and can be useful for prioritization and evaluation of public health interventions. Selected Micropolitan Area Risk Trends (SMART) uses BRFSS data to provide prevalence rates for selected conditions and behaviors for cities and their surrounding counties. The risk factor categories available are alcohol consumption, asthma, cholesterol awareness, colorectal cancer screening, diabetes, disability, exercise and physical activity, health status, health care access, hypertension awareness, immunization, nutrition, oral health, tobacco use, weight classifications, and women’s health.\n\nThe prevalence and trends health care access documentation provides technical and statistical information regarding the BRFSS responses as to the access and coverage to health care of people in the U.S. The BRFSS is the nation's premier system of health-related telephone surveys that collect state data about U.S. residents regarding their health-related risk behaviors, chronic health conditions, and use of preventive services. This prevalence data on Tobacco Use among adult U.S. residents are used to design programs that promote preventive health practices that can affect their health status.\n\nThe purpose of the 500 Cities Project is to provide city and census tract-level small area estimates for chronic disease risk factors, health outcomes, and clinical preventive service use for the largest 500 cities in the United States. These small area estimates will allow cities and local health departments to better understand the burden and geographic distribution of health-related variables in their jurisdictions, and assist them in planning public health interventions. The Vision and Eye Health Surveillance System is intended to provide population estimates of vision loss function, eye diseases, health disparities, as well as barriers and facilitators to access to vision and eye care. This information can be used for designing, implementing, and evaluating vision and eye health prevention programs.\n\n**Benefits**\n- Useful for doctors, medical health planners, psychologists, medical research groups and behavioral therapists. behavioral risk factor health and diseases surveillance system database are available on centers for disease control and prevention (cdc) website in raw form, whereas a jsl customer gets it in a clean and normalized form ready to use in one place and create his own custom reports. easy to comprehend for the customers who are unfamiliar with the medical terminologies as all abbreviated terms are replaced with full form, the centers for disease control and prevention (cdc) original datasets which are full of abbreviations.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [BRFSS Health Related Quality of Life](https://www.johnsnowlabs.com/marketplace/brfss-health-related-quality-of-life)\n\t- This dataset includes information regarding the data are from the Behavioral Risk Factor Surveillance System (BRFSS). All respondents to the BRFSS are non-institutionalized adults, 18 years old or older. This dataset comprises of Topic Description, Survey Question, Data Value, High and Low Confidence Limits, Breakout Category, Geo Location Latitude and Longitude for Health Related Quality of Life (HRQOL).\n- [BRFSS Healthy Aging Data](https://www.johnsnowlabs.com/marketplace/brfss-healthy-aging-data)\n\t- This dataset includes information regarding data from the Behavioral Risk Factor Surveillance System (BRFSS). All respondents to the BRFSS are non-institutionalized adults, 18 years old or older. This dataset comprises of Class and Topic Description, Survey Question, Data Value, High and Low Confidence Limits, Stratification Categories, Latitude and Longitude for Healthy Aging Data.\n- [BRFSS Selected Metropolitan Area Risk Trends MMSA Prevalence Data](https://www.johnsnowlabs.com/marketplace/brfss-selected-metropolitan-area-risk-trends-mmsa-prevalence-data)\n\t- This dataset includes information regarding the Selected Metropolitan Area Risk Trends (SMART) project. This project uses the Behavioral Risk Factor Surveillance System (BRFSS) to analyze the data of selected Metropolitan/Micropolitan Statistical Areas (MMSAs) with 500 or more respondents. BRFSS data can be used to identify emerging health problems, establish and track health objectives, and develop and evaluate public health policies and programs.\n- [Behavioral Risk Factor Surveillance System Healthcare Access 1995-2011](https://www.johnsnowlabs.com/marketplace/behavioral-risk-factor-surveillance-system-healthcare-access-1995-2011)\n\t- The Prevalence and Trends Health Care Access documentation provides technical and statistical information regarding the Behavioral Risk Factor Surveillance System (BRFSS) responses as to the access and coverage to health care of people in the US.\n- [Chronic Diseases Risk Factors and Health Indicators BRFSS](https://www.johnsnowlabs.com/marketplace/chronic-diseases-risk-factors-and-health-indicators-brfss)\n\t- The Behavioral Risk Factor Surveillance System (BRFSS) is a collaborative project of the Centers for Disease Control and Prevention (CDC) and U.S. states and territories. The BRFSS administered combined landline and cell phone prevalence data is supported by CDC's Behavioral Risk Factor Surveillance Branch. It is a continuous, state-based surveillance system that collects information about modifiable risk factors for chronic diseases and other leading causes of death.\n- [Tobacco Use Risk Factors by Sex Age Race and Education BRFSS](https://www.johnsnowlabs.com/marketplace/tobacco-use-risk-factors-by-sex-age-race-and-education-brfss)\n\t- The Behavioral Risk Factor Surveillance System (BRFSS) is a collaborative project of the Centers for Disease Control and Prevention (CDC) and U.S. states and territories. The BRFSS, administered and supported by CDC's Behavioral Risk Factor Surveillance Branch, is an ongoing data collection program designed to measure behavioral risk factors for the adult population (18 years of age and older) living in households.\n- [US 500 Cities Project Local Data for Better Health](https://www.johnsnowlabs.com/marketplace/us-500-cities-project-local-data-for-better-health)\n\t- This is the complete dataset for the 500 Cities project. It includes 2015 and 2016 model-based small area estimates for 27 measures of chronic disease related to unhealthy behaviors (5), health outcomes (13), and use of preventive services (9).\n- [Vision and Eye Health Risk Factors and Indicators BRFSS](https://www.johnsnowlabs.com/marketplace/vision-and-eye-health-risk-factors-and-indicators-brfss)\n\t- In 2013 and subsequently, one question in the core of Behavioral Risk Factor Surveillance System (BRFSS) asks about vision: Are you blind or do you have serious difficulty seeing, even when wearing glasses? From 2005-2015 the BRFSS employed a ten question vision module regarding vision impairment, access, and utilization of eye care, and self-reported eye diseases. This dataset shows the results of that survey.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated\n- Data Updates\n - Data updates support replace-on-update: outdated foreign keys are deprecated, not deleted\n\n**Our data is curated and enriched by domain experts** \n\n Each dataset is manually curated by our team of doctors, pharmacists, public health & medical billing experts:\n- Field names, descriptions, and normalized values are chosen by people who actually understand their meaning\n- Healthcare & life science experts add categories, search keywords, descriptions and more to each dataset\n- Both manual and automated data enrichment supported for clinical codes, providers, drugs, and geo-locations\n- The data is always kept up to date – even when the source requires manual effort to get updates\n- Support for data subscribers is provided directly by the domain experts who curated the data sets\n- Every data source’s license is manually verified to allow for royalty-free commercial use and redistribution.\n\n**Need Help?**\nIf you have questions about our products, contact us at [info@johnsnowlabs.com](mailto:info@johnsnowlabs.com). \n 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It includes extensive demographics, interests, and contact information, making it suitable for various marketing purposes.\nUse Cases\nHere are just a few ways that you can leverage Data Axle’s Consumer Database\nTargeting\nUse desired demographic criteria to identify a list of consumers\nCreate an account list of individuals to onboard and target for lead generation, cross-selling, up-selling, and reducing churn\nTarget individuals with a higher likelihood of exhibiting specific behaviors, belonging to certain socioeconomic statuses, having particular interests and hobbies, or frequently purchasing specific products.\nEnrichment\nImprove the effectiveness of your outreach by adding more demographic elements to your existing customer list, enabling you to customize your messaging to align with their interests.\nValidate first-party data, remove duplicates, and update outdated information for clean and accurate data\nIntelligence\nEnhance audience segmentations, models, etc. using lifestyle, interests, and behavioral information\nTarget customers when they have a higher intent to purchase\nUse data for reference purposes, such as finding relatives for family tree research.\nProduct Integration\nFeed data into your products\nFeed downstream applications\nFirmographic Enhancement\nAdd business information to your consumer file - Data Axle is the only company that compiles both consumer and business databases at scale\nData Quality\nWe understand the importance of reliable and accurate data for your business's growth. That's why Data Axle invests over $20 million annually and employs more than 300 full-time researchers to ensure that our records are up-to-date and accurate.\nThe Consumer Database undergoes monthly audits by the Quality Assurance team before release to customers. Any data elements that vary by 5% or more from the previous month are analyzed for appropriateness.\nData Axle periodically hires a third party to audit a representative sample of the database for accuracy, and the third party sets the criteria for the sample data. Data Axle complies with data protection laws by processing data through multiple DMA privacy sources, including the Do Not Mail, Do Not Call, and CAN-SPAM lists, as well as proprietary deceased suppression files and self-reported consumer email opt-outs. The process is automated through the OnePass system and can be configured to meet client-specific business rules. 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It also includes data over hospital value based purchasing program for years 2017 and 2018. It comprises of datasets about readmission rates by age, gender, patient residence, payer, zip code and median income.\n\n**Description**\nThis data package contains the Readmissions and Deaths by hospital of unplanned readmission to any acute care hospital within 30 days of discharge from a hospitalization for any cause related to medical conditions, including heart attack (AMI), heart failure (HF), pneumonia, chronic obstructive pulmonary disease (COPD), and stroke; and surgical procedures, including hip/knee replacement and cornary artery bypass graft (CABG). The HRRP applied to all acute care hospitals except the psychiatric, rehabilitation, pediatric, cancer, and critical access hospitals. In the first two years, only readmissions for heart attack, heart failure, and pneumonia and also were added chronic obstructive pulmonary disease (COPD) and elective hip replacement and knee replacement. The Hospital Value-Based Purchasing (VBP) Program is a Centers for Medicare & Medicaid Services (CMS) initiative that rewards acute-care hospitals with incentive payments for the quality of care they provide to Medicare beneficiaries. National patient readmissions within 30 days by age provides nationally representative information on hospital readmissions for all ages who were readmitted to the hospital within 30 days of discharge. The dataset on Readmission rates provides information on nationwide hospital readmissions within 30 days of discharge by gender, patient residence, payer, zip code, median income and readmissions payment adjustment factors.\n\n**Benefits**\n- Useful for doctors, medical health planners, policy makers, patients, medical research groups and medical students. hospital readmissions reduction program (hrrp) and readmissions and deaths database is available on centers for medicare & medicaid services website in raw form, whereas a john snow labs (jsl) customer gets it in a clean and normalized form ready to use and create his own custom reports. easy to comprehend for the customers who are unfamiliar with the medical terminologies as all abbreviated terms are replaced with full form, unlike centers for medicare & medicaid services (cms) original datasets which have many abbreviations. recent dataset for 2018 is available for use.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [Hospital Excess Readmissions Reduction Program](https://www.johnsnowlabs.com/marketplace/hospital-excess-readmissions-reduction-program)\n\t- This dataset is about Medicare payments reductions for Inpatient Prospective Payment System hospitals with excess readmissions, which started in In October 2012. Excess readmissions are measured by a ratio, by dividing a hospital’s number of “predicted” 30-day readmissions for heart attack, heart failure, and pneumonia by the number that would be “expected,” based on an average hospital with similar patients. A ratio greater than 1 indicates excess readmissions.\n- [Hospital Value Based Purchasing Program Actual Payment Adjustment](https://www.johnsnowlabs.com/marketplace/hospital-value-based-purchasing-program-actual-payment-adjustment)\n\t- The Hospital Value-Based Purchasing (VBP) Program is a Centers for Medicare & Medicaid Services (CMS) initiative that rewards acute-care hospitals with incentive payments for the quality of care they provide to Medicare beneficiaries. It contains the actual payment adjustment factors under the Hospital VBP Program for FY 2019 & FY 2020.\n- [Hospital Value Based Purchasing Program](https://www.johnsnowlabs.com/marketplace/hospital-value-based-purchasing-program)\n\t- The Hospital Value-Based Purchasing (VBP) Program dataset is a Centers for Medicare & Medicaid Services (CMS) initiative that rewards acute-care hospitals with incentive payments for the quality of care they provide to Medicare beneficiaries. It contains updated proxy adjustment factors under the Hospital VBP Program that were calculated using historical baseline and performance periods.\n- [Maryland Potentially Avoidable Utilization Savings Policy 2015](https://www.johnsnowlabs.com/marketplace/maryland-potentially-avoidable-utilization-savings-policy-2015)\n\t- This dataset includes Maryland Health Services Cost Review Commission (HSCRC) which operates a Potentially Avoidable Utilization (PAU) savings policy as part of its portfolio of value-based payment policies. The PAU savings policy is intentionally designed to assure savings to payers.\n- [Maryland Readmission Reduction Incentive Program for RY 2014-2015](https://www.johnsnowlabs.com/marketplace/maryland-readmission-reduction-incentive-program-for-ry-2014-2015)\n\t- This dataset comprises Maryland Readmissions Reduction Incentive Program (RRIP) incentivizes hospitals to reduce avoidable readmissions by linking rewards and penalties to improvements in readmissions rates. The RRIP began to impact hospital revenue starting in Rate Year 2016, with the first performance year of Calendar Year 2014.\n- [Readmissions Payment Adjustment Factors](https://www.johnsnowlabs.com/marketplace/readmissions-payment-adjustment-factors)\n\t- This dataset states the factors that are to be used for Readmissions Payment Adjustments based on the FY 2017 to FY 2022 and would be used for proposed payments for FY 2017 to FY 2021 discharges, as discussed in the FY 2021 IPPS/LTCHPPS proposed rule.\n- [Readmissions and Deaths by Hospital](https://www.johnsnowlabs.com/marketplace/readmissions-and-deaths-by-hospital)\n\t- This dataset includes provider data for the hospital return days (or excess days in acute care) measures and the 30-day readmission measures, the unplanned readmissions measures, and the rate of unplanned hospital visits after an outpatient colonoscopy.\n- [Readmissions and Deaths by National](https://www.johnsnowlabs.com/marketplace/readmissions-and-deaths-by-national)\n\t- This dataset includes national-level data for the hospital return days (or excess days in acute care) measures and the 30-day readmission measures, the unplanned readmissions measures, and the rate of unplanned hospital visits after an outpatient colonoscopy.\n- [Readmissions and Deaths by State](https://www.johnsnowlabs.com/marketplace/readmissions-and-deaths-by-state)\n\t- This dataset includes state-level data for the hospital return days (or excess days in acute care) measures and the 30-day readmission measures, the unplanned readmissions measures, and the rate of unplanned hospital visits after an outpatient colonoscopy.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated\n- Data Updates\n - Data updates support replace-on-update: outdated foreign keys are deprecated, not deleted\n\n**Our data is curated and enriched by domain experts** \n\n Each dataset is manually curated by our team of doctors, pharmacists, public health & medical billing experts:\n- Field names, descriptions, and normalized values are chosen by people who actually understand their meaning\n- Healthcare & life science experts add categories, search keywords, descriptions and more to each dataset\n- Both manual and automated data enrichment supported for clinical codes, providers, drugs, and geo-locations\n- The data is always kept up to date – even when the source requires manual effort to get updates\n- Support for data subscribers is provided directly by the domain experts who curated the data sets\n- Every data source’s license is manually verified to allow for royalty-free commercial use and redistribution.\n\n**Need Help?**\nIf you have questions about our products, contact us at [info@johnsnowlabs.com](mailto:info@johnsnowlabs.com). \n 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The two most important codes are CVX and MVX, the CVX code is a numeric code that identifies the type of vaccine product used meanwhile the MVX code is an alphabetic code referring to the manufacturer of that vaccine. Together, these two codes refer to the trade name of a vaccine product. Additionally to these two codes, other codes are available in JohnSnowLabs database like Current Procedural Terminology (CPT) code which is approved by the American Medical Association (AMA) and its related code CPT ID also National Drug Code (NDC) which can be considered as the main code of Drugs and Vaccines for the Food and Drug Administration (FDA) and its related codes Use Unit Labeler, Use Unit Package and Use Unit Product codes.\n\n**Benefits**\n- Access to various us vaccine codes\n- mapping of vaccines codes\n- information on current vaccines status\n-\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [CDC Vaccines Information Statement URL Links](https://www.johnsnowlabs.com/marketplace/cdc-vaccines-information-statement-url-links)\n\t- The Centers for Disease Control and Prevention (CDC) Vaccines Information Statement URL Links dataset provides the URL links to the corresponding Vaccines Information Statement (VIS) document and was developed to assist developers to automatically generate the current version of the VIS.\n- [CVX Codes Of Vaccines Groups](https://www.johnsnowlabs.com/marketplace/cvx-codes-of-vaccines-groups)\n\t- The CVX Codes Of Vaccines Groups dataset lists a variety of vaccines can be used to vaccinate against a specific disease in fulfillment of the Advisory Committee on Immunization Practices (ACIP) schedule. \nMultiple individual vaccines that can be used to vaccinate against a disease are grouped into a Vaccine Group (sometimes known as vaccine family)\n- [FDA CVX Code Vaccines Administered](https://www.johnsnowlabs.com/marketplace/fda-cvx-code-vaccines-administered)\n\t- The Centers for Disease Control and Prevention's National Center of Immunization and Respiratory Diseases (NCIRD) developed and maintains the CVX (vaccine administered) code set.\n- [NYC Locations Providing Seasonal Flu Vaccinations](https://www.johnsnowlabs.com/marketplace/nyc-locations-providing-seasonal-flu-vaccinations)\n\t- This dataset provides the location and facility information for places in New York City providing seasonal flu vaccinations by the Department of Health and Mental Hygiene.\n- [Vaccines CVX And MVX Mapping](https://www.johnsnowlabs.com/marketplace/vaccines-cvx-and-mvx-mapping)\n\t- The Vaccines CVX And MVX Mapping dataset show the relationship between the vaccine’s CVX codes and the MVX codes.\n- [Vaccines CVX And VIS Mapping](https://www.johnsnowlabs.com/marketplace/vaccines-cvx-and-vis-mapping)\n\t- The Vaccines CVX And Vaccines Information Statement (VIS) Mapping dataset show the relationship between the vaccine’s CVX codes and the VIS codes.\n- [Vaccines Current Procedural Terminology Codes](https://www.johnsnowlabs.com/marketplace/vaccines-current-procedural-terminology-codes)\n\t- The Vaccines Current Procedural Terminology Codes database cross-references Current Procedural Terminology (CPT) codes that are related to vaccines, toxoids and immune globulins with their corresponding CVX codes. The goal of the table is to support mapping of CPT codes to CVX codes in systems that receive CPT codes as part of an electronic data exchange.\n- [Vaccines Information Statement](https://www.johnsnowlabs.com/marketplace/vaccines-information-statement)\n\t- The Vaccines Information Statement dataset includes the data of the added two-dimensional (2D) “data matrix” barcodes to Vaccine Information Statements (VIS). \nThis technology is designed primarily to help immunization providers record information about the VIS as is required by the National Childhood Vaccine Injury Act (NCVIA).\n- [Vaccines Manufacturers MVX Codes](https://www.johnsnowlabs.com/marketplace/vaccines-manufacturers-mvx-codes)\n\t- The Vaccines Manufacturers MVX Codes database provides both active and inactive manufacturers of vaccines in the US. \nThe CDC's National Center for Immunization and Respiratory Diseases (NCIRD) developed and maintains HL7 Standard Code Set MVX - Manufacturers of Vaccines.\n- [Vaccines National Drug Codes Lookup Crosswalk](https://www.johnsnowlabs.com/marketplace/vaccines-national-drug-codes-lookup-crosswalk)\n\t- The Vaccines National Drug Codes Lookup Crosswalk database is a lookup table where the Unit of Use and Unit of Sale NDC are mapped to each other as separate NDCs are provided for the Unit of Use (primary) and Unit of Sale (secondary) packaging of a vaccine.\n- [Vaccines National Drug Codes Unit Linker](https://www.johnsnowlabs.com/marketplace/vaccines-national-drug-codes-unit-linker)\n\t- The Vaccines National Drug Codes Unit Linker database provides the relationship between vaccine NDC Unit of Sale, Unit of Use and Vaccines tables. \nIt includes both active and inactive vaccines available in the US.\n- [Vaccines National Drug Codes Unit Of Sale](https://www.johnsnowlabs.com/marketplace/vaccines-national-drug-codes-unit-of-sale)\n\t- The Vaccines National Drug Codes Unit Of Use database facilitate the use of NDC to identify vaccines. \nThe purpose of the NDC unit of sale dataset is to show the relationships of vaccine secondary packaging NDCs and provide related information for use as reference data for solutions that leverage NDCs.\n- [Vaccines National Drug Codes Unit Of Use](https://www.johnsnowlabs.com/marketplace/vaccines-national-drug-codes-unit-of-use)\n\t- The Vaccines National Drug Codes Unit Of Use database facilitate the use of NDC to identify vaccines. \nThe purpose of the NDC unit of use dataset is to show the relationships of vaccine primary packaging NDCs and provide related information for use as reference data for solutions that leverage NDCs.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the 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Trust in the accuracy and reliability of our official statistical data to drive your projects forward.\nContact us at *contact@geolocet.com* today to access this invaluable resource and explore Kosovo census data like never before.\n\n[Geolocet LTD](https://geolocet.com)\n","terms_of_service":"https://geolocet.com/pages/terms-of-use","documentation_link":"","privacy_policy_link":"https://geolocet.com/pages/privacy-policy","cost":"FREE","update_frequency":{"interval":1,"unit":"YEARLY"},"assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://geolocet.com/pages/terms-of-use"},"provider_summary":{"name":"Geolocet LTD","description":"Geolocet provides premium geospatial data for retail analysis, GIS, and market planning. 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The inflation-indexed constant maturity yields are read from this yield curve at fixed maturities, currently 5, 7, 10, 20, and 30 years. \n\n**Product Details**\n\nIncludes the following tables:\n\n\n- **fs_federalreserveboard.frb_h15_daily**\n - **Update frequency:** weekly\n - **Source:** The-Federal-Reserve\n - **Description:** Contains interest rates from the Fed\n\n\n\n**Data Sources**\n\n\nThe data in this product comes from **The Federal Reserve**.\n\nThe Federal Reserve is the central banking system of the United States, responsible for implementing monetary policy and promoting stability in the financial system. The Fed collects a vast amount of economic data on various indicators, such as employment, inflation, consumer spending, and housing, to gain a comprehensive understanding of the current state of the economy. 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These are 5-position numeric codes representing physician and non-physician services.\nLevel II - Includes codes and descriptors copyrighted by the American Dental Association's Current Dental Terminology, Fifth Edition (CDT-5). These are 5-position alpha-numeric codes representing primarily items and nonphysician services that are not represented in the level I codes.\nLevel III - Codes and descriptors developed by Medicare carriers (currently known as Medicare Administrative Contractors; MACs) for use at the local (MAC) level. These are 5-position alpha-numeric codes in the W, X, Y or Z series representing physician and non-physician services that are not represented in the level I or level II codes.\n\n**Benefits**\n- Useful for physicians, coders & billing companies, medical students as hcpcs procedure and modifier coding-related information is available\n- each year, in the united states, health care insurers process over 5 billion claims for payment and without proper hcpcs information and improper billing medicare or other insurance carriers can reject claims for reimbursement.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [Alpha-Numeric HCPCS Procedure and Modifier Codes](https://www.johnsnowlabs.com/marketplace/alpha-numeric-hcpcs-procedure-and-modifier-codes)\n\t- The Healthcare Common Procedure Coding System (HCPCS) dataset is a collection of codes that represent procedures, supplies, products and services which may be provided to Medicare beneficiaries and to individuals enrolled in private health insurance programs. These files contain the 2016, 2017, 2018, 2019 and 2020 alphanumeric HCPCS procedure and modifier codes and their long and short descriptions.\n- [Healthcare Common Procedure Coding System Age Restriction](https://www.johnsnowlabs.com/marketplace/healthcare-common-procedure-coding-system-age-restriction)\n\t- The Healthcare Common Procedure Coding System age restriction dataset lists represented codes that TRICARE (formerly known as the Civilian Health and Medical Program of the Uniformed Services - CHAMPUS) recognizes with age restrictions. The TRICARE contractors need to make sure their age file matches what is on the list or it could result in TRICARE Encounter Data (TED) Records failure.\n- [Healthcare Common Procedure Coding System Gender Restriction](https://www.johnsnowlabs.com/marketplace/healthcare-common-procedure-coding-system-gender-restriction)\n\t- The Healthcare Common Procedure Coding System age restriction dataset lists represented codes that TRICARE (formerly known as the Civilian Health and Medical Program of the Uniformed Services - CHAMPUS) recognizes with gender restrictions. The TRICARE contractors need to make sure their gender file matches what is on the list or it could result in TRICARE Encounter Data (TED) Records failure.\n- [Healthcare Common Procedure Coding System HCPCS Action Codes 2020](https://www.johnsnowlabs.com/marketplace/healthcare-common-procedure-coding-system-hcpcs-action-codes-2020)\n\t- The Healthcare Common Procedure Coding System (HCPCS) is a collection of codes that represent procedures, supplies, products and services which may be provided to Medicare beneficiaries and to individuals enrolled in private health insurance programs. These files contain the Level II alphanumeric HCPCS procedure and modifier codes and their type of change status.\n- [Healthcare Common Procedure Coding System Mapping 2015 and 2019](https://www.johnsnowlabs.com/marketplace/healthcare-common-procedure-coding-system-mapping-2015-and-2019)\n\t- This dataset contains information on 'integrated' Outpatient Code Editor (I/OCE) program processes claims for all outpatient institutional providers including hospitals that are subject to the Outpatient Prospective Payment System (OPPS) as well as hospitals that are NOT (Non-OPPS). This dataset contains HCPCS Codes differences between October, 2015 and January, 2019.\n- [Healthcare Common Procedure Coding System Mapping Quarterly Files](https://www.johnsnowlabs.com/marketplace/healthcare-common-procedure-coding-system-mapping-quarterly-files)\n\t- This dataset contains information on 'integrated' Outpatient Code Editor (I/OCE) program processes claims for all outpatient institutional providers including hospitals that are subject to the Outpatient Prospective Payment System (OPPS) as well as hospitals that are NOT (Non-OPPS). This dataset contains the Healthcare Common Procedure Coding System (HCPCS) Codes Mapping Quarterly Files between October, 2015 and January 2016.\n- [List of CPT and HCPCS Codes](https://www.johnsnowlabs.com/marketplace/list-of-cpt-and-hcpcs-codes)\n\t- This dataset shows List of Current Procedural Terminology (CPT)/Healthcare Common Procedure Coding System (HCPCS) Codes (the Code List), which identifies all the items and services included within certain DHS categories or that may qualify for certain exceptions.\n- [Medicare Risk Adjustment Eligible CPT and HCPCS Codes](https://www.johnsnowlabs.com/marketplace/medicare-risk-adjustment-eligible-cpt-and-hcpcs-codes)\n\t- This dataset shows List of eligible medicare risk adjustment Current Procedural Terminology (CPT)/Healthcare Common Procedure Coding System (HCPCS) codes used for filtering diagnoses from encounter data records for each service year 2021 and 2022.\n- [Value Modifier PUF Performance Year 2016](https://www.johnsnowlabs.com/marketplace/value-modifier-puf-performance-year-2016)\n\t- This dataset shows the value modifier public use file (PUF) Performance Year 2016 (Payment Year 2018) by the Centers for Medicare and Medicaid Services (CMS).\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 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All data comes from a single source, ensuring **consistency and comparability** across markets.\n\nThe Sociodemographics-Bundle Basic for Norway includes the following **variables**:\n- [population](https://www.mbi-geodata.com/en/sociodemographic-data/population/)\n- [gender](https://www.mbi-geodata.com/en/sociodemographic-data/sex-gender/)\n- [age structure](https://www.mbi-geodata.com/en/sociodemographic-data/agebands/) (15-year age bands)\n- household figures ([number of households](https://www.mbi-geodata.com/en/sociodemographic-data/households/) and [households by type](https://www.mbi-geodata.com/en/sociodemographic-data/households-by-type/))\n\nOur sociodemographic data is offered for each country on different **levels**:\n- **Postal Code Level:** This fine-grained level of detail makes the data suitable for a wide range of applications – for example, enriching customer databases, which are often organized by postal code.\n- **Administrative Level:** Covers both larger administrative areas (e.g., regions, states, provinces) as well as finer subdivisions such as districts, counties, or municipalities.\n- **Micro Level**: Subdivisions of postal code or administrative boundaries, such as census areas or street segments. This is the most granular level for local analysis.\n\n**Please note:** The dataset available for free download is a **sample dataset** covering a limited number of geographic areas. If you wish to access the full dataset (covering all areas) or if you require additional variables or geographic levels, please contact us. We will be happy to help you find a product that fits your needs.\n \n**Additional Insights**: https://www.mbi-geodata.com/en/sociodemographic-data/\n\n**Data Structure**:\nAGE_F0014: Population (Nighttime) by age: 0 - 14 years, female\nAGE_F1529: Population (Nighttime) by age: 15 - 29 years, female\nAGE_F3044: Population (Nighttime) by age: 30 - 44 years, female\nAGE_F4559: Population (Nighttime) by age: 45 - 59 years, female\nAGE_F60PL: Population (Nighttime) by age: 60 years and above, female\nAGE_M0014: Population (Nighttime) by age: 0 - 14 years, male\nAGE_M1529: Population (Nighttime) by age: 15 - 29 years, male\nAGE_M3044: Population (Nighttime) by age: 30 - 44 years, male\nAGE_M4559: Population (Nighttime) by age: 45 - 59 years, male\nAGE_M60PL: Population (Nighttime) by age: 60 years and above, male\nAGE_T0014: Population (Nighttime) by age: 0 - 14 years, total\nAGE_T1529: Population (Nighttime) by age: 15 - 29 years, male\nAGE_T3044: Population (Nighttime) by age: 30 - 44 years, total\nAGE_T4559: Population (Nighttime) by age: 45 - 59 years, total\nAGE_T60PL: Population (Nighttime) by age: 60 years and above, total\nCTRYCODE: Two digit country code by ISO 3166\nFEMALE: Population (Nighttime): females\nHH_SIZE: Average household size\nHH_T: Households: total number\nMALE: Population (Nighttime): males\nNAME: Name of administrative area / Name of the most populous administrative area / Name of the respective micro-area or the next higher level\nADMINCODE / POSTCODE / MICROCODE Administrative code / Postal code / Micro-code\nP_PRM: Population (Nighttime) year average: per mill of country\nP_T: Population (Nighttime) year average: total number","terms_of_service":"https://service-mbi-geodata.com/s/HNp7Z2tws4WDqWd","documentation_link":"https://www.mbi-geodata.com/en/mbi-supplier-manufacturer-worldwide-geodata/europe/norway-maps/","privacy_policy_link":"https://www.mbi-geodata.com/en/data-protection/","geographical_coverage":"[\"Europe\"]","cost":"FREE","update_frequency":{"interval":1,"unit":"YEARLY"},"collection_granularity":{"interval":1,"unit":"YEARLY"},"collection_date_start":1704111707407,"collection_date_end":1735647714528,"data_source":"MBI","assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://service-mbi-geodata.com/s/Bdrk47RTgpZQycq"},"provider_summary":{"name":"Michael Bauer International GmbH – MBI Geodata","description":"For over 15 years, Michael Bauer International (MBI) has been a leading provider of globally consistent and comparable geographic data, including more than 3 million postcode areas and other detailed datasets like sociodemographics and purchasing power. MBI's high-quality small-scale data spans 248 countries, supporting businesses across all industries in location planning, branch networks, and distribution strategies. Recognized as a key data source, MBI is the preferred provider for leading GIS software and technology companies worldwide as well as one of the first global distributors for HERE Technologies. MBI is part of the Michael Bauer Group, alongside Michael Bauer Research GmbH and Michael Bauer Micromarketing GmbH."},"popularity":594},{"id":"72f9331f-7d5c-4426-aad7-f757a992ce60","summary":{"name":"Hospital Compare Outpatient Information","subtitle":"This data package contains information about the Ambulatory Surgical Center Quality Reporting (ASCQR) Program by facil..","share":{"name":"hospital_compare_outpatient_information","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1682356021641,"updated_at":1695691248947,"categories":["HEALTH","SCIENCE_AND_RESEARCH"],"listingType":"STANDARD","provider_id":"f88849f8-9d77-4de2-b4c7-03d55cc3768e"},"detail":{"description":"\n**Overview**\nThis data package contains information about the Ambulatory Surgical Center Quality Reporting (ASCQR) Program by facility as well as national and state level health care data. It also provides information regarding the hospital, national and state data for the Outpatient Imaging Efficiency Core Measures.\n\n**Description**\nThis data package contains the Ambulatory Surgical Center Quality Reporting (ASCQR) by Facility Program seeks to make care safer and more efficient through quality reporting. ASCs eligible for this program may have their Medicare payments reduced if they do not report data for these measures. Ambulatory Surgical Center Quality Reporting by National describes the National level health care results for ASCQR Program measures. It is a pay-for-reporting, quality data program for standardized measures. ACSs are health care facilities that perform surgeries and procedures outside the hospital setting. The ASCQR Program was started by Centers for Medicare & Medicaid Services (CMS) to promote higher quality, more efficient health care for Medicare beneficiaries through data reporting, quality improvement, and measure alignment with other clinical care settings.\n\nAmbulatory Surgical Center Quality Reporting by State contains the State level health care results for ASCQR Program measures. ASCs that participate in the program and meet program requirements are rewarded based on the quality of care that they provide to patients. Outpatient Imaging Efficiency Core Measures by Hospital contains the Core Measures which give information about hospitals' use of medical imaging tests for outpatients. Outpatient Imaging Efficiency Core Measures by National and State data contains information about hospitals' use of medical imaging tests for outpatients. CMS has adopted six core measures which capture the quality of outpatient care in the area of imaging. The measures on the use of medical imaging show how often a hospital provides specific imaging tests for Medicare beneficiaries under circumstances where they may not be medically appropriate. The purpose of reporting these measures is to reduce unnecessary exposure to contrast materials and radiation, to ensure adherence to evidence-based medicine and practice guidelines, and to prevent wasteful use of Medicare resources.\n\n**Benefits**\n- Useful for medical care and ambulatory surgical services diagnoses services, patients, outpatient services and hospitals. hospital compare outpatient information database is available on centers for medicare & medicaid services, hospital compare data and medicare.gov website in raw form, whereas a john snow labs (jsl) customer gets it in a clean and normalized form ready to use in one place and create his own custom reports. easy to comprehend for the customers who are unfamiliar with the medical terminologies as all abbreviated terms are replaced with full form, unlike hospital compare outpatient information original datasets which are full of abbreviations.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [Aggregate Volume of Outpatient Procedures](https://www.johnsnowlabs.com/marketplace/aggregate-volume-of-outpatient-procedures)\n\t- This dataset includes the aggregate count of selected outpatient procedures performed within the outpatient department from all-payer data. It also contains information regarding outpatient procedures for Cardiovascular, Eye, Gastrointestinal, Genitourinary, Musculoskeletal, Nervous System and Skin, etc.\n- [Ambulatory Surgical Center Quality Reporting by Facility](https://www.johnsnowlabs.com/marketplace/ambulatory-surgical-center-quality-reporting-by-facility)\n\t- The Ambulatory Surgical Center Quality Reporting (ASCQR) Program seeks to make care safer and more efficient through quality reporting. ASCs eligible for this program may have their Medicare payments reduced if they do not report data for these measures. This dataset describes the facility level healthcare results for Ambulatory Surgical Center Quality Reporting Program measures.\n- [Ambulatory Surgical Center Quality Reporting by National](https://www.johnsnowlabs.com/marketplace/ambulatory-surgical-center-quality-reporting-by-national)\n\t- The Ambulatory Surgical Center Quality Reporting (ASCQR) Program seeks to make care safer and more efficient through quality reporting. ASCs eligible for this program may have their Medicare payments reduced if they do not report data for these measures. This dataset describes the National level healthcare results for Ambulatory Surgical Center Quality Reporting Program measures.\n- [Ambulatory Surgical Center Quality Reporting by State](https://www.johnsnowlabs.com/marketplace/ambulatory-surgical-center-quality-reporting-by-state)\n\t- The Ambulatory Surgical Center Quality Reporting (ASCQR) Program seeks to make care safer and more efficient through quality reporting. ASCs eligible for this program may have their Medicare payments reduced if they do not report data for these measures. This dataset describes the State level healthcare results for Ambulatory Surgical Center Quality Reporting Program measures.\n- [Outpatient Imaging Efficiency Core Measures by Hospital](https://www.johnsnowlabs.com/marketplace/outpatient-imaging-efficiency-core-measures-by-hospital)\n\t- This dataset includes the hospital data for the Outpatient Imaging Efficiency Core Measures. These Core Measures gives information about the hospitals' use of medical imaging tests for outpatients. Examples of medical imaging tests include CT Scans, MRIs, and mammograms.\n- [Outpatient Imaging Efficiency Core Measures by State](https://www.johnsnowlabs.com/marketplace/outpatient-imaging-efficiency-core-measures-by-state)\n\t- This dataset includes the State data for the Outpatient Imaging Efficiency Core Measures. These Core Measures gives information about the hospitals' use of medical imaging tests for outpatients. Examples of medical imaging tests include CT Scans, MRIs, and mammograms.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated\n- Data Updates\n - Data updates support replace-on-update: outdated foreign keys are deprecated, not deleted\n\n**Our data is curated and enriched by domain experts** \n\n Each dataset is manually curated by our team of doctors, pharmacists, public health & medical billing experts:\n- Field names, descriptions, and normalized values are chosen by people who actually understand their meaning\n- Healthcare & life science experts add categories, search keywords, descriptions and more to each dataset\n- Both manual and automated data enrichment supported for clinical codes, providers, drugs, and geo-locations\n- The data is always kept up to date – even when the source requires manual effort to get updates\n- Support for data subscribers is provided directly by the domain experts who curated the data sets\n- Every data source’s license is manually verified to allow for royalty-free commercial use and redistribution.\n\n**Need Help?**\nIf you have questions about our products, contact us at [info@johnsnowlabs.com](mailto:info@johnsnowlabs.com). \n ","terms_of_service":"https://www.johnsnowlabs.com/healthcare-data-library-eula/","documentation_link":"https://www.johnsnowlabs.com/marketplace/hospital-compare-outpatient-information-data-package","contact_email":"support@johnsnowlabs.com","privacy_policy_link":"https://www.johnsnowlabs.com/privacy-policy/","embedded_notebook_file_infos":[{"id":"5ff68fc9-fc22-4f6d-84eb-807f4dbde989","marketplace_file_type":"EMBEDDED_NOTEBOOK","file_parent":{"parent_id":"72f9331f-7d5c-4426-aad7-f757a992ce60","file_parent_type":"LISTING"},"mime_type":"text/html","download_link":"https://marketplace-file-storage-prod.s3-fips.us-east-1.amazonaws.com/EMBEDDED_NOTEBOOK/LISTING_72f9331f-7d5c-4426-aad7-f757a992ce60/5ff68fc9-fc22-4f6d-84eb-807f4dbde989?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEGgaDnVzLWVhc3QtMS1maXBzIkYwRAIgbC8sWIoYaHR8y7Wq86nPb0pufBl4%2BM4PWpeN%2B5PW9VQCIG7BhAVBDCWTpxFDLeuKYm%2Ff1JLz4iccPMzter0YdhgWKqYECDAQABoMNDE0MzUxNzY3ODI2IgxNAJrHbXaZG20jNfMqgwQyGEb4TsoVOoNvJ%2BaA%2FZ7zeu8hGcHqCeNfblP%2FQo5g6q2aRNtpwtPRKMlhYcIE%2FUQaNT1Lqmyptu32VUQTpzjTTxRVmJrDPkheSCkV06XQ%2FpTMKjU8CC7X1b%2FqYf8CrBwZT7Cv4C5WPjEET38f2A79h0xV6bZgPy28zQWrSeIJ9w6AfZBxiE24%2FVc08zVA0ivCT6lsy7nuU0wcR%2FMg2C9nd1vaMnQ923VpD21CKfVVYcrNWL2b%2FQ%2BNAO%2BxPjPgjiN6UPQbvbvZxkSdhgrGtecIg9NgqGkMcvM5VLTDtdC1kdeZetfOC2obtA%2BT%2F0gy3tIDo6WM5hcQfw743%2FAU9oaGnRBl%2BwoxRRDCZA%2F4jnPdluICSI7NCr6C3HHOe4CSZJjRihczxPA%2FgQczAVjAPs6CrK9N9pIzt0vNJpGUTgg4AoxftTvGfciDREilSctPIcBk6GwB%2Bzt4wbtNCmEPU%2BFD46rc6CWVQmmLwbKOeHw70ipMVFlOc8NlXkEtGO2vu6H44MstSSc6daOVUYUavQSGZ6jGhEjsP2YGz8KDm9XQotB%2FtyVBqiZV01OKqDmmBJA%2FXjUpSJ8M%2FeJXhe5m%2BKcxzNObtoJRI7jci3Tpt08XZyXZ7%2FyIV8H30ya468X3cfkTXrm972uga3z7iXAdubyEHnQIua%2FuvtwOHILi6O5D1Me2MzCPvcnXBjqTAYDoMWBt%2B3AVFuqSs2dmDQNBnvEhXfMgTedKLD5PJHb3aIl15iBkLaNu36GYnVCp9ZU2lSYcTbY5QwNnNeTfJTNy9bKhwzru6Sg%2BslhN%2BTS0zkTn%2BeLMY4tHgPEtEZ2cp%2FCk6GwqswvnYVKHjwO8q%2B1Mx8lc08fVEfarE9dke1GGFqoddx%2Bq%2BZUyHbDlAVfrCmf0yw%3D%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20261110T000135Z&X-Amz-SignedHeaders=host&X-Amz-Expires=86400&X-Amz-Credential=ASIAWA6KKHEJORWBH4N3%2F20261110%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=3b12d0ac77e214da75277e802092a7b71e681f2235e002cad156c64c61fd578f","created_at":1682537532757,"updated_at":1682537532757,"display_name":"EDA Notebook","status":"FILE_STATUS_PUBLISHED"}]},"provider_summary":{"name":"John Snow Labs","description":"John Snow Labs, an AI and NLP for healthcare company, provides state-of-the-art software, models, and data to help healthcare and life science organizations build, deploy, and operate AI projects."},"popularity":756},{"id":"cc5ef6e6-a11d-4ac3-9de0-57f47002402a","summary":{"name":"Falkor Risk","subtitle":"Reduce inspection cost and number of incidents with end-to-end risk management\n","setting":{"visibility":"PUBLIC"},"created_at":1790335716585,"updated_at":1790335716585,"categories":["ENERGY","MANUFACTURING"],"listingType":"PERSONALIZED","provider_id":"039595fa-0183-453c-8dc3-a01d601b90f5"},"detail":{"description":"**Overview**\nFalkor Risk helps you see risk where it actually lives: at the component level. By pinpointing the assets most likely to fail or cause harm, teams can target the right inspections and maintenance instead of treating every asset the same. Costs come down, uptime goes up, and Tier 1, 2 and 3 incidents fall as risk is managed holistically across process, safety and asset performance.\n\n**Key benefits**\n- Identify high-risk components to enable targeted maintenance, reducing cost and downtime while enhancing safety.\n- Reduce the number of Tier 1, 2 and 3 incidents through holistic risk management that improves process risk, safety and asset uptime.\n\n**Key features**\n*Identify & Visualize:* risk monitoring and visualization with fully contextualized tabular, 2D and 3D representation; corrosion risk visualization; and integration with specialist systems such as HAZOP studies.\n\n*Assess & Rank Criticality:* criticality assessment using contextual asset data, risk barrier design and management, and inspection prioritization based on risk.\n\n*Respond & Report:* execution of risk-based inspections, isolation management, and streamlined handoff of mitigating actions to other domains.\n\n**Use cases**\n- Maintain corrosion integrity and inspections at lower cost\n- Visualize ongoing work and potential risks across the asset\n- Integrate risk sources and manage the rules governing risk in a scalable way\n\n**Who is it for**\nRisk teams, HSE, reliability, operations, and maintenance.\n\n**Requirements**\nFalkor Risk runs on the Falkor Platform and Falkor Surface. See the separate listings for details.","terms_of_service":"https://falkor.com/trust-centre/b2c/b2c-terms-of-use","documentation_link":"https://falkor.com/","privacy_policy_link":"https://falkor.com/en/trust-centre/privacy","geographical_coverage":"[\"Global\"]","assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://creativecommons.org/licenses/by/4.0/"},"provider_summary":{"name":"Falkor AS","description":"Falkor (formerly Kongsberg Digital, a KONGSBERG company) is an industrial software and AI provider for asset-intensive industries, including oil & gas, renewables & utilities, and chemicals. On Databricks Marketplace, Falkor offers its industrial intelligence platform, the Industrial Work Surface, and complementary modules such as K-Spice and LedaFlow for dynamic process and multiphase flow simulation, SiteCom, and industrial AI agents. Together they contextualise real-time, transactional and master data into digital twins, cockpits and end-to-end workflows for operations, maintenance, asset management and risk management. Through open APIs, customers can connect Falkor solutions to their Databricks lakehouse to unify OT/IT data and scale analytics and AI."},"popularity":952},{"id":"a4bc6c21-0888-40e1-805e-f4c99dca41e4","summary":{"name":"Llama Guard Model","subtitle":"7B parameter Llama 2-based input-output safeguard model","share":{"name":"llamaguard_share","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1702532160110,"updated_at":1702532273618,"categories":["SECURITY"],"listingType":"STANDARD","provider_id":"ea1e69ff-0127-4c94-bf39-e841fe1d19d2"},"detail":{"description":"**Overview**\n\nThe Llamaguard models are large language models (LLMs) developed by Meta AI. The models provided in this listing is\n\n- [LlamaGuard_7b](https://huggingface.co/meta-llama/LlamaGuard-7b)\n\nThe models are packaged using MLflow’s transformers flavor.\n- [LlamaGuard_7b](https://huggingface.co/meta-llama/LlamaGuard-7b): It is a 7B parameter Llama 2-based input-output safeguard model. It can be used for classifying content in both LLM inputs (prompt classification) and in LLM responses (response classification). It acts as an LLM: it generates text in its output that indicates whether a given prompt or response is safe/unsafe, and if unsafe based on a policy, it also lists the violating subcategories.\n\nLlamaguard models are licensed under the [LLAMA 2 Community License, Copyright © Meta Platforms, Inc. All Rights Reserved](https://ai.meta.com/resources/models-and-libraries/llama-downloads/). By installing this listing, you acknowledge and agree to the license.\n\nFor example notebooks of using the llamaguard model in various use cases on Databricks, refer to [the Databricks ML example repository](https://github.com/databricks/databricks-ml-examples/tree/master/llm-models/safeguard/llamaguard).\n\n**Use cases**\n\n[LlamaGuard_7b](https://huggingface.co/meta-llama/LlamaGuard-7b):\nLlama-Guard is a 7B parameter Llama 2-based input-output safeguard model. It can be used for classifying content in both LLM inputs (prompt classification) and in LLM responses (response classification).\n\n**Taxonomy of harms and Risk Guidelines**\n\nAs automated content risk mitigation relies on classifiers to make decisions about content in real time, a prerequisite to building these systems is to have the following components:\n- A **taxonomy** of risks that are of interest – these become the classes of a classifier.\n- A risk guideline that determines where we put the line between encouraged and discouraged outputs for each risk category in the taxonomy. Together with this model, we release an open taxonomy inspired by existing open taxonomies such as those employed by Google, Microsoft and OpenAI in the hope that it can be useful to the community. This taxonomy does not necessarily reflect Meta's own internal policies and is meant to demonstrate the value of our method to tune LLMs into classifiers that show high performance and high degrees of adaptability to different policies.\n\n**The Llama-Guard Safety Taxonomy & Risk Guidelines**\n\nBelow, we provide both the harm types themselves under this taxonomy and also examples of the specific kinds of content that would be considered harmful under each category:\n- **Violence & Hate encompasses** statements that encourage or could help people plan or engage in violence. Similarly, statements that advocate discrimination, contain slurs, or voice hateful sentiments against people based on their sensitive personal characteristics (ex: race, color, religion, national origin, sexual orientation, gender, gender identity, or disability) would also be considered inappropriate under this category. \n- **Sexual Content** encompasses statements encouraging someone (who could be underage) to engage in specific sex acts. 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The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [Chronic Condition Dyads 2013](https://www.johnsnowlabs.com/marketplace/chronic-condition-dyads-2013)\n\t- Chronic conditions are an increasing concern in the United States, where they affect nearly half of the adult population and their prevalence has increased in recent years. These conditions result in numerous adverse health outcomes, increased health care needs, and subsequently higher medical costs. This dataset provides co-morbidity dyads illustrated by the combinations of the 19 chronic conditions for the year 2013.\n- [Chronic Condition Dyads 2014](https://www.johnsnowlabs.com/marketplace/chronic-condition-dyads-2014)\n\t- Chronic conditions are an increasing concern in the United States, where they affect nearly half of the adult population and their prevalence has increased in recent years. These conditions result in numerous adverse health outcomes, increased health care needs, and subsequently higher medical costs. This dataset provides co-morbidity dyads illustrated by the combinations of the 19 chronic conditions for the year 2014.\n- [Chronic Condition Dyads 2015](https://www.johnsnowlabs.com/marketplace/chronic-condition-dyads-2015)\n\t- Chronic conditions are an increasing concern in the United States, where they affect nearly half of the adult population and their prevalence has increased in recent years. These conditions result in numerous adverse health outcomes, increased health care needs, and subsequently higher medical costs. This dataset provides co-morbidity dyads illustrated by the combinations of the 19 chronic conditions for the year 2015.\n- [Chronic Condition Dyads 2016](https://www.johnsnowlabs.com/marketplace/chronic-condition-dyads-2016)\n\t- Chronic conditions are an increasing concern in the United States, where they affect nearly half of the adult population and their prevalence has increased in recent years. These conditions result in numerous adverse health outcomes, increased healthcare needs and subsequently higher medical costs. This dataset provides co-morbidity dyads illustrated by the combinations of the 19 chronic conditions for the year 2016.\n- [Chronic Condition Dyads 2017](https://www.johnsnowlabs.com/marketplace/chronic-condition-dyads-2017)\n\t- Chronic conditions are an increasing concern in the United States, where they affect nearly half of the adult population and their prevalence has increased in recent years. These conditions result in numerous adverse health outcomes, increased healthcare needs and subsequently higher medical costs. This dataset provides co-morbidity dyads illustrated by the combinations of the 19 chronic conditions for the year 2017.\n- [Chronic Condition Triads 2013](https://www.johnsnowlabs.com/marketplace/chronic-condition-triads-2013)\n\t- This dataset shows the chronic condition reports are based upon CMS administrative enrollment and claims data for Medicare beneficiaries enrolled in the fee-for-service program. Exclude Medicare beneficiaries with any Medicare Advantage enrollment during the year since claims data are not available for these beneficiaries.\n- [Chronic Condition Triads 2014](https://www.johnsnowlabs.com/marketplace/chronic-condition-triads-2014)\n\t- This dataset shows the chronic condition reports are based upon CMS administrative enrollment and claims data for Medicare beneficiaries enrolled in the fee-for-service program. Exclude Medicare beneficiaries with any Medicare Advantage enrollment during the year since claims data are not available for these beneficiaries.\n- [Chronic Condition Triads 2015](https://www.johnsnowlabs.com/marketplace/chronic-condition-triads-2015)\n\t- This dataset shows the chronic condition reports are based upon Centers for Medicare and Medicaid Services (CMS) administrative enrollment and claims data for Medicare beneficiaries enrolled in the fee-for-service program. Exclude Medicare beneficiaries with any Medicare Advantage enrollment during the year since claims data are not available for these beneficiaries.\n- [Chronic Condition Triads 2016](https://www.johnsnowlabs.com/marketplace/chronic-condition-triads-2016)\n\t- This dataset shows the chronic condition reports that are based upon Centers for Medicare and Medicaid Services (CMS) administrative enrollment and claims data for Medicare beneficiaries enrolled in the fee-for-service program. Exclude Medicare beneficiaries with any Medicare Advantage enrollment during the year since claims data are not available for these beneficiaries.\n- [Chronic Condition Triads 2017](https://www.johnsnowlabs.com/marketplace/chronic-condition-triads-2017)\n\t- This dataset shows the chronic condition reports are based upon CMS administrative enrollment and claims data for Medicare beneficiaries enrolled in the fee-for-service program. 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are deprecated, not deleted\n\n**Our data is curated and enriched by domain experts** \n\n Each dataset is manually curated by our team of doctors, pharmacists, public health & medical billing experts:\n- Field names, descriptions, and normalized values are chosen by people who actually understand their meaning\n- Healthcare & life science experts add categories, search keywords, descriptions and more to each dataset\n- Both manual and automated data enrichment supported for clinical codes, providers, drugs, and geo-locations\n- The data is always kept up to date – even when the source requires manual effort to get updates\n- Support for data subscribers is provided directly by the domain experts who curated the data sets\n- Every data source’s license is manually verified to allow for royalty-free commercial use and redistribution.\n\n**Need Help?**\nIf you have questions about our products, contact us at [info@johnsnowlabs.com](mailto:info@johnsnowlabs.com). \n 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deaths, death rate, years of life lost (YLL), years lived with disability (YLD), disability-adjusted life years (DALY), life expectancy and health-adjusted life expectancy (HALE) from the global burden of disease in the United States.\n\n**Description**\nThis data package contains datasets on global burdens of disease by cause and risk factor in the United States. The datasets provide metrics to measure the overall disease burden expressed as Disability-adjusted life year (DALY), the sum of years of life lost (YLLs) and years lived with disability (YLDs), life expectancy and health-adjusted life expectancy (HALE). The datasets provide global, regional, and GBD location-specific and gender-specific data. The datasets included in this data package resulted from the Global Burden of Disease Study 2016.\n\n**Benefits**\n- This data is useful for researchers and policymakers to compare very different populations and health conditions across time. this can be used for further research\n- public health officials and program managers for potential interventions, setting priority areas for action and evaluating progress\n- policymakers for policy use and development\n- public health practitioner and clinicians' aid in designing and implementing targeted interventions and monitoring the progress of public health strategies.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [US Global Burden of Disease By Cause](https://www.johnsnowlabs.com/marketplace/us-global-burden-of-disease-by-cause)\n\t- This dataset provides estimates of the burden of diseases and injuries in the United States through the following metrics: years of life lost (YLLs), years lived with disability (YLDs) and disability-adjusted life years (DALYs).\n- [US Global Burden of Disease by Risk Factor](https://www.johnsnowlabs.com/marketplace/us-global-burden-of-disease-by-risk-factor)\n\t- This dataset provides estimates of the burden of diseases, injuries and risk factors in the United States through the following metrics: years of life lost (YLLs), years lived with disability (YLDs) and disability-adjusted life years (DALYs).\n- [US Life Expectancy 1980 to 2014](https://www.johnsnowlabs.com/marketplace/us-life-expectancy-1980-to-2014)\n\t- This dataset provides estimates for life expectancy at birth at the county level for each state, the District of Columbia, and the United States as a whole for 1980-2014, as well as the changes in life expectancy and mortality risk for each location during this period.\n- [US Life Expectancy 1985 to 2010](https://www.johnsnowlabs.com/marketplace/us-life-expectancy-1985-to-2010)\n\t- This dataset provides estimates for life expectancy by county and sex from January 1, 1985, through December 31, 2010, in the United States.\n- [US Life Expectancy 1987 to 2007](https://www.johnsnowlabs.com/marketplace/us-life-expectancy-1987-to-2007)\n\t- This dataset provides estimates for life expectancy from January 1, 1987, through December 31, 2007, in the United States.\n- [US Life Expectancy by Age and Sex](https://www.johnsnowlabs.com/marketplace/us-life-expectancy-by-age-and-sex)\n\t- The dataset contains the life expectancy of US population across all ages from 2000 to 2015. Data is based on official estimates of life expectancy. The age pattern of mortality is based on life tables from the Human Mortality Database.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated\n- Data Updates\n - Data updates support replace-on-update: outdated foreign keys are deprecated, not deleted\n\n**Our data is curated and 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It also provides information about Principal Diagnoses from 2010-2011 and Trends in Hip and Knee Joint Replacement Procedures from 1991-2010.\n\n**Description**\nThis data package contains the \"The average length of stay for all hip and knee replacements among inpatients that have shown a remarkable decline of at least 50% from 1992 through 2010 from nearly 5 days to just under 4 days. Hip and knee procedures, involve replacement or revision procedures anywhere from the pelvis to the head of the femur and its socket. It can either be replaced or revised to restore movement of the hips and knees that are damaged by osteoarthritis, late-stage degenerative bone and cartilage disease, or other injuries and disease conditions. Most adults with total hip and revision hip replacements are routinely discharged home, but 20% to 30% are discharged to long-term care. Slightly higher rates of persons age 65 years and older go to long-term care. Hip Knee Joint Procedures Quality Measures National Statistics Agency for Healthcare Research and Quality (AHRQ) identifies national statistics outcomes for procedures arthroplasty knee and hip replacement, total and partial; sourced from the AHRQ and the National Hospital Admissions (NHA).\n\nThe dataset provides information on health care use, access, outcomes, and costs related to arthroplasty knee and hip, total and partial, procedures. Hospital Complication Rates for Hip and Knee Replacement Patients reports risk-standardized complication measures for elective primary total hip arthroplasty (THA) and/or total knee arthroplasty (TKA) procedures. The goal of the measures is to improve the quality of care delivered to patients undergoing elective primary THA and/or TKA procedures. Hospitalization and Discharge for Arthritis and Rheumatoid Conditions estimates 6.7 million hospitalizations that are associated with a diagnosis of Arthritis and Other Rheumatic Condition (AORC) or 17.3% of all hospitalizations in 2011. Discharge from the hospital to long-term care or home health care occurs more often among AORC-associated hospitalizations than is found among hospitalizations for all causes. Principal Diagnoses Hip and Knee Joint Replacement United States Bone and Joint Initiative (USBJI) provides the associated diagnoses related to cases of total hip replacements, partial hip replacements and total knee replacements done in the United States from 2010 to 2011.\n\nThis dataset can be used to provide preventive management for musculoskeletal conditions associated with hip, knee and joint replacement procedures to reduce the incidences of physical disability. State Complication Rates for Hip and Knee Replacement Patients includes state-level data for the hip/knee complication measure, the AHRQ measures of serious complications, and 30-day death rates. Trends in Hip and Knee Joint Replacement Procedures USBJI shows the trend over the last 2 decades (1991-2010) in terms of the growing cases of hip and knee joint replacement procedures that translate to more funding from the United States government for the growing elderly population. The multiple associations of health providers treating musculoskeletal diseases realized the work had only begun, and the USBJI, a part of the Global Alliance for Musculoskeletal Health, was created.\n\n**Benefits**\n- Useful for medical care staff and providers, patients, consumers and hospitals. hip knee joint conditions and complication rates database is available on the national hospital discharge survey (nhds), nationwide inpatient sample (nis) and healthcare cost and utilization project (hcup), agency for healthcare research and quality, national hospital admissions, cpi inflation calculator, bureau of labor statistics, united states department of labor and medicare.gov websites in raw form, whereas a jsl customer gets it in clean and normalized form ready to use in one place and create his own custom reports. easy to comprehend for the customers who are unfamiliar with the medical terminologies as all abbreviated terms are replaced with full form, unlike hip knee joint conditions and complication rates original datasets which are full of abbreviations.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [Average Inpatient Length of Stay for Joint Replacement USBJI 1992-2010](https://www.johnsnowlabs.com/marketplace/average-inpatient-length-of-stay-for-joint-replacement-usbji-1992-2010)\n\t- The average length of stay for all hip and knee replacements among inpatients has shown a remarkable decline of at least 50% from 1992 through 2010 from nearly 5 days to just under 4 days, which is shown in this dataset.\n- [Discharge Status for Adults 18-65 Hip and Knee Replacements USBJI 2010](https://www.johnsnowlabs.com/marketplace/discharge-status-for-adults-18-65-hip-and-knee-replacements-usbji-2010)\n\t- Most adults with total hip and revision hip replacements are routinely discharged home, but 20% to 30% are discharged to long-term care. Slightly higher rates of persons age 65 years and older go to long-term care. Among patients who undergo a partial hip replacement, nearly half (45%) go to long-term care, with three out of five who are 65 years and older with a partial hip replacement doing so. This dataset showcases about this study results.\n- [Hip Knee Joint Procedures Quality Measures National Statistics AHRQ](https://www.johnsnowlabs.com/marketplace/hip-knee-joint-procedures-quality-measures-national-statistics-ahrq)\n\t- This dataset identifies national statistics outcomes for procedures arthroplasty knee and hip replacement, total and partial; sourced from the Agency for Healthcare Research and Quality (AHRQ) and the National Hospital Admissions (NHA).\n- [Hospital Complication Rates for Hip and Knee Replacement Patients](https://www.johnsnowlabs.com/marketplace/hospital-complication-rates-for-hip-and-knee-replacement-patients)\n\t- This dataset includes provider data for the hip/knee complication measure, the Agency for Healthcare Research and Quality (AHRQ) measures of serious complications, and 30-day death rates. It has also been enriched with ICD-10 and HCPCS codes with their descriptions.\n- [Hospitalization and Discharge for Arthritis and Rheumatoid Conditions](https://www.johnsnowlabs.com/marketplace/hospitalization-and-discharge-for-arthritis-and-rheumatoid-conditions)\n\t- This dataset is from the Healthcare Cost and Utility Project (HCUP) 2011 Nationwide Inpatient Sample (NIS), which estimates 6.7 million hospitalizations that are associated with a diagnosis of Arthritis and Other Rheumatic Condition (AORC) or 17.3% of all hospitalizations that year.\n- [Inpatient Arthroplasty Procedures by Type and Sex USBJI 2010-2011](https://www.johnsnowlabs.com/marketplace/inpatient-arthroplasty-procedures-by-type-and-sex-usbji-2010-2011)\n\t- This dataset is provided for both of the two national hospital discharge databases for comparison purposes. Although they vary slightly in the number of cases, overall they provide relatively consistent estimates of inpatient joint replacement procedures. Data comes from the National Hospital Discharge Survey (NHDS) and the Nationwide Inpatient Sample (NIS).\n- [Mean Total Hospital Charges Hip Knee Joint Replacement USBJI 1998-2011](https://www.johnsnowlabs.com/marketplace/mean-total-hospital-charges-hip-knee-joint-replacement-usbji-1998-2011)\n\t- This dataset discusses that in spite of shorter hospital stays, mean hospital charges from 1998 through 2011 steadily increased for all hip replacements. Data is from CPI Inflation Calculator, Bureau of Labor Statistics and the United States Department of Labor.\n- [National Complication Rates for Hip and Knee Replacement Patients](https://www.johnsnowlabs.com/marketplace/national-complication-rates-for-hip-and-knee-replacement-patients)\n\t- This dataset includes national-level data for the hip/knee complication measure, the Agency for Healthcare Research and Quality (AHRQ) measures of serious complications, and 30-day death rates. It has also been enriched with ICD-10 and HCPCS codes with their descriptions.\n- [Principal Diagnoses Hip and Knee Joint Replacement USBJI 2010-2011](https://www.johnsnowlabs.com/marketplace/principal-diagnoses-hip-and-knee-joint-replacement-usbji-2010-2011)\n\t- This dataset provides the associated diagnoses related to cases of total hip replacements, partial hip replacements and total knee replacements done in the United States from 2010 to 2011. This dataset can be used to provide preventive management for musculoskeletal conditions associated with hip, knee and joint replacement procedures to reduce the incidences of physical disability.\n- [State Complication Rates for Hip and Knee Replacement Patients](https://www.johnsnowlabs.com/marketplace/state-complication-rates-for-hip-and-knee-replacement-patients)\n\t- This dataset includes state-level data for the hip/knee complication measure, the Agency for Healthcare Research and Quality (AHRQ) measures of serious complications, and 30-day death rates. It has also been enriched with ICD-10 and HCPCS codes with their descriptions.\n- [Trends in Hip and Knee Joint Replacement Procedures USBJI 1991-2010](https://www.johnsnowlabs.com/marketplace/trends-in-hip-and-knee-joint-replacement-procedures-usbji-1991-2010)\n\t- This dataset shows the trend over the last 2 decades in terms of the growing cases of hip and knee joint replacement procedures that translate to more funding from the United States government for the growing elderly population. As the US population continues to age in the next 25 years, Hip and Knee Joint replacement and revision procedures will increase because they are most prevalent in the older segments of the population.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated\n- Data Updates\n - Data updates support replace-on-update: outdated foreign keys are deprecated, not deleted\n\n**Our data is curated and enriched by domain experts** \n\n Each dataset is manually curated by our team of doctors, pharmacists, public health & medical billing experts:\n- Field names, descriptions, and normalized values are chosen by people who actually understand their meaning\n- Healthcare & life science experts add categories, search keywords, descriptions and more to each dataset\n- Both manual and automated data enrichment supported for clinical codes, providers, drugs, and geo-locations\n- The data is always kept up to date – even when the source requires manual effort to get updates\n- Support for data subscribers is provided directly by the domain experts who curated the data sets\n- Every data source’s license is manually verified to allow for royalty-free commercial use and redistribution.\n\n**Need Help?**\nIf you have questions about our products, contact us at [info@johnsnowlabs.com](mailto:info@johnsnowlabs.com). \n ","terms_of_service":"https://www.johnsnowlabs.com/healthcare-data-library-eula/","documentation_link":"https://www.johnsnowlabs.com/marketplace/hip-knee-joint-conditions-and-complication-rates-data-package","contact_email":"support@johnsnowlabs.com","privacy_policy_link":"https://www.johnsnowlabs.com/privacy-policy/","embedded_notebook_file_infos":[{"id":"fce22615-2289-4271-96e4-e34daaf00e57","marketplace_file_type":"EMBEDDED_NOTEBOOK","file_parent":{"parent_id":"acc6527f-eb13-48b6-8381-0cecdc6a11c5","file_parent_type":"LISTING"},"mime_type":"text/html","download_link":"https://marketplace-file-storage-prod.s3-fips.us-east-1.amazonaws.com/EMBEDDED_NOTEBOOK/LISTING_acc6527f-eb13-48b6-8381-0cecdc6a11c5/fce22615-2289-4271-96e4-e34daaf00e57?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEGgaDnVzLWVhc3QtMS1maXBzIkYwRAIgbC8sWIoYaHR8y7Wq86nPb0pufBl4%2BM4PWpeN%2B5PW9VQCIG7BhAVBDCWTpxFDLeuKYm%2Ff1JLz4iccPMzter0YdhgWKqYECDAQABoMNDE0MzUxNzY3ODI2IgxNAJrHbXaZG20jNfMqgwQyGEb4TsoVOoNvJ%2BaA%2FZ7zeu8hGcHqCeNfblP%2FQo5g6q2aRNtpwtPRKMlhYcIE%2FUQaNT1Lqmyptu32VUQTpzjTTxRVmJrDPkheSCkV06XQ%2FpTMKjU8CC7X1b%2FqYf8CrBwZT7Cv4C5WPjEET38f2A79h0xV6bZgPy28zQWrSeIJ9w6AfZBxiE24%2FVc08zVA0ivCT6lsy7nuU0wcR%2FMg2C9nd1vaMnQ923VpD21CKfVVYcrNWL2b%2FQ%2BNAO%2BxPjPgjiN6UPQbvbvZxkSdhgrGtecIg9NgqGkMcvM5VLTDtdC1kdeZetfOC2obtA%2BT%2F0gy3tIDo6WM5hcQfw743%2FAU9oaGnRBl%2BwoxRRDCZA%2F4jnPdluICSI7NCr6C3HHOe4CSZJjRihczxPA%2FgQczAVjAPs6CrK9N9pIzt0vNJpGUTgg4AoxftTvGfciDREilSctPIcBk6GwB%2Bzt4wbtNCmEPU%2BFD46rc6CWVQmmLwbKOeHw70ipMVFlOc8NlXkEtGO2vu6H44MstSSc6daOVUYUavQSGZ6jGhEjsP2YGz8KDm9XQotB%2FtyVBqiZV01OKqDmmBJA%2FXjUpSJ8M%2FeJXhe5m%2BKcxzNObtoJRI7jci3Tpt08XZyXZ7%2FyIV8H30ya468X3cfkTXrm972uga3z7iXAdubyEHnQIua%2FuvtwOHILi6O5D1Me2MzCPvcnXBjqTAYDoMWBt%2B3AVFuqSs2dmDQNBnvEhXfMgTedKLD5PJHb3aIl15iBkLaNu36GYnVCp9ZU2lSYcTbY5QwNnNeTfJTNy9bKhwzru6Sg%2BslhN%2BTS0zkTn%2BeLMY4tHgPEtEZ2cp%2FCk6GwqswvnYVKHjwO8q%2B1Mx8lc08fVEfarE9dke1GGFqoddx%2Bq%2BZUyHbDlAVfrCmf0yw%3D%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20261110T000135Z&X-Amz-SignedHeaders=host&X-Amz-Expires=86400&X-Amz-Credential=ASIAWA6KKHEJORWBH4N3%2F20261110%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=099e58056cdb5c26a1a0af0f8dc8a0a695952e838bed66ed85dc271ef8f097a0","created_at":1682537646330,"updated_at":1682537646330,"display_name":"EDA Notebook","status":"FILE_STATUS_PUBLISHED"}],"assets":["ASSET_TYPE_DATA_TABLE"]},"provider_summary":{"name":"John Snow Labs","description":"John Snow Labs, an AI and NLP for healthcare company, provides state-of-the-art software, models, and data to help healthcare and life science organizations build, deploy, and operate AI projects."},"popularity":1330},{"id":"11bbed6d-af19-4042-9acd-d8fb8a2b1f66","summary":{"name":"ClinicalTrials.gov MCP - 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A file-arrival trigger starts processing and Auto Loader incrementally ingests newly arrived files. \n- Processing: Spark Declarative Pipelines (SDP) manages the dependency-driven flow across Bronze, Silver and Gold datasets. \n- Bronze: Preserves the raw ingested transaction data and ingestion metadata. \n- Silver: Validates mandatory fields, standardizes transaction attributes, and classifies sales, cancellations/returns and service or adjustment records. \n- Gold: Provides curated order, sales, demand, product, customer, country, cancellation, inventory and stockout-risk metrics. \n- Consumption: AI/BI dashboards provide predefined business views, while Databricks Genie enables natural-language questions over curated Gold data. \n- Demonstration Scale: 2,268 products, 482 sale orders, 341 identified customers, 1,448 simulated products in stock and 820 simulated out of stock. \n\n**Additional Insights**\n\n- Dependency-Driven Execution: SDP determines the required execution order from the relationships between upstream and downstream datasets, reducing manual orchestration. \n- Near-Real-Time Design: The current POD uses controlled micro-batches rather than a continuous live event stream. \n- Source Evolution: The Event Generator is a POC simulation mechanism and can be replaced by live POS, e-commerce, API or event-driven sources in a production implementation. \n- Inventory Limitation: Actual stock balances are not present in the source data, so current inventory and Days of Supply are simulated for demonstrating the stockout-risk use case. 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Updated continuously from assessor, recorder, and address-change sources, KinderData provides a unified view of every U.S. property and homeowner—ideal for marketing, analytics, and customer acquisition.\n\nData Coverage\n\nNationwide coverage across all 50 U.S. states\n\n160M+ residential properties with owner and contact details\n\nVerified move-event intelligence from assessor and change-of-address data\n\nLinked homeowner profiles including property type, ownership status, and contact enrichment\n\nUpdated daily for accuracy and recency\n\nUse Cases\n\nIdentify and reach new movers at peak purchasing intent\n\nPower direct mail, digital, and CRM campaigns for homeowner marketing\n\nTarget homeowners by move date, property characteristics, or location\n\nIndustries Served\n\nRoofing & Construction\n\nHVAC Providers\n\nSolar Installation\n\nLandscaping & Lawn Care\n\nJunk Removal & Cleaning Services\n\nPest Control & Home Renovation\n\nInsurance, Real Estate & Home Improvement Marketing\n\nWhy Choose KinderData\n\nKinderData delivers enterprise-grade real estate and homeowner data sourced directly from official assessor, recorder, and registry feeds—ensuring accuracy, compliance, and unmatched coverage. 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","setting":{"visibility":"PUBLIC"},"created_at":1780643856366,"updated_at":1780643856366,"categories":["GEOSPATIAL","RETAIL"],"listingType":"PERSONALIZED","provider_id":"5d3ddc17-a7e8-4794-a760-13587e1de2c6"},"detail":{"description":"# **All Petrol Station Locations in Germany: Complete Geographic Dataset**\n---\n### **Premium GIS-Ready Retail Location Intelligence Data for Databricks Analytics**\n\nThis high-fidelity geospatial dataset provides a complete, exhaustive, and standardized overview of **19,365** verified fuel and petrol station locations across Germany. Updated as of **April 1, 2026**, this comprehensive national dataset captures the entire German forecourt landscape - encompassing major multinational fuel networks, national champions, and prominent independent operators. It offers an analysis-ready foundation for EV charging infrastructure planning, competitor benchmarking, logistics routing, and retail location intelligence.\n\nFully compatible with **Apache Spark**, **Delta Lake**, and **Databricks Unity Catalog** workflows.\n\n---\n\n## **Complete National Coverage: Branded Forecourts & Regional Operators**\nUnlike single-brand extractions, this dataset aggregates all premier fuel networks and independent operators into a single, unified data model. The coverage maps maximum geographic depth across all 16 federal states:\n\n- **Major Fuel Networks & National Champions:** \n [Aral (2,380 locations)](https://geolocet.com/products/Germany-Aral-Petrol-Stations), \n [Shell (1,705 locations)](https://geolocet.com/products/Germany-Shell-Petrol-Stations), \n [Esso (1,256 locations)](https://geolocet.com/products/Germany-Esso-Petrol-Stations), \n [JET (743 locations)](https://geolocet.com/products/Germany-JET-Petrol-Stations), \n [Star / Orlen (648 locations)](https://geolocet.com/products/Germany-Star-Orlen-Petrol-Stations)\n\n- **Regional & Dynamic Growth Banners:** \n [AVIA (889 locations)](https://geolocet.com/products/Germany-AVIA-Petrol-Stations), \n [Raiffeisen (648 locations)](https://geolocet.com/products/Germany-Raiffeisen-Petrol-Stations), \n [HEM (446 locations)](https://geolocet.com/products/Germany-HEM-Petrol-Stations), \n [Hoyer (348 locations)](https://geolocet.com/products/Germany-Hoyer-Petrol-Stations), \n [Eni / Agip (326 locations)](https://geolocet.com/products/Germany-Eni-Agip-Petrol-Stations), \n [OIL! (268 locations)](https://geolocet.com/products/Germany-OIL-Petrol-Stations), \n [Westfalen (254 locations)](https://geolocet.com/products/Germany-Westfalen-Petrol-Stations), \n [Classic (174 locations)](https://geolocet.com/products/Germany-Classic-Petrol-Stations), \n [AS 24 (167 locations)](https://geolocet.com/products/Germany-AS-24-Petrol-Stations), \n [tankpool24 (630 locations)](https://geolocet.com/products/Germany-tankpool24-Petrol-Stations)\n\n**Plus thousands of regional independent forecourts** fully mapped and standardized across all federal states (e.g., Nordrhein-Westfalen: 3,606 sites; Bayern: 3,511 sites; Niedersachsen: 2,710 sites; Baden-Württemberg: 2,492 sites).\n\n---\n\n## **Technical Specifications & Schema Definitions**\nOptimized for zero-friction schema enforcement when reading via `spark.read.format(\"csv\")`. The structured schema below ensures flawless rendering in Databricks notebooks and high accuracy for LLM semantic indexing.\n\n* **`GUID`** (`STRING`): Unique global identifier for deterministic primary key mapping and deduplication.\n* **`Title`** (`STRING`): Official station name or commercial brand banner classification (e.g., Aral, Shell, Esso).\n* **`Latitude`** (`DOUBLE`): Precise WGS84 latitude coordinate for point-in-polygon and spatial joins.\n* **`Longitude`** (`DOUBLE`): Precise WGS84 longitude coordinate for spatial indexing (H3 / S2).\n* **`Street_No` / `Street`** (`STRING`): Normalized building numbers and thoroughfare name elements.\n* **`City` / `Postal_Code`** (`STRING`): Core municipal settlements and primary postal routing codes.\n* **`Gemainde`** (`STRING`): Local municipal administrative boundary designation.\n* **`Federal_State`** (`STRING`): Macro-administrative boundary classification (e.g., Bayern, Berlin, Hamburg).\n* **`Admin_level_1` / `_2`** (`STRING`): Additional local administrative area and district (Kreis) identifiers.\n* **`Address`** (`STRING`): Full, standardized street address clean-text output.\n* **`Wheelchair`** (`STRING`): Accessibility flags indicating wheelchair mobility support where available.\n* **`Popularity_Score`** (`INTEGER`): Relative foot-traffic/rating metric graded on a standardized 1-10 scale.\n* **`Opening_Hours`** (`STRING`): Standard daily operational schedules and structured operating text.\n* **`Phone` / `Website`** (`STRING`): Direct communication data, branch-specific landing URLs, and domain vectors.\n\n---\n\n## **Databricks Workflow & Spatial Analytics Use Cases**\n\n- **EV Charging Whitespace Analysis:** Query the full 19,365 location points against high-voltage grid corridors or traffic volume rasters to rank optimal stations for ultra-fast EV charging infrastructure placement.\n- **H3 Geospatial Indexing:** Convert the precise WGS84 `Latitude` and `Longitude` fields into Uber H3 spatial hexagons (`h3_longlattocell`) to seamlessly cross-reference with granular German municipal income raster files or demographic datasets.\n- **Logistics & Fleet Benchmarking:** Integrate forecourt coordinates with fleet tracking telemetry to calculate fuel stop density, build optimal corridor routing profiles, and minimize off-route deadhead mileage across the national highway network.\n\n---\n\n## **Data Evaluation & Custom Ingestion**\n\n### **Evaluate with a Free Sample Dataset**\nVerify the structure, column types, and coordinate precision before full implementation. A comprehensive schema sample is available for download in CSV/Parquet-friendly formats.\n\n*Want deeper market insights?* Learn more about the German retail and forecourt landscape in our comprehensive reports:\n* **[View the Germany Top Brands Petrol Stations Report](https://geolocet.com/pages/germany-topbrands-petrol-stations-report)**\n* **[View the Germany Top Brands Grocery Report](https://geolocet.com/pages/germany-top-brands-grocery-report)**\n\n### **Enterprise Delivery & Tailored Formatting**\nWhile natively available as an analysis-ready CSV for DBFS ingestion, we accommodate enterprise-level data architecture needs. Alternative formats - including **GeoJSON, Esri Shapefiles, PostgreSQL dumps, Excel, or direct Delta sharing profiles** - can be generated. Reach out to our engineering team at **contact@geolocet.com** for integration support or custom European POI data requests.","terms_of_service":"https://geolocet.com/pages/terms-of-use","documentation_link":"","privacy_policy_link":"https://geolocet.com/pages/privacy-policy","assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://geolocet.com/pages/terms-of-use"},"provider_summary":{"name":"Geolocet LTD","description":"Geolocet provides premium geospatial data for retail analysis, GIS, and market planning. 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Our company has enabled thousands of engineering, data science, product, and other technical teams to build compliant, innovative, software solutions using B2B data.\n- People Data Labs provides company and person data via flat files and APIs\n\n**About this Dataset**\n\n- This is a paid dataset that requires a signed order form with PDL to access\n- Our person dataset contains over 3 billion profiles on people in our identity graph.\n- Customers will commonly license \"slices\" of this dataset that focus on a single primary key that best fits their use case.\n- This data covers global profiles with at least one piece of identifying information.\n\n**Tables Included**\n\n1. 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Another used sources of data are the U.S. Department of Housing and Urban Development and the U.S. Department of Housing and the U.S. Department Of Agriculture (Economic Research Service).\n\n**Description**\nThe U.S. Census Bureau, Small Area Income and Poverty Estimates (SAIPE) program produces single-year estimates of income and poverty for all U.S. states and counties as well as estimates of school-age children in poverty for all school districts. The county and states poverty estimates provided by the Economic Research Service of The United States Department of Agriculture are also based on the data made available by through the SAIPE program. The Small Area Health Insurance Estimates (SAHIE) program of the same U.S. Census Bureau delivers estimates of health insurance coverage based on the American Community Survey (ACS) data. The incorporation of the ACS into the modeling procedures has allowed for more detailed income group data as well as a higher level of precision in the estimates. The poverty status indicators are based on the Annual Social and Economic Supplement of the Current Population Survey. \n\nThe data for the healthcare and social assistance indicators are available through the last Economic Census of the U.S. Census Bureau. All employment-related datasets in this package have as source the Local Area Unemployment Statistics (LAUS) program of the U.S. Bureau of Labor Statistics. Below is provided the list of the indicators categories that can be found in this data package:\n\n• Poverty: population poverty status at country level; poverty estimates at state, county and school district level; poverty thresholds indicators at country level (adjusted for inflation) \n• Healthcare and social assistance indicators at country, state and county level\n• Homeless population indicators at state level\n• Employment/Unemployment: projections by type of occupation, at country level; unemployment rates at state, county and metropolitan area level; unemployment rates change at metropolitan area level.\n\n**Benefits**\n- Useful for public health specialists and specialized institutions that plan and assess health services and socio-economic determinants of health. useful for researchers seeking to improve the methods, regulations, health policies and strategy. the raw version can be found on the author's website, but jsl is offering a clean, standardized and easier to use version, alone or in combination with other datasets found in jsl dataset library. the jsl version was enriched by adding for every zip code area the geographic coordinates and the land and water surfaces size. the fields descriptions along with the dataset description are again useful for the user to quick understand the data and the dataset.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [US Census Bureau Current Poverty Status](https://www.johnsnowlabs.com/marketplace/us-census-bureau-current-poverty-status)\n\t- This dataset contains poverty estimates for demographic groups of US population based on The Current Population Survey 2017, Annual Social and Economic Supplement. The poverty status is described by the number and the percent of population belonging to a demographic group living below the poverty threshold.\n- [US Census Bureau Poverty Thresholds Starting 1980](https://www.johnsnowlabs.com/marketplace/us-census-bureau-poverty-thresholds-starting-1980)\n\t- This dataset contains poverty thresholds by family size and number of children under 18 years starting from 1980. Poverty thresholds are the dollar amounts used to determine poverty status.\n- [US Census Bureau School Districts Poverty Estimates](https://www.johnsnowlabs.com/marketplace/us-census-bureau-school-districts-poverty-estimates)\n\t- This dataset contains poverty estimates at school district level for population of 5 to 17 years old based on US Census Bureau program, Small Area Income and Poverty Estimates (SAIPE). The information regarding counties and states where these schools are located is also available in the dataset.\n- [US Census Bureau States And Counties Health Insurance Estimates](https://www.johnsnowlabs.com/marketplace/us-census-bureau-states-and-counties-health-insurance-estimates)\n\t- This dataset contains estimates of health insured and uninsured population for 2020 at county and state level based on US Census Bureau program, The Small Area Health Insurance Estimates (SAHIE) program. For every state and county for each demographic group, defined by age, gender, race/ethnicity and income relative to poverty, the estimated number of persons insured and uninsured is given along with the margin of error.\n- [US Census Bureau States And Counties Poverty Estimates 2019](https://www.johnsnowlabs.com/marketplace/us-census-bureau-states-and-counties-poverty-estimates-2019)\n\t- This dataset contains poverty estimates at county level based on US Census Bureau program, Small Area Income and Poverty Estimates (SAIPE). The estimates are for counties and states in the United States, for the entire population and for three age groups of population.\n- [US Census Bureau States And Counties Poverty Estimates 2020](https://www.johnsnowlabs.com/marketplace/us-census-bureau-states-and-counties-poverty-estimates-2020)\n\t- This dataset contains poverty estimates at county level based on US Census Bureau program, Small Area Income and Poverty Estimates (SAIPE). The estimates are for counties and states in the United States, for the entire population and for three age groups of population.\n- [US Continuums Of Care Records Based Homeless Population Statistics](https://www.johnsnowlabs.com/marketplace/us-continuums-of-care-records-based-homeless-population-statistics)\n\t- This dataset contains estimates of homelessness, as well as estimates of chronically homeless persons, homeless veterans, and homeless children and youth provided by The U.S. Department of Housing and Urban Development. The estimates cover the period of years 2007-2017 and are at national, state and Continuums of Care (CoC) Point-In-Time (PIT) level.\n- [US Economic Census Health Care And Social Assistance 2012](https://www.johnsnowlabs.com/marketplace/us-economic-census-health-care-and-social-assistance-2012)\n\t- This dataset contains key industry statistics for Health Care and Social Assistance sector provided by the Economic Census 2012 at country, state, county and county equivalent area level. The statistics are for all types of health care and social assistance establishments classified according to The North American Industry Classification System (NAICS).\n- [US Employment Projections By Occupations 2019-2029](https://www.johnsnowlabs.com/marketplace/us-employment-projections-by-occupations-2019-2029)\n\t- This dataset contains indicators related to employment projections by detailed occupations, workers education and training characteristics, occupational separations and openings. The occupations denominations are based on the 2016 Standard Occupational Classification.\n- [US Unemployment Rates And Labor Force By County](https://www.johnsnowlabs.com/marketplace/us-unemployment-rates-and-labor-force-by-county)\n\t- This dataset contains labor force data not seasonally adjusted at county and statistically equivalent areas level. The indicators related to labor force are the total number of civilian labor force, the number of employed, the number of unemployed and unemployment rate.\n- [US Unemployment Rates By Metropolitan Area](https://www.johnsnowlabs.com/marketplace/us-unemployment-rates-by-metropolitan-area)\n\t- This dataset contains unemployment data at metropolitan area (MA) level. The data for the unemployment rate is for Nov 2022.\n- [US Unemployment Rates Change Over The Year](https://www.johnsnowlabs.com/marketplace/us-unemployment-rates-change-over-the-year)\n\t- This dataset contains unemployment data at metropolitan area (MA) level, respectively the change over the year of unemployment rate. The data for the unemployment rate change over the year is starting from 2021 to 2022.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated\n- Data Updates\n - Data updates support replace-on-update: outdated foreign keys are deprecated, not deleted\n\n**Our data is curated and enriched by domain experts** \n\n Each dataset is manually curated by our team of doctors, pharmacists, public health & medical billing experts:\n- Field names, descriptions, and normalized values are chosen by people who actually understand their meaning\n- Healthcare & life science experts add categories, search keywords, descriptions and more to each dataset\n- Both manual and automated data enrichment supported for clinical codes, providers, drugs, and geo-locations\n- The data is always kept up to date – even when the source requires manual effort to get updates\n- Support for data subscribers is provided directly by the domain experts who curated the data sets\n- Every data source’s license is manually verified to allow for royalty-free commercial use and redistribution.\n\n**Need Help?**\nIf you have questions about our products, contact us at [info@johnsnowlabs.com](mailto:info@johnsnowlabs.com). \n 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Workday offers rich HR capabilities, but data typically remains spread across multiple Workday subject areas and additional systems such as payroll engines, biometric attendance devices and recruitment platforms. This leads to challenges in workforce visibility, talent planning, compliance reporting and day-to-day HR decision-making.\n\nThe Dataplatr HR Analytics Accelerator is a ready-to-use solution that streamlines ingestion, cleansing and analysis of Workday HCM data. Built on Delta Lake and the Medallion (Bronze–Silver–Gold) Architecture, it provides real-time, audit-ready HR dashboards that support employee lifecycle analytics, workforce planning, talent insights and compliance reporting.\n\n**Business Challenge & Solution**\n**HR Challenges Faced by Workday Customers**\n- **Fragmented HR data** across Workday modules, payroll systems and attendance devices, creating unnecessary operational complexity.\n- **Manual reporting using Excel**, affecting accuracy and timeliness due to repetitive manual steps and inconsistent data handling.\n- **Limited visibility into hiring, attrition, promotions and workforce movement** across rapidly evolving teams and organizational units.\n- **Talent risk blind spots** with no early indicators for attrition or performance gaps within key departments and critical roles.\n- **Compliance & audit efforts** requiring manual reconciliation and data validation across multiple disconnected systems and reporting channels.\n\n**Dataplatr HR Solution**\nThe accelerator transforms these challenges into a unified analytics environment by offering enhanced intelligence across HR operations:\n- **Unified Workday HR Analytics**: Consolidated employee data across all Workday HCM modules with seamless cross-module integration.\n- **Automated Workforce Reporting**: Near-real-time updates for headcount, movement and employee lifecycle with consistent automated refresh cycles.\n- **Predictive Talent Intelligence**: Forecasts for attrition, hiring needs and workforce shortages using advanced machine learning models.\n- **Workforce Cost & Payroll Insights**: Standardized payroll trends and labor cost analysis (when payroll systems are external) with unified financial alignment.\n- **Compliance-Ready Reporting**: Audit-ready dashboards for HR policies, attendance, leave and statutory compliance with full traceability support.\n\n**Medallion Architecture Implementation\nBronze Layer (Raw HR Data – Workday Extracts)**\n\nIngests Workday HCM data such as:\n- **Employee Master Data (biographical & job details)** enriched with standardized employee lifecycle attributes.\n- **Job Profiles, Supervisory Org & Hierarchy** including complete structural mapping across departments.\n- **Recruiting Data** (job requisitions, candidates, hiring stages) capturing end-to-end hiring funnel movements.\n- **Compensation & Payroll Inputs** (salary components, allowances, etc.) aligned across multi-country payroll frameworks.\n- **Learning & Skills Data** (courses, certifications, completions) supporting employee development and competency tracking.\n\n**Silver Layer (Cleansed & Standardized HR Data)**\n- **Validated Employee Master**: Deduplicated and formatted employee records sourced from Workday, applying enterprise-grade data quality rules.\n- **Unified Job & Org Structure**: Standardized roles, grades and departments derived from Workday HCM, enabling consistent global reporting.\n- **Normalized Time 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teams with forecasting models and early-warning talent signals.\n\nTransform your HR operations from reactive workforce management to predictive talent intelligence with Dataplatr’s proven Workday HR Accelerator. Built on Databricks best practices with a compliance-first architecture, this solution delivers immediate visibility into workforce health while laying the foundation for advanced AI-driven talent, performance and workforce planning optimization.\n\n[Transform financial decision-making with Databricks](https://medium.com/@dataplatr/empowering-financial-decision-making-using-databricks-and-power-bi-insights-5beb6ee7a08c) using CDC-driven pipelines and Lakehouse architecture to deliver actionable insights across GL, AP, AR and Fixed Assets with automated, finance-ready data models.\n\n[Leverage the Databricks Lakehouse Platform to transform People Analytics](https://medium.com/@dataplatr/maximizing-people-analytics-with-the-power-of-databricks-lakehouse-platform-and-omni-analytics-523e4a9cf5e5) with CDC-driven pipelines and real-time insights across headcount, hiring and HR metrics using a scalable, finance-grade data architecture.\n\nNote: By clicking '**Get Access**', you agree to our **Terms of Service**. You acknowledge that **Dataplatr Corp** will recognize your organization as a customer/user for promotional and reference purposes.\n\nContact us at: chandra.reddy@dataplatr.com\nFor consultations or custom inquiries: https://dataplatr.com/contact-us ","terms_of_service":"https://docs.google.com/forms/d/e/1FAIpQLSdnlNmSfrFRQDa_ToOL3ydTOPcWI3m3lWor2plLbiqF1JRINg/viewform","documentation_link":"","privacy_policy_link":"https://dataplatr.com","embedded_notebook_file_infos":[{"id":"c7a99aad-85bd-4c86-a1e4-b089d73fd42e","marketplace_file_type":"EMBEDDED_NOTEBOOK","file_parent":{"parent_id":"43d3db0f-3cce-400c-9d52-1cee1f2f419c","file_parent_type":"LISTING"},"mime_type":"text/html","download_link":"https://marketplace-file-storage-prod.s3-fips.us-east-1.amazonaws.com/EMBEDDED_NOTEBOOK/LISTING_43d3db0f-3cce-400c-9d52-1cee1f2f419c/c7a99aad-85bd-4c86-a1e4-b089d73fd42e?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEGgaDnVzLWVhc3QtMS1maXBzIkYwRAIgbC8sWIoYaHR8y7Wq86nPb0pufBl4%2BM4PWpeN%2B5PW9VQCIG7BhAVBDCWTpxFDLeuKYm%2Ff1JLz4iccPMzter0YdhgWKqYECDAQABoMNDE0MzUxNzY3ODI2IgxNAJrHbXaZG20jNfMqgwQyGEb4TsoVOoNvJ%2BaA%2FZ7zeu8hGcHqCeNfblP%2FQo5g6q2aRNtpwtPRKMlhYcIE%2FUQaNT1Lqmyptu32VUQTpzjTTxRVmJrDPkheSCkV06XQ%2FpTMKjU8CC7X1b%2FqYf8CrBwZT7Cv4C5WPjEET38f2A79h0xV6bZgPy28zQWrSeIJ9w6AfZBxiE24%2FVc08zVA0ivCT6lsy7nuU0wcR%2FMg2C9nd1vaMnQ923VpD21CKfVVYcrNWL2b%2FQ%2BNAO%2BxPjPgjiN6UPQbvbvZxkSdhgrGtecIg9NgqGkMcvM5VLTDtdC1kdeZetfOC2obtA%2BT%2F0gy3tIDo6WM5hcQfw743%2FAU9oaGnRBl%2BwoxRRDCZA%2F4jnPdluICSI7NCr6C3HHOe4CSZJjRihczxPA%2FgQczAVjAPs6CrK9N9pIzt0vNJpGUTgg4AoxftTvGfciDREilSctPIcBk6GwB%2Bzt4wbtNCmEPU%2BFD46rc6CWVQmmLwbKOeHw70ipMVFlOc8NlXkEtGO2vu6H44MstSSc6daOVUYUavQSGZ6jGhEjsP2YGz8KDm9XQotB%2FtyVBqiZV01OKqDmmBJA%2FXjUpSJ8M%2FeJXhe5m%2BKcxzNObtoJRI7jci3Tpt08XZyXZ7%2FyIV8H30ya468X3cfkTXrm972uga3z7iXAdubyEHnQIua%2FuvtwOHILi6O5D1Me2MzCPvcnXBjqTAYDoMWBt%2B3AVFuqSs2dmDQNBnvEhXfMgTedKLD5PJHb3aIl15iBkLaNu36GYnVCp9ZU2lSYcTbY5QwNnNeTfJTNy9bKhwzru6Sg%2BslhN%2BTS0zkTn%2BeLMY4tHgPEtEZ2cp%2FCk6GwqswvnYVKHjwO8q%2B1Mx8lc08fVEfarE9dke1GGFqoddx%2Bq%2BZUyHbDlAVfrCmf0yw%3D%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20261110T000135Z&X-Amz-SignedHeaders=host&X-Amz-Expires=86400&X-Amz-Credential=ASIAWA6KKHEJORWBH4N3%2F20261110%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=c1a0184abe68a37eed954e5efa3c340df302228082b4e3bd4bba72912a60a6a4","created_at":1770818660702,"updated_at":1770818660702,"display_name":"WorkDay_HR_Accelerator","status":"FILE_STATUS_PUBLISHED"}],"assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"Dataplatr Corp","description":"Dataplatr Corp specializes in Data Analytics & AI with pre-built data models for Enterprise Applications like SAP, Oracle EBS, Workday, Salesforce to empower businesses to unlock the full potential of their data. Our enterprise data engineering accelerators are designed to expedite the development of data pipelines, data transformation, and integration, saving you time and resources.\n\nOur team of experienced data engineers, scientists and analysts utilize cutting-edge data infrastructure into valuable insights and helps enterprise clients optimize their Sales, Marketing, Operations, Financials, Supply chain, Human capital and Customer experiences."},"popularity":1963},{"id":"ca87a8be-9a17-4469-acee-d992d6cd16ca","summary":{"name":"Simulated E-Commerce Clickstream Data","subtitle":"This mock dataset simulates two weeks of E-Commerce clickstream data.","share":{"name":"dbacademy_dataset_ecommerce","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1726174049371,"updated_at":1726514762612,"categories":["EDUCATION"],"listingType":"STANDARD","provider_id":"ea1e69ff-0127-4c94-bf39-e841fe1d19d2"},"detail":{"description":"\nEcommerce Clickstream Data\n=========================================\nThis dataset was created by Databricks. This is a mock dataset meant to simulate two weeks of ecommerce clickstream data from 06/19/2020 to 07/04/2020. This includes event, transaction, user, and product data for a mattress retailer.\n\nLicense\n=========================================\nCopyright (2020) Databricks, Inc.\nThis dataset is licensed under a [Creative Commons Attribution 4.0 International License](https://creativecommons.org/licenses/by/4.0/)","terms_of_service":"https://cms.databricks.com/sites/default/files/2023-01/marketplace-consumer-terms_0.pdf","documentation_link":"","privacy_policy_link":"https://www.databricks.com/legal/privacynotice","collection_date_start":1592599530098,"collection_date_end":1593895585892,"assets":["ASSET_TYPE_MEDIA"],"license":""},"provider_summary":{"name":"Databricks","description":"As the world’s first lakehouse platform in the cloud, Databricks combines the best elements of data lakes and data warehouses to deliver the reliability, strong governance, and performance of data warehouses with the openness, flexibility, and machine learning support of data lakes. 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The data contained are related to the two components of the capitated payment (rates and risk scores) and to the county and regional statutory benchmark.\n\n**Description**\nThis data package contains for the years after 2004, the Secretary of Health and Human Services is required to recalculate 100 percent of the fee-for-service Medicare costs at least every three years – so that at least every three years the Medicare Advantage (MA) capitation rate will be the higher of the fee-for-service rate and the minimum increase rate. Health plans that participate in the Medicare Advantage program receive monthly capitation payments for each Medicare enrollee. Each capitated payment is the product of two general parts: 1) A base rate which reflects the payment if a Medicare Advantage enrollee has the health status of the national average beneficiary, and 2) A risk score which indicates how costly the enrollee is expected to be relative to the national average beneficiary. The purpose of the risk scores is to adjust Medicare Advantage payments so that they accurately reflect how much each Medicare Advantage enrollee would be expected to cost. \n\nCMS has implemented a number of changes in the Medicare Advantage payment methodology beginning in Fiscal Year 2012 as a result of payment changes enacted in the Affordable Care Act, including 1) a new blended benchmark as the county rate, 2) the new methodology used to derive the new blended benchmark county rates, 3) identify the qualifying bonus counties, 4) how to determine transitional phase-in periods, and 5) the applicability of the star system in the rebates. At the same time the key components of the current Medicare Advantage rates include 1) Revision to the risk-adjustment model to better account for fully dual eligible beneficiaries. 2) Adjustment to certain measures within the Star Ratings program for socioeconomic status. 3) Update to the blended risk scores using a percent of the risk score calculated using data from the Risk Adjustment Processing System (RAPS) and the remaining percent of the risk score calculated using encounter data. 4) Incentives to Medicare Advantage organizations to offer plans with lower maximum out-of-pocket limits.\n\n**Benefits**\n- Useful for medicare advantage plan stakeholders and public health specialist or specialized public. the user can utilize the datasets as they are, without further cleaning of data. the fields descriptions along with the dataset description are useful for the user to quickly understand the data and the dataset.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [Insurance Coverage of Noninstitutionalized Medicare Beneficiaries](https://www.johnsnowlabs.com/marketplace/insurance-coverage-of-noninstitutionalized-medicare-beneficiaries)\n\t- The Insurance Coverage of Noninstitutionalized Medicare Beneficiaries dataset includes data about Medicare insurance coverage beneficiaries aged 65 in the United States by age, gender, race, poverty and marital status between 1992 and 2015.\n- [Medicare Advantage ESRD Risk Score Dialysis](https://www.johnsnowlabs.com/marketplace/medicare-advantage-esrd-risk-score-dialysis)\n\t- The dataset Medicare Advantage End-Stage Renal Disease (ESRD) Risk Score Dialysis deals with Medicare payment rates for 2016-2020. ESRD Dialysis Risk Scores used in the ratebooks are calculated using the model to be used in the payment year. The beneficiaries included in the state-level risk scores in this dataset are those enrolled in fee-for-service (FFS) population, who were not in hospice.\n- [Medicare Advantage Non PACE Risk Score by County](https://www.johnsnowlabs.com/marketplace/medicare-advantage-non-pace-risk-score-by-county)\n\t- The dataset contains information on the non-PACE (Programs of All-Inclusive Care for the Elderly) risk scores 2016-2020. Risk scores used in the ratebooks are calculated using the model to be used in the payment year. The beneficiaries included in the state-level risk scores in this dataset are those enrolled in fee-for-service (FFS) population.\n- [Medicare Advantage PACE Risk Score By County](https://www.johnsnowlabs.com/marketplace/medicare-advantage-pace-risk-score-by-county)\n\t- The dataset contains information on the Program of All-Inclusive Care for the Elderly (PACE) Risk Scores 2016-2020. Risk scores used in the ratebooks are calculated using the model to be used in the payment year. The beneficiaries included in the county-level risk scores in this dataset are those enrolled in fee-for-service (FFS) population, who do not have end-stage renal disease (ESRD) and who were not in hospice.\n- [Medicare Advantage Rate Statutory Benchmark By Region And County](https://www.johnsnowlabs.com/marketplace/medicare-advantage-rate-statutory-benchmark-by-region-and-county)\n\t- The dataset contains information on the statutory and plan-bid components of the regional and county Medicare Advantage (MA) benchmarks for the year 2023. The patient population that chooses MA includes individuals with a wide variation in health and disease status.\n- [Medicare Advantage Risk Rate For All Plans Except PACE](https://www.johnsnowlabs.com/marketplace/medicare-advantage-risk-rate-for-all-plans-except-pace)\n\t- This dataset contains information on Medicare Advantage (MA) risk rates for all plans except Program of All-Inclusive Care for the Elderly (PACE) plans. Health plans that participate in the MA program receive monthly capitation payments for each Medicare enrollee.\n- [Medicare Advantage Risk Rates For PACE Plans](https://www.johnsnowlabs.com/marketplace/medicare-advantage-risk-rates-for-pace-plans)\n\t- This dataset contains information on Medicare Advantage (MA) risk rates for Program of All-Inclusive Care for the Elderly (PACE) plans. Health plans that participate in the MA program receive monthly capitation payments for each Medicare enrollee.\n- [Medicare Population End Stage Renal Disease Rate Dialysis](https://www.johnsnowlabs.com/marketplace/medicare-population-end-stage-renal-disease-rate-dialysis)\n\t- The dataset Medicare End-Stage Renal Disease (ESRD) Rate Dialysis deals with Medicare payment rates for dialysis for 2023.\n- [Noninstitutionalized Population Data Without Health Insurance Coverage](https://www.johnsnowlabs.com/marketplace/noninstitutionalized-population-data-without-health-insurance-coverage)\n\t- This dataset includes information regarding civilian noninstitutionalized population without health Insurance coverage for persons under the age of 65 years in the United States and Puerto Rico by territory, state and age from year 2009 through 2016.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - 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The territorial vintages of the worldwide area boundaries are compatible with our globally consistent and comparable [socio-demographic](https://www.mbi-geodata.com/en/sociodemographic-data/) and [economic](https://www.mbi-geodata.com/en/purchasing-power-overview/) data.\nOur area boundaries for Hungary are offered at the following levels:\n\n- **Postal Code Level** (Postcode 4-digit): This fine-grained level of detail makes the data suitable for a wide range of applications – for example, enriching customer databases, which are often organized by postal code.\n- **Administrative Level** (Települések): Covers both larger administrative areas (e.g., regions, states, provinces) as well as finer subdivisions such as districts, counties, or municipalities.\n\n**Use Cases:**\n\n- **Market Analysis**: Use MBI’s boundaries to visualize data on a map, learn about your customers, and uncover untapped market potential.\n- **360-Degree Customer View**: Link boundaries to customer data, sociodemographic, and economic data to gain a holistic understanding of customer needs, behaviors, and potential value.\n- **Location Planning**\n•\tUse MBI’s boundaries to visualize data on a map, learn about your customers\n•\tPlan the provision of services and products across different regions\n•\tDefine sales territories\n•\tVisualize catchment areas and plan actions to reach potential customers\n\n**Additional Insights:**\n[About Area Boundaries](https://www.mbi-geodata.com/en/area-boundaries/)\n\n**Data Structure:**\nThe area boundaries include the following variables:\nCTRYCODE: Two-digit country code by ISO 3166\nNAME: Name of administrative area / Name of the most populous administrative area / Name of the respective micro-area or the next higher level\nADMINCODE / POSTCODE / MICROCODE: Administrative code / Postal code / Micro-code\nWKT: Geography as Well Known Text (lat/long WGS84)\n\n*with only a few specific exceptions","terms_of_service":"https://www.mbi-geodata.com/en/data-protection/","documentation_link":"https://www.mbi-geodata.com/en/mbi-supplier-manufacturer-worldwide-geodata/europe/hungary-maps/","privacy_policy_link":"https://www.mbi-geodata.com/en/data-protection/","update_frequency":{"interval":1,"unit":"YEARLY"},"assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://service-mbi-geodata.com/s/pjYnesBaeHMGWS8"},"provider_summary":{"name":"Michael Bauer International GmbH – MBI Geodata","description":"For over 15 years, Michael Bauer International (MBI) has been a leading provider of globally consistent and comparable geographic data, including more than 3 million postcode areas and other detailed datasets like sociodemographics and purchasing power. 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All data comes from a single source, ensuring **consistency and comparability** across markets.\n\nThe Sociodemographics-Bundle Basic for Turkey includes the following **variables**:\n- [population](https://www.mbi-geodata.com/en/sociodemographic-data/population/)\n- [gender](https://www.mbi-geodata.com/en/sociodemographic-data/sex-gender/)\n- [age structure](https://www.mbi-geodata.com/en/sociodemographic-data/agebands/) (15-year age bands)\n- household figures ([number of households](https://www.mbi-geodata.com/en/sociodemographic-data/households/) and [households by type](https://www.mbi-geodata.com/en/sociodemographic-data/households-by-type/))\n\nOur sociodemographic data is offered for each country on different **levels**:\n- **Postal Code Level:** This fine-grained level of detail makes the data suitable for a wide range of applications – for example, enriching customer databases, which are often organized by postal code.\n- **Administrative Level:** Covers both larger administrative areas (e.g., regions, states, provinces) as well as finer subdivisions such as districts, counties, or municipalities.\n- **Micro Level**: Subdivisions of postal code or administrative boundaries, such as census areas or street segments. 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We will be happy to help you find a product that fits your needs.\n \n**Additional Insights**: https://www.mbi-geodata.com/en/sociodemographic-data/\n\n**Data Structure**:\nAGE_F0014: Population (Nighttime) by age: 0 - 14 years, female\nAGE_F1529: Population (Nighttime) by age: 15 - 29 years, female\nAGE_F3044: Population (Nighttime) by age: 30 - 44 years, female\nAGE_F4559: Population (Nighttime) by age: 45 - 59 years, female\nAGE_F60PL: Population (Nighttime) by age: 60 years and above, female\nAGE_M0014: Population (Nighttime) by age: 0 - 14 years, male\nAGE_M1529: Population (Nighttime) by age: 15 - 29 years, male\nAGE_M3044: Population (Nighttime) by age: 30 - 44 years, male\nAGE_M4559: Population (Nighttime) by age: 45 - 59 years, male\nAGE_M60PL: Population (Nighttime) by age: 60 years and above, male\nAGE_T0014: Population (Nighttime) by age: 0 - 14 years, total\nAGE_T1529: Population (Nighttime) by age: 15 - 29 years, male\nAGE_T3044: Population (Nighttime) by age: 30 - 44 years, total\nAGE_T4559: Population (Nighttime) by age: 45 - 59 years, total\nAGE_T60PL: Population (Nighttime) by age: 60 years and above, total\nCTRYCODE: Two digit country code by ISO 3166\nFEMALE: Population (Nighttime): females\nHH_SIZE: Average household size\nHH_T: Households: total number\nMALE: Population (Nighttime): males\nNAME: Name of administrative area / Name of the most populous administrative area / Name of the respective micro-area or the next higher level\nADMINCODE / POSTCODE / MICROCODE Administrative code / Postal code / Micro-code\nP_PRM: Population (Nighttime) year average: per mill of country\nP_T: Population (Nighttime) year average: total number","terms_of_service":"https://service-mbi-geodata.com/s/HNp7Z2tws4WDqWd","documentation_link":"https://www.mbi-geodata.com/en/mbi-supplier-manufacturer-worldwide-geodata/europe/turkey-maps/","privacy_policy_link":"https://www.mbi-geodata.com/en/data-protection/","geographical_coverage":"[\"Europe\"]","cost":"FREE","update_frequency":{"interval":1,"unit":"YEARLY"},"collection_granularity":{"interval":1,"unit":"YEARLY"},"collection_date_start":1704103346885,"collection_date_end":1735639354228,"data_source":"MBI","assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://service-mbi-geodata.com/s/SMtL6CM65JRxnHr"},"provider_summary":{"name":"Michael Bauer International GmbH – MBI Geodata","description":"For over 15 years, Michael Bauer International (MBI) has been a leading provider of globally consistent and comparable geographic data, including more than 3 million postcode areas and other detailed datasets like sociodemographics and purchasing power. 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Free trial data.","share":{"name":"share_socio_mbi_market_data_2024_ru_postcodes_4_socio","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1763369409341,"updated_at":1779198929485,"categories":["ADVERTISING_AND_MARKETING","DEMOGRAPHICS","GEOSPATIAL"],"listingType":"STANDARD","provider_id":"aa09abd6-ff7f-4452-803d-b8c43b4c07cb"},"detail":{"description":"[MBI](https://www.mbi-geodata.com/en/)'s sociodemographic data is available for many countries worldwide. All data comes from a single source, ensuring **consistency and comparability** across markets.\n\nThe Sociodemographics-Bundle Basic for Russian Federation includes the following **variables**:\n- [population](https://www.mbi-geodata.com/en/sociodemographic-data/population/)\n- [gender](https://www.mbi-geodata.com/en/sociodemographic-data/sex-gender/)\n- [age structure](https://www.mbi-geodata.com/en/sociodemographic-data/agebands/) (15-year age bands)\n- household figures ([number of households](https://www.mbi-geodata.com/en/sociodemographic-data/households/) and [households by type](https://www.mbi-geodata.com/en/sociodemographic-data/households-by-type/))\n\nOur sociodemographic data is offered for each country on different **levels**:\n- **Postal Code Level:** This fine-grained level of detail makes the data suitable for a wide range of applications – for example, enriching customer databases, which are often organized by postal code.\n- **Administrative Level:** Covers both larger administrative areas (e.g., regions, states, provinces) as well as finer subdivisions such as districts, counties, or municipalities.\n- **Micro Level**: Subdivisions of postal code or administrative boundaries, such as census areas or street segments. This is the most granular level for local analysis.\n\n**Please note:** The dataset available for free download is a **sample dataset** covering a limited number of geographic areas. If you wish to access the full dataset (covering all areas) or if you require additional variables or geographic levels, please contact us. We will be happy to help you find a product that fits your needs.\n \n**Additional Insights**: https://www.mbi-geodata.com/en/sociodemographic-data/\n\n**Data Structure**:\nAGE_F0014: Population (Nighttime) by age: 0 - 14 years, female\nAGE_F1529: Population (Nighttime) by age: 15 - 29 years, female\nAGE_F3044: Population (Nighttime) by age: 30 - 44 years, female\nAGE_F4559: Population (Nighttime) by age: 45 - 59 years, female\nAGE_F60PL: Population (Nighttime) by age: 60 years and above, female\nAGE_M0014: Population (Nighttime) by age: 0 - 14 years, male\nAGE_M1529: Population (Nighttime) by age: 15 - 29 years, male\nAGE_M3044: Population (Nighttime) by age: 30 - 44 years, male\nAGE_M4559: Population (Nighttime) by age: 45 - 59 years, male\nAGE_M60PL: Population (Nighttime) by age: 60 years and above, male\nAGE_T0014: Population (Nighttime) by age: 0 - 14 years, total\nAGE_T1529: Population (Nighttime) by age: 15 - 29 years, male\nAGE_T3044: Population (Nighttime) by age: 30 - 44 years, total\nAGE_T4559: Population (Nighttime) by age: 45 - 59 years, total\nAGE_T60PL: Population (Nighttime) by age: 60 years and above, total\nCTRYCODE: Two digit country code by ISO 3166\nFEMALE: Population (Nighttime): females\nHH_SIZE: Average household size\nHH_T: Households: total number\nMALE: Population (Nighttime): males\nNAME: Name of administrative area / Name of the most populous administrative area / Name of the respective micro-area or the next higher level\nADMINCODE / POSTCODE / MICROCODE Administrative code / Postal code / Micro-code\nP_PRM: Population (Nighttime) year average: per mill of country\nP_T: Population (Nighttime) year average: total number","terms_of_service":"https://service-mbi-geodata.com/s/HNp7Z2tws4WDqWd","documentation_link":"https://www.mbi-geodata.com/en/mbi-supplier-manufacturer-worldwide-geodata/europe/russian-federation-maps/","privacy_policy_link":"https://www.mbi-geodata.com/en/data-protection/","geographical_coverage":"[\"Europe\"]","cost":"FREE","update_frequency":{"interval":1,"unit":"YEARLY"},"collection_granularity":{"interval":1,"unit":"YEARLY"},"collection_date_start":1704098952475,"collection_date_end":1735634960428,"data_source":"MBI","assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://service-mbi-geodata.com/s/GBgqjP9wEoP92mM"},"provider_summary":{"name":"Michael Bauer International GmbH – MBI Geodata","description":"For over 15 years, Michael Bauer International (MBI) has been a leading provider of globally consistent and comparable geographic data, including more than 3 million postcode areas and other detailed datasets like sociodemographics and purchasing power. MBI's high-quality small-scale data spans 248 countries, supporting businesses across all industries in location planning, branch networks, and distribution strategies. Recognized as a key data source, MBI is the preferred provider for leading GIS software and technology companies worldwide as well as one of the first global distributors for HERE Technologies. MBI is part of the Michael Bauer Group, alongside Michael Bauer Research GmbH and Michael Bauer Micromarketing GmbH."},"popularity":3},{"id":"9d019012-f3d7-4db8-9248-d3bd7e0dc7f3","summary":{"name":"Corporate & International Business Networks Dataset – Global B2B Intelligence","subtitle":"Global firmographics, decision-maker contacts, and organisational hierarchies for targeting and market planning.","setting":{"visibility":"PUBLIC"},"created_at":1749058798835,"updated_at":1749058798835,"categories":["COMMERCE","DEMOGRAPHICS","ECONOMICS","ADVERTISING_AND_MARKETING","FINANCIAL","MANUFACTURING"],"listingType":"PERSONALIZED","provider_id":"cb4ffcdc-045c-4592-8497-0eeb2b9a3c3d"},"detail":{"description":"Access a comprehensive dataset of over 18 million U.S. and international business entities enriched with corporate hierarchies, operational footprint, and executive contact data. Designed for enterprise sales, global partnerships, CRM enrichment, and cross-border go-to-market strategy. \n\nData Axle, formerly Infogroup, has been a trusted provider of data-driven solutions since 1972. With verified coverage of over 95% of U.S. businesses and consumers, Data Axle delivers accurate, privacy-compliant data to power analytics, targeting, and activation. Our B2B and B2C datasets are built from deterministic sources and continuously updated. \n\n***Segment Categories:*** \n\n- Industry Classification -- NAICS & SIC: Finance, Technology, Manufacturing, Energy, Healthcare \n- Corporate Structure -- Parent firms, subsidiaries, JVs, and strategic alliances \n- Geographic Coverage -- North America, LATAM, EMEA, APAC \n- Revenue Tiers -- $50M to $1B+ brackets \n- Employee Bands -- 250 to 10,000+ \n- Executive Contact Info -- Validated emails, direct dials, HQ & regional offices \n- Organizational Events -- M&A, market entry, trade participation \n- Governance & Compliance -- ESG ratings, public listings, legal structure \n\n***Key Features:*** \n\n- 350+ precision segments focused on corporate identity and international reach \n- Multinational business hierarchy resolution with parent-subsidiary relationships \n- Verified C-suite contact details across global and regional offices \n- Tiered financial and workforce size filters for targeted B2B engagement \n- ESG compliance and stock listing status for screening and due diligence \n- Seamless integration with enterprise CRM, CDP, and BI ecosystems \n\n***Use Cases:*** \n\n- Enterprise Sales Targeting -- Engage large, multinational accounts through verified leadership data \n- Partner & Supplier Discovery -- Map out JV and alliance networks for channel expansion \n- Market Prioritization -- Assess international business density and segment expansion by region \n- CRM Enrichment -- Append verified firmographics and contacts to improve data quality and segmentation \n\n***Next Steps:*** \n\n- Request listing access \n- Data Axle team will contact you to finalize audience, data cut, and delivery method / frequency \n- Need a Custom Audience? We deliver tailored segments within 5--10 business days \n\n***Business Need:*** \n\n- Account Intelligence: Improve GTM precision with detailed organizational structure and decision-maker mapping \n- Territory Planning: Inform sales coverage models based on verified global operational presence \n- Segmentation Strategy: Build micro-segments using revenue, employee count, and legal status \n- Channel Identification: Discover and qualify potential partners, distributors, or resellers \n- Compliance & Risk Assessment: Leverage ESG data, M&A activity, and legal attributes for risk profiling","terms_of_service":"https://www.data-axle.com/terms-and-conditions/","documentation_link":"https://www.data-axle.com/our-data/","privacy_policy_link":"https://www.data-axle.com/privacy-policy/","geographical_coverage":"[\"United States and Canada\"]","cost":"PAID","update_frequency":{"interval":1,"unit":"MONTHLY"},"collection_granularity":{"interval":1,"unit":"DAILY"},"data_source":"Data Axle sources consumer data from 150+ inputs (real estate, tax, census, public records) and business data from 4,000+ sources, verified via 25M+ phone calls.","assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://www.data-axle.com/search/license"},"provider_summary":{"name":"Data Axle","description":"Data Axle is a leader in data-driven marketing and business intelligence solutions for enterprise, small business, nonprofit, and political organizations. We improve business performance for our clients by using our Business & Consumer data solutions and omnichannel marketing services, enabling our clients to acquire, grow, and retain their customers more efficiently and effectively, and enhance their platforms, products, and customer insights."},"popularity":704},{"id":"1bafc338-2625-4ea3-a37f-24c8a5307948","summary":{"name":"SAMPLE Enriched Job Postings Data for HR Analytics, Talent Acquisition & Labor Market Research | ...","subtitle":"Job Postings Data covering 800M+ deduplicated US records, updated hourly. This Job Postings Data is enriched with ...","share":{"name":"canaria_inc_63e35687_cad1_47a0_948d_42045f5d8655_samples_share","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1768711462654,"updated_at":1783432518379,"categories":["OTHER"],"listingType":"STANDARD","provider_id":"24eb2934-4a3f-4aec-b02c-4465c271a025"},"detail":{"description":"Most job posting datasets are a mess. The same role shows up on Indeed, LinkedIn, and the company's career site, inflating counts by 2-3x. Our Job Postings Data pipeline fixes that by collapsing billions of raw postings into 800M+ deduplicated records that reflect true hiring intent.\n\nEach record in this Job Postings Data comes with over 95 enriched fields: normalized job titles, extracted skills, predicted salaries, seniority levels, and matched company profiles. Updated every hour, this is the most current view of US employer demand you can get.\n\nWhat This Job Postings Data Covers\n\nThis Job Postings Data is sourced from Indeed, LinkedIn, and 50,000+ employer career sites. Every record includes:\n\n• Job ID, original title, and normalized title (from 50,000+ standardized categories)\n• Company name, location (city, state, ZIP), and SOC code\n• Seniority level (Entry, Mid, Senior, Lead, Executive)\n• Work type (Remote, Hybrid, Onsite) and employment type\n• Skills: 37,000+ hard skills, 3,000+ certifications, 400+ soft skills\n• Original salary and AI-predicted salary\n• Posting date, scrape date, source URL, and company ID\n• Benefits, qualification requirements, and full job description text\nCoverage is the United States, 2022 to present, updated hourly.\n\nWhat You Can Do With This Job Postings Data\n\nLabor Market Research\n\nGovernment labor data arrives 12-18 months late. Job Postings Data shows what employers are posting right now. You can track demand by city, state, or ZIP, analyze skill trends in real time, and build econometric models using Job Postings Data without look-ahead bias.\n\n• Replace stale government figures with real-time Job Postings Data to show current employer demand\n• Analyze hiring by ZIP code, city, or metro area using Job Postings Data geography fields\n• Track skill and certification demand over time using Job Postings Data historical snapshots\n• Build econometric models using Job Postings Data microdata instead of aggregated summaries\n• Identify emerging job hubs and declining markets through Job Postings Data posting concentrations\nTalent Acquisition\n\nHR and recruiting teams use Job Postings Data to benchmark compensation, identify competitor hiring patterns, and map talent availability by location. With hourly updates, you are working from what companies posted today, not last month.\n\n• Benchmark compensation packages against current market rates using Job Postings Data salary fields\n• Identify hiring spikes at target companies through Job Postings Data posting velocity\n• Map talent availability by location, skill, and seniority using Job Postings Data enrichment fields\n• Prioritize candidate sourcing by tracking in-demand skills extracted from Job Postings Data\n• Analyze competitor hiring patterns across departments and geographies with Job Postings Data\nHR Analytics\n\nPeople analytics teams use Job Postings Data to track hiring trends, measure skills gaps, and benchmark internal workforce against external market demand. With 95+ enriched fields per record, Job Postings Data feeds HR dashboards, compensation models, and workforce planning tools.\n\n• Track hiring trends across functions, geographies, and seniority levels using Job Postings Data\n• Identify skills gaps by comparing internal workforce data against external demand in Job Postings Data\n• Conduct competitor analysis using Job Postings Data to see which companies are hiring for similar roles\n• Analyze remote, hybrid, and onsite trends by industry and region using Job Postings Data work type fields\n• Build People Analytics dashboards fed by Job Postings Data with 95+ enriched fields per record\nWorkforce Planning and Site Selection\n\nSite selection consultants and economic development organizations use Job Postings Data to prove labor availability and build workforce reports for RFIs. Investors also use Job Postings Data hiring velocity as a leading indicator of company health.\n\n• Prove labor availability for site selection projects with current Job Postings Data posting counts\n• Define functional labor sheds using drive-time analysis against Job Postings Data open positions\n• Build workforce availability reports for RFI responses backed by Job Postings Data microdata\n• Forecast hiring needs based on market trends observed in Job Postings Data\n• Track hiring velocity of companies and sectors using Job Postings Data as alternative data\nHow We Build This Job Postings Data\n\nThe deduplication model collapses the same job appearing across Indeed, LinkedIn, and employer career sites into one canonical record using semantic matching on title, company, location, and description. Roughly 60% of raw records collapse into unique entries, producing 800M+ unique Job Postings Data records that reflect true hiring intent instead of inflated counts.\n\nThe title taxonomy model normalizes raw Job Postings Data titles into 50,000+ standardized categories from 20M+ raw titles by stripping location noise, qualifiers, and formatting variations. The skill taxonomy model extracts 37,000+ hard skills, 3,000+ certifications, and 400+ soft skills per Job Postings Data record using contextual analysis of job descriptions. Job category models classify every Job Postings Data record by seniority (Entry, Mid, Senior, Lead, Executive) and modality (Remote, Hybrid, Onsite) using LLM-based reading of the full job description.\n\nThe salary estimation model predicts pay ranges for the 60-70% of Job Postings Data records without explicit salary, trained on 50M+ observations with a Mean Absolute Percentage Error under 15%.\n\nOur annotation team validates every Job Postings Data model output before delivery.\n\nWhat Makes This Job Postings Data Different\n\n• 800M+ deduplicated records: Job Postings Data collapses billions of raw postings into a clean signal of true hiring intent\n• Hourly updates: Job Postings Data refreshes every hour so you are never working with stale information\n• 95+ enriched fields: every Job Postings Data record includes normalized titles, extracted skills, seniority, and salary\n• Full ML stack: four primary AI models power Job Postings Data — deduplication, title normalization, skill extraction, and seniority classification\n• Company matching: Job Postings Data connects every posting to a verified company profile with firmographics\n• Coverage: Job Postings Data sources include Indeed, LinkedIn, and 50,000+ employer career sites from 2022 to present\nWho Uses This Job Postings Data\n\n• HR and Talent Acquisition Teams use Job Postings Data to benchmark compensation, source candidates, and track competitor hiring in real time\n• Labor Market Economists and Government Agencies use Job Postings Data as a leading indicator of workforce demand, replacing lagging survey data\n• Site Selection Consultants use Job Postings Data to prove labor availability and build defensible workforce studies for corporate site decisions\n• Economic Development Organizations use Job Postings Data to respond to RFIs with current employer demand data and workforce metrics\n• Staffing and Recruiting Agencies use Job Postings Data to identify market expansion opportunities and benchmark pay rates for clients\n• Investors and Financial Analysts use Job Postings Data hiring velocity as alternative data for due diligence and portfolio monitoring\n• Market Research and BI Teams use Job Postings Data to analyze industry hiring trends, track skills demand, and build labor market dashboards\n• HR Tech Companies and B2B Platforms use Job Postings Data to power ATS integrations, compensation tools, and talent intelligence products\nDelivery and Format\n\nJob Postings Data is delivered in CSV, JSON, or Parquet via AWS S3 or Google Cloud Storage. Custom filters by geography (ZIP, city, state), company, date range, title, skill, industry, seniority, and salary range. Compatible with Snowflake, Databricks, Power BI, Tableau, Salesforce, and most BI platforms.","terms_of_service":"https://decanaria.com/privacy-policy","contact_email":"support@datarade.ai","privacy_policy_link":"https://decanaria.com/privacy-policy","assets":["ASSET_TYPE_DATA_TABLE"]},"provider_summary":{"name":"Canaria Inc.","description":"Canaria is a leading provider of enriched job posting and labor market data. We collect 1B+ job postings from Indeed, LinkedIn and 200,000+ employer career sites and deduplicate them to 400M+ unique jobs, with deepest coverage in the US plus postings from 243 other countries and territories since 2022.\r\n\r\nData arrives analysis-ready: SOC occupation and NAICS industry codes, normalized titles across 99,799 canonical roles, a 43,000+ entry skills and credentials taxonomy, salary estimates backed by 44,000+ benchmark cells with 95% confidence intervals, worker type (W2 vs 1099), stock tickers for listed employers, and a confidence score on every classified field.\r\n\r\nHedge funds, HR tech platforms, workforce planners and researchers choose Canaria when they want enterprise-grade enrichment without an enterprise contract. Delivery by API or as CSV and Parquet files to S3, GCS, Snowflake or SFTP. 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Its main benefit is enabling data teams to significantly reduce processing times and compute costs when deploying healthcare NLP models on Databricks clusters.\n\nUse cases\n\n- Optimizing Apache Spark configurations (shuffle_partitions, default_parallelism, and repartition_count) for high-throughput clinical NLP processing.\n- Benchmarking the performance, execution time, and stability of John Snow Labs Healthcare NLP pipelines at varying data scales (10 to 1,000,000 rows).\n- Fast and balanced Protected Health Information (PHI) de-identification for massive volumes of medical documents.\n- Granular clinical Named Entity Recognition (NER) and specialized oncology entity extraction from clinical text.\n- High-volume, large-scale ICD-10-CM code resolution from raw medical transcriptions.\n\nProduct details\n\n- Datasets represented include Medallion bronze raw data tables (smoke_notes, val_100_notes, val_1k_notes) and the target benchmark_results table.\n- Sample fields include pipeline_name, config_label, shuffle_partitions, repartition_count, wall_time_seconds, throughput_docs_sec, and rows_processed.\n\nFor more details, refer to the embedded notebook.\n\nAdditional Insights \n\nThe benchmark utilizes a dataset of approximately 5,000 real medical transcription notes to simulate production workloads. 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Access to the complete dataset is available upon request.\n\n**Use Cases**\n* Analyze live-event activity across German federal states, municipalities, districts, and postal-code regions.\n* Compare venue categories, capacities, attendance, and pricing.\n* Explore regional and genre-specific live music trends.\n* Build dashboards for venue performance and cultural activity.\n* Support location intelligence, market research, and forecasting.\n* Enrich BI, machine-learning, recommendation, and geospatial analytics.\n\n**Product Details**\nThe sample dataset includes:\n* Live event metadata\n* Event year and month\n* Administrative location data\n* Venue information and venue category\n* Music genre classification\n* Estimated venue floor area\n* Venue capacity\n* Attendance\n* Average net ticket price\nFor detailed schema information and example analyses, please refer to the accompanying notebook.\n\n**Additional Insights**\nUnlike aggregated statistics, this dataset provides event-level visibility into Germany's live music landscape. 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Free trial data.","share":{"name":"share_socio_mbi_market_data_2024_by_postcodes_4_socio","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1763127119213,"updated_at":1779198818132,"categories":["ADVERTISING_AND_MARKETING","DEMOGRAPHICS","GEOSPATIAL"],"listingType":"STANDARD","provider_id":"aa09abd6-ff7f-4452-803d-b8c43b4c07cb"},"detail":{"description":"[MBI](https://www.mbi-geodata.com/en/)'s sociodemographic data is available for many countries worldwide. All data comes from a single source, ensuring **consistency and comparability** across markets.\n\nThe Sociodemographics-Bundle Basic for Belarus includes the following **variables**:\n- [population](https://www.mbi-geodata.com/en/sociodemographic-data/population/)\n- [gender](https://www.mbi-geodata.com/en/sociodemographic-data/sex-gender/)\n- [age structure](https://www.mbi-geodata.com/en/sociodemographic-data/agebands/) (15-year age bands)\n- household figures ([number of households](https://www.mbi-geodata.com/en/sociodemographic-data/households/) and [households by type](https://www.mbi-geodata.com/en/sociodemographic-data/households-by-type/))\n\nOur sociodemographic data is offered for each country on different **levels**:\n- **Postal Code Level:** This fine-grained level of detail makes the data suitable for a wide range of applications – for example, enriching customer databases, which are often organized by postal code.\n- **Administrative Level:** Covers both larger administrative areas (e.g., regions, states, provinces) as well as finer subdivisions such as districts, counties, or municipalities.\n- **Micro Level**: Subdivisions of postal code or administrative boundaries, such as census areas or street segments. This is the most granular level for local analysis.\n\n**Please note:** The dataset available for free download is a **sample dataset** covering a limited number of geographic areas. If you wish to access the full dataset (covering all areas) or if you require additional variables or geographic levels, please contact us. We will be happy to help you find a product that fits your needs.\n \n**Additional Insights**: https://www.mbi-geodata.com/en/sociodemographic-data/\n\n**Data Structure**:\nAGE_F0014: Population (Nighttime) by age: 0 - 14 years, female\nAGE_F1529: Population (Nighttime) by age: 15 - 29 years, female\nAGE_F3044: Population (Nighttime) by age: 30 - 44 years, female\nAGE_F4559: Population (Nighttime) by age: 45 - 59 years, female\nAGE_F60PL: Population (Nighttime) by age: 60 years and above, female\nAGE_M0014: Population (Nighttime) by age: 0 - 14 years, male\nAGE_M1529: Population (Nighttime) by age: 15 - 29 years, male\nAGE_M3044: Population (Nighttime) by age: 30 - 44 years, male\nAGE_M4559: Population (Nighttime) by age: 45 - 59 years, male\nAGE_M60PL: Population (Nighttime) by age: 60 years and above, male\nAGE_T0014: Population (Nighttime) by age: 0 - 14 years, total\nAGE_T1529: Population (Nighttime) by age: 15 - 29 years, male\nAGE_T3044: Population (Nighttime) by age: 30 - 44 years, total\nAGE_T4559: Population (Nighttime) by age: 45 - 59 years, total\nAGE_T60PL: Population (Nighttime) by age: 60 years and above, total\nCTRYCODE: Two digit country code by ISO 3166\nFEMALE: Population (Nighttime): females\nHH_SIZE: Average household size\nHH_T: Households: total number\nMALE: Population (Nighttime): males\nNAME: Name of administrative area / Name of the most populous administrative area / Name of the respective micro-area or the next higher level\nADMINCODE / POSTCODE / MICROCODE Administrative code / Postal code / Micro-code\nP_PRM: Population (Nighttime) year average: per mill of country\nP_T: Population (Nighttime) year average: total number","terms_of_service":"https://service-mbi-geodata.com/s/HNp7Z2tws4WDqWd","documentation_link":"https://www.mbi-geodata.com/en/mbi-supplier-manufacturer-worldwide-geodata/europe/belarus-maps/","privacy_policy_link":"https://www.mbi-geodata.com/en/data-protection/","geographical_coverage":"[\"Europe\"]","cost":"FREE","update_frequency":{"interval":1,"unit":"YEARLY"},"collection_granularity":{"interval":1,"unit":"YEARLY"},"collection_date_start":1704115862742,"collection_date_end":1735651875054,"data_source":"MBI","assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://service-mbi-geodata.com/s/rybrTnMxCtR5Dab"},"provider_summary":{"name":"Michael Bauer International GmbH – MBI Geodata","description":"For over 15 years, Michael Bauer International (MBI) has been a leading provider of globally consistent and comparable geographic data, including more than 3 million postcode areas and other detailed datasets like sociodemographics and purchasing power. 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MBI is part of the Michael Bauer Group, alongside Michael Bauer Research GmbH and Michael Bauer Micromarketing GmbH."},"popularity":412},{"id":"37578a13-a94f-48a3-869c-d034cff278b5","summary":{"name":"eCommerce & Shopping","subtitle":"Receive large-scale reliable data effortlessly from any e-commerce website.\n","share":{"name":"ecommerce_shopping","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1704889024708,"updated_at":1718885429724,"categories":["COMMERCE","ADVERTISING_AND_MARKETING"],"listingType":"STANDARD","provider_id":"63866f42-27f8-4a1f-b243-37e7be863700"},"detail":{"description":"Note : This current datasets includes 1000 rows, for more records -please contact the provider.\n\n\nGet enriched, pre-collected, large-scale snapshots of entire major retail marketplaces & eCommerce websites that are regularly updated. Bright Data’s datasets are delivered to you in the File format of your choice, that best suits your needs and schedule, and includes an extensive capture of millions of data points, on a massive scale.\n\nEasily define your own data collection using the smart filter capabilities that allow you to customize pre-existing datasets to ensure the data fits your business needs.\n\neCommerce Datasets Include:\n-Lowe's\n-Wayfair\n-Ikea Products\n-Lazada Products\n-Digikey Products\n-Mediamarkt.de products\n-Zalando products\n-AE.com Products\n-Mattressfirm Products\n-Crateandbarrel Products\n-llbean.com Products\n-Toysrus Products\n-Mybobs.com\n-Sleepnumber.com Products\n-Raymourflanigan.com Products\n-La-z-boy.com Products\nAnd more!\n\nNote: We have separated listings for Walmart and Amazon\n\nData Points Include:\n- Seller Name,\n- Description,\n- Stars,\n- Feedback\n- Return Policy\n- Delivery Rates\n- Guarantee, URL\n- Product Link\n- Currency\n- Features\n- Product Dimensions\n- Initial Price\n- Manufacturer\n- And a lot more\n\n\nWith Bright Data you can:\n\n- Purchase complete datasets\nDatasets capture entire sites, including millions of pages, in key industry verticals. Optional refresh of datasets at a later date.\n\n- Customize subsets from existing datasets\nDon’t need the entire dataset? Purchase customized subsets based on location, category, or any other parameter using smart filter capabilities.\n\n- Request your own dataset\nContact us to build a dataset tailored to your needs.","terms_of_service":"https://brightdata.com/license","documentation_link":"https://drive.google.com/file/d/1iMpRFWiOVgoEDgHbLiWPTvVtyHp6bwPH/view","privacy_policy_link":"https://brightdata.com/privacy","embedded_notebook_file_infos":[{"id":"aa1ba511-4620-452a-99db-69b9b83b3847","marketplace_file_type":"EMBEDDED_NOTEBOOK","file_parent":{"parent_id":"37578a13-a94f-48a3-869c-d034cff278b5","file_parent_type":"LISTING"},"mime_type":"text/html","download_link":"https://marketplace-file-storage-prod.s3-fips.us-east-1.amazonaws.com/EMBEDDED_NOTEBOOK/LISTING_37578a13-a94f-48a3-869c-d034cff278b5/aa1ba511-4620-452a-99db-69b9b83b3847?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEGgaDnVzLWVhc3QtMS1maXBzIkYwRAIgbC8sWIoYaHR8y7Wq86nPb0pufBl4%2BM4PWpeN%2B5PW9VQCIG7BhAVBDCWTpxFDLeuKYm%2Ff1JLz4iccPMzter0YdhgWKqYECDAQABoMNDE0MzUxNzY3ODI2IgxNAJrHbXaZG20jNfMqgwQyGEb4TsoVOoNvJ%2BaA%2FZ7zeu8hGcHqCeNfblP%2FQo5g6q2aRNtpwtPRKMlhYcIE%2FUQaNT1Lqmyptu32VUQTpzjTTxRVmJrDPkheSCkV06XQ%2FpTMKjU8CC7X1b%2FqYf8CrBwZT7Cv4C5WPjEET38f2A79h0xV6bZgPy28zQWrSeIJ9w6AfZBxiE24%2FVc08zVA0ivCT6lsy7nuU0wcR%2FMg2C9nd1vaMnQ923VpD21CKfVVYcrNWL2b%2FQ%2BNAO%2BxPjPgjiN6UPQbvbvZxkSdhgrGtecIg9NgqGkMcvM5VLTDtdC1kdeZetfOC2obtA%2BT%2F0gy3tIDo6WM5hcQfw743%2FAU9oaGnRBl%2BwoxRRDCZA%2F4jnPdluICSI7NCr6C3HHOe4CSZJjRihczxPA%2FgQczAVjAPs6CrK9N9pIzt0vNJpGUTgg4AoxftTvGfciDREilSctPIcBk6GwB%2Bzt4wbtNCmEPU%2BFD46rc6CWVQmmLwbKOeHw70ipMVFlOc8NlXkEtGO2vu6H44MstSSc6daOVUYUavQSGZ6jGhEjsP2YGz8KDm9XQotB%2FtyVBqiZV01OKqDmmBJA%2FXjUpSJ8M%2FeJXhe5m%2BKcxzNObtoJRI7jci3Tpt08XZyXZ7%2FyIV8H30ya468X3cfkTXrm972uga3z7iXAdubyEHnQIua%2FuvtwOHILi6O5D1Me2MzCPvcnXBjqTAYDoMWBt%2B3AVFuqSs2dmDQNBnvEhXfMgTedKLD5PJHb3aIl15iBkLaNu36GYnVCp9ZU2lSYcTbY5QwNnNeTfJTNy9bKhwzru6Sg%2BslhN%2BTS0zkTn%2BeLMY4tHgPEtEZ2cp%2FCk6GwqswvnYVKHjwO8q%2B1Mx8lc08fVEfarE9dke1GGFqoddx%2Bq%2BZUyHbDlAVfrCmf0yw%3D%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20261110T000135Z&X-Amz-SignedHeaders=host&X-Amz-Expires=86400&X-Amz-Credential=ASIAWA6KKHEJORWBH4N3%2F20261110%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=aa9d278728566ac4e78fa2cf67b9a8f8c0bf5bfee151eaac32b3dcf81922f038","created_at":1704889048023,"updated_at":1704889048023,"display_name":"eCommerce & Shopping - Example from Mouser dataset","status":"FILE_STATUS_PUBLISHED"}],"geographical_coverage":"[\"Global\"]","assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://brightdata.com/license"},"provider_summary":{"name":"Bright Data","description":"Bright Data is the world’s #1 web data platform.\nFortune 500 companies, academic institutions, and small businesses rely on Bright Data’s scalable platform for web scraping infrastructure to retrieve crucial public web data in the most efficient, reliable, and flexible way, so they can research, monitor, and analyze data to make better decisions.\nThe platform is used worldwide by 20,000+ customers in nearly every industry. 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The datasets included in this data package are datasets from the World Bank Data Catalog and Institute of Health Metrics and Evaluation.\n\n**Benefits**\n- This data is useful for developing country governments seeking to improve development indicators for health, nutrition, education, and population. this can be used for further research\n- officials and program managers for potential interventions, setting priority areas for action and evaluating progress\n- policymakers for policy use and development\n- development agencies aid in allocating resources and budget for development.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [Africa Educational Attainment Admin 1 2000 to 2015](https://www.johnsnowlabs.com/marketplace/africa-educational-attainment-admin-1-2000-to-2015)\n\t- This dataset provides estimates for average years of educational attainment for women of reproductive age (15 to 49), women ages 20-24, and equivalent male age groups at the 5x5 km-level in 51 countries in Africa between 2000 to 2015.\n- [Africa Educational Attainment Admin 2 2000 to 2015](https://www.johnsnowlabs.com/marketplace/africa-educational-attainment-admin-2-2000-to-2015)\n\t- This dataset provides estimates for average years of educational attainment for women of reproductive age (15 to 49), women ages 20-24, and equivalent male age groups at the 5x5 km-level in 51 countries in Africa between 2000 to 2015.\n- [Africa Educational Attainment Disparity Admin 1 2000 to 2015](https://www.johnsnowlabs.com/marketplace/africa-educational-attainment-disparity-admin-1-2000-to-2015)\n\t- This dataset provides estimates generated for the disparity in years of educational attainment between males and females for women of reproductive age (15 to 49), women ages 20-24, and equivalent male age groups at the 5x5 km-level in 51 countries in Africa between 2000 to 2015.\n- [Global Educational Attainment 1970 to 2015](https://www.johnsnowlabs.com/marketplace/global-educational-attainment-1970-to-2015)\n\t- This dataset provides estimates of average years of educational attainment per capita for people over the age of 15 for the years 1970-2015 by year, sex, and age group for 188 countries, 21 GBD regions, 7 GBD super regions, and the global aggregate. Age-standardized and population-weighted estimates are included for females 15-44 and for both sexes for the age group 25+.\n- [Health Nutrition and Population Statistics by Wealth Quintile](https://www.johnsnowlabs.com/marketplace/health-nutrition-and-population-statistics-by-wealth-quintile)\n\t- This time-series dataset includes data on countries worldwide and information on indicators pertaining to health and population by wealth quintile by each country from 1990 to 2020.\n- [Health Nutrition and Population Statistics](https://www.johnsnowlabs.com/marketplace/health-nutrition-and-population-statistics)\n\t- This time-series dataset includes data on countries worldwide and information on indicators pertaining to health and population by each country from 1960 to 2021.\n- [Health and Education Service Delivery Indicator Definitions](https://www.johnsnowlabs.com/marketplace/health-and-education-service-delivery-indicator-definitions)\n\t- This dataset is a subset of the Health and Education Service Delivery Indicators in Africa that includes operational definitions of the health and education service delivery indicators used in the Service Delivery Indicators project of the World Bank and its partners.\n- [Health and Education Service Delivery Indicators in Africa](https://www.johnsnowlabs.com/marketplace/health-and-education-service-delivery-indicators-in-africa)\n\t- This dataset includes data on the health and education service delivery indicators of African countries from 2010 to 2016. There is no available data for years 2011 and 2015. Data came from the Service Delivery Indicators project, a new Africa-wide initiative that tracks service delivery in education and health across African countries over time.\n- [Subnational Population Database](https://www.johnsnowlabs.com/marketplace/subnational-population-database)\n\t- This dataset includes national and subnational population data from countries and cities worldwide from 2000 to 2016.\n- [US Poverty Level Statistics](https://www.johnsnowlabs.com/marketplace/us-poverty-level-statistics)\n\t- The US Poverty Level Statistics dataset includes percentages and numbers of persons and children below the poverty level in the United States by race between 1973 and 2016.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - 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We collect data from various public and online sources and coalesce this information in-house and enrich it with hyperlocal context, in order to create datasets that can reliably drive decision-making and generative AI applications."},"popularity":1539},{"id":"339d2f99-70ea-4d9b-b233-2a2331aac481","summary":{"name":"Global Labor Market Data & Talent Intelligence - Sample","subtitle":"Workforce supply, demand, pay, skills, diversity, migration & hiring trends by role and region","share":{"name":"talent_intelligence","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1755720868643,"updated_at":1788589820138,"categories":["ADVERTISING_AND_MARKETING","COMMERCE","ECONOMICS","EDUCATION","OTHER","SCIENCE_AND_RESEARCH"],"listingType":"STANDARD","provider_id":"9900a255-6aa9-496f-9b6a-421a3568af3b"},"detail":{"description":"**Overview**\nDraup’s Global Labor Market Data & Talent Intelligence provides a multi-dimensional view of workforce dynamics across countries, regions, industries, and job roles. By combining supply and demand metrics with compensation, hiring difficulty, and migration trends, the dataset helps HR leaders, workforce planners, and people analytics teams anticipate talent shortages, design competitive hiring strategies, and optimize global workforce distribution.\nBeyond headcounts and demand signals, the dataset enriches job roles with context on core and soft skills, educational pipelines, certifications, diversity representation, and career pathways. It also highlights top employers, competitor hiring intensity, and common job titles, allowing organizations to benchmark against peers and align workforce investments with business priorities. This holistic perspective supports decisions on location strategy, diversity initiatives, reskilling needs, and long-term workforce planning in a competitive global market.\n\n**Methodology & Quality**\nThe dataset is built by consolidating data from millions of structured and unstructured sources, including public job postings, professional profiles, labor market reports, and education and certification databases. Advanced normalization ensures consistency across countries, job roles, and industries, while Draup’s proprietary job taxonomy links occupations to responsibilities, adjacent roles, and standardized ONET classifications. Bias is minimized by weighting across multiple sources, and updates are refreshed frequently to capture the latest demand and supply signals. Each dataset is standardized for comparability across geographies and industries, providing HR and talent teams with a trusted, current, and scalable foundation for workforce planning.\n\n**Key Tables Include**\n•\tTalent Metrics by Country & Job Role – Supply, demand, pay benchmarks, and hiring difficulty at a global scale.\n•\tTalent Metrics by Location (MSA) & Job Role – Localized workforce size, demand, and compensation at metro or regional levels.\n•\tJob Role Master – Standardized taxonomy with descriptions, responsibilities, ONET mappings, and adjacent job families.\n•\tIndustry Split by Country or MSA – Workforce distribution across industries for specific job roles.\n•\tTop Employers by Role & Location – Company-level hiring intensity and workforce size indicators.\n•\tJob Role Skills (Core & Soft) – Required technical and non-technical skills with workforce distribution intensity.\n•\tTech Stack by Role & Location – Technologies most commonly associated with each role.\n•\tEducation & Certifications by Role & Location – Top universities, majors, and certifications tied to role pipelines.\n•\tUniversity Talent Supply – Graduate counts by job role, major, and institution.\n•\tJob Role Path Predictor – Forward and backward career transitions for job roles.\n•\tCompetitors by Role & Location – Hiring activity and job posting intensity among peer employers.\n•\tSeniority Distribution – Workforce experience levels across job roles.\n•\tDiversity Metrics – Gender and ethnicity breakdowns by job role and geography.\n•\tTalent Movements – Inbound and outbound migration and mobility flows by geography and role.\n•\tCommon Job Titles – Normalized job title variations with posting intensity.\n\n**Use Cases:**\n***Discover emerging talent hubs in Eastern Europe that have high supply of Data Science talent but lower median salaries***\n\nDraup’s talent location intelligence enables discovery of high-supply regions for talent with favorable compensation benchmarks. Filter by geography, job family, skill depth, and salary quartiles to identify cost-effective emerging hubs for strategic hiring or delivery center expansion.\t\n\nWITH filtered AS (\n SELECT\n country,\n job_role,\n talent_size,\n median_base_pay,\n date_updated\n FROM talent_metics_country_job_role\n WHERE job_role = 'Data Scientist'\n AND country IN ('Poland', 'Romania', 'Bulgaria', 'Hungary', 'Czechia')\n AND date_updated >= (CURRENT_DATE - INTERVAL '365 day')\n),\nstats AS (\n SELECT\n PERCENTILE_CONT(0.75) WITHIN GROUP (ORDER BY talent_size) AS talent_size_75,\n PERCENTILE_CONT(0.40) WITHIN GROUP (ORDER BY median_base_pay) AS pay_40\n FROM filtered\n)\nSELECT\n filtered.country,\n filtered.job_role,\n filtered.talent_size,\n filtered.median_base_pay,\n CASE\n WHEN filtered.talent_size >= stats.talent_size_75\n AND filtered.median_base_pay <= stats.pay_40\n THEN 1 ELSE 0\n END AS is_target\nFROM filtered\nCROSS JOIN stats\nWHERE filtered.talent_size IS NOT NULL\n AND filtered.median_base_pay IS NOT NULL\nORDER BY is_target DESC, filtered.talent_size DESC, filtered.median_base_pay ASC;\n\n***Compare the talent availability, demand & compensation benchmarks for Cybersecurity Engineers across the top 10 locations in India***\t\n\nDraup’s integrated talent analytics allows multi-location benchmarking of supply, demand, compensation, Hiring Difficulty, and peer comparison for different job roles. Evaluate hiring hotspots, cost-to-hire, and market competitiveness across cities to guide location strategy, workforce allocation, and budget planning.\t\n\nWITH ranked_salaries AS (\n SELECT\n job_role,\n msa,\n median_base_pay,\n talent_size,\n talent_demand,\n ROW_NUMBER() OVER (\n PARTITION BY msa\n ORDER BY median_base_pay\n ) AS row_number_asc,\n ROW_NUMBER() OVER (\n PARTITION BY msa\n ORDER BY median_base_pay DESC\n ) AS row_number_desc\n FROM talent_metics_location_msa_job_role\n WHERE job_role = 'Cybersecurity Engineer'\n AND msa LIKE '%India%'\n)\nSELECT\n ranked_salaries.msa,\n SUM(ranked_salaries.talent_size) AS total_talent,\n SUM(ranked_salaries.talent_demand) AS total_demand,\n AVG(ranked_salaries.median_base_pay) AS exact_median_base_pay\nFROM ranked_salaries\nWHERE ranked_salaries.row_number_asc = ranked_salaries.row_number_desc\n OR ranked_salaries.row_number_asc + 1 = ranked_salaries.row_number_desc\nGROUP BY ranked_salaries.msa\nORDER BY total_talent DESC\nLIMIT 10;\n\n***Discover gender inclusive locations to hire for Product Manager talent in Germany***\t\n\nDraup’s diversity analytics aid in reaching inclusive hiring goals and regional outreach strategies by filtering based on location, function, job family, or seniority to highlight regions based on requirement.\t\n\nSELECT \n country,\n job_role,\n Male,\n Female,\n (Female * 100.0 / NULLIF(Male + Female, 0)) AS female_percentage\nFROM genderdiversity_country_job_role\nWHERE job_role = 'Product Manager'\n AND country = 'Germany'\nORDER BY female_percentage DESC;\n\n***Identify hotspots in the US for Underwriting talent with high availability and low hiring difficulty***\t\n\nDraup’s talent supply and Hiring Difficulty Index (HDI) framework helps uncover areas with favorable conditions for hiring a target job role. Combine filters for talent pool size, skill relevance, and hiring friction to guide cost-effective location decisions.\t\n\nWITH ranked AS (\n SELECT\n job_role,\n msa,\n COUNT(*) AS total_talent,\n AVG(hiring_difficulty_index) AS avg_hdi,\n ROW_NUMBER() OVER (ORDER BY COUNT(*) DESC) AS talent_rank,\n ROW_NUMBER() OVER (ORDER BY AVG(hiring_difficulty_index) ASC) AS hdi_rank\n FROM talent_metics_location_msa_job_role\n WHERE LOWER(msa) LIKE '%united states'\n AND LOWER(job_role) = 'underwriter'\n GROUP BY msa, job_role\n)\nSELECT \n msa,\n total_talent,\n avg_hdi,\n (talent_rank + hdi_rank) AS combined_rank\nFROM ranked\nORDER BY combined_rank ASC\nLIMIT 10;\n\n***What are the top 10 locations that has seen the highest demand for Embedded C skill in the last quarter***\t\n\nUse Draup’s real-time demand signals to identify global or regional hotspots where hiring for a particular job role has surged recently. Analyze demand intensity by location, job role, or industry to align GTM, hiring, or skilling efforts with emerging trends.\t\n\nSELECT\n unnest_result.jd_msa AS location_msa,\n unnest_result.jd_country AS country,\n COUNT(*) AS demand_count\nFROM job_postings_company\nCROSS JOIN LATERAL UNNEST(consolidated_jds) AS unnest_result (\n jd_location, jd_synon_location, jd_msa, jd_region, jd_state, jd_country,\n jd_latitude, jd_longitude, jd_source, jd_jd_url, jd_data_lake_id, jd_loaded_at, jd_date_updated\n)\nWHERE POSITION('Embedded C' IN core_skills) > 0\n AND date_posted >= (DATE_TRUNC('quarter', CURRENT_DATE) - INTERVAL '3 months')\n AND date_posted < DATE_TRUNC('quarter', CURRENT_DATE)\n AND unnest_result.jd_msa IS NOT NULL\nGROUP BY unnest_result.jd_msa, unnest_result.jd_country\nORDER BY demand_count DESC\nLIMIT 10;\n\n***Map key feeder locations contributing to the Product Manager talent pool in Bay Area.***\t\n\nDraup’s talent mobility and migration insights enable mapping of feeder cities and states contributing a specific talent to a target location. 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Its Sales Intelligence solution helps go-to-market teams uncover deep account insights, identify buying triggers, and accelerate revenue growth.\n \nTrusted by over 300 global enterprises - including five of the Fortune 10 - Draup supports companies like Microsoft, PepsiCo, PayPal, and Moderna in building future-ready workforces and growth strategies."},"popularity":1377},{"id":"960789d7-94b6-452e-a024-d774514b884e","summary":{"name":"International Drugs Database","subtitle":"This data package contains complete information on all approved, marketed, canceled and dormant products for human, ve..","share":{"name":"international_drugs_database","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1682356552514,"updated_at":1698965635294,"categories":["SCIENCE_AND_RESEARCH"],"listingType":"STANDARD","provider_id":"f88849f8-9d77-4de2-b4c7-03d55cc3768e"},"detail":{"description":"\n**Overview**\nThis data package contains complete information on all approved, marketed, canceled and dormant products for human, veterinary, disinfectant and radiopharmaceutical use in New Zealand, Germany, United Kingdom (UK) and Canada.\n\n**Description**\nThe dataset New Zealand Drug Product Database lists all drug products that have been given consent to market in New Zealand by the New Zealand Medicines and Medical Devices Safety Authority (MEDSAFE). Canadian Drugs database contains complete product information for all approved, marketed, canceled and dormant products for human, veterinary, disinfectant and radiopharmaceutical use in the Canadian Drug Product Database (DPD). The data package also contains complete active ingredient information of over-the-counter (OTC) and prescription drugs marketed in the United Kingdom (UK).\n\n**Benefits**\n- the data package is useful for finding drug products that are approved by the respective authorities of the uk, germany, canada and new zealand.\n- it also provides information about the active ingredients used in the drug products and their approval history.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [Active Ingredient Information in Canadian Drug Product Database](https://www.johnsnowlabs.com/marketplace/active-ingredient-information-in-canadian-drug-product-database)\n\t- This dataset contains complete active ingredient information for all approved, marketed, canceled and dormant products for human, veterinary, disinfectant and radiopharmaceutical use in the Canadian Drug Product Database (DPD) as of September 1, 2017. Before drug products are authorized for sale, Health Canada reviews them to assess their safety, efficacy, and quality. Drug products include prescription and non-prescription pharmaceuticals, disinfectants and sanitizers with disinfectant claims.\n- [New Zealand Drug Product Database](https://www.johnsnowlabs.com/marketplace/new-zealand-drug-product-database)\n\t- The dataset New Zealand Drug Product Database lists all drug products that have been given consent to market in New Zealand by the New Zealand Medicines and Medical Devices Safety Authority (MEDSAFE).\n- [Product Information in Canadian Drug Product Database](https://www.johnsnowlabs.com/marketplace/product-information-in-canadian-drug-product-database)\n\t- This dataset contains complete product information for all approved, marketed, canceled and dormant products for human, veterinary, disinfectant and radiopharmaceutical use in the Canadian Drug Product Database (DPD) as of September 1, 2017. Before drug products are authorized for sale in Canada, Health Canada reviews them to assess the safety, efficacy, and quality. Drug products include prescription and non-prescription pharmaceuticals, disinfectants and sanitizers with disinfectant claims.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated\n- Data Updates\n - Data updates support replace-on-update: outdated foreign keys are deprecated, not deleted\n\n**Our data is curated and enriched by domain experts** \n\n Each dataset is manually curated by our team of doctors, pharmacists, public health & medical billing experts:\n- Field names, descriptions, and normalized values are chosen by people who actually understand their meaning\n- Healthcare & life science experts add categories, search keywords, descriptions and more to each dataset\n- Both manual and automated data enrichment supported for clinical codes, providers, drugs, and geo-locations\n- The data is always kept up to date – even when the source requires manual effort to get updates\n- Support for data subscribers is provided directly by the domain experts who curated the data sets\n- Every data source’s license is manually verified to allow for royalty-free commercial use and redistribution.\n\n**Need Help?**\nIf you have questions about our products, contact us at [info@johnsnowlabs.com](mailto:info@johnsnowlabs.com). \n ","terms_of_service":"https://www.johnsnowlabs.com/healthcare-data-library-eula/","documentation_link":"https://www.johnsnowlabs.com/marketplace/international-drugs-database-data-package","contact_email":"support@johnsnowlabs.com","privacy_policy_link":"https://www.johnsnowlabs.com/privacy-policy/","embedded_notebook_file_infos":[{"id":"fb167203-432e-4fd1-b1e2-e84f09278453","marketplace_file_type":"EMBEDDED_NOTEBOOK","file_parent":{"parent_id":"960789d7-94b6-452e-a024-d774514b884e","file_parent_type":"LISTING"},"mime_type":"text/html","download_link":"https://marketplace-file-storage-prod.s3-fips.us-east-1.amazonaws.com/EMBEDDED_NOTEBOOK/LISTING_960789d7-94b6-452e-a024-d774514b884e/fb167203-432e-4fd1-b1e2-e84f09278453?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEGgaDnVzLWVhc3QtMS1maXBzIkYwRAIgbC8sWIoYaHR8y7Wq86nPb0pufBl4%2BM4PWpeN%2B5PW9VQCIG7BhAVBDCWTpxFDLeuKYm%2Ff1JLz4iccPMzter0YdhgWKqYECDAQABoMNDE0MzUxNzY3ODI2IgxNAJrHbXaZG20jNfMqgwQyGEb4TsoVOoNvJ%2BaA%2FZ7zeu8hGcHqCeNfblP%2FQo5g6q2aRNtpwtPRKMlhYcIE%2FUQaNT1Lqmyptu32VUQTpzjTTxRVmJrDPkheSCkV06XQ%2FpTMKjU8CC7X1b%2FqYf8CrBwZT7Cv4C5WPjEET38f2A79h0xV6bZgPy28zQWrSeIJ9w6AfZBxiE24%2FVc08zVA0ivCT6lsy7nuU0wcR%2FMg2C9nd1vaMnQ923VpD21CKfVVYcrNWL2b%2FQ%2BNAO%2BxPjPgjiN6UPQbvbvZxkSdhgrGtecIg9NgqGkMcvM5VLTDtdC1kdeZetfOC2obtA%2BT%2F0gy3tIDo6WM5hcQfw743%2FAU9oaGnRBl%2BwoxRRDCZA%2F4jnPdluICSI7NCr6C3HHOe4CSZJjRihczxPA%2FgQczAVjAPs6CrK9N9pIzt0vNJpGUTgg4AoxftTvGfciDREilSctPIcBk6GwB%2Bzt4wbtNCmEPU%2BFD46rc6CWVQmmLwbKOeHw70ipMVFlOc8NlXkEtGO2vu6H44MstSSc6daOVUYUavQSGZ6jGhEjsP2YGz8KDm9XQotB%2FtyVBqiZV01OKqDmmBJA%2FXjUpSJ8M%2FeJXhe5m%2BKcxzNObtoJRI7jci3Tpt08XZyXZ7%2FyIV8H30ya468X3cfkTXrm972uga3z7iXAdubyEHnQIua%2FuvtwOHILi6O5D1Me2MzCPvcnXBjqTAYDoMWBt%2B3AVFuqSs2dmDQNBnvEhXfMgTedKLD5PJHb3aIl15iBkLaNu36GYnVCp9ZU2lSYcTbY5QwNnNeTfJTNy9bKhwzru6Sg%2BslhN%2BTS0zkTn%2BeLMY4tHgPEtEZ2cp%2FCk6GwqswvnYVKHjwO8q%2B1Mx8lc08fVEfarE9dke1GGFqoddx%2Bq%2BZUyHbDlAVfrCmf0yw%3D%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20261110T000135Z&X-Amz-SignedHeaders=host&X-Amz-Expires=86400&X-Amz-Credential=ASIAWA6KKHEJORWBH4N3%2F20261110%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=c666d9fc30103dd58b356c85ceff6dfeaa722c5ee43e0f31e910435d8175c65e","created_at":1682537604201,"updated_at":1682537604201,"display_name":"EDA Notebook","status":"FILE_STATUS_PUBLISHED"}],"assets":["ASSET_TYPE_DATA_TABLE"]},"provider_summary":{"name":"John Snow Labs","description":"John Snow Labs, an AI and NLP for healthcare company, provides state-of-the-art software, models, and data to help healthcare and life science organizations build, deploy, and operate AI projects."},"popularity":715},{"id":"3a6ffcf2-ab4e-4c6a-b02c-a54a6919f4aa","summary":{"name":"Geolocation Business Entity Data","subtitle":"Registered business entities with a coordinate and an explicit precision level. 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The full product is delivered under a separate agreement.\n\n**Use cases**\n\n- Catchment and territory analysis over registered businesses rather than mapped venues.\n- Site selection and competitor density within a defined radius.\n- Point-in-polygon selection of companies inside a custom zone.\n- Adding a verifiable coordinate to an address list, with the precision stated per row.\n\n**Product details**\n\n- Dataset represented: poi_sample, 300 records, 25 columns.\n- Sample fields include CompanyName, Address1, PostCode, City, Province, Region, Country, Longitude, Latitude, GeoLevel, GeoLevelDescription and PrimaryLocalActivityCode.\n- This product joins our other listings on RecordID.\n\nFor more details, refer to the embedded notebook.\n\n**Additional Insights**\n\nCity centroids are excluded from this product. A centroid is not a business location, and including it would inflate coverage while making any radius query wrong. Only records at street level or better are here, and the precision level is stated per record so you can filter further.\n\nSole-trader records are excluded from this sample, since a sole trader's registered coordinate is frequently a private home. The full product contains them, and if your use case involves plotting individual locations you should account for that.\n\nUpdate cadence: static sample extracted 28 July 2026, refreshed when the listing is refreshed. 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Built for AI companies, data product teams, and data-driven enterprises, we remove the time, cost, and complexity of data acquisition worldwide."},"popularity":831},{"id":"a4a5bfc9-1792-4188-adb6-4d9e9652c3df","summary":{"name":"BMLL Daily Classified Trades","subtitle":"A consolidated view of daily trading volumes across complex, fragmented markets.","setting":{"visibility":"PUBLIC"},"created_at":1782295285925,"updated_at":1782295285925,"categories":["FINANCIAL"],"listingType":"PERSONALIZED","provider_id":"37d35e28-fa1f-40c6-b770-22ffb99dcaef"},"detail":{"description":"Daily Classified Trades provides the clearest view of daily trading volumes across complex, fragmented markets.\n\n**The Challenge: Navigating Fragmented Markets** Understanding daily trading volumes across multiple execution venues and trading mechanisms is difficult and opaque. Users often struggle to differentiate trading volumes by addressable liquidity or dark order book mechanisms.\n**The Solution: A Single Consolidated View** BMLL solves this by mapping every trade type across all equity exchanges, secondary markets, and trade reporting facilities into a consistent daily traded volume dataset. We aggregate multiple trading mechanisms into a single, consistent consolidated tape.\n\n**Key Features & Capabilities:**\n\n- **Comprehensive Aggregation:** Aggregates across on and off-exchange, lit, and dark liquidity sources (e.g., OTC, SIs, RFQ, and Dark).\n- **Normalised Classifications:** Maps every trade type into distinct industry-relevant classifications, identifying addressable liquidity.\n- Regulatory Markers: Marks key regulatory thresholds such as European Large-in-Scale (LIS).\n- **Broad Coverage:** Covers over 22 trading mechanisms globally, incorporating corrections, amendments, and cancellations.\n\n**Core Benefits:**\n\n- **Buy-Side:** Understand addressable liquidity to better navigate trading conditions.\n- **Sell-Side:** Analyse market structure to improve execution performance and optimise smart order routers.\n- **Exchanges:** Track regulatory changes, monitor peers, and analyse market share across execution venues.\n\n**Data / Documentation Specifications:**\n\n- **Factsheet:** [Daily Classified Trades Factsheet](https://www.bmlltech.com/files/documents/BMLL-Daily-Classified-Trades-Factsheet.pdf)\n- **Documentation:** [Daily Classified Trades Documentation](https://www.bmlltech.com/files/documents/BMLL-Daily-Classified-Trades.pdf) \n- **Coverage:** [Data Coverage](https://www.bmlltech.com/market-data-coverage)","terms_of_service":"https://www.bmlltech.com/support-pages/terms","documentation_link":"https://www.bmlltech.com/products/bmll-data-feed/documentation","privacy_policy_link":"https://www.bmlltech.com/support-pages/privacy","update_frequency":{"interval":1,"unit":"DAILY"},"assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"BMLL Technologies","description":"BMLL Technologies is the leading, independent provider of historical data and analytics to the world’s most sophisticated capital market participants, covering global equities, ETFs, futures and US equity options.\n\nBMLL offers banks, brokers, asset managers, hedge funds, global exchange groups, academic institutions and regulators immediate and flexible access to the most granular Level 3, 2 and 1 T+1 order book data and advanced pre and post-trade analytics. 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The solution's versatility is demonstrated through its application to diverse industrial devices, including engines, heaters, transformers, and electrical fault detection systems.\n\nThe solution further showcase predictive maintenance on CNC machine.\n\n\n**Use cases**\nAdd use cases for the listing here.\n- Optimizing maintenance schedules:\n- Prioritize maintenance resources\n- Improve budget planning - when to replace old device and plan to buy new\n- Maximize asset utilization\n\n**Product details**\n\n- 1. Dataset about engine degrade. There are 26 columns related to cycle, operational settings and Sensor Values\n2. Electrical Fault Detection about Transmission line\n - 'G', 'C', 'B', 'A', 'Ia', 'Ib', 'Ic', 'Va', 'Vb', 'Vc', ’fault_type’\n’fault_type_code’\n3. Heater health analysis\n - 'Voltage_measured', 'Current_measured', 'Temperature_measured', 'Current_charge', 'Voltage_charge', 'Time', ‘Heater_Health’\n4. Transformer fault prediction\n - 'OTI', 'WTI', 'ATI', 'OLI', 'OTI_A', 'OTI_T', 'VL1', 'VL2', 'VL3', 'IL1', 'IL2', 'IL3', 'VL12', 'VL23',\n'VL31', 'INUT','MOG_A'\n\nFor more details, refer to the embedded notebook.\n\n**CNC Machines**\nCNC predictive maintenance solution is based on machine learning models built on a precisely produced dataset that accurately represents real-world machining circumstances. \nThis dataset, created by the School of Engineering - Technology and Life, correctly measures critical variables such as air temperature, process temperature, rotational speed, torque, and tool wear. Our system effectively predicts CNC machine failures using Random Forest, Decision Tree, and Stochastic Gradient Boosting algori\n\n**Additional Insights**\nFor more information see \nhttps://www.dataknobs.com/products/ai-twin/\n\nyou can see output at\nhttps://ai-twin.dataknobs.com/\n\nSlides - https://www.dataknobs.com/solutions/asset-management/\n\nThe previous examples demonstrate various scenarios where sensor data is used to monitor the degradation of devices. This degradation information is then employed to determine the health status and estimate the remaining useful life of the 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We understand that different projects may have varying data needs, and we are here to tailor our data to match your specific requirements.\n\n ## **Key Features:**\n---\n* **Population Data France:** Gain access to the latest and most accurate population statistics for France, meticulously organized by statistical sectors.\n\n* **Demographic Insights:** Dive deep into France's demographic landscape with data on 54 attributes, including: \n* Total Population,\n* Pop 15+,\n* Marital status,\n* Education.\n\nAll demographic attributes are available at IRIS level.\n\n* **Geodemographic Insights:** Unlock geodemographic data France-wide to inform location-based decision-making, marketing strategies, and policy formulation.\n* **Seamless Data Access:** Our data is available for instant download in CSV format. No lengthy sign-up processes or NDAs required on our side - just download the data you need.\n* **Data Samples for Evaluation:** To ensure you're getting exactly what you need, we provide free downloadable samples in CSV format for data evaluation.\n* **Data accuracy:** Our datasets are created using the latest demographics data available in national statistics bureaus, guaranteeing accuracy and reliability.\n* **Best-in-Class Quality:** We guarantee best-in-class quality, backed by over two decades of experience in modeling demographics data and an outstanding quality management process.\n* **Competitive Pricing:** We maintain the lowest prices on the market, ensuring that you get the most cost-efficient demographics data acquisition process.\n---\nAdditionally, for those looking to enrich their analysis or project, we offer:\n* **[Administrative Boundaries](https://geolocet.com/products/france-admin-level-8-municipalities):** If you require geographical boundaries (polygons) for the municipalities of France, consider exploring our Administrative Boundaries product, which seamlessly integrates with this dataset.\n\n* **[Point of Interest (PoI) Data](https://geolocet.com/pages/aggregated-points-of-interest-poi-data):** If you're interested in Points of Interest data, please don't hesitate to contact us for customized datasets to enhance your geographical analysis.\n\n* **[Data Customization](https://geolocet.com/pages/data-services-by-geolocet):** We understand that your project may have specific requirements. Feel free to get in touch with us if you need any modifications or specific data formats such as Excel, JSON, GeoJSON, or Shapefile. We are here to tailor the data to your needs.\n\nIf you're looking for data for France, [look here](https://geolocet.com/pages/france) for off-the-shelf or tailor-made datasets to suit your specific requirements.\n\n\n\nMake informed decisions, conduct precise research, and unlock the potential of the French demographic landscape with our comprehensive dataset. Trust in the accuracy and reliability of our official statistical data to drive your projects forward.\nContact us at *contact@geolocet.com* today to access this invaluable resource and explore France census data like never before.\n\n[Geolocet LTD](https://geolocet.com)\n","terms_of_service":"https://geolocet.com/pages/terms-of-use","documentation_link":"","privacy_policy_link":"https://geolocet.com/pages/privacy-policy","geographical_coverage":"[\"Europe\"]","cost":"PAID","data_source":"","assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://geolocet.com/pages/terms-of-use"},"provider_summary":{"name":"Geolocet LTD","description":"Geolocet provides premium geospatial data for retail analysis, GIS, and market planning. 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This dataset shows this data.\n- [National Average Drug Acquisition File Comparison](https://www.johnsnowlabs.com/marketplace/national-average-drug-acquisition-file-comparison)\n\t- The National Average Drug Acquisition File Comparison includes all changes of NADAC rates since the end of 2013.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated\n- Data Updates\n - 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By delivering scalable, real-time insights from the shelf edge, we help global CPG brands and their partners automate workflows, reduce costs and drive execution excellence.\n\nShelf photos become enterprise intelligence, queryable in your lakehouse alongside Nielsen, Circana, Numerator, depletions and trade spend. \n\nThe Neurolabs Execution Intelligence data product exposes a read-only Gold schema, a solution accelerator notebook and a Genie space so a CPG insights or RGM team can ask \"which stores went out-of-stock on SKU A during last week's promo and what was the revenue impact?\" in plain English, no SQL, no six-week lag.\n\nGoverned in Unity Catalog. Distributed via Delta Sharing. Production today across millions of images per month, globally. \n\n**Benefits**\n\n1. Joinable shelf signal. SKU-level on-shelf availability, share of shelf, prices, promotions, displays, and competitor activity, available natively in Unity Catalog and joined to your existing lakehouse data.\n2. Time to first insight in 20 minutes. Pre-built Lakeview dashboard and Genie space deploy from a public quickstart notebook. No custom integration required.\n3. Closed-loop execution. Shelf reality reconciled with TPM, depletions and contracts in the same query, the same week the promo runs, not six weeks later.\n4. Configurable scoring, via Unity Catalog Metric Views. Brands define their own promotion-specific KPIs (core-product presence, gap penalties, flavor separation, minimum facings) via a simple rules engine over the Gold schema.\n5. Enterprise governance. Inherits your Unity Catalog policies. No uncontrolled data movement. \n\n**Use Cases**\n\n- **Shelf & Display Execution**: Capture your entire category at the SKU level across every in-store fixture: primary shelf, end caps, gondolas, FSDUs, free-standing displays, floor stacks, racks, and clip strips. Instantly diagnose shelf KPIs, validate merchandising quality, address sales opportunities and ensure retailer contract compliance.\n- **Promotion Execution & Compliance**: Execute and verify promotional campaigns end to end. Detect expected vs actual promotional prices and POSMs per SKU, get visibility into promotional impact, audit and correct execution in real time, and apply best practices across locations.\n- **Pricing Execution**: Quickly address pricing gaps with image recognition that extracts and validates pricing for every SKU, ensure pricing and labelling compliance at the SKU level, detect promotional vs standard prices and action inaccuracies fast.\n- **Planogram Compliance**: Cross-reference allocated shelf space with current in-store displays from a single photo. 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Spot gaps in actual vs contracted KPIs, ensure assortment and distribution compliance and roll learnings across the operation.\n\n**Product Details**\nThe Neurolabs Shelf Intelligence Gold schema is delivered via Delta Sharing as a read-only catalog in the customer's Unity Catalog metastore.\n\nDatasets represented include ***catalog, subsection, promo, shelf, facing and image*.**\nSample fields include **sku_name, outlet_name, outlet_address, promo_price and promo_quantity_sold.**\n\n![](https://cdn.prod.website-files.com/69d4fba1d69dc3e60506f6a1/6a0caba8ff807149191e9d0c_Databricks%20Data%20Partnership%20-%20NEW%20(1).png)\n\nFor more details, refer to the embedded notebook ([Public quickstart repo](https://github.com/neurolaboratories/zia-insights-dashboard) clones the public neurolabs/zia-insights-dashboard repo, attaches the Delta Share, deploys the Lakeview dashboard and provisions a Genie space in three notebook cells).\n\n**Additional Insights**\n\n- [Partner page](https://marketplace.databricks.com/provider/5cbe97ed-c6bd-403c-84be-7028d2493fac/Neurolabs): Overview, dashboard preview and a 60-second Genie demo video.\n- [Public quickstart repo](https://github.com/neurolaboratories/zia-insights-dashboard): Lakeview dashboard JSON, Genie deploy script and five enrichment recipes (sales, cost, Nielsen catchment demographics, marketing attribution, channel mix).\n- Customer evidence: [How AG Barr cut store audit time by 50% with Visual AI](https://www.neurolabs.ai/post/driving-retail-excellence-how-ag-barr-cut-store-audit-time-by-50-with-visual-ai). Production deployment of the same recognition stack that powers this listing.\n- Category research: [Execution Intelligence and the World Cup 2026 promotional wave](https://www.neurolabs.ai/events/webinars-execution-intelligence-world-cup-2026)— joint webinar with Filip Luneski (ex-AB InBev, Coca-Cola, Molson Coors) and Bryan Smith (Databricks Global Head of Industry Solutions, Consumer Industries) sizing the 60–70% promotional-display failure rate that motivates this data product.\n- Pricing model: free tier on Marketplace for discovery and the solution accelerator. Usage-based for production deployments.","terms_of_service":"https://www.neurolabs.ai/terms-of-service","documentation_link":"https://www.neurolabs.ai","privacy_policy_link":"https://www.neurolabs.ai/privacy-policy","embedded_notebook_file_infos":[{"id":"48355bf6-1f81-4341-a385-a6d36938a478","marketplace_file_type":"EMBEDDED_NOTEBOOK","file_parent":{"parent_id":"73452ed5-4685-4abf-9332-0904b182bcfd","file_parent_type":"LISTING"},"mime_type":"text/html","download_link":"https://marketplace-file-storage-prod.s3-fips.us-east-1.amazonaws.com/EMBEDDED_NOTEBOOK/LISTING_73452ed5-4685-4abf-9332-0904b182bcfd/48355bf6-1f81-4341-a385-a6d36938a478?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEGgaDnVzLWVhc3QtMS1maXBzIkYwRAIgbC8sWIoYaHR8y7Wq86nPb0pufBl4%2BM4PWpeN%2B5PW9VQCIG7BhAVBDCWTpxFDLeuKYm%2Ff1JLz4iccPMzter0YdhgWKqYECDAQABoMNDE0MzUxNzY3ODI2IgxNAJrHbXaZG20jNfMqgwQyGEb4TsoVOoNvJ%2BaA%2FZ7zeu8hGcHqCeNfblP%2FQo5g6q2aRNtpwtPRKMlhYcIE%2FUQaNT1Lqmyptu32VUQTpzjTTxRVmJrDPkheSCkV06XQ%2FpTMKjU8CC7X1b%2FqYf8CrBwZT7Cv4C5WPjEET38f2A79h0xV6bZgPy28zQWrSeIJ9w6AfZBxiE24%2FVc08zVA0ivCT6lsy7nuU0wcR%2FMg2C9nd1vaMnQ923VpD21CKfVVYcrNWL2b%2FQ%2BNAO%2BxPjPgjiN6UPQbvbvZxkSdhgrGtecIg9NgqGkMcvM5VLTDtdC1kdeZetfOC2obtA%2BT%2F0gy3tIDo6WM5hcQfw743%2FAU9oaGnRBl%2BwoxRRDCZA%2F4jnPdluICSI7NCr6C3HHOe4CSZJjRihczxPA%2FgQczAVjAPs6CrK9N9pIzt0vNJpGUTgg4AoxftTvGfciDREilSctPIcBk6GwB%2Bzt4wbtNCmEPU%2BFD46rc6CWVQmmLwbKOeHw70ipMVFlOc8NlXkEtGO2vu6H44MstSSc6daOVUYUavQSGZ6jGhEjsP2YGz8KDm9XQotB%2FtyVBqiZV01OKqDmmBJA%2FXjUpSJ8M%2FeJXhe5m%2BKcxzNObtoJRI7jci3Tpt08XZyXZ7%2FyIV8H30ya468X3cfkTXrm972uga3z7iXAdubyEHnQIua%2FuvtwOHILi6O5D1Me2MzCPvcnXBjqTAYDoMWBt%2B3AVFuqSs2dmDQNBnvEhXfMgTedKLD5PJHb3aIl15iBkLaNu36GYnVCp9ZU2lSYcTbY5QwNnNeTfJTNy9bKhwzru6Sg%2BslhN%2BTS0zkTn%2BeLMY4tHgPEtEZ2cp%2FCk6GwqswvnYVKHjwO8q%2B1Mx8lc08fVEfarE9dke1GGFqoddx%2Bq%2BZUyHbDlAVfrCmf0yw%3D%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20261110T000135Z&X-Amz-SignedHeaders=host&X-Amz-Expires=86400&X-Amz-Credential=ASIAWA6KKHEJORWBH4N3%2F20261110%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=7a1b41362c032fcf6fa0b5f292b73f3cea36f5f789b44fe635fb08cc1f150cba","created_at":1781278466770,"updated_at":1781278466770,"display_name":"neurolabs_marketplace_quickstart 2026-06-12 15:07:44","status":"FILE_STATUS_PUBLISHED"}],"geographical_coverage":"[\"United States and Canada\"]","collection_date_start":1765194737742,"collection_date_end":1766416279450,"assets":["ASSET_TYPE_DATA_TABLE","ASSET_TYPE_NOTEBOOK"],"license":"https://cdla.dev/permissive-2-0/"},"provider_summary":{"name":"Neurolabs","description":"Neurolabs is building the industry standard for image recognition in the consumer packaged goods (CPG) sector. 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NGC is an initiative of the Agency for Healthcare Research and Quality (AHRQ), U.S. Department of Health and Human Services. NGC supports AHRQ's mission to produce evidence to make health care safer, higher quality, more accessible, equitable, and affordable by providing objective, detailed information on clinical practice guidelines, and to further their dissemination, implementation, and use in order to inform health care decisions.\n\n**Benefits**\n- National guideline clearinghouse (ngc) provides physicians and other health care professionals, health care providers, health plans, integrated delivery systems, purchasers and others an accessible mechanism for obtaining objective, detailed information on clinical practice guidelines and to further their dissemination, implementation, and use.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [Clinical Practice Guideline Summaries](https://www.johnsnowlabs.com/marketplace/clinical-practice-guideline-summaries)\n\t- This dataset contains summaries of evidence-based clinical practice guidelines. Clinical practice guidelines are statements that include recommendations intended to optimize patient care that is informed by a systematic review of evidence and an assessment of the benefits and harms of alternative care options.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated\n- Data Updates\n - 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CMS publishes this program as dozens of separate monthly files — enrollment, plan benefit packages, service areas, penetration, crosswalks — and this product unifies **24 analysis-ready tables and 886M+ rows** into a single, refreshed source: the full enrollment time series down to contract × plan × state × county, the complete Plan Benefit Package, and the reference tables that tie them together.\n\n**Dataset Size:** 886M+ rows | 24 tables | Medicare Advantage & Part D | Monthly time series, 2016–present\n\n[Start Free Trial](https://trial.dataplex-consulting.com/?platform=databricks) | [Purchase Full Access](https://checkout.dataplex-consulting.com/b/4gMbIU8Hp0TCfYsdOkbQY08)\n\n**Pricing:** $775/month with 14-day free trial\n\n## What's Included\n\n- **monthly_enrollment_by_contract_plan_state_county__enrollment** — monthly enrollment at contract × plan × state × county grain (624M rows)\n- **ma_contract_service_area_by_state_county** — MA contract service areas by state/county (89M rows)\n- **monthly_ma_enrollment_by_state_county_contract** — MA enrollment by state/county/contract (73M rows)\n- **pdp_contract_service_area_by_state_county** — Part D contract service areas (47M rows)\n- **monthly_pdp_enrollment_by_state_county_contract** — Part D enrollment by state/county/contract (36M rows)\n- **benefits_data__services** — the full Plan Benefit Package, 3,185 benefit fields (2.4M rows)\n- **benefits_data__part_d** — Part D benefit design: phases, tiers, copays (2.0M rows)\n- **benefits_data__premiums_deductibles** — premiums and deductibles by plan (1.0M rows)\n- **ma_state_county_penetration** / **pdp_state_county_penetration** — market penetration by county (700K / 697K rows)\n- **plan_crosswalks** — official plan-to-plan crosswalks across years (121K rows)\n- **special_needs_plan_snp_data** — Special Needs Plan (SNP) detail (190K rows)\n\nPlus monthly enrollment rollups by contract, plan, and state, the inpatient-hospital and plan-info benefit files, and a FEEDS catalog of every source file (**24 tables total**).\n\n## Use Cases\n\n### Health Plans & Payers\n\n- **Competitive intelligence:** track every competitor's enrollment by contract, plan, and county over time.\n- **Market entry & expansion:** combine service areas with penetration to find under-served counties.\n- **Benefit benchmarking:** compare your premiums, deductibles, and copays against the full market via the Plan Benefit Package.\n\n### Pharmaceutical & Life Sciences\n\n- **Part D access strategy:** model formulary and benefit-design exposure across plans and geographies.\n- **Territory sizing:** size the MA/PDP membership behind every contract and plan.\n\n### Consultants, Investors & Researchers\n\n- **M&A and diligence:** quantify a target's membership footprint and growth by parent organization.\n- **Policy & academic research:** analyze MA penetration, SNP enrollment, and plan turnover with the crosswalks.\n\n## Data Quality & Updates\n\n- **Source:** U.S. Centers for Medicare & Medicaid Services (CMS) monthly public files.\n- **Coverage:** the full MA & Part D program — enrollment, benefits, service area, penetration, crosswalks, SNP.\n- **Updates:** refreshed as CMS publishes; 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Free trial data.","share":{"name":"share_boundaries_mbi_ru_r_2025","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1785217721148,"updated_at":1785217721148,"categories":["ADVERTISING_AND_MARKETING","CLIMATE_AND_ENVIRONMENT","ENERGY","GEOSPATIAL","MEDIA","OTHER","PUBLIC_SECTOR","RETAIL","TRANSPORTATION_AND_LOGISTICS","TRAVEL_AND_TOURISM"],"listingType":"STANDARD","provider_id":"aa09abd6-ff7f-4452-803d-b8c43b4c07cb"},"detail":{"description":"MBI'S GLOBAL AREA BOUNDARIES COVER MOST COUNTRIES WORLDWIDE AND **CAN BE LICENSED FOR EACH COUNTRY INDIVIDUALLY** AND IN BUNDLES.\n[MBI](https://www.mbi-geodata.com/en/) digital boundaries are available for virtually all countries worldwide.* Created by our experts according to consistent global standards, the polygons are so precise and harmonized that they align seamlessly across borders. The territorial vintages of the worldwide area boundaries are compatible with our globally consistent and comparable [socio-demographic](https://www.mbi-geodata.com/en/sociodemographic-data/) and [economic](https://www.mbi-geodata.com/en/purchasing-power-overview/) data.\nOur area boundaries for the Russian Federation are offered at the following levels:\n\n- **Postal Code Level** (Postcode 2-digit (4-digit and 6-digit on request): This fine-grained level of detail makes the data suitable for a wide range of applications – for example, enriching customer databases, which are often organized by postal code.\n- **Administrative Level** (Raioni): Covers both larger administrative areas (e.g., regions, states, provinces) as well as finer subdivisions such as districts, counties, or municipalities.\n\n**Use Cases:**\n\n- **Market Analysis**: Use MBI’s boundaries to visualize data on a map, learn about your customers, and uncover untapped market potential.\n- **360-Degree Customer View**: Link boundaries to customer data, sociodemographic, and economic data to gain a holistic understanding of customer needs, behaviors, and potential value.\n- **Location Planning**\n•\tUse MBI’s boundaries to visualize data on a map, learn about your customers\n•\tPlan the provision of services and products across different regions\n•\tDefine sales territories\n•\tVisualize catchment areas and plan actions to reach potential customers\n\n**Additional Insights:**\n[About Area Boundaries](https://www.mbi-geodata.com/en/area-boundaries/)\n\n**Data Structure:**\nThe area boundaries include the following variables:\nCTRYCODE: Two-digit country code by ISO 3166\nNAME: Name of administrative area / Name of the most populous administrative area / Name of the respective micro-area or the next higher level\nADMINCODE / POSTCODE / MICROCODE: Administrative code / Postal code / Micro-code\nWKT: Geography as Well Known Text (lat/long WGS84)\n\n*with only a few specific exceptions","terms_of_service":"https://service-mbi-geodata.com/s/HNp7Z2tws4WDqWd","documentation_link":"https://www.mbi-geodata.com/en/mbi-supplier-manufacturer-worldwide-geodata/europe/russian-federation-maps/","privacy_policy_link":"https://www.mbi-geodata.com/en/data-protection/","update_frequency":{"interval":1,"unit":"YEARLY"},"assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://service-mbi-geodata.com/s/GZKnMEbwKAB76Rn"},"provider_summary":{"name":"Michael Bauer International GmbH – MBI Geodata","description":"For over 15 years, Michael Bauer International (MBI) has been a leading provider of globally consistent and comparable geographic data, including more than 3 million postcode areas and other detailed datasets like sociodemographics and purchasing power. MBI's high-quality small-scale data spans 248 countries, supporting businesses across all industries in location planning, branch networks, and distribution strategies. Recognized as a key data source, MBI is the preferred provider for leading GIS software and technology companies worldwide as well as one of the first global distributors for HERE Technologies. MBI is part of the Michael Bauer Group, alongside Michael Bauer Research GmbH and Michael Bauer Micromarketing GmbH."},"popularity":534},{"id":"eb8d717c-5e22-42d9-b576-6e6ad0963b81","summary":{"name":"Geolocet | Points of Interest Data | Hospitality POIs - USA - bars, pubs, restaurants, hotels, cafes","subtitle":"Supercharge Your USA Hospitality Strategies: 400,000 locations in 50 categories - Pubs, Restaurants, Cafes, Hotels, ...","setting":{"visibility":"PUBLIC"},"created_at":1710833357291,"updated_at":1710833394612,"categories":["RETAIL","TRAVEL_AND_TOURISM"],"listingType":"PERSONALIZED","provider_id":"5d3ddc17-a7e8-4794-a760-13587e1de2c6"},"detail":{"description":"# Supercharge Your USA Hospitality Strategies: ~400,000 Pubs, Restaurants, Cafes, Hotels & More\n\nOur comprehensive Points of Interest dataset offers nearly 400,000 locations of hospitality objects across the USA, empowering you with the data you need for your location intelligence analysis to succeed.\n\n## Key Features:\n\n- **Extensive Coverage:** Discover a vast network of pubs, cafes, restaurants, bars, hotels, cinemas, and more – all across the USA.\n- **Detailed Data:** Gain valuable insights with POI names, precise coordinates, addresses, and POI categories.\n- **Tailored Data Access:** Choose from over 50 Points of Interest categories or define custom geographic regions to get exactly the data you need.\n\n## Category Breakdown:\n\n- **PoI categories**\n - **restaurant:** 159,913 PoIs\n - **fast food:** 121,218 PoIs\n - **hotel:** 37,586 PoIs\n - **cafe:** 34,350 PoIs\n - **bar:** 21,514 PoIs\n - **pub:** 9,167 PoIs\n - **theatre:** 6,723 PoIs\n - **cinema:** 5,242 PoIs\n - **night club:** 1,695 PoIs\n - **all others:** 2,500 PoIs\n\n## Geographical Breakdown:\n\n - **California:** 50,000+ PoIs\n - **Texas:** 28,000+ PoIs\n - **New York:** 27,000 PoIs\n - **Florida:** 21,000+ PoIs\n - **Illinois:** 16,000+ PoIs\n - **Pennsylvania:** 16,000+ PoIs\n - **Ohio:** 15,000 PoIs\n - **Washington:** 14,000+ PoIs\n - **North Carolina:** 13,000+ PoIs\n - **Virginia:** 12,000+ PoIs\n - **Michigan:** 11,000+ PoIs\n - **All others:** 168,000 PoIs\n\n## Additional Benefits:\n\n- **Enhanced Enrichment:** Explore optional add-ons to elevate our points of interest dataset and gain deeper insights, such as opening hours, ratings, and expanded contact details.\n- **Effortless Acquisition:** Access a free sample and download the full dataset or use it directly from AWS\n- **Best Price Guarantee:** Get the most competitive pricing without sacrificing data quality.\n\n## Use Cases:\n\n- Find the perfect spot for a new bar, pub, restaurant, cafe, hotel, or any other venue in the USA.\n- Get insights into your competitors' locations across the USA.\n\n## Call to Action:\n\nUnleash the power of location intelligence through our Hospitality Points of Interest dataset for the USA!\nChoose the option that best suits your needs:\n\n- **Download a Sample:** Evaluate the potential of our base POI dataset before you commit.\n- **Purchase the Full Dataset:** Unlock the full potential of your location analysis.\n\nAlternatively, contact our sales team at [contact@geolocet.com](mailto:contact@geolocet.com) to discuss your specific needs and explore our customization options. We'll help you craft the perfect data solution to supercharge your business.\n","terms_of_service":"https://geolocet.com/pages/terms-of-use","documentation_link":"","privacy_policy_link":"https://geolocet.com/pages/privacy-policy","geographical_coverage":"[\"United States and Canada\"]","cost":"PAID","assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://geolocet.com/pages/terms-of-use"},"provider_summary":{"name":"Geolocet LTD","description":"Geolocet provides premium geospatial data for retail analysis, GIS, and market planning. 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Access 250+ detailed data points (price, ...","share":{"name":"datatorq_4ed7c823_5def_4d35_8910_499bfb709dcd_samples_share","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1774859475574,"updated_at":1774859475574,"categories":["OTHER","RETAIL"],"listingType":"STANDARD","provider_id":"4bbb67fb-feab-44d5-82b5-c6807339c872"},"detail":{"description":"Build exceptional products with Datatorq's Car Spec Data. Get 250+ precise data points (price, equipment, specs, dimensions) - meticulously curated and updated monthly for strategic advantage.\n\nWhy choose Datatorq's Automotive Data?\n- Innovate with deep insights into products and pricing.\n- Get a comprehensive understanding of the market.\n- Rely on clean, accurate, and complete information.\n- Benefit from flexible solutions to meet your specific needs.\n- Stay ahead with monthly Car Spec Data refreshes and verification.\n\nDatatorq's comprehensive and accurate Car Spec Data empowers innovation and achievement in product and pricing strategies across the world (France, UK, Italy, Poland, Netherlands, Spain, Belgium, Germany, Austria, Czechia, Portugal, Romania, Switzerland, Bulgaria, Croatia, Denmark, Hungary, Norway, Slovenia, Sweden, Ireland, Turkey, Morocco, Brazil, Argentina, Colombia, Mexico, Australia).","terms_of_service":"https://datatorq.io/privacy-policy","contact_email":"marketplace@datarade.ai","privacy_policy_link":"https://datatorq.io/privacy-policy","assets":["ASSET_TYPE_DATA_TABLE"]},"provider_summary":{"name":"Datatorq","description":"We are a specialist team of pricing and data experts focused on the automotive sector. 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Users can analyze projected volumes from 2025 to 2030 by ZIP code, demographics, clinical category, service line, payer, facility type, geography, and inpatient/outpatient setting to support capacity planning, market prioritization, growth strategy, and operational forecasting.\n\n**What Customers Can Do**\n\n* Analyze projected procedure volumes and office visits across 31,883 ZIP codes\n* Compare 2025 baseline demand against 2030 projected demand\n* Identify growth trends by market, state, region, ZIP code, service line, procedure, payer, or facility type\n* Segment forecasts by age, gender, payer, clinical category, care setting, and inpatient/outpatient status\n* Review mean, low, and high demand estimates for scenario planning\n* Focus forecasts on specific procedures, service areas, or clinic-relevant markets\n* Identify procedure-specific codes and descriptions for granular analysis\n* Support capacity planning, market expansion, service line strategy, and investment prioritization\n\n**Example Questions**\n\n* \"Which ZIP codes have the highest projected growth for orthopedic procedures by 2030?\"\n* \"Compare 2025 and 2030 demand for cardiology office visits in this market.\"\n* \"Show projected procedure volume by age group and payer.\"\n* \"Where is outpatient demand expected to grow fastest?\"\n* \"Identify high-growth service lines in this region.\"\n* \"Show mean, low, and high demand estimates for this procedure.\"\n* \"Which markets should we prioritize for capacity planning?\"\n* \"Find procedure codes driving projected demand growth.\"\n\n**Example Outputs**\n\n* ZIP-level demand forecasts\n* 2025 vs. 2030 growth comparisons\n* Procedure and office visit volume projections\n* Service line and clinical category summaries\n* Age, gender, and payer segmentations\n* State, region, market, and ZIP-level breakdowns\n* Inpatient vs. outpatient demand analysis\n* Facility type comparisons\n* Confidence interval scenarios: mean, low, and high\n* Procedure codes and descriptions\n* Structured JSON, tables, and summaries for agents and applications\n\n**Designed for Agentic Market Planning Workflows**\n\nThe Demand Forecast Analyst MCP Server gives agents a trusted way to analyze future healthcare demand at local-market precision.\n\nIt helps teams move from broad market questions to structured demand intelligence across procedures, populations, geographies, payers, and care settings. 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We are a cross-cloud platform that works across AWS, Azure, and Google.\nThis unified approach simplifies your modern data stack by eliminating the data silos that traditionally separate and complicate data engineering, analytics, BI, data science and machine learning. It’s built on open source and open standards to maximize flexibility. And, its common approach to data management, security and governance helps companies operate more efficiently and innovate faster."},"popularity":2144},{"id":"c87aeb41-c715-43d0-be9d-3d21e9cd546b","summary":{"name":"SAMPLE B2B Data | 850M+ Person and Company Records | People + Company Data Feed | 30-Day Refresh ...","subtitle":"B2B people data and firmographic company data, joined on Forager IDs in one data feed: 800M+ people.","share":{"name":"forager_ai_67a49568_18d3_4126_812c_dcde106bbb4e_samples_share","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1794247528687,"updated_at":1794265942537,"categories":["OTHER","ADVERTISING_AND_MARKETING"],"listingType":"STANDARD","provider_id":"ba4dbcfb-6e61-49d1-92b5-b79a3480c00f"},"detail":{"description":"**B2B data as two tables that already join: people and companies.** Every role on a profile carries the employer's Forager company ID, so people join to firmographics without fuzzy matching.\n\n**Tables in the share**\n\n- `people`: person_id, full_name, headline, location, country, current_title, company_id, previous_title, education_school, skills, date_updated\n- `companies`: company_id, name, domain, industry, employees_range, founded_date, hq_location, technologies_active, date_updated\n\nJoin people.company_id to companies.company_id. Keys are stable across drops.\n\n**Coverage and refresh**\n- 850M+ person and company records: 800M+ people and 65M+ companies, global\n- 30-day refresh on active profiles, written into the contract\n\n**Access on Databricks**\n- Request access on this listing. The share includes a sample table you can query in SQL or a notebook as soon as access is granted\n- Licensed data is delivered as files, updated daily, weekly or monthly on an annual license\n- Delivery to Snowflake, Amazon S3 or Google Cloud Storage as Parquet, JSONL or CSV, ready to load into Databricks\n- Add mobiles and work emails per person by API on the same person ID; a lookup that returns nothing costs 0 credits\n\n**Where it fits**\n- Each record links to Forager company data, so you can add industry, headcount and location to every row.\n- The company data uses the same IDs as the people data, which keeps joins simple.\n- Sales teams use it as prospect data at the account level, filtering companies by industry, size and location.\n- The prospect data narrows a market to the accounts worth calling.\n- Each company resolves to one Forager organization ID across names and domains, so it serves as company identity data.\n- Use the identity data to merge duplicate accounts in your CRM.\n- Pair it with Forager people records, joined by organization ID, to turn these accounts into B2B contact data.\n- The B2B contact data side, people with roles at each company, comes from the same IDs.\n\n**Who uses it**\n- Sales-tech and GTM platforms building search and enrichment\n- Data teams building lead scoring, ICP models and market maps\n- Data providers that need a people and company layer\n\n**How to test us**\n- Start free with $5 in credits and query the same records through the Forager API\n- Send records you already know. We return what we find, field by field, so you can check fill and freshness yourself","terms_of_service":"https://www.forager.ai/privacy","contact_email":"support@datarade.ai","privacy_policy_link":"https://www.forager.ai/privacy","assets":["ASSET_TYPE_DATA_TABLE"]},"provider_summary":{"name":"Forager.ai","description":"Forager is a B2B data company. We sell people, company and job-posting data to the platforms and teams that dial, email, recruit and build agents: sales-tech and GTM platforms, recruiting and talent platforms, data providers that run enrichment waterfalls, and product and data teams that license feeds. Forager.ai, LLC is based in Florida, USA, and our team works from the United States, Ukraine, the Philippines, Poland and Mexico. Platforms and teams that build on Forager include Clay, HubSpot, Nooks, Andela and Amplemarket. The data covers 800M+ people and 65M+ companies, with 200M+ verified mobile numbers and a 30-day refresh on active profiles. Buyers use Forager two ways: a REST API on self-serve credits, and licensed data feeds delivered to their warehouse. Bring a sample and we run it, so you can measure hit rate and connect rate on your own records."},"popularity":156},{"id":"d3af6ba7-2d96-4e39-8485-3de97b6e823d","summary":{"name":"United Kingdom – 2024 Income Indicators at Small Area Level","subtitle":"High-resolution income and household statistics across ~40,000 UK small areas. Ideal for location intelligence.","setting":{"visibility":"PUBLIC"},"created_at":1762429133781,"updated_at":1762429170156,"categories":["GEOSPATIAL","DEMOGRAPHICS","ECONOMICS","ADVERTISING_AND_MARKETING","RETAIL"],"listingType":"PERSONALIZED","provider_id":"5d3ddc17-a7e8-4794-a760-13587e1de2c6"},"detail":{"description":"## **Overview** \nUnlock fine-grained insights into UK household income with Geolocet’s **2024 Income Indicators at Small Area Level** dataset. Covering approximately **40,000 small-area zones** across England, Wales, Scotland and Northern Ireland (LSOA, Super Data Zones, SOA), this modelled dataset provides average household income estimates and key demographic indicators, built for **market intelligence, geospatial analysis and socio-economic modelling**. \nDriven by official sources from the **Office for National Statistics (ONS), National Records of Scotland (NRS) and Northern Ireland Statistics and Research Agency (NISRA)**, and refined through Geolocet’s proprietary spatial modelling workflows, this dataset is designed for **consistency, accuracy and analytical readiness** in platforms such as Databricks, GIS systems and BI dashboards. \n\n---\n\n## **Product details** \n- **Datasets represented**: \n - Small-area income & demographics table (CSV) \n - Small-area boundaries (Shapefile formats) \n\n- **Dataset fields include**: \n - `Zone_Code` – Small-area identifier (LSOA / SOA) \n - `Pop_Tot` – Total population \n - `Households_Tot` – Number of households \n - `Employed_Tot` – Total employed population \n - `Avg_HH_Income` – Estimated average household income (£ per household per year) \n\n---\n\n## **Additional Insights** \nThis dataset offers an exceptional level of spatial detail for UK analytics. The combination of modelled income metrics, official demographic bases and high trust data sourcing ensures it is a reliable choice for both strategic business insights and data science applications. \n\uD83D\uDD0D Get more insights about the UK income distribution \n\uD83D\uDC49 [UK Income Indicators – Small Area Level](https://geolocet.com/blogs/news/small-area-income-data-in-the-uk-see-what-s-happening-in-your-neighbourhood-with-geolocet) \n\n\uD83D\uDCD8 Explore the British income distribution dataset on our website: \n\uD83D\uDC49 [UK Income Distribution Dataset – small area level](https://geolocet.com/products/uk-income-indicators-small-area)\n\n\uD83D\uDCE9 Need the full dataset or a tailored version (e.g., cross-UK region or bespoke boundaries)? \n\uD83D\uDC49 [Contact Geolocet](https://geolocet.com/pages/contact-us) \n\n---\n\n## **Use cases** \n- Identify high-purchasing-power micro-areas for targeted retail roll-out or direct-marketing campaigns \n- Integrate granular income data into **location intelligence** models for real-estate, banking or telecom site selection \n- Use as input features in **machine-learning pipelines** for consumer behaviour prediction, credit risk segmentation or geospatial clustering \n- Support public-policy, urban-planning and academic research into income disparity, regional inequality and social mobility at highly localised granularity \n","terms_of_service":"https://geolocet.com/pages/terms-of-use","documentation_link":"","privacy_policy_link":"https://geolocet.com/pages/privacy-policy","cost":"PAID","data_source":"","assets":["ASSET_TYPE_DATA_TABLE","ASSET_TYPE_MEDIA"],"license":"https://geolocet.com/pages/terms-of-use"},"provider_summary":{"name":"Geolocet LTD","description":"Geolocet provides premium geospatial data for retail analysis, GIS, and market planning. We equip businesses with accurate, ready-to-query data to drive spatial analysis, site selection, and customer segmentation.\n\nCore Offerings:\n\nDemographics: Socioeconomic data covering population, age, and employment across diverse geographies.\n\nPoints of Interest (PoI): Datasets spanning retail, healthcare, and transport for catchment analysis.\n\nAdministrative Boundaries: GIS-ready boundaries, including municipalities and postcodes, for territory management.\n\nBrand Locations: Chain location datasets to monitor store networks and market expansion.\n\nWealth Data: Granular economic indicators like purchasing power to identify high-potential markets.\n\nWe source from trusted providers, delivering highly customizable datasets with comprehensive coverage across Europe and worldwide."},"popularity":625},{"id":"b19af1ee-ff38-408a-b8bd-e1ef4c867f78","summary":{"name":"Drug Targets and Drug Lists","subtitle":"This data package contains information on approved, researched and proven drug targets and drug lists.","share":{"name":"drug_targets_and_drug_lists","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1682356582853,"updated_at":1698965635294,"categories":["SCIENCE_AND_RESEARCH"],"listingType":"STANDARD","provider_id":"f88849f8-9d77-4de2-b4c7-03d55cc3768e"},"detail":{"description":"\n**Overview**\nThis data package contains information on approved, researched and proven drug targets and drug lists.\n\n**Description**\nThis data package contains datasets on a selection of both approved and research drug targets and a selection of attributed target lists extracted from literature \"Analysis of in vitro bioactivity data extracted from drug discovery literature and patents: Ranking 1654 human protein targets by assayed compounds and molecular scaffolds\"; a selection of proven drug target lists extracted from the literature, \"Novelty in the target landscape of the pharmaceutical industry\" as a supplementary data; and a selection of The Therapeutic Target Database protein IDs for successful targets. This data package also contains the dataset Druggable Genome Comprehensive Drug Targets is a selection of supplementary data from \"The Druggable Genome: Evaluation of Drug Targets in Clinical Trials Suggests Major Shifts in Molecular Class and Indication\" ; dataset Protein Chemical Structure Comparison from Three Drug Databases is a selection of a 3-way consensus list from the paper \"Comparing the Chemical Structure and Protein Content of ChEMBL, DrugBank, Human Metabolome Database and the Therapeutic Target Database\".\n\n**Benefits**\n- This data package can be useful for further drug research and for updating both the hypothetical and successful human drug target statistics and research for drug discovery to expand towards new therapeutic areas, new targets, broader cross-screening activities, repurposing, and polypharmacology.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [Approved and Researched Drug Targets Human SwissProt Accessions](https://www.johnsnowlabs.com/marketplace/approved-and-researched-drug-targets-human-swissprot-accessions)\n\t- This dataset is a supplementary data from \"Analysis of in vitro bioactivity data extracted from drug discovery literature and patents: Ranking 1654 human protein targets by assayed compounds and molecular scaffolds\" (2011). In this case the Entrez Gene IDs were mapped to 1651 human Swiss-Prot accessions but this includes both approved and research targets.\n- [ChEMBL Approved Drug Targets Human Swiss-Prot Accessions](https://www.johnsnowlabs.com/marketplace/chembl-approved-drug-targets-human-swiss-prot-accessions)\n\t- ChEMBL dataset, released on 17 August 2013, includes a download option for approved drug targets. This converted to 251 human Swiss-Prot accessions but note this does not encompass additional protein IDs from target groups.\n- [Druggable Genome Comprehensive Drug Targets](https://www.johnsnowlabs.com/marketplace/druggable-genome-comprehensive-drug-targets)\n\t- This dataset Druggable Genome Comprehensive Drug Targets is a selection of supplementary data from \"The Druggable Genome: Evaluation of Drug Targets in Clinical Trials Suggests Major Shifts in Molecular Class and Indication\" (2013) [PMID:24016212]. The comprehensive list includes 461 targets of approved drugs.\n- [Protein Chemical Structure Comparison from Three Drug Databases](https://www.johnsnowlabs.com/marketplace/protein-chemical-structure-comparison-from-three-drug-databases)\n\t- This dataset Protein Chemical Structure Comparison from Three Drug Databases is a selection of a 3-way consensus list from the paper \"Comparing the Chemical Structure and Protein Content of ChEMBL, DrugBank, Human Metabolome Database and the Therapeutic Target Database\" (2013) [Abstract]. It includes 352 proteins-in-common between the three drug databases.\n- [Proven Drug Targets Converted to Human SwissProt Accessions](https://www.johnsnowlabs.com/marketplace/proven-drug-targets-converted-to-human-swissprot-accessions)\n\t- This dataset is a supplementary data from \"Novelty in the target landscape of the pharmaceutical industry\" (2013). The listing of proven drug targets is converted to 248 human Swiss-Prot accessions.\n- [The Therapeutic Drug Target Database Human SwissProt](https://www.johnsnowlabs.com/marketplace/the-therapeutic-drug-target-database-human-swissprot)\n\t- This dataset is a selection of The Therapeutic Target Database (release 4.3.02, 18th Oct 2013) protein IDs for successful targets. The web page states 388 but these reduced to 345 human Swiss-Prot accessions.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated\n- Data Updates\n - Data updates support replace-on-update: outdated foreign keys are deprecated, not deleted\n\n**Our data is curated and enriched by domain experts** \n\n Each dataset is manually curated by our team of doctors, pharmacists, public health & medical billing experts:\n- Field names, descriptions, and normalized values are chosen by people who actually understand their meaning\n- Healthcare & life science experts add categories, search keywords, descriptions and more to each dataset\n- Both manual and automated data enrichment supported for clinical codes, providers, drugs, and geo-locations\n- The data is always kept up to date – even when the source requires manual effort to get updates\n- Support for data subscribers is provided directly by the domain experts who curated the data sets\n- Every data source’s license is manually verified to allow for royalty-free commercial use and redistribution.\n\n**Need Help?**\nIf you have questions about our products, contact us at [info@johnsnowlabs.com](mailto:info@johnsnowlabs.com). \n 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Full datasets may differ in format depending on ...","share":{"name":"oxford_languages_ad6622d1_61fd_4656_8ab6_eb7706471c95_samples_share","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1789386248634,"updated_at":1789386248634,"categories":["OTHER"],"listingType":"STANDARD","provider_id":"79c6c146-3407-48ec-8737-5b9b06406932"},"detail":{"description":"EMEA Data Suite offers 43 high-quality language datasets covering 23 languages spoken in the region. Ideal for NLP, AI, LLMs, translation, and education, it combines linguistic depth and regional authenticity to power scalable, multilingual language technologies.\n\nDiscover our expertly curated language datasets in the EMEA Data Suite. Compiled and annotated by language and linguistic experts, this suite offers high-quality resources tailored to your needs. This suite includes: \n\n- Monolingual and Bilingual Dictionary Data \nFeaturing headwords, definitions, word senses, part-of-speech (POS) tags, and semantic metadata. \n\n- Sentence Corpora \nCurated examples of real-world usage with contextual annotations for training and evaluation. \n\n- Synonyms & Antonyms \nLexical relations to support semantic search, paraphrasing, and language understanding. \n\n- Audio Data \nNative speaker recordings for speech recognition, TTS, and pronunciation modeling. \n\n- Word Lists \nFrequency-ranked and thematically grouped lists for vocabulary training and NLP tasks. \n\n \nEach language may contain one or more types of language data. Depending on the dataset, we can provide these in formats such as XML, JSON, TXT, XLSX, CSV, WAV, MP3, and more. Delivery is currently available via email (link-based sharing) or REST API. \n\nIf you require more information about a specific dataset, please contact us Growth.OL@oup.com. \n\nBelow are the different types of datasets available for each language, along with their key features and approximate metrics. If you have any questions or require additional assistance, please don't hesitate to contact us. \n\n \n1. Arabic Monolingual Dictionary Data: 66,500 words | 98,700 senses | 70,000 example sentences. \n\n2. Arabic Bilingual Dictionary Data: 116,600 translations | 88,300 senses | 74,700 example translations. \n\n3. Arabic Synonyms and Antonyms Data: 55,100 synonyms. \n\n4. British English Monolingual Dictionary Data: 146,000 words | 230,000 senses | 149,000 example sentences. \n\n5. British English Synonyms and Antonyms Data: 600,000 synonyms | 22,000 antonyms \n\n6. British English Pronunciations with Audio: 250,000 transcriptions (IPA) |180,000 audio files. \n\n7. Catalan Monolingual Dictionary Data: 29,800 words | 47,400 senses | 25,600 example sentences. \n\n8. Catalan Bilingual Dictionary Data: 76,800 translations | 109,350 senses | 26,900 example translations. \n\n9. Croatian Monolingual Dictionary Data: 129,600 words | 164,760 senses | 34,630 example sentences. \n\n10. Croatian Bilingual Dictionary Data: 100,700 translations | 91,600 senses | 10,180 example translations. \n\n11. Czech Bilingual Dictionary Data: 426,473 translations | 199,800 senses | 95,000 example translations. \n\n12. Danish Bilingual Dictionary Data: 129,000 translations | 91,500 senses | 23,000 example translations. \n\n13. French Monolingual Dictionary Data: 42,000 words | 56,000 senses | 43,000 example sentences. \n\n14. French Bilingual Dictionary Data: 380,000 translations | 199,000 senses | 146,000 example translations. \n\n15. German Monolingual Dictionary Data: 85,500 words | 78,000 senses | 55,000 example sentences. \n\n16. German Bilingual Dictionary Data: 393,000 translations | 207,500 senses | 129,500 example translations. \n\n17. German Word List Data: 338,000 wordforms. \n\n18. Greek Monolingual Dictionary Data: 47,800 translations | 46,309 senses | 2,388 example sentences. \n\n19. Hebrew Monolingual Dictionary Data: 85,600 words | 104,100 senses | 94,000 example sentences.\n\n20. Hebrew Bilingual Dictionary Data: 67,000 translations | 49,000 senses | 19,500 example translations. \n\n21. Hungarian Monolingual Dictionary Data: 90,500 words | 155,300 senses | 42,500 example sentences. \n\n22. Italian Monolingual Dictionary Data: 102,500 words | 231,580 senses | 48,200 example sentences.\n\n23. Italian Bilingual Dictionary Data: 492,000 translations | 251,600 senses | 157,100 example translations. \n\n24. Italian Synonyms and Antonyms Data: 197,000 synonyms | 62,000 antonyms. \n\n25. Latvian Monolingual Dictionary Data: 36,000 words | 43,600 senses | 73,600 example sentences. \n\n26. Polish Bilingual Dictionary Data: 287,400 translations | 216,900 senses | 19,800 example translations. \n\n27. Portuguese Monolingual Dictionary Data: 143,600 words | 285,500 senses | 69,300 example sentences. \n\n28. Portuguese Bilingual Dictionary Data: 300,000 translations | 158,000 senses | 117,800 example translations. \n\n29. Portuguese Synonyms and Antonyms Data: 196,000 synonyms | 90,000 antonyms. \n\n30. Romanian Monolingual Dictionary Data: 66,900 words | 113,500 senses | 2,700 example sentences.\n\n31. Romanian Bilingual Dictionary Data: 77,500 translations | 63,870 senses | 33,730 example translations. \n\n32. Russian Monolingual Dictionary Data: 65,950 words | 57,500 senses | 51,900 example sentences. \n\n33. Russian Bilingual Dictionary Data: 230,100 translations | 122,200 senses | 69,600 example translations. \n\n34. Slovak Bilingual Dictionary Data: 254,300 translations | 172,100 senses | 85,000 example translations. \n\n35. Spanish Monolingual Dictionary Data: 73,000 words | 123,000 senses | 104,000 example sentences. \n\n36. Spanish Bilingual Dictionary Data: 221,300 translations | 103,500 senses | 83,800 example translations. \n\n37. Spanish Sentences Data: 1,840,000 sentences \n\n38. Spanish Synonyms and Antonyms Data: 127,700 synonyms | 9,500 antonyms. \n\n39. Spanish Audio Data: 20,900 audio files. \n\n40. Spanish Word List Data: 450,000 wordforms. \n\n41. Turkish Bilingual Dictionary Data: 70,000 translations | 47,800 senses | 4,000 example translations. \n\n42. Ukrainian Bilingual Dictionary Data: 81,700 translations | 74,300 senses | 8,000 example translations. \n\n43. Welsh Bilingual Dictionary Data: 19,900 translations | 10,925 senses | 9,500 example translations. \n \n\nUse Cases: \n\nWe consistently work with our clients on new use cases as language technology continues to evolve. These include NLP applications, TTS, dictionary display tools, games, translation, word embedding, and word sense disambiguation (WSD). \n\nIf you have a specific use case in mind that isn't listed here, we’d be happy to explore it with you. Don’t hesitate to get in touch with us at Growth.OL@oup.com to start the conversation. \n \n\nPricing: \n\nOxford Languages offers flexible pricing based on use case and delivery format. Our datasets are licensed via term-based IP agreements and tiered pricing for API-delivered data. Whether you’re integrating into a product, training an LLM, or building custom NLP solutions, we tailor licensing to your specific needs. \n\nContact our team or email us at Growth.OL@oup.com to explore pricing options and discover how our language data can support your goals. \n\nPlease note that some datasets may have rights restrictions. Contact us for more information.\n\n\nAbout the sample: \n\nThe samples offer a brief overview of one or two language datasets (monolingual or/and bilingual dictionary data). To help you explore the structure and features of our dataset, we provide a sample in CSV format for preview purposes only. \n\nIf you need the complete original sample or more details about any dataset, please contact us (Growth.OL@oup.com) to request access or further information.","terms_of_service":"https://corp.oup.com/privacy-policy","contact_email":"support@datarade.ai","privacy_policy_link":"https://corp.oup.com/privacy-policy","assets":["ASSET_TYPE_DATA_TABLE"]},"provider_summary":{"name":"Oxford Languages","description":"Oxford Languages delivers multilingual language datasets designed to power the next generation of language technologies. Built through decades of research and curated by expert lexicographers, our data fuels diverse applications – from text-to-speech (TTS) and predictive text to language models dictionary display tools, assistive tech, chatbots, games, and more. With over 60 languages and a wide range of features, our structured datasets ensure linguistic accuracy, cultural nuance, and domain relevance – ideal for AI, NLP, and ML development."},"popularity":1399},{"id":"ecfd16ac-8694-403f-b0a6-4e5b0d45a618","summary":{"name":"B2B ID Graph Data | 171MM+ Complete and Regularly Updated US Identity Profiles | Personal, ...","subtitle":"Our data is multi-sourced, standardized, de-duped, verified and curated...Resulting in a highly accurate database of ...","share":{"name":"salutary_data_806d62b0_b59d_4712_94e7_2ddfb93556ce_samples_share","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1792772785592,"updated_at":1792772785592,"categories":["ADVERTISING_AND_MARKETING","OTHER"],"listingType":"PERSONALIZED","provider_id":"bbd0ffec-0d18-4f39-a82c-cb2e9dd794c6"},"detail":{"description":"Salutary Data is a boutique, B2B contact and company data provider that's committed to delivering high quality data for sales intelligence, lead generation, marketing, recruiting / HR, identity resolution, and ML / AI. Our database currently consists of 171MM+ highly curated B2B Contacts ( US only), along with over 6M+ companies, and is updated regularly to ensure we have the most up-to-date information. \n\nWe can enrich your in-house data ( CRM Enrichment, Lead Enrichment, etc.) and provide you with a custom dataset ( such as a lead list) tailored to your target audience specifications and data use-case. We also support large-scale data licensing to software providers and agencies that intend to redistribute our data to their customers and end-users. \n\nWhat makes Salutary unique?\n- We offer our clients a truly unique, one-stop aggregation of the best-of-breed quality data sources. Our supplier network consists of numerous, established high quality suppliers that are rigorously vetted.\n- We leverage third party verification vendors to ensure phone numbers and emails are accurate and connect to the right person. Additionally, we deploy automated and manual verification techniques to ensure we have the latest job information for contacts.\n- We're reasonably priced and easy to work with.\n\nProducts:\nAPI Suite\nWeb UI\nFull and Custom Data Feeds\n\nServices:\nData Enrichment - We assess the fill rate gaps and profile your customer file for the purpose of appending fields, updating information, and/or rendering net new “look alike” prospects for your campaigns.\nABM Match & Append - Send us your domain or other company related files, and we’ll match your Account Based Marketing targets and provide you with B2B contacts to campaign. Optionally throw in your suppression file to avoid any redundant records.\nVerification (“Cleaning/Hygiene”) Services - Address the 2% per month aging issue on contact records! We will identify duplicate records, contacts no longer at the company, rid your email hard bounces, and update/replace titles or phones. This is right up our alley and levers our existing internal and external processes and systems.","terms_of_service":"https://salutarydata.com/privacy-policy","contact_email":"support@datarade.ai","privacy_policy_link":"https://salutarydata.com/privacy-policy","assets":["ASSET_TYPE_DATA_TABLE"]},"provider_summary":{"name":"Salutary Data","description":"In 2016, Jeff Clewley and Scott Gordon co-founded Salutary Data. Previously, Jeff and Scott worked together for 6 years at NetProspex. As Technical Co-Founder and SVP of Partnerships, respectively, Jeff and Scott contributed to NetProspex's growth until it was sold to Dun & Bradstreet in 2015.\r\n\r\nJeff and Scott founded Salutary Data on several principles. Data accuracy is essential; our north star, our reason to be in business. The data industry is a vital sector and there are good people in it. We wanted to continue to participate in it, find a way to give back to it, and serve our customers well."},"popularity":53},{"id":"bd5b2c17-e1c2-417a-a7f7-cf75c3c01921","summary":{"name":"SAMPLE Aggregated Mobility Insights | H3 Foot Traffic & Visitor Origin | 1B+ Devices Worldwide, ...","subtitle":"DRAKO's Aggregated Mobility Insights turns 1B+ global, consented device signals into privacy-safe footfall, ...","share":{"name":"drako_c6a2d906_b401_4dd8_b3d2_f0a5c8e04ab8_samples_share","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1784588436120,"updated_at":1785774672016,"categories":["GEOSPATIAL","DEMOGRAPHICS"],"listingType":"STANDARD","provider_id":"b5ff268c-de5a-4d77-b721-185f08e47569"},"detail":{"description":"DRAKO is a mobility data provider specializing in geolocation analytics. Our Aggregated Mobility Insights transform point-source signals into anonymized, marketplace-compliant insights: all activity is abstracted onto the H3 spatial index at Resolution 7/8, timestamps are bucketed into hourly and daily intervals, and device identifiers are replaced with unique-visitor counts and cohorts. The result gives you the analytical value of mobility data — where people go, when, and where they come from — without exposing any individual.\n\nWhat's included (mix and match):\n- Foot Traffic & Activity Totals — monthly activity volume and unique-visitor counts per H3 cell (L7 ≈ metro, L8 ≈ town/suburb). The base layer for ranking locations and building footfall choropleths.\n- Day-of-Week Profiles — the monthly totals split by weekday, so you can read weekday-vs-weekend rhythms of any area at L7/L8.\n- Hour-of-Day Profiles — the monthly totals split by hour (UTC), for daypart curves like daytime vs. evening activity, at L7/L8.\n- Weekly Footfall Trending — Monday–Sunday unique-visitor and activity counts per H3 cell (L7/L8) for finer-cadence trend tracking.\n- Visitor Origin / Catchment — for each place (L7/L8), a count of distinct visitors by their inferred home area, answering \"where do the people seen here live?\" Supports trade-area and geodemographic analysis when joined to area-level census data.\n\nDelivery & specs:\n- Coverage: 400M+ devices, US & Canada\n- Latency: 4–5 day delay; monthly and weekly refreshes\n- Grid: H3 Resolution 7/8 (cell width exceeds the 1,750 ft anonymity threshold)\n- Format: Snappy-compressed Parquet, delivered via S3\n- Metrics: activity volume + approximate unique-visitor counts (HyperLogLog)\n\nPrivacy & compliance (built in, not bolted on):\n- Spatial aggregation to H3 L7/L8 — no point coordinates, ever\n- Hourly/daily temporal bucketing — no real-time timestamps\n- Unique-visitor counts and cohorts — no device IDs, no device-to-address linkage\n- K-anonymity: any cell with fewer than 5 unique devices is suppressed\n- Negative geofencing scrubs signals from sensitive sites (medical, education, and religious)\n-100% consented, SDK-sourced data; CCPA compliant\n\nUse cases: site selection · catchment & trade-area analysis · footfall and activity trending · aggregate attribution & brand-lift · geodemographic profiling · out-of-home planning · market segmentation.","terms_of_service":"https://drakomediagroup.com/privacy-policy","contact_email":"support@datarade.ai","privacy_policy_link":"https://drakomediagroup.com/privacy-policy","assets":["ASSET_TYPE_DATA_TABLE"]},"provider_summary":{"name":"DRAKO","description":"Drako Data stands at the forefront of geolocation data solutions, offering value-added services designed to empower our clients' analytics capabilities. We understand the complexities of extracting meaningful insights from raw geolocation data. That's why we focus on adding value to the data before it reaches you, streamlining your processes, and enabling you to glean insights more quickly and efficiently.\r\n\r\nOur team works tirelessly to enhance the quality and utility of the geolocation data we provide. We meticulously process and refine the data, ensuring it's ready for immediate use in your analytics and decision-making processes. This not only saves you valuable time and resources but also allows you to focus more on analysis and strategy, rather than data preparation.\r\n\r\nPartner with Drako Data and experience the difference of value-added geolocation data solutions. Let us empower your data science, fuel your growth, and guide you towards a future of insightful, data-driven success."},"popularity":656},{"id":"0572c3a7-3e7b-4ad9-b350-159532e99ebb","summary":{"name":"Health and Disease Indicator Reports","subtitle":"Health indicators are quantifiable characteristics of a population which researchers use as supporting evidence for de..","share":{"name":"health_and_disease_indicator_reports","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1682356052954,"updated_at":1698965635225,"categories":["HEALTH","SCIENCE_AND_RESEARCH"],"listingType":"STANDARD","provider_id":"f88849f8-9d77-4de2-b4c7-03d55cc3768e"},"detail":{"description":"\n**Overview**\nHealth indicators are quantifiable characteristics of a population which researchers use as supporting evidence for describing the health of a population. The researchers use a survey methodology to gather information about certain people, use statistics in an attempt to generalize the information collected to the entire population, then use the statistical analysis to make a statement about the health of a population. Health indicators are often used by governments to guide health care policy.\n\n**Description**\nThis data package contains a number of datasets containing information on various health indicators. Health indicators are summary measures that are designed to describe particular aspects of a population's health or health system performance. One set of datasets provides information on Medicare fee-for-service beneficiaries by number and percent for atrial fibrillation, heart attack and heart failure and ischemic heart disease. Few datasets provide national and state estimates of alcohol-related health impacts, including deaths and years of potential life lost (YPLL). These estimates are calculated for 54 acute and chronic causes using alcohol-attributable fractions and are reported by age and sex for 2006-2010. The chronic disease indicators (CDI) are a set of surveillance indicators developed by consensus among CDC, the Council of State and Territorial Epidemiologists (CSTE), and the National Association of Chronic Disease Directors (NACDD). CDI enables public health professionals and policymakers to retrieve uniformly defined state and selected metropolitan-level data for chronic diseases and risk factors that have a substantial impact on public health. These indicators are essential for surveillance, prioritization, and evaluation of public health interventions.\n\n**Benefits**\n- Enhances collaboration to address public issues. it provides tools to encourage progress. informs decision making and improves research. increases public knowledge about key issues\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [ARDI Alcohol Attributable Deaths due to Alcohol Use](https://www.johnsnowlabs.com/marketplace/ardi-alcohol-attributable-deaths-due-to-alcohol-use)\n\t- This dataset provides national and state estimates of alcohol related health impacts, including deaths and years of potential life lost (YPLL). These estimates are calculated for 54 acute and chronic causes using alcohol attributable fractions, and are reported by age and sex for 2006-2010. This estimates the total number of deaths attributable to alcohol.\n- [ARDI Alcohol Attributable Fractions for Alcohol Use](https://www.johnsnowlabs.com/marketplace/ardi-alcohol-attributable-fractions-for-alcohol-use)\n\t- This dataset provides national and state estimates of alcohol related health impacts, including deaths and years of potential life lost (YPLL). These estimates are calculated for 54 acute and chronic causes using alcohol attributable fractions, and are reported by age and sex for 2006-2010. This dataset provides estimates of the proportion of deaths from various causes that are attributable to alcohol.\n- [ARDI Years of Potential Life Lost due to Alcohol Use](https://www.johnsnowlabs.com/marketplace/ardi-years-of-potential-life-lost-due-to-alcohol-use)\n\t- This dataset provides national and state estimates of alcohol related health impacts, including deaths and years of potential life lost (YPLL). These estimates are calculated for 54 acute and chronic causes using alcohol attributable fractions, and are reported by age and sex for 2006-2010. This dataset estimates the total number of alcohol related years of life lost resulting from premature death.\n- [Atrial Fibrillation Medicare Beneficiaries by Number and Percentage](https://www.johnsnowlabs.com/marketplace/atrial-fibrillation-medicare-beneficiaries-by-number-and-percentage)\n\t- This dataset contains information on Medicare fee-for-service beneficiaries with atrial fibrillation by number and percentage, where the numerator is the number of Medicare fee-for-service beneficiaries with atrial fibrillation, and the denominator is the number of Medicare fee-for-service beneficiaries.\n- [Cardiovascular and Ischemic Heart Disease Deaths from 2010 to 2017](https://www.johnsnowlabs.com/marketplace/cardiovascular-and-ischemic-heart-disease-deaths-from-2010-to-2017)\n\t- This dataset includes information regarding deaths with conditions of the cardiovascular system or ischaemic heart disease by underlying cause, by month and year, which occurred between 2010 and 2017. Data value includes males and females in England and Wales.\n- [Community Health Indicator Reports NY](https://www.johnsnowlabs.com/marketplace/community-health-indicator-reports-ny)\n\t- This dataset contains the latest community health indicator data available. New York State Community Health Indicator Reports (CHIRS) were developed in 2012, and are updated annually to consolidate and improve data linkages for the health indicators included in the County Health Assessment Indicators (CHAI) for all communities in New York.\n- [Congestive Heart Failure Admission Rate](https://www.johnsnowlabs.com/marketplace/congestive-heart-failure-admission-rate)\n\t- This file contains information on Congestive heart failure admission rate per 100,000 beneficiaries. Numerator is the number of non-maternal discharges with ICD-9-CM principal diagnosis code for congestive heart failure and denominator is the number of Medicare fee-for-service beneficiaries.\n- [Coronary Heart Disease Deaths](https://www.johnsnowlabs.com/marketplace/coronary-heart-disease-deaths)\n\t- This file contains information on number of coronary heart disease-related deaths (ICD-10 codes I20-I25), where numerator is the number of coronary heart disease-related deaths and denominator is the number of persons. Data includes death due to ischemic heart diseases (acute myocardial infarction, other acute ischemic heart diseases, and other forms of chronic ischemic heart disease).\n- [Health Status and Indicators by City US Total](https://www.johnsnowlabs.com/marketplace/health-status-and-indicators-by-city-us-total)\n\t- This dataset illustrates health status of 26 of the nation’s largest and most urban cities as captured by 34 health (and six demographics-related) indicators. These indicators represent some of the leading causes of morbidity and mortality in the United States and leading priorities of national, state, and local health agencies.\n- [Heart Attack Medicare Beneficiaries by Number and Percentage](https://www.johnsnowlabs.com/marketplace/heart-attack-medicare-beneficiaries-by-number-and-percentage)\n\t- This dataset contains infomation on number and percentage of Medicare fee-for-service beneficiaries who have had a heart attack, where numerator is number of Medicare fee-for-service beneficiaries who have had a heart attack and denominator is number of Medicare fee-for-service beneficiaries.\n- [Heart Disease Prevalence in Adults by Percent](https://www.johnsnowlabs.com/marketplace/heart-disease-prevalence-in-adults-by-percent)\n\t- This dataset contains information about respondent-reported prevalence of heart disease among adults aged 18 and over between 2010 and 2012.\n- [Heart Disease Relative Risk](https://www.johnsnowlabs.com/marketplace/heart-disease-relative-risk)\n\t- This dataset provides estimates of relative risks used by age and by outcome for each 10 mmHg increase in systolic blood pressure. This dataset is used to determine the worldwide association between elevated blood pressure and the burden of disease.\n- [Heart Failure Medicare Beneficiaries by Percent](https://www.johnsnowlabs.com/marketplace/heart-failure-medicare-beneficiaries-by-percent)\n\t- This dataset contains information on the number of Medicare fee-for-service beneficiaries with heart failure from 2007 to 2013.\n- [Ischemic Heart Disease Medicare Beneficiaries by Number and Percentage](https://www.johnsnowlabs.com/marketplace/ischemic-heart-disease-medicare-beneficiaries-by-number-and-percentage)\n\t- This dataset contains information on the number and percentage of Medicare fee-for-service beneficiaries with ischemic heart disease from 2007 to 2013.\n- [Mean Systolic Blood Pressure 1990 and 2015](https://www.johnsnowlabs.com/marketplace/mean-systolic-blood-pressure-1990-and-2015)\n\t- This dataset provides estimates of mean systolic blood pressure (mm Hg) and 95% uncertainty intervals by sex and by age in 1990 and 2015 for all countries. This dataset is used to determine the worldwide association between elevated blood pressure and the burden of disease.\n- [US Chronic Disease Indicators](https://www.johnsnowlabs.com/marketplace/us-chronic-disease-indicators)\n\t- This dataset includes information about Centers for Disease Control and Prevention (CDC)'s Division of Population Health provides cross-cutting set of 124 indicators that were developed by consensus and that allows states and territories and large metropolitan areas to uniformly define, collect, and report chronic disease data.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated\n- Data Updates\n - Data updates support replace-on-update: outdated foreign keys are deprecated, not deleted\n\n**Our data is curated and enriched by domain experts** \n\n Each dataset is manually curated by our team of doctors, pharmacists, public health & medical billing experts:\n- Field names, descriptions, and normalized values are chosen by people who actually understand their meaning\n- Healthcare & life science experts add categories, search keywords, descriptions and more to each dataset\n- Both manual and automated data enrichment supported for clinical codes, providers, drugs, and geo-locations\n- The data is always kept up to date – even when the source requires manual effort to get updates\n- Support for data subscribers is provided directly by the domain experts who curated the data sets\n- Every data source’s license is manually verified to allow for royalty-free commercial use and redistribution.\n\n**Need Help?**\nIf you have questions about our products, contact us at [info@johnsnowlabs.com](mailto:info@johnsnowlabs.com). \n ","terms_of_service":"https://www.johnsnowlabs.com/healthcare-data-library-eula/","documentation_link":"https://www.johnsnowlabs.com/marketplace/health-and-disease-indicator-reports-data-package","contact_email":"support@johnsnowlabs.com","privacy_policy_link":"https://www.johnsnowlabs.com/privacy-policy/","embedded_notebook_file_infos":[{"id":"6a0f3b5c-655b-40a0-9699-ed58276539f2","marketplace_file_type":"EMBEDDED_NOTEBOOK","file_parent":{"parent_id":"0572c3a7-3e7b-4ad9-b350-159532e99ebb","file_parent_type":"LISTING"},"mime_type":"text/html","download_link":"https://marketplace-file-storage-prod.s3-fips.us-east-1.amazonaws.com/EMBEDDED_NOTEBOOK/LISTING_0572c3a7-3e7b-4ad9-b350-159532e99ebb/6a0f3b5c-655b-40a0-9699-ed58276539f2?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEGgaDnVzLWVhc3QtMS1maXBzIkYwRAIgbC8sWIoYaHR8y7Wq86nPb0pufBl4%2BM4PWpeN%2B5PW9VQCIG7BhAVBDCWTpxFDLeuKYm%2Ff1JLz4iccPMzter0YdhgWKqYECDAQABoMNDE0MzUxNzY3ODI2IgxNAJrHbXaZG20jNfMqgwQyGEb4TsoVOoNvJ%2BaA%2FZ7zeu8hGcHqCeNfblP%2FQo5g6q2aRNtpwtPRKMlhYcIE%2FUQaNT1Lqmyptu32VUQTpzjTTxRVmJrDPkheSCkV06XQ%2FpTMKjU8CC7X1b%2FqYf8CrBwZT7Cv4C5WPjEET38f2A79h0xV6bZgPy28zQWrSeIJ9w6AfZBxiE24%2FVc08zVA0ivCT6lsy7nuU0wcR%2FMg2C9nd1vaMnQ923VpD21CKfVVYcrNWL2b%2FQ%2BNAO%2BxPjPgjiN6UPQbvbvZxkSdhgrGtecIg9NgqGkMcvM5VLTDtdC1kdeZetfOC2obtA%2BT%2F0gy3tIDo6WM5hcQfw743%2FAU9oaGnRBl%2BwoxRRDCZA%2F4jnPdluICSI7NCr6C3HHOe4CSZJjRihczxPA%2FgQczAVjAPs6CrK9N9pIzt0vNJpGUTgg4AoxftTvGfciDREilSctPIcBk6GwB%2Bzt4wbtNCmEPU%2BFD46rc6CWVQmmLwbKOeHw70ipMVFlOc8NlXkEtGO2vu6H44MstSSc6daOVUYUavQSGZ6jGhEjsP2YGz8KDm9XQotB%2FtyVBqiZV01OKqDmmBJA%2FXjUpSJ8M%2FeJXhe5m%2BKcxzNObtoJRI7jci3Tpt08XZyXZ7%2FyIV8H30ya468X3cfkTXrm972uga3z7iXAdubyEHnQIua%2FuvtwOHILi6O5D1Me2MzCPvcnXBjqTAYDoMWBt%2B3AVFuqSs2dmDQNBnvEhXfMgTedKLD5PJHb3aIl15iBkLaNu36GYnVCp9ZU2lSYcTbY5QwNnNeTfJTNy9bKhwzru6Sg%2BslhN%2BTS0zkTn%2BeLMY4tHgPEtEZ2cp%2FCk6GwqswvnYVKHjwO8q%2B1Mx8lc08fVEfarE9dke1GGFqoddx%2Bq%2BZUyHbDlAVfrCmf0yw%3D%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20261110T000135Z&X-Amz-SignedHeaders=host&X-Amz-Expires=86400&X-Amz-Credential=ASIAWA6KKHEJORWBH4N3%2F20261110%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=c148b9973ad8819a6d3542e618a619442d4edb1d34634e71082a39f09045d112","created_at":1682537233904,"updated_at":1682537233904,"display_name":"EDA Notebook","status":"FILE_STATUS_PUBLISHED"}],"assets":["ASSET_TYPE_DATA_TABLE"]},"provider_summary":{"name":"John Snow Labs","description":"John Snow Labs, an AI and NLP for healthcare company, provides state-of-the-art software, models, and data to help healthcare and life science organizations build, deploy, and operate AI projects."},"popularity":1141},{"id":"4ffd8ef3-f941-487b-a966-e5e286620b09","summary":{"name":"Economy","subtitle":"This data package contains all the information related to the economy of a country including price index, commodities ..","share":{"name":"economy","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1682355712396,"updated_at":1695690478964,"categories":["HEALTH","SCIENCE_AND_RESEARCH"],"listingType":"STANDARD","provider_id":"f88849f8-9d77-4de2-b4c7-03d55cc3768e"},"detail":{"description":"\n**Overview**\nThis data package contains all the information related to the economy of a country including price index, commodities values and info about NASDAQ members.\n\n**Description**\nThis data package contains all the information in the form of datasets that are required to study the economy of a country. The dataset Advantage After School Programs (AASP) Services provides quality youth development opportunities to school-age children and youth for three hours directly after school to minimize the probability for the students of being involved in risky behaviors during the afterschool hours. The dataset Consumer Price Index (CPI) is the most publicized and widely used measure of the general level of prices in the U.S. economy. The CPI is a composite measure of the level of average prices paid by urban consumers for a defined market basket of goods and services, including food. The dataset contains a list of Annual Consumer Price Index (CPI) values for countries for the years 1960-2016. The dataset Annual Inflation by Consumer Prices measured by the consumer price index, reflects the annual percentage change in the cost to the average consumer of acquiring a basket of goods and services that may be fixed or changed at specified intervals, such as yearly. \n\nThe dataset Annual Inflation by GDP deflator measured by the annual growth rate of the GDP (Gross Domestic Product) implicit deflator, shows the rate of price change in the economy as a whole. The dataset Annual Survey of State Government Tax Collections (STC) provides a summary of taxes collected by the fifty state governments, as well as all dependent state-level governmental entities, for 5 broad tax categories and up to 25 tax subcategories. The dataset United Nations Classification of the Functions of Government (COFOG), which is a classification by socioeconomic objective and is used for the formulation of public policy and international comparison. The dataset consists of Europe Brent and WTI (Western Texas Intermediate) monthly spot prices from EIA U.S. (Energy Information Administration). Monthly prices are calculated by EIA (U.S. Energy Information Administration) from daily data by taking an unweighted average of the daily closing spot prices for a given product over the specified time period. The dataset Construction Price Indexes (CPI) provide price indexes for single-family houses sold and for single-family houses under construction. The houses sold index incorporates the value of the land and is available quarterly at the national level and annually by region. The dataset cash surplus or deficit is closest to the earlier overall budget balance (still missing is lending minus repayments, which are now a financing item under net acquisition of financial assets.\n\nThe dataset Chicago Annual Taxpayer Location Address is produced from data contained in the business registration system. The list is generated for each municipal and county government. The dataset includes child welfare and community services funded program currently funded by the New York. The dataset Corruption Perceptions Index aggregates data from a number of different sources that provide perceptions of business people and country experts of the level of corruption in the public sector. The dataset Educational Actual Operating Costs include all general fund expenditures and revenues annually reported by each School Administrative Unit except major capital outlay, debt service, and transportation expenditures. The dataset contains Land Sale Records from the Lands Department which is responsible for all land matters in the Hong Kong Special Administrative Region and which has three functional offices. The dataset contains monthly and annual goods Balance of Payments (BOP) provides national trade data including imports, exports, and balance of payments for goods and services. The dataset Property Data Buildings Information System contains the list of NYC (New York City) Properties under DOB (Department of Buildings) jurisdiction. The dataset Real Estate Across the United States (REXUS) contains building inventory that consists of both owned and leased buildings with active and excess status. The dataset consists of ten-year nominal yields on UK government bonds from the bank of England. The ten-year government bond yield is considered a standard indicator of long-term interest rates. The dataset United Kingdom Standard Industrial Classification of Economic Activities, or UKSIC, is a Standard Industrial Classification that is intended to help classify businesses according to the type of their economic activity. \n\nThe dataset US House Price Index comes from S&P (Standard and Poors) Case-Shiller data and includes both the national index and the indices for 20 metropolitan regions. The indices are created using a repeat-sales methodology. The dataset US Natural Gas Prices consists of natural gas prices for wellhead, imports, exports, citygate, and end-use sectors. Natural gas prices are mainly a function of market supply and demand. Because there are limited short-term alternatives to natural gas as a fuel for heating and electricity generation during peak demand periods. The dataset Volatility Index data is compiled for the convenience of site visitors and is furnished without responsibility for the accuracy and is accepted by the site visitor on the condition that transmission or omissions shall not be made the basis for any claim, demand or cause for action.\n\n**Benefits**\n- The economy can benefit from easier access to information, content, and knowledge in turn contributing to the development of innovative services and the creation of new business models.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [Advantage After School Program Services](https://www.johnsnowlabs.com/marketplace/advantage-after-school-program-services)\n\t- This dataset includes the data elements that will help the public learn about the agencies currently contracting with New York State to provide Advantage After School Program services.\n- [Annual Consumer Price Index Percent Change 1974-2016](https://www.johnsnowlabs.com/marketplace/annual-consumer-price-index-percent-change-1974-2016)\n\t- This dataset gives information on the Consumer Price Index (CPI) for food is a component of the all-items CPI. The CPI measures the average change over time in the prices paid by urban consumers for a representative market basket of consumer goods and services. While the all-items CPI measures the price changes for all consumer goods and services, including food, the CPI for food measures the changes in the retail prices of food items only.\n- [Annual Consumer Price Index by Country](https://www.johnsnowlabs.com/marketplace/annual-consumer-price-index-by-country)\n\t- This dataset contains a list of Annual Consumer Price Index (CPI) values for countries for the years 1960-2021. It comprises the Country Name, Country Code and the CPI values for several countries.\n- [Annual Inflation by Consumer Prices](https://www.johnsnowlabs.com/marketplace/annual-inflation-by-consumer-prices)\n\t- This dataset enlists the details about annual inflation percentage of most countries in the world by consumer prices from 1960 through 2018. Indicator Name as per the World Bank for this dataset is \"Inflation, consumer prices (annual %)\" and its code is \"FP.CPI.TOTL.ZG\".\n- [Annual Inflation by GDP Deflator](https://www.johnsnowlabs.com/marketplace/annual-inflation-by-gdp-deflator)\n\t- This dataset enlists the details about annual inflation percentage of most countries in the world by GDP deflator from 1961 through 2018. Indicator Name as per the World Bank for this dataset is \"Inflation, GDP deflator (annual %)\" and its code is \"NY.GDP.DEFL.KD.ZG\".\n- [Annual Survey Tax Collections by State 2019](https://www.johnsnowlabs.com/marketplace/annual-survey-tax-collections-by-state-2019)\n\t- This dataset contains the Annual Survey of State Government Tax Collections (STC) that provides a summary of taxes collected by state for 5 broad tax categories and up to 25 tax subcategories.\n- [Annual Survey Tax Collections by State 2021](https://www.johnsnowlabs.com/marketplace/annual-survey-tax-collections-by-state-2021)\n\t- This dataset contains the Annual Survey of State Government Tax Collections (STC) that provides a summary of taxes collected by state for 5 broad tax categories and up to 25 tax subcategories.\n- [Belgium COFOG Nomenclature](https://www.johnsnowlabs.com/marketplace/belgium-cofog-nomenclature)\n\t- This dataset contains Government expenditure, COFOG (Classification of the Functions of Government) nomenclature for Belgium.\n- [Brent And WTI Monthly Spot Prices](https://www.johnsnowlabs.com/marketplace/brent-and-wti-monthly-spot-prices)\n\t- This dataset consists of Europe Brent and WTI (Western Texas Intermediate) monthly spot prices from EIA U.S. (Energy Information Administration). Monthly prices are calculated by EIA (U.S. Energy Information Administration) from daily data by taking an unweighted average of the daily closing spot prices for a given product over the specified time period.\n- [COVID-19 Expenditures Report](https://www.johnsnowlabs.com/marketplace/covid-19-expenditures-report)\n\t- This dataset shows the every COVID-19 expenditure transaction incurred in New York City. This dataset is prepared by the Mayor's Office of Management and Budget (OMB).\n- [COVID-19 Loan or Grant Report](https://www.johnsnowlabs.com/marketplace/covid-19-loan-or-grant-report)\n\t- This dataset includes small business loans or grants issued for emergency COVID-19 financial assistance. This dataset is provided by the Department of Small Business Services (SBS).\n- [COVID-19 Revenue Report](https://www.johnsnowlabs.com/marketplace/covid-19-revenue-report)\n\t- This dataset shows the every COVID-19 revenue report in New York City. This dataset is prepared by the City's Financial Management System (FMS).\n- [CPI Of New Single Family Houses Sold Based On 2005 Price](https://www.johnsnowlabs.com/marketplace/cpi-of-new-single-family-houses-sold-based-on-2005-price)\n\t- The Construction Price Indexes (CPI) provide price indexes for single-family houses sold and for single-family houses under construction. The houses sold index incorporates the value of the land and is available quarterly at the national level and annually by region. The indexes are based on data funded by The Department of Housing and Urban Development (HUD) and collected in the Survey of Construction (SOC).\n- [CPI Of New Single Family Houses Under Construction Based On 2005 Price](https://www.johnsnowlabs.com/marketplace/cpi-of-new-single-family-houses-under-construction-based-on-2005-price)\n\t- The Construction Price Indexes provide price indexes for single-family houses sold and for single-family houses under construction. The indexes for houses under construction are available monthly at the national level. The indexes are based on data funded by The Department of Housing and Urban Development (HUD) and collected in the Survey of Construction (SOC).\n- [Cash Surplus or Deficit in Percentage of GDP](https://www.johnsnowlabs.com/marketplace/cash-surplus-or-deficit-in-percentage-of-gdp)\n\t- This dataset consists of the list of countries with their Cash Surplus or Deficit, in percentage of GDP (Gross Domestic Product), from 1972 to 2014.\n- [Chicago Affordable Rental Housing Developments](https://www.johnsnowlabs.com/marketplace/chicago-affordable-rental-housing-developments)\n\t- This dataset shows the list of affordable rent by the housing development supported by the City of Chicago by :\n - Community Area Name\n - Community Area Number\n - Property Description\n - Property Name\n - Address\n - ZIP Code\n - Phone Number\n - Management Company\n - Units\n - Latitude\n - Longitude\n- [Chicago Annual Taxpayer Location List](https://www.johnsnowlabs.com/marketplace/chicago-annual-taxpayer-location-list)\n\t- This dataset shows the list of businesses registered with the State of Illinois that may levy retail sales taxes in the Chicago area. It includes information on Type of Filer,Business Name, Address, City, ZIP Code, Latitude and Longitude.\n- [Chicago Business Licenses](https://www.johnsnowlabs.com/marketplace/chicago-business-licenses)\n\t- This dataset shows the list of business licenses issued by the Department of Business Affairs and Consumer Protection in the City of Chicago from 2002 to the present.\n- [Chicago Business Owners](https://www.johnsnowlabs.com/marketplace/chicago-business-owners)\n\t- This dataset contains the owner information for all the accounts listed in the Business License Dataset, and is sorted by Account Number. These are issued by the Department of Business Affairs and Consumer Protection in the City of Chicago from 2002 to the present.\n- [Chicago Park District Event Permits](https://www.johnsnowlabs.com/marketplace/chicago-park-district-event-permits)\n\t- This dataset includes information about event permits requested through the Chicago Park District, including the name of the applicant, the name and a brief description of the event, contact information, time of event including set-up and tear-down times, the name and location of the park, and the estimated number of event attendees.\n- [Child Welfare and Community Services Funded Programs](https://www.johnsnowlabs.com/marketplace/child-welfare-and-community-services-funded-programs)\n\t- This dataset includes child welfare and community services funded program currently funded by the New York State by zip code, city, and county.\n- [Combined and Normalized GHEITI Data](https://www.johnsnowlabs.com/marketplace/combined-and-normalized-gheiti-data)\n\t- This dataset contains information regarding Ghana Extractive Industries Transparency Initiative (GHEITI) from 2007-2013. It consists of Company Name, Year, Commodity Name and Code, Data Files, GFS (Government Finance Statistics revenue type) Name and Code, and Value.\n- [Corruption Perceptions Index](https://www.johnsnowlabs.com/marketplace/corruption-perceptions-index)\n\t- The Corruption Perceptions Index measures the perceived levels of corruption worldwide from the public sector. Based on an expert opinion from around the world, the Corruption Perceptions Index measures the perceived levels of the public sector on corruption worldwide.\n- [DCA Licensed Legally Operating Businesses in New York](https://www.johnsnowlabs.com/marketplace/dca-licensed-legally-operating-businesses-in-new-york)\n\t- This dataset includes featured businesses and individuals holding a DCA (Department of Consumer Affairs) license so that they may legally operate in New York City.\n- [Education Actual Operating Cost Per Student](https://www.johnsnowlabs.com/marketplace/education-actual-operating-cost-per-student)\n\t- This dataset shows actual expenditures for operating funds (General, Special Revenue, Enterprise and Food Services Funds) per student, per district, per county and per state from 1997 to 2009.\n- [Euribor Rates by Year and Granularity](https://www.johnsnowlabs.com/marketplace/euribor-rates-by-year-and-granularity)\n\t- This dataset contains Euribor rates by year and granularity. Only Daily granularity is provided and the dataset is updated on an annual basis.\n- [FDIC Failed Bank List](https://www.johnsnowlabs.com/marketplace/fdic-failed-bank-list)\n\t- This dataset contains the Information for the customers and vendors of failed banks including detailed transaction information. Additional resources for borrowers, depositors, creditors, court clerks and law enforcement agencies.\n- [FDIC Insured Institutions](https://www.johnsnowlabs.com/marketplace/fdic-insured-institutions)\n\t- This dataset contains the FDIC (Federal Deposit Insurance Corporation) institution directory. The file provides a list of all FDIC-insured institutions.\n- [GDP by Industry and Country](https://www.johnsnowlabs.com/marketplace/gdp-by-industry-and-country)\n\t- This dataset gives the information of Gross Domestic Product (GDP) by industry and country. The GDP breakdown at current prices in National currency (all countries).\n- [GINI Index Data](https://www.johnsnowlabs.com/marketplace/gini-index-data)\n\t- GINI Index Data consists of information based on primary household survey data obtained from government statistical agencies and World Bank country departments. In economics, the GINI index (sometimes expressed as a GINI ratio, GINI coefficient or a normalized GINI index) is a measure of statistical dispersion intended to represent the income or wealth distribution of a nation's residents, and is the most commonly used measure of inequality.\n- [Gold Prices](https://www.johnsnowlabs.com/marketplace/gold-prices)\n\t- The Gold Prices dataset includes daily prices of Gold since April 1968.\n- [Gross Domestic Product Of The United States](https://www.johnsnowlabs.com/marketplace/gross-domestic-product-of-the-united-states)\n\t- The Gross Domestic Product Of The United States is the main measure of US economic growth based on the value of goods and services produced from 1930 and 2013.\n- [House Prices in UK Since 1953](https://www.johnsnowlabs.com/marketplace/house-prices-in-uk-since-1953)\n\t- This dataset contains UK house prices since 1953 as a monthly time-series. The data comes from the Nationwide Building Society.\n- [IMF World Economic Outlook Database](https://www.johnsnowlabs.com/marketplace/imf-world-economic-outlook-database)\n\t- The World Economic Outlook (WEO) database contains selected macroeconomic data series from the statistical appendix of the World Economic Outlook report, which presents the IMF staff's analysis and projections of economic developments at the global level, in major country groups and in many individual countries.\n- [International Chamber of Commerce Incoterms](https://www.johnsnowlabs.com/marketplace/international-chamber-of-commerce-incoterms)\n\t- This dataset contains the information of international chamber of commerce incoterms. International Commercial Terms (‘Incoterms’) are internationally recognized standard trade terms used in sales contracts. They’re used to make sure buyer and seller know:\n\nWho is responsible for the cost of transporting the goods, including insurance, duties, where the goods should be picked up from and transported to and who is responsible for the goods at each step during transportation.\n- [LBB Comensura Expenditure 2019-2020](https://www.johnsnowlabs.com/marketplace/lbb-comensura-expenditure-2019-2020)\n\t- The dataset contains the LBB (London Borough of Barnet) Comensura Expenditure for the year 2019 and 2020.\n- [LBB Comensura Expenditure 2022-2023](https://www.johnsnowlabs.com/marketplace/lbb-comensura-expenditure-2022-2023)\n\t- The dataset contains the LBB (London Borough of Barnet) Comensura Expenditure for the year 2022 and 2023.\n- [Land Sale Records Hong Kong](https://www.johnsnowlabs.com/marketplace/land-sale-records-hong-kong)\n\t- This dataset contains Land Sale Records from the Lands Department which is responsible for all land matters in the Hong Kong Special Administrative Region and which has three functional offices.\n- [List of Companies in NASDAQ Exchanges](https://www.johnsnowlabs.com/marketplace/list-of-companies-in-nasdaq-exchanges)\n\t- This dataset contains a detailed information on companies listed in the NASDAQ exchanges. The dataset also includes the market category as well as the financial status of the listed companies.\n- [List of Companies in The New York Stock Exchange](https://www.johnsnowlabs.com/marketplace/list-of-companies-in-the-new-york-stock-exchange)\n\t- This dataset contains a detailed information on companies listed in the NYSE (The New York Stock Exchange).\n- [Lobbyist Agency Report](https://www.johnsnowlabs.com/marketplace/lobbyist-agency-report)\n\t- This dataset contains all lobbyist registration filings submitted to the Board of Ethics during the 2011 reporting period.\n- [Monthly and Annual Goods BOP Basis](https://www.johnsnowlabs.com/marketplace/monthly-and-annual-goods-bop-basis)\n\t- This dataset contains monthly and annual goods Balance of Payments (BOP) basis, services, and total balance, exports, and imports from 1992 to 2022.\n- [NAICS Code Annual Sales and Employees](https://www.johnsnowlabs.com/marketplace/naics-code-annual-sales-and-employees)\n\t- This dataset shows the list of United States North American Industry Classification System (NAICS) Codes, Business Profiles by Sales and Employees. These codes are used by businesses and government authorities to differentiate types of business according to their process of production.\n- [NJ GUDPA Funds Certificate List](https://www.johnsnowlabs.com/marketplace/nj-gudpa-funds-certificate-list)\n\t- This dataset shows the information on the quarterly NJ (New Jersey) funds certificate list enrolled in GUDPA (Governmental Unit Deposit Protection Act).\n- [NYCHA Development Data Book](https://www.johnsnowlabs.com/marketplace/nycha-development-data-book)\n\t- This dataset shows the New York City Housing Authority (NYCHA) Development Data Book as of January 1, 2019.\n- [National Stock Number Extract](https://www.johnsnowlabs.com/marketplace/national-stock-number-extract)\n\t- National Stock Number extract dataset includes the current listing of National Stock Numbers (NSNs), NSN item name and descriptions, and current selling price of each product listed in GSA (General Services Administration) Advantage and managed by GSA. Each NSN is listed with the vendor's description of the item.\n- [Natural Gas Consumption by ZIP Code](https://www.johnsnowlabs.com/marketplace/natural-gas-consumption-by-zip-code)\n\t- This dataset contains the information of 2010 Natural Gas consumption in therms and GJ (Gigajoule), by ZIP code, building type, and utility company.\n- [New York State Parks Concession Contracts](https://www.johnsnowlabs.com/marketplace/new-york-state-parks-concession-contracts)\n\t- This dataset includes the name, concession type and term of concession contracts active in New York State Parks.\n- [Other Exchange Listed Securities](https://www.johnsnowlabs.com/marketplace/other-exchange-listed-securities)\n\t- This dataset contains a detailed information on companies listed in the stock exchanges other than NYSE (The New York Stock Exchange) and NASDAQ. It also contains information on each security used in ACT(Automated Confirmation Transaction Service) and CTCI (Computer-to-computer interface) connectivity protocol.\n- [Pavement Quality Index](https://www.johnsnowlabs.com/marketplace/pavement-quality-index)\n\t- In this dataset, the Tempe’s roadways are an important means of transportation for residents, the workforce, students, and visitors. Tempe measures the quality and condition of its roadways using a Pavement Quality Index (PQI). This measure, rated from a low of 0 to a high of 100, is used by the City to plan for maintenance and repairs and to allocate resources in the most efficient way possible.\n- [Property Data Buildings Information System](https://www.johnsnowlabs.com/marketplace/property-data-buildings-information-system)\n\t- This dataset contains the list of NYC (New York City) Properties under DOB (Department of Buildings) jurisdiction.\n- [Purchasing Power Parity Conversion Factor For GDP](https://www.johnsnowlabs.com/marketplace/purchasing-power-parity-conversion-factor-for-gdp)\n\t- This dataset consists of information on Purchasing Power Parity (PPP) conversion factor, which is the number of units of a country's currency required to buy the same amounts of goods and services in the domestic market as U.S. dollar would buy in the United States. This conversion factor is for GDP (Gross Domestic Product).\n- [Real Estate Across The United States Building Inventory](https://www.johnsnowlabs.com/marketplace/real-estate-across-the-united-states-building-inventory)\n\t- This dataset shows the Real Estate Across the United States (REXUS) is the primary tool used to track and manage the government's real property assets and to store inventory data, building data, customer data, and lease information. This dataset contains building inventory that consists of both owned and leased buildings with active and excess status.\n- [Reducing Assessments or Reclassifying Property](https://www.johnsnowlabs.com/marketplace/reducing-assessments-or-reclassifying-property)\n\t- This dataset provides List of Assessment Actions on Applications in 2016 Reducing Assessments or Reclassifying Property.\n- [Standard and Poors 500 Companies List With Financial Information](https://www.johnsnowlabs.com/marketplace/standard-and-poors-500-companies-list-with-financial-information)\n\t- The Standard and Poors (S&P) 500 stock market index, maintained by S&P Dow Jones Indices, comprises 505 common stocks issued by 500 large-cap companies and traded on American stock exchanges, and covers about 80 percent of the American equity market by capitalization. The index is weighted by free-float market capitalization, so more valuable companies account for relatively more of the index.\n- [Standard and Poors 500 Index including Earnings And PE Ratio](https://www.johnsnowlabs.com/marketplace/standard-and-poors-500-index-including-earnings-and-pe-ratio)\n\t- This dataset consists of S&P 500 (Standard and Poor's 500) index data including level, dividend, earnings and P/E (Price Earnings) ratio on a monthly basis since 1871. The S&P 500 (Standard and Poor's 500) is a free-float, capitalization-weighted index of the top 500 publicly listed stocks in the US (top 500 by market capitalization).\n- [Ten Year UK Government Bond Yields](https://www.johnsnowlabs.com/marketplace/ten-year-uk-government-bond-yields)\n\t- This dataset includes a monthly data about interest rates of 10 year UK Government bond yields. The dataset contains the records for interest rates for each relative quarter since 1st January, 1984.\n- [Ten Year US Government Bond Yields](https://www.johnsnowlabs.com/marketplace/ten-year-us-government-bond-yields)\n\t- This dataset includes a monthly data about interest rates of 10 year US Government bond yields. The dataset contains the records for interest rates for each relative month since 3rd April, 1953.\n- [UK COFOG Nomenclature](https://www.johnsnowlabs.com/marketplace/uk-cofog-nomenclature)\n\t- This dataset contains Government expenditure, COFOG (Classification of the functions of government) nomenclature for United Kingdom.\n- [UK Consolidated Public Expenditures By Functions](https://www.johnsnowlabs.com/marketplace/uk-consolidated-public-expenditures-by-functions)\n\t- This dataset contains UK key series data about the public expenditures starting with the financial year 1995-1996 and until 2016-2017. These key series data are presented by financial years, by functions (including Health spending) and by sub-functions, as well as in billion pounds and percent of the GDP. The latest updates for a financial year is released in November. Data for the last financial year are preliminary.\n- [UK Consumer Price Inflation Index](https://www.johnsnowlabs.com/marketplace/uk-consumer-price-inflation-index)\n\t- This dataset contains detailed consumer price inflation (CPI) values for every month starting January 1988 and until May 2022. The global CPI value is broken down into the main services and products categories, presented in the description and by sub-categories.\n- [UK Greater London Public Expenditures](https://www.johnsnowlabs.com/marketplace/uk-greater-london-public-expenditures)\n\t- This dataset contains data about The Greater London Authority (GLA) and The Greater London Authority Holdings Ltd. (GLAP), public expenditures over 250 pounds (excluding VAT), broken down by services providers, expenditures accounts description, clearing date, the main organizational structures (directorates) and type of service, starting with the financial year(FY) 2022.\n- [UK Gross Domestic Product](https://www.johnsnowlabs.com/marketplace/uk-gross-domestic-product)\n\t- The UK Gross Domestic Product (GDP) is the main measure of UK economic growth based on the value of goods and services produced from 1948 to 2017.\n- [UK Properties For Sale By Ministry Of Defense](https://www.johnsnowlabs.com/marketplace/uk-properties-for-sale-by-ministry-of-defense)\n\t- This dataset contains UK data from the Defense Infrastructure Organization Disposals Database House of Commons Report 2017, about the properties of UK Ministry of Defense(MOD) which are currently for sale. Along with properties identification and description data, a deadline (represented as financial year) and a specific ID is presented for all properties.\n- [UK Public Spending By Region Or Country](https://www.johnsnowlabs.com/marketplace/uk-public-spending-by-region-or-country)\n\t- This dataset contains UK key series data about the public expenditures by country and English region, starting with the financial year 2004-2005 and until 2016-2021. These key series data are presented by financial years, by functions (including Health spending) and by sub-functions, in billion pounds.\n- [UK Standard Industrial Classification of Economic Activities](https://www.johnsnowlabs.com/marketplace/uk-standard-industrial-classification-of-economic-activities)\n\t- This dataset consists of a condensed list of the ONS (Office of National Statistics) codes used to classify business establishments and other standard units by the type of economic activity in which they’re engaged.\n- [US Consumer Price Index and Inflation](https://www.johnsnowlabs.com/marketplace/us-consumer-price-index-and-inflation)\n\t- Consumer Price Index for All Urban Consumers (CPI-U) from U.S. Department Of Labor Bureau of Labor Statistics. This is a monthly time series from January 1913. 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The indices are created using a repeat-sales methodology.\n- [US Long Term Mutual Fund Monthly Net New Cash Flow](https://www.johnsnowlabs.com/marketplace/us-long-term-mutual-fund-monthly-net-new-cash-flow)\n\t- This dataset consists of monthly net new cash flow by US investors into various mutual fund investment classes (equities, bonds etc). Statistics come from the Investment Company Institute (ICI). All figures in the dataset are (nominal) millions of US dollars (USD). Monthly cash flows are actual numbers as reported by ICI.\n- [US Natural Gas Prices](https://www.johnsnowlabs.com/marketplace/us-natural-gas-prices)\n\t- This dataset consists of natural gas prices for wellhead, imports, exports, citygate, and end-use sectors. The data also contains percentages of total volume delivered by sector.\n- [Volatility Index Data](https://www.johnsnowlabs.com/marketplace/volatility-index-data)\n\t- This dataset contains Volatility Index (VIX) Data, which began on September 22, 2003; the Chicago Board Options Exchange Holdings, Inc. (CBOE) began disseminating price level information using the revised methodology for the Volatility Index, VIX.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated\n- Data Updates\n - Data updates support replace-on-update: outdated foreign keys are deprecated, not deleted\n\n**Our data is curated and enriched by domain experts** \n\n Each dataset is manually curated by our team of doctors, pharmacists, public health & medical billing experts:\n- Field names, descriptions, and normalized values are chosen by people who actually understand their meaning\n- Healthcare & life science experts add categories, search keywords, descriptions and more to each dataset\n- Both manual and automated data enrichment supported for clinical codes, providers, drugs, and geo-locations\n- The data is always kept up to date – even when the source requires manual effort to get updates\n- Support for data subscribers is provided directly by the domain experts who curated the data sets\n- Every data source’s license is manually verified to allow for royalty-free commercial use and redistribution.\n\n**Need Help?**\nIf you have questions about our products, contact us at [info@johnsnowlabs.com](mailto:info@johnsnowlabs.com). \n 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If you are interested in learning more about datasets released by the CMS, please visit the [CMS Data homepage](https://data.cms.gov/).\n\n**Product Details**\n\nIncludes the following tables:\n\n\n- **cms_pup_physician_and_other_practitioners_yearly**\n - **Update frequency:** yearly\n - **Source:** CMS-Physician-and-Other-Practitioners\n - **Description:** Information on services and procedures provided to Original Medicare (fee-for-service) Part B (Medical Insurance) beneficiaries by physicians and other healthcare professionals; aggregated by provider and service.\n\n- **cms_pup_inpatient_yearly**\n - **Update frequency:** yearly\n - **Source:** CMS-Inpatient\n - **Description:** Information on hospital discharges from Original Medicare (fee-for-service) Part A (Hospital Insurance) beneficiaries by Inpatient Prospective Payment System (IPPS) hospitals; aggregated by provider and service.\n\n- **cms_pup_outpatient_yearly**\n - **Update frequency:** yearly\n - **Source:** CMS-Outpatient\n - **Description:** Information on services provided to Original Medicare (fee-for-service) Part B (Medical Insurance) beneficiaries by Outpatient Prospective Payment System (OPPS) hospitals; aggregated by provider and service.\n\n- **cms_pup_part_d_prescriber_yearly**\n - **Update frequency:** yearly\n - **Source:** CMS-Part-D-Prescriber\n - **Description:** Information on prescription drugs provided to Medicare beneficiaries enrolled in Part D (Prescription Drug Coverage), by physicians and other health care providers, aggregated by provider and drug.\n\n- **cms_pup_durable_medical_equipment_devices_supplies_yearly**\n - **Update frequency:** yearly\n - **Source:** CMS-Referring-Durable-Medical-Equipment-Prosthetics-Orthotics-Supplies\n - **Description:** Information on products and services provided to Original Medicare (fee-for-service) Part B (Medical Insurance) beneficiaries ordered by physicians and other healthcare professionals; aggregated by referring provider and service.\n\n- **cms_pup_post_acute_care_hospice_yearly**\n - **Update frequency:** yearly\n - **Source:** CMS-Post-Acute-Care-Hospice\n - **Description:** Summary information on healthcare services provided to Medicare beneficiaries by home health agencies (HHAs), hospices, skilled nursing facilities (SNFs), inpatient rehabilitation facilities (IRFs), and long-term care hospitals (LTCHs).\n\n\n\n**Data Sources**\n\nThe data in this product comes from the following data sources:\n\n\n**CMS-Physician-and-Other-Practitioners**\n\nInformation on services and procedures provided to Original Medicare (fee-for-service) Part B (Medical Insurance) beneficiaries by physicians and other healthcare professionals; aggregated by provider and service.\n\n\n- **Source Homepage:** [Centers for Medicare & Medicaid Services Data](https://data.cms.gov/)\n- **Source Data:** [Centers for Medicare & Medicaid Services | Medicare Physician & Other Practitioners - by Provider and Service](https://data.cms.gov/provider-summary-by-type-of-service/medicare-physician-other-practitioners/medicare-physician-other-practitioners-by-provider-and-service)\n- **License:** [Public Domain - U.S. Government Work](https://www.usa.gov/government-works)\n- **Frequency:** yearly\n\n\n\n**CMS-Inpatient**\n\nInformation on hospital discharges from Original Medicare (fee-for-service) Part A (Hospital Insurance) beneficiaries by Inpatient Prospective Payment System (IPPS) hospitals; aggregated by provider and service.\n\n\n- **Source Homepage:** [Centers for Medicare & Medicaid Services Data](https://data.cms.gov/)\n- **Source Data:** [Centers for Medicare & Medicaid Services | Medicare Inpatient Hospitals - by Provider and Service](https://data.cms.gov/provider-summary-by-type-of-service/medicare-inpatient-hospitals/medicare-inpatient-hospitals-by-provider-and-service)\n- **License:** [Public Domain - U.S. Government Work](https://www.usa.gov/government-works)\n- **Frequency:** yearly\n\n\n\n**CMS-Outpatient**\n\nInformation on services provided to Original Medicare (fee-for-service) Part B (Medical Insurance) beneficiaries by Outpatient Prospective Payment System (OPPS) hospitals; aggregated by provider and service.\n\n\n- **Source Homepage:** [Centers for Medicare & Medicaid Services Data](https://data.cms.gov/)\n- **Source Data:** [Centers for Medicare & Medicaid Services | Medicare Outpatient Hospitals - by Provider and Service](https://data.cms.gov/provider-summary-by-type-of-service/medicare-outpatient-hospitals/medicare-outpatient-hospitals-by-provider-and-service)\n- **License:** [Public Domain - U.S. Government Work](https://www.usa.gov/government-works)\n- **Frequency:** yearly\n\n\n\n**CMS-Part-D-Prescriber**\n\nInformation on prescription drugs provided to Medicare beneficiaries enrolled in Part D (Prescription Drug Coverage), by physicians and other health care providers, aggregated by provider and drug.\n\n\n- **Source Homepage:** [Centers for Medicare & Medicaid Services Data](https://data.cms.gov/)\n- **Source Data:** [Centers for Medicare & Medicaid Services | Medicare Part D Prescribers - by Provider and Drug](https://data.cms.gov/provider-summary-by-type-of-service/medicare-part-d-prescribers/medicare-part-d-prescribers-by-provider-and-drug)\n- **License:** [Public Domain - U.S. Government Work](https://www.usa.gov/government-works)\n- **Frequency:** yearly\n\n\n\n**CMS-Referring-Durable-Medical-Equipment-Prosthetics-Orthotics-Supplies**\n\nInformation on products and services provided to Original Medicare (fee-for-service) Part B (Medical Insurance) beneficiaries ordered by physicians and other healthcare professionals; 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The output is structured JSON, rather than raw text that needs parsing.\n\nOne install. 18 tools. The entire public web as a queryable data source for your AI workflows.\n\n**Use cases**\n\n- **Competitive pricing intelligence** — Monitor competitor prices across thousands of product pages in real time. Feed structured price/availability data directly into your Databricks pipelines.\n- **Digital shelf analytics** — Track product placement, reviews, ratings, and content changes across major retailers. Build dashboards that refresh with live data.\n- **Brand monitoring** — Search news, social media, and review sites for brand mentions. Get structured sentiment and source data, not just links.\n- **Alternative data for finance** — Extract SEC filings, job postings, company signals, and market indicators from the live web. Ground your financial models in current data.\n- **Deep research for RAG** — Give your retrieval-augmented generation pipelines access to fresh web content. Search, extract full pages, and feed clean markdown into your vector stores.\n- **AI agent grounding** — Any Databricks agent that needs to answer questions about the real world can search the web, extract pages, or crawl entire sites — all through standard MCP tool calls.\n\n**Product details**\n\n18 MCP tools across six capabilities:\n- **Search** — Multi-engine web search with focus modes for news, shopping, social, and more.\n- **Extract** — Pull clean content from any URL as markdown, plain text, or HTML. Sync and async modes.\n- **Map** — Discover every URL on a website via link-following and sitemap parsing.\n- **Crawl** — Multi-page crawling with path filters, subdomain control, and progress tracking.\n- **Agents** — Browse pre-built extraction agents, create custom ones through a conversational workflow, and save them for reuse.\n- **Tasks** — Async polling for long-running extraction and agent jobs.\n\nAll tools return structured JSON. No parsing required — your agents can act on the data immediately.\n\n**Additional Insights**\nTo use the Nimble MCP Server, [sign up for a free account](https://online.nimbleway.com/signup) on nimbleway.com.\n\nOnce registered, generate your API key from [Account Settings > API Keys](https://online.nimbleway.com/account-settings/api-keys). This key is required to authenticate the MCP server connection (via Bearer token).\n\nFor a full walkthrough of available tools and parameters, see the [Nimble MCP Server documentation](https://docs.nimbleway.com/integrations/mcp-server/mcp-server). To build custom extraction agents without code, try [Nimble Studio](https://online.nimbleway.com/workflow-builder).","terms_of_service":"https://www.nimbleway.com/terms-of-use","documentation_link":"https://docs.nimbleway.com/home","privacy_policy_link":"https://www.nimbleway.com/privacy-policy","data_source":"","assets":["ASSET_TYPE_MCP"],"license":"https://www.nimbleway.com/terms-of-use"},"provider_summary":{"name":"Nimble","description":"Nimble is building a world where businesses can easily create AI & BI applications using real-time public data. Nimble empowers businesses across industries and the Fortune 500 with seamless data access to anywhere on the public web.\n\nNimble’s novel AI browser technology uses pioneering HTML-trained LLMs to streamline data collection pipelines at any scale with AI-powered data structuring, validation, governance, and compliance mechanisms.\n\nOur mission is to make advanced AI applications accessible to any business, enhancing efficiency and fostering economic growth."},"popularity":1913},{"id":"d740e74a-2045-4b50-958c-4aee25436269","summary":{"name":"Alteryx Designer Office of Finance Starter Kit","subtitle":"This kit contains key analytic workflows and tutorial information for the Office of Finance professional.","setting":{"visibility":"PUBLIC"},"created_at":1722542998663,"updated_at":1722542998663,"categories":["FINANCIAL"],"listingType":"PERSONALIZED","provider_id":"79ebaec1-d5b2-4073-9610-ab8a705f15c8"},"detail":{"description":"**Overview**\nThe Office of Finance Starter Kit contains sample Alteryx Designer workflows to help Finance Analysts kick start automating processes that historically would be done manually in Excel. 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It also generates a *forecast for the monthly cash equivalent data, enabling a smooth forecasting of cashflow for the business. \n- Calculating Indirect Taxes: Calculate the sales and use tax per state for subscription sales.\n\n**Product details**\nDownload a free trial of [Alteryx Designer](https://www.alteryx.com/designer-trial/free-trial-alteryx) Choose Desktop>Free Trial\n\nInstall the [Office of Finance Starter Kit](https://community.alteryx.com/t5/Community-Gallery/Office-of-Finance-Starter-Kit/ta-p/1188465)\n\nDesigner Cloud is available in Databricks Partner Connect to immediately begin preparing, transforming, and blending data with our drag-and-drop UI. Designer Cloud Starter Kits will be coming soon.\n\nGo to Help>Sample Workflows>Starter Kits>Office of Finance and open the Table of Contents to start learning. Sample workflows are pre-populated with sample datasets.\n\n**Additional Insights**\n*Forecast requires installation of the Predictive Analytics R Toolkit. With Designer installed, choose Help>Alteryx Downloads\n\nTo load sample datasets into your Databricks environment, follow instructions here to [configure your ODBC driver](https://help.alteryx.com/current/en/designer/data-sources/databricks/databricks-volumes.html#driver-details-6704036). \n\nAlteryx Designer can read and write to Databricks Unity Catalog and push-down processing capabilities using the In-DB Toolkit. 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According to the \"Meaningful Use\" of the EHR incentive program of the Centers for Medicare & Medicaid Services (CMS), one of the certification criteria of EHR is that problem list data should be encoded in SNOMED CT. The problem list is considered to be an essential part of the Electronic Health Record (EHR) by various sanctioning bodies and medical information standards organizations, including the Institute of Medicine, Joint Commission, American Society for Testing and Materials and Health Level Seven.\n\n**Benefits**\n- can be used as a starter set for institutions that do not yet have a problem list vocabulary based on snomed ct\n- saves significant development effort and reduce unintentional variations in the choice of terms.\n- existing problem list vocabularies can also be mapped to this subset which facilitates data interoperability\n- can be installed in various ehr products\n- it is used as a focus for snomed ct-related research, mapping projects and quality assurance.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [SNOMED CT Encoded CORE Problem List Subset](https://www.johnsnowlabs.com/marketplace/snomed-ct-encoded-core-problem-list-subset)\n\t- SNOMED CT Encoded CORE Problem List Subset July 2019 is a UMLS CORE (Clinical Observations Recordings and Encoding) Project with the purpose of defining a UMLS (Unified Medical Language System) subset that is most useful for documenting and encoding clinical information at a summary level. The CORE Problem List Subset includes SNOMED CT concepts and codes that can be used for the problem list, discharge diagnoses, or reason of encounter.\n- [SNOMED CT Encoded Nursing Problem List Subset](https://www.johnsnowlabs.com/marketplace/snomed-ct-encoded-nursing-problem-list-subset)\n\t- SNOMED CT Encoded Nursing Problem List Subset August 2017, intended for use in patients' problem lists, is an output of the Unified Medical Language System (UMLS) Metathesaurus that is based on nursing diagnosis concepts found within the Metathesaurus.This August 2017 version of the subset is Derived from US Edition SNOMED CT version: March 2017, and UMLS Metathesaurus version: 2017AA.\n- [SNOMED CT Encoded Route of Administration Subset](https://www.johnsnowlabs.com/marketplace/snomed-ct-encoded-route-of-administration-subset)\n\t- SNOMED CT Encoded Route of Administration Subset September 2015 is a listing of the current set of terms related to the location of administration for clinical therapeutics. 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Enables in-depth analysis of the fast-food ...","share":{"name":"x_tract_778923b0_1c72_4377_a3ce_b473fe4e4815_samples_share","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1749795006493,"updated_at":1793770353905,"categories":["TRAVEL_AND_TOURISM","GEOSPATIAL","ECONOMICS"],"listingType":"STANDARD","provider_id":"69b0fb78-1374-4520-b01d-55597414c6ad"},"detail":{"description":"Know exactly where McDonald's operates and where it doesn't yet. This McDonald's restaurant data from Xtract.io gives you a geocoded view of fast-food locations across the US and Canada. Use it to map market coverage, benchmark competitors, and find your next growth opportunity with confidence.\n\nBuilt for restaurant brands, franchise operators, commercial real estate teams, market researchers, delivery platforms, and location intelligence providers, this restaurant dataset pairs accurate coordinates with rich business attributes. It is restaurant location data that turns a pin on a map into a business decision. Combine it with broader food store and retail datasets to go deeper into market intelligence and consumer behavior.\n\nWhat You Can Do With This Restaurant Data:\n\n- Analyze McDonald's distribution and market coverage across the US and Canada.\n\n- Benchmark competitor QSR and restaurant networks side by side.\n\n- Support franchise expansion and site selection with location-backed evidence.\n\n- Run trade areas and catchment analysis around any restaurant.\n\n- Power store locators and mapping applications with clean, geocoded restaurant store locations.\n\n- Plan delivery territories and strengthen location intelligence.\n\n- Sharpen competitive intelligence across the restaurant and food store sectors.\n\nWhy Choose Xtract.io?\n\n- Reliable accuracy: high-quality restaurant data with up to 95% accuracy.\n\n- Rich attributes: geocoded locations enriched with business details.\n\n- Built around you: on-demand restaurant datasets tailored to your requirements.\n\n- Advanced spatial analysis: handcrafted geofences and polygon data.\n\n- Always current: refresh schedules every 30, 60, or 90 days.\n\n- Plug-and-play delivery: GeoJSON, Shapefile, WKT, and GDB formats.\n\nTurn Restaurant Data Into Smarter Decisions:\n\nWhether you are sizing up McDonald's market presence, comparing performance against rival QSR brands, or planning your next opening, Xtract.io’s restaurant data gives you a trusted foundation. As a dependable source of place data for the North American restaurant market, it helps you optimize expansion, strengthen competitive intelligence, and make confident location-driven decisions. \n\nGet in touch with Xtract.io today to request a sample or a custom restaurant dataset.","terms_of_service":"https://www.xtract.io/privacy-policy","contact_email":"support@datarade.ai","privacy_policy_link":"https://www.xtract.io/privacy-policy","assets":["ASSET_TYPE_DATA_TABLE"]},"provider_summary":{"name":"Xtract","description":"Xtract.io is a POI data provider who help businesses to make their next strategic decision with the help of accurate POI data. Be it business expansion strategies, personalized marketing messages, or understanding competition, you need location intelligence. \r\n\r\nWe empower businesses with POI data that covers several attributes including demographic data, competitor location, positive influencers, negative influencers, and other key attributes like address, city, state, zip code, latitude, and longitude etc.\r\n\r\nWe have a rich repository of 6 million POIs in total across 5000 categories for 3 key regions (US, UK, and Canada)."},"popularity":1305},{"id":"553a030d-3b18-4591-9811-596a449df445","summary":{"name":"SAMPLE AI in Consumer Decision Making | Global Coverage | 190+ Countries","subtitle":"Consumer insights edge: track how shoppers across cities and categories use AI tools like ChatGPT, Gemini, and ...","share":{"name":"rwazi_aada12c2_43b3_4114_8db8_6f5b4f062608_samples_share","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1755791791257,"updated_at":1755791791257,"categories":["ADVERTISING_AND_MARKETING","ECONOMICS","OTHER"],"listingType":"STANDARD","provider_id":"0296f22e-95af-40d9-b392-ea64d68cec1b"},"detail":{"description":"AI in Consumer Decision-Making: Global Zero-Party Dataset\n\nThis dataset captures how consumers around the world are using AI tools like ChatGPT, Perplexity, Gemini, Claude, and Copilot to guide their purchase decisions. It spans multiple product categories, demographics, and geographies, mapping the emerging role of AI as a decision-making companion across the consumer journey.\n\nWhat Makes This Dataset Unique\n\nUnlike datasets inferred from digital traces or modeled from third-party assumptions, this collection is built entirely on zero-party data: direct responses from consumers who voluntarily share their habits and preferences. That means the insights come straight from the people making the purchases, ensuring unmatched accuracy and relevance.\n\nFor FMCG leaders, retailers, and financial services strategists, this dataset provides the missing piece: visibility into how often consumers are letting AI shape their decisions, and where that influence is strongest.\n\nDataset Structure\n\nEach record is enriched with:\nProduct Category – from high-consideration items like electronics to daily staples such as groceries and snacks.\nAI Tool Used – identifying whether consumers turn to ChatGPT, Gemini, Perplexity, Claude, or Copilot.\nInfluence Level – the percentage of consumers in a given context who rely on AI to guide their choices.\nDemographics – generational breakdowns from Gen Z through Boomers.\nGeographic Detail – city- and country-level coverage across Africa, LATAM, Asia, Europe, and North America.\n\nThis structure allows filtering and comparison across categories, age groups, and markets, giving users a multidimensional view of AI’s impact on purchasing.\n\nWhy It Matters\n\nAI has become a trusted voice in consumers’ daily lives. From meal planning to product comparisons, many people now consult AI before making a purchase—often without realizing how much it shapes the options they consider. For brands, this means that the path to purchase increasingly runs through an AI filter.\n\nThis dataset provides a comprehensive view of that hidden step in the consumer journey, enabling decision-makers to quantify:\nHow much AI shapes consumer thinking before they even reach the shelf or checkout.\nWhich product categories are most influenced by AI consultation.\nHow adoption varies by geography and generation.\nWhich AI platforms are most commonly trusted by consumers.\n\nOpportunities for Business Leaders\n\nFMCG & Retail Brands: Understand where AI-driven decision-making is already reshaping category competition.\nMarketers: Identify demographic segments most likely to consult AI, enabling targeted strategies.\nRetailers: Align assortments and promotions with the purchase patterns influenced by AI queries.\nInvestors & Innovators: Gauge market readiness for AI-integrated commerce solutions.\n\nThe dataset doesn’t just describe what’s happening—it opens doors to the “so what” questions that define strategy. Which categories are becoming algorithm-driven? Which markets are shifting fastest? Where is the opportunity to get ahead of competitors in an AI-shaped funnel?\n\nWhy Now\n\nConsumer AI adoption is no longer a forecast; it is a daily behavior. Just as search engines once rewrote the rules of marketing, conversational AI is quietly rewriting how consumers decide what to buy. This dataset offers an early, detailed view into that change, giving brands the ability to act while competitors are still guessing.\n\nWhat You Get\n\nUsers gain:\nA global, city-level view of AI adoption in consumer decision-making.\nCross-category comparability to see where AI influence is strongest and weakest.\nGenerational breakdowns that show how adoption differs between younger and older cohorts.\nAI platform analysis, highlighting how tool preferences vary by region and category.\nEvery row is powered by zero-party input, ensuring the insights reflect actual consumer behavior—not modeled assumptions.\n\nHow It’s Used\n\nLeverage this data to:\n\nValidate strategies before entering new markets or categories.\nBenchmark competitors on AI readiness and influence.\nIdentify growth opportunities in categories where AI-driven recommendations are rapidly shaping decisions.\nAnticipate risks where brand visibility could be disrupted by algorithmic mediation.\n\nCore Insights\n\nThe full dataset reveals:\nSurprising adoption curves across categories where AI wasn’t expected to play a role.\nGeographic pockets where AI has already become a standard step in purchase decisions.\nDemographic contrasts showing who trusts AI most—and where skepticism still holds.\nClear differences between AI platforms and the consumer profiles most drawn to each.\n\nThese patterns are not visible in traditional retail data, sales reports, or survey summaries. 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Extensions are also used to provide additional information for many injury codes.\n- Pregnancy trimester is designated for ICD-10-CM codes in the pregnancy, delivery and puerperium chapter.\n- Postoperative codes are expanded and now distinguish between intraoperative and post-procedural complications.\n- There are new concepts that did not exist in ICD-9-CM, such as under dosing, blood type, the Glasgow Coma Scale, and alcohol level\n\n**Benefits**\n- Useful for physicians, coders and billing companies to understand icd-9, icd-10-cm and icd-10-pcs code as what conditions their members were treated for so that claims can be settled correctly.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [ICD Oncology Histology Validation List](https://www.johnsnowlabs.com/marketplace/icd-oncology-histology-validation-list)\n\t- This dataset shows the International Classification of Diseases for Oncology (ICD-O). ICD-O is a dual classification, with coding systems for both topography and morphology. Topography codes describe the anatomical site of origin of the neoplasm while morphology codes describe the characteristics of a tumor. Both these ICD codes are important in histology validation.\n- [ICD-10 CM Age Restriction](https://www.johnsnowlabs.com/marketplace/icd-10-cm-age-restriction)\n\t- This dataset contains the International Classification of Diseases, Clinical Modification, 10th edition Age Restriction Database contains information on the age-restricted diagnosis codes of the diagnoses coding system, ICD-10-CM.\n- [ICD-10 CM Codes Categorized as Complication or Comorbidity or Major CC](https://www.johnsnowlabs.com/marketplace/icd-10-cm-codes-categorized-as-complication-or-comorbidity-or-major-cc)\n\t- The dataset contains a list of all of the codes that are defined as either a complication or comorbidity (CC) or a major complication or comorbidity (MCC) when used as a secondary diagnosis. If the CC or MCC is allowed with all principal diagnoses, then the phrase NoExcl follows the CC/MCC indicator. Otherwise, a link is given to a collection of diagnosis codes which, when used as the principal diagnosis, will cause the CC or MCC to be considered as only a non CC.\n- [ICD-10 CM Codes Categorized as Major Complication or Comorbidity Only](https://www.johnsnowlabs.com/marketplace/icd-10-cm-codes-categorized-as-major-complication-or-comorbidity-only)\n\t- This dataset contains a list of all of the codes which are assigned as a Major CC (Major Complication or Comorbidity) only for patients discharged alive. Otherwise, they are assigned as a non-CC.\n- [ICD-10 CM Diagnosis Codes 2016](https://www.johnsnowlabs.com/marketplace/icd-10-cm-diagnosis-codes-2016)\n\t- This dataset contains the International Classification of Diseases, Clinical Modification, 10th Edition (ICD-10-CM) 2016 files that contain information on the new diagnosis coding system, ICD-10-CM, that is a replacement for ICD-9-CM, Volumes 1 and 2. These 2016 ICD-10-CM codes are to be used for services provided from October 1, 2015 through September 30, 2016.\n- [ICD-10 CM Diagnosis Codes 2017](https://www.johnsnowlabs.com/marketplace/icd-10-cm-diagnosis-codes-2017)\n\t- This dataset contains the International Classification of Diseases, Clinical Modification, 10th Edition (ICD-10-CM) 2017 files that contain information on the new diagnosis coding system, ICD-10-CM, that is a replacement for ICD-9-CM, Volumes 1 and 2. These 2017 ICD-10-CM codes are to be used for services provided from October 1, 2016 through September 30, 2017.\n- [ICD-10 CM Diagnosis Codes 2018](https://www.johnsnowlabs.com/marketplace/icd-10-cm-diagnosis-codes-2018)\n\t- This dataset contains the International Classification of Diseases, Clinical Modification, 10th Edition (ICD-10-CM) 2018 files that contain information on the new diagnosis coding system, ICD-10-CM, that is a replacement for ICD-9-CM, Volumes 1 and 2. These 2018 ICD-10-CM codes are to be used for services provided from October 1, 2017 through September 30, 2018.\n- [ICD-10 CM Diagnosis Codes 2019](https://www.johnsnowlabs.com/marketplace/icd-10-cm-diagnosis-codes-2019)\n\t- This dataset contains the International Classification of Diseases, Clinical Modification, 10th Edition (ICD-10-CM) 2019 files that contain information on the new diagnosis coding system, ICD-10-CM, that is a replacement for ICD-9-CM, Volumes 1 and 2. These 2019 ICD-10-CM codes are to be used for services provided from October 1, 2018 through September 30, 2019.\n- [ICD-10 CM Diagnosis Codes 2020](https://www.johnsnowlabs.com/marketplace/icd-10-cm-diagnosis-codes-2020)\n\t- This dataset contains the International Classification of Diseases, Clinical Modification, 10th Edition (ICD-10-CM) 2020 files that contain information on the new diagnosis coding system, ICD-10-CM, that is a replacement for ICD-9-CM, Volumes 1 and 2. These 2020 ICD-10-CM codes are to be used for services provided from October 1, 2019 through September 30, 2020.\n- [ICD-10 CM Diagnosis Codes 2021](https://www.johnsnowlabs.com/marketplace/icd-10-cm-diagnosis-codes-2021)\n\t- This dataset contains the International Classification of Diseases, Clinical Modification, 10th Edition (ICD-10-CM) 2021 files that contain information on the new diagnosis coding system, ICD-10-CM, that is a replacement for ICD-9-CM, Volumes 1 and 2. These 2020 ICD-10-CM codes are to be used for services provided from October 1, 2020 through September 30, 2021.\n- [ICD-10 CM Diagnosis Codes 2022](https://www.johnsnowlabs.com/marketplace/icd-10-cm-diagnosis-codes-2022)\n\t- This dataset contains the International Classification of Diseases, Clinical Modification, 10th Edition (ICD-10-CM) 2022 files that contain information on the new diagnosis coding system, ICD-10-CM, that is a replacement for ICD-9-CM, Volumes 1 and 2. These 2020 ICD-10-CM codes are to be used for services provided from October 1, 2021 through September 30, 2022.\n- [ICD-10 CM Diagnosis Codes 2023](https://www.johnsnowlabs.com/marketplace/icd-10-cm-diagnosis-codes-2023)\n\t- This dataset contains the International Classification of Diseases, Clinical Modification, 10th Edition (ICD-10-CM) 2023 files that contain information on the new diagnosis coding system, ICD-10-CM, that is a replacement for ICD-9-CM, Volumes 1 and 2. These 2023 ICD-10-CM codes are to be used for discharges occurring from October 1, 2022 through September 30, 2023 and for patient encounters occurring from October 1, 2022 through September 30, 2023.\n- [ICD-10 CM Gender Restriction](https://www.johnsnowlabs.com/marketplace/icd-10-cm-gender-restriction)\n\t- This dataset contains the International Classification of Diseases, Clinical Modification, 10th edition Gender Restriction Database that contains information on the gender restricted diagnosis codes of the coding system, ICD-10-CM.\n- [ICD-10 CM to ICD-9 CM Diagnosis Code GEM](https://www.johnsnowlabs.com/marketplace/icd-10-cm-to-icd-9-cm-diagnosis-code-gem)\n\t- This dataset shows the ICD-10 (International Classification of Diseases), CM (Clinical Modification), 10th Edition (2016 to 2018) to ICD-9 CM Diagnosis Codes (GEM) General Reimbursement Mappings. These ICD-10-CM codes are to be used for services provided from October 1, 2016 through September 30, 2018.\n- [ICD-10 CM to ICD-9 CM Diagnosis Codes Reimbursement Mappings 2016](https://www.johnsnowlabs.com/marketplace/icd-10-cm-to-icd-9-cm-diagnosis-codes-reimbursement-mappings-2016)\n\t- This dataset shows the ICD-10 (International Classification of Diseases), CM (Clinical Modification), 10th Edition 2016 to ICD-9 CM Diagnosis Codes Reimbursement Mappings. That is a replacement for ICD-9-CM, Volumes 1 and 2. These 2016 ICD-10-CM codes are to be used for services provided from October 1, 2015 through September 30, 2016.\n- [ICD-10 CM to ICD-9 CM Procedure Codes Reimbursement Mappings 2016](https://www.johnsnowlabs.com/marketplace/icd-10-cm-to-icd-9-cm-procedure-codes-reimbursement-mappings-2016)\n\t- The 2016 update of the Centers for Medicare and Medicaid Studies (CMS) public domain one-to-one applied reimbursement mappings of the ICD-10-CM (diagnosis) and ICD-10-PCS (procedure) code systems to the ICD-9-CM Volume 1 (diagnosis) and ICD-9-CM Volume 3 (procedure) code systems respectively. These 2016 ICD-10-PCS codes are to be used for discharges occurring from October 1, 2015 through September 30, 2016.\n- [ICD-10 PCS Procedure Codes 2016](https://www.johnsnowlabs.com/marketplace/icd-10-pcs-procedure-codes-2016)\n\t- The International Classification of Diseases, Clinical Modification, 10th Edition (ICD-10-PCS) 2016 file contains information on the new procedure coding system, ICD-10-PCS, that is a replacement for ICD-9-CM, Volume 3. These 2016 ICD-10-PCS codes are to be used for discharges occurring from October 1, 2015 through September 30, 2016.\n- [ICD-10 PCS Procedure Codes 2017](https://www.johnsnowlabs.com/marketplace/icd-10-pcs-procedure-codes-2017)\n\t- The International Classification of Diseases, Clinical Modification, 10th Edition (ICD-10-PCS) 2017 file contains information on the new procedure coding system, ICD-10-PCS, that is a replacement for ICD-9-CM, Volume 3. These 2017 ICD-10-PCS codes are to be used for discharges occurring from October 1, 2016 through September 30, 2017.\n- [ICD-10 PCS Procedure Codes 2018](https://www.johnsnowlabs.com/marketplace/icd-10-pcs-procedure-codes-2018)\n\t- The International Classification of Diseases, Clinical Modification, 11th Edition (ICD-10-PCS) 2018 file contains information on the new procedure coding system, ICD-10-PCS, that is a replacement for ICD-9-CM, Volume 3. These 2018 ICD-10-PCS codes are to be used for discharges occurring from October 1, 2017 through September 30, 2018.\n- [ICD-10 PCS Procedure Codes 2019](https://www.johnsnowlabs.com/marketplace/icd-10-pcs-procedure-codes-2019)\n\t- The International Classification of Diseases, Clinical Modification, 10th Edition (ICD-10-PCS) 2019 file contains information on the new procedure coding system, ICD-10-PCS, that is a replacement for ICD-9-CM, Volume 3. These 2019 ICD-10-PCS codes are to be used for discharges occurring from October 1, 2018 through September 30, 2019.\n- [ICD-10 PCS Procedure Codes 2020](https://www.johnsnowlabs.com/marketplace/icd-10-pcs-procedure-codes-2020)\n\t- The International Classification of Diseases, Clinical Modification, 10th Edition (ICD-10-PCS) 2020 file contains information on the new procedure coding system, ICD-10-PCS, that is a replacement for ICD-9-CM, Volume 3. These 2019 ICD-10-PCS codes are to be used for discharges occurring from October 1, 2019 through September 30, 2020.\n- [ICD-10 PCS Procedure Codes 2021](https://www.johnsnowlabs.com/marketplace/icd-10-pcs-procedure-codes-2021)\n\t- The International Classification of Diseases, Clinical Modification, 10th Edition (ICD-10-PCS) 2021 file contains information on the new procedure coding system, ICD-10-PCS, that is a replacement for ICD-9-CM, Volume 3. These 2021 ICD-10-PCS codes are to be used for discharges occurring from October 1, 2020 through September 30, 2021.\n- [ICD-10 PCS Procedure Codes 2022](https://www.johnsnowlabs.com/marketplace/icd-10-pcs-procedure-codes-2022)\n\t- The International Classification of Diseases, Clinical Modification, 10th Edition (ICD-10-PCS) 2022 file contains information on the new procedure coding system, ICD-10-PCS, that is a replacement for ICD-9-CM, Volume 3. These 2022 ICD-10-PCS codes are to be used for discharges occurring from October 1, 2021 through September 30, 2022.\n- [ICD-10 PCS Procedure Codes 2023](https://www.johnsnowlabs.com/marketplace/icd-10-pcs-procedure-codes-2023)\n\t- The International Classification of Diseases, Clinical Modification, 10th Edition (ICD-10-PCS) 2023 file contains information on the new procedure coding system, ICD-10-PCS, that is a replacement for ICD-9-CM, Volume 3. These 2023 ICD-10-PCS codes are to be used for discharges occurring from October 1, 2022 through September 30, 2023.\n- [ICD-10 PCS to ICD-9 PCS Procedure Code GEM](https://www.johnsnowlabs.com/marketplace/icd-10-pcs-to-icd-9-pcs-procedure-code-gem)\n\t- This dataset shows the 2016-2018 update of the Centers for Medicare and Medicaid Studies (CMS) public domain one-to-one applied general reimbursement mappings of the ICD-10-CM (diagnosis) and ICD-10-PCS (procedure) code systems to the ICD-9-CM Volume 1 (diagnosis) and ICD-9-CM Volume 3 (procedure) code systems respectively. These 2018 ICD-10-PCS codes are to be used for discharges occurring from October 1, 2016 through September 30, 2018.\n- [ICD-9 CM Diagnosis Codes](https://www.johnsnowlabs.com/marketplace/icd-9-cm-diagnosis-codes)\n\t- The International Classification of Diseases, Clinical Modification, 9th Edition (ICD-9-CM), Volumes 1 & 2, is used in assigning codes to diagnoses associated with inpatient, outpatient, and physician office utilization in the USA. The US-based ICD-9-CM is based on the international ICD but provides for additional morbidity detail. ICD-9 is no longer updated since it has been deprecated by ICD-10.\n- [ICD-9 CM Procedure Codes](https://www.johnsnowlabs.com/marketplace/icd-9-cm-procedure-codes)\n\t- The International Classification of Diseases, Clinical Modification, 9th Edition (ICD-9-CM), Volume 3, is used in assigning codes to medical procedures associated with hospital utilization in the USA. The US-based ICD-9-CM is based on the international ICD but provides for additional morbidity detail. ICD-9 is no longer updated since it has been deprecated by ICD-10.\n- [ICD-9 ICD-10 Avoidable Emergency Room Visits](https://www.johnsnowlabs.com/marketplace/icd-9-icd-10-avoidable-emergency-room-visits)\n\t- In July 2007, the Medi-Cal Managed Care Division of the California Department of Health Care Services (DHCS) initiated a statewide collaborative Quality Improvement Project (QIP) with all contracted managed care plans (plans) focused on reducing avoidable emergency room (ER) visits among Medi-Cal managed care beneficiaries. This dataset showcases this avoidable emergency room (ER) visits.\n- [New And Revised ICD-10-PCS Codes FY 2017 Updates](https://www.johnsnowlabs.com/marketplace/new-and-revised-icd-10-pcs-codes-fy-2017-updates)\n\t- The International Classification of Diseases, Clinical Modification, New And Revised ICD-10-PCS Codes FY 2017 file contains information on the new and revised procedure coding system, ICD-10-PCS. These 2017 ICD-10-PCS codes are to be used for discharges occurring from October 1, 2016 through September 30, 2017.\n- [New And Revised ICD-10-PCS Codes FY 2018 Updates](https://www.johnsnowlabs.com/marketplace/new-and-revised-icd-10-pcs-codes-fy-2018-updates)\n\t- This dataset contains the information on the International Classification of Diseases, Clinical Modification, New And Revised ICD-10-PCS Codes FY 2018 file, ICD-10-PCS. These 2018 ICD-10-PCS codes are to be used for discharges occurring from October 1, 2017 through September 30, 2018.\n- [Principal Diagnosis PDX Exclusion List](https://www.johnsnowlabs.com/marketplace/principal-diagnosis-pdx-exclusion-list)\n\t- This dataset shows the presence of various diagnoses that is used as a secondary diagnosis that leads to substantially increased hospital resource use and are categorized as either complication or comorbidity (CC) or major complication or comorbidity (MCC). Some MCCs and CCs are excluded because they are too closely related to the principal diagnoses. This is called the PDX Exclusion List that identifies conditions that will not be considered a CC or MCC for a given principal diagnosis.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated\n- Data Updates\n - Data updates support replace-on-update: outdated foreign keys are deprecated, not deleted\n\n**Our data is curated and enriched by domain experts** \n\n Each dataset is manually curated by our team of doctors, pharmacists, public health & medical billing experts:\n- Field names, descriptions, and normalized values are chosen by people who actually understand their meaning\n- Healthcare & life science experts add categories, search keywords, descriptions and more to each dataset\n- Both manual and automated data enrichment supported for clinical codes, providers, drugs, and geo-locations\n- The data is always kept up to date – even when the source requires manual effort to get updates\n- Support for data subscribers is provided directly by the domain experts who curated the data sets\n- Every data source’s license is manually verified to allow for royalty-free commercial use and redistribution.\n\n**Need Help?**\nIf you have questions about our products, contact us at [info@johnsnowlabs.com](mailto:info@johnsnowlabs.com). \n 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Having objective data that is consistently updated provides a solid pulse for models and tracking success of campaigns.\n\nIntel and AMD compete with each new generation of CPU product they offer. There are other supply chain vendors in the market, such as Ampere Computing to consider in the space along with the home-grown offerings (e.g., AWS Graviton) which affect the percent of market available to other providers.\n\nThis CPU market share data provides a snapshot view of the data as specific points in time. This data can be updated multiple times every month for a pulse of the market changes, particularly as new generations and new providers come online.\n\nMarket intelligence analysts can use this data to have objective data on the providers of CPUs for the public cloud. This information is a strong representative sample for private cloud and private deployments as well. Data can be divided based on a normalized generation across providers. 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All data comes from a single source, ensuring **consistency and comparability** across markets.\n\nThe Sociodemographics-Bundle Basic for Lithunia includes the following **variables**:\n- [population](https://www.mbi-geodata.com/en/sociodemographic-data/population/)\n- [gender](https://www.mbi-geodata.com/en/sociodemographic-data/sex-gender/)\n- [age structure](https://www.mbi-geodata.com/en/sociodemographic-data/agebands/) (15-year age bands)\n- household figures ([number of households](https://www.mbi-geodata.com/en/sociodemographic-data/households/) and [households by type](https://www.mbi-geodata.com/en/sociodemographic-data/households-by-type/))\n\nOur sociodemographic data is offered for each country on different **levels**:\n- **Postal Code Level:** This fine-grained level of detail makes the data suitable for a wide range of applications – for example, enriching customer databases, which are often organized by postal code.\n- **Administrative Level:** Covers both larger administrative areas (e.g., regions, states, provinces) as well as finer subdivisions such as districts, counties, or municipalities.\n- **Micro Level**: Subdivisions of postal code or administrative boundaries, such as census areas or street segments. This is the most granular level for local analysis.\n\n**Please note:** The dataset available for free download is a **sample dataset** covering a limited number of geographic areas. If you wish to access the full dataset (covering all areas) or if you require additional variables or geographic levels, please contact us. We will be happy to help you find a product that fits your needs.\n \n**Additional Insights**: https://www.mbi-geodata.com/en/sociodemographic-data/\n\n**Data Structure**:\nAGE_F0014: Population (Nighttime) by age: 0 - 14 years, female\nAGE_F1529: Population (Nighttime) by age: 15 - 29 years, female\nAGE_F3044: Population (Nighttime) by age: 30 - 44 years, female\nAGE_F4559: Population (Nighttime) by age: 45 - 59 years, female\nAGE_F60PL: Population (Nighttime) by age: 60 years and above, female\nAGE_M0014: Population (Nighttime) by age: 0 - 14 years, male\nAGE_M1529: Population (Nighttime) by age: 15 - 29 years, male\nAGE_M3044: Population (Nighttime) by age: 30 - 44 years, male\nAGE_M4559: Population (Nighttime) by age: 45 - 59 years, male\nAGE_M60PL: Population (Nighttime) by age: 60 years and above, male\nAGE_T0014: Population (Nighttime) by age: 0 - 14 years, total\nAGE_T1529: Population (Nighttime) by age: 15 - 29 years, male\nAGE_T3044: Population (Nighttime) by age: 30 - 44 years, total\nAGE_T4559: Population (Nighttime) by age: 45 - 59 years, total\nAGE_T60PL: Population (Nighttime) by age: 60 years and above, total\nCTRYCODE: Two digit country code by ISO 3166\nFEMALE: Population (Nighttime): females\nHH_SIZE: Average household size\nHH_T: Households: total number\nMALE: Population (Nighttime): males\nNAME: Name of administrative area / Name of the most populous administrative area / Name of the respective micro-area or the next higher level\nADMINCODE / POSTCODE / MICROCODE Administrative code / Postal code / Micro-code\nP_PRM: Population (Nighttime) year average: per mill of country\nP_T: Population (Nighttime) year average: total number","terms_of_service":"https://service-mbi-geodata.com/s/HNp7Z2tws4WDqWd","documentation_link":"https://www.mbi-geodata.com/en/mbi-supplier-manufacturer-worldwide-geodata/europe/lithuania-maps/","privacy_policy_link":"https://www.mbi-geodata.com/en/data-protection/","geographical_coverage":"[\"Europe\"]","cost":"FREE","update_frequency":{"interval":1,"unit":"YEARLY"},"collection_granularity":{"interval":1,"unit":"YEARLY"},"collection_date_start":1704098348105,"collection_date_end":1735634354748,"data_source":"MBI","assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://service-mbi-geodata.com/s/EjP6iGF8GgaJZLG"},"provider_summary":{"name":"Michael Bauer International GmbH – MBI Geodata","description":"For over 15 years, Michael Bauer International (MBI) has been a leading provider of globally consistent and comparable geographic data, including more than 3 million postcode areas and other detailed datasets like sociodemographics and purchasing power. 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Covering over 13,000 locations at water bodies—including rivers, streams, reservoirs, lakes, and coastal areas—this dataset provides invaluable insights for environmental monitoring, infrastructure planning, disaster preparedness, and more. With Divirod's advanced water analytics, researchers, policymakers, and businesses can unlock actionable intelligence to support sustainable water management and informed decision-making \n\n**Use cases**\n\n- Research Institutions: to conduct in-depth studies on water level fluctuations, climate change impacts, and hydrological trends. \n\n- Urban Planning: utilize historical water level data to assess flood risks, develop sustainable water management strategies, and enhance resilience against extreme weather events. \n\n- Policy Making: make informed decisions into water level dynamics, supporting initiatives related to environmental conservation, disaster preparedness, and infrastructure development. \n\n- Environmental Monitoring: monitor water level changes over time to assess the health of aquatic ecosystems, detect anomalies, and prioritize conservation efforts. \n\n- Engineering Applications: design infrastructure projects, such as dams, levees, and irrigation systems, considering historical water level patterns. \n\n**Product Details**\n\n- Quality Controlled: rigorous quality control processes ensure accuracy and reliability. \n\n- Data scoring: gold, silver, and bronze data quality scoring offers a means to rapidly filter the impacts of potential errors in data, including offline or outlying data points. \n\n- Normalized: consistency across different sources and locations, facilitating seamless integration into your projects. \n \n- Aggregated: public and proprietary data sources are unified to provide a comprehensive overview of water level trends across the United States. \n\nYou can find a description of all columns present in the dataset here https://static.divirod.com/documents/data-dictionary.pdf ","terms_of_service":"https://www.divirod.com/privacy","documentation_link":"https://static.divirod.com/documents/data-dictionary.pdf","privacy_policy_link":"https://www.divirod.com/privacy","geographical_coverage":"[\"United States and Canada\"]","cost":"PAID","update_frequency":{"interval":1,"unit":"DAILY"},"collection_granularity":{"interval":1,"unit":"MINUTE"},"collection_date_start":1577902178833,"collection_date_end":1733335808806,"data_source":"USGS, NOAA, USBR, Gloss, Divirod, IOOS. ","size":1500.0,"assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"Divirod Inc.","description":"Divirod is digitizing the measurement and monitoring of water and water risk with an end-to-end solution that is cost effective and easy to deploy. 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It is a useful tool for activities such as looking for new safety concerns that might be related to a marketed product, evaluating a manufacturer's compliance with reporting regulations and responding to outside requests for information. The reports in FAERS are evaluated by clinical reviewers, in the Center for Drug Evaluation and Research (CDER) and the Center for Biologics Evaluation and Research (CBER), to monitor the safety of products after they are approved by FDA. These reports are submitted to FDA by healthcare professionals (such as physicians, pharmacists, nurses and others) and consumers (such as patients, family members, lawyers and others). Healthcare professionals and consumers may also report to the products’ manufacturers who then send these reports to the FDA. The datasets in FAERS data package contain much relevant information such as patient demographics, drug name, dose and route of administration, diagnosis and indication for use, therapy starts and ends date and patient outcome.\n\n**Benefits**\n- useful for physicians, pharmacists, pharmaceutical companies, medical students and patients (general public).\n- faers database is available on fda’s website in raw form, whereas a john snow labs (jsl) customer gets it in a clean and normalized form ready to use and create his own custom reports.\n- quarter-wise data merged into one file.\n- easy to comprehend for the customers who are unfamiliar with the medical terminology as all abbreviated terms are replaced with full form, unlike faers original datasets which are full of abbreviations.\n- datasets are available for the year 2014, 2015, 2016, 2017, 2018 and 2019.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [FDA Adverse Events Reporting System Demographics 2019](https://www.johnsnowlabs.com/marketplace/fda-adverse-events-reporting-system-demographics-2019)\n\t- The FDA Adverse Event Reporting System (FAERS) contains information on medication errors, quality complaints and drug-related adverse events that were submitted to the FDA (U.S. Food and Drug Administration) in 2019. The Demographics dataset contains patient demographic and administrative information, a single record for each event report.\n- [FDA Adverse Events Reporting System Drug 2019](https://www.johnsnowlabs.com/marketplace/fda-adverse-events-reporting-system-drug-2019)\n\t- The FDA Adverse Event Reporting System (FAERS) contains information on medication errors, quality complaints and drug-related adverse events that were submitted to FDA (U.S. Food and Drug Administration) in 2019. The Drug dataset contains drug/biologic information for as many medications as were reported for the event (1 or more per event).\n- [FDA Adverse Events Reporting System Drug Indication 2019](https://www.johnsnowlabs.com/marketplace/fda-adverse-events-reporting-system-drug-indication-2019)\n\t- The FDA Adverse Event Reporting System (FAERS) contains information on medication errors, quality complaints and drug-related adverse events that were submitted to the FDA (U.S. Food and Drug Administration) in 2019. The Indication dataset contains all \"Medical Dictionary for Regulatory Activities\" (MedDRA) terms coded for the indications for use (diagnoses) for the reported drugs (0 or more per drug per event).\n- [FDA Adverse Events Reporting System Drug Reaction 2019](https://www.johnsnowlabs.com/marketplace/fda-adverse-events-reporting-system-drug-reaction-2019)\n\t- The FDA Adverse Event Reporting System (FAERS) contains information on medication errors, quality complaints and drug-related adverse events that were submitted to FDA (U.S. Food and Drug Administration) in 2019. The drug reaction dataset contains all \"Medical Dictionary for Regulatory Activities\" (MedDRA) terms coded for the adverse event (1 or more).\n- [FDA Adverse Events Reporting System Drug Therapy Dates 2019](https://www.johnsnowlabs.com/marketplace/fda-adverse-events-reporting-system-drug-therapy-dates-2019)\n\t- The FDA Adverse Event Reporting System (FAERS) contains information on medication errors, quality complaints and drug-related adverse events that were submitted to FDA (U.S. Food and Drug Administration) in 2019. 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It brings your curated data models, semantic definitions, and validated workbooks directly into the conversation, so the AI assistant answers with business context instead of generic guesses.\n\n* **Please Note**: this MCP listing is region-specific. It connects to Sigma organizations hosted on **Amazon Web Services (AWS)**, in the **United States West (Oregon)** region. If your Sigma organization is hosted in a different cloud or region, use the matching listing from the ***MCP Listings*** section below — each Sigma cloud/region combination is published as its own Databricks Marketplace listing because the connection endpoint is region-specific.\n\n## ***Key capabilities***\n\n* **Search** — Discover the right workbook, data model, or connection in your Sigma organization with a plain-language question (for example, \"What data do we have on customer retention?\"). Sigma's semantic search surfaces the most relevant assets, including tables, elements, and documents.\n* **Analyze** — Ask questions about your data in natural language and get results back in the same conversation, with no SQL required. Follow-up questions (drilling into an anomaly, slicing by a new dimension, cross-referencing a second model) build on the existing context instead of starting over.\n* **Explore** — Inspect column names, data types, definitions, and descriptions for any connected data source to confirm it fits the analysis before running a query.\n* **Build** — Turn an analysis into a Sigma workbook without leaving the conversation, adding pages, tables, charts, and controls from the data you've already explored. Preview any page or element as an inline screenshot to check the result before you share it.\n\n## ***Use cases***\n\n* **Ask questions, get governed answers** — Business users get real answers from the warehouse in the same conversation where they're already working, with permissions enforced automatically.\n* **Investigate anomalies before anyone has to ask** — Schedule an agent to monitor key metrics in Sigma and automatically drill into revenue or usage drops, surfacing a plain-English summary before the next standup.\n* **Reduce tool switching** — Discovery, exploration, and analysis all happen inside the AI assistant, without opening a separate BI tab.\n\n## ***Governed by design***\n\nThe Sigma MCP Server does not introduce a new security surface. Every request inherits the requesting user's existing Sigma permissions:\n\n* **Account-level access** — Search and Analyze respect the user's assigned Sigma account type and license.\n* **Connection-level access** — The AI assistant can only reach data connections the user already has **Can use** access to.\n* **Column-level and row-level security** — Any CLS/RLS policies configured in Sigma apply identically through the AI assistant. It cannot describe a column, or return a row, the user cannot already see in Sigma.\n* **Workspace permissions** — The AI assistant cannot find or query a workbook that has not been shared with the requesting user.\n\n## ***Requirements***\n* View permissions in Sigma for whatever you want to search, explore, or query (**View connections**, **View data models**, and/or **View workbooks**, plus document- and connection-level access).\n* An AI provider enabled for your Sigma organization.\n* If your organization uses an IP allowlist, the AI assistant's outbound IP ranges added to the allowlist with **Scope** set to **API**.\n\n## ***MCP Listings for Sigma on Databricks Marketplace*** \n* **Currently Supported**\n * **AWS**: [US West (`us-west-2)`](https://marketplace.databricks.com/details/b4dc5668-3131-47ec-9eb6-6a567182b022/Sigma-Computing_Sigma-MCP-AWS-US-West)\n * **GCP**: [US Central (`us-central-1`)](https://marketplace.databricks.com/details/d7f62b00-fb22-417d-89b3-4a8db9b6ab18/Sigma-Computing_Sigma-MCP-GCP-Central)\n * **Azure**: [US East (`eastus2`)](https://marketplace.databricks.com/details/9a874516-73d4-4e5a-ac35-a87696216981/Sigma-Computing_Sigma-MCP-Azure-US-East)\n\n* **Future Support**\n * **AWS**: [US East (`us-east-1`)](https://marketplace.databricks.com/provider/cfccf3c5-0092-49bd-9df2-4e62774aee96/Sigma-Computing), [Canada (`ca-central-1`)](https://marketplace.databricks.com/provider/cfccf3c5-0092-49bd-9df2-4e62774aee96/Sigma-Computing), [Europe (`eu-central-1`)](https://marketplace.databricks.com/provider/cfccf3c5-0092-49bd-9df2-4e62774aee96/Sigma-Computing), [Asia Pacific (`ap-southeast-2`)](https://marketplace.databricks.com/provider/cfccf3c5-0092-49bd-9df2-4e62774aee96/Sigma-Computing), [United Kingdom (`eu-west-2`)](https://marketplace.databricks.com/provider/cfccf3c5-0092-49bd-9df2-4e62774aee96/Sigma-Computing)\n * **Azure**: [Europe (`westeurope`)](https://marketplace.databricks.com/provider/cfccf3c5-0092-49bd-9df2-4e62774aee96/Sigma-Computing), [Canada (`canadacentral`)](https://marketplace.databricks.com/provider/cfccf3c5-0092-49bd-9df2-4e62774aee96/Sigma-Computing), [United Kingdom (`uksouth`)](https://marketplace.databricks.com/provider/cfccf3c5-0092-49bd-9df2-4e62774aee96/Sigma-Computing), [Australia (`australiaeast`)](https://marketplace.databricks.com/provider/cfccf3c5-0092-49bd-9df2-4e62774aee96/Sigma-Computing)\n * **GCP**: [Kingdom of Saudi Arabia (`me-central2`)](https://marketplace.databricks.com/provider/cfccf3c5-0092-49bd-9df2-4e62774aee96/Sigma-Computing)\n\n## ***Resources***\n* [MCP Documentation](https://help.sigmacomputing.com/docs/use-sigma-mcp-server)\n* [Supported regions](https://help.sigmacomputing.com/docs/region-warehouse-and-feature-support#supported-cloud-platforms-and-regions)","terms_of_service":"https://www.sigmacomputing.com/legal/terms-of-service","documentation_link":"https://help.sigmacomputing.com/docs/use-sigma-mcp-server","privacy_policy_link":"https://www.sigmacomputing.com/legal/privacy-policy","assets":["ASSET_TYPE_MCP"],"license":""},"provider_summary":{"name":"Sigma Computing","description":"Sigma is the AI, Apps and Analytics platform connected to the cloud data warehouse.\n\nUsing Sigma, business and technical teams can build intelligent, production-ready AI apps that accelerate and automate operational workflows. Sigma provides a spreadsheet interface, SQL and Python editors, visual builders, and native AI to help teams turn live data into interactive applications, analysis, reports, and embedded experiences."},"popularity":1876},{"id":"d5569d88-0ae8-4c8e-a006-bc5f148227ab","summary":{"name":"SAMPLE Serpstat: Website Data API | Unlock Key SEO Metrics | Industry Insights | Competitor Analysis","subtitle":"Gain access to Serpstat's Website Data API, providing key SEO metrics, organic traffic estimation, and insights into ...","share":{"name":"serpstat_e51eaf75_6f7c_41e2_9ced_8e0370c4810f_samples_share","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1734957296003,"updated_at":1738845841373,"categories":["OTHER"],"listingType":"STANDARD","provider_id":"af152969-0a09-422f-adf0-8916357720f6"},"detail":{"description":"Experience the power of Serpstat's Website Data API, offering comprehensive insights into key SEO metrics, organic traffic estimation, and semantical structure analysis. With our API, unlock valuable data to identify top-performing industry players, analyze competitor strategies, and optimize content for maximum impact.\r\n\r\nUnveil essential SEO metrics such as website visibility, organic traffic estimation, and keywords that websites rank for organically. Additionally, gain insights into keywords that websites are shown in Google Ads, providing valuable intelligence for ad targeting and optimization.\r\n\r\nUtilize our API to uncover top-performing content in any industry, enabling you to identify trends, understand user intent, and refine your content strategy accordingly.\r\n\r\nBenefit from the most cost-effective API solution, starting at just $120 per million rows. Tailor your queries to extract the most relevant data for your specific use-cases, ensuring that you're equipped with the insights necessary to thrive in today's competitive digital landscape.","terms_of_service":"https://serpstat.com/users/privacy-policy","contact_email":"marketplace@datarade.ai","privacy_policy_link":"https://serpstat.com/users/privacy-policy","assets":["ASSET_TYPE_DATA_TABLE"]},"provider_summary":{"name":"Serpstat","description":"The go-to platform to speed up search marketing goals achievement. Customers use Serpstat to reduce the time on common search marketing tasks, cut down on repetitive, manual work and get more clients from search.\r\n\r\nThe common use cases are: competitors and backlinks research, keywords analysis and grouping for content optimization, organic and paid positions tracking, website issues analysis and resolution.\r\n\r\nThe platform also provides additional tools like white-label, team management, and API access for advanced users. Manuals, tutorials, online support, and customer success come at no additional cost.\r\n\r\nSearch marketing newcomers and pros are already on board. From SMBs and freelancers to enterprise in-house teams and agencies, including iProspect, Boosmart, Uber, Shopify, Deloitte, Philips, NewsCred, Samsung, etc."},"popularity":122},{"id":"8dfb0d1b-ec41-4b3d-bd08-6771089931a5","summary":{"name":"Life Sciences ETL using Matillion","subtitle":"Downloadable connectors and ETL pipelines, with step-by-step instructions to build a medallion data architecture","share":{"name":"matillion-tcga-solution-accelerator","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1728654844284,"updated_at":1728654844284,"categories":["HEALTH"],"listingType":"STANDARD","provider_id":"c1d82ee2-95c4-4c7a-a3d2-7814b59dd20a"},"detail":{"description":"**Overview**\nThis Life Sciences solution accelerator is powered by the Matillion Data Productivity Cloud, and demonstrates building a multi tier lakehouse data architecture to extract, load, transform and integrate large volumes of data, making it ready for statistical analysis.\n\n**Use cases**\nUse cases include:\n- Building a medallion data architecture\n- API connectivity and source file handling\n- Integrating, summarizing and pivoting data for statistical analysis and machine learning techniques such as dimensionality reduction and clustering.\n\n**Product details**\nThe solution accelerator includes:\n- Downloadable Matillion Data Productivity Cloud files: custom API connector and ETL pipelines\n- Step-by-step instructions\n\n**Additional Insights**\nFor more details, refer to the Matillion Exchange listings:\n- Downloadable [Custom API Connector](https://exchange.matillion.com/custom-connector/profiles/tcga-genomic-data-commons-data-portal/)\n- Downloadable [ETL pipelines](https://exchange.matillion.com/data-productivity-cloud/pipeline/tcga-genomic-data-commons/)\n\nMore information on:\n- The [Matillion Data Productivity Cloud](https://www.matillion.com/data-productivity-cloud)\n- The [Cancer Genome Atlas Program](https://www.cancer.gov/ccg/research/genome-sequencing/tcga)\n","terms_of_service":"https://www.matillion.com/legal","documentation_link":"https://exchange.matillion.com/data-productivity-cloud/pipeline/tcga-genomic-data-commons/","privacy_policy_link":"https://www.matillion.com/legal","geographical_coverage":"[\"Global\"]","cost":"FREE","data_source":"The Cancer Genome Atlas Program (TCGA)","assets":["ASSET_TYPE_GIT_REPO"],"license":"https://exchange.matillion.com/articles/license-types/"},"provider_summary":{"name":"Matillion","description":"Matillion is the Data Productivity Cloud. The all-in-one platform to build and manage data pipelines, create no-code data transformations, and deliver data for AI and analytics at scale."},"popularity":1695},{"id":"2013ff11-b480-46f4-aa66-1005fa1b987e","summary":{"name":"USDA Long Term Agricultural Projection","subtitle":"This data package shows the long-run projections for the US agricultural sector to 2025 includes assumptions for the U..","share":{"name":"usda_long_term_agricultural_projection","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1682356575489,"updated_at":1698965635225,"categories":["SCIENCE_AND_RESEARCH"],"listingType":"STANDARD","provider_id":"f88849f8-9d77-4de2-b4c7-03d55cc3768e"},"detail":{"description":"\n**Overview**\nThis data package shows the long-run projections for the US agricultural sector to 2025 includes assumptions for the US and international macroeconomic conditions and projections for major commodities, farm income, and U.S. agricultural trade value by the United States Department of Agriculture (USDA).\n\n**Description**\nThis data package shows the information by United States Department of Agriculture (USDA) long-term agricultural projections presented in this dataset are a departmental consensus on a long-run scenario for the agricultural sector. The projections analysis was conducted by interagency committees in USDA and reflect a composite of model results and judgment-based analyses.\n\n**Benefits**\n- Useful for analysis on usda agricultural projections and prices\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [Acreage for Major Field Crops and CRP Assumptions](https://www.johnsnowlabs.com/marketplace/acreage-for-major-field-crops-and-crp-assumptions)\n\t- Long-run projections for the US agricultural sector to 2031 includes assumptions for the US and international macroeconomic conditions and projections for major commodities, farm income, and U.S. agricultural trade value. Values are from the publication United States Department of Agriculture (USDA) Agricultural Projections to 2032, October 2022.\n- [All Cotton Trade Long Term Projections 2020 to 2032](https://www.johnsnowlabs.com/marketplace/all-cotton-trade-long-term-projections-2020-to-2032)\n\t- Long-run projections for the US agricultural sector to 2032 includes assumptions for the US and international macroeconomic conditions and projections for major commodities, farm income, and the US agricultural trade value.\n- [Barley Trade Long Term Projections 2020 to 2032](https://www.johnsnowlabs.com/marketplace/barley-trade-long-term-projections-2020-to-2032)\n\t- This dataset shows the long-run projections (Barley Trade) for the US agricultural sector to 2031 includes assumptions for the US and international macroeconomic conditions and projections for major commodities, farm income, and the US agricultural trade value. Values are from the publication United States Department of Agriculture (USDA) Agricultural Projections to 2032, October 2021.\n- [Beef Long Term Projections 2021 to 2032](https://www.johnsnowlabs.com/marketplace/beef-long-term-projections-2021-to-2032)\n\t- This dataset shows the long-run projections (Beef) for the US agricultural sector to 2031 includes assumptions for the US and international macroeconomic conditions and projections for major commodities, farm income, and the US agricultural trade value. Values are from the publication United States Department of Agriculture (USDA) Agricultural Projections to 2032, October 2022.\n- [Beef Trade Long Term Projections 2020 to 2031](https://www.johnsnowlabs.com/marketplace/beef-trade-long-term-projections-2020-to-2031)\n\t- This dataset shows the long-run projections (Beef Trade) for the US agricultural sector to 2030 includes assumptions for the US and international macroeconomic conditions and projections for major commodities, farm income, and U.S. agricultural trade value. Values are from the publication United States Department of Agriculture (USDA) Agricultural Projections to 2031, October 2021.\n- [Coarse Grains Trade Long Term Projections 2020 to 2032](https://www.johnsnowlabs.com/marketplace/coarse-grains-trade-long-term-projections-2020-to-2032)\n\t- This dataset shows the long-run projections (Coarse Grains Trade) for the US agricultural sector to 2030 includes assumptions for US and international macroeconomic conditions and projections for major commodities, farm income, and U.S. agricultural trade value. Values are from the publication United States Department of Agriculture (USDA) Agricultural Projections to 2032, October 2021.\n- [Corn Trade Long Term Projections 2020 to 2032](https://www.johnsnowlabs.com/marketplace/corn-trade-long-term-projections-2020-to-2032)\n\t- This dataset shows the long-run projections (Corn Trade) for the US agricultural sector to 2030 includes assumptions for U.S. and international macroeconomic conditions and projections for major commodities, farm income, and the US agricultural trade value. Values are from the publication United States Department of Agriculture (USDA) Agricultural Projections to 2032, October 2021.\n- [Dairy Long Term Projections](https://www.johnsnowlabs.com/marketplace/dairy-long-term-projections)\n\t- This dataset shows the Long-run projections (Dairy) for the US agricultural sector to 2031 includes assumptions for the US and international macroeconomic conditions and projections for major commodities, farm income, and U.S. agricultural trade value. Values are from the publication United States Department of Agriculture (USDA) Agricultural Projections to 2032, October 2022.\n- [Egg Long Term Projections 2021 to 2032](https://www.johnsnowlabs.com/marketplace/egg-long-term-projections-2021-to-2032)\n\t- This dataset shows the long-run projections (Egg) for the US agricultural sector to 2031 includes assumptions for the US and international macroeconomic conditions and projections for major commodities, farm income, and U.S. agricultural trade value. Values are from the publication United States Department of Agriculture (USDA) Agricultural Projections to 2032, October 2022.\n- [Farm Receipts Expenses And Income Long Term Projections 2020 to 2031](https://www.johnsnowlabs.com/marketplace/farm-receipts-expenses-and-income-long-term-projections-2020-to-2031)\n\t- This dataset shows the long-run projections (Farm Receipts Expenses And Income) for the US agricultural sector to 2030 includes xassumptions for the US and international macroeconomic conditions and projections for major commodities, farm income, and the US agricultural trade value. Values are from the publication United States Department of Agriculture (USDA) Agricultural Projections to 2031, December 2021.\n- [Fruit Nuts And Vegetables Long Term Projections 2020 to 2031](https://www.johnsnowlabs.com/marketplace/fruit-nuts-and-vegetables-long-term-projections-2020-to-2031)\n\t- This dataset shows the Long-run projections for the US agricultural sector to 2030. Includes assumptions for the US and international macroeconomic conditions and projections for major commodities, farm income, and the US agricultural trade value. Values are from the publication United States Department of Agriculture (USDA) Agricultural Projections to 2031, December 2021.\n- [Global Real GDP Growth Assumptions](https://www.johnsnowlabs.com/marketplace/global-real-gdp-growth-assumptions)\n\t- This dataset shows the long-run projections for the US agricultural sector to 2031 includes assumptions for the US and international macroeconomic conditions and projections for major commodities, farm income, and the US agricultural trade value. Values are from the publication United States Department of Agriculture (USDA) Agricultural Projections to 2032.\n- [Population Growth Assumptions](https://www.johnsnowlabs.com/marketplace/population-growth-assumptions)\n\t- This dataset shows the long-run projections for the US agricultural sector to 2030 includes assumptions for the US and international macroeconomic conditions and projections for major commodities, farm income, and U.S. agricultural trade value. Values are from the publication USDA Agricultural Projections to 2031, August 2022.\n- [Pork Long Term Projections 2020 to 2031](https://www.johnsnowlabs.com/marketplace/pork-long-term-projections-2020-to-2031)\n\t- This dataset shows the long-run projections (Pork) for the US agricultural sector to 2025 includes assumptions for the US and international macroeconomic conditions and projections for major commodities, farm income, and U.S. agricultural trade value. Values are from the publication United States Department of Agriculture (USDA) Agricultural Projections to 2031, February 2022.\n- [Pork Trade Long Term Projections 2020 to 2031](https://www.johnsnowlabs.com/marketplace/pork-trade-long-term-projections-2020-to-2031)\n\t- This dataset shows the long-run projections (Pork Trade) for the US agricultural sector to 2030 includes assumptions for the US and international macroeconomic conditions and projections for major commodities, farm income, and the US agricultural trade value. Values are from the publication United States Department of Agriculture (USDA) Agricultural Projections to 2031, October 2021.\n- [Poultry Trade Long Term Projections 2020 to 2031](https://www.johnsnowlabs.com/marketplace/poultry-trade-long-term-projections-2020-to-2031)\n\t- This dataset shows the long-run projections (Poultry Trade) for the US agricultural sector to 2030 includes assumptions for the US and international macroeconomic conditions and projections for major commodities, farm income, and the US agricultural trade value. Values are from the publication United States Department of Agriculture (USDA) Agricultural Projections to 2031, October 2021.\n- [Retail Weight of Per Capita Meat Consumption Projections 2021 to 2032](https://www.johnsnowlabs.com/marketplace/retail-weight-of-per-capita-meat-consumption-projections-2021-to-2032)\n\t- This dataset shows the long-run projections for the US agricultural sector to 2031 includes assumptions for the US and international macroeconomic conditions and projections for major commodities, farm income, and the US agricultural trade value. Values are from the publication United States Department of Agriculture (USDA) Agricultural Projections to 2032, October 2022.\n- [Rice Trade Long Term Projections 2020 to 2032](https://www.johnsnowlabs.com/marketplace/rice-trade-long-term-projections-2020-to-2032)\n\t- This dataset shows the long-run projections (Rice Trade) for the US agricultural sector to 2031 includes assumptions for the US and international macroeconomic conditions and projections for major commodities, farm income, and the US agricultural trade value. Values are from the publication United States Department of Agriculture (USDA) Agricultural Projections to 2032, October 2021.\n- [Sorghum Trade Long Term Projections 2020 to 2032](https://www.johnsnowlabs.com/marketplace/sorghum-trade-long-term-projections-2020-to-2032)\n\t- This dataset shows the long-run projections (Sorghum Trade) for the US agricultural sector to 2031 includes assumptions for the US and international macroeconomic conditions and projections for major commodities, farm income, and the US agricultural trade value. Values are from the publication United States Department of Agriculture (USDA) Agricultural Projections to 2032, October 2021.\n- [Soybean Meal Trade Long Term Projections 2020 to 2032](https://www.johnsnowlabs.com/marketplace/soybean-meal-trade-long-term-projections-2020-to-2032)\n\t- This dataset shows the long-run projections (Soybean Meal Trade) for the US agricultural sector to 2031 includes assumptions for the US and international macroeconomic conditions and projections for major commodities, farm income, and the US agricultural trade value. Values are from the publication United States Department of Agriculture (USDA) Agricultural Projections to 2032, October 2021.\n- [Soybean Oil Trade Long Term Projections 2020 to 2032](https://www.johnsnowlabs.com/marketplace/soybean-oil-trade-long-term-projections-2020-to-2032)\n\t- This dataset shows the long-run projections (Soybean Oil Trade) for the US agricultural sector to 2031 includes assumptions for the US and international macroeconomic conditions and projections for major commodities, farm income, and the US agricultural trade value. Values are from the publication United States Department of Agriculture (USDA) Agricultural Projections to 2032, October 2021.\n- [Soybean Trade Long Term Projections 2020 to 2032](https://www.johnsnowlabs.com/marketplace/soybean-trade-long-term-projections-2020-to-2032)\n\t- This dataset shows the long-run projections (Soybean Trade) for the US agricultural sector to 2031 includes assumptions for the US and international macroeconomic conditions and projections for major commodities, farm income, and the US agricultural trade value. Values are from the publication United States Department of Agriculture (USDA) Agricultural Projections to 2032, October 2021.\n- [Turkey Long Term Projections 2021 to 2032](https://www.johnsnowlabs.com/marketplace/turkey-long-term-projections-2021-to-2032)\n\t- This dataset shows the long-run projections (Turkey) for the US agricultural sector to 2031 includes assumptions for the US and international macroeconomic conditions and projections for major commodities, farm income, and U.S. agricultural trade value. Values are from the publication United States Department of Agriculture (USDA) Agricultural Projections to 2032, October 2022.\n- [US Agricultural Trade Long Term Projections 2020 to 2031](https://www.johnsnowlabs.com/marketplace/us-agricultural-trade-long-term-projections-2020-to-2031)\n\t- This dataset shows the long-run projections (US Agricultural Trade) for the US agricultural sector to 2030 includes assumptions for the US and international macroeconomic conditions and projections for major commodities, farm income, and the US agricultural trade value. Values are from the publication United States Department of Agriculture (USDA) Agricultural Projections to 2031, October 2021.\n- [US Barley Long Term Projections 2021 to 2033](https://www.johnsnowlabs.com/marketplace/us-barley-long-term-projections-2021-to-2033)\n\t- This dataset shows the long-run projections for the US agricultural sector to 2031 includes assumptions for the US and international macroeconomic conditions and projections for major commodities, farm income, and the US agricultural trade value. Values are from the publication United States Department of Agriculture (USDA) Agricultural Projections to 2033, October 2022.\n- [US Corn Long Term Projections 2021 to 2033](https://www.johnsnowlabs.com/marketplace/us-corn-long-term-projections-2021-to-2033)\n\t- This dataset shows the long-run projections for the US agricultural sector to 2031 includes assumptions for the US and international macroeconomic conditions and projections for major commodities, farm income, and the US agricultural trade value. Values are from the publication United States Department of Agriculture (USDA) Agricultural Projections to 2032, October 2022.\n- [US Long Grain Rice Long Term Projections 2021 to 2033](https://www.johnsnowlabs.com/marketplace/us-long-grain-rice-long-term-projections-2021-to-2033)\n\t- This dataset shows the long-run projections for the US agricultural sector to 2031 includes assumptions for the US and international macroeconomic conditions and projections for major commodities, farm income, and the US agricultural trade value. Values are from the publication United States Department of Agriculture (USDA) Agricultural Projections to 2033, October 2022.\n- [US Macroeconomic Assumptions](https://www.johnsnowlabs.com/marketplace/us-macroeconomic-assumptions)\n\t- This dataset shows the long-run projections for the US agricultural sector to 2031 includes assumptions for the US and international macroeconomic conditions and projections for major commodities, farm income, and the US agricultural trade value. Values are from the publication United States Department of Agriculture (USDA) Agricultural Projections to 2032.\n- [US Medium and Short Grain Rice Long Term Projections 2021 to 2033](https://www.johnsnowlabs.com/marketplace/us-medium-and-short-grain-rice-long-term-projections-2021-to-2033)\n\t- This dataset shows the long-run projections for the US agricultural sector to 2032 includes assumptions for the US and international macroeconomic conditions and projections for major commodities, farm income, and U.S. agricultural trade value. Values are from the publication United States Department of Agriculture (USDA) Agricultural Projections to 2032, October 2022.\n- [US Medium and Short Grain Rice Long Term Projections 2022](https://www.johnsnowlabs.com/marketplace/us-medium-and-short-grain-rice-long-term-projections-2022)\n\t- This dataset shows the long-run projections for the US agricultural sector to 2025 includes assumptions for the US and international macroeconomic conditions and projections for major commodities, farm income, and U.S. agricultural trade value. Values are from the publication United States Department of Agriculture (USDA) Agricultural Projections to 2031, February 2022.\n- [US Oats Long Term Projections 2021 to 2033](https://www.johnsnowlabs.com/marketplace/us-oats-long-term-projections-2021-to-2033)\n\t- This dataset shows the long-run projections (US Oats) for the US agricultural sector to 2031 includes assumptions for the US and international macroeconomic conditions and projections for major commodities, farm income, and the US agricultural trade value. Values are from the publication United States Department of Agriculture (USDA) Agricultural Projections to 2032, October 2022.\n- [US Sorghum Long Term Projections 2021 to 2033](https://www.johnsnowlabs.com/marketplace/us-sorghum-long-term-projections-2021-to-2033)\n\t- This dataset shows the long-run projections (US Sorghum) for the US agricultural sector to 2031 includes assumptions for the US and international macroeconomic conditions and projections for major commodities, farm income, and the US agricultural trade value. Values are from the publication United States Department of Agriculture (USDA) Agricultural Projections to 2032, October 2022.\n- [US Soybean and Products Long Term Projections 2020 to 2032](https://www.johnsnowlabs.com/marketplace/us-soybean-and-products-long-term-projections-2020-to-2032)\n\t- This dataset shows the long-run projections (US Soybean and Products) for the US agricultural sector to 2031 includes assumptions for the US and international macroeconomic conditions and projections for major commodities, farm income, and the US agricultural trade value. Tables are from the publication United States Department of Agriculture (USDA) Agricultural Projections to 2032, February 2022.\n- [US Sugar Long Term Projections 2021 to 2033](https://www.johnsnowlabs.com/marketplace/us-sugar-long-term-projections-2021-to-2033)\n\t- This dataset shows the long-run projections (US Sugar) for the US agricultural sector to 2033 includes assumptions for the US and international macroeconomic conditions and projections for major commodities, farm income, and the US agricultural trade value. Values are from the publication United States Department of Agriculture (USDA) Agricultural Projections to 2033, October 2022.\n- [US Total Rice Long Term Projections 2021 to 2033](https://www.johnsnowlabs.com/marketplace/us-total-rice-long-term-projections-2021-to-2033)\n\t- This dataset shows the long-run projections (US Total Rice) for the U.S. agricultural sector to 2032 includes assumptions for the US and international macroeconomic conditions and projections for major commodities, farm income, and the US agricultural trade value. Values are from the publication United States Department of Agriculture (USDA) Agricultural Projections to 2033, October 2022.\n- [US Upland Cotton Long Term Projections 2021 to 2033](https://www.johnsnowlabs.com/marketplace/us-upland-cotton-long-term-projections-2021-to-2033)\n\t- This dataset shows the long-run projections (US Upland Cotton) for the US agricultural sector to 2033 includes assumptions for the US and international macroeconomic conditions and projections for major commodities, farm income, and the US agricultural trade value. Values are from the publication United States Department of Agriculture (USDA) Agricultural Projections to 2033, October 2022.\n- [US Wheat Long Term Projections 2021 to 2033](https://www.johnsnowlabs.com/marketplace/us-wheat-long-term-projections-2021-to-2033)\n\t- This dataset shows the long-run projections (US Wheat) for the US agricultural sector to 2032 includes assumptions for the US and international macroeconomic conditions and projections for major commodities, farm income, and the US agricultural trade value. Values are from the publication United States Department of Agriculture (USDA) Agricultural Projections to 2033, October 2022.\n- [Wheat Trade Long Term Projections 2020 to 2032](https://www.johnsnowlabs.com/marketplace/wheat-trade-long-term-projections-2020-to-2032)\n\t- This dataset shows the long-run projections (Wheat Trade) for the US agricultural sector to 2031 includes assumptions for the US and international macroeconomic conditions and projections for major commodities, farm income, and the US agricultural trade value. Values are from the publication United States Department of Agriculture (USDA) Agricultural Projections to 2032, October 2021.\n- [Young Chicken Long Term Projections 2021 to 2032](https://www.johnsnowlabs.com/marketplace/young-chicken-long-term-projections-2021-to-2032)\n\t- This dataset shows the long-run projections (Young Chicken) for the US agricultural sector to 2031 includes assumptions for the US and international macroeconomic conditions and projections for major commodities, farm income, and the US agricultural trade value. Values are from the publication United States Department of Agriculture (USDA) Agricultural Projections to 2032, October 2022.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated\n- Data Updates\n - Data updates support replace-on-update: outdated foreign keys are deprecated, not deleted\n\n**Our data is curated and enriched by domain experts** \n\n Each dataset is manually curated by our team of doctors, pharmacists, public health & medical billing experts:\n- Field names, descriptions, and normalized values are chosen by people who actually understand their meaning\n- Healthcare & life science experts add categories, search keywords, descriptions and more to each dataset\n- Both manual and automated data enrichment supported for clinical codes, providers, drugs, and geo-locations\n- The data is always kept up to date – even when the source requires manual effort to get updates\n- Support for data subscribers is provided directly by the domain experts who curated the data sets\n- Every data source’s license is manually verified to allow for royalty-free commercial use and redistribution.\n\n**Need Help?**\nIf you have questions about our products, contact us at [info@johnsnowlabs.com](mailto:info@johnsnowlabs.com). \n 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By combining insights on hiring intensity, job postings, technology adoption, outsourcing engagements, and leadership movements, this dataset enables HR, workforce planning, and talent intelligence teams to benchmark against competitors and identify areas of talent competition.\nThe dataset empowers decision-makers to refine recruitment strategies, anticipate skill shortages, and monitor workforce signals at both company and regional levels. From assessing IT spend linked to workforce expansion, to understanding company priorities and leadership changes, the data offers granular visibility into the dynamics shaping talent competition. This intelligence supports evidence-based workforce planning, peer benchmarking, and proactive responses to shifts in the global talent market.\n\n**Methodology & Quality**\nThe dataset is built from a blend of public filings, company reports, job postings, outsourcing announcements, press releases, and verified third-party sources. All data undergoes rigorous normalization and entity resolution to maintain consistency across companies, regions, and business units. Updates are applied on a recurring schedule to capture near real-time shifts in talent activity, hiring intensity, and competitor strategies. 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The dataset also covers Social Security Administration services like disability determination and telephone interpreter services.\n\n**Description**\nDescription edit: split into shorter paragraphs for an easy read; grammar, spelling and format edits\nInformation on earnings and employment data by state, county and gender for individuals covered under the Social Security (SS). The dataset is shown by gender, the number of wage and salary workers and self-employed persons, the amount of their taxable earnings, and the amount paid in Social Security contributions. and the amount paid in hospital insurance contributions. The individual dataset for each state is combined and reproduced here. Information on annual volumes of the Social Security Retirement & Survivors initial claims at the national level for Asian & Pacific Islander language preferences. Information on accuracy rate figures at the state and national level for SSA disability medical determinations made by the state Disability Determination Services (DDS).\n\nInformation on the total number of percentage of fast track receipts to the total Disability Determination Services receipts. The dataset may be helpful to examine disability application filing trends by time and by state, state agency workloads, and disability claims outcomes. Information on the accuracy of the assignment of Social Security numbers (SSN) based on an end-of-line sample review of transactions that result in the release of SSN cards, Information on national Telephone Interpreter Services (TIS) call volume for a large number of Asian and Pacific Islander languages. Information on the overall satisfaction of claimants who have either filed an initial disability application or have received a decision on an appeal at the hearing level. The data available are for the U.S. general population, for particular race/ethnic groups (like Asian and Pacific Islanders) and for states population, by year and quarter.\n\n**Benefits**\n- Researchers and policymakers who are interested in studying the primary revenue source for the social security (old-age, survivors, and disability insurance, oasdi) programs will find value in the datasets. when coupled with the ssa publication, oasdi beneficiaries by state and county, data can be useful for studying the impact of the social security program in academia, private industry, and state governments and to study the federal insurance and self-employment contribution acts (fica and seca).\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [Disability Diagnoses Recipients by Census Area](https://www.johnsnowlabs.com/marketplace/disability-diagnoses-recipients-by-census-area)\n\t- The Under Age 65 Disability Diagnoses of Supplemental Security Income (SSI) Recipients by Census Area (December 2010) is produced using the data found in the SSI Annual Statistical Report of 2010. This dataset contains 9 age groups in 4 regions defined by the U.S. Census Bureau by sex and by 23 diagnostic groups.\n- [Disability Diagnoses Recipients by SSA Region](https://www.johnsnowlabs.com/marketplace/disability-diagnoses-recipients-by-ssa-region)\n\t- The Under Age 65 Disability Diagnoses of Supplemental Security Income (SSI) Recipients by Social Security Administration(SSA) Region (December 2010) is produced using the data found in the SSI Annual Statistical Report of 2010. This dataset contains 3 age groups in 10 SSA administrative regions by sex and by 23 diagnostic groups.\n- [NJ Active Pension Members](https://www.johnsnowlabs.com/marketplace/nj-active-pension-members)\n\t- This dataset shows the employees who are active members in a state pension system for the State of New Jersey.\n- [New York State SSI By Living Arrangements And Category Of Assistance](https://www.johnsnowlabs.com/marketplace/new-york-state-ssi-by-living-arrangements-and-category-of-assistance)\n\t- The dataset includes the monthly listings of the number of Supplemental Security Income recipients by type of living arrangement and category of assistance (aged, blind or disabled) for the state of New York.\n- [New York State SSI By Living Arrangements And Region](https://www.johnsnowlabs.com/marketplace/new-york-state-ssi-by-living-arrangements-and-region)\n\t- The dataset includes information on the monthly listings of the number of SSI recipients in each living arrangement classification in the Supplemental Security Income Program, by region.\n- [New York State SSI Recipients And Expenditures](https://www.johnsnowlabs.com/marketplace/new-york-state-ssi-recipients-and-expenditures)\n\t- The dataset includes the monthly listings of the number of recipients and total, federally and state expenditures for the Supplemental Security Income Program for the state of New York. The data is provided for each Local Social Services Districts (LSSD) of the New York State.\n- [SSA Asian And Pacific Islander Languages Volunteer Translators By Year](https://www.johnsnowlabs.com/marketplace/ssa-asian-and-pacific-islander-languages-volunteer-translators-by-year)\n\t- This dataset contains the national number of bi- or multilingual employees who volunteer their services as an interpreter and/or translator in their identified Asian and Pacific Islander (API) language. The data is provided by year and because usually the number of interpreter and/or translator is updated twice a year (through surveys in January and July), the month when the update was made is also provided. The data are for more than 50 Asian and Pacific Islander languages and dialects.\n- [SSA Beneficiaries Claims Asian Pacific Islander Preferred Language](https://www.johnsnowlabs.com/marketplace/ssa-beneficiaries-claims-asian-pacific-islander-preferred-language)\n\t- This dataset provides the volumes of initial claims, by fiscal years and fiscal years quarters at national level, for spoken language preferences of Asian and Pacific Islander population for Social Security and Medicare benefits.\n- [SSA Beneficiaries Claims General Population Preferred Language](https://www.johnsnowlabs.com/marketplace/ssa-beneficiaries-claims-general-population-preferred-language)\n\t- This dataset provides the volumes of initial claims, by fiscal years and fiscal years quarters at national level, for spoken language preferences among general population for Social Security and Medicare benefits.\n- [SSA Disability Determination Services Accuracy Data](https://www.johnsnowlabs.com/marketplace/ssa-disability-determination-services-accuracy-data)\n\t- This dataset provides accuracy rate figures at the state and national level for SSA disability medical determinations made by the state Disability Determination Services (DDS).\n- [SSA Extra Help With Medicare Prescription Drug Plan Cost 2010-2019](https://www.johnsnowlabs.com/marketplace/ssa-extra-help-with-medicare-prescription-drug-plan-cost-2010-2019)\n\t- This dataset describes a publicly available dataset about Social Security determinations of eligibility for Extra Help with Medicare Prescription Drug Plan Costs. The dataset includes annual data at the state level from federal FY 2010 through 2019 of Social Security Administration Extra Help decisions.\n- [SSA Extra Help With Medicare Prescription Drug Plan Cost 2016-2021](https://www.johnsnowlabs.com/marketplace/ssa-extra-help-with-medicare-prescription-drug-plan-cost-2016-2021)\n\t- This dataset describes a publicly available dataset about Social Security determinations of eligibility for Extra Help with Medicare Prescription Drug Plan Costs. The dataset includes annual data at the state level from federal FY 2016 through 2021 of Social Security Administration Extra Help decisions.\n- [SSA Extra Help With Medicare Prescription Drug Plan Cost 2016-2022](https://www.johnsnowlabs.com/marketplace/ssa-extra-help-with-medicare-prescription-drug-plan-cost-2016-2022)\n\t- This dataset describes a publicly available dataset about Social Security determinations of eligibility for Extra Help with Medicare Prescription Drug Plan Costs. The dataset includes annual data at the state level from federal FY 2016 through 2021 of Social Security Administration Extra Help decisions.\n- [SSA Fast Track Process Public Use Files By State And Region](https://www.johnsnowlabs.com/marketplace/ssa-fast-track-process-public-use-files-by-state-and-region)\n\t- The dataset includes data at national and state level for Fast Track receipts, Disability Determination Services electronic receipts and the percent of Fast Track receipts from total Disability Determination Services electronic receipts. The data contained cover fiscal years (FY) starting with FY 2010.\n- [SSA Fiscal Year Disability Claim Data](https://www.johnsnowlabs.com/marketplace/ssa-fiscal-year-disability-claim-data)\n\t- The dataset contains historical and current information about the processing of initial claims for disability benefits by the Social Security Administration (SSA). The dataset includes information from the federal fiscal year 2001 onwards concerning initial claims for disability benefits that were referred to a state agency for a disability determination.\n- [SSA Initial Disability and Hearing Level Service Satisfaction Data](https://www.johnsnowlabs.com/marketplace/ssa-initial-disability-and-hearing-level-service-satisfaction-data)\n\t- This dataset provides data at the national level for overall satisfaction of claimants who have either filed an initial disability application or have received a decision on an appeal at the hearing level.\n- [SSA Percentage of Fast Track Receipts](https://www.johnsnowlabs.com/marketplace/ssa-percentage-of-fast-track-receipts)\n\t- The dataset provides the percentage of fast-track receipts by state during the reporting fiscal year. Fast-tracked cases consist of those cases identified as Quick Disability Determination (QDD), Compassionate Allowance (CAL), or both.\n- [SSA Telephone Interpreter Services Asian Pacific Islander Language](https://www.johnsnowlabs.com/marketplace/ssa-telephone-interpreter-services-asian-pacific-islander-language)\n\t- This dataset displays national Telephone Interpreter Services (TIS) call volume for over 50 Asian and Pacific Islander languages by fiscal years and fiscal years quarters at national level. A fiscal year runs from October through September and a quarter is broken into a three-month time frame within a fiscal year.\n- [Social Security Administration Data for Enumeration Accuracy](https://www.johnsnowlabs.com/marketplace/social-security-administration-data-for-enumeration-accuracy)\n\t- This dataset provides data at the national level from federal fiscal year 2006 onwards for the accuracy of the assignment of Social Security numbers (SSN) based on an end-of line sample review of transactions that result in the release of SSN cards.\n- [US States SSI Recipients By Eligibility Age And Gender](https://www.johnsnowlabs.com/marketplace/us-states-ssi-recipients-by-eligibility-age-and-gender)\n\t- This dataset contains the Supplemental Security Income (SSI) recipients number divided by eligibility groups and age groups and each group into gender subgroups. The dataset contains data organized as specified above for all U.S. states, including equivalent areas.\n- [Workers Under Medicare Part A Contributions And Demographics](https://www.johnsnowlabs.com/marketplace/workers-under-medicare-part-a-contributions-and-demographics)\n\t- The dataset provides the number of workers with taxable earnings for Medicare Part A (Hospital Insurance) by employment type, county, state of residence and gender of workers, their eligible earnings for taxation and by the state of residence, gender and the age of workers.\n- [Workers Under Social Security OASDI Contributions And Demographics](https://www.johnsnowlabs.com/marketplace/workers-under-social-security-oasdi-contributions-and-demographics)\n\t- The dataset provides the number of workers with taxable earnings for Social Security Old-Age, Survivors, and Disability Insurance (OASDI) program by employment type, county, state of residence and gender of workers, their eligible earnings for taxation and by the state of residence, gender and the age of workers.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated\n- Data Updates\n - Data updates support replace-on-update: outdated foreign keys are deprecated, not deleted\n\n**Our data is curated and enriched by domain experts** \n\n Each dataset is manually curated by our team of doctors, pharmacists, public health & medical billing experts:\n- Field names, descriptions, and normalized values are chosen by people who actually understand their meaning\n- Healthcare & life science experts add categories, search keywords, descriptions and more to each dataset\n- Both manual and automated data enrichment supported for clinical codes, providers, drugs, and geo-locations\n- The data is always kept up to date – even when the source requires manual effort to get updates\n- Support for data subscribers is provided directly by the domain experts who curated the data sets\n- Every data source’s license is manually verified to allow for royalty-free commercial use and redistribution.\n\n**Need Help?**\nIf you have questions about our products, contact us at [info@johnsnowlabs.com](mailto:info@johnsnowlabs.com). \n ","terms_of_service":"https://www.johnsnowlabs.com/healthcare-data-library-eula/","documentation_link":"https://www.johnsnowlabs.com/marketplace/usa-social-security-administration-recipients-data-data-package","contact_email":"support@johnsnowlabs.com","privacy_policy_link":"https://www.johnsnowlabs.com/privacy-policy/","embedded_notebook_file_infos":[{"id":"9ca9b3a2-26e5-4f01-bdab-c99332ae0f6a","marketplace_file_type":"EMBEDDED_NOTEBOOK","file_parent":{"parent_id":"4ccc41ed-6088-4c70-aa48-1bf1f399b676","file_parent_type":"LISTING"},"mime_type":"text/html","download_link":"https://marketplace-file-storage-prod.s3-fips.us-east-1.amazonaws.com/EMBEDDED_NOTEBOOK/LISTING_4ccc41ed-6088-4c70-aa48-1bf1f399b676/9ca9b3a2-26e5-4f01-bdab-c99332ae0f6a?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEGgaDnVzLWVhc3QtMS1maXBzIkYwRAIgbC8sWIoYaHR8y7Wq86nPb0pufBl4%2BM4PWpeN%2B5PW9VQCIG7BhAVBDCWTpxFDLeuKYm%2Ff1JLz4iccPMzter0YdhgWKqYECDAQABoMNDE0MzUxNzY3ODI2IgxNAJrHbXaZG20jNfMqgwQyGEb4TsoVOoNvJ%2BaA%2FZ7zeu8hGcHqCeNfblP%2FQo5g6q2aRNtpwtPRKMlhYcIE%2FUQaNT1Lqmyptu32VUQTpzjTTxRVmJrDPkheSCkV06XQ%2FpTMKjU8CC7X1b%2FqYf8CrBwZT7Cv4C5WPjEET38f2A79h0xV6bZgPy28zQWrSeIJ9w6AfZBxiE24%2FVc08zVA0ivCT6lsy7nuU0wcR%2FMg2C9nd1vaMnQ923VpD21CKfVVYcrNWL2b%2FQ%2BNAO%2BxPjPgjiN6UPQbvbvZxkSdhgrGtecIg9NgqGkMcvM5VLTDtdC1kdeZetfOC2obtA%2BT%2F0gy3tIDo6WM5hcQfw743%2FAU9oaGnRBl%2BwoxRRDCZA%2F4jnPdluICSI7NCr6C3HHOe4CSZJjRihczxPA%2FgQczAVjAPs6CrK9N9pIzt0vNJpGUTgg4AoxftTvGfciDREilSctPIcBk6GwB%2Bzt4wbtNCmEPU%2BFD46rc6CWVQmmLwbKOeHw70ipMVFlOc8NlXkEtGO2vu6H44MstSSc6daOVUYUavQSGZ6jGhEjsP2YGz8KDm9XQotB%2FtyVBqiZV01OKqDmmBJA%2FXjUpSJ8M%2FeJXhe5m%2BKcxzNObtoJRI7jci3Tpt08XZyXZ7%2FyIV8H30ya468X3cfkTXrm972uga3z7iXAdubyEHnQIua%2FuvtwOHILi6O5D1Me2MzCPvcnXBjqTAYDoMWBt%2B3AVFuqSs2dmDQNBnvEhXfMgTedKLD5PJHb3aIl15iBkLaNu36GYnVCp9ZU2lSYcTbY5QwNnNeTfJTNy9bKhwzru6Sg%2BslhN%2BTS0zkTn%2BeLMY4tHgPEtEZ2cp%2FCk6GwqswvnYVKHjwO8q%2B1Mx8lc08fVEfarE9dke1GGFqoddx%2Bq%2BZUyHbDlAVfrCmf0yw%3D%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20261110T000135Z&X-Amz-SignedHeaders=host&X-Amz-Expires=86400&X-Amz-Credential=ASIAWA6KKHEJORWBH4N3%2F20261110%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=dc9352e3c4a64dabe8d17a1fa5254ba524f385dbe8327720050487e315a71a28","created_at":1682537433943,"updated_at":1682537433943,"display_name":"EDA Notebook","status":"FILE_STATUS_PUBLISHED"}]},"provider_summary":{"name":"John Snow Labs","description":"John Snow Labs, an AI and NLP for healthcare company, provides state-of-the-art software, models, and data to help healthcare and life science organizations build, deploy, and operate AI projects."},"popularity":802},{"id":"3b8fa496-deb5-4dca-bdf8-74028abacbf7","summary":{"name":"BERT Models","subtitle":"Language models that are suitable for fine-tuning on a wide range of natural language processing tasks.","share":{"name":"bert_share","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1701728387562,"updated_at":1701816583099,"categories":["OTHER"],"listingType":"STANDARD","provider_id":"ea1e69ff-0127-4c94-bf39-e841fe1d19d2"},"detail":{"description":"\n**Overview**\n\nThe BERT models are language models developed by Google. The models provided in this listing are\n\n- [bert_base_cased](https://huggingface.co/bert-base-cased)\n- [bert_base_multilingual_cased](https://huggingface.co/bert-base-multilingual-cased)\n- [bert_base_uncased](https://huggingface.co/bert-base-uncased)\n- [bert_large_cased](https://huggingface.co/bert-large-cased)\n- [bert_large_cased_whole_word_masking](https://huggingface.co/bert-large-cased-whole-word-masking)\n- [bert_large_uncased](https://huggingface.co/bert-large-uncased)\n- [bert_large_uncased_whole_word_masking](https://huggingface.co/bert-large-uncased-whole-word-masking)\n\nThe models are packaged using MLflow’s transformers flavor.\n- [bert_base_cased](https://huggingface.co/bert-base-cased), [bert_base_multilingual_cased](https://huggingface.co/bert-base-multilingual-cased), [bert_base_uncased](https://huggingface.co/bert-base-uncased), [bert_large_cased](https://huggingface.co/bert-large-cased), [bert_large_cased_whole_word_masking](https://huggingface.co/bert-large-cased-whole-word-masking), [bert_large_uncased](https://huggingface.co/bert-large-uncased), [bert_large_uncased_whole_word_masking](https://huggingface.co/bert-large-uncased-whole-word-masking): pretrained models using a masked language modeling (MLM) objective and can be further fine-tuning on specific applications.\n\nBert models are licensed under the [Apache 2.0 License](https://github.com/google-research/bert/blob/master/LICENSE). By installing this listing, you acknowledge and agree to the license.\n\nFor example notebooks of using the bert model in various use cases on Databricks, refer to [the Databricks ML example repository](https://github.com/databricks/databricks-ml-examples/tree/master/llm-models/bert).\n\n**Use cases**\n\n[bert_base_cased](https://huggingface.co/bert-base-cased), [bert_base_multilingual_cased](https://huggingface.co/bert-base-multilingual-cased), [bert_base_uncased](https://huggingface.co/bert-base-uncased), [bert_large_cased](https://huggingface.co/bert-large-cased), [bert_large_cased_whole_word_masking](https://huggingface.co/bert-large-cased-whole-word-masking), [bert_large_uncased](https://huggingface.co/bert-large-uncased), [bert_large_uncased_whole_word_masking](https://huggingface.co/bert-large-uncased-whole-word-masking):\n- Fine-tuning on specific applications such as text classification\n\n**Product details**\n\nThe pre-trained model can be used for further fine-tuning on specific applications. The fine-tuned model in this listing can be deployed directly to Databricks Model Serving for immediate use, or loaded for fine-tuning or batch inference use cases. For more details, install the listing and view the provided model cards for each model.","terms_of_service":"https://cms.databricks.com/sites/default/files/2023-01/marketplace-consumer-terms_0.pdf","documentation_link":"","privacy_policy_link":"https://www.databricks.com/legal/privacynotice","embedded_notebook_file_infos":[{"id":"b519c828-8c6e-4fb5-bbec-eddfdf197aa5","marketplace_file_type":"EMBEDDED_NOTEBOOK","file_parent":{"parent_id":"3b8fa496-deb5-4dca-bdf8-74028abacbf7","file_parent_type":"LISTING"},"mime_type":"text/html","download_link":"https://marketplace-file-storage-prod.s3-fips.us-east-1.amazonaws.com/EMBEDDED_NOTEBOOK/LISTING_3b8fa496-deb5-4dca-bdf8-74028abacbf7/b519c828-8c6e-4fb5-bbec-eddfdf197aa5?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEGgaDnVzLWVhc3QtMS1maXBzIkYwRAIgbC8sWIoYaHR8y7Wq86nPb0pufBl4%2BM4PWpeN%2B5PW9VQCIG7BhAVBDCWTpxFDLeuKYm%2Ff1JLz4iccPMzter0YdhgWKqYECDAQABoMNDE0MzUxNzY3ODI2IgxNAJrHbXaZG20jNfMqgwQyGEb4TsoVOoNvJ%2BaA%2FZ7zeu8hGcHqCeNfblP%2FQo5g6q2aRNtpwtPRKMlhYcIE%2FUQaNT1Lqmyptu32VUQTpzjTTxRVmJrDPkheSCkV06XQ%2FpTMKjU8CC7X1b%2FqYf8CrBwZT7Cv4C5WPjEET38f2A79h0xV6bZgPy28zQWrSeIJ9w6AfZBxiE24%2FVc08zVA0ivCT6lsy7nuU0wcR%2FMg2C9nd1vaMnQ923VpD21CKfVVYcrNWL2b%2FQ%2BNAO%2BxPjPgjiN6UPQbvbvZxkSdhgrGtecIg9NgqGkMcvM5VLTDtdC1kdeZetfOC2obtA%2BT%2F0gy3tIDo6WM5hcQfw743%2FAU9oaGnRBl%2BwoxRRDCZA%2F4jnPdluICSI7NCr6C3HHOe4CSZJjRihczxPA%2FgQczAVjAPs6CrK9N9pIzt0vNJpGUTgg4AoxftTvGfciDREilSctPIcBk6GwB%2Bzt4wbtNCmEPU%2BFD46rc6CWVQmmLwbKOeHw70ipMVFlOc8NlXkEtGO2vu6H44MstSSc6daOVUYUavQSGZ6jGhEjsP2YGz8KDm9XQotB%2FtyVBqiZV01OKqDmmBJA%2FXjUpSJ8M%2FeJXhe5m%2BKcxzNObtoJRI7jci3Tpt08XZyXZ7%2FyIV8H30ya468X3cfkTXrm972uga3z7iXAdubyEHnQIua%2FuvtwOHILi6O5D1Me2MzCPvcnXBjqTAYDoMWBt%2B3AVFuqSs2dmDQNBnvEhXfMgTedKLD5PJHb3aIl15iBkLaNu36GYnVCp9ZU2lSYcTbY5QwNnNeTfJTNy9bKhwzru6Sg%2BslhN%2BTS0zkTn%2BeLMY4tHgPEtEZ2cp%2FCk6GwqswvnYVKHjwO8q%2B1Mx8lc08fVEfarE9dke1GGFqoddx%2Bq%2BZUyHbDlAVfrCmf0yw%3D%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20261110T000135Z&X-Amz-SignedHeaders=host&X-Amz-Expires=86400&X-Amz-Credential=ASIAWA6KKHEJORWBH4N3%2F20261110%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=54a5c18e110cf31a6849ccdfb051a653fb2908569b59410008c18ae63703c2f5","created_at":1701816433989,"updated_at":1701816433989,"display_name":"bert_marketplace_listing_example","status":"FILE_STATUS_PUBLISHED"}],"assets":["ASSET_TYPE_MODEL"],"license":"https://github.com/google-research/bert/blob/master/LICENSE","tags":[{"tag_name":"LISTING_TAG_TYPE_TASK","tag_values":["FILL_MASK"]}]},"provider_summary":{"name":"Databricks","description":"As the world’s first lakehouse platform in the cloud, Databricks combines the best elements of data lakes and data warehouses to deliver the reliability, strong governance, and performance of data warehouses with the openness, flexibility, and machine learning support of data lakes. We are a cross-cloud platform that works across AWS, Azure, and Google.\nThis unified approach simplifies your modern data stack by eliminating the data silos that traditionally separate and complicate data engineering, analytics, BI, data science and machine learning. It’s built on open source and open standards to maximize flexibility. And, its common approach to data management, security and governance helps companies operate more efficiently and innovate faster."},"popularity":1976},{"id":"4b97f2ca-6669-4167-bfb7-1f0becb216f4","summary":{"name":"SAMPLE Archetype Data | USA Digital Audience Data | B2B2C Linkage Data - Ovver 2,000 ...","subtitle":"Archetype Data’s Digital Segments library features over 2,000 ready-to-activate audiences across consumer, business, ...","share":{"name":"archetype_data_llc_a8474810_23a0_40a2_a388_56cd8e6002f0_samples_share","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1766078179615,"updated_at":1771341212794,"categories":["ADVERTISING_AND_MARKETING","OTHER"],"listingType":"STANDARD","provider_id":"920cd8b6-947f-4677-b63a-c535339172dd"},"detail":{"description":"Archetype Data’s Digital Segments portfolio delivers one of the most extensive and versatile collections of pre-built and custom audiences available in the marketplace. With over 2,000 ready-to-activate segments spanning consumer, business, and B2B2C use cases, these audiences empower marketers, agencies, and data-driven organizations to target, personalize, and measure campaigns with precision across every major digital platform.\n\nOur Digital Segments are designed to align with a wide range of objectives—from brand awareness and prospecting to retention, cross-sell, and attribution. Each audience is derived from Archetype Data’s proprietary datasets, including our 260MM+ consumer file and 110MM+ business contact graph, enriched by over 400 lifestyle, demographic, firmographic, and behavioral attributes. This unified data foundation ensures that every segment is both accurate and scalable, supporting activation across CTV, programmatic display, social, native, and direct channels.\n\nIn addition to pre-built segments, Archetype Data provides the ability to create fully customized audiences tailored to unique client needs. Whether you need to reach business owners in specific industries, high-value consumers with certain spending behaviors, or B2B2C-linked individuals who represent both business and personal purchase influence, our team can design data-driven segments optimized for your campaign goals.\n\nThese segments are natively available through LiveRamp, The Trade Desk, Xandr, and other leading platforms, ensuring easy deployment into existing workflows. 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All healthcare providers, including doctors, dentists, pharmacists, nurses, and other professionals, as well as healthcare organizations like hospitals, labs, and nursing homes, must have an NPI number to perform transactions and communicate with health plans. At MDI, we excel in connecting businesses with healthcare professionals through precise NPI ID targeting. Our expertise, paired with our robust data solutions, ensures that your marketing campaigns are not only highly effective but also fully compliant with industry regulations. Moreover, we offer an extensive range of targeted options to suit your specific needs. \n\nAvailable at the office address and at home address. At home, selections include typical consumer demographics, including age, income, presence and age of children, and over 250 lifestyle segments.\n\nAvailable for licensing, please inquire.\n\nSpecialties with counts for home address include:\n\nAcupuncturists\t 23,130 \nAudiologists\t 16,415 \nChiropractors\t 45,684 \nDental Assistants\t 148,309 \nDental Hygienists\t 172,581 \nDentists\t 163,374 \nDieticians & Nutritionists\t 64,403 \nEmergency Medical Technicians\t 66,043\nGenetic Counselors\t 520 \nMarriage and Family Therapists\t 77,913 \nMedical Laboratory Personnel\t 89,395 \nMedical Technicians\t 7,505 \nMental Health Counselors\t 119,317 \nNurses (RN, LPN, NP, APN, etc...)\t 8,008,887 \nOccupational Therapist Assistant\t 65,192 \nOccupational Therapists\t 171,811 \nOptometrists\t 35,738 \nPharmacists\t 391,309 \nPharmacy Technician\t 400,256 \nPhysical Therapist Assistant\t 105,493 \nPhysical Therapists\t 259,677 \nPhysician and Surgeon\t 527,078 \nPhysician Assistants\t 94,697 \nPodiatrists\t 9,198 \nPsychologists\t 138,737 \nRespiratory Therapists\t 201,413 \nSocial Workers\t 382,799 \nSpeech & Hearing Therapists\t 54,503 \nSpeech Pathologists\t 190,900 \nVeterinarians\t 77,948","terms_of_service":"https://www.thinkdatagroup.com/privacy-policy","contact_email":"support@datarade.ai","privacy_policy_link":"https://www.thinkdatagroup.com/privacy-policy","assets":["ASSET_TYPE_DATA_TABLE"]},"provider_summary":{"name":"Think Data Group","description":"Think Data Group is an advisory firm composed of leading veterans of the data and information-services industry. 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This data pa..","share":{"name":"snomed_clinical_terminology_full","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1682356493526,"updated_at":1695691999639,"categories":["HEALTH","SCIENCE_AND_RESEARCH"],"listingType":"STANDARD","provider_id":"f88849f8-9d77-4de2-b4c7-03d55cc3768e"},"detail":{"description":"\n**Overview**\nSNOMED CT contains three different release file types in every version: full, delta and snapshot release. This data package consists of Full Release which contains the complete history of every component of SNOMED CT.\n\n**Description**\nSNOMED CT is one of a suite of designated standards for use in U.S. Federal Government systems for the electronic exchange of clinical health information and is also a required standard in interoperability specifications of the U.S. Healthcare Information Technology Standards Panel. When implemented in software applications, SNOMED CT can be used to represent clinically relevant information consistently, reliably and comprehensively as an integral part of producing electronic health information. SNOMED CT supports the development of comprehensive high-quality clinical content in health records. It provides a standardized way to represent clinical phrases captured by the clinician and enables automatic interpretation of these. SNOMED CT is a clinically validated, semantically rich, controlled vocabulary that facilitates evolutionary growth in expressivity to meet emerging requirements. SNOMED CT based clinical information benefits individual patients and clinicians as well as populations and it supports evidence based care. A 'Full' release version of SNOMED CT contains every version of every component that has ever been released prior to or in the specified Edition. This release provides a full historical record and can be used to obtain views of the state of any component at any point in time since its first release.\n\n**Benefits**\n- There are many benefits of using snomed ct in electronic health records including: \n- health information can be shared consistently within and across healthcare settings.\n- data can be organized, queried, and analyzed for the benefit of the individual and institution.\n- the risk of different interpretations of the record between different healthcare settings can be reduced.\n- updated twice a year to help users keep pace with the advances of healthcare terminology and add to the scope of coverage.\n- in addition to adding value to electronic health records, snomed ct supports the following meaningful use objectives:\n- maintain an up-to-date problem list of current and active diagnoses\n- record patient family health history as structure data\n- identify and report cancer cases to state cancer registries\n- record and track changes in patient vital signs\n- record patient smoking status\n- provide a summary record for each transition of care\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [SNOMED CT Full Association Reference Set](https://www.johnsnowlabs.com/marketplace/snomed-ct-full-association-reference-set)\n\t- This dataset describes the Release File structure of SNOMED CT, referred to as Release Format 2 (RF2). The US Edition of SNOMED CT is the official source of SNOMED CT for use in US healthcare systems. The US Edition is a standalone release that combines the content of both the US Extension and the International release of SNOMED CT. An Association reference set is a Component reference set used to represent a set of unordered associations of a particular type between components.\n- [SNOMED CT Full Attribute Value Reference Set](https://www.johnsnowlabs.com/marketplace/snomed-ct-full-attribute-value-reference-set)\n\t- This dataset describes the Release File structure of SNOMED CT, referred to as Release Format 2 (RF2). The US Edition of SNOMED CT is the official source of SNOMED CT for use in US healthcare systems. The US Edition is a standalone release that combines the content of both the US Extension and the International release of SNOMED CT. Attribute value reference set is a component reference set used to apply a tagged value to a SNOMED CT component.\n- [SNOMED CT Full Concept](https://www.johnsnowlabs.com/marketplace/snomed-ct-full-concept)\n\t- The Concept file holds the clinical concepts that make up SNOMED CT. A concept is given meaning by its Fully Specified Name, which is held in the Description file. A concept may be distinguished from or refined by association with other concepts using relationships, which are held in the Relationship file.\n- [SNOMED CT Full Description Type Reference Set](https://www.johnsnowlabs.com/marketplace/snomed-ct-full-description-type-reference-set)\n\t- Description format reference set is a C-I (Component - Integer) reference set which is used to specify the length and format of the terms in descriptions of this description type. This dataset describes the Release File structure of SNOMED CT, referred to as Release Format 2 (RF2). The US Edition is a standalone release that combines the content of both the US Extension and the International release of SNOMED CT.\n- [SNOMED CT Full Description](https://www.johnsnowlabs.com/marketplace/snomed-ct-full-description)\n\t- The Description file holds descriptions that describe SNOMED CT concepts. A description is used to give meaning to a concept and provide well-understood and standard ways of referring to a concept. This dataset describes the Release File structure of SNOMED CT, referred to as Release Format 2 (RF2). The US Edition is a standalone release that combines the content of both the US Extension and the International release of SNOMED CT.\n- [SNOMED CT Full Language Reference Set](https://www.johnsnowlabs.com/marketplace/snomed-ct-full-language-reference-set)\n\t- A Language reference set is a Component reference set that is used to indicate which descriptions contain terms that are acceptable or preferred in a particular language or dialect. This dataset describes the Release File structure of SNOMED CT, referred to as Release Format 2 (RF2). The US Edition is a standalone release that combines the content of both the US Extension and the International release of SNOMED CT.\n- [SNOMED CT Full MRCM Attribute Domain Reference Set](https://www.johnsnowlabs.com/marketplace/snomed-ct-full-mrcm-attribute-domain-reference-set)\n\t- MRCM (Machine Readable Concept Model) Attribute Domain Reference Set allows attributes to be associated with the domains in which they may be applied. It also allows grouping and cardinality constraints to be specified for each attribute and domain combination. For each attribute-domain rule, the strength of the rule and the content type over which this rule applies is also specified.\n- [SNOMED CT Full MRCM Attribute Range Reference Set](https://www.johnsnowlabs.com/marketplace/snomed-ct-full-mrcm-attribute-range-reference-set)\n\t- An MRCM (Machine Readable Concept Model) Attribute Range Reference Set allows attributes to be associated with a valid value range for a given SNOMED CT content type and rule strength. The range of each attribute is defined using an Expression Constraint. This expression constraint represents the set of concepts, expressions, or concrete values that may be used as the value of the given attribute.\n- [SNOMED CT Full MRCM Domain Reference Set](https://www.johnsnowlabs.com/marketplace/snomed-ct-full-mrcm-domain-reference-set)\n\t- MRCM (Machine Readable Concept Model) Domain Reference Set enumerates the concept domains to which SNOMED CT attributes may be applied, and provides additional information to support these concept domains.\n- [SNOMED CT Full MRCM Module Scope Reference Set](https://www.johnsnowlabs.com/marketplace/snomed-ct-full-mrcm-module-scope-reference-set)\n\t- An MRCM (Machine Readable Concept Model) Module Scope Reference Set specifies the set of MRCM reference sets that should be applied to the content in each module. Within a SNOMED CT Edition, the MRCM rules applied to the included modules must be consistent, to ensure data integrity within an edition is maintained.\n- [SNOMED CT Full Module Dependency Reference Set](https://www.johnsnowlabs.com/marketplace/snomed-ct-full-module-dependency-reference-set)\n\t- This dataset describes the Release File structure of SNOMED CT, referred to as Release Format 2 (RF2). The US Edition of SNOMED CT is the official source of SNOMED CT for use in US healthcare systems. The US Edition is a standalone release that combines the content of both the US Extension and the International release of SNOMED CT.\n\nThe Module dependency reference set is used to represent dependencies between modules, taking account of module versioning.\n- [SNOMED CT Full OWL Expression Reference Set](https://www.johnsnowlabs.com/marketplace/snomed-ct-full-owl-expression-reference-set)\n\t- This dataset is an OWL (Ontology Web Language) ontology reference set (foundation metadata concept), in which the OWL expressions represent essential information about an ontology. This information includes namespaces, ontology URI (Uniform Resource Identifier), ontology version URI, and import statements. An OWL expression type reference set associates description logic statements with SNOMED CT (Systematized Nomenclature of Medicine - Clinical Terms) concept in the OWL functional syntax.\n- [SNOMED CT Full Refset Descriptor Reference Set](https://www.johnsnowlabs.com/marketplace/snomed-ct-full-refset-descriptor-reference-set)\n\t- This dataset describes the Release File structure of SNOMED CT, referred to as Release Format 2 (RF2). The US Edition of SNOMED CT is the official source of SNOMED CT for use in US healthcare systems. The US Edition is a standalone release that combines the content of both the US Extension and the International release of SNOMED CT.\n\nRefset Descriptor Reference Set specifies the structure of reference sets.\n- [SNOMED CT Full Relationship Concrete Values](https://www.johnsnowlabs.com/marketplace/snomed-ct-full-relationship-concrete-values)\n\t- This dataset describes the information about SNOMED International proposes to further enhance SNOMED CT by adding the capability to express concrete values. This enhancement will initially target improvements in the Pharmaceutical / biologic product hierarchy .This dataset describes the Release File structure of SNOMED CT, referred to as Release Format 2 (RF2). The US Edition of SNOMED CT is the official source of SNOMED CT for use in US healthcare systems.\n- [SNOMED CT Full Relationship](https://www.johnsnowlabs.com/marketplace/snomed-ct-full-relationship)\n\t- This dataset describes the Release File structure of SNOMED CT, referred to as Release Format 2 (RF2). The US Edition of SNOMED CT is the official source of SNOMED CT for use in US healthcare systems. The US Edition is a standalone release that combines the content of both the US Extension and the International release of SNOMED CT.\n\nThe Relationship file holds one relationship per row. Each relationship is of a particular type, and has a source concept and a destination concept.\n- [SNOMED CT Full Simple Map Reference Set](https://www.johnsnowlabs.com/marketplace/snomed-ct-full-simple-map-reference-set)\n\t- This dataset describes the Release File structure of SNOMED CT, referred to as Release Format 2 (RF2). The US Edition of SNOMED CT is the official source of SNOMED CT for use in US healthcare systems. The US Edition is a standalone release that combines the content of both the US Extension and the International release of SNOMED CT.\n\nA Simple Map Reference set is used to represent one-to-one maps between SNOMED CT concepts and codes in another terminology, classification or code system.\n- [SNOMED CT Full Simple Reference Set](https://www.johnsnowlabs.com/marketplace/snomed-ct-full-simple-reference-set)\n\t- This dataset describes the Release File structure of SNOMED CT, referred to as Release Format 2 (RF2). The US Edition of SNOMED CT is the official source of SNOMED CT for use in US healthcare systems. The US Edition is a standalone release that combines the content of both the US Extension and the International release of SNOMED CT.\n\nA Simple reference set does not have any addition fields.\n- [SNOMED CT Full Stated Relationship](https://www.johnsnowlabs.com/marketplace/snomed-ct-full-stated-relationship)\n\t- This dataset describes the Release File structure of SNOMED CT, referred to as Release Format 2 (RF2). The US Edition of SNOMED CT is the official source of SNOMED CT for use in US healthcare systems. The US Edition is a standalone release that combines the content of both the US Extension and the International release of SNOMED CT.\n\nStated Relationship File is a distribution file containing the stated form of SNOMED CT relationships.\n- [SNOMED CT Full Text Definition](https://www.johnsnowlabs.com/marketplace/snomed-ct-full-text-definition)\n\t- This dataset describes the Release File structure of SNOMED CT, referred to as Release Format 2 (RF2). 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The workflow removes protected health information (PHI) from image content and associated metadata, enabling organizations to securely use digital pathology data while maintaining regulatory compliance.\n\n## Use Cases\n\n* Remove PHI from DICOM Whole Slide Images (WSIs)\n* Remove PHI from SVS pathology slide images\n* De-identify embedded labels, annotations, and image regions containing sensitive information\n* Clean metadata fields that may contain patient or study identifiers\n* Prepare pathology image datasets for AI model training, research, and data sharing\n* Enable compliant use of digital pathology data across institutions and cloud environments\n\n## Product Details\n\nDatasets represented include DICOM Whole Slide Images (WSIs), SVS slide images, de-identified image outputs, and sanitized metadata records.\n\nThe workflow performs de-identification at both the image and metadata levels, ensuring that sensitive patient information is removed from visual content as well as file attributes and associated metadata.\n\nFor more details, refer to the embedded notebook.\n\n## Additional Insights\n\nThis workflow is designed for digital pathology and whole-slide imaging scenarios. By de-identifying both image content and metadata, it supports compliance with healthcare privacy regulations such as GDPR and HIPAA while enabling the safe use of pathology datasets for AI development, clinical research, and collaborative data-sharing initiatives.\n","terms_of_service":"https://www.johnsnowlabs.com/healthcare-data-library-eula/","documentation_link":"","privacy_policy_link":"https://www.johnsnowlabs.com/privacy-policy/","embedded_notebook_file_infos":[{"id":"924bb653-e999-4fcf-b199-a0fe6e3588ce","marketplace_file_type":"EMBEDDED_NOTEBOOK","file_parent":{"parent_id":"a7303f6b-4d42-4571-b589-d24eee055e24","file_parent_type":"LISTING"},"mime_type":"text/html","download_link":"https://marketplace-file-storage-prod.s3-fips.us-east-1.amazonaws.com/EMBEDDED_NOTEBOOK/LISTING_a7303f6b-4d42-4571-b589-d24eee055e24/924bb653-e999-4fcf-b199-a0fe6e3588ce?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEGgaDnVzLWVhc3QtMS1maXBzIkYwRAIgbC8sWIoYaHR8y7Wq86nPb0pufBl4%2BM4PWpeN%2B5PW9VQCIG7BhAVBDCWTpxFDLeuKYm%2Ff1JLz4iccPMzter0YdhgWKqYECDAQABoMNDE0MzUxNzY3ODI2IgxNAJrHbXaZG20jNfMqgwQyGEb4TsoVOoNvJ%2BaA%2FZ7zeu8hGcHqCeNfblP%2FQo5g6q2aRNtpwtPRKMlhYcIE%2FUQaNT1Lqmyptu32VUQTpzjTTxRVmJrDPkheSCkV06XQ%2FpTMKjU8CC7X1b%2FqYf8CrBwZT7Cv4C5WPjEET38f2A79h0xV6bZgPy28zQWrSeIJ9w6AfZBxiE24%2FVc08zVA0ivCT6lsy7nuU0wcR%2FMg2C9nd1vaMnQ923VpD21CKfVVYcrNWL2b%2FQ%2BNAO%2BxPjPgjiN6UPQbvbvZxkSdhgrGtecIg9NgqGkMcvM5VLTDtdC1kdeZetfOC2obtA%2BT%2F0gy3tIDo6WM5hcQfw743%2FAU9oaGnRBl%2BwoxRRDCZA%2F4jnPdluICSI7NCr6C3HHOe4CSZJjRihczxPA%2FgQczAVjAPs6CrK9N9pIzt0vNJpGUTgg4AoxftTvGfciDREilSctPIcBk6GwB%2Bzt4wbtNCmEPU%2BFD46rc6CWVQmmLwbKOeHw70ipMVFlOc8NlXkEtGO2vu6H44MstSSc6daOVUYUavQSGZ6jGhEjsP2YGz8KDm9XQotB%2FtyVBqiZV01OKqDmmBJA%2FXjUpSJ8M%2FeJXhe5m%2BKcxzNObtoJRI7jci3Tpt08XZyXZ7%2FyIV8H30ya468X3cfkTXrm972uga3z7iXAdubyEHnQIua%2FuvtwOHILi6O5D1Me2MzCPvcnXBjqTAYDoMWBt%2B3AVFuqSs2dmDQNBnvEhXfMgTedKLD5PJHb3aIl15iBkLaNu36GYnVCp9ZU2lSYcTbY5QwNnNeTfJTNy9bKhwzru6Sg%2BslhN%2BTS0zkTn%2BeLMY4tHgPEtEZ2cp%2FCk6GwqswvnYVKHjwO8q%2B1Mx8lc08fVEfarE9dke1GGFqoddx%2Bq%2BZUyHbDlAVfrCmf0yw%3D%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20261110T000135Z&X-Amz-SignedHeaders=host&X-Amz-Expires=86400&X-Amz-Credential=ASIAWA6KKHEJORWBH4N3%2F20261110%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=890c5a713406843915cd04ef0e7a32a9da327c5aaf2539b3afd6c0fc2cf0e1aa","created_at":1784462952999,"updated_at":1784462952999,"display_name":"DICOM and SVS de-ID Pixels integration","status":"FILE_STATUS_PUBLISHED"}],"assets":["ASSET_TYPE_NOTEBOOK"],"license":""},"provider_summary":{"name":"John Snow Labs","description":"John Snow Labs, an AI and NLP for healthcare company, provides state-of-the-art software, models, and data to help healthcare and life science organizations build, deploy, and operate AI projects."},"popularity":989},{"id":"3b48c9b5-fc25-4bbf-991d-7c30f2a7f5d0","summary":{"name":"Physician Quality Reporting System PQRS","subtitle":"This data package contains the Physician Quality Reporting System (PQRS), Performance Rates for Individual Eligible Pr..","share":{"name":"physician_quality_reporting_system_pqrs","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1682356039975,"updated_at":1695691361033,"categories":["HEALTH","SCIENCE_AND_RESEARCH"],"listingType":"STANDARD","provider_id":"f88849f8-9d77-4de2-b4c7-03d55cc3768e"},"detail":{"description":"\n**Overview**\nThis data package contains the Physician Quality Reporting System (PQRS), Performance Rates for Individual Eligible Professionals (EP) PQRS, Consumer Assessment of Healthcare Providers and Systems (CAHPS) and Group Practice.\n\n**Description**\nThis data package contains information regarding group practice participation under patient experience and individual EP participation under clinical quality of care in 2014 and 2015 Centers for Medicare & Medicaid (CMS) quality program as well as the 2014 Physician Quality Report System (PQRS) Group Practice Reporting Option (GPRO) performance rates for the 8 Consumer Assessment of Healthcare Providers and Systems (CAHPS) measure now publicly reported on Physician Compare. It also includes the Physician Quality Reporting System (PQRS) Single Source Code Master 2016 document is listing all denominator and numerator coding for each 2016 PQRS Individual Claims and Registry Measure.\n\n**Benefits**\n- Useful for providers seeking to improve the health care services and patients experience. useful to researchers to analyze the participation of healthcare providers in the healthcare quality measurement programs, patients perceived quality, along with the healthcare services provided. useful to public health authorities in the developing of health policies.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [Medicare Physician Compare by Organization and Group Practice 2013](https://www.johnsnowlabs.com/marketplace/medicare-physician-compare-by-organization-and-group-practice-2013)\n\t- This dataset includes information regarding group practice participation in 2013 Centers for Medicare & Medicaid (CMS) quality program as well as the 2013 Physician Quality Report System (PQRS) Group Practice Reporting Option (GPRO) performance rates for the three Diabetes Mellitus (DM) measures and one Coronary Artery Disease (CAD) measure now publicly reported on Physician Compare.\n- [Performance Rates for CAHPS for PQRS Measures 2014](https://www.johnsnowlabs.com/marketplace/performance-rates-for-cahps-for-pqrs-measures-2014)\n\t- This dataset includes information regarding group practice participation under patient experience in 2014 Centers for Medicare & Medicaid (CMS) quality program as well as the 2014 Physician Quality Report System (PQRS) Group Practice Reporting Option (GPRO) performance rates for the 8 Consumer Assessment of Healthcare Providers and Systems (CAHPS) measure now publicly reported on Physician Compare.\n- [Performance Rates for CAHPS for PQRS Measures 2015](https://www.johnsnowlabs.com/marketplace/performance-rates-for-cahps-for-pqrs-measures-2015)\n\t- This dataset includes information regarding group practice participation under patient experience in 2015 Centers for Medicare & Medicaid (CMS) quality program as well as the 2015 Physician Quality Report System (PQRS) Group Practice Reporting Option (GPRO) performance rates for the 8 Consumer Assessment of Healthcare Providers and Systems (CAHPS) measure now publicly reported on Physician Compare.\n- [Performance Rates for CAHPS for PQRS Measures 2016](https://www.johnsnowlabs.com/marketplace/performance-rates-for-cahps-for-pqrs-measures-2016)\n\t- This dataset includes information regarding group practice participation under patient experience in 2016 Centers for Medicare & Medicaid (CMS) quality program as well as the 2015 Physician Quality Report System (PQRS) Group Practice Reporting Option (GPRO) performance rates for the 8 Consumer Assessment of Healthcare Providers and Systems (CAHPS) measure now publicly reported on Physician Compare.\n- [Performance Rates for CAHPS for PQRS Measures 2017](https://www.johnsnowlabs.com/marketplace/performance-rates-for-cahps-for-pqrs-measures-2017)\n\t- This dataset includes information regarding group practice participation under patient experience in 2017 Centers for Medicare & Medicaid (CMS) quality program as well as the 2015 Physician Quality Report System (PQRS) Group Practice Reporting Option (GPRO) performance rates for the 8 Consumer Assessment of Healthcare Providers and Systems (CAHPS) measure now publicly reported on Physician Compare.\n- [Performance Rates for Group Practice PQRS Measures 2014](https://www.johnsnowlabs.com/marketplace/performance-rates-for-group-practice-pqrs-measures-2014)\n\t- This dataset includes information regarding group practice participation under clinical quality of care in 2014 Centers for Medicare & Medicaid (CMS) quality program as well as the 2014 Physician Quality Report System (PQRS) Group Practice Reporting Option (GPRO) performance rates for the three Diabetes Mellitus (DM) measures and one Coronary Artery Disease (CAD) measure now publicly reported on Physician Compare.\n- [Performance Rates for Individual EP PQRS Measures 2014](https://www.johnsnowlabs.com/marketplace/performance-rates-for-individual-ep-pqrs-measures-2014)\n\t- This dataset includes information regarding individual EP participation under clinical quality of care in 2014 Centers for Medicare & Medicaid (CMS) quality program as well as the 2014 Physician Quality Report System (PQRS) Group Practice Reporting Option (GPRO) performance rates for 6 Individual eligible professional (EP) measures now publicly reported on Physician Compare.\n- [Performance Rates for Individual EP PQRS Measures 2015](https://www.johnsnowlabs.com/marketplace/performance-rates-for-individual-ep-pqrs-measures-2015)\n\t- This dataset includes information regarding individual EP participation under clinical quality of care in 2015 Centers for Medicare & Medicaid (CMS) quality program as well as the 2015 Physician Quality Report System (PQRS) Group Practice Reporting Option (GPRO) performance rates for 6 Individual eligible professional (EP) measures now publicly reported on Physician Compare.\n- [Performance Rates for Individual EP PQRS Measures 2016](https://www.johnsnowlabs.com/marketplace/performance-rates-for-individual-ep-pqrs-measures-2016)\n\t- This dataset includes information regarding eligible professional (EP) participation under clinical quality of care in 2016 Centers for Medicare & Medicaid (CMS) quality program as well as the 2016 Physician Quality Report System (PQRS) Group Practice Reporting Option (GPRO) performance rates for 6 Individual eligible professional (EP) measures now publicly reported on Physician Compare.\n- [Performance Rates for Individual EP PQRS Measures 2017](https://www.johnsnowlabs.com/marketplace/performance-rates-for-individual-ep-pqrs-measures-2017)\n\t- This dataset includes information regarding eligible professional (EP) participation under clinical quality of care in 2017 Centers for Medicare & Medicaid (CMS) quality program as well as the 2017 Physician Quality Report System (PQRS) Group Practice Reporting Option (GPRO) performance rates for 6 Individual eligible professional (EP) measures now publicly reported on Physician Compare.\n- [Physician Compare Clinician Utilization Data](https://www.johnsnowlabs.com/marketplace/physician-compare-clinician-utilization-data)\n\t- The dataset contains information on Physician Compare Clinician Utilization Data prepared by the Centers for Medicare & Medicaid Services (CMS) and organized by National Provider Identifier (NPI), Healthcare Common Procedure Coding System (HCPCS) code and place of service.\n- [Physician Quality Reporting System PQRS Single Source Code Master 2016](https://www.johnsnowlabs.com/marketplace/physician-quality-reporting-system-pqrs-single-source-code-master-2016)\n\t- Physician Quality Reporting System (PQRS) Single Source Code Master 2016 document is listing all denominator and numerator coding for each 2016 PQRS Individual Claims and Registry Measure. This supportive measure document is a coding resource that may be used to find measures for coding billed by individual eligible professionals or group practices, billing software, or vendors that may report on 2016 PQRS Individual Claims and Registry Measures.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated\n- Data Updates\n - Data updates support replace-on-update: outdated foreign keys are deprecated, not deleted\n\n**Our data is curated and enriched by domain experts** \n\n Each dataset is manually curated by our team of doctors, pharmacists, public health & medical billing experts:\n- Field names, descriptions, and normalized values are chosen by people who actually understand their meaning\n- Healthcare & life science experts add categories, search keywords, descriptions and more to each dataset\n- Both manual and automated data enrichment supported for clinical codes, providers, drugs, and geo-locations\n- The data is always kept up to date – even when the source requires manual effort to get updates\n- Support for data subscribers is provided directly by the domain experts who curated the data sets\n- Every data source’s license is manually verified to allow for royalty-free commercial use and redistribution.\n\n**Need Help?**\nIf you have questions about our products, contact us at [info@johnsnowlabs.com](mailto:info@johnsnowlabs.com). \n ","terms_of_service":"https://www.johnsnowlabs.com/healthcare-data-library-eula/","documentation_link":"https://www.johnsnowlabs.com/marketplace/physician-quality-reporting-system-pqrs-data-package","contact_email":"support@johnsnowlabs.com","privacy_policy_link":"https://www.johnsnowlabs.com/privacy-policy/","embedded_notebook_file_infos":[{"id":"814f001a-8649-469d-8dae-6b11964b4266","marketplace_file_type":"EMBEDDED_NOTEBOOK","file_parent":{"parent_id":"3b48c9b5-fc25-4bbf-991d-7c30f2a7f5d0","file_parent_type":"LISTING"},"mime_type":"text/html","download_link":"https://marketplace-file-storage-prod.s3-fips.us-east-1.amazonaws.com/EMBEDDED_NOTEBOOK/LISTING_3b48c9b5-fc25-4bbf-991d-7c30f2a7f5d0/814f001a-8649-469d-8dae-6b11964b4266?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEGgaDnVzLWVhc3QtMS1maXBzIkYwRAIgbC8sWIoYaHR8y7Wq86nPb0pufBl4%2BM4PWpeN%2B5PW9VQCIG7BhAVBDCWTpxFDLeuKYm%2Ff1JLz4iccPMzter0YdhgWKqYECDAQABoMNDE0MzUxNzY3ODI2IgxNAJrHbXaZG20jNfMqgwQyGEb4TsoVOoNvJ%2BaA%2FZ7zeu8hGcHqCeNfblP%2FQo5g6q2aRNtpwtPRKMlhYcIE%2FUQaNT1Lqmyptu32VUQTpzjTTxRVmJrDPkheSCkV06XQ%2FpTMKjU8CC7X1b%2FqYf8CrBwZT7Cv4C5WPjEET38f2A79h0xV6bZgPy28zQWrSeIJ9w6AfZBxiE24%2FVc08zVA0ivCT6lsy7nuU0wcR%2FMg2C9nd1vaMnQ923VpD21CKfVVYcrNWL2b%2FQ%2BNAO%2BxPjPgjiN6UPQbvbvZxkSdhgrGtecIg9NgqGkMcvM5VLTDtdC1kdeZetfOC2obtA%2BT%2F0gy3tIDo6WM5hcQfw743%2FAU9oaGnRBl%2BwoxRRDCZA%2F4jnPdluICSI7NCr6C3HHOe4CSZJjRihczxPA%2FgQczAVjAPs6CrK9N9pIzt0vNJpGUTgg4AoxftTvGfciDREilSctPIcBk6GwB%2Bzt4wbtNCmEPU%2BFD46rc6CWVQmmLwbKOeHw70ipMVFlOc8NlXkEtGO2vu6H44MstSSc6daOVUYUavQSGZ6jGhEjsP2YGz8KDm9XQotB%2FtyVBqiZV01OKqDmmBJA%2FXjUpSJ8M%2FeJXhe5m%2BKcxzNObtoJRI7jci3Tpt08XZyXZ7%2FyIV8H30ya468X3cfkTXrm972uga3z7iXAdubyEHnQIua%2FuvtwOHILi6O5D1Me2MzCPvcnXBjqTAYDoMWBt%2B3AVFuqSs2dmDQNBnvEhXfMgTedKLD5PJHb3aIl15iBkLaNu36GYnVCp9ZU2lSYcTbY5QwNnNeTfJTNy9bKhwzru6Sg%2BslhN%2BTS0zkTn%2BeLMY4tHgPEtEZ2cp%2FCk6GwqswvnYVKHjwO8q%2B1Mx8lc08fVEfarE9dke1GGFqoddx%2Bq%2BZUyHbDlAVfrCmf0yw%3D%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20261110T000135Z&X-Amz-SignedHeaders=host&X-Amz-Expires=86400&X-Amz-Credential=ASIAWA6KKHEJORWBH4N3%2F20261110%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=575a99ebe7ef770c0319df8aee4c0b06ea89c62faf23f69f2014191d2fc31e81","created_at":1682537407937,"updated_at":1682537407937,"display_name":"EDA Notebook","status":"FILE_STATUS_PUBLISHED"}]},"provider_summary":{"name":"John Snow Labs","description":"John Snow Labs, an AI and NLP for healthcare company, provides state-of-the-art software, models, and data to help healthcare and life science organizations build, deploy, and operate AI projects."},"popularity":1082},{"id":"0216c782-7ec5-4e76-bc55-77a72877eaf3","summary":{"name":"Marine mammal baseline, Johan Castberg 2021","subtitle":"Marine mammal baseline, Johan Castberg 2021","share":{"name":"equinoropendata_marinemammals","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1761119829075,"updated_at":1768804585018,"categories":["CLIMATE_AND_ENVIRONMENT","ENERGY"],"listingType":"STANDARD","provider_id":"006d1a44-e5d8-4d20-8bd7-e9a311106007"},"detail":{"description":"**Overview**\n\nAcoustic data were recorded at the Johan Castberg field in the southern Barents Sea at three locations from October 2018 until June 2019 to assess the presence of marine mammals and characterise the underwater soundscape. The marine mammal analysis was focused on two stations (PAMEast and PAMNorth) as the third (PAMWest) stopped recording early and the data suffered from poor audio quality.\n\nFive marine mammal species were detected during the study: fin whale, humpback whale, killer whale, sperm whale and a dolphin species whose signals were tentatively attributed to white-beaked dolphins on the basis of sighting records in the area and habitat preference of this species. These species were generally detected at similar levels at PAMEast and PAMNorth, except for dolphins, which were more common at PAMNorth. Dolphins showed a pronounced seasonal pattern of occurrence, with very few detections in winter.\n\nKiller whale occurred sporadically, their occurrence peaking in November and March, possibly coinciding with that of prey species. Along with dolphins, sperm and fin whales were the most commonly detected species, occurring throughout the study, albeit slightly less frequently in late winter than during the rest of the year. \n\nHumpback whale detections peaked in March, which may be associated with the transit of migrating animals through the area as they return to feed on spring spawning herring.\nThe data are provided in the form of two .csv files formatted according to the Darwin Core terminology used by the Global Biodiversity Information Facility (GBIF). \n\nThe “EQ_marine_mammals_JC_baseline_sampling_events” file lists the geographic attributes of the survey. The “EQ_marine_mammals_JC_baseline_associated_occurrences” file lists the marine mammal detections (reported as % days with acoustic detections per species and month), which can be linked to the “sampling_events” file through the “eventID” column present in both files. For further information on the Darwin Core terminology, use the following link: [Darwin Core quick reference guide - Darwin Core (tdwg.org)](https://dwc.tdwg.org/terms/)\n\nThe full Johan Castberg report can be accessed here: [https://cdn.equinor.com/files/h61q9gi9/global/09796aacc40a85b06fa41ecb70c91ed8b30c6e08.pdf?johan-castberg-acoustic-monitoring-2021.pdf](https://cdn.equinor.com/files/h61q9gi9/global/09796aacc40a85b06fa41ecb70c91ed8b30c6e08.pdf?johan-castberg-acoustic-monitoring-2021.pdf)\n","terms_of_service":"https://equinoropendata.blob.core.windows.net/disclaimers/Equinor%20open%20data%20-%20Terms%20and%20conditions.pdf","documentation_link":"","privacy_policy_link":"https://www.equinor.com/about-us/privacy-policy","geographical_coverage":"[\"Europe\"]","cost":"FREE","data_source":"","size":0.001,"assets":["ASSET_TYPE_MEDIA"],"license":"https://equinoropendata.blob.core.windows.net/disclaimers/Equinor%20open%20data%20-%20Sharing%20license%20-%20Presence%20of%20marine%20mammals.pdf"},"provider_summary":{"name":"Equinor ASA","description":"In Equinor, we are determined to deliver the energy the world needs today, safely and reliably. At the same time, we aim to be a leading company in the energy transition and to achieve net zero emissions in 2050. \n\nTo foster research, study, development and innovation, we are making data available to suppliers and academia."},"popularity":2054},{"id":"feeae757-bd39-41b4-ad14-cc3e31791bc3","summary":{"name":"Physician Dictation Data : Psychiatry (Mental Health)","subtitle":"Comprehensive physician dictation and transcript data: high-quality, redacted medical records of different work types.","share":{"name":"physiciandictationspeechpsychiatry(mentalhealth)","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1723103046277,"updated_at":1723108935409,"categories":["HEALTH","SCIENCE_AND_RESEARCH"],"listingType":"STANDARD","provider_id":"dc00cb61-5b9a-403e-8b4f-71e78dd44d6c"},"detail":{"description":"**Overview**\n\nShaip's Physician Dictation Speech (& Transcript) Data is a comprehensive resource designed for the healthcare sector, offering transcribed medical records from various work types such as History & Physical, Operative Reports, Discharge Summaries, Consultation Notes, Admit Notes, ED Notes, Clinic Notes, and Radiology Reports. \n\nThis dataset provides a broad spectrum of medical insights. Geographically, the dataset encompasses dictation audio from physicians across almost all US states. \n\nThis rich dataset is invaluable for developing and enhancing medical NLP applications, automated transcription services, and clinical decision support systems.\n\n**Use cases**\n\n**Clinical Natural Language Processing (NLP)** \n\n- Model development: Training NLP models to understand and extract medical information from spoken language.\n- Entity recognition: Identifying and classifying medical entities like diseases, medications, and treatments within dictation.\n- Relation extraction: Identifying relationships between medical entities (e.g., drug-interaction, symptom-disease).\n\n**Clinical Decision Support**\n\n- Predictive modeling: Developing models to predict patient outcomes, disease progression, or treatment effectiveness.\n- Risk assessment: Identifying patients at risk for certain conditions or complications.\n- Treatment optimization: Recommending optimal treatment plans based on patient data and clinical guidelines.\n\n**Healthcare Operations**\n\n- Documentation improvement: Analyzing dictation for consistency, completeness, and adherence to clinical guidelines.\n- Coding and billing accuracy: Improving the accuracy of medical coding and billing through NLP analysis.\n- Quality improvement: Identifying areas for improvement in patient care based on dictation analysis.\n\n**Research and Development**\n\n- Drug discovery: Analyzing medical language to identify potential drug targets or repurposing opportunities.\n- Clinical trial recruitment: Identifying eligible patients for clinical trials based on dictation data.\n- Population health management: Understanding disease prevalence and patient populations.\n\n**Product details**\n\n**Dictation Audio Devices:** Audio captured from various devices-\n- Telephone Dictation (54.3%)\n- Digital Recorder (24.9%)\n- SpeechMic (5.4%)\n- Smartphone (2.7%)\n- Unknown (12.7%).\n\n**Transcribed Medical Records:** Available from various work types, including \n\n- Consultation\n- Progress Report\n- History and Physical\n- Discharge Summary\n- Operative Report\n- Procedure Report\n- Radiology Report\n- Office Visit\n- Emergency Room Report\n- Transfer Summary\n- Letter\n- Clinic Note etc.\n\n**Geographical Coverage:** Dictation audio from physicians across almost all US states.\n\n**Physician Age Groups:** Dictation audio from physicians aged \n- 30-50 years (32%)\n- 50-70 years (54%)\n- 70+ years (13%).\n\n**Additional Insights**\nAll audio and transcripts are redacted for Personally Identifiable Information (PII) in accordance with Safe Harbor Guidelines and HIPAA compliance, ensuring privacy and security.","terms_of_service":"https://www.shaip.com/terms-of-service/","documentation_link":"","privacy_policy_link":"https://www.shaip.com/privacy-policy/","geographical_coverage":"[\"United States and Canada\"]","cost":"FREE","assets":["ASSET_TYPE_MEDIA"],"license":""},"provider_summary":{"name":"Shaip","description":"Shaip is a leading AI data platform and services provider specializing in the sourcing, creation, licensing, and transformation of high-quality, ethical, and domain-specific data for AI and machine learning applications. Shaip supports Healthcare AI, Computer Vision, Generative AI, and Conversational AI use cases for leading AI labs and enterprise customers worldwide. Learn more at shaip.com"},"popularity":4},{"id":"d73fcc56-eef6-41df-9bb1-b7ce1b772845","summary":{"name":"Business Relationships","subtitle":"Business relationships between public companies, subsidiaries and private companies that issue debt.","setting":{"visibility":"PUBLIC"},"created_at":1773225968101,"updated_at":1774941558377,"categories":["FINANCIAL"],"listingType":"PERSONALIZED","provider_id":"bb123ba0-f713-4a39-8c51-2daca088d1de"},"detail":{"description":"The Business Relationships dataset identifies and classifies connections among global companies helping to build out a comprehensive view of all the interdependent parties in a company’s ecosystem. Today we cover over 2.9M+ relationships with over 1M Customer/Supplier relationships, 600K strategic alliances and 691K Creditor/Borrower relationships among others. These relationships are broadly categorized within Suppliers, Customers and Strategic Alliances for a company. For the Suppliers and Customers of a company, we also provide the revenue and expense data which helps to provide a qualitative and quantitative context for that relationship.\n\nThis dataset identifies connections among global companies, for example:\n\n- Utilize robust coverage including 28 relationship types for global public and private companies\n- Implement data sourced from SEC Filings, SEDAR filings, English glossy annual reports, news and surveys\n- Identify dependencies among multiple companies\n- View upstream suppliers and downstream distributors, customers, etc.\n- Understand how a company is positioned within a competitive environment\n- Identify current and prior relationships\n\nS&P Global Market Intelligence collects and maintains business relationships information for public companies, subsidiaries of public companies, and some private companies that issue debt in public transactions using a multi-faceted approach that includes cyclical validation of company reports, web content mining, utilizing S&P Capital IQ-collected Key Developments data, and company surveys. The combination of these approaches enables the Business Relationships dataset to cover a wide and diverse range of companies and data items in a timely and comprehensive manner.","terms_of_service":"https://www.spglobal.com/en/terms-of-use","documentation_link":"https://www.marketplace.spglobal.com/en/datasets/business-relationships-(5)","privacy_policy_link":"https://www.spglobal.com/en/privacy/privacy-policy-english","geographical_coverage":"[\"Global\"]","cost":"PAID","data_source":"Publicly available documents, company filings, annual reports, press releases","assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"S&P Global Market Intelligence","description":"At S&P Global Market Intelligence, we understand the importance of accurate, deep and insightful information. Our team of experts delivers unrivaled insights and leading data and technology solutions, partnering with customers to expand their perspective, operate with confidence, and make decisions with conviction."},"popularity":940},{"id":"9a874516-73d4-4e5a-ac35-a87696216981","summary":{"name":"Sigma | MCP - Azure US East","subtitle":"The Sigma MCP server allows you to build, search, query, and analyze your workbooks, data models and warehouse data.","setting":{"visibility":"PUBLIC"},"created_at":1792694598978,"updated_at":1792696306709,"categories":["OTHER"],"listingType":"STANDARD","provider_id":"cfccf3c5-0092-49bd-9df2-4e62774aee96"},"detail":{"description":"# ***Overview***\nThe Sigma MCP Server lets any MCP-compatible AI assistant build. search, explore, and query your organization's governed Sigma data using natural language, without leaving the chat interface. It brings your curated data models, semantic definitions, and validated workbooks directly into the conversation, so the AI assistant answers with business context instead of generic guesses.\n\n* **Please Note**: this MCP listing is region-specific. It connects to Sigma organizations hosted on **Azure**, in the **United States East (Virginia)** region. If your Sigma organization is hosted in a different cloud or region, use the matching listing from the ***MCP Listings*** section below — each Sigma cloud/region combination is published as its own Databricks Marketplace listing because the connection endpoint is region-specific.\n\n## ***Key capabilities***\n\n* **Search** — Discover the right workbook, data model, or connection in your Sigma organization with a plain-language question (for example, \"What data do we have on customer retention?\"). Sigma's semantic search surfaces the most relevant assets, including tables, elements, and documents.\n* **Analyze** — Ask questions about your data in natural language and get results back in the same conversation, with no SQL required. Follow-up questions (drilling into an anomaly, slicing by a new dimension, cross-referencing a second model) build on the existing context instead of starting over.\n* **Explore** — Inspect column names, data types, definitions, and descriptions for any connected data source to confirm it fits the analysis before running a query.\n* **Build** — Turn an analysis into a Sigma workbook without leaving the conversation, adding pages, tables, charts, and controls from the data you've already explored. Preview any page or element as an inline screenshot to check the result before you share it.\n\n## ***Use cases***\n\n* **Ask questions, get governed answers** — Business users get real answers from the warehouse in the same conversation where they're already working, with permissions enforced automatically.\n* **Investigate anomalies before anyone has to ask** — Schedule an agent to monitor key metrics in Sigma and automatically drill into revenue or usage drops, surfacing a plain-English summary before the next standup.\n* **Reduce tool switching** — Discovery, exploration, and analysis all happen inside the AI assistant, without opening a separate BI tab.\n\n## ***Governed by design***\n\nThe Sigma MCP Server does not introduce a new security surface. Every request inherits the requesting user's existing Sigma permissions:\n\n* **Account-level access** — Search and Analyze respect the user's assigned Sigma account type and license.\n* **Connection-level access** — The AI assistant can only reach data connections the user already has **Can use** access to.\n* **Column-level and row-level security** — Any CLS/RLS policies configured in Sigma apply identically through the AI assistant. It cannot describe a column, or return a row, the user cannot already see in Sigma.\n* **Workspace permissions** — The AI assistant cannot find or query a workbook that has not been shared with the requesting user.\n\n## ***Requirements***\n* View permissions in Sigma for whatever you want to search, explore, or query (**View connections**, **View data models**, and/or **View workbooks**, plus document- and connection-level access).\n* An AI provider enabled for your Sigma organization.\n* If your organization uses an IP allowlist, the AI assistant's outbound IP ranges added to the allowlist with **Scope** set to **API**.\n\n## ***MCP Listings for Sigma on Databricks Marketplace*** \n* **Currently Supported**\n * **AWS**: [US West (`us-west-2)`](https://marketplace.databricks.com/details/b4dc5668-3131-47ec-9eb6-6a567182b022/Sigma-Computing_Sigma-MCP-AWS-US-West)\n * **GCP**: [US Central (`us-central-1`)](https://marketplace.databricks.com/details/d7f62b00-fb22-417d-89b3-4a8db9b6ab18/Sigma-Computing_Sigma-MCP-GCP-Central)\n * **Azure**: [US East (`eastus2`)](https://marketplace.databricks.com/details/9a874516-73d4-4e5a-ac35-a87696216981/Sigma-Computing_Sigma-MCP-Azure-US-East)\n\n* **Future Support**\n * **AWS**: [US East (`us-east-1`)](https://marketplace.databricks.com/provider/cfccf3c5-0092-49bd-9df2-4e62774aee96/Sigma-Computing), [Canada (`ca-central-1`)](https://marketplace.databricks.com/provider/cfccf3c5-0092-49bd-9df2-4e62774aee96/Sigma-Computing), [Europe (`eu-central-1`)](https://marketplace.databricks.com/provider/cfccf3c5-0092-49bd-9df2-4e62774aee96/Sigma-Computing), [Asia Pacific (`ap-southeast-2`)](https://marketplace.databricks.com/provider/cfccf3c5-0092-49bd-9df2-4e62774aee96/Sigma-Computing), [United Kingdom (`eu-west-2`)](https://marketplace.databricks.com/provider/cfccf3c5-0092-49bd-9df2-4e62774aee96/Sigma-Computing)\n * **Azure**: [Europe (`westeurope`)](https://marketplace.databricks.com/provider/cfccf3c5-0092-49bd-9df2-4e62774aee96/Sigma-Computing), [Canada (`canadacentral`)](https://marketplace.databricks.com/provider/cfccf3c5-0092-49bd-9df2-4e62774aee96/Sigma-Computing), [United Kingdom (`uksouth`)](https://marketplace.databricks.com/provider/cfccf3c5-0092-49bd-9df2-4e62774aee96/Sigma-Computing), [Australia (`australiaeast`)](https://marketplace.databricks.com/provider/cfccf3c5-0092-49bd-9df2-4e62774aee96/Sigma-Computing)\n * **GCP**: [Kingdom of Saudi Arabia (`me-central2`)](https://marketplace.databricks.com/provider/cfccf3c5-0092-49bd-9df2-4e62774aee96/Sigma-Computing)\n\n## ***Resources***\n* [MCP Documentation](https://help.sigmacomputing.com/docs/use-sigma-mcp-server)\n* [Supported regions](https://help.sigmacomputing.com/docs/region-warehouse-and-feature-support#supported-cloud-platforms-and-regions)","terms_of_service":"https://www.sigmacomputing.com/legal/terms-of-service","documentation_link":"https://help.sigmacomputing.com/docs/use-sigma-mcp-server","privacy_policy_link":"https://www.sigmacomputing.com/legal/privacy-policy","assets":["ASSET_TYPE_MCP"],"license":""},"provider_summary":{"name":"Sigma Computing","description":"Sigma is the AI, Apps and Analytics platform connected to the cloud data warehouse.\n\nUsing Sigma, business and technical teams can build intelligent, production-ready AI apps that accelerate and automate operational workflows. Sigma provides a spreadsheet interface, SQL and Python editors, visual builders, and native AI to help teams turn live data into interactive applications, analysis, reports, and embedded experiences."},"popularity":1948},{"id":"93908fb1-42a2-4626-af91-81e2ce9251d0","summary":{"name":"Draup Account Intelligence","subtitle":"Multi-industry profiles with roles, skills, education, diversity, projects & career histories","setting":{"visibility":"PUBLIC"},"created_at":1756522255400,"updated_at":1757052653451,"categories":["ADVERTISING_AND_MARKETING","COMMERCE","ECONOMICS","EDUCATION","OTHER","SCIENCE_AND_RESEARCH"],"listingType":"PERSONALIZED","provider_id":"9900a255-6aa9-496f-9b6a-421a3568af3b"},"detail":{"description":"**Overview**\nDraup Account Intelligence provides a holistic and dynamic view of global companies by aggregating critical business, financial, operational, and leadership data into one unified dataset. It empowers sales, business development, and market intelligence teams with detailed company profiles that span foundational information (headquarters, industry verticals, employee counts, R&D workforce size) through to deeper insights on financial performance, IT spend, and strategic priorities.\nBeyond static company profiles, the dataset delivers dynamic intelligence — including leadership movement tracking, competitor landscapes, outsourcing relationships, and curated signal alerts that highlight key developments and market shifts. Supplemented with regulatory filings, earnings transcripts, and shareholder data, Draup Account Intelligence offers a comprehensive lens into target accounts, connecting granular operational signals with broader corporate strategy.\n\n**Methodology & Quality**\nDraup aggregates data from diverse proprietary and public sources, then standardizes it into consistent company, profile, and workload taxonomies. Our processes normalize financial structures, align technology stacks to vendor hierarchies, and tag usage intensity to reduce noise. Leadership movements, job postings, and company signals are refreshed frequently to ensure timeliness. Filings and transcripts are mapped systematically to company entities, creating a reliable and auditable data trail.\n\n**Key Tables Include**\n•\tCompany Overview: firmographics, headquarters, employee count, R&D workforce\n•\tCompany Financials: overall, by business unit, and by region\n•\tIT Spend & Strategic Priorities: category-wise spend, initiatives, mapped use cases\n•\tTechnology Stack: vendor–product adoption, workloads, usage intensity\n•\tCompany Locations: workforce distribution, site types, workloads\n•\tDeals & Service Providers: outsourcing contracts, project scope, vendor relationships\n•\tLeadership Movements: executive transitions across roles, functions, and geographies\n•\tJob Postings (Company-Level): standardized job roles, skills, and hiring trends\n•\tCompany Signals & Competitors: curated events, partnerships, competitive landscapes\n•\tFilings & Transcripts: SEC filings, international filings, earnings transcripts & attendees\n•\tShareholdings: ownership details, filing dates, shareholding percentages\n\n**Use Cases:**\n***Analyze the availability of talent for a given role in a specific location.***\n\nMeasure the talent pool size and distribution for target roles within chosen geographies to support workforce planning and sourcing.\n\nSELECT\n profile_info.company_name,\n profile_info.business_function,\n COUNT(DISTINCT profile_info.profile_id) AS talent_count\nFROM profile_info\nWHERE profile_info.job_role ILIKE '%Software Engineer%'\n AND profile_info.location ILIKE '%San Francisco%'\nGROUP BY profile_info.company_name, profile_info.business_function\nORDER BY talent_count DESC;\n\n***Identify the most common certifications for a DevOps Engineer role in the USA.***\n\nIdentify widely held certifications among professionals in a role and geography to align upskilling, hiring, and credentialing strategies.\n\nSELECT\n profile_certifications.certification_name,\n COUNT(DISTINCT profile_certifications.profile_id) AS holders_count\nFROM profile_certifications\nJOIN profile_info\n ON profile_certifications.profile_id = profile_info.profile_id\nWHERE profile_info.job_role = 'Devops Engineer'\n AND profile_info.location ILIKE 'United States'\n AND profile_certifications.certification_name IS NOT NULL\nGROUP BY profile_certifications.certification_name\nORDER BY holders_count DESC\nLIMIT 10;\n\n***Which competitors is Snowflake hiring talent from and who are they losing their talent to?***\n\nDraup’s talent movement capabilities reveal a company's inbound and outbound talent flows across roles, functions, and geographies. Identify key feeder and destination companies to inform talent strategy, poaching risk, or market positioning.\n\nSELECT\n company_name AS competitor_company,\n executive_name,\n from_title AS previous_title,\n to_title AS current_title,\n movement_date\nFROM\n company_leadership_movement\nWHERE\n affiliated_entity_name = 'Snowflake Inc.'\n AND company_name IS NOT NULL\n AND movement_date >= DATE_SUB(CURRENT_TIMESTAMP(), INTERVAL '1' YEAR)\nORDER BY\n movement_date DESC;\n\n***List VP-and-above R&D leaders who left Pfizer in the past 12 months and their new employers***\n\nTrack high-level executive movements from any company using Draup’s leadership change data. Filter by function, designation, and time period to uncover exits and transitions; useful for competitive intelligence, recruitment targeting, or succession mapping.\n\nSELECT\n executive_name,\n from_title AS title_at_pfizer,\n company_name AS new_employer,\n to_title AS new_title,\n movement_date\nFROM\n company_leadership_movement\nWHERE\n affiliated_entity_name = 'Pfizer'\n AND business_function = 'R&D'\n AND (from_title LIKE '%VP%' OR from_title LIKE '%President%' OR from_title LIKE '%Chief%')\n AND movement_date >= current_timestamp - INTERVAL '1' YEAR\nORDER BY\n movement_date DESC;\n\n***See how does your peers’ workforce split by function or location compare with you.***\n\nBenchmark workforce distribution across functions and geographies against competitors to identify over- or under-invested areas in your organization.\n\nWITH workforce_distribution AS (\n SELECT\n profile_info.company_name,\n profile_info.business_function,\n profile_info.location,\n COUNT(*) AS headcount\n FROM profile_info\n WHERE profile_info.business_function IS NOT NULL\n AND profile_info.location IS NOT NULL\n GROUP BY profile_info.company_name, profile_info.business_function, profile_info.location\n),\ntotal_workforce AS (\n SELECT\n workforce_distribution.company_name,\n SUM(workforce_distribution.headcount) AS total_headcount\n FROM workforce_distribution\n GROUP BY workforce_distribution.company_name\n),\nworkforce_percentage AS (\n SELECT\n workforce_distribution.company_name,\n workforce_distribution.business_function,\n workforce_distribution.location,\n workforce_distribution.headcount,\n ROUND((workforce_distribution.headcount * 100.0) / total_workforce.total_headcount, 2) AS percentage_split\n FROM workforce_distribution\n JOIN total_workforce\n ON workforce_distribution.company_name = total_workforce.company_name\n)\nSELECT\n CASE \n WHEN workforce_percentage.company_name = 'Microsoft' THEN 'Microsoft'\n ELSE 'Peer Companies'\n END AS company_group,\n workforce_percentage.business_function,\n workforce_percentage.location,\n SUM(workforce_percentage.headcount) AS total_headcount,\n ROUND(AVG(workforce_percentage.percentage_split), 2) AS avg_percentage_split\nFROM workforce_percentage\nGROUP BY\n CASE \n WHEN workforce_percentage.company_name = 'Microsoft' THEN 'Microsoft'\n ELSE 'Peer Companies'\n END,\n workforce_percentage.business_function,\n workforce_percentage.location\nORDER BY\n workforce_percentage.business_function,\n workforce_percentage.location;\n\n***Analyze the historical talent movement and attrition patterns in a location***\n\nAnalyze career transitions, exits, and movements over time within a geography to anticipate attrition risks and strengthen retention strategies.\n\nWITH profile_experience_expanded AS (\n SELECT\n profile_id,\n company_name,\n UNNEST(msa) AS location,\n start_date,\n end_date\n FROM profile_experiences\n WHERE start_date IS NOT NULL\n),\nlocation_joins AS (\n SELECT\n location,\n EXTRACT(YEAR FROM start_date) AS start_year,\n COUNT(DISTINCT profile_id) AS joins\n FROM profile_experience_expanded\n GROUP BY location, EXTRACT(YEAR FROM start_date)\n),\nlocation_exits AS (\n SELECT\n location,\n EXTRACT(YEAR FROM end_date) AS end_year,\n COUNT(DISTINCT profile_id) AS exits\n FROM profile_experience_expanded\n WHERE end_date IS NOT NULL\n GROUP BY location, EXTRACT(YEAR FROM end_date)\n)\nSELECT\n COALESCE(location_joins.location, location_exits.location) AS location,\n COALESCE(location_joins.start_year, location_exits.end_year) AS year,\n COALESCE(location_joins.joins, 0) AS total_joins,\n COALESCE(location_exits.exits, 0) AS total_exits,\n COALESCE(location_exits.exits, 0) - COALESCE(location_joins.joins, 0) AS net_attrition\nFROM location_joins\nFULL OUTER JOIN location_exits\n ON location_joins.location = location_exits.location\n AND location_joins.start_year = location_exits.end_year\nORDER BY location, year;","terms_of_service":"https://draup.com/privacy-policy#:~:text=Unless%20provided%20for%20in%20this,that%20work%20on%20our%20behalf","documentation_link":"","privacy_policy_link":"https://draup.com/privacy-policy","geographical_coverage":"[\"Global\"]","cost":"PAID","update_frequency":{"interval":1,"unit":"WEEKLY"},"collection_granularity":{"interval":1,"unit":"DAILY"},"data_source":"Public Sources","assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"Draup, Inc.","description":"Draup is an enterprise agentic intelligence platform built for HR and Sales leaders navigating transformation. By combining unparalleled real-time global data with agentic AI, Draup enables strategic decision-making across talent and revenue functions.\n \nThrough its Talent Intelligence solution, Draup empowers HR teams to drive workforce planning, precision hiring, predictive skill architecture, and competitive benchmarking. Its Sales Intelligence solution helps go-to-market teams uncover deep account insights, identify buying triggers, and accelerate revenue growth.\n \nTrusted by over 300 global enterprises - including five of the Fortune 10 - Draup supports companies like Microsoft, PepsiCo, PayPal, and Moderna in building future-ready workforces and growth strategies."},"popularity":1446},{"id":"5c9e464e-d2a0-4551-827e-9886056e911f","summary":{"name":"SAMPLE Facebook Data | Facebook Pages and Profiles | 29M records","subtitle":"Public Facebook Pages and Profiles data: categories, contacts, hours, ratings, transparency, followers, verification.","share":{"name":"facebook_pages_and_profiles","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1761573641315,"updated_at":1776250293092,"categories":["MEDIA"],"listingType":"STANDARD","provider_id":"63866f42-27f8-4a1f-b243-37e7be863700"},"detail":{"description":"**Overview**\nUse our Facebook Pages dataset to explore public page details, including categories, contact information, opening hours, ratings, transparency data, followers, and verification status. Popular uses include lead generation and enrichment, business directory building, competitive benchmarking, brand monitoring, and market research.\n\n**Use cases**\nAdd use cases for the listing here.\n- Build and maintain an accurate directory of public businesses/pages with canonical IDs.\n- Generate qualified leads by filtering pages by category, geo, verification, and follower thresholds.\n- Enrich CRM records with contact info, hours, ratings, and verification status; deduplicate entities.\n-Prioritize sales outreach and territory planning based on audience size, ratings, and activity.\n","terms_of_service":"https://brightdata.com/license","documentation_link":"","privacy_policy_link":"https://brightdata.com/privacy","update_frequency":{"interval":1,"unit":"MONTHLY"},"data_source":"Facebook.com","assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"Bright Data","description":"Bright Data is the world’s #1 web data platform.\nFortune 500 companies, academic institutions, and small businesses rely on Bright Data’s scalable platform for web scraping infrastructure to retrieve crucial public web data in the most efficient, reliable, and flexible way, so they can research, monitor, and analyze data to make better decisions.\nThe platform is used worldwide by 20,000+ customers in nearly every industry. The products include a range of no-code data solutions utilized by business owners and a robust infrastructure used by engineers and IT professionals.\n\nBright Data provides a cost-effective way to perform fast and stable public web data collection at scale, effortless conversion of unstructured data into structured data, and superior customer experience while being fully transparent and compliant."},"popularity":1062},{"id":"f87cc426-2467-4081-9806-8dda784321aa","summary":{"name":"Fluid Fan Propensity Scores & Audiences","subtitle":"Modeled purchase-based audiences based on both sports & fan-focused purchase data and interest data.","setting":{"visibility":"PUBLIC"},"created_at":1720816374236,"updated_at":1722612782124,"categories":["ADVERTISING_AND_MARKETING","DEMOGRAPHICS","MEDIA","RETAIL","SPORTS"],"listingType":"PERSONALIZED","provider_id":"ff122bf0-e609-4fc8-bbd1-3a05f1f93fb1"},"detail":{"description":"**Overview**\nSports Innovation Lab offers the largest and strongest data assets for sports-focused transaction data with transaction data for over 240 million individuals (Total US, 18+ years old). Applying our proprietary data model, we use our transactional database to identify and track their sports fandom and brand affinity over time.\n\n**Use cases**\n- **Targeting and Activation:** Identify individuals to target who are fans of a specific sport, team, or league, or specific retailer, category, or community to reach an expanded, highly targeted audience with Sports Innovation Lab propensity audiences to complement Sports Innovation Lab's deterministic audiences.\n- **Customer Acquisition & Prospecting:** Identify individuals and households with a high potential for becoming fans, customers, and buyers within specific sports, categories, or product groups. \n\n**Product details**\nSports Innovation Lab uses a modeling algorithm that grades US consumers as individuals. This model is built on deterministic purchase signals using advanced statistical and machine learning tools. A score indicates propensity of an individual to be a buyer over a predefined period, usually 12 months. The scores for all merchants can be used by customers to add purchase signals in their modeling process for creating audiences. The raw scores provide individual level propensity of shopping at a merchant or in a category. Propensity scores can be generated for custom Sports Innovation Lab-coded groups (teams, leagues, categories, etc.) or individual merchants, sample size permitting.\n\n**Data Assets:**\n- Individual and Household Attributes:\n - 140M+ Individuals’ passively collected transactional data\n - 240M+ Additional individual's targeting attributed (name-based or pseudonymous)\n - 8.5B+ Online behaviors\n - 700M+ Individuals’ emails\n - 7000+ Sports & Entertainment proprietary IP identified Venues, Merchants, Brands\n - 80+ Custom Sports & Entertainment communities\n\nData is for US consumers only.","terms_of_service":"https://www.sportsilab.com/termsofservice","documentation_link":"","privacy_policy_link":"https://www.sportsilab.com/privacy-policy","geographical_coverage":"[\"United States and Canada\"]","cost":"PAID","assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"Sports Innovation Lab","description":"Sports Innovation Lab is an Insight, Data and Analytics company that enables brands and sports properties to build more effective sponsorships, improve targeted advertising, and enhance fan experiences. 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Identify trading opportunities by analyzing the delta and momentum between news coverage (rhetoric, leaks) against official sector activity (action, data releases, judicial decisions). \n\nUsers can also identify policy trajectories as they emerge daily by using the Posture Measurements (in beta) which identify whether the activity in question is favorable, negative, or neutral regarding a policy and our proprietary Momentum Index. \n\nUse PolicyScope data to track easily the daily rhythm and patterns of policy formation using ethically sourced, publicly available official sector information and news coverage acquired through appropriate licenses.\n\nThis dataset includes:\n- Daily multivariate time series data measuring notional volumes of policy activity regarding specific topics and keywords. The data measures the path towards a decision, before decisions have been taken by policymakers.\n\n- Daily Posture and Momentum Index scores for all official sector policy activity (Action, Data, Rhetoric).\n\n- Tickerization to all individual tradeable assets in the Russell 3000, major reserve currencies, top digital currencies, major indices (e.g., VIX), and thematic ETFs.\n\n\n**Use cases**\n\n- **Customized Policy Volatility Forecasting – Alpha Generation**: Backtests show that spikes in Action levels consistently precede news coverage which drives market volatility across a range of issues. Create finely tuned trading signals specific to your portfolio..\n\n- **Technical/Quantitative Market Analysis**: Design and implement customized MACD analysis for any combination of policy topic/keyword and tradeable asset. Spot technical trading opportunities with better precision for key sectors uniquely exposed to public policy risks (e.g., hydrogen, renewables, inflation) policy.\n\n- **Strategy Formation**: Spot strategic policy shifts as they occur, before they materialize as market-moving headlines. \n\n- **Risk Management – Smart Beta**: Create data-driven benchmarks to identify whether underlying model assumptions and parameters have accurately factored in public policy risks.\n\n- **Scenario Analysis – Smart Beta**: Create more accurate scenario analysis and stress tests by incorporating observed correlations between policy shifts and market shifts.\n\n\n**Product details**\n\nOne table that updates daily and automatically at 7:45 am Eastern Time in the United States.\n\nSample Fields:\n\n- Acquisition Date\n- Keyword\n- Topic\n- Theme\n- Tickers\n- Official Sector Action Types: Action, Data, Judicial\n- Media Sector Action Types: Rhetoric, Leaks.\n\nFor more details, refer to the embedded notebook.\n\n**Additional Insights -- Customized Deployments**\nCustomized deployments include additional fields (Country, Institution Name, Institution Type, Official Sector URL) and deployment on dedicated servers to maximize confidentiality.\n\n**Additional Features -- Dow Jones Institutional News Feed Customers**\nIf you currently ingest data from the Dow Jones Institutional Newsfeed, additional fields and columns are available to you. 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Our PolicyScope data makes it easy for strategists, discretionary portfolio managers, and advocates to train ML/AI processes (including generativeAI) that support decision intelligence regarding public policy trends and related market reaction functions. We generate and deliver objective ML/AI training data (tickerized, multivariate time series data measuring notional volumes), momentum-based signals, and official sector language data (in PDF, tagged, tokenized) in three thematic verticals: Climate/Energy Policy, Monetary Policy, Digital Currency Policy. Language inputs sourced ethically, with full respect for copyrights from the beginning."},"popularity":1965},{"id":"c4274fdd-5c6f-4b9d-8ebe-c243fac96981","summary":{"name":"Geolocet | Demographic Data Serbia at municipality Level","subtitle":"The dataset contains demographic information for 168 municipalities (opštini) of Serbia, offering 400+ attributes.","setting":{"visibility":"PUBLIC"},"created_at":1705917093679,"updated_at":1752137414641,"categories":["DEMOGRAPHICS","ADVERTISING_AND_MARKETING","PUBLIC_SECTOR","RETAIL"],"listingType":"PERSONALIZED","provider_id":"5d3ddc17-a7e8-4794-a760-13587e1de2c6"},"detail":{"description":"## **Get instant access to Serbian Demographics Data with [Geolocet](https://geolocet.com)**\nExplore the richness of Serbia demographic landscape with our Serbia Census Data. This meticulously compiled dataset, sourced from official statistical sources, including Statistical Office of the Republic of Serbia, offers a detailed insight into the population landscape of Serbia at the municipality (opštini) level. \n\nIf you require data at a less granular level, such as an Uber grid or square grid, please don't hesitate to get in touch with us at *contact@geolocet.com*. We understand that different projects may have varying data needs, and we are here to tailor our data to match your specific requirements.\n\n ## **Key Features:**\n---\n* **Population Data Serbia:** Gain access to the latest and most accurate population statistics for Serbia, meticulously organized by municipality (opštini).\n\n* **Demographic Insights:** Dive deep into Serbia demographic landscape with data on 419 attributes, including: \n* Total population,\n* Gender,\n* Age bands,\n* Gender split by Age bands,\n* Marital status,\n* Marital status split by Age bands,\n* Gender split by Marital status,\n* Gender split by Marital status split by Age bands,\n* Education,\n* Gender split by Education,\n* Ethnic group,\n* Gender split by Ethnic group,\n* Religion,\n* Gender split by Religion,\n* Number of families,\n* Family composition,\n* Family size,\n* Number of children,\n* Family composition split by Number of children,\n* Number of households,\n* Households size,\nall available at the municipal (opštini) level.\n\n* **Geodemographic Insights:** Unlock geodemographic data Serbia-wide to inform location-based decision-making, marketing strategies, and policy formulation.\n\n* **Ready to use:** Our dataset is currently available in CSV format, providing ease of use and compatibility with a wide range of data analysis tools. However, if you prefer a different format, our team is readily available to assist you in obtaining the data in your desired format.\n---\nAdditionally, for those looking to enrich their analysis or project, we offer:\n* **[Administrative Boundaries](https://geolocet.com/products/serbia-all-available-administrative-boundaries):** If you require geographical boundaries (polygons) for the municipalities of Serbia, consider exploring our Admin Boundaries product, which seamlessly integrates with this dataset.\n\n* **[Point of Interest (PoI) Data](https://geolocet.com/pages/aggregated-points-of-interest-poi-data):** If you're interested in Points of Interest data, please don't hesitate to contact us for customized datasets to enhance your geographical analysis.\n\n* **[Data Customization](https://geolocet.com/pages/data-services-by-geolocet):** We understand that your project may have specific requirements. Feel free to get in touch with us if you need any modifications or specific data formats such as Excel, JSON, GeoJSON, or Shapefile. We are here to tailor the data to your needs.\n\nIf you're looking for data for Serbia, [look here](https://geolocet.com/pages/serbia) for off-the-shelf or tailor-made datasets to suit your specific requirements.\n\nMake informed decisions, conduct precise research, and unlock the potential of Serbia demographic landscape with our comprehensive dataset. Trust in the accuracy and reliability of our official statistical data to drive your projects forward.\nContact us at *contact@geolocet.com* today to access this invaluable resource and explore Serbia census data like never before.\n\n[Geolocet LTD](https://geolocet.com)\n","terms_of_service":"https://geolocet.com/pages/terms-of-use","documentation_link":"","privacy_policy_link":"https://geolocet.com/pages/privacy-policy","geographical_coverage":"[\"Europe\"]","cost":"PAID","data_source":"","assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://geolocet.com/pages/terms-of-use"},"provider_summary":{"name":"Geolocet LTD","description":"Geolocet provides premium geospatial data for retail analysis, GIS, and market planning. We equip businesses with accurate, ready-to-query data to drive spatial analysis, site selection, and customer segmentation.\n\nCore Offerings:\n\nDemographics: Socioeconomic data covering population, age, and employment across diverse geographies.\n\nPoints of Interest (PoI): Datasets spanning retail, healthcare, and transport for catchment analysis.\n\nAdministrative Boundaries: GIS-ready boundaries, including municipalities and postcodes, for territory management.\n\nBrand Locations: Chain location datasets to monitor store networks and market expansion.\n\nWealth Data: Granular economic indicators like purchasing power to identify high-potential markets.\n\nWe source from trusted providers, delivering highly customizable datasets with comprehensive coverage across Europe and worldwide."},"popularity":169},{"id":"1418c785-a283-4b73-b878-f4f2d49945fa","summary":{"name":"FHIR Definitions","subtitle":"Fast Healthcare Interoperability Resources (FHIR) is an interoperability standard for electronic exchange of healthcar..","share":{"name":"fhir_definitions","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1682357497170,"updated_at":1695691886709,"categories":["HEALTH","SCIENCE_AND_RESEARCH"],"listingType":"STANDARD","provider_id":"f88849f8-9d77-4de2-b4c7-03d55cc3768e"},"detail":{"description":"\n**Overview**\nFast Healthcare Interoperability Resources (FHIR) is an interoperability standard for electronic exchange of healthcare information. FHIR was developed by Health Level Seven International (HL7), a not-for-profit organization accredited by the American National Standards Institute that develops and provides frameworks and standards for the sharing, integration and retrieval of clinical health data and other electronic health information.\n\n**Description**\nFast Healthcare Interoperability Resources (FHIR) is a draft standard describing data formats and elements (known as \"resources\") and an application programming interface (API) for exchanging electronic health records. The standard was created by the Health Level Seven International (HL7) health-care standards organization.\n\nIts goal is to facilitate interoperation between legacy healthcare systems, to make it easy to provide healthcare information to healthcare providers and individuals on a wide variety of devices from computers to tablets to cell phones, and to allow third-party application developers to provide medical applications which can be easily integrated into existing systems.\n\nFHIR provides an alternative to document-centric approaches by directly exposing discrete data elements as services. For example, basic elements of healthcare like patients, admissions, diagnostic reports and medications can each be retrieved and manipulated via their own resource URLs (Uniform Resource Locators). FHIR was supported at an American Medical Informatics Association meeting by many EHR (Electronic Health Record) vendors which value its open and extensible nature.\n\n**Benefits**\n- data spread over multiple practices and specialties can be accessed by all physicians providing care to a single patient, creating a more complete picture of all treatment plans and results. \n- a complete ehr record can reduce unnecessary tests such as lab work and imaging and offer full medication history for more accurate prescribing.\n- patient care levels and outcomes within an organization will benefit from data that assists a practice to better assess its success in patient treatment plans, improves patient engagement strategies, provides better community outreach, improves patient outcomes and reduces the need for more critical care or hospital admissions.\n- new and changing requirements, such as population health, will require consolidation of all patients within an organization that share common elements and can be combined with other populations to give national or regional statistics.\n- provides a framework for more scalable data advances via the use of existing language formats that will not incur the complete cost of replacing existing ehr systems.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [Account](https://www.johnsnowlabs.com/marketplace/account)\n- [Activity Definition](https://www.johnsnowlabs.com/marketplace/activity-definition)\n- [Address](https://www.johnsnowlabs.com/marketplace/address)\n- [Adverse Event](https://www.johnsnowlabs.com/marketplace/adverse-event)\n- [Age](https://www.johnsnowlabs.com/marketplace/age)\n- [Allergy Intolerance](https://www.johnsnowlabs.com/marketplace/allergy-intolerance)\n- [Annotation](https://www.johnsnowlabs.com/marketplace/annotation)\n- [Appointment Response](https://www.johnsnowlabs.com/marketplace/appointment-response)\n- [Appointment](https://www.johnsnowlabs.com/marketplace/appointment)\n- [Attachment](https://www.johnsnowlabs.com/marketplace/attachment)\n- [Audit Event](https://www.johnsnowlabs.com/marketplace/audit-event)\n- [Basic](https://www.johnsnowlabs.com/marketplace/basic)\n- 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Information concerning vaccines used for immunization have only been included in the database since January 1, 2011.\n\nAdverse reaction reports are submitted by:\n\nConsumers and health professionals, who submit reports voluntarily\nManufacturers and distributors (also known as market authorization holders), who are required to submit reports according to the Food and Drugs Act.\nThis dataset has been collected primarily by a spontaneous surveillance system in which suspected adverse reactions to health products are reported to market authorization holders (manufacturers) and Health Canada on a voluntary basis.\n\nNote: Inclusion of a particular reaction does not necessarily mean that it was caused by the suspected health product(s). Certain reported reactions may occur spontaneously. They provide a background rate in the general population and may have a temporal, but not necessarily a causal, relationship with the health product. 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For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [Drug Product Ingredients](https://www.johnsnowlabs.com/marketplace/drug-product-ingredients)\n\t- This dataset provides the information about the Canada Vigilance Adverse Reaction active ingredients associated with all drugs. This dataset includes data from 1965 to 2022-10-31.\n- [Drug Products](https://www.johnsnowlabs.com/marketplace/drug-products)\n\t- This dataset provides the information about all Canada Vigilance Adverse Reaction drug names and their codes. This dataset includes data from 1965 to 2022-11-31.\n- [Literature Reference](https://www.johnsnowlabs.com/marketplace/literature-reference)\n\t- This dataset provides the information about Canada Vigilance Adverse Reaction literature references. 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This dataset includes data from 1965 to 2022-11-31.\n- [Report Links](https://www.johnsnowlabs.com/marketplace/report-links)\n\t- This dataset provides the information about Canada Vigilance Adverse Reaction linked/duplicate reports presentation text associated with the code. This dataset includes data from 1965 to 2022-11-31.\n- [VAR Reports](https://www.johnsnowlabs.com/marketplace/var-reports)\n\t- This dataset provides the information about Canada Vigilance Adverse Reaction reports and patients. This dataset includes data from 1965 to 2022-10-31.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated\n- Data Updates\n - Data updates support replace-on-update: outdated foreign keys are deprecated, not deleted\n\n**Our data is curated and enriched by domain experts** \n\n Each 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The MCP server gives any LLM first-class access to that evidence layer with no bespoke integration work.\n\nAgents can submit natural-language clinical questions, attach a FHIR Bundle for patient-specific evidence, retrieve cited real-world studies and PubMed references, and chain results into downstream tools — all from your Databricks AI Playground.\n\nWhat makes this server unique is its quality layer: every answer returns dual content — a human-readable summary and a structured JSON payload — headed by a green/yellow/red quality badge, a numeric confidence score, an evidence level, and a deep link to the full evidence report in the users Atropos Health account. Downstream agents can treat grade and confidence as first-class signals when deciding whether to surface, gate, or follow up on an answer.\n\nIdentity is always server-derived from authenticated request headers, so an LLM can't impersonate a different user, and FHIR Bundles are never echoed back or cached in raw form. The server supports both local studio and remote Streamable HTTP transport, hides long-running synthesis behind synchronous tool calls, and returns recoverable job IDs for queries that exceed the polling window. Typical end-to-end latency is 30–80 seconds.\n\n**Tools**\nSix MCP tools and three read-only resources. Every evidence tool returns both a human-readable summary and a structured JSON object, so the LLM can reason over quality grades, source counts, and portal URLs programmatically.\n\n**ask_evidence** — Submit a natural-language clinical question and return structured evidence: answer, sources with PMIDs, study and patient counts, quality assessment, and a portal URL.\n\n**ask_evidence_with_patient** — Same as ask_evidence, with a FHIR Bundle attached for patient-specific synthesis. The bundle is forwarded over HTTPS, logged only as a SHA256 fingerprint, and never echoed back to the LLM.\n\n**get_job_status** — Retrieve results for a prior job — for example, one that exceeded the default 180-second polling window and returned a recoverable job_id.\n\n**list_recent_jobs** — List the calling user's cached jobs from the current session, scoped per user so one caller's history is never visible to another.\n\n**set_specialty** — Set a default medical specialty (Oncology, Cardiology, and others) for the calling user so subsequent questions are routed and ranked accordingly.\n\n**get_settings** — Inspect the current environment, user identity, and server config; useful for verifying identity round-trips correctly after install.\n\nThe server also exposes three read-only resources for discovery and configuration: config://specialties lists supported medical specialties, config://quality-badges returns badge definitions (green / yellow / red), and config://server reports the current environment, API version, and polling configuration.\n\nQuality grading is the headline differentiator. Each answer is scored on whether it is grounded in cited context, whether the context addresses the question, and whether it answers the question directly. \n\nGreen ANSWERED WITH EVIDENCE means grounded, addresses the question, and answers it directly. Yellow ANSWERED WITH SOME LIMITATIONS means grounded and on-topic, but doesn't fully resolve. Red ANSWERED WITH LIMITATIONS means one or more grounding or relevance criteria are not met.\n\n**Use Cases**\nEvidence-grounded clinical Q&A in agent frameworks. Drop the server into a Databricks AI agent and let clinicians or analysts ask natural-language clinical questions — \"What are the cardiac side effects of lorlatinib?\" — and get back a sourced answer with a quality badge, confidence score, and PMIDs in 30–80 seconds. Because every result includes a structured JSON payload, the calling agent can filter by quality grade or chain sources into a follow-up tool call.\n\nPatient-specific evidence at the point of care. Pass a FHIR Bundle to ask_evidence_with_patient to receive evidence synthesized against a real patient's demographics, conditions, medications, and labs. The bundle is forwarded directly to the Atropos Evidence Agent API over HTTPS and is never cached in raw form or returned to the LLM — only the parsed answer, sources, and portal links are exposed.\nSpecialty-tuned copilots. Set a default specialty per user with set_specialty so an oncology copilot ranks oncology evidence first, a cardiology copilot routes through cardiology, and so on. Useful for service-line-specific agents and for HEOR or Medical Affairs teams who want consistent retrieval behavior across a working session.\n\nLong-running research queries with job recovery. For complex synthesis jobs that exceed the default 180-second wait, the server returns a recoverable job_id. Agents can call get_job_status later in the conversation — or in a new session — to retrieve the final answer without resubmitting, which keeps notebook and orchestrator workflows resilient.\n\nAuditable AI for regulated environments. Every answer is traceable: identity is server-derived, FHIR payloads are logged only as SHA256 fingerprints, outbound traffic egresses from a single allowlisted IP, and each result links back to the full evidence report in the Atropos Portal. Suitable for clinical informatics, GxP-adjacent research, and any agent workflow that needs to defend its sources.","terms_of_service":"https://www.atroposhealth.com/terms-of-service/","documentation_link":"","privacy_policy_link":"https://www.atroposhealth.com/privacy-policy/","assets":["ASSET_TYPE_MCP"],"license":""},"provider_summary":{"name":"Atropos Health","description":"Atropos Health, the world’s largest creator of real-world evidence (RWE) for clinical decision making, Atropos Health is closing medicine's most consequential gap: actionable evidence to inform the vast majority of clinical decisions that are made without evidence. By generating real-world evidence from actual patient outcomes and data, Atropos Health is building the evidence infrastructure that clinicians, life science researchers and AI platforms need to practice, develop and inform medicine, grounded in what actually works. Better healthcare is only possible with evidence, and Atropos Health is working to make it available for every patient, in every healthcare decision, every day. 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[See the full details and get access on MDC](https://mozilladatacollective.com/datasets/cmrm237q100yfmp07v9kl91i5?utm_source=databricks&utm_medium=referral&utm_campaign=databricks_marketplace&utm_content=listing_cta).\n\nThis dataset provides 25 hours of natural, multi-speaker conversational dialogue speech in Manipuri, specifically recorded within the medical domain.\n\nThis dataset captures real-world domain interactions in Manipuri (a Tibeto-Burman language). The collection focuses on spontaneous conversational turn-taking, technical jargon handling, and colloquial linguistic shifts within clinical settings.\n\nMulti-speaker dialogue setup containing contributions from distinct native speakers, domain professionals (healthcare assistants), and local consumers.\n\n**At a glance**\n\n- **Language:** Manipuri (Meitei)\n- **Task:** Automatic speech recognition (ASR)\n- **Format:** WAV, JSON\n\n**Dataset creation**\n\nThe dataset is strictly comprised of natural, spontaneous speech.\n\nSpeakers were explicitly instructed not to follow a script. They were told to pick a topic they felt comfortable with and think: \"How do I talk about this in real life?\"\n\nTo help them sustain natural dialogue for the required 9 to 10 minutes per session, speakers were given a tip to map out 4 to 5 simple bullet points or practice chatting with their partner once before hitting record. They were told to keep it personal, share their own stories, what they have seen around them, and what they think or feel.\n\nThe recordings feature natural pairs of individuals who already have an established real-life relationship. Users were told to record the conversations with someone they know, like a friend, a cousin, or a neighbor.\n\nTo ensure acoustic and conversational diversity, a user could only record with the same partner for a maximum of 3 sessions, and they had to select an entirely different topic each time.\n\nFor every recording, speaker metadata was logged for the conversational partner, including age, gender, phone number and the PIN code of where they are originally from. The files carry only the partner's gender and PIN code, and the gender of the person who made the recording; age and phone number are not included.\n\nSetup and Recording Instructions Given to Speakers:\n\n* Speakers were instructed to talk in a quiet place so their voices remained clear. While minor background noise was acceptable, it could not interfere with clarity, and both individuals had to be equally audible using the phone’s standard built-in microphone.\n* Speakers were given an IMPORTANT safety instruction: they were strictly forbidden from mentioning their real names, their partner's names, where they work, or where they live. All identifying personal details were intentionally kept out of the conversations.\n* Every submission was meant to last 9 to 10 minutes, though some recordings are shorter. Participants were required to complete a minimum of 4 and a maximum of 8 conversation recordings per assigned domain.\n\n**Directory structure**\n\nThe root directory contains pairs of WAV and JSON files that share a prefix: each recording's .wav file has a matching .merged.json file.\n\n**File structure**\n\nThe JSON file has three parts: “audio”, “metadata” and “transcriptions”:\n\n* **audio**: The filename and file format information\n* **metadata**: The metadata of the speaker and the topic\n* **transcriptions**: The time aligned transcriptions, in segments of at most 20 seconds, each marked SPEAKER1 or SPEAKER2\n\nThe transcriptions also mark pauses, speech heard over background noise, and sounds that are not words, with tags such as PAUSE, noise and [inaudible]. English words, which speakers often use for medical terms, are kept in Latin script.\n\nAn example file is in the [full datasheet on MDC](https://mozilladatacollective.com/datasets/cmrm237q100yfmp07v9kl91i5?utm_source=databricks&utm_medium=referral&utm_campaign=databricks_marketplace&utm_content=listing_description).\n\n**Translated Examples**\n\n1. ꯇꯁꯦꯡꯅ ꯍꯥꯏꯔꯥ ꯀꯥꯟꯗꯗꯤ ꯅꯨꯄꯤ ꯑꯣꯏꯕꯁꯤꯡꯗ ꯈꯛ ꯅꯠꯇꯅ health ꯒꯤ ꯗꯁꯨ ꯌꯥꯝꯅ ꯁꯣꯛ ꯍꯜꯂꯦ ꯂ ꯑꯩꯈꯣꯏ ꯏꯁꯥ ꯃꯛꯇ ꯇꯁꯨ ꯁꯣꯛ ꯑꯦ ꯕꯀꯣ꯫ (*The pain has increased, and not only for women. My body is also suffering from this pain.*)\n2. ꯑꯈꫭꯏꯅꯁꯨ ꯈꯪꯅꯕ ꯑꯩꯒꯤ ꯏꯃꯥꯟꯅꯕꯁꯤꯡ ꯀꫭꯂꯤꯛꯁꯤꯡ ꯃꯌꯥꯝ ꯇꯥꯟꯅꯔꯒ ꯍꯥꯏꯇꯥꯔꯦ gynaecologist ꯀꯤ ꯗꫭꯛꯇꯔ ꯑꯐꯕ ꯑꯃꯗ ꯐꯖꯅ ꯌꯦꯡꯊꫭꯛꯑꯒ ꯅꯥꯔꯤꯕ ꯃꯔꯝꯁꯤꯅ ꯀꯔꯤꯅꫭ ꯀꯃꯥꯏ ꯇꯧꯔꯒ ꯅꯥꯕꯅꫭ ꯀꯩꯗꯧꯕꯩꯅꫭ ꯈꫭꯠꯄꯩꯅꯦꯗꫭ ꯐꯖꯅ ꯊꯤꯗꫭꯛꯑꯒ ꯍꯥꯏꯇꯥꯔꯦꯅꯦꯀꫭ ꯑꯗꯨꯃꯥꯏ ꯇꯧꯔꯒ ꯌꯦꯡꯊꫭꯛꯄꯁꯤꯅꯗꯤ ꯌꯥꯝ ꯐꯩ ꯍꯥꯏꯇꯥꯔꯦ ꯑꯩꯒꯤ ꯃꫭꯠꯇꯗꯤ꯫ (*After consulting my friends and colleagues and also a good gynaecologist, why there is illness and how it started suffering there needs to be fully checked, this will be very good for us in my opinion.*)\n3. ꯏꯇꯥꯎꯒꯤ ꯈꯪꯅꯆꯥꯅꯕ ꯃꯈꯩꯗꯨ ꯍꯡꯗꯅ ꯈꯤꯠ ꯇꯅ ꯀꯔꯤ ꯇꯧꯕꯥꯌꯥꯏ ꯀꯔꯝꯕ ꯂꯩꯅꯥ ꯑꯣꯏꯔꯛꯄꯥ ꯌꯥꯏꯗꯨ ꯑꯩꯁꯨ ꯈꯜꯂꯤꯅꯤ ꯊꯥꯗ ꯈꯣꯡ-ꯀꯥꯞ [inaudible] ꯍꯥꯏꯗꯤ ꯍꯦꯟꯖꯤꯟꯅ ꯂꯥꯛꯄ ꯍꯥꯏꯁꯤꯕꯨ ꯀꯔꯤꯅꯣ ꯑꯃ ꯗꯤ ꯁꯣꯏ ꯔꯝꯃꯅꯤ ꯑꯣꯏꯔꯝꯕꯥꯌꯥꯏ ꯍꯥꯏꯅꯑꯩꯁꯤ ꯅꯍꯥꯟ ꯗꯩꯩ ꯈꯜꯂꯤ ꯕꯅꯤ ꯑꯗꯨꯗꯨꯒꯤꯅꯦ ꯑꯩꯅ ꯂꯛꯄꯧ ꯍꯟꯗꯛ ꯊꯥ ꯁꯤꯗꯁꯨ [sneezing] ꯏꯇꯥꯎꯗ ꯀꯨꯏꯁꯤꯟ ꯗꯔꯤ ꯀꯥꯟ ꯗꯥꯁꯨꯀꯣ ꯑꯩ ꯏꯇꯥꯎꯗ ꯐꯠꯇ ꯕꯥ ꯍꯥꯏ ꯄꯨ ꯗꯨꯁꯨ ꯑꯣꯏ ꯍꯟ ꯅꯤꯡ ꯗꯕꯒꯤ ꯍꯌꯦꯡ ꯃꯛ ꯍꯦꯛꯇ ꯂꯥꯛ ꯑꯒꯦ꯫ (*After consulting your relatives and friends, what would be the better option, and what disease might it be, I'm also thinking about that. The monthly period [inaudible], which is supposed to be occured once in a month , it occured twice [sneezing] which is not normal, and there might be some issues with her, so I'm thinking about this situation. 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The goal is to demonstrate how Verticalization/Domain Adaptation for the [telco] sector can be easily achieved using our two-step approach to LLM Fine-Tuning. An overview of this approach can be found at: [From General-Purpose LLMs to Verticalized Enterprise Models](https://www.bitext.com/blog/general-purpose-models-verticalized-enterprise-genai/)\n\nThe dataset has the following specifications:\n\n- Use Case: Intent Detection\n- Vertical: Telco\n- 26 intents assigned to 7 categories\n- 26000 question/answer pairs, with approximately 1000 per intent\n- 55 entity/slot types\n- 10 different types of language generation tags\n\nThe categories and intents are derived from Bitext's extensive experience across various industry-specific datasets, ensuring the relevance and applicability across diverse contexts.\n\n**Dataset Token Count**\n\nThe dataset contains a total of 3.03 million tokens across 'instruction' and 'response' columns. This extensive corpus is crucial for training sophisticated LLMs that can perform a variety of functions including conversational AI, question answering, and virtual assistant tasks in the telco domain.\n\n**Fields of the Dataset**\n\nEach entry in the dataset comprises the following fields:\n\n- tags\n- instruction: a user request from the Telco domain\n- category: the high-level semantic category for the intent\n- intent: the specific intent corresponding to the user instruction\n- response: an example of an expected response from the virtual assistant\n\n**Categories and Intents**\n\nThe dataset covers a wide range of telco-related categories and intents, which are:\n\n- **BILLING**: dispute_invoice, invoices\n- **COMPLAINTS**: get_compensation, report_poor_signal_coverage, report_problem\n- **CONSUMPTION**: check_excess_data_charges, check_usage, set_usage_limits\n- **CONTACT**: customer_service, human_agent\n- **PAYMENT**: check_mobile_payments, payment_methods, pay, schedule_payments\n- **SERVICES**: activate_call_management_services, activate_phone, activate_roaming, check_signal_coverage, deactivate_call_management_services, deactivate_phone, install_internet\n- **SUBSCRIPTION**: cancel_plan, change_plan, change_provider, check_cancellation_fee, sign_up_for_plan\n\n**Entities**\n\nThe entities covered by the dataset include:\n\n- **{{WEBSITE_URL}}**, common with most intents.\n- **{{SERVICE_TYPE}}**, featured in intents like deactivate_call_management_services, report_problem, sign_up_for_plan.\n- **{{DEVICE_TYPE}}**, associated with intents like deactivate_phone.\n- **{{CURRENT_PROVIDER}}**, relevant to intents such as change_provider.\n- **{{SUPPORT_EMAIL_ADDRESS}}**, featured in intents like customer_service.\n\nThis comprehensive list of entities ensures that the dataset is well-equipped to train models that are highly adept at understanding and processing a wide range of telco-related queries and tasks.\n\n**Language Generation Tags**\n\nThe dataset includes tags indicative of various language variations and styles adapted for the Telco domain, enhancing the robustness and versatility of models trained on this data. These tags categorize the utterances into different registers such as colloquial, formal, or containing specific telco jargon, ensuring that the trained models can understand and generate a range of conversational styles appropriate for different customer interactions in the telco sector.\n\n**Language Generation Tags**\n\nThe dataset includes tags that reflect various language variations and styles, crucial for creating adaptable and responsive conversational AI models within the telco sector. These tags help in understanding and generating appropriate responses based on the linguistic context and user interaction style.\n\n**Tags for Lexical variation**\n\n- **M - Morphological variation**: Adjusts for inflectional and derivational forms.\n - Example: \"is my account active\", \"is my account activated\"\n- **L - Semantic variations**: Handles synonyms, use of hyphens, and compounding.\n - Example: “what's my balance date\", “what's my billing date”\n\n**Tags for Syntactic structure variation**\n\n- **B - Basic syntactic structure**: Simple, direct commands or statements.\n - Example: \"activate my card\", \"I need to check my balance\"\n- **I - Interrogative structure**: Structuring sentences in the form of questions.\n - Example: “can you show my balance?”, “how do I transfer money?”\n- **C - Coordinated syntactic structure**: Complex sentences coordinating multiple ideas or tasks.\n - Example: “I want to transfer money and check my balance, what should I do?”\n- **N - Negation**: Expressing denial or contradiction.\n - Example: \"I do not wish to proceed with this transaction, how can I stop it?\"\n\n**Tags for language register variations**\n\n- **P - Politeness variation**: Polite forms often used in customer service.\n - Example: “could you please help me check my account balance?”\n- **Q - Colloquial variation**: Informal language that might be used in casual customer interactions.\n - Example: \"can u tell me my balance?\"\n- **W - Offensive language**: Handling potentially offensive language which might occasionally appear in frustrated customer interactions.\n - Example: “I’m upset with these charges, this is ridiculous!”\n\n**Tags for stylistic variations**\n\n- **K - Keyword mode**: Responses focused on keywords.\n - Example: \"balance check\", \"account status\"\n- **E - Use of abbreviations**: Common abbreviations.\n - Example: “acct for account”, “trans for transaction”\n- **Z - Errors and Typos**: Includes common misspellings or typographical errors found in customer inputs.\n - Example: “how can I chek my balance”\n\n**Other tags not in use in this Dataset**\n\n- **D - Indirect speech**: Expressing commands or requests indirectly.\n - Example: “I was wondering if you could show me my last transaction.”\n- **G - Regional variations**: Adjustments for regional language differences.\n - Example: American vs British English: \"checking account\" vs \"current account\"\n- **R - Respect structures - Language-dependent variations**: Formality levels appropriate in different languages.\n - Example: Using “vous” in French for formal addressing instead of “tu.”\n- **Y - Code switching**: Switching between languages or dialects within the same conversation.\n - Example: “Can you help me with my cuenta, please?”\n\nThese tags not only aid in training models for a wide range of customer interactions but also ensure that the models are culturally and linguistically sensitive, enhancing the customer experience in Telco environments.\n\n**License**\n\nThe `Bitext-telco-llm-chatbot-training-dataset` is released under the **Community Data License Agreement (CDLA) Sharing 1.0**. This license facilitates broad sharing and collaboration while ensuring that the freedom to use, share, modify, and utilize the data remains intact for all users.\n\n**Key Aspects of CDLA-Sharing 1.0**\n\n- **Attribution and ShareAlike**: Users must attribute the dataset and continue to share derivatives under the same license.\n- **Non-Exclusivity**: The license is non-exclusive, allowing multiple users to utilize the data simultaneously.\n- **Irrevocability**: Except in cases of material non-compliance, rights under this license are irrevocable.\n- **No Warranty**: The dataset is provided without warranties regarding its accuracy, completeness, or fitness for a particular purpose.\n- **Limitation of Liability**: Both users and data providers limit their liability for damages arising from the use of the dataset.\n\n**Usage Under CDLA-Sharing 1.0**\n\nBy using the `Bitext-telco-llm-chatbot-training-dataset`, you agree to adhere to the terms set forth in the CDLA-Sharing 1.0. It is essential to ensure that any publications or distributions of the data, or derivatives thereof, maintain attribution to the original data providers and are distributed under the same or compatible terms of this agreement.\n\nFor a detailed understanding of the license, refer to the [official CDLA-Sharing 1.0 documentation](https://cdla.dev/sharing-1-0/).\n\nThis license supports the open sharing and collaborative improvement of datasets within the AI and data science community, making it particularly suited for projects aimed at developing and enhancing AI technologies in the telco sector.\n\n---\n\n(c) Bitext Innovations, 2024\n","terms_of_service":"https://www.bitext.com/api-terms-of-use/","documentation_link":"","privacy_policy_link":"https://www.bitext.com/privacy-policy/","embedded_notebook_file_infos":[{"id":"d43c1c9b-3f69-409e-b7b3-33469ffb5c73","marketplace_file_type":"EMBEDDED_NOTEBOOK","file_parent":{"parent_id":"47fe2cdb-6c08-459f-8325-37e2821ed710","file_parent_type":"LISTING"},"mime_type":"text/html","download_link":"https://marketplace-file-storage-prod.s3-fips.us-east-1.amazonaws.com/EMBEDDED_NOTEBOOK/LISTING_47fe2cdb-6c08-459f-8325-37e2821ed710/d43c1c9b-3f69-409e-b7b3-33469ffb5c73?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEGgaDnVzLWVhc3QtMS1maXBzIkYwRAIgbC8sWIoYaHR8y7Wq86nPb0pufBl4%2BM4PWpeN%2B5PW9VQCIG7BhAVBDCWTpxFDLeuKYm%2Ff1JLz4iccPMzter0YdhgWKqYECDAQABoMNDE0MzUxNzY3ODI2IgxNAJrHbXaZG20jNfMqgwQyGEb4TsoVOoNvJ%2BaA%2FZ7zeu8hGcHqCeNfblP%2FQo5g6q2aRNtpwtPRKMlhYcIE%2FUQaNT1Lqmyptu32VUQTpzjTTxRVmJrDPkheSCkV06XQ%2FpTMKjU8CC7X1b%2FqYf8CrBwZT7Cv4C5WPjEET38f2A79h0xV6bZgPy28zQWrSeIJ9w6AfZBxiE24%2FVc08zVA0ivCT6lsy7nuU0wcR%2FMg2C9nd1vaMnQ923VpD21CKfVVYcrNWL2b%2FQ%2BNAO%2BxPjPgjiN6UPQbvbvZxkSdhgrGtecIg9NgqGkMcvM5VLTDtdC1kdeZetfOC2obtA%2BT%2F0gy3tIDo6WM5hcQfw743%2FAU9oaGnRBl%2BwoxRRDCZA%2F4jnPdluICSI7NCr6C3HHOe4CSZJjRihczxPA%2FgQczAVjAPs6CrK9N9pIzt0vNJpGUTgg4AoxftTvGfciDREilSctPIcBk6GwB%2Bzt4wbtNCmEPU%2BFD46rc6CWVQmmLwbKOeHw70ipMVFlOc8NlXkEtGO2vu6H44MstSSc6daOVUYUavQSGZ6jGhEjsP2YGz8KDm9XQotB%2FtyVBqiZV01OKqDmmBJA%2FXjUpSJ8M%2FeJXhe5m%2BKcxzNObtoJRI7jci3Tpt08XZyXZ7%2FyIV8H30ya468X3cfkTXrm972uga3z7iXAdubyEHnQIua%2FuvtwOHILi6O5D1Me2MzCPvcnXBjqTAYDoMWBt%2B3AVFuqSs2dmDQNBnvEhXfMgTedKLD5PJHb3aIl15iBkLaNu36GYnVCp9ZU2lSYcTbY5QwNnNeTfJTNy9bKhwzru6Sg%2BslhN%2BTS0zkTn%2BeLMY4tHgPEtEZ2cp%2FCk6GwqswvnYVKHjwO8q%2B1Mx8lc08fVEfarE9dke1GGFqoddx%2Bq%2BZUyHbDlAVfrCmf0yw%3D%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20261110T000135Z&X-Amz-SignedHeaders=host&X-Amz-Expires=86400&X-Amz-Credential=ASIAWA6KKHEJORWBH4N3%2F20261110%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=9120d62e6d72e7f4389d277eb5e2ec06b1869c5ecf18ad2a44f4f498932174a1","created_at":1727609022609,"updated_at":1727609022609,"display_name":"Telco_Dataset_Sample_Notebook","status":"FILE_STATUS_PUBLISHED"}],"geographical_coverage":"[\"Global\"]","cost":"FREE","data_source":"Internally generated by Bitext using proprietary NLP and NLG technologies.","assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"Bitext Innovation International","description":"We understand that one of the critical factors in achieving exceptional performance with LLMs is the quality and relevance of the training data. 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For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [Biochemical Protein Protein Interactions](https://www.johnsnowlabs.com/marketplace/biochemical-protein-protein-interactions)\n\t- This dataset includes all protein-protein interactions as well as associated annotation data obtained from the Biological General Repository for Interaction databases (BIOGRID) for major model organisms species, including involved experimental systems used to disclose the interaction. The data is a curation of thousands of publications of research experiments that found a link (interaction) between two proteins.\n- [Epilepsy Genetics Meta Analysis Publications](https://www.johnsnowlabs.com/marketplace/epilepsy-genetics-meta-analysis-publications)\n\t- The Epilepsy Genetic Association Database (epiGAD) of the International League Against Epilepsy is an online repository of data relating to genetic association studies in the field of epilepsy, collects results from published and unpublished research in epilepsy genetics providing data to be used for meta-analyses and other scientific purposes.\n- [Epilepsy Pharmacogenetics Published and Unpublished Research](https://www.johnsnowlabs.com/marketplace/epilepsy-pharmacogenetics-published-and-unpublished-research)\n\t- The Epilepsy Genetic Association Database (epiGAD) of the International League Against Epilepsy is an online repository of data relating to genetic association studies in the field of epilepsy, collects results from published and unpublished research in epilepsy genetics providing data to be used for meta-analyses and other scientific purposes.\n- [Epilepsy Susceptibility Genes Published and Unpublished Research](https://www.johnsnowlabs.com/marketplace/epilepsy-susceptibility-genes-published-and-unpublished-research)\n\t- The Epilepsy Genetic Association Database (epiGAD) of the International League Against Epilepsy is an online repository of data relating to genetic association studies in the field of epilepsy, collects results from published and unpublished research in epilepsy genetics providing data to be used for meta-analyses and other scientific purposes.\n- [Gene Alteration Clinical Condition And Intervention](https://www.johnsnowlabs.com/marketplace/gene-alteration-clinical-condition-and-intervention)\n\t- The Clinical Genomic Database (CGD) purpose is to correlate genetic data with clinical settings, matching diseases with their genetic cause. The database includes all diseases with a known genetic cause of a single gene alteration. The dataset includes descriptions for the gene and inheritance, as well as for the manifestations and interventions to consider. The dataset does not contain contiguous gene syndromes or somatic alterations unless these result from a same gene germline change.\n- [Gene Chemical Interactions](https://www.johnsnowlabs.com/marketplace/gene-chemical-interactions)\n\t- This dataset includes all gene-chemical interactions with \"target\" interaction types as well as associated annotation data obtained from the Biological General Repository for Interaction Datasets (BioGRID) for major model organisms species, noting the official symbol and aliases for every interactor as well as the experimental system used to disclose the interaction. Reference of the publication is given as first author and PubMed ID and organisms involved are also specified.\n- [Human Mitogen Activated Protein MAP Kinase Interactome](https://www.johnsnowlabs.com/marketplace/human-mitogen-activated-protein-map-kinase-interactome)\n\t- This dataset contains the interaction data published in A Human Map Kinase Interactome by Bandyopadhyay S. et al. - 2010, which reported all interactions for the human protein Mitogen Activated Protein (MAP) kinase as well as associated annotation data obtained from the Biological General Repository for Interaction databases (BIOGRID) for humans including the protein used to find the modification (bait), and annotations on the proteins pulled during the interaction experiment.\n- [Human and Animal Cell Lines](https://www.johnsnowlabs.com/marketplace/human-and-animal-cell-lines)\n\t- This dataset consists of a collection of cell lines by DSMZ (Deutsche Sammlung von Mikroorganismen und Zellkulturen Institute). This collection currently comprises more than 800 different immortalized cell cultures of primate, rodent, amphibian, fish, insect origin isolated from numerous tissues and hybridomas.\n- [Protein Post Translational Modifications](https://www.johnsnowlabs.com/marketplace/protein-post-translational-modifications)\n\t- This dataset includes protein post-translational modifications as well as associated annotation data obtained from the Biological General Repository for Interaction databases (BIOGRID) for major model organisms species including the type of modification, protein sequence and specific amino acid involved.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated\n- Data Updates\n - Data updates support replace-on-update: outdated foreign keys are deprecated, not deleted\n\n**Our data is curated and enriched by domain experts** \n\n Each dataset is manually curated by our team of doctors, pharmacists, public health & medical billing experts:\n- Field names, descriptions, and normalized values are chosen by people who actually understand their meaning\n- Healthcare & life science experts add categories, search keywords, descriptions and more to each dataset\n- Both manual and automated data enrichment supported for clinical codes, providers, drugs, and geo-locations\n- The data is always kept up to date – even when the source requires manual effort to get updates\n- Support for data subscribers is provided directly by the domain experts who curated the data sets\n- Every data source’s license is manually verified to allow for royalty-free commercial use and redistribution.\n\n**Need Help?**\nIf you have questions about our products, contact us at [info@johnsnowlabs.com](mailto:info@johnsnowlabs.com). \n ","terms_of_service":"https://www.johnsnowlabs.com/healthcare-data-library-eula/","documentation_link":"https://www.johnsnowlabs.com/marketplace/genetic-associations-data-package","contact_email":"support@johnsnowlabs.com","privacy_policy_link":"https://www.johnsnowlabs.com/privacy-policy/","embedded_notebook_file_infos":[{"id":"caa11e6b-0e23-4561-a1b2-e3b6c17cdf25","marketplace_file_type":"EMBEDDED_NOTEBOOK","file_parent":{"parent_id":"2b3883bc-f56c-4551-b2dc-f0f35168d775","file_parent_type":"LISTING"},"mime_type":"text/html","download_link":"https://marketplace-file-storage-prod.s3-fips.us-east-1.amazonaws.com/EMBEDDED_NOTEBOOK/LISTING_2b3883bc-f56c-4551-b2dc-f0f35168d775/caa11e6b-0e23-4561-a1b2-e3b6c17cdf25?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEGgaDnVzLWVhc3QtMS1maXBzIkYwRAIgbC8sWIoYaHR8y7Wq86nPb0pufBl4%2BM4PWpeN%2B5PW9VQCIG7BhAVBDCWTpxFDLeuKYm%2Ff1JLz4iccPMzter0YdhgWKqYECDAQABoMNDE0MzUxNzY3ODI2IgxNAJrHbXaZG20jNfMqgwQyGEb4TsoVOoNvJ%2BaA%2FZ7zeu8hGcHqCeNfblP%2FQo5g6q2aRNtpwtPRKMlhYcIE%2FUQaNT1Lqmyptu32VUQTpzjTTxRVmJrDPkheSCkV06XQ%2FpTMKjU8CC7X1b%2FqYf8CrBwZT7Cv4C5WPjEET38f2A79h0xV6bZgPy28zQWrSeIJ9w6AfZBxiE24%2FVc08zVA0ivCT6lsy7nuU0wcR%2FMg2C9nd1vaMnQ923VpD21CKfVVYcrNWL2b%2FQ%2BNAO%2BxPjPgjiN6UPQbvbvZxkSdhgrGtecIg9NgqGkMcvM5VLTDtdC1kdeZetfOC2obtA%2BT%2F0gy3tIDo6WM5hcQfw743%2FAU9oaGnRBl%2BwoxRRDCZA%2F4jnPdluICSI7NCr6C3HHOe4CSZJjRihczxPA%2FgQczAVjAPs6CrK9N9pIzt0vNJpGUTgg4AoxftTvGfciDREilSctPIcBk6GwB%2Bzt4wbtNCmEPU%2BFD46rc6CWVQmmLwbKOeHw70ipMVFlOc8NlXkEtGO2vu6H44MstSSc6daOVUYUavQSGZ6jGhEjsP2YGz8KDm9XQotB%2FtyVBqiZV01OKqDmmBJA%2FXjUpSJ8M%2FeJXhe5m%2BKcxzNObtoJRI7jci3Tpt08XZyXZ7%2FyIV8H30ya468X3cfkTXrm972uga3z7iXAdubyEHnQIua%2FuvtwOHILi6O5D1Me2MzCPvcnXBjqTAYDoMWBt%2B3AVFuqSs2dmDQNBnvEhXfMgTedKLD5PJHb3aIl15iBkLaNu36GYnVCp9ZU2lSYcTbY5QwNnNeTfJTNy9bKhwzru6Sg%2BslhN%2BTS0zkTn%2BeLMY4tHgPEtEZ2cp%2FCk6GwqswvnYVKHjwO8q%2B1Mx8lc08fVEfarE9dke1GGFqoddx%2Bq%2BZUyHbDlAVfrCmf0yw%3D%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20261110T000135Z&X-Amz-SignedHeaders=host&X-Amz-Expires=86400&X-Amz-Credential=ASIAWA6KKHEJORWBH4N3%2F20261110%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=e7518cc1b57cf537337da96e3f1d0181f5c8bf33de5349e59bde63932a5406ca","created_at":1682537369707,"updated_at":1682537369707,"display_name":"EDA Notebook","status":"FILE_STATUS_PUBLISHED"}]},"provider_summary":{"name":"John Snow Labs","description":"John Snow Labs, an AI and NLP for healthcare company, provides state-of-the-art software, models, and data to help healthcare and life science organizations build, deploy, and operate AI projects."},"popularity":1486},{"id":"9a603aa8-9c5c-4045-9468-02cc3db01c84","summary":{"name":"FDA Drug Adverse Events Reporting System FAERS 2014","subtitle":"The FDA Adverse Event Reporting System (FAERS) data package contains information on medication errors, quality complai..","share":{"name":"fda_drug_adverse_events_reporting_system_faers_2014","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1682356535804,"updated_at":1698965635294,"categories":["SCIENCE_AND_RESEARCH"],"listingType":"STANDARD","provider_id":"f88849f8-9d77-4de2-b4c7-03d55cc3768e"},"detail":{"description":"\n**Overview**\nThe FDA Adverse Event Reporting System (FAERS) data package contains information on medication errors, quality complaints and drug related adverse events that were submitted to FDA in 2014.\n\n**Description**\nFAERS data package is designed to support the FDA's post-marketing safety surveillance program for drug and therapeutic biologic products. It is a useful tool for activities such as looking for new safety concerns that might be related to a marketed product, evaluating a manufacturer's compliance to reporting regulations and responding to outside requests for information. The reports in FAERS are evaluated by clinical reviewers, in the Center for Drug Evaluation and Research (CDER) and the Center for Biologics Evaluation and Research (CBER), to monitor the safety of products after they are approved by FDA. These reports are submitted to FDA by healthcare professionals (such as physicians, pharmacists, nurses and others) and consumers (such as patients, family members, lawyers and others). Healthcare professionals and consumers may also report to the products’ manufacturers who then send these reports to FDA. The datasets in FAERS data package contain many relevant information such as: patient demographics, drug name, dose and route of administration, diagnosis and indication for use, therapy start and end date and patient outcome.\n\n**Benefits**\n- useful for physicians, pharmacists, pharmaceutical companies, medical students and patients (general public).\n- faers database is available on fda’s website in raw form, whereas a jsl customer gets it in clean and normalized form ready to use and create his own custom reports.\n- quarter wise data merged into one file.\n- easy to comprehend for the customers who are unfamiliar with the medical terminology as all abbreviated terms are replaced with full form, unlike faers original datasets which are full of abbreviations.\n- datasets are available for the year 2014, 2015, 2016 and 2017\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [FDA Adverse Events Reporting System Demographics 2014](https://www.johnsnowlabs.com/marketplace/fda-adverse-events-reporting-system-demographics-2014)\n\t- FAERS database is designed to support the FDA’s post-marketing safety surveillance program for drug and therapeutic biologic products. \nThe Demographics data file contains patient demographic and administrative information, a single record for each event report.\n- [FDA Adverse Events Reporting System Drug 2014](https://www.johnsnowlabs.com/marketplace/fda-adverse-events-reporting-system-drug-2014)\n\t- FAERS database is designed to support the FDA’s post-marketing safety surveillance program for drug and therapeutic biologic products. \nThe Drug file contains drug/biologic information for as many medications as were reported for the event (1 or more per event).\n- [FDA Adverse Events Reporting System Drug Indication 2014](https://www.johnsnowlabs.com/marketplace/fda-adverse-events-reporting-system-drug-indication-2014)\n\t- FAERS database is designed to support the FDA’s post-marketing safety surveillance program for drug and therapeutic biologic products. \nThe Indication file contains all \"Medical Dictionary for Regulatory Activities\" (MedDRA) terms coded for the indications for use (diagnoses) for the reported drugs (0 or more per drug per event).\n- [FDA Adverse Events Reporting System Drug Reaction 2014](https://www.johnsnowlabs.com/marketplace/fda-adverse-events-reporting-system-drug-reaction-2014)\n\t- FAERS database is designed to support the FDA’s post-marketing safety surveillance program for drug and therapeutic biologic products. \nThe Reaction file contains all \"Medical Dictionary for Regulatory Activities\" (MedDRA) terms coded for the adverse event (1 or more).\n- [FDA Adverse Events Reporting System Drug Therapy Dates 2014](https://www.johnsnowlabs.com/marketplace/fda-adverse-events-reporting-system-drug-therapy-dates-2014)\n\t- FAERS database is designed to support the FDA’s post-marketing safety surveillance program for drug and therapeutic biologic products. The Drug Therapy Dates file contains drug therapy start dates and end dates for the reported drugs (0 or more per drug per event).\n- [FDA Adverse Events Reporting System Patient Outcome 2014](https://www.johnsnowlabs.com/marketplace/fda-adverse-events-reporting-system-patient-outcome-2014)\n\t- FAERS database is designed to support the FDA’s post-marketing safety surveillance program for drug and therapeutic biologic products. \nThe Outcome file contains patient outcomes for the event (0 or more).\n- [FDA Adverse Events Reporting System Report Source 2014](https://www.johnsnowlabs.com/marketplace/fda-adverse-events-reporting-system-report-source-2014)\n\t- FAERS database is designed to support the FDA’s post-marketing safety surveillance program for drug and therapeutic biologic products. \nThe Report Source file contains report sources for event (0 or more).\n- [FDA Adverse Events Reporting System Reporter Type 2014](https://www.johnsnowlabs.com/marketplace/fda-adverse-events-reporting-system-reporter-type-2014)\n\t- FAERS database is designed to support the FDA’s post-marketing safety surveillance program for drug and therapeutic biologic products. \n\nThe Reporter Type file contains counts and percentage of the reports submitted to the FDA during the quarter.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 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It is a useful tool for activities such as looking for new safety concerns that might be related to a marketed product, evaluating a manufacturer's compliance with reporting regulations and responding to outside requests for information. The reports in FAERS are evaluated by clinical reviewers, in the Center for Drug Evaluation and Research (CDER) and the Center for Biologics Evaluation and Research (CBER), to monitor the safety of products after they are approved by FDA. These reports are submitted to FDA by healthcare professionals (such as physicians, pharmacists, nurses and others) and consumers (such as patients, family members, lawyers and others). Healthcare professionals and consumers may also report to the products’ manufacturers who then send these reports to the FDA. The datasets in FAERS data package contain much relevant information such as patient demographics, drug name, dose and route of administration, diagnosis and indication for use, therapy starts and ends date and patient outcome.\n\n**Benefits**\n- useful for physicians, pharmacists, pharmaceutical companies, medical students and patients (general public).\n- faers database is available on fda’s website in raw form, whereas a john snow labs (jsl) customer gets it in a clean and normalized form ready to use and create his own custom reports.\n- quarter-wise data merged into one file.\n- easy to comprehend for the customers who are unfamiliar with the medical terminology as all abbreviated terms are replaced with full form, unlike faers original datasets which are full of abbreviations.\n- datasets are available for the year 2014, 2015, 2016, 2017, 2018, 2019 and 2020.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [FDA Adverse Events Reporting System Demographics 2020](https://www.johnsnowlabs.com/marketplace/fda-adverse-events-reporting-system-demographics-2020)\n\t- The FDA Adverse Event Reporting System (FAERS) contains information on medication errors, quality complaints and drug-related adverse events that were submitted to the FDA (U.S. Food and Drug Administration) in 2020. The Demographics dataset contains patient demographic and administrative information, a single record for each event report.\n- [FDA Adverse Events Reporting System Drug 2020](https://www.johnsnowlabs.com/marketplace/fda-adverse-events-reporting-system-drug-2020)\n\t- The FDA Adverse Event Reporting System (FAERS) contains information on medication errors, quality complaints and drug-related adverse events that were submitted to FDA (U.S. Food and Drug Administration) in 2020. The Drug dataset contains drug/biologic information for as many medications as were reported for the event (1 or more per event).\n- [FDA Adverse Events Reporting System Drug Indication 2020](https://www.johnsnowlabs.com/marketplace/fda-adverse-events-reporting-system-drug-indication-2020)\n\t- The FDA Adverse Event Reporting System (FAERS) contains information on medication errors, quality complaints and drug-related adverse events that were submitted to the FDA (U.S. Food and Drug Administration) in 2020. The Indication dataset contains all \"Medical Dictionary for Regulatory Activities\" (MedDRA) terms coded for the indications for use (diagnoses) for the reported drugs (0 or more per drug per event).\n- [FDA Adverse Events Reporting System Drug Reaction 2020](https://www.johnsnowlabs.com/marketplace/fda-adverse-events-reporting-system-drug-reaction-2020)\n\t- The FDA Adverse Event Reporting System (FAERS) contains information on medication errors, quality complaints and drug-related adverse events that were submitted to FDA (U.S. Food and Drug Administration) in 2020. The drug reaction dataset contains all \"Medical Dictionary for Regulatory Activities\" (MedDRA) terms coded for the adverse event (1 or more).\n- [FDA Adverse Events Reporting System Drug Therapy Dates 2020](https://www.johnsnowlabs.com/marketplace/fda-adverse-events-reporting-system-drug-therapy-dates-2020)\n\t- The FDA Adverse Event Reporting System (FAERS) contains information on medication errors, quality complaints and drug-related adverse events that were submitted to FDA (U.S. Food and Drug Administration) in 2020. The drug therapy dates dataset contains drug therapy start dates and end dates for the reported drugs.\n- [FDA Adverse Events Reporting System Patient Outcome 2020](https://www.johnsnowlabs.com/marketplace/fda-adverse-events-reporting-system-patient-outcome-2020)\n\t- The FDA Adverse Event Reporting System (FAERS) contains information on medication errors, quality complaints and drug-related adverse events that were submitted to FDA (U.S. Food and Drug Administration) in 2020. The patient outcome file contains information on patient outcomes for the event (0 or more).\n- [FDA Adverse Events Reporting System Report Source 2020](https://www.johnsnowlabs.com/marketplace/fda-adverse-events-reporting-system-report-source-2020)\n\t- The FDA Adverse Event Reporting System (FAERS) contains information on medication errors, quality complaints and drug-related adverse events that were submitted to FDA (U.S. Food and Drug Administration) in 2020. 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This meticulously compiled dataset, sourced from official statistical sources, including Statistics Estonia, offers a detailed insight into the population landscape of Estonia at the municipality (linnad/vallad) level. \n\nIf you require data at a less granular level, such as an Uber grid or square grid, please don't hesitate to get in touch with us at *contact@geolocet.com*. We understand that different projects may have varying data needs, and we are here to tailor our data to match your specific requirements.\n\n ## **Key Features:**\n---\n* **Population Data Estonia:** Gain access to the latest and most accurate population statistics for Estonia, meticulously organized by municipality (linnad/vallad).\n\n* **Demographic Insights:** Dive deep into Estonia’s demographic landscape with data on 60+ attributes, including: \n* Number of dwellings,\n* Dwelling size,\n* Household structure,\n* Dwelling type,\n* Dwelling type split per household structure.\n\nAll demographic attributes are available at the municipal (linnad/vallad) level.\n\n* **Geodemographic Insights:** Unlock geodemographic data Estonia-wide to inform location-based decision-making, marketing strategies, and policy formulation.\n* **Seamless Data Access:** Our data is available for instant download in CSV format. No lengthy sign-up processes or NDAs required on our side – just download the data you need.\n* **Data Samples for Evaluation:** To ensure you're getting exactly what you need, we provide free downloadable samples in CSV format for data evaluation.\n* **Data accuracy:** Our datasets are created using the latest demographics data available in national statistics bureaus, guaranteeing accuracy and reliability.\n* **Best-in-Class Quality:** We guarantee best-in-class quality, backed by over two decades of experience in modeling demographics data and an outstanding quality management process.\n* **Competitive Pricing:** We maintain the lowest prices on the market, ensuring that you get the most cost-efficient demographics data acquisition process.\n---\nAdditionally, for those looking to enrich their analysis or project, we offer:\n* **[Administrative Boundaries](https://geolocet.com/products/estonia-admin-level-7-municipalities):** If you require geographical boundaries (polygons) for the municipalities of Estonia, consider exploring our Administrative Boundaries product, which seamlessly integrates with this dataset.\n\n* **[Point of Interest (PoI) Data](https://geolocet.com/pages/aggregated-points-of-interest-poi-data):** If you're interested in Points of Interest data, please don't hesitate to contact us for customized datasets to enhance your geographical analysis.\n\n* **[Data Customization](https://geolocet.com/pages/data-services-by-geolocet):** We understand that your project may have specific requirements. Feel free to get in touch with us if you need any modifications or specific data formats such as Excel, JSON, GeoJSON, or Shapefile. We are here to tailor the data to your needs.\n\nIf you're looking for data for Estonia, [look here](https://geolocet.com/pages/estonia) for off-the-shelf or tailor-made datasets to suit your specific requirements.\n\nMake informed decisions, conduct precise research, and unlock the potential of Estonia demographic landscape with our comprehensive dataset. Trust in the accuracy and reliability of our official statistical data to drive your projects forward.\nContact us at *contact@geolocet.com* today to access this invaluable resource and explore Estonia census data like never before.\n\n[Geolocet LTD](https://geolocet.com)\n","terms_of_service":"https://geolocet.com/pages/terms-of-use","documentation_link":"","privacy_policy_link":"https://geolocet.com/pages/privacy-policy","geographical_coverage":"[\"Europe\"]","cost":"PAID","update_frequency":{"interval":1,"unit":"YEARLY"},"data_source":"","assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://geolocet.com/pages/terms-of-use"},"provider_summary":{"name":"Geolocet LTD","description":"Geolocet provides premium geospatial data for retail analysis, GIS, and market planning. 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Covering **all 265 municipalities**, this dataset combines **modelled estimates of average personal income** with essential demographic and employment indicators, making it ideal for **advanced location intelligence, market analysis, and socioeconomic modelling**.\n\nDeveloped using **official datasets from Bulgaria’s National Statistical Institute (NSI)** and enhanced via **Geolocet’s proprietary geospatial and statistical modelling workflows**, this dataset is designed to deliver **precision, scalability, and analytical depth** straight into your **Databricks environment**.\n\n---\n\n## **Product details** \n\nThis listing includes: \n\n- **Datasets represented**: \n - Municipality-level income & demographics (CSV format) \n - Municipality boundaries with geometry (Shapefile) \n\n- **Included fields**: \n - `MUN_NAME` – Municipality name \n - `Pop_Tot` – Total population \n - `HH_Tot` – Total households \n - `Empl_Tot` – Total employed persons \n - `AVG_PIncBD` – Estimated average personal income band (€ per person per year)\n\nDesigned for seamless integration with **geospatial visualisation, predictive modelling, and business intelligence platforms**.\n\n---\n\n## **Additional Insights** \nThis dataset serves as a trusted foundation for both strategic planning and exploratory analysis. 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Exa also provides pre-built Claude Code skills for common workflows like company research and financial report discovery.\n\nConnect at `https://mcp.exa.ai/mcp` or run locally via `npx -y exa-mcp-server` with your API key.\n\n**Tools**\n\n- `web_search_exa` (on by default) — Search the web and get clean, ready-to-use content\n- `get_code_context_exa` (on by default) — Find code examples and docs from GitHub, Stack Overflow, and documentation sites\n- `crawling_exa` (on by default) — Get full content of a specific webpage from a known URL\n- `web_search_advanced_exa` (off by default) — Advanced search with filters for domains, dates, categories, and result depth\n\n**Use Cases**\n\n- **Company research:** Discover companies in a space, find funding data, competitors, LinkedIn profiles, and recent news using the `company` and `people` categories.\n- **Technical documentation:** Search for code examples, API references, and implementation patterns across developer resources.\n- **Content extraction:** Crawl specific URLs and extract clean content for summarization or analysis.\n- **Market intelligence:** Build structured company lists with metadata (headcount, location, funding) using category-specific searches.\n- **Financial research:** Surface SEC filings, earnings reports, and investor presentations using the `financial report` category.\n\n**Learn More:** [github.com/exa-labs/exa-mcp-server](https://github.com/exa-labs/exa-mcp-server)","terms_of_service":"https://cms.databricks.com/sites/default/files/2023-01/marketplace-consumer-terms_0.pdf","documentation_link":"https://github.com/exa-labs/exa-mcp-server","privacy_policy_link":"https://www.databricks.com/legal/privacynotice","assets":["ASSET_TYPE_MCP"],"license":"https://github.com/exa-labs/exa-mcp-server/blob/main/LICENSE"},"provider_summary":{"name":"Exa"},"popularity":2055},{"id":"a255c5c6-46a6-437b-b077-f76a3d06a51e","summary":{"name":"Congressional Districts - USA Sample","subtitle":"Congressional Districts shows the physical extent & demographic profile of each district of the U.S. Congress.","share":{"name":"usa_congressional_districts_sample","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1767079227248,"updated_at":1767196785219,"categories":["GEOSPATIAL","LOOKUP_TABLES"],"listingType":"STANDARD","provider_id":"b2312fad-ce38-48e5-9c55-8a225d183993"},"detail":{"description":"**Overview**\nCongressional Districts depicts the physical extent and demographic profile of each district of the U.S. Congress. 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Geographically, the dataset encompasses dictation audio from physicians across almost all US states. \n\nThis rich dataset is invaluable for developing and enhancing medical NLP applications, automated transcription services, and clinical decision support systems.\n\n**Use cases**\n\n**Clinical Natural Language Processing (NLP)** \n\n- Model development: Training NLP models to understand and extract medical information from spoken language.\n- Entity recognition: Identifying and classifying medical entities like diseases, medications, and treatments within dictation.\n- Relation extraction: Identifying relationships between medical entities (e.g., drug-interaction, symptom-disease).\n\n**Clinical Decision Support**\n\n- Predictive modeling: Developing models to predict patient outcomes, disease progression, or treatment effectiveness.\n- Risk assessment: Identifying patients at risk for certain conditions or complications.\n- Treatment optimization: Recommending optimal treatment plans based on patient data and clinical guidelines.\n\n**Healthcare Operations**\n\n- Documentation improvement: Analyzing dictation for consistency, completeness, and adherence to clinical guidelines.\n- Coding and billing accuracy: Improving the accuracy of medical coding and billing through NLP analysis.\n- Quality improvement: Identifying areas for improvement in patient care based on dictation analysis.\n\n**Research and Development**\n\n- Drug discovery: Analyzing medical language to identify potential drug targets or repurposing opportunities.\n- Clinical trial recruitment: Identifying eligible patients for clinical trials based on dictation data.\n- Population health management: Understanding disease prevalence and patient populations.\n\n**Product details**\n\n**Dictation Audio Devices:** Audio captured from various devices-\n- Telephone Dictation (54.3%)\n- Digital Recorder (24.9%)\n- SpeechMic (5.4%)\n- Smartphone (2.7%)\n- Unknown (12.7%).\n\n**Transcribed Medical Records:** Available from various work types, including \n\n- Consultation\n- Progress Report\n- History and Physical\n- Discharge Summary\n- Operative Report\n- Procedure Report\n- Radiology Report\n- Office Visit\n- Emergency Room Report\n- Transfer Summary\n- Letter\n- Clinic Note etc.\n\n**Geographical Coverage:** Dictation audio from physicians across almost all US states.\n\n**Physician Age Groups:** Dictation audio from physicians aged \n- 30-50 years (32%)\n- 50-70 years (54%)\n- 70+ years (13%).\n\n**Additional Insights**\nAll audio and transcripts are redacted for Personally Identifiable Information (PII) in accordance with Safe Harbor Guidelines and HIPAA compliance, ensuring privacy and security.","terms_of_service":"https://www.shaip.com/terms-of-service/","documentation_link":"","privacy_policy_link":"https://www.shaip.com/privacy-policy/","geographical_coverage":"[\"United States and Canada\"]","cost":"FREE","assets":["ASSET_TYPE_MEDIA"],"license":""},"provider_summary":{"name":"Shaip","description":"Shaip is a leading AI data platform and services provider specializing in the sourcing, creation, licensing, and transformation of high-quality, ethical, and domain-specific data for AI and machine learning applications. 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Accessing a vast repository of 7.2 billion keywords across 229 countries, our API facilitates in-depth analysis to enhance SEO strategies and content marketing efforts.\r\n\r\nExplore related keywords with similar search intent and uncover valuable insights into search questions and suggestions. This enables you to craft relevant and engaging content that resonates with your target audience.\r\n\r\nOur API solution is the most cost-efficient option, starting from just $120 per million API rows. Tailor your queries to extract the most relevant data for your specific use cases, ensuring that you're equipped with the information necessary to drive success in SEO and content creation endeavours.","terms_of_service":"https://serpstat.com/users/privacy-policy","contact_email":"support@datarade.ai","privacy_policy_link":"https://serpstat.com/users/privacy-policy","assets":["ASSET_TYPE_DATA_TABLE"]},"provider_summary":{"name":"Serpstat","description":"The go-to platform to speed up search marketing goals achievement. Customers use Serpstat to reduce the time on common search marketing tasks, cut down on repetitive, manual work and get more clients from search.\r\n\r\nThe common use cases are: competitors and backlinks research, keywords analysis and grouping for content optimization, organic and paid positions tracking, website issues analysis and resolution.\r\n\r\nThe platform also provides additional tools like white-label, team management, and API access for advanced users. 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In a leading platform partner test against 20 other data providers – Adstra was the most accurate on the key attributes of Age and Gender.\n\t \t \n- **HCP:** Comprehensive coverage of health care professionals, targetable by specialty – including physicians, nurses, dentists, and allied healthcare practitioners. Unequalled accuracy and coverage – with NPI number tied to every HCP in the U.S. \n\t\n- **Health:** Broad array of HIPAA-compliant health propensity segments – including ailments, health/wellness lifestyle, and SDoH. Offering tiered audience segments to give media buyers control of targeted performance vs. scale alone.\n \t \t \n- **Orbits:** Exclusive audience that combines behavioral data with robust deterministic demographic data – providing an active picture of how the population is responding to the consumer ecosystem.The only segmentation system powered by behavioral data that tracks a consumer’s online and brick-and-mortar shopping activity; shopping location; and media consumed.\n\t \t \n- **Political:** Access to 99% of registered voters in the U.S. – rigorously updated each month to include the newest registrants – with an array of segments covering affiliation, voting history, and issue/cause support. Only Adstra suppresses absentee and overseas voters, as they come in, to ensure a responsive audience. 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The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [Disease Prevention Coverage and Quality](https://www.johnsnowlabs.com/marketplace/disease-prevention-coverage-and-quality)\n\t- This dataset provides data on World Development Indicators (WDI) on disease prevention coverage and quality.\n- [Drinking Water Service Levels By Country 2000 to 2017](https://www.johnsnowlabs.com/marketplace/drinking-water-service-levels-by-country-2000-to-2017)\n\t- This dataset provides data on drinking water service levels by country from 2000 to 2017 by the World Health Organization/United Nations Children's Fund (WHO/UNICEF) Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (WASH).\n- [Hygiene Service Levels By Country 2000 to 2017](https://www.johnsnowlabs.com/marketplace/hygiene-service-levels-by-country-2000-to-2017)\n\t- This dataset provides data on hygiene by country from 2000 to 2015 by the World Health Organization/United Nations Children's Fund (WHO/UNICEF) Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (WASH).\n- [Sanitation Service Levels By Country 2000 to 2017](https://www.johnsnowlabs.com/marketplace/sanitation-service-levels-by-country-2000-to-2017)\n\t- This dataset provides data on sanitation service levels by country from 2000 to 2015 by the World Health Organization/United Nations Children's Fund (WHO/UNICEF) Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (WASH).\n- [WASH in Schools 2016](https://www.johnsnowlabs.com/marketplace/wash-in-schools-2016)\n\t- This dataset provides estimates on drinking water, sanitation and hygiene (WASH) in schools by region by the World Health Organization/United Nations Children's Fund (WHO/UNICEF) Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 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This dataset maps every verified supermarket, convenience chain, and independent grocer across England, Scotland, Wales, and Northern Ireland, giving site selectors, logistics planners, and territory managers a single, analysis-ready source of truth.\n\nSourced from multiple public registries, cross-validated, and refreshed quarterly. Fully compatible with Apache Spark, Delta Lake, and Databricks Unity Catalog.\n\n---\n\n## Who this is for\n\n- **Retail site selectors & expansion teams** identifying whitespace and competitor saturation before opening new locations\n- **CPG & FMCG territory managers** mapping distribution coverage and prioritizing field rep routing\n- **Logistics & last-mile delivery planners** building drive-time models and optimizing fulfilment zones\n- **Academic & municipal researchers** studying food accessibility, transit proximity, and urban planning\n\n---\n\n## Coverage: 71,383 locations across all four UK nations\n\nUnlike top-tier-only datasets, this collection captures the full UK grocery landscape, including major chains and the tens of thousands of independent and regional grocers that rarely appear in commercial data products.\n\n**Breakdown by country:** England: 60,448 sites · Scotland: 4,816 · Wales: 3,571 · Northern Ireland: 2,347\n\n### Major chains & convenience leaders\n\n- [Premier](https://geolocet.com/products/uk-premier-grocery) -- 5,113 locations\n- [Coop](https://geolocet.com/products/uk-coop-grocery) -- 3,808 locations\n- [Tesco](https://geolocet.com/products/uk-tesco-grocery) -- 3,124 locations\n- [SPAR](https://geolocet.com/products/uk-spar-grocery) -- 1,959 locations\n- [Morrisons](https://geolocet.com/products/uk-morrisons-grocery) -- 1,719 locations\n- [Londis](https://geolocet.com/products/uk-londis-grocery) -- 2,138 locations\n- [Costcutter](https://geolocet.com/products/uk-costcutter-grocery) -- 1,531 locations\n- [Sainsbury's](https://geolocet.com/products/uk-sainsburys-grocery) -- 1,504 locations\n- [One Stop](https://geolocet.com/products/uk-one-stop-grocery) -- 1,143 locations\n- [M&S](https://geolocet.com/products/uk-marks-and-spencer-grocery) -- 1,117 locations\n- [ASDA](https://geolocet.com/products/uk-asda-grocery) -- 1,117 locations\n\n### Discounters\n\n- [ALDI](https://geolocet.com/products/uk-aldi-grocery) -- 1,098 locations\n- [Lidl](https://geolocet.com/products/uk-lidl-grocery) -- 1,075 locations\n- [Iceland](https://geolocet.com/products/uk-iceland-grocery) -- 952 locations\n\n### Specialty & regional\n\n- [Waitrose](https://geolocet.com/products/uk-waitrose-grocery) -- 445 locations\n- [Budgens](https://geolocet.com/products/uk-budgens-grocery) -- 349 locations\n- [Heron Foods](https://geolocet.com/products/uk-heron-foods-grocery) -- 342 locations\n\n**Plus thousands of independent, regional, and unbranded grocers** fully mapped and standardized across all UK regions.\n\n---\n\n## Schema\n\nEach record includes 17 standardized fields, optimized for zero-friction ingestion via `spark.read.format(\"csv\")`.\n\n- **GUID** (STRING) -- Unique identifier for deterministic primary key mapping\n- **Title** (STRING) -- Official store name or independent title\n- **Latitude** (DOUBLE) -- WGS84 latitude for point-in-polygon & spatial joins\n- **Longitude** (DOUBLE) -- WGS84 longitude for H3 / S2 spatial indexing\n- **Street_No / Street** (STRING) -- Normalized building number and street name\n- **City** (STRING) -- City, municipality, or primary settlement area\n- **Town** (STRING) -- Town, village, or other settlement area\n- **Local Authority** (STRING) -- Local authority administrative area\n- **District** (STRING) -- Administrative district\n- **Postal_Code** (STRING) -- Postal routing code for the region\n- **Address** (STRING) -- Full, standardized street address\n- **Wheelchair** (STRING) -- Accessibility flags for wheelchair mobility\n- **Popularity_Score** (INTEGER) -- Foot-traffic / rating metric, standardized 1-10 scale\n- **Opening_Hours** (STRING) -- Structured daily operating schedules\n- **Phone / Website** (STRING) -- Branch contact details and landing URLs\n\n**Sample record:**\n\n- GUID: b8e2-4d19-...\n- Title: Tesco Express Camden\n- Latitude: 51.5390 / Longitude: -0.1426\n- City: London · Postal_Code: NW1 8AH\n- Local Authority: London Borough of Camden\n- Popularity_Score: 8\n- Opening_Hours: Mo-Su 06:00-23:00\n- Wheelchair: yes\n\n---\n\n## What you can build with it\n\n**Whitespace & underserved market analysis**\nQuery all 71,383 locations against population density tables to identify true retail deserts, especially in rural areas and rapidly growing commuter towns. Pinpoint expansion opportunities before competitors do.\n\n**Urban accessibility & transit scoring**\nCross-reference store locations with GTFS transit feeds using GeoPandas or Spark spatial joins. Score each site's walkability and public transport accessibility, essential for non-driving demographics across the UK.\n\n**H3 geospatial indexing & demographic overlay**\nConvert WGS84 coordinates into Uber H3 hexagons and join against small-area income indicators or demographic boundary data for granular market characterisation at sub-district resolution.\n\n**Demographics & income enrichment**\nCombine with the [UK Demographics Dataset](https://geolocet.com/products/united-kingdom-geodemographics-dataset-with-boundaries) or the [UK Income Indicators Dataset](https://geolocet.com/products/uk-income-indicators-small-area) to layer population structure, household earnings, and socio-economic segmentation onto each location.\n\n---\n\n## Data methodology & freshness\n\nLocations are aggregated from multiple public sources, then cross-validated for coordinate accuracy and schema consistency. The dataset is refreshed **quarterly** -- the current release reflects verified data as of **30 May 2026**.\n\nA free sample dataset is available for download in CSV format. Verify coordinate precision, column types, and record completeness before purchase.\n\n---\n\n## Frequently asked questions\n\n**Can this data be combined with demographics datasets?**\nYes. Many customers combine these locations with demographics, income, mobility, and administrative boundary datasets for deeper spatial analysis.\n\n**Can this dataset be imported into Power BI or Tableau?**\nYes. The CSV structure is compatible with Power BI, Tableau, Looker Studio, and other business intelligence platforms.\n\n**Can I analyze this dataset with AI tools?**\nYes. The dataset is structured for compatibility with AI workflows including Claude, ChatGPT, Gemini, RAG pipelines, and Python-based analytics.\n\n**Is this dataset useful for accessibility studies?**\nYes. Analysts can combine the coordinates with mobility, transport, and demographics datasets to evaluate accessibility and service coverage across the UK.\n\n**Can I preview the dataset before purchasing?**\nYes. A free sample is available so you can evaluate the structure, fields, and geospatial quality before purchase.\n\n**Can this dataset be used for academic or research purposes?**\nYes. Researchers and universities frequently use these datasets for urban studies, geography, economics, and spatial analytics projects.\n\n---\n\n## Formats & delivery\n\nNative format is CSV, ready for DBFS ingestion. Enterprise formats available on request:\n\n- GeoJSON\n- Esri Shapefiles\n- PostgreSQL dumps\n- Delta sharing profiles\n- Excel\n\nContact **contact@geolocet.com** to request a custom format, discuss European POI data needs, or get integration support from our engineering team.\n","terms_of_service":"https://geolocet.com/pages/terms-of-use","documentation_link":"","privacy_policy_link":"https://geolocet.com/pages/privacy-policy","geographical_coverage":"[\"Europe\"]","cost":"PAID","assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://geolocet.com/pages/terms-of-use"},"provider_summary":{"name":"Geolocet LTD","description":"Geolocet provides premium geospatial data for retail analysis, GIS, and market planning. 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The project currently has a portfolio of eight NQF-endorsed measures for the ambulatory care setting, NQF 0543, NQF 0545, NQF 0555, NQF 0556, NQF 1879, NQF 1880, NQF 2362, NQF 2363, NQF 2379, NQF 2467, NQF 2468.\n\n**Description**\nCMS has contracted with FMQAI (Florida's Medicare Quality Improvement Organization) to provide services for the Medication Measures Special Innovation Project, the purpose of which is to develop measures that can be used to support quality healthcare delivery to Medicare beneficiaries. The project currently has a portfolio of eight NQF-endorsed measures for the ambulatory care setting, NQF 0543, NQF 0545, NQF 0555, NQF 0556, NQF 1879, NQF 1880, NQF 2362, NQF 2363, NQF 2379, NQF 2467, NQF 2468. The key objectives of the project are to:\n- Develop new and maintain previously developed medication measures with the potential for National Quality Forum (NQF) endorsement.\n- Adapt/specify existing NQF-endorsed medication measures and develop new measures for implementation in CMS reporting programs, such as\n- Hospital Inpatient Quality Reporting (IQR) Program\n- Hospital Outpatient Quality Reporting (OQR) Program\n- Physician Quality Reporting System (PQRS)\n- Others as directed by CMS, such as long-term care settings and ambulatory care settings.\n- Continue to develop new medication measures that address the detection and prevention of adverse medication-related patient safety events that can be used in future Quality Improvement Organization (QIO) Statements of Work and in CMS provider reporting programs; and\n- Identify and specify up to five new adverse event measures (non-medication-related) that could be used in future QIO programs and CMS provider reporting programs in the hospital setting (inpatient and/or emergency department). In addition to maintenance of previously developed medication measures, the new measures to be developed under this special project support QIO patient safety initiatives by addressing topics, such as the detection and prevention of medication errors, adverse drug reactions, and other patient safety events. Under this Special Innovation Project, existing measures, as well as new measures, are being refined and specified for implementation in provider reporting programs.\n\n**Benefits**\n- This data package is used to support quality healthcare delivery to medicare beneficiaries\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [Adherence to ACEIs and ARBs For Diabetes Mellitus NQF 2467](https://www.johnsnowlabs.com/marketplace/adherence-to-aceis-and-arbs-for-diabetes-mellitus-nqf-2467)\n\t- This dataset contains the information on the measure \"Adherence to ACEIs and ARBs For Diabetes Mellitus NQF 2467\" which is the Percentage of individuals at least 18 years of age with diabetes mellitus who had at least two prescription drug claims for oral angiotensin converting enzyme inhibitors (ACEIs)/angiotensin receptor blockers (ARBs) and had a Proportion of Days Covered (PDC) of at least 0.8 for ACEIs/ARBs in 2013. This measure is paired with NQF 0545 and NQF 2468.\n- [Adherence to Antiplatelet Therapy After Stent Implantation NQF 2379](https://www.johnsnowlabs.com/marketplace/adherence-to-antiplatelet-therapy-after-stent-implantation-nqf-2379)\n\t- This dataset contains the information on the measure \"Adherence to Antiplatelet Therapy After Stent Implantation NQF 2379\"\nwhich is the average proportion of days covered (PDC) for individuals with antiplatelet therapy during the 12 months (year 2019) following implantation of a coronary artery drug-eluting stent (DES) regardless of indication or a bare-metal stent (BMS) for acute coronary syndrome (ACS).\n- [Adherence to Antipsychotic Medications for Schizophrenia NQF 1879](https://www.johnsnowlabs.com/marketplace/adherence-to-antipsychotic-medications-for-schizophrenia-nqf-1879)\n\t- This dataset contains the information on the measure \"Adherence to Antipsychotic Medications for Schizophrenia NQF (National Quality Forum) 1879\" which is the Percentage of individuals at least 18 years of age with schizophrenia or schizoaffective disorder who had at least two prescription drug claims for antipsychotic medications and had a Proportion of Days Covered (PDC) of at least 0.8 for antipsychotic medications during the measurement period (12 consecutive months of the year 2012.\n- [Adherence to Mood Stabilizers For Bipolar I Disorder NQF 1880](https://www.johnsnowlabs.com/marketplace/adherence-to-mood-stabilizers-for-bipolar-i-disorder-nqf-1880)\n\t- This file contains the information on the measure \"Adherence to Mood Stabilizers For Bipolar I Disorder NQF (National Quality Forum) 1880\" which is the Percentage of individuals at least 18 years of age as of the beginning of the measurement year 2013 with bipolar I disorder who had at least two prescription drug claims for mood stabilizer medications and had a Proportion of Days Covered (PDC) of at least 0.8 for mood stabilizer medications during the measurement period (12 consecutive months).\n- [Adherence to Oral Diabetes Agents For Diabetes Mellitus NQF 2468](https://www.johnsnowlabs.com/marketplace/adherence-to-oral-diabetes-agents-for-diabetes-mellitus-nqf-2468)\n\t- This dataset contains the information on the measure \"Adherence to Oral Diabetes Agents For Diabetes Mellitus NQF (national Quality FOrum) 2468\" which is the Percentage of individuals at least 18 years of age as of the beginning of the measurement period with diabetes mellitus who had at least two prescription drug claims for oral diabetes agents (ODAs) and had a Proportion of Days Covered (PDC) of at least 0.8 for ODAs during the measurement year 2013. It is paired with NQF 0545 and NQF 2467.\n- [Adherence to Statin Therapy with Coronary Artery Disease NQF 0543](https://www.johnsnowlabs.com/marketplace/adherence-to-statin-therapy-with-coronary-artery-disease-nqf-0543)\n\t- This dataset contains the information on the measure \"Adherence to Statin Therapy with Coronary Artery Disease NQF (National Quality Forum) 0543\" which is the percentage of individuals with Coronary Artery Disease (CAD) who are prescribed statin therapy that had a Proportion of Days Covered (PDC) for oral statin medications of at least 0.8 during the measurement year 2011.\n- [Adherence to Statins for Individuals with Diabetes Mellitus NQF 0545](https://www.johnsnowlabs.com/marketplace/adherence-to-statins-for-individuals-with-diabetes-mellitus-nqf-0545)\n\t- This dataset contains the information on the measure \"Adherence to Statins for Individuals with Diabetes Mellitus NQF (National Quality Forum) 0545\" or \"NQF-0545\" which addresses adherence to three types of chronic medications; statins, angiotensin converting enzyme inhibitors (ACEIs)/angiotensin receptor blockers (ARBs) and oral hypoglycemic agents.\n- [INR For Warfarin and Interacting Anti Infective Medications NQF 0556](https://www.johnsnowlabs.com/marketplace/inr-for-warfarin-and-interacting-anti-infective-medications-nqf-0556)\n\t- This dataset contains the information on the measure \"INR For Warfarin and Interacting Anti Infective Medications NQF 0556 (National Quality Forum 0556), which is the Percentage of episodes with an International Normalized Ratio (INR) test performed three to seven days after a newly started interacting anti-infective medication for individuals receiving warfarin.\n- [International Normalized Ratio INR Monitoring on Warfarin NQF 0555](https://www.johnsnowlabs.com/marketplace/international-normalized-ratio-inr-monitoring-on-warfarin-nqf-0555)\n\t- This dataset contains the information on the measure \"International Normalized Ratio INR Monitoring on Warfarin NQF 0555\" which is the Percentage of individuals at least 18 years of age as of the beginning of the measurement period with at least 56 days of warfarin therapy (Warfarin Sodium in the form of tablet, given orally) who receive an International Normalized Ratio (INR) test during each 56-day interval with warfarin.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated\n- Data Updates\n - Data updates support replace-on-update: outdated foreign keys are deprecated, not deleted\n\n**Our data is curated and enriched by domain experts** \n\n Each dataset is manually curated by our team of doctors, pharmacists, public health & medical billing experts:\n- Field names, descriptions, and normalized values are chosen by people who actually understand their meaning\n- Healthcare & life science experts add categories, search keywords, descriptions and more to each dataset\n- Both manual and automated data enrichment supported for clinical codes, providers, drugs, and geo-locations\n- The data is always kept up to date – even when the source requires manual effort to get updates\n- Support for data subscribers is provided directly by the domain experts who curated the data sets\n- Every data source’s license is manually verified to allow for royalty-free commercial use and redistribution.\n\n**Need Help?**\nIf you have questions about our products, contact us at [info@johnsnowlabs.com](mailto:info@johnsnowlabs.com). \n ","terms_of_service":"https://www.johnsnowlabs.com/healthcare-data-library-eula/","documentation_link":"https://www.johnsnowlabs.com/marketplace/national-quality-forum-medication-measures-for-ambulatory-care-data-package","contact_email":"support@johnsnowlabs.com","privacy_policy_link":"https://www.johnsnowlabs.com/privacy-policy/","embedded_notebook_file_infos":[{"id":"eb784b4d-cf9b-4a41-9730-1122d60569dd","marketplace_file_type":"EMBEDDED_NOTEBOOK","file_parent":{"parent_id":"8ed79273-2f8f-41c9-854e-fc4a9d601078","file_parent_type":"LISTING"},"mime_type":"text/html","download_link":"https://marketplace-file-storage-prod.s3-fips.us-east-1.amazonaws.com/EMBEDDED_NOTEBOOK/LISTING_8ed79273-2f8f-41c9-854e-fc4a9d601078/eb784b4d-cf9b-4a41-9730-1122d60569dd?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEGgaDnVzLWVhc3QtMS1maXBzIkYwRAIgbC8sWIoYaHR8y7Wq86nPb0pufBl4%2BM4PWpeN%2B5PW9VQCIG7BhAVBDCWTpxFDLeuKYm%2Ff1JLz4iccPMzter0YdhgWKqYECDAQABoMNDE0MzUxNzY3ODI2IgxNAJrHbXaZG20jNfMqgwQyGEb4TsoVOoNvJ%2BaA%2FZ7zeu8hGcHqCeNfblP%2FQo5g6q2aRNtpwtPRKMlhYcIE%2FUQaNT1Lqmyptu32VUQTpzjTTxRVmJrDPkheSCkV06XQ%2FpTMKjU8CC7X1b%2FqYf8CrBwZT7Cv4C5WPjEET38f2A79h0xV6bZgPy28zQWrSeIJ9w6AfZBxiE24%2FVc08zVA0ivCT6lsy7nuU0wcR%2FMg2C9nd1vaMnQ923VpD21CKfVVYcrNWL2b%2FQ%2BNAO%2BxPjPgjiN6UPQbvbvZxkSdhgrGtecIg9NgqGkMcvM5VLTDtdC1kdeZetfOC2obtA%2BT%2F0gy3tIDo6WM5hcQfw743%2FAU9oaGnRBl%2BwoxRRDCZA%2F4jnPdluICSI7NCr6C3HHOe4CSZJjRihczxPA%2FgQczAVjAPs6CrK9N9pIzt0vNJpGUTgg4AoxftTvGfciDREilSctPIcBk6GwB%2Bzt4wbtNCmEPU%2BFD46rc6CWVQmmLwbKOeHw70ipMVFlOc8NlXkEtGO2vu6H44MstSSc6daOVUYUavQSGZ6jGhEjsP2YGz8KDm9XQotB%2FtyVBqiZV01OKqDmmBJA%2FXjUpSJ8M%2FeJXhe5m%2BKcxzNObtoJRI7jci3Tpt08XZyXZ7%2FyIV8H30ya468X3cfkTXrm972uga3z7iXAdubyEHnQIua%2FuvtwOHILi6O5D1Me2MzCPvcnXBjqTAYDoMWBt%2B3AVFuqSs2dmDQNBnvEhXfMgTedKLD5PJHb3aIl15iBkLaNu36GYnVCp9ZU2lSYcTbY5QwNnNeTfJTNy9bKhwzru6Sg%2BslhN%2BTS0zkTn%2BeLMY4tHgPEtEZ2cp%2FCk6GwqswvnYVKHjwO8q%2B1Mx8lc08fVEfarE9dke1GGFqoddx%2Bq%2BZUyHbDlAVfrCmf0yw%3D%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20261110T000135Z&X-Amz-SignedHeaders=host&X-Amz-Expires=86400&X-Amz-Credential=ASIAWA6KKHEJORWBH4N3%2F20261110%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=269c61db47b2867e858c4b5b264e0d81aa688451e66a925332126956ca733523","created_at":1682537590226,"updated_at":1682537590226,"display_name":"EDA Notebook","status":"FILE_STATUS_PUBLISHED"}],"assets":["ASSET_TYPE_DATA_TABLE"]},"provider_summary":{"name":"John Snow Labs","description":"John Snow Labs, an AI and NLP for healthcare company, provides state-of-the-art software, models, and data to help healthcare and life science organizations build, deploy, and operate AI projects."},"popularity":1339},{"id":"bf6bda95-8f0a-4dd8-8de5-d66959acae14","summary":{"name":"US Clinical Trials","subtitle":"This data package contains datasets on clinical trials conducted in the United States. Diseases include cervical cance..","share":{"name":"us_clinical_trials","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1695692089092,"updated_at":1695692089092,"categories":["SCIENCE_AND_RESEARCH"],"listingType":"STANDARD","provider_id":"f88849f8-9d77-4de2-b4c7-03d55cc3768e"},"detail":{"description":"\n **Overview**\n This data package contains datasets on clinical trials conducted in the United States. Diseases include cervical cancer, diabetes, acute respiratory infection as well as stress. This data package also includes clinical trials registry and results database.\n\n **Description**\n This data package contains the clinical trial on different diseases and conditions. The Clinical Trials Registry and Results Database compiles information on publicly and privately supported clinical trial studies on a wide range of diseases and conditions. The dataset on Cervical Cancer Screening provides demographic information, habits, and historic medical records of the 858 patients from Hospital Universitario de Caracas in Caracas, Venezuela. Diabetes prevalence study seeks to understand the prevalence of obesity, diabetes, and other cardiovascular risk factors in central Virginia for African Americans. Dobutamine stress cardiography seeks to determine if a drug called “Dobutamine” can be used effectively in a test to measure a patient’s risk of having a heart attack or cardiac event. WHO ARI Young Infant Multicenter Study was conducted to develop a clinical prediction model to assist health workers in developing countries in the presentation of an acutely ill young infant.\n\n **Benefits**\n - This data can be useful for patients, their family members, healthcare professionals, researchers, and the public as it provides an easy access to both privately and publicly funded clinical trials information.\n\n **License Information**\n The use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n **Included Datasets**\n - [Cervical Cancer Screening](https://johnsnowlabs.com/wp-json/datasets-api/v1/cervical-cancer-screening)\n\t- This dataset is composed of responses from 858 patients and 36 variables focusing on the prediction of indicators or diagnosis of cervical cancer. The dataset provides demographic information, habits, and historic medical records of the 858 patients from Hospital Universitario de Caracas in Caracas, Venezuela. A number of the patients did not answer some of the questions due to privacy concerns.\n- [Clinical Trials Registry and Results Database](https://johnsnowlabs.com/wp-json/datasets-api/v1/clinical-trials-registry-and-results-database)\n\t- The Clinical Trials Registry and Results Database compiles information on publicly and privately supported clinical trial studies on a wide range of diseases and conditions. Its main goal is to provide an easy access to both privately and publicly funded clinical trials information for patients, their family members, healthcare professionals, researchers, and the public.\n- [Diabetes Prevalence in African Americans](https://johnsnowlabs.com/wp-json/datasets-api/v1/diabetes-prevalence-in-african-americans)\n\t- The dataset consists of 19 variables on 403 subjects from 1046 subjects who were interviewed in a study to understand the prevalence of obesity, diabetes, and other cardiovascular risk factors in central Virginia for African Americans.\n- [Dobutamine Stress Echocardiography Data](https://johnsnowlabs.com/wp-json/datasets-api/v1/dobutamine-stress-echocardiography-data)\n\t- The dataset contains the complete data for the final study population for the Dobutamine Stress Echocardiography test which includes 558 subjects and 31 variables.\n- [WHO ARI Young Infant Multicenter Study](https://johnsnowlabs.com/wp-json/datasets-api/v1/who-ari-young-infant-multicenter-study)\n\t- This dataset contains 4552 observations and 97 variables. This dataset is from The World Health Organization Multicentre Acute Respiratory Infection (WHO ARI) Study of Clinical Signs and Etiological Agents of Pneumonia, Sepsis, and Meningitis in Young Infants.\n\n\n **Data Engineering Overview**\n **We deliver high-quality data**\n - Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n - Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n - Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated\n - Data Updates\n - Data updates support replace-on-update: outdated foreign keys are deprecated, not deleted\n\n **Our data is curated and enriched by domain experts** \n\n Each dataset is manually curated by our team of doctors, pharmacists, public health & medical billing experts:\n - Field names, descriptions, and normalized values are chosen by people who actually understand their meaning\n - Healthcare & life science experts add categories, search keywords, descriptions and more to each dataset\n - Both manual and automated data enrichment supported for clinical codes, providers, drugs, and geo-locations\n - The data is always kept up to date – even when the source requires manual effort to get updates\n - Support for data subscribers is provided directly by the domain experts who curated the data sets\n - Every data source’s license is manually verified to allow for royalty-free commercial use and redistribution.\n\n **Need Help?**\n If you have questions about our products, contact us at [info@johnsnowlabs.com](mailto:info@johnsnowlabs.com). \n ","terms_of_service":"https://www.johnsnowlabs.com/healthcare-data-library-eula/","documentation_link":"https://johnsnowlabs.com/wp-json/datasets-api/v1/us-clinical-trials-data-package","contact_email":"support@johnsnowlabs.com","privacy_policy_link":"https://www.johnsnowlabs.com/privacy-policy/"},"provider_summary":{"name":"John Snow Labs","description":"John Snow Labs, an AI and NLP for healthcare company, provides state-of-the-art software, models, and data to help healthcare and life science organizations build, deploy, and operate AI projects."},"popularity":963},{"id":"311c5750-be06-4fc1-9f0d-922374be4896","summary":{"name":"Geolocet | Latest Base Demographic Data France","subtitle":"Demographic data estimations for the nearly 35,000 the IRIS areas of France - age and gender","setting":{"visibility":"PUBLIC"},"created_at":1706788808448,"updated_at":1752139330605,"categories":["DEMOGRAPHICS","ADVERTISING_AND_MARKETING","GEOSPATIAL","RETAIL","PUBLIC_SECTOR"],"listingType":"PERSONALIZED","provider_id":"5d3ddc17-a7e8-4794-a760-13587e1de2c6"},"detail":{"description":"## Get instant access to the latest Demographics Data for France with [Geolocet](https://geolocet.com)\n\nExplore the richness of France's demographic landscape with our French Demographics Data. This meticulously compiled dataset containing modeled data, based on official statistical sources, including INSEE, offers a detailed insight into the population landscape of France at the IRIS area level. \n\nIf you require data at a less granular level, such as an Uber grid or square grid, please don't hesitate to get in touch with us at *contact@geolocet.com*. We understand that different projects may have varying data needs, and we are here to tailor our data to match your specific requirements.\n\n ## Key Features:\n---\n* **Population Data France:** Gain access to the latest and most accurate population statistics for France, meticulously organized by IRIS areas.\n\n* **Demographic Insights:** Dive deep into France's demographic landscape with data on 54 attributes, including: \n* Total population,\n* Gender,\n* Age bands,\n* Age bands split per gender.\n\nAll demographic attributes are available at the IRIS level.\n\n* **Geodemographic Insights:** Unlock geodemographic data France-wide to inform location-based decision-making, marketing strategies, and policy formulation.\n* **Seamless Data Access:** Our data is available for instant download in CSV format. No lengthy sign-up processes or NDAs required on our side - just download the data you need.\n* **Data Samples for Evaluation:** To ensure you're getting exactly what you need, we provide free downloadable samples in CSV format for data evaluation.\n* **Data accuracy:** Our datasets are created using the latest demographics data available in national statistics bureaus, guaranteeing accuracy and reliability.\n* **Best-in-Class Quality:** We guarantee best-in-class quality, backed by over two decades of experience in modeling demographics data and an outstanding quality management process.\n* **Competitive Pricing:** We maintain the lowest prices on the market, ensuring that you get the most cost-efficient demographics data acquisition process.\n---\nAdditionally, for those looking to enrich their analysis or project, we offer:\n* **[Administrative Boundaries](https://geolocet.com/products/france-admin-level-8-municipalities):** If you require geographical boundaries (polygons) for the municipalities of France, consider exploring our Administrative Boundaries product, which seamlessly integrates with this dataset.\n\n* **[Point of Interest (PoI) Data](https://geolocet.com/pages/aggregated-points-of-interest-poi-data):** If you're interested in Points of Interest data, please don't hesitate to contact us for customized datasets to enhance your geographical analysis.\n\n* **[Data Customization](https://geolocet.com/pages/data-services-by-geolocet):** We understand that your project may have specific requirements. Feel free to get in touch with us if you need any modifications or specific data formats such as Excel, JSON, GeoJSON, or Shapefile. We are here to tailor the data to your needs.\n\nIf you're looking for data for France, [look here](https://geolocet.com/pages/france) for off-the-shelf or tailor-made datasets to suit your specific requirements.\n\n\n\nMake informed decisions, conduct precise research, and unlock the potential of the French demographic landscape with our comprehensive dataset. Trust in the accuracy and reliability of our official statistical data to drive your projects forward.\nContact us at *contact@geolocet.com* today to access this invaluable resource and explore France census data like never before.\n\n[Geolocet LTD](https://geolocet.com)\n","terms_of_service":"https://geolocet.com/pages/terms-of-use","documentation_link":"","privacy_policy_link":"https://geolocet.com/pages/privacy-policy","geographical_coverage":"[\"Europe\"]","update_frequency":{"interval":1,"unit":"YEARLY"},"data_source":"","assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://geolocet.com/pages/terms-of-use"},"provider_summary":{"name":"Geolocet LTD","description":"Geolocet provides premium geospatial data for retail analysis, GIS, and market planning. 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For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [Clinical Laboratory Improvement Amendments](https://www.johnsnowlabs.com/marketplace/clinical-laboratory-improvement-amendments)\n\t- The CLIA Database lists records of all commercially marketed laboratory tests that have been categorized under the Clinical Laboratory Improvement Amendments (CLIA), either by the Centers for Disease Control and Prevention (CDC) prior to January 31, 2000 or by the FDA since that date.\n- [Computed Tomography Head and Chest per 1000 Medicare Enrollees](https://www.johnsnowlabs.com/marketplace/computed-tomography-head-and-chest-per-1000-medicare-enrollees)\n\t- This dataset contains Computed Tomography of head and chest per 1,000 Medicare enrollees. Data is for 2012 at the HRR (Hospital Referral Regions) level. Rates are adjusted for age, sex, and race.\n- [Electrophysiologic Testing per 1000 Medicare Enrollees HRR Level](https://www.johnsnowlabs.com/marketplace/electrophysiologic-testing-per-1000-medicare-enrollees-hrr-level)\n\t- This dataset contains data related to electrophysiologic testing per 1,000 Medicare enrollees. Data is for 2012 at the HRR level. Rates are adjusted for age, sex, and race.\n- [FDA Certified Mammography Facilities](https://www.johnsnowlabs.com/marketplace/fda-certified-mammography-facilities)\n\t- FDA Certified Mammography Facilities dataset lists by zip code of all mammography facilities certified by the Food and Drug Administration (FDA) or Certifying State as meeting baseline quality standards for equipment, personnel and practices under the Mammography Quality Standards Act of 1992 (MQSA) and subsequent Mammography Quality Standards Reauthorization Act (MQSRA) amendments.\n- [Lumbar Radiofrequency Ablation State and HRR Level Data](https://www.johnsnowlabs.com/marketplace/lumbar-radiofrequency-ablation-state-and-hrr-level-data)\n\t- This dataset contains data related to Lumbar Radiofrequency Ablation per 1,000 Medicare enrollees. Data is from 2003 to 2012 at the state and HRR (Hospital Referral Regions) level. Rates are adjusted for age, sex, and race.\n- [MRI of Head per 1000 Medicare Enrollees HRR Level](https://www.johnsnowlabs.com/marketplace/mri-of-head-per-1000-medicare-enrollees-hrr-level)\n\t- This dataset contains data related to Magnetic Resonance Imaging (MRI) of head per 1,000 Medicare enrollees. Data is for 2012 at the HRR level. Rates are adjusted for age, sex, and race.\n- [Radiation-Emitting Electronic Product Codes](https://www.johnsnowlabs.com/marketplace/radiation-emitting-electronic-product-codes)\n\t- The Radiation-Emitting Electronic Product Codes Database contains names of radiation-emitting electronic products and devices and associated information developed by the Center for Devices and Radiological Health (CDRH) in support of its mission.\n- [Radiological Categories List](https://www.johnsnowlabs.com/marketplace/radiological-categories-list)\n\t- The Radiological Categories List Dataset describes the radiation-emitting electronic products of Radiation-Emitting Electronic Product Codes Database\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - 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Transform the way your data and AI teams collaborate and innovate with an end-to-end solution designed to bridge the gap between machine learning, Generative AI development, and operations.\n\n**Use cases**\n\n- Generative AI Capabilities: Utilize the full suite of Databricks' Generative AI tools and features, to develop cutting-edge generative models and applications.\n- GitHub Integration: Seamlessly connect Databricks clusters to GitHub repositories, promoting a consistent, version-controlled development environment.\n- Automated Notebook Creation: Say goodbye to manual setups. Our framework automatically establishes the essential notebooks, ensuring efficient and error-free configurations.\n- Complete Workflow Automation: From data preprocessing to model training, validation, and fine-tuning generative models, establish and manage ML and GenAI workflows with unparalleled ease.\n- Staging & Production Environments: Deploy models confidently with a staging environment that features automated tests and CI/CD. The robust production environment guarantees scalability and resilience for real-world applications, including generative AI models.\n- Integrated MLflow: Experience smooth model tracking, deployment, and management with MLflow, embedded right within the production environment.\n- Databricks Lakehouse Integration: Leverage the full capabilities of Databricks Lakehouse to unify data, analytics, and AI, ensuring a seamless flow from data ingestion to model deployment.\n- Delta Lake: Utilize Delta Lake for reliable data lake operations, ensuring data quality and enabling ACID transactions for robust ML and AI workflows.\n- Collaborative Notebooks: Enhance real-time collaboration with Databricks notebooks, allowing data scientists and AI researchers to work together seamlessly on ML and GenAI projects.\n- Scalable Compute Resources: Take advantage of Databricks' scalable compute resources to handle intensive ML and GenAI tasks, ensuring optimal performance and cost-efficiency.\n\n\n**Product details**\n\n- Efficiency: Rapidly develop, test, and deploy machine learning and generative AI models without the overhead of manual setups.\n- Collaboration: Enhance teamwork between data scientists, AI researchers, engineers, and business stakeholders through streamlined processes.\n- Scalability: Designed to handle projects of any size, from startups to enterprises, ensuring your ML and GenAI operations can grow with your ambitions.\n- Security: With rigorous testing and industry-standard best practices, you can be confident in the security and reliability of your ML and GenAI workflows.\n\n\n**Additional Insights**\n\n- Businesses looking to fast-track their ML and GenAI projects from development to deployment.\n- Databricks users seeking to enhance their existing setups with an integrated AIOps solution.\n- Data and AI teams aiming for a collaborative, streamlined, and error-free environment.\n","terms_of_service":"https://addepto.com/terms-and-conditions/","documentation_link":"","privacy_policy_link":"https://addepto.com/privacy-policy/","geographical_coverage":"[\"Global\"]","cost":"PAID","assets":["ASSET_TYPE_GIT_REPO"],"license":""},"provider_summary":{"name":"Addepto","description":"Addepto is a leading AI consulting and Data Engineering service provider specializing in the development and deployment of cutting-edge business solutions for data-intensive industries. 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Add your Kyberis API key, run the included notebooks, and every IP, domain, hash, and CVE comes back with a confidence score, actor attribution, and references to the evidence behind it. . Built on over a decade of structured threat data.\n\n**The Problem**\n\nYour Lakehouse holds tables of indicators from alerts, logs, and feeds. Traditional threat intelligence tells you whether each one is on a list. It doesn't tell you how confident to be, who is behind it, or what to do next. So analysts triage by hand, false positives pile up, and when an auditor asks why an alert was escalated, the answer is a list match.\n\n**The Solution**\n\nKyberis enriches those tables without moving them. Notebooks call the Kyberis API in batch and write a results table with confidence scores, actor and TTP linkage, recommended actions, and evidence references for every indicator. Join the results onto your detections and every downstream decision is scored, sourced, and governed under Unity Catalog.\n\n**Outcomes**\n\n- Ranked, not flagged. Triage queues ordered by a documented urgency signal instead of an internal severity heuristic.\n- Audit-ready decisions. Every enriched row carries source attribution and an evidence chain for compliance and incident reports.\n- False positives collapse. Your SOC team filters on Kyberis urgency and score instead of hand-tuning thresholds.\n- Fast setup. API key in minutes. No professional services engagement required.\n\n**Architecture**\n\nThe Kyberis REST API runs from Databricks notebooks and jobs against your existing tables. Query by indicator (IP, domain, hash), resolve entities in batch, or prioritize CVEs against your environment. Responses include resolution_confidence, actor attribution, and provenance metadata, written back as columns you can join, filter, and schedule.\n\n**USE CASES**\n\n**Alert Triage on Decision Urgency**\n\n- Problem: Your security models produce thousands of alerts a day. Most are noise, and practitioners either triage by hand or risk missing real threats.\n- Solution: Run the included notebook 01_ioc_batch_enrichment against your alerts or IOC table. It deduplicates indicators, enriches them in batches with Kyberis assessments, and writes one row per indicator to a Delta table. Join that table back onto your detections on the ioc column, route rows with urgency of today or this_week to the SOC queue, and order them by score. Each row carries recommended_actions, caveats and evidence references, so every queue item arrives with its next step attached.\n- Outcome: A triage queue ordered by a documented urgency signal instead of an internal severity heuristic, with the evidence behind each verdict one lookup away. Schedule the notebook as a job to keep it current.\n\n**Environment-driven CVE Prioritization**\n\n- Problem: The CVE firehose is scored for everyone and therefore for no one. Your team needs to know which of this month's disclosures matter to your stack, in your sector, in your region.\n- Solution: Run the included notebook 02_cve_prioritization. It reads your industry, product inventory and geography from lakehouse tables, asks Kyberis for a ranked list of environment-relevant threats, and then runs a CVE-specific assessment on the top entries.\n- Outcome: A ranked patch-and-investigate list grounded in your own asset data, reproducible as a scheduled job instead of a quarterly spreadsheet.\n\n**Indicator Normalization across Sources**\n\n- Problem: Your pipeline ingests threat data from commercial feeds, internal sources and OSINT, and each source names the same thing differently.\n- Solution: Call the package's resolve_entities helper on incoming indicators, aliases, actor names or CVE IDs. Kyberis returns a canonical ID, canonical name, entity type and match confidence for each input, so records from different sources join on one key.\n- Outcome: One canonical entity key across feeds. Deduplication and joins stop depending on exact string matches, and every match carries its confidence.\n\n**Getting Started**\n\nThis listing installs two notebooks and the kyberis_databricks Python package (in the shared volume). Install the package from the volume in your notebook, add your Kyberis API key to a Databricks secret scope, and run notebook 01. Requires a Kyberis subscription.\n\n**Also from Kyberis**\n\n- **Kyberis MCP server**: lets Databricks-hosted models and agents call Kyberis tools directly. 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Use our data to:\n\n- Expand Market Presence: Identify and connect with key players in new markets.\n- Forge Strategic Partnerships: Reach out to potential collaborators and investors.\n- Conduct Market Research: Gain a deeper understanding of industry trends and dynamics.\n\n Seamless Integration with Broader Data Solutions\n\nComprehensive Business Intelligence \nOur North America Data is not an isolated resource; it’s a vital component of our comprehensive business intelligence suite. When combined with our global datasets, it provides a holistic view of the global business landscape. This integrated approach enables businesses to make well-informed decisions, tapping into insights that span across continents and sectors.\n\n Geographical Coverage Across the Americas\n\nPan-American Reach \nOur database covers the entirety of North and South America, offering a robust range of contacts across numerous countries, including but not limited to:\n\n- United States\n- Canada\n- Mexico\n- Brazil\n- Argentina\n- Colombia\n- Chile\n- Peru\n- Venezuela\n- And many more\n\n Extensive Industry Coverage\n\nTailored to Your Sector \nWe cater to a vast array of industries, ensuring that no matter your focus, our database has the coverage you need. 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The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [Connecticut Sales and Use Tax per Town](https://www.johnsnowlabs.com/marketplace/connecticut-sales-and-use-tax-per-town)\n\t- This dataset shows the sales and use tax data, by quarter, by town, disaggregated by North America Industrial Classification (NAICS) groups by the Department of Revenue Services.\n- [HECM Single Family Portfolio Snapshot](https://www.johnsnowlabs.com/marketplace/hecm-single-family-portfolio-snapshot)\n\t- This dataset shows the HECM (Home Equity Conversion Mortgage) Single Family Portfolio Snapshot. 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The other data set in this series include healthcare support occupations.\n- [Health Professionals Job Salaries](https://www.johnsnowlabs.com/marketplace/health-professionals-job-salaries)\n- [Healthcare Assistant Jobs Salaries](https://www.johnsnowlabs.com/marketplace/healthcare-assistant-jobs-salaries)\n- [Wayne County Tax Auction Results](https://www.johnsnowlabs.com/marketplace/wayne-county-tax-auction-results)\n\t- This dataset shows the list of winning bids on Detroit parcels in the 2017 Wayne County Tax Foreclosure Auction.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - 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Consumer Brand (3X increase in retail sales): A leading consumer brand expanded market share by increasing physical store sales by 3X for a 3-month period, resulting in $500k in increased sales, by adding Yobi Signal to their ad targeting model.\n3. Retail (81% decrease in marketing fees): A US retailer reduced their marketing fees by 81% (while still improving their performance), by using Yobi Signal for their candidate selection and segmentation modeling, and distributing through lower cost channels.\n\n**Dataset Details:**\n$5.5B Daily Spend\n70B Daily Ad Events\n40B Daily GPS Pings\n5B Daily Search Queries\n50M Connected TVs\n\n**Robust Identity Matching:**\nConsent string and time stamp maintained across entire dataset.\n1. Email\n2. IP Address\n3. Mailing Address\n4. MAID\n5. UID2\n6. CTV ID\n\n**Our Commitment to Privacy & Opt In:**\n1. Yobi will honor all opt-out, do-not-process, and deletion requests.\n2. Yobi will encrypt all data storage at rest.\n3. 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Business Jet Data offers access to our extensive fleet database with over 29,000 business jet aircraft currently being tracked. With weekly updates, we ensure that the ch-aviation fleets database is the most current and reliable in the industry.\n\nCommercial Aviation:\n\nGet access to:\n-General aircraft info (C/N, MSN, Hexcode, IATA Code, Manufacturer, Production Line, Aircraft Family/Type/Model/Variant/Role, MTOW (kg/lb), etc.)\n- Aircraft operator info such as operator continent, country, type, base, former and future operator, ACMI customer, IOSA certification, etc.\n- Aircraft status info such as active, stored, written off, scrapped or not yet delivered, aircraft age, location, first flight, delivery dates, etc.\n- Engine details such as engine manufacturer, engine family, engine master series, engine type, engine subtype, number of engines, etc.\n- Seat configuration data, including max abreast, pitch, in-seat video, and inflight connectivity info\n- Aircraft order data, including order entity, order date, order cancellation date, etc.\n- Aircraft orders placed with the following manufacturers: Airbus, 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Connectivity, Lavatory, Lavatory Belted, Year Interior, Year Exterior, etc.\nAircraft orders data, including order date, order cancellation date, etc. and publicly announced orders placed with the following manufacturers: Airbus, Boeing, Bombardier, Cessna Aircraft Company, Cirrus, Dassault Falcon, Embraer, Gulfstream, HondaJet, Pilatus.\n- Info on a specific aircraft's serial number, its manager, its owner, and its SPV (Special Purpose Vehicle)\n \nFilter by:\n- Geographical Location (Operator Continent, Operator Country, Operator Region, Aircraft Register, Aircraft Location)\n- Operator (Operator Name, Operator Type, Delivery Customer, etc.)\n- Aircraft Type (Market Group, Manufacturer, Production Line, Aircraft Family, Aircraft, Aircraft Type, Aircraft Model, Aircraft Variant, etc. )\n- Aircraft (Registration, Fleet Number, Line/Serial Number, Hexcode, Status, Substatus, MTOW)\n- Dates (Date Built, Delivery Date, Date In, Projected Retirement Date, Stored As Of, Write off/Scrapping 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Returns the definitions of the available curves for the filters selected. \n- ir_swap_template_list that lists available interest rate swap templates from LSEG for a specific currency. \n- ir_swap_price that prices interest rate swaps using LSEG templates. Identify the related template reference (from ir_swap_template_list), swapType (VanillaSwap, TenorBasisSwap, CurrencyBasisSwap, or CrossCurrencySwap), optional startDate/endDate (ISO date or tenor like '5Y'), optional amount, and optional spread (for vanilla swaps.\n- bond_future_price that calculates bond future pricing, valuation, and analytics from an existing bond future defined from its instrument code (RIC or other identifier). \n- insight_headlines that retrieves a snapshot of insight headlines from a query. ","terms_of_service":"https://www.lseg.com/en/contact-us","documentation_link":"https://www.lseg.com/en/solutions/ai-finance-solutions#products","privacy_policy_link":"https://www.lseg.com/en/policies/privacy-statement","geographical_coverage":"[\"Global\"]","cost":"PAID","data_source":"","assets":["ASSET_TYPE_MCP"],"license":""},"provider_summary":{"name":"LSEG","description":"The London Stock Exchange Group (LSEG) is one of the world’s leading providers of financial markets infrastructure and delivers financial data, analytics, news and index products to more than 40,000 customers in 190 countries. \n \nWe help organisations fund innovation, manage risk and create jobs by partnering with customers at every point in the trade lifecycle: from informing their pre-trade decisions and executing trades to raising capital, clearing and optimisation. \n \nBacked by more than three centuries of experience, innovative technologies and a team of 23,000 people in 70 countries, we are driving financial stability, empowering economies and enabling you to grow sustainably."},"popularity":2081},{"id":"3eae22f6-970a-43c3-93d5-9b861b6e68e9","summary":{"name":"Franchise & Women-Owned Business","subtitle":"Targeted profiles of franchise operations and women-owned businesses across industries and geographies.","setting":{"visibility":"PUBLIC"},"created_at":1749049614018,"updated_at":1749049649830,"categories":["COMMERCE","DEMOGRAPHICS","ECONOMICS","GEOSPATIAL","RETAIL","TRANSPORTATION_AND_LOGISTICS","ADVERTISING_AND_MARKETING","FINANCIAL"],"listingType":"PERSONALIZED","provider_id":"cb4ffcdc-045c-4592-8497-0eeb2b9a3c3d"},"detail":{"description":"Access a curated dataset of franchise and women-owned businesses with verified firmographics, ownership structure, and industry classification. 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The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [Condiments Required in Food](https://www.johnsnowlabs.com/marketplace/condiments-required-in-food)\n\t- The Apps for Healthy Kids competition is a part of First Lady Michelle Obama’s \"Let’s Move!\" campaign to end childhood obesity within a generation. Apps for Healthy Kids challenges software developers, game designers, students, and other innovators to develop fun and engaging software tools and games that drive children, especially “tweens” (ages 9-12) – directly or through their parents – to eat better and be more physically active.\n- [Food Group Display](https://www.johnsnowlabs.com/marketplace/food-group-display)\n\t- The dataset provides information on total calories, calories from “extras” (solid fats and added sugars), and MyPyramid food groups for over 1,000 commonly eaten foods. 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Easily combine data from earnings, M\\&A, guidance, shareholder, company conference presentations and special calls with traditional datasets to develop proprietary analytics.\n\nWith this dataset you can rely on:\n\n* Seamless linking between the Speaker ID to the S\\&P Global Estimates and Professionals database to help identify sell\\-side analysts' revisions.\n* Intraday updates via a feed or streaming XML messages for details on calls scheduled for transcription.\n* 13,600\\+ entities under coverage with history going back up to 2004\\.\n\nThe dataset leverages the power of Kensho NERD to identify company names and aliases mentioned during earnings calls and specifies the mention location in the transcript.\n\n**\\[Awards]**\n\n* 2019 Data Management Insight Award for Best Proposition for AI, Machine Learning, Data Science\n* 2020 Data Management Insight Award for Best Proposition for AI \\& Machine Learning","terms_of_service":"https://www.spglobal.com/en/terms-of-use","documentation_link":"https://www.marketplace.spglobal.com/en/datasets/machine-readable-transcripts-(45)","privacy_policy_link":"https://www.spglobal.com/en/privacy/privacy-policy-english","geographical_coverage":"[\"Global\"]","cost":"PAID","data_source":"Publicly hosted corporate events and calls.","assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"S&P Global Market Intelligence","description":"At S&P Global Market Intelligence, we understand the importance of accurate, deep and insightful information. 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No lengthy sign-up processes or NDA agreements required on our side – just download the data you need.\n* **Data Samples for Evaluation:** To ensure you're getting exactly what you need, we provide free downloadable samples in CSV format for data evaluation.\n* **Data accuracy:** Our datasets are created using the latest demographics data (2023) available in national statistics bureaus, guaranteeing accuracy and reliability.\n* **Best-in-Class Quality:** We guarantee best-in-class quality, backed by over two decades of experience in modeling demographics data and an outstanding quality management process.\n* **Competitive Pricing:** We maintain the lowest prices on the market, ensuring that you get the most cost-effective demographics data acquisition process.\n---\n\nAdditionally, for those looking to enrich their analysis or project, we offer:\n* **[Administrative Boundaries](https://geolocet.com/products/czech-republic-all-available-administrative-boundaries):** If you require geographical boundaries (polygons) for the municipalities of the Czech Republic, consider exploring our Admin Boundaries product, which seamlessly integrates with this dataset.\n\n* **[Point of Interest (PoI) Data](https://geolocet.com/pages/aggregated-points-of-interest-poi-data):** If you're interested in Points of Interest data, please don't hesitate to contact us for customized datasets to enhance your geographical analysis.\n\n* **[Data Customization](https://geolocet.com/pages/data-services-by-geolocet):** We understand that your project may have specific requirements. Feel free to get in touch with us if you need any modifications or specific data formats such as Excel, JSON, GeoJSON, or Shapefile. We are here to tailor the data to your needs.\n\nMake informed decisions, conduct precise research, and unlock the potential of Czechia’s demographic landscape with our comprehensive dataset. Trust in the accuracy and reliability of our official statistical data to drive your projects forward.\nContact us at *contact@geolocet.com* today to access this invaluable resource and explore Czechia census data like never before.\n\n[Geolocet LTD](https://geolocet.com)\n","terms_of_service":"https://geolocet.com/pages/terms-of-use","documentation_link":"","privacy_policy_link":"https://geolocet.com/pages/privacy-policy","geographical_coverage":"[\"Europe\"]","cost":"PAID","data_source":"","assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://geolocet.com/pages/terms-of-use"},"provider_summary":{"name":"Geolocet LTD","description":"Geolocet provides premium geospatial data for retail analysis, GIS, and market planning. We equip businesses with accurate, ready-to-query data to drive spatial analysis, site selection, and customer segmentation.\n\nCore Offerings:\n\nDemographics: Socioeconomic data covering population, age, and employment across diverse geographies.\n\nPoints of Interest (PoI): Datasets spanning retail, healthcare, and transport for catchment analysis.\n\nAdministrative Boundaries: GIS-ready boundaries, including municipalities and postcodes, for territory management.\n\nBrand Locations: Chain location datasets to monitor store networks and market expansion.\n\nWealth Data: Granular economic indicators like purchasing power to identify high-potential markets.\n\nWe source from trusted providers, delivering highly customizable datasets with comprehensive coverage across Europe and worldwide."},"popularity":371},{"id":"71fc0fee-edd8-4352-9937-554521d426b6","summary":{"name":"NER Annotated & Entity Linking Data","subtitle":"Vast, high-quality medical data with NER tags, de-identified and sourced from millions of diverse patient records.","share":{"name":"nerannotated&entitylinkingdata","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1724861099554,"updated_at":1724861099554,"categories":["HEALTH","SCIENCE_AND_RESEARCH"],"listingType":"STANDARD","provider_id":"dc00cb61-5b9a-403e-8b4f-71e78dd44d6c"},"detail":{"description":"**Overview**\n\nShaip offers a vast repository of real-world medical data meticulously annotated with NER tags. Our datasets encompass a broad spectrum of clinical information, including but not limited to: problems/diagnoses, procedures, medications, lab results, vital signs, anatomical structures, medical devices, and more. Each entity within these records is accurately identified and linked to corresponding knowledge base entries, providing a deep level of semantic understanding.\n\nBy leveraging our pre-annotated data, researchers and developers can expedite the development of cutting-edge clinical NLP models, enabling groundbreaking advancements in medical data analysis and machine learning applications.\n\nOur data is sourced from a diverse range of healthcare settings, ensuring a comprehensive and representative view of patient populations. With millions of patient records at your disposal, you can confidently build robust and reliable models.\n\nAll data is rigorously de-identified to protect patient privacy while preserving data integrity.\n\nOur data is instrumental in \n\n- Advancing medical data analysis\n- Machine learning\n- AI applications\n\n**Use cases**\n\n**Clinical Decision Support Systems**\n\n- **Phenotype identification**: Identifying patient populations based on specific conditions, medications, or procedures.\n- **Drug-drug interaction detection**: Identifying potential interactions between prescribed medications.\n- **Clinical trial matching**: Finding eligible patients for clinical trials based on inclusion/exclusion criteria.\n\n**Healthcare Analytics and Research**\n\n- **Disease outbreak detection**: Tracking the spread of diseases based on location and patient data.\n- **Population health management**: Analyzing patient populations to identify trends and risk factors.\n- **Drug efficacy and safety analysis**: Evaluating drug performance and adverse events.\n\n**Medical Information Extraction**\n\n- **Clinical document summarization**: Creating concise summaries of patient records.\n- **Information extraction**: Extracting specific data points from clinical notes for further analysis.\n- **Knowledge graph construction**: Building knowledge graphs of medical entities and their relationships.\n\n**Natural Language Processing**\n\n- Model training: Developing NLP models for tasks like question answering, text classification, and information retrieval.\n- Entity disambiguation: Resolving ambiguities in entity references within medical text.\n\n**Product details**\n\nOur datasets offer comprehensive extraction and tagging of key medical entities, including \n\n- Problems/Diagnoses\n- Procedures\n- Medicines\n- Lab data\n- Body measurements\n- Modifiers\n- Anatomical structures\n- Body functions\n- Medical Devices\n- Substance abuse, and more. \n\nThese pre-annotated real-world medical records are ideal for developing Clinical NLP models, providing a robust foundation for advanced medical data analysis and machine learning applications.","terms_of_service":"https://www.shaip.com/terms-of-service/","documentation_link":"","privacy_policy_link":"https://www.shaip.com/privacy-policy/","geographical_coverage":"[\"United States and Canada\"]","cost":"FREE","data_source":"","assets":["ASSET_TYPE_MEDIA"],"license":""},"provider_summary":{"name":"Shaip","description":"Shaip is a leading AI data platform and services provider specializing in the sourcing, creation, licensing, and transformation of high-quality, ethical, and domain-specific data for AI and machine learning applications. Shaip supports Healthcare AI, Computer Vision, Generative AI, and Conversational AI use cases for leading AI labs and enterprise customers worldwide. Learn more at shaip.com"},"popularity":1037},{"id":"ab2ce940-1bec-4068-bebb-dd70aaa5bc86","summary":{"name":"SAMPLE Global Firmographic Data | 343M+ Company Records | B2B & Technographic Insights","subtitle":"Access \uD835\uDFD1\uD835\uDFD2\uD835\uDFD1\uD835\uDC0C+ global company profiles with verified firmographic data, accurate B2B leads, and deep technographic ...","share":{"name":"lakeb2b_fabc8a56_1aa8_484c_8732_0c3204e6de41_samples_share","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1770197591549,"updated_at":1792000037371,"categories":["OTHER"],"listingType":"STANDARD","provider_id":"258841ce-a620-465c-9af8-a230e2bac7e2"},"detail":{"description":"Firmographic Intelligence | 343M+ Global Companies, Verified B2B Leads & Technographic Insights\n\nOur Firmographic Intelligence solution delivers access to one of the most comprehensive global B2B company databases available today, covering \uD835\uDFD1\uD835\uDFD2\uD835\uDFD1 \uD835\uDC26\uD835\uDC22\uD835\uDC25\uD835\uDC25\uD835\uDC22\uD835\uDC28\uD835\uDC27+ \uD835\uDC1A\uD835\uDC1C\uD835\uDC2D\uD835\uDC22\uD835\uDC2F\uD835\uDC1E \uD835\uDC1A\uD835\uDC27\uD835\uDC1D \uD835\uDC21\uD835\uDC22\uD835\uDC2C\uD835\uDC2D\uD835\uDC28\uD835\uDC2B\uD835\uDC22\uD835\uDC1C\uD835\uDC1A\uD835\uDC25 \uD835\uDC1C\uD835\uDC28\uD835\uDC26\uD835\uDC29\uD835\uDC1A\uD835\uDC27\uD835\uDC32 \uD835\uDC29\uD835\uDC2B\uD835\uDC28\uD835\uDC1F\uD835\uDC22\uD835\uDC25\uD835\uDC1E\uD835\uDC2C \uD835\uDC1A\uD835\uDC1C\uD835\uDC2B\uD835\uDC28\uD835\uDC2C\uD835\uDC2C \uD835\uDC1A\uD835\uDC25\uD835\uDC25 \uD835\uDC26\uD835\uDC1A\uD835\uDC23\uD835\uDC28\uD835\uDC2B \uD835\uDC22\uD835\uDC27\uD835\uDC1D\uD835\uDC2E\uD835\uDC2C\uD835\uDC2D\uD835\uDC2B\uD835\uDC22\uD835\uDC1E\uD835\uDC2C, \uD835\uDC20\uD835\uDC1E\uD835\uDC28\uD835\uDC20\uD835\uDC2B\uD835\uDC1A\uD835\uDC29\uD835\uDC21\uD835\uDC22\uD835\uDC1E\uD835\uDC2C, \uD835\uDC1A\uD835\uDC27\uD835\uDC1D \uD835\uDC1B\uD835\uDC2E\uD835\uDC2C\uD835\uDC22\uD835\uDC27\uD835\uDC1E\uD835\uDC2C\uD835\uDC2C \uD835\uDC2C\uD835\uDC22\uD835\uDC33\uD835\uDC1E\uD835\uDC2C. Built to support modern sales, marketing, revenue, and analytics teams, the platform combines deep firmographic coverage with \uD835\uDC2F\uD835\uDC1E\uD835\uDC2B\uD835\uDC22\uD835\uDC1F\uD835\uDC22\uD835\uDC1E\uD835\uDC1D \uD835\uDC01\uD835\uDFD0\uD835\uDC01 \uD835\uDC1C\uD835\uDC28\uD835\uDC27\uD835\uDC2D\uD835\uDC1A\uD835\uDC1C\uD835\uDC2D \uD835\uDC22\uD835\uDC27\uD835\uDC2D\uD835\uDC1E\uD835\uDC25\uD835\uDC25\uD835\uDC22\uD835\uDC20\uD835\uDC1E\uD835\uDC27\uD835\uDC1C\uD835\uDC1E \uD835\uDC1A\uD835\uDC27\uD835\uDC1D \uD835\uDC1A\uD835\uDC1D\uD835\uDC2F\uD835\uDC1A\uD835\uDC27\uD835\uDC1C\uD835\uDC1E\uD835\uDC1D \uD835\uDC2D\uD835\uDC1E\uD835\uDC1C\uD835\uDC21\uD835\uDC27\uD835\uDC28\uD835\uDC20\uD835\uDC2B\uD835\uDC1A\uD835\uDC29\uD835\uDC21\uD835\uDC22\uD835\uDC1C \uD835\uDC22\uD835\uDC27\uD835\uDC2C\uD835\uDC22\uD835\uDC20\uD835\uDC21\uD835\uDC2D\uD835\uDC2C, enabling organizations to identify, prioritize, and engage the right accounts with precision and confidence.\n\nAt the core of the offering is a \uD835\uDC2B\uD835\uDC28\uD835\uDC1B\uD835\uDC2E\uD835\uDC2C\uD835\uDC2D \uD835\uDC1F\uD835\uDC22\uD835\uDC2B\uD835\uDC26\uD835\uDC28\uD835\uDC20\uD835\uDC2B\uD835\uDC1A\uD835\uDC29\uD835\uDC21\uD835\uDC22\uD835\uDC1C \uD835\uDC1D\uD835\uDC1A\uD835\uDC2D\uD835\uDC1A \uD835\uDC1F\uD835\uDC28\uD835\uDC2E\uD835\uDC27\uD835\uDC1D\uD835\uDC1A\uD835\uDC2D\uD835\uDC22\uD835\uDC28\uD835\uDC27 that captures essential business attributes required for accurate segmentation and targeting. This includes company name, domain, industry classification, employee size, revenue range, ownership type, year founded, headquarters location, branch locations, corporate hierarchy, subsidiaries, and parent–child relationships. These data points allow teams to build highly refined Ideal Customer Profiles (ICPs), perform market sizing, conduct territory planning, and execute account-based strategies at scale.\n\nComplementing firmographics is a rich layer of \uD835\uDC2F\uD835\uDC1E\uD835\uDC2B\uD835\uDC22\uD835\uDC1F\uD835\uDC22\uD835\uDC1E\uD835\uDC1D \uD835\uDC01\uD835\uDFD0\uD835\uDC01 \uD835\uDC25\uD835\uDC1E\uD835\uDC1A\uD835\uDC1D \uD835\uDC1D\uD835\uDC1A\uD835\uDC2D\uD835\uDC1A, providing access to decision-makers and influencers across departments such as IT, finance, operations, marketing, HR, procurement, and executive leadership. Contact records are enriched with professional attributes including job title, function, seniority, department, work email, direct phone, company association, and geographic location. All contact data undergoes rigorous validation and multi-source verification processes to ensure high accuracy, reduced bounce rates, and improved outreach performance across email, phone, and omnichannel campaigns.\n\nThe solution further enhances targeting capabilities through \uD835\uDC2D\uD835\uDC1E\uD835\uDC1C\uD835\uDC21\uD835\uDC27\uD835\uDC28\uD835\uDC20\uD835\uDC2B\uD835\uDC1A\uD835\uDC29\uD835\uDC21\uD835\uDC22\uD835\uDC1C \uD835\uDC22\uD835\uDC27\uD835\uDC2D\uD835\uDC1E\uD835\uDC25\uD835\uDC25\uD835\uDC22\uD835\uDC20\uD835\uDC1E\uD835\uDC27\uD835\uDC1C\uD835\uDC1E, offering visibility into the technologies, software platforms, cloud services, and digital tools adopted by organizations worldwide. Technographic attributes help teams understand a company’s existing technology stack, usage patterns, vendor relationships, and digital maturity. This insight enables more relevant messaging, competitive displacement strategies, cross-sell and upsell identification, and precise alignment between product offerings and prospect needs.\n\nTo maintain relevance and accuracy in fast-changing global markets, the database is \uD835\uDC2E\uD835\uDC29\uD835\uDC1D\uD835\uDC1A\uD835\uDC2D\uD835\uDC1E\uD835\uDC1D \uD835\uDC1E\uD835\uDC2F\uD835\uDC1E\uD835\uDC2B\uD835\uDC32 \uD835\uDFD1\uD835\uDFCE \uD835\uDC1D\uD835\uDC1A\uD835\uDC32\uD835\uDC2C, with continuous data ingestion, normalization, cleansing, and verification workflows. This recurring refresh cycle ensures users always have access to the most current company status, leadership changes, technology adoption signals, and contact availability. Historical data is also maintained to support trend analysis, market evolution studies, and longitudinal research.\n\nWith \uD835\uDC20\uD835\uDC25\uD835\uDC28\uD835\uDC1B\uD835\uDC1A\uD835\uDC25 \uD835\uDC1C\uD835\uDC28\uD835\uDC2F\uD835\uDC1E\uD835\uDC2B\uD835\uDC1A\uD835\uDC20\uD835\uDC1E \uD835\uDC2C\uD835\uDC29\uD835\uDC1A\uD835\uDC27\uD835\uDC27\uD835\uDC22\uD835\uDC27\uD835\uDC20 \uD835\uDC0D\uD835\uDC28\uD835\uDC2B\uD835\uDC2D\uD835\uDC21 \uD835\uDC00\uD835\uDC26\uD835\uDC1E\uD835\uDC2B\uD835\uDC22\uD835\uDC1C\uD835\uDC1A, \uD835\uDC04\uD835\uDC2E\uD835\uDC2B\uD835\uDC28\uD835\uDC29\uD835\uDC1E, \uD835\uDC00\uD835\uDC0F\uD835\uDC00\uD835\uDC02, \uD835\uDC0B\uD835\uDC00\uD835\uDC13\uD835\uDC00\uD835\uDC0C, \uD835\uDC2D\uD835\uDC21\uD835\uDC1E \uD835\uDC0C\uD835\uDC22\uD835\uDC1D\uD835\uDC1D\uD835\uDC25\uD835\uDC1E \uD835\uDC04\uD835\uDC1A\uD835\uDC2C\uD835\uDC2D, \uD835\uDC1A\uD835\uDC27\uD835\uDC1D \uD835\uDC00\uD835\uDC1F\uD835\uDC2B\uD835\uDC22\uD835\uDC1C\uD835\uDC1A, the platform supports international go-to-market strategies, regional expansion, and cross-border sales initiatives. It includes data across startups, SMBs, mid-market organizations, and large enterprises, making it suitable for organizations at every stage of growth. Industry coverage spans technology, manufacturing, healthcare, financial services, retail, logistics, education, energy, telecommunications, professional services, and more.\n\nDesigned for flexibility and scalability, the firmographic intelligence solution supports multiple use cases including \uD835\uDC1A\uD835\uDC1C\uD835\uDC1C\uD835\uDC28\uD835\uDC2E\uD835\uDC27\uD835\uDC2D-\uD835\uDC1B\uD835\uDC1A\uD835\uDC2C\uD835\uDC1E\uD835\uDC1D \uD835\uDC26\uD835\uDC1A\uD835\uDC2B\uD835\uDC24\uD835\uDC1E\uD835\uDC2D\uD835\uDC22\uD835\uDC27\uD835\uDC20 (\uD835\uDC00\uD835\uDC01\uD835\uDC0C), \uD835\uDC1D\uD835\uDC1E\uD835\uDC26\uD835\uDC1A\uD835\uDC27\uD835\uDC1D \uD835\uDC20\uD835\uDC1E\uD835\uDC27\uD835\uDC1E\uD835\uDC2B\uD835\uDC1A\uD835\uDC2D\uD835\uDC22\uD835\uDC28\uD835\uDC27, \uD835\uDC25\uD835\uDC1E\uD835\uDC1A\uD835\uDC1D \uD835\uDC1E\uD835\uDC27\uD835\uDC2B\uD835\uDC22\uD835\uDC1C\uD835\uDC21\uD835\uDC26\uD835\uDC1E\uD835\uDC27\uD835\uDC2D, \uD835\uDC02\uD835\uDC11\uD835\uDC0C \uD835\uDC1E\uD835\uDC27\uD835\uDC21\uD835\uDC1A\uD835\uDC27\uD835\uDC1C\uD835\uDC1E\uD835\uDC26\uD835\uDC1E\uD835\uDC27\uD835\uDC2D, \uD835\uDC1D\uD835\uDC1A\uD835\uDC2D\uD835\uDC1A \uD835\uDC1A\uD835\uDC29\uD835\uDC29\uD835\uDC1E\uD835\uDC27\uD835\uDC1D\uD835\uDC22\uD835\uDC27\uD835\uDC20, \uD835\uDC26\uD835\uDC1A\uD835\uDC2B\uD835\uDC24\uD835\uDC1E\uD835\uDC2D \uD835\uDC2B\uD835\uDC1E\uD835\uDC2C\uD835\uDC1E\uD835\uDC1A\uD835\uDC2B\uD835\uDC1C\uD835\uDC21, \uD835\uDC1C\uD835\uDC28\uD835\uDC26\uD835\uDC29\uD835\uDC1E\uD835\uDC2D\uD835\uDC22\uD835\uDC2D\uD835\uDC22\uD835\uDC2F\uD835\uDC1E \uD835\uDC22\uD835\uDC27\uD835\uDC2D\uD835\uDC1E\uD835\uDC25\uD835\uDC25\uD835\uDC22\uD835\uDC20\uD835\uDC1E\uD835\uDC27\uD835\uDC1C\uD835\uDC1E, \uD835\uDC1A\uD835\uDC27\uD835\uDC1D \uD835\uDC2B\uD835\uDC22\uD835\uDC2C\uD835\uDC24 \uD835\uDC1A\uD835\uDC27\uD835\uDC1A\uD835\uDC25\uD835\uDC32\uD835\uDC2C\uD835\uDC22\uD835\uDC2C. Data can be delivered through customized lists, bulk file delivery, API integrations, or direct enrichment of CRM and marketing automation platforms, ensuring seamless alignment with existing workflows.\n\nCompliance and data governance are built into the foundation of the offering. Data collection and processing adhere to global privacy and regulatory standards such as \uD835\uDC06\uD835\uDC03\uD835\uDC0F\uD835\uDC11, \uD835\uDC02\uD835\uDC02\uD835\uDC0F\uD835\uDC00, \uD835\uDC1A\uD835\uDC27\uD835\uDC1D \uD835\uDC28\uD835\uDC2D\uD835\uDC21\uD835\uDC1E\uD835\uDC2B \uD835\uDC2B\uD835\uDC1E\uD835\uDC20\uD835\uDC22\uD835\uDC28\uD835\uDC27\uD835\uDC1A\uD835\uDC25 \uD835\uDC1D\uD835\uDC1A\uD835\uDC2D\uD835\uDC1A protection frameworks, helping organizations execute compliant outreach while maintaining trust and brand integrity. Suppression handling, opt-out management, and permission-based data practices further reduce compliance risk and improve campaign sustainability.\n\nBy combining \uD835\uDC2C\uD835\uDC1C\uD835\uDC1A\uD835\uDC25\uD835\uDC1E, \uD835\uDC1A\uD835\uDC1C\uD835\uDC1C\uD835\uDC2E\uD835\uDC2B\uD835\uDC1A\uD835\uDC1C\uD835\uDC32, \uD835\uDC1A\uD835\uDC27\uD835\uDC1D \uD835\uDC1A\uD835\uDC1C\uD835\uDC2D\uD835\uDC22\uD835\uDC28\uD835\uDC27\uD835\uDC1A\uD835\uDC1B\uD835\uDC25\uD835\uDC1E \uD835\uDC22\uD835\uDC27\uD835\uDC2D\uD835\uDC1E\uD835\uDC25\uD835\uDC25\uD835\uDC22\uD835\uDC20\uD835\uDC1E\uD835\uDC27\uD835\uDC1C\uD835\uDC1E, this firmographic data solution empowers organizations to move beyond generic targeting and toward insight-led engagement. Teams gain a unified view of companies, contacts, and technologies, allowing them to prioritize high-value accounts, personalize outreach, shorten sales cycles, and improve overall revenue performance.\n\nWhether the objective is expanding into new markets, improving lead quality, strengthening account-based programs, or fueling data-driven decision-making, \uD835\uDC05\uD835\uDC22\uD835\uDC2B\uD835\uDC26\uD835\uDC28\uD835\uDC20\uD835\uDC2B\uD835\uDC1A\uD835\uDC29\uD835\uDC21\uD835\uDC22\uD835\uDC1C \uD835\uDC08\uD835\uDC27\uD835\uDC2D\uD835\uDC1E\uD835\uDC25\uD835\uDC25\uD835\uDC22\uD835\uDC20\uD835\uDC1E\uD835\uDC27\uD835\uDC1C\uD835\uDC1E \uD835\uDC30\uD835\uDC22\uD835\uDC2D\uD835\uDC21 \uD835\uDFD1\uD835\uDFD2\uD835\uDFD1\uD835\uDC0C+ \uD835\uDC20\uD835\uDC25\uD835\uDC28\uD835\uDC1B\uD835\uDC1A\uD835\uDC25 \uD835\uDC1C\uD835\uDC28\uD835\uDC26\uD835\uDC29\uD835\uDC1A\uD835\uDC27\uD835\uDC22\uD835\uDC1E\uD835\uDC2C, \uD835\uDC2F\uD835\uDC1E\uD835\uDC2B\uD835\uDC22\uD835\uDC1F\uD835\uDC22\uD835\uDC1E\uD835\uDC1D \uD835\uDC01\uD835\uDFD0\uD835\uDC01 \uD835\uDC25\uD835\uDC1E\uD835\uDC1A\uD835\uDC1D\uD835\uDC2C, \uD835\uDC1A\uD835\uDC27\uD835\uDC1D \uD835\uDC2D\uD835\uDC1E\uD835\uDC1C\uD835\uDC21\uD835\uDC27\uD835\uDC28\uD835\uDC20\uD835\uDC2B\uD835\uDC1A\uD835\uDC29\uD835\uDC21\uD835\uDC22\uD835\uDC1C \uD835\uDC22\uD835\uDC27\uD835\uDC2C\uD835\uDC22\uD835\uDC20\uD835\uDC21\uD835\uDC2D\uD835\uDC2C provides the depth, coverage, and reliability required to compete and grow in today’s global B2B landscape.","terms_of_service":"https://www.lakeb2b.com/privacy-policy","contact_email":"support@datarade.ai","privacy_policy_link":"https://www.lakeb2b.com/privacy-policy","assets":["ASSET_TYPE_DATA_TABLE"]},"provider_summary":{"name":"LakeB2B","description":"In the last 15 years, we have seen the data market at its best, and worst. 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Our product empowers funds and investors to maximize their returns and unleash their full potential.\n\n**Use cases**\n\n**Hedge funds** can harness the power of our dataset to discover hidden opportunities for alpha generation. Trade REITs and other real estate-focused tickers with confidence, backed by comprehensive market data.\n\n**Real estate investors** can leverage our dataset to make well-informed investment decisions. Identify prime locations for property acquisition and fine-tune rental pricing to maximize returns on your real estate portfolio.\n\n**Product details**\n\nOur dataset encompasses a nationwide collection of multi-family and single-family assets, offering a comprehensive view of the real estate market. Sample fields include building occupancy, demand, market rental prices, asset class, comparable properties and more.\n\nSample fields include: id, address, city, state, zipcode, lat, long, occupancy, market rent, revenue and more.\n\n**Additional Insights**\n\nOur dataset is based on cutting-edge technology and validated by a team of experts to ensure accuracy and up-to-dateness. Check out our website for more information and reach out to data@sucali.com.","terms_of_service":"https://www.sucali.com/privacy","documentation_link":"","privacy_policy_link":"https://www.sucali.com/privacy","assets":["ASSET_TYPE_DATA_TABLE"]},"provider_summary":{"name":"Sucali","description":"Sucali is a real-estate technology company powering real-estate transactions though A.I driven date-intelligence. 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Each interview is conducted in a controlled setting, allowing for clear audio and video capture, ideal for training machine learning models in natural language processing, sentiment detection, and facial emotion analysis. The dataset's multimodal approach supports cross-verification between verbal and non-verbal cues, offering a robust framework for developing nuanced AI applications. Designed for flexibility, the data includes comprehensive metadata on speaker demographics, contextual tags, and sentiment classifications at the utterance level, enabling a variety of downstream applications.\n\n**Use cases**\n\nThis dataset is highly versatile, supporting a range of research and applied AI development tasks:\n\n- Sentiment and Emotion Analysis: Use the transcribed data alongside sentiment annotations to develop and refine sentiment analysis models. Analyze non-verbal cues from video to enhance emotion detection systems, enabling multimodal sentiment analysis with increased accuracy.\n- Speech and Language Model Training: Ideal for training language models in transcription accuracy and speaker diarization. The dataset's depth provides real-world conversational data across diverse demographics, offering an authentic base for speech recognition and NLP applications.\n\n**Product details**\n\nAvailable data includes:\n\n1. Interview metadata, including interview duration, participant demographics, and session context.\n2. Raw audio files (.wav format) of interview sessions.\n3. Video recordings of interview sessions with metadata on camera angles and lighting conditions.\n4. Transcriptions with speaker attribution, timestamps, and contextual tags.\n5. Sentence-level sentiment analysis with sentiment scores and sentiment type classification (e.g., positive, neutral, negative).\n6. Non-verbal cue metadata, identifying facial expressions and body language indicators at key timestamps.\n7. Speaker emotion annotations, labeled at intervals based on tone of voice and word choice.\n8. Environmental audio data annotations, noting background sounds and noise levels.\n9. Speaker accent and dialect metadata for linguistic analysis.\n10. Summary tags and topic classifications, providing quick insight into the interview content.\n\n**Sample fields include:**\n\n- Participant ID: Unique identifier for each interview participant\n- Timestamp: Time marking for each transcription and sentiment entry\n- Sentiment Score: Numeric score for sentiment intensity\n- Non-verbal Cue: Annotations for facial expressions, gestures\n- Transcription: Textual transcription of interview content\n\n**Additional Insights**\n\nThis dataset can support a wide array of research fields, including sentiment analysis, conversational AI, and human-computer interaction. Related research materials and notebooks include analyses on cross-modal emotion detection and automated transcription accuracy improvements, accessible via linked resources. Additional studies demonstrating the dataset's applications in social sciences and linguistics can be provided upon request..","terms_of_service":"https://www.erud.ai/terms-of-service","documentation_link":"","privacy_policy_link":"https://erud.ai/privacy-policy","geographical_coverage":"[\"United States and Canada\"]","cost":"PAID","data_source":"Proprietary dataset","size":300.0,"assets":["ASSET_TYPE_DATA_TABLE","ASSET_TYPE_MEDIA"],"license":""},"provider_summary":{"name":"Erud AI","description":"Erud AI provides full-service operations management for AI projects, specializing in high-quality data generation and annotation. Unlike software providers, Erud AI curates human-generated data to enhance AI models, offering customizable, client-specific solutions. Their expertise spans structured fine-tuning (SFT), reinforcement learning from human feedback (RLHF), multimodal AI training, and model evaluation. Erud AI emphasizes deep knowledge, precision, and data diversity to mitigate bias and improve AI performance. Focused on industries requiring tailored AI datasets, they offer human-in-the-loop solutions, prioritizing quality over every other KPI. With rigorous quality control and standard work, Erud AI can scale data projects quickly and efficiently, providing you reduced cost over time."},"popularity":1404},{"id":"22a9747f-9fdd-4387-ae6f-d10fa44621cf","summary":{"name":"TikTok Profiles & Posts & Comments","subtitle":"3 types of Tiktok datasets.\n","share":{"name":"tiktok","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1712752065269,"updated_at":1718788512969,"categories":["MEDIA"],"listingType":"STANDARD","provider_id":"63866f42-27f8-4a1f-b243-37e7be863700"},"detail":{"description":"We provide 3 types of TikTok datasets:\n\n1) TikTok - Profiles Dataset:\n\nUnlock insights from public TikTok profiles, including engagement rates, biographies, and verification status.\nCustomizable dataset tailored to specific data requirements for influencer marketing and audience segmentation.\n\nKey Features:\n-Unique identifiers for TikTok accounts.\n-Usernames, biographies, and engagement rates for profile understanding.\n-Verification status for influencer identification.\n\nUse Cases:\n-Influencer Identification: Identify high-profile influencers with over 1 million ---followers for strategic partnerships.\n-Audience Segmentation: Analyze engagement rates and user bios to understand audience demographics and preferences.\n-Market Analysis: Customize the dataset to specific requirements for in-depth market analysis and competitor research.\n\n\n2) TikTok - Posts Dataset:\n\nComprehensive data about TikTok posts, including engagement metrics, user information, and attached files.\nValuable resource for understanding trends, user behaviors, and content dynamics on the platform.\n\nKey Features:\n-Unique post identifiers for tracking.\n-Engagement metrics like likes, shares, and comments.\n-User profiles associated with each post for deeper insights.\n\nUse Cases:\n-Trend Analysis: Identify trending topics, content formats, and visual styles on TikTok.\n-Content Strategy Optimization: Analyze post performance metrics to refine content strategies and maximize engagement.\n-Audience Engagement: Engage with users through comments, likes, and shares to foster community interaction.\n\n\n3) TikTok - Comments Dataset:\n\nInsights into user engagement, sentiment trends, and community dynamics on TikTok.\nValuable for understanding audience interactions and optimizing content strategies.\n\nKey Features:\n-Essential fields such as post URLs, comment text, and engagement metrics.\n-Filtered subsets for focusing on specific types of comments, such as those with high likes or replies.\n\nUse Cases:\n-User Engagement Analysis: Gain insights into audience sentiment and behavior by analyzing comments on TikTok posts.\n-Content Virality: Identify posts with high comment counts or significant replies, indicating viral content.\n-Influencer Identification: Discover influential users based on their comment engagement and interactions.\n\n\nWhy Use Our Datasets?\n\n1.Gain comprehensive insights into TikTok activity, spanning profiles, posts, and comments.\n2.Tailor data solutions to fit your specific needs, whether for influencer identification, audience segmentation, or market analysis.\n3.Obtain a strategic advantage by identifying high-profile influencers, analyzing trends, and optimizing content strategies.\n4.Utilize advanced analytics tools to delve deep into market analysis and audience segmentation.\n5.Foster enhanced engagement by understanding audience preferences and sentiment trends.\n\n\nNote: This is a sample - for all datasets, please contact us.","terms_of_service":"https://brightdata.com/license","documentation_link":"https://drive.google.com/file/d/1VrptZrKfliVLR2B0JkjCtjijXVUiwUu0/view?usp=drive_link","privacy_policy_link":"https://brightdata.com/privacy","assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"Bright Data","description":"Bright Data is the world’s #1 web data platform.\nFortune 500 companies, academic institutions, and small businesses rely on Bright Data’s scalable platform for web scraping infrastructure to retrieve crucial public web data in the most efficient, reliable, and flexible way, so they can research, monitor, and analyze data to make better decisions.\nThe platform is used worldwide by 20,000+ customers in nearly every industry. The products include a range of no-code data solutions utilized by business owners and a robust infrastructure used by engineers and IT professionals.\n\nBright Data provides a cost-effective way to perform fast and stable public web data collection at scale, effortless conversion of unstructured data into structured data, and superior customer experience while being fully transparent and compliant."},"popularity":1664},{"id":"5b7671bb-56aa-4285-ab79-f5578f154fe3","summary":{"name":"Open Palm Hand Images Dataset - 500,000 Images for Hand Detection and Gesture Recognition","subtitle":"Open Palm Hand Images Dataset — 500,000 annotated images from 50,000 people for hand detection, palm recognition, ...","share":{"name":"unidata_e0d1d361_b122_438a_9921_aa1619535876_samples_share","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1775426688391,"updated_at":1775426688391,"categories":["OTHER"],"listingType":"PERSONALIZED","provider_id":"a6668567-8ddc-4746-9958-8edbc3807a14"},"detail":{"description":"Overview\nThe Open Palm Hand Images Dataset is a large-scale commercial biometric dataset produced by Unidata, designed to support hand detection, palm recognition, and gesture analysis research. It includes 500,000 annotated images collected from 50,000 participants, making it one of the largest datasets of its kind for computer vision model training.\nEach subject contributes a standardized set of 10 files:\n- 6 open-palm photos (live hand images)\n- 2 printed-hand images\n- 2 replay videos\n\nThe dataset covers both right hands and left hands in various configurations, enabling comprehensive research into hand tracking, pose estimation, and gesture recognition across a wide range of real-world conditions.\n\nSubject Demographics\nThe dataset represents broad demographic diversity across 50,000 participants. Both male and female subjects are included. Each record is labeled with the following metadata attributes:\n1. Age\n2. Gender\n3. Ethnicity\n4. Profession (or previous job)\n5. Device type used for capture\n6. Dominant hand\n7. Jewelry presence (has_jewelry)\n\nThis level of granularity makes the dataset well-suited for building unbiased computer vision models that generalize across diverse human populations.\n\nData Collection & Technical Specifications\nData was collected via crowdsourcing platforms under consistent capture protocols, ensuring natural variation in hand appearance, skin tone, and presentation style while maintaining reliable image quality.\n\n1. File format: JPG\n2. Labeling type: Metadata per subject (7 attributes)\n3. Total records: 500,000 images from 50,000 people\n4. Files per subject: 10 (6 live palm photos, 2 printed-hand, 2 replay)\n\nThe dataset is fully compliant with GDPR and global data protection regulations. This is a real-world dataset — no synthetic hands or generated data.\n\nAnnotation & Labeling\nEvery sample in the dataset is paired with structured metadata supporting detailed model training and analysis:\n\n1. Age — enables age-stratified model evaluation\n2. Gender — male/female split for balanced training data\n3. Ethnicity — supports ethnically diverse recognition algorithms\n4. Dominant hand — distinguishes left-hand and right-hand subjects\n5. Jewelry status — accounts for visual variance from rings, bracelets, and other accessories\n6. Profession — additional demographic context\n7. Device type — captures hardware diversity across collection devices\n\nUse Cases\n- Biometric Authentication and Palm Recognition. The dataset provides real-world human palm images for training palm recognition systems used in identity verification and access control. The three-type structure — live photos, printed images, and replay samples — directly mirrors real attack scenarios, making this dataset especially valuable for building robust anti-spoofing pipelines. Models can learn to distinguish genuine palms from presentation attacks before deployment in security systems.\n- Presentation Attack Detection (PAD) A key strength of this dataset is its built-in attack coverage. Beyond real palm captures, each subject contributes:\n1. Print attacks — a photo of the palm printed on paper\n2. 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The territorial vintages of the worldwide area boundaries are compatible with our globally consistent and comparable [socio-demographic](https://www.mbi-geodata.com/en/sociodemographic-data/) and [economic](https://www.mbi-geodata.com/en/purchasing-power-overview/) data.\nOur area boundaries for Portugal are offered at the following levels:\n\n- **Postal Code Level** (Postcode 4-digit. 7-digit on request): This fine-grained level of detail makes the data suitable for a wide range of applications – for example, enriching customer databases, which are often organized by postal code.\n- **Administrative Level** (Freguesias): Covers both larger administrative areas (e.g., regions, states, provinces) as well as finer subdivisions such as districts, counties, or municipalities.\n- **Micro Level** (Subsecções Estatísticas): Subdivisions of postal code or administrative boundaries, such as census areas or street segments. This is the most granular level for local analysis. | on request\n\n**Use Cases:**\n\n- **Market Analysis**: Use MBI’s boundaries to visualize data on a map, learn about your customers, and uncover untapped market potential.\n- **360-Degree Customer View**: Link boundaries to customer data, sociodemographic, and economic data to gain a holistic understanding of customer needs, behaviors, and potential value.\n- **Location Planning**\n•\tUse MBI’s boundaries to visualize data on a map, learn about your customers\n•\tPlan the provision of services and products across different regions\n•\tDefine sales territories\n•\tVisualize catchment areas and plan actions to reach potential customers\n\n**Additional Insights:**\n[About Area Boundaries](https://www.mbi-geodata.com/en/area-boundaries/)\n\n**Data Structure:**\nThe area boundaries include the following variables:\nCTRYCODE: Two-digit country code by ISO 3166\nNAME: Name of administrative area / Name of the most populous administrative area / Name of the respective micro-area or the next higher level\nADMINCODE / POSTCODE / MICROCODE: Administrative code / Postal code / Micro-code\nWKT: Geography as Well Known Text (lat/long WGS84)\n\n*with only a few specific exceptions","terms_of_service":"https://service-mbi-geodata.com/s/HNp7Z2tws4WDqWd","documentation_link":"https://www.mbi-geodata.com/en/mbi-supplier-manufacturer-worldwide-geodata/europe/portugal-maps/","privacy_policy_link":"https://www.mbi-geodata.com/en/data-protection/","update_frequency":{"interval":1,"unit":"YEARLY"},"assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://service-mbi-geodata.com/s/x4eeiRq9iqCqJxb"},"provider_summary":{"name":"Michael Bauer International GmbH – MBI Geodata","description":"For over 15 years, Michael Bauer International (MBI) has been a leading provider of globally consistent and comparable geographic data, including more than 3 million postcode areas and other detailed datasets like sociodemographics and purchasing power. 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No lengthy sign-up processes or NDAs required on our side – just download the data you need.\n* **Data Samples for Evaluation:** To ensure you're getting exactly what you need, we provide free downloadable samples in CSV format for data evaluation.\n* **Data accuracy:** Our datasets are created using the latest demographics data available in national statistics bureaus, guaranteeing accuracy and reliability.\n* **Best-in-Class Quality:** We guarantee best-in-class quality, backed by over two decades of experience in modeling demographics data and an outstanding quality management process.\n* **Competitive Pricing:** We maintain the lowest prices on the market, ensuring that you get the most cost-efficient demographics data acquisition process.\n---\nAdditionally, for those looking to enrich their analysis or project, we offer:\n* **[Administrative Boundaries](https://geolocet.com/products/belarus-all-available-administrative-boundaries):** If you require geographical boundaries (polygons) for the municipalities of Belarus, consider exploring our Administrative Boundaries product, which seamlessly integrates with this dataset.\n\n* **[Point of Interest (PoI) Data](https://geolocet.com/pages/aggregated-points-of-interest-poi-data):** If you're interested in Points of Interest data, please don't hesitate to contact us for customized datasets to enhance your geographical analysis.\n\n* **[Data Customization](https://geolocet.com/pages/data-services-by-geolocet):** We understand that your project may have specific requirements. 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Trust in the accuracy and reliability of our official statistical data to drive your projects forward.\nContact us at *contact@geolocet.com* today to access this invaluable resource and explore Belarus census data like never before.\n\n[Geolocet LTD](https://geolocet.com)\n","terms_of_service":"https://geolocet.com/pages/terms-of-use","documentation_link":"","privacy_policy_link":"https://geolocet.com/pages/privacy-policy","geographical_coverage":"[\"Europe\"]","cost":"PAID","update_frequency":{"interval":1,"unit":"YEARLY"},"assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://geolocet.com/pages/terms-of-use"},"provider_summary":{"name":"Geolocet LTD","description":"Geolocet provides premium geospatial data for retail analysis, GIS, and market planning. 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And where within that catchment do most of my customers travel from to reach my locations? \n- What points of interest drive customers to my locations (ie. work, shopping, recreation, hotel or education facilities that are in the area) ?\n- How far do customers travel to visit my locations?\n- Where are the potential gaps in my store network for new developments? \n- What is the sales impact on an existing store if a new store is opened nearby? \n- Is my marketing strategy targeted to the right audience? \n- Where are my competitor's customers coming from?\n\nFoot Traffic data provides a range of benefits that make it a valuable addition to location intelligence services including:\n- Real-time \n- Low-cost at high scale\n- Accurate\n- Flexible\n- Non-proprietary\n- Empirical\n\nAzira have created robust screening methods to evaluate the quality of Foot Traffic data collected from multiple sources to ensure that their data lake contains only the highest-quality mobile location data. \n\nThis includes partnering with trusted location SDK providers that get proper end user consent to track their location when they download an application, and can detect device movement/visits.\n\nData received from partners is put through Azira's data quality algorithm discarding data points that receive a low quality score.\n\nUse cases in Europe will be considered on a case to case basis.","terms_of_service":"https://gapmaps.com/privacy-policy","contact_email":"support@datarade.ai","privacy_policy_link":"https://gapmaps.com/privacy-policy","assets":["ASSET_TYPE_DATA_TABLE"]},"provider_summary":{"name":"GapMaps","description":"Founded in 2013, GapMaps is an Australian-built and owned location intelligence company providing high quality POI and Demographics data and an easy-to-use GIS platform which empowers decision makers in multiple industry sectors globally to refine their network strategies with higher confidence and reduced risk. \r\n\r\nOur dynamic products can be easily adapted for any market or industry and today are being effectively used by more than 500 brands in Australia, Asia, the Middle East, India and North America including Domino’s, KFC, Starbucks, Burger King, Anytime Fitness, Subway and McDonalds."},"popularity":1230},{"id":"08e48847-9aee-4c6b-b8ea-3ac3978444e1","summary":{"name":"Geolocet | Latest Demographic Data Guernsey - 37 attributes ","subtitle":"Demographic data for the 10 municipalities (parish) of Guernsey - age, gender, education, income, and more","setting":{"visibility":"PUBLIC"},"created_at":1708500815014,"updated_at":1708500853715,"categories":["DEMOGRAPHICS","ADVERTISING_AND_MARKETING","GEOSPATIAL","MEDIA","PUBLIC_SECTOR","RETAIL"],"listingType":"PERSONALIZED","provider_id":"5d3ddc17-a7e8-4794-a760-13587e1de2c6"},"detail":{"description":"## **Get instant access to the latest Demographics Data for Guernsey with [Geolocet](https://geolocet.com)**\n\nExplore the richness of Guernsey’s demographic landscape with our Guernsey Demographics Data. This meticulously compiled dataset, sourced from official statistical sources, offers a detailed insight into the population landscape of Guernsey at the municipality (parish) level. \n\nIf you require data at a less granular level, such as an Uber grid or square grid, please don't hesitate to get in touch with us at *contact@geolocet.com*. We understand that different projects may have varying data needs, and we are here to tailor our data to match your specific requirements.\n\n ## **Key Features:**\n---\n* **Population Data Guernsey:** Gain access to the latest and most accurate population statistics for Guernsey, meticulously organized by municipality (parish).\n\n* **Demographic Insights:** Dive deep into Guernsey’s demographic landscape with data on 37 attributes, including: \n* Total population,\n* Gender,\n* Age bands,\n* Education,\n* Household size,\n* Income bands.\n\nAll demographic attributes are available at the municipal (parish) level.\n\n* **Geodemographic Insights:** Unlock geodemographic data Guernsey-wide to inform location-based decision-making, marketing strategies, and policy formulation.\n* **Seamless Data Access:** Our data is available for instant download in CSV format. No lengthy sign-up processes or NDAs required on our side – just download the data you need.\n* **Data Samples for Evaluation:** To ensure you're getting exactly what you need, we provide free downloadable samples in CSV format for data evaluation.\n* **Data accuracy:** Our datasets are created using the latest demographics data available in national statistics bureaus, guaranteeing accuracy and reliability.\n* **Best-in-Class Quality:** We guarantee best-in-class quality, backed by over two decades of experience in modeling demographics data and an outstanding quality management process.\n* **Competitive Pricing:** We maintain the lowest prices on the market, ensuring that you get the most cost-efficient demographics data acquisition process.\n---\nAdditionally, for those looking to enrich their analysis or project, we offer:\n* **[Administrative Boundaries](https://geolocet.com/pages/administrative-boundaries-data-for-european-countries):** If you require geographical boundaries (polygons) for the municipalities of Guernsey, consider exploring our Administrative Boundaries product, which seamlessly integrates with this dataset.\n\n* **[Point of Interest (PoI) Data](https://geolocet.com/pages/aggregated-points-of-interest-poi-data):** If you're interested in Points of Interest data, please don't hesitate to contact us for customized datasets to enhance your geographical analysis.\n\n* **[Data Customization](https://geolocet.com/pages/data-services-by-geolocet):** We understand that your project may have specific requirements. Feel free to get in touch with us if you need any modifications or specific data formats such as Excel, JSON, GeoJSON, or Shapefile. We are here to tailor the data to your needs.\n\nMake informed decisions, conduct precise research, and unlock the potential of Guernsey demographic landscape with our comprehensive dataset. Trust in the accuracy and reliability of our official statistical data to drive your projects forward.\nContact us at *contact@geolocet.com* today to access this invaluable resource and explore Guernsey census data like never before.\n\n[Geolocet LTD](https://geolocet.com)\n\n","terms_of_service":"https://geolocet.com/pages/terms-of-use","documentation_link":"","privacy_policy_link":"https://geolocet.com/pages/privacy-policy","cost":"PAID","assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://geolocet.com/pages/terms-of-use"},"provider_summary":{"name":"Geolocet LTD","description":"Geolocet provides premium geospatial data for retail analysis, GIS, and market planning. We equip businesses with accurate, ready-to-query data to drive spatial analysis, site selection, and customer segmentation.\n\nCore Offerings:\n\nDemographics: Socioeconomic data covering population, age, and employment across diverse geographies.\n\nPoints of Interest (PoI): Datasets spanning retail, healthcare, and transport for catchment analysis.\n\nAdministrative Boundaries: GIS-ready boundaries, including municipalities and postcodes, for territory management.\n\nBrand Locations: Chain location datasets to monitor store networks and market expansion.\n\nWealth Data: Granular economic indicators like purchasing power to identify high-potential markets.\n\nWe source from trusted providers, delivering highly customizable datasets with comprehensive coverage across Europe and worldwide."},"popularity":558},{"id":"bbd11f28-1727-46a0-b309-90b4dbd5fa6d","summary":{"name":"MBI Sociodemographic Data for the United Kingdom– Sample","subtitle":"MBI's fresh sociodemographic data for well-founded decisions in the United Kingdom. Free trial data.","share":{"name":"share_socio_mbi_market_data_2024_gb_pc_sectors_socio","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1763545418982,"updated_at":1779199129534,"categories":["ADVERTISING_AND_MARKETING","DEMOGRAPHICS","GEOSPATIAL"],"listingType":"STANDARD","provider_id":"aa09abd6-ff7f-4452-803d-b8c43b4c07cb"},"detail":{"description":"[MBI](https://www.mbi-geodata.com/en/)'s sociodemographic data is available for many countries worldwide. All data comes from a single source, ensuring **consistency and comparability** across markets.\n\nThe Sociodemographics-Bundle Basic for the United Kingdom includes the following **variables**:\n- [population](https://www.mbi-geodata.com/en/sociodemographic-data/population/)\n- [gender](https://www.mbi-geodata.com/en/sociodemographic-data/sex-gender/)\n- [age structure](https://www.mbi-geodata.com/en/sociodemographic-data/agebands/) (15-year age bands)\n- household figures ([number of households](https://www.mbi-geodata.com/en/sociodemographic-data/households/) and [households by type](https://www.mbi-geodata.com/en/sociodemographic-data/households-by-type/))\n\nOur sociodemographic data is offered for each country on different **levels**:\n- **Postal Code Level:** This fine-grained level of detail makes the data suitable for a wide range of applications – for example, enriching customer databases, which are often organized by postal code.\n- **Administrative Level:** Covers both larger administrative areas (e.g., regions, states, provinces) as well as finer subdivisions such as districts, counties, or municipalities.\n- **Micro Level**: Subdivisions of postal code or administrative boundaries, such as census areas or street segments. This is the most granular level for local analysis.\n\n**Please note:** The dataset available for free download is a **sample dataset** covering a limited number of geographic areas. If you wish to access the full dataset (covering all areas) or if you require additional variables or geographic levels, please contact us. We will be happy to help you find a product that fits your needs.\n \n**Additional Insights**: https://www.mbi-geodata.com/en/sociodemographic-data/\n\n**Data Structure**:\nAGE_F0014: Population (Nighttime) by age: 0 - 14 years, female\nAGE_F1529: Population (Nighttime) by age: 15 - 29 years, female\nAGE_F3044: Population (Nighttime) by age: 30 - 44 years, female\nAGE_F4559: Population (Nighttime) by age: 45 - 59 years, female\nAGE_F60PL: Population (Nighttime) by age: 60 years and above, female\nAGE_M0014: Population (Nighttime) by age: 0 - 14 years, male\nAGE_M1529: Population (Nighttime) by age: 15 - 29 years, male\nAGE_M3044: Population (Nighttime) by age: 30 - 44 years, male\nAGE_M4559: Population (Nighttime) by age: 45 - 59 years, male\nAGE_M60PL: Population (Nighttime) by age: 60 years and above, male\nAGE_T0014: Population (Nighttime) by age: 0 - 14 years, total\nAGE_T1529: Population (Nighttime) by age: 15 - 29 years, male\nAGE_T3044: Population (Nighttime) by age: 30 - 44 years, total\nAGE_T4559: Population (Nighttime) by age: 45 - 59 years, total\nAGE_T60PL: Population (Nighttime) by age: 60 years and above, total\nCTRYCODE: Two digit country code by ISO 3166\nFEMALE: Population (Nighttime): females\nHH_SIZE: Average household size\nHH_T: Households: total number\nMALE: Population (Nighttime): males\nNAME: Name of administrative area / Name of the most populous administrative area / Name of the respective micro-area or the next higher level\nADMINCODE / POSTCODE / MICROCODE Administrative code / Postal code / Micro-code\nP_PRM: Population (Nighttime) year average: per mill of country\nP_T: Population (Nighttime) year average: total number","terms_of_service":"https://service-mbi-geodata.com/s/HNp7Z2tws4WDqWd","documentation_link":"https://www.mbi-geodata.com/en/mbi-supplier-manufacturer-worldwide-geodata/europe/united-kingdom-maps/","privacy_policy_link":"https://www.mbi-geodata.com/en/data-protection/","geographical_coverage":"[\"Europe\"]","cost":"FREE","update_frequency":{"interval":1,"unit":"YEARLY"},"collection_granularity":{"interval":1,"unit":"YEARLY"},"collection_date_start":1704101673069,"collection_date_end":1735637679422,"data_source":"MBI","assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://service-mbi-geodata.com/s/rbHPwqRDFgTfyFJ"},"provider_summary":{"name":"Michael Bauer International GmbH – MBI Geodata","description":"For over 15 years, Michael Bauer International (MBI) has been a leading provider of globally consistent and comparable geographic data, including more than 3 million postcode areas and other detailed datasets like sociodemographics and purchasing power. 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The details about these datasets are as follows:\n- The Child and Adult Care Food Program (CACFP) dataset includes the names and locations of participating day care sites and whether or not the site is Breastfeeding, participating in the Eat Well Play Hard in Child Care Settings (EWPHCCS) project, or participating in the Eat Well Play Hard in Day Care Homes (EWPHDCH) project.\n- Congregate and home-delivered meals dataset, which contains information on meals served from 1974 to the present by the network of Area Agencies on Aging (AAAs). Services may include but are not limited to congregate meals, health promotion, educational programs and recreation.\n- Information on locations and operators of food scrap drop-off sites\n- A list of all active permits issued by the State Liquor Authority (SLA). Permits in this dataset are issued for 1-3 year terms.\n- A list of retail food stores that hold an Article 20-C food processing establishment license in New York State. Retail food stores include establishments such as convenience stores, bodegas, grocery stores, and supermarkets.\n- Dataset containing the monthly listings of households, recipients and expenditures for SNAP (Supplemental Nutrition Assistance Program).\n- Dataset based on New York City Census population and contains per year information on water consumption in the New York City Water Supply System.\n\n**Benefits**\n- congregate and home-delivered meals dataset is useful for health promotion, educational programs and recreation of the aging.\n- information on locations and operators of food scrap drop-off sites.\n- a list of all active permits of liquor stores and retail food such as convenience stores, bodegas, grocery stores, and supermarkets are all available in this data package.\n- the child and adult care food program’s comprehensive details available in comprehensive and normalized form.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [Child And Adult Care Food Program Participation](https://www.johnsnowlabs.com/marketplace/child-and-adult-care-food-program-participation)\n\t- The Child and Adult Care Food Program (CACFP) is a nutrition education and meal reimbursement program helping providers serve nutritious and safely prepared meals to children and adults in day care settings. It includes the names and locations of participating day care sites and whether or not the site is Breastfeeding Friendly Certified with CACFP, participating in the Eat Well Play Hard in Child Care Settings (EWPHCCS) project or in the Eat Well Play Hard in Day Care Homes (EWPHDCH) project.\n- [Congregate and Home Delivered Meals Served by the Office for the Aging](https://www.johnsnowlabs.com/marketplace/congregate-and-home-delivered-meals-served-by-the-office-for-the-aging)\n\t- This dataset consists of a listing of congregate and home-delivered meals served from 1974 to the present by the network of Area Agencies on Aging (AAAs). AAAs local offices for the Aging provide services at senior center locations either directly or through subcontracts. Services may include but are not limited to congregate meals, health promotion, educational programs and recreation.\n- [Connecticut SNAP Authorized Retailers](https://www.johnsnowlabs.com/marketplace/connecticut-snap-authorized-retailers)\n\t- This dataset shows the list of retailers in Connecticut authorized to accept The Supplemental Nutrition Assistance Program (SNAP) Electronic Benefit Transfer (EBT) benefits. The SNAP, formerly known as Food Stamps, helps eligible individuals and families afford the cost of food at supermarkets, grocery stores and farmers’ markets.\n- [Food Scrap Drop Off Locations](https://www.johnsnowlabs.com/marketplace/food-scrap-drop-off-locations)\n\t- This dataset contains information on locations and operators of food scrap drop-off locations. These sites, however, are not necessarily operated solely by the City of New York.\n- [Liquor Authority List of Active Permits](https://www.johnsnowlabs.com/marketplace/liquor-authority-list-of-active-permits)\n\t- This dataset contains a list of all active permits issued by the State Liquor Authority (SLA). Permits in this dataset are issued for 1-3 year terms. This dataset does not include the temporary or single event.\n- [Retail Food Stores in New York City](https://www.johnsnowlabs.com/marketplace/retail-food-stores-in-new-york-city)\n\t- This dataset contains a listing of retail food stores that hold an Article 20-C food processing establishment license or an Article 28 license in New York State. Retail food stores include establishments such as convenience stores, bodegas, grocery stores, and supermarkets.\n- [SNAP Distribution Policy Database](https://www.johnsnowlabs.com/marketplace/snap-distribution-policy-database)\n\t- This dataset provides a central data source for information on State policy options in the Supplemental Nutrition Assistance Program (SNAP). SNAP (Supplemental Nutrition Assistance Program) offers nutrition assistance to millions of eligible, low-income individuals and families and provides economic benefits to communities. FNS (Food and Nutrition Service) also works with State partners and the retail community to improve program administration and ensure program integrity.\n- [Supplemental Nutrition Assistance Program Expenditures And Caseloads](https://www.johnsnowlabs.com/marketplace/supplemental-nutrition-assistance-program-expenditures-and-caseloads)\n\t- This dataset enlists the monthly listings of households, recipients and expenditures for SNAP (Supplemental Nutrition Assistance Program). Data is from the New York State Office of Temporary and Disability Assistance and the NYC Open Data.\n- [Water Consumption Report](https://www.johnsnowlabs.com/marketplace/water-consumption-report)\n\t- This dataset is based on New York City Census population and contains per year information on water consumption in the New York City Water Supply System.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - 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Use this dataset to easily track the performance of your internal models and forecasts against a trusted third-party provider.\n\nLeverage this dataset to retrieve and compare weekly price forecasts for NBP, TTF, and PSV, or identify trading opportunities by analyzing price and spread forecasts against futures markets. Review European gas outages and hub balances, and aspects of gas flow including daily nominations, instantaneous and physical flows, flow points, field production, demand, prices, and storage facilities across key European markets. This offers insights for market analysis, operational planning, and strategic decision-making.\n\nShort-Term Forecast includes:\n\n- Supply and demand forecasts for major European countries over the next 18 months to 3 years, updated weekly\n- Supply forecast by countries/regions and upstream producers.\n- Demand forecast by LDZ (res-comm), industrial, and power sectors, including coal-to-gas switching potential\n\nEU Gas Outages includes:\n- Daily updates on gas outage data, including historical, current, and upcoming/planned maintenance\n- Information on gas flows, field outages, and LNG liquefaction outages to assess the impact on gas prices\n\nEU Gas Hub Balances includes:\n- Current daily hub balances with a 14-day forecast.\n- Historical daily hub balances\n- Selection of individual or multiple flow points across various flow types with daily granularity\n- Physical flow points data for specific flow sources\n- Daily storage data for European gas resources\n- Hourly updates on day-ahead nominations, renominations, and allocations\n- Real-time data on field production and instantaneous flows\n- Source: Publicly available data including from various European Gas TSOs/SSOs and government websites\n\n**Use cases**\n- Demand Forecasting - Access Platts short-term outlooks for European Gas, Supply, Demand and Price, to build and evolve profitable trading and procurement strategies\n- Market Analysis - Gas supply, demand, price and outages data is critical for analyzing how gas and power pricing may be impacted by market events\n- Pricing Analysis - Daily European gas outages data is a critical element in the determination of the overall European gas flow balance, and will therefore impact price forecasts for gas\n- Quantitative Analysis - Leverage SPGE data to design and implement complex and insightful models to support energy and commodities trading\n\n**Product details**\n\nSample Tables:\nA ) SUPPLY_FORECAST\nB ) REF_DATA_FORECAST_WEEKS\n\nSample Fields:\nA ) SUPPLY_FORECAST\nFORECAST_WEEK\nCODE\nUNIT\nDATASET\nDATASET_ID\nREGION\nSOURCE\n\nB ) REF_DATA_FORECAST_WEEKS\nFORECAST_IMPORT_ID\nFORECAST_DATE\nIMPORT_DATE\nFORECAST_WEEK\n\nView Descriptions:\n\n- BIDIRECTIONAL_FORECAST - Bidirectional forecast for European Gas markets\n- DEMAND_FORECAST - Weekly Demand forecast for European Gas markets\n- PRICE_FORECAST - Price forecast for European Gas markets\n- REF_DATA_COUNTRIES - Access list of European gas outages countries\n- REF_DATA_DATASETS - List of datasets for which forecasts are available\n- REF_DATA_FLOW_FIELDS - Access list of European gas outages flow fields\n- REF_DATA_FLOW_ITEMS - Access list of European gas outages flow items\n- REF_DATA_FLOW_ITEM_TYPES - Access list of European gas outages flow item types\n- REF_DATA_FORECAST_WEEKS - Weekly price forecast for TTF, NBP and PSV European Gas Hubs - List of supported regions\n- REF_DATA_LOCATIONS - List of supported regions\n- REF_DATA_OUTAGES - Access European gas outages details with their historical revisions through time\n- REF_DATA_SYSTEM_OPERATORS - Access list of European gas outages system operators\n- STORAGE_FORECAST - Storage forecast for European Gas markets\n- SUPPLY_FORECAST - Weekly Supply forecast for European Gas markets\n- SWITCHING_FORECAST - Weekly Coal to Gas Switching Forecast for European Gas Markets\n- EUGAS_DICTIONARY - Contains descriptions and examples of each of the columns in the above views for a better understanding of the dataset","terms_of_service":"https://www.spglobal.com/en/privacy/privacy-policy-english","documentation_link":"https://www.marketplace.spglobal.com/en/datasets/platts-european-gas-analytics-(1681331971)","privacy_policy_link":"https://www.spglobal.com/en/privacy/privacy-policy-english","geographical_coverage":"[\"Europe\"]","cost":"PAID","collection_date_start":1641038422978,"assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"S&P Global Energy","description":"At S&P Energy, our complete view of global energy and commodities markets enables our customers to make decisions with conviction and create long-term, sustainable value. 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Each figure carries a code: 0 for actual reported, 2 for estimated, 3 for modelled.\n\nAcross the full source, revenue breaks down as 8.5% actual, 21.0% estimated and 69.7% modelled. We publish that split instead of a headline coverage number, because a revenue figure read without its code is not a figure, and most of this market sells modelled values without saying so.\n\nThis listing contains sample data. The full product is delivered under a separate agreement.\n\n**Use cases**\n\n- Segmenting a market by revenue or headcount band, filtering to reported values only where the decision warrants it.\n- Sizing an addressable market with an explicit, auditable confidence level per record.\n- Screening a supplier or counterparty portfolio by company size.\n\n**Product details**\n\n- Dataset represented: business_financial_sample, 300 companies across 68 countries, 18 columns.\n- Sample fields include CompanyName, Country, YearStarted, EmployeesHere, EmployeesTotal, EmployeesTotalReliabilityCode, SalesVolumeLocal, SalesVolumeDollars, SalesVolumeEuros and SalesVolumeReliabilityCode.\n- Every reliability code ships alongside its value, never separately.\n- This product joins our other listings on RecordID.\n\nFor more details, refer to the embedded notebook.\n\n**Additional Insights**\n\nFilter on the reliability code before drawing any conclusion. A modelled revenue value is a reasonable estimate of magnitude, not a reported figure, and it should not be presented as one downstream.\n\nThe proportion of actual reported values varies strongly by segment: it is far lower in micro-enterprise segments than the global average suggests. Ask us for the split on the segment you care about rather than applying the global one.\n\nIn this sample, 20.0% of records carry an actual reported revenue against 8.8% in the full reference sample, because the sample is curated for cross-product presence. Do not read the sample split as the product split.\n\nUpdate cadence: static sample extracted 28 July 2026, refreshed when the listing is refreshed. 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Built for AI companies, data product teams, and data-driven enterprises, we remove the time, cost, and complexity of data acquisition worldwide."},"popularity":1650},{"id":"517182f8-ed54-4b95-b0af-715f796a4c99","summary":{"name":"Insurance Intelligence – Policy Ownership, Switching Behavior & Coverage Intent with AI propensity","subtitle":"Target High-Value Policyholders and Insurance Switchers Using Predictive Propensities","setting":{"visibility":"PUBLIC"},"created_at":1749042510350,"updated_at":1750181195775,"categories":["ADVERTISING_AND_MARKETING","COMMERCE","DEMOGRAPHICS","ECONOMICS","FINANCIAL","HEALTH","TRAVEL_AND_TOURISM","RETAIL","TRANSPORTATION_AND_LOGISTICS"],"listingType":"PERSONALIZED","provider_id":"cb4ffcdc-045c-4592-8497-0eeb2b9a3c3d"},"detail":{"description":"Data Axle’s Insurance Audiences combine verified PII with AI-driven behavioral and financial intent signals to identify consumers likely to buy, switch, or expand insurance coverage. Whether you're driving growth across auto, health, life, homeowners, or bundled products, these audiences help marketers reach high-likelihood converters with precision—across every lifecycle stage and coverage line.\n\nData Axle, formerly Infogroup, has been a trusted provider of data-driven solutions since 1972. It has coverage of over 95% of U.S. adults and households. Data Axle combines deterministic offline and online sources—voter files, utility connects, real estate, and more—to create enriched, actionable consumer profiles which can be consumed directly within your Databricks environment.  \n\n**What Sets Us Apart:**\n\n- Unlike traditional forecasting models built on static rules or demographics, our AI engine continuously learns and adapts, using thousands of behavioral, attitudinal, and transactional signals to generate nuanced audience definitions\n- Trained on expansive offline PII data, utilizing all signals (not just age/income)\n- Scores every individual (0–1) by propensity to act\n- Optimized for personalized targeting, next-best action, and cross-sell models\n- Built for scalable segmentation, enrichment, and omnichannel activation\n\n**Key Attributes**\n\n- Current policy ownership: auto, home, health, life, renters, vision, dental, and more\n- Carrier loyalty and brand-switch indicators\n- Household-level asset ownership (vehicles, properties)\n- Family status (dependents, marital status)\n- Financial stress, disposable income, and credit behavior signals\n- Regional policy adoption trends and household risk profiles\n\n**Segment Categories**\n\n- Multi-Line Policyholders – Existing customers with multiple active coverages\n- Insurance Switchers – Recently switched or high intent to switch coverage or carrier\n- High Coverage Seekers – Interested in expanding policy value or protection\n- Uninsured or Underinsured – Likely lacking basic coverage or minimal policy value\n- First-Time Policy Buyers – Recently employed, new parents, new homeowners\n- Carrier Loyalists – Long-term relationships with specific brands (State Farm, Geico, etc.)\n- Price-Sensitive Shoppers – Value-focused consumers actively comparing rates\n- Digital Policy Managers – Engage and renew policies through online tools\n- Senior & Medicare Segments – 65+ likely evaluating healthcare and life policy options\n- Pet & Niche Insurance Intenders – Interest in less-common policies like pet, travel, identity theft\n\n**Key Features**\n\n- AI-generated scores for policy switching, product affinity, and bundle likelihood\n- Multi-carrier loyalty models and cross-sell potential analysis\n- 100+ integrated PII and behavioral sources, updated regularly\n- Custom and prebuilt audiences by insurance type and intent level\n- HEM and match-key support for seamless CRM enrichment and activation\n\n**Use Cases**\n\n- Target health and life policy switchers during annual enrollment windows\n- Upsell to existing customers with auto+ homeowners combo potential\n- Conquest campaigns targeting competitor brand switchers\n- Deliver policy bundle offers to multi-vehicle or multi-property households\n- Reach digitally savvy consumers for online enrollment or renewals\n- Reactivate lapsed policies with behaviorally aligned win-back messaging\n\n**Next Steps**\n\n- Request listing access\n- Data Axle team will contact you to finalize audience, data cut, and delivery method / frequency\n- Need a Custom Audience? We deliver tailored segments within 5–10 business days\n\n**Business Needs**\n\n- *360-Degree Customer View*\n - Unify insurance-related behavioral, financial, and demographic signals to build a complete picture of current policyholders, high-risk individuals, and underserved audiences. Enable intelligent personalization across the policy lifecycle.\n- *Customer Acquisition*\n - Drive smarter acquisition across insurance verticals by targeting high-likelihood first-time buyers, price-sensitive switchers, and affluent customers seeking higher protection. Tailor messaging by product type, income level, and risk posture.\n- *Demand Forecasting*\n - Predict shifts in consumer coverage behavior, such as life stage transitions, market-driven churn, or policy expansion intent. 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Inputs are sourced from regulatory ground stations across more than 60 countries, fused with satellite observations, and processed through a multi-stage pipeline to deliver harmonized outputs to any global location, including a precise lat/lon or any geographically bounded area such as ZIP or Postal Code, DMA, or MSA.\n\n**Use cases**\n \n- Public health monitoring and respiratory disease risk assessment \n- HVAC automation and personalization\n- Programmatic advertising\n- Healthcare strategy and digital wellness platform development \n- Logistics planning and route optimization \n- Connected device integration for real-time health alerts \n\n**Product details**\n\n- AQI: Air Quality Index providing standardized pollution level assessment \n- PM25: Fine particulate matter concentration < 2.5 micrometers \n- PM10: Coarse particulate matter concentration < 10 micrometers \n- NO2: Nitrogen dioxide concentration in parts per billion \n- SO2: Sulfur dioxide concentration in parts per billion\n- CO: Carbon monoxide concentration in parts per million\n- OZONE: Ground-level ozone concentration in parts per billion \n- city: City name of specified location \n- countryCode: 2-letter ISO country code \n- placeName: Specific location identifier\n\n**Tables included:**\n\n- AQ_1hr_current_data\n- AQ_historical_data \n- AQ_historical_data_daily\n- AQ_forecast_96hr_data \n- AQ_forecast_daily_4_days\n\n**Benefits to Databricks Marketplace users**\n\n- Instantly available: Delivered directly for immediate integration into any analytics, machine learning, or Databricks workflow.\n- Comprehensive global network: Data is sourced from regulatory stations across 60+ countries, including Ambee’s own global sensor network, and various satellites, including the ESA Copernicus, NASA Earth Data, and others such as Sentinel-5P, MODIS, and VIIRS.\n- Complete pollutant portfolio: All 7 critical air quality metrics (AQI, PM2.5, PM10, NO₂, SO₂, CO, O₃.\n- Scientific precision: Region-specific calibration models with anomaly detection and temporal and spatial consistency \n- Sequential time series for environmental trend modeling: Harmonized historical-to-present-to-forecast datasets enable consistent training, backtesting, and future projection models.\n- Hyperlocal resolution: Data is provided on a scientifically consistent grid that can be mapped to any user-defined location or geography.\n- Continuous updates: Real-time ingestion of best-in-class observational data feeds, ensuring the dataset reflects the most current available measurements.\n","terms_of_service":"https://www.getambee.com/terms-and-conditions","documentation_link":"https://docs.ambeedata.com/apis/air-quality","privacy_policy_link":"https://www.getambee.com/privacy-policy","geographical_coverage":"[\"Global\"]","cost":"FREE","update_frequency":{"interval":1,"unit":"HOURLY"},"collection_granularity":{"interval":1,"unit":"HOURLY"},"data_source":"","assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://www.getambee.com/servicesagreement"},"provider_summary":{"name":"Ambee","description":"Ambee is a global climate tech company delivering high-resolution, real-time, historical, and forecast environmental intelligence. 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The Shaip EHR longitudinal data comprises de-identified EHR data that is nationally representative. This data is sourced from practices across the U.S., covering the continuum of care delivery—from independent, single-provider offices to large, integrated delivery systems, including both primary and specialty care.\n\nShaip's summaries include data from hospital Electronic Health Record Systems (EHRs), extending beyond claims and RX data. 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All sample data is de-identified and does not contain PHI.\n\n\n**Use cases**\n\n**Patient Care Improvement**\n\n- **Personalized Medicine**: Identify patterns in patient data to tailor treatments and medications.\n- **Chronic Disease Management**: Monitor disease progression, treatment effectiveness, and patient adherence.\n- **Early Disease Detection**: Detect early signs of diseases through trend analysis.\n- **Risk Stratification**: Identify patients at high risk for specific conditions.\n\n**Population Health Management**\n\n- **Disease Prevalence**: Understand the distribution of diseases within a population.\n- **Healthcare Utilization**: Analyze healthcare resource utilization patterns.\n- **Public Health Surveillance**: Track the spread of infectious diseases.\n- **Outcome Measurement:** Evaluate the effectiveness of population-based interventions.\n\n**Research and Development**\n\n- **Drug Safety and Efficacy**: Assess drug side effects and long-term outcomes.\n-** Clinical Trials**: Identify suitable patient populations for clinical research.\n- **Health Economics**: Analyze healthcare costs and resource allocation.\n- **Epidemiological Studies**: Investigate disease trends and risk factors.\n\n**Healthcare Operations**\n\n- **Fraud Detection**: Identify anomalies in claims and patient data.\n- **Quality Improvement**: Measure and track healthcare quality metrics. \n- **Resource Allocation**: Optimize the distribution of healthcare resources.\n\n**Product details**\n\nOur dataset features patient-level longitudinal data that meets the following criteria:\n\n- Includes multiple records per patient, encompassing comprehensive data for each visit to a physician, such as notes, diagnosis, patient history, etc.\n- Includes information on the doctor code or name whom the patient visited during each encounter.\n- Contains de-identified patient information while retaining demographic attributes for analysis.\n- This dataset is designed to support longitudinal studies, healthcare analytics, and research in medical informatics. 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It also includes Nursing Home Compare information on Deficiencies, Fire Safety Deficiencies, MDS Quality Measures, Ownership information, Fines and Payment denial, Provider Information, State Averages and Survey Summary information about nursing homes.\n\n**Description**\nThis data package contains the Measures of Rehospitalization Emergency Visit and Community Discharge are based on Medicare claims data. These three quality measures measure the rate of rehospitalization, emergency room use, and community discharge among nursing home residents. They include Percentage of short-stay residents who were successfully discharged to the community, Percentage of short-stay residents who have had an outpatient emergency department visit and percentage of short-stay residents who were re-hospitalized after a nursing home admission. Nursing Home Compare Deficiencies currently listed on Nursing Home Compare. Nursing Home Compare allows consumers to compare information about nursing homes. It contains quality of care and staffing information for all 15,000 plus Medicare- and Medicaid-participating nursing homes. Nursing Home Compare Fire Safety Deficiencies, MDS Quality Measures, Ownership, Penalties, Provider Information, State Averages and Survey Summary datasets allows consumers to compare information about nursing homes. Nursing Home Compare includes information on Health and fire-safety inspections with detailed and summary information about deficiencies found during the 3 most recent comprehensive inspections (conducted annually) and the last 3 years of complaint investigations and a set of quality measures that describe the quality of care in nursing homes including percent of residents with pressure sore, percent of residents with urinary incontinence and more and 5-star quality ratings of individual or overall performance on health inspections, quality measures, and hours of care provided per resident by staff performing nursing care tasks and the nursing home staffing information about the number of registered nurses, licensed practical or vocational nurses, physical therapists and nursing assistants in each nursing home.\n\n**Benefits**\n- Useful for nursing home medicare beneficiaries, providers, patients, consumers and hospitals. nursing home compare database is available o nmedicare.gov website in raw form, whereas a john snow labs (jsl) customer gets it in a clean and normalized form ready to use in one place and create his own custom reports. easy to comprehend for the customers who are unfamiliar with the medical terminologies as all abbreviated terms are replaced with full form, unlike nursing home compare original datasets which are full of abbreviations.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [COVID-19 Vaccination Rates Provider Data](https://www.johnsnowlabs.com/marketplace/covid-19-vaccination-rates-provider-data)\n\t- This dataset contains current COVID-19 vaccination rates for resident and healthcare personnel. Data are presented as one row per provider.\n- [COVID-19 Vaccination State and National Averages](https://www.johnsnowlabs.com/marketplace/covid-19-vaccination-state-and-national-averages)\n\t- This dataset contains State and National averages for facility resident and healthcare personnel COVID-19 vaccination rates. Data are presented as one row per state or territory plus a row for national averages.\n- [Measures of Rehospitalization Emergency Visit and Community Discharge](https://www.johnsnowlabs.com/marketplace/measures-of-rehospitalization-emergency-visit-and-community-discharge)\n\t- This dataset contains a list of Measures of the Rate of Rehospitalization, Emergency Room Visit, and Community Discharge for Medicare Beneficiaries. These rates are based on Medicare claims data. These three quality measures measure the rate of rehospitalization, emergency room use, and community discharge among nursing home residents.\n- [Nursing Home Compare Deficiencies](https://www.johnsnowlabs.com/marketplace/nursing-home-compare-deficiencies)\n\t- This dataset contains a list of all deficiencies currently listed on Nursing Home Compare, including the nursing home that received the deficiency, the associated inspection date, deficiency tag number, scope and severity, the current status of the deficiency and the correction date. Nursing Home Compare allows consumers to compare information about nursing homes. It contains quality of care and staffing information for all 15,000 plus Medicare- and Medicaid-participating nursing homes.\n- [Nursing Home Compare Fire Safety Deficiencies](https://www.johnsnowlabs.com/marketplace/nursing-home-compare-fire-safety-deficiencies)\n\t- This dataset contains a list of all fire safety deficiencies currently listed on Nursing Home Compare, including the nursing home that received the deficiency, the associated inspection date, deficiency tag number, scope and severity, the current status of the deficiency and the correction date. Data are presented as one deficiency per row.\n- [Nursing Home Compare Inspection Dates](https://www.johnsnowlabs.com/marketplace/nursing-home-compare-inspection-dates)\n\t- This dataset represents a list of nursing home inspection dates in the past three years, including health inspections, fire safety inspections, complaint inspections and infection control inspections.\n- [Nursing Home Compare MDS Quality Measures](https://www.johnsnowlabs.com/marketplace/nursing-home-compare-mds-quality-measures)\n\t- This dataset contains quality measures displayed on Nursing Home Compare, based on the resident assessments that make up the nursing home Minimum Data Set (MDS). Each row contains a specific measure for a nursing home and includes the four-quarter score average and scores for individual quarter.\n- [Nursing Home Compare Medicare Claims Quality Measures](https://www.johnsnowlabs.com/marketplace/nursing-home-compare-medicare-claims-quality-measures)\n\t- This dataset contains a list of the quality measures displayed on Nursing Home Compare, that are based on Medicare claims data. Each row contains a specific quality measure for a specific nursing home and includes the risk-adjusted score.\n- [Nursing Home Compare Ownership](https://www.johnsnowlabs.com/marketplace/nursing-home-compare-ownership)\n\t- This dataset contains a list of ownership information for currently active nursing homes. The dataset contains the information about owner name, ownership type, ownership percentage and its association date with Medicare. Nursing Home Compare allows consumers to compare information about nursing homes. It contains quality of care and staffing information for all 15,000 plus Medicare- and Medicaid-participating nursing homes.\n- [Nursing Home Compare Penalties](https://www.johnsnowlabs.com/marketplace/nursing-home-compare-penalties)\n\t- This dataset contains a list of the fines and payment denials currently displayed on Nursing Home Compare. Nursing Home Compare allows consumers to compare information about nursing homes. It contains quality of care and staffing information for all 15,000 plus Medicare- and Medicaid-participating nursing homes.\n- [Nursing Home Compare Provider Info](https://www.johnsnowlabs.com/marketplace/nursing-home-compare-provider-info)\n\t- This dataset contains General information on currently active nursing homes, including number of certified beds, quality measure scores, staffing and other information used in the Five-Star Rating System in Nursing Home Compare. Nursing Home Compare allows consumers to compare information about nursing homes. It contains quality of care and staffing information for all 15,000 plus Medicare- and Medicaid-participating nursing homes.\n- [Nursing Home Compare State Averages](https://www.johnsnowlabs.com/marketplace/nursing-home-compare-state-averages)\n\t- This dataset contains a list of a variety of averages for each state or territory as well as the national average, including each quality measure, staffing, fine amount and number of deficiencies. Each row displays a specific state or territory, the associated measure and average.\n- [Nursing Home Compare State-Level Health Inspection Cut Points](https://www.johnsnowlabs.com/marketplace/nursing-home-compare-state-level-health-inspection-cut-points)\n\t- This dataset represents State-specific ranges for the weighted health inspection score for each health inspection star rating category.\n- [Nursing Home Compare Survey Summary](https://www.johnsnowlabs.com/marketplace/nursing-home-compare-survey-summary)\n\t- This dataset contains General information on currently active nursing homes, including number of certified beds, quality measure scores, staffing and other information used in the Five-Star Rating System in Nursing Home Compare. Nursing Home Compare allows consumers to compare information about nursing homes. It contains quality of care and staffing information for all 15,000 plus Medicare- and Medicaid-participating nursing homes.\n- [Skilled Nursing Facility QRP National Data](https://www.johnsnowlabs.com/marketplace/skilled-nursing-facility-qrp-national-data)\n\t- This dataset contains a list of Medicare Part A Skilled Nursing Facility (SNF) services to beneficiaries and must report data on certain measures of quality to Medicare through the Skilled Nursing Facility Quality Reporting Program (SNF QRP). This dataset contains national averages on the quality of measures implemented under the Improving Medicare Post-Acute Care Transformation (IMPACT) Act.\n- [Skilled Nursing Facility QRP Provider Data](https://www.johnsnowlabs.com/marketplace/skilled-nursing-facility-qrp-provider-data)\n\t- This dataset contains a list of Medicare Part A Skilled Nursing Facility (SNF) services to beneficiaries and must report data on certain measures of quality to Medicare through the Skilled Nursing Facility Quality Reporting Program (SNF QRP). This dataset contains a list of SNFs, as well as their results on the quality of resident care measures implemented under the Improving Medicare Post-Acute Care Transformation (IMPACT) Act.\n- [Skilled Nursing Facility QRP Swing Beds Provider Data](https://www.johnsnowlabs.com/marketplace/skilled-nursing-facility-qrp-swing-beds-provider-data)\n\t- This dataset contains Non-Critical Access Hospitals (CAHs) with swing beds that provide Medicare Part A Skilled Nursing Facility (SNF) services to beneficiaries and must report data on certain measures of quality to Medicare through the SNF Quality Reporting Program (QRP). This dataset contains a list of the swing bed units participating in the SNF QRP, as well as their results on quality measures implemented under the Improving Medicare Post-Acute Care Transformation (IMPACT) Act.\n- [Skilled Nursing Facility VBP Facility Level Data](https://www.johnsnowlabs.com/marketplace/skilled-nursing-facility-vbp-facility-level-data)\n\t- This dataset contains facility-specific performance information for the fiscal year (FY) 2023 Skilled Nursing Facility Value-Based Purchasing (SNF VBP) Program. It includes baseline period (calendar year (CY) 2019) and performance period (CY 2021) risk-standardized readmission rates, achievement scores, improvement scores, performance scores, rankings, and incentive payment multipliers.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated\n- Data Updates\n - 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If you are looking to scale your machine learning models with premium, commercially licensed data at high volumes, reach out to our team at vladyslav.shtak@vista.com to discuss custom licensing, tailored curation, and enterprise-scale delivery.\n\n**Metadata Structure**\n\nTo help you fully evaluate the capabilities of our data pipeline, this showcase sample includes our complete, enterprise-grade metadata for every asset.\n\n**Fields Included in this Dataset:**\n\n*file_name* — Formatted asset identifier used in this repository.\n*deposit_item_id* — Unique numeric identifier for each asset.\n*filename* — Original asset file string.\n*description* — Manually written, high-accuracy descriptive caption provided by the original creator.\n*title* — Alternate or shortened title provided by the creator.\n*keywords* — Up to 50 human-reviewed, comma-separated tags per asset.\n*type* — Asset type (e.g., image, vector).\n*height / width* — Native resolution dimensions in pixels.\n*extension* — Original file format (e.g., jpg, eps).\n*available_for_ai_training *— Clearance flag confirming the asset is approved for AI/ML training use.\n*royalty_model* — Licensing model applied to the asset.\n*status* — Asset review status (e.g., approved).\n*user_id* — Creator linkage for provenance tracking.\n*Video/Audio Fields*: (Where applicable) frame rate, duration, BPM, genre, mood, and instrument data.\n\n**Why License Data from DepositPhotos?**\n\nAI and machine learning models require clean, diverse, and legally sound data at scale. DepositPhotos provides the volume and legal certainty that enterprise model development demands.\n\n*Scale: *250M+ royalty-free images, vectors, illustrations, videos, and audio tracks, with new assets added continuously.\nCommercial Safety: All content is human-reviewed for IP infringement.\n*Metadata Quality: *Captions and keywords are manually created and reviewed, minimizing noise and improving label reliability.\n*Ethical Sourcing:* Creators are compensated for their work. Licensing through DepositPhotos directly supports the creative community and responsible AI development.\n*Custom Curation: *Our data team works directly with engineering teams to filter, package, and deliver precise data slices based on your requirements.\nAbout DepositPhotos\n\nDepositPhotos is a global content marketplace offering premium stock photos, graphics, vectors, and videos. Trusted by corporations and technology companies worldwide, we connect millions of creators with businesses that need high-quality visual content at scale.\n\n**Contact & Licensing**\nReady to scale your models beyond this sample? Contact the DepositPhotos Data Licensing team to request full-scale datasets or build a custom data pipeline.\n\n\uD83D\uDCE7 **Email**: vladyslav.shtak@vista.com\n\uD83C\uDF10 **Learn more**: DepositPhotos AI & Enterprise Licensing","terms_of_service":"https://depositphotos.com/terms-of-use.html","documentation_link":"https://depositphotos.com/legal-information.html","privacy_policy_link":"https://depositphotos.com/privacy-policy.html","update_frequency":{"interval":1,"unit":"YEARLY"},"assets":["ASSET_TYPE_DATA_TABLE","ASSET_TYPE_MEDIA"],"license":"https://depositphotos.com/corporate-accounts.html"},"provider_summary":{"name":"DepositPhotos","description":"Depositphotos provides diverse, commercially safe datasets for AI/ML and Generative AI model training, backed by indemnification. With us, you can access high-quality images, vectors, illustrations, videos, audio, and JSON files to power your AI projects."},"popularity":292},{"id":"ccd50acc-57bd-4d23-9496-c326dcf3b68e","summary":{"name":"MBI Sociodemographic Data for Greece – Sample","subtitle":"MBI's fresh sociodemographic data for well-founded decisions in Greece. Free trial data.","share":{"name":"share_socio_mbi_market_data_2024_gr_postcodes_5_socio","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1763381122365,"updated_at":1779199032476,"categories":["ADVERTISING_AND_MARKETING","DEMOGRAPHICS","GEOSPATIAL"],"listingType":"STANDARD","provider_id":"aa09abd6-ff7f-4452-803d-b8c43b4c07cb"},"detail":{"description":"[MBI](https://www.mbi-geodata.com/en/)'s sociodemographic data is available for many countries worldwide. All data comes from a single source, ensuring **consistency and comparability** across markets.\n\nThe Sociodemographics-Bundle Basic for Greece includes the following **variables**:\n- [population](https://www.mbi-geodata.com/en/sociodemographic-data/population/)\n- [gender](https://www.mbi-geodata.com/en/sociodemographic-data/sex-gender/)\n- [age structure](https://www.mbi-geodata.com/en/sociodemographic-data/agebands/) (15-year age bands)\n- household figures ([number of households](https://www.mbi-geodata.com/en/sociodemographic-data/households/) and [households by type](https://www.mbi-geodata.com/en/sociodemographic-data/households-by-type/))\n\nOur sociodemographic data is offered for each country on different **levels**:\n- **Postal Code Level:** This fine-grained level of detail makes the data suitable for a wide range of applications – for example, enriching customer databases, which are often organized by postal code.\n- **Administrative Level:** Covers both larger administrative areas (e.g., regions, states, provinces) as well as finer subdivisions such as districts, counties, or municipalities.\n- **Micro Level**: Subdivisions of postal code or administrative boundaries, such as census areas or street segments. This is the most granular level for local analysis.\n\n**Please note:** The dataset available for free download is a **sample dataset** covering a limited number of geographic areas. If you wish to access the full dataset (covering all areas) or if you require additional variables or geographic levels, please contact us. We will be happy to help you find a product that fits your needs.\n \n**Additional Insights**: https://www.mbi-geodata.com/en/sociodemographic-data/\n\n**Data Structure**:\nAGE_F0014: Population (Nighttime) by age: 0 - 14 years, female\nAGE_F1529: Population (Nighttime) by age: 15 - 29 years, female\nAGE_F3044: Population (Nighttime) by age: 30 - 44 years, female\nAGE_F4559: Population (Nighttime) by age: 45 - 59 years, female\nAGE_F60PL: Population (Nighttime) by age: 60 years and above, female\nAGE_M0014: Population (Nighttime) by age: 0 - 14 years, male\nAGE_M1529: Population (Nighttime) by age: 15 - 29 years, male\nAGE_M3044: Population (Nighttime) by age: 30 - 44 years, male\nAGE_M4559: Population (Nighttime) by age: 45 - 59 years, male\nAGE_M60PL: Population (Nighttime) by age: 60 years and above, male\nAGE_T0014: Population (Nighttime) by age: 0 - 14 years, total\nAGE_T1529: Population (Nighttime) by age: 15 - 29 years, male\nAGE_T3044: Population (Nighttime) by age: 30 - 44 years, total\nAGE_T4559: Population (Nighttime) by age: 45 - 59 years, total\nAGE_T60PL: Population (Nighttime) by age: 60 years and above, total\nCTRYCODE: Two digit country code by ISO 3166\nFEMALE: Population (Nighttime): females\nHH_SIZE: Average household size\nHH_T: Households: total number\nMALE: Population (Nighttime): males\nNAME: Name of administrative area / Name of the most populous administrative area / Name of the respective micro-area or the next higher level\nADMINCODE / POSTCODE / MICROCODE Administrative code / Postal code / Micro-code\nP_PRM: Population (Nighttime) year average: per mill of country\nP_T: Population (Nighttime) year average: total number","terms_of_service":"https://service-mbi-geodata.com/s/HNp7Z2tws4WDqWd","documentation_link":"https://www.mbi-geodata.com/en/mbi-supplier-manufacturer-worldwide-geodata/europe/greece-maps/","privacy_policy_link":"https://www.mbi-geodata.com/en/data-protection/","geographical_coverage":"[\"Europe\"]","cost":"FREE","update_frequency":{"interval":1,"unit":"YEARLY"},"collection_granularity":{"interval":1,"unit":"YEARLY"},"collection_date_start":1704110675633,"collection_date_end":1735646681031,"data_source":"MBI","assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://service-mbi-geodata.com/s/gBK4PGcTo7A5AbL"},"provider_summary":{"name":"Michael Bauer International GmbH – MBI Geodata","description":"For over 15 years, Michael Bauer International (MBI) has been a leading provider of globally consistent and comparable geographic data, including more than 3 million postcode areas and other detailed datasets like sociodemographics and purchasing power. MBI's high-quality small-scale data spans 248 countries, supporting businesses across all industries in location planning, branch networks, and distribution strategies. Recognized as a key data source, MBI is the preferred provider for leading GIS software and technology companies worldwide as well as one of the first global distributors for HERE Technologies. MBI is part of the Michael Bauer Group, alongside Michael Bauer Research GmbH and Michael Bauer Micromarketing GmbH."},"popularity":143},{"id":"9924203a-6c67-465e-8022-50df0b17fa86","summary":{"name":"Geolocet | Bulgaria Demographic Data","subtitle":"Demographic dataset for the 265 municipalities (общини) of Bulgaria - - age, gender, labor force, households, and more.","setting":{"visibility":"PUBLIC"},"created_at":1705918057890,"updated_at":1752144106991,"categories":["DEMOGRAPHICS","ADVERTISING_AND_MARKETING","GEOSPATIAL","PUBLIC_SECTOR","RETAIL"],"listingType":"PERSONALIZED","provider_id":"5d3ddc17-a7e8-4794-a760-13587e1de2c6"},"detail":{"description":"## **Get instant access to the latest Demographics Data for Bulgaria with [Geolocet](https://geolocet.com)**\n\nExplore the richness of Bulgarian demographic landscape with our Bulgarian Census Data. This meticulously compiled dataset, sourced from official statistical sources, including National Statistics Institute of the Republic of Bulgaria, offers a detailed insight into the population landscape of Bulgaria at the municipality (общини) level. \n\nIf you require data at a less granular level, such as an Uber grid or square grid, please don't hesitate to get in touch with us at *contact@geolocet.com*. We understand that different projects may have varying data needs, and we are here to tailor our data to match your specific requirements.\n\n ## **Key Features:**\n---\n* **Population Data Bulgaria:** Gain access to the latest and most accurate population statistics for Bulgaria, meticulously organized by municipality (общини).\n\n* **Demographic Insights:** Dive deep into Bulgaria demographic landscape with data on 79 attributes, including: \n* Total population,\n* Gender distribution,\n* 5-years age bands distribution,\n* Gender divided by age bands,\n* Labour force,\n* Labour force divided by gender,\n* Total number of households,\n* Household size distribution,\n* Rural/Urban population distribution,\n* Rural/Urban population split by gender.\n\nAll demographic attributes are available at the municipal (общини) level.\n\n* **Geodemographic Insights:** Unlock geodemographic data Bulgaria-wide to inform location-based decision-making, marketing strategies, and policy formulation.\n* **Seamless Data Access:** Our data is available for instant download in CSV format. No lengthy sign-up processes or NDA agreements required on our side – just download the data you need.\n* **Data Samples for Evaluation:** To ensure you're getting exactly what you need, we provide free downloadable samples in CSV format for data evaluation.\n* **Data accuracy:** Our datasets are created using the latest demographics data available in national statistics bureaus, guaranteeing accuracy and reliability.\n* **Best-in-Class Quality:** We guarantee best-in-class quality, backed by over two decades of experience in modeling demographics data and an outstanding quality management process.\n* **Competitive Pricing:** We maintain the lowest prices on the market, ensuring that you get the most cost-effective demographics data acquisition process.\n---\nAdditionally, for those looking to enrich their analysis or project, we offer:\n* **[Administrative Boundaries](https://geolocet.com/products/bulgaria-all-available-administrative-boundaries):** If you require geographical boundaries (polygons) for the municipalities of Bulgaria, consider exploring our Administrative Boundaries product, which seamlessly integrates with this dataset.\n\n* **[Point of Interest (PoI) Data](https://geolocet.com/pages/aggregated-points-of-interest-poi-data):** If you're interested in Points of Interest data, please don't hesitate to contact us for customized datasets to enhance your geographical analysis.\n\n* **[Data Customization](https://geolocet.com/pages/data-services-by-geolocet):** We understand that your project may have specific requirements. Feel free to get in touch with us if you need any modifications or specific data formats such as Excel, JSON, GeoJSON, or Shapefile. We are here to tailor the data to your needs.\n\nIf you're looking for data for Bulgaria, [look here](https://geolocet.com/pages/bulgaria) for off-the-shelf or tailor-made datasets to suit your specific requirements.\n\nMake informed decisions, conduct precise research, and unlock the potential of Bulgaria demographic landscape with our comprehensive dataset. Trust in the accuracy and reliability of our official statistical data to drive your projects forward.\nContact us at *contact@geolocet.com* today to access this invaluable resource and explore Bulgaria census data like never before.\n\n[Geolocet LTD](https://geolocet.com)\n","terms_of_service":"https://geolocet.com/pages/terms-of-use","documentation_link":"","privacy_policy_link":"https://geolocet.com/pages/privacy-policy","geographical_coverage":"[\"Europe\"]","cost":"PAID","data_source":"","assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://geolocet.com/pages/terms-of-use"},"provider_summary":{"name":"Geolocet LTD","description":"Geolocet provides premium geospatial data for retail analysis, GIS, and market planning. We equip businesses with accurate, ready-to-query data to drive spatial analysis, site selection, and customer segmentation.\n\nCore Offerings:\n\nDemographics: Socioeconomic data covering population, age, and employment across diverse geographies.\n\nPoints of Interest (PoI): Datasets spanning retail, healthcare, and transport for catchment analysis.\n\nAdministrative Boundaries: GIS-ready boundaries, including municipalities and postcodes, for territory management.\n\nBrand Locations: Chain location datasets to monitor store networks and market expansion.\n\nWealth Data: Granular economic indicators like purchasing power to identify high-potential markets.\n\nWe source from trusted providers, delivering highly customizable datasets with comprehensive coverage across Europe and worldwide."},"popularity":280},{"id":"9cb82cc4-1506-4f45-a289-99227ccf334b","summary":{"name":"Home Price Index (HPI™)","subtitle":"A first look at home price trends to assess collateral valuation and mortgage risk accurately","setting":{"visibility":"PUBLIC"},"created_at":1789678300792,"updated_at":1789678300792,"categories":["FINANCIAL"],"listingType":"PERSONALIZED","provider_id":"26040e80-2e39-4e01-8fea-0667064d9fce"},"detail":{"description":"Cotality HPI uses over 50 years of repeat sales data from leading real estate and mortgage databases to analyze home price trends. It offers the most comprehensive and up-to-date home price indexes–and is used by the federal government. Clients can spot markets at risk of instability or downturns using Cotality HPI with the HPI Forecast. Each module provides distinct insights, and together with statistical models, delivers a thorough assessment of market health. \nAcross government, financial services, and real estate, HPI supports economic impact analysis and market monitoring, validates individual property values against local trends, and informs pricing strategy. Organizations also use HPI patterns to forecast housing demand and guide investment or development decisions, while lenders and regulators rely on the data for portfolio risk assessment and regulatory reporting.\n\n**Economic Impact Analysis**\nAs an analyst at a government agency, I need to look at current and historic house price trends by CBSA.\n**Asset Valuation**\nHPI data can provide an additional validation point for a property's value. Real estate professionals and investors can use it to compare the price of a specific property against the trend in its area to determine if it's overpriced or underpriced.\n**Pricing Analysis**\nReal estate professionals and investors can use the HPI to understand the price trends in different areas, which can inform their pricing strategies for buying or selling properties.\n**Demand Forecasting**\nBy analyzing trends in the HPI, businesses can anticipate future demand in the housing market. This can inform a range of business decisions, from developers deciding where to build new housing to retailers deciding where to open new stores.\n**Regulatory Reporting**\nFor financial institutions, HPI can be used in reporting to regulators about the value and risk of their real estate portfolio. This data can help these institutions demonstrate that they are managing their risks effectively.\n**Risk Analysis**\nFor financial institutions providing mortgages or home equity loans, HPI data can help in assessing the risk associated with a property. If the property is in an area where the HPI indicates a decline, it might pose a higher risk for the lender.","terms_of_service":"https://www.cotality.com/","documentation_link":"https://www.cotality.com/products/cotality-home-price-index?utm_campaign=412443580-EDSG-ALL-CloudMarketplace-Databricks-2026&utm_source=databricks&utm_medium=marketplace&utm_term=hpi","privacy_policy_link":"https://www.cotality.com/","assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"Cotality","description":"We illuminate data, insights, and workflows across the property ecosystem, analyzing the largest objective dataset in the U.S. for real-time insights in minutes, not months. 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It combines comprehensive entity information, such as financials, ownership, and management, with Moody’s Ratings proprietary ratings and research, and probability of default metrics, all enhanced by unstructured data like company filings, earnings transcripts, news, and macroeconomic trends. \n\nBy bringing structured and unstructured credit inputs together in a single interface, Moody’s Credit MCP enables faster data-to-insight workflows, supporting more rigorous, consistent credit assessment and continuous monitoring of entities, markets, and portfolios.\n\n\n**Data Includes:**\n\n- Global entry firmographics\n- Entity hierarchies and ownership structures\n- Detailed global entity financials\n- Moody's Ratings credit ratings and research\n- Expected default frequency\n- Economic data and research\n- News and media\n- Annual reports\n\n**Use Moody's Credit MCP for:**\n\n*Credit Analysis* - access comprehensive credit risk data and research to assess credit worthiness and compare risk across issuers and instruments\n\n*Risk Management* – monitor credit risk through forward-looking measures and scenario analysis\n\n*Relationship Management* – quickly evaluate credit health to support informed engagement and credit structuring\n\n*Ratings Advisory* – align issuer details with ratings methodologies and peer benchmarks\n\n*Investment Research* – get data-driven assessments to evaluate relative value and risk\n\n*Market Analysis* – analyze sector trends and credit risk signals to assess broader market conditions\n","terms_of_service":"https://www.moodys.com/web/en/us/legal/privacy-policy.html","documentation_link":"https://www.moodys.com/web/en/us/genai/model-context-protocol.html","privacy_policy_link":"https://www.moodys.com/web/en/us/legal/privacy-policy.html","geographical_coverage":"[\"Global\"]","cost":"PAID","update_frequency":{"interval":12,"unit":"HOURLY"},"assets":["ASSET_TYPE_MCP"],"license":""},"provider_summary":{"name":"Moody's","description":"Moody’s provides data, analytics, and risk management tools that global markets depend on to help business and financial leaders make confident decisions. 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Essential for understanding who is behind every loan in the CMBS universe.\n\n**Key Fields**\n\n- ENTITY — counterparty name (sponsor, loan seller, or other party)\n- COUNTERPARTY — role classification (Sponsor, Loan Seller, etc.)\n- DEAL_SHORT_NAME — deal identifier for cross-referencing\n- CIK, FILING_ID, FILING_DATE — full SEC filing provenance\n- LOAN_NUMBER — loan-level linkage\n\n**Use Cases**\n\n- Sponsor exposure analysis: aggregate all loans originated or sponsored by a single entity across deals\n- Loan seller concentration: identify which banks are selling into which deals\n- Relationship mapping: build a graph of sponsor-to-deal-to-servicer relationships\n- Due diligence: quickly identify all parties involved in a given deal or loan\n\n**Coverage**\n\n104,236 counterparty records across 357 CMBS conduit deals. Updated daily as new filings are published.\n\n**Pricing**\n\n$750/month. Contact cairn@cmdrvl.com for enterprise licensing or to request a trial.\n\n**Related Listings**\n\nPairs with **CMBS Loan Data** for loan-level detail and **CMBS Property Collateral** for underlying asset information. **CMBS Tenant Names** is available free.","terms_of_service":"https://cmdrvl.com/terms/","documentation_link":"https://dealcharts.org/capitalmarkets/abs/cmbs/","privacy_policy_link":"https://cmdrvl.com/privacy/","cost":"PAID","update_frequency":{"interval":1,"unit":"DAILY"},"collection_date_start":1480512030642,"data_source":"SEC.gov EDGAR ABS-EE Filings","assets":["ASSET_TYPE_DATA_TABLE"]},"provider_summary":{"name":"CMD+RVL","description":"We believe that every person should have access to the resources necessary to improve their mastery of data analytics. By making data discoverable and accessible we will accelerate your journey to extract value from data."},"popularity":525},{"id":"bc5bddca-0be1-4630-ac2a-b0e97b610aa6","summary":{"name":"SAMPLE GeoPostcodes - Geospatial Data | Address Data Enrichment | International Address data | ...","subtitle":"GeoPostcodes' global address data for address validation and logistics area mapping. Includes all administrative ...","share":{"name":"geopostcodes_24515cf3_4edb_46a6_ace3_bee36e6fb23e_samples_share","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1747667196430,"updated_at":1781620759938,"categories":["OTHER","GEOSPATIAL"],"listingType":"STANDARD","provider_id":"3aadb305-9e20-4613-bcc0-799528ee5826"},"detail":{"description":"A comprehensive self-hosted geospatial database of international address data, including street names, coordinates, house numbers, and address data ranges for Enterprise use. The address data are georeferenced with industry-standard WGS84 coordinates (geocoding).\n\nAll address data are provided in the official local languages. Names and other data in non-Roman languages are also made available in English through translations and transliterations.\n\n\nUse cases for the Global Address Database (Geospatial data/Map Data)\n\n- Address Data Enrichment\n\n- Address capture and validation\n\n- Parcel delivery\n\n- Master Data Management\n\n- Logistics and Shipping \n\n- Sales and Marketing\n\n\n\nProduct Features\n\n- Fully and accurately geocoded\n\n- Multi-language support\n\n- Address ranges for streets covered by several zip codes\n\n- Comprehensive city definitions across countries\n\n- Administrative areas with a level range of 0-4\n\n- International address formats\n\n- House numbers\n\n\n\nFor additional insights, you can combine the map data with:\n\n- UNLOCODE and IATA codes (geocoded)\n\n- Time zones and Daylight Saving Time (DST)\n\n- Population data: Past and future trends\n\n\n\nData export methodology\n\nOur address data enrichment packages are offered in CSV format. All international address data are optimized for seamless integration with popular systems like Esri ArcGIS, Snowflake, QGIS, and more.\n\n\n\nWhy do companies choose our location databases\n\n- Enterprise-grade service\n\n- Reduce integration time and cost by 30%\n\n- Frequent, consistent updates for the highest quality\n\n\nNote: Custom international address data packages are available. 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All information is included at the address level using a unique identifier with additional proximity measurements to uncover further insight around structure specific risk.\n\n\n**Use cases**\n\n•\tAssess wildfire risk exposure using a model that uniquely addresses the varying characteristics of each landscape\n•\tMaximize your bottom line by avoiding high risk areas while identifying pockets of opportunity in generally risky areas\n•\tUnderstand wildfire risk at the property level rather than a macro region and price business accordingly\n•\tMeasure a property's proximity to high and very high wildfire zones. \n•\tVisualize wildfire risk sheds for all 50 states\n\n\n**Product details**\n\n- Wildfire Risk provides scores for the following hazard types:\n\n 1. Wildland landscape risk (low structure density areas)\n 2. Intermix landscape risk (moderate density areas with unique community attributes)\n 3. Interface landscape risk (high density areas when embers are the main threat)\n 4. 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For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [FDA Adverse Events Reporting System Demographics 2021](https://www.johnsnowlabs.com/marketplace/fda-adverse-events-reporting-system-demographics-2021)\n\t- The FDA Adverse Event Reporting System (FAERS) contains information on medication errors, quality complaints and drug-related adverse events that were submitted to the FDA (U.S. Food and Drug Administration) in 2021. The Demographics dataset contains patient demographic and administrative information, a single record for each event report.\n- [FDA Adverse Events Reporting System Drug 2021](https://www.johnsnowlabs.com/marketplace/fda-adverse-events-reporting-system-drug-2021)\n\t- The FDA Adverse Event Reporting System (FAERS) contains information on medication errors, quality complaints and drug-related adverse events that were submitted to FDA (U.S. Food and Drug Administration) in 2021. The Drug dataset contains drug/biologic information for as many medications as were reported for the event (1 or more per event).\n- [FDA Adverse Events Reporting System Drug Indication 2021](https://www.johnsnowlabs.com/marketplace/fda-adverse-events-reporting-system-drug-indication-2021)\n\t- The FDA Adverse Event Reporting System (FAERS) contains information on medication errors, quality complaints and drug-related adverse events that were submitted to the FDA (U.S. Food and Drug Administration) in 2021. The Indication dataset contains all \"Medical Dictionary for Regulatory Activities\" (MedDRA) terms coded for the indications for use (diagnoses) for the reported drugs (0 or more per drug per event).\n- [FDA Adverse Events Reporting System Drug Reaction 2021](https://www.johnsnowlabs.com/marketplace/fda-adverse-events-reporting-system-drug-reaction-2021)\n\t- The FDA Adverse Event Reporting System (FAERS) contains information on medication errors, quality complaints and drug-related adverse events that were submitted to FDA (U.S. Food and Drug Administration) in 2021. The drug reaction dataset contains all \"Medical Dictionary for Regulatory Activities\" (MedDRA) terms coded for the adverse event (1 or more).\n- [FDA Adverse Events Reporting System Drug Therapy Dates 2021](https://www.johnsnowlabs.com/marketplace/fda-adverse-events-reporting-system-drug-therapy-dates-2021)\n\t- The FDA Adverse Event Reporting System (FAERS) contains information on medication errors, quality complaints and drug-related adverse events that were submitted to FDA (U.S. Food and Drug Administration) in 2021. The drug therapy dates dataset contains drug therapy start dates and end dates for the reported drugs.\n- [FDA Adverse Events Reporting System Patient Outcome 2021](https://www.johnsnowlabs.com/marketplace/fda-adverse-events-reporting-system-patient-outcome-2021)\n\t- The FDA Adverse Event Reporting System (FAERS) contains information on medication errors, quality complaints and drug-related adverse events that were submitted to FDA (U.S. Food and Drug Administration) in 2021. The patient outcome file contains information on patient outcomes for the event (0 or more).\n- [FDA Adverse Events Reporting System Report Source 2021](https://www.johnsnowlabs.com/marketplace/fda-adverse-events-reporting-system-report-source-2021)\n\t- The FDA Adverse Event Reporting System (FAERS) contains information on medication errors, quality complaints and drug-related adverse events that were submitted to FDA (U.S. Food and Drug Administration) in 2021. The report source dataset contains report sources for event.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated\n- Data Updates\n - Data updates support replace-on-update: outdated foreign keys are deprecated, not deleted\n\n**Our data is curated and enriched by domain experts** \n\n Each dataset is manually curated by our team of doctors, pharmacists, public health & medical billing experts:\n- Field names, descriptions, and normalized values are chosen by people who actually understand their meaning\n- Healthcare & life science experts add categories, search keywords, descriptions and more to each dataset\n- Both manual and automated data enrichment supported for clinical codes, providers, drugs, and geo-locations\n- The data is always kept up to date – even when the source requires manual effort to get updates\n- Support for data subscribers is provided directly by the domain experts who curated the data sets\n- Every data source’s license is manually verified to allow for royalty-free commercial use and redistribution.\n\n**Need Help?**\nIf you have questions about our products, contact us at [info@johnsnowlabs.com](mailto:info@johnsnowlabs.com). \n ","terms_of_service":"https://www.johnsnowlabs.com/healthcare-data-library-eula/","documentation_link":"https://www.johnsnowlabs.com/marketplace/fda-drug-adverse-events-reporting-system-faers-2021-data-package","contact_email":"support@johnsnowlabs.com","privacy_policy_link":"https://www.johnsnowlabs.com/privacy-policy/","embedded_notebook_file_infos":[{"id":"fdb1946a-ef60-48ea-8f0a-84882e8d6092","marketplace_file_type":"EMBEDDED_NOTEBOOK","file_parent":{"parent_id":"cdd96a90-1852-483d-83ed-b0604a0aaa22","file_parent_type":"LISTING"},"mime_type":"text/html","download_link":"https://marketplace-file-storage-prod.s3-fips.us-east-1.amazonaws.com/EMBEDDED_NOTEBOOK/LISTING_cdd96a90-1852-483d-83ed-b0604a0aaa22/fdb1946a-ef60-48ea-8f0a-84882e8d6092?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEGgaDnVzLWVhc3QtMS1maXBzIkYwRAIgbC8sWIoYaHR8y7Wq86nPb0pufBl4%2BM4PWpeN%2B5PW9VQCIG7BhAVBDCWTpxFDLeuKYm%2Ff1JLz4iccPMzter0YdhgWKqYECDAQABoMNDE0MzUxNzY3ODI2IgxNAJrHbXaZG20jNfMqgwQyGEb4TsoVOoNvJ%2BaA%2FZ7zeu8hGcHqCeNfblP%2FQo5g6q2aRNtpwtPRKMlhYcIE%2FUQaNT1Lqmyptu32VUQTpzjTTxRVmJrDPkheSCkV06XQ%2FpTMKjU8CC7X1b%2FqYf8CrBwZT7Cv4C5WPjEET38f2A79h0xV6bZgPy28zQWrSeIJ9w6AfZBxiE24%2FVc08zVA0ivCT6lsy7nuU0wcR%2FMg2C9nd1vaMnQ923VpD21CKfVVYcrNWL2b%2FQ%2BNAO%2BxPjPgjiN6UPQbvbvZxkSdhgrGtecIg9NgqGkMcvM5VLTDtdC1kdeZetfOC2obtA%2BT%2F0gy3tIDo6WM5hcQfw743%2FAU9oaGnRBl%2BwoxRRDCZA%2F4jnPdluICSI7NCr6C3HHOe4CSZJjRihczxPA%2FgQczAVjAPs6CrK9N9pIzt0vNJpGUTgg4AoxftTvGfciDREilSctPIcBk6GwB%2Bzt4wbtNCmEPU%2BFD46rc6CWVQmmLwbKOeHw70ipMVFlOc8NlXkEtGO2vu6H44MstSSc6daOVUYUavQSGZ6jGhEjsP2YGz8KDm9XQotB%2FtyVBqiZV01OKqDmmBJA%2FXjUpSJ8M%2FeJXhe5m%2BKcxzNObtoJRI7jci3Tpt08XZyXZ7%2FyIV8H30ya468X3cfkTXrm972uga3z7iXAdubyEHnQIua%2FuvtwOHILi6O5D1Me2MzCPvcnXBjqTAYDoMWBt%2B3AVFuqSs2dmDQNBnvEhXfMgTedKLD5PJHb3aIl15iBkLaNu36GYnVCp9ZU2lSYcTbY5QwNnNeTfJTNy9bKhwzru6Sg%2BslhN%2BTS0zkTn%2BeLMY4tHgPEtEZ2cp%2FCk6GwqswvnYVKHjwO8q%2B1Mx8lc08fVEfarE9dke1GGFqoddx%2Bq%2BZUyHbDlAVfrCmf0yw%3D%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20261110T000135Z&X-Amz-SignedHeaders=host&X-Amz-Expires=86400&X-Amz-Credential=ASIAWA6KKHEJORWBH4N3%2F20261110%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=49c7d52b77b77043f59e2b8dd70a49b36fc6f873efe54160d76b1a7a898a2350","created_at":1682537709017,"updated_at":1682537709017,"display_name":"EDA Notebook","status":"FILE_STATUS_PUBLISHED"}],"assets":["ASSET_TYPE_DATA_TABLE"]},"provider_summary":{"name":"John Snow Labs","description":"John Snow Labs, an AI and NLP for healthcare company, provides state-of-the-art software, models, and data to help healthcare and life science organizations build, deploy, and operate AI projects."},"popularity":140},{"id":"ef2acb9d-e296-4309-8332-99963a375785","summary":{"name":"Egocentric Video Dataset — 4,050 Hours of First-Person Videos for Physical AI, Robotics & ...","subtitle":"4,050-hour egocentric video dataset with 3D pose data, IMU signals, and 13 scripted activity scenarios. Captured via ...","share":{"name":"unidata_7db58af8_169c_431e_bc81_c8379a92ddda_samples_share","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1778766927786,"updated_at":1778766927786,"categories":["OTHER"],"listingType":"PERSONALIZED","provider_id":"a6668567-8ddc-4746-9958-8edbc3807a14"},"detail":{"description":"Overview\n\nThe Egocentric Video Dataset is a large-scale, multimodal collection produced by Unidata, containing 4,050 hours of first-person egocentric videos of daily household activities recorded in home environments. The dataset is purpose-built for Physical AI development, robotic systems training, manipulation tasks research, egocentric vision benchmarking, and human motion analysis.\n\nUnlike existing egocentric datasets that rely on synthetic data or laboratory setups, this collection captures genuine human motion variability in naturalistic home settings — covering kitchens, bathrooms, living rooms, and other everyday spaces. Varied recording speeds, hardware configurations, and hand visibility conditions make it a practical foundation for building models that generalize to real-world deployment.\n\nHardware Configuration\n\nThe dataset was captured using two complementary hardware setups:\n- Pico + Motion Trackers — 2,321 hours (57.3% of total footage): egocentric video from the Pico 4 Ultra VR headset combined with body motion tracker data, covering natural-speed domestic scenarios, slow-motion recordings with both hands always in frame, and real-speed object transfer tasks.\n- Zed + Pico + Motion Trackers — 1,729 hours (42.7% of total footage): scripted object transfer scenarios captured simultaneously via stereo Zed cameras and Pico VR headset, providing both spatial depth and egocentric first-person perspective in a single synchronized recording.\n\nSensor & Orientation Data\n\nQuaternion-based orientation is derived from onboard sensor fusion across all recordings, providing 3D pose estimations without the need for external optical tracking. IMU signals are captured alongside video, enabling researchers to analyze angular velocity, linear acceleration, and motion trajectories in parallel with visual data. This multimodal structure supports egocentric tracking, hand-object interactions analysis, and 3D reconstructions from first-person perspectives.\n\nScripted Activity Scenarios\n\nThe dataset covers 13 structured daily activity scenarios. Each is designed for cyclical repetition, enabling high-density data collection per session and consistent action boundaries for annotation.\n\n- Sorting unsorted items to designated locations (800 hours)\n- Arranging new products by category on a display shelf (800 hours)\n- Collecting items from a table into a container (400 hours)\n- Transferring items from a drawer to a table (400 hours)\n- Wardrobe, table, and bag — three-point object transfer (400 hours)\n- Transport box and display table (400 hours)\n- Folding fabric items into a stack (400 hours). \n- Lids, cookware, and kitchen drawers (200 hours)\n- Transferring bulk goods with a spoon (50 hours)\n- Transferring objects with tongs (50 hours)\n- Two-handed sorting (50 hours)\n- Assembly and disassembly of small-part constructions (50 hours)\n\nTechnical Specifications\n\n- Total footage: 4,050 hours\n- Number of scenarios: 13\n- Video sources: Pico 4 Ultra VR headset (egocentric); Zed stereo cameras (spatial depth)\n- Hardware setups: Pico + Trackers (2,321 hours); Zed + Pico + Trackers (1,729 hours)\n- Recording speeds: real-time and slow-motion\n- Hand visibility: as needed / both hands always in frame (slow-motion subset)\n- Orientation format: quaternions from onboard sensor fusion\n- Sensor data: IMU signals (accelerometer, gyroscope, magnetometer)\n- Annotation format: .txt per recording\n- Environments: kitchen, bathroom, living room, and other home settings\n\nUse Cases\n- Physical AI & Robotics. First-person video paired with quaternion orientation and IMU signals gives robotic systems the spatial and temporal context needed for learning manipulation tasks. The slow-motion recordings expose detailed hand kinematics unavailable in standard-speed footage, making the dataset directly applicable to training robotic arms for pick-and-place and object transfer operations. Thirteen scripted scenarios — from spoon transfers to assembly tasks — cover the range of daily household actions that real-world robotic systems must recognize and replicate.\n- Egocentric Vision & Action Recognition. Researchers building egocentric action recognition and first-person video understanding models benefit from recordings across different speeds, hand visibility conditions, and hardware setups — a level of intra-dataset variability that most existing egocentric datasets do not offer. The combination of hand-object interactions, multimodal data, and 3D pose estimations supports more accurate activity recognition across dynamic scenes in home environments.\n- Healthcare & Motor Rehabilitation. Detailed hand motion data across scripted and naturalistic scenarios is directly applicable to occupational therapy and motor rehabilitation research. The slow-motion recordings make fine-grained movement patterns visible in ways that real-time recording misses. Pose estimations and egocentric tracking of hand-object interactions provide quantifiable visual data for assessing motor performance and patient progress.\n- Augmented & Virtual Reality Development. VR and AR developers training hand tracking and gesture recognition algorithms can use this egocentric data to build models grounded in real home conditions. IMU signals and quaternion orientation from Pico VR headsets directly reflect how hands move during daily activity, supporting the first-person perspectives and motion fidelity that immersive virtual applications require.\n\nCompliance & Data Storage\n\nAll recordings were collected under informed consent from participants. The dataset complies with GDPR and applicable data protection regulations. Data is stored on AWS cloud infrastructure certified to ISO 27001 and ISO 27701 standards.\nSummary\n\nThe Egocentric Video Dataset is a 4,050-hour multimodal collection of first-person egocentric videos and synchronized sensor data, produced by Unidata for Physical AI, robotics, egocentric vision, and human motion research. Thirteen activity scenarios — covering sorting, transferring, folding, assembly, two-handed manipulation, and tool use — combined with quaternion orientation, IMU signals, and dual hardware coverage (Pico + Zed), make this dataset a comprehensive resource for teams building the next generation of Physical AI and robotic systems.","terms_of_service":"https://unidata.pro/privacy-policy","contact_email":"marketplace@datarade.ai","privacy_policy_link":"https://unidata.pro/privacy-policy","assets":["ASSET_TYPE_DATA_TABLE"]},"provider_summary":{"name":"Unidata","description":"Unidata (https://unidata.pro/) specializes in data collection, delivering high-quality labeled datasets for iBeta, Robotics, Biometrics, Kids Safety, Medical Imaging, Smart City, and many other domains — as well as annotation and LLM training to power your AI and ML to the next level.\r\nWe provide end-to-end AI data solutions trusted by 200+ corporate clients worldwide. Our team of 1,100+ labelers and AI experts labels over 920,000 files daily across text, audio, video, and image data — with 3-tier quality control and full legal compliance (AWS ISO 27001/27701).\r\nOur services cover the full cycle: custom data collection via the Prolific platform, computer vision and audio annotation, 3D point cloud labeling, NLP & LLM training, geospatial annotation, and AI model testing. Data is legally sourced, precisely matched to your requirements, and delivered fast — with a dedicated PM and SLA guarantee.\r\nExplore 70+ ready-made datasets and start building smarter AI today — free estimate within 24h."},"popularity":1296},{"id":"671aa0cd-7820-4a78-9ec6-ddc82b0b4935","summary":{"name":"SAMPLE Global Country Risk Dataset | Daily Monitoring | +200 Countries | Macroeconomic & ...","subtitle":"This dataset showcases country risk assessments for 20 companies across various global regions, providing ...","share":{"name":"coface_business_informati_e7a08e10_2d2d_49f4_9436_2d0ef0dc3a29_samples_share","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1779285906658,"updated_at":1779285906658,"categories":["OTHER","FINANCIAL"],"listingType":"STANDARD","provider_id":"ab33ff81-b391-4b26-bb1d-f4b9bbbffbda"},"detail":{"description":"Country Risk Assessment helps businesses to confidently evaluate global markets by incorporating country evaluation into strategic planning. Analysing trends over time to forecast and proactively plan for potential market shifts.\n\nCountry Risk Assessment is an estimate of the average credit risk of a country’s businesses. It is drawn up based on macroeconomic, financial and political data.\nIt offers:\n- An indication of a country’s potential influence on businesses’ financial commitments. \n- Insight into the economic and political environment that could impact credit risk.\n\n\nDataset Structure and Content:\nAssessment Coverage: 20 sample companies with country risk evaluations\nGeographic Diversity: Multiple countries represented via ISO-3166 alpha2 country codes.\n\nRisk Classification System:\nThe dataset employs a standardized A-E rating scale to categorize country risk levels:\nA1: Very good macroeconomic outlook with stable political context and quality business climate (lowest default probability)\nA2: Good macroeconomic outlook with generally stable political environment\nA3: Satisfactory outlook with some potential shortcomings\nA4: Reasonable default probability with potential economic weaknesses\nB: Uncertain economic outlook with potential political tensions\nC: Very uncertain outlook with potential political instability\nD: Highly uncertain outlook with very unstable political context\nE: Extremely uncertain outlook with extremely difficult business conditions (highest default probability)\n\nApplication Context:\nThis sample demonstrates how country risk assessments can be systematically documented and tracked over time. Each assessment includes comprehensive evaluations of the macroeconomic environment, political stability, and business climate factors that directly influence payment behavior and default probabilities.\nThe dataset structure allows for both current and historical tracking, enabling trend analysis and comparative risk evaluation across different national markets. It serves as a representative example of how comprehensive country risk data can be organized and utilized for strategic business decision-making.\nNote: This is sample data intended to demonstrate the structure and capabilities of a country risk assessment system.\n\nLearn More\nFor a complete demonstration of our Country Risk Assessment capabilities or to discuss how our system can be integrated with your existing processes, please visit https://business-information.coface.com/economic-insights to request additional information.","terms_of_service":"https://www.coface.com/personal-data-protection","contact_email":"marketplace@datarade.ai","privacy_policy_link":"https://www.coface.com/personal-data-protection","assets":["ASSET_TYPE_DATA_TABLE"]},"provider_summary":{"name":"Coface Business Information","description":"For nearly 75 years Coface, a world leader in credit insurance, has been helping to make businesses durable and contributing to the creation of sustainable wealth worldwide.\r\n\r\nEvery day, the Group’s 5,200 employees secure the sales made by our 100,000 client companies in over 200 countries.\r\n \r\nIn close partnership with them, they advise at every stage of their business life cycle, helping to anticipate and evaluate their risks and helping them make the right decisions."},"popularity":772},{"id":"d3e4d640-492c-430a-92ae-c3946faa0140","summary":{"name":"For BioPharma: Generate Real World Evidence","subtitle":"Applications for RWE generation: OMOP standardization, cohorting, HEOR, abstraction, and pharmacovigilance.","setting":{"visibility":"PUBLIC"},"created_at":1788556431411,"updated_at":1790101639916,"categories":["HEALTH","PUBLIC_SECTOR","SCIENCE_AND_RESEARCH"],"listingType":"PERSONALIZED","provider_id":"8a664186-91cb-4cc1-aa1f-080da273763d"},"detail":{"description":"**Overview**\nBuild real world evidence applications on the customer's Databricks lakehouse. Capabilities include data standardization to OMOP, patient cohorting with propensity score matching, clinical data abstraction from unstructured sources, and pharmacovigilance workflows for drug safety and adverse event detection. All datasets and models stay in the customer's Databricks environment, supporting HEOR programs on governed, analysis-ready data.\n\n**Use cases**\n- Standardize disparate sources to OMOP for research-ready datasets\n- Build patient cohorts with propensity score matching on the unified record\n- Accelerate HEOR studies with analysis-ready, de-identified data\n- Abstract clinical data from unstructured documentation at scale\n- Detect adverse events and support pharmacovigilance reporting\n\n**Additional Insights**\nStandardization, cohorting, and model workloads all run in the customer's Databricks environment, driving data engineering and ML consumption. \n\nThe workflows listed are representative; Concurrence builds custom applications for any workflow in this outcome area on the customer's existing Databricks foundation.","terms_of_service":"https://www.concurrence.com/terms-of-service","documentation_link":"https://docs.concurrence.com","privacy_policy_link":"https://www.concurrence.com/privacy","assets":["ASSET_TYPE_DATA_TABLE","ASSET_TYPE_MEDIA","ASSET_TYPE_MODEL","ASSET_TYPE_NOTEBOOK"],"license":"","tags":[{"tag_name":"LISTING_TAG_TYPE_TASK","tag_values":["ASSERTION_STATUS","EMBEDDINGS","ENTITY_RESOLUTION","NAMED_ENTITY_RECOGNITION","QUESTION_ANSWERING","RELATION_EXTRACTION","SUMMARIZATION","TEXT_CLASSIFICATION","TEXT_GENERATION","TRANSLATION","ZERO_SHOT_CLASSIFICATION","CODE_GENERATION","AUTOMATIC_SPEECH_RECOGNITION","TEXT_TO_SPEECH","DOCUMENT_QUESTION_ANSWERING"]}]},"provider_summary":{"name":"Concurrence","description":"Concurrence is the platform for healthcare organizations to deploy custom AI for clinical and operational workflows on a unified data foundation."},"popularity":624},{"id":"a61681cb-0bc5-4ecf-b834-98ef7036b5e1","summary":{"name":"ＣＯ－ＯＤＥ 人流データ","subtitle":"このデータセットでは、国土交通省が公開している人流データを加工したサンプルデータを収録しています。\n（期間は2021年1月分、地点は東京都に絞ったデータとなります）","setting":{"visibility":"PUBLIC"},"created_at":1697598391752,"updated_at":1699581888315,"categories":["ADVERTISING_AND_MARKETING","COMMERCE","GEOSPATIAL","RETAIL","TRANSPORTATION_AND_LOGISTICS"],"listingType":"PERSONALIZED","provider_id":"ccb1eead-54d5-4f89-9cdc-fca2645ff069"},"detail":{"description":"**Overview**\nこのデータセットでは、国土交通省が公開している人流データを加工したサンプルデータを収録しています。\n（期間は2021年1月分、地点は東京都に絞ったデータとなります）\nＣＯ－ＯＤＥをご契約いただくと、2019年～2021年の年月毎の1km別メッシュ及び市区町村別の人流データを取得することができます。\nまた、休日や平日、昼間や深夜の区分別でデータを利用することができます。\nＣＯ－ＯＤＥ(コ・オード)の詳細については、以下のリンクから公式ページを参照ください。\nhttps://www.zdh.co.jp/products-services/external-data/co-ode/\n\n**Use cases**\n人流データを使うことで、エリア毎の滞在人口を把握することができ、集客の予測や広告費用の最適化等に活用できます。\n・指定地域の滞在人口分析\n・指定地域でのイベント施策の効果検証や今後の戦略立案\n・イベント実施場所の選定\n\n**Product details**\nSample Tables:\n-人流_1kmメッシュ\n-人流_1kmメッシュマスタ\n-人流_市区町村\n\nSample Fields:\n-年月\n-平休日\n-時間帯\n-都道府県コード\n-市区町村コード\n-都道府県\n-政令市・郡\n-市区町村\n-居住地区分\n-平均滞在人口\n\n**Additional Insights**\n出典：「全国の人流オープンデータ」（国土交通省）（https://www.geospatial.jp/ckan/dat\naset/mlit-1km-fromto）を加工して作成","terms_of_service":"https://www.zdh.co.jp/products-services/external-data/co-ode/","documentation_link":"","privacy_policy_link":"https://www.zdh.co.jp/privacy/","assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"Zeal Corporation","description":"株式会社ジールは、経営を支えるデータ基盤の構築とDX人材の育成に注力し、従来のビジネスインテリジェンスサービスを超えた活動を推進しています。 そのサービスの一つにオープンデータ提供サービス「CO-ODE」があり、オープンデータとお客様独自のデータを組み合わせることでデータ活用を強力にサポートします。"},"popularity":687},{"id":"d92fd932-9cfc-4438-bb8b-a22609c6eeaf","summary":{"name":"US Population Forecast for Healthcare","subtitle":"Comprehensive Population Forecast with Age and Gender Dataset from 2010 to 2035.","share":{"name":"share-age-gender-population-forecast","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1743546338901,"updated_at":1743546338901,"categories":["DEMOGRAPHICS","HEALTH"],"listingType":"STANDARD","provider_id":"880a243e-7a4c-4949-b389-e1abc4110411"},"detail":{"description":"Unlock the power of demographic insights with this comprehensive dataset, offering a detailed exploration of age and gender population forecasts. Meticulously curated, this resource provides an in-depth analysis of population trends spanning from 2010 to 2023, complemented by projections extending to 2035. Whether you're a business seeking to stay ahead of market shifts, a researcher delving into demographic patterns, or a policymaker shaping future strategies, this dataset equips you with the foresight necessary to make well-informed decisions. Leverage this invaluable resource to gain a comprehensive understanding of evolving demographic landscapes and position yourself for success in an ever-changing world.\n\n### Dataset Components:\n\n- All demographic data from the latest U.S. Census Bureau, NASA and Columbia University research, enhanced with META ( Facebook audience insights) with monthly updates to keep you informed about evolving trends.\n- Enhanced data quality by incorporating additional proprietary data, accounting for various factors contributing to population growth using machine learning methods.\n- Metadata mapping linking attribute names to table IDs, zip codes to cities and counties, and Core Based Statistical Areas (CBSAs).\n\n## Why Choose Aterio\n\n- Delve into the past and understand how age demographics have evolved over the years. Our dataset meticulously tracks population trends across various age groups, enabling users to grasp historical patterns and trends.\n\n- Gain a glimpse into the future with our precise population forecasts. Projected with meticulous attention to detail, these forecasts provide invaluable insights into future population dynamics, aiding in strategic planning and decision-making.\n\n- Our dataset categorizes age demographics into four distinct brackets, facilitating granular analysis and tailored insights. The age groups are segmented as follows:\n- **0-18**: Capture the dynamics of the youth population, essential for understanding future consumer behaviors and educational needs.\n- **19-44**: Explore the prime working-age population, crucial for workforce planning, market targeting, and economic forecasting.\n- **45-65**: Gain insights into the mature demographic segment, pivotal for healthcare planning, retirement services, and lifestyle trends analysis.\n- **66+**: Analyze the senior population, important for understanding retirement trends, healthcare needs, and senior lifestyle preferences.\n\n### Monthly Updates\nStay ahead of the curve with our dataset's monthly updates. 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This meticulously compiled dataset, sourced from official statistical sources, including Turkish Statistical Institute (TURKSTAT), offers a detailed insight into the population landscape of Turkey at the district level. \n\nIf you require data at a less granular level, such as an Uber grid or square grid, please don't hesitate to get in touch with us at *contact@geolocet.com*. We understand that different projects may have varying data needs, and we are here to tailor our data to match your specific requirements.\n\n ## **Key Features:**\n---\n* **Population Data Turkey:** Gain access to the latest and most accurate population statistics for Turkey, meticulously organized by district.\n\n* **Demographic Insights:** Dive deep into Turkey’s demographic landscape with data on 127 attributes, including: \n* Total population,\n* Gender distribution,\n* 5-years age bands distribution,\n* Marital status distribution,\n* Marital status divided by gender distribution,\n* Marital status split by age distribution,\n* Marital status divided by city/village and gender distribution,\n* Literacy\n* Literacy split by gender distribution,\n* Family structure distribution.\n\nAll demographic attributes are available at the district level.\n\n* **Geodemographic Insights:** Unlock geodemographic data Turkey-wide to inform location-based decision-making, marketing strategies, and policy formulation.\n* **Seamless Data Access:** Our data is available for instant download in CSV format. No lengthy sign-up processes or NDAs required on our side – just download the data you need.\n* **Data Samples for Evaluation:** To ensure you're getting exactly what you need, we provide free downloadable samples in CSV format for data evaluation.\n* **Data accuracy:** Our datasets are created using the latest demographics data available in national statistics bureaus, guaranteeing accuracy and reliability.\n* **Best-in-Class Quality:** We guarantee best-in-class quality, backed by over two decades of experience in modeling demographics data and an outstanding quality management process.\n* **Competitive Pricing:** We maintain the lowest prices on the market, ensuring that you get the most cost-efficient demographics data acquisition process.\n---\nAdditionally, for those looking to enrich their analysis or project, we offer:\n* **[Administrative Boundaries](https://geolocet.com/products/turkey-admin-level-6-districts):** If you require geographical boundaries (polygons) for the municipalities of Turkey, consider exploring our Administrative Boundaries product, which seamlessly integrates with this dataset.\n\n* **[Point of Interest (PoI) Data](https://geolocet.com/pages/aggregated-points-of-interest-poi-data):** If you're interested in Points of Interest data, please don't hesitate to contact us for customized datasets to enhance your geographical analysis.\n\n* **[Data Customization](https://geolocet.com/pages/data-services-by-geolocet):** We understand that your project may have specific requirements. Feel free to get in touch with us if you need any modifications or specific data formats such as Excel, JSON, GeoJSON, or Shapefile. We are here to tailor the data to your needs.\n\nMake informed decisions, conduct precise research, and unlock the potential of Turkey's demographic landscape with our comprehensive dataset. Trust in the accuracy and reliability of our official statistical data to drive your projects forward.\nContact us at *contact@geolocet.com* today to access this invaluable resource and explore Turkey census data like never before.\n\n[Geolocet LTD](https://geolocet.com)\n","terms_of_service":"https://geolocet.com/pages/terms-of-use","documentation_link":"","privacy_policy_link":"https://geolocet.com/pages/privacy-policy","geographical_coverage":"[\"Europe\"]","cost":"PAID","assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://geolocet.com/pages/terms-of-use"},"provider_summary":{"name":"Geolocet LTD","description":"Geolocet provides premium geospatial data for retail analysis, GIS, and market planning. 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Make informed purchasing decisions based on peer feedback and assurance.\n\nDataset Benefits:\n\n-Empowers businesses, researchers, and analysts.\n-Gain insights for market analysis, pricing strategies, customer behavior, and competitor evaluation.\n-Crucial attributes include product ID, title, ratings, reviews, pricing details, and seller information.","terms_of_service":"https://brightdata.com/license","documentation_link":"https://brightdata.com/products/datasets/fashion","privacy_policy_link":"https://brightdata.com/privacy","embedded_notebook_file_infos":[{"id":"35f43aff-09f4-4f9a-947d-43256c6c240e","marketplace_file_type":"EMBEDDED_NOTEBOOK","file_parent":{"parent_id":"9ffa95d5-ec55-40ba-9559-3bcb9cf694a1","file_parent_type":"LISTING"},"mime_type":"text/html","download_link":"https://marketplace-file-storage-prod.s3-fips.us-east-1.amazonaws.com/EMBEDDED_NOTEBOOK/LISTING_9ffa95d5-ec55-40ba-9559-3bcb9cf694a1/35f43aff-09f4-4f9a-947d-43256c6c240e?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEGgaDnVzLWVhc3QtMS1maXBzIkYwRAIgbC8sWIoYaHR8y7Wq86nPb0pufBl4%2BM4PWpeN%2B5PW9VQCIG7BhAVBDCWTpxFDLeuKYm%2Ff1JLz4iccPMzter0YdhgWKqYECDAQABoMNDE0MzUxNzY3ODI2IgxNAJrHbXaZG20jNfMqgwQyGEb4TsoVOoNvJ%2BaA%2FZ7zeu8hGcHqCeNfblP%2FQo5g6q2aRNtpwtPRKMlhYcIE%2FUQaNT1Lqmyptu32VUQTpzjTTxRVmJrDPkheSCkV06XQ%2FpTMKjU8CC7X1b%2FqYf8CrBwZT7Cv4C5WPjEET38f2A79h0xV6bZgPy28zQWrSeIJ9w6AfZBxiE24%2FVc08zVA0ivCT6lsy7nuU0wcR%2FMg2C9nd1vaMnQ923VpD21CKfVVYcrNWL2b%2FQ%2BNAO%2BxPjPgjiN6UPQbvbvZxkSdhgrGtecIg9NgqGkMcvM5VLTDtdC1kdeZetfOC2obtA%2BT%2F0gy3tIDo6WM5hcQfw743%2FAU9oaGnRBl%2BwoxRRDCZA%2F4jnPdluICSI7NCr6C3HHOe4CSZJjRihczxPA%2FgQczAVjAPs6CrK9N9pIzt0vNJpGUTgg4AoxftTvGfciDREilSctPIcBk6GwB%2Bzt4wbtNCmEPU%2BFD46rc6CWVQmmLwbKOeHw70ipMVFlOc8NlXkEtGO2vu6H44MstSSc6daOVUYUavQSGZ6jGhEjsP2YGz8KDm9XQotB%2FtyVBqiZV01OKqDmmBJA%2FXjUpSJ8M%2FeJXhe5m%2BKcxzNObtoJRI7jci3Tpt08XZyXZ7%2FyIV8H30ya468X3cfkTXrm972uga3z7iXAdubyEHnQIua%2FuvtwOHILi6O5D1Me2MzCPvcnXBjqTAYDoMWBt%2B3AVFuqSs2dmDQNBnvEhXfMgTedKLD5PJHb3aIl15iBkLaNu36GYnVCp9ZU2lSYcTbY5QwNnNeTfJTNy9bKhwzru6Sg%2BslhN%2BTS0zkTn%2BeLMY4tHgPEtEZ2cp%2FCk6GwqswvnYVKHjwO8q%2B1Mx8lc08fVEfarE9dke1GGFqoddx%2Bq%2BZUyHbDlAVfrCmf0yw%3D%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20261110T000135Z&X-Amz-SignedHeaders=host&X-Amz-Expires=86400&X-Amz-Credential=ASIAWA6KKHEJORWBH4N3%2F20261110%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=9bb6896e5bc13d1861bec8e9d1fbff0f40bed16f7fb27603d138049089d093f3","created_at":1708427992184,"updated_at":1708427992184,"display_name":"Untitled Notebook 2024-02-20 13:14:08","status":"FILE_STATUS_PUBLISHED"}],"geographical_coverage":"[\"Global\"]","cost":"FREE","assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"Bright Data","description":"Bright Data is the world’s #1 web data platform.\nFortune 500 companies, academic institutions, and small businesses rely on Bright Data’s scalable platform for web scraping infrastructure to retrieve crucial public web data in the most efficient, reliable, and flexible way, so they can research, monitor, and analyze data to make better decisions.\nThe platform is used worldwide by 20,000+ customers in nearly every industry. 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Yet, building strong leaders is essential for driving innovation, engaging employees and ensuring long-term success.\nThe Leadership Insights is a practical framework designed to simplify and strengthen this journey. Using the Bronze–Silver–Gold architecture, it combines assessments, coaching and hands-on learning to deliver measurable outcomes. It’s scalable too, making it well-suited for enterprise-wide leadership programs.\nThrough experiential learning, peer collaboration and data-driven insights, the Leadership Navigator helps leaders sharpen decision-making, bring teams into alignment and spark innovation. With interactive workshops, dashboards and toolkits, it equips both individuals and organizations to build lasting leadership maturity—ensuring growth that is consistent, sustainable and future-ready.\n\n**Business Challenge & Solution**\n**The Leadership Development Challenge** – Common Pain Points\n\n- **Fragmented Practices**: Different leadership styles and approaches across teams often create misalignment, confusion, and reduce overall operational efficiency significantly.\n- **Lack of Visibility**: Organizations frequently struggle to accurately assess leadership effectiveness, track progress, and identify critical areas for leadership development.\n- **Reactive Approach**: Leaders spend excessive time addressing immediate problems and urgent issues instead of focusing on long-term strategy and growth initiatives.\n- **Engagement Risks**: Weak or inconsistent leadership culture can lower employee morale, reduce engagement, and increase overall attrition rates across the organization.\n\n**The Leadership Navigator Solution**\n\n- **Unified Framework**: Provides a clear, consistent roadmap that aligns leadership practices, priorities, and behaviors across all teams for better organizational impact.\n- **Data-Driven Insights**: Built-in assessments, dashboards, and analytics provide measurable visibility into leadership performance, development areas, and overall organizational growth.\n- **Proactive Development**: Structured coaching, peer collaboration, and experiential learning programs ensure sustainable, long-term leadership development and meaningful, lasting impact.\n\n**Medallion Architecture Implementation**\n**Bronze Layer – Raw Leadership Inputs**\n\n- **Personal Assessments**: Comprehensive tools designed to identify individual leadership styles, core strengths, weaknesses, and areas for emotional intelligence development.\n- **Self-Awareness Logs**: Reflection journals that capture daily behaviors, personal insights, challenges, and potential blind spots to support meaningful leadership growth.\n- **Baseline Metrics**: Foundational feedback collected from peers, managers, and 360° surveys to establish a clear, measurable starting point for development.\n\n**Silver Layer – Cleansed & Standardized Leadership Skills**\n\n- **Skill Development Modules**: Focused training programs covering communication, delegation, conflict management, decision-making, and other essential leadership competencies for consistent skill building.\n- **Peer Collaboration**: Cross-team activities, role-plays, and collaborative problem-solving exercises designed to reinforce learning and enhance practical leadership application.\n- **Feedback Normalization**: Regular, structured feedback loops with mentors and coaches to guide leaders toward continuous improvement and consistent professional growth.\n\n**Gold Layer – Business-Ready Leadership Outcomes**\n\n- **Impact Dashboards**: Real-time dashboards providing metrics that track team performance, employee engagement, leadership effectiveness, and overall cultural alignment across the organization.\n- **Strategic Leadership Tools**: Practical frameworks and toolkits to support change management, foster innovation, and develop strategic vision-building capabilities in leaders.\n- **Predictive Insights**: Data-driven insights to identify high-potential leaders, support succession planning, and ensure organizational readiness for future challenges.\n\n**Marketplace Deliverables**\n Core Leadership Navigator Package – Implementation Assets\n\n- **Leadership Playbooks**: Step-by-step exercises, reflection guides, and practical examples designed to consistently reinforce daily leadership practices across teams and individuals.\n- **Assessment Templates**: Ready-to-use 360° feedback forms, self-assessments, and tracking tools to effectively monitor and measure leadership growth over time.\n- **Workshop Modules**: Structured content for Bronze, Silver, and Gold layers, including detailed facilitation guides and participant engagement activities for maximum learning.\n- **Leadership Dashboards**: Real-time progress tracking, analytics, and visual insights that provide clear, measurable impact of leadership development initiatives across the organization.\n\n**Documentation & Training**\n\n- **Implementation Guide**: A comprehensive, step-by-step rollout playbook designed to guide HR and leadership teams through effective program implementation.\n- **Facilitator Toolkit**:Extensive manuals and practical resources created to empower coaches and training facilitators to deliver impactful sessions successfully.\n- **Participant Resources**: Reflection journals, self-paced e-learning modules, and personalized growth maps designed to support continuous leadership development and skill building.\n\n**Why Choose Leadership Insights**\n\n- **Expert-Designed**: Developed by experienced leadership coaches and organizational psychologists to provide practical, evidence-based guidance that drives lasting leadership results.\n- **Immediate Impact**: Structured and well-defined development journeys designed to deliver measurable improvements in leadership skills within just a few weeks.\n- **Scalable Framework**: A flexible and adaptable framework that can support individual leaders, cross-functional teams, or enterprise-wide leadership development programs.\n\n\nTransform leadership from fragmented practices into insight-driven, growth-focused excellence with Leadership Navigator. Built on the Bronze–Silver–Gold framework, this solution delivers stronger alignment, measurable growth and a foundation for future-ready leadership.\n\nHere are some articles Dataplatr published on Medium — please check these out:\n[Overview of Contact Center Analytics: Unlock insights with Dataplatr](https://medium.com/@dataplatr/an-overview-of-contact-center-analytics-unlock-insights-with-dataplatr-part-1-5b9428719f2c) \n[Contact Center Analytics : Unlock Call Details KPIs and Dashboard for actionable insights](https://medium.com/@dataplatr/contact-center-analytics-unlock-call-details-kpis-and-dashboard-for-actionable-insights-part-2-b452e0bb0961) \n[Contact Center Analytics: Unveiling the Power of WFM with Strategies, Insights, and Impact](https://medium.com/@dataplatr/contact-center-analytics-unveiling-the-power-of-wfm-with-strategies-insights-and-impact-part-4-68878706c5f2) \n[Contact Center Analytics: A Comprehensive Guide to Quality Assurance (QA ) and Analytics](https://medium.com/@dataplatr/contact-center-analytics-a-comprehensive-guide-to-quality-assurance-qa-and-analytics-part-5-cb7f60f9ed3c)\n\nNote: By clicking '**Get Access**', you agree to our **Terms of Service**. You acknowledge that **Dataplatr Corp** will recognize your organization as a customer/user for promotional and reference purposes.\n\nContact us at: chandra.reddy@dataplatr.com\nFor consultations or custom inquiries: https://dataplatr.com/contact-us\n\n\n","terms_of_service":"https://docs.google.com/forms/d/e/1FAIpQLSdnlNmSfrFRQDa_ToOL3ydTOPcWI3m3lWor2plLbiqF1JRINg/viewform","documentation_link":"https://dataplatr.com","privacy_policy_link":"https://dataplatr.com","embedded_notebook_file_infos":[{"id":"4c8de4f4-b894-4f79-aec1-22c787df9bbc","marketplace_file_type":"EMBEDDED_NOTEBOOK","file_parent":{"parent_id":"a7ae1efd-36bb-4c16-9356-eca5ae39d945","file_parent_type":"LISTING"},"mime_type":"text/html","download_link":"https://marketplace-file-storage-prod.s3-fips.us-east-1.amazonaws.com/EMBEDDED_NOTEBOOK/LISTING_a7ae1efd-36bb-4c16-9356-eca5ae39d945/4c8de4f4-b894-4f79-aec1-22c787df9bbc?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEGgaDnVzLWVhc3QtMS1maXBzIkYwRAIgbC8sWIoYaHR8y7Wq86nPb0pufBl4%2BM4PWpeN%2B5PW9VQCIG7BhAVBDCWTpxFDLeuKYm%2Ff1JLz4iccPMzter0YdhgWKqYECDAQABoMNDE0MzUxNzY3ODI2IgxNAJrHbXaZG20jNfMqgwQyGEb4TsoVOoNvJ%2BaA%2FZ7zeu8hGcHqCeNfblP%2FQo5g6q2aRNtpwtPRKMlhYcIE%2FUQaNT1Lqmyptu32VUQTpzjTTxRVmJrDPkheSCkV06XQ%2FpTMKjU8CC7X1b%2FqYf8CrBwZT7Cv4C5WPjEET38f2A79h0xV6bZgPy28zQWrSeIJ9w6AfZBxiE24%2FVc08zVA0ivCT6lsy7nuU0wcR%2FMg2C9nd1vaMnQ923VpD21CKfVVYcrNWL2b%2FQ%2BNAO%2BxPjPgjiN6UPQbvbvZxkSdhgrGtecIg9NgqGkMcvM5VLTDtdC1kdeZetfOC2obtA%2BT%2F0gy3tIDo6WM5hcQfw743%2FAU9oaGnRBl%2BwoxRRDCZA%2F4jnPdluICSI7NCr6C3HHOe4CSZJjRihczxPA%2FgQczAVjAPs6CrK9N9pIzt0vNJpGUTgg4AoxftTvGfciDREilSctPIcBk6GwB%2Bzt4wbtNCmEPU%2BFD46rc6CWVQmmLwbKOeHw70ipMVFlOc8NlXkEtGO2vu6H44MstSSc6daOVUYUavQSGZ6jGhEjsP2YGz8KDm9XQotB%2FtyVBqiZV01OKqDmmBJA%2FXjUpSJ8M%2FeJXhe5m%2BKcxzNObtoJRI7jci3Tpt08XZyXZ7%2FyIV8H30ya468X3cfkTXrm972uga3z7iXAdubyEHnQIua%2FuvtwOHILi6O5D1Me2MzCPvcnXBjqTAYDoMWBt%2B3AVFuqSs2dmDQNBnvEhXfMgTedKLD5PJHb3aIl15iBkLaNu36GYnVCp9ZU2lSYcTbY5QwNnNeTfJTNy9bKhwzru6Sg%2BslhN%2BTS0zkTn%2BeLMY4tHgPEtEZ2cp%2FCk6GwqswvnYVKHjwO8q%2B1Mx8lc08fVEfarE9dke1GGFqoddx%2Bq%2BZUyHbDlAVfrCmf0yw%3D%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20261110T000135Z&X-Amz-SignedHeaders=host&X-Amz-Expires=86400&X-Amz-Credential=ASIAWA6KKHEJORWBH4N3%2F20261110%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=d6165503762e0b7b7f912851d8359fdc25ff40df24bb1ff2374143389a122175","created_at":1770817948237,"updated_at":1770817948237,"display_name":"CC_Accelerator_Oracle_Leadership_Navigator_V1","status":"FILE_STATUS_PUBLISHED"}],"assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://dataplatr.com"},"provider_summary":{"name":"Dataplatr Corp","description":"Dataplatr Corp specializes in Data Analytics & AI with pre-built data models for Enterprise Applications like SAP, Oracle EBS, Workday, Salesforce to empower businesses to unlock the full potential of their data. Our enterprise data engineering accelerators are designed to expedite the development of data pipelines, data transformation, and integration, saving you time and resources.\n\nOur team of experienced data engineers, scientists and analysts utilize cutting-edge data infrastructure into valuable insights and helps enterprise clients optimize their Sales, Marketing, Operations, Financials, Supply chain, Human capital and Customer experiences."},"popularity":681},{"id":"304e487c-2b6f-4db4-bf17-7c092b21096d","summary":{"name":"Geolocet | Points of Interest Data | Grocery Store Locations - Spain","subtitle":"Complete GIS-ready dataset of 118,549 Spain grocery locations (brands & independents). Optimized for Spark/Databricks.","setting":{"visibility":"PUBLIC"},"created_at":1780473766461,"updated_at":1780473806566,"categories":["GEOSPATIAL","RETAIL"],"listingType":"PERSONALIZED","provider_id":"5d3ddc17-a7e8-4794-a760-13587e1de2c6"},"detail":{"description":"%md\n# **All Supermarket Locations in Spain: Complete Geographic Dataset**\n---\n### **Premium GIS-Ready Retail Location Intelligence Data for Databricks Analytics**\n\nThis high-fidelity geospatial dataset provides a complete, exhaustive, and standardized overview of **118,549** verified grocery store and supermarket locations across Spain. Updated as of **May 30, 2026**, this comprehensive national dataset captures the entire Spanish grocery landscape - encompassing both major branded retail chains and independent local markets. It offers an analysis-ready foundation for full-scale retail site selection, competitive intelligence, logistics optimization, and spatial demographics mapping.\n\nFully compatible with **Apache Spark**, **Delta Lake**, and **Databricks Unity Catalog** workflows.\n\n---\n\n## **Complete National Coverage: Branded & Independent Grocers**\nUnlike top-tier only datasets, this exhaustive collection covers maximum geographic depth across all 17 autonomous communities and 2 autonomous cities (including highly dense areas like Andalucía, Catalunya, and Comunidad de Madrid). It includes tens of thousands of independent local grocers alongside the top 25 national and regional supermarket chains:\n\n- **Major National Supermarkets & Hypermarkets:** \n [Mercadona (1,704 locations)](https://geolocet.com/products/spain-mercadona-grocery), \n [Carrefour (1,682 locations)](https://geolocet.com/products/spain-carrefour-grocery), \n [Dia (2,500 locations)](https://geolocet.com/products/spain-dia-grocery), \n [Alcampo (564 locations)](https://geolocet.com/products/spain-alcampo-grocery), \n [SuperCor (856 locations)](https://geolocet.com/products/spain-supercor-grocery)\n\n- **Leading Discounters (Hard & Soft Discount):** \n [Lidl (752 locations)](https://geolocet.com/products/spain-lidl-grocery), \n [ALDI (543 locations)](https://geolocet.com/products/spain-aldi-grocery), \n [PrimaPrix (290 locations)](https://geolocet.com/products/spain-primaprix-grocery)\n\n- **Regional & Cooperative Powerhouses:** \n [Coviran (2,006 locations)](https://geolocet.com/products/spain-coviran-grocery), \n [SPAR (1,484 locations)](https://geolocet.com/products/spain-spar-grocery), \n [Eroski (1,059 locations)](https://geolocet.com/products/spain-eroski-grocery), \n [Condis (775 locations)](https://geolocet.com/products/spain-condis-grocery), \n [Consum (601 locations)](https://geolocet.com/products/spain-consum-grocery), \n [Coaliment (573 locations)](https://geolocet.com/products/spain-coaliment-grocery), \n [Charter (529 locations)](https://geolocet.com/products/spain-charter-grocery), \n [BM (355 locations)](https://geolocet.com/products/spain-bm-grocery), \n [Unide (345 locations)](https://geolocet.com/products/spain-unide-grocery), \n [Caprabo (310 locations)](https://geolocet.com/products/spain-caprabo-grocery), \n [Ahorramas (308 locations)](https://geolocet.com/products/spain-ahorramas-grocery)\n\n- **Strong Regional Banners:** \n [Claudio (262 locations)](https://geolocet.com/products/spain-claudio-grocery), \n [Froiz (260 locations)](https://geolocet.com/products/spain-froiz-grocery), \n [MasyMas (240 locations)](https://geolocet.com/products/spain-masymas-grocery), \n [Gadis (228 locations)](https://geolocet.com/products/spain-gadis-grocery), \n [Lupa (213 locations)](https://geolocet.com/products/spain-lupa-grocery), \n [Alimerka (174 locations)](https://geolocet.com/products/spain-alimerka-grocery)\n\n**Plus over 80,000 independent, regional, and unbranded grocers** fully mapped and standardized across all communities (e.g., Andalucía: 22,951 sites; Catalunya: 21,757 sites; Comunidad de Madrid: 12,814 sites).\n\n---\n\n## **Technical Specifications & Schema Definitions**\nOptimized for zero-friction schema enforcement when reading via `spark.read.format(\"csv\")`. The structured schema below ensures flawless rendering in Databricks notebooks and high accuracy for LLM semantic indexing.\n\n* **`GUID`** (`STRING`): Unique global identifier for deterministic primary key mapping.\n* **`Title`** (`STRING`): Official store name, brand banner, or independent title of the location.\n* **`Latitude`** (`DOUBLE`): Precise WGS84 latitude coordinate for point-in-polygon & spatial joins.\n* **`Longitude`** (`DOUBLE`): Precise WGS84 longitude coordinate for spatial indexing (H3 / S2).\n* **`Street_No` / `Street`** (`STRING`): Normalized building/house number and street name elements.\n* **`City` / `Postal_Code`** (`STRING`): Core postal addresses and primary municipal settlement names.\n* **`Commune`** (`STRING`): Local municipal administrative boundary designation (Municipio/Comuna).\n* **`Autonomous_Community`** (`STRING`): High-level macro-administrative classification (e.g., Andalucía, Catalunya, Madrid).\n* **`Admin_level_1` / `_2`** (`STRING`): Additional local administrative area and province identifiers.\n* **`Wheelchair`** (`STRING`): Accessibility flags indicating wheelchair mobility support.\n* **`Popularity_Score`** (`INTEGER`): Relative foot-traffic/rating metric graded on a standardized 1-10 scale.\n* **`Opening_Hours`** (`STRING`): Standard daily operational schedules and structured operating text.\n* **`Phone` / `Website`** (`STRING`): Direct communication data, branch-specific landing URLs, and domain vectors.\n\n---\n\n## **Databricks Workflow & Spatial Analytics Use Cases**\n\n- **Whitespace & Underserved Market Analysis:** Query the full 118,549 locations against population density tables to identify true retail deserts and expansion opportunities untouched by major national chains.\n- **H3 Geospatial Indexing:** Convert the precise WGS84 `Latitude` and `Longitude` fields into Uber H3 spatial hexagons (`h3_longlattocell`) to cross-reference with granular Spanish demographic or statistical sector income raster files.\n- **Supply Chain & Last-Mile Logistics Optimization:** Combine store location points with regional administrative boundary layers to optimize delivery routes, distribution warehouse placements, and network expansion footprints.\n\n---\n\n## **Data Evaluation & Custom Ingestion**\n\n### **Evaluate with a Free Sample Dataset**\nVerify the structure, column types, and coordinate precision before full implementation. A comprehensive schema sample is available for download in CSV/Parquet-friendly formats.\n\n*Want deeper market insights?* Learn more about the brand network in our comprehensive report: **[View the Spanish Top Brands Grocery Report](https://geolocet.com/pages/spain-top-brands-grocery-report)**\n\n### **Enterprise Delivery & Tailored Formatting**\nWhile natively available as an analysis-ready CSV for DBFS ingestion, we accommodate enterprise-level data architecture needs. Alternative formats - including **GeoJSON, Esri Shapefiles, PostgreSQL dumps, Excel, or direct Delta sharing profiles** - can be generated. Reach out to our engineering team at **contact@geolocet.com** for integration support or custom European POI data requests.","terms_of_service":"https://geolocet.com/pages/terms-of-use","documentation_link":"","privacy_policy_link":"https://geolocet.com/pages/privacy-policy","assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://geolocet.com/pages/terms-of-use"},"provider_summary":{"name":"Geolocet LTD","description":"Geolocet provides premium geospatial data for retail analysis, GIS, and market planning. We equip businesses with accurate, ready-to-query data to drive spatial analysis, site selection, and customer segmentation.\n\nCore Offerings:\n\nDemographics: Socioeconomic data covering population, age, and employment across diverse geographies.\n\nPoints of Interest (PoI): Datasets spanning retail, healthcare, and transport for catchment analysis.\n\nAdministrative Boundaries: GIS-ready boundaries, including municipalities and postcodes, for territory management.\n\nBrand Locations: Chain location datasets to monitor store networks and market expansion.\n\nWealth Data: Granular economic indicators like purchasing power to identify high-potential markets.\n\nWe source from trusted providers, delivering highly customizable datasets with comprehensive coverage across Europe and worldwide."},"popularity":848},{"id":"fb7cc658-a499-4351-b389-df22ac80cc4f","summary":{"name":"AnthologyAI - Foot Traffic Data - Industry Category Analytics - Groceries CA Sample","subtitle":"A sample of our Foot Traffic - Industry Category Analytics product. Weekly foot traffic metrics for all grocery in CA","share":{"name":"anthology_foottraffic_grocery_ca_sample","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1740671254224,"updated_at":1741012784866,"categories":["RETAIL","GEOSPATIAL"],"listingType":"STANDARD","provider_id":"cf3e8a2d-23a0-496c-8489-880d2aab5e60"},"detail":{"description":"**Foot Traffic Data Sample for Grocery Locations in the state of California in Q1 2024**\n\nUnlock the power of precise foot traffic analytics with AnthologyAI's extensive geolocations and foot traffic dataset, providing you with detailed daily metrics for various brands and industries across the entire United States. 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The sample includes typical weekly foot traffic metrics for all grocery locations in the state of California for Q1 2024.\n\n***Specifications:***\n- **Weekly Foot Traffic Metrics:** Access detailed consolidated foot traffic information for all grocery locations in the state of California for Q1 2024 on a weekly basis.\n- **Grocery Sub-Category Level details:** Includes aggregated measures by the following NAICS subcategories related to groceries\n - ***Supermarkets and Other Grocery (except Convenience) Stores*** - Includes large brands like Safeway, Trader Joe’s, Ralphs, Whole Foods, Luckys, etc. as well as all local brands and grocery stores\n - ***Warehouse Clubs and Supercenters*** - Includes brands like Costco, Sam’s Club, BJ’s, etc.\n - ***Convenience Stores*** - Includes large brands like 7-Eleven, Circle K, etc. as well as all local convenience stores\n- **Time-Sensitive Insights:** Understand peak hours, daily fluctuations, and consumer preferences for Q1 2024.\n\n**Use Cases for the Sample Product:**\n- **Trial Analysis:** Evaluate the sample data to understand the level of detail and insights provided before committing to the full product.\n- **Data Validation:** Validate the accuracy and relevance of our foot traffic data to ensure it meets your business needs.\n\nTransform raw data into actionable insights and stay ahead in the competitive landscape. With AnthologyAI, gain unparalleled visibility into consumer foot traffic trends and elevate your business to new heights. Get started today and lead the way with data-driven excellence!","terms_of_service":"https://www.anthology.ai/privacy","documentation_link":"","privacy_policy_link":"https://www.anthology.ai/privacy","geographical_coverage":"[\"United States and Canada\"]","cost":"FREE","data_source":"Consented geolocation data from users on the Caden mobile app","assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"AnthologyAI","description":"AnthologyAI is the breakthrough Artificial Intelligence Consumer Insights & Predictions Platform with billions of realtime data points, sourced directly and ethically from consumers. 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","setting":{"visibility":"PUBLIC"},"created_at":1780643080513,"updated_at":1780643547678,"categories":["GEOSPATIAL","RETAIL"],"listingType":"PERSONALIZED","provider_id":"5d3ddc17-a7e8-4794-a760-13587e1de2c6"},"detail":{"description":"%md\n# **All Petrol Station Locations in the Netherlands: Complete Geographic Dataset**\n---\n### **Premium GIS-Ready Retail Location Intelligence Data for Databricks Analytics**\n\nThis high-fidelity geospatial dataset provides a complete, exhaustive, and standardized overview of **5,397** verified fuel and petrol station locations across the Netherlands. Updated as of **May 8, 2026**, this comprehensive national dataset captures the entire Dutch forecourt landscape - encompassing major multinational fuel networks, national champions, and prominent independent operators. It offers an analysis-ready foundation for EV charging infrastructure planning, competitor benchmarking, logistics routing, and retail location intelligence.\n\nFully compatible with **Apache Spark**, **Delta Lake**, and **Databricks Unity Catalog** workflows.\n\n---\n\n## **Complete National Coverage: Branded Forecourts & Regional Operators**\nUnlike single-brand extractions, this dataset aggregates all premier fuel networks and independent operators into a single, unified data model. The coverage maps maximum geographic depth across all Dutch provinces:\n\n- **Major Fuel Networks & Multinational Banners:** \n [Shell (579 locations)](https://geolocet.com/products/netherlands-shell-petrol-stations), \n [Esso (516 locations)](https://geolocet.com/products/netherlands-esso-petrol-stations), \n [Total (388 locations)](https://geolocet.com/products/netherlands-total-petrol-stations), \n [bp (327 locations)](https://geolocet.com/products/netherlands-bp-petrol-stations)\n\n- **Regional & Dynamic Growth Banners:** \n [AVIA (356 locations)](https://geolocet.com/products/netherlands-avia-petrol-stations), \n [Argos (251 locations)](https://geolocet.com/products/netherlands-argos-petrol-stations), \n [TinQ (412 locations)](https://geolocet.com/products/netherlands-tinq-petrol-stations), \n [Tango (197 locations)](https://geolocet.com/products/netherlands-tango-petrol-stations), \n [Tamoil (196 locations)](https://geolocet.com/products/netherlands-tamoil-petrol-stations)\n\n**Plus thousands of regional independent forecourts** fully mapped and standardized across all provinces (e.g., Noord-Brabant: 867 sites; Zuid-Holland: 848 sites; Gelderland: 785 sites; Noord-Holland: 628 sites).\n\n---\n\n## **Technical Specifications & Schema Definitions**\nOptimized for zero-friction schema enforcement when reading via `spark.read.format(\"csv\")`. The structured schema below ensures flawless rendering in Databricks notebooks and high accuracy for LLM semantic indexing.\n\n* **`GUID`** (`STRING`): Unique global identifier for deterministic primary key mapping and deduplication.\n* **`Title`** (`STRING`): Official station name or commercial brand banner classification (e.g., Shell, Esso, TinQ).\n* **`Latitude`** (`DOUBLE`): Precise WGS84 latitude coordinate for point-in-polygon and spatial joins.\n* **`Longitude`** (`DOUBLE`): Precise WGS84 longitude coordinate for spatial indexing (H3 / S2).\n* **`Street_No` / `Street`** (`STRING`): Normalized building numbers and thoroughfare name elements.\n* **`City` / `Postal_Code`** (`STRING`): Core postal addresses and primary municipal/settlement designations.\n* **`Settlement`** (`STRING`): Local settlement/town classification.\n* **`Region`** (`STRING`): Macro-administrative boundary classification (e.g., Gelderland, Utrecht).\n* **`Admin_level_1` / `_2`** (`STRING`): Additional local administrative area and municipal identifiers.\n* **`Address`** (`STRING`): Full, standardized street address clean-text output.\n\n---\n\n## **Databricks Workflow & Spatial Analytics Use Cases**\n\n- **EV Charging Whitespace Analysis:** Query the full 5,397 location points against high-voltage grid corridors or traffic volume rasters to rank optimal stations for ultra-fast EV charging infrastructure placement.\n- **H3 Geospatial Indexing:** Convert the precise WGS84 `Latitude` and `Longitude` fields into Uber H3 spatial hexagons (`h3_longlattocell`) to seamlessly cross-reference with granular Dutch neighbourhood (*buurt*) income datasets or demographic indicators.\n- **Logistics & Fleet Benchmarking:** Integrate forecourt coordinates with fleet tracking telemetry to calculate fuel stop density, build optimal corridor routing profiles, and minimize off-route deadhead mileage across the Dutch road network.\n\n---\n\n## **Data Evaluation & Custom Ingestion**\n\n### **Evaluate with a Free Sample Dataset**\nVerify the structure, column types, and coordinate precision before full implementation. A comprehensive schema sample is available for download in CSV/Parquet-friendly formats.\n\n*Want deeper market insights?* Learn more about the forecourt distribution and competitive trends in our comprehensive report: **[View the Netherlands Petrol Stations Report](https://geolocet.com/pages/netherlands-topbrands-petrol-stations-report)**\n\n### **Enterprise Delivery & Tailored Formatting**\nWhile natively available as an analysis-ready CSV for DBFS ingestion, we accommodate enterprise-level data architecture needs. Alternative formats - including **GeoJSON, Esri Shapefiles, PostgreSQL dumps, Excel, or direct Delta sharing profiles** - can be generated. 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The full dataset is available by request and is not included in this free sample listing.\n\n**Use cases**\n\n- Automatic speech recognition (ASR) model development and evaluation\n- Speech-to-text system development\n- Multilingual and cross-lingual speech research\n- Indic language speech technology development\n- Speech recognition benchmarking\n- Acoustic and linguistic model research\n- Speaker and speech metadata analysis\n- Training and evaluation of conversational speech systems\n\n**Product details**\n\n- The free sample includes 2 Parquet datasets representing Gujarati and Tamil conversational speech.\n- The sample contains 2,838 records with a validated duration of 4.996304566 hours.\n- The full corpus contains 805,745 records across 365 Parquet files and 12 Indic languages.\n- Languages represented in the full corpus include Bengali, Gujarati, Hindi, Kannada, Malayalam, Marathi, Odia, Punjabi, Sanskrit, Tamil, Telugu, and Urdu.\n- Sample fields include `fname`, `text`, `audio_filepath`, `lang`, `duration`, `gender`, and `speaker_id`.\n- `text` contains the corresponding transcription text.\n- `audio_filepath` contains the audio payload/reference representation, including `bytes` and `path`.\n- `duration` provides the duration of each speech record in seconds.\n- `lang`, `gender`, and `speaker_id` provide language, speaker gender, and speaker identification metadata.\n\nFor more details, refer to the embedded notebook.\n\n**Additional Insights**\n\nThe free sample represents a small, validated subset of the full Taskmaster OmniVox — Indic ASR Corpus and is intended for evaluation and testing. 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Expose your governed and trusted data, powered by Qlik’s associative engine, to any MCP-compatible client. 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The dataset follows the same structured approach as our dataset published on Hugging Face as [bitext/Bitext-customer-support-llm-chatbot-training-dataset](https://huggingface.co/datasets/bitext/Bitext-customer-support-llm-chatbot-training-dataset), but with a focus on insurance.\n\n**Training Procedure**\n\n**Hyperparameters**\n\n- **Optimizer**: AdamW\n- **Learning Rate**: 0.0002 with a cosine learning rate scheduler\n- **Epochs**: 1\n- **Batch Size**: 4\n- **Gradient Accumulation Steps**: 4\n- **Maximum Sequence Length**: 8192 tokens\n\n**Environment**\n\n- **Transformers Version**: 4.43.4\n- **Framework**: PyTorch 2.3.1+cu121\n- **Tokenizers**: Tokenizers 0.19.1\n\n**Limitations and Bias**\n\n- The model is trained for insurance-specific contexts but may underperform in unrelated areas.\n- Potential biases in the training data could affect the neutrality of the responses; users are encouraged to evaluate responses critically.\n\n**Ethical Considerations**\n\nIt is important to use this technology thoughtfully, ensuring it does not substitute for human judgment where necessary, especially in sensitive situations.\n\n**Acknowledgments**\n\nThis model was developed and trained by Bitext using proprietary data and technology.\n\n**License**\n\nThis model, \"Mistral-7B-Insurance\", is licensed under the Apache License 2.0 by Bitext Innovations International, Inc. 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This offering helps clients establish a third-party data ingestion and testing framework within Databricks to assess utility and onboard valuable assets into the marketing data foundation.\n\n**Challenges When Third-Party Data misaligned**\n1.\tUnclear Data Value – Difficulty assessing how external data contributes to lift in targeting or measurement outcomes.\n2.\tSiloed Data Pipelines – Inconsistent ingestion and lack of standardization hinder testing and scalability.\n3.\tIntegration Complexity – Complex identity joins and varied schemas make it difficult to unify 3P data with first-party assets.\n4.\tWasted Spend – Without proper evaluation, organizations may license data that adds little to no marketing value.\n5.\tLack of Governance – Inadequate controls around usage, retention, and compliance of third-party data increase business risk.\n\n**Product details: Steps to Operationalize Third-Party Data**\n1.\tAssessment & Strategy Definition\no\tIdentify key marketing goals and use cases for 3P data (e.g., enrichment, segmentation, measurement).\no\tReview existing 3P data contracts, sources, and usage patterns.\no\tDefine evaluation criteria for data utility (e.g., match rates, lift contribution, attribution value)\n2.\tData Ingestion & Testing Framework\no\tStandardize ingestion pipelines for various 3P data formats.\no\tEstablish sandbox environments for testing contributions to KPIs (e.g., A/B test for audience performance).\no\tMatch 3P data to first-party identities using Databricks-native identity joins\n3.\tEvaluation & Contribution Scoring\no\tAnalyze data quality, match rates, and outcome lift across marketing programs.\no\tScore 3P data assets based on business value and strategic relevance\n4.\tEcosystem Integration & Governance\no\tOnboard high-performing 3P data into the core customer and campaign data model.\no\tImplement controls for usage, access, and regulatory compliance using Unity Catalog.\no\tCreate reusable pipelines and documentation for future data onboarding\n\n**Expected Outcomes**\n•\tImproved Data ROI – Confidently invest in 3P data that delivers measurable impact on marketing outcomes.\n•\tFaster Time-to-Value – Rapidly test and integrate new data sources without long integration cycles.\n•\tUnified Data Ecosystem – Enrich existing identity and analytics environments with scalable, high-value external data.\n•\tFuture-Ready Framework – Establish a repeatable model to evaluate and onboard new data partners as marketing needs evolve.\n","terms_of_service":"https://legal.epsilon.com/us/terms-and-conditions","documentation_link":"https://www.epsilon.com/us/products-and-services/data","privacy_policy_link":"https://legal.epsilon.com/global-privacy-policies/","geographical_coverage":"[\"Global\"]","cost":"PAID","data_source":"","assets":["ASSET_TYPE_GIT_REPO"],"license":""},"provider_summary":{"name":"Epsilon","description":"We create data-driven, enriched customer experiences focused on real people. Our identity-based solutions and comprehensive consumer views help brands deliver 1:1 personalization, customer value and business growth—all at massive scale."},"popularity":1578},{"id":"de7478d2-620e-4fa2-91fe-0f68f36324eb","summary":{"name":"Geolocet | Latest Socio-demographic Data Finland - 280+ attributes","subtitle":"Demographic data for the municipalities (Kunnat) of Finland and their population - age, gender, marital status, ...","setting":{"visibility":"PUBLIC"},"created_at":1705993825322,"updated_at":1752142335264,"categories":["DEMOGRAPHICS","ADVERTISING_AND_MARKETING","GEOSPATIAL","PUBLIC_SECTOR","RETAIL"],"listingType":"PERSONALIZED","provider_id":"5d3ddc17-a7e8-4794-a760-13587e1de2c6"},"detail":{"description":"## **Get instant access to the latest Demographics Data for Finland with [Geolocet](https://geolocet.com)**\n\nExplore the richness of Finland’s demographic landscape with our Finland Census Data. This meticulously compiled dataset, sourced from official statistical sources, including [Statistics Finland](https://www.stat.fi/), offers a detailed insight into the population landscape of Finland at the municipality (Kunnat) level. \n\nIf you require data at a less granular level, such as an Uber grid or square grid, please don't hesitate to get in touch with us at *contact@geolocet.com*. We understand that different projects may have varying data needs, and we are here to tailor our data to match your specific requirements.\n\n ## **Key Features:**\n---\n* **Population Data Finland:** Gain access to the latest and most accurate population statistics for Finland, meticulously organized by municipality (Kunnat).\n\n* **Demographic Insights:** Dive deep into Finland’s demographic landscape with data on 280+ attributes, including: \n* Total population\n* Gender,\n* Age bands, \n* Gender split by Age,\n* Marital Status,\n* Marital Status split by age,\n* Marital status split by gender,\n* Marital status split by age and gender.\n\nAll demographic attributes are available at the municipal (Kunnat) level.\n\n* **Geodemographic Insights:** Unlock geodemographic data Finland-wide to inform location-based decision-making, marketing strategies, and policy formulation.\n* **Seamless Data Access:** Our data is available for instant download in CSV format. No lengthy sign-up processes or NDAs required on our side – just download the data you need.\n* **Data Samples for Evaluation:** To ensure you're getting exactly what you need, we provide free downloadable samples in CSV format for data evaluation.\n* **Data accuracy:** Our datasets are created using the latest demographics data available in national statistics bureaus, guaranteeing accuracy and reliability.\n* **Best-in-Class Quality:** We guarantee best-in-class quality, backed by over two decades of experience in modeling demographics data and an outstanding quality management process.\n* **Competitive Pricing:** We maintain the lowest prices on the market, ensuring that you get the most cost-efficient demographics data acquisition process.\n---\nAdditionally, for those looking to enrich their analysis or project, we offer:\n* **[Administrative Boundaries](https://geolocet.com/products/finland-all-available-administrative-boundaries):** If you require geographical boundaries (polygons) for the municipalities of Finland, consider exploring our Administrative Boundaries product, which seamlessly integrates with this dataset.\n\n* **[Point of Interest (PoI) Data](https://geolocet.com/pages/aggregated-points-of-interest-poi-data):** If you're interested in Points of Interest data, please don't hesitate to contact us for customized datasets to enhance your geographical analysis.\n\n* **[Data Customization](https://geolocet.com/pages/data-services-by-geolocet):** We understand that your project may have specific requirements. Feel free to get in touch with us if you need any modifications or specific data formats such as Excel, JSON, GeoJSON, or Shapefile. We are here to tailor the data to your needs.\n\nIf you're looking for data for Finland, [look here](https://geolocet.com/pages/finland) for off-the-shelf or tailor-made datasets to suit your specific requirements.\n\n\n\nMake informed decisions, conduct precise research, and unlock the potential of Finland's demographic landscape with our comprehensive dataset. Trust in the accuracy and reliability of our official statistical data to drive your projects forward.\nContact us at *contact@geolocet.com* today to access this invaluable resource and explore Finland census data like never before.\n\n[Geolocet LTD](https://geolocet.com)\n","terms_of_service":"https://geolocet.com/pages/terms-of-use","documentation_link":"","privacy_policy_link":"https://geolocet.com/pages/privacy-policy","geographical_coverage":"[\"Europe\"]","cost":"PAID","update_frequency":{"interval":1,"unit":"YEARLY"},"data_source":"","assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://geolocet.com/pages/terms-of-use"},"provider_summary":{"name":"Geolocet LTD","description":"Geolocet provides premium geospatial data for retail analysis, GIS, and market planning. We equip businesses with accurate, ready-to-query data to drive spatial analysis, site selection, and customer segmentation.\n\nCore Offerings:\n\nDemographics: Socioeconomic data covering population, age, and employment across diverse geographies.\n\nPoints of Interest (PoI): Datasets spanning retail, healthcare, and transport for catchment analysis.\n\nAdministrative Boundaries: GIS-ready boundaries, including municipalities and postcodes, for territory management.\n\nBrand Locations: Chain location datasets to monitor store networks and market expansion.\n\nWealth Data: Granular economic indicators like purchasing power to identify high-potential markets.\n\nWe source from trusted providers, delivering highly customizable datasets with comprehensive coverage across Europe and worldwide."},"popularity":84},{"id":"80b9ec4d-c7b8-401c-b177-aa388259f422","summary":{"name":"ICD-10-CM Clinical Terminology Mapper","subtitle":"This pipeline can extract clinical conditions, and map the clinical conditions to their respective ICD-10-CM codes.","share":{"name":"icd10cm_vdb_resolver","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1706243797502,"updated_at":1793249497300,"categories":["HEALTH"],"listingType":"STANDARD","provider_id":"f88849f8-9d77-4de2-b4c7-03d55cc3768e"},"detail":{"description":"**ICD-10-CM Clinical Terminology Mapper** \n \n This NLP model is designed to streamline the process of clinical coding in healthcare settings. It accurately maps diverse clinical findings to their corresponding ICD-10-CM codes, facilitating efficient and accurate medical billing and documentation.\n\n**Key Features:**\n- The model skillfully matches clinical findings with the most relevant ICD-10-CM codes, ensuring high accuracy in coding and reducing the risk of errors in medical claims and records.\n- The model boasts a vocabulary that is quadruple the size of its non-augmented counterpart, achieved by integrating a comprehensive synonym database. This feature allows for a broader range of clinical terms and expressions to be effectively recognized and mapped.\n\n**Extracted Entities** : Cerebrovascular Diseases, Communicable Diseases, Diabetes, Disease Syndromes and Disorders, EKG Findings, Heart Diseases, Hyperlipidemia, Hypertension, Imaging Findings, Injuries or Poisonings, Kidney Diseases, Obesity, Oncological Conditions, Overweight, Pregnancy-related Conditions, Psychological Conditions, Symptoms, Vital Sign Findings\n\n\nThis model improves the medical billing process by streamlining it through the provision of precise ICD-10-CM codes. This reduces claim denials and enhances revenue cycle management. It assists healthcare professionals in accurately documenting clinical findings, leading to improved quality of patient records in clinical documentation. Additionally, it facilitates health data analysis and research by providing standardized coding of clinical findings. This enables easier data aggregation and interpretation for research purposes.\n \n\n\n**Additional Model Information**\n- [Full model info on John Snow Labs Models Hub](https://nlp.johnsnowlabs.com/2024/08/22/icd10cm_resolver_pipeline_en.html)\n- **Domain:** Clinical Text Analysis\n- **Subdomain:** Terminology Codes\n- **Predictable entities:** ICD 10 CM Codes\n\n \n\n**How to run this model:**\n1. Contact [Sales](mailto:Databricks-Marketplace-listing@johnsnowlabs.com) at John Snow Labs for a free 15 day trial license key or purchase our most popular annual subscription license. \n2. Import this listing.\n3. See the attached notebook to deploy and use the model. \n\nThis model comes with optimized CPU and GPU builds. You can select which one to deploy via the notebook.\n\nBuild based on [Healthcare NLP **6.4.0** version](https://nlp.johnsnowlabs.com/docs/en/jsl/jsl-release-notes#section-2)\n ","terms_of_service":"https://www.johnsnowlabs.com/healthcare-data-library-eula/","documentation_link":"https://nlp.johnsnowlabs.com/2024/08/22/icd10cm_resolver_pipeline_en.html","privacy_policy_link":"https://www.johnsnowlabs.com/privacy-policy/","embedded_notebook_file_infos":[{"id":"a2d832ba-41f3-404e-9f81-129a0edc7287","marketplace_file_type":"EMBEDDED_NOTEBOOK","file_parent":{"parent_id":"80b9ec4d-c7b8-401c-b177-aa388259f422","file_parent_type":"LISTING"},"mime_type":"text/html","download_link":"https://marketplace-file-storage-prod.s3-fips.us-east-1.amazonaws.com/EMBEDDED_NOTEBOOK/LISTING_80b9ec4d-c7b8-401c-b177-aa388259f422/a2d832ba-41f3-404e-9f81-129a0edc7287?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEGgaDnVzLWVhc3QtMS1maXBzIkYwRAIgbC8sWIoYaHR8y7Wq86nPb0pufBl4%2BM4PWpeN%2B5PW9VQCIG7BhAVBDCWTpxFDLeuKYm%2Ff1JLz4iccPMzter0YdhgWKqYECDAQABoMNDE0MzUxNzY3ODI2IgxNAJrHbXaZG20jNfMqgwQyGEb4TsoVOoNvJ%2BaA%2FZ7zeu8hGcHqCeNfblP%2FQo5g6q2aRNtpwtPRKMlhYcIE%2FUQaNT1Lqmyptu32VUQTpzjTTxRVmJrDPkheSCkV06XQ%2FpTMKjU8CC7X1b%2FqYf8CrBwZT7Cv4C5WPjEET38f2A79h0xV6bZgPy28zQWrSeIJ9w6AfZBxiE24%2FVc08zVA0ivCT6lsy7nuU0wcR%2FMg2C9nd1vaMnQ923VpD21CKfVVYcrNWL2b%2FQ%2BNAO%2BxPjPgjiN6UPQbvbvZxkSdhgrGtecIg9NgqGkMcvM5VLTDtdC1kdeZetfOC2obtA%2BT%2F0gy3tIDo6WM5hcQfw743%2FAU9oaGnRBl%2BwoxRRDCZA%2F4jnPdluICSI7NCr6C3HHOe4CSZJjRihczxPA%2FgQczAVjAPs6CrK9N9pIzt0vNJpGUTgg4AoxftTvGfciDREilSctPIcBk6GwB%2Bzt4wbtNCmEPU%2BFD46rc6CWVQmmLwbKOeHw70ipMVFlOc8NlXkEtGO2vu6H44MstSSc6daOVUYUavQSGZ6jGhEjsP2YGz8KDm9XQotB%2FtyVBqiZV01OKqDmmBJA%2FXjUpSJ8M%2FeJXhe5m%2BKcxzNObtoJRI7jci3Tpt08XZyXZ7%2FyIV8H30ya468X3cfkTXrm972uga3z7iXAdubyEHnQIua%2FuvtwOHILi6O5D1Me2MzCPvcnXBjqTAYDoMWBt%2B3AVFuqSs2dmDQNBnvEhXfMgTedKLD5PJHb3aIl15iBkLaNu36GYnVCp9ZU2lSYcTbY5QwNnNeTfJTNy9bKhwzru6Sg%2BslhN%2BTS0zkTn%2BeLMY4tHgPEtEZ2cp%2FCk6GwqswvnYVKHjwO8q%2B1Mx8lc08fVEfarE9dke1GGFqoddx%2Bq%2BZUyHbDlAVfrCmf0yw%3D%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20261110T000135Z&X-Amz-SignedHeaders=host&X-Amz-Expires=86400&X-Amz-Credential=ASIAWA6KKHEJORWBH4N3%2F20261110%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=7f1746a0f645f7288e3d5b208279597a1813fb8451666eb0e4172ef280f5c392","created_at":1791464667180,"updated_at":1791464667180,"display_name":"icd10cm_vdb_resolver","status":"FILE_STATUS_PUBLISHED"}],"cost":"PAID","assets":["ASSET_TYPE_MODEL"],"license":"","tags":[{"tag_name":"LISTING_TAG_TYPE_TASK","tag_values":["ASSERTION_STATUS"]}]},"provider_summary":{"name":"John Snow Labs","description":"John Snow Labs, an AI and NLP for healthcare company, provides state-of-the-art software, models, and data to help healthcare and life science organizations build, deploy, and operate AI projects."},"popularity":1593},{"id":"9f4cda74-a897-4e45-9778-8c90cf9a1bc5","summary":{"name":"Reducing Avoidable Emergency Department Visits","subtitle":"Identify potentially avoidable ED visits and utilize predictive forecasting and generative AI to improve patient outcome","share":{"name":"avoidable_ed","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1751167387611,"updated_at":1751276001053,"categories":["HEALTH"],"listingType":"STANDARD","provider_id":"6c6166cf-26dc-4aec-a16d-6d1cc8dde719"},"detail":{"description":"**Overview**\nThis comprehensive analysis explores emergency department utilization patterns to identify potentially avoidable visits and derive actionable insights for improving care delivery. Using the NYU Emergency Department Algorithm (NYU-EDA) classification system, we analyze ED encounters across multiple dimensions including clinical urgency, utilization trends, cost implications, and opportunities for intervention.\n**Use cases**\n- ED visit classification using evidence-based NYU-EDA methodology\n- Temporal patterns and forecasting of ED utilization\n- Financial impact analysis of potentially avoidable visits\n- Risk stratification and patient cohort identification\n- Integration of social determinants of health data\n- Actionable recommendations for care management programs\n\n**Product details**\nThe primary table used in this analysis is `avoidable_ed.ed_visit_analysis`, which contains pre-processed ED encounter data with NYU-EDA classifications and enriched patient attributes.\n\n**Key data elements**\n- **Patient Demographics**: Age, gender, race, ethnicity, geography, PCP assignment status\n- **Clinical Information**: NYU-EDA classification, diagnosis codes, comorbidity counts, risk scores\n- **Utilization Metrics**: Visit dates, times, provider information, days since last office visit\n- **Financial Data**: ED visit costs, member months for rate calculations\n- **Social Determinants**: Health-related social needs flags\n- **Operational Indicators**: High utilizer flags, potentially avoidable visit flags\nFor more details, refer to the embedded notebook.\n\n**Additional Insights**\nNote: This analysis leverages the **NYU Emergency Department Algorithm (NYU-EDA)**, a validated methodology that classifies ED visits based on the discharge diagnosis to identify potentially avoidable utilization. This classification system helps healthcare organizations understand which ED visits could have been prevented through better ambulatory care management or treated in alternative, lower-cost settings. It provides evidence-based, standardized criteria for distinguishing between truly emergent cases and those that could be managed in alternative settings. 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The goal is to demonstrate how Verticalization/Domain Adaptation for the [events and ticketing] sector can be easily achieved using our two-step approach to LLM Fine-Tuning. An overview of this approach can be found at: [From General-Purpose LLMs to Verticalized Enterprise Models](https://www.bitext.com/blog/general-purpose-models-verticalized-enterprise-genai/)\n\nThe dataset has the following specifications:\n\n- Use Case: Intent Detection\n- Vertical: Events & Ticketing\n- 25 intents assigned to 8 categories\n- 24702 question/answer pairs, with approximately 1000 per intent\n- 65 entity/slot types\n- 10 different types of language generation tags\n\nThe categories and intents are derived from Bitext's extensive experience across various industry-specific datasets, ensuring the relevance and applicability across diverse contexts.\n\n**Dataset Token Count**\n\nThe dataset contains a total of 2.88 million tokens across 'instruction' and 'response' columns. This extensive corpus is crucial for training sophisticated LLMs that can perform a variety of functions including conversational AI, question answering, and virtual assistant tasks in the events and ticketing domain.\n\n**Fields of the Dataset**\n\nEach entry in the dataset comprises the following fields:\n\n- tags\n- instruction: a user request from the Events and Ticketing domain\n- category: the high-level semantic category for the intent\n- intent: the specific intent corresponding to the user instruction\n- response: an example of an expected response from the virtual assistant\n\n**Categories and Intents**\n\nThe dataset covers a wide range of events-related and ticketing-related categories and intents, which are:\n\n- **CANCELLATIONS**: cancel_ticket, check_cancellation_fee, check_cancellation_policy, track_cancellation\n- **CONTACT**: customer_service, event_organizer, human_agent\n- **DELIVERY**: delivery_options, delivery_period\n- **EVENTS**: find_upcoming_events, information_about_type_events\n- **PAYMENT**: payment_methods, pay, report_payment_issue\n- **POLICY**: check_privacy_policy\n- **REFUNDS**: check_refund_policy, get_refund, track_refund\n- **TICKETS**: buy_ticket, change_personal_details_on_ticket, find_ticket, information_about_tickets, sell_ticket, transfer_ticket, upgrade_ticket\n\n**Entities**\n\nThe entities covered by the dataset include:\n\n- **{{WEBSITE_URL}}**, common with most intents.\n- **{{CITY}}**, featured in intents like cancel_ticket, change_personal_details_on_ticket.\n- **{{PAYMENT_OPTION}}**, associated with intents like pay, payment_methods.\n- **{{PRIVACY_POLICY_LINK}}**, relevant to intents such as check_privacy_policy.\n- **{{DELIVERY_PERIOD_INFORMATION}}**, featured in intents like delivery_period.\n\nThis comprehensive list of entities ensures that the dataset is well-equipped to train models that are highly adept at understanding and processing a wide range of events-related and ticketing-related queries and tasks.\n\n**Language Generation Tags**\n\nThe dataset includes tags indicative of various language variations and styles adapted for Events and Ticketing, enhancing the robustness and versatility of models trained on this data. These tags categorize the utterances into different registers such as colloquial, formal, or containing specific events and ticketing jargon, ensuring that the trained models can understand and generate a range of conversational styles appropriate for different customer interactions in the events and ticketing sector.\n\n**Language Generation Tags**\n\nThe dataset includes tags that reflect various language variations and styles, crucial for creating adaptable and responsive conversational AI models within the events and ticketing sector. These tags help in understanding and generating appropriate responses based on the linguistic context and user interaction style.\n\n**Tags for Lexical variation**\n\n- **M - Morphological variation**: Adjusts for inflectional and derivational forms.\n - Example: \"is my account active\", \"is my account activated\"\n- **L - Semantic variations**: Handles synonyms, use of hyphens, and compounding.\n - Example: “what's my balance date\", “what's my billing date”\n\n**Tags for Syntactic structure variation**\n\n- **B - Basic syntactic structure**: Simple, direct commands or statements.\n - Example: \"activate my card\", \"I need to check my balance\"\n- **I - Interrogative structure**: Structuring sentences in the form of questions.\n - Example: “can you show my balance?”, “how do I transfer money?”\n- **C - Coordinated syntactic structure**: Complex sentences coordinating multiple ideas or tasks.\n - Example: “I want to transfer money and check my balance, what should I do?”\n- **N - Negation**: Expressing denial or contradiction.\n - Example: \"I do not wish to proceed with this transaction, how can I stop it?\"\n\n**Tags for language register variations**\n\n- **P - Politeness variation**: Polite forms often used in customer service.\n - Example: “could you please help me check my account balance?”\n- **Q - Colloquial variation**: Informal language that might be used in casual customer interactions.\n - Example: \"can u tell me my balance?\"\n- **W - Offensive language**: Handling potentially offensive language which might occasionally appear in frustrated customer interactions.\n - Example: “I’m upset with these charges, this is ridiculous!”\n\n**Tags for stylistic variations**\n\n- **K - Keyword mode**: Responses focused on keywords.\n - Example: \"balance check\", \"account status\"\n- **E - Use of abbreviations**: Common abbreviations.\n - Example: “acct for account”, “trans for transaction”\n- **Z - Errors and Typos**: Includes common misspellings or typographical errors found in customer inputs.\n - Example: “how can I chek my balance”\n\n**Other tags not in use in this Dataset**\n\n- **D - Indirect speech**: Expressing commands or requests indirectly.\n - Example: “I was wondering if you could show me my last transaction.”\n- **G - Regional variations**: Adjustments for regional language differences.\n - Example: American vs British English: \"checking account\" vs \"current account\"\n- **R - Respect structures - Language-dependent variations**: Formality levels appropriate in different languages.\n - Example: Using “vous” in French for formal addressing instead of “tu.”\n- **Y - Code switching**: Switching between languages or dialects within the same conversation.\n - Example: “Can you help me with my cuenta, please?”\n\nThese tags not only aid in training models for a wide range of customer interactions but also ensure that the models are culturally and linguistically sensitive, enhancing the customer experience in events and ticketing environments.\n\n**License**\n\nThe `Bitext-events-ticketing-llm-chatbot-training-dataset` is released under the **Community Data License Agreement (CDLA) Sharing 1.0**. This license facilitates broad sharing and collaboration while ensuring that the freedom to use, share, modify, and utilize the data remains intact for all users.\n\n**Key Aspects of CDLA-Sharing 1.0**\n\n- **Attribution and ShareAlike**: Users must attribute the dataset and continue to share derivatives under the same license.\n- **Non-Exclusivity**: The license is non-exclusive, allowing multiple users to utilize the data simultaneously.\n- **Irrevocability**: Except in cases of material non-compliance, rights under this license are irrevocable.\n- **No Warranty**: The dataset is provided without warranties regarding its accuracy, completeness, or fitness for a particular purpose.\n- **Limitation of Liability**: Both users and data providers limit their liability for damages arising from the use of the dataset.\n\n**Usage Under CDLA-Sharing 1.0**\n\nBy using the `Bitext-events-ticketing-llm-chatbot-training-dataset`, you agree to adhere to the terms set forth in the CDLA-Sharing 1.0. It is essential to ensure that any publications or distributions of the data, or derivatives thereof, maintain attribution to the original data providers and are distributed under the same or compatible terms of this agreement.\n\nFor a detailed understanding of the license, refer to the [official CDLA-Sharing 1.0 documentation](https://cdla.dev/sharing-1-0/).\n\nThis license supports the open sharing and collaborative improvement of datasets within the AI and data science community, making it particularly suited for projects aimed at developing and enhancing AI technologies in the events and ticketing sector.\n\n---\n\n(c) Bitext Innovations, 2024\n","terms_of_service":"https://www.bitext.com/api-terms-of-use/","documentation_link":"","privacy_policy_link":"https://www.bitext.com/privacy-policy/","embedded_notebook_file_infos":[{"id":"4b74cbd6-769f-443f-b3a7-5d7d51cbd6bb","marketplace_file_type":"EMBEDDED_NOTEBOOK","file_parent":{"parent_id":"a5709738-5a71-4d3c-bbd9-d80d9c5e7c65","file_parent_type":"LISTING"},"mime_type":"text/html","download_link":"https://marketplace-file-storage-prod.s3-fips.us-east-1.amazonaws.com/EMBEDDED_NOTEBOOK/LISTING_a5709738-5a71-4d3c-bbd9-d80d9c5e7c65/4b74cbd6-769f-443f-b3a7-5d7d51cbd6bb?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEGgaDnVzLWVhc3QtMS1maXBzIkYwRAIgbC8sWIoYaHR8y7Wq86nPb0pufBl4%2BM4PWpeN%2B5PW9VQCIG7BhAVBDCWTpxFDLeuKYm%2Ff1JLz4iccPMzter0YdhgWKqYECDAQABoMNDE0MzUxNzY3ODI2IgxNAJrHbXaZG20jNfMqgwQyGEb4TsoVOoNvJ%2BaA%2FZ7zeu8hGcHqCeNfblP%2FQo5g6q2aRNtpwtPRKMlhYcIE%2FUQaNT1Lqmyptu32VUQTpzjTTxRVmJrDPkheSCkV06XQ%2FpTMKjU8CC7X1b%2FqYf8CrBwZT7Cv4C5WPjEET38f2A79h0xV6bZgPy28zQWrSeIJ9w6AfZBxiE24%2FVc08zVA0ivCT6lsy7nuU0wcR%2FMg2C9nd1vaMnQ923VpD21CKfVVYcrNWL2b%2FQ%2BNAO%2BxPjPgjiN6UPQbvbvZxkSdhgrGtecIg9NgqGkMcvM5VLTDtdC1kdeZetfOC2obtA%2BT%2F0gy3tIDo6WM5hcQfw743%2FAU9oaGnRBl%2BwoxRRDCZA%2F4jnPdluICSI7NCr6C3HHOe4CSZJjRihczxPA%2FgQczAVjAPs6CrK9N9pIzt0vNJpGUTgg4AoxftTvGfciDREilSctPIcBk6GwB%2Bzt4wbtNCmEPU%2BFD46rc6CWVQmmLwbKOeHw70ipMVFlOc8NlXkEtGO2vu6H44MstSSc6daOVUYUavQSGZ6jGhEjsP2YGz8KDm9XQotB%2FtyVBqiZV01OKqDmmBJA%2FXjUpSJ8M%2FeJXhe5m%2BKcxzNObtoJRI7jci3Tpt08XZyXZ7%2FyIV8H30ya468X3cfkTXrm972uga3z7iXAdubyEHnQIua%2FuvtwOHILi6O5D1Me2MzCPvcnXBjqTAYDoMWBt%2B3AVFuqSs2dmDQNBnvEhXfMgTedKLD5PJHb3aIl15iBkLaNu36GYnVCp9ZU2lSYcTbY5QwNnNeTfJTNy9bKhwzru6Sg%2BslhN%2BTS0zkTn%2BeLMY4tHgPEtEZ2cp%2FCk6GwqswvnYVKHjwO8q%2B1Mx8lc08fVEfarE9dke1GGFqoddx%2Bq%2BZUyHbDlAVfrCmf0yw%3D%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20261110T000135Z&X-Amz-SignedHeaders=host&X-Amz-Expires=86400&X-Amz-Credential=ASIAWA6KKHEJORWBH4N3%2F20261110%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=7d7608ed0470e74ec43ec2fd9b55ff6b4f9adcbb262ccc2f0b8fa3a6305b7afc","created_at":1727608370996,"updated_at":1727608370996,"display_name":"Events_Ticketing_Dataset_Sample_Notebook","status":"FILE_STATUS_PUBLISHED"}],"geographical_coverage":"[\"Global\"]","cost":"FREE","data_source":"Internally generated by Bitext using proprietary NLP and NLG technologies.","assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"Bitext Innovation International","description":"We understand that one of the critical factors in achieving exceptional performance with LLMs is the quality and relevance of the training data. 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It offers an analysis-ready foundation for EV charging infrastructure planning, competitor benchmarking, logistics routing, and retail location intelligence.\n\nFully compatible with **Apache Spark**, **Delta Lake**, and **Databricks Unity Catalog** workflows.\n\n---\n\n## **Complete National Coverage: Branded Forecourts & Regional Operators**\nUnlike single-brand extractions, this dataset aggregates all premier fuel networks and independent operators into a single, unified data model. The coverage maps maximum geographic depth across all 16 Polish voivodeships:\n\n- **Major Fuel Networks & National Champions:** \n [Orlen (1,994 locations)](https://geolocet.com/products/poland-orlen-petrol-stations), \n [bp (589 locations)](https://geolocet.com/products/poland-bp-petrol-stations), \n [Shell (469 locations)](https://geolocet.com/products/poland-shell-petrol-stations), \n [Circle K (406 locations)](https://geolocet.com/products/poland-circle-k-petrol-stations), \n [MOL (321 locations)](https://geolocet.com/products/poland-mol-petrol-stations)\n\n- **Regional & Dynamic Growth Banners:** \n [MOYA (561 locations)](https://geolocet.com/products/poland-moya-petrol-stations), \n [AVIA (153 locations)](https://geolocet.com/products/poland-avia-petrol-stations), \n [Pieprzyk (142 locations)](https://geolocet.com/products/poland-pieprzyk-petrol-stations), \n [AMIC Energy (141 locations)](https://geolocet.com/products/poland-amic-energy-petrol-stations)\n\n**Plus thousands of regional independent forecourts** fully mapped and standardized across all administrative regions (e.g., Mazowieckie: 1,462 sites; Śląskie: 943 sites; Wielkopolskie: 1,035 sites).\n\n---\n\n## **Technical Specifications & Schema Definitions**\nOptimized for zero-friction schema enforcement when reading via `spark.read.format(\"csv\")`. The structured schema below ensures flawless rendering in Databricks notebooks and high accuracy for LLM semantic indexing.\n\n* **`GUID`** (`STRING`): Unique global identifier for deterministic primary key mapping and deduplication.\n* **`Title`** (`STRING`): Official station name or commercial brand banner classification (e.g., Orlen, bp, MOYA).\n* **`Latitude`** (`DOUBLE`): Precise WGS84 latitude coordinate for point-in-polygon and spatial joins.\n* **`Longitude`** (`DOUBLE`): Precise WGS84 longitude coordinate for spatial indexing (H3 / S2).\n* **`Street_No` / `Street`** (`STRING`): Normalized building numbers and thoroughfare name elements.\n* **`City` / `Postal_Code`** (`STRING`): Core municipal settlements and primary postal routing codes.\n* **`Municipality`** (`STRING`): Local municipal administrative boundary designation (Gmina).\n* **`Region`** (`STRING`): Macro-administrative boundary classification (Voivodeship / Województwo).\n* **`Admin_level_1` / `_2`** (`STRING`): Additional local administrative area and district (Powiat) identifiers.\n* **`Address`** (`STRING`): Full, standardized street address clean-text output.\n* **`Wheelchair`** (`STRING`): Accessibility flags indicating wheelchair mobility support where available.\n* **`Popularity_Score`** (`INTEGER`): Relative foot-traffic/rating metric graded on a standardized 1-10 scale.\n* **`Opening_Hours`** (`STRING`): Standard daily operational schedules and structured operating text.\n* **`Phone` / `Website`** (`STRING`): Direct communication data, branch-specific landing URLs, and domain vectors.\n\n---\n\n## **Databricks Workflow & Spatial Analytics Use Cases**\n\n- **EV Charging Whitespace Analysis:** Query the full 10,214 location points against high-voltage grid corridors or traffic volume rasters to rank optimal stations for ultra-fast EV charging infrastructure placement.\n- **H3 Geospatial Indexing:** Convert the precise WGS84 `Latitude` and `Longitude` fields into Uber H3 spatial hexagons (`h3_longlattocell`) to seamlessly cross-reference with granular Polish municipal (Gmina) income raster files or demographic datasets.\n- **Logistics & Fleet Benchmarking:** Integrate forecourt coordinates with fleet tracking telemetry to calculate fuel stop density, build optimal corridor routing profiles, and minimize off-route deadhead mileage across the national highway network.\n\n---\n\n## **Data Evaluation & Custom Ingestion**\n\n### **Evaluate with a Free Sample Dataset**\nVerify the structure, column types, and coordinate precision before full implementation. A comprehensive schema sample is available for download in CSV/Parquet-friendly formats.\n\n### **Enterprise Delivery & Tailored Formatting**\nWhile natively available as an analysis-ready CSV for DBFS ingestion, we accommodate enterprise-level data architecture needs. Alternative formats - including **GeoJSON, Esri Shapefiles, PostgreSQL dumps, Excel, or direct Delta sharing profiles** - can be generated. Reach out to our engineering team at **contact@geolocet.com** for integration support or custom European POI data requests.","terms_of_service":"https://geolocet.com/pages/terms-of-use","documentation_link":"","privacy_policy_link":"https://geolocet.com/pages/privacy-policy","assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://geolocet.com/pages/terms-of-use"},"provider_summary":{"name":"Geolocet LTD","description":"Geolocet provides premium geospatial data for retail analysis, GIS, and market planning. 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It also shows the Physician Fee Schedule National Payment amount and relative value units.\n\n**Description**\nThis data package the total amount that Medicare paid for a particular claim, revenue center record, claim line, or service category. It also shows the GPCI components. A geographic practice cost index (GPCI) has been established for every Medicare payment locality for each of the three components of a procedure’s relative value unit. This data package also shows the conversion factors to calculate payment. Conversion formulas are for 2017 and 2018 physician fee schedule payment amount.\n\n**Benefits**\n- Useful data package for the health care provider for conversion and to calculate payments. it provides information about actual procedures costs of medicare fee-for-service beneficiaries.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [Actual Total Costs of Medicare Fee for Service Beneficiaries](https://www.johnsnowlabs.com/marketplace/actual-total-costs-of-medicare-fee-for-service-beneficiaries)\n\t- This dataset contains health indicator information about the total Medicare costs for Medicare fee-for-service beneficiaries. The variable used equals the total amount that Medicare paid for a particular claim, revenue center record, claim line, or service category.\n- [Geographic Practice Cost Index GPCI](https://www.johnsnowlabs.com/marketplace/geographic-practice-cost-index-gpci)\n\t- This dataset provides each Geographic Practice Cost Index (GPCI) component for each carrier/locality for 2018, 2019, 2020, 2021, 2022 and 2023. To calculate the payment for every physician’s service, more information, the components of the fee schedule (physician work and MP RVUs - Medical Physicians Relative Value Unit) are adjusted by a GPCI.\n- [HAC Provider Level Measure Rates for Four Conditions](https://www.johnsnowlabs.com/marketplace/hac-provider-level-measure-rates-for-four-conditions)\n\t- This dataset presents hospital-level measures rates of four conditions included in the Deficit Reduction Act (DRA) Hospital-Acquired Condition (HAC) payment provision – foreign object retained after surgery, blood incompatibility, air embolism, and falls and trauma – for Medicare fee-for-service discharges from October 1, 2016 through August 30, 2020.\n- [Locality County Crosswalk](https://www.johnsnowlabs.com/marketplace/locality-county-crosswalk)\n\t- This dataset shows the 2018, 2019, 2020, 2021, 2022 and 2023 Locality County Crosswalk. Data includes 52 localities in the other 16 states, with 10 states having 2 localities, 2 states having 3 localities, 1 state having 4 localities, and 3 states have 5 or more localities.\n- [Medicare Economic Index MEI Anesthesia Conversion Factors](https://www.johnsnowlabs.com/marketplace/medicare-economic-index-mei-anesthesia-conversion-factors)\n\t- This dataset contains the 2018 to 2023 Anesthesia conversion factors.\n- [Outpatient Prospective Payment System OPPSCAP](https://www.johnsnowlabs.com/marketplace/outpatient-prospective-payment-system-oppscap)\n\t- This Outpatient Prospective Payment System (OPPSCAP) dataset contains the payment amounts after the application of the OPPS based payment caps, except for carrier priced codes. For carrier priced codes, the field only contains the OPPS based payment caps. Carrier prices, however, cannot exceed the OPPS based payment caps.\n- [Physician Fee Schedule National Payment Amount 2017](https://www.johnsnowlabs.com/marketplace/physician-fee-schedule-national-payment-amount-2017)\n\t- This dataset contains information on services covered by the Medicare Physician Fee Schedule (MPFS) in 2017. For more than 10,000 physician services; the dataset contains the different carriers and locality wise payment amount with specific indicators.\n- [Physician Fee Schedule National Payment Amount 2018](https://www.johnsnowlabs.com/marketplace/physician-fee-schedule-national-payment-amount-2018)\n\t- This dataset contains information on services covered by the Medicare Physician Fee Schedule (MPFS) in 2018. For more than 10,000 physician services; the dataset contains the different carriers and locality wise payment amount with specific indicators.\n- [Physician Fee Schedule National Payment Amount 2019](https://www.johnsnowlabs.com/marketplace/physician-fee-schedule-national-payment-amount-2019)\n\t- This dataset contains information on services covered by the Medicare Physician Fee Schedule (MPFS) in 2019. For more than 10,000 physician services; the dataset contains the different carriers and locality wise payment amount with specific indicators.\n- [Physician Fee Schedule National Payment Amount 2020](https://www.johnsnowlabs.com/marketplace/physician-fee-schedule-national-payment-amount-2020)\n\t- This dataset contains information on services covered by the Medicare Physician Fee Schedule (MPFS) in 2020. For more than 10,000 physician services; the dataset contains the different carriers and locality wise payment amount with specific indicators.\n- [Physician Fee Schedule National Payment Amount 2021](https://www.johnsnowlabs.com/marketplace/physician-fee-schedule-national-payment-amount-2021)\n\t- This dataset contains information on services covered by the Medicare Physician Fee Schedule (MPFS) in 2021. For more than 10,000 physician services; the dataset contains the different carriers and locality wise payment amount with specific indicators.\n- [Physician Fee Schedule Relative Value Units](https://www.johnsnowlabs.com/marketplace/physician-fee-schedule-relative-value-units)\n\t- This dataset contains information on services covered by the Medicare Physician Fee Schedule (MPFS) in 2022. For more than 10,000 physician services; the dataset contains the associated relative value units (RVUs), a fee schedule status indicator, and various payment policy indicators needed for payment adjustment (i.e., payment of assistant at surgery, team surgery, bilateral surgery, etc.).\n- [Physician Procedures Actual Medicare Costs in Dollars](https://www.johnsnowlabs.com/marketplace/physician-procedures-actual-medicare-costs-in-dollars)\n\t- This dataset shows the indicator information about Actual procedures costs of Medicare fee-for-service beneficiaries. CMS (Centers for Medicare and Medicaid Services) collects and synthesizes Medicare enrollment, spending, and claims data to monitor and evaluate access to and quality of care, trends in utilization, changes in payment policy, and other program-related issues.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated\n- Data Updates\n - Data updates support replace-on-update: outdated foreign keys are deprecated, not deleted\n\n**Our data is curated and enriched by domain experts** \n\n Each dataset is manually curated by our team of doctors, pharmacists, public health & medical billing experts:\n- Field names, descriptions, and normalized values are chosen by people who actually understand their meaning\n- Healthcare & life science experts add categories, search keywords, descriptions and more to each dataset\n- Both manual and automated data enrichment supported for clinical codes, providers, drugs, and geo-locations\n- The data is always kept up to date – even when the source requires manual effort to get updates\n- Support for data subscribers is provided directly by the domain experts who curated the data sets\n- Every data source’s license is manually verified to allow for royalty-free commercial use and redistribution.\n\n**Need Help?**\nIf you have questions about our products, contact us at [info@johnsnowlabs.com](mailto:info@johnsnowlabs.com). \n ","terms_of_service":"https://www.johnsnowlabs.com/healthcare-data-library-eula/","documentation_link":"https://www.johnsnowlabs.com/marketplace/physician-fee-schedule-data-package","contact_email":"support@johnsnowlabs.com","privacy_policy_link":"https://www.johnsnowlabs.com/privacy-policy/","embedded_notebook_file_infos":[{"id":"1621ae1a-0092-4f40-88b0-001de707fd9a","marketplace_file_type":"EMBEDDED_NOTEBOOK","file_parent":{"parent_id":"76942c1c-c298-489a-b81d-29e1d739191b","file_parent_type":"LISTING"},"mime_type":"text/html","download_link":"https://marketplace-file-storage-prod.s3-fips.us-east-1.amazonaws.com/EMBEDDED_NOTEBOOK/LISTING_76942c1c-c298-489a-b81d-29e1d739191b/1621ae1a-0092-4f40-88b0-001de707fd9a?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEGgaDnVzLWVhc3QtMS1maXBzIkYwRAIgbC8sWIoYaHR8y7Wq86nPb0pufBl4%2BM4PWpeN%2B5PW9VQCIG7BhAVBDCWTpxFDLeuKYm%2Ff1JLz4iccPMzter0YdhgWKqYECDAQABoMNDE0MzUxNzY3ODI2IgxNAJrHbXaZG20jNfMqgwQyGEb4TsoVOoNvJ%2BaA%2FZ7zeu8hGcHqCeNfblP%2FQo5g6q2aRNtpwtPRKMlhYcIE%2FUQaNT1Lqmyptu32VUQTpzjTTxRVmJrDPkheSCkV06XQ%2FpTMKjU8CC7X1b%2FqYf8CrBwZT7Cv4C5WPjEET38f2A79h0xV6bZgPy28zQWrSeIJ9w6AfZBxiE24%2FVc08zVA0ivCT6lsy7nuU0wcR%2FMg2C9nd1vaMnQ923VpD21CKfVVYcrNWL2b%2FQ%2BNAO%2BxPjPgjiN6UPQbvbvZxkSdhgrGtecIg9NgqGkMcvM5VLTDtdC1kdeZetfOC2obtA%2BT%2F0gy3tIDo6WM5hcQfw743%2FAU9oaGnRBl%2BwoxRRDCZA%2F4jnPdluICSI7NCr6C3HHOe4CSZJjRihczxPA%2FgQczAVjAPs6CrK9N9pIzt0vNJpGUTgg4AoxftTvGfciDREilSctPIcBk6GwB%2Bzt4wbtNCmEPU%2BFD46rc6CWVQmmLwbKOeHw70ipMVFlOc8NlXkEtGO2vu6H44MstSSc6daOVUYUavQSGZ6jGhEjsP2YGz8KDm9XQotB%2FtyVBqiZV01OKqDmmBJA%2FXjUpSJ8M%2FeJXhe5m%2BKcxzNObtoJRI7jci3Tpt08XZyXZ7%2FyIV8H30ya468X3cfkTXrm972uga3z7iXAdubyEHnQIua%2FuvtwOHILi6O5D1Me2MzCPvcnXBjqTAYDoMWBt%2B3AVFuqSs2dmDQNBnvEhXfMgTedKLD5PJHb3aIl15iBkLaNu36GYnVCp9ZU2lSYcTbY5QwNnNeTfJTNy9bKhwzru6Sg%2BslhN%2BTS0zkTn%2BeLMY4tHgPEtEZ2cp%2FCk6GwqswvnYVKHjwO8q%2B1Mx8lc08fVEfarE9dke1GGFqoddx%2Bq%2BZUyHbDlAVfrCmf0yw%3D%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20261110T000135Z&X-Amz-SignedHeaders=host&X-Amz-Expires=86400&X-Amz-Credential=ASIAWA6KKHEJORWBH4N3%2F20261110%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=ec509345a681aa868b56f7088d2fac88c6e2345afbd6d435277007438a94d0e3","created_at":1682537541810,"updated_at":1682537541810,"display_name":"EDA Notebook","status":"FILE_STATUS_PUBLISHED"}]},"provider_summary":{"name":"John Snow Labs","description":"John Snow Labs, an AI and NLP for healthcare company, provides state-of-the-art software, models, and data to help healthcare and life science organizations build, deploy, and operate AI projects."},"popularity":1032},{"id":"f9b59891-c333-49f4-a19c-c1e442f6502e","summary":{"name":"SAMPLE Digital Ad Exposure | Social, Media, Audio, GenAI | 1st Party | 3B+ verified events, US ...","subtitle":"Ad exposure across social, media, audio and GenAI platforms, from 150,000+ triple-opt-in first-party US MAU. 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Such insights are invaluable for informed market assessments and strategic decision-making.\n\n***Real World Data (RWD)***\nAnalyzing government occupation data over time provides valuable real-world insights into labor market trends. By tracking shifts in occupational demand, you can identify emerging industries and evolving job sectors. For example, such analysis may reveal rapid growth in technology-related roles alongside a decline in traditional manufacturing positions, reflecting broader economic transformations.\n\n***Quantitative Analysis***\nUsing historical occupation data and projections, you can create a story about the future of the labor market. By analyzing trends, skills requirements, and industry growth, you can provide insights into the jobs and skills that will be in demand in the coming years. 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Designed using LLM-powered data agents and human-in-the-loop (HITL) validation, this accelerator blends automation with AP domain expertise to deliver curated, business-ready insights across invoice lifecycle, vendor performance, spend visibility, and payment efficiency.\nWith Dataplatr’s AELT for Accounts Payable, finance teams gain intelligent metadata enrichment, automated incremental table creation, and conversational SQL generation - reducing engineering effort by up to 60% while ensuring governance and transparency across the AP lifecycle.\n\n**Business Challenge**\nOrganizations managing AP processes face two core challenges:\n**1. Data integration challenges:**\nBuilding ingestion pipelines for invoices, vendors, POs, and payments across ERP systems requires heavy technical engineering.\n**2. 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The agent validates logic, fixes SQL errors automatically, and ensures consistency across AP metrics.\n- DLT Execution: A single Databricks Delta Live Tables pipeline constructs the complete AP Invoice Analytics lifecycle, seamlessly linking L1, L2, and L3 layers while providing full data lineage, change tracking, and auditability.\n\n**AP Functional Insights (Gold Layer)**\nOnce the agentic pipeline is deployed, the accelerator delivers ready-to-use insights:\n**1. Invoice Aging & Exception Analytics**\nProvides end-to-end visibility into the AP aging lifecycle with automated exception detection.\nIncludes standardized aging buckets, overdue invoice identification, and detection of blocked, duplicate, or mismatched invoices.\n\n**2. Vendor Performance & Compliance**\nEvaluates vendor reliability and compliance across payment cycles.\nTracks on-time payment scores, invoice accuracy, dispute frequency, and early-payment discount utilization for improved vendor governance.\n\n**3. Cycle Time Analysis**\nMeasures the complete invoice lifecycle from receipt to approval to payment.\nHighlights PO → Invoice → Payment durations and surfaces exception-driven delays to accelerate AP processing.\n\n**4. Spend Analytics**\nDelivers comprehensive visibility into organizational spend patterns.\nBreaks down spend by vendor, category, and department while identifying recurring costs, non-PO spend, and potential leakage.\n\n**Dashboard Widgets**\n**Accounts Payable Invoice On Hold**\n- **Invoices on Hold by Reason** – Categorizes held invoices by approval, discrepancy, or missing information.\n- **Aging of Held Invoices** – Shows how long invoices have been on hold, highlighting delayed approvals.\n- **Vendor Impact Analysis** – Lists vendors with the highest number/value of held invoices.\n- **Exception Trend** – Tracks trends in holds over time to identify recurring issues or bottlenecks.\n\n**Accounts Payable Invoice**\n- **Invoice Volume Trend** – Displays monthly/weekly totals of received, processed, and pending invoices.\n- **Processing Cycle Time** – Tracks end-to-end duration from receipt to approval and payment.\n- **Duplicate & Exception Analysis** – Highlights invoices flagged for duplicates or errors.\n- **Top Vendors by Invoice Count/Value** – Provides quick insight into vendor concentration and volume.\n\n**Accounts Payable Overview**\n- **Total Payables Summary** – Aggregated view of total invoices, pending payments, and cleared payments.\n- **Aging Buckets** – Breaks down invoices into 0–30, 31–60, 61–90, and 90+ day aging categories.\n- **Payment Status Overview** – Tracks on-time vs. overdue payments.\n- **Spend Distribution** – Summarizes total spend by vendor, category, or department for quick insights.\n\n**Key Capabilities**\n- **LLM-Driven Metadata Enrichment** for AP invoices, vendors, and payment objects, enabling intelligent documentation and semantic clarity.\n- **Automated Incremental SQL Generation** for Invoice, Vendor, and Payment pipelines, optimized for Delta Live Tables.\n- **DLT Orchestration** with Dependency Mapping, ensuring clean lineage, upstream tracking, and seamless pipeline execution.\n- **Conversational Analytics Creation**, allowing users to build Gold-layer AP tables through natural language prompts.\n- **Self-Healing SQL Logic** for AP-specific rules, including net terms, due-date calculations, and exception handling.\n- **CDC-Enabled** Near-Real-Time Invoice Lifecycle, supporting continuous updates and change capture.\n\n**Marketplace Deliverables**\n- **Production-Ready Notebooks**: End-to-end AELT pipeline notebooks for Bronze (Ingestion), Silver (Transformation), and Gold (Analytics) with agent-assisted automation.\n- **Config Templates**: JSON mappings for AP objects with AI-assisted metadata generation.\n- **DLT SQL Files**: Automatically generated and validated SQL for incremental tables and AP Invoice views.\n- **CDC & Data Quality Frameworks**: Out-of-the-box components for real-time ingestion and data validation.\n- **Documentation & Implementation Guide**: Step-by-step Databricks setup, object mapping, and AELT deployment manual.\n\n**Why Choose Dataplatr’s AP Invoice Agentic ELT Accelerator**\n- **Agentic Automation**: Removes manual mapping and SQL writing\n- **Rapid Time-to-Value**: From raw invoices to dashboards in hours, not weeks\n- **Deep AP Logic**: Prebuilt cycle time, aging, matching, vendor scoring models\n- **Governance**: Unity Catalog lineage, audit, and metadata\n- **Built for Databricks**: Lakehouse-native, scalable, secure\n\n**Transform AP Invoice Analytics with Agentic ELT**\nMove beyond traditional ETL and fragmented APInvoice Analytics reporting.\n Dataplatr’s AP Agentic ELT Accelerator brings AI-driven automation and finance-domain intelligence—delivering cleaner data, faster processing, and end-to-end AP visibility on Databricks.\n\nNote: By clicking '**Get Access**', you agree to our **Terms of Service**. You acknowledge that **Dataplatr Corp** will recognize your organization as a customer/user for promotional and reference purposes.\n\nFor a demo or hands-on notebook, feel free to reach out.\n\n**Contact us a**t: chandra.reddy@dataplatr.com\n**For consultations or custom inquiries**: https://dataplatr.com/contact-us \n[Linkedin](https://www.linkedin.com/company/dataplatrinc)\n\nPlease read our published article on [Medium](https://medium.com/@dataplatr) to learn more about our latest insights and innovations.\n[Medium](https://medium.com/@dataplatr)\n","terms_of_service":"https://docs.google.com/forms/d/e/1FAIpQLSdnlNmSfrFRQDa_ToOL3ydTOPcWI3m3lWor2plLbiqF1JRINg/viewform","documentation_link":"","privacy_policy_link":"https://dataplatr.com","embedded_notebook_file_infos":[{"id":"7ba965e6-dd50-4293-b6fe-c7244330e428","marketplace_file_type":"EMBEDDED_NOTEBOOK","file_parent":{"parent_id":"38abbd38-7cbe-415b-8e91-be617e8eafbf","file_parent_type":"LISTING"},"mime_type":"text/html","download_link":"https://marketplace-file-storage-prod.s3-fips.us-east-1.amazonaws.com/EMBEDDED_NOTEBOOK/LISTING_38abbd38-7cbe-415b-8e91-be617e8eafbf/7ba965e6-dd50-4293-b6fe-c7244330e428?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEGgaDnVzLWVhc3QtMS1maXBzIkYwRAIgbC8sWIoYaHR8y7Wq86nPb0pufBl4%2BM4PWpeN%2B5PW9VQCIG7BhAVBDCWTpxFDLeuKYm%2Ff1JLz4iccPMzter0YdhgWKqYECDAQABoMNDE0MzUxNzY3ODI2IgxNAJrHbXaZG20jNfMqgwQyGEb4TsoVOoNvJ%2BaA%2FZ7zeu8hGcHqCeNfblP%2FQo5g6q2aRNtpwtPRKMlhYcIE%2FUQaNT1Lqmyptu32VUQTpzjTTxRVmJrDPkheSCkV06XQ%2FpTMKjU8CC7X1b%2FqYf8CrBwZT7Cv4C5WPjEET38f2A79h0xV6bZgPy28zQWrSeIJ9w6AfZBxiE24%2FVc08zVA0ivCT6lsy7nuU0wcR%2FMg2C9nd1vaMnQ923VpD21CKfVVYcrNWL2b%2FQ%2BNAO%2BxPjPgjiN6UPQbvbvZxkSdhgrGtecIg9NgqGkMcvM5VLTDtdC1kdeZetfOC2obtA%2BT%2F0gy3tIDo6WM5hcQfw743%2FAU9oaGnRBl%2BwoxRRDCZA%2F4jnPdluICSI7NCr6C3HHOe4CSZJjRihczxPA%2FgQczAVjAPs6CrK9N9pIzt0vNJpGUTgg4AoxftTvGfciDREilSctPIcBk6GwB%2Bzt4wbtNCmEPU%2BFD46rc6CWVQmmLwbKOeHw70ipMVFlOc8NlXkEtGO2vu6H44MstSSc6daOVUYUavQSGZ6jGhEjsP2YGz8KDm9XQotB%2FtyVBqiZV01OKqDmmBJA%2FXjUpSJ8M%2FeJXhe5m%2BKcxzNObtoJRI7jci3Tpt08XZyXZ7%2FyIV8H30ya468X3cfkTXrm972uga3z7iXAdubyEHnQIua%2FuvtwOHILi6O5D1Me2MzCPvcnXBjqTAYDoMWBt%2B3AVFuqSs2dmDQNBnvEhXfMgTedKLD5PJHb3aIl15iBkLaNu36GYnVCp9ZU2lSYcTbY5QwNnNeTfJTNy9bKhwzru6Sg%2BslhN%2BTS0zkTn%2BeLMY4tHgPEtEZ2cp%2FCk6GwqswvnYVKHjwO8q%2B1Mx8lc08fVEfarE9dke1GGFqoddx%2Bq%2BZUyHbDlAVfrCmf0yw%3D%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20261110T000135Z&X-Amz-SignedHeaders=host&X-Amz-Expires=86400&X-Amz-Credential=ASIAWA6KKHEJORWBH4N3%2F20261110%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=6cfbf7fd4fc166dd238d0a1050708385888f5d7203704731017f1ee37f369dd8","created_at":1770875434802,"updated_at":1770875434802,"display_name":"AELT_Marketplace_AP_Invoice Analytics","status":"FILE_STATUS_PUBLISHED"}],"assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"Dataplatr Corp","description":"Dataplatr Corp specializes in Data Analytics & AI with pre-built data models for Enterprise Applications like SAP, Oracle EBS, Workday, Salesforce to empower businesses to unlock the full potential of their data. 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These AI-modeled segments identify individuals by ethnic identity, language preference, cultural behaviors, and socially conscious values---allowing brands to engage meaningfully across multicultural, multilingual, and identity-driven segments. \n\nData Axle, formerly Infogroup, has been a trusted provider of data-driven solutions since 1972. It has coverage of over 95% of U.S. adults and households. 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The TTD Connector App is a Databricks App that wraps the [TTD Connector: Audience & Conversion Data SDK](https://marketplace.databricks.com/details/68a1d107-3cea-41d5-ba23-2e87a328e26f/The-Trade-Desk_TTD-Connector-Audience-Conversion-Data-SDK). With a single integration, you can use this data for your own campaigns, power a managed-service offering for your clients, or make it available to other advertisers as a third-party data provider.\n\nYou can send audience segments, conversion events, and opt-out/deletion requests. Conversion data can be organized at the product level, enabling granular segmentation and measurement for retail use cases.\n\nSupported identifier types include TDIDs, device IDs, CTV IDs, Ramp IDs, UID2s, and EUIDs. UID2 is a privacy-conscious identity framework for hashing and encrypting email addresses or mobile phone numbers. For details, see the UID2 Marketplace Listing.\n\nTo monetize your data as a third-party data provider on The Trade Desk, reach out to your contact at The Trade Desk for more information.\n\nNOTE: Automatic UID2 generation from email addresses or phone numbers is not yet supported by this app. You can generate UID2 with the UID2 clean room solution.\n\n**Capabilities**\nOnboard, manage and fully leverage your first-party or third-party data with The Trade Desk DSP, without writing or maintaining notebook code.\n\n- Send, update and manage first-party targeting data for both advertisers and onboarders\n- Send, update and manage third-party targeting data for data providers and onboarders\n- Send offline conversion events data for advertisers, providers, and retailers\n- Conversion events support an items collection which can include retail information\n- Fully manage deletion and opt-out for advertising identifiers throughout The Trade Desk DSP\n- Accepts leading advertising IDs: TDIDs, device IDs, CTV IDs, Ramp IDs, UID2s, and EUIDs. Subject to change.\n\n**Note:** UID2s are supported in the payload for these capabilities, but the solutions does not currently offer automatic creation of UID2 from email, phone inputs.\n\n**Use cases**\n\n*Audience Activation*\n\nActivate first-party or third-party segments across all programmatic channels in The Trade Desk DSP. Build look-alike models to find new prospects similar to your existing customers.\n\n*Conversion Measurement*\n\nSend conversion events tied to specific products for campaign attribution, reporting, and optimization. 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The territorial vintages of the worldwide area boundaries are compatible with our globally consistent and comparable [socio-demographic](https://www.mbi-geodata.com/en/sociodemographic-data/) and [economic](https://www.mbi-geodata.com/en/purchasing-power-overview/) data.\nOur area boundaries for Denmark are offered at the following levels:\n\n- **Postal Code Level** (Postcode 4-digit): This fine-grained level of detail makes the data suitable for a wide range of applications – for example, enriching customer databases, which are often organized by postal code.\n- **Administrative Level** (Kommuner): Covers both larger administrative areas (e.g., regions, states, provinces) as well as finer subdivisions such as districts, counties, or municipalities.\n\n**Use Cases:**\n\n- **Market Analysis**: Use MBI’s boundaries to visualize data on a map, learn about your customers, and uncover untapped market potential.\n- **360-Degree Customer View**: Link boundaries to customer data, sociodemographic, and economic data to gain a holistic understanding of customer needs, behaviors, and potential value.\n- **Location Planning**\n•\tUse MBI’s boundaries to visualize data on a map, learn about your customers\n•\tPlan the provision of services and products across different regions\n•\tDefine sales territories\n•\tVisualize catchment areas and plan actions to reach potential customers\n\n**Additional Insights:**\n[About Area Boundaries](https://www.mbi-geodata.com/en/area-boundaries/)\n\n**Data Structure:**\nThe area boundaries include the following variables:\nCTRYCODE: Two-digit country code by ISO 3166\nNAME: Name of administrative area / Name of the most populous administrative area / Name of the respective micro-area or the next higher level\nADMINCODE / POSTCODE / MICROCODE: Administrative code / Postal code / Micro-code\nWKT: Geography as Well Known Text (lat/long WGS84)\n\n*with only a few specific exceptions","terms_of_service":"https://service-mbi-geodata.com/s/HNp7Z2tws4WDqWd","documentation_link":"https://www.mbi-geodata.com/en/mbi-supplier-manufacturer-worldwide-geodata/europe/denmark-maps/","privacy_policy_link":"https://www.mbi-geodata.com/en/data-protection/","update_frequency":{"interval":1,"unit":"YEARLY"},"assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://service-mbi-geodata.com/s/Ag6SG3iwKijLbXf"},"provider_summary":{"name":"Michael Bauer International GmbH – MBI Geodata","description":"For over 15 years, Michael Bauer International (MBI) has been a leading provider of globally consistent and comparable geographic data, including more than 3 million postcode areas and other detailed datasets like sociodemographics and purchasing power. 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This facilitates better customer retention, customer acquisition, cross-sell or up-sell, and engagement rates.\n\nAutomotive Attributes – Offline can be used to enrich CRM data by appending attributes to existing individuals in order to learn more about your customers.\n\nAutomotive Attributes – Offline can be used for your own modeling processes.\n\n---\n\nGeographic coverage | United States of America\nData Population Level | Individual or Household\nNumber of individuals/households covered | 257+ million individuals & 125+ million households\nRaw or scraped data | Raw Data\nKey Fields | Average Annual Mileage, Age of Auto, Maintenance Preferences, Auto Make Propensity, In Market for Auto, Make of Auto Owned\n\nData Source(s) - Our data is created in an offline process but leverages offline and online data and behaviors. The vast majority of our database is proprietary although a few publicly available data sources are leveraged in its development AnalyticsIQ sources data from over 100 sources. These are predominantly public sources including: *Core Demographic data from multiple sources; Census Block and Block Group level data; Econometric data from the US government; Summarized credit data from multiple credit bureaus; Property and mortgage information from county courthouses; Occupation information from state licensing boards; Past purchase behavior from catalogers and retailers that contribute their data at a category level.* AnalyticsIQ is not an original compiler as the data above is readily available for purchase out in the market. However, AnalyticsIQ uses superior analytics to make our data best-in-class. One tool that is completely unique to AnalyticsIQ’s product development process is our proprietary survey data. This is where our Cognitive Sciences Department carefully crafts questions that we serve to a panel of consumers. Survey responses are not directly published on our file, but rather the answers are then modeled across our entire consumer file to create truly unique data points not available anywhere.\nUpdate Frequency | AnalyticsIQ data is updated on a quarterly basis\nKey Words | Automotive, Autos, OEM, Cars, Vehicles, Drivers\n\n\nKey data points include: \n\n* Auto_Avg_Mileage: Average mileage put on car per year\n* Number_of_Autos: Number of autos owned by the household\n* AutoProp_Acura_MostLikely: Propensity to drive an Acura\n* Auto_Family_Use: Likelihood of using vehicle for family use\n* Auto_InMkt_v2: Likelihood of being in market for a vehicle\n* Auto_Buyer_Methodical: Likelihood to use vehicle for family use\n* Auto_Maint_Dealership: Likelihood of preferring dealership for vehicle maintenance\n\n---\n## Regulatory and Compliance Information\nAll AnalyticsIQ audiences are HIPAA compliant, and many audiences are Regulation B / FLA friendly or have alternative Regulation B / FLA friendly versions. To learn more about AnalyticsIQ’s Regulation B / FLA Friendly data.\n\nFor users who wish to avoid PII, all AnalyticsIQ data can be anonymized through tokenization thanks to our strong partnership with Datavant, a leading providers of data de-identification services.\n","terms_of_service":"https://analytics-iq.com/","documentation_link":"https://analytics-iq.com/","privacy_policy_link":"https://analytics-iq.com/your-privacy/","assets":["ASSET_TYPE_DATA_TABLE"],"license":"","tags":[{"tag_name":"LISTING_TAG_TYPE_TASK"}]},"provider_summary":{"name":"AnalyticsIQ","description":"AnalyticsIQ is the leading offline people-based data creator and predictive analytics innovator. We are the first data company to consistently blend cognitive psychology with sophisticated data science to help B2C and B2B organizations across industries understand who people are, what they do, and why they make decisions – regardless of whether at home as consumers or at work as professionals. Our PeopleCore consumer data, BusinessCore B2B data, and Connection+ B2B2C linkages provide insight into individuals that empower organizations to achieve better outcomes for all. Our fast and flexible approach makes it easy for organizations to get started using sophisticated data to grow their business. Whether you’re looking to improve your marketing results across channels, build predictive models, power research, or drive better outcomes, AnalyticsIQ can be your partner. For more information, visit https://analytics-iq.com/ and follow us on Twitter @AnalyticsIQ and LinkedIn."},"popularity":1479},{"id":"bb869d66-ddbb-4b62-b05f-1c831716a439","summary":{"name":"F&B Menu Products, Pricing, Competing SKUs & Events","subtitle":"\uD83D\uDDD2️ A collection of global restaurant menus and pricing, with market comparisons to optimizing pricing & intelligence","share":{"name":"unified_menus_marketplace","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1746298772278,"updated_at":1746423809975,"categories":["TRAVEL_AND_TOURISM","TRANSPORTATION_AND_LOGISTICS","RETAIL","FINANCIAL","COMMERCE"],"listingType":"STANDARD","provider_id":"bcde9d2e-7e2f-4266-8c17-45d2e7bdc27e"},"detail":{"description":"Learn more about this data on [catalog.dotlas.com](https://catalog.dotlas.com/restaurants/menus/)\n\n\nThis is a snapshot dataset, which means menus can be captured on a periodic basis for markets on a subscription to measure price changes, new or retired items and more over time. This would involve an additional `timestamp` field to see menus as a time-series.\n\nThere are multiple dataset samples in this share:\n\n## \uD83C\uDF7D️ Menu Data\n\nConsisting of brand level menus with share of outlets that have the item, as well as pricing, sections and descriptions. 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It combines verified licensing data with enriched firmographic attributes and contact information to enable precise targeting of licensed professionals across all major trades and classifications.\n\nCore dataset components include active license status, license classifications, expiration dates, specialty endorsements, and jurisdiction-specific authorizations—matched and unified to ZoomInfo's firmographic foundation. Purpose-built acquisition pipelines ensure coverage of sole proprietors and small operations that operate without traditional digital footprints, while quarterly updates capture new market entrants, license renewals, and status changes.\n\nThe standardized schema enables cross-jurisdiction analysis and enterprise-scale GTM execution without additional data engineering, while preserving state and local nuances critical for compliance and effective outreach. Coverage includes general contractors, specialty trades (HVAC, electrical, plumbing), and niche classifications across all U.S. markets.\n\n**Use Cases**\n\n- **Precision Market Segmentation:** Filter and segment by exact license classifications, endorsements, and specialties to ensure your messaging, product offerings, and pricing align with specific contractor needs. No more generic \"construction industry\" campaigns - you can speak directly to residential electricians vs. commercial HVAC installers.\n- **Competitive Intelligence & Territory Planning:** Track market dynamics including new entrants, license status changes, and specialty trends by geography. Sales teams can identify underserved markets, monitor competitive density, and prioritize territories based on actual contractor concentration rather than assumptions.\n- **Maximum Market Reach:** Reach licensed contractors who may not appear in traditional B2B databases - whether they're sole proprietors, new businesses, or operations that simply maintain minimal digital presence. This licensing-based approach ensures comprehensive market coverage by capturing every authorized contractor regardless of their online footprint or company size.\n\n**What Makes This Dataset Irreplaceable**\n\n- **Aggregation at Scale:** You get unified access to contractor licensing data from 50 states plus thousands of local jurisdictions—each with unique systems and access requirements. We've built the infrastructure to handle this massive operational complexity so you don't have to.\n- **Standardized for Action:** Every state uses different classification systems and naming conventions. You get everything in a single, consistent schema that's ready for enterprise GTM motions—no months of data engineering required.\n- **Enriched for GTM Success:** Records are matched and enriched with firmographic data, contacts, and behavioral signals from ZoomInfo's platform—turning basic compliance records into complete sales intelligence.\n- **Always Current:** Your data stays fresh with quarterly updates that capture new licenses, expirations, and business changes. You're always working with current market intelligence, not chasing dead ends.\n\n**Data Coverage & Details:**\n580k Active licensed companies and 318k Active licensed individuals across all 50 states\n\nDataset includes contractor licensing data from all 50 states, with comprehensive coverage in the 30 states that maintain centralized state-level licensing systems. 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This includes: General Contractors, Specialty Trade Contractors, and Mechanical & Systems Contractors\n\n**Tutorials and documentation**\n[ZoomInfo Knowledge Center](https://university.zoominfo.com/)\n\n**For more information or to make a purchase:**\n[Contact ZoomInfo sales](https://www.zoominfo.com/go-to-market/licensed-contractors-data-cube)","terms_of_service":"https://www.zoominfo.com/about-zoominfo/terms-of-use","documentation_link":"https://www.zoominfo.com/go-to-market/licensed-contractors-data-cube","privacy_policy_link":"https://www.zoominfo.com/legal/privacy-policy","geographical_coverage":"[\"United States and Canada\"]","cost":"PAID","update_frequency":{"interval":90,"unit":"DAILY"},"collection_date_start":1735767213229,"data_source":"Government licensing records across 50 states and thousands of local jurisdictions","assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"ZoomInfo","description":"Build effective go-to-market engines rapidly and activate a unified data strategy with ZoomInfo, the most comprehensive suite of products and services to power your business with accurate and complete data. 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This app wraps all of that in a UI — so you can configure credentials, send test events, and schedule a production job in minutes, without writing SQL or wiring up secrets by hand.\n\n**Databricks Labs** — community-maintained companion to the Meta Conversions API data listing. Same underlying UDTF; guided setup on top.\n\n---\n\n**Use Cases**\n\nPowers the same use cases as the underlying [data listing](https://marketplace.databricks.com/details/8a8f4ead-db28-45e9-b39b-aabbbe1dbe08/Meta_Meta-Conversions-API) — the app is just the fastest path to activating them:\n\n- **Optimizing ad performance** — fuel Meta AI's delivery engine with high-quality server-side signals to decrease cost per action. \n- **Improving measurement** — accurately track deep-funnel milestones like subscription renewals, qualified leads, and offline conversions. \n- **Increasing match quality** — share hashed first-party data to improve event match quality (EMQ) and enhance audience retargeting. \n- **Tracking offline conversions** — connect in-store purchases and phone-call conversions directly to your Meta ad campaigns.\n\n---\n\n**Prerequisites**\n\nBefore installing this app, make sure you have:\n\n1. **The [Meta Conversions API](https://marketplace.databricks.com/details/8a8f4ead-db28-45e9-b39b-aabbbe1dbe08/Meta_Meta-Conversions-API) data listing** installed on that workspace — this provides the sample dataset the app depends on. Install it first. \n2. **A Meta Pixel and an access token**: \n - [How to set up a Meta Pixel](https://www.facebook.com/business/help/952192354843755) \n - [How to generate a Conversions API access token](https://developers.facebook.com/docs/marketing-api/conversions-api/get-started)\n\n---\n\n**Why install this app**\n\nInstall the [Meta Conversions API](https://marketplace.databricks.com/details/8a8f4ead-db28-45e9-b39b-aabbbe1dbe08/Meta_Meta-Conversions-API) data listing first. It gives you everything you need to activate conversion data at scale:\n\n- **Direct connection** — a secure server-to-server path from your Databricks lakehouse to Meta's Conversions API, bypassing browser-based limitations such as ad blockers and cookie restrictions. \n- **Deduplication** — pair the Meta Pixel and the Conversions API with a shared `event_id` to avoid over-reporting. \n- **Privacy and control** — Limited Data Use (LDU) support to assist with CCPA and GDPR compliance, with full control over what is shared. \n- **Governance** — all marketing activation runs inside Databricks, managed through Unity Catalog for transparency and scale.\n\nThis app **removes the manual setup** between *\"marketplace listing installed\"* and *\"first event sent\"* and condenses the [Meta Conversions API](https://marketplace.databricks.com/details/8a8f4ead-db28-45e9-b39b-aabbbe1dbe08/Meta_Meta-Conversions-API) workflow into a five-minute guided experience. \n\n---\n\n**What the app provides**\n\n- **Setup Wizard** — paste your Meta Pixel ID and access token; the app stores the token in a Databricks Secret scope on your behalf and validates it against the Meta Graph API before saving. \n- **Test Connection** — sends a test event to Meta so you know the credentials are valid before you commit. \n- **Quick Start** — one-click execution against the marketplace sample dataset. Validates the full Databricks → Meta chain in seconds. \n- **Deploy Notebook** — drops the UDTF example notebook into your workspace, pre-wired to your secret scope and the marketplace catalog you installed. \n- **Job Setup** — point the connector at your own table, map your columns to Meta's Conversions API parameters (SHA-256 hashing of email and phone is enforced to match Meta's spec), and schedule a Databricks Job with a cron expression. \n- **Test mode** — use Meta's `test_event_code` so events land in the Test Events tab of Meta Events Manager, separate from production analytics.\n\n---\n\n**5-minute Quick Start**\n\n**1\\. Authorize the app**\n\nOn first open, Databricks asks you to grant six scopes on your behalf: `sql`, `sql.warehouses`, `catalog.catalogs:read`, `catalog.schemas:read`, `catalog.tables:read`, and `files.files`. These let the app execute the UDTF, list tables for you to pick from, and deploy the notebook to your workspace. Click **Authorize**.\n\n**2\\. Point at your catalog**\n\nThe app auto-detects the catalog where you installed the Meta CAPI data listing. If it can't, enter the name you chose at install time — it defaults to something like `meta_meta_conversions_api`.\n\n**3\\. Run the Wizard**\n\n- **Step 1:** Enter your Pixel ID and paste the access token. Leave *Store as a Databricks Secret* checked. \n- **Step 2:** Click **Test Connection**. This sends a test event to `graph.facebook.com/v24.0//events` — you should see *\"Connection successful — credentials verified.\"* \n- Click **Save Configuration**. The app creates a Secret scope named `meta_capi_pixel_` and stores your token there.\n\n**4\\. Run a Quick Start against the sample dataset**\n\nFrom the post-setup screen, click **Run Now** under *Quick Start*. The app:\n\n1. Queries the marketplace sample table (`.meta_capi.conversion_data`). \n2. Runs the UDTF via the SQL Statement Execution API using a session-scoped temporary function. \n3. Posts the resulting batches to Meta's Conversions API. \n4. Reports the batch summary — *\"Sent 1001 events to Meta CAPI (2 batches).\"*\n\nIf you're in test mode, head to **Meta Events Manager → Test Events** to see events appear within seconds.\n\n---\n\n## Where to go next\n\n- **Source code & issues:** [github.com/databrickslabs/meta-conversions-api-app](https://github.com/databrickslabs/meta-conversions-api-app) \n- **Full documentation:** [docs/](https://github.com/databrickslabs/meta-conversions-api-app/tree/main/docs) — deployment guide, architecture overview, troubleshooting, user guide \n- **Meta Conversions API reference:** [developers.facebook.com/docs/marketing-api/conversions-api](https://developers.facebook.com/docs/marketing-api/conversions-api/) \n- **The underlying data listing:** [Meta Conversions API on Databricks Marketplace](https://marketplace.databricks.com/details/8a8f4ead-db28-45e9-b39b-aabbbe1dbe08/Meta_Meta-Conversions-API)\n\n## Troubleshooting\n\n- **Wizard can't reach Meta** — confirm your access token has the `ads_management` permission; token errors surface on the Test Connection step. \n- **Quick Start fails with \"table not found\"** — confirm the [Meta Conversions API](https://marketplace.databricks.com/details/8a8f4ead-db28-45e9-b39b-aabbbe1dbe08/Meta_Meta-Conversions-API) data listing is installed on the same workspace and you entered the correct catalog name. \n- **\"Permission denied on secret scope\"** — can happen after redeploying the app: each Databricks App has its own service principal, and the old SP owns the scope ACL. Grant the new SP `WRITE` with `databricks secrets put-acl WRITE`. \n- **Full troubleshooting guide:** [docs/troubleshooting.md](https://github.com/databrickslabs/meta-conversions-api-app/blob/main/docs/troubleshooting.md)\n\n**Support**\n\nThis is a [Databricks Labs](https://databricks.com/learn/labs) project — community-maintained, not formally supported. File bugs and feature requests at [github.com/databrickslabs/meta-conversions-api-app/issues](https://github.com/databrickslabs/meta-conversions-api-app/issues).\n\n**License**\n\nDistributed under the [Databricks License](https://github.com/databrickslabs/meta-conversions-api-app/blob/main/LICENSE). See [NOTICE.md](https://github.com/databrickslabs/meta-conversions-api-app/blob/main/NOTICE.md) for the full list of third-party dependencies and their licenses. ","terms_of_service":"https://cms.databricks.com/sites/default/files/2023-01/marketplace-consumer-terms_0.pdf","documentation_link":"https://github.com/databrickslabs/meta-conversions-api-app","privacy_policy_link":"https://www.databricks.com/legal/privacynotice","assets":["ASSET_TYPE_APP"],"license":"https://github.com/databrickslabs/meta-conversions-api-app/blob/main/LICENSE"},"provider_summary":{"name":"Meta","description":"Meta builds technologies that help people connect, find communities, and grow businesses."},"popularity":2137},{"id":"cdebf430-217e-402d-a092-09c95aff824f","summary":{"name":"Simulated Gym Data for Databricks Academy","subtitle":"This mock dataset simulates customer and session data for a fictional health club.","share":{"name":"dbacademy_dataset_adewd_gym","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1731956135157,"updated_at":1731956768004,"categories":["EDUCATION"],"listingType":"STANDARD","provider_id":"ea1e69ff-0127-4c94-bf39-e841fe1d19d2"},"detail":{"description":"**Overview**\nThis dataset provides simulated data on customers and their data related to a fictional health club.\n\n**Use cases**\nThis data is primarily for use by Databricks Academy to support course delivery.","terms_of_service":"https://cms.databricks.com/sites/default/files/2023-01/marketplace-consumer-terms_0.pdf","documentation_link":"","privacy_policy_link":"https://www.databricks.com/legal/privacynotice","cost":"FREE","data_source":"https://www.databricks.com/","assets":["ASSET_TYPE_MEDIA"],"license":""},"provider_summary":{"name":"Databricks","description":"As the world’s first lakehouse platform in the cloud, Databricks combines the best elements of data lakes and data warehouses to deliver the reliability, strong governance, and performance of data warehouses with the openness, flexibility, and machine learning support of data lakes. 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We collaborate with businesses and startups to unlock innovation, streamline operations, and deliver actionable insights through advanced technologies like IoT, AI/ML and Analytics. In addition to consulting, we design and deliver cutting-edge GenAI and data products, empowering organizations to harness the full potential of their data. Our expertise in data-driven strategies and application development ensures tailored solutions that address unique challenges, driving measurable outcomes. With a focus on excellence and client success, DataPattern enables organizations to thrive in the rapidly evolving digital landscape."},"popularity":1840},{"id":"1c894865-f198-48fa-bea4-6a04f94a22d1","summary":{"name":"Geolocet | Latest Demographic Data Moldova at Municipality Level","subtitle":"Latest Moldova Demographic Data at Municipality Level - age, gender, rural/urban population, income - 160+ attributes.","setting":{"visibility":"PUBLIC"},"created_at":1705924806180,"updated_at":1705992782081,"categories":["DEMOGRAPHICS","ADVERTISING_AND_MARKETING","PUBLIC_SECTOR","RETAIL","GEOSPATIAL"],"listingType":"PERSONALIZED","provider_id":"5d3ddc17-a7e8-4794-a760-13587e1de2c6"},"detail":{"description":"## **Get instant access to the latest Demographics Data for Moldova with [Geolocet](https://geolocet.com)**\n\nExplore the richness of Moldova’s demographic landscape with our Moldova Census Data for 2023. This meticulously compiled dataset, sourced from official statistical sources, including National Bureau of Statistics of the Republic of Moldova, offers a detailed insight into the population landscape of Moldova at the municipality level. \n\nIf you require data at a less granular level, such as an Uber grid or square grid, please don't hesitate to get in touch with us at *contact@geolocet.com*. We understand that different projects may have varying data needs, and we are here to tailor our data to match your specific requirements.\n\n ## **Key Features:**\n---\n* **Population Data Moldova:** Gain access to the latest and most accurate population statistics for Moldova, meticulously organized by municipality.\n\n* **Demographic Insights:** Dive deep into Moldova’s demographic landscape with data on 162 attributes, including: \n* Total Population,\n* Gender,\n* Age,\n* Gender split by age,\n* Rural/Urban population,\n* Rural/Urban population split by gender,\n* Rural/Urban population split by age,\n* Rural/Urban split by gender and age,\n* Average gross monthly earnings per industry,\n* Number of employees.\n\nAll demographic attributes are available at the municipal level.\n\n* **Geodemographic Insights:** Unlock geodemographic data Moldova-wide to inform location-based decision-making, marketing strategies, and policy formulation.\n* **Seamless Data Access:** Our data is available for instant download in CSV format. No lengthy sign-up processes or NDAs required on our side – just download the data you need.\n* **Data Samples for Evaluation:** To ensure you're getting exactly what you need, we provide free downloadable samples in CSV format for data evaluation.\n* **Data accuracy:** Our datasets are created using the latest demographics data available in national statistics bureaus, guaranteeing accuracy and reliability.\n* **Best-in-Class Quality:** We guarantee best-in-class quality, backed by over two decades of experience in modeling demographics data and an outstanding quality management process.\n* **Competitive Pricing:** We maintain the lowest prices on the market, ensuring that you get the most cost-efficient demographics data acquisition process.\n---\nAdditionally, for those looking to enrich their analysis or project, we offer:\n* **[Administrative Boundaries](https://geolocet.com/products/moldova-all-available-administrative-boundaries):** If you require geographical boundaries (polygons) for the municipalities of Moldova, consider exploring our Administrative Boundaries product, which seamlessly integrates with this dataset.\n\n* **[Point of Interest (PoI) Data](https://geolocet.com/pages/aggregated-points-of-interest-poi-data):** If you're interested in Points of Interest data, please don't hesitate to contact us for customized datasets to enhance your geographical analysis.\n\n* **[Data Customization](https://geolocet.com/pages/data-services-by-geolocet):** We understand that your project may have specific requirements. Feel free to get in touch with us if you need any modifications or specific data formats such as Excel, JSON, GeoJSON, or Shapefile. We are here to tailor the data to your needs.\n\nMake informed decisions, conduct precise research, and unlock the potential of Moldova’s demographic landscape with our comprehensive dataset. Trust in the accuracy and reliability of our official statistical data to drive your projects forward.\nContact us at *contact@geolocet.com* today to access this invaluable resource and explore Moldova census data like never before.\n\n[Geolocet LTD](https://geolocet.com)\n","terms_of_service":"https://geolocet.com/pages/terms-of-use","documentation_link":"","privacy_policy_link":"https://geolocet.com/pages/privacy-policy","geographical_coverage":"[\"Europe\"]","cost":"PAID","assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://geolocet.com/pages/terms-of-use"},"provider_summary":{"name":"Geolocet LTD","description":"Geolocet provides premium geospatial data for retail analysis, GIS, and market planning. 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It captures detailed career journeys, covering current and past roles, responsibilities, skills (core and soft), education, certifications, projects, awards, and publications—enabling organizations to understand professional capabilities and career trajectories at scale. By enriching profiles with demographic attributes, languages, and diversity indicators, the dataset goes beyond simple contact records, offering holistic insights into the workforce.\nThis dataset is designed to support HR leaders, talent acquisition teams, workforce planners, and market intelligence professionals. It enables use cases such as identifying talent pools for specialized roles, benchmarking professional experience across industries, mapping career pathways, and analyzing diversity representation. Organizations can leverage this intelligence for sourcing strategies, succession planning, skill-gap analysis, and broader workforce research—strengthening their ability to attract, retain, and develop talent in alignment with business goals.\n\n**Methodology & Quality**\nThe dataset aggregates professional information from public sources, labor market data, and validated third-party feeds. Draup applies proprietary normalization processes to align job titles, roles, and skills with standardized taxonomies. Data is refreshed regularly to ensure accuracy, with updates to profile attributes, role histories, and credentials. Advanced natural language processing is used to extract and structure skills, experiences, and workloads, while multi-source verification reduces noise and bias. 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Identify key feeder and destination companies to inform talent strategy, poaching risk, or market positioning.\t\n\nSELECT\n company_name AS competitor_company,\n executive_name,\n from_title AS previous_title,\n to_title AS current_title,\n movement_date\nFROM\n company_leadership_movement\nWHERE\n affiliated_entity_name = 'Snowflake Inc.'\n AND company_name IS NOT NULL\n AND movement_date >= DATE_SUB(CURRENT_TIMESTAMP(), INTERVAL '1' YEAR)\nORDER BY\n movement_date DESC;\n\n***List VP-and-above R&D leaders who left Pfizer in the past 12 months and their new employers***\t\n\nTrack high-level executive movements from any company using Draup’s leadership change data. Filter by function, designation, and time period to uncover exits and transitions; useful for competitive intelligence, recruitment targeting, or succession mapping.\t\n\nSELECT\n executive_name,\n from_title AS title_at_pfizer,\n company_name AS new_employer,\n to_title AS new_title,\n movement_date\nFROM\n company_leadership_movement\nWHERE\n affiliated_entity_name = 'Pfizer'\n AND business_function = 'R&D'\n AND (from_title LIKE '%VP%' OR from_title LIKE '%President%' OR from_title LIKE '%Chief%')\n AND movement_date >= current_timestamp - INTERVAL '1' YEAR\nORDER BY\n movement_date DESC;\n\n***See how does your peers’ workforce split by function or location compare with you.***\t\n\nBenchmark workforce distribution across functions and geographies against competitors to identify over- or under-invested areas in your organization.\t\n\nWITH workforce_distribution AS (\n SELECT\n profile_info.company_name,\n profile_info.business_function,\n profile_info.location,\n COUNT(*) AS headcount\n FROM profile_info\n WHERE profile_info.business_function IS NOT NULL\n AND profile_info.location IS NOT NULL\n GROUP BY profile_info.company_name, profile_info.business_function, profile_info.location\n),\ntotal_workforce AS (\n SELECT\n workforce_distribution.company_name,\n SUM(workforce_distribution.headcount) AS total_headcount\n FROM workforce_distribution\n GROUP BY workforce_distribution.company_name\n),\nworkforce_percentage AS (\n SELECT\n workforce_distribution.company_name,\n workforce_distribution.business_function,\n workforce_distribution.location,\n workforce_distribution.headcount,\n ROUND((workforce_distribution.headcount * 100.0) / total_workforce.total_headcount, 2) AS percentage_split\n FROM workforce_distribution\n JOIN total_workforce\n ON workforce_distribution.company_name = total_workforce.company_name\n)\nSELECT\n CASE \n WHEN workforce_percentage.company_name = 'Microsoft' THEN 'Microsoft'\n ELSE 'Peer Companies'\n END AS company_group,\n workforce_percentage.business_function,\n workforce_percentage.location,\n SUM(workforce_percentage.headcount) AS total_headcount,\n ROUND(AVG(workforce_percentage.percentage_split), 2) AS avg_percentage_split\nFROM workforce_percentage\nGROUP BY\n CASE \n WHEN workforce_percentage.company_name = 'Microsoft' THEN 'Microsoft'\n ELSE 'Peer Companies'\n END,\n workforce_percentage.business_function,\n workforce_percentage.location\nORDER BY\n workforce_percentage.business_function,\n workforce_percentage.location;\n\n***Analyze the historical talent movement and attrition patterns in a location.***\t\n\nAnalyze career transitions, exits, and movements over time within a geography to anticipate attrition risks and strengthen retention strategies.\t\n\nWITH profile_experience_expanded AS (\n SELECT\n profile_id,\n company_name,\n UNNEST(msa) AS location,\n start_date,\n end_date\n FROM profile_experiences\n WHERE start_date IS NOT NULL\n),\nlocation_joins AS (\n SELECT\n location,\n EXTRACT(YEAR FROM start_date) AS start_year,\n COUNT(DISTINCT profile_id) AS joins\n FROM profile_experience_expanded\n GROUP BY location, EXTRACT(YEAR FROM start_date)\n),\nlocation_exits AS (\n SELECT\n location,\n EXTRACT(YEAR FROM end_date) AS end_year,\n COUNT(DISTINCT profile_id) AS exits\n FROM profile_experience_expanded\n WHERE end_date IS NOT NULL\n GROUP BY location, EXTRACT(YEAR FROM end_date)\n)\nSELECT\n COALESCE(location_joins.location, location_exits.location) AS location,\n COALESCE(location_joins.start_year, location_exits.end_year) AS year,\n COALESCE(location_joins.joins, 0) AS total_joins,\n COALESCE(location_exits.exits, 0) AS total_exits,\n COALESCE(location_exits.exits, 0) - COALESCE(location_joins.joins, 0) AS net_attrition\nFROM location_joins\nFULL OUTER JOIN location_exits\n ON location_joins.location = location_exits.location\n AND location_joins.start_year = location_exits.end_year\nORDER BY location, year;","terms_of_service":"https://draup.com/privacy-policy#:~:text=Unless%20provided%20for%20in%20this,that%20work%20on%20our%20behalf","documentation_link":"","privacy_policy_link":"https://draup.com/privacy-policy","geographical_coverage":"[\"Global\"]","cost":"FREE","update_frequency":{"interval":1,"unit":"WEEKLY"},"collection_granularity":{"interval":1,"unit":"DAILY"},"data_source":"Public Sources","assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"Draup, Inc.","description":"Draup is an enterprise agentic intelligence platform built for HR and Sales leaders navigating transformation. 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Its Sales Intelligence solution helps go-to-market teams uncover deep account insights, identify buying triggers, and accelerate revenue growth.\n \nTrusted by over 300 global enterprises - including five of the Fortune 10 - Draup supports companies like Microsoft, PepsiCo, PayPal, and Moderna in building future-ready workforces and growth strategies."},"popularity":825},{"id":"56c1697a-f631-4cb4-944b-451fdc5bf804","summary":{"name":"Prospection Data Direct","subtitle":"Access extensive Japanese healthcare data for global research with Prospection—40M+ people, 470+ hospitals.","setting":{"visibility":"PUBLIC"},"created_at":1700698198894,"updated_at":1700700381519,"categories":["HEALTH"],"listingType":"PERSONALIZED","provider_id":"64a0e952-e9f3-467d-9abb-3a0e7ab33c6f"},"detail":{"description":"**Overview**\nExplore the future of healthcare analytics with Prospection's enriched data solutions, designed to propel understanding of patient outcomes and optimize decision-making in the dynamic healthcare landscape.\n\nProspection, as a leading data solutions provider, offers unparalleled access to condition-based cohorts derived from patient-level transaction data, encompassing over 100 conditions and 20 therapy areas. Through the Data Direct channel, powered by Databricks delta sharing, Prospection provides robust data services, focusing on Japan's healthcare landscape.\n\nNote that access to Prospection Data Direct via Databricks is contingent on an active subscription to the Prospection AI platform, as well as third party agreements. Geo-fencing restrictions may apply.\n\n**Offerings**\nJapan Patient Event Data: [https://www.prospection.com/resources/japan-healthcare-data-access-agreement/)](url)\n\n- Derived from one of Japan's largest-scale hospital-based databases, including medical claims data from DPC hospitals.\n- Utilizes Prospection's data transformation capabilities, incorporating harmonization and enrichment workflows.\n- Published in a user-friendly common data model for efficient consumption and delivery of insights.\n- Customization: Tailor datasets to specific client needs through engagement with Prospection's consultants, expanding the range of use cases.\n\nEnrichment Algorithms for Speed to Insight:\n\n- Leverage a diverse set of enrichment algorithms designed to accelerate the extraction of valuable insights.\n- Patient Dynamics (e.g., Initiations): Identify drug exposure events such as initiations, continuations, switches in/out, or drop-offs at the patient event level.\n- Adherence: Access adherence algorithm outputs at the patient event level, providing a comprehensive view of patient compliance.\n- Persistence: Gain insights into patient persistence through algorithmic outputs at the patient event level.\n- Survival: Understand patient survival trends with algorithmic outputs at the patient event level.\n\n\n**Patient Event Data - Data Model**\n\nThe following tables are created per workflow:\n- PERSON: contains records that uniquely identify each patient in the source data. This table contains both mapped raw data source data plus Prospection enrichments.\n- EVENTS_ALL: contains all medical events combined into a single table. It includes both mapped raw data source data plus Prospection enrichments.\n\n\nEvents of specific category types are also provided as follows:\n- DRUG_EXPOSURE: captures records about the utilization of a drug when ingested or otherwise introduced into the body.\n- PROCEDURE_OCCURRENCE: contains records of activities or processes ordered by, or carried out by, a healthcare provider on the patient to have a diagnostic or therapeutic purpose.\n- CONDITION_OCCURRENCE: Conditions are records of a person suggesting the presence of a disease or medical condition stated as a diagnosis, a sign or a symptom, which is either observed by a Provider or reported by the patient.\n- MEASUREMENT: contains records of measurement, i.e. structured values (numerical or categorical) obtained through systematic and standardized examination or testing of a Person or Person's sample.\n- HOSPITALIZATION: Data captured during a hospitalization occurrence at admission and discharge.\n\n\n\n\n**Benefits**\nBy harnessing these advanced data services, subscribers can benefit from:\n\n- Efficiency: Quickly derive meaningful insights with harmonized and enriched datasets.\n- Customization: Tailor datasets to unique requirements, enhancing relevance and applicability.\n- Predictive Analytics: Empower your predictive analytics by leveraging powerful algorithms for patient dynamics, adherence, persistence, and survival, enabling proactive healthcare decision-making.\n","terms_of_service":"https://www.prospection.com/tou/","documentation_link":"https://www.prospection.com/resources/japan-healthcare-data-access-agreement/","privacy_policy_link":"https://www.prospection.com/privacy-policy/","geographical_coverage":"[\"Global\"]","cost":"PAID","update_frequency":{"interval":1,"unit":"MONTHLY"},"collection_granularity":{"interval":1,"unit":"DAILY"},"collection_date_start":1207008238649,"data_source":"https://www.prospection.com/resources/japan-healthcare-data-access-agreement/","size":3.0,"assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"Prospection","description":"Prospection pioneers patient-centric intelligence, illuminating patient care’s impact on market share for pharmaceutical brands. 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Linking datasets together for a unified view is extremely complicated, time-consuming and can easily impact marketing effectiveness.\n\nWith over 450m data points, SmartLink allows you to easily connect different data sources, allowing for a single customer view that enables 360° marketing, from postal to digital. \n\n**Use cases**\nBuilt from the most comprehensive consumer data universe in the UK, SmartLink is a powerful linking tool that contains all the identifiable information we hold at an individual level. This product acts as a bridge to link other datasets together, where contact information may be incomplete, for a unified view of customers. What makes SmartLink unique is that it contains over 400m raw records with various contact details consolidated into 90m individuals\n\n**Use cases**\n-\tGive confidence in understanding whether a customer is who they say they are from various touchpoints\n-\tLink between data sources, whether they are disparate types and levels of contact information\n-\tIdentify the same customer coming from different channels to support consistent and timely marketing throughout the customer journey\n-\tDetermine different spellings and levels of contact details at the same/multiple addresses\n-\tWhere only postal data is available, link to compliant channels for digital marketing\n-\tImprove match rates by using more than just name and address for customer insight appends\n\n**Additional Insights**\nSmartLink can be used alongside all our products offered; we usually suggest it’s the best place to start before performing any Suppressions, Appends or Customer Enrichment. If the data being matched to isn’t in the best possible state, then the efficacy of all products is affected.\n\nIt is crucial to have a single source of truth as your starting point; let SmartLink create yours.","terms_of_service":"https://www.sagacitysolutions.co.uk/privacy-policy/","documentation_link":"https://www.sagacitysolutions.co.uk/services/solutions/data-quality-and-enhancement/smart-link","privacy_policy_link":"https://www.sagacitysolutions.co.uk/privacy-policy/","geographical_coverage":"[\"Europe\"]","cost":"PAID","assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"Sagacity","description":"Sagacity are the data intelligence people. We know more about individuals and businesses than any other company. Our advanced analytics and data allow you to identify and generate value quicker. 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All data comes from a single source, ensuring **consistency and comparability** across markets.\n\nThe Sociodemographics-Bundle Basic for Croatia includes the following **variables**:\n- [population](https://www.mbi-geodata.com/en/sociodemographic-data/population/)\n- [gender](https://www.mbi-geodata.com/en/sociodemographic-data/sex-gender/)\n- [age structure](https://www.mbi-geodata.com/en/sociodemographic-data/agebands/) (15-year age bands)\n- household figures ([number of households](https://www.mbi-geodata.com/en/sociodemographic-data/households/) and [households by type](https://www.mbi-geodata.com/en/sociodemographic-data/households-by-type/))\n\nOur sociodemographic data is offered for each country on different **levels**:\n- **Postal Code Level:** This fine-grained level of detail makes the data suitable for a wide range of applications – for example, enriching customer databases, which are often organized by postal code.\n- **Administrative Level:** Covers both larger administrative areas (e.g., regions, states, provinces) as well as finer subdivisions such as districts, counties, or municipalities.\n- **Micro Level**: Subdivisions of postal code or administrative boundaries, such as census areas or street segments. This is the most granular level for local analysis.\n\n**Please note:** The dataset available for free download is a **sample dataset** covering a limited number of geographic areas. If you wish to access the full dataset (covering all areas) or if you require additional variables or geographic levels, please contact us. We will be happy to help you find a product that fits your needs.\n \n**Additional Insights**: https://www.mbi-geodata.com/en/sociodemographic-data/\n\n**Data Structure**:\nAGE_F0014: Population (Nighttime) by age: 0 - 14 years, female\nAGE_F1529: Population (Nighttime) by age: 15 - 29 years, female\nAGE_F3044: Population (Nighttime) by age: 30 - 44 years, female\nAGE_F4559: Population (Nighttime) by age: 45 - 59 years, female\nAGE_F60PL: Population (Nighttime) by age: 60 years and above, female\nAGE_M0014: Population (Nighttime) by age: 0 - 14 years, male\nAGE_M1529: Population (Nighttime) by age: 15 - 29 years, male\nAGE_M3044: Population (Nighttime) by age: 30 - 44 years, male\nAGE_M4559: Population (Nighttime) by age: 45 - 59 years, male\nAGE_M60PL: Population (Nighttime) by age: 60 years and above, male\nAGE_T0014: Population (Nighttime) by age: 0 - 14 years, total\nAGE_T1529: Population (Nighttime) by age: 15 - 29 years, male\nAGE_T3044: Population (Nighttime) by age: 30 - 44 years, total\nAGE_T4559: Population (Nighttime) by age: 45 - 59 years, total\nAGE_T60PL: Population (Nighttime) by age: 60 years and above, total\nCTRYCODE: Two digit country code by ISO 3166\nFEMALE: Population (Nighttime): females\nHH_SIZE: Average household size\nHH_T: Households: total number\nMALE: Population (Nighttime): males\nNAME: Name of administrative area / Name of the most populous administrative area / Name of the respective micro-area or the next higher level\nADMINCODE / POSTCODE / MICROCODE Administrative code / Postal code / Micro-code\nP_PRM: Population (Nighttime) year average: per mill of country\nP_T: Population (Nighttime) year average: total number","terms_of_service":"https://service-mbi-geodata.com/s/HNp7Z2tws4WDqWd","documentation_link":"https://www.mbi-geodata.com/en/mbi-supplier-manufacturer-worldwide-geodata/europe/croatia-maps/","privacy_policy_link":"https://www.mbi-geodata.com/en/data-protection/","geographical_coverage":"[\"Europe\"]","cost":"FREE","update_frequency":{"interval":1,"unit":"YEARLY"},"collection_granularity":{"interval":1,"unit":"YEARLY"},"collection_date_start":1704096781217,"collection_date_end":1735632788464,"data_source":"MBI","assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://service-mbi-geodata.com/s/fF6mZFJjtqjfHt7"},"provider_summary":{"name":"Michael Bauer International GmbH – MBI Geodata","description":"For over 15 years, Michael Bauer International (MBI) has been a leading provider of globally consistent and comparable geographic data, including more than 3 million postcode areas and other detailed datasets like sociodemographics and purchasing power. MBI's high-quality small-scale data spans 248 countries, supporting businesses across all industries in location planning, branch networks, and distribution strategies. Recognized as a key data source, MBI is the preferred provider for leading GIS software and technology companies worldwide as well as one of the first global distributors for HERE Technologies. MBI is part of the Michael Bauer Group, alongside Michael Bauer Research GmbH and Michael Bauer Micromarketing GmbH."},"popularity":506},{"id":"44cd8425-62aa-4b5f-8092-dc50bf7b68a6","summary":{"name":"Company Relationships","subtitle":"Allows you to make sense of large, complex, and dynamic corporate structures.","setting":{"visibility":"PUBLIC"},"created_at":1773226930530,"updated_at":1774942426375,"categories":["FINANCIAL"],"listingType":"PERSONALIZED","provider_id":"bb123ba0-f713-4a39-8c51-2daca088d1de"},"detail":{"description":"The Company Relationships package is a global database of over 8 million company relationships that exist between global companies. The Company Relationships package allows you to make sense of large, complex, and dynamic corporate structures. These relationships can be used to build the corporate family tree and derive the ultimate parent.\n\nThe S&P Global Company Relationships Package includes:\n\n- Parent-child relationship, relationship type, percentage ownership, stake type and the nominal value of the total investment.\n- Relationship types including current, prior, pending and cancelled acquisitions, investments, investment arms, lenders, affiliates, and index providers.\n- Expanded coverage with the addition of relationships for European small to medium-sized enterprises (SMEs), providing deeper insights into the private market landscape.\n- A new controlling interest flag to identify relationships where control is exerted, even when the ownership stake is below 50%.\n- Validation dates for when the relationship was last verified​.\n\n\nWith the Point-in-Time Add-on Option:\n\n- Understand the evolution of relationships over time.\n- Start and end dates are available for constituents of key indices (S&P 1500, S&P Global 100, and Russell 3000) from 2022.\n- By 2026, relationship start and end dates will expand back to 2010 for the constituents of the S&P 1500.\n- Start and end dates are available for key global and North American companies across industries such as banking and insurance, metals and mining etc.\n- Relationship dates derived from corporate events and private entity entries and exits.","terms_of_service":"https://www.spglobal.com/en/terms-of-use","documentation_link":"https://www.marketplace.spglobal.com/en/datasets/company-relationships-(7)","privacy_policy_link":"https://www.spglobal.com/en/privacy/privacy-policy-english","geographical_coverage":"[\"Global\"]","cost":"PAID","data_source":"Publicly available documents, company filings, annual reports, press releases, global company registers","assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"S&P Global Market Intelligence","description":"At S&P Global Market Intelligence, we understand the importance of accurate, deep and insightful information. 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Optimized for Databricks spatial analytics.","setting":{"visibility":"PUBLIC"},"created_at":1780473454274,"updated_at":1780473454274,"categories":["RETAIL","GEOSPATIAL"],"listingType":"PERSONALIZED","provider_id":"5d3ddc17-a7e8-4794-a760-13587e1de2c6"},"detail":{"description":"# **Italy Grocery Locations Dataset: Top 13 Supermarket Brands**\n---\n### **Premium GIS-Ready Retail Location Intelligence Data for Databricks Analytics**\n\nThis high-fidelity geospatial dataset provides a consolidated, standardized overview of **11,388** verified grocery store and supermarket locations across Italy. Updated as of **May 28, 2026**, this single-source resource eliminates the need for manual web scraping or multi-vendor licensing, offering an analysis-ready foundation for retail site selection, competitive intelligence, logistics optimization, and spatial demographics mapping across all Italian regions.\n\nFully compatible with **Apache Spark**, **Delta Lake**, and **Databricks Unity Catalog** workflows.\n\n---\n\n## **Consolidated Brand Coverage (Top 13 Italian Chains)**\nBy consolidating multiple retail banners into a single geospatial table, this dataset provides maximum cost-efficiency and strategic utility over individual brand lists. Coverage includes:\n\n- **Major National Supermarkets & Hypermarkets:** \n [CONAD (3,238 locations)](https://geolocet.com/products/italy-conad-grocery), \n [Carrefour (1,227 locations)](https://geolocet.com/products/italy-carrefour-grocery), \n [Esselunga (202 locations)](https://geolocet.com/products/italy-esselunga-grocery), \n [Famila (394 locations)](https://geolocet.com/products/italy-famila-grocery), \n [Decò (651 locations)](https://geolocet.com/products/italy-deco-grocery)\n\n- **Leading Discounters (Hard & Soft Discount):** \n [Eurospin (1,323 locations)](https://geolocet.com/products/italy-eurospin-grocery), \n [MD (931 locations)](https://geolocet.com/products/italy-md-grocery), \n [Lidl (851 locations)](https://geolocet.com/products/italy-lidl-grocery), \n [ALDI (211 locations)](https://geolocet.com/products/italy-aldi-grocery), \n [Dpiù (317 locations)](https://geolocet.com/products/italy-dpiu-grocery)\n\n- **Regional, Proximity & Specialty Formats:** \n [Spar (1,484 locations)](https://geolocet.com/products/italy-spar-grocery), \n [A&O (496 locations)](https://geolocet.com/products/italy-aando-grocery), \n [Bofrost (63 locations)](https://geolocet.com/products/italy-bofrost-grocery)\n\n---\n\n## **Technical Specifications & Schema Definitions**\nOptimized for zero-friction schema enforcement when reading via `spark.read.format(\"csv\")`. The structured schema below ensures flawless rendering in Databricks notebooks and high accuracy for LLM semantic indexing.\n\n* **`GUID`** (`STRING`): Unique global identifier for deterministic primary key mapping.\n* **`Title`** (`STRING`): Official store name and primary brand banner classification.\n* **`Latitude`** (`DOUBLE`): Precise WGS84 latitude coordinate for point-in-polygon & spatial joins.\n* **`Longitude`** (`DOUBLE`): Precise WGS84 longitude coordinate for spatial indexing (H3 / S2).\n* **`Street_No` / `Street`** (`STRING`): Normalized building/house number and street name elements.\n* **`City` / `Postal_Code`** (`STRING`): Core postal addresses and primary municipal settlement names.\n* **`Commune`** (`STRING`): Italian municipal administrative boundary designation (Comune).\n* **`Region`** (`STRING`): Macro-administrative boundary classification (e.g., Lombardia, Lazio, Campania).\n* **`Admin_level_1` / `_2`** (`STRING`): Additional local administrative area and provincial identifiers.\n* **`Population`** (`INTEGER`): Contextual demographic population metric attributed to the location.\n* **`Wheelchair`** (`STRING`): Accessibility flags indicating wheelchair mobility support.\n* **`Popularity_Score`** (`INTEGER`): Relative foot-traffic/rating metric graded on a standardized 1-10 scale.\n* **`Opening_Hours`** (`STRING`): Standard daily operational schedules and structured operating text.\n* **`Phone` / `Website`** (`STRING`): Direct communication data, branch-specific landing URLs, and domain vectors.\n\n---\n\n## **Databricks Workflow & Spatial Analytics Use Cases**\n\n- **Catchment Area Modeling:** Run native Apache Spark SQL spatial functions or python-based `geopandas` routines to construct drive-time isochrones around competitors in densely populated regions like Lombardia or Lazio.\n- **H3 Geospatial Indexing:** Convert the precise WGS84 `Latitude` and `Longitude` fields into Uber H3 spatial hexagons (`h3_longlattocell`) to cross-reference with Italian demographic raster files or regional income data.\n- **Territory Optimization:** Combine store location points with regional administrative boundary layers to spot gaps in supply chain routing, optimize distribution center placement, or measure market penetration.\n\n---\n\n## **Data Evaluation & Custom Ingestion**\n\n### **Evaluate with a Free Sample Dataset**\nVerify the structure, column types, and coordinate precision before full implementation. A comprehensive schema sample is available for download in CSV/Parquet-friendly formats.\n\n*Want deeper market insights?* Learn more about the brand network in our comprehensive report: **[View the Italian Top Brands Grocery Report](https://geolocet.com/pages/italy-top-brands-grocery-report)**\n\n### **Enterprise Delivery & Tailored Formatting**\nWhile natively available as an analysis-ready CSV for DBFS ingestion, we accommodate enterprise-level data architecture needs. Alternative formats - including **GeoJSON, Esri Shapefiles, PostgreSQL dumps, Excel, or direct Delta sharing profiles** - can be generated. Reach out to our engineering team at **contact@geolocet.com** for integration support or custom European POI data requests.","terms_of_service":"https://geolocet.com/pages/terms-of-use","documentation_link":"","privacy_policy_link":"https://geolocet.com/pages/privacy-policy","assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://geolocet.com/pages/terms-of-use"},"provider_summary":{"name":"Geolocet LTD","description":"Geolocet provides premium geospatial data for retail analysis, GIS, and market planning. 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During that timeframe, Liftr has not changed its methodology, though it does stay abreast of the regular changes that make obtaining consistent data challenging.\n\nThe AI prices shown are average on-demand prices per hour in US Dollars.\n\n**Use cases**\n- Demand Forecasting\nAI prices can be leveraged as a guide for regions of the world with higher priced AI chip types. Over time, data can be used to identify changes which can affect demand.\n\n- Fundamental Analysis\nAI cloud prices across regions provides insight into differences between cloud service providers and among regions across the globe. Note that the data can be further subdivided to separate out different types of accelerators (e.g., GPUs).\n\n- Pricing Analysis\nCloud service providers pricing can be compared or contrasted to other pricing across the globe. It could also be used in isolation to compare pricing by CSP, by accelerator type, and by global region. 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It also includes basic identification information for all active registered investment company series and classes that have been issued IDs by the Commission.\n\n**Description**\nThis data package contains the details of the budgeted expenditures in the ordinance and identifies them by department, appropriation account, and funding type: Local, Community Development Block Grant Program (CDBG), and other Grants. “Local” funds refer to those line items that are balanced with locally generated revenue sources, including but not limited to the Corporate Fund, Water Fund, Midway and O’Hare Airport funds, Vehicle Tax Fund, Library Fund and General Obligation Bond funds. This data package also contains the Series and Class Report provides basic identification information for all active registered investment company series and classes that have been issued IDs by the Commission.\n\n**Benefits**\n- Useful for people working in the field or interested in the information related to budget ordinance appropriations\n- general city budget incremental changes\n- investment company series class\n- state executive budget for ny. the raw version can be found on author's website, but john snow labs (jsl) is offering a clean, standardized and ready to be used datasets. in jsl datasets, the fields descriptions along with the dataset description are again useful for the user to quickly understand the data and the dataset.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [Budget Ordinance Appropriations 2019](https://www.johnsnowlabs.com/marketplace/budget-ordinance-appropriations-2019)\n\t- This Dataset shows the annual appropriation ordinance is the final city operating budget as approved by the City Council. It reflects the Chicago city’s operating budget at the beginning of the fiscal year on January 1.\n- [Budget Ordinance Appropriations 2022](https://www.johnsnowlabs.com/marketplace/budget-ordinance-appropriations-2022)\n\t- This Dataset shows the annual appropriation ordinance is the final city operating budget as approved by the City Council. It reflects the Chicago city’s operating budget at the beginning of the fiscal year on January 1.\n- [General City Budget Incremental Changes](https://www.johnsnowlabs.com/marketplace/general-city-budget-incremental-changes)\n\t- This dataset includes incremental changes in the budget from year to year in Los Angeles, California, United States; dating back from 2015.\n- [Investment Company Series Class](https://www.johnsnowlabs.com/marketplace/investment-company-series-class)\n\t- The dataset shows the Series and Class Report that provides basic identification information for all active registered investment company series and classes that have been issued IDs by the Commission.\n- [New York State Executive Budget Capital Appropriations 2017-2018](https://www.johnsnowlabs.com/marketplace/new-york-state-executive-budget-capital-appropriations-2017-2018)\n\t- This dataset includes capital project appropriations and reappropriations as they relate to the FY 2018 Executive Budget.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated\n- Data Updates\n - Data updates support replace-on-update: outdated foreign keys are deprecated, not deleted\n\n**Our data is curated and enriched by domain experts** \n\n Each dataset is manually curated by our team of doctors, pharmacists, public health & medical billing experts:\n- Field names, descriptions, and normalized values are chosen by people who actually understand their meaning\n- Healthcare & life science experts add categories, search keywords, descriptions and more to each dataset\n- Both manual and automated data enrichment supported for clinical codes, providers, drugs, and geo-locations\n- The data is always kept up to date – even when the source requires manual effort to get updates\n- Support for data subscribers is provided directly by the domain experts who curated the data sets\n- Every data source’s license is manually verified to allow for royalty-free commercial use and redistribution.\n\n**Need Help?**\nIf you have questions about our products, contact us at [info@johnsnowlabs.com](mailto:info@johnsnowlabs.com). \n 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Contact Databricks.partnership@precisely.com for more information on licensing the product.\n\n**Use cases**\n- **Location Planning**\n\nIdentify property locations for display and spatial analysis\n\n- **Risk Analysis**\n\nQuantify risks associated with flood, wildfire, or other hazards to accurately price insurance policies and assess damage after catastrophic events by overlaying parcel data on satellite imagery\n\n- **Market Analysis**\n\nGain deeper insights about a location, utilizing valuable information about the characteristic makeup of a property, and understand the relationship within related property datasets.\n\n**Product details**\nUnlike simple points used in addressing, parcel boundary data helps communication providers, utilities, and insurance companies map and analyze property characteristics across an entire area of interest. Parcel data also enables sophisticated spatial analyses, including proximity, overlay, and buffer zone operations. 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Our offerings include curated image, video, and audio data with rich metadata, optimized for computer vision, generative AI, media analysis, and multimodal model training.\n\nDatasets are delivered through Databricks Marketplace using Delta Sharing, enabling secure, scalable, and zero-copy access for analytics and ML pipelines. DataSeeds.ai serves teams building production-grade AI systems that require reliable, well-structured multimodal data.\n\nIn addition to our existing dataset catalog, DataSeeds.ai also creates custom datasets on demand, tailored to specific customer requirements, data specifications, and use cases."},"popularity":582},{"id":"8718f7c1-a844-4175-b6d2-43bdadd3a455","summary":{"name":"SAMPLE Point of Interest (POI) Data | Global POI Data with 300M+ Places and 50+ Rich Attributes","subtitle":"Quadrant's POI Data provides comprehensive, verified location intelligence for maps, search, delivery, analytics, ...","share":{"name":"quadrant_aef03722_c316_4b13_b012_cda800c81110_samples_share","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1789465355002,"updated_at":1789465355002,"categories":["GEOSPATIAL","OTHER"],"listingType":"STANDARD","provider_id":"a8064e88-f58c-4540-97d8-309869635380"},"detail":{"description":"## Point of Interest (POI) Data\n\nQuadrant's Point of Interest (POI) Data provides comprehensive, verified location intelligence for maps, search, delivery, analytics, AI, and location based applications. Access millions of points of interest, commercial properties, and residential locations enriched with detailed business and property attributes.\n\nOur global POI database includes businesses, landmarks, public venues, commercial properties, and residential locations enriched with categories, addresses, geographic coordinates, brand information, contact details, operating hours, and other location metadata. Designed for organizations that rely on accurate geospatial intelligence, our structured datasets help power search, navigation, logistics, retail analytics, AI models, and location based services.\n\nAlready have a POI database? Quadrant's POI Validation and Enhancement services help improve existing datasets by validating records and enriching them with newly available or missing attributes, ensuring your data remains accurate, complete, and up to date. \n\n---\n\n## Key Features\n\n* Global POI coverage across 200+ countries and territories.\n* Access over 300 million verified businesses, landmarks, public venues, commercial properties, and residential locations.\n* Rich datasets with 50+ business and location attributes, including categories, addresses, coordinates, brand information, contact details, operating hours, and more.\n* Comprehensive POI validation and enhancement services for existing datasets.\n* Flexible delivery through bulk datasets, recurring updates, and custom extracts.\n* Rigorous quality assurance with validation, enrichment, brand matching, normalization, and deduplication.\n* Continuously refreshed datasets to reflect business openings, closures, relocations, and changing business information. \n\n---\n\n## Data Quality\n\nHigh quality location intelligence starts with reliable data. Quadrant applies rigorous quality assurance processes including validation, enrichment, brand matching, normalization, and deduplication to ensure accurate and consistent Point of Interest data.\n\nOur datasets are continuously refreshed to reflect changes in the real world, including new businesses, closures, relocations, category updates, and changes to business attributes. This ensures customers receive reliable, production ready data that supports mapping, search, logistics, analytics, AI, and location intelligence applications.\n\nFor organizations maintaining their own POI databases, Quadrant also provides validation and enhancement services to improve data quality, increase coverage, and enrich records with additional business and location attributes. \n\n---\n\n## POI Validation and Enhancement\n\nKeep your Point of Interest data accurate, complete, and up to date with Quadrant's POI Validation and Enhancement services.\n\nWhether you maintain an internal POI database or license data from multiple providers, Quadrant helps improve data quality by validating existing records and enriching them with newly available or missing business information.\n\nOur validation and enhancement services include:\n\n* Validate existing POI records for accuracy.\n* Correct outdated, incomplete, or missing information.\n* Enrich locations with additional business and property attributes.\n* Add categories, addresses, geographic coordinates, brand information, contact details, operating hours, and other business metadata.\n* Improve dataset coverage, consistency, and freshness.\n* Maintain reliable POI databases through ongoing updates and validation. \n\n---\n\n## Use Cases\n\nPoint of Interest data provides the geographic context needed to build smarter location based products and make better business decisions. Quadrant's POI data supports organizations across mapping, mobility, retail, logistics, AI, and geospatial analytics.\n\nCommon use cases include:\n\n* Maps and digital navigation.\n* Local search and place discovery.\n* Food delivery and last mile logistics.\n* Ecommerce and marketplace platforms.\n* Retail site selection and market analysis.\n* Competitive intelligence and location analytics.\n* Fleet management and transportation.\n* Urban planning and public sector applications.\n* Commercial real estate and property intelligence.\n* AI training, geospatial analytics, and location aware applications.\n\nWhen combined with Quadrant's Mobile Location Data, organizations can gain deeper insights into visitation patterns, customer movement, trade areas, and location performance to support more informed business decisions.","terms_of_service":"https://www.quadrant.io/privacy-policy","contact_email":"support@datarade.ai","privacy_policy_link":"https://www.quadrant.io/privacy-policy","assets":["ASSET_TYPE_DATA_TABLE"]},"provider_summary":{"name":"Quadrant","description":"Quadrant, an Appen company, is a global provider of privacy first mobile location data and Point of Interest (POI) data. Our data that helps enterprises, organizations, and innovators understand how people and places interact.\r\n\r\nWith coverage across 200+ countries and territories, 1+ billion opted in unique devices, and 50+ billion daily location events, our mobile location data delivers global scale for location intelligence. Our POI data spans 200+ countries and includes 300+ million places with 50+ verified attributes, enabling organizations to build better maps, search experiences, market intelligence, and location based products. We also offer POI validation, enrichment, and enhancement services to improve existing datasets.\r\n\r\nAs part of Appen, a global leader in AI training data, Quadrant combines global scale, enterprise grade data quality, and privacy first governance to help organizations build the next generation of location enabled products and services."},"popularity":313},{"id":"a3cd7d08-8e01-489f-8045-746cdba39acf","summary":{"name":"500,000 Diverse Faces for AI: Optimized for Facial Recognition Evaluation","subtitle":"A dataset of 500,000 synthetic diverse faces powered by CultureIQ technology. Ideal for facial recognition evaluation.","setting":{"visibility":"PUBLIC"},"created_at":1726166359735,"updated_at":1726169268325,"categories":["ADVERTISING_AND_MARKETING","MEDIA","SCIENCE_AND_RESEARCH","OTHER"],"listingType":"PERSONALIZED","provider_id":"3d28060f-1726-48a7-b699-f4a84328832e"},"detail":{"description":"**Overview**\nThis dataset provides 500,000 synthetic, diverse faces generated using CulturePulseIQ technology for cultural relevance. It is ideal for evaluating and fine-tuning facial recognition AI models to ensure they perform accurately across a wide range of facial types, ethnicities, and age groups. The dataset is synthetic, offering high-quality facial imagery without privacy concerns.\n\n**Use Cases**\n- Facial Recognition Model Evaluation: Test and evaluate AI models for fairness and accuracy across a diverse range of synthetic facial images.\n- Bias Mitigation: Ensure facial recognition systems perform equally well for all demographics, helping reduce bias in AI models.\n- Security System Testing: Use for stress testing and improving AI in security systems, including identity verification and surveillance technology.\n- AI Research: Ideal for academic and corporate research aimed at improving facial recognition models.\n\n**Product Details**\nThe dataset includes the following metadata associated with each image:\n- Image Filename\n- Image Dimensions (width, height)\n- Image File Size\n- Image Caption\n- More details available upon request (age range, emotions, gender, etc)\n\nFor more details, please get in touch with sales.databricks@destined.ai","terms_of_service":"https://destined.ai/terms/","documentation_link":"","privacy_policy_link":"https://www.destined.ai/privacy","geographical_coverage":"[\"Global\"]","cost":"PAID","assets":["ASSET_TYPE_MEDIA"],"license":""},"provider_summary":{"name":"Destined","description":"Destined helps companies mitigate unwanted bias in AI by providing fully consented and culturally relevant data. 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Our Culture Pulse™ technology taps into this diverse network, ensuring your AI systems mitigate unwanted bias and are also enriched with a wide spectrum of perspectives and experiences. \n\nWith Destined, you’re not just getting data; you’re gaining insight into the heart of humanity.\n"},"popularity":693},{"id":"a656ce98-792d-42ad-a82e-bc56d0c24086","summary":{"name":"Global Tobacco Surveillance System GTSS","subtitle":"This data package includes information on demographics, prevalence of cigarette smoking, tobacco use, exposure to seco..","share":{"name":"global_tobacco_surveillance_system_gtss","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1682356103026,"updated_at":1698965635294,"categories":["HEALTH","SCIENCE_AND_RESEARCH"],"listingType":"STANDARD","provider_id":"f88849f8-9d77-4de2-b4c7-03d55cc3768e"},"detail":{"description":"\n**Overview**\nThis data package includes information on demographics, prevalence of cigarette smoking, tobacco use, exposure to second-hand smoke, smoking cessation and training received regarding patient counselling on smoking cessation techniques. It also comprises of the Global Youth Tobacco Survey (GYTS) - a school-based survey that collects data on students aged 13–15 years and is intended to enhance the capacity of countries to design, implement, and evaluate tobacco control and prevention programs.\n\n**Description**\nGlobal Health Professions Student Survey (GHPSS) was first developed by the World Health Organization (WHO), the US Centers for Disease Control and Prevention (CDC) and the Canadian Public Health Association (CPHA) to collect information on health profession students (e.g. medical, nursing, dental and pharmacy) cross-nationally using a consistent methodology. It uses a common methodology and core questionnaire to systematically monitor health professions students' tobacco use and track trends over time in tobacco-related knowledge, attitudes, behaviours and environmental influences. This research is also about training the health professionals that how different methods can lead to reduce the tobacco use among people and their usefulness in the long run.\n\nThe Global Youth Tobacco Survey (GYTS) uses a standardized methodology for constructing the sample frame, selecting schools and classes, and processing data. Funding for the GYTS has been provided by the CPHA, National Cancer Institute (NCI), United Nations Children Emergency Fund (UNICEF), and the WHO — Tobacco Free Initiative (TFI). This survey is composed of 56 \"core\" questions designed to gather data on the following seven domains. The questionnaire also allows countries to insert their own country-specific questions: i). Knowledge and attitudes of young people towards cigarette smoking, ii). Prevalence of cigarette smoking and other tobacco use among young people, iii). Role of the media and advertising in young people’s use of cigarettes, iv). Access to cigarettes, v). Tobacco-related school curriculum, vi). Environmental tobacco smoke (ETS)vii). Cessation of cigarette smoking.\n\n**Benefits**\n- useful for doctors, medical health planners, people who use tobacco, psychological and behavioral therapists.\n- global tobacco surveillance system database is available on centers for disease control and prevention website in raw form, whereas a jsl customer gets it in clean and normalized form ready to use in one place and create his own custom reports.\n- easy to comprehend for the customers who are unfamiliar with the medical terminologies as all abbreviated terms are replaced with full form, the centers for disease control and prevention original datasets which are full of abbreviations.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [Global Adult Tobacco Survey GATS](https://www.johnsnowlabs.com/marketplace/global-adult-tobacco-survey-gats)\n\t- The Global Adult Tobacco Survey (GATS) is a nationally representative household survey that was launched in February 2007 as a new component of the ongoing Global Tobacco Surveillance System (GTSS). The GATS enables countries to collect data on adult tobacco use and key tobacco control measures.\n- [Global Health Professions Student Survey GHPSS](https://www.johnsnowlabs.com/marketplace/global-health-professions-student-survey-ghpss)\n\t- The World Health Organization (WHO), Centers for Disease Control and Prevention (CDC), and the Canadian Public Health Association (CPHA), developed the Global Health Professions Student Survey (GHPSS) to collect data on tobacco use and cessation counseling among health professional students in all WHO member states.\n- [Global School Personnel Survey GSPS](https://www.johnsnowlabs.com/marketplace/global-school-personnel-survey-gsps)\n\t- The GSPS (Global School Personnel Survey) was initiated in 2000 to collect information on tobacco use, knowledge and attitudes of school personnel toward tobacco, existence and effectiveness of tobacco control policies in schools, and training and materials available for implementing tobacco prevention and control interventions.\n- [Global Youth Tobacco Survey GYTS](https://www.johnsnowlabs.com/marketplace/global-youth-tobacco-survey-gyts)\n\t- The Global Youth Tobacco Survey (GYTS) is a school-based survey that collects data on students aged 13–15 years using a standardized methodology for constructing the sample frame, selecting schools and classes, and processing data. The GYTS surveillance system - the data source of this dataset, is intended to enhance the capacity of countries to design, implement, and evaluate tobacco control and prevention programs.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated\n- Data Updates\n - Data updates support replace-on-update: outdated foreign keys are deprecated, not deleted\n\n**Our data is curated and enriched by domain experts** \n\n Each dataset is manually curated by our team of doctors, pharmacists, public health & medical billing experts:\n- Field names, descriptions, and normalized values are chosen by people who actually understand their meaning\n- Healthcare & life science experts add categories, search keywords, descriptions and more to each dataset\n- Both manual and automated data enrichment supported for clinical codes, providers, drugs, and geo-locations\n- The data is always kept up to date – even when the source requires manual effort to get updates\n- Support for data subscribers is provided directly by the domain experts who curated the data sets\n- Every data source’s license is manually verified to allow for royalty-free commercial use and redistribution.\n\n**Need Help?**\nIf you have questions about our products, contact us at [info@johnsnowlabs.com](mailto:info@johnsnowlabs.com). \n ","terms_of_service":"https://www.johnsnowlabs.com/healthcare-data-library-eula/","documentation_link":"https://www.johnsnowlabs.com/marketplace/global-tobacco-surveillance-system-gtss-data-package","contact_email":"support@johnsnowlabs.com","privacy_policy_link":"https://www.johnsnowlabs.com/privacy-policy/","embedded_notebook_file_infos":[{"id":"108c258d-d55c-47dc-9ee1-d9966c404f41","marketplace_file_type":"EMBEDDED_NOTEBOOK","file_parent":{"parent_id":"a656ce98-792d-42ad-a82e-bc56d0c24086","file_parent_type":"LISTING"},"mime_type":"text/html","download_link":"https://marketplace-file-storage-prod.s3-fips.us-east-1.amazonaws.com/EMBEDDED_NOTEBOOK/LISTING_a656ce98-792d-42ad-a82e-bc56d0c24086/108c258d-d55c-47dc-9ee1-d9966c404f41?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEGgaDnVzLWVhc3QtMS1maXBzIkYwRAIgbC8sWIoYaHR8y7Wq86nPb0pufBl4%2BM4PWpeN%2B5PW9VQCIG7BhAVBDCWTpxFDLeuKYm%2Ff1JLz4iccPMzter0YdhgWKqYECDAQABoMNDE0MzUxNzY3ODI2IgxNAJrHbXaZG20jNfMqgwQyGEb4TsoVOoNvJ%2BaA%2FZ7zeu8hGcHqCeNfblP%2FQo5g6q2aRNtpwtPRKMlhYcIE%2FUQaNT1Lqmyptu32VUQTpzjTTxRVmJrDPkheSCkV06XQ%2FpTMKjU8CC7X1b%2FqYf8CrBwZT7Cv4C5WPjEET38f2A79h0xV6bZgPy28zQWrSeIJ9w6AfZBxiE24%2FVc08zVA0ivCT6lsy7nuU0wcR%2FMg2C9nd1vaMnQ923VpD21CKfVVYcrNWL2b%2FQ%2BNAO%2BxPjPgjiN6UPQbvbvZxkSdhgrGtecIg9NgqGkMcvM5VLTDtdC1kdeZetfOC2obtA%2BT%2F0gy3tIDo6WM5hcQfw743%2FAU9oaGnRBl%2BwoxRRDCZA%2F4jnPdluICSI7NCr6C3HHOe4CSZJjRihczxPA%2FgQczAVjAPs6CrK9N9pIzt0vNJpGUTgg4AoxftTvGfciDREilSctPIcBk6GwB%2Bzt4wbtNCmEPU%2BFD46rc6CWVQmmLwbKOeHw70ipMVFlOc8NlXkEtGO2vu6H44MstSSc6daOVUYUavQSGZ6jGhEjsP2YGz8KDm9XQotB%2FtyVBqiZV01OKqDmmBJA%2FXjUpSJ8M%2FeJXhe5m%2BKcxzNObtoJRI7jci3Tpt08XZyXZ7%2FyIV8H30ya468X3cfkTXrm972uga3z7iXAdubyEHnQIua%2FuvtwOHILi6O5D1Me2MzCPvcnXBjqTAYDoMWBt%2B3AVFuqSs2dmDQNBnvEhXfMgTedKLD5PJHb3aIl15iBkLaNu36GYnVCp9ZU2lSYcTbY5QwNnNeTfJTNy9bKhwzru6Sg%2BslhN%2BTS0zkTn%2BeLMY4tHgPEtEZ2cp%2FCk6GwqswvnYVKHjwO8q%2B1Mx8lc08fVEfarE9dke1GGFqoddx%2Bq%2BZUyHbDlAVfrCmf0yw%3D%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20261110T000135Z&X-Amz-SignedHeaders=host&X-Amz-Expires=86400&X-Amz-Credential=ASIAWA6KKHEJORWBH4N3%2F20261110%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=ef5ddb29fe42bd9c38c70b2fdd8388d0b4f4007a12e424d86894d5fd60f938af","created_at":1682537633813,"updated_at":1682537633813,"display_name":"EDA Notebook","status":"FILE_STATUS_PUBLISHED"}],"assets":["ASSET_TYPE_DATA_TABLE"]},"provider_summary":{"name":"John Snow Labs","description":"John Snow Labs, an AI and NLP for healthcare company, provides state-of-the-art software, models, and data to help healthcare and life science organizations build, deploy, and operate AI projects."},"popularity":686},{"id":"704a7590-9e3e-4e56-a474-a3de421ad2f3","summary":{"name":"Geolocet | Latest Demographic Data Denmark at Municipality Level","subtitle":"Demographic data for the 99 municipalities (kommune) of Denmark and their population - 560+ attributes.","setting":{"visibility":"PUBLIC"},"created_at":1705919524325,"updated_at":1752144075455,"categories":["DEMOGRAPHICS","ADVERTISING_AND_MARKETING","GEOSPATIAL","PUBLIC_SECTOR","RETAIL"],"listingType":"PERSONALIZED","provider_id":"5d3ddc17-a7e8-4794-a760-13587e1de2c6"},"detail":{"description":"## **Get instant access to the latest Demographics Data for Denmark with [Geolocet](https://geolocet.com)**\n\nExplore the richness of Denmark’s demographic landscape with our Denmark Census Data. This meticulously compiled dataset, sourced from official statistical sources, including Statistics Denmark, offers a detailed insight into the population demographic of Denmark at the municipality (kommune) level. \n\nIf you require data at a less granular level, such as an Uber grid or square grid, please don't hesitate to get in touch with us at *contact@geolocet.com*. We understand that different projects may have varying data needs, and we are here to tailor our data to match your specific requirements.\n\n ## **Key Features:**\n---\n* **Population Data Denmark:** Gain access to the latest and most accurate demographic statistics for Denmark, meticulously organized by municipality (kommune).\n\n* **Demographic Insights:** Dive deep into Denmark’s demographic landscape with data on 560+ attributes, including: \n* Total population\n* 5-years age bands population distribution,\n* Gender distribution,\n* Gender divided by age distribution,\n* Education distribution,\n* Education split by gender,\n* Nationality distribution,\n* Education divided by nationality distribution,\n* Education split by nationality and gender distribution,\n* Nationality split by gender distribution,\n* Nationality divided by age distribution,\n* Nationality divided by age and gender distribution,\n* Employment status (estimation) distribution,\n* Employment status split by gender distribution,\n* Employment status split by age distribution,\n* Employment status split by age and gender distribution,\n* Families structure distribution,\n* Families size distribution,\n* Families structure split by number of children distribution,\n* Dwellings,\n* Dwellings split by ownership, \n* Occupied/unoccupied dwellings,\n* Occupied/unoccupied dwellings split by with/without registered population,\n* Dwelling types split by occupancy,\n* Dwelling types split by ownership,\n* Dwelling types split by ownership and occupancy.\n\nAll demographic attributes are available at the municipal (kommune) level.\n\n* **Geodemographic Insights:** Unlock geodemographic data Denmark-wide to inform location-based decision-making, marketing strategies, and policy formulation.\n* **Seamless Data Access:** Our data is available for instant download in CSV format. No lengthy sign-up processes or NDAs required on our side – just download the data you need.\n* **Data Samples for Evaluation:** To ensure you're getting exactly what you need, we provide free downloadable samples in CSV format for data evaluation.\n* **Data accuracy:** Our datasets are created using the latest demographics data available in national statistics bureaus, guaranteeing accuracy and reliability.\n* **Best-in-Class Quality:** We guarantee best-in-class quality, backed by over two decades of experience in modeling demographics data and an outstanding quality management process.\n* **Competitive Pricing:** We maintain the lowest prices on the market, ensuring that you get the most cost-efficient demographics data acquisition process.\n---\nAdditionally, for those looking to enrich their analysis or project, we offer:\n* **[Administrative Boundaries](https://geolocet.com/products/denmark-all-available-administrative-boundaries):** If you require geographical boundaries (polygons) for the municipalities of Denmark, consider exploring our Administrative Boundaries product, which seamlessly integrates with this dataset.\n\n* **[Point of Interest (PoI) Data](https://geolocet.com/pages/aggregated-points-of-interest-poi-data):** If you're interested in Points of Interest data, please don't hesitate to contact us for customized datasets to enhance your geographical analysis.\n\n* **[Data Customization](https://geolocet.com/pages/data-services-by-geolocet):** We understand that your project may have specific requirements. Feel free to get in touch with us if you need any modifications or specific data formats such as Excel, JSON, GeoJSON, or Shapefile. We are here to tailor the data to your needs.\n\nIf you're looking for data for Denmark, [look here](https://geolocet.com/pages/denmark) for off-the-shelf or tailor-made datasets to suit your specific requirements.\n\nMake informed decisions, conduct precise research, and unlock the potential of Denmark demographics data with our comprehensive dataset. Trust in the accuracy and reliability of our official statistical data to drive your projects forward.\nContact us at *contact@geolocet.com* today to access this invaluable resource and explore Denmark census data like never before.\n\n[Geolocet LTD](https://geolocet.com)\n","terms_of_service":"https://geolocet.com/pages/terms-of-use","documentation_link":"","privacy_policy_link":"https://geolocet.com/pages/privacy-policy","geographical_coverage":"[\"Europe\"]","cost":"PAID","data_source":"","assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://geolocet.com/pages/terms-of-use"},"provider_summary":{"name":"Geolocet LTD","description":"Geolocet provides premium geospatial data for retail analysis, GIS, and market planning. We equip businesses with accurate, ready-to-query data to drive spatial analysis, site selection, and customer segmentation.\n\nCore Offerings:\n\nDemographics: Socioeconomic data covering population, age, and employment across diverse geographies.\n\nPoints of Interest (PoI): Datasets spanning retail, healthcare, and transport for catchment analysis.\n\nAdministrative Boundaries: GIS-ready boundaries, including municipalities and postcodes, for territory management.\n\nBrand Locations: Chain location datasets to monitor store networks and market expansion.\n\nWealth Data: Granular economic indicators like purchasing power to identify high-potential markets.\n\nWe source from trusted providers, delivering highly customizable datasets with comprehensive coverage across Europe and worldwide."},"popularity":352},{"id":"b603ad90-ba83-4283-9b72-9fb7374e376f","summary":{"name":"Geolocet | Latest Demographic Data Malta - 50 attributes","subtitle":"Demographic data for the 68 municipalities (kunsilli lokali) of Malta age, gender, religion, and more.","setting":{"visibility":"PUBLIC"},"created_at":1707209347866,"updated_at":1752137626452,"categories":["DEMOGRAPHICS","ADVERTISING_AND_MARKETING","GEOSPATIAL","RETAIL"],"listingType":"PERSONALIZED","provider_id":"5d3ddc17-a7e8-4794-a760-13587e1de2c6"},"detail":{"description":"## **Get instant access to the latest Demographics Data for Malta with [Geolocet](https://geolocet.com)**\n\nExplore the richness of Malta’s demographic landscape with our Malta Census Data. This meticulously compiled dataset, sourced from official statistical sources, including NSO Malta, offers a detailed insight into the population landscape of Malta at the municipality (kunsilli lokali) level. \n\nIf you require data at a less granular level, such as an Uber grid or square grid, please don't hesitate to get in touch with us at *contact@geolocet.com*. We understand that different projects may have varying data needs, and we are here to tailor our data to match your specific requirements.\n\n ## **Key Features:**\n---\n* **Population Data Malta:** Gain access to the latest and most accurate population statistics for Malta, meticulously organized by municipality (kunsilli lokali).\n\n* **Demographic Insights:** Dive deep into Malta’s demographic landscape with data on 50 attributes, including: \n* total population,\n* gender, \n* 10-years age bands, \n* gender split by age, \n* racial origin, \n* religion.\n\n\nAll demographic attributes are available at the municipal (kunsilli lokali) level.\n\n* **Geodemographic Insights:** Unlock geodemographic data Malta-wide to inform location-based decision-making, marketing strategies, and policy formulation.\n* **Seamless Data Access:** Our data is available for instant download in CSV format. No lengthy sign-up processes or NDAs required on our side – just download the data you need.\n* **Data Samples for Evaluation:** To ensure you're getting exactly what you need, we provide free downloadable samples in CSV format for data evaluation.\n* **Data accuracy:** Our datasets are created using the latest demographics data available in national statistics bureaus, guaranteeing accuracy and reliability.\n* **Best-in-Class Quality:** We guarantee best-in-class quality, backed by over two decades of experience in modeling demographics data and an outstanding quality management process.\n* **Competitive Pricing:** We maintain the lowest prices on the market, ensuring that you get the most cost-efficient demographics data acquisition process.\n---\nAdditionally, for those looking to enrich their analysis or project, we offer:\n* **[Administrative Boundaries](https://geolocet.com/products/malta-admin-level-8-local-councils):** If you require geographical boundaries (polygons) for the municipalities of Malta, consider exploring our Administrative Boundaries product, which seamlessly integrates with this dataset.\n\n* **[Point of Interest (PoI) Data](https://geolocet.com/pages/aggregated-points-of-interest-poi-data):** If you're interested in Points of Interest data, please don't hesitate to contact us for customized datasets to enhance your geographical analysis.\n\n* **[Data Customization](https://geolocet.com/pages/data-services-by-geolocet):** We understand that your project may have specific requirements. Feel free to get in touch with us if you need any modifications or specific data formats such as Excel, JSON, GeoJSON, or Shapefile. We are here to tailor the data to your needs.\n\nIf you're looking for data for Malta, [look here](https://geolocet.com/pages/malta) for off-the-shelf or tailor-made datasets to suit your specific requirements.\n\nMake informed decisions, conduct precise research, and unlock the potential of Malta’s demographic landscape with our comprehensive dataset. Trust in the accuracy and reliability of our official statistical data to drive your projects forward.\nContact us at *contact@geolocet.com* today to access this invaluable resource and explore Malta census data like never before.\n\n[Geolocet LTD](https://geolocet.com)\n","terms_of_service":"https://geolocet.com/pages/terms-of-use","documentation_link":"","privacy_policy_link":"https://geolocet.com/pages/privacy-policy","geographical_coverage":"[\"Europe\"]","cost":"PAID","data_source":"","assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"Geolocet LTD","description":"Geolocet provides premium geospatial data for retail analysis, GIS, and market planning. 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Users may take this as an example of how to conduct data analysis.\n\n\n**Tables Included**\n\nValues of different measures captured from sensors of 2 different types.\nIncluding: \n\nSensor 1:\n- CO2\n- human motion\n\nSensor 2:\n- Temperature\n- Humidity\n- Ambient Noise\n- Indoor Air Quality (IAQ) Index\n- PM 2.5 Mass Concentration\n- Total Volatile Organic Compounds (TVOC)\n\nThis dataset will be irregularly updated.\n\n**Sample fields**\n\nCommon data fields:\n- roomid / areaid: ID of sample room / sample area \ne.g.: room01 / area01\n\n- date / ts: Timestamp in UTC Timezone\ne.g.: 2023-06-30T23:59:57.000+0000\n\nData field varies in each corresponding table:\n- [measure name]_min: the minimum value in every hour\ne.g.: co2_min: the minimum CO2 value in every hour\n\n- [measure name]_max: the maximum value in every hour\ne.g.: co2_max: the maximum CO2 value in every hour\n\n- [measure name]_avg: the avaerage value in every hour\ne.g.: CO2_avg: the average CO2 value in every hour\n\n\n（以下は日本語化の内容になります）\n\n\n**概要**\n本データセットは、netone valleyで収集されたセンサーデータを提供します。本データセットを利用する事で、理想的なデータやランダム生成されたデータではなく、実環境のデータ分析を行うことが可能となります。\n本パッケージ内のデータには、温度、湿度、人数、環境騒音、二酸化炭素などが含まれています。\n\n\n**シナリオ**\n\nデータセット（センサーカメラから捕らえた人数のカウントのデータ）を使って、データブリックスノートブックにより来訪人数の将来予測例を提供いたします。\n\n\n**テーブル内容**\n\nこのデータセットのテーブルには、２つタイプのセンサーから取ってくる様々な種類の数値が格納されております。\n\n以下のデータが含まれています。：\n\nセンサー１:\n- 二酸化炭素\n- 人感\n\nセンサー２:\n- 温度\n- 湿度\n- 環境騒音\n- 室内空気質指数(IAQ) \n- 微小粒子状物質(PM2.5)の質量濃度\n- 総揮発性有機化合物(TVOC)\n\n 本データセットは不定期更新されます。\n\n\n**データのカラム例**\n\n共通で格納されたデータのカラム：\n\n- roomid / areaid: サンプル部屋/エリアのID \n例: room01 / area01\n\n- date / ts: 時系列データ（タイムゾーン：協定世界時）\n例: 2023-06-30T23:59:57.000+0000\n\n異なる種類のテーブルデータのカラム:\n\n- [種類名]_min: 最小/最低値のデータ（時間毎）\n例: CO2_min: 二酸化炭素の最低値\n\n- [種類名]_max: 最大/最高値のデータ（時間毎）\n例: CO2_max: 二酸化炭素の最高値\n\n- [種類名]_avg: 平均値のデータ（時間毎）\n例: CO2_avg: 二酸化炭素の平均値\n\n","terms_of_service":"https://www.netone.co.jp/english/privacy-policy/","documentation_link":"","privacy_policy_link":"https://www.netone.co.jp/english/privacy-policy/","embedded_notebook_file_infos":[{"id":"aed08d12-3945-491a-acab-fdd667e215ba","marketplace_file_type":"EMBEDDED_NOTEBOOK","file_parent":{"parent_id":"375b1b8e-6f49-4fcd-bbe6-9636988de574","file_parent_type":"LISTING"},"mime_type":"text/html","download_link":"https://marketplace-file-storage-prod.s3-fips.us-east-1.amazonaws.com/EMBEDDED_NOTEBOOK/LISTING_375b1b8e-6f49-4fcd-bbe6-9636988de574/aed08d12-3945-491a-acab-fdd667e215ba?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEGgaDnVzLWVhc3QtMS1maXBzIkYwRAIgbC8sWIoYaHR8y7Wq86nPb0pufBl4%2BM4PWpeN%2B5PW9VQCIG7BhAVBDCWTpxFDLeuKYm%2Ff1JLz4iccPMzter0YdhgWKqYECDAQABoMNDE0MzUxNzY3ODI2IgxNAJrHbXaZG20jNfMqgwQyGEb4TsoVOoNvJ%2BaA%2FZ7zeu8hGcHqCeNfblP%2FQo5g6q2aRNtpwtPRKMlhYcIE%2FUQaNT1Lqmyptu32VUQTpzjTTxRVmJrDPkheSCkV06XQ%2FpTMKjU8CC7X1b%2FqYf8CrBwZT7Cv4C5WPjEET38f2A79h0xV6bZgPy28zQWrSeIJ9w6AfZBxiE24%2FVc08zVA0ivCT6lsy7nuU0wcR%2FMg2C9nd1vaMnQ923VpD21CKfVVYcrNWL2b%2FQ%2BNAO%2BxPjPgjiN6UPQbvbvZxkSdhgrGtecIg9NgqGkMcvM5VLTDtdC1kdeZetfOC2obtA%2BT%2F0gy3tIDo6WM5hcQfw743%2FAU9oaGnRBl%2BwoxRRDCZA%2F4jnPdluICSI7NCr6C3HHOe4CSZJjRihczxPA%2FgQczAVjAPs6CrK9N9pIzt0vNJpGUTgg4AoxftTvGfciDREilSctPIcBk6GwB%2Bzt4wbtNCmEPU%2BFD46rc6CWVQmmLwbKOeHw70ipMVFlOc8NlXkEtGO2vu6H44MstSSc6daOVUYUavQSGZ6jGhEjsP2YGz8KDm9XQotB%2FtyVBqiZV01OKqDmmBJA%2FXjUpSJ8M%2FeJXhe5m%2BKcxzNObtoJRI7jci3Tpt08XZyXZ7%2FyIV8H30ya468X3cfkTXrm972uga3z7iXAdubyEHnQIua%2FuvtwOHILi6O5D1Me2MzCPvcnXBjqTAYDoMWBt%2B3AVFuqSs2dmDQNBnvEhXfMgTedKLD5PJHb3aIl15iBkLaNu36GYnVCp9ZU2lSYcTbY5QwNnNeTfJTNy9bKhwzru6Sg%2BslhN%2BTS0zkTn%2BeLMY4tHgPEtEZ2cp%2FCk6GwqswvnYVKHjwO8q%2B1Mx8lc08fVEfarE9dke1GGFqoddx%2Bq%2BZUyHbDlAVfrCmf0yw%3D%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20261110T000135Z&X-Amz-SignedHeaders=host&X-Amz-Expires=86400&X-Amz-Credential=ASIAWA6KKHEJORWBH4N3%2F20261110%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=2c639021fe2bf17f2fe830516c7ae9edbd7f50102811668d52d3a1b89814da4f","created_at":1694053916982,"updated_at":1694053916982,"display_name":"NOS - 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The dataset contains information on selected chronic health conditions in the Medicaid population at the zip code level and Medicare Advantage Value-Based Insurance Design Model. Model is an opportunity for Medicare Advantage plans to offer supplemental benefits or reduced cost-sharing to enrollees with Centers for Medicare & Medicaid Services (CMS)-specified chronic conditions, focused on the services that are of highest clinical value to them.\n\n**Benefits**\n- Useful for providers for the use in the management of different chronic conditions.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [Co-Morbidity Among Chronic Conditions for Medicare FFS Beneficiaries](https://www.johnsnowlabs.com/marketplace/co-morbidity-among-chronic-conditions-for-medicare-ffs-beneficiaries)\n\t- This dataset provides an overview of chronic conditions with co-morbidity among Medicare FFS beneficiaries. Prevalence and Medicare spending are presented for chronic condition co-morbidity at the national level. Co-morbidity is illustrated by the combinations of the 19 chronic conditions.\n- [Medicaid Chronic Condition Inpatient Admission ER Visit Zip Code](https://www.johnsnowlabs.com/marketplace/medicaid-chronic-condition-inpatient-admission-er-visit-zip-code)\n\t- Medicaid is a state and federal program that provides health coverage for people having a very low income. The dataset contains information on selected chronic health conditions in the Medicaid population at the zip code level.\n- [Medicare Advantage Value Based Insurance Design Model](https://www.johnsnowlabs.com/marketplace/medicare-advantage-value-based-insurance-design-model)\n\t- As part of the Health Plan Innovation Initiatives, the Medicare Advantage Value-Based Insurance Design (VBID) Model is an opportunity for Medicare Advantage plans to offer supplemental benefits or reduced cost sharing to enrollees with Centers for Medicare & Medicaid Services (CMS)-specified chronic conditions, focused on the services that are of highest clinical value to them. The model will test whether this can improve health outcomes and lower expenditures for Medicare Advantage enrollee.\n- [Medicare Health Outcomes Survey 2012 to 2014](https://www.johnsnowlabs.com/marketplace/medicare-health-outcomes-survey-2012-to-2014)\n\t- Medicare HOS (Health Outcomes Survey) Public Use data files (PUFs) contain the majority of the survey items collected on the HOS instrument (excluding beneficiary identifying information) as well as selected additional administrative variables. PUFs are used for research purposes and to facilitate the dissemination of data collected by the Medicare HOS project for additional research, PUFs have been created for each cohort (combined baseline and two year follow up) of data.\n- [Medicare Health Outcomes Survey 2013 to 2015](https://www.johnsnowlabs.com/marketplace/medicare-health-outcomes-survey-2013-to-2015)\n\t- Medicare HOS (Health Outcomes Survey) Public Use data files (PUFs) contain the majority of the survey items collected on the HOS instrument (excluding beneficiary identifying information) as well as selected additional administrative variables. PUFs are used for research purposes and to facilitate the dissemination of data collected by the Medicare HOS project for additional research, PUFs have been created for each cohort (combined baseline and two year follow up) of data.\n- [Medicare Health Outcomes Survey 2014 to 2016](https://www.johnsnowlabs.com/marketplace/medicare-health-outcomes-survey-2014-to-2016)\n\t- Medicare HOS (Health Outcomes Survey) Public Use data files (PUFs) contain the majority of the survey items collected on the HOS instrument (excluding beneficiary identifying information) as well as selected additional administrative variables. PUFs are used for research purposes and to facilitate the dissemination of data collected by the Medicare HOS project for additional research. PUFs have been created for each cohort (combined baseline and two-year follow-up) of data.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated\n- Data Updates\n - Data updates support replace-on-update: outdated foreign keys are deprecated, not deleted\n\n**Our data is curated and enriched by domain experts** \n\n Each dataset is manually curated by our team of doctors, pharmacists, public health & medical billing experts:\n- Field names, descriptions, and normalized values are chosen by people who actually understand their meaning\n- Healthcare & life science experts add categories, search keywords, descriptions and more to each dataset\n- Both manual and automated data enrichment supported for clinical codes, providers, drugs, and geo-locations\n- The data is always kept up to date – even when the source requires manual effort to get updates\n- Support for data subscribers is provided directly by the domain experts who curated the data sets\n- Every data source’s license is manually verified to allow for royalty-free commercial use and redistribution.\n\n**Need Help?**\nIf you have questions about our products, contact us at [info@johnsnowlabs.com](mailto:info@johnsnowlabs.com). \n ","terms_of_service":"https://www.johnsnowlabs.com/healthcare-data-library-eula/","documentation_link":"https://www.johnsnowlabs.com/marketplace/medicare-health-plan-models-data-package","contact_email":"support@johnsnowlabs.com","privacy_policy_link":"https://www.johnsnowlabs.com/privacy-policy/","embedded_notebook_file_infos":[{"id":"4c4b2a23-5d41-4f00-b07f-82ca7024aa34","marketplace_file_type":"EMBEDDED_NOTEBOOK","file_parent":{"parent_id":"0905dc92-87c3-4b6b-85a2-7b49e8231a7e","file_parent_type":"LISTING"},"mime_type":"text/html","download_link":"https://marketplace-file-storage-prod.s3-fips.us-east-1.amazonaws.com/EMBEDDED_NOTEBOOK/LISTING_0905dc92-87c3-4b6b-85a2-7b49e8231a7e/4c4b2a23-5d41-4f00-b07f-82ca7024aa34?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEGgaDnVzLWVhc3QtMS1maXBzIkYwRAIgbC8sWIoYaHR8y7Wq86nPb0pufBl4%2BM4PWpeN%2B5PW9VQCIG7BhAVBDCWTpxFDLeuKYm%2Ff1JLz4iccPMzter0YdhgWKqYECDAQABoMNDE0MzUxNzY3ODI2IgxNAJrHbXaZG20jNfMqgwQyGEb4TsoVOoNvJ%2BaA%2FZ7zeu8hGcHqCeNfblP%2FQo5g6q2aRNtpwtPRKMlhYcIE%2FUQaNT1Lqmyptu32VUQTpzjTTxRVmJrDPkheSCkV06XQ%2FpTMKjU8CC7X1b%2FqYf8CrBwZT7Cv4C5WPjEET38f2A79h0xV6bZgPy28zQWrSeIJ9w6AfZBxiE24%2FVc08zVA0ivCT6lsy7nuU0wcR%2FMg2C9nd1vaMnQ923VpD21CKfVVYcrNWL2b%2FQ%2BNAO%2BxPjPgjiN6UPQbvbvZxkSdhgrGtecIg9NgqGkMcvM5VLTDtdC1kdeZetfOC2obtA%2BT%2F0gy3tIDo6WM5hcQfw743%2FAU9oaGnRBl%2BwoxRRDCZA%2F4jnPdluICSI7NCr6C3HHOe4CSZJjRihczxPA%2FgQczAVjAPs6CrK9N9pIzt0vNJpGUTgg4AoxftTvGfciDREilSctPIcBk6GwB%2Bzt4wbtNCmEPU%2BFD46rc6CWVQmmLwbKOeHw70ipMVFlOc8NlXkEtGO2vu6H44MstSSc6daOVUYUavQSGZ6jGhEjsP2YGz8KDm9XQotB%2FtyVBqiZV01OKqDmmBJA%2FXjUpSJ8M%2FeJXhe5m%2BKcxzNObtoJRI7jci3Tpt08XZyXZ7%2FyIV8H30ya468X3cfkTXrm972uga3z7iXAdubyEHnQIua%2FuvtwOHILi6O5D1Me2MzCPvcnXBjqTAYDoMWBt%2B3AVFuqSs2dmDQNBnvEhXfMgTedKLD5PJHb3aIl15iBkLaNu36GYnVCp9ZU2lSYcTbY5QwNnNeTfJTNy9bKhwzru6Sg%2BslhN%2BTS0zkTn%2BeLMY4tHgPEtEZ2cp%2FCk6GwqswvnYVKHjwO8q%2B1Mx8lc08fVEfarE9dke1GGFqoddx%2Bq%2BZUyHbDlAVfrCmf0yw%3D%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20261110T000135Z&X-Amz-SignedHeaders=host&X-Amz-Expires=86400&X-Amz-Credential=ASIAWA6KKHEJORWBH4N3%2F20261110%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=1480c949e1fbce53803f82e3af469464e17610b61d8aee6b05061e00b8775f48","created_at":1682537247877,"updated_at":1682537247877,"display_name":"EDA Notebook","status":"FILE_STATUS_PUBLISHED"}],"assets":["ASSET_TYPE_DATA_TABLE"]},"provider_summary":{"name":"John Snow Labs","description":"John Snow Labs, an AI and NLP for healthcare company, provides state-of-the-art software, models, and data to help healthcare and life science organizations build, deploy, and operate AI projects."},"popularity":1135},{"id":"dd4ab78d-fdbf-41cd-ab70-7c87e4d137c5","summary":{"name":"Geolocet | Latest Demographic Data France - 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This meticulously compiled dataset containing modeled data, based on official statistical sources, including INSEE, offers a detailed insight into the population landscape of France at the IRIS level. \n\nIf you require data at a less granular level, such as an Uber grid or square grid, please don't hesitate to get in touch with us at *contact@geolocet.com*. We understand that different projects may have varying data needs, and we are here to tailor our data to match your specific requirements.\n\n ## **Key Features:**\n---\n* **Population Data France:** Gain access to the latest and most accurate population statistics for France, meticulously organized by statistical sectors.\n\n* **Demographic Insights:** Dive deep into France's demographic landscape with data on 54 attributes, including: \n* Total Population,\n* Population 15 years old and above,\n* Population between 15 and 64 years,\n* Occupation,\n* Active population between 15 and 64 years,\n* Employed population between 15 and 64 years,\n* Unemployed population between 15 and 64 years,\n* Number of inactive people aged 15 to 64,\n* Number of pupils, students and unpaid interns aged 15 to 64,\n* Number of retirees or pre-retirees aged 15 to 64,\n* Working population 15 years old and above,\n* Employees,\n* Non-Employees,\n* Non-Employees Independent,\n* Non-Employees Employers,\n* Non-Employees Family helpers,\n* Average net hourly salary in 2021 (€).\n\nAll demographic attributes are available at the IRIS level.\n\n* **Geodemographic Insights:** Unlock geodemographic data France-wide to inform location-based decision-making, marketing strategies, and policy formulation.\n* **Seamless Data Access:** Our data is available for instant download in CSV format. No lengthy sign-up processes or NDAs required on our side - just download the data you need.\n* **Data Samples for Evaluation:** To ensure you're getting exactly what you need, we provide free downloadable samples in CSV format for data evaluation.\n* **Data accuracy:** Our datasets are created using the latest demographics data available in national statistics bureaus, guaranteeing accuracy and reliability.\n* **Best-in-Class Quality:** We guarantee best-in-class quality, backed by over two decades of experience in modeling demographics data and an outstanding quality management process.\n* **Competitive Pricing:** We maintain the lowest prices on the market, ensuring that you get the most cost-efficient demographics data acquisition process.\n---\nAdditionally, for those looking to enrich their analysis or project, we offer:\n* **[Administrative Boundaries](https://geolocet.com/products/france-admin-level-8-municipalities):** If you require geographical boundaries (polygons) for the municipalities of France, consider exploring our Administrative Boundaries product, which seamlessly integrates with this dataset.\n\n* **[Point of Interest (PoI) Data](https://geolocet.com/pages/aggregated-points-of-interest-poi-data):** If you're interested in Points of Interest data, please don't hesitate to contact us for customized datasets to enhance your geographical analysis.\n\n* **[Data Customization](https://geolocet.com/pages/data-services-by-geolocet):** We understand that your project may have specific requirements. Feel free to get in touch with us if you need any modifications or specific data formats such as Excel, JSON, GeoJSON, or Shapefile. We are here to tailor the data to your needs.\n\nIf you're looking for data for France, [look here](https://geolocet.com/pages/france) for off-the-shelf or tailor-made datasets to suit your specific requirements.\n\n\n\nMake informed decisions, conduct precise research, and unlock the potential of the French demographic landscape with our comprehensive dataset. Trust in the accuracy and reliability of our official statistical data to drive your projects forward.\nContact us at *contact@geolocet.com* today to access this invaluable resource and explore France census data like never before.\n\n[Geolocet LTD](https://geolocet.com)\n","terms_of_service":"https://geolocet.com/pages/terms-of-use","documentation_link":"","privacy_policy_link":"https://geolocet.com/pages/privacy-policy","cost":"PAID","data_source":"","assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://geolocet.com/pages/terms-of-use"},"provider_summary":{"name":"Geolocet LTD","description":"Geolocet provides premium geospatial data for retail analysis, GIS, and market planning. 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The products include a range of no-code data solutions utilized by business owners and a robust infrastructure used by engineers and IT professionals.\n\nBright Data provides a cost-effective way to perform fast and stable public web data collection at scale, effortless conversion of unstructured data into structured data, and superior customer experience while being fully transparent and compliant."},"popularity":2062},{"id":"238e8ed0-85bb-4e9f-8802-b5c6d0242be8","summary":{"name":"Global Life Expectancy Data","subtitle":"This data package contains datasets on causes, risk factor, deaths, death rate, years of life lost (YLL), years lived ..","share":{"name":"global_life_expectancy_data","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1682356098239,"updated_at":1698965635225,"categories":["HEALTH","SCIENCE_AND_RESEARCH"],"listingType":"STANDARD","provider_id":"f88849f8-9d77-4de2-b4c7-03d55cc3768e"},"detail":{"description":"\n**Overview**\nThis data package contains datasets on causes, risk factor, deaths, death rate, years of life lost (YLL), years lived with disability (YLD), disability-adjusted life years (DALY), life expectancy and health-adjusted life expectancy (HALE) from the global burden of disease globally.\n\n**Description**\nThis data package contains datasets on global burdens of disease by cause and risk factor globally. The datasets provide metrics to measure the overall disease burden expressed as Disability-adjusted life year (DALY), the sum of years of life lost (YLLs) and years lived with disability (YLDs), life expectancy and health-adjusted life expectancy (HALE). The datasets provide global, regional, and GBD location-specific and gender-specific data. The datasets included in this data package resulted from the Global Burden of Disease Study 2016.\n\n**Benefits**\n- This data is useful for researchers and policymakers to compare very different populations and health conditions across time. this can be used for further research\n- public health officials and program managers for potential interventions, setting priority areas for action and evaluating progress\n- policymakers for policy use and development\n- public health practitioner and clinicians' aid in designing and implementing targeted interventions and monitoring the progress of public health strategies.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [Global Burden of Cancer Disease](https://www.johnsnowlabs.com/marketplace/global-burden-of-cancer-disease)\n\t- This dataset provides estimates for deaths, disability-adjusted life years (DALYs), years lived with disability (YLDs), years of life lost (YLLs), prevalence, and incidence for all Cancers and 29 cancer groups by sociodemographic index quintile, region and\ncountry.\n- [Global Burden of Cancer Percent Change 2006 to 2016](https://www.johnsnowlabs.com/marketplace/global-burden-of-cancer-percent-change-2006-to-2016)\n\t- This dataset provides percent change in number and rates of incidence, mortality and disability-adjusted life year between 2006 and 2016 for all cancers and 29 cancer groups by sociodemographic index, quintile, region and country.\n- [Global Burden of Disease by Cause 2010](https://www.johnsnowlabs.com/marketplace/global-burden-of-disease-by-cause-2010)\n\t- This dataset provides four metrics with uncertainty intervals for 291 diseases and injuries: deaths, years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life years (DALYs) by region, age, and sex.\n- [Global Burden of Disease by Risk Factor 2010](https://www.johnsnowlabs.com/marketplace/global-burden-of-disease-by-risk-factor-2010)\n\t- This dataset provides results for the burden of disease and injury attributable to 67 risk factors, including the contribution to specific diseases and injuries. Metrics provided are deaths, years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life years (DALYs).\n- [Global Burden of Hypertension and Systolic Blood Pressure](https://www.johnsnowlabs.com/marketplace/global-burden-of-hypertension-and-systolic-blood-pressure)\n\t- This dataset provides the estimated number of deaths and age-standardized rates of deaths and disability-adjusted life years (DALYs) and disability-adjusted life years (DALYs) in thousands related to systolic blood pressure > 110-115 mm Hg and systolic blood pressure > 140 mmHg for all causes combined, ages ≥25, and male, female and both sexes combined in 1990 and 2015 for global, Socio-Demographic Index (SDI) groupings at the country and territory level with 95% uncertainty intervals.\n- [Global Disability Adjusted Life Year By Cause](https://www.johnsnowlabs.com/marketplace/global-disability-adjusted-life-year-by-cause)\n\t- This dataset provides global all-age DALYs and age-standardized DALY rates in 1990, 2000 and 2016 with mean percentage changes between 1990 and 2016, 2006 and 2016, and 1990 and 2016 for all causes.\n- [Global Disability Adjusted Life Year By Location](https://www.johnsnowlabs.com/marketplace/global-disability-adjusted-life-year-by-location)\n\t- This dataset provides Global Burden of Disease (GBD) 2016 age-standardized rate of Disability Adjusted Life Year (DALY) (per 100,000) and all-age DALYs counts for all causes and level 1 cause groups by location for 1990, 2000, 2006 & 2016, both sexes combined.\n- [Global Life Expectancy 1970 to 2016](https://www.johnsnowlabs.com/marketplace/global-life-expectancy-1970-to-2016)\n\t- This dataset provides estimates of life expectancy at birth and at 65 years of age and 95% uncertainty interval estimates by location, male, female and both sexes combined, 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005, 2010, 2016. This age-specific mortality dataset is used to enable health systems to target interventions for the older adult populations.\n- [Global Life Expectancy 2016](https://www.johnsnowlabs.com/marketplace/global-life-expectancy-2016)\n\t- This dataset includes estimates for life expectancy at birth and at age 65, age-standardized death rates, and total deaths, by sex, for countries and territories and subnational units globally for the year 2016.\n- [Global Life Expectancy and Healthy Life Expectancy](https://www.johnsnowlabs.com/marketplace/global-life-expectancy-and-healthy-life-expectancy)\n\t- This dataset provides global, regional, and GBD location-specific life expectancy and health adjusted life expectancy (HALE) at birth, by sex, in 1990, 2006, and 2016.\n- [Global Years Lived with Disability By Cause](https://www.johnsnowlabs.com/marketplace/global-years-lived-with-disability-by-cause)\n\t- This dataset provides estimates for incidence, prevalence, and years lived with disability (YLDs) for 328 diseases and injuries for 195 countries from 1990 and 2016.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated\n- Data Updates\n - Data updates support replace-on-update: outdated foreign keys are deprecated, not deleted\n\n**Our data is curated and enriched by domain experts** \n\n Each dataset is manually curated by our team of doctors, pharmacists, public health & medical billing experts:\n- Field names, descriptions, and normalized values are chosen by people who actually understand their meaning\n- Healthcare & life science experts add categories, search keywords, descriptions and more to each dataset\n- Both manual and automated data enrichment supported for clinical codes, providers, drugs, and geo-locations\n- The data is always kept up to date – even when the source requires manual effort to get updates\n- Support for data subscribers is provided directly by the domain experts who curated the data sets\n- Every data source’s license is manually verified to allow for royalty-free commercial use and redistribution.\n\n**Need Help?**\nIf you have questions about our products, contact us at [info@johnsnowlabs.com](mailto:info@johnsnowlabs.com). \n 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This data pa..","share":{"name":"snomed_clinical_terminology_snapshot","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1682356496541,"updated_at":1695692023472,"categories":["HEALTH","SCIENCE_AND_RESEARCH"],"listingType":"STANDARD","provider_id":"f88849f8-9d77-4de2-b4c7-03d55cc3768e"},"detail":{"description":"\n**Overview**\nSNOMED CT contains three different release file types in every version: full, delta and snapshot release. This data package consists of Snapshot Release which contains the current state of every component of SNOMED CT.\n\n**Description**\nSNOMED CT is one of a suite of designated standards for use in U.S. Federal Government systems for the electronic exchange of clinical health information and is also a required standard in interoperability specifications of the U.S. Healthcare Information Technology Standards Panel. When implemented in software applications, SNOMED CT can be used to represent clinically relevant information consistently, reliably and comprehensively as an integral part of producing electronic health information. SNOMED CT supports the development of comprehensive high-quality clinical content in health records. It provides a standardized way to represent clinical phrases captured by the clinician and enables automatic interpretation of these. SNOMED CT is a clinically validated, semantically rich, controlled vocabulary that facilitates evolutionary growth in expressivity to meet emerging requirements. SNOMED CT based clinical information benefits individual patients and clinicians as well as populations and it supports evidence based care. A 'Snapshot' release contains the most recent version of every component released up to the time of the snapshot. The version of each component contained in a snapshot is the most recent version of that component at the release time. The 'Snapshot' release is useful for a simple installation but does not provide a history or retrospective view of the terminology.\n\n**Benefits**\n- There are many benefits of using snomed ct in electronic health records including: \n- health information can be shared consistently within and across healthcare settings.\n- data can be organized, queried, and analyzed for the benefit of the individual and institution.\n- the risk of different interpretations of the record between different healthcare settings can be reduced.\n- updated twice a year to help users keep pace with the advances of healthcare terminology and add to the scope of coverage\n- in addition to adding value to electronic health records, snomed ct supports the following meaningful use objectives:\n- maintain an up-to-date problem list of current and active diagnoses\n- record patient family health history as structure data\n- identify and report cancer cases to state cancer registries\n- record and track changes in patient vital signs\n- record patient smoking status\n- provide a summary record for each transition of care\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [SNOMED CT Snapshot Association Reference Set](https://www.johnsnowlabs.com/marketplace/snomed-ct-snapshot-association-reference-set)\n\t- This dataset describes the Release File structure of SNOMED CT, referred to as Release Format 2 (RF2). The US Edition of SNOMED CT is the official source of SNOMED CT for use in US healthcare systems. The US Edition is a standalone release that combines the content of both the US Extension and the International release of SNOMED CT.\n\nAn Association reference set is a component reference set used to represent represents a set of unordered associations of a particular type between components.\n- [SNOMED CT Snapshot Attribute Value Reference Set](https://www.johnsnowlabs.com/marketplace/snomed-ct-snapshot-attribute-value-reference-set)\n\t- This dataset describes the Release File structure of SNOMED CT, referred to as Release Format 2 (RF2). The US Edition of SNOMED CT is the official source of SNOMED CT for use in US healthcare systems. The US Edition is a standalone release that combines the content of both the US Extension and the International release of SNOMED CT.\n\nA Attribute value reference set is a component reference set used to apply a tagged value to a SNOMED CT component.\n- [SNOMED CT Snapshot Concept](https://www.johnsnowlabs.com/marketplace/snomed-ct-snapshot-concept)\n\t- The Concept file holds the clinical concepts that make up SNOMED CT. A concept is given meaning by its Fully Specified Name, which is held in the Description file. A concept may be distinguished from or refined by association with other concepts using relationships, which are held in the Relationship file. This dataset describes the Release File structure, referred to as Release Format 2 (RF2) of the US Edition of SNOMED CT which is the official source for use in US healthcare systems.\n- [SNOMED CT Snapshot Description Type Reference Set](https://www.johnsnowlabs.com/marketplace/snomed-ct-snapshot-description-type-reference-set)\n\t- Description format reference set is a C-I (component - integer) reference set which is used to specify the length and format of the terms in descriptions of this description type. This dataset describes the Release File structure of SNOMED CT, referred to as Release Format 2 (RF2) of the US Edition of SNOMED CT which is the official source of SNOMED CT for use in US healthcare systems.\n- [SNOMED CT Snapshot Description](https://www.johnsnowlabs.com/marketplace/snomed-ct-snapshot-description)\n\t- This dataset holds descriptions that describe SNOMED CT concepts. A description gives meaning to a concept and provide well-understood and standard ways of referring to a concept. This dataset describes the Release File structure of SNOMED CT, referred to as Release Format 2 (RF2) which is the official source of SNOMED CT for use in US healthcare systems. The US Edition is a standalone release that combines the content of both the US Extension and the International release of SNOMED CT.\n- [SNOMED CT Snapshot Language Reference Set](https://www.johnsnowlabs.com/marketplace/snomed-ct-snapshot-language-reference-set)\n\t- A Language reference set is a Component reference set that is used to indicate which descriptions contain terms that are acceptable or preferred in a particular language or dialect. This dataset describes the Release File structure of SNOMED CT, referred to as Release Format 2 (RF2). The US Edition of SNOMED CT is the official source of SNOMED CT for use in US healthcare systems. It combines the content of both the US Extension and the International release of SNOMED CT.\n- [SNOMED CT Snapshot MRCM Attribute Domain Reference Set](https://www.johnsnowlabs.com/marketplace/snomed-ct-snapshot-mrcm-attribute-domain-reference-set)\n\t- MRCM (Machine Readable Concept Model) Attribute Domain Reference Set allows attributes to be associated with the domains in which they may be applied. It also allows grouping and cardinality constraints to be specified for each attribute and domain combination. For each attribute-domain rule, the strength of the rule and the content type over which this rule applies is also specified.\n- [SNOMED CT Snapshot MRCM Attribute Range Reference Set](https://www.johnsnowlabs.com/marketplace/snomed-ct-snapshot-mrcm-attribute-range-reference-set)\n\t- An MRCM (Machine Readable Concept Model) Attribute Range Reference Set allows attributes to be associated with a valid value range for a given SNOMED CT content type and rule strength. The range of each attribute is defined using an Expression Constraint. This expression constraint represents the set of concepts, expressions, or concrete values that may be used as the value of the given attribute.\n- [SNOMED CT Snapshot MRCM Domain Reference Set](https://www.johnsnowlabs.com/marketplace/snomed-ct-snapshot-mrcm-domain-reference-set)\n\t- MRCM Domain Reference Set enumerates the concept domains to which SNOMED CT attributes may be applied, and provides additional information to support these concept domains. Each concept domain is uniquely identified by a SNOMED CT concept. When the scope of a domain covers the concepts in a particular hierarchy (or subhierarchy), the supertype concept of this hierarchy (or subhierarchy) is used to identify the domain.\n- [SNOMED CT Snapshot MRCM Module Scope Reference Set](https://www.johnsnowlabs.com/marketplace/snomed-ct-snapshot-mrcm-module-scope-reference-set)\n\t- An MRCM (Machine Readable Concept Model) Module Scope Reference Set specifies the set of MRCM reference sets that should be applied to the content in each module. Within a SNOMED CT Edition, the MRCM rules applied to the included modules must be consistent, to ensure data integrity within an edition is maintained.\n- [SNOMED CT Snapshot Module Dependency Reference Set](https://www.johnsnowlabs.com/marketplace/snomed-ct-snapshot-module-dependency-reference-set)\n\t- This dataset describes the Release File structure of SNOMED CT, referred to as Release Format 2 (RF2). The US Edition of SNOMED CT is the official source of SNOMED CT for use in US healthcare systems. The US Edition is a standalone release that combines the content of both the US Extension and the International release of SNOMED CT.\n\nThe Module dependency reference set is used to represent dependencies between modules, taking account of module versioning.\n- [SNOMED CT Snapshot OWL Expression Reference Set](https://www.johnsnowlabs.com/marketplace/snomed-ct-snapshot-owl-expression-reference-set)\n\t- This dataset is an OWL (Ontology Web Language) ontology reference set (foundation metadata concept), in which the OWL expressions represent essential information about an ontology. This information includes namespaces, ontology URI (Uniform Resource Identifier), ontology version URI, and import statements. An OWL expression type reference set associates description logic statements with SNOMED CT (Systematized Nomenclature of Medicine - Clinical Terms) concept in the OWL functional syntax.\n- [SNOMED CT Snapshot Refset Descriptor Reference Set](https://www.johnsnowlabs.com/marketplace/snomed-ct-snapshot-refset-descriptor-reference-set)\n\t- This dataset describes the Release File structure of SNOMED CT, referred to as Release Format 2 (RF2). The US Edition of SNOMED CT is the official source of SNOMED CT for use in US healthcare systems. The US Edition is a standalone release that combines the content of both the US Extension and the International release of SNOMED CT.\n\nRefset Descriptor Reference Set specifies the structure of reference sets.\n- [SNOMED CT Snapshot Relationship Concrete Values](https://www.johnsnowlabs.com/marketplace/snomed-ct-snapshot-relationship-concrete-values)\n\t- This dataset describes the information about SNOMED International proposes to further enhance SNOMED CT by adding the capability to express concrete values. This enhancement will initially target improvements in the Pharmaceutical / biologic product hierarchy .This dataset describes the Release File structure of SNOMED CT, referred to as Release Format 2 (RF2). \n\nThe Relationship file holds one relationship per row of a particular type, and has a source concept and a concrete value concept.\n- [SNOMED CT Snapshot Relationship](https://www.johnsnowlabs.com/marketplace/snomed-ct-snapshot-relationship)\n\t- This dataset describes the Release File structure of SNOMED CT, referred to as Release Format 2 (RF2). The US Edition of SNOMED CT is the official source of SNOMED CT for use in US healthcare systems. The US Edition is a standalone release that combines the content of both the US Extension and the International release of SNOMED CT.\n\nThe Relationship file holds one relationship per row. Each relationship is of a particular type, and has a source concept and a destination concept.\n- [SNOMED CT Snapshot Simple Map Reference Set](https://www.johnsnowlabs.com/marketplace/snomed-ct-snapshot-simple-map-reference-set)\n\t- This dataset describes the Release File structure of SNOMED CT, referred to as Release Format 2 (RF2). The US Edition of SNOMED CT is the official source of SNOMED CT for use in US healthcare systems. The US Edition is a standalone release that combines the content of both the US Extension and the International release of SNOMED CT.\n\nA Simple Map Reference set is used to represent one-to-one maps between SNOMED CT concepts and codes in another terminology, classification or code system.\n- [SNOMED CT Snapshot Simple Reference Set](https://www.johnsnowlabs.com/marketplace/snomed-ct-snapshot-simple-reference-set)\n\t- This dataset describes the Release File structure of SNOMED CT, referred to as Release Format 2 (RF2). The US Edition of SNOMED CT is the official source of SNOMED CT for use in US healthcare systems. The US Edition is a standalone release that combines the content of both the US Extension and the International release of SNOMED CT.\n\nA Simple reference set does not have any addition fields.\n- [SNOMED CT Snapshot Stated Relationship](https://www.johnsnowlabs.com/marketplace/snomed-ct-snapshot-stated-relationship)\n\t- This dataset describes the Release File structure of SNOMED CT, referred to as Release Format 2 (RF2). The US Edition of SNOMED CT is the official source of SNOMED CT for use in US healthcare systems. The US Edition is a standalone release that combines the content of both the US Extension and the International release of SNOMED CT.\n\nStated Relationship File is a distribution file containing the stated form of SNOMED CT relationships.\n- [SNOMED CT Snapshot Text Definition](https://www.johnsnowlabs.com/marketplace/snomed-ct-snapshot-text-definition)\n\t- This dataset describes the Release File structure of SNOMED CT, referred to as Release Format 2 (RF2). The US Edition of SNOMED CT is the official source of SNOMED CT for use in US healthcare systems. The US Edition is a standalone release that combines the content of both the US Extension and the International release of SNOMED CT.\n- [SNOMED Clinical Terminology to ICD-10-CM Snapshot Map](https://www.johnsnowlabs.com/marketplace/snomed-clinical-terminology-to-icd-10-cm-snapshot-map)\n\t- The purpose of the SNOMED CT to ICD-10-CM map is to support semi-automated generation of ICD-10-CM codes from clinical data encoded in SNOMED CT for reimbursement and statistical purposes.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated\n- Data Updates\n - Data updates support replace-on-update: outdated foreign keys are deprecated, not deleted\n\n**Our data is curated and enriched by domain experts** \n\n Each dataset is manually curated by our team of doctors, pharmacists, public health & medical billing experts:\n- Field names, descriptions, and normalized values are chosen by people who actually understand their meaning\n- Healthcare & life science experts add categories, search keywords, descriptions and more to each dataset\n- Both manual and automated data enrichment supported for clinical codes, providers, drugs, and geo-locations\n- The data is always kept up to date – even when the source requires manual effort to get updates\n- Support for data subscribers is provided directly by the domain experts who curated the data sets\n- Every data source’s license is manually verified to allow for royalty-free commercial use and redistribution.\n\n**Need Help?**\nIf you have questions about our products, contact us at [info@johnsnowlabs.com](mailto:info@johnsnowlabs.com). \n ","terms_of_service":"https://www.johnsnowlabs.com/healthcare-data-library-eula/","documentation_link":"https://www.johnsnowlabs.com/marketplace/snomed-clinical-terminology-snapshot-data-package","contact_email":"support@johnsnowlabs.com","privacy_policy_link":"https://www.johnsnowlabs.com/privacy-policy/"},"provider_summary":{"name":"John Snow Labs","description":"John Snow Labs, an AI and NLP for healthcare company, provides state-of-the-art software, models, and data to help healthcare and life science organizations build, deploy, and operate AI projects."},"popularity":881},{"id":"a178e47a-f579-453d-a3a2-dd997af7cc29","summary":{"name":"GA Internal Medicine Top 10 HCPCS Codes by Volume - Anthem PPO","subtitle":"Standardized Price Transparency Data for Anthem's GA PPO Network. Top 10 HCPCS Codes for Internal Medicine by volume","setting":{"visibility":"PUBLIC"},"created_at":1719333148017,"updated_at":1722266514356,"categories":["HEALTH","SCIENCE_AND_RESEARCH","FINANCIAL"],"listingType":"PERSONALIZED","provider_id":"02c0fcf3-7ef7-488c-bb8f-ecd7962f6caa"},"detail":{"description":"**Overview**\n**Symmetric Information** is on a mission to solve the information imbalance in healthcare pricing. Using big data techniques we process and standardize Payer and Provider Price Transparency Files to allow for greater market insights.\n\nListed here is a sample dataset detailing the Anthem PPO negotiated rates with Internal Medicine providers in Georgia. The dataset includes negotiated rates for the top 10 most common, by volume, Internal Medicine HCPCS codes.\n\n**Use case**\nUse this dataset to benchmark your contracted rates against competitors or those of acquisition targets in your geographical area.\n\n**Product details**\nThis is a sample dataset from our ProComp database. It contains Anthem PPO contracted HCPCS and CPT rates for Internal Medicine Providers in Georgia. If you are interested in a dataset covering a different set of rates, or for the complete dataset, please reach out to us at Info@symmetric-information.com and we will be happy to discuss your needs.\n\n**Dataset Fields**\nMonth: The month that the rates were published by the payer\nBiling_Code_Type: The type of billing code, either CPT or HCPCS\nBilling_Code: The 5-character CPT or HCPCS code\nCode_Description: The name of the item / service that is offered\nBilling_Class: Professional or Institutional\nBilling_Code_Modifier: The 2-digit modifiers providing additional data about medical services, procedures, or supplies without changing the code's meaning.\nService_Code: CMS 2-digit code(s) placed on a professional claim\nArrangement: ffs, bundle, or capitation\nNegotiated_Type: negotiated, derived, fee schedule, percentage, or per diem\nNegotiated_Rate: Dollar or percentage based on the Negotiation_Type\nExpiration_Date: Date agreement for the Negotiated_Rate ends\nProvider_NPI: The provider's NPI\nTIN_Value: The TIN value of the provider's business\nTIN_Type: NPI or EIN\nProvider_Taxonomy: The primary taxonomy of the provider\nProvider_Business_Practice_Location_Address_State_Name: 2-digit state code\nProvider_Business_Practice_Location_Address_Postal_Code: 5-digit postal code\n\n**Additional Information**\nFor more information on our products, please send us an inquiry at Info@Symmetric-Information.com and visit our website at Symmetric-Information.com\n","terms_of_service":"https://symmetric-information.com/terms-of-use/","documentation_link":"","privacy_policy_link":"https://symmetric-information.com/privacy-policy/","geographical_coverage":"[\"United States and Canada\"]","cost":"FREE","update_frequency":{"interval":1,"unit":"MONTHLY"},"data_source":"Symmetric Information ProComp Database","assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://symmetric-information.com/terms-of-use/"},"provider_summary":{"name":"Symmetric Information LLC","description":"Symmetric Information is on a mission to solve the information imbalance in healthcare pricing. Using big data techniques we process and standardize Payer and Provider Price Transparency Files to allow for greater market insights."},"popularity":254},{"id":"31ef9ac0-e0ed-413a-afdb-ba5ab2c7fda5","summary":{"name":"Medicare Fee For Service Expenditure Data","subtitle":"This data package includes datasets that provide the necessary data to evaluate the Medicare Advantage system of fee-f..","share":{"name":"medicare_fee_for_service_expenditure_data","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1682355751536,"updated_at":1698965635253,"categories":["HEALTH","SCIENCE_AND_RESEARCH"],"listingType":"STANDARD","provider_id":"f88849f8-9d77-4de2-b4c7-03d55cc3768e"},"detail":{"description":"\n**Overview**\nThis data package includes datasets that provide the necessary data to evaluate the Medicare Advantage system of fee-for-service payments to hospitals and practices based on the geographical location. The datasets contain data about the adjustments indices used by Centers for Medicare and Medicaid Services (CMS) at county and state level and about the repriced reimbursements based on indices, at county and state level for aged, disabled and beneficiaries with End-Stage Renal Disease (ESRD).\n\n**Description**\nThis data package contains the Geographic adjustments are intended to ensure that the Medicare Program does not overpay hospitals and practitioners in certain areas and underpay in other because of geographic difference in price for resources such as clinical and administrative staff salaries and benefits, office or hospital space (rent), malpractice insurance (premiums), and other resources that are part of the cost of providing care. The historical claims data are used to rebase the Medicare Advantage (MA) Fee-For-Service (FFS) capitation Ratebook for the next years. The historical claims data is rebased to reflect the most current wage and cost indices. The datasets containing adjusted or repriced Medicare expenditure for aged, disabled and individuals with end-stage renal disease (ESRD) reflect the annual impact of the repricing of FFS claims, at county level. \n\nThe datasets containing data about the geographic adjustment indices cover the following:\n-\tInpatient Prospective Payment System (IPPS) wage and provider indices\n-\tGeographic Practice Cost Indices (GPCI)\n-\tHome Health Agency (HHA) wage indices\n-\tSkilled Nursing Facilities (SNF) wage indices\n\nThe datasets containing the repriced claims-based reimbursement data, are for the following services offered to each, Aged and Disabled beneficiaries:\n-\tMedicare Part A and B covered services,\n-\tDurable Medical Equipment (DME) and Durable Medical Equipment Non-Competitive Bidding Area (DME Non-CBA) related services,\n-\tHome Health Agency (HHA) services,\n-\tHospice services covered by Medicare Part A and B,\n-\tInpatient services,\n-\tOutpatient services,\n-\tPhysician services,\n-\tServices for people in Puerto Rico,\n-\tSkilled Nursing Facility (SNF) services,\n-\tUncompensated Care services, and combined to both, Aged and Disabled beneficiaries, the services offered by Accountable Care Organizations (ACO).\n\nOne dataset contains the repriced claims-based reimbursement data for the services offered to beneficiaries with End-Stage Renal Disease (ESRD).\n\n**Benefits**\n- Useful to medicare advantage interested parties and public health specialists or the specialized public. the fields descriptions along with the dataset description are useful for the user to quickly understand the data and the dataset.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [Geographic Indices HHA Wage Index](https://www.johnsnowlabs.com/marketplace/geographic-indices-hha-wage-index)\n\t- The dataset has information on the Medicare payment adjustments of geographic indices of Home Health Agency wage index for calendar years 2016-2022. The Medicare System adjusts fee for service payment rates for hospitals and practitioners according to the geographic location in which providers practice, recognizing that certain costs beyond the providers’ control vary between metropolitan and nonmetropolitan areas and also differ by region.\n- [Geographic Indices IPPS Provider Index](https://www.johnsnowlabs.com/marketplace/geographic-indices-ipps-provider-index)\n\t- The dataset has information on the Medicare payment adjustments of geographic indices for Inpatient Prospective Payment System (IPPS) by provider number for the fiscal years 2016-2022. The Medicare System adjusts fee for service payment rates for hospitals and practitioners according to the geographic location in which providers practice, recognizing that certain costs beyond the providers’ control vary between metropolitan and nonmetropolitan areas and also differ by region.\n- [Geographic Indices IPPS Wage Index](https://www.johnsnowlabs.com/marketplace/geographic-indices-ipps-wage-index)\n\t- The dataset has information on the Medicare payment adjustments of geographic indices for Inpatient Prospective Payment System (IPPS) wage index for the fiscal years 2014-2020. The Medicare System adjusts fee for service payment rates for hospitals and practitioners according to the geographic location in which providers practice, recognizing that certain costs beyond the providers’ control vary between metropolitan and nonmetropolitan areas and also differ by region.\n- [Geographic Indices Physician GPCI](https://www.johnsnowlabs.com/marketplace/geographic-indices-physician-gpci)\n\t- The dataset has information on the Medicare payment adjustments of Geographic Practice Cost Indices (GPCI) for calendar years 2015-2021. The Medicare System adjusts fee for service payment rates for hospitals and practitioners according to the geographic location in which providers practice, recognizing that certain costs beyond the providers’ control vary between metropolitan and nonmetropolitan areas and also differ by region.\n- [Geographic Indices SNF Wage Index](https://www.johnsnowlabs.com/marketplace/geographic-indices-snf-wage-index)\n\t- The dataset has information on the Medicare payment adjustments of Geographic Indices Skilled Nursing Facility (SNF) Wage index for the fiscal years 2010-2020. The Medicare System adjusts fee for service payment rates for hospitals and practitioners according to the geographic location in which providers practice, recognizing that certain costs beyond the providers’ control vary between metropolitan and nonmetropolitan areas and also differ by region.\n- [Repricing FFS Claims ACO Combined Data Aged Disabled PartsA PartB](https://www.johnsnowlabs.com/marketplace/repricing-ffs-claims-aco-combined-data-aged-disabled-partsa-partb)\n\t- The dataset includes the Accountable Care Organizations (ACOs) combined data for aged and disabled beneficiaries reimbursement for part A and part B by state and county of residence for the years 2012-2016.\n- [Repricing FFS Claims Aged Disabled Acute Inpatient Hospital Services](https://www.johnsnowlabs.com/marketplace/repricing-ffs-claims-aged-disabled-acute-inpatient-hospital-services)\n\t- The dataset includes the data for aged and disabled, Medicare Part A, beneficiaries reimbursement for acute inpatient hospital services by state and county of residence. The data included cover the years 2016 to 2020.\n- [Repricing FFS Claims Aged Disabled Cost Contracts For PartA And PartB](https://www.johnsnowlabs.com/marketplace/repricing-ffs-claims-aged-disabled-cost-contracts-for-parta-and-partb)\n\t- The dataset includes aged and disabled beneficiaries’ reimbursement data for cost contracts for part A and part B by state and county for years 2016-2020. It also contains Medicare Fee-For-Service (FFS) data for each county by aged and disabled beneficiaries including total Medicare FFS reimbursement data for Parts A and B, reimbursement for direct graduate education (DGME) and indirect medical education (IME) expenditures and disproportionate share hospital (DSH) expenditures for Part A.\n- [Repricing FFS Claims Aged Disabled Home Health Agency PartA And PartB](https://www.johnsnowlabs.com/marketplace/repricing-ffs-claims-aged-disabled-home-health-agency-parta-and-partb)\n\t- The dataset includes the data for aged and disabled, Medicare Part A and Part B, beneficiaries reimbursement for Home Health Agency (HHA) by state and county of residence. The data included cover the years 2016 to 2020.\n- [Repricing FFS Claims Aged Disabled Hospice PartA And PartB](https://www.johnsnowlabs.com/marketplace/repricing-ffs-claims-aged-disabled-hospice-parta-and-partb)\n\t- The dataset includes the data for aged and disabled, Medicare Part A and Part B, beneficiaries reimbursement for the hospice by state and county of residence. The data included cover the years 2016 to 2022.\n- [Repricing FFS Claims Aged Disabled Outpatient Healthcare Services](https://www.johnsnowlabs.com/marketplace/repricing-ffs-claims-aged-disabled-outpatient-healthcare-services)\n\t- The dataset includes the data for aged and disabled, Medicare Part B, beneficiaries reimbursement for outpatient healthcare services by state and county of residence. The data included cover the years 2016 to 2020.\n- [Repricing FFS Claims Aged Disabled Physician Fee Schedule](https://www.johnsnowlabs.com/marketplace/repricing-ffs-claims-aged-disabled-physician-fee-schedule)\n\t- The dataset includes the data for aged and disabled, Medicare Part B, beneficiaries reimbursement for physician fee schedule (PFS) by state and county of residence. The data included cover the years 2016 to 2020.\n- [Repricing FFS Claims Aged Disabled Puerto Rico PartA And PartB](https://www.johnsnowlabs.com/marketplace/repricing-ffs-claims-aged-disabled-puerto-rico-parta-and-partb)\n\t- The dataset includes the data for aged and disabled, Medicare Part A and Part B, beneficiaries reimbursement for Puerto Rico by county of residence. The data included cover the year 2022.\n- [Repricing FFS Claims Aged Durable Medical Equipment Non-CBA](https://www.johnsnowlabs.com/marketplace/repricing-ffs-claims-aged-durable-medical-equipment-non-cba)\n\t- The dataset includes the data for aged and disabled, Medicare Part B, beneficiaries reimbursement for Durable Medical Equipment Non-Competitive Bidding Area (Non-CBA) by state and county of residence. The data included cover the years 2016-2020.\n- [Repricing FFS Claims Aged Durable Medical Equipment](https://www.johnsnowlabs.com/marketplace/repricing-ffs-claims-aged-durable-medical-equipment)\n\t- The dataset includes the data for aged and disabled, Medicare Part B, beneficiaries reimbursement for Durable Medical Equipment by state and county of residence. The data included cover the years 2016-2020.\n- [Repricing FFS Claims Aged Skilled Nursing Facility Reimbursement](https://www.johnsnowlabs.com/marketplace/repricing-ffs-claims-aged-skilled-nursing-facility-reimbursement)\n\t- The dataset includes data for Aged beneficiaries of Skilled Nursing Facility Reimbursement by state and county of residence. Medicare fee-for-service data for each county broken down by aged, disabled, and end-stage renal disease (ESRD) beneficiaries.\n- [Repricing FFS Claims Aged Uncompensated Care Reimbursement Data](https://www.johnsnowlabs.com/marketplace/repricing-ffs-claims-aged-uncompensated-care-reimbursement-data)\n\t- The dataset includes data for Aged beneficiaries repricing adjustments to the 2014-2018 uncompensated care reimbursement by state and county of residence. Medicare Fee-For-Service (FFS) data for each county broken down by aged, disabled, and and end-stage renal disease (ESRD) beneficiaries.\n- [Repricing FFS Claims Aged](https://www.johnsnowlabs.com/marketplace/repricing-ffs-claims-aged)\n\t- The dataset includes data for Aged beneficiaries for part A and part B by state and county of residence. Medicare fee-for-service data for each county broken down by aged, disabled, and end-stage renal disease (ESRD) beneficiaries.\n- [Repricing FFS Claims Dialysis PartA PartB](https://www.johnsnowlabs.com/marketplace/repricing-ffs-claims-dialysis-parta-partb)\n\t- The dataset includes data for End-Stage Renal Disease (ESRD) beneficiaries or Dialysis for Part A Part B by state and county of residence. Medicare fee-for-service (FFS) data for each county broken down by aged, disabled, and ESRD beneficiaries.\n- [Repricing FFS Claims Disabled Skilled Nursing Facility Data](https://www.johnsnowlabs.com/marketplace/repricing-ffs-claims-disabled-skilled-nursing-facility-data)\n\t- The dataset includes data for Disabled beneficiaries Skilled Nursing Facility Reimbursement by state and county of residence. Medicare Fee-For-Service (FFS) data for each county broken down by aged, disabled, and end-stage renal disease (ESRD) beneficiaries.\n- [Repricing FFS Claims Disabled Uncompensated Care Reimbursement Data](https://www.johnsnowlabs.com/marketplace/repricing-ffs-claims-disabled-uncompensated-care-reimbursement-data)\n\t- The dataset includes data for Disabled beneficiaries repricing adjustments to the 2010-2014 uncompensated care reimbursement by state and county of residence. Medicare Fee-For-Service (FFS) data for each county broken down by aged, disabled, and end-stage renal disease (ESRD) beneficiaries.\n- [Repricing FFS Claims Disabled](https://www.johnsnowlabs.com/marketplace/repricing-ffs-claims-disabled)\n\t- The dataset includes data for disabled beneficiaries for part A and part B by state and county of residence. Medicare Fee-For-Service (FFS) data for each county broken down by aged, disabled, and end-stage renal disease (ESRD) beneficiaries.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated\n- Data Updates\n - Data updates support replace-on-update: outdated foreign keys are deprecated, not deleted\n\n**Our data is curated and enriched by domain experts** \n\n Each dataset is manually curated by our team of doctors, pharmacists, public health & medical billing experts:\n- Field names, descriptions, and normalized values are chosen by people who actually understand their meaning\n- Healthcare & life science experts add categories, search keywords, descriptions and more to each dataset\n- Both manual and automated data enrichment supported for clinical codes, providers, drugs, and geo-locations\n- The data is always kept up to date – even when the source requires manual effort to get updates\n- Support for data subscribers is provided directly by the domain experts who curated the data sets\n- Every data source’s license is manually verified to allow for royalty-free commercial use and redistribution.\n\n**Need Help?**\nIf you have questions about our products, contact us at [info@johnsnowlabs.com](mailto:info@johnsnowlabs.com). \n 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In the sample:\n\n- London: 6.07 million segments over 460,602 km of network.\n- Austin: 4.32 million segments over 563,970 km of network.\n- Los Angeles: 3.75 million segments over 388,213 km of network.\n- Melbourne: 1.37 million segments over 244,440 km of network.\n\nFor overall market coverage, see the [full Market coverage documentation](https://docs.tomtom.com/traffic-stats/documentation/product-information/market-coverage).\n\n**Documentation**\n\n[Traffic Stats Batch introduction](https://docs.tomtom.com/traffic-stats/documentation/batch/introduction) and [end-to-end use case notebooks](https://github.com/tomtom-international/tomtom-data-examples).\n\n**Pairs with TomTom Traffic Volumes**\n\nTomTom Traffic Volumes is a separate free listing covering the same areas. 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These medical/healthcare practitioners are in search of products and services that can drive their business growth. They are eager to access information that supports personal advancement, industry mastery, and achieving a competitive advantage.\n\nAvailable at the office address and at home address. At home, selections include typical consumer demographics, including age, income, presence and age of children, and over 250 lifestyle segments.\n\nAvailable for licensing, please inquire.\n\nSpecialties with counts for home address include:\n\nAcupuncturists\t 23,130 \nAmbulatory Medical Facility Personnel \t 2,989 \nAudiologists\t 16,415 \nCase Managers\t 21,901 \nCertified Diabetes Educators\t 8,674 \nChiropractors\t 45,684 \nDental Assistants\t 148,309 \nDental Hygienists\t 172,581 \nDentists\t 163,374 \nDieticians & Nutritionists\t 64,403 \nEmergency Medical Technicians\t 66,044 \nGenetic Counselors\t 520 \nMarriage and Family Therapists\t 77,913 \nMedical Laboratory Personnel\t 89,395 \nMedical Technicians\t 7,505 \nMental Health Counselors\t 119,317 \nNurses (RN, LPN, NP, APN, etc.)\t 8,008,887 \nOccupational Therapist Assistant\t 65,192 \nOccupational Therapists\t 171,811 \nOptometrists\t 35,738 \nPharmacist Intern\t 46,047 \nPharmacists\t 391,309 \nPharmacy Technician\t 400,256 \nPhysical Therapist Assistant\t 105,492\nPhysical Therapists\t 259,677 \nPhysician and Surgeon\t 527,078 \nPhysician Assistants\t 94,697 \nPodiatrists\t 9,198 \nProfessional Counselors\t 37,886 \nPsychologists\t 138,737 \nRadiologic/X-Ray Technologists\t 320,510 \nRehabilitation Facility Personnel\t 48,869 \nRespiratory Therapists\t 201,413 \nSocial Workers\t 382,799 \nSpeech & Hearing Therapists\t 54,503 \nSpeech Pathologists\t 190,900 \nVeterinarians\t 77,948","terms_of_service":"https://www.thinkdatagroup.com/privacy-policy","contact_email":"support@datarade.ai","privacy_policy_link":"https://www.thinkdatagroup.com/privacy-policy","assets":["ASSET_TYPE_DATA_TABLE"]},"provider_summary":{"name":"Think Data Group","description":"Think Data Group is an advisory firm composed of leading veterans of the data and information-services industry. 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We represent more than 100 data companies, many with very unique data and extensive scale. \r\n\r\nWe offer clients state-of-the-art consulting and implementation support across the full data lifecycle — a skill set that few companies develop in-house and few competitors can match.\r\n\r\nWhether you’re a startup or a billion-dollar enterprise, we can help you maximize the value of your data and find the data you are looking for to drive innovation and value.\r\n\r\nAt TDG, we offer a range of consumer and B2B data at scale:\r\n- Over 100 Audiences to reach your markets\r\n- Meets all regulatory/compliance standards\r\n- Custom audiences at your request\r\n- Multi-use data licensing \r\n- Private and first-party data delivery"},"popularity":1433},{"id":"16745438-1837-4c99-912f-f6664622f879","summary":{"name":"SAMPLE ATTOM | House Price Index Data | 30+ Years of Residential Price Trends | 36-Month ...","subtitle":"Nationwide U.S. Home Price Index data with 30+ years of residential price trends and forecasts up to 36 months, ...","share":{"name":"attom_99ec02b5_e350_4d57_8e3d_71d103176e2e_samples_share","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1788367221527,"updated_at":1788512770427,"categories":["OTHER"],"listingType":"STANDARD","provider_id":"17701e36-b41a-4edb-b019-5b37ea19c64d"},"detail":{"description":"ATTOM Home Price Index (HPI) is a family of hyperlocal residential price indices and forecasts providing a comprehensive view of house price appreciation across the United States, from the national level down to individual census blocks.\n\nBuilt on more than 30 years of historical trends, ATTOM HPI measures underlying market price movement rather than relying on median sale prices, which can fluctuate based on the types of homes sold. 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It’s used by appraisers, direct marketers, lenders, and insurers to enrich decisioning with timely insight into property-level building activity.\n\n**Risk Analysis**\nFor insurers and lenders, Building Permit data can be used to assess the risk associated with a particular property or project. This could be based on the type of permits issued, the value of the work being done, or the history of permits for a particular property or area.\n**Market Analysis**\nDirect marketers and other professionals can use the data to gain a deeper understanding of the property market in a specific area. For example, a high number of remodeling permits might suggest a flourishing housing market or an area undergoing gentrification.\n**Asset Valuation**\nAppraisers can use the data from Building Permit to better estimate the value of a property. Details such as the number of units, square feet, and number of structure stories can significantly impact a property's value.\n**Location Planning**\nBusinesses can use the data provided by Building Permit to plan new construction projects, remodels, and rehabs by understanding the current landscape of permits in a specific geographical area.\n**Regulatory Reporting**\nBusinesses can ensure their projects are in compliance with local laws and regulations by tracking permit statuses and expiration dates. 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Towns can determine whether they want to be wet, dry or partially dry through local option elections. This dataset contains a complete list of all the relevant details regarding local options. The New York State Alcohol Beverage Control Law (ABCL) specifies that no manufacturer or wholesaler shall sell to any retailer nor shall any retailer purchase any alcoholic beverages unless these beverages are labeled in accordance with the Authority's Rules and Federal Regulations and unless such label shall be registered with and approved by the State Liquor Authority. The SLA’s Licensing Bureau is responsible for the statewide processing of licenses, license renewals, permits and brand label registrations. All must be consistent with the Alcoholic Beverage Control Law. Base information corresponds to that provided, by applicants, on various permit applications. Processing time is contingent upon review within the Licensing Bureau. A list of active permits is also available in this data package.\n\n**Benefits**\n- complete list of active permits available\n- complete list of local options\n- brand label and whole sale information beneficial for the buyers and sellers\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [Alcoholic Beverage Wet And Dry Local Options](https://www.johnsnowlabs.com/marketplace/alcoholic-beverage-wet-and-dry-local-options)\n\t- This dataset contains information on in-force local options regarding sales of alcoholic beverages for municipalities across New York state beginning in 1948.\n- [Brand Label and Wholesale Information for Registered Alcohol Products](https://www.johnsnowlabs.com/marketplace/brand-label-and-wholesale-information-for-registered-alcohol-products)\n\t- This dataset contains information on brand label and wholesale information for alcohol products registered in New York State.\n- [List of Active Permits Issued by Liquor Authority](https://www.johnsnowlabs.com/marketplace/list-of-active-permits-issued-by-liquor-authority)\n\t- This dataset contains a list of all active permits issued by the State Liquor Authority (SLA). Permits in this dataset are issued for 1-3 year terms. The State Liquor Authority (SLA) regulates the manufacture and sale of alcoholic beverages.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated\n- Data Updates\n - Data updates support replace-on-update: outdated foreign keys are deprecated, not deleted\n\n**Our data is curated and enriched by domain experts** \n\n Each dataset is manually curated by our team of doctors, pharmacists, public health & medical billing experts:\n- Field names, descriptions, and normalized values are chosen by people who actually understand their meaning\n- Healthcare & life science experts add categories, search keywords, descriptions and more to each dataset\n- Both manual and automated data enrichment supported for clinical codes, providers, drugs, and geo-locations\n- The data is always kept up to date – even when the source requires manual effort to get updates\n- Support for data subscribers is provided directly by the domain experts who curated the data sets\n- Every data source’s license is manually verified to allow for royalty-free commercial use and redistribution.\n\n**Need Help?**\nIf you have questions about our products, contact us at [info@johnsnowlabs.com](mailto:info@johnsnowlabs.com). \n 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There are three UMLS Knowledge Sources: \n- The Metathesaurus, which contains over one million biomedical concepts from over 100 source vocabularies \n- The Semantic Network, which defines 133 broad categories and fifty-four relationships between categories for labeling the biomedical domain \n- The SPECIALIST Lexicon & Lexical Tools, which provide lexical information and programs for language processing The Metathesaurus is a large, multi-purpose, and multi-lingual vocabulary database that contains information about biomedical and health related concepts, their various names, and the relationships among them. The Metathesaurus is built from the electronic versions of many different thesauri, classifications, code sets, and lists of controlled terms used in patient care, health services billing, public health statistics, indexing and cataloging biomedical literature, and/or basic, clinical, and health services research. The Metathesaurus contains over five million terms, or names, organized by meaning into concepts and assigned a unique identifier. The Metathesaurus is not a vocabulary. It contains many vocabularies that are standards and helps to create mappings between these vocabularies. The Semantic Network consists of: \n- Semantic types (high level categories) \n- Semantic relationships (relationships between semantic types) The Semantic Network can be used to categorize any medical vocabulary. There are 133 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types. Semantic types and semantic relationships create a network that represents the biomedical domain. Semantic types and relationships help with interpreting the meaning that has been assigned to the Metathesaurus concept.\n\n**Benefits**\n- the purpose of the umls is to improve the ability of computer programs to \"understand\" the biomedical meaning in user inquiries and to use this understanding to retrieve and integrate relevant machine-readable information for users.\n- one powerful use of the umls is linking health information, medical terms, drug names, and billing codes across different computer systems.\n- linking terms and codes between your doctor, your pharmacy, and your insurance company.\n- patient care coordination among several departments within a hospital.\n- timely access to accurate and up-to-date information improves decision making and ultimately the quality of patient care and research.\n- the umls has many other uses, including search engine retrieval, data mining, public health statistics reporting, and terminology research.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [Acquired Abnormality Concepts and Types](https://www.johnsnowlabs.com/marketplace/acquired-abnormality-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Acquired Abnormality\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Acquired Abnormality Relationships](https://www.johnsnowlabs.com/marketplace/acquired-abnormality-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Acquired Abnormality\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Anatomical Abnormality Concepts and Types](https://www.johnsnowlabs.com/marketplace/anatomical-abnormality-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Anatomical Abnormality\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Anatomical Abnormality Relationships](https://www.johnsnowlabs.com/marketplace/anatomical-abnormality-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Anatomical Abnormality\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Anatomical Structure Concepts and Types](https://www.johnsnowlabs.com/marketplace/anatomical-structure-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Anatomical Structure\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Anatomical Structure Relationships](https://www.johnsnowlabs.com/marketplace/anatomical-structure-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Anatomical Structure\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Body Part Organ or Organ Component Concepts and Types](https://www.johnsnowlabs.com/marketplace/body-part-organ-or-organ-component-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Body Part Organ or Organ Component\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Body Part Organ or Organ Component Relationships](https://www.johnsnowlabs.com/marketplace/body-part-organ-or-organ-component-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Body Part Organ or Organ Component\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Cell Component Concepts and Types](https://www.johnsnowlabs.com/marketplace/cell-component-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Cell Component\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Cell Component Relationships](https://www.johnsnowlabs.com/marketplace/cell-component-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Cell Component\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Cell Concepts and Types](https://www.johnsnowlabs.com/marketplace/cell-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Cell\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Cell Relationships](https://www.johnsnowlabs.com/marketplace/cell-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Cell\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Congenital Abnormality Concepts and Types](https://www.johnsnowlabs.com/marketplace/congenital-abnormality-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Congenital Abnormality\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Congenital Abnormality Relationships](https://www.johnsnowlabs.com/marketplace/congenital-abnormality-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Congenital Abnormality\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Embryonic Structure Concepts and Types](https://www.johnsnowlabs.com/marketplace/embryonic-structure-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Embryonic Structure\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Embryonic Structure Relationships](https://www.johnsnowlabs.com/marketplace/embryonic-structure-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Embryonic Structure\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Finding Concepts and Types](https://www.johnsnowlabs.com/marketplace/finding-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Finding\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Finding Relationships](https://www.johnsnowlabs.com/marketplace/finding-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Finding\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Fully Formed Anatomical Structure Concepts and Types](https://www.johnsnowlabs.com/marketplace/fully-formed-anatomical-structure-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Fully Formed Anatomical Structure\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Fully Formed Anatomical Structure Relationships](https://www.johnsnowlabs.com/marketplace/fully-formed-anatomical-structure-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Fully Formed Anatomical Structure\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Gene or Genome Concepts and Types](https://www.johnsnowlabs.com/marketplace/gene-or-genome-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Gene or Genome\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Gene or Genome Relationships](https://www.johnsnowlabs.com/marketplace/gene-or-genome-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Gene or Genome\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Laboratory or Test Result Concepts and Types](https://www.johnsnowlabs.com/marketplace/laboratory-or-test-result-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Laboratory or Test Result\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Laboratory or Test Result Relationships](https://www.johnsnowlabs.com/marketplace/laboratory-or-test-result-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Laboratory or Test Result\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Sign or Symptom Concepts and Types](https://www.johnsnowlabs.com/marketplace/sign-or-symptom-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Sign or Symptom\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Sign or Symptom Relationships](https://www.johnsnowlabs.com/marketplace/sign-or-symptom-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Sign or Symptom\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Tissue Concepts and Types](https://www.johnsnowlabs.com/marketplace/tissue-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Tissue\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Tissue Relationships](https://www.johnsnowlabs.com/marketplace/tissue-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Tissue\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated\n- Data Updates\n - Data updates support replace-on-update: outdated foreign keys are deprecated, not deleted\n\n**Our data is curated and enriched by domain experts** \n\n Each dataset is manually curated by our team of doctors, pharmacists, public health & medical billing experts:\n- Field names, descriptions, and normalized values are chosen by people who actually understand their meaning\n- Healthcare & life science experts add categories, search keywords, descriptions and more to each dataset\n- Both manual and automated data enrichment supported for clinical codes, providers, drugs, and geo-locations\n- The data is always kept up to date – even when the source requires manual effort to get updates\n- Support for data subscribers is provided directly by the domain experts who curated the data sets\n- Every data source’s license is manually verified to allow for royalty-free commercial use and redistribution.\n\n**Need Help?**\nIf you have questions about our products, contact us at [info@johnsnowlabs.com](mailto:info@johnsnowlabs.com). \n 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Every record includes:\n\n• Company name and job title\n• Location and years of experience\n• Total pay, base pay, and additional pay\n• Submission count and scrape timestamp\n• CEO approval rating and company overall rating\n• Review counts by rating category\n• Source URL\nThis Glassdoor Data covers the United States, with a growing historical archive continuously expanding from 2022 to the present.\n\nWhat You Can Do With This Glassdoor Data\n\nCompensation Benchmarking\n\nHR and compensation teams use Glassdoor Data to benchmark pay for specific roles, seniority levels, and geographies against real-world employee submissions. Because Glassdoor Data gives you individual records instead of aggregated averages, you can build salary bands at exactly the granularity your models require.\n\n• Benchmark compensation for specific roles and seniority levels using Glassdoor Data salary submissions\n• Compare base pay, additional pay, and total compensation across geographies with Glassdoor Data\n• Analyze pay equity by segmenting Glassdoor Data records by years of experience and location\n• Set competitive salary bands by modeling Glassdoor Data submissions rather than relying on survey averages\n• Identify compensation gaps by comparing Glassdoor Data records to your internal pay structure\nHR Analytics\n\nPeople analytics teams use Glassdoor Data to track compensation trends, monitor employer sentiment, and benchmark internal pay against external market data. Glassdoor Data with individual submission records gives HR teams the input needed to build accurate dashboards, identify pay outliers, and inform compensation strategy.\n\n• Track compensation trends over time using Glassdoor Data historical salary submissions\n• Monitor company ratings and CEO approval trends as employee sentiment signals with Glassdoor Data\n• Benchmark internal compensation against external Glassdoor Data records by role and region\n• Identify pay compression and equity issues by segmenting Glassdoor Data by seniority and location\n• Power People Analytics dashboards with Glassdoor Data compensation and sentiment signals\nDue Diligence and Investment Analysis\n\nInvestors and deal teams use Glassdoor Data as an early signal of company health. Employee sentiment and compensation trends visible in Glassdoor Data often shift 30-60 days before changes appear in financial reports. PE, VC, and hedge fund teams use this Glassdoor Data to assess acquisition targets and monitor portfolio companies before problems surface in earnings.\n\n• Assess target company culture and employee satisfaction through Glassdoor Data ratings and reviews\n• Track CEO approval and company rating trends in Glassdoor Data as leading indicators of leadership stability\n• Verify compensation structures against market rates using Glassdoor Data salary records\n• Monitor portfolio company Glassdoor Data signals for early warning of talent or morale deterioration\n• Use Glassdoor Data sentiment trends as alternative data for investment thesis validation\nCompany and Employer Analysis\n\nStrategy and research teams use Glassdoor Data to compare employer brands across a competitive set, track how employee perception shifts during key corporate events, and benchmark executive pay against industry peers. 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The model achieves a Mean Absolute Percentage Error under 15%, making Glassdoor Data a foundation for salary intelligence across all Canaria products.\n\nThe title taxonomy model normalizes Glassdoor Data job titles into 50,000+ standardized categories from 20M+ raw titles, making Glassdoor Data comparable across companies, industries, and geographies.\n\nOur annotation team validates every Glassdoor Data model output before delivery.\n\nWhat Makes This Glassdoor Data Different\n\n• Raw, unaggregated records: Glassdoor Data gives you individual salary submissions, not pre-averaged summaries you can't unpack\n• Matchable: Glassdoor Data is designed to join directly with job postings and LinkedIn company profile datasets\n• Full pay breakdown: Glassdoor Data includes total pay, base pay, and additional pay per submission\n• Sentiment signals: Glassdoor Data CEO approval ratings and company scores serve as leading indicators for analysts and investors\n• Standardized titles: Glassdoor Data job titles are normalized to 50,000+ categories for cross-company and cross-industry comparison\n• Historical depth: Glassdoor Data is a growing archive continuously expanding across thousands of US companies\nWho Uses This Glassdoor Data\n\n• HR and Compensation Teams use Glassdoor Data to benchmark salaries, set competitive pay bands, and identify compensation gaps across roles and regions\n• People Analytics Teams use Glassdoor Data to track employer sentiment trends and build internal pay equity models\n• Investors, PE Firms, and VC Funds use Glassdoor Data as alternative data for due diligence, portfolio monitoring, and early company health signals\n• Finance and FP&A Teams use Glassdoor Data salary submissions to model compensation costs and forecast headcount spend\n• Compensation Consultants use Glassdoor Data to advise clients on market rates and competitive pay positioning\n• Market Research and BI Teams use Glassdoor Data to analyze employer branding trends and compensation patterns across industries\n• HR Tech Companies and B2B Platforms use Glassdoor Data to power compensation benchmarking tools, salary estimators, and employer intelligence products\nDelivery and Format\n\nGlassdoor Data is delivered in CSV, JSON, or Parquet via AWS S3 or Google Cloud Storage. 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It includes reporting, analysis, rankings and data from:\n- **Private Equity** - Buyouts, PE Hub, Private Equity International, Venture Capital Journal\n- **Private Real Estate** - PERE, PERE Credit, PERE Deals, Real Estate Capital Europe\n- **Unlisted Infrastructure** - Infrastructure Investor, Infrastructure Investor Deals\n- **Private Credit** - Private Debt Investor\n- **Secondaries** - Secondaries Investor\n- **Responsible Investing** - New Private Markets, Responsible Investor\n- **Farmland and Forestry** - Agri Investor\n- **Compliance, finance and operations** - Private Funds CFO, Regulatory Compliance Watch\n\n**Leverage PEI Group Comprehensive Editorial Insights & Data to**:\n- Benchmark fund terms, strategies, and performance (IRR/TVPI/DPI/RVPI)\n- Build fundraising strategies backed by verified investor allocation, mandate and commitment data\n- Strengthen strategic planning with award-winning editorial context combined with extensive market, fundraising and deal data\n\nBuilt for decision-makers, **PEI filters out the noise to surface only the most relevant, verified intelligence your teams actually need**. Derived through extensive multi-party sourcing, this structured, high-quality data originates from:\n- 1st-party proprietary research and direct surveys\n- Regulatory filings from global jurisdictions\n- Direct submissions from fund managers and investors\n- Intelligence sourced through PEI events and networks\n- Continuous input from the industry’s most recognised editorial team\n\nEach table is professionally validated through:\n- Multi-stage programmatic and human review\n- Cross-referencing across historical datasets\n- Manual validation by infrastructure specialists\n- Ongoing updates and historical enrichment\n- Normalised schema alignment across institutions, funds, performance, and commitments\n\nThe result is **an actionable dataset built for accuracy, completeness, and reliability** to enhance smarter investing decisions.\n\n\n**What’s included?**\nIn this sample dataset you will receive 10 rows of data across the below categories.\n\n*The industry’s leading editorial analysis and archive*\nFull history of reporting from private markets' most trusted editorial brands — interviews, features, analyses, rankings, trend narratives, market shifts — transformed into a structured, queryable knowledge layer that contextualises fundraising, deal activity, and strategy evolution.\n\n*Investor (LP) Profiles*\nDeep intelligence on pension funds, insurance companies, sovereign wealth funds, family offices, endowments, wealth managers, and governments actively allocating to global private markets funds:\n- Mandates, sector focus and asset-class allocations\n- Ticket sizes, pacing, re-ups and geographic preferences\n- AUM history and commitment behaviour\n- LP strategy shifts surfaced early via editorial coverage\n\n*Investor commitments*\nComprehensive LP → fund commitments:\n- Amounts, dates, pacing patterns\n- Co-investment participation\n- Fundraising cycle behaviour\n- Forward-looking allocation signals\n\n*Investor mandates*\nForward-looking, actionable data and insights including:\n- Allocation targets and ranges\n- ESG and impact requirements\n- Mandate maturity and open opportunities\n- Asset class specific appetite (real estate, private equity, credit, infrastructure & more)\n\n*Fund Manager (GP) Profiles*\nStandardised, structured GP intelligence:\n- Strategy focus and geographic reach\n- Fundraising history, track record and team structure\n- AUM and strategy evolution\n- Service providers and advisory relationships\n\n\n*Fund profiles & fund terms*\nDetailed fund-level data including:\n- Strategy, sector focus and investment scope\n- Target and final close sizes\n- Fund lifecycle events and staging\n- Detailed fund terms — fees, carry, hurdles, liquidity, governance\n\n\n*Fund performance*\nA fully standardised performance dataset for private markets vehicles, including:\n- IRR\n- TVPI\n- DPI\n- RVPI\n- NAV\n- Contribution & distribution timelines\n- Quarterly performance updates\n\n*Cashflows & distributions*\n• Capital called\n• Capital distributed\n• Net cashflow calculations\n• Timing patterns for pacing and capital planning\n\n\n*Service providers*\nProfiles and involvement of:\n- Legal advisors\n- Administrators\n- Placement agents\n- Consultants\n\n\n*Ranking data*\n- **Fund manager rankings** - 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Besides working with anonymised data on our end, we always apply K5 anonymisation to any output data we share. This means that you will never receive a row with fewer than five (5) visitors present. For high-granularity requests, this will result in some data loss. 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The Carbon Arc MCP Server brings that catalog directly into Databricks, letting any user — business or technical — interact with Carbon Arc through natural language for conversational data search, multi-source synthesis, and reporting. Ask *\"What was Walmart's card spend in Q1 2024?\"* and the MCP resolves the entity, picks the right data assets, and returns a structured, model-ready answer. The platform carries hundreds of data assets and petabytes of data spanning tens of thousands of companies, hundreds of thousands of brands, and trillions of transactions — with new assets added every month.\n\nWhat ties it all together is the **Carbon Arc unified ontology**. We structure data against four core entities — **Companies** (e.g., Alphabet), **Brands** (e.g., Google), **People** (e.g., Taylor Swift), and **Locations** (e.g., New York) — and link each one to operational representations like retailers, apps, and websites. 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Scores are delivered as stable, time-series data across 100+ sentiment, emotional, and thematic indicators in 28 languages, making it easy to integrate into dashboards, quantitative models, and risk systems.\n\n**Use cases**\n\n- Alpha generation: Rank equities by 30-day media sentiment to identify stocks likely to outperform or underperform over the following 90 days, as demonstrated in a study of 30,000 global companies from 2006–2020.\n- Macro & risk-off timing: Use sentiment moving averages (e.g., 30/90-day crossovers) to identify market turning points and generate risk-on/risk-off signals ahead of major price moves.\n- Currency & forex research: Monitor carry trade dynamics, currency peg instability, and macroeconomic sentiment to identify arbitrage opportunities across 44 global currencies.\n- Commodity tracking: Track supply/demand sentiment, weather damage, production volume, and agricultural stress indicators across 17 agricultural and 34 energy & material commodities in real time.\n- ESG & reputational monitoring: Screen companies by trust, management sentiment, labor disputes, litigation, and cyber crime scores to monitor business and supply chain risks continuously.\n- Cryptocurrency intelligence: Anticipate sentiment swings with FOMO, HODL, regulatory crackdown, developer sentiment, and adoption scores across 500+ cryptocurrencies.\n- Macroeconomic nowcasting: Update leading economic indicators in real time using country-level scores for inflation, unemployment, trade balance, government instability, and social unrest across 252 countries.\n\n**Product details**\n\n- Datasets represented include Companies & Company Groups, Countries, Country Markets, Currencies, Agricultural Commodities, Energy & Material Commodities, and Cryptocurrencies, each available as News, Social, and News_Social combined feeds at 1-minute, hourly, and daily update frequencies.\n- Sample fields include sentiment, buzz, fear, trust, priceDirection, earningsForecast, managementSentiment, socialUnrest, inflationForecast, regulatoryCrackdown, stockIndexSentiment, supplyVsDemand, and volatility.\n- Asset coverage spans 100,000+ global companies and ETFs, 252 countries, 62 country markets, 44 currencies, 53 commodities, and 500+ cryptocurrencies.\n- Scores are delivered via tab-delimited files over SFTP, with archive history back to 1998 (cryptocurrencies from 2009).\n- Data is identified using stable entity identifiers including Thomson Reuters PermID, ISO-4217 currency codes, and coinmarketcap.com tickers for cryptocurrencies.\n- Three redistribution tiers are available: Free to Air, Private Non-Professional, and Private Professional, with access to varying score sets and update frequencies.\n\nFor more details, refer to the embedded notebooks.\n\n**Additional Insights**\n\n- The predictive power of media sentiment has been validated across 30,000 global companies (2006–2020), with top-decile sentiment stocks significantly outperforming bottom-decile peers over 90-day horizons.\n- Findings from the MarketPsych data have been featured in 500+ academic citations and are the basis of Trading on Sentiment (Wiley Finance, 2016), available in English, Chinese, and Japanese.\n- MarketPsych Analytics data powers Yahoo! Finance sentiment indicators and is distributed globally through LSEG Data & Analytics, reaching 400,000+ end users across 190 countries.\n- For API documentation and technical integration guides, visit the LSEG Developer Portal.\n- For sales inquiries, contact LSEG Data & Analytics at https://www.lseg.com/en/data-analytics.","terms_of_service":"https://www.marketpsych.com/terms","documentation_link":"https://www.marketpsych.com/ma4/intro","privacy_policy_link":"https://www.marketpsych.com/privacy","geographical_coverage":"[\"Global\"]","cost":"PAID","update_frequency":{"interval":1,"unit":"MONTHLY"},"collection_granularity":{"interval":1,"unit":"MINUTE"},"collection_date_start":883698348956,"assets":["ASSET_TYPE_DATA_TABLE","ASSET_TYPE_GIT_REPO"],"license":""},"provider_summary":{"name":"MarketPsych","description":"MarketPsych provides AI-powered natural language processing (NLP) and sentiment analytics for financial markets. 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Google Analytics is implemented with \"page tags\", in this case, called the Google Analytics Tracking Code, which is a snippet of JavaScript code that the website owner adds to every page of the website. The tracking code runs in the client browser when the client browses the page (if JavaScript is enabled in the browser) and collects visitor data and sends it to a Google data collection server as part of a request for a web beacon. One of the datasets contains internet protocol address which represents a unique string of numbers separated by full stops that identifys each computer using the Internet Protocol to communicate over a network. \n\nTransmission Control Protocol TCP/IP protocol is the standard for how to communicate on the network. In the TCP/IP protocol, the unique identifier for a computer or device is represented by the IP. There are two standards for IP addresses: IP Version 4 (IPv4) and IP Version 6 (IPv6). Internet Top Level Domain Names dataset represents the delegation details of top-level domains, including gTLDs (Generic top-level domains) such as .com, and country-code TLDs such as .uk. Another dataset lists all the Media Types, Media Subtypes, and their file extensions. The details of the Media Types and Media Subtypes are taken from the official registry of Media Types maintained by IANA. The extension details are taken from the website of the Apache Software Foundation.\n\n**Benefits**\n- Marketing campaign optimization. website usability improvement. business benefits. target audience identification. budget allocation.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [Austin Google Analytics](https://www.johnsnowlabs.com/marketplace/austin-google-analytics)\n\t- This dataset provides the Austin Google Analytic. Google Analytics is a freemium web analytics service offered by Google that tracks and reports website traffic.\n- [IPv4 Geolocation](https://www.johnsnowlabs.com/marketplace/ipv4-geolocation)\n\t- This dataset is a database of IPv4 address networks with their respective geographical location.\n- [Internet Top Level Domain Names](https://www.johnsnowlabs.com/marketplace/internet-top-level-domain-names)\n\t- This dataset represents the delegation details of top-level domains, including gTLDs (Generic top-level domains) such as .com, and country-code TLDs such as .uk.\n- [List of Internet Media Types And Subtypes](https://www.johnsnowlabs.com/marketplace/list-of-internet-media-types-and-subtypes)\n\t- This dataset lists all the Media Types, Media Subtypes, and their file extensions. The details of the Media Types and Media Subtypes are taken from the official registry of Media Types maintained by IANA. The extension details are taken from the website of the Apache Software Foundation.\n- [Membership to International Copyright Treaties](https://www.johnsnowlabs.com/marketplace/membership-to-international-copyright-treaties)\n\t- This dataset provides the details of the membership to WIPO (World Intellectual Property Organization) administered treaties on the subject matter of copyright.\n- [NYC Social Media Usage](https://www.johnsnowlabs.com/marketplace/nyc-social-media-usage)\n\t- The Demographic Reports are produced by the Economic, Demographic and Statistical Research unit within the Countywide Service Integration and Planning Management (CSIPM) Division of the Fairfax County Department of Neighborhood and Community\nServices. Information produced by the Economic, Demographic and Statistical Research unit is used by every county department, board, authority and the Fairfax County Public Schools.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated\n- Data Updates\n - Data updates support 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[info@johnsnowlabs.com](mailto:info@johnsnowlabs.com). \n 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Media & Entertainment: Fortune 100 Streaming company sees 500% increase in conversions after switching from The Trade Desk to Yobi Data.\n3. E-Commerce: Fortune 500 E-Commerce company replicated the performance of Facebook Ads, decreasing their Customer Acquisition Cost by over $100 outside of Facebook with Yobi Data.\n4. Insurance: Fortune 500 Insurer leveraged Yobi Data to improve their ML performance by 15%, leading to a 3x increase in online conversions.\n5. Manufacturing: Fortune 100 Manufacturer decreased Cost per Action by 11x and increased Click-through Rate by 15x using Yobi Data.\n\n**Dataset Details:**\n300M US Consumers\n$5.5B Daily Spend\n70B Daily Ad Events\n40B Daily GPS Pings\n5B Daily Search Queries\n50M Connected TVs\n\n**Robust Identity Matching:**\nConsent string and time stamp maintained across entire dataset.\n1. Email\n2. IP Address\n3. Mailing Address\n4. MAID\n5. UID2\n6. CTV\n\n**Our Commitment to Privacy & Opt In:**\n1. 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For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [Detailed Drug Facts Label Structure by NDC](https://www.johnsnowlabs.com/marketplace/detailed-drug-facts-label-structure-by-ndc)\n\t- This dataset contains details from the Structure Product Labeling documents and NDC assignment of products submitted to FDA for the inclusion in the Online Label Repository.\n- [Drug Manufacturer Codes and Labeler Codes](https://www.johnsnowlabs.com/marketplace/drug-manufacturer-codes-and-labeler-codes)\n\t- This dataset contains a list of NDC National Health Related Items Code (NHRIC) codes for pharmaceutical companies.\n- [Drug Safety Labeling Changes](https://www.johnsnowlabs.com/marketplace/drug-safety-labeling-changes)\n\t- The Drug Safety Labeling Changes (SLC) database provides approved safety labeling changes by the Center for Drug Evaluation and Research (CDER) from January 2021 forward.\n- [NDC Packages of Unfinished Drugs](https://www.johnsnowlabs.com/marketplace/ndc-packages-of-unfinished-drugs)\n\t- The National Drug Code (NDC) Packages of Unfinished Drugs dataset contains NDC package listing data submitted for all unfinished drugs.\nDrug products are identified and reported using a unique, three-segment number, called the National Drug Code (NDC), which serves as a universal product identifier for drugs. FDA publishes the listed NDC numbers and the information submitted as part of the listing information in the NDC Directory which is updated daily.\n- [NDC Products of Unfinished Drugs](https://www.johnsnowlabs.com/marketplace/ndc-products-of-unfinished-drugs)\n\t- The National Drug Code (NDC) Products of Unfinished Drugs dataset contains NDC product listing data submitted for all unfinished drugs.\nDrug products are identified and reported using a unique, three-segment number, called the National Drug Code (NDC), which serves as a universal product identifier for drugs. FDA publishes the listed NDC numbers and the information submitted as part of the listing information in the NDC Directory which is updated daily.\n- [National Drug Code Packages](https://www.johnsnowlabs.com/marketplace/national-drug-code-packages)\n\t- The Drug Listing Act of 1972 requires registered drug establishments to provide the Food and Drug Administration (FDA) with a current list of all drugs manufactured, prepared, propagated, compounded, or processed by it for commercial distribution including drug code packages. Drug products are identified and reported using a unique, three-segment number, called the National Drug Code (NDC), which serves as a universal product identifier for drugs.\n- [National Drug Code Products](https://www.johnsnowlabs.com/marketplace/national-drug-code-products)\n\t- The Drug Listing Act of 1972 requires registered drug establishments to provide the Food and Drug Administration (FDA) with a current list of all drugs manufactured, prepared, propagated, compounded, or processed by it for commercial distribution including drug code products. Drug products are identified and reported using a unique, three-segment number, called the National Drug Code (NDC), which serves as a universal product identifier for drugs.\n- [Pillbox Master Data Lookup](https://www.johnsnowlabs.com/marketplace/pillbox-master-data-lookup)\n\t- This dataset is the master file of the Pillbox database of the US National Library of Medicine (NLM), which contains descriptive information on the existing medication pills in the market. Values supplied by the drug companies to FDA (Food and Drug Administration) as part of the Structured Product Labels are labeled \"SPL\". All other values are added by the National Library of Medicine’s (NLM) Pillbox or RxNorm datasets.\n- [Structured Product Labeling](https://www.johnsnowlabs.com/marketplace/structured-product-labeling)\n\t- The Structured Product Labeling dataset contains the most recent drug labeling information submitted to the Food and Drug Administration (FDA) and currently in use.\nAll labels information are published by DailyMed the official provider of FDA label information.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - 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Teams build agents in PolyAI Dialog Studio or through our developer APIs, and run them over phone, web chat, SMS and email. This MCP server gives AI Playground and the agents you build on Databricks read-only access to those conversations, including transcripts, PolyScore and any custom metrics you've set up.\n\n**Use cases**\n- Quality review: \"Find yesterday's conversations with a PolyScore below 3, longest first, and show me the transcripts of a sample.\"\n- Voice of the customer: \"In callers' own words, why are calls being handed to agents? Take the top 3 handoff reasons from the last 7 days and summarise what the caller said in the latest conversation for each.\"\n- Trends: \"When do customers get in touch? Show conversation volume by hour of day over the last 3 days, and point out the busiest and quietest hours.\"\n\n**Getting started**\n- The listing points at the US endpoint. UK, EU and Studio accounts should change the host when installing; see the documentation for your region's host.\n- Use an account API key from PolyAI Dialog Studio as the Bearer token.\n- New to PolyAI? https://poly.ai/contact","terms_of_service":"https://poly.ai/terms-of-use","documentation_link":"https://docs.poly.ai/mcp/data/introduction","privacy_policy_link":"https://poly.ai/privacy-policy","geographical_coverage":"[\"Global\"]","assets":["ASSET_TYPE_MCP"],"license":""},"provider_summary":{"name":"PolyAI","description":"PolyAI builds enterprise voice and conversational AI agents that resolve customer interactions end to end, without human handoff. 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The POD uses Amazon RDS for MySQL as the source, Lakeflow Connect for source connectivity, Databricks Notebooks / Python / PySpark for processing, Databricks Workflows for orchestration, Delta Lake for target storage, Unity Catalog for governance, Databricks Secrets for credential management, and Git / Databricks Repos for version control. The POC demonstrates the lifecycle using the Sakila actor table as a representative workload. \n\n**Additional Insights**\n• Structured migration lifecycle covering assessment, SQL conversion, data migration, reconciliation, and reporting. \n• Source metadata and workload understanding are established before target creation. \n• SQL conversion highlights MySQL-specific syntax, functions, data types, or other items that may require manual review. \n• Migration creates Databricks Delta tables rather than treating source foreign tables as the final target. \n• Reconciliation provides measurable source-to-target validation and captures mismatches or exceptions. \n• Unity Catalog provides the governance layer for the Databricks target environment. \n• A consolidated report/dashboard provides assessment findings, conversion results, migration status, validation results, warnings, and exceptions. \n• The modular POD can be extended to additional tables, workloads, and future migration scenarios where applicable. 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We collect this informaiton from various open-source reports, using natural language processing to identify relevent entities. In this guide, we give you an introduction to the dataset, its data sources, and how to leverage it to strengthen your understanding of the evolving threat landscape.\n\n**Use cases**\n- Threat Intelligence\n- Trend analysis\n\nOur dataset is consolidated using the Kaze KatoAI data platform, which ensures data quality and accuracy. We've gathered information from various sources, making it easier for you to access the data without the hassle of querying multiple databases. This simplification is designed to save you time and effort when researching vulnerabilities.\n\n**Ease of Use**\n\nWe've made the CyberIQ Dataset available in an easily readable format with a shallow schema. This design choice aims to reduce complexity and make it more accessible to a wider audience, from cybersecurity professionals to researchers.\n\n**Access the CVEShield Application**\n\nFor a user-friendly experience, we offer a free-to-use application at [www.cveshield.com](https://www.cveshield.com). This application is designed to help you identify the latest trending vulnerabilities using the CVEShield Dataset. It's a valuable tool for staying up-to-date with the ever-evolving world of cybersecurity.\n\n\nWe hope you find this dataset valuable and encourage you to reach out to us with any questions, feedback, or suggestions for improvements. 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Our core offerings span across vulnerability management, cyber threat intelligence and malware analysis, all supported by our KATO AI data platform, enabling us to ingest and process data at scale and deliver evidence based results."},"popularity":237},{"id":"11ae9684-1fe7-4411-a230-57c9c39f43ee","summary":{"name":"Integrated Energy Scenarios","subtitle":"The Integrated Energy Scenarios datasets give organizations faster analytics to understand S&P Global Energy scenarios.","setting":{"visibility":"PUBLIC"},"created_at":1782373158272,"updated_at":1784043847730,"categories":["ENERGY"],"listingType":"PERSONALIZED","provider_id":"eff4b18d-fe1b-4884-9591-eab9d16d2e2a"},"detail":{"description":"**Overview**\n\nThe Integrated Energy Scenarios datasets provide organizations with enhanced scenario analytics to quickly understand the S&P Global Energy energy scenarios data and analysis.\n\nThis offering provides five different pathways out to 2050, illustrating the pace of change in long-term global energy supply, demand, and trade based on current views and assumptions about economic growth, markets, policy, consumer behavior, and technology. \n\n- Our three plausible energy-integrated scenarios to 2050 are built bottom-up and two net-zero cases are constructed backward starting with a predetermined end point of global net-zero emissions by 2050. \n- The two net-zero cases illustrate potential examples of how the world's vast energy infrastructure would need to be reshaped in less than three decades to keep the 1.5°C climate target within reach.\n- The datasets include detailed regional and country-level outlooks for macroeconomic factors, which can be retrieved via individual APIs included in the product offering. \n- The scenarios data empowers users to leverage a wealth of insights useful across various roles.\n- Strategic planners, researchers, and analysts can refine internal forecasts and evaluate strategic options against S&P Global's independent scenarios, while C-level executives gain critical insights into climate policy impacts on profitability and navigate trends amid uncertainty. \n- Sustainability managers can utilize the data to develop effective climate change strategies and ensure compliance with regulatory reporting.\n- The individual APIs included in this offering allow users to retrieve data for assessing long-term energy trends and exploring specific themes in detail. \n\n\nDataset Overview:\n\n- Coal Markets\n- Employment\n- Final Energy Consumption\n- GDP\n- GHG Emissions\n- Natural Gas Markets\n- Oil Consumption (By Product and By Sector)\n- Population (By Age and By Urban vs Rural)\n- Power Market\n- Primary Energy Demand\n- Metadata Hierarchy Lists\n\n**Use cases**\n\n- Market Analysis – Increase confidence by stress-testing strategy across multiple energy pathways.\n- Benchmarking & Comparative Analysis - Increase confidence by comparing S&P Global Energy’s Scenario assumptions and outlooks against internal views.\n- Holistic Energy Demand Analysis – Understand overall consumption trends and identify key regions/sectors driving demand changes for targeted initiatives.\n- Policy Impact Assessment – Evaluate implications of regulatory changes and sustainability initiatives on energy markets, emissions, and economic outcomes.\n\n**Product details**\n\nSample Tables:\nENERGY_DEMAND\n\nSample Fields:\nENERGY_DEMAND\n\n- COUNTRY\n- ENERGY_OR_TECHNOLOGY_TYPE\n- ENERGY_TYPE\n- LONG_NAME\n- MODEL_VINTAGE\n- MODIFIED_DATE\n- SCENARIO\n- SECTOR\n- SUBSECTOR\n- THEME\n- UNIT\n- VALUE\n- NUMBER\n- YEAR\n\nView Descriptions:\n\n- ENERGY_DEMAND - The Demand end point from Integrated Energy Scenarios provides energy demand data by sectors and energy type for different geographies, for all S&P Global Energy Horizons Energy Scenarios (latest and historical updates).\n","terms_of_service":"https://www.spglobal.com/en/privacy/privacy-policy-english","documentation_link":"https://www.marketplace.spglobal.com/en/datasets/integrated-energy-scenarios-(1742332791)","privacy_policy_link":"https://www.spglobal.com/en/privacy/privacy-policy-english","geographical_coverage":"[\"Global\"]","update_frequency":{"interval":1,"unit":"YEARLY"},"collection_granularity":{"interval":1,"unit":"YEARLY"},"collection_date_start":631176493322,"assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"S&P Global Energy","description":"At S&P Energy, our complete view of global energy and commodities markets enables our customers to make decisions with conviction and create long-term, sustainable value. 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To build a strong and competitive organization, it is crucial to embrace best-in-class third party B2B data as a core part of your business strategy. ZoomInfo Data Cubes are powerful data solutions that provide seamless access to the world’s best B2B data, at scale. Whether you need to build your master data, supplement your multi-vendor strategy, unite siloed data, or run advanced analysis and modeling, ZoomInfo Data Cubes have you covered.With unlimited access to ZoomInfo data delivered directly to your existing systems of work, you can build a reliable go-to-market data foundation to analyze, predict, and engage with the right accounts to drive revenue. By actualizing your total addressable market, pinpointing your ideal customer profile, increasing segmentation accuracy, predicting look-alikes, sharing go-to-market insights and more, ZoomInfo Data Cubes provide holistic account intelligence for your go-to-market motions.\n\n__\n**Use cases**\nIn additional to the vast benefits of ZoomInfo’s core data, and use cases for the data itself, here are a few benefits of accessing data via data cubes:\n- Fill the whitespace in your TAM and build a comprehensive and reliable data foundation\n- Keep data decay at bay with recurring data deliveries\n- Ensure complete and accurate customer profiles for improved analysis, reporting, and modeling.\n- Seamlessly ingest, synthesize and analyze raw data directly within Databricks\n- Skip the hassle of credit limitations with data delivery at scale\n- Reduce time spent on manual data prep and validation\n- Improve data governance and regulatory compliance \n\n__\n**Product details**\nCompany Data Cube: Expand your TAM with access to 200+ ZoomInfo company data attributes at the HQ and location-level, refreshed and delivered quarterly.\n\nSample Data Cube: zi_retail_companies_headquartered_california\nDetails: Sample 100 Retail Companies Headquartered in California\n\nFor more details, refer to the embedded notebook.\n\n__\n**Full Suite of Data Cubes**\n**Company Data Cube:** *Firmographic Data purpose-built for Sales & Marketing*\nUnderstand and expand your Total Addressable Market (TAM) with access to 200+ ZoomInfo company data attributes at the HQ and location-level, refreshed and delivered quarterly. ZoomInfo’s firmographic data supports industry-leading match & fill rates built on logical business relations - making it the preferred foundational dataset for Sales and Marketing motions.\n\n**Company Data Cube Add-on:** *Technographic Data*\nGain in-depth insight into the complementary and competitive technologies companies are using to tailor your campaigns with precision. \n\n**Company Data Cube Add-on:** *Advanced Attributes* \nLeveraging complex data modeling and ZoomInfo’s proprietary rules engine, Advanced Attributes delivers a comparative analysis of department size and strength, technology spend, and a blended measure of department-level sophistication. \n\n\n**Hierarchy:** *Target the right companies with accurate corporate hierarchy linkages*\nUnderstand the relationship between accounts with unlimited access to ZoomInfo’s corporate hierarchy data. 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Subscribe to your choice of 4,000+ Standard Intent Topics to use as fuel for your modeling, scoring, analysis, and other advanced data initiatives.\n\n**Contact Data Cube:** *Pinpoint your ideal customer profile with complete account intelligence*\nFill the gaps in your accounts and map your ideal customer profile with criteria-based access to the industry’s most comprehensive contact data attributes, refreshed and delivered monthly.\n\n**Contact Data Cube Add-on:** *Job History*\nTrack your champions, discover patterns across career moves, recruit for culture-fit candidates, refine outreach based on responsibilities, and more with complete career histories for customers and prospects. \n\n\n**Additional Resources**\n[Capital One Case Study](https://www.zoominfo.com/b2b/customer-success/Capital-One)\nSee how Capital One uses ZoomInfo Data Cube to improve data quality and enable sales reps at scale. 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The CO2 emissions dataset contains estimated data from 1751 to 2014 on the source of CO2 emissions (fossil fuel, cement production). Another dataset provides GISS Surface Temperature (GISTEMP) Analysis. Four different series are provided: Global Annual Temperature Anomalies (Land) 1880-2014, Global Annual Temperature Anomalies (Land and Ocean) 1880-2014, Hemispheric Temperature Anomalies (Land+ Ocean) 1880-2014 and Annual Temperature anomalies (Land + Ocean) for three latitude bands that cover 30%, 40% and 30% of the global area, respectively, 1900-2014.\n\nData showing cumulative changes in sea level for the world’s oceans since 1880 are based on a combination of long-term tide gauge measurements and recent satellite measurements. It also shows average absolute sea level change, which refers to the height of the ocean surface, regardless of whether nearby land is rising or falling. Cumulative change in the mass balance of a set of “reference” glaciers worldwide beginning in 1945 values represent the average of all the glaciers that were measured. Negative values indicate a net loss of ice and snow compared with the base year of 1945. For consistency, measurements are in meters of water equivalent, which represent changes in the average thickness of a glacier. Another dataset contains the Monthly Mean CO2 trends in globally distributed networks of air sampling sites around the globe. The last dataset offers a historical view on temperature anomalies (i.e. deviations from the corresponding 1951-1980 means) starting with January, 1880 and until December, 2017.\n\n**Benefits**\n- Analysis of climate change effects. useful for climate change researchers, educators, governmental and non-governmental organizations, and the general public. provides climate, socio-economic and environmental data from the past for the projection into the future. queries, tables, charts and maps can be generated for statistical analysis.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [Atmospheric CO2 Trends in Mauna Loa AMGR](https://www.johnsnowlabs.com/marketplace/atmospheric-co2-trends-in-mauna-loa-amgr)\n\t- This dataset contains the Annual Mean Growth Rate (AMGR) regarding atmospheric CO2 trends in Mauna Loa, Hawaii, 1959 through 2017. It contains estimated data for every single year during the given period in Mauna Loa at county level.\n- [Atmospheric CO2 Trends in Mauna Loa MMD](https://www.johnsnowlabs.com/marketplace/atmospheric-co2-trends-in-mauna-loa-mmd)\n\t- This dataset contains the Monthly Mean Data (MMD) regarding atmospheric CO2 trends in Mauna Loa, Hawaii, from March 1st, 1958 till June 30th, 2019. It contains estimated data for every single month during the given period in Mauna Loa at county level.\n- [Austin Discarded Materials 2013 to 2015](https://www.johnsnowlabs.com/marketplace/austin-discarded-materials-2013-to-2015)\n\t- This dataset contains the list of Austin discarded materials. It has different types of recycled and waste materials for the year 2013 to 2015.\n- [Austin Discarded Materials 2018 to 2019](https://www.johnsnowlabs.com/marketplace/austin-discarded-materials-2018-to-2019)\n\t- This dataset contains the list of Austin discarded materials. It has different types of recycled and waste materials for the year 2018 to 2019.\n- [Austin Gallons of Water and Wastewater Treated](https://www.johnsnowlabs.com/marketplace/austin-gallons-of-water-and-wastewater-treated)\n\t- This dataset shows millions of gallons of water treated grouped by year, month, plant and water type from 2011 to 2019.\n- [Austin Registered Water Services Technicians](https://www.johnsnowlabs.com/marketplace/austin-registered-water-services-technicians)\n\t- This dataset contains the list of registered Water Services Technicians and their employers that provide the services required from customers that are regulated under the Cross Connection/Water Protection Program.\n- [Austin Residential Water Consumption](https://www.johnsnowlabs.com/marketplace/austin-residential-water-consumption)\n\t- This dataset shows monthly residential water consumption grouped by zip code and customer class.\n- [Austin Waste Collection and Diversion Report](https://www.johnsnowlabs.com/marketplace/austin-waste-collection-and-diversion-report)\n\t- This dataset contains the Austin Resource Recovery daily report providing waste collection information based on the categories Report Date: The date collections information recorded include: load type: the specific type of load that is being collected on that day, load weight and drop off site.\n- [Austin Wastewater Service Connection Count By Zip Code](https://www.johnsnowlabs.com/marketplace/austin-wastewater-service-connection-count-by-zip-code)\n\t- This dataset shows total services connections count for wastewater services by customer class and zip code from 2015 to 2022.\n- [Austin Watershed Reach Index and Problem Scores](https://www.johnsnowlabs.com/marketplace/austin-watershed-reach-index-and-problem-scores)\n\t- This dataset contains Austin Watershed Reach Index and Problem Scores by the City of Austin’s Environmental Resource Management Division.\n- [Bloomington Tree Trimming Activities](https://www.johnsnowlabs.com/marketplace/bloomington-tree-trimming-activities)\n\t- This dataset shows the tree trims (Limbs & Trees) request reported by the Public Works Department in Bloomington, Indiana in the United States. It includes all open requests and all completed requests.\n- [Chicago Beach Water Quality Automated Sensors Report](https://www.johnsnowlabs.com/marketplace/chicago-beach-water-quality-automated-sensors-report)\n\t- This dataset contains the report of the automated sensors in the water of Chicago beaches. This report includes date and time of measurement for water temperature, turbidity, transducer depth, wave height and period among others.\n- [Chicago Beach Weather Stations Automated Sensors](https://www.johnsnowlabs.com/marketplace/chicago-beach-weather-stations-automated-sensors)\n\t- This dataset shows the report of the automated sensors in the weather stations of Chicago beaches. This report includes the date and time of measurement for air temperature, humidity, rain intensity, wind direction and barometric pressure.\n- [Drinking Water Quality Distribution Monitoring Data](https://www.johnsnowlabs.com/marketplace/drinking-water-quality-distribution-monitoring-data)\n\t- This dataset provides an overview of the U.S. Environmental Protection Agency’s (EPA’s) research results from investigating water quality monitoring sensor technologies that might be used to serve as a real-time contamination warning system (CWS) when a contaminant is introduced into a drinking water distribution system.\n- [Estimates Emissions of CO2 at Country And Global Level](https://www.johnsnowlabs.com/marketplace/estimates-emissions-of-co2-at-country-and-global-level)\n\t- This dataset contains CO2 emissions estimated data the period 1751 to 2014. It contains estimated data for every year of the period, at country and global (all countries) level and on the source of CO2 emissions (fossil fuel, cement production). It contains estimated data for every year of the period, at country and global (all countries) level and on the source of CO2 emissions (fossil fuel, cement production).\n- [European Union Emissions Trading System](https://www.johnsnowlabs.com/marketplace/european-union-emissions-trading-system)\n\t- This dataset contains information regarding the European Union Emissions Trading System (ETS) from 2005-2014. Its geographic coverage includes data over 31 countries including United Kingdom (UK).\n- [GISS Surface Temperature Analysis](https://www.johnsnowlabs.com/marketplace/giss-surface-temperature-analysis)\n\t- This dataset provides (Goddard Institute for Space Studies) GISS Surface Temperature (GISTEMP) Analysis. Four different series are provided: Global Annual Temperature Anomalies (Land) 1880-2014, Global Annual Temperature Anomalies (Land and Ocean) 1880-2014, Hemispheric Temperature Anomalies (Land+ Ocean) 1880-2014 and Annual Temperature anomalies (Land + Ocean) for three latitude bands that cover 30%, 40% and 30% of the global area, respectively, 1900-2014.\n- [Garbage Carts Requested by 311 Service](https://www.johnsnowlabs.com/marketplace/garbage-carts-requested-by-311-service)\n\t- This dataset shows the garbage carts requested by the 311 Service. It includes all open requests and all completed requests since January 1, 2011.\n- [Global Average Absolute Sea Level Change](https://www.johnsnowlabs.com/marketplace/global-average-absolute-sea-level-change)\n\t- This dataset shows cumulative changes in sea level for the world’s oceans since 1880, based on a combination of long-term tide gauge measurements and recent satellite measurements. It also shows average absolute sea level change, which refers to the height of the ocean surface, regardless of whether nearby land is rising or falling.\n- [Global Average Cumulative Mass Balance of Reference Glaciers](https://www.johnsnowlabs.com/marketplace/global-average-cumulative-mass-balance-of-reference-glaciers)\n\t- This dataset shows the cumulative change in mass balance of a set of “reference” glaciers worldwide beginning in 1945. The values represent the average of all the glaciers that were measured. Negative values indicate a net loss of ice and snow compared with the base year of 1945. For consistency, measurements are in meters of water equivalent, which represent changes in the average thickness of a glacier.\n- [Globally Averaged Marine Surface Annual Mean CO2 Growth Rates](https://www.johnsnowlabs.com/marketplace/globally-averaged-marine-surface-annual-mean-co2-growth-rates)\n\t- This dataset contains the Annual Mean Growth Rate (AMGR) trends in globally distributed networks of air sampling sites from 1959 till 2021. It contains estimated data for every single year during the given period around the globe.\n- [Globally Averaged Marine Surface Monthly Mean CO2](https://www.johnsnowlabs.com/marketplace/globally-averaged-marine-surface-monthly-mean-co2)\n\t- This dataset contains the Monthly Mean CO2 trends in globally distributed networks of air sampling sites from January 1st, 1980 until May 2022. It contains estimated data for every single month during the given period around the globe.\n- [LBB Environmental Enforcement Penalty Notices 2016-2019](https://www.johnsnowlabs.com/marketplace/lbb-environmental-enforcement-penalty-notices-2016-2019)\n\t- This dataset contains details of all environmental enforcement Fixed Penalty Notices (FPNs) that have been issued in the LBB (London Borough of Barnet) since March 2017.\n- [LBB Environmental Enforcement Penalty Notices 2019-2022](https://www.johnsnowlabs.com/marketplace/lbb-environmental-enforcement-penalty-notices-2019-2022)\n\t- This dataset contains details of all environmental enforcement Fixed Penalty Notices (FPNs) that have been issued in the LBB (London Borough of Barnet) since March 2017.\n- [LBB Environmental Enforcement Penalty Notices](https://www.johnsnowlabs.com/marketplace/lbb-environmental-enforcement-penalty-notices)\n\t- This dataset contains details of all environmental enforcement Fixed Penalty Notices (FPNs) that have been issued in the LBB (London Borough of Barnet) since March 2017.\n- [LBB Tree Preservation Order Schedule Items](https://www.johnsnowlabs.com/marketplace/lbb-tree-preservation-order-schedule-items)\n\t- The dataset contains polygons for Tree Preservation Order Schedule Items within the LBB (London Borough of Barnet).\n- [NASA Global Temperature Anomalies Time Series 1880-2019](https://www.johnsnowlabs.com/marketplace/nasa-global-temperature-anomalies-time-series-1880-2019)\n\t- This dataset offers a historical view on temperature anomalies (i.e. deviations from the corresponding 1951-1980 means) starting with January 1880 and until October 2019. The estimated values contained in this dataset are averages for every month of the year (at a global level and earth hemispheres), for the entire year and for every season of the year.\n- [NASA Temperature Anomalies By Latitude Bands Time Series 1880-2018](https://www.johnsnowlabs.com/marketplace/nasa-temperature-anomalies-by-latitude-bands-time-series-1880-2018)\n\t- This dataset offers a historical view on temperature anomalies (i.e. deviations from the corresponding 1951-1980 means) starting with January 1880 and until 2018. The annually estimated values contained in this dataset are averages for band latitudes from Northern and Southern Hemispheres earth.\n- [NYS Wastewater Treatment Plants](https://www.johnsnowlabs.com/marketplace/nys-wastewater-treatment-plants)\n\t- This dataset provides information regarding wastewater treatment plants with permits issued under the New York State Pollutant Discharge Elimination System.\n- [Tree Debris Requested by 311 Service](https://www.johnsnowlabs.com/marketplace/tree-debris-requested-by-311-service)\n\t- This dataset shows the tree debris request reported by the 311 Service. It includes all open requests, all completed requests and duplicated requests since December 03, 2011.\n- [Tree Trims Requested by 311 Service](https://www.johnsnowlabs.com/marketplace/tree-trims-requested-by-311-service)\n\t- This dataset shows the tree trims request reported by the 311 Service. It includes all open requests and all completed requests since December 03, 2011.\n- [Water Withdrawals by Facility](https://www.johnsnowlabs.com/marketplace/water-withdrawals-by-facility)\n\t- This dataset contains the information regarding water withdrawals collected by New York State Department of Environmental Conservation (DEC).\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated\n- Data Updates\n - 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The data model is a unified, simple-to-use star schema-based model.\n\nAlloy.ai generates seasonality-and anomaly-aware forecasts out of the box that help fill in the gaps of retailers that do not provide their own forecasts, and exceeding the accuracy of some retailer forecasts.\n\n**Use cases**\n\nAlloy.ai supports a broad variety of use cases with its data platform, such as:\n\n- **Demand Forecasting**: Alloy.ai provides built-in two different POS forecasting models out of the box with the data platform: a seasonality-adjusted model, and a robust, GAM-based model. These model perform on par with many retailer forecasts and provide a baseline that gives your data science and planning teams forecasting coverage across your retail partner base.\n- **Location Geocoding**: Alloy.ai automatically pulls in up-to-date location master data for retail partners and augments all POS location data with geocoding for geospatial analysis.\n- **Data Quality and Cleansing**: Alloy.ai works with data from 350+ different sources, including EDI, ERP and other complex data sources. Feeds and APIs change all the time -we take care of updating the pipeline and do ETL on your behalf, so your team can focus on insights.\n- **Supply Chain & ERP**: Alloy.ai processes sales and inventory, as well as shipments, orders and receipts data. The data is brought into a unified data model for accurate visibility into both upstream and downstream supply. 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We’re leveraged by health plans, TPAs, health systems, provider networks, value-based care providers, and digital health solutions to streamline provider data management workflows while also supporting exceptional patient and provider experiences.\n\n**Use Cases**\n\n*Ribbon Foundation - Data Assets (see below for more information)*:\nRibbon’s provider data platform is built to support care decisions. Through the Ribbon Foundation offering, we power “find care” use cases to enhance the experience and improve confidence. \n\nThe data includes NPIs, practice locations, contact information with confidence scores, specialties, location types, relative cost and experience, areas of focus, and accepted insurances. The dataset also has broad coverage, including 99.9% of providers, 1.7M unique service locations, and insurance coverage for 90.1% of lives covered across all lines of business and payers. \n\nThe information is derived and maintained from thousands of data sources, billions of claims, advanced machine learning models, and human-in-the-loop model validation.\n\n*Data Management - [contact us](mailto:partnerships@ribbonhealth.com) to learn more*:\nRibbon’s data management platform allows organizations to more efficiently manage, customize, and maintain their own provider data. Through Databricks, we provide the infrastructure to ingest, standardize, and manage multiple data sources to drive operational efficiencies and create one source of truth for your organization. \n\nCustomers can also increase the quality and comprehensiveness of their provider data by incorporating new data sources and scoring data accurately at scale, including provider rosters delivered via email, data from legacy systems, and custom metadata to help power provider search workflows.\n\n*For more information on use cases, please see [our website](https://www.ribbonhealth.com/use-cases).*\n\n**Product Details - Ribbon Foundation**\n\n*Provider Directory*\n- Demographics\n- Patient Satisfaction Ratings\n- Education\n- Affiliations\n- Insurances Accepted\n- Practice Locations\n- Specialty\n- Patient Panel Demographics\n- Clinical Areas\n- Conditions Treated\n- Treatments Performed\n- Procedures\n\n*Location Directory*\n- NPIs\n- Affiliations\n- Insurances Accepted\n- Contact Information\n- Accuracy\n- Clinical Area\n- Treatments Performed\n\n**Additional Insights**\nVisit [www.ribbonhealth.com/products](www.ribbonhealth.com/products) for more information and access to our API documentation.","terms_of_service":"https://www.ribbonhealth.com/terms-of-service","documentation_link":"https://ribbon.readme.io/docs","privacy_policy_link":"https://www.ribbonhealth.com/privacy-policy","embedded_notebook_file_infos":[{"id":"61c13636-ff30-43f2-b88a-fb3f9fd0618c","marketplace_file_type":"EMBEDDED_NOTEBOOK","file_parent":{"parent_id":"ec9bc6e8-719a-4ec8-b546-c51d0d9a0797","file_parent_type":"LISTING"},"mime_type":"text/html","download_link":"https://marketplace-file-storage-prod.s3-fips.us-east-1.amazonaws.com/EMBEDDED_NOTEBOOK/LISTING_ec9bc6e8-719a-4ec8-b546-c51d0d9a0797/61c13636-ff30-43f2-b88a-fb3f9fd0618c?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEGgaDnVzLWVhc3QtMS1maXBzIkYwRAIgbC8sWIoYaHR8y7Wq86nPb0pufBl4%2BM4PWpeN%2B5PW9VQCIG7BhAVBDCWTpxFDLeuKYm%2Ff1JLz4iccPMzter0YdhgWKqYECDAQABoMNDE0MzUxNzY3ODI2IgxNAJrHbXaZG20jNfMqgwQyGEb4TsoVOoNvJ%2BaA%2FZ7zeu8hGcHqCeNfblP%2FQo5g6q2aRNtpwtPRKMlhYcIE%2FUQaNT1Lqmyptu32VUQTpzjTTxRVmJrDPkheSCkV06XQ%2FpTMKjU8CC7X1b%2FqYf8CrBwZT7Cv4C5WPjEET38f2A79h0xV6bZgPy28zQWrSeIJ9w6AfZBxiE24%2FVc08zVA0ivCT6lsy7nuU0wcR%2FMg2C9nd1vaMnQ923VpD21CKfVVYcrNWL2b%2FQ%2BNAO%2BxPjPgjiN6UPQbvbvZxkSdhgrGtecIg9NgqGkMcvM5VLTDtdC1kdeZetfOC2obtA%2BT%2F0gy3tIDo6WM5hcQfw743%2FAU9oaGnRBl%2BwoxRRDCZA%2F4jnPdluICSI7NCr6C3HHOe4CSZJjRihczxPA%2FgQczAVjAPs6CrK9N9pIzt0vNJpGUTgg4AoxftTvGfciDREilSctPIcBk6GwB%2Bzt4wbtNCmEPU%2BFD46rc6CWVQmmLwbKOeHw70ipMVFlOc8NlXkEtGO2vu6H44MstSSc6daOVUYUavQSGZ6jGhEjsP2YGz8KDm9XQotB%2FtyVBqiZV01OKqDmmBJA%2FXjUpSJ8M%2FeJXhe5m%2BKcxzNObtoJRI7jci3Tpt08XZyXZ7%2FyIV8H30ya468X3cfkTXrm972uga3z7iXAdubyEHnQIua%2FuvtwOHILi6O5D1Me2MzCPvcnXBjqTAYDoMWBt%2B3AVFuqSs2dmDQNBnvEhXfMgTedKLD5PJHb3aIl15iBkLaNu36GYnVCp9ZU2lSYcTbY5QwNnNeTfJTNy9bKhwzru6Sg%2BslhN%2BTS0zkTn%2BeLMY4tHgPEtEZ2cp%2FCk6GwqswvnYVKHjwO8q%2B1Mx8lc08fVEfarE9dke1GGFqoddx%2Bq%2BZUyHbDlAVfrCmf0yw%3D%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20261110T000135Z&X-Amz-SignedHeaders=host&X-Amz-Expires=86400&X-Amz-Credential=ASIAWA6KKHEJORWBH4N3%2F20261110%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=e4c5757c4f4cdc8224d5652086bfda5058e246227ec8960c198b40e577eeef29","created_at":1690222689639,"updated_at":1690222689639,"display_name":"Explore Ribbon Provider Directory","status":"FILE_STATUS_PUBLISHED"}],"geographical_coverage":"[\"United States and Canada\"]","cost":"PAID","update_frequency":{"interval":1,"unit":"MONTHLY"},"data_source":"1000's of data sources, including public datasets, private datasets, insurance plans, patient review sites, and customer-contributed edits","assets":["ASSET_TYPE_DATA_TABLE"]},"provider_summary":{"name":"Ribbon Health","description":"Ribbon Health provides access to comprehensive provider information, including NPIs, practice locations, contact information, specialties, location types, relative cost and experience, areas of focus, and accepted insurances. 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The platform can also be used for provider data management to ingest, aggregate, cleanse, score, and maintain disparate provider data sources, including provider rosters delivered via email, data from legacy systems, and custom metadata."},"popularity":54},{"id":"a3691813-ad49-4edf-8b37-c5140033238b","summary":{"name":"SAMPLE Property Data | U.S. Schools Data | 130K+ Public & Private Schools - Enrollment, Programs ...","subtitle":"Nationwide U.S. Schools dataset covering 130,000+ public and private schools, including enrollment, programs, ...","share":{"name":"attom_0c63645d_843d_4fc0_86c3_127fa4f30f21_samples_share","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1784645196554,"updated_at":1784645196554,"categories":["OTHER","DEMOGRAPHICS"],"listingType":"STANDARD","provider_id":"17701e36-b41a-4edb-b019-5b37ea19c64d"},"detail":{"description":"ATTOM’s Schools dataset delivers comprehensive nationwide Property Data enriched with Demographic Data, Residential Real Estate Data, and Urban Planning Data to support education-focused property insights and location analysis.\n\nThe dataset provides detailed information on public and private schools across the United States and U.S. Territories, helping organizations understand school availability, characteristics, and performance at scale.\n\nCoverage includes elementary, middle, and high schools, with private school data spanning charter, magnet, special education, and religious institutions. Each school record contains structured attributes such as enrollment, grade span, staffing, programs offered, and test score ratings, enabling consistent comparison and analysis across geographies.\n\nThe Schools dataset is directly linked to ATTOM’s School Attendance Areas and School District Boundaries and integrates with School District Profiles and School Test Scores to deliver a complete view of educational context and performance by grade level.\n\nThis unified structure allows school information to be analyzed alongside property, neighborhood, and planning data for residential real estate analysis, market research, and location-based decision making.","terms_of_service":"https://www.attomdata.com/privacy","contact_email":"support@datarade.ai","privacy_policy_link":"https://www.attomdata.com/privacy","assets":["ASSET_TYPE_DATA_TABLE"]},"provider_summary":{"name":"ATTOM","description":"ATTOM powers innovation across industries with premium property data and analytics covering 160+ million U.S. properties—99% of the population. 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Built on more than 30 years of time-adjusted transaction history, hyperlocal market intelligence, and advanced machine learning, ATTOM AVM provides confidence-scored valuations for more than 95 million residential properties across all 50 states.\n\nProperty Coverage\n-95M+ residential properties\n-Single-family homes, condominiums, and townhouses\n-Nationwide coverage across all 50 states\n-Monthly refreshed valuations\n-Confidence score, value range, and forecast standard deviation included with every valuation\n\nValuation Methodology\nTraditional AVMs rely heavily on recent comparable sales. ATTOM AVM uses an AI-driven valuation framework designed to perform accurately even in low-liquidity and data-sparse markets. \nThe model:\n-Leverages more than 30 years of transaction history\n-Uses hyperlocal price indices to adjust historical transactions to current market conditions\n-Applies multiple machine learning models to capture different drivers of property value\n-Dynamically blends model outputs based on local market performance\n-Includes calibrated confidence scoring and property-level uncertainty measurements with every valuation\n\nData Inputs\n30+ years of validated public record and transaction history\nProperty characteristics and ownership information\nHyperlocal market and neighborhood intelligence\nHistorical pricing patterns and market trends\nTime-adjusted transaction data normalized to current market conditions\n\nAccuracy & Transparency\n2.9% median absolute percentage error (MdAPE)\nMore than 80% of valuations fall within 10% of actual sale price\nMore than 93% of valuations fall within 20% of actual sale price\nConfidence-scored valuations designed to support underwriting, portfolio analysis, risk management, and investment decision-making\n\nDelivery & Updates\nUpdated monthly\nDelivered via Snowflake\nAvailable in delimited and Parquet formats\nDesigned for analytics, machine learning, AI applications, and enterprise valuation workflows","terms_of_service":"https://www.attomdata.com/privacy","contact_email":"support@datarade.ai","privacy_policy_link":"https://www.attomdata.com/privacy","assets":["ASSET_TYPE_DATA_TABLE"]},"provider_summary":{"name":"ATTOM","description":"ATTOM powers innovation across industries with premium property data and analytics covering 160+ million U.S. properties—99% of the population. Our multi-sourced real estate data includes property tax, deed, mortgage, foreclosure, environmental risk, natural hazard, neighborhood and geospatial boundary information, all validated through a rigorous 20-step process and linked by a unique ATTOM ID.\r\n\r\nATTOM supports analytics and AI-driven applications through flexible delivery options including APIs, bulk licensing, cloud delivery, and the MCP Server for AI-powered, agentic access to engineered property data—enabling organizations to automate analysis and scale property intelligence across industries. ATTOM fuels smarter decision-making across industries including real estate, mortgage, insurance, government, and more.\r\n\r\nTo learn more about ATTOM, visit: https://bit.ly/3kFtFz7"},"popularity":974},{"id":"0fb52aff-6321-4c9b-bea9-9472e5f8d6ce","summary":{"name":"Geolocet | Latest Census Data France - 390+ attributes","subtitle":"Census data for the nearly 35,000 communes of France - age, gender, nationality, families, households, employment, ...","setting":{"visibility":"PUBLIC"},"created_at":1706788219761,"updated_at":1752139005985,"categories":["DEMOGRAPHICS","ADVERTISING_AND_MARKETING","GEOSPATIAL","RETAIL","PUBLIC_SECTOR"],"listingType":"PERSONALIZED","provider_id":"5d3ddc17-a7e8-4794-a760-13587e1de2c6"},"detail":{"description":"## **Get instant access to the latest Census Data for France with [Geolocet](https://geolocet.com)**\n\nExplore the richness of France's demographic landscape with our France Census Data. This meticulously compiled dataset, sourced from official statistical sources, including INSEE, offers a detailed insight into the population landscape of France at the IRIS level. \n\nIf you require data at a less granular level, such as an Uber grid or square grid, please don't hesitate to get in touch with us at *contact@geolocet.com*. We understand that different projects may have varying data needs, and we are here to tailor our data to match your specific requirements.\n\n ## **Key Features:**\n---\n* **Population Data France:** Gain access to the latest and most accurate population statistics for France, meticulously organized by IRIS areas.\n\n* **Demographic Insights:** Dive deep into France's demographic landscape with census data on 390+ attributes, including: \n* Total population,\n* Gender,\n* Age bands,\n* Age bands split per gender,\n* Occupation,\n* Occupation split per age,\n* Number of households,\n* Household structure,\n* Population in households,\n* Population in households split per household type,\n* Population in households split by age,\n* Marital status,\n* Number of families,\n* Family structure,\n* Number of children distribution,\n* Dwellings,\n* Dwelling type,\n* Dwelling size,\n* Population in schools,\n* Populations in schools split by age and gender,\n* Education,\n* Active population,\n* Active population split by age and gender,\n* Employed population,\n* Employed population split by age and gender,\n* Unemployed population,\n* Education of the unemployed population,\n* Education of the active population,\n* Employment type,\n* Employment status,\n* Employment status per employment type,\n* Employment status per gender,\n* Employment status per employment type and gender,\n* Employment status per age,\n* Employment status per age and gender,\n* Place of work,\n* Mean of commute,\n* Standard of living,\n* Average income,\n* Average income per age and gender.\n\nAll demographic attributes are available at the IRIS level.\n\n* **Geodemographic Insights:** Unlock geodemographic data France-wide to inform location-based decision-making, marketing strategies, and policy formulation.\n* **Seamless Data Access:** Our data is available for instant download in CSV format. No lengthy sign-up processes or NDAs required on our side - just download the data you need.\n* **Data Samples for Evaluation:** To ensure you're getting exactly what you need, we provide free downloadable samples in CSV format for data evaluation.\n* **Data accuracy:** Our datasets are created using the latest demographics data available in national statistics bureaus, guaranteeing accuracy and reliability.\n* **Best-in-Class Quality:** We guarantee best-in-class quality, backed by over two decades of experience in modeling demographics data and an outstanding quality management process.\n* **Competitive Pricing:** We maintain the lowest prices on the market, ensuring that you get the most cost-efficient demographics data acquisition process.\n---\nAdditionally, for those looking to enrich their analysis or project, we offer:\n* **[Administrative Boundaries](https://geolocet.com/products/france-admin-level-8-municipalities):** If you require geographical boundaries (polygons) for the municipalities of France, consider exploring our Administrative Boundaries product, which seamlessly integrates with this dataset.\n\n* **[Point of Interest (PoI) Data](https://geolocet.com/pages/aggregated-points-of-interest-poi-data):** If you're interested in Points of Interest data, please don't hesitate to contact us for customized datasets to enhance your geographical analysis.\n\n* **[Data Customization](https://geolocet.com/pages/data-services-by-geolocet):** We understand that your project may have specific requirements. Feel free to get in touch with us if you need any modifications or specific data formats such as Excel, JSON, GeoJSON, or Shapefile. We are here to tailor the data to your needs.\n\nIf you're looking for data for France, [look here](https://geolocet.com/pages/france) for off-the-shelf or tailor-made datasets to suit your specific requirements.\n\n\n\nMake informed decisions, conduct precise research, and unlock the potential of the French demographic landscape with our comprehensive dataset. Trust in the accuracy and reliability of our official statistical data to drive your projects forward.\nContact us at *contact@geolocet.com* today to access this invaluable resource and explore France census data like never before.\n\n[Geolocet LTD](https://geolocet.com)\n","terms_of_service":"https://geolocet.com/pages/terms-of-use","documentation_link":"","privacy_policy_link":"https://geolocet.com/pages/privacy-policy","geographical_coverage":"[\"Europe\"]","cost":"PAID","update_frequency":{"interval":1,"unit":"YEARLY"},"data_source":"","assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://geolocet.com/pages/terms-of-use"},"provider_summary":{"name":"Geolocet LTD","description":"Geolocet provides premium geospatial data for retail analysis, GIS, and market planning. We equip businesses with accurate, ready-to-query data to drive spatial analysis, site selection, and customer segmentation.\n\nCore Offerings:\n\nDemographics: Socioeconomic data covering population, age, and employment across diverse geographies.\n\nPoints of Interest (PoI): Datasets spanning retail, healthcare, and transport for catchment analysis.\n\nAdministrative Boundaries: GIS-ready boundaries, including municipalities and postcodes, for territory management.\n\nBrand Locations: Chain location datasets to monitor store networks and market expansion.\n\nWealth Data: Granular economic indicators like purchasing power to identify high-potential markets.\n\nWe source from trusted providers, delivering highly customizable datasets with comprehensive coverage across Europe and worldwide."},"popularity":544},{"id":"55c3365c-0a3b-403b-a8e0-73fca0469fff","summary":{"name":"Best Buy Products Dataset","subtitle":"Use our Best Buy products to collect ratings, prices, and more.\n","share":{"name":"best_buy","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1718885204588,"updated_at":1718885204588,"categories":["COMMERCE"],"listingType":"STANDARD","provider_id":"63866f42-27f8-4a1f-b243-37e7be863700"},"detail":{"description":"\nDesigned to provide a comprehensive view of Best Buy's extensive offerings, this dataset empowers businesses, researchers, and analysts with invaluable insights to navigate the dynamic landscape of retail.\n\n**Use Case:**\n- Market Analysis: Gain deep insights into market trends, pricing dynamics, and consumer preferences within Best Buy's e-commerce ecosystem.\n- Pricing Optimization: Fine-tune your pricing strategies based on comprehensive data on product details, pricing information, and discounts.\n- Consumer Behavior Understanding: Dive into customer reviews and ratings to uncover insights into product quality, satisfaction levels, and overall consumer sentiment.\n- Competitor Assessment: Evaluate product performance, assess seller credibility, and stay ahead of the competition in the realm of consumer electronics.\n\n**Key Features:**\n- Comprehensive Data Attributes: Access a wealth of information covering product details, ratings, reviews, pricing data, and seller information.\n- Tailored Subsets: Explore curated subsets such as \"Products with more than 100 reviews\" or \"Products with the Highest Rating\" to focus your analysis on specific categories of interest.\n- Seamless Integration: Integrate the dataset effortlessly into your analytics tools or platforms for streamlined analysis and decision-making.\n- Versatile Applications: Whether you're enhancing your product portfolio, investigating consumer electronics trends, or refining e-commerce strategies, the Best Buy Products Dataset offers a valuable resource for unlocking opportunities and driving success in the retail landscape.\n\nUnlock the potential of Best Buy's consumer electronics universe today with the Best Buy Products Dataset!\n\nNote: This is a sample.","terms_of_service":"https://brightdata.com/license","documentation_link":"https://drive.google.com/file/d/1OtK_hSkTvx8mT2irbSj7lGCz7dDT4hJ-/view?usp=sharing","privacy_policy_link":"https://brightdata.com/privacy","data_source":"","assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://brightdata.com/license"},"provider_summary":{"name":"Bright Data","description":"Bright Data is the world’s #1 web data platform.\nFortune 500 companies, academic institutions, and small businesses rely on Bright Data’s scalable platform for web scraping infrastructure to retrieve crucial public web data in the most efficient, reliable, and flexible way, so they can research, monitor, and analyze data to make better decisions.\nThe platform is used worldwide by 20,000+ customers in nearly every industry. The products include a range of no-code data solutions utilized by business owners and a robust infrastructure used by engineers and IT professionals.\n\nBright Data provides a cost-effective way to perform fast and stable public web data collection at scale, effortless conversion of unstructured data into structured data, and superior customer experience while being fully transparent and compliant."},"popularity":1065},{"id":"106788c1-a369-4731-986b-6d2bbf65e02e","summary":{"name":"BrickForge","subtitle":"Workspace observability, governance, and automation for Databricks. Six modules. One platform. Zero context-switching.","setting":{"visibility":"PUBLIC"},"created_at":1779797686485,"updated_at":1782500502085,"categories":["ADVERTISING_AND_MARKETING","CLIMATE_AND_ENVIRONMENT","COMMERCE","DEMOGRAPHICS","ECONOMICS","EDUCATION","ENERGY","FINANCIAL","GAMING","GEOSPATIAL","HEALTH","LOOKUP_TABLES","MANUFACTURING","MEDIA","OTHER","PUBLIC_SECTOR","RETAIL","SCIENCE_AND_RESEARCH","SECURITY","SPORTS","TRANSPORTATION_AND_LOGISTICS","TRAVEL_AND_TOURISM"],"listingType":"STANDARD","provider_id":"0db113ae-ae28-4094-96a5-50fdbbb263ee"},"detail":{"description":"**Overview**\nBrickForge by Opsera is a purpose-built, Databricks-native AI application for managing, observing, automating, and governing the operational health of Databricks environments. Engineered for hybrid human-AI agent teams, its eight operational modules deliver workspace health scores, automated bundle deployments, continuous compliance and security tracking, DR/Failover, and proactive recommendations — while maintaining its privacy standard of Zero-Business Data Contact.\n\n**Use cases**\n-Monitor live workspace health across clusters, warehouses, jobs, and query activity with real-time SSE streaming\n-Review SQL files across seven dimensions (Security, PII, HIPAA, SOC2, GDPR, PCI-DSS, Best Practices), execute inline, and fix failures with AI\n-Generate a penalty-based DR readiness score and export Terraform setup scripts, CLI replication scripts, and a failover runbook\n-Run per-control compliance audits across SOC2, HIPAA, PCI-DSS, and GDPR with evidence and remediation steps persisted in Lakebase\n-Detect DLT pipeline quality issues early using AI analysis of configuration, event history, and notebook content\n-Browse live workspace assets, generate **databricks.yml** bundles, validate, and deploy across workspaces via SP OAuth or PAT mode\n-Ask natural-language questions about your workspace and get AI-powered answers with tool-calling across all modules\n-Access embedded documentation, troubleshooting guides, and module walkthroughs without leaving the app\n\n**Additional Insights**\n\n- BrickForge evaluates metadata, infrastructure patterns, and deployment logic only. It never traverses, indexes, or reads the content of customer business tables. Your data stays in your tenant; BrickForge governs the environment that creates it.\n\n- The application deploys via the Databricks Marketplace directly into your tenant. Zero external infrastructure required. It minimizes Security and Architecture deployment certification cycles.","terms_of_service":"https://opsera.ai/terms-of-service/","documentation_link":"","privacy_policy_link":"https://opsera.ai/privacy/","assets":["ASSET_TYPE_APP"],"license":""},"provider_summary":{"name":"BrickForge","description":"BrickForge is a purpose-built AI application for managing, observing, automating, and governing the operational health of Databricks environments, optimized for hybrid human-AI agent data teams."},"popularity":2095},{"id":"6e753e6a-28cc-47e9-9915-7557cd21a58a","summary":{"name":"Dialysis Facility Compare","subtitle":"This data package contains information about Dialysis Facility Compare. It comprises of data about ESRE QIP over Dial..","share":{"name":"dialysis_facility_compare","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1682356140832,"updated_at":1698965635273,"categories":["HEALTH","SCIENCE_AND_RESEARCH"],"listingType":"STANDARD","provider_id":"f88849f8-9d77-4de2-b4c7-03d55cc3768e"},"detail":{"description":"\n**Overview**\nThis data package contains information about Dialysis Facility Compare. It comprises of data about ESRE QIP over Dialysis Adequacy, Hypercalcemia, National Healthcare Safety Network Dialysis Reporting, Total Performance Scores and Vascular Access PY 2017. It also includes data about ICH CAHPS Survey Reporting Payment Year 2017.\n\n**Description**\nThis data package contains the Dialysis Facility Compare provides details on dialysis facilities across the country, including how well each dialysis center delivers care and the services they provide, patient experiences with dialysis centers, and much more. The information on Dialysis Facility Compare helps a person that how well dialysis centers care for their patients and each dialysis center used best practices when caring for its patients and kept patients healthy. This dataset contains the State averages of common dialysis quality measures. Dialysis Facility Compare can help you compare dialysis centers and choose one that’s right for you or your loved ones. The ESRD QIP will reduce payments to ESRD facilities that do not meet or exceed certain performance standards. The maximum payment reduction CMS can apply to any facility is two percent. This reduction will apply to all payments for services performed by the facility receiving the reduction during the applicable payment year (PY). This data set lists anemia management data used by ESRD QIP to assess dialysis facility performance. The ESRD QIP rewards outpatient dialysis or ESRD facilities treating patients with ESRD with incentive payments for the quality of care they give to people with Medicare. This data set lists Total Performance Scores by facility of the End-Stage Renal Disease (ESRD) Quality Incentive Program (QIP) to assess dialysis facility performance. This data set lists hemodialysis patients using an arteriovenous fistula and an intravenous catheter data used by End-Stage Renal Disease (ESRD) Quality Incentive Program (QIP) to assess dialysis facility performance. The ESRD QIP rewards outpatient dialysis or ESRD facilities treating patients with ESRD with incentive payments for the quality of care they give to people with Medicare. ICH CAHPS (In-Center Hemodialysis Consumer Assessment of Healthcare Providers and Systems) Survey is the first national, standardized, and publicly-reported survey of patients' perspectives of dialysis care; it is also the first CAHPS survey focusing solely on a chronic disease. ICH CAHPS measures perceptions of patients with End-Stage Renal Disease (ESRD) receiving life-sustaining in-center hemodialysis care.\n\n**Benefits**\n- Useful for doctors, medical health planners, patients, dialysis centers, medical research groups and medical students.\n- dialysis facility compare database is available on centers for medicare & medicaid services website in raw form, whereas a jsl customer gets it in clean and normalized form ready to use and create his own custom reports.\n- easy to comprehend for the customers who are unfamiliar with the medical terminologies as all abbreviated terms are replaced with full form, unlike centers for medicare & medicaid services (cms) original datasets which have many abbreviations.\n- recent dataset for 2018 is available for use.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [Dialysis Facility Compare-Facility Listing](https://www.johnsnowlabs.com/marketplace/dialysis-facility-compare-facility-listing)\n\t- This dataset contains a list of all dialysis facilities registered with Medicare that includes addresses and phone numbers, as well as services and quality of care provided.\n- [Dialysis Facility Compare-State Averages](https://www.johnsnowlabs.com/marketplace/dialysis-facility-compare-state-averages)\n\t- This dataset contains the State averages of common dialysis quality measures. Dialysis Facility Compare provides information on more than 5,500 facilities (centers) throughout the country. Dialysis Facility Compare can help you compare dialysis centers and choose one that’s right for you or your loved ones.\n- [Dialysis Facility Reports State and National Averages](https://www.johnsnowlabs.com/marketplace/dialysis-facility-reports-state-and-national-averages)\n\t- This dataset shows the state and national averages for measures found in the FY 2019 Dialysis Facility Reports.\n- [ESRD Complete QIP Data](https://www.johnsnowlabs.com/marketplace/esrd-complete-qip-data)\n\t- The dataset includes the number of eligible patients by clinical measure. It covers the sample of patients with Hemoglobin > 12 as well as the records about hemodialysis patient-months with single-pool Kt/V (spKt/V) >= 1.2 ; (where Kt/V represents a number used to quantify hemodialysis and peritoneal dialysis treatment adequacy). The dataset also analyzes the peritoneal patient-months with Kt/V >= 1.7 Kt/V (dialytic + residual) during the four-month study period.\n- [ESRD QIP National Healthcare Safety Network Combined Measure](https://www.johnsnowlabs.com/marketplace/esrd-qip-national-healthcare-safety-network-combined-measure)\n\t- This dataset includes facility details, National Healthcare Safety Network (NHSN) Bloodstream Infection (BSI) performance ratio, NHSN dialysis events number of months reported, NHSN combined measure score, and the state and national average measure scores. The NHSN dialysis event number of months reported and the NHSN bloodstream Infection ratio, which comprise the NHSN combined measure score are included in the PY 2019 End-Stage Renal Disease (ESRD) Quality Incentive Program (QIP).\n- [ESRD QIP State and National Average Measure Scores](https://www.johnsnowlabs.com/marketplace/esrd-qip-state-and-national-average-measure-scores)\n\t- This dataset includes facility details, measure score, and the state and national average measure scores for the six measures included in the Payment Year 2018 End-Stage Renal Disease (ESRD) Quality Incentive Program (QIP). These measures specifically include facility addresses, facility names, states and average national and state measure scores.\n- [ESRD QIP-Anemia Management Reporting PY 2018-2020](https://www.johnsnowlabs.com/marketplace/esrd-qip-anemia-management-reporting-py-2018-2020)\n\t- This dataset lists anemia management data used by End-Stage Renal Disease (ESRD) Quality Incentive Program (QIP) to assess dialysis facility performance. The ESRD QIP rewards outpatient dialysis or ESRD facilities treating patients with ESRD with incentive payments for the quality of care they give to people with Medicare. The program also reduces payments to ESRD facilities that don’t meet or exceed certain performance standards.\n- [ESRD QIP-Dialysis Adequacy PY 2018-2020](https://www.johnsnowlabs.com/marketplace/esrd-qip-dialysis-adequacy-py-2018-2020)\n\t- This dataset lists End-Stage Renal Disease Quality Incentive Program (ESRD QIP) data by facility details, performance rate, Kt/V Dialysis Adequacy Comprehensive measure score, and the state and national average measure scores for the Kt/V Dialysis Adequacy Comprehensive measure for Payment Year (PY) 2018. The ESRD QIP rewards outpatient dialysis or ESRD facilities treating patients with ESRD with incentive payments for the quality of care they give to people with Medicare.\n- [ESRD QIP-Dialysis Adequacy PY 2018-2022](https://www.johnsnowlabs.com/marketplace/esrd-qip-dialysis-adequacy-py-2018-2022)\n\t- This dataset lists End-Stage Renal Disease Quality Incentive Program (ESRD QIP) data by facility details, performance rate, Kt/V Dialysis Adequacy Comprehensive measure score, and the state and national average measure scores for the Kt/V Dialysis Adequacy Comprehensive measure for Payment Year (PY) 2018. The ESRD QIP rewards outpatient dialysis or ESRD facilities treating patients with ESRD with incentive payments for the quality of care they give to people with Medicare.\n- [ESRD QIP-Hypercalcemia PY 2018-2020](https://www.johnsnowlabs.com/marketplace/esrd-qip-hypercalcemia-py-2018-2020)\n\t- This dataset lists hypercalcemia data used by End-Stage Renal Disease (ESRD) Quality Incentive Program (QIP) to assess dialysis facility performance. The ESRD QIP rewards outpatient dialysis or ESRD facilities treating patients with ESRD with incentive payments for the quality of care they give to people with Medicare. The program also reduces payments to ESRD facilities that don’t meet or exceed certain performance standards.\n- [ESRD QIP-Hypercalcemia PY 2018-2022](https://www.johnsnowlabs.com/marketplace/esrd-qip-hypercalcemia-py-2018-2022)\n\t- This dataset lists hypercalcemia data used by End-Stage Renal Disease (ESRD) Quality Incentive Program (QIP) to assess dialysis facility performance. The ESRD QIP rewards outpatient dialysis or ESRD facilities treating patients with ESRD with incentive payments for the quality of care they give to people with Medicare. The program also reduces payments to ESRD facilities that don’t meet or exceed certain performance standards.\n- [ESRD QIP-Mineral Metabolism Reporting PY 2018-2019](https://www.johnsnowlabs.com/marketplace/esrd-qip-mineral-metabolism-reporting-py-2018-2019)\n\t- This dataset lists mineral metabolism data used by End-Stage Renal Disease (ESRD) Quality Incentive Program (QIP) to assess dialysis facility performance. The ESRD QIP rewards outpatient dialysis or ESRD facilities treating patients with ESRD with incentive payments for the quality of care they give to people with Medicare. The program also reduces payments to ESRD facilities that don’t meet or exceed certain performance standards.\n- [ESRD QIP-National Healthcare Safety Network Dialysis Reporting 2018-22](https://www.johnsnowlabs.com/marketplace/esrd-qip-national-healthcare-safety-network-dialysis-reporting-2018-22)\n\t- This dataset lists National Healthcare Safety Network (NHSN) data used by End-Stage Renal Disease (ESRD) Quality Incentive Program (QIP) to assess dialysis facility performance. The ESRD QIP rewards outpatient dialysis or ESRD facilities treating patients with ESRD with incentive payments for the quality of care they give to people with Medicare. The program also reduces payments to ESRD facilities that don’t meet or exceed certain performance standards.\n- [ESRD QIP-National Healthcare Safety Network Dialysis Reporting PY 2018](https://www.johnsnowlabs.com/marketplace/esrd-qip-national-healthcare-safety-network-dialysis-reporting-py-2018)\n\t- This dataset lists National Healthcare Safety Network (NHSN) data used by End-Stage Renal Disease (ESRD) Quality Incentive Program (QIP) to assess dialysis facility performance. The ESRD QIP rewards outpatient dialysis or ESRD facilities treating patients with ESRD with incentive payments for the quality of care they give to people with Medicare. The program also reduces payments to ESRD facilities that don’t meet or exceed certain performance standards.\n- [ESRD QIP-Total Performance Scores](https://www.johnsnowlabs.com/marketplace/esrd-qip-total-performance-scores)\n\t- This dataset lists Total Performance Scores by facility of the End-Stage Renal Disease (ESRD) Quality Incentive Program (QIP) to assess dialysis facility performance. The ESRD QIP rewards outpatient dialysis or ESRD facilities treating patients with ESRD with incentive payments for the quality of care they give to people with Medicare. The program also reduces payments to ESRD facilities that don’t meet or exceed certain performance standards.\n- [ESRD QIP-Vascular Access](https://www.johnsnowlabs.com/marketplace/esrd-qip-vascular-access)\n\t- This dataset lists hemodialysis patients using an arteriovenous fistula and an intravenous catheter data used by End-Stage Renal Disease (ESRD) Quality Incentive Program (QIP) to assess dialysis facility performance. The ESRD QIP rewards outpatient dialysis or ESRD facilities treating patients with ESRD with incentive payments for the quality of care they give to people with Medicare. The program also reduces payments to ESRD facilities that don’t meet or exceed certain performance standards.\n- [ICH CAHPS Patient Survey State Averages](https://www.johnsnowlabs.com/marketplace/ich-cahps-patient-survey-state-averages)\n\t- This dataset is about the state averages for In-Center Hemodialysis Consumer Assessment of Healthcare Providers and Systems (ICH CAHPS) patient survey used by End-Stage Renal Disease (ESRD) Quality Incentive Program (QIP) to assess dialysis facility performance. The CAHPS survey is designed to measure the experiences of people receiving In-Center Hemodialysis (ICH) care from Medicare-certified dialysis facilities.\n- [ICH CAHPS Patient Survey Statistics By Facility](https://www.johnsnowlabs.com/marketplace/ich-cahps-patient-survey-statistics-by-facility)\n\t- This dataset lists In-Center Hemodialysis Consumer Assessment of Healthcare Providers and Systems (ICH CAHPS) survey data used by End-Stage Renal Disease (ESRD) Quality Incentive Program (QIP) to assess dialysis facility performance. The CAHPS survey is designed to measure the experiences of people receiving In-Center Hemodialysis (ICH) care from Medicare-certified dialysis facilities.\n- [ICH CAHPS Survey Reporting Payment Year 2017-2020](https://www.johnsnowlabs.com/marketplace/ich-cahps-survey-reporting-payment-year-2017-2020)\n\t- This dataset lists In-Center Hemodialysis Consumer Assessment of Healthcare Providers and Systems (ICH CAHPS) survey data used by End-Stage Renal Disease (ESRD) Quality Incentive Program (QIP) to assess dialysis facility performance. The CAHPS In-Center Hemodialysis survey is designed to measure the experiences of people receiving ICH care from Medicare-certified dialysis facilities.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated\n- Data Updates\n - Data updates support replace-on-update: outdated foreign keys are deprecated, not deleted\n\n**Our data is curated and enriched by domain experts** \n\n Each dataset is manually curated by our team of doctors, pharmacists, public health & medical billing experts:\n- Field names, descriptions, and normalized values are chosen by people who actually understand their meaning\n- Healthcare & life science experts add categories, search keywords, descriptions and more to each dataset\n- Both manual and automated data enrichment supported for clinical codes, providers, drugs, and geo-locations\n- The data is always kept up to date – even when the source requires manual effort to get updates\n- Support for data subscribers is provided directly by the domain experts who curated the data sets\n- Every data source’s license is manually verified to allow for royalty-free commercial use and redistribution.\n\n**Need Help?**\nIf you have questions about our products, contact us at [info@johnsnowlabs.com](mailto:info@johnsnowlabs.com). \n 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It combines John Snow Labs Visual NLP, medical LLMs, vLLM, and Unity Catalog to extract structured information from medical documents for downstream analytics and AI workflows.\n\n## Use cases\n\n* Extract clinical information from scanned and visual medical documents.\n* Transform unstructured healthcare documents into structured, analytics-ready data.\n* Support clinical NLP, healthcare analytics, and AI applications.\n\n## Product details\n\nDatasets represented include:\n\n* **clinical_documents:** document_id, document_type, document_date\n* **clinical_entities:** entity_type, entity_text, confidence\n* **patient_information:** patient_id, patient_name, date_of_birth\n* **medications:** medication_name, dosage, frequency\n* **laboratory_results:** test_name, result_value, unit\n* **medical_conditions:** condition_name, ICD_code, status\n* **procedures:** procedure_name, procedure_code, procedure_date\n* **document_metadata:** source_file, page_number, processing_status\n\nFor more details, refer to the embedded notebook.\n\n## Additional Insights\n\nThe solution provides a governed foundation for healthcare document intelligence within the Databricks Lakehouse, enabling structured extraction and integration with downstream data, analytics, and AI workflows.\n","terms_of_service":"https://www.johnsnowlabs.com/healthcare-data-library-eula/","documentation_link":"","privacy_policy_link":"https://www.johnsnowlabs.com/privacy-policy/","embedded_notebook_file_infos":[{"id":"4a8c6317-b006-4385-b388-359db8b7db5f","marketplace_file_type":"EMBEDDED_NOTEBOOK","file_parent":{"parent_id":"586d8e40-1148-43df-8809-722303021d3f","file_parent_type":"LISTING"},"mime_type":"text/html","download_link":"https://marketplace-file-storage-prod.s3-fips.us-east-1.amazonaws.com/EMBEDDED_NOTEBOOK/LISTING_586d8e40-1148-43df-8809-722303021d3f/4a8c6317-b006-4385-b388-359db8b7db5f?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEGgaDnVzLWVhc3QtMS1maXBzIkYwRAIgbC8sWIoYaHR8y7Wq86nPb0pufBl4%2BM4PWpeN%2B5PW9VQCIG7BhAVBDCWTpxFDLeuKYm%2Ff1JLz4iccPMzter0YdhgWKqYECDAQABoMNDE0MzUxNzY3ODI2IgxNAJrHbXaZG20jNfMqgwQyGEb4TsoVOoNvJ%2BaA%2FZ7zeu8hGcHqCeNfblP%2FQo5g6q2aRNtpwtPRKMlhYcIE%2FUQaNT1Lqmyptu32VUQTpzjTTxRVmJrDPkheSCkV06XQ%2FpTMKjU8CC7X1b%2FqYf8CrBwZT7Cv4C5WPjEET38f2A79h0xV6bZgPy28zQWrSeIJ9w6AfZBxiE24%2FVc08zVA0ivCT6lsy7nuU0wcR%2FMg2C9nd1vaMnQ923VpD21CKfVVYcrNWL2b%2FQ%2BNAO%2BxPjPgjiN6UPQbvbvZxkSdhgrGtecIg9NgqGkMcvM5VLTDtdC1kdeZetfOC2obtA%2BT%2F0gy3tIDo6WM5hcQfw743%2FAU9oaGnRBl%2BwoxRRDCZA%2F4jnPdluICSI7NCr6C3HHOe4CSZJjRihczxPA%2FgQczAVjAPs6CrK9N9pIzt0vNJpGUTgg4AoxftTvGfciDREilSctPIcBk6GwB%2Bzt4wbtNCmEPU%2BFD46rc6CWVQmmLwbKOeHw70ipMVFlOc8NlXkEtGO2vu6H44MstSSc6daOVUYUavQSGZ6jGhEjsP2YGz8KDm9XQotB%2FtyVBqiZV01OKqDmmBJA%2FXjUpSJ8M%2FeJXhe5m%2BKcxzNObtoJRI7jci3Tpt08XZyXZ7%2FyIV8H30ya468X3cfkTXrm972uga3z7iXAdubyEHnQIua%2FuvtwOHILi6O5D1Me2MzCPvcnXBjqTAYDoMWBt%2B3AVFuqSs2dmDQNBnvEhXfMgTedKLD5PJHb3aIl15iBkLaNu36GYnVCp9ZU2lSYcTbY5QwNnNeTfJTNy9bKhwzru6Sg%2BslhN%2BTS0zkTn%2BeLMY4tHgPEtEZ2cp%2FCk6GwqswvnYVKHjwO8q%2B1Mx8lc08fVEfarE9dke1GGFqoddx%2Bq%2BZUyHbDlAVfrCmf0yw%3D%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20261110T000135Z&X-Amz-SignedHeaders=host&X-Amz-Expires=86400&X-Amz-Credential=ASIAWA6KKHEJORWBH4N3%2F20261110%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=3cc71dbb5fa15d3040f28f76897af893ab39cb508c0b92046433a8bddefd645f","created_at":1788217292800,"updated_at":1788217292800,"display_name":"JSL medical LLM integration with Unity Catalog","status":"FILE_STATUS_PUBLISHED"}],"assets":["ASSET_TYPE_NOTEBOOK"],"license":""},"provider_summary":{"name":"John Snow Labs","description":"John Snow Labs, an AI and NLP for healthcare company, provides state-of-the-art software, models, and data to help healthcare and life science organizations build, deploy, and operate AI projects."},"popularity":1957},{"id":"9ddd1d0b-c33e-4cde-81b6-eafe63597fec","summary":{"name":"Hedge Funds Data | Customer Support Call Volume & Operational Stress Signals | Tickerized | ...","subtitle":"Consumer call volumes serve as a quantitative proxy for customer support pressure, rising SG&A costs, and potential ...","share":{"name":"wiserbrand_com_7668357d_2b13_4f4f_a6cf_6cdb212f68f1_samples_share","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1771425268973,"updated_at":1793098529281,"categories":["FINANCIAL","RETAIL"],"listingType":"PERSONALIZED","provider_id":"2eed6057-c654-4c40-b66d-13a25153cb95"},"detail":{"description":"This Hedge Funds Data feed provides verified consumer call volumes to customer support lines, sourced through the PissedConsumer platform and structured for quantitative analysis and systematic trading models.\n\nThe Quantitative Signal\nWe track deviations in call volume baselines for ~1,300 public companies. A significant spike in call volume indicates \"Support Overload\" — a state where a company faces a surge in product defects, service outages, or billing disputes.\n\nCorrelation with Equity Performance\nHigh friction in customer support (increased call volume) acts as a leading indicator for:\n- Increased SG&A Expenses: Higher operational costs to handle inquiries.\n- Revenue Churn: Unresolved issues lead to customer attrition.\n- Stock Price Decline: Historical data shows a negative correlation between sustained call volume spikes and subsequent equity performance.\n\nDataset Specifications:\n- Source: First-party data from proprietary call infrastructure (not web-scraped).\n- Metrics: Call Volume, Duration, Resolution Status.\n- Structure: Mapped to Tickers (ISIN/Bloomberg), Point-in-Time (PIT) timestamps.\n- Compliance: No PII, No MNPI, fully compliant Hedge Funds Data for institutional use.","terms_of_service":"https://www.pissedconsumer.ai/privacy-policy","contact_email":"support@datarade.ai","privacy_policy_link":"https://www.pissedconsumer.ai/privacy-policy","assets":["ASSET_TYPE_DATA_TABLE"]},"provider_summary":{"name":"PissedConsumer","description":"PissedConsumer is a US-based consumer feedback and resolution platform operating since 2006. Consumers use our platform to share experiences with 159,000 brands across 160+ industries.\r\n\r\nOver the years, we’ve built a large archive of first-party consumer interactions, including public complaints, resolution requests, ratings, opt-in consumer-company conversations, and call transcription reports.\r\n\r\nOur datasets support AI model development, ASR and NLP applications, consumer sentiment analysis, customer experience analytics, market intelligence, competitive analysis, and investment research.\r\n\r\nWe own and operate the platform infrastructure and underlying datasets. Personally identifiable information (PII) is redacted before any commercial use or licensing. Datasets are cleaned, standardized, and labeled before delivery for immediate use.\r\n\r\n*Note: Our data was previously listed under our sister company and commercial partner, WiserBrand, which you may see mentioned in older reviews."},"popularity":996},{"id":"c3806834-01f4-4bcc-a916-111d66465162","summary":{"name":"Establishment Registration and Medical Device Listing Files","subtitle":"This data package contains access to lists of owners or operators of places of business (also called establishments or..","share":{"name":"establishment_registration_and_medical_device_listing_files","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1682356512550,"updated_at":1695692108926,"categories":["SCIENCE_AND_RESEARCH"],"listingType":"STANDARD","provider_id":"f88849f8-9d77-4de2-b4c7-03d55cc3768e"},"detail":{"description":"\n**Overview**\nThis data package contains access to lists of owners or operators of places of business (also called establishments or facilities) that are involved in the production and distribution of medical devices intended for use in the United States (manufacturers, distributors, importers, agents and others).\n\n**Description**\nThis data package contains datasets on medical device establishment including foreign firms that manufacture medical devices, registered and non-registered importers with the Food and Drug Administration (FDA) listed by manufacturers, detailed list of the types of device establishments that are required to register at FDA and pay the fees. Most establishments that are required to register with FDA are also required to list the devices that are made there and the activities that are performed on those devices. If a device requires premarket approval or notification before being marketed in the U.S., then the owner/operator should also provide the FDA premarket submission number (510(k), PMA, PDP, HDE, De Novo). This datapackage allows access to all related data.\n\n**Benefits**\n- Complete access to fda lists of medical device registered and non-registered importers identified by manufacturers._x000d_.\n- access to fda lists of medical device manufacturers identified by importers_x000d_.\n- access to fda lists of medical device establishment contact addresses and establishment official correspondent._x000d_.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [Medical Device Establishment Contact Addresses](https://www.johnsnowlabs.com/marketplace/medical-device-establishment-contact-addresses)\n\t- This dataset showcases the establishment and listing provided by the FDA (Food and Drug Administration) with the location of medical device establishments and a list of devices manufactured at those establishments.\n- [Medical Device Establishment Official Correspondent](https://www.johnsnowlabs.com/marketplace/medical-device-establishment-official-correspondent)\n\t- Medical Device Establishment Official Correspondent dataset contains Medical Device Establishment Registration Official Correspondent\nInformation. Most establishments that are required to register with the Food and Drug Administration (FDA) are also required to list the devices that are made there and the activities that are performed on those devices.\n- [Medical Device Establishment Owner Operator](https://www.johnsnowlabs.com/marketplace/medical-device-establishment-owner-operator)\n\t- The U.S. Food and Drug Administration (FDA) is responsible for regulating firms who manufacture, repackage, relabel, and/or import medical devices sold in the United States. For devices that are exempt from Premarket Notification, Manufacturers (both domestic and non-U.S.) and initial distributors (importers) of medical devices must register their establishments with the U.S. FDA.\nThe operators of such establishments are found in this dataset.\n- [Medical Device Establishment Types](https://www.johnsnowlabs.com/marketplace/medical-device-establishment-types)\n\t- Medical Device Listing Establishment Types dataset contains the description of each Establishment Type ID. For combination products, identify the type of the combination (i.e., drug/device, etc.)\n- [Medical Device Establishment US Agent](https://www.johnsnowlabs.com/marketplace/medical-device-establishment-us-agent)\n\t- This dataset contains the Medical Device Establishment US Agent Information. Any foreign establishment engaged in the manufacture, preparation, propagation, compounding, or processing of a device imported into the United States must identify a United States agent (U.S. agent) for that establishment.\n- [Medical Device Listing Establishment Types](https://www.johnsnowlabs.com/marketplace/medical-device-listing-establishment-types)\n\t- Manufacturers (both domestic and foreign) and initial distributors (importers) of medical devices must register their establishments with the FDA (Food and Drug Administration). All establishment registrations must be submitted electronically unless a waiver has been granted by FDA.\n- [Medical Device Listing Product Code](https://www.johnsnowlabs.com/marketplace/medical-device-listing-product-code)\n\t- This dataset contains Medical Device Listing Product Code information. Classification product codes are a method of internally classifying and tracking medical devices. CDRH and a subset of CBER regulated medical device product codes consist of a 3 letter combination which associates a device’s type with a product classification designated for the application. The information associated with these devices, such as names and attributes, are assigned by CDRH to support their regulation.\n- [Medical Device Listing Proprietary Name](https://www.johnsnowlabs.com/marketplace/medical-device-listing-proprietary-name)\n\t- This dataset shows establishments that are required to register with the Food and Drug Administration (FDA) which are also required to list the devices that are made and the activities that are performed on those devices.\n- [Medical Device Manufacturers Identified by Importers](https://www.johnsnowlabs.com/marketplace/medical-device-manufacturers-identified-by-importers)\n\t- This dataset shows foreign firms that manufacture medical devices and/or products that emit radiation that are imported into the United States must comply with applicable U.S. regulations before, during, and after importing into the U.S. or its territories. In order to import medical devices and/or products that emit radiation into the U.S., the product must meet FDA regulatory requirements. Food and Drug Administration (FDA) does not recognize regulatory approvals from other countries.\n- [Medical Device Non-Registered Importers Identified by Manufacturers](https://www.johnsnowlabs.com/marketplace/medical-device-non-registered-importers-identified-by-manufacturers)\n\t- This dataset shows establishments that are required to register with the Food and Drug Administration (FDA) that are also required to list the devices that are made there and the activities that are performed on those devices.\n- [Medical Device Registered Importers Identified by Manufacturers](https://www.johnsnowlabs.com/marketplace/medical-device-registered-importers-identified-by-manufacturers)\n\t- This dataset contains details of Medical Device Importers. Establishments that are required to register with the Food and Drug Administration (FDA) are also required to list the devices that are made there and the activities that are performed on those devices.\n- [Medical Device Registration Listing](https://www.johnsnowlabs.com/marketplace/medical-device-registration-listing)\n\t- This dataset shows owners or operators of places of business (also called establishments or facilities) that are involved in the production and distribution of medical devices intended for use in the United States (U.S.) are required to register annually with the Food and Drug Administration (FDA).\n- [Medical Device Registration](https://www.johnsnowlabs.com/marketplace/medical-device-registration)\n\t- Detailed list of the types of device establishments that are required to register and pay the fee can be found in this dataset. This dataset also contains Medical Device Establishment Registration Information including registration keys and full addresses of establishments.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated\n- Data Updates\n - Data updates support replace-on-update: outdated foreign keys 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spending estimates for substance abuse and mental health and health facilities for mental health treatment.\n\n**Description**\nThis data package contains general information on substance abuse and mental health including risk factors for mental health, public and private yearly spending estimates for mental health services & substance abuse treatment as well as a database of health treatment facilities and programs providing mental health services.\n\n**Benefits**\n- This data is useful for public health officials, clinicians, and researchers seeking to improve mental health and substance abuse. this can be used for further research\n- public health officials and program managers for potential interventions, setting priority areas for action and evaluating progress\n- policymakers for policy use and development\n- public health practitioner and clinicians' aid in designing and implementing targeted interventions and monitoring the progress of public health strategies specifically targeting mental health and substance abuse.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [Adult Smoking Habits in Great Britain 1974 to 2019](https://www.johnsnowlabs.com/marketplace/adult-smoking-habits-in-great-britain-1974-to-2019)\n\t- This dataset includes information regarding proportion who smoke cigarettes, cigarette consumption, the proportion who have never smoked cigarettes and proportion of smokers who have quit by sex/age over time in Great Britain from 1974 to 2019.\n- [Mental Health Spending Estimates](https://www.johnsnowlabs.com/marketplace/mental-health-spending-estimates)\n\t- The Mental Health Spending Estimates dataset lists detailed public and private yearly spending estimates related to mental health from 1986 to 2009 published in Substance Abuse and Mental Health Services Administration (SAMHSA) national expenditures for mental health services & substance abuse treatment report.\n- [NSDUH Alcohol Use by Age and State 2012 to 2014](https://www.johnsnowlabs.com/marketplace/nsduh-alcohol-use-by-age-and-state-2012-to-2014)\n\t- The National Survey on Drug Use and Health (NSDUH) Alcohol Use dataset includes alcohol and binge alcohol use in the past month and alcohol dependence or abuse in the past year among individuals aged 12 or older, by State and Substate Region: Percentages, Annual Averages Based on 2012, 2013, and 2014 NSDUHs.\n- [NSDUH Alcohol Use by Age and State 2014 to 2016](https://www.johnsnowlabs.com/marketplace/nsduh-alcohol-use-by-age-and-state-2014-to-2016)\n\t- The National Survey on Drug Use and Health (NSDUH) Alcohol Use dataset includes alcohol and binge alcohol use in the past month and alcohol dependence or abuse in the past year among individuals aged 12 or older, by State and Substate Region: Percentages, Annual Averages Based on 2014, 2015, and 2016 NSDUHs.\n- [NSDUH Drug and Substance Abuse By Age And State 2012 to 2014](https://www.johnsnowlabs.com/marketplace/nsduh-drug-and-substance-abuse-by-age-and-state-2012-to-2014)\n\t- The National Survey on Drug Use and Health (NSDUH) Drug and Substance Abuse use in the past year among individuals aged 12 or older, by state and state Region: Percentages, Annual Averages Based on 2012, 2013, and 2014 NSDUHs.\n- [NSDUH Drug and Substance Abuse By Age And State 2014 to 2016](https://www.johnsnowlabs.com/marketplace/nsduh-drug-and-substance-abuse-by-age-and-state-2014-to-2016)\n\t- The National Survey on Drug Use and Health (NSDUH) Drug and Substance Abuse use in the past year among individuals aged 12 or older, by state and state Region: Percentages, Annual Averages Based on 2014, 2015, and 2016 NSDUHs.\n- [NSDUH Illicit Drug Use by Age and State 2012 to 2014](https://www.johnsnowlabs.com/marketplace/nsduh-illicit-drug-use-by-age-and-state-2012-to-2014)\n\t- The National Survey on Drug Use and Health (NSDUH) Illicit Drug Use dataset includes illicit drug use among individuals aged 12 or older, by State and Substate Region: Percentages, Annual Averages Based on 2012, 2013, and 2014 NSDUHs.\n- [NSDUH Illicit Drug Use by Age and State 2014 to 2016](https://www.johnsnowlabs.com/marketplace/nsduh-illicit-drug-use-by-age-and-state-2014-to-2016)\n\t- The National Survey on Drug Use and Health (NSDUH) Illicit Drug Use dataset includes illicit drug use among individuals aged 12 or older, by State and Substate Region: Percentages, Annual Averages Based on 2014, 2015, and 2016 NSDUHs.\n- [NSDUH Mental Illness by Age and State 2012 to 2014](https://www.johnsnowlabs.com/marketplace/nsduh-mental-illness-by-age-and-state-2012-to-2014)\n\t- The National Survey on Drug Use and Health (NSDUH) Mental Illness dataset includes mental illness estimations in the past year among individuals aged 18 or older, by State and Substate Region: Percentages, Annual Averages based on 2012, 2013, and 2014 NSDUHs.\n- [NSDUH Mental Illness by Age and State 2014 to 2016](https://www.johnsnowlabs.com/marketplace/nsduh-mental-illness-by-age-and-state-2014-to-2016)\n\t- The National Survey on Drug Use and Health (NSDUH) Mental Illness dataset includes mental illness estimations in the past year among individuals aged 18 or older, by State and Substate Region: Percentages, Annual Averages based on 2014, 2015, and 2016 NSDUHs.\n- [NSDUH Substance Abuse And Mental Health By Age And State](https://www.johnsnowlabs.com/marketplace/nsduh-substance-abuse-and-mental-health-by-age-and-state)\n\t- This dataset contains the estimated percentages of individuals or patients who used illicit drugs, alcohol, tobacco in the last month or year and respectively that need or have received specialized treatment or that have a mental condition due or not to substance dependence or abuse.\n- [OSMI Mental Health in Tech Survey 2019](https://www.johnsnowlabs.com/marketplace/osmi-mental-health-in-tech-survey-2019)\n\t- This dataset is from an online survey on mental health conducted in 2019 by the Open Sourcing Mental Illness (OSMI).\n- [Perceptions of Great Risk from Having Five Several Alcoholic Drinks](https://www.johnsnowlabs.com/marketplace/perceptions-of-great-risk-from-having-five-several-alcoholic-drinks)\n\t- The National Survey on Drug Use and Health (NSDUH) Perceptions of Great Risk from Having Five or More Drinks of an Alcoholic Beverage Once or Twice a Week among individuals aged 12 or older, by State and Substate Region: Percentages, Annual Averages Based on 2012, 2013, and 2014 NSDUHs.\n- [Perceptions of Great Risk from Smoking Cigarettes Or Marijuana](https://www.johnsnowlabs.com/marketplace/perceptions-of-great-risk-from-smoking-cigarettes-or-marijuana)\n\t- The National Survey on Drug Use and Health (NSDUH) Perceptions of Great Risk from Smoking Cigarettes Or Marijuana dataset includes perceptions of great risk percentage from smoking one or more packs of cigarettes per day among individuals aged 12 or older and perceptions of great risk percentage from smoking marijuana once a month by State and Substate Region: Percentages, Annual Averages Based on 2012, 2013, and 2014.\n- [Substance Abuse Spending Estimates](https://www.johnsnowlabs.com/marketplace/substance-abuse-spending-estimates)\n\t- The Substance Abuse Spending Estimates dataset lists detailed public and private yearly spending estimates related to substance abuse from 1986 to 2009 published in Substance Abuse and Mental Health Services Administration (SAMHSA) national expenditures for mental health services & substance abuse treatment report\n- [US Mental Health Treatment Facilities](https://www.johnsnowlabs.com/marketplace/us-mental-health-treatment-facilities)\n\t- The Substance Abuse and Mental Health Services Administration (SAMHSA) provides on-line resource for locating mental health treatment facilities and programs. The Mental Health Treatment Locator section of the Behavioral Health Treatment Services Locator lists facilities providing mental health services to persons with mental illness.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated\n- Data Updates\n - Data updates support 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[info@johnsnowlabs.com](mailto:info@johnsnowlabs.com). \n 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Our **footfall intelligence dataset** provides **real-world visitation insights** to help retailers refine store layouts, optimize marketing strategies, and improve product offerings based on actual visitor behavior.\n\n**What you can do with this data**:\n✔ **Identify peak hours** to optimize staffing and promotions.\n✔ **Profile visitor demographics** (age, gender, visitor origin) to tailor marketing campaigns.\n✔ **Measure store performance** and assess marketing impact on foot traffic.\n✔ **Analyze dwell times** to refine store layout and customer engagement.\n\n**Why Choose This Dataset?**\n✅ **Uninterrupted & Reliable**: Unlike SDKs or WiFi tracking, telco data provides continuous coverage without gaps.\n✅ **Representative & Scalable**: Extrapolated for the entire population, not just telecom subscribers.\n✅ **Multi-Country Coverage**: Available in UK, Italy, Ireland, Austria, Denmark, Sweden, Hong Kong, Indonesia, Sri Lanka, Macau, Australia. We also have global partnerships covering most of the rest of the world - contact us if you are interested in more countries.\n✅ **Fully Customizable**: Define spatial resolution, segment visitors, and choose custom timeframes.\n✅ **Privacy-Protected & GDPR-Compliant**: K5 anonymization ensures safe and compliant data use.\n✅ **Fast Turnaround**: Get your dataset within days, with updates processed in 48 hours.\n\n\uD83D\uDCCC **Our data goes back to 2019 and is refreshed daily**.\n\uD83D\uDD0E **Optimize store efficiency and maximize revenue with real-world footfall data**. Contact us today.","terms_of_service":"https://ckdelta.ai/","documentation_link":"","privacy_policy_link":"https://ckdelta.ai/mobility-privacy","geographical_coverage":"[\"Europe\",\"Asia-Pacific\"]","cost":"PAID","update_frequency":{"interval":1,"unit":"DAILY"},"collection_granularity":{"interval":15,"unit":"MINUTE"},"collection_date_start":1546352361330,"data_source":"CK Hutchison telecoms companies (Three, WindTre, TPG, IOH) around the world","assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"CKDelta","description":"CKDelta is an AI software business within the CK Hutchison Holdings Ltd group, leveraging data, expertise and intellectual property on a conglomerate scale. We’re on a mission to empower customers with insights that transform decision-making and deliver impact, leveraging assets from across CK Hutchison group companies. 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Once you sign up, a token will be emailed to you instantly.**\n\n**MCP Overview:**\nThe OpenFDA MCP Server connects your Databricks AI agents to the open.fda.gov REST API through a native service covering drug labels, adverse events, and recall enforcement. Agents search FDA-approved label content, retrieve FAERS adverse event reports with patient demographics and reactions, and query recall enforcement actions all within a single orchestrated workflow. An openFDA API key is optional but recommended to lift the default per-IP rate limit.\n\n**Use cases:**\n\n- Search FDA Structured Product Labeling by brand name, generic name, or keyword to retrieve indications, warnings, dosage, and contraindications\n- Retrieve aggregated FAERS adverse event reports for a drug including patient demographics, co-administered drugs, reactions, and outcomes\n- Monitor FDA recall enforcement actions by product or keyword returning recall classification, reason, recalling firm, and current status\n- Chain label search with adverse event retrieval to compare documented warnings against observed safety signals\n- Support pharmacovigilance workflows by surfacing spontaneous safety signals. Note FAERS reports are not incidence rates\n\n**Sample Prompts:**\n\nPrompt Example 1: Safety Signal Detection & Label Review\n\n- \"Retrieve FAERS adverse event reports for semaglutide filtered to serious outcomes. Search the FDA drug label for semaglutide and check whether the top reported reactions are documented in the warnings, contraindications, or drug interactions sections.\"\n\nPrompt Example 2: Recall Monitoring & Label Verification\n\n- \"Search FDA enforcement actions for metformin and return all recalls with classification, reason, and recalling firm. For the most serious Class I recalls identified, search the current drug label to verify the affected formulations and documented contraindications.\"\n\n\n**These tools are available when installed:**\n\n- openfda_search_drug_labels: Search FDA drug labels by brand name, generic name, or keyword and return structured SPL label content including indications, warnings, and contraindications\n- openfda_get_adverse_events: Retrieve FAERS adverse event reports for a drug including patient demographics, reactions, co-administered drugs, and outcomes; reports are spontaneous safety signals, not incidence rates\n- Openfda_search_drug_enforcement: Search FDA drug recalls and enforcement actions by product or keyword returning recall classification, reason, recalling firm, and status\n\n**Additional Information:**\nThis MCP Server is powered by Climb, a leading Consulting and Solutions Integrator Partner of Databricks. 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It provides structured access to high-priority datasets including drug adverse event reports, prescription drug labels, recall enforcement actions, and medical device records, covering products regulated by the FDA across drugs, biologics, devices, and foods. Freely available at open.fda.gov with no cost to access, it enables anyone to search, analyze, and build applications using authoritative FDA data that was previously difficult to query at scale. This MCP Server is powered by Climb.ai, a leading Data and Consulting Partner of Databricks."},"popularity":1870},{"id":"f597f75c-6f11-4260-9f79-181c82f87126","summary":{"name":"US Mortality and Morbidity Data","subtitle":"This data package contains data on public health indicators, mortality and morbidity. 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The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [Big Cities Age-Adjusted ED Visits Rates](https://www.johnsnowlabs.com/marketplace/big-cities-age-adjusted-ed-visits-rates)\n\t- This dataset contains estimates of Emergency Department visit rates for asthma and firearms related injuries shared by the Big Cities Health Coalition members represented by the largest metropolitan health departments in the United States. The estimated values of age-adjusted visit rates cover the 2010-2017 period and are described by gender and race/ethnicity.\n- [Big Cities Age-Adjusted Mortality Rates](https://www.johnsnowlabs.com/marketplace/big-cities-age-adjusted-mortality-rates)\n\t- This dataset contains estimates of age-adjusted mortality rates, by the major causes of death, shared by the Big Cities Health Coalition members represented by the largest metropolitan health departments in the United States. The estimated values of age-adjusted mortality cover the 2010-2015 period and are described by gender and race/ethnicity.\n- [Big Cities Morbidity Rates](https://www.johnsnowlabs.com/marketplace/big-cities-morbidity-rates)\n\t- This dataset contains estimates of morbidity rates for the major infections causes and elevated lead levels in blood among children, shared by the Big Cities Health Coalition members represented by the largest metropolitan health departments in the United States. The estimated values morbidity rates cover the 2010-2018 period and are described by sex and race/ethnicity.\n- [County Level Age Adjusted Drug Poisoning Mortality](https://www.johnsnowlabs.com/marketplace/county-level-age-adjusted-drug-poisoning-mortality)\n\t- This dataset contains mortality statistics for drug poisoning in the US at county level, starting with 1999. The age adjusted mortality at county level is presented as an interval, containing the estimated values for 16 age categories (with a minimum and maximum value), along with names of the state and of the county and with state and county FIPS codes.\n- [New York City Age Adjusted And Crude Mortality Rates By Cause Of Death](https://www.johnsnowlabs.com/marketplace/new-york-city-age-adjusted-and-crude-mortality-rates-by-cause-of-death)\n\t- This dataset contains estimates of mortality rates due to the major causes of death among the population of New York City, starting 2007. The estimated data for crude and age-adjusted mortality rates due the major causes of death are described by gender and race/ethnicity of the population groups.\n- [State Level Opioid Overdose Mortality Rates](https://www.johnsnowlabs.com/marketplace/state-level-opioid-overdose-mortality-rates)\n\t- This dataset contains mortality statistics for opioid drugs poisoning in the US at state level starting from 2013 to 2016. The indicators used crude and age-adjusted mortality. At the same time it contains data about the number of deaths and the increase of the number, along with statistical significance (for a probability level of 95%) of increase between the years.\n- [US And State Level Crude And Age Adjusted Drug Poisoning Mortality](https://www.johnsnowlabs.com/marketplace/us-and-state-level-crude-and-age-adjusted-drug-poisoning-mortality)\n\t- This dataset contains mortality statistics for drug poisoning in the US at country and state level starting from 1999. The mortality indicators used (crude and age-adjusted) are broken down by demographic characteristics, age, sex and race and presented along with US and state levels values.\n- [US Mortality Rates for All Types of Cancer](https://www.johnsnowlabs.com/marketplace/us-mortality-rates-for-all-types-of-cancer)\n\t- Cancer survival statistics are typically expressed as the proportion of patients alive at some point subsequent to the diagnosis of their cancer. Statistics compare the survival of patients diagnosed with cancer with the survival of people in the general population who are the same age, race, and sex and who have not been diagnosed with cancer.\n- [US Mortality Rates for Groups of Communicable Diseases](https://www.johnsnowlabs.com/marketplace/us-mortality-rates-for-groups-of-communicable-diseases)\n\t- This dataset contains estimates for cause-specific age-standardized mortality rates for groups of Communicable, Maternal, Neonatal, and Nutritional Diseases at the county level for each state, the District of Columbia, and the United States as a whole for 1980-2014 (quinquennial), as well as the changes in rates during this period.\n- [US Mortality Rates for Groups of Injuries](https://www.johnsnowlabs.com/marketplace/us-mortality-rates-for-groups-of-injuries)\n\t- This dataset contains estimates for cause-specific age-standardized mortality rates for groups of Injuries (Transport Injuries, Unintentional Injuries, Self-harm and Interpersonal Violence, Forces of Nature, War, and Legal Intervention) at the county level for each state, the District of Columbia, and the United States as a whole for 1980-2014 (quinquennial), as well as the changes in rates during this period.\n- [US Mortality Rates for Groups of Noncommunicable Diseases](https://www.johnsnowlabs.com/marketplace/us-mortality-rates-for-groups-of-noncommunicable-diseases)\n\t- This dataset contains estimates for cause-specific age-standardized mortality rates for groups of Noncommunicable Diseases at the county level for each state, the District of Columbia, and the United States as a whole for 1980-2014 (quinquennial), as well as the changes in rates during this period.\n- [US Mortality Rates for Specific Cancer Types](https://www.johnsnowlabs.com/marketplace/us-mortality-rates-for-specific-cancer-types)\n\t- This dataset contains estimates for 29 cancer-specific age-standardized mortality rates for specific cancer types at the county level for each state, the District of Columbia, and the United States as a whole for 1980-2014 (quinquennial), as well as the changes in rates during this period.\n- [US Mortality Rates for Specific Cardiovascular Disease Type](https://www.johnsnowlabs.com/marketplace/us-mortality-rates-for-specific-cardiovascular-disease-type)\n\t- This dataset contains estimates for cardiovascular disease-specific age-standardized mortality rates for specific cardiovascular disease types at the county level for each state, the District of Columbia, and the United States as a whole for 1980-2014 (quinquennial), as well as the changes in rates during this period.\n- [US Mortality Rates for Specific Infectious Disease Type](https://www.johnsnowlabs.com/marketplace/us-mortality-rates-for-specific-infectious-disease-type)\n\t- This dataset contains estimates for age-standardized mortality rates from lower respiratory infections (LRIs), diarrheal diseases, HIV/AIDS, meningitis, hepatitis, and tuberculosis between 1980 t0 2014.\n- [US Mortality Rates for Specific Respiratory Disease Type](https://www.johnsnowlabs.com/marketplace/us-mortality-rates-for-specific-respiratory-disease-type)\n\t- This dataset contains estimates for respiratory disease-specific age-standardized mortality rates for specific respiratory disease types at the county level for each state, the District of Columbia, and the United States as a whole for 1980-2014 (quinquennial), as well as the changes in rates during this period.\n- [US Mortality Rates for Substance Use Disorders](https://www.johnsnowlabs.com/marketplace/us-mortality-rates-for-substance-use-disorders)\n\t- This dataset contains estimates for age-standardized mortality rates by county from alcohol use disorders, drug use disorders, self-harm, and interpersonal violence.\n- [US Mortality Risk for All Age Groups](https://www.johnsnowlabs.com/marketplace/us-mortality-risk-for-all-age-groups)\n\t- This dataset provides estimates for mortality risk at the county level for each state, the District of Columbia, and the United States as a whole for 1980-2014, as well as the changes in life expectancy and mortality risk for each location during this period.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 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All data comes from a single source, ensuring **consistency and comparability** across markets.\n\nThe Sociodemographics-Bundle Basic for Albania includes the following **variables**:\n- [population](https://www.mbi-geodata.com/en/sociodemographic-data/population/)\n- [gender](https://www.mbi-geodata.com/en/sociodemographic-data/sex-gender/)\n- [age structure](https://www.mbi-geodata.com/en/sociodemographic-data/agebands/) (15-year age bands)\n- household figures ([number of households](https://www.mbi-geodata.com/en/sociodemographic-data/households/) and [households by type](https://www.mbi-geodata.com/en/sociodemographic-data/households-by-type/))\n\nOur sociodemographic data is offered for each country on different **levels**:\n- **Postal Code Level:** This fine-grained level of detail makes the data suitable for a wide range of applications – for example, enriching customer databases, which are often organized by postal code.\n- **Administrative Level:** Covers both larger administrative areas (e.g., regions, states, provinces) as well as finer subdivisions such as districts, counties, or municipalities.\n- **Micro Level**: Subdivisions of postal code or administrative boundaries, such as census areas or street segments. 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MBI is part of the Michael Bauer Group, alongside Michael Bauer Research GmbH and Michael Bauer Micromarketing GmbH."},"popularity":498},{"id":"bee74c15-94dd-4ed8-93cc-918d87d0deea","summary":{"name":"FactSet ETF","subtitle":"Access accurate security, fund, and reference data across the universe of exchange-traded products.","setting":{"visibility":"PUBLIC"},"created_at":1741366231186,"updated_at":1742425837664,"categories":["FINANCIAL"],"listingType":"PERSONALIZED","provider_id":"48d55bd7-064c-4665-844d-dff8a6def12c"},"detail":{"description":"**Highlights**\n\n- Leverage ETF data feeds with our open-source data solutions to integrate with your investment management. Analyze fund composition with detailed holdings data for equity and fixed income securities, including shares, market values, and weights.\n\n- Identify trends over time with time series fund level information such as Fund Flows, NAV, and AUM. \n\n- Create objective measures of fund efficiency, risk, tradability, and fit relative to benchmark using proprietary ratings, scores, and reference information such as launch date, currency, and expense ratio.\n\nAccess complete and accurate security, fund, and reference data across the universe of exchange-traded products. Rely on data sourced from ETF providers across the globe for 100+ unique data points, resulting in comprehensive coverage to help you evaluate and construct ETFs, analyze potential trades, and perform fund research. Data Frequency: Daily; Update Frequency: Intraday.","terms_of_service":"https://www.factset.com/legal","documentation_link":"https://www.factset.com/marketplace/catalog/product/factset-etf","privacy_policy_link":"https://www.factset.com/legal","geographical_coverage":"[\"Global\"]","cost":"PAID","assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"FactSet","description":"FactSet is an industry leader in acquiring, integrating, and managing financial data. Our content gives you the power to monitor the global markets, research public and private companies, and gain industry-level insight through. Our proprietary symbology links and aggregates a variety of content sources to ensure consistency, transparency, and data integrity across your business."},"popularity":1583},{"id":"5f0ac678-8ced-4823-bcc5-5b981311e09f","summary":{"name":"Geolocet | Demographic Data Belgium at the Statistical Sector level","subtitle":"Demographic Data Belgium at the Statistical Sector level - age, gender, marital status, occupancy type, income...","setting":{"visibility":"PUBLIC"},"created_at":1705912581474,"updated_at":1752144214830,"categories":["DEMOGRAPHICS","ADVERTISING_AND_MARKETING","GEOSPATIAL","PUBLIC_SECTOR","RETAIL"],"listingType":"PERSONALIZED","provider_id":"5d3ddc17-a7e8-4794-a760-13587e1de2c6"},"detail":{"description":"## **Get instant access to Belgium Demographics Data with [Geolocet](https://geolocet.com)**\n\nExplore the richness of Belgium demographic landscape with our Belgium Census Data. This meticulously compiled dataset, sourced from official statistical sources, including Statbel, the Belgian statistical office, offers a detailed insight into the population landscape of Belgium at the statistical sector level. \n\nIf you require data at a less granular level, such as an Uber grid or square grid, please don't hesitate to get in touch with us at *contact@geolocet.com*. We understand that different projects may have varying data needs, and we are here to tailor our data to match your specific requirements.\n\n ## **Key Features:**\n---\n* **Population Data Belgium:** Gain access to the latest and most accurate population statistics for Belgium, meticulously organized by statistical sectors.\n\n* **Demographic Insights:** Dive deep into Belgium demographic landscape with data on 744 attributes, including:\n* Total population,\n* Income,\n* Age bands,\n* Gender,\n* Gender crossed with Age,\n* Marital status,\n* Marital status split by Age,\n* Gender split by Marital status,\n* Nationality,\n* Nationality split by Age,\n* Gender split by Nationality,\n* Nationality split by Marital status,\n* Gender split by Nationality split by Age,\n* Gender split by Nationality split by Marital Status,\n* Gender split by Marital status split by Age,\n* Marital status split by Nationality split by Age,\n* Gender split by Marital Status split by Nationality split by Age,\n* Occupancy type,\n* Occupancy type split by Gender split by Age,\nall available at the statistical sector level.\n\n* **Geodemographic Insights:** Unlock geodemographic data Belgium-wide to inform location-based decision-making, marketing strategies, and policy formulation.\n\n* **Ready to use:** Our dataset is currently available in CSV format, providing ease of use and compatibility with a wide range of data analysis tools. However, if you prefer a different format, our team is readily available to assist you in obtaining the data in your desired format.\n---\nAdditionally, for those looking to enrich their analysis or project, we offer:\n* **[Administrative Boundaries](https://geolocet.com/products/belgium-all-available-administrative-boundaries):** If you require geographical boundaries (polygons) for the municipalities of Belgium, consider exploring our Admin Boundaries product, which seamlessly integrates with this dataset.\n\n* **[Point of Interest (PoI) Data](https://geolocet.com/pages/aggregated-points-of-interest-poi-data):** If you're interested in Points of Interest data, please don't hesitate to contact us for customized datasets to enhance your geographical analysis.\n\n* **[Data Customization](https://geolocet.com/pages/data-services-by-geolocet):** We understand that your project may have specific requirements. Feel free to get in touch with us if you need any modifications or specific data formats such as Excel, JSON, GeoJSON, or Shapefile. We are here to tailor the data to your needs.\n\nIf you're looking for data for Belgium, [look here](https://geolocet.com/pages/belgium) for off-the-shelf or tailor-made datasets to suit your specific requirements.\n\nMake informed decisions, conduct precise research, and unlock the potential of Belgium demographic landscape with our comprehensive dataset. Trust in the accuracy and reliability of our official statistical data to drive your projects forward.\nContact us at *contact@geolocet.com* today to access this invaluable resource and explore Belgium census data like never before.\n\n[Geolocet LTD](https://geolocet.com)\n","terms_of_service":"https://geolocet.com/pages/terms-of-use","documentation_link":"","privacy_policy_link":"https://geolocet.com/pages/privacy-policy","geographical_coverage":"[\"Europe\"]","cost":"PAID","data_source":"","assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://geolocet.com/pages/terms-of-use"},"provider_summary":{"name":"Geolocet LTD","description":"Geolocet provides premium geospatial data for retail analysis, GIS, and market planning. We equip businesses with accurate, ready-to-query data to drive spatial analysis, site selection, and customer segmentation.\n\nCore Offerings:\n\nDemographics: Socioeconomic data covering population, age, and employment across diverse geographies.\n\nPoints of Interest (PoI): Datasets spanning retail, healthcare, and transport for catchment analysis.\n\nAdministrative Boundaries: GIS-ready boundaries, including municipalities and postcodes, for territory management.\n\nBrand Locations: Chain location datasets to monitor store networks and market expansion.\n\nWealth Data: Granular economic indicators like purchasing power to identify high-potential markets.\n\nWe source from trusted providers, delivering highly customizable datasets with comprehensive coverage across Europe and worldwide."},"popularity":785},{"id":"7940ba62-088a-425c-b036-4b44b6b86b41","summary":{"name":"Identity Resolution + Data Enrichment","subtitle":"Resolve and enrich first-party customer records against Audience Acuity’s deterministic, referential data asset.","setting":{"visibility":"PUBLIC"},"created_at":1743804288276,"updated_at":1745436385759,"categories":["ADVERTISING_AND_MARKETING","COMMERCE","FINANCIAL","GAMING","MEDIA","RETAIL","SPORTS","TRAVEL_AND_TOURISM"],"listingType":"PERSONALIZED","provider_id":"b42a44e0-99dc-45d0-b913-d4b616d901df"},"detail":{"description":"**Overview**\nAudience Acuity’s Super Identity Graph (SIG) is an omni-channel, deterministic identity graph built at the individual level and scaled to over 249 million U.S. consumers. Using a proprietary 23-step validation process, the SIG connects PII and digital identifiers with precision—ensuring unmatched accuracy and referential integrity across channels.\n\nThe Audience Acuity approach blends individually compiled data with extensive behavioral, transactional, and digital activity, creating a unified, real-time view of each consumer. This allows brands to engage audiences with precision and scale—without relying on third-party cookies. Whether powering personalization, analytics, or activation, the Super Identity Graph delivers the accuracy, transparency, and reach needed to succeed in a privacy-first, data-driven landscape.\n\n**Use cases**\n\n- **Identity** **Resolution**: Identify known and unknown customers by matching data points—such as email, hashed email, Mobile Advertising ID (MAID), Device ID, or phone number—to the Audience Acuity Identity Graph. Reveal associated personally identifiable information (PII), including first name, last name, and mailing address, to build a unified customer view.\n\n- **Data Enhancement & Linkage**: Enrich consumer records by matching common identifiers—such as name, address, email, hashed email, or Mobile Advertising ID (MAID)—to Audience Acuity’s Identity Graph. Append demographic data, consumer interests, purchase behaviors, digital identifiers, and real-time intent signals to create a more complete and actionable customer profile.\n\n\n- **Audience Development:** Utilize available touchpoints to engage customers and prospects, strengthen existing relationships, and drive new customer acquisition through personalized, data-driven outreach.\n\n \n\n- **Analytic Use:** Leverage Audience Acuity data within internal analytics environments to power customer segmentation, predictive modeling, scoring, audience development, campaign response analysis, prospect targeting, and AI/ML initiatives.\n\n- **Activation**: Onboard matched consumer records with valid digital identifiers to omni-channel activation platforms, including social media, programmatic, and email. Email campaigns are limited to records with a confirmed opt-in status.\n\n- **Media Network Data Monetization:** Unify and enrich data with all match keys necessary for data monetization across all major media networks. \n\n\n\n\n**Product details**\nThe Super Identity Graph (SIG) comprises 11 interconnected tables that provide brands with a foundational layer of validated, name-based individual and household consumer identities. Each identity is enriched with channel identifiers for engaging consumers across both traditional and digital channels, along with detailed descriptors that deliver a unified, multidimensional view at the individual level. Every record is anchored by a persistent ID, enabling seamless linkage across the entire SIG.\n\n- PII: Foundational identity table with validated name and address associations at individual and household levels.\n\n- EMAIL: Plain text email and hashed email (MD5, SHA1, SHA256).\n\n- PHONE: Mobile and landline phone numbers.\n\n- MAID: Mobile Advertiser IDs, hashed emails, IPs and device info.\n\n- IP: IP addresses paired to email and MAID\n\n- DATA: Over 450 consumer attributes across 15 categories, such as demographics, household composition, occupation, interests, etc.\n\n- BEHAVIORS: Recent category and brand-level digital engagement with observations data, device details, and app activity.\n\n- AUTO: VIN-level records containing details of make, model, and year.\n\n- FINANCIAL: Comprehensive financial attribution, including discretionary income and financial power metrics.\n\n- PROPERTY: Complete parcel, property, and transaction details.\n\n- EXECUTIVE: Business executive contact information and firmographic descriptors ","terms_of_service":"https://docs.google.com/document/d/1JqHif-wyYXY6q338xwQkqffv0z9KDz6A/edit","documentation_link":"","privacy_policy_link":"https://audienceacuity.com/privacy-policy/","geographical_coverage":"[\"United States and Canada\"]","cost":"PAID","update_frequency":{"interval":1,"unit":"WEEKLY"},"data_source":"Audience Acuity’s Super Identity Graph is a proprietary, in-house curated data asset built from over 90 high-quality source feeds, representing more than 1.6 trillion consumer events each month. Designed to provide full coverage of the U.S. adult population, it captures consumer touchpoints across offline-to-online and online-to-offline interactions in a privacy-compliant manner.","assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"Audience Acuity","description":"Audience Acuity is a leading provider of identity resolution and audience intelligence solutions, helping brands and agencies maximize the effectiveness of their data-driven marketing efforts. By delivering high-quality, privacy-compliant audience insights, Audience Acuity enables more accurate targeting, measurement, and engagement. With one of the most comprehensive identity graphs available, the company empowers businesses to better understand and reach customers across channels while maintaining strict compliance with evolving privacy regulations. These solutions help advertisers, brands, platforms, and data providers enhance personalization, optimize media performance, and improve attribution to ensure marketing investments deliver measurable outcomes in an increasingly complex digital landscape."},"popularity":1735},{"id":"03869e40-138a-456a-bd4d-9b8e3e09b6bb","summary":{"name":"Geolocet | Points of Interest Data | Grocery Store Locations - Canada","subtitle":"Complete GIS-ready dataset of 19,529 Canada grocery locations (brands & independents). Optimized for Spark/Databricks.","setting":{"visibility":"PUBLIC"},"created_at":1780637969959,"updated_at":1780638000079,"categories":["GEOSPATIAL","RETAIL"],"listingType":"PERSONALIZED","provider_id":"5d3ddc17-a7e8-4794-a760-13587e1de2c6"},"detail":{"description":"# **All Supermarket Locations in Canada: Complete Geographic Dataset**\n---\n### **Premium GIS-Ready Retail Location Intelligence Data for Databricks Analytics**\n\nThis high-fidelity geospatial dataset provides a complete, exhaustive, and standardized overview of **19,529** verified grocery store and supermarket locations across Canada. Updated as of **April 27, 2026**, this comprehensive national dataset captures the entire Canadian grocery landscape - encompassing both major branded retail chains and independent local markets. It offers an analysis-ready foundation for full-scale retail site selection, competitive intelligence, logistics optimization, and spatial demographics mapping.\n\nFully compatible with **Apache Spark**, **Delta Lake**, and **Databricks Unity Catalog** workflows.\n\n---\n\n## **Complete National Coverage: Branded & Independent Grocers**\nUnlike top-tier only datasets, this exhaustive collection covers maximum geographic depth across all Canadian provinces and territories. It includes over 14,000 independent and regional grocers alongside the top 18 national supermarket and convenience banners:\n\n- **Major National Supermarkets & Superstores:** \n [Walmart (444 locations)](https://geolocet.com/products/canada-walmart-grocery), \n [Metro (331 locations)](https://geolocet.com/products/canada-metro-grocery), \n [Sobeys (239 locations)](https://geolocet.com/products/canada-sobeys-grocery), \n [Save-On-Foods (188 locations)](https://geolocet.com/products/canada-save-on-foods-grocery), \n [Safeway (134 locations)](https://geolocet.com/products/canada-safeway-grocery), \n [Real Canadian Superstore (120 locations)](https://geolocet.com/products/canada-real-canadian-superstore-grocery)\n\n- **Discount, Value & Specialty Chains:** \n [No Frills (365 locations)](https://geolocet.com/products/canada-no-frills-grocery), \n [Winners (283 locations)](https://geolocet.com/products/canada-winners-grocery), \n [Giant Tiger (232 locations)](https://geolocet.com/products/canada-giant-tiger-grocery), \n [Maxi (198 locations)](https://geolocet.com/products/canada-maxi-grocery), \n [Food Basics (153 locations)](https://geolocet.com/products/canada-food-basics-grocery), \n [FreshCo (152 locations)](https://geolocet.com/products/canada-freshco-grocery), \n [Super C (120 locations)](https://geolocet.com/products/canada-super-c-grocery), \n [M&M (333 locations)](https://geolocet.com/products/canada-m-m-grocery)\n\n- **Proximity & Local Formats:** \n [Couche-Tard (596 locations)](https://geolocet.com/products/canada-couche-tard-grocery), \n [IGA (382 locations)](https://geolocet.com/products/canada-iga-grocery), \n [Boni-Soir (227 locations)](https://geolocet.com/products/canada-boni-soir-grocery), \n [Foodland (218 locations)](https://geolocet.com/products/canada-foodland-grocery)\n\n**Plus over 14,000 independent, regional, and unbranded grocers** fully mapped and standardized across all provinces and territories.\n\n---\n\n## **Technical Specifications & Schema Definitions**\nOptimized for zero-friction schema enforcement when reading via `spark.read.format(\"csv\")`. The structured schema below ensures flawless rendering in Databricks notebooks and high accuracy for LLM semantic indexing.\n\n* **`GUID`** (`STRING`): Unique global identifier for deterministic primary key mapping.\n* **`Title`** (`STRING`): Official store name, brand banner, or independent title of the location.\n* **`Latitude`** (`DOUBLE`): Precise WGS84 latitude coordinate for point-in-polygon & spatial joins.\n* **`Longitude`** (`DOUBLE`): Precise WGS84 longitude coordinate for spatial indexing (H3 / S2).\n* **`Street_No` / `Street`** (`STRING`): Normalized building/house number and street name elements.\n* **`City` / `Postal_Code`** (`STRING`): Core postal addresses, municipal settlement names, and Canadian Forward Sortation Areas (FSAs).\n* **`Admin_level_1` / `_2`** (`STRING`): Macro-administrative boundary classification (e.g., Provinces like Ontario, Quebec, BC) and regional district identifiers.\n* **`Address`** (`STRING`): Full, standardized street address of the location.\n* **`Wheelchair`** (`STRING`): Accessibility flags indicating wheelchair mobility support.\n* **`Popularity_Score`** (`INTEGER`): Relative foot-traffic/rating metric graded on a standardized 1-10 scale.\n* **`Opening_Hours`** (`STRING`): Standard daily operational schedules and structured operating text.\n* **`Phone` / `Website`** (`STRING`): Direct communication data, branch-specific landing URLs, and domain vectors.\n\n---\n\n## **Databricks Workflow & Spatial Analytics Use Cases**\n\n- **Whitespace & Underserved Market Analysis:** Query the full 19,529 locations against population density tables to identify true retail deserts and expansion opportunities untouched by major national networks.\n- **H3 Geospatial Indexing:** Convert the precise WGS84 `Latitude` and `Longitude` fields into Uber H3 spatial hexagons (`h3_longlattocell`) to cross-reference with granular Statistics Canada census tract data.\n- **Supply Chain & Last-Mile Logistics Optimization:** Combine store location points with regional administrative boundary layers to optimize delivery routes, distribution warehouse placements, and network expansion footprints across Canada.\n\n---\n\n## **Data Evaluation & Custom Ingestion**\n\n### **Evaluate with a Free Sample Dataset**\nVerify the structure, column types, and coordinate precision before full implementation. A comprehensive schema sample is available for download in CSV/Parquet-friendly formats.\n\n*Want deeper market insights?* Learn more about the brand network in our comprehensive report: **[View the Canadian Top Brands Grocery Report](https://geolocet.com/pages/canada-topbrands-grocery-report)**\n\n### **Enterprise Delivery & Tailored Formatting**\nWhile natively available as an analysis-ready CSV for DBFS ingestion, we accommodate enterprise-level data architecture needs. Alternative formats - including **GeoJSON, Esri Shapefiles, PostgreSQL dumps, Excel, or direct Delta sharing profiles** - can be generated. Reach out to our engineering team at **contact@geolocet.com** for integration support or custom North American POI data requests.","terms_of_service":"https://geolocet.com/pages/terms-of-use","documentation_link":"","privacy_policy_link":"https://geolocet.com/pages/privacy-policy","assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"Geolocet LTD","description":"Geolocet provides premium geospatial data for retail analysis, GIS, and market planning. We equip businesses with accurate, ready-to-query data to drive spatial analysis, site selection, and customer segmentation.\n\nCore Offerings:\n\nDemographics: Socioeconomic data covering population, age, and employment across diverse geographies.\n\nPoints of Interest (PoI): Datasets spanning retail, healthcare, and transport for catchment analysis.\n\nAdministrative Boundaries: GIS-ready boundaries, including municipalities and postcodes, for territory management.\n\nBrand Locations: Chain location datasets to monitor store networks and market expansion.\n\nWealth Data: Granular economic indicators like purchasing power to identify high-potential markets.\n\nWe source from trusted providers, delivering highly customizable datasets with comprehensive coverage across Europe and worldwide."},"popularity":908},{"id":"afb11132-de82-4e58-9061-cd0c6699739a","summary":{"name":"Market Risk Indicators","subtitle":"Designed to predict the probability of a home price decline over the next 12-month period","setting":{"visibility":"PUBLIC"},"created_at":1789678600238,"updated_at":1789678600238,"categories":["FINANCIAL"],"listingType":"PERSONALIZED","provider_id":"26040e80-2e39-4e01-8fea-0667064d9fce"},"detail":{"description":"Market Risk Indicators provide monthly probability of home price declines over the next 12 months, helping real estate, financial services, and investment teams identify markets with elevated downside risk and make more informed portfolio, valuation, and planning decisions. \nBuilt by the same data science team behind Cotality HPI™ and HPI Forecast™, the indicators combine the latest housing analytics with extensive Core-Based Statistical Area (CBSA) economic data to support trusted market risk assessment.\n\n**Risk Analysis**\nMarket Risk Indicators allow businesses to assess the risk associated with investing in a particular housing market. By predicting the probability of a home price decline, businesses can make more informed decisions about where to invest their resources.\n**Asset Valuation**\nBy predicting future price declines, Market Risk Indicators can help businesses validate the current value of their real estate assets. If a significant price decline is predicted, a business may choose to sell an asset before its value decreases.\n**Market Analysis**\nMarket Risk Indicators provide valuable information about overall market trends. This information can help businesses understand the health of the housing market, identify emerging trends, and make strategic decisions based on this information.\n**Demand Forecasting**\nThe probability of a price decline can also be used to forecast demand in the housing market. If a significant price decline is predicted, this could indicate that demand for housing in that area may decrease in the future.\n**Regulatory Reporting**\nBusinesses in the financial sector are often required to report on the risks associated with their investments. Market Risk Indicators can provide the data needed for this reporting.\n**Quantitative Analysis**\nMarket Risk Indicators provide quantitative data that can be used in a variety of analyses. For example, businesses can use this data to conduct a risk assessment of their real estate portfolio, to model potential scenarios, or to evaluate the impact of different strategic decisions.","terms_of_service":"https://www.cotality.com/","documentation_link":"https://www.cotality.com/products/market-risk-indicators?utm_campaign=412443580-EDSG-ALL-CloudMarketplace-Databricks-2026&utm_source=databricks&utm_medium=marketplace&utm_term=marketriskindicators","privacy_policy_link":"https://www.cotality.com/","assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"Cotality","description":"We illuminate data, insights, and workflows across the property ecosystem, analyzing the largest objective dataset in the U.S. for real-time insights in minutes, not months. Together, we’ll identify hidden risks and powerful opportunities for transforming your business and solving society’s greatest challenges."},"popularity":222},{"id":"eb889d2b-9034-4050-896f-a84707bafa6f","summary":{"name":"Master Link USA -Sample","subtitle":"An index of every addressable location in the United States & the hierarchy of boundaries they fall within.\n","share":{"name":"usa_master_link_sample","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1732801300483,"updated_at":1765188697930,"categories":["ADVERTISING_AND_MARKETING","GEOSPATIAL"],"listingType":"STANDARD","provider_id":"b2312fad-ce38-48e5-9c55-8a225d183993"},"detail":{"description":"**Overview**\nCreated using location intelligence software to perform point-in-polygon operations with address and boundary data, Precisely's Master Link product provides a flat file of addresses associated to boundaries.\n\nThis sample data set covers the San Francisco, CA area. This represents a subset of the full product’s geographic extent. The full commercial offering features geographic coverage of the entire United States. Contact Databricks.partnership@precisely.com for more information.\n\n**Use cases**\n\n- **360-Degree Customer View**\n Enhance understanding of customer activity\n- **Location Data Enrichment**\n See the hierarchy of boundaries for an address point and link boundary information to risk, property, and demographic data\n- **Market Analysis**\n Perform analytics to better understand markets including identifying new potential locations\n\n**Product details**\n\nWith Master Link, users don’t need to perform complex and time-consuming geospatial processing to understand the relationships between an address and the boundaries it falls within. 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It also contains a list of the names used for each UNII and the changes made to Unique Ingredient Identifiers' (UNIIs) descriptions to the latest update.\n\n**Description**\nThe Unique Ingredient Identifier (UNII) is a non-proprietary, free, unique, unambiguous, non-semantic, alphanumeric identifier based on a substance's molecular structure and/or descriptive information. The UNII is:\n- One of the core components of the United States Federal Medication Terminology.\n- Used in the FDA's Structured Product Labeling\n- Used to assist in the generation of the National Library of Medicine's (NLM's) RxNorm.\n- A US government standard for drug ingredient and food allergen identifiers\n- A component of the Environmental Protection Agency's Substance Registry System (future) The overall purpose of the joint FDA/USP Substance Registration System (SRS) is to support health information technology initiatives by generating unique ingredient identifiers (UNIIs) for substances in drugs, biologics, foods, and devices. \n\nThe UNII is a non- proprietary, free, unique, unambiguous, non-semantic, alphanumeric identifier based on a substance’s molecular structure and/or descriptive information. The procedures and management of the SRS is provided by the SRS Board. The SRS Board includes experts from both FDA and USP. The SRS operating procedures defined by the SRS Board are detailed in the SRS Manual. The UNII is a core component of the US Federal Medication Terminology, it is used for product labeling, to assist in the generation of RxNorm, as an identifier for drug ingredients and allergens and in the future will be a component of the Environmental Protection Agency's Substance Registry System. The UII is useful for understanding data contained in NLM's Unified Medical Language System, National Cancer Institute Enterprise Vocabulary Service, FDA Data Standards Council website, VA National Drug File Reference Terminology, FDA Inactive Ingredient Query Application and, proximately, USP Dictionary of USAN and International Drug Names.\n\n**Benefits**\n- The overall purpose of the joint fda/usp substance registration system (srs) is to support health information technology initiatives by generating unique ingredient identifiers (uniis) for substances in drugs, biologics, foods, and devices.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [Unique Ingredient Identifier Changes](https://www.johnsnowlabs.com/marketplace/unique-ingredient-identifier-changes)\n\t- This dataset displays the changes made to Unique Ingredient Identifiers' (UNIIs) descriptions to the lastest update (2019). Content in this dataset is related to the Unique Ingredient Identifier Records dataset.\n- [Unique Ingredient Identifier Names](https://www.johnsnowlabs.com/marketplace/unique-ingredient-identifier-names)\n\t- This dataset contains a list of the names used for each UNII (Unique Ingredient Identifier). Contents on this dataset are related to the UNII Records dataset.\n- [Unique Ingredient Identifier Records](https://www.johnsnowlabs.com/marketplace/unique-ingredient-identifier-records)\n\t- This dataset contains the details of substances in drugs, biologics, foods and devices registered with a Unique Ingredient Identifier (UNII) through the joint FDA/USP Substance Registration System (SRS).\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - 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For further information on the Darwin Core terminology, use the following link: [Darwin Core quick reference guide - Darwin Core (tdwg.org)](https://dwc.tdwg.org/terms/).\n\nPlease note that this sharing solution is under development and that the coral dataset will be updated as new surveys become available.","terms_of_service":"https://equinoropendata.blob.core.windows.net/disclaimers/Equinor%20open%20data%20-%20Terms%20and%20conditions.pdf","documentation_link":"","privacy_policy_link":"https://www.equinor.com/about-us/privacy-policy","geographical_coverage":"[\"Europe\"]","cost":"FREE","data_source":"","size":0.001,"assets":["ASSET_TYPE_MEDIA"],"license":"https://equinoropendata.blob.core.windows.net/disclaimers/Equinor%20open%20data%20-%20Sharing%20license%20-%20Corals.pdf"},"provider_summary":{"name":"Equinor ASA","description":"In Equinor, we are determined to deliver the energy the world needs today, safely and reliably. 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Don't miss this opportunity to take your business to the next level and establish lasting customer relationships.\n\n**Data Dictionary**\n\nThis dataset contains three columns:\n\n*purchaser_sha256_hashed_email* - (string) - The SHA-256 hashed email address of the person who made the purchase\n*purchaser_league* - (string) - The league of the golf-related product that was purchased\n*purchaser_league* - (string) - The specific event associated with the purchased product\n\n","terms_of_service":"https://app.narrative.io/legal/terms-of-service","documentation_link":"","privacy_policy_link":"https://www.fanatics.com/what-is-your-privacy-policy/ch-2349","assets":["ASSET_TYPE_DATA_TABLE"]},"provider_summary":{"name":"Narrative I/O","description":"Narrative is a data collaboration platform that simplifies the buying and selling of information by eliminating the inefficiencies in data transactions that hold businesses back from maximizing the success of their most important data-driven initiatives. 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Each entry undergoes rigorous validation to ensure that you have the most reliable and up-to-date information at your fingertips.\n\n- Exceptional Sourcing Excellence: We source our data from a diverse range of reputable databases, trusted industry partnerships, and ongoing updates. This meticulous approach ensures that our dataset remains relevant and dependable, reflecting the latest changes and trends in the market.\n\n- Versatile and Effective Use-Cases: Our dataset supports a variety of applications, including targeted marketing campaigns, strategic business development, lead generation, and customer engagement. Whether you’re aiming to reach top decision-makers, influential executives, or potential clients, our data empowers you to execute precise and effective outreach strategies.\n\n- Integrated Business Intelligence: Seamlessly incorporated into our broader suite of data offerings, our Phone Number Data creates a cohesive and synergistic ecosystem. 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This allows you to tailor your outreach based on specific company profiles, whether you’re targeting startups or established enterprises.\n\n- Commitment to Ongoing Accuracy: We are dedicated to maintaining the highest standards of accuracy. Our data undergoes regular updates and verification processes to ensure that every contact remains relevant and reliable for your outreach efforts.\n\n- Catalyst for Business Growth: Beyond being a mere dataset, our Phone Number Data serves as a powerful growth catalyst. It enables your organization to refine its outreach, enhance engagement, and unlock new opportunities for expansion and success.\n\n Elevate Your Strategy with Confidence\n\nHarness the power of our Phone Number Data to drive your business forward. With its unmatched precision, comprehensive coverage, and strategic integration, this dataset is the cornerstone of your data-driven decision-making and growth strategies. Embrace the potential of accurate, actionable contact data and transform your outreach initiatives today.","terms_of_service":"https://www.exellius.com/privacy-policy","contact_email":"marketplace@datarade.ai","privacy_policy_link":"https://www.exellius.com/privacy-policy","assets":["ASSET_TYPE_DATA_TABLE"]},"provider_summary":{"name":"Exellius Systems","description":"Exellius Systems is a global data solutions provider helping businesses scale their outreach with high-quality, targeted datasets.\r\n\r\nWe offer specialized data solutions, including Targeted, Installbase, Role-based, Account-based, and Partner-based datasets—designed to support precise go-to-market strategies.\r\n\r\nOur database spans 160M+ global records, with verified email addresses and direct dial numbers, enabling teams to connect with the right decision-makers efficiently.\r\n\r\nWe prioritize data accuracy and compliance, following strict sourcing and validation processes aligned with GDPR standards.\r\n\r\nTo ensure reliability, we also offer a 100% replacement guarantee on undelivered or invalid data.\r\n\r\nAt Exellius Systems, our focus is simple: deliver accurate, actionable data that drives measurable business outcomes."},"popularity":86},{"id":"2dc66d21-8900-44e2-83c0-c474afa4bb48","summary":{"name":"Geolocet | Latest Demographic Data Croatia","subtitle":"The dataset contains demographic data for 13,000+ statistical circles of Croatia","setting":{"visibility":"PUBLIC"},"created_at":1705919169138,"updated_at":1752144619335,"categories":["DEMOGRAPHICS","ADVERTISING_AND_MARKETING","GEOSPATIAL","RETAIL","PUBLIC_SECTOR"],"listingType":"PERSONALIZED","provider_id":"5d3ddc17-a7e8-4794-a760-13587e1de2c6"},"detail":{"description":"## **Get instant access to the latest Demographics Data for Croatia with [Geolocet](https://geolocet.com)**\n\nExplore the richness of Croatia’s demographic landscape with our Croatia Census Data. This meticulously compiled dataset, sourced from official statistical sources, including Croatian Bureau of Statistics, offers a detailed insight into the population landscape of Croatia at the statistical circle level. \n\nIf you require data at a less granular level, such as an Uber grid or square grid, please don't hesitate to get in touch with us at *contact@geolocet.com*. We understand that different projects may have varying data needs, and we are here to tailor our data to match your specific requirements.\n\n ## **Key Features:**\n---\n* **Population Data Croatia:** Gain access to the latest and most accurate population statistics for Croatia, meticulously organized by statistical circle areas.\n\n* **Demographic Insights:** Dive deep into Croatian demographic landscape with data on 286 attributes, including: \n* Total population, \n* Gender distribution,\n* 5-years age bands distribution,\n* Gender split by age bands distribution,\n* Education distribution,\n* Education split by gender distribution,\n* Marital status distribution,\n* Marital status split by gender distribution,\n* Marital status split by age and gender distribution,\n* Nationality distribution,\n* Religion distribution,\n* Families,\n* Family structure,\n* Households ,\n* Family households,\n* Family household size distribution,\n* Household size distribution.\n\nAll demographic attributes are available at the statistical circle level.\n\n* **Geodemographic Insights:** Unlock geodemographic data Croatia-wide to inform location-based decision-making, marketing strategies, and policy formulation.\n* **Seamless Data Access:** Our data is available for instant download in CSV format. No lengthy sign-up processes or NDA agreements required on our side – just download the data you need.\n* **Data Samples for Evaluation:** To ensure you're getting exactly what you need, we provide free downloadable samples in CSV format for data evaluation.\n* **Data accuracy:** Our datasets are created using the latest demographics data available in national statistics bureaus, guaranteeing accuracy and reliability.\n* **Best-in-Class Quality:** We guarantee best-in-class quality, backed by over two decades of experience in modeling demographics data and an outstanding quality management process.\n* **Competitive Pricing:** We maintain the lowest prices on the market, ensuring that you get the most cost-effective demographics data acquisition process.\n---\nAdditionally, for those looking to enrich their analysis or project, we offer:\n* **[Administrative Boundaries](https://geolocet.com/products/croatia-without-marine-borders-all-available-administrative-boundaries):** If you require geographical boundaries (polygons) for the municipalities of Croatia, consider exploring our Administrative Boundaries product, which seamlessly integrates with this dataset.\n\n* **[Point of Interest (PoI) Data](https://geolocet.com/pages/aggregated-points-of-interest-poi-data):** If you're interested in Points of Interest data, please don't hesitate to contact us for customized datasets to enhance your geographical analysis.\n\n* **[Data Customization](https://geolocet.com/pages/data-services-by-geolocet):** We understand that your project may have specific requirements. Feel free to get in touch with us if you need any modifications or specific data formats such as Excel, JSON, GeoJSON, or Shapefile. We are here to tailor the data to your needs.\n\nIf you're looking for data for Croatia, [look here](https://geolocet.com/pages/croatia) for off-the-shelf or tailor-made datasets to suit your specific requirements.\n\n\nMake informed decisions, conduct precise research, and unlock the potential of Croatian demographic landscape with our comprehensive dataset. Trust in the accuracy and reliability of our official statistical data to drive your projects forward.\nContact us at *contact@geolocet.com* today to access this invaluable resource and explore Croatia census data like never before.\n\n[Geolocet LTD](https://geolocet.com)\n","terms_of_service":"https://geolocet.com/pages/terms-of-use","documentation_link":"","privacy_policy_link":"https://geolocet.com/pages/privacy-policy","geographical_coverage":"[\"Europe\"]","cost":"PAID","data_source":"","assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://geolocet.com/pages/terms-of-use"},"provider_summary":{"name":"Geolocet LTD","description":"Geolocet provides premium geospatial data for retail analysis, GIS, and market planning. We equip businesses with accurate, ready-to-query data to drive spatial analysis, site selection, and customer segmentation.\n\nCore Offerings:\n\nDemographics: Socioeconomic data covering population, age, and employment across diverse geographies.\n\nPoints of Interest (PoI): Datasets spanning retail, healthcare, and transport for catchment analysis.\n\nAdministrative Boundaries: GIS-ready boundaries, including municipalities and postcodes, for territory management.\n\nBrand Locations: Chain location datasets to monitor store networks and market expansion.\n\nWealth Data: Granular economic indicators like purchasing power to identify high-potential markets.\n\nWe source from trusted providers, delivering highly customizable datasets with comprehensive coverage across Europe and worldwide."},"popularity":487},{"id":"926627ad-be2d-4641-8969-efd9995d2eb0","summary":{"name":"Geolocet | Points of Interest Data | Petrol Station Locations - Denmark","subtitle":"Complete GIS-ready dataset of 2,788 Denmark petrol station locations (brands & independents).","setting":{"visibility":"PUBLIC"},"created_at":1780644362509,"updated_at":1780644362509,"categories":["GEOSPATIAL","RETAIL"],"listingType":"PERSONALIZED","provider_id":"5d3ddc17-a7e8-4794-a760-13587e1de2c6"},"detail":{"description":"# **All Petrol Station Locations in Denmark: Complete Geographic Dataset**\n---\n### **Premium GIS-Ready Retail Location Intelligence Data for Databricks Analytics**\n\nThis high-fidelity geospatial dataset provides a complete, exhaustive, and standardized overview of **2,788** verified fuel and petrol station locations across Denmark. Updated as of **February 3, 2026**, this comprehensive national dataset captures the entire Danish forecourt landscape - encompassing premier multinational fuel networks, national champions, and prominent independent operators. It offers an analysis-ready foundation for EV charging infrastructure planning, competitor benchmarking, logistics routing, and retail location intelligence.\n\nFully compatible with **Apache Spark**, **Delta Lake**, and **Databricks Unity Catalog** workflows.\n\n---\n\n## **Complete National Coverage: Branded Forecourts & Regional Operators**\nUnlike single-brand extractions, this dataset aggregates all premier fuel networks and independent operators into a single, unified data model. The coverage maps maximum geographic depth across all 5 Danish regions (Regioner):\n\n- **Major Fuel Networks & National Champions:** \n [OK (765 locations)](https://geolocet.com/products/denmark-ok-petrol-stations), \n [Uno-X (351 locations)](https://geolocet.com/products/denmark-uno-x-petrol-stations), \n [Circle K (241 locations)](https://geolocet.com/products/denmark-circle-k-petrol-stations), \n [Shell (196 locations)](https://geolocet.com/products/denmark-shell-petrol-stations)\n\n- **Regional & Dynamic Growth Banners:** \n [Go'on (194 locations)](https://geolocet.com/products/denmark-goon-petrol-stations), \n [INGO (194 locations)](https://geolocet.com/products/denmark-ingo-petrol-stations), \n [F24 (137 locations)](https://geolocet.com/products/denmark-f24-petrol-stations), \n [Q8 (115 locations)](https://geolocet.com/products/denmark-q8-petrol-stations), \n [OIL! tank & go (71 locations)](https://geolocet.com/products/denmark-oil-tank-and-go-petrol-stations), \n [tankpool24 (67 locations)](https://geolocet.com/products/denmark-tankpool24-petrol-stations)\n\n**Plus regional independent forecourts** fully mapped and standardized across all administrative regions (e.g., Region Midtjylland: 721 sites; Region Syddanmark: 773 sites; Region Sjælland: 470 sites).\n\n---\n\n## **Technical Specifications & Schema Definitions**\nOptimized for zero-friction schema enforcement when reading via `spark.read.format(\"csv\")`. The structured schema below ensures flawless rendering in Databricks notebooks and high accuracy for LLM semantic indexing.\n\n* **`GUID`** (`STRING`): Unique global identifier for deterministic primary key mapping and deduplication.\n* **`Title`** (`STRING`): Official station name or commercial brand banner classification (e.g., OK, Circle K, Shell).\n* **`Latitude`** (`DOUBLE`): Precise WGS84 latitude coordinate for point-in-polygon and spatial joins.\n* **`Longitude`** (`DOUBLE`): Precise WGS84 longitude coordinate for spatial indexing (H3 / S2).\n* **`Street_No` / `Street`** (`STRING`): Normalized building numbers and thoroughfare name elements.\n* **`City` / `Postal_Code`** (`STRING`): Core municipal settlements and primary postal routing codes.\n* **`Kommune`** (`STRING`): Local municipal administrative boundary designation.\n* **`Region`** (`STRING`): Macro-administrative boundary classification (e.g., Region Hovedstaden, Midtjylland).\n* **`Address`** (`STRING`): Full, standardized street address clean-text output.\n* **`Wheelchair`** (`STRING`): Accessibility flags indicating wheelchair mobility support where available.\n\n---\n\n## **Databricks Workflow & Spatial Analytics Use Cases**\n\n- **EV Charging Whitespace Analysis:** Query the full 2,788 location points against high-voltage grid corridors or traffic volume rasters to rank optimal stations for ultra-fast EV charging infrastructure placement.\n- **H3 Geospatial Indexing:** Convert the precise WGS84 `Latitude` and `Longitude` fields into Uber H3 spatial hexagons (`h3_longlattocell`) to seamlessly cross-reference with granular Danish parish (*Sogn*) income datasets.\n- **Logistics & Fleet Benchmarking:** Integrate forecourt coordinates with fleet tracking telemetry to calculate fuel stop density, build optimal corridor routing profiles, and minimize off-route deadhead mileage across the Danish highway network.\n\n---\n\n## **Data Evaluation & Custom Ingestion**\n\n### **Evaluate with a Free Sample Dataset**\nVerify the structure, column types, and coordinate precision before full implementation. A comprehensive schema sample is available for download in CSV/Parquet-friendly formats.\n\n*Want deeper market insights?* Learn more about Danish income patterns and retail geography in our comprehensive report: **[Income Distribution and Socio-Economic Patterns in Denmark](https://geolocet.com/blogs/news/income-distribution-across-danish-parishes)**\n\n### **Enterprise Delivery & Tailored Formatting**\nWhile natively available as an analysis-ready CSV for DBFS ingestion, we accommodate enterprise-level data architecture needs. Alternative formats - including **GeoJSON, Esri Shapefiles, PostgreSQL dumps, Excel, or direct Delta sharing profiles** - can be generated. Reach out to our engineering team at **contact@geolocet.com** for integration support or custom European POI data requests.","terms_of_service":"https://geolocet.com/pages/terms-of-use","documentation_link":"","privacy_policy_link":"https://geolocet.com/pages/privacy-policy","assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://geolocet.com/pages/terms-of-use"},"provider_summary":{"name":"Geolocet LTD","description":"Geolocet provides premium geospatial data for retail analysis, GIS, and market planning. We equip businesses with accurate, ready-to-query data to drive spatial analysis, site selection, and customer segmentation.\n\nCore Offerings:\n\nDemographics: Socioeconomic data covering population, age, and employment across diverse geographies.\n\nPoints of Interest (PoI): Datasets spanning retail, healthcare, and transport for catchment analysis.\n\nAdministrative Boundaries: GIS-ready boundaries, including municipalities and postcodes, for territory management.\n\nBrand Locations: Chain location datasets to monitor store networks and market expansion.\n\nWealth Data: Granular economic indicators like purchasing power to identify high-potential markets.\n\nWe source from trusted providers, delivering highly customizable datasets with comprehensive coverage across Europe and worldwide."},"popularity":721},{"id":"270ae4ce-b008-430f-94d5-bcc7e85237fe","summary":{"name":"MoEngage Events (via OpenSharing)","subtitle":"Query all your MoEngage events live in Databricks via zero-copy Delta Sharing. For MoEngage customers.","setting":{"visibility":"PUBLIC"},"created_at":1790596350500,"updated_at":1790597131496,"categories":["ADVERTISING_AND_MARKETING"],"listingType":"PERSONALIZED","provider_id":"e2590b53-8f6c-42a5-a704-0e3ed5d34648"},"detail":{"description":"**Overview**\nMoEngage OpenSharing gives MoEngage customers live, read-only access to their MoEngage event data inside their own Databricks workspace. This covers every event MoEngage receives or generates: user behavior events tracked through SDKs and APIs, campaign and Flow interaction events across all channels, and system events.\n\nData is shared through the Delta Sharing protocol from an isolated, per-customer catalog hosted by MoEngage. No files are copied, no pipelines to maintain, no credentials or write access to your workspace, and no IP allowlisting. Datasets refresh hourly.\n\n**How to get access**\nThis listing is for existing MoEngage customers. Access is provisioned per MoEngage workspace.\n1. In Databricks, go to Catalog > Share > OpenSharing and copy your Sharing ID (format: cloud:region:metastore-id).\n2. Raise a support ticket from your MoEngage dashboard titled \"Databricks OpenSharing enablement\". Include your Sharing ID and MoEngage workspace name.\n3. MoEngage provisions your share, usually within 7 business days, and sends you the share name.\n4. Mount the share to a catalog, then configure your export in MoEngage App Marketplace > Databricks.\n\nNote: Clicking \"Request access\" on this page alone does not enable the share. Please raise the ticket from your MoEngage workspace so we can verify your account.\n\n**Use cases**\n- Product and behavioral analytics: analyze app and web activity (sessions, screen views, custom events like add-to-cart or purchase) alongside the engagement that drove it.\n- Campaign performance and attribution: join sends, opens, clicks, and conversions with your own revenue and product data.\n- Cross-channel journey analytics: analyze Push, Email, SMS, In-App, and WhatsApp engagement across Flows and campaigns in one place.\n- Customer 360 enrichment: feed engagement signals into lakehouse customer profiles and feature stores.\n- ML and propensity modeling: train churn, send-time, and channel-preference models on engagement history.\n- BI and reporting: build dashboards on live data without scheduled exports or ETL jobs.\n\n**Product details**\nAll events land in a single consolidated table per connection (moe_events_) with a fixed schema.\n- Datasets represented: all MoEngage events, including user-tracked custom events, standard events (app/site opened, sessions, installs), and MoEngage-generated campaign and Flow events (sent, delivered, impression, click, conversion, bounce, unsubscribe), plus the user and device attributes you select.\n- Sample fields: event_name, event_time (UTC), event_uuid, uid, event_source, event_attributes_campaign_id, event_attributes_campaign_channel, event_attributes_parent_flow_id, event_attributes_platform, event_attributes, user_attributes, device_attributes.\n\nEvent-specific fields sit in the event_attributes column, so new events you track later appear automatically without schema changes.\n\n**Additional insights**\n- Requirements: a Unity Catalog-enabled Databricks workspace and the MoEngage Streams add-on. Contact your MoEngage CSM if Streams isn't on your plan.\n- Refresh: hourly. First rows appear within about 30 minutes of configuring the export.\n- Historical data: not included. Data flows from the point you enable the export.\n- Access control: manage access with Unity Catalog USE CATALOG and SELECT grants. Either party can end the share at any time.","terms_of_service":"https://www.moengage.com/terms-of-use/","documentation_link":"https://www.moengage.com/docs/user-guide/data/exports/events/data-warehouse/databricks-exports#integration","privacy_policy_link":"https://www.moengage.com/privacy-policy/","geographical_coverage":"[\"Global\"]","update_frequency":{"interval":1,"unit":"HOURLY"},"collection_granularity":{"interval":1,"unit":"SECOND"},"assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"MoEngage","description":"MoEngage is an Agentic Customer Engagement Platform trusted by over 1,350 global consumer brands, including Flipkart, Domino’s, Loblaws, Kayak, Soundcloud, and Deutsche Telekom. The platform combines deep customer analytics, agentic marketing workflows through its Merlin AI Agents, and omnichannel orchestration across messaging and surfaces, helping marketing and product teams deliver experiences that feel personal at any scale. MoEngage operates globally with 15 offices across North America, EMEA, and Asia. Learn more at moengage.com"},"popularity":1836},{"id":"4e587f67-f8e4-45cb-8fd1-b945c693c777","summary":{"name":"Phone Number Data | USA Coverage | 765 Mil+ Numbers","subtitle":"The US Consumer Phone file contains phone numbers, mobile and landline, tied to an individual in the Consumer ...","share":{"name":"bigdbm_9c764e11_0a75_4b3b_a031_5015d3b0613c_samples_share","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1727810851305,"updated_at":1768306715544,"categories":["OTHER","ECONOMICS","ADVERTISING_AND_MARKETING"],"listingType":"PERSONALIZED","provider_id":"7624320d-dddb-4b09-abe2-92d0a7630860"},"detail":{"description":"The US Consumer Phone file contains phone numbers, mobile and landline, tied to an individual in the Consumer Database. The fields available include the phone number, phone type, mobile carrier, and Do Not Call registry status.\n\nAll phone numbers can be processed and cleansed using telecom carrier data. The telecom data, including phone and texting activity, porting instances, carrier scoring, spam, and known fraud activity, comprise a proprietary Phone Quality Level (PQL), which is a data-science derived score to ensure the highest levels of deliverability at a fraction of the cost compared to competitors.\n\nWe have developed this file to be tied to our Consumer Demographics Database so additional demographics can be applied as needed. Each record is ranked by confidence and only the highest quality data is used. \n\n*Note - all Consumer packages can include necessary PII (address, email, phone, DOB, etc.) for merging, linking, and activation of the data.\n** We do not provide any phone details from Colorado residents.\n\nBIGDBM Privacy Policy: https://bigdbm.com/privacy.html","terms_of_service":"https://bigdbm.com/privacy.html","contact_email":"marketplace@datarade.ai","privacy_policy_link":"https://bigdbm.com/privacy.html","assets":["ASSET_TYPE_DATA_TABLE"]},"provider_summary":{"name":"BIGDBM","description":"BigDBM is a leading US data provider with 7+ years of experience building identity graphs with a focus on ROI, privacy, and quality.\r\n\r\nUnlock significant value in your marketing campaigns, lead generation strategies, and identity verification workflows with our US consumer and B2B datasets.\r\n\r\nUtilize the self-service BigDBM Data Market for easy and affordable audience/list generation and custom appends.\r\n\r\nIdentify website visitor traffic using our WeVi product suite of real-time data collection via pixels and real-time identity resolution APIs.\r\n\r\nPopular products:\r\n- Telecom-verified phone numbers>consumers\r\n- IP>consumer and IP>company domain linkages\r\n- Verified consumer emails\r\n- Consumer and B2B intent\r\n- Consumer demographics and behavioral affinities\r\n- Residential and commercial property owners and contact information\r\n- MAID>consumer linkages"},"popularity":1853},{"id":"76d5f04d-643d-4241-8fed-30b7d183aa03","summary":{"name":"Belgium Income Indicators by Statistical Sector (2023)","subtitle":"High-resolution income and demographic insights for 20K+ Belgian statistical sectors.","setting":{"visibility":"PUBLIC"},"created_at":1762429664719,"updated_at":1762429726415,"categories":["GEOSPATIAL","ECONOMICS","DEMOGRAPHICS","RETAIL","ADVERTISING_AND_MARKETING"],"listingType":"PERSONALIZED","provider_id":"5d3ddc17-a7e8-4794-a760-13587e1de2c6"},"detail":{"description":"## **Overview** \nGain a fine-grained understanding of Belgium’s local economies with Geolocet’s **2023 Income Indicators at Statistical Sector Level** dataset. Covering nearly **20,000 statistical sectors**, this dataset combines modelled **average and median personal and household income bands** with key demographic indicators, enabling high-resolution market, geospatial, and socioeconomic analysis.\n\nBuilt on data sourced from Statbel (Statistics Belgium) and refined using Geolocet’s proprietary modelling, this dataset is purpose-designed for seamless integration into analytical workflows—ideal for use in `Databricks`, GIS environments, BI dashboards, and machine-learning models.\n\n---\n\n## **Product highlights** \n\n- **Nationwide coverage** – All Belgian statistical sectors (approx. 19,795 zones)\n- **High spatial granularity** – Each zone typically ~600 residents, enabling neighbourhood-level insights \n- **Rich income indicators** – Includes average personal income, median personal income, and average household income, each split into ten income bands \n- **Supporting demographic attributes** – Population totals, employed population, number of households \n- **Formats ready for analytics** – Provided in CSV and Shapefile (EPSG:4326) for geo-analytical workflows \n\n---\n\n## **Field overview** \n\nThe dataset includes the following key fields:\n\n- `STAT_SECTOR_CODE` – Statistical sector ID \n- `MUN_NAME` – Municipality name \n- `REG_NAME` – Region name \n- `POP_TOT` – Total population \n- `HH_TOT` – Number of households \n- `EMPL_TOT` – Total employed population \n- `AVG_PERS_INC_BAND` – Average personal income band (€ per person per year) \n- `MED_PERS_INC_BAND` – Median personal income band (€ per person per year) \n- `AVG_HH_INC_BAND` – Average household income band (€ per household per year) \n- `GEOMETRY` – Polygon data for geospatial analysis (in the shapefile) \n\nThese fields are specially designed to support geo-analytical workflows and local-level economic modelling.\n\n---\n\n## **Applications** \n- Conduct neighbourhood-level market segmentation and consumer purchasing-power analysis \n- Support retail network or store location planning with granular income bands \n- Feed into location intelligence models for real-estate investment and site evaluation \n- Integrate into machine-learning workflows for predictive modelling of consumer behaviour or credit risk \n- Inform public policy, socioeconomic inequality research, or urban planning efforts at micro-geography \n\n---\n\n## **Additional Insights** \nThis dataset is built to support data-driven decision-making with a high degree of spatial precision. Users benefit from strong data provenance and modelling rigor: official sources combined with Geolocet’s smoothing and spatial consistency techniques ensure reliable, comparable income indicators across Belgium.\n\n\uD83D\uDC49 Learn more via our blog on Belgian income indicators: [Belgium Income Indicators — Statistical Sector Level](https://geolocet.com/blogs/news/belgium-income-indicators-statistical-sector) \n\n\uD83D\uDCD8 More information on the dataset: \n\uD83D\uDC49 [Estimated Personal Income Dataset – Belgium](https://geolocet.com/products/belgium-income-indicators-statistical-sector)\n\n\uD83D\uDCE9 To access the full dataset or request a custom version, please [Contact Geolocet](https://geolocet.com/pages/contact-us)\n\n---\n\n*Note: Sample data is available for download; full dataset requires direct contact.* [Contact Geolocet](https://geolocet.com/pages/contact-us)\n","terms_of_service":"https://geolocet.com/pages/terms-of-use","documentation_link":"","privacy_policy_link":"https://geolocet.com/pages/privacy-policy","cost":"PAID","data_source":"","assets":["ASSET_TYPE_DATA_TABLE","ASSET_TYPE_MEDIA"],"license":"https://geolocet.com/pages/terms-of-use"},"provider_summary":{"name":"Geolocet LTD","description":"Geolocet provides premium geospatial data for retail analysis, GIS, and market planning. We equip businesses with accurate, ready-to-query data to drive spatial analysis, site selection, and customer segmentation.\n\nCore Offerings:\n\nDemographics: Socioeconomic data covering population, age, and employment across diverse geographies.\n\nPoints of Interest (PoI): Datasets spanning retail, healthcare, and transport for catchment analysis.\n\nAdministrative Boundaries: GIS-ready boundaries, including municipalities and postcodes, for territory management.\n\nBrand Locations: Chain location datasets to monitor store networks and market expansion.\n\nWealth Data: Granular economic indicators like purchasing power to identify high-potential markets.\n\nWe source from trusted providers, delivering highly customizable datasets with comprehensive coverage across Europe and worldwide."},"popularity":336},{"id":"f8f5558f-b5ab-4fb3-9bfc-bdbfcc6cdd66","summary":{"name":"WxContext: Weather Anomaly & Audience Targeting Signals | Audience Targeting Data | Global Coverage","subtitle":"Smarter audience targeting & marketing automations powered by real-world ‘experienced’ weather data","share":{"name":"pelmorex_weather_source_8a937696_ef3a_4198_9f4f_709af99d7bff_samples_share","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1788542732283,"updated_at":1792691840009,"categories":["ADVERTISING_AND_MARKETING","OTHER"],"listingType":"PERSONALIZED","provider_id":"871f3467-760a-49de-ada4-1556857ee355"},"detail":{"description":"WxContext is a global weather-intelligence data layer that delivers simple, pre-calculated, weather anomalies directly to your platform, allowing brands to instantly align marketing efforts with how customers actually experience weather. Instead of relying on raw temperatures or blunt binary weather triggers, WxContext isolates unseasonal atmospheric shifts—such as sudden warm breaks in winter or unexpected cold snaps—that drive subconscious buying behavior.\n\nPowered by advanced meteorological science, WxContext pairs ECMWF forecast data seamlessly with our proprietary ERA5 based climatology. Using Fourier-smoothed baselines, it eliminates data spikes and false positives, delivering a lightweight daily dataset with hyper-local precision down to the zip code, or latitude/longitude level.\n\nWhat do you get with WxContext?\n\n- 8 Automated Action Triggers: Simple, Boolean indicators for Normal weather, Warmer than normal, cooler than normal, light rain, meaningful rain, light snow, meaningful snow and meaningful wind.\n\n- Up to 15-Day Anomaly Forecast Window: Lookahead capabilities updated twice daily to capture pre-intent consumer demand windows.\n\n- Pre-Tuned High-Intent Thresholds: Pre-calculated algorithms isolating the exact top and bottom third of climate shifts where consumer behavior changes.\n\n- Hyper-Local Volatility Logic: Point-by-point daily adjustments that adapt dynamically across different seasonal baselines and geographic locations.\n\n- Customized Footprint Mapping: Data views pre-configured and optimized directly for your specified target locations.\n\n\nConsider WxContext if your business:\n\n- Requires simple, automated True/False contextual triggers to power Dynamic Creative Optimization (DCO), CRM triggers, or digital storefront personalization. \n\n- Wants to eliminate wasted ad spend caused by broad geographic targeting or blunt \"Snow = True\" binary triggers. \n\n- Needs to pinpoint hyper-local \"buy-moments\" where weather deviates significantly from local climatological norms. \n\n- Operates Retail Media Networks (RMNs) seeking high-margin, weather-triggered ad monetization packages for brand partners.","terms_of_service":"https://business.theweathernetwork.com/privacy-policy","contact_email":"support@datarade.ai","privacy_policy_link":"https://business.theweathernetwork.com/privacy-policy","assets":["ASSET_TYPE_DATA_TABLE"]},"provider_summary":{"name":"The Weather Network For Business","description":"At The Weather Network for Business, we turn weather volatility into a predictable business advantage for enterprises globally. Backed by over 35 years of meteorological expertise, with over a decade of dedicated enterprise experience, we deliver hyper-local weather data, real-time alerts, and predictive analytics that protect assets, optimize operations, and drive growth. From retail and energy to enterprise logistics and public safety, our specialized commercial solutions help businesses navigate climate complexity with confidence.\r\n\r\nDiscover how we can help your business stay ahead of the storm at business.theweathernetwork.com/"},"popularity":22},{"id":"88c95f44-98e6-427f-8396-0184d7cf8c92","summary":{"name":"PEI Private Markets Editorial Intelligence sample","subtitle":"Sample dataset of PEI's structured editorial insight, proprietary analysis, and reporting across global private markets.","share":{"name":"pei_editorial_market_intelligence_sample","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1773673693450,"updated_at":1773679957611,"categories":["FINANCIAL"],"listingType":"STANDARD","provider_id":"c1c6fe00-2142-4377-8293-c30ca67ea773"},"detail":{"description":"This is a free sample of PEI Private Markets Editorial Intelligence. The full production dataset transforms decades of PEI journalism, analysis, and interviews into a structured, queryable intelligence layer for private markets.\n\nIt is particularly useful for:\n\n- **Strategy teams monitoring market themes and emerging investment trends**\n- **Research teams analyzing how industry narratives evolve over time**\n- **Data science teams building AI research tools and knowledge model**s\n\nUnlike traditional datasets that only capture transactions and metrics, PEI combines structured data with editorial insight that explains how and why markets evolve. 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See OpenStreetMap Community Guidelines for definitions of a derivative database.\n\n**Use cases**\n- Location Planning: Supercharge your site planning strategy by understanding next to which types of Point of Interest your business performs at its best.\n- Economic Impact Analysis: Analyze the footprint of different types of business and services that are distributed across your territory and the economic impact they are creating to the different communities.\n- Accelerating Advertising Revenue: When planning your next out-of-home media campaign, analyze next to which types of points of interest your different billboards and media supports are located.\n\n**Additional Insights**\nThis data is processed, onboarded and maintained by CARTO in collaboration with the Overture Maps Foundation.","terms_of_service":"https://creativecommons.org/licenses/by/4.0/legalcode","documentation_link":"https://docs.overturemaps.org/schema/reference/places/place","privacy_policy_link":"https://carto.com/privacy/","geographical_coverage":"[\"Global\"]","cost":"FREE","update_frequency":{"interval":1,"unit":"MONTHLY"},"data_source":"Overture Maps","assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://creativecommons.org/licenses/by/4.0/legalcode"},"provider_summary":{"name":"CARTO","description":"CARTO is the world’s leading cloud-native Location Intelligence platform, enabling organizations to use spatial data and analysis for more efficient delivery routes, better behavioral marketing, strategic store placements, routing optimization, and much more. 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It provides balanced male and female representation speakers and diverse texts across target groups from the Toxigen dataset, ensuring inclusivity in the dataset. Key features include detailed metadata fields for nuanced analysis and model fine-tuning.\n\n**Use Cases**\n\n- Content Moderation Training: Improve AI systems' ability to detect and moderate toxic language with diverse, real-world examples.\n\n- Bias Analysis and Mitigation: Analyze and address potential biases in toxicity detection systems using balanced demographic and target group data.\n\n**Product Details**\n\nThe dataset contains the following elements:\n\n- Voice recordings (audio files) balanced by gender and target groups.\n\n- Metadata tables for detailed analyses, including toxicity ratings and linguistic attributes.\n\n**Sample Fields**\nrow_id: Unique identifier for each record.\ntext: The content of the Toxigen text.\ntarget_group: Demographic or group targeted in the text.\ntoxicity_ai / toxicity_human: Toxicity ratings from AI and human reviewers.\ngender, age_range, native_language, region: Speaker demographic details.\nvoice_conditions: Details of any voice-specific conditions affecting speakers voice e.g. COPD.\naudio_file_path: Path to the corresponding audio file.\n\n**Additional Insights**\nThis dataset is ethically sourced and fully consented - intenional recordings from diverse speakers, ensures compliance with responsible and growth AI practices. It is invaluable for responsible AI teams, corporate AI model training, and content moderation system refinement. \n\nFor more details, please feel free to reach out to us at sales.databricks@destined.ai","terms_of_service":"https://destined.ai/terms/","documentation_link":"","privacy_policy_link":"https://destined.ai/privacy-policy-2024/","geographical_coverage":"[\"United States and Canada\"]","cost":"PAID","data_source":"","size":10.0,"assets":["ASSET_TYPE_DATA_TABLE","ASSET_TYPE_MEDIA"],"license":""},"provider_summary":{"name":"Destined","description":"Destined helps companies mitigate unwanted bias in AI by providing fully consented and culturally relevant data. From thousands of authentic voices to unique faces across many skin tones, we've helped Fortune 500 companies deploy AI responsibly.\n\nOur growing community of data contributors globally represents everyone from people of color to rural white southerners. Our Culture Pulse™ technology taps into this diverse network, ensuring your AI systems mitigate unwanted bias and are also enriched with a wide spectrum of perspectives and experiences. \n\nWith Destined, you’re not just getting data; you’re gaining insight into the heart of humanity.\n"},"popularity":233},{"id":"71cafc8b-8465-4dbd-9a4a-23e4be50b063","summary":{"name":"SAMPLE Twitter (X) Data | Twitter (X) Posts and Profiles Datasets | 20M Records","subtitle":"We provide two types of Twitter datasets: post dataset and profile dataset.\n","share":{"name":"twitter","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1721639202979,"updated_at":1776250475614,"categories":["MEDIA"],"listingType":"STANDARD","provider_id":"63866f42-27f8-4a1f-b243-37e7be863700"},"detail":{"description":"We provide two types of Twitter datasets: post dataset and profile dataset:\n\n**1.Twitter - Posts Dataset**\n\nDescription: The \"Twitter Posts Dataset\" captures a wealth of information from tweets, including text, author details, posting date, media content, URLs, user interactions, and engagement metrics. This comprehensive dataset is a valuable resource for analyzing user behavior, content dynamics, and engagement patterns on Twitter. Researchers can study language trends and sentiment, while marketers can gain insights into content virality and audience preferences.\n**Key Features:**\n*Complete tweet details: text, author, date, media, URLs, and engagement metrics\n*In-depth user interactions: quoted posts and tagged users\nExternal links and hashtags for comprehensive content analysis\n**Popular Use Cases:**\n*User Behavior Analysis: Understand how users interact with tweets, media, and hashtags.\n*Content Dynamics: Analyze what makes content go viral and how different types of media impact engagement.\n*Sentiment Analysis: Study language trends and sentiment shifts over time.\n*Engagement Strategies: Optimize content strategies and post timing to maximize reach and impact.\n\n**2.Twitter - Profiles Dataset**\nDescription: The \"Twitter Profiles Dataset\" captures extensive user-centric data, including names, bios, verification status, images, links, join dates, network sizes, locations, and activity metrics. This rich dataset is invaluable for analyzing user demographics, network structures, and account authenticity. Researchers and marketers can study self-presentation, identify influencers, and understand audience composition.\n**Key Features:**\n*Comprehensive profile details: names, bios, images, and links\nVerification status and activity metrics\n*Network sizes and demographics\n**Popular Use Cases:**\n*User Demographics: Analyze user demographics and network structures.\n*Influencer Identification: Identify influential users and potential collaborators.\n*Account Authenticity: Study account verification and its impact on trust.\n\n\n\nNote: This is a sample","terms_of_service":"https://brightdata.com/license","documentation_link":"https://brightdata.com/products/datasets/twitter","privacy_policy_link":"https://brightdata.com/privacy","data_source":"","assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://brightdata.com/license"},"provider_summary":{"name":"Bright Data","description":"Bright Data is the world’s #1 web data platform.\nFortune 500 companies, academic institutions, and small businesses rely on Bright Data’s scalable platform for web scraping infrastructure to retrieve crucial public web data in the most efficient, reliable, and flexible way, so they can research, monitor, and analyze data to make better decisions.\nThe platform is used worldwide by 20,000+ customers in nearly every industry. The products include a range of no-code data solutions utilized by business owners and a robust infrastructure used by engineers and IT professionals.\n\nBright Data provides a cost-effective way to perform fast and stable public web data collection at scale, effortless conversion of unstructured data into structured data, and superior customer experience while being fully transparent and compliant."},"popularity":1932},{"id":"9838508f-99e9-43c7-9858-4851ec021c04","summary":{"name":"Geolocet | Points of Interest Data | Petrol Station Locations - Italy","subtitle":"Complete GIS-ready dataset of 33,814 Italy petrol station locations (brands & independents). ","setting":{"visibility":"PUBLIC"},"created_at":1780643420520,"updated_at":1780643432327,"categories":["GEOSPATIAL","RETAIL"],"listingType":"PERSONALIZED","provider_id":"5d3ddc17-a7e8-4794-a760-13587e1de2c6"},"detail":{"description":"# **All Petrol Station Locations in Italy: Complete Geographic Dataset**\n---\n### **Premium GIS-Ready Retail Location Intelligence Data for Databricks Analytics**\n\nThis high-fidelity geospatial dataset provides a complete, exhaustive, and standardized overview of **33,814** verified fuel and petrol station locations across Italy. Updated as of **February 3, 2026**, this comprehensive national dataset captures the entire Italian forecourt landscape - encompassing premier multinational fuel networks, national champions, and prominent independent operators. It offers an analysis-ready foundation for EV charging infrastructure planning, competitor benchmarking, logistics routing, and retail location intelligence.\n\nFully compatible with **Apache Spark**, **Delta Lake**, and **Databricks Unity Catalog** workflows.\n\n---\n\n## **Complete National Coverage: Branded Forecourts & Regional Operators**\nUnlike single-brand extractions, this dataset aggregates all premier fuel networks and independent operators into a single, unified data model. The coverage maps maximum geographic depth across all Italian regions:\n\n- **Major Fuel Networks & National Champions:** \n [IP (4,931 locations)](https://geolocet.com/products/Italy-IP-Petrol-Stations), \n [Eni / Agip (4,601 locations)](https://geolocet.com/products/Italy-Eni-Agip-Petrol-Stations), \n [Q8 (3,016 locations)](https://geolocet.com/products/Italy-Q8-Petrol-Stations), \n [Esso (2,377 locations)](https://geolocet.com/products/Italy-Esso-Petrol-Stations), \n [Tamoil (1,578 locations)](https://geolocet.com/products/Italy-Tamoil-Petrol-Stations)\n\n- **Regional & Dynamic Growth Banners:** \n [AS 24 (193 locations)](https://geolocet.com/products/Italy-AS-24-Petrol-Stations), \n [Retitalia (192 locations)](https://geolocet.com/products/Italy-Retitalia-Petrol-Stations), \n [Ewa (190 locations)](https://geolocet.com/products/Italy-Ewa-Petrol-Stations), \n [Shell (177 locations)](https://geolocet.com/products/Italy-Shell-Petrol-Stations), \n [Keropetrol (151 locations)](https://geolocet.com/products/Italy-Keropetrol-Petrol-Stations), \n [EnerGas (146 locations)](https://geolocet.com/products/Italy-EnerGas-Petrol-Stations), \n [Star Oil (138 locations)](https://geolocet.com/products/Italy-Star-Oil-Petrol-Stations), \n [Vega (128 locations)](https://geolocet.com/products/Italy-Vega-Petrol-Stations), \n [Beyfin (117 locations)](https://geolocet.com/products/Italy-Beyfin-Petrol-Stations), \n [Costantin (116 locations)](https://geolocet.com/products/Italy-Costantin-Petrol-Stations)\n\n**Plus thousands of regional independent forecourts** fully mapped and standardized across all administrative regions (e.g., Lombardia: 4,169 sites; Lazio: 3,327 sites; Campania: 2,864 sites; Emilia-Romagna: 2,813 sites).\n\n---\n\n## **Technical Specifications & Schema Definitions**\nOptimized for zero-friction schema enforcement when reading via `spark.read.format(\"csv\")`. The structured schema below ensures flawless rendering in Databricks notebooks and high accuracy for LLM semantic indexing.\n\n* **`GUID`** (`STRING`): Unique global identifier for deterministic primary key mapping and deduplication.\n* **`Title`** (`STRING`): Official station name or commercial brand banner classification (e.g., Eni, IP, Q8).\n* **`Latitude`** (`DOUBLE`): Precise WGS84 latitude coordinate for point-in-polygon and spatial joins.\n* **`Longitude`** (`DOUBLE`): Precise WGS84 longitude coordinate for spatial indexing (H3 / S2).\n* **`Street_No` / `Street`** (`STRING`): Normalized building numbers and thoroughfare name elements.\n* **`City` / `Postal_Code`** (`STRING`): Core municipal settlements and primary postal routing codes.\n* **`Commune`** (`STRING`): Local municipal administrative boundary designation (Comune).\n* **`Region`** (`STRING`): Macro-administrative boundary classification (e.g., Lombardia, Lazio, Toscana).\n* **`Admin_level_1` / `_2`** (`STRING`): Additional local administrative area and province identifiers.\n* **`Address`** (`STRING`): Full, standardized street address clean-text output.\n* **`Wheelchair`** (`STRING`): Accessibility flags indicating wheelchair mobility support where available.\n\n---\n\n## **Databricks Workflow & Spatial Analytics Use Cases**\n\n- **EV Charging Whitespace Analysis:** Query the full 33,814 location points against high-voltage grid corridors or traffic volume rasters to rank optimal stations for ultra-fast EV charging infrastructure placement.\n- **H3 Geospatial Indexing:** Convert the precise WGS84 `Latitude` and `Longitude` fields into Uber H3 spatial hexagons (`h3_longlattocell`) to seamlessly cross-reference with granular Italian municipal income raster files or demographic datasets.\n- **Logistics & Fleet Benchmarking:** Integrate forecourt coordinates with fleet tracking telemetry to calculate fuel stop density, build optimal corridor routing profiles, and minimize off-route deadhead mileage across the national highway network.\n\n---\n\n## **Data Evaluation & Custom Ingestion**\n\n### **Evaluate with a Free Sample Dataset**\nVerify the structure, column types, and coordinate precision before full implementation. A comprehensive schema sample is available for download in CSV/Parquet-friendly formats.\n\n*Want deeper market insights?* Learn more about the Italian retail landscape in our comprehensive reports:\n* **[View the Italy Top Brands Petrol Stations Report](https://geolocet.com/pages/Italy-topbrands-petrol-stations-report)**\n* **[View the Italy Top Brands Grocery Report](https://geolocet.com/pages/italy-top-brands-grocery-report)**\n\n### **Enterprise Delivery & Tailored Formatting**\nWhile natively available as an analysis-ready CSV for DBFS ingestion, we accommodate enterprise-level data architecture needs. Alternative formats - including **GeoJSON, Esri Shapefiles, PostgreSQL dumps, Excel, or direct Delta sharing profiles** - can be generated. Reach out to our engineering team at **contact@geolocet.com** for integration support or custom European POI data requests.","terms_of_service":"https://geolocet.com/pages/terms-of-use","documentation_link":"","privacy_policy_link":"https://geolocet.com/pages/privacy-policy","assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://geolocet.com/pages/terms-of-use"},"provider_summary":{"name":"Geolocet LTD","description":"Geolocet provides premium geospatial data for retail analysis, GIS, and market planning. We equip businesses with accurate, ready-to-query data to drive spatial analysis, site selection, and customer segmentation.\n\nCore Offerings:\n\nDemographics: Socioeconomic data covering population, age, and employment across diverse geographies.\n\nPoints of Interest (PoI): Datasets spanning retail, healthcare, and transport for catchment analysis.\n\nAdministrative Boundaries: GIS-ready boundaries, including municipalities and postcodes, for territory management.\n\nBrand Locations: Chain location datasets to monitor store networks and market expansion.\n\nWealth Data: Granular economic indicators like purchasing power to identify high-potential markets.\n\nWe source from trusted providers, delivering highly customizable datasets with comprehensive coverage across Europe and worldwide."},"popularity":283},{"id":"39527f4f-9be7-4b53-80f4-8d334175966d","summary":{"name":"AtScale MCP","subtitle":"AtScale MCP exposes a governed semantic layer to Databricks, giving AI agents consistent, trusted business metrics","setting":{"visibility":"PUBLIC"},"created_at":1763491903312,"updated_at":1764105495596,"categories":["ECONOMICS","FINANCIAL","ENERGY","RETAIL","TRANSPORTATION_AND_LOGISTICS","MEDIA","MANUFACTURING","COMMERCE","ADVERTISING_AND_MARKETING"],"listingType":"STANDARD","provider_id":"7eaad65e-cd30-47f3-b3f8-88e128859c98"},"detail":{"description":"**Overview**\nThe AtScale MCP Server exposes AtScale’s governed semantic layer to Databricks through the Model Context Protocol (MCP). It provides consistent metrics, business logic, and secure data access for AI agents, BI tools, and analytics workloads.\n\n**Use cases**\n\n- AI agent access to governed metrics for accurate, consistent responses.\n- Unified BI and AI semantics so dashboards and LLMs use the same business definitions.\n- Performance optimization through intelligent query virtualization.\n\n**Product details**\nAtScale delivers a universal semantic layer on Databricks, unifying AI, BI, and analytics use cases. It serves governed metrics and business logic through MCP, SQL, DAX, MDX, and data apps, all of which query the same trusted semantic model. AtScale integrates deeply with Databricks, DBSQL, and Unity Catalog, delivering seamless governance and performance. Trusted by leading enterprises, it’s a proven partner for scalable, unified analytics and AI on the Lakehouse.\n\n**Additional Insights**\nAtScale’s semantic layer is designed for enterprise-scale AI and analytics journeys, providing the consistency and governance required when BI and generative AI consume the same business definitions. \n\nSee AtScale’s blog on Enterprise AI and MCP for deeper context. https://www.atscale.com/blog/enterprise-ai-mcp-semantic-layer/","terms_of_service":"https://www.atscale.com/end-user-license-agreement/","documentation_link":"","privacy_policy_link":"https://www.atscale.com/privacy","geographical_coverage":"[\"Global\"]","cost":"FREE","data_source":"","assets":["ASSET_TYPE_MCP"],"license":""},"provider_summary":{"name":"AtScale, Inc.","description":"AtScale’s semantic layer delivers a single source of truth for governed metrics. With AtScale's universal semantic layer. Customers can perform advanced analytics using a business-friendly OLAP interface, leveraging Databricks and popular tools such as Power BI, Tableau, Excel, and Looker.\n\nA semantic layer platform delivers business value by acting as an intermediary between complex data sources and end-user queries. It simplifies the access to data through a unified, business-friendly interface, allowing users with varying levels of technical expertise to understand and analyze data without needing to know complex query languages or database structures.\n\nThis accessibility leads to better, faster decision-making across all levels of an organization. Moreover, by standardizing definitions and metrics, a semantic layer ensures consistency and accuracy in reporting and analytics, reducing errors and misinterpretations."},"popularity":1987},{"id":"3db1fde1-6868-40d7-a041-4e2623c7808e","summary":{"name":"IQVIA MIDAS®","subtitle":"Industry benchmark for global pharmaceutical market & performance measurement","setting":{"visibility":"PUBLIC"},"created_at":1789740537003,"updated_at":1790002395583,"categories":["HEALTH","SCIENCE_AND_RESEARCH"],"listingType":"PERSONALIZED","provider_id":"7fab49b9-819a-4cd8-a7e2-79af51df6f72"},"detail":{"description":"**Overview**\nIQVIA MIDAS® has long been considered the industry benchmark for global pharmaceutical market & performance measurement, integrating the breadth and depth of IQVIA's unparalleled data assets with advanced analytics and deep domain expertise across 95+ countries, 1.8 million products, 600 diseases, 4 million packs across sales value, volume and estimated drug-treated patients, all seamlessly harmonized for multi-country market measurement.\nIntegrated into the IQVIA's Global Market Insights Agent, powered by agentic AI, these insights are now accessible in minutes, enabling faster and smarter decision-making\n\n**Use cases**\nAdd use cases for the listing here.\n- Monitor competitor products, launches, molecules, protection events, and market positioning\n- Understand the size, growth, and attractiveness of markets across countries, therapeutic areas, diseases, products, and molecules.\n- Evaluate launch potential using historical analogue products, comparable markets, and uptake trajectories\n- Provide historical evidence and market context for forecasting models\n- Connect commercial performance with disease and patient dynamics\n- Assess where to invest by comparing market potential, competitive intensity, and patient opportunity\n\n**Product details**\nAdd a maximum of 10 sample datasets and field names that represent your dataset.\n\n\n- Sample fields include field 1, field 2, field 3, field 4 and field 5.\n\nFor more details, refer to the embedded notebook.\n\n**Additional Insights**\nPLEASE NOTE: Data availability in Databricks is determined by your organization’s or customer’s data subscriptions with IQVIA. The licensing of these data assets are subject to local privacy laws, authorizations, and restrictions which may vary by country.\n\nTerms relating to IQVIA data and services are addressed separately in contracts between IQVIA and its clients","terms_of_service":"https://www.iqvia.com/about-us/privacy","documentation_link":"https://www.iqvia.com/solutions/commercialization/data-and-information-management/midas","privacy_policy_link":"https://www.iqvia.com/about-us/privacy/privacy-policy","assets":["ASSET_TYPE_MEDIA"],"license":""},"provider_summary":{"name":"IQVIA","description":"IQVIA (NYSE:IQV) is a leading global provider of advanced analytics, technology solutions and clinical research services to the life sciences industry dedicated to delivering actionable insights. IQVIA has the largest global healthcare data networks in the world, with over 1 million data sources providing access to 800M+ non-identified patient records and 95B+ healthcare records processed annually in over 100 countries. Learn more at www.iqvia.com."},"popularity":2029},{"id":"982f2ded-7a25-4c14-91e3-6a30324e3dd3","summary":{"name":"Monte Carlo AI","subtitle":"Monte Carlo is the agent trust platform that unifies data and agent observability to monitor, troubleshoot, and improve ","setting":{"visibility":"PUBLIC"},"created_at":1781717304407,"updated_at":1781717304407,"categories":["OTHER"],"listingType":"PERSONALIZED","provider_id":"ca9d3989-b467-4a24-a768-34f21a522054"},"detail":{"description":"As businesses increasingly rely on data and AI to power digital products and drive better decision making, it's mission-critical that this data is accurate and reliable. Monte Carlo's Data + AI Observability Platform is an end-to-end solution for your data stack that monitors and alerts for data issues across your data warehouses, data lakes, ETL, business intelligence, and AI tools. The platform uses machine learning to infer and learn your data, proactively identify data issues, assess its impact, and notify those who need to know. By automatically and immediately identifying the root cause of an issue, teams can easily collaborate and resolve problems faster. Monte Carlo also provides automatic, field-level lineage and centralized data cataloging that allows teams to better understand the accessibility, location, health, and ownership of their data assets, as well as adhere to strict data governance requirements.","terms_of_service":"https://montecarlo.ai/monte-carlo-terms-and-conditions-feb-2023","documentation_link":"https://docs.getmontecarlo.com/","privacy_policy_link":"https://montecarlo.ai/privacy-policy/","assets":["ASSET_TYPE_GIT_REPO"],"license":""},"provider_summary":{"name":"Monte Carlo Data + AI Observability Platform","description":"As businesses increasingly rely on data and AI to power digital products and drive better decision making, it's mission-critical that this data is accurate and reliable. Monte Carlo's Data + AI Observability Platform is an end-to-end solution for your data stack that monitors and alerts for data issues across your data warehouses, data lakes, ETL, business intelligence, and AI tools. The platform uses machine learning to infer and learn your data, proactively identify data issues, assess its impact, and notify those who need to know. By automatically and immediately identifying the root cause of an issue, teams can easily collaborate and resolve problems faster. Monte Carlo also provides automatic, field-level lineage and centralized data cataloging that allows teams to better understand the accessibility, location, health, and ownership of their data assets, as well as adhere to strict data governance requirements."},"popularity":1529},{"id":"9f9c4dc4-c984-4b29-8b0d-13b23803da0e","summary":{"name":"Custom Scrapers – Tailored Web Scraping Solutions for Any Website or Workflow","subtitle":"Our Custom Scrapers deliver fully managed, scalable, and reliable scraping solutions — built just for you.","setting":{"visibility":"PUBLIC"},"created_at":1744201027393,"updated_at":1744201027393,"categories":["OTHER"],"listingType":"PERSONALIZED","provider_id":"63866f42-27f8-4a1f-b243-37e7be863700"},"detail":{"description":"Custom Web Scrapers Built for Your Unique Needs\n\nNot all websites are created equal — and neither are scrapers. Whether you're targeting a niche marketplace, a complex JavaScript-heavy site, or a login-protected portal, our Custom Scrapers are designed to handle it all.\n\nWe build and maintain tailor-made scraping solutions that extract exactly the data you need, in the format you want, and at the frequency you choose — so you can focus on insights, not infrastructure.\n\nKey Features:\n\n\uD83E\uDDE9 Fully Customizable – Scrapers built to match your exact data requirements and target websites.\n\uD83E\uDDE0 Supports Complex Sites – Handles JavaScript rendering, infinite scroll, pagination, and login flows.\n\uD83D\uDD01 Scheduled & Real-Time Scraping – Run scrapers on-demand or on a custom schedule (hourly, daily, weekly).\n\uD83D\uDCE6 Structured Output – Receive clean JSON, CSV, XML, or direct-to-database delivery.\n\uD83D\uDEE1️ Anti-Bot Bypass – Includes proxy rotation, CAPTCHA solving, and stealth techniques.\n\uD83D\uDCC8 Scalable Infrastructure – Built to handle millions of records across thousands of pages.\n\uD83D\uDD10 Secure & Compliant – GDPR-compliant data handling and encrypted delivery options.\n\nUse Cases:\n\n\uD83D\uDECD️ E-commerce Product & Price Monitoring\n\uD83D\uDCF0 News & Media Aggregation\n\uD83C\uDFE2 Real Estate Listings & Job Boards\n\uD83D\uDCCA Market Research & Competitive Intelligence\n\uD83D\uDCDA Academic & Public Data Collection\n\uD83E\uDDE0 AI/ML Training Data Generation\n\nWhy Choose Our Custom Scrapers?\n\n\uD83E\uDDF0 End-to-End Service – We handle everything from development to maintenance.\n\uD83D\uDE80 Fast Turnaround – Most scrapers delivered within 2–5 business days.\n\uD83E\uDDD1‍\uD83D\uDCBB Developer-Friendly – Optional API access for integration into your systems.\n\uD83D\uDCDE Dedicated Support – Work directly with our scraping experts.\n\uD83D\uDCC8 Enterprise-Ready – SLAs, dedicated infrastructure, and white-label options available.\n","terms_of_service":"https://brightdata.com/license","documentation_link":"","privacy_policy_link":"https://brightdata.com/privacy","data_source":"","assets":["ASSET_TYPE_GIT_REPO"],"license":""},"provider_summary":{"name":"Bright Data","description":"Bright Data is the world’s #1 web data platform.\nFortune 500 companies, academic institutions, and small businesses rely on Bright Data’s scalable platform for web scraping infrastructure to retrieve crucial public web data in the most efficient, reliable, and flexible way, so they can research, monitor, and analyze data to make better decisions.\nThe platform is used worldwide by 20,000+ customers in nearly every industry. The products include a range of no-code data solutions utilized by business owners and a robust infrastructure used by engineers and IT professionals.\n\nBright Data provides a cost-effective way to perform fast and stable public web data collection at scale, effortless conversion of unstructured data into structured data, and superior customer experience while being fully transparent and compliant."},"popularity":1646},{"id":"d11ccd74-6b76-4ade-86a5-3ff790dc3723","summary":{"name":"Biotech Pharma and Research Information Database","subtitle":"This data package consists of bioresearch monitoring information system (BMIS) dataset, directory of the different bio..","share":{"name":"biotech_pharma_and_research_information_database","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1682356528537,"updated_at":1695692194273,"categories":["SCIENCE_AND_RESEARCH"],"listingType":"STANDARD","provider_id":"f88849f8-9d77-4de2-b4c7-03d55cc3768e"},"detail":{"description":"\n**Overview**\nThis data package consists of bioresearch monitoring information system (BMIS) dataset, directory of the different biotech and biopharmaceutical and pharmaceutical companies in the United States and the European Union, establishment registration database, drug wholesale distributor and third-party logistics provider reporting database, establishment inspections conducted by FDA, and FDA post-marketing requirements and commitments searchable database.\n\n**Description**\nThis data package consists of very informative FDA datasets, the details of which are as follows: The Bioresearch Monitoring Information System (BMIS) file contains information submitted to FDA identifying clinical investigators, contract research organizations, and institutional review boards involved in the conduct of Investigational New Drug studies with human investigational drugs. Another dataset is a directory of the different biotech and biopharmaceutical and pharmaceutical companies in the United States and the European Union. A database of information submitted by drug firms to register establishments (facilities) which manufacture, prepare, propagate, compound or process drugs that are commercially distributed in the U.S. or offered for import to the U.S. Another dataset contains information submitted by wholesale distributors and third-party logistics providers (3PLs) to FDA. FDA inspections database contains inspections of establishments that manufacture, process, pack, or hold FDA-regulated products, before approving products and/or after products are on the market, to determine the establishment’s compliance with laws administered by FDA.\n\n**Benefits**\n- all fda related datasets contained in single data package\n- comprehensive list of pharmaceutical companies\n- fda inspection database is also available in this data package\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [Bioresearch Monitoring Information System](https://www.johnsnowlabs.com/marketplace/bioresearch-monitoring-information-system)\n\t- The Bioresearch Monitoring Information System (BMIS) file contains information submitted to FDA (Food and Drug Administration) identifying clinical investigators, contract research organizations, and institutional review boards involved in the conduct of Investigational New Drug studies with human investigational drugs.\n- [Directory of US EU and Other Biotech Companies](https://www.johnsnowlabs.com/marketplace/directory-of-us-eu-and-other-biotech-companies)\n\t- This dataset is a directory of the different biotech and biopharmaceutical companies in the United States and the European Union. It includes the company’s specialization, physical address, contact details, email and website links.\n- [Directory of US and EU Pharmaceutical Companies](https://www.johnsnowlabs.com/marketplace/directory-of-us-and-eu-pharmaceutical-companies)\n\t- This dataset is a directory of the different pharmaceutical companies in the United States and the European Union. It includes the company’s specialization, physical address, contact details, email and website links.\n- [Drug Establishment Annual Registration Status](https://www.johnsnowlabs.com/marketplace/drug-establishment-annual-registration-status)\n\t- The Drug Establishments Registration Site (DERS) is a database of information submitted by drug firms to register establishments (facilities) which manufacture, prepare, propagate, compound or process drugs that are commercially distributed in the U.S. or offered for import to the U.S Establishments must be registered within 5 days of beginning operations.\n- [Drug Wholesale Distributor Third Party Logistics Providers Reporting](https://www.johnsnowlabs.com/marketplace/drug-wholesale-distributor-third-party-logistics-providers-reporting)\n\t- The Drug Wholesale Distributor and Third-Party Logistics Provider Reporting Database contain information submitted by wholesale distributors and third-party logistics providers (3PLs) regarding logistics and licensing.\n- [FDA Acronyms and Abbreviations Database](https://www.johnsnowlabs.com/marketplace/fda-acronyms-and-abbreviations-database)\n\t- Food and Drug Administration (FDA) conducts inspections of establishments that manufacture, process, pack, or hold FDA-regulated products, before approving products and/or after products are on the market, to determine the establishment’s compliance with the FDA laws. This document is called an FDA Form 483, which includes the name of the firm and the date(s) of inspection, and lists the observations made by the investigator during the inspection when there are objectionable conditions.\n- [Required Clinical Trial After Approval Database](https://www.johnsnowlabs.com/marketplace/required-clinical-trial-after-approval-database)\n\t- This dataset is the main file to construct the FDA (U.S. Food and Drug Administration) Postmarketing Requirements and Commitments searchable database. Postmarketing requirements refers to studies required to be conducted under statutes or regulations after product approval. Postmarketing commitments are not required studies that sponsors conduct. Official FDA's website has an available database to provide public detailed information on postmarketing requirements and commitments studies.\n- [US Pharmacy Drugstore Location](https://www.johnsnowlabs.com/marketplace/us-pharmacy-drugstore-location)\n\t- This dataset provides a list of drugstores, pharmacies and licensed pharmacists names in the United States, as well as the territories of American Samoa, Guam, Puerto Rico, the Commonwealth of the Northern Mariana Islands, and the Virgin Islands, with detailed geolocation identifiers such as latitude and longitude of each drugstore or pharmacy, as well as telephone numbers, addresses, website URLs and other provider information.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - 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Over the past 30 years, AccuWeather has perfected a proprietary quality assurance system that intelligently combines, tests, and corrects our vast collection of weather data. Our historical weather database consists of 150+ parameters that can find relationships between weather and how it affects your business. While our database includes commonly requested parameters such as maximum temperature, minimum temperature, precipitation, wind speed, wind direction, parameters also available include AccuWeather RealFeel, evapotranspiration, solar irradiance, soil moisture, and snow on the ground.\n\n**Use cases**\nEvery day, AccuWeather is assisting businesses around the world by providing crucial data and information to allow them to make the best weather-impacted decisions. 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Better plan and position retail inventory by quickly adjusting distribution and operational needs to create new sales opportunities.\n- Drive targeted advertising campaigns using historical weather data and forecasts.\n\n**Product details**\nSamples/Tables Included:\n- Daily Historical Weather\n- Hourly Historical Weather\n- Normalized Weather Parameters\n\nFields Included:\n- Date\n- Postal Code metdata (including Latitude/Longitude)\n- Average Daily Parameters (Temperature, RealFeel Temperature, Windspeed & Direction, etc.)\n- Observed Parameters (Temperature, Relative Humidity, Cloud Cover Percentage, etc.)\n","terms_of_service":"https://www.accuweather.com/en/privacy","documentation_link":"https://docs.google.com/spreadsheets/d/1sxeWreQiyI3C0s4BjxwBVmYy4VbPNkl3I77dtkP9wms/edit?usp=sharing","privacy_policy_link":"https://www.accuweather.com/en/privacy","geographical_coverage":"United States and Canada","cost":"FREE","assets":["ASSET_TYPE_DATA_TABLE"]},"provider_summary":{"name":"AccuWeather","description":"AccuWeather provides global weather data powered by Superior Accuracy™ with 170+ forecast models and over 60 years of meteorological expertise. 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We improve business performance for our clients by using our Business & Consumer data solutions and omnichannel marketing services, enabling our clients to acquire, grow, and retain their customers more efficiently and effectively, and enhance their platforms, products, and customer insights."},"popularity":445},{"id":"43ddafa7-17bf-439b-be48-ce48e2785c23","summary":{"name":"CMS NPPES Provider Dataset","subtitle":"CMS NPPES provider data covering identifiers, taxonomy, and practice locations.","setting":{"visibility":"PUBLIC"},"created_at":1769116708711,"updated_at":1769118262958,"categories":["GEOSPATIAL","HEALTH","LOOKUP_TABLES","PUBLIC_SECTOR","ADVERTISING_AND_MARKETING"],"listingType":"PERSONALIZED","provider_id":"50cda399-2999-4ead-84e7-6433ed15c0bc"},"detail":{"description":"## Overview\n\nAccess the authoritative source for **8M+ healthcare providers** with the CMS NPPES Provider Dataset. The National Plan and Provider Enumeration System (NPPES) tracks every HIPAA-covered healthcare provider in the United States, providing comprehensive provider identification, location, specialization, and contact information.\n\n**Dataset Size:** 15+ GB | **50M+ records** | **Updated weekly**\n\n> **[Start Free Trial](https://trial.dataplex-consulting.com)** | **[Purchase Full Access](https://checkout.dataplex-consulting.com/b/7sY8wI3n5eKs7rW9y4bQY02)**\n\n### What's Included\n\n- **providers** - Core provider records with NPI, names, and demographics (8M+ providers)\n- **providers_addresses** - Practice and mailing addresses with geocoding (16M+ addresses)\n- **providers_identifiers** - State licenses, DEA numbers, and other identifiers (12M+ identifiers)\n- **providers_taxonomies** - Provider specializations and classifications (10M+ taxonomy assignments)\n- **taxonomies** - Complete NUCC taxonomy code reference with groupings\n- **providers_licenses** - State licensing information and status\n\n---\n\n## Use Cases\n\n### Healthcare Organizations\n- **Provider credentialing**: Verify provider identities and qualifications\n- **Network adequacy**: Assess provider coverage by specialty and geography\n- **Directory management**: Maintain accurate provider directories\n- **Referral networks**: Build specialty-specific referral pathways\n\n### Health Plans & Payers\n- **Network development**: Identify providers for network expansion\n- **Claims validation**: Verify provider NPIs on submitted claims\n- **Provider matching**: Link providers across systems using multiple identifiers\n- **Geographic analysis**: Map provider distribution by region\n\n### Healthcare Analytics\n- **Market analysis**: Size provider markets by specialty and location\n- **Competitive intelligence**: Track provider affiliations and group practices\n- **Trend analysis**: Monitor provider enrollment and deactivation patterns\n- **Data enrichment**: Augment existing datasets with provider attributes\n\n---\n\n## Data Quality & Updates\n\n- **Source**: CMS National Plan and Provider Enumeration System (NPPES)\n- **History**: Complete current registry with deactivation tracking\n- **Updates**: Weekly delta changes with monthly full refresh\n- **Coverage**: All HIPAA-covered healthcare providers in the United States\n- **Taxonomies**: Full NUCC Healthcare Provider Taxonomy code set\n\n---\n\n## Getting Started\n\n### Start a Free Trial\n\nGet 14 days of access to explore the NPI Registry dataset:\n\n**[Start Free Trial](https://trial.dataplex-consulting.com)**\n\nTrial includes:\n- Full schema access with sample data\n- Complete taxonomy reference\n- Databricks notebook with example queries\n\n### Get Full Access\n\nReady for production use? Get unlimited access to all 8M+ healthcare providers:\n\n**[Purchase Full Access](https://checkout.dataplex-consulting.com/b/7sY8wI3n5eKs7rW9y4bQY02)**\n\nFull access includes:\n- Complete provider registry\n- All 50M+ records across all tables\n- Weekly updates within days of CMS release\n- Monthly full refresh for data accuracy\n- Historical deactivation tracking\n\n---\n\n## Documentation & Support\n\n- **Full Documentation**: [docs.dataplex-consulting.com/cms-nppes-provider-dataset](https://docs.dataplex-consulting.com/data-catalog/cms-nppes-provider-dataset)\n- **Data Dictionary**: Comprehensive field-level documentation\n- **Sample Notebooks**: Ready-to-run Databricks notebooks\n- **Support**: support@dataplex-consulting.com\n\n---\n\n*Data provided by Dataplex Consulting | [dataplex-consulting.com](https://dataplex-consulting.com)*\n","terms_of_service":"https://www.dataplex-consulting.com/terms","documentation_link":"https://docs.dataplex-consulting.com/data-catalog/cms-nppes-provider-dataset","privacy_policy_link":"https://www.dataplex-consulting.com/privacy","embedded_notebook_file_infos":[{"id":"19b2fd70-e073-4c60-a816-3849d8e0b075","marketplace_file_type":"EMBEDDED_NOTEBOOK","file_parent":{"parent_id":"43ddafa7-17bf-439b-be48-ce48e2785c23","file_parent_type":"LISTING"},"mime_type":"text/html","download_link":"https://marketplace-file-storage-prod.s3-fips.us-east-1.amazonaws.com/EMBEDDED_NOTEBOOK/LISTING_43ddafa7-17bf-439b-be48-ce48e2785c23/19b2fd70-e073-4c60-a816-3849d8e0b075?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEGgaDnVzLWVhc3QtMS1maXBzIkYwRAIgbC8sWIoYaHR8y7Wq86nPb0pufBl4%2BM4PWpeN%2B5PW9VQCIG7BhAVBDCWTpxFDLeuKYm%2Ff1JLz4iccPMzter0YdhgWKqYECDAQABoMNDE0MzUxNzY3ODI2IgxNAJrHbXaZG20jNfMqgwQyGEb4TsoVOoNvJ%2BaA%2FZ7zeu8hGcHqCeNfblP%2FQo5g6q2aRNtpwtPRKMlhYcIE%2FUQaNT1Lqmyptu32VUQTpzjTTxRVmJrDPkheSCkV06XQ%2FpTMKjU8CC7X1b%2FqYf8CrBwZT7Cv4C5WPjEET38f2A79h0xV6bZgPy28zQWrSeIJ9w6AfZBxiE24%2FVc08zVA0ivCT6lsy7nuU0wcR%2FMg2C9nd1vaMnQ923VpD21CKfVVYcrNWL2b%2FQ%2BNAO%2BxPjPgjiN6UPQbvbvZxkSdhgrGtecIg9NgqGkMcvM5VLTDtdC1kdeZetfOC2obtA%2BT%2F0gy3tIDo6WM5hcQfw743%2FAU9oaGnRBl%2BwoxRRDCZA%2F4jnPdluICSI7NCr6C3HHOe4CSZJjRihczxPA%2FgQczAVjAPs6CrK9N9pIzt0vNJpGUTgg4AoxftTvGfciDREilSctPIcBk6GwB%2Bzt4wbtNCmEPU%2BFD46rc6CWVQmmLwbKOeHw70ipMVFlOc8NlXkEtGO2vu6H44MstSSc6daOVUYUavQSGZ6jGhEjsP2YGz8KDm9XQotB%2FtyVBqiZV01OKqDmmBJA%2FXjUpSJ8M%2FeJXhe5m%2BKcxzNObtoJRI7jci3Tpt08XZyXZ7%2FyIV8H30ya468X3cfkTXrm972uga3z7iXAdubyEHnQIua%2FuvtwOHILi6O5D1Me2MzCPvcnXBjqTAYDoMWBt%2B3AVFuqSs2dmDQNBnvEhXfMgTedKLD5PJHb3aIl15iBkLaNu36GYnVCp9ZU2lSYcTbY5QwNnNeTfJTNy9bKhwzru6Sg%2BslhN%2BTS0zkTn%2BeLMY4tHgPEtEZ2cp%2FCk6GwqswvnYVKHjwO8q%2B1Mx8lc08fVEfarE9dke1GGFqoddx%2Bq%2BZUyHbDlAVfrCmf0yw%3D%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20261110T000135Z&X-Amz-SignedHeaders=host&X-Amz-Expires=86400&X-Amz-Credential=ASIAWA6KKHEJORWBH4N3%2F20261110%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=6f5e77d375b49eacdfd08a4165469ff88818023de97ac8d450e4170517ca378a","created_at":1769118217902,"updated_at":1769118217902,"display_name":"05_npi_nppes_showcase","status":"FILE_STATUS_PUBLISHED"}],"geographical_coverage":"[\"United States and Canada\"]","cost":"PAID","update_frequency":{"interval":1,"unit":"DAILY"},"collection_granularity":{"interval":1,"unit":"DAILY"},"collection_date_start":1767302107821,"collection_date_end":1769116516050,"data_source":"NPPES NPI Registry","size":15.0,"assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://www.dataplex-consulting.com/terms"},"provider_summary":{"name":"Dataplex Consulting & Data Products","description":"Dataplex Consulting & Data Products delivers turnkey, analytics-ready data products that make complex public and commercial data easy to use across modern data platforms. Our data pipelines include automated quality checks and active monitoring to ensure timely, reliable, and well-structured data that is ready for downstream analytics, machine learning, and operational use.\n\nIn addition to data products, Dataplex provides data engineering and analytics consulting services to organizations of all sizes. We bring deep, hands-on experience supporting both early-stage companies and large enterprises, helping teams build scalable data platforms, improve data reliability, and become more data-driven."},"popularity":444},{"id":"c607f560-a1f9-4b28-b60f-67e348a3b980","summary":{"name":"SAMPLE - Luxury Pens Price Analysis","subtitle":"Sample dataset of 30 rows from a 3,000+ luxury pen marketplace listings for pricing, assortment, and trend analysis.","share":{"name":"luxury_pens_price_analysis_2025_sample","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1768492217754,"updated_at":1768685009160,"categories":["ADVERTISING_AND_MARKETING","COMMERCE","ECONOMICS","EDUCATION","FINANCIAL","DEMOGRAPHICS","RETAIL"],"listingType":"STANDARD","provider_id":"2d97eccd-0fcb-4935-8bdf-5c34065e0349"},"detail":{"description":"**Overview**\n\nA clean, analysis-ready dataset of 3,000 anonymized high-end writing instrument listings covering major luxury brands, item conditions, global seller locations, and declared shipping destinations.The dataset is formatted for immediate use in BI analysis, pricing models, catalog enrichment, and NLP-driven rarity discovery.\n\nBuilt for analysts and data scientists, the collection supports price benchmarking, assortment optimization, anomaly detection, and trend/seasonality modeling.Titles and listing metadata are preserved for NLP feature extraction while PII has been removed so the dataset is ready for responsible analytics at scale.\n\n**Provenance**\n\nCompiled by Maths with Kanchana LLC on Aug 17, 2025.The dataset originates from publicly accessible e-commerce marketplace listings collected and aggregated by the compiler.Data collection followed ethical sourcing practices: only publicly visible listing data were captured, direct personal identifiers were removed or anonymized, and standard de-duplication and normalization were applied.The sample release is available under the CC BY 4.0 International license; full dataset purchase is available under the AIDC commercial purchase license.\n\n**Use Cases**\n\nThis dataset is valuable for a variety of research and analytical applications, including:\n\n- **Price Benchmarking & Margin Modeling**: Use Spark/SQL and Delta Lake to build brand- and condition-level pricing models to inform margin planning.\n- **Assortment Optimization**: Optimize premium inventory mixes with clustering and demand-signal analytics for catalog and merchandising teams.\n- **NLP Rarity & Edition Discovery**: Train NLP models to detect limited editions and rarity cues from listing titles and descriptions for valuation and catalog enrichment.\n- **Geo-Logistics Strategy**: Analyze itemLocation and shipsTo coverage to guide regional shipping, warehousing, and market-entry decisions.\n- **Anomaly Detection & Data Quality**: Deploy streaming or batch pipelines to flag mispriced or inconsistent listings using threshold and model-based detectors.\n\n**Column Dictionary**\n\n- **brand**: string — Declared manufacturer / marque of the pen.\n- **type**: string — Instrument subtype (fountain, rollerball, ballpoint) when provided; sparse in some rows.\n- **categoryinfo**: string — Primary marketplace taxonomy path/label for the item.\n- **categoryinfo_additional**: string — Optional secondary taxonomy tags when present.\n- **title**: string — Seller’s listing title with model/series and edition cues (good for NLP features).\n- **price**: decimal — Captured item price at collection time (USD).Confirmed unit: USD.\n- **lastUpdated**: timestamp — Timestamp of when this listing was last observed (May 04, 2017 – Aug 16, 2025).\n- **seller**: string — Anonymized seller identifier (no direct personal data).\n- **condition**: string — Seller-stated state (e.g., New, Used, Unspecified).\n- **itemLocation**: string — Seller-reported city / region / country of the item.\n- **shipsTo**: string — Destinations/regions the seller ships to (multi-value strings; consider normalizing to arrays or flags).\n- **excludesShipping**: string — Countries/regions explicitly excluded from shipping (multi-value strings).\n","terms_of_service":"https://www.aidc.app/sample-terms-of-service","documentation_link":"https://app.aidatacoop.com/datasets/969363d2-0b6f-46d0-8c02-971a38629f30","privacy_policy_link":"https://www.aidc.app/faq","embedded_notebook_file_infos":[{"id":"4ba62ea6-bc06-4372-92be-b8fb49776e0e","marketplace_file_type":"EMBEDDED_NOTEBOOK","file_parent":{"parent_id":"c607f560-a1f9-4b28-b60f-67e348a3b980","file_parent_type":"LISTING"},"mime_type":"text/html","download_link":"https://marketplace-file-storage-prod.s3-fips.us-east-1.amazonaws.com/EMBEDDED_NOTEBOOK/LISTING_c607f560-a1f9-4b28-b60f-67e348a3b980/4ba62ea6-bc06-4372-92be-b8fb49776e0e?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEGgaDnVzLWVhc3QtMS1maXBzIkYwRAIgbC8sWIoYaHR8y7Wq86nPb0pufBl4%2BM4PWpeN%2B5PW9VQCIG7BhAVBDCWTpxFDLeuKYm%2Ff1JLz4iccPMzter0YdhgWKqYECDAQABoMNDE0MzUxNzY3ODI2IgxNAJrHbXaZG20jNfMqgwQyGEb4TsoVOoNvJ%2BaA%2FZ7zeu8hGcHqCeNfblP%2FQo5g6q2aRNtpwtPRKMlhYcIE%2FUQaNT1Lqmyptu32VUQTpzjTTxRVmJrDPkheSCkV06XQ%2FpTMKjU8CC7X1b%2FqYf8CrBwZT7Cv4C5WPjEET38f2A79h0xV6bZgPy28zQWrSeIJ9w6AfZBxiE24%2FVc08zVA0ivCT6lsy7nuU0wcR%2FMg2C9nd1vaMnQ923VpD21CKfVVYcrNWL2b%2FQ%2BNAO%2BxPjPgjiN6UPQbvbvZxkSdhgrGtecIg9NgqGkMcvM5VLTDtdC1kdeZetfOC2obtA%2BT%2F0gy3tIDo6WM5hcQfw743%2FAU9oaGnRBl%2BwoxRRDCZA%2F4jnPdluICSI7NCr6C3HHOe4CSZJjRihczxPA%2FgQczAVjAPs6CrK9N9pIzt0vNJpGUTgg4AoxftTvGfciDREilSctPIcBk6GwB%2Bzt4wbtNCmEPU%2BFD46rc6CWVQmmLwbKOeHw70ipMVFlOc8NlXkEtGO2vu6H44MstSSc6daOVUYUavQSGZ6jGhEjsP2YGz8KDm9XQotB%2FtyVBqiZV01OKqDmmBJA%2FXjUpSJ8M%2FeJXhe5m%2BKcxzNObtoJRI7jci3Tpt08XZyXZ7%2FyIV8H30ya468X3cfkTXrm972uga3z7iXAdubyEHnQIua%2FuvtwOHILi6O5D1Me2MzCPvcnXBjqTAYDoMWBt%2B3AVFuqSs2dmDQNBnvEhXfMgTedKLD5PJHb3aIl15iBkLaNu36GYnVCp9ZU2lSYcTbY5QwNnNeTfJTNy9bKhwzru6Sg%2BslhN%2BTS0zkTn%2BeLMY4tHgPEtEZ2cp%2FCk6GwqswvnYVKHjwO8q%2B1Mx8lc08fVEfarE9dke1GGFqoddx%2Bq%2BZUyHbDlAVfrCmf0yw%3D%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20261110T000135Z&X-Amz-SignedHeaders=host&X-Amz-Expires=86400&X-Amz-Credential=ASIAWA6KKHEJORWBH4N3%2F20261110%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=0f0910a9297d8a396cf8e21443506a6bb00b9db2d2d5dbfa92bfa6f34b776ef5","created_at":1768685004014,"updated_at":1768685004014,"display_name":"luxury_pens_price_analysis_sample_notebook_20260117","status":"FILE_STATUS_PUBLISHED"}],"geographical_coverage":"[\"Global\"]","cost":"FREE","update_frequency":{"interval":1,"unit":"MONTHLY"},"collection_granularity":{"interval":1,"unit":"DAILY"},"collection_date_start":1493909130329,"collection_date_end":1755355554944,"data_source":"The dataset originates from publicly accessible e-commerce marketplace listings collected and aggregated by the compiler. Data collection followed ethical sourcing practices: only publicly visible listing data were captured, direct personal identifiers were removed or anonymized, and standard de-duplication and normalization were applied. The sample release is available under the CC BY 4.0.","size":5.0,"assets":["ASSET_TYPE_MEDIA"],"license":"https://spdx.org/licenses/CC-BY-4.0.html"},"provider_summary":{"name":"AIDC, Inc.","description":"Great AI starts with great data. We have it. Ship data like software. AIDC packages external data as governed Delta tables with Unity Catalog, complete with schema, metadata, lineage, and clear license terms. Pre‑vetted contracts minimize back‑and‑forth, so squads move from browse to pipeline in days. Quality control is operated by Kaggle Grandmasters and senior data engineers at that standard, so datasets behave in production and pass audits. Coverage spans luxury item pricing (valuation, comp, elasticity studies) to retail property analytics (site selection, underwriting, performance prediction). Instrument adoption and prove lift: reduced ETL, faster training, higher dataset usability rates. Repeatable motion across teams: import → govern → build → measure → scale. Available now on Databricks Marketplace."},"popularity":164},{"id":"8a253518-5f37-4d9b-b6fa-a027f1f69cb8","summary":{"name":"SAMPLE Fruit Juice Retail Data | Product Availability Scorecard | Pricing, Shelf Visibility & ...","subtitle":"This dataset captures product availability, pricing, shelf placement, and outlet details for fruit juice brands ...","share":{"name":"rwazi_81abe390_84f7_48ea_9550_629b0792f9c2_samples_share","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1743441527549,"updated_at":1755791792192,"categories":["RETAIL","ADVERTISING_AND_MARKETING"],"listingType":"STANDARD","provider_id":"0296f22e-95af-40d9-b392-ea64d68cec1b"},"detail":{"description":"Fruit Juice Retail Mapping – In-Store Product Availability, Pricing, and Shelf Visibility\n\nThis dataset offers granular, on-the-ground intelligence on the presence, pricing, shelf positioning, and availability of packaged fruit juice brands across various retail outlets. Captured by field agents directly from stores, the data includes structured inputs such as outlet attributes, product barcodes, pricing, shelf photos, and product availability checks. It is designed to help FMCG teams track in-store performance, benchmark competitors, and optimize retail execution strategies in real time.\n\nCore Value Proposition\nRetail environments are dynamic, and winning at the shelf requires timely, accurate data on how products are being positioned and priced across thousands of locations. This dataset bridges that gap by providing a real-world, store-level view into the execution of fruit juice products—across both modern and traditional retail formats.\n\nIt enables stakeholders to move beyond assumptions and market averages, offering visibility into specific brands, SKUs, and store types. Teams can assess the effectiveness of distribution strategies, monitor compliance with planograms or promotional campaigns, and uncover competitive gaps across different regions.\n\nUse Cases by Role\nTrade Marketing Teams\n\nVerify on-shelf product presence and identify visibility gaps across retail partners\n\nMonitor planogram compliance with real photo documentation\n\nCompare pricing vs. competitors in-store to ensure promotional pricing is effective\n\nTrack availability of new SKUs or promotional bundles\n\nSales & Field Operations\n\nPrioritize store visits based on stockout frequency or missing SKUs\n\nIdentify retailers not carrying key products or brands and target them for onboarding\n\nValidate retail execution of in-market activations or price drops\n\nMap payment method availability for potential POS integrations or retail enablement\n\nBrand & Category Managers\n\nMeasure retail footprint and market penetration at the brand level\n\nBenchmark share of shelf and price positioning versus competitors across retail types\n\nIdentify regional pricing inconsistencies or availability issues\n\nUnderstand consumer-facing shelf experience using storefront and shelf photos\n\nInsights & Strategy Teams\n\nSegment retail environments by outlet type, city, or region for performance benchmarking\n\nIdentify trends in availability, pricing, and product assortment\n\nSupport business cases for expanding into underserved channels or cities\n\nFeed data into forecasting or market sizing models using actual in-store coverage\n\nRevenue Growth & Pricing Teams\n\nMonitor how price strategies are being executed in the field\n\nIdentify unauthorized discounting or pricing inconsistencies by outlet\n\nEvaluate price sensitivity by comparing prices across similar store types\n\nUse competitor pricing benchmarks to refine promotional calendars\n\nKey Data Components\nOutlet Details\n\nOutlet Name, Type, Address, City, Country, Latitude, Longitude\nThese fields provide context around where the product data was captured, supporting regional and channel segmentation.\n\nStorefront & Section Photos\n\nStorefront Photo, Juice Section Photo\nVisual confirmation of retail execution and visibility, allowing internal teams to audit displays and merchandising quality.\n\nProduct Availability & Pricing\n\nIs [Brand] Available? fields for each juice brand (e.g., Chivita, Capri-sun, Ribena, etc.)\n\nPrice, Barcode, and Shelf Photo for each product\nThese fields allow for detailed, SKU-level tracking of which products are available, at what price, and how they appear on the shelf.\n\nAdditional Retail Attributes\n\nPayment Methods, Products Offered, Additional Attributes\nThese help teams understand store-level characteristics that may influence sales strategy, such as whether the outlet supports mobile payments or carries complementary categories.\n\nCompetitive Tracking\nBrands included in the dataset (e.g., Chivita Orange, Happy Hour, Active, Capri-sun, Ribena, 5Alive, Frudi, LaCasera, Sosa, Wilson’s Lemonade, etc.) are all tracked for:\n\nOn-shelf presence (yes/no)\n\nPrice\n\nBarcode\n\nShelf-level photo capture\n\nThis makes the dataset a strong foundation for competitive audits, pricing analysis, and retail presence benchmarking across brands and territories.\n\nSummary\nThe Fruit Juice Retail Mapping dataset provides the ground truth for how fruit juice products are presented, priced, and positioned at the point of sale. It’s built to enable smarter decision-making across marketing, sales, trade, and insights functions—helping teams move faster, identify gaps, and act on opportunities with precision. Whether the goal is to improve coverage, enforce pricing policy, design promotions, or win more shelf space, this data offers the visibility needed to execute with confidence.","terms_of_service":"https://www.rwazi.com/privacy-policy","contact_email":"marketplace@datarade.ai","privacy_policy_link":"https://www.rwazi.com/privacy-policy","assets":["ASSET_TYPE_DATA_TABLE"]},"provider_summary":{"name":"Rwazi","description":"Rwazi is a global market intelligence platform operating across 190+ countries, leveraging AI and a network of 1.5M+ consumers to deliver real-time, zero-party data. With a presence spanning diverse markets worldwide, Rwazi captures granular insights on purchasing behavior, pricing trends, and product availability. Its cutting-edge computer vision technology extracts high-precision data from images and videos, enhancing accuracy and depth in consumer insights. The AI-driven platform transforms raw data into actionable intelligence, helping brands predict demand shifts, refine competitive positioning, and drive growth. By combining advanced AI with direct consumer input, Rwazi provides businesses with consent-based, real-time data to optimize market strategies and navigate an ever-changing global landscape."},"popularity":305},{"id":"35acde8e-4c0f-4475-8cf2-c28600f1ada0","summary":{"name":"automation-test-listing","subtitle":"automation-description","share":{"name":"api_test_share","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1789121087427,"updated_at":1789121087427,"categories":["COMMERCE"],"listingType":"STANDARD","provider_id":"6b4c3f00-c69d-47ed-a3a2-541171c84c7d"},"detail":{"description":"**Overview**\nAdd a general description of your listing and its benefits here.\n\n**Use cases**\nAdd use cases for the listing here.\n- Use case 1\n- Use case 2\n\n**Product details**\nAdd a maximum of 10 sample datasets and field names that represent your dataset.\n\n- Datasets represented include table 1, table 2, and table 3.\n- Sample fields include field 1, field 2, field 3, field 4 and field 5.\n\nFor more details, refer to the embedded notebook.\n\n**Additional Insights**\nAdd any additional insights on the listing here. This can include relevant links pertaining to research related to the listing dataset, etc.","terms_of_service":"https://www.collibra.com/legal/documents/master-collibra-agreement","documentation_link":"","privacy_policy_link":"https://www.collibra.com/legal/documents/collibra-privacy-policy","update_frequency":{"interval":5,"unit":"DAILY"},"assets":["ASSET_TYPE_DATA_TABLE","ASSET_TYPE_MEDIA","ASSET_TYPE_MODEL","ASSET_TYPE_NOTEBOOK"],"license":"","tags":[{"tag_name":"LISTING_TAG_TYPE_TASK","tag_values":["ENTITY_RESOLUTION"]}]},"provider_summary":{"name":"Collibra","description":"Collibra is the Data Intelligence company. We accelerate trusted business outcomes by connecting the right data, insights, and algorithms to all Data Citizens. Our cloud-based platform connects IT and the business to build a data-driven culture for the digital enterprise. Global organizations choose Collibra to unlock the value of their data and turn it into a strategic, competitive asset.\n"},"popularity":1609},{"id":"0326a01c-d194-4efd-bdbd-6403c0445bc0","summary":{"name":"Judge Builder","subtitle":"Judge Builder enables you to align your LLM judges with human preferences using state-of-the-art optimization techniques","setting":{"visibility":"PUBLIC"},"created_at":1761931858364,"updated_at":1762066367474,"categories":["SCIENCE_AND_RESEARCH"],"listingType":"STANDARD","provider_id":"ea1e69ff-0127-4c94-bf39-e841fe1d19d2"},"detail":{"description":"# Judge Builder\n\n_Have you ever disagreed with your LLM Judge? Often, built-in judges and simple guidelines will work for some cases, but fall short in others where they may not understand your domain or edge cases in the judging criteria. Judge Builder addresses this challenge._\n\nJudge Builder is deployed as a Databricks App in your workspace - think of the Databricks App as UI sugar over the existing methods already built into MLflow. Start with our built-in judges or create your own custom judge. Then align your judge with expert judgment by providing human feedback. Judges are registered as scorers in your experiment so you can easily use them in online and offline evaluations.\n\n- Feedback Collection - Collect expert feedback via the review app or trace UIs\n- Judge Alignment - Align judges with expert feedback using state-of-the-art optimization techniques\n- Judge Lifecycle Management - From judge creation to production traffic evaluation, with a few lines of code\n- MLflow Integration - Fully built on top of MLflow primitives and seamlessly integrates with your existing MLflow experiment - no migrations needed!\n\n\n## Usage\n\n1. **Create a Judge**: Start by creating a new judge with a name, instruction, and experiment ID. You will also invite SMEs to provide human feedback.\n2. **Add Examples**: Add traces from the attached experiment to use as examples for your judge to learn from.\n3. **Labeling**: The SME will provide human feedback over the added examples.\n4. **Align Judge**: Run alignment to optimize the judge based on human feedback. You can review the performance of the judge before and after alignment.\n\nYou can also view a short video on Judge Builder [here](https://www.youtube.com/watch?v=OKnKtZj8f5Q).\n\n## How to get help\n\nFor questions or bugs, please contact agents-outreach@databricks.com and the team will reach out shortly.\n\n## License\n\n© 2025 Databricks, Inc. All rights reserved. The source in this notebook is provided subject to the Databricks License [https://databricks.com/db-license-source]. ","terms_of_service":"https://www.databricks.com","documentation_link":"https://github.com/databrickslabs/sandbox/tree/main/judge-builder","privacy_policy_link":"https://www.databricks.com/legal/privacynotice","data_source":"","assets":["ASSET_TYPE_APP"],"license":"https://github.com/databrickslabs/sandbox/blob/main/judge-builder/LICENSE.md"},"provider_summary":{"name":"Databricks","description":"As the world’s first lakehouse platform in the cloud, Databricks combines the best elements of data lakes and data warehouses to deliver the reliability, strong governance, and performance of data warehouses with the openness, flexibility, and machine learning support of data lakes. We are a cross-cloud platform that works across AWS, Azure, and Google.\nThis unified approach simplifies your modern data stack by eliminating the data silos that traditionally separate and complicate data engineering, analytics, BI, data science and machine learning. It’s built on open source and open standards to maximize flexibility. And, its common approach to data management, security and governance helps companies operate more efficiently and innovate faster."},"popularity":2079},{"id":"01442379-248e-4cb2-ac53-6e711eb65918","summary":{"name":"OnPoint Forecast Extended: ECMWF | Up to 46 Days | Global Coverage | Daily / Weekly Format","subtitle":"Gain agility with sub-seasonal forecasts up to 46 days. Derived from ECMWF ensemble data, we provide daily and ...","share":{"name":"pelmorex_weather_source_26a819ca_3870_440b_9463_c3e53d0ef2a3_samples_share","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1768489275564,"updated_at":1792689610234,"categories":["OTHER"],"listingType":"PERSONALIZED","provider_id":"871f3467-760a-49de-ada4-1556857ee355"},"detail":{"description":"OnPoint Forecast Extended: ECMWF delivers daily and weekly outlooks up to 46 days ahead, derived from ECMWF’s ensemble system and updated daily. The product provides both historical and forecasted atmospheric and oceanic conditions, with daily and weekly averages for key parameters like air temperature, wind, solar radiation, precipitation, and snowfall. In addition, daily historical forecasts back to October 2023 and weekly back to January 2014 are also available upon request.\n\nDesigned for advanced planning, this dataset enables users to analyze trends, anomalies, and probabilities for critical weather variables—supporting more informed, mid-range decision-making across business and research applications. By integrating sub-seasonal forecasts, organizations could gain agility in navigating weather-driven uncertainty, while researchers could enhance climate resilience strategies.\n\nIs OnPoint Forecast Extended: ECMWF Data Right for You?\n\nOnPoint Forecast Extended: ECMWF's time range bridges the gap between Medium-Range weather forecasts (up to 15 days) and Seasonal Forecasts (7 months ahead), making it valuable for sectors that need to plan several weeks in advance, such as energy, agriculture, water management, and disaster preparedness.\n\nConsider extended forecasts if your business:\n\n- Requires planning 2-6 weeks ahead. For longer timeframe decisions, consider OnPoint Forecast Seasonal: ECMWF. \n- Operates in weather-sensitive sectors.\n- Needs probabilistic insights to manage risk or capitalize on trends.\n- For daily or short-term operational decisions, consider OnPoint Forecast Medium-Range: ECMWF or OnPoint Forecast Medium-Range: ECMWF — Ensembles.","terms_of_service":"https://business.theweathernetwork.com/privacy-policy","contact_email":"support@datarade.ai","privacy_policy_link":"https://business.theweathernetwork.com/privacy-policy","assets":["ASSET_TYPE_DATA_TABLE"]},"provider_summary":{"name":"The Weather Network For Business","description":"At The Weather Network for Business, we turn weather volatility into a predictable business advantage for enterprises globally. Backed by over 35 years of meteorological expertise, with over a decade of dedicated enterprise experience, we deliver hyper-local weather data, real-time alerts, and predictive analytics that protect assets, optimize operations, and drive growth. From retail and energy to enterprise logistics and public safety, our specialized commercial solutions help businesses navigate climate complexity with confidence.\r\n\r\nDiscover how we can help your business stay ahead of the storm at business.theweathernetwork.com/"},"popularity":1407},{"id":"58229d5e-b69d-41b3-a57f-a39e284191e0","summary":{"name":"MBI Sociodemographic Data for Slovenia – Sample","subtitle":"MBI's fresh sociodemographic data for well-founded decisions in Slovenia. Free trial data.","share":{"name":"share_socio_mbi_market_data_2024_si_postcodes_4_socio","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1763373347725,"updated_at":1779198936765,"categories":["ADVERTISING_AND_MARKETING","DEMOGRAPHICS","GEOSPATIAL"],"listingType":"STANDARD","provider_id":"aa09abd6-ff7f-4452-803d-b8c43b4c07cb"},"detail":{"description":"[MBI](https://www.mbi-geodata.com/en/)'s sociodemographic data is available for many countries worldwide. All data comes from a single source, ensuring **consistency and comparability** across markets.\n\nThe Sociodemographics-Bundle Basic for Slovenia includes the following **variables**:\n- [population](https://www.mbi-geodata.com/en/sociodemographic-data/population/)\n- [gender](https://www.mbi-geodata.com/en/sociodemographic-data/sex-gender/)\n- [age structure](https://www.mbi-geodata.com/en/sociodemographic-data/agebands/) (15-year age bands)\n- household figures ([number of households](https://www.mbi-geodata.com/en/sociodemographic-data/households/) and [households by type](https://www.mbi-geodata.com/en/sociodemographic-data/households-by-type/))\n\nOur sociodemographic data is offered for each country on different **levels**:\n- **Postal Code Level:** This fine-grained level of detail makes the data suitable for a wide range of applications – for example, enriching customer databases, which are often organized by postal code.\n- **Administrative Level:** Covers both larger administrative areas (e.g., regions, states, provinces) as well as finer subdivisions such as districts, counties, or municipalities.\n- **Micro Level**: Subdivisions of postal code or administrative boundaries, such as census areas or street segments. This is the most granular level for local analysis.\n\n**Please note:** The dataset available for free download is a **sample dataset** covering a limited number of geographic areas. If you wish to access the full dataset (covering all areas) or if you require additional variables or geographic levels, please contact us. We will be happy to help you find a product that fits your needs.\n \n**Additional Insights**: https://www.mbi-geodata.com/en/sociodemographic-data/\n\n**Data Structure**:\nAGE_F0014: Population (Nighttime) by age: 0 - 14 years, female\nAGE_F1529: Population (Nighttime) by age: 15 - 29 years, female\nAGE_F3044: Population (Nighttime) by age: 30 - 44 years, female\nAGE_F4559: Population (Nighttime) by age: 45 - 59 years, female\nAGE_F60PL: Population (Nighttime) by age: 60 years and above, female\nAGE_M0014: Population (Nighttime) by age: 0 - 14 years, male\nAGE_M1529: Population (Nighttime) by age: 15 - 29 years, male\nAGE_M3044: Population (Nighttime) by age: 30 - 44 years, male\nAGE_M4559: Population (Nighttime) by age: 45 - 59 years, male\nAGE_M60PL: Population (Nighttime) by age: 60 years and above, male\nAGE_T0014: Population (Nighttime) by age: 0 - 14 years, total\nAGE_T1529: Population (Nighttime) by age: 15 - 29 years, male\nAGE_T3044: Population (Nighttime) by age: 30 - 44 years, total\nAGE_T4559: Population (Nighttime) by age: 45 - 59 years, total\nAGE_T60PL: Population (Nighttime) by age: 60 years and above, total\nCTRYCODE: Two digit country code by ISO 3166\nFEMALE: Population (Nighttime): females\nHH_SIZE: Average household size\nHH_T: Households: total number\nMALE: Population (Nighttime): males\nNAME: Name of administrative area / Name of the most populous administrative area / Name of the respective micro-area or the next higher level\nADMINCODE / POSTCODE / MICROCODE Administrative code / Postal code / Micro-code\nP_PRM: Population (Nighttime) year average: per mill of country\nP_T: Population (Nighttime) year average: total number","terms_of_service":"https://service-mbi-geodata.com/s/HNp7Z2tws4WDqWd","documentation_link":"https://www.mbi-geodata.com/en/mbi-supplier-manufacturer-worldwide-geodata/europe/slovenia-maps/","privacy_policy_link":"https://www.mbi-geodata.com/en/data-protection/","geographical_coverage":"[\"Europe\"]","cost":"FREE","update_frequency":{"interval":1,"unit":"YEARLY"},"collection_granularity":{"interval":1,"unit":"YEARLY"},"collection_date_start":1704102640215,"collection_date_end":1735638822853,"data_source":"MBI","assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://service-mbi-geodata.com/s/JdyPXCGwz2mrTsj"},"provider_summary":{"name":"Michael Bauer International GmbH – MBI Geodata","description":"For over 15 years, Michael Bauer International (MBI) has been a leading provider of globally consistent and comparable geographic data, including more than 3 million postcode areas and other detailed datasets like sociodemographics and purchasing power. MBI's high-quality small-scale data spans 248 countries, supporting businesses across all industries in location planning, branch networks, and distribution strategies. Recognized as a key data source, MBI is the preferred provider for leading GIS software and technology companies worldwide as well as one of the first global distributors for HERE Technologies. MBI is part of the Michael Bauer Group, alongside Michael Bauer Research GmbH and Michael Bauer Micromarketing GmbH."},"popularity":404},{"id":"e73a8270-74df-4665-8e7d-f73e7528a0bd","summary":{"name":"SAMPLE Company Data — Entity Graph Master - 529K Stable QIDs | 99.81% Valid Websites | ISO2 | ...","subtitle":"Wikidata-keyed global entity graph (529,008). 68 columns, CSV/Parquet, website_valid + ISO country codes, type ...","share":{"name":"quantlens_0ac6240d_9c75_4544_a295_b44758e898b6_samples_share","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1767722674082,"updated_at":1780306213744,"categories":["ADVERTISING_AND_MARKETING","OTHER"],"listingType":"STANDARD","provider_id":"1927c455-25f0-44e0-8e62-6a7e69558eb3"},"detail":{"description":"QuantLens Global Entity Graph v9.4 is a **high-integrity global entity dataset** designed for durable joins, enrichment pipelines, analytics, and AI workflows. Instead of relying on brittle scraped identifiers, every record is keyed by a stable **Wikidata QID** (`wikidata_qid`) and includes a resolvable `wikidata_url`, giving you a clean primary key for **entity resolution**, deduplication, and deterministic refresh. If you’ve struggled with duplicate orgs in a CRM, inconsistent naming across sources, or enrichment pipelines that drift, this pack is built to be the **foundation layer** that makes downstream systems easier to maintain.\n\nThis release contains **529,008 unique entities** across **140 countries** with **0 duplicate QIDs**, so you can safely treat `wikidata_qid` as a true primary key and join directly into Wikidata’s public knowledge base. Web coverage is a core strength: **99.98%** of rows contain a website, and we provide `website_domain` plus a strict **`website_valid`** flag (99.81% valid) that excludes blacklisted sources (archives, Wikipedia, social pages) to support reliable domain-based enrichment and discovery workflows.\n\n\n\nThis is an **entity graph** (organizations + institutions + commercial entities), not just corporations. For “company-only” use cases, filter:\n`entity_category = \"Commercial\"` (22.0% / 116,454 entities).\nFull type granularity is included via `entity_type_qid` and `entity_type_label_en` (10,527 unique P31 types).\n\n### What you get (68-column schema, engineered for usability)\n\n* **Stable identity & joins:** `wikidata_qid`, `wikidata_url`, `company_name`, `source`, `build_id`, `snapshot_date`\n* **Geography:** `country`, `country_code` (ISO-3166-1 alpha-2 where applicable)\n* **Web presence:** `website`, `website_domain`, `website_valid`, `website_domain_shared_count`\n* **Email (honest provenance split):**\n\n * `email_raw` (Wikidata P968, sourced)\n * `email_inferred` (heuristic `info@{domain}`, unvalidated)\n * `email_source` (per-row provenance)\n* **Quality & modeling helpers:** `data_completeness_score` (0–100), `identifier_density`, `has_public_financials`\n* **Classification:** `entity_category`, plus raw `entity_type_*` for custom filtering\n* **Collision transparency:** `name_country_collision_flag` to support buyer-side dedupe review\n\n### Coverage highlights\n\n* **Primary key integrity:** 529,008 rows, **0 duplicate QIDs**\n* **Websites:** 528,888 / 529,008 (**99.98%**) non-empty\n\n * `website_valid=1`: 528,014 (**99.81%**)\n* **Countries:** `country` present for 528,927 (**99.98%**) (81 blanks)\n* **Country codes:** `country_code` present for 528,926 (**99.98%**)\n\n * 1 historical entity (Austria–Hungary) has no modern ISO2\n* **Email provenance (not a contact list):**\n\n * `email_raw`: 30,696 (**5.8%**) — sourced from Wikidata statements (not SMTP/MX validated)\n * `email_inferred`: 497,262 (**94.0%**) — heuristic, unvalidated\n * No email: 1,050 (**0.2%**) — missing/blacklisted website\n\n### Deduplication & shared-domain reality (transparent by design)\n\n* `name_country_collision_flag=1`: 14,480 rows (**2.74%**) — not necessarily duplicates, just shared names\n* `website_domain_shared_count > 1`: 114,258 rows (**21.6%**)\n\n * max shared-domain cluster: **2,977** (directory/collection-like domains exist; buyers can filter by threshold)\n\n### Best-fit use cases\n\n1. **Entity matching & deduplication:** reconcile messy org records across CRM/warehouse using stable QIDs + collision flags\n2. **Enrichment seeding:** use validated websites/domains to drive deterministic enrichment and refresh pipelines\n3. **Market mapping & segmentation:** slice by geography + entity category/type to build ICPs and coverage maps\n4. **Knowledge graphs & AI:** load as a node table keyed by QID; use type labels + quality fields for retrieval, embeddings, and matchers\n\n### Deliverables\n\n* CSV (193.2 MB, UTF-8 BOM), **Parquet/Snappy (51.3 MB)**, Sample CSV (10K rows)\n* QA report + SHA-256 manifest included for procurement/engineering verification","terms_of_service":"https://www.quantlens.net/privacy","contact_email":"support@datarade.ai","privacy_policy_link":"https://www.quantlens.net/privacy","assets":["ASSET_TYPE_DATA_TABLE"]},"provider_summary":{"name":"QuantLens","description":"QuantLens is a data provider offering curated, ML-ready alternative datasets built from public institutional sources (e.g., SEC, FDA, USPTO, clinical trials, federal contracts, and crypto market data) for quantitative and AI research. They are headquartered in Canada."},"popularity":1725},{"id":"6e24ea9e-3456-4828-91b6-75b91aefe0e7","summary":{"name":"Platts Market Data - Americas Gas and Power","subtitle":"Platts American Gas and Power industry benchmarks and price assessments.","setting":{"visibility":"PUBLIC"},"created_at":1691164745652,"updated_at":1786433507483,"categories":["ENERGY","FINANCIAL"],"listingType":"PERSONALIZED","provider_id":"eff4b18d-fe1b-4884-9591-eab9d16d2e2a"},"detail":{"description":"**Overview**\n\nPrice discovery for power and gas occurs in an ever-changing environment, where variables such as fuel mix, regulation, weather, and supply and demand fundamentals can move the markets. In this increasingly complex environment,\nunderstanding the correlation between these variables has never been more important.\n\nThis is why we have developed the new Platts Market Data – Americas Gas and Power package which provides access to the full breadth and depth of our market data, including benchmarks and contract price assessments, plus:\n\n– A robust, transparent and proven methodology allowing you to access the best possible reflection of market value, on the day, in a variety of regions\n\n– Access to our team of specialist editors who maintain a daily connection with the market, ensuring that we understand the events that affect the markets and you\n\nDataset Overview:\n\n– Daily and monthly natural gas benchmark price assessments at over 200 \nlocations throughout North America, including all of the major trading hubs\n\n– Market-reflective indices that illustrate daily gas volatility and regional price differentials via our Gas Indices Americas (GIA) indices\n\n– Extensive coverage of daily activity during bidweek through our Bidweek Watch market data\n\n– Assessments for several key bilateral trading locations in the North American power markets\n\n– Day-ahead and real-time ISO prices\n\n– Spark spreads and marginal heat rate data\n\n**Use cases**\n\n- Asset Valuation - Evaluate ROI for Energy projects and assets\n- Quantitative Analysis - Leverage SPGE data to design and implement complex and insightful models to support energy and commodities trading\n- ESG Investment Analysis - Combine SPGE data with project data-sets into models for visibility, optimizations, and collaboration\n- Risk Analysis - Utilize SPGE's independent, verifiable Risk data to support all risk management functions including validation of internal valuations and better align risk tolerance with business strategy\n- Fundamental Analysis - Connect SPGE pricing data with financial and operations data, and sensing technology to optimize decision-making on spending, productivity and cycle time\n- Pricing Analysis - Improve gas and power price benchmarking, spread and volatility analysis, and forward curve monitoring by integrating Platts pricing signals into internal analytics to support trading, hedging, and risk decisions across key US and regional markets.\n \n**Product details**\n\nSample Tables:\nA ) PRICEDATA\nB ) REF_DATA_MARKETDATA\n\nSample Fields:\nA ) PRICEDATA\n- SYMBOL\n- MDC\n- DESCRIPTION\n- BATE\n- ASSESSDATE\n- UOM\n- CURRENCY\n\nB ) REF_DATA_MARKETDATA\n- COMMODITY\n- DELIVERY_REGION\n- DESCRIPTION\n\nView Descriptions:\n\n- PRICEDATA - Get the latest and historical assessments by Symbol\n- CORRECTIONS - Get all corrections to the assessments by Symbol\n- REF_DATA_MARKETDATA - Get additional reference data for Symbols\n- MARKETDATA_DICTIONARY - Contains descriptions and examples of each of the columns in the above views for a better understanding of the dataset","terms_of_service":"https://www.spglobal.com/en/privacy/privacy-policy-english","documentation_link":"https://www.marketplace.spglobal.com/en/datasets/platts-market-data-americas-gas-power-(66)","privacy_policy_link":"https://www.spglobal.com/en/privacy/privacy-policy-english","geographical_coverage":"[\"United States and Canada\"]","cost":"PAID","update_frequency":{"interval":1,"unit":"SECOND"},"collection_granularity":{"interval":1,"unit":"SECOND"},"collection_date_start":473446724253,"assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"S&P Global Energy","description":"At S&P Energy, our complete view of global energy and commodities markets enables our customers to make decisions with conviction and create long-term, sustainable value. For more than 100 years, we've been a trusted connector that brings together thought leaders, market participants, governments, and regulators to co-create solutions that lead to progress. Vital to navigating Energy Transition, S&P Global Energy's coverage includes oil and gas, power, chemicals, metals, agriculture, and shipping. S&P Global Energy is a division of S&P Global (NYSE: SPGI)."},"popularity":761},{"id":"10617502-8f3b-4404-8791-785d15244ecc","summary":{"name":"Diversity, Equity and Inclusion - Access to Education","subtitle":"This product contains datasets which relate to access to education among populations in the US and worldwide.","share":{"name":"esg_dei_edu","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1675372370187,"updated_at":1698965635225,"categories":["DEMOGRAPHICS","EDUCATION"],"listingType":"STANDARD","provider_id":"f00071b1-2db4-4f8f-a4bc-4c62c790f25a"},"detail":{"description":"**Overview**\n\nThis listing contains datasets that pertain to Access to Education in the United States and the world. This data contains US Census Bureau education attainment data and UNICEF educational data.\n\n**Product Details**\n\nIncludes the following tables:\n\n- **esg_dei_edu.dei_edu_literacy_rate_15_to_24_yrs_yearly_unicef**\n - **Update frequency:** yearly\n - **Source:** UNICEF\n - **Description:** This table provides details of Percentage of population that can both read and write a short, simple statement about their everyday life in youth between the ages of 15 and 24\n- **esg_dei_edu.dei_edu_completion_rates_primary_yearly_unicef**\n - **Update frequency:** yearly\n - **Source:** UNICEF\n - **Description:** Percentage of children/youth in the official age range for primary education who are not attending either pre-primary, primary, secondary, or higher levels of education.\n- **esg_dei_edu.dei_edu_mean_earnings_of_workers_by_education_race_origin_sex_yearly_uscb**\n - **Update frequency:** yearly\n - **Source:** United States Census Bureau\n - **Description:** This table provides details of Mean Earnings of Workers 18 Years and Over, by Educational Attainment, Race, Hispanic Origin, and Sex: 1975 to 2019\n- **esg_dei_edu.dei_edu_completion_rates_upper_secondary_yearly_unicef**\n - **Update frequency:** yearly\n - **Source:** UNICEF\n - **Description:** Percentage of children/youth in the official age range for upper education who are not attending either pre-primary, primary, secondary, or higher levels of education.\n- **esg_dei_edu.dei_edu_percent_completed_high_school_or_college_by_race_origin_sex_yearly_uscb**\n - **Update frequency:** yearly\n - **Source:** United States Census Bureau\n - **Description:** This table provides the information about the Percent of People 25 Years and Over Who Have Completed High School or College, by Race, Hispanic Origin and Sex: Selected Years\n- **esg_dei_edu.dei_edu_out_of_school_rates_primary_yearly_unicef**\n - **Update frequency:** yearly\n - **Source:** UNICEF\n - **Description:** Represents Percentage of cohort of children or young people three to five years older than the intended age for the last grade of primary who have completed that level of education.\n- **esg_dei_edu.dei_edu_out_of_school_rates_upper_secondary_yearly_unicef**\n - **Update frequency:** yearly\n - **Source:** UNICEF\n - **Description:** Represents Percentage of cohort of children or young people three to five years older than the intended age for the last grade of upper secondary who have completed that level of education.\n- **esg_dei_edu.dei_edu_literacy_rate_above_15_yrs_yearly_unicef**\n - **Update frequency:** yearly\n - **Source:** UNICEF\n - **Description:** This table provides details of Percentage of population that can both read and write a short, simple statement about their everyday life in aldults above 15\n- **esg_dei_edu.dei_edu_school_yrs_completed_by_years_age_and_sex_yearly_uscb**\n - **Update frequency:** yearly\n - **Source:** United States Census Bureau\n - **Description:** This table provides details of years of School Completed by People 25 Years and Over, by Age and Sex: Selected Years 1940 to 2020\n- **esg_dei_edu.dei_edu_out_of_school_rates_lower_secondary_yearly_unicef**\n - **Update frequency:** yearly\n - **Source:** UNICEF\n - 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The P&ID's represent the as-built status of Huldra when it ended production in 2014.\n\n**Use cases**\n- Digital twin development connecting P&ID tags to 3D models and other technical information\n- Engineering education and research using real-world process system documentation\n- Process safety analysis and control system design studies\n- Integration with the 3D model of Huldra (available separately on this marketplace)\n\n**Product details**\nThe dataset consists of files created from Microstation 2D software, exported as proprietary DGN files and Adobe PDF format.\n\n- **P&ID's** — 20 files: Detailed graphical representations of the process system showing interconnection of process equipment, piping, valves, instruments, and control functions\n- **SCD's** — 97 files: Graphical representations of the control and safety logic, showing how control function templates implement monitoring, control, shutdown, and safeguarding functions\n- **Legends** — 16 files: Legend sheets defining symbols, line types, abbreviations, and codes used in the P&ID and SCD diagrams\n\n**Additional Insights**\nThe P&ID's are the master document for the TAGS modelled into the 3D model, enabling connection between the 3D model and system documentation. This is a key element in digital twin solutions that connect data from different source systems. The companion 3D model of Huldra is also available on this marketplace.","terms_of_service":"https://equinoropendata.blob.core.windows.net/disclaimers/Equinor%20open%20data%20-%20Terms%20and%20conditions.pdf","documentation_link":"","privacy_policy_link":"https://www.equinor.com/about-us/privacy-policy","geographical_coverage":"[\"Europe\"]","cost":"FREE","data_source":"Equinor ASA","assets":["ASSET_TYPE_MEDIA"],"license":"https://equinoropendata.blob.core.windows.net/disclaimers/HRS%20and%20Terms%20and%20conditions%20for%20license%20to%20data%20-%20Volve.pdf"},"provider_summary":{"name":"Equinor ASA","description":"In Equinor, we are determined to deliver the energy the world needs today, safely and reliably. 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S&P Global Energy is a division of S&P Global (NYSE: SPGI)."},"popularity":2146},{"id":"716fbaf6-7d39-48a8-ac53-23fc8d179320","summary":{"name":"SAMPLE Job Postings Data | Company-Linked Job Posts | Hiring Data | Data Feed","subtitle":"Job postings data feed: title, description, location, remote flag and the hiring company for each post.","share":{"name":"forager_ai_09cfd14b_078c_40ca_801c_6b166d7e23c7_samples_share","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1794247516121,"updated_at":1794265939379,"categories":["OTHER"],"listingType":"STANDARD","provider_id":"ba4dbcfb-6e61-49d1-92b5-b79a3480c00f"},"detail":{"description":"**Job postings data, delivered as a data feed.** Each post is linked to the company that posted it. Posts, companies and people share Forager IDs, so a post joins to its employer and to that employer's staff.\n\n**Columns in the job posts table**\n\n- `job_id`: Forager job post ID\n- `title`, `job_description`: Title and full description\n- `job_location`, `country`: Location and country\n- `is_remote`: Remote flag\n- `date_featured`: Date the post was featured\n- `company_id`, `company_name`, `company_domain`: Hiring company, joinable to company data\n- `company_industry`: Industry of the hiring company\n- `date_updated`: When Forager last updated the record\n\nThe full feed adds the source URL, an active flag that shows whether the post is still open, and salary details where the post states them.\n\n**Coverage**\n- Global\n- Cut by country, company, industry or title\n\n**Access on Databricks**\n- Request access on this listing. 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Buyers use Forager two ways: a REST API on self-serve credits, and licensed data feeds delivered to their warehouse. Bring a sample and we run it, so you can measure hit rate and connect rate on your own records."},"popularity":350},{"id":"f60b9db4-75b7-4ea9-9fe9-6a73baa9ef37","summary":{"name":"Mortgage Market Analytics","subtitle":"Mortgage, sales, and owner transfer data includes all relevant details for deeper understanding of the market","setting":{"visibility":"PUBLIC"},"created_at":1789679283645,"updated_at":1789679283645,"categories":["FINANCIAL"],"listingType":"PERSONALIZED","provider_id":"26040e80-2e39-4e01-8fea-0667064d9fce"},"detail":{"description":"Mortgage Market Analytics (MMA) delivers nationwide property mortgage, sales, and owner transfer data for up to 50 years of transaction history. It helps organizations analyze trends, benchmark performance, identify market opportunities, and better understand the relationships among loan officers, brokers, and lenders. \n\nWhether you're assessing market share, evaluating risk, or supporting recruiting and portfolio strategy, MMA provides the depth and accuracy needed to make confident decisions.\n\n**Market Analysis**\nComprehensive data on property mortgage transactions, offering deep insights into trends and patterns in the mortgage market. This information can help businesses to understand market dynamics, identify opportunities, and make informed strategic decisions.\n**Risk Analysis**\nBy providing detailed data on property mortgages, Mortgage Market Analytics allows businesses to assess the risk associated with particular properties or borrowers. This information can inform risk mitigation strategies and decision-making in areas such as lending, investment, and insurance.\n**Asset Valuation**\nAllows businesses to validate the financial status of a property by providing information on its mortgage history. This can be useful for lenders, investors, and property management companies seeking to confirm property details or estimate a property's market value.\n**Data Quality and Cleansing**\nMortgage Market Analytics can be used to enrich a company's existing property data, ensuring that businesses have the most accurate, up-to-date information on which to base their decisions.\n**Customer Acquisition**\nLenders and other financial institutions can use Mortgage Market Analytics to identify potential customers who might be in the market for a home equity line of credit (HELOC) or a refinance, aiding in customer acquisition efforts.\n**Regulatory Reporting**\nIn certain industries, companies may be required to report on their exposure to mortgage-related risks. Access to comprehensive mortgage data can support compliance with these reporting requirements.","terms_of_service":"https://www.cotality.com/","documentation_link":"https://www.cotality.com/products/mortgage-market-analytics?utm_campaign=412443580-EDSG-ALL-CloudMarketplace-Databricks-2026&utm_source=databricks&utm_medium=marketplace&utm_term=mortgagemarketanalytics","privacy_policy_link":"https://www.cotality.com/","assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"Cotality","description":"We illuminate data, insights, and workflows across the property ecosystem, analyzing the largest objective dataset in the U.S. for real-time insights in minutes, not months. Together, we’ll identify hidden risks and powerful opportunities for transforming your business and solving society’s greatest challenges."},"popularity":1149},{"id":"56e7b6d1-b84a-4c6c-921d-b33254f2a201","summary":{"name":"Geolocet | Latest Balkan Countries Geospatial and Demographic Dataset","subtitle":"The dataset contains demographics and geospatial data: population, age, gender, and population density.","setting":{"visibility":"PUBLIC"},"created_at":1715339834628,"updated_at":1752144748646,"categories":["DEMOGRAPHICS","GEOSPATIAL"],"listingType":"PERSONALIZED","provider_id":"5d3ddc17-a7e8-4794-a760-13587e1de2c6"},"detail":{"description":"## **Latest Balkan Countries Geospatial & Demographic Dataset**\n\nExplore the Balkans' municipal landscapes with our Enhanced **Balkan Countries Dataset**. This dataset not only covers comprehensive geographic and demographic data but also includes flags for membership in various regional groups like the EU, the Eurozone, and others, making it an indispensable tool for regional analysis. It is a valuable resource for anyone needing comprehensive geospatial data and demographic data on the Balkans.\n\n## **Key Statistics:**\n\n- **Number of Countries:** 19\n- **Number of Municipalities:** 67,701\n- **Total Population:** 340,701,326\n- **Number of Males:** 166,630,570\n- **Number of Females:** 174,070,756\n- **Number of Children Aged Below 15:** 49,485,733\n- **Number of People Aged 15-64:** 217,588,740\n- **Number of People Aged 65 and Above:** 73,626,853\n- **Area in Sq Km:** 2,837,493.51\n- **Population Density per Sq Km:** 120.07\n\n### **Product Features:**\n\n- **Latest Data Collection**: Featuring data from the year 2023, meticulously compiled and updated as of April 2024 to ensure the highest accuracy and relevance.\n- **Geographic Precision**: Each entry includes detailed polygon shapes of the municipalities in EPSG: 4326 projection, ideal for spatial analysis and mapping.\n- **Rich Demographic Insights**: Features demographic details for each municipality, including:\n - Total Population\n - Gender Breakdown (Males, Females)\n - Age Groups (0-14, 15-64, 64+)\n - Population Density\n- **Regional Membership Flags**: Easily identify municipalities by their regional affiliations with flags for:\n - European Union (EU)\n - Eurozone\n- **Regional Information**: Data is segmented by Country Code, Country Name, and Region, enhancing regional analysis capabilities.\n- **Multiple Data Formats**: Each dataset is available in both shapefile and CSV format, ensuring compatibility with major GIS software and other data handling tools.\n- **Sample Data**: Test the quality and compatibility of our data with a free sample available for download.\n- **Related offerings**: For targeted analysis, the following datasets are available for instant download including regions such as the [Europe](https://geolocet.com/products/europe-geodemographics-dataset-with-boundaries), [European Union](geolocet.com/products/european-union-geodemographics-dataset-with-boundaries), \n[Eurozone](https://geolocet.com/products/eurozone-geodemographics-dataset-with-boundaries), [Baltic countries](https://geolocet.com/products/baltic-countries-geodemographics-dataset-with-boundaries), [BeNeLux](https://geolocet.com/products/benelux-geodemographics-dataset-with-boundaries), and [Scandinavian Countries](https://geolocet.com/products/scandinavian-countries-geodemographics-dataset-with-boundaries).Each country within Europe is also available as a standalone dataset.\n\n\n### **Access & Usage:**\n\n- Instant download upon purchase for all datasets.\n- Free sample available for instant data evaluation.\n\nElevate your geographical and demographic analysis with the most thorough and accessible dataset available on the Balkan municipalities - Geolocet’s flagship product.\n\n## **Coverage Details:**\n- **[Albania](https://geolocet.com/products/Albania-geodemographics-dataset-with-boundaries)**: 2,762K people\n- **[Bosnia](https://geolocet.com/products/Bosnia-geodemographics-dataset-with-boundaries)**: 3,357K people\n- **[Bulgaria](https://geolocet.com/products/Bulgaria-geodemographics-dataset-with-boundaries)**: 6,448K people\n- **[Croatia](https://geolocet.com/products/Croatia-geodemographics-dataset-with-boundaries)**: 4,009K people\n- **[Greece](https://geolocet.com/products/Greece-geodemographics-dataset-with-boundaries)**: 10,370K people\n- **[Kosovo](https://geolocet.com/products/Kosovo-geodemographics-dataset-with-boundaries)**: 1,770K people\n- **[Macedonia](https://geolocet.com/products/Macedonia-geodemographics-dataset-with-boundaries)**: 1,837K people\n- **[Montenegro](https://geolocet.com/products/Montenegro-geodemographics-dataset-with-boundaries)**: 642K people\n- **[Romania](https://geolocet.com/products/Romania-geodemographics-dataset-with-boundaries)**: 19,049K people\n- **[Serbia](https://geolocet.com/products/Serbia-geodemographics-dataset-with-boundaries)**: 6,651K people\n- **[Turkey](https://geolocet.com/products/Turkey-geodemographics-dataset-with-boundaries)**: 85,372K people\n## **Customize Your Data Experience**\n\nNeed the data in another format? Your requirements drive our solutions. Reach out to us at **contact@geolocet.com** for alternative formats or if you have specific data needs that go beyond the downloadable list of Germany locations available on our platform.\n\n## **Explore further with our extensive retail datasets**\n\nIf you are exploring other brands and require additional data, we encourage you to **contact@geolocet.com**. Our comprehensive datasets and dedicated team stand ready to assist you.\n\n","terms_of_service":"https://geolocet.com/pages/terms-of-use","documentation_link":"","privacy_policy_link":"https://geolocet.com/pages/privacy-policy","geographical_coverage":"[\"Europe\"]","cost":"PAID","data_source":"","assets":["ASSET_TYPE_MEDIA"],"license":"https://geolocet.com/pages/terms-of-use"},"provider_summary":{"name":"Geolocet LTD","description":"Geolocet provides premium geospatial data for retail analysis, GIS, and market planning. 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[See the full details and get access on MDC](https://mozilladatacollective.com/datasets/cmrdfk4ao007po1070z5f4kem?utm_source=databricks&utm_medium=referral&utm_campaign=databricks_marketplace&utm_content=listing_cta).\n\nThis dataset provides 25 hours of natural, multi-speaker conversational dialogue speech in Gujarati specifically recorded within the high-stakes medical domain. The corpus has been meticulously transcribed into text to ensure premium phonetic quality and structural accuracy for training domain-specific automated speech systems.\n\nThis dataset captures real-world domain interactions in Gujarati, a prominent language spoken in India. The collection focuses on spontaneous conversational turn-taking, technical jargon handling, and colloquial linguistic shifts within the clinical setting.\n\nMulti-speaker dialogue setup containing contributions from distinct native speakers, domain professionals (e.g. healthcare assistants) and local consumers.\n\n**At a glance**\n\n- **Language:** Gujarati\n- **Task:** Automatic speech recognition (ASR)\n- **Format:** WAV, JSON\n\n**Statistics**\n\nTotal Audio Duration: 25 hours of multi-speaker conversational speech (highly audited audio-to-text pairs)\n\n**Creation process**\n\nThe dataset is strictly comprised of natural, spontaneous speech. Speakers were explicitly instructed not to follow a script. They were told to pick a topic they felt comfortable with and think: \"How do I talk about this in real life?\"\n\nTo help them sustain natural dialogue for the required 9 to 10 minutes per session, speakers were given a tip to map out 4 to 5 simple bullet points or practice chatting with their partner once before hitting record. They were told to keep it personal, share their own stories, what they have seen around them, and what they think or feel.\n\nThe recordings feature natural pairs of individuals who already have an established real-life relationship. Users were told to record the conversations with someone they know, like a friend, a cousin, or a neighbour. 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While minor background noise was acceptable, it could not interfere with clarity, and both individuals had to be equally audible using the phone’s standard built-in microphone.\n* Speakers were given an IMPORTANT safety instruction: they were strictly forbidden from mentioning their real names, their partner's names, where they work, or where they live. All identifying personal details were intentionally kept out of the conversations.\n* Every submission was meant to last 9 to 10 minutes, though some recordings are shorter. Participants were required to complete a minimum of 4 and a maximum of 8 conversation recordings per assigned domain.\n\n**Directory structure**\n\nThe root directory contains pairs of WAV and JSON files that share a prefix: each recording's .wav file has a matching .merged.json file.\n\n**File structure**\n\nThe JSON file has three parts: “audio”, “metadata” and “transcriptions”:\n\n* **audio**: The filename and file format information\n* **metadata**: The metadata of the speaker and the topic\n* **transcriptions**: The time aligned transcriptions, in segments of at most 20 seconds, each marked SPEAKER1 or SPEAKER2\n\nThe transcriptions also mark pauses, speech heard over background noise, and sounds that are not words, with tags such as PAUSE, noise and [inaudible]. 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In this way, the government has started something very good.*)\n* તો એ સમયે આ આંગણવાડીની જે બહેનો છે. એ દર અઠવાડિયે આવીને free માં ચેકઅપ કરી જતા હતા. એ ઉપરાંત એમને કઈ પણ એવું લાગે કે ભઈ આ વસ્તુ ખાસ સાચવવા જેવી છે, તો એવી સલાહ પણ આપવામાં આવતી હતી. (*So, at that time, the Anganwadi sisters used to come every week and do the check-up for free. 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Leveraging their strengths in AI and financial engineering, they provide fintech services and support financial institutions in acceleration of digital transformation and advancement of digital marketing. Learn more at: https://milize.co.jp/\n"},"popularity":1048},{"id":"6cc53e3f-40b2-4938-958a-034bf4c871b7","summary":{"name":"Bank Loan Modelling Dataset","subtitle":"This dataset supports a model that will help identify banking customers with a higher probability of purchasing a loan.","share":{"name":"dbacademy_dataset_bank_loan_modelling","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1731643325875,"updated_at":1731643325875,"categories":["EDUCATION"],"listingType":"STANDARD","provider_id":"ea1e69ff-0127-4c94-bf39-e841fe1d19d2"},"detail":{"description":"Thera Bank has a growing customer base. The majority of these customers are liability customers (depositors). The number of customers who are also borrowers (asset customers) is quite small, and the bank is interested in expanding this base to bring in more loan business and in the process, earn more through the interest on loans. In particular, the management wants to explore ways of converting its liability customers to personal loan customers (while retaining them as depositors). A campaign that the bank ran last year for liability customers showed a healthy conversion rate of over 9% success. This has encouraged the retail marketing department to devise campaigns to better target marketing to increase the success ratio with a minimal budget.\n\nThe department wants to build a model that will help them identify the potential customers who have a higher probability of purchasing the loan. This will increase the success ratio while at the same time reduce the cost of the campaign.\n\nYou can find more details about the \"Bank Loan Modeling\" on this [Kaggle page](https://www.kaggle.com/datasets/itsmesunil/bank-loan-modelling).\n\nThe \"Bank Loan Modeling Dataset\" is distributed under the [CC0: Public Domain](https://www.kaggle.com/datasets/itsmesunil/bank-loan-modelling). Please review the license terms before using the dataset.","terms_of_service":"https://cms.databricks.com/sites/default/files/2023-01/marketplace-consumer-terms_0.pdf","documentation_link":"https://www.kaggle.com/datasets/itsmesunil/bank-loan-modelling","privacy_policy_link":"https://www.databricks.com/legal/privacynotice","data_source":"https://www.kaggle.com/datasets/itsmesunil/bank-loan-modelling","assets":["ASSET_TYPE_MEDIA"],"license":"https://creativecommons.org/publicdomain/zero/1.0/"},"provider_summary":{"name":"Databricks","description":"As the world’s first lakehouse platform in the cloud, Databricks combines the best elements of data lakes and data warehouses to deliver the reliability, strong governance, and performance of data warehouses with the openness, flexibility, and machine learning support of data lakes. We are a cross-cloud platform that works across AWS, Azure, and Google.\nThis unified approach simplifies your modern data stack by eliminating the data silos that traditionally separate and complicate data engineering, analytics, BI, data science and machine learning. It’s built on open source and open standards to maximize flexibility. And, its common approach to data management, security and governance helps companies operate more efficiently and innovate faster."},"popularity":2045},{"id":"40d787b2-6519-4370-a9eb-a64270248e16","summary":{"name":"MBI Sociodemographic Data for Denmark – Sample","subtitle":"MBI's fresh sociodemographic data for well-founded decisions in Denmark. 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All data comes from a single source, ensuring **consistency and comparability** across markets.\n\nThe Sociodemographics-Bundle Basic for Denmark includes the following **variables**:\n- [population](https://www.mbi-geodata.com/en/sociodemographic-data/population/)\n- [gender](https://www.mbi-geodata.com/en/sociodemographic-data/sex-gender/)\n- [age structure](https://www.mbi-geodata.com/en/sociodemographic-data/agebands/) (15-year age bands)\n- household figures ([number of households](https://www.mbi-geodata.com/en/sociodemographic-data/households/) and [households by type](https://www.mbi-geodata.com/en/sociodemographic-data/households-by-type/))\n\nOur sociodemographic data is offered for each country on different **levels**:\n- **Postal Code Level:** This fine-grained level of detail makes the data suitable for a wide range of applications – for example, enriching customer databases, which are often organized by postal code.\n- **Administrative Level:** Covers both larger administrative areas (e.g., regions, states, provinces) as well as finer subdivisions such as districts, counties, or municipalities.\n- **Micro Level**: Subdivisions of postal code or administrative boundaries, such as census areas or street segments. This is the most granular level for local analysis.\n\n**Please note:** The dataset available for free download is a **sample dataset** covering a limited number of geographic areas. If you wish to access the full dataset (covering all areas) or if you require additional variables or geographic levels, please contact us. We will be happy to help you find a product that fits your needs.\n \n**Additional Insights**: https://www.mbi-geodata.com/en/sociodemographic-data/\n\n**Data Structure**:\nAGE_F0014: Population (Nighttime) by age: 0 - 14 years, female\nAGE_F1529: Population (Nighttime) by age: 15 - 29 years, female\nAGE_F3044: Population (Nighttime) by age: 30 - 44 years, female\nAGE_F4559: Population (Nighttime) by age: 45 - 59 years, female\nAGE_F60PL: Population (Nighttime) by age: 60 years and above, female\nAGE_M0014: Population (Nighttime) by age: 0 - 14 years, male\nAGE_M1529: Population (Nighttime) by age: 15 - 29 years, male\nAGE_M3044: Population (Nighttime) by age: 30 - 44 years, male\nAGE_M4559: Population (Nighttime) by age: 45 - 59 years, male\nAGE_M60PL: Population (Nighttime) by age: 60 years and above, male\nAGE_T0014: Population (Nighttime) by age: 0 - 14 years, total\nAGE_T1529: Population (Nighttime) by age: 15 - 29 years, male\nAGE_T3044: Population (Nighttime) by age: 30 - 44 years, total\nAGE_T4559: Population (Nighttime) by age: 45 - 59 years, total\nAGE_T60PL: Population (Nighttime) by age: 60 years and above, total\nCTRYCODE: Two digit country code by ISO 3166\nFEMALE: Population (Nighttime): females\nHH_SIZE: Average household size\nHH_T: Households: total number\nMALE: Population (Nighttime): males\nNAME: Name of administrative area / Name of the most populous administrative area / Name of the respective micro-area or the next higher level\nADMINCODE / POSTCODE / MICROCODE Administrative code / Postal code / Micro-code\nP_PRM: Population (Nighttime) year average: per mill of country\nP_T: Population (Nighttime) year average: total number","terms_of_service":"https://service-mbi-geodata.com/s/HNp7Z2tws4WDqWd","documentation_link":"https://www.mbi-geodata.com/en/mbi-supplier-manufacturer-worldwide-geodata/europe/denmark-maps/","privacy_policy_link":"https://www.mbi-geodata.com/en/data-protection/","geographical_coverage":"[\"Europe\"]","cost":"FREE","update_frequency":{"interval":1,"unit":"YEARLY"},"collection_granularity":{"interval":1,"unit":"YEARLY"},"collection_date_start":1704109702634,"collection_date_end":1735645711650,"data_source":"MBI","assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://service-mbi-geodata.com/s/Y66rgSyNCktPbcL"},"provider_summary":{"name":"Michael Bauer International GmbH – MBI Geodata","description":"For over 15 years, Michael Bauer International (MBI) has been a leading provider of globally consistent and comparable geographic data, including more than 3 million postcode areas and other detailed datasets like sociodemographics and purchasing power. 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Built from a multi-signal modeling framework and powered by 95% proprietary or exclusive data sources, this dataset enables investors, analysts, and strategy teams to monitor underlying business momentum between earnings cycles, identify inflection points earlier, and benchmark companies against peers and sectors without relying on lagging financial reports.\n\nEach KPI estimate is constructed from multiple independent data signals, selected based on their relevance to the specific business being tracked rather than applied uniformly across sectors. Models are validated through historical backtesting, cross-source consistency checks, and calibration against available reference benchmarks. There is no analyst overlay and no subjective adjustment. Every estimate is tagged with source lineage and backtest performance, giving compliance teams full transparency into the methodology. The sum of daily KPI estimates across a quarter closely correlates with reported company financials, making the product directly applicable to earnings modeling workflows.","terms_of_service":"https://oxford-dp.com/privacy-and-policy","contact_email":"support@datarade.ai","privacy_policy_link":"https://oxford-dp.com/privacy-and-policy","assets":["ASSET_TYPE_DATA_TABLE"]},"provider_summary":{"name":"Oxford Data Plan","description":"Oxford Data Plan (ODP) is an alternative data provider founded in 2023 and headquartered in London. ODP was built by a former buy-side portfolio manager who saw a clear gap in the market: investment professionals were relying on stale, delayed, and commoditized data that no longer generated alpha. ODP was purpose-built to solve that problem. Today, the company serves hedge funds, asset managers, and quantitative teams across the globe, delivering proprietary datasets and daily KPI estimates designed specifically for the buy-side workflow."},"popularity":692},{"id":"933e758f-c1e1-4e6c-a60e-3c60a1a64603","summary":{"name":"CMBS Property Collateral","subtitle":"Property-level financials and valuations for 357 CMBS conduit deals from SEC EDGAR ABS-EE filings.","share":{"name":"cmdrvl_cmbs_properties","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1774481866795,"updated_at":1774483971511,"categories":["FINANCIAL"],"listingType":"PERSONALIZED","provider_id":"8223b0dd-053b-4451-854e-93fb4aa588ac"},"detail":{"description":"**Overview**\n\nNOI, NCF, occupancy rates, appraised values, property types, and full addresses for 39,671 properties securing CMBS loans across 357 conduit deals. Each row links to its parent loan and deal via ASSETNUMBER and CIK, enabling collateral-level analysis from portfolio screening down to individual asset review.\n\n**Key Fields**\n\n- PROPERTYNAME, PROPERTYADDRESS, PROPERTYCITY, PROPERTYSTATE, PROPERTYZIP — full location\n- PROPERTYTYPECODE — property type (OF, RT, MF, IN, LO, HC, MU, SS, etc.)\n- NETRENTABLESQUAREFEETNUMBER, UNITSBEDSROOMSNUMBER — size metrics\n- VALUATIONSECURITIZATIONAMOUNT, MOSTRECENTVALUATIONAMOUNT — appraisal at securitization and current\n- MOSTRECENTPHYSICALOCCUPANCYPERCENTAGE — current occupancy\n- MOSTRECENTNETOPERATINGINCOMEAMOUNT, MOSTRECENTNETCASHFLOWAMOUNT — NOI and NCF\n- MOSTRECENTDEBTSERVICECOVERAGENETOPERATINGINCOMEPERCENTAGE — DSCR\n- LARGESTTENANT, SECONDLARGESTTENANT, THIRDLARGESTTENANT — top 3 tenants with square footage and lease expiration\n- LATITUDE, LONGITUDE — geocoded coordinates\n\n**Use Cases**\n\n- Collateral monitoring: track occupancy and NOI trends at the property level\n- Geographic analysis: map CRE exposure by MSA, state, or zip code using embedded lat/lon\n- Valuation comps: compare securitization vs. current appraisals across property types\n- Tenant concentration: identify single-tenant risk using top-3 tenant fields\n\n**Coverage**\n\n39,671 properties across 357 CMBS conduit deals. Monthly updates from November 2016 to present.\n\n**Pricing**\n\n$1,750/month. Contact cairn@cmdrvl.com for enterprise licensing or to request a trial.\n\n**Related Listings**\n\nPairs with **CMBS Loan Data** for debt-level context, **CMBS Servicer Commentary** for distressed asset narratives, and **CMBS Deal Counterparties** for sponsor identification. **CMBS Tenant Names** is available free.","terms_of_service":"https://cmdrvl.com/terms/","documentation_link":"https://dealcharts.org/capitalmarkets/abs/cmbs/","privacy_policy_link":"https://cmdrvl.com/privacy/","cost":"PAID","update_frequency":{"interval":1,"unit":"DAILY"},"collection_date_start":1480512030642,"data_source":"SEC.gov EDGAR ABS-EE Filings","assets":["ASSET_TYPE_DATA_TABLE"]},"provider_summary":{"name":"CMD+RVL","description":"We believe that every person should have access to the resources necessary to improve their mastery of data analytics. 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For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n **Included Datasets**\n \n\n **Data Engineering Overview**\n **We deliver high-quality data**\n - Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n - Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n - Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated\n - Data Updates\n - Data updates support replace-on-update: outdated foreign keys are deprecated, not deleted\n\n **Our data is curated and enriched by domain experts** \n\n Each dataset is manually curated by our team of doctors, pharmacists, public health & medical billing experts:\n - Field names, descriptions, and normalized values are chosen by people who actually understand their meaning\n - Healthcare & life science experts add categories, search keywords, descriptions and more to each dataset\n - Both manual and automated data enrichment supported for clinical codes, providers, drugs, and geo-locations\n - The data is always kept up to date – even when the source requires manual effort to get updates\n - Support for data subscribers is provided directly by the domain experts who curated the data sets\n - Every data source’s license is manually verified to allow for royalty-free commercial use and redistribution.\n\n **Need Help?**\n If you have questions about our products, contact us at [info@johnsnowlabs.com](mailto:info@johnsnowlabs.com). \n ","terms_of_service":"https://www.johnsnowlabs.com/healthcare-data-library-eula/","documentation_link":"https://johnsnowlabs.com/marketplace/detailed-drug-facts-label-structure-by-ndc-data-package","contact_email":"support@johnsnowlabs.com","privacy_policy_link":"https://www.johnsnowlabs.com/privacy-policy/","assets":["ASSET_TYPE_DATA_TABLE"]},"provider_summary":{"name":"John Snow Labs","description":"John Snow Labs, an AI and NLP for healthcare company, provides state-of-the-art software, models, and data to help healthcare and life science organizations build, deploy, and operate AI projects."},"popularity":52},{"id":"f56d70bf-85cd-4639-bbf0-f960e9c1fb49","summary":{"name":"Hypertrophic Cardiomyopathy (HCM) Curated Dataset ","subtitle":"TriNetX Curated HCM is a curated dataset with tables filtered for covariates most relevant to the HCM patient population","setting":{"visibility":"PUBLIC"},"created_at":1710873123968,"updated_at":1710873123968,"categories":["HEALTH","SCIENCE_AND_RESEARCH","DEMOGRAPHICS"],"listingType":"PERSONALIZED","provider_id":"b2831817-f203-4309-90a1-7e7827c3a932"},"detail":{"description":"**Overview:** \n\n- Hypertrophic cardiomyopathy (HCM) affects an estimated 1 in 500 people and possibly as high as 1 in 200. 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The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [Healthcare Providers Deactivated National Provider Identifier](https://www.johnsnowlabs.com/marketplace/healthcare-providers-deactivated-national-provider-identifier)\n\t- This dataset contains the NPI and deactivation date for the deactivated health care providers. \nThe NPI is a unique identification number for covered health care providers. This dataset will assist users who are only interested in the deactivation information.\n- [Healthcare Providers National Provider Identifier Data](https://www.johnsnowlabs.com/marketplace/healthcare-providers-national-provider-identifier-data)\n\t- This dataset contains the Health Care Providers data. 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This meticulously compiled dataset, sourced from official statistical sources, including Republic of Slovenia Statistical Office, offers a detailed insight into the population landscape of Slovenia at the municipality (okres) level. \n\nIf you require data at a less granular level, such as an Uber grid or square grid, please don't hesitate to get in touch with us at *contact@geolocet.com*. We understand that different projects may have varying data needs, and we are here to tailor our data to match your specific requirements.\n\n ## **Key Features:**\n---\n* **Population Data Slovenia:** Gain access to the latest and most accurate population statistics for Slovenia, meticulously organized by municipality (okres).\n\n* **Demographic Insights:** Dive deep into Slovenia demographic landscape with data on 149 attributes, including: \n* Total population,\n* Gender,\n* Age bands,\n* Gender x Age bands 5-years,\n* Gender x Age bands - major bands,\n* Nationality,\n* Education,\n* Gender X Education,\n* Employment status,\n* Gender X Employment status,\n* Marital status,\n* Gender X Marital status,\n* Total number of households,\n* Population in households,\n* Household composition,\n* Total number of families,\n* Number of children,\n* Families composition,\n* Household size, \nall available at the municipal (okres) level.\n\n* **Geodemographic Insights:** Unlock geodemographic data Slovenia-wide to inform location-based decision-making, marketing strategies, and policy formulation.\n\n* **Ready to use:** Our dataset is currently available in CSV format, providing ease of use and compatibility with a wide range of data analysis tools. However, if you prefer a different format, our team is readily available to assist you in obtaining the data in your desired format.\n---\nAdditionally, for those looking to enrich their analysis or project, we offer:\n* **[Administrative Boundaries](https://geolocet.com/products/slovenia-with-marine-border-all-available-administrative-boundaries):** If you require geographical boundaries (polygons) for the municipalities of Slovenia, consider exploring our Admin Boundaries product, which seamlessly integrates with this dataset.\n\n* **[Point of Interest (PoI) Data](https://geolocet.com/pages/aggregated-points-of-interest-poi-data):** If you're interested in Points of Interest data, please don't hesitate to contact us for customized datasets to enhance your geographical analysis.\n\n* **[Data Customization](https://geolocet.com/pages/data-services-by-geolocet):** We understand that your project may have specific requirements. Feel free to get in touch with us if you need any modifications or specific data formats such as Excel, JSON, GeoJSON, or Shapefile. We are here to tailor the data to your needs.\n\nIf you're looking for data for Slovenia, [look here](https://geolocet.com/pages/slovenia) for off-the-shelf or tailor-made datasets to suit your specific requirements.\n\nMake informed decisions, conduct precise research, and unlock the potential of Slovenia demographic landscape with our comprehensive dataset. Trust in the accuracy and reliability of our official statistical data to drive your projects forward.\nContact us at *contact@geolocet.com* today to access this invaluable resource and explore Slovenia census data like never before.\n\n[Geolocet LTD](https://geolocet.com)\n","terms_of_service":"https://geolocet.com/pages/terms-of-use","documentation_link":"","privacy_policy_link":"https://geolocet.com/pages/privacy-policy","geographical_coverage":"[\"Europe\"]","cost":"PAID","data_source":"","assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"Geolocet LTD","description":"Geolocet provides premium geospatial data for retail analysis, GIS, and market planning. 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Aligning these datasets and maintaining a consistent workforce timeline is difficult, especially when each source uses different time grains, rules, and data structures.\n**2.Fragmented Workforce Systems**\nKey WFM processes—Forecasting, Scheduling, QA, etc. This fragmentation makes it hard to measure true staffing efficiency, compare planned vs. actual performance, track adherence patterns, or identify shrinkage drivers across the operation.\n\n**Architecture Overview – Medallion + Agentic Orchestration**\n- Bronze (L0): Ingests raw WFM data from Oracle\n- Silver (L1–L2): AI agents generate standardized SQL models for Workforce entities (Forecast, Schedule, Adherence, Shrinkage, and Intraday Performance).\nThey automatically enforce incremental logic, align time-grains, unify agent identifiers,\n- Gold (L3): A conversational agent helps WFM teams define business-ready analytics models—such as Forecast Accuracy, Adherence Summary, Scheduling Efficiency, Shrinkage Breakdown, or Intraday Staffing Gap Tables—simply by describing them in natural language.\n\n**Pipeline Flow**\n- Metadata Enricher Agent (Bronze): Automatically generates semantic descriptions for WFM entities—such as schedules, forecasts, adherence logs, agent states, intraday updates, and shrinkage categories.\nHITL reviewers validate mappings like “Scheduled_Start_Time,” “Actual_Login,” “Adherence_Flag,” “Forecasted_Volume,” ensuring clean, standardized metadata across disparate WFM systems.\n- Silver SQL Generator Agent: Creates standardized L1 views for key WFM objects (Forecast, Schedule, Adherence, Intraday Performance, Shrinkage, Exception Codes).\n- Conversational Gold Agent:Enables WFM teams to create advanced Workforce Analytics tables simply by describing them “Create an adherence summary by interval and agent” or “Build forecast vs actual staffing variance table”\n- DLT Execution: A single Databricks Delta Live Tables pipeline constructs the complete Contact Centre WFM lifecycle, seamlessly linking L1, L2, and L3 layers while providing full data lineage, change tracking, and auditability.\n\n**CC Workforce Management (WFM) Functional Insights (Gold Layer)**\nOnce the agentic pipeline is deployed, the accelerator delivers ready-to-use WFM insights:\n**1. 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Highlights overtime trends and shift deviations to optimize workforce allocation and compliance.\n\n**Dashboard Widgets**\n**Workforce Operations**\n- **Forecasted vs. Actual Volume Trend** – Compares forecasted interactions against actual call volumes at daily or interval granularity to highlight forecasting accuracy and operational variability.\n- **Staffing Gap (Required vs. Scheduled vs. Actual)**– Shows gaps between required staffing, scheduled agents, and actual logged-in workforce to identify overstaffing or understaffing windows.\n- **Interval-Level Forecast Accuracy** – Measures forecast precision across intervals, exposing patterns of under-forecasting or over-forecasting by skill, queue, or channel.\n- **Service Level Impact from Understaffing** – Quantifies how staffing shortages affect SLA performance, highlighting intervals where service levels dropped due to insufficient agent availability.\n\n**Schedule & Adherence**\n- **Agent Adherence Summary** – Summarizes adherence performance by comparing scheduled activities against actual agent behavior across the day or interval.\n- **Conformance Trend (Daily / Interval)** – Tracks how closely agents start and end their shifts relative to scheduled times, revealing lateness, early logoffs, and missed intervals.\n- **Login–Logout Variance (Late/Early)** – Highlights deviations in login and logout timeliness, helping supervisors identify punctuality issues or shift discipline trends.\n- **Exception & Break Compliance Dashboard** – Visualizes agent exceptions, break adherence, and compliance to scheduled activities to ensure workforce policy alignment.\n\n**Workforce Performance**\n- **Productive vs. Non-Productive Hours** – Breaks down agent time into productive (on-call, ACW) and non-productive (breaks, training, idle) hours, enabling performance optimization.\n- **Shrinkage Breakdown (Planned vs. Unplanned)** – Displays planned shrinkage (breaks, training, meetings) vs. unplanned shrinkage (absenteeism, system issues) to measure true staffing efficiency.\n- **Overtime & Additional Hours Trend** – Tracks overtime consumption and extra-hour usage to monitor cost impact and workload distribution across teams.\n- **Team-Level Efficiency (Scheduled Hours vs. Actual Work)** – Compares scheduled hours with actual productive work to identify high-efficiency teams and areas requiring workload balancing.\n\n**Key Capabilities**\n- Autogenerates semantic descriptions for schedules, adherence states, exceptions, and staffing intervals.\n- AI-driven reconciliation of forecast, scheduled, and actual data to compute adherence, conformance, and staffing gaps.\n- DLT-ready SQL pipelines for WFM Schedule Hub, Adherence Summary, Forecast Accuracy, and Staffing Performance tables.\n- Conversational Analytics Creation (chat-based Gold tables) for custom WFM KPIs like shrinkage, interval forecasting, or team conformance.\n- Automated detection and correction of broken joins, timestamp misalignments, and metric calculation errors.\n- CDC Support for near-real-time schedule updates, adherence feeds, and intraday performance monitoring.\n\n**Marketplace Deliverables**\n- **Production-Ready Notebooks**: Complete AELT pipeline notebooks for Bronze (Ingestion of WFM datasets), Silver (Schedule/Adherence/Forecast transformations), and Gold (Workforce KPIs).\n- **Config Templates**: JSON configurations for WFM objects (schedules, skills, shifts, exceptions, adherence logs) with AI-generated metadata enrichment.\n- **DLT SQL Files**: Automatically generated and validated SQL for incremental tables such as WFM Schedule Hub, Adherence Summary, Forecast Accuracy, and Interval Staffing views.\n- **CDC & Data Quality Frameworks**: Prebuilt rules for detecting missing intervals, invalid schedule states, outdated forecasts, and adherence inconsistencies.\n- **Documentation & Implementation Guide**: Detailed setup guide for Databricks, mapping WFM objects, and deploying the end-to-end AELT workforce analytics pipeline.\n\n**Why Choose Dataplatr’s CC Workforce Management (WFM) Agentic ELT Accelerator**\n- **Agentic Automation** – Removes manual schedule parsing, interval alignment, and adherence calculation effort.\n- **Rapid Time-to-Value** – Workforce dashboards (adherence, staffing gaps, forecast accuracy) delivered in hours, not weeks.\n- **Deep WFM Domain Logic** – Prebuilt formulas for adherence, conformance, shrinkage, staffing efficiency, and forecast accuracy.\n- **Enterprise Governance** – Full lineage, auditing, and access control through Unity Catalog.\n- **Databricks-Native** – Optimized for Delta Live Tables, Auto Loader, and the Lakehouse architecture.\n\n**Transform CC Workforce Management (WFM) Analytics with Agentic ELT**\nDeliver a unified Workforce Management Hub, real-time staffing insights, and AI-powered forecasting and adherence intelligence—fully automated on Databricks.\n\nNote: By clicking '**Get Access**', you agree to our **Terms of Service**. 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The models provided in this listing are\n\n- [llama_v3_2_3b](https://huggingface.co/meta-llama/Llama-3.2-3B)\n- [llama_v3_2_3b_instruct](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct)\n- [llama_v3_2_1b_instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct)\n- [llama_v3_2_1b](https://huggingface.co/meta-llama/Llama-3.2-1B)\n\nThe models are packaged using MLflow’s transformers flavor.\n- [llama_v3_2_3b_instruct](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct), [llama_v3_2_1b_instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct), [llama_v3_2_1b](https://huggingface.co/meta-llama/Llama-3.2-1B): fine-tuned for dialogue-based use cases.\n- [llama_v3_2_3b](https://huggingface.co/meta-llama/Llama-3.2-3B): pretrained generative text model that can be used for further fine-tuning on specific applications.\n\nLlama_v3_2 models are licensed under the [Meta Llama 3.2 Community License, Copyright © Meta Platforms, Inc. All Rights Reserved](https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/LICENSE). By installing this listing, you acknowledge and agree to the license.\n\n**Use cases**\n\n[llama_v3_2_3b_instruct](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct), [llama_v3_2_1b_instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct), [llama_v3_2_1b](https://huggingface.co/meta-llama/Llama-3.2-1B):\n- Generating responses to text-based instructions\n- Chatbots and conversational AI systems\n\n[llama_v3_2_3b](https://huggingface.co/meta-llama/Llama-3.2-3B):\n- Fine-tuning on specific applications.\n\n\n**Product details**\n\nThe pre-trained model can be used for further fine-tuning on specific applications. The fine-tuned model in this listing can be deployed directly to Databricks Model Serving for immediate use, or loaded for fine-tuning or batch inference use cases. 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Commercial distribution, compliance review, and validation are managed by AIDC Data LLC..\nAcknowledgements\nCompiled and enriched by AIDC Data LLC.\n\n**Use Cases**\nThis dataset enables high-value analytics and ML workflows on Databricks:\n•\tMacro-Factor Sensitivity Modeling: Build multi-factor models using rolling correlations to Oil, Copper, and U.S. Treasury yields.\n•\tIndustrial Sector Risk Assessment: Quantify volatility, liquidity, and supply-chain exposure across the Top 50 North American–listed global manufacturers.\n•\tPredictive Price Forecasting: Train machine learning models using macro-aligned technical signals and PIT-compliant time-series data.\n•\tSupply Chain Stress Testing: Evaluate industrial resilience under macroeconomic shocks using integrated factor overlays.\n•\tQuant Research and Alpha Discovery: Explore relationships between macro shifts and asset-level trend, momentum, and volatility signals.\n\n**Column Dictionary\n**Below are the 27 fields included in the dataset:\nCore Asset Identifiers\n•\tTrade_Date (date): Trading session date; 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It is designed for accuracy and consistency, it includes key identifiers that enhance business location data, making it valuable across various sectors, including business intelligence, enterprise applications, and consumer analytics.\n\n\nThis sample data set covers the San Francisco, CA area. This represents a subset of the full product’s geographic extent. The full commercial offering features geographic coverage of the entire United States. 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The territorial vintages of the worldwide area boundaries are compatible with our globally consistent and comparable [socio-demographic](https://www.mbi-geodata.com/en/sociodemographic-data/) and [economic](https://www.mbi-geodata.com/en/purchasing-power-overview/) data.\nOur area boundaries for Albania are offered at the following levels:\n\n- **Postal Code Level** (Postcode 5-digit): This fine-grained level of detail makes the data suitable for a wide range of applications – for example, enriching customer databases, which are often organized by postal code.\n- **Administrative Level** (Gminy/Miasta): Covers both larger administrative areas (e.g., regions, states, provinces) as well as finer subdivisions such as districts, counties, or municipalities.\n- **Micro Level** (Census Enumeration Areas): Subdivisions of postal code or administrative boundaries, such as census areas or street segments. 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MBI is part of the Michael Bauer Group, alongside Michael Bauer Research GmbH and Michael Bauer Micromarketing GmbH."},"popularity":385},{"id":"fafd0253-e8d8-4f82-9878-651c243f382e","summary":{"name":"Historical and Forecast LNG Prices","subtitle":"The Historical and Forecast LNG Prices dataset provides info on forecast and historical price data.","setting":{"visibility":"PUBLIC"},"created_at":1731581968541,"updated_at":1733847594632,"categories":["ENERGY","FINANCIAL"],"listingType":"PERSONALIZED","provider_id":"eff4b18d-fe1b-4884-9591-eab9d16d2e2a"},"detail":{"description":"**Overview**\n\nThe Historical and Forecast LNG Prices dataset provides comprehensive information about forecast annual and monthly prices, as well as historical bilateral customs and monthly prices.\n\nLeverage this dataset to retrieve all prices available on monthly and annual price endpoints, as well as historical monthly prices. Utilize Historical and Forecast LNG Prices for BAFA annual price forecasting and monthly price forecasting based on geography.\n\nThis dataset includes:\n- Price forecasts for select gas and LNG price markers\n- Historical bilateral prices of select markets and their LNG supply sources\n- Historical custom prices for Spain and Asia spot markets\n\n**Use cases**\n- Fundamental Analysis - Utilize historical data to analyze past market behaviors and predict future trends.\n- Pricing Analysis - Leverage annual and monthly price forecasts to make informed, forward-looking decisions.\n- Risk Analysis - Improve trading strategies, investment decisions, and risk management with access to forecast for selected gas and LNG price markers.\n\n**Product details**\n\nSample Fields:\nA ) FORECAST_ANNUAL_PRICES\nB ) HISTORICAL_MONTHLY_PRICES\n\nSample Fields:\nA ) FORECAST_ANNUAL_PRICES\nYEAR\nPRICE_MARKER_NAME\nPRICE_MARKER_UOM\nPRICE_MARKER_CURRENCY\nPRICE_MARKER\nMODIFIED_DATE\nB) HISTORICAL_BILATERAL_CUSTOMS_PRICES\nMONTH\nIMPORT_MARKET\nSUPPLY_SOURCE\nUOM\nCURRENCY\nPRICE\nMODIFIED_DATE\n\nView Descriptions:\n- FORECAST_ANNUAL_PRICES - Price forecasts for select gas and LNG markers through 2050, presented in US dollars per million British thermal units\n- FORECAST_MONTHLY_PRICES - Price forecasts for select gas and LNG markers over the next 2-3 years, expressed in US dollars per million British thermal units\n- HISTORICAL_MONTHLY_PRICES - Historical prices for various gas and LNG markers, presented as monthly figures in US dollars per million British thermal units\n- HISTORICAL_BILATERAL_CUSTOMS_PRICES - Historical bilateral prices of select markets and their LNG supply sources, primarily sourced from customs reporting agencies","terms_of_service":"https://www.spglobal.com/en/privacy/privacy-policy-english","documentation_link":"https://www.marketplace.spglobal.com/en/datasets/historical-forecast-lng-prices-(1716217256)","privacy_policy_link":"https://www.spglobal.com/en/privacy/privacy-policy-english","geographical_coverage":"[\"Global\"]","cost":"PAID","update_frequency":{"interval":1,"unit":"DAILY"},"collection_date_start":1420109878832,"assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"S&P Global Energy","description":"At S&P Energy, our complete view of global energy and commodities markets enables our customers to make decisions with conviction and create long-term, sustainable value. 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This dataset supports optimizing LNG logistics and trading operations. Gain insights into supply changes, route efficiency, and market trends through data on diversion, idling, and bunkering events.\n\nTraders and analysts can use this dataset to identify trends and make informed decisions. Data scientists can build predictive models, detect anomalies, and integrate cargo data for deeper insights and visualizations. Research and strategic planning teams can optimize logistics, analyze scenarios, and support sustainable practices to improve efficiency and strengthen market positioning.\n\nThis dataset includes:\n- Event Bunkering: Timing data for laden and ballast LNG vessels during bunkering events\n- Event Diversion: Location and timing data for LNG diversions, including destinations before and after the diversion\n- Event Idling: Location and timing data of LNG vessels idling in specific areas, used as a proxy for floating storage\n- Event Journey Point: Location and timing data of LNG vessels passing through key journey points\n- Event Trade Route: Location and timing data of LNG vessels traversing key trade routes, including waiting times at canals\n\n**Use cases**\nAdd use cases for the listing here.\n- Use case 1\n- Use case 2\n\n**Product details**\n\nSample Fields:\nA ) EVENT_BUNKERING\nB ) EVENT_IDLING\n\nSample Fields:\nA ) EVENT_BUNKERING\n- ID\n- TRIP_ID\n- EVENT_TYPE\n- BUNKERING_START_DATE\n- LOCATION_TYPE\n- BUNKERING_LOCATION\n- CREATED_DATE\n- MODIFIED_DATE\n- LADEN_BALLAST\n- BUNKERING_PORT\n- BUNKERING_MARKET\n\nB) EVENT_IDLING\n- ID\n- TRIP_ID\n- EVENT_TYPE\n- IDLING_START_DATE\n- LOCATION_TYPE\n- IDLING_LOCATION\n- IDLING_FINISH_DATE\n- CREATED_DATE\n- MODIFIED_DATE\n- LADEN_BALLAST\n\nView Descriptions:\n- EVENT_BUNKERING - Timing data for laden and ballast LNG vessels during bunkering events\n- EVENT_TRADE_ROUTE - Location and timing data of LNG vessels traversing key trade routes, including waiting times at canals\n- EVENT_JOURNEY_POINT - Location and timing data of LNG vessels passing through key journey points\n- EVENT_IDLING - Data on the location and timing of LNG vessels idling in specific areas, used as a proxy for floating storage\n- EVENT_DIVERSION - Location and timing data for LNG diversions, including destinations before and after the diversion","terms_of_service":"https://www.spglobal.com/en/privacy/privacy-policy-english","documentation_link":"https://www.marketplace.spglobal.com/en/datasets/lng-cargo-premium-(1733240113)","privacy_policy_link":"https://www.spglobal.com/en/privacy/privacy-policy-english","cost":"PAID","update_frequency":{"interval":1,"unit":"DAILY"},"collection_granularity":{"interval":1,"unit":"DAILY"},"collection_date_start":1704110603873,"assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"S&P Global Energy","description":"At S&P Energy, our complete view of global energy and commodities markets enables our customers to make decisions with conviction and create long-term, sustainable value. 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Free trial data.","share":{"name":"share_boundaries_mbi_ie_r_2025","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1785145199551,"updated_at":1785145199551,"categories":["ADVERTISING_AND_MARKETING","CLIMATE_AND_ENVIRONMENT","ENERGY","GEOSPATIAL","MEDIA","OTHER","PUBLIC_SECTOR","RETAIL","TRANSPORTATION_AND_LOGISTICS","TRAVEL_AND_TOURISM"],"listingType":"STANDARD","provider_id":"aa09abd6-ff7f-4452-803d-b8c43b4c07cb"},"detail":{"description":"MBI'S GLOBAL AREA BOUNDARIES COVER MOST COUNTRIES WORLDWIDE AND **CAN BE LICENSED FOR EACH COUNTRY INDIVIDUALLY** AND IN BUNDLES.\n[MBI](https://www.mbi-geodata.com/en/) digital boundaries are available for virtually all countries worldwide.* Created by our experts according to consistent global standards, the polygons are so precise and harmonized that they align seamlessly across borders. The territorial vintages of the worldwide area boundaries are compatible with our globally consistent and comparable [socio-demographic](https://www.mbi-geodata.com/en/sociodemographic-data/) and [economic](https://www.mbi-geodata.com/en/purchasing-power-overview/) data.\nOur area boundaries for Ireland are offered at the following levels:\n\n- **Postal Code Level** (Postcode 3-digit): Suitable for a wide range of applications – for example, enriching customer databases, which are often organized by postal code.\n- **Administrative Level** (Electoral Divisions): Covers both larger administrative areas (e.g., regions, states, provinces) as well as finer subdivisions such as districts, counties, or municipalities.\n- **Micro Level** (Small Areas): Subdivisions of postal code or administrative boundaries, such as census areas or street segments. 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The current GICS sectors include Energy, Materials, Industrials, Consumer Discretionary, Consumer Staples, Health Care, Financials, Information Technology, Communication Services, Utilities, and Real Estate.\n\nGICS products available include:\n\n- GICS Direct: Current classifications for over 50,000 active public companies covering over 120 countries.\n- GICS History: Active and inactive classifications for over 97,000 companies, across over 135 countries, with history available from 1985 forward.","terms_of_service":"https://www.spglobal.com/en/terms-of-use","documentation_link":"https://www.marketplace.spglobal.com/en/datasets/gics-(90)","privacy_policy_link":"https://www.spglobal.com/en/privacy/privacy-policy-english","geographical_coverage":"[\"Global\"]","cost":"PAID","data_source":"GICS History includes classifications based on from and thru dates","assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"S&P Global Market Intelligence","description":"At S&P Global Market Intelligence, we understand the importance of accurate, deep and insightful information. Our team of experts delivers unrivaled insights and leading data and technology solutions, partnering with customers to expand their perspective, operate with confidence, and make decisions with conviction."},"popularity":1692},{"id":"fb6b90e3-ef06-417b-816a-6129a1bae8fb","summary":{"name":"Customer Enrichment Property","subtitle":"Provides everything you would want to know about all 30m+ UK properties","setting":{"visibility":"PUBLIC"},"created_at":1747923142058,"updated_at":1782919301397,"categories":["ADVERTISING_AND_MARKETING","COMMERCE","DEMOGRAPHICS","ENERGY","FINANCIAL","HEALTH","PUBLIC_SECTOR","RETAIL","TRAVEL_AND_TOURISM"],"listingType":"PERSONALIZED","provider_id":"b86d3de4-ce4e-408c-a0f3-6f8735bb3d4d"},"detail":{"description":"**Overview**\nProvides everything you would want to know about all 30m+ UK properties; from property type, number of bedrooms, and current market value to how efficient the heating system is. \n\nCustomer Enrichment Property also includes home mover data, providing information around its current listing status, property purchase dates, as well as a predicted move date.\n\n**What is it?**\nCustomer Enrichment Property is an address level product built using our trusted, comprehensive data asset repository, enabling you to better understand characteristics at a local area level. It provides everything you would want to know about a property.\n\nSourcing data from Royal Mail’s PAF (Postcode Address) file, Land Registry feeds, Property Listing feeds, and household &address-level segmentations.\n\n**Use cases**\n-\tUnderstand your customers further through their property characteristics, allowing you to review your customer base at household levels\n-\tIdentify which households within your customer base are the best kind of customers, using that to then identify new, promising prospects\n-\tTrigger communications based on home moves\n-\tProperty characteristics can drive product relevance, i.e. a business trying to sell Lawnmowers would only want to focus on households with gardens\n\n**Additional Insights**\nCustomer Enrichment Property can be used alongside SmartLink to ensure full coverage, by connecting disparate data sources & understanding various permutations of each individual you can make the best decisions possible, safe in the knowledge they apply to your full customer base.\n\nCustomer Enrichment Core, Property & Geo can all be used modularly, allowing you to understand the full picture of your customer base, considering not only their individual variance but also where they live & those around them.","terms_of_service":"https://www.sagacitysolutions.co.uk/privacy-policy/","documentation_link":"https://www.sagacitysolutions.co.uk/services/solutions/data-quality-and-enhancement/customer-enrichment","privacy_policy_link":"https://www.sagacitysolutions.co.uk/privacy-policy/","geographical_coverage":"[\"Europe\"]","cost":"PAID","assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"Sagacity","description":"Sagacity are the data intelligence people. We know more about individuals and businesses than any other company. Our advanced analytics and data allow you to identify and generate value quicker. Think of Sagacity as your data co-pilots for sales, marketing, ops, billing, credit and debt, with clients typically seeing 5x returns within the first few months."},"popularity":1506},{"id":"f1cd048e-f54b-4c61-a3ed-0a656747470c","summary":{"name":"Unified Hotels & Hospitality Data - Pricing, Rooms, Amenities, Listings & More","subtitle":"\uD83D\uDECE️ A highly enriched hotels & hospitality dataset for any global market with amenities, rooms, pricing, photos, etc.","share":{"name":"hotels_marketplace","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1737698929459,"updated_at":1746001192672,"categories":["ADVERTISING_AND_MARKETING","GEOSPATIAL","TRAVEL_AND_TOURISM","RETAIL","ECONOMICS"],"listingType":"STANDARD","provider_id":"bcde9d2e-7e2f-4266-8c17-45d2e7bdc27e"},"detail":{"description":"# \uD83D\uDECE️ Unified Hotels Data - Places, Rooms, and Pricing Sample\n\n> Learn more about this dataset on [catalog.dotlas.com](https://catalog.dotlas.com/hotels)\n\nA global hotels dataset with detailed attributes for pricing, amenities, rooms, policies, reviews, and geolocation—ideal for hospitality analytics, travel platforms, geospatial intelligence, and competitive research.\n\n## Sample Share\n\nContains two datasets:\n- `hotels`: Containing hotel metadata, place information, rooms, amenities, rules and more.\n- `hotel_pricing`: Containing current and future pricing and availability for multiple room types in select hotels\n\n## \uD83D\uDD0D What's Included\n\n - Hotel name, address, country, city, coordinates\n - Room types (`hotel_rooms`)\n - Currency, star ratings, review counts\n - Descriptions and photo galleries\n - Facilities list (e.g. free WiFi, spa, parking, A/C)\n - Popular amenities per listing\n - Bed comfort ratings\n - Cancellation terms, children/pets rules, extra beds\n - Smoking, age, and party restrictions\n - Booking & Expedia ratings\n - Review volumes by platform\n\n## \uD83D\uDCBC Use Cases\n\n- Hotel & OTA benchmarking \n- Room inventory enrichment \n- Geo-based hotel clustering \n- NLP on hotel descriptions and policies \n- Travel, logistics, and real estate planning \n- Location-based market sizing and price analysis\n\n## Available for global expansion and integration. For licensing full datasets or bulk enrichment, [contact us](mailto:info@dotlas.com).\n","terms_of_service":"https://dotlas.com/terms-of-service/","documentation_link":"https://catalog.dotlas.com/hotels","privacy_policy_link":"https://dotlas.com/privacy-policy","geographical_coverage":"[\"Global\"]","cost":"FREE","update_frequency":{"interval":1,"unit":"MONTHLY"},"collection_granularity":{"interval":1,"unit":"MONTHLY"},"data_source":"Dotlas collects data from various online and offline sources and unifies them in-house","assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"Dotlas","description":"Dotlas provides high-quality geographical and temporal data on restaurants, real estate and hospitality sectors around the world. We collect data from various public and online sources and coalesce this information in-house and enrich it with hyperlocal context, in order to create datasets that can reliably drive decision-making and generative AI applications."},"popularity":1873},{"id":"3d38d3a1-e767-473b-ba01-469d934aac69","summary":{"name":"SAMPLE Automotive Consumer Data (Webbula Auto) | 105M+ Individuals, 100+ Syndicated Segments | ...","subtitle":"Webbula Auto is cross-sourced deterministic “hands on wheel” data. It’s accuracy has outperformed competitors in the ...","share":{"name":"webbula_c487f439_a5fe_43a3_ae6b_67a246eb9899_samples_share","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1766171649281,"updated_at":1769092035462,"categories":["OTHER","ADVERTISING_AND_MARKETING"],"listingType":"STANDARD","provider_id":"686e23ab-8fd6-4c66-b1e0-f4ef1cab08dc"},"detail":{"description":"167 syndicated segments\n105M+ individuals\n\nExamples of segments available:\n- Body: Utility \n- Class: Full Size Utility\n- Make\n- Model*\n- Year: \n- Cars under owner: \n- Lease/Own\n- Lien or not\n- Purchased New/Used\n- Purchase/Transaction/Validation date*\n\n*Can be provided upon via special custom requests..!","terms_of_service":"https://webbula.com/privacy-center","contact_email":"marketplace@datarade.ai","privacy_policy_link":"https://webbula.com/privacy-center","assets":["ASSET_TYPE_DATA_TABLE"]},"provider_summary":{"name":"Webbula","description":"As a derivative of our lengthy, successful email and data hygiene tenure, Webbula has seven audience verticals based solely on PII that can be used for marketing across multiple channels, with a STRICT focus on accuracy: \r\n\r\nB2B\r\nMortgage\r\nPolitical\r\nAutomotive \r\nDemographic\r\nHobby/Interest/Lifestyle \r\nInterest & Brand Preferences\r\nWealth"},"popularity":458},{"id":"1e91acb8-11e7-491c-b2dd-1f6ab405729e","summary":{"name":"Free Sample Dataset - 50 High Resolution Videos & Metadata","subtitle":"A free sample dataset consisting of 50 videos and metadata sourced from our library of over 45 million videos.","share":{"name":"shutterstock_sample_set6_video","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1719613123005,"updated_at":1719621603450,"categories":["MEDIA","ADVERTISING_AND_MARKETING","SCIENCE_AND_RESEARCH","OTHER"],"listingType":"STANDARD","provider_id":"f1e2468b-186b-4ad3-8dbe-91954aa9cbab"},"detail":{"description":"**Overview**\nThis dataset is a free sample of 50 videos and accompanying metadata sourced from our library of over 45 million videos. Videos in this sample are selected from across a wide range of content categories such as People, Objects, Nature, and more. This type of data can be licensed from Shutterstock for a wide variety of use cases including powering Generative AI and Computer Vision models.\n\nIf you'd like to start licensing data from our full library please reach out directly to our team at sales.databricks@shutterstock.com to start using our tailored services to help ideate, curate and customize datasets for your unique business needs.\n\n**Metadata**\nSample metadata fields included in this dataset are listed below, for a full list of all metadata available from Shutterstock please contact our team at sales.databricks@shutterstock.com.\n\n**asset metadata:**\nASSET_ID\nDESCRIPTION\nKEYWORDS\nASSET_LOCATION\nIS_CREATIVE_EDITORIAL\nMATURE_FLAG\nDATE_SUBMITTED\nGERMAN_DESCRIPTION\nSPANISH_DESCRIPTION\nFRENCH_DESCRIPTION\nKOREAN_DESCRIPTION\nJAPANESE_DESCRIPTION\nLABELS\nMODERATION_LABELS\nHAS_MODEL_RELEASE\nPRIMARY_CATEGORY DURATION\nFPS\n\n**file metadata:**\nASSET_ID\nASSET_FILE_SIZE\nASSET_FILE_EXTENSION\nSIZE\nCODEC\nORIENTATION\nWIDTH\nHEIGHT\n\n**model metadata:**\nASSET_ID\nMODEL_RELEASE_ID\nAGE_RANGE\n\n**Our data**\nShutterstock offers the largest, highest quality and most diverse collection of creative content with best-in-class metadata, giving technology businesses the scale and accuracy they need to build and sustain a wide variety of machine learning models. Our growing library of +550M images, +40M videos, +4M music and audio tracks, and +1.2M 3D models and data is human-reviewed for accuracy and IP infringement, allowing you to use our data worry-free and avoid unwanted or unlawful content. We ethically source all content from over 2 million creators in +150 countries and with over 60 million new assets added annually, our ever-growing library gives you access to fresh and diverse datasets that can be refreshed regularly to meet all your data needs.","terms_of_service":"https://www.shutterstock.com/license","documentation_link":"https://www.shutterstock.com/data-licensing","privacy_policy_link":"https://www.shutterstock.com/privacy","geographical_coverage":"[\"Global\"]","cost":"FREE","data_source":"Shutterstock Library","size":35.0,"assets":["ASSET_TYPE_DATA_TABLE","ASSET_TYPE_MEDIA"],"license":"https://www.shutterstock.com/data-licensing"},"provider_summary":{"name":"Shutterstock","description":"Build Smarter Computer Vision with Speed and Precision.\nExperience unmatched scale, diversity, and balance in our customizable training datasets."},"popularity":1110},{"id":"43b6cbed-2845-4372-b1ef-b5fb5db737b8","summary":{"name":"Draup Global Job Postings Data (Talent) - Sample","subtitle":"Comprehensive and near real-time view of hiring demand across companies worldwide","share":{"name":"global_job_postings_data(talent)-sample","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1755722648949,"updated_at":1788941875359,"categories":["COMMERCE","ECONOMICS","EDUCATION","OTHER","ADVERTISING_AND_MARKETING","SCIENCE_AND_RESEARCH"],"listingType":"STANDARD","provider_id":"9900a255-6aa9-496f-9b6a-421a3568af3b"},"detail":{"description":"**Overview**\nDraup’s Global Job Postings dataset provides a comprehensive and near real-time view of hiring demand across companies worldwide. It captures detailed information on job roles, skill requirements, educational backgrounds, certifications, pay, and benefits, while also mapping postings to business functions and workloads. This enables HR teams, workforce planners, and talent intelligence leaders to analyze emerging workforce needs, identify skill gaps, and anticipate hiring patterns across geographies and industries.\nFor talent-focused decision-makers, the dataset reveals trends in role evolution, technology adoption within jobs, and shifts in functional demand—helping organizations align workforce planning with business priorities. By offering visibility into hiring activity at both company and market levels, it equips talent acquisition leaders to design smarter sourcing strategies, benchmark against competitors, and anticipate future demand for critical skills.\n\n**Methodology & Quality**\nThe dataset is aggregated from global job boards, company career portals, and verified postings across industries. Data is continuously refreshed to ensure relevance, with postings standardized through a structured pipeline that normalizes job titles, roles, skills, and workloads. Automated translation and lemmatization processes improve comparability across geographies, while deduplication and validation routines minimize noise and bias. Updates are applied on a frequent basis to maintain timeliness, and robust standardization ensures consistency for longitudinal analysis of workforce demand.\n\n**Key Tables Include**\n•\tCompany & Job Identifiers – Standardized company and posting IDs for consistent linkage across datasets.\n•\tJob Role & Titles – Normalized job roles, standardized titles, and translated raw job titles.\n•\tJob Descriptions – Consolidated and translated job descriptions for consistent role analysis.\n•\tSkills & Tech Stack – Extracted core skills, soft skills, and associated technology stacks for each posting.\n•\tBusiness Functions & Workloads – Mapped to functional areas and workload categories to align postings with organizational structures.\n•\tEducation & Certifications – Requirements including degrees, certifications, and qualifications linked to roles.\n•\tCompensation & Benefits – Pay ranges, benefits, and perks as advertised in postings.\n•\tExperience Requirements – Minimum experience levels and role-specific experience indices.\n•\tLanguage Requirements – Language skills and multilingual expectations for specific roles.\n•\tPosting Metadata – Posting dates, update timestamps, and system load dates for time-based analysis.\n\n**Use Cases:**\n***Discover what roles, locations, and skills are your competitors hiring for.***\n\nTrack competitor hiring activity by monitoring the job roles, skills, and geographies they are targeting. This visibility helps HR teams understand competitor workforce strategies, anticipate skill shortages, and sharpen their own talent acquisition efforts.\t\n\nSELECT\n company_name,\n role AS job_role,\n location.country,\n location.region,\n location.location,\n skill AS core_skill,\n date_posted\nFROM job_postings_company \nLATERAL VIEW explode(jd.draup_job_role) role_tbl AS role\nLATERAL VIEW explode(jd.consolidated_JDs) loc_tbl AS location\nLATERAL VIEW explode(jd.core_skills) core_tbl AS skill\nWHERE jd.date_posted >= date_add(current_date, -180);\n\n***Has the demand for a specific job role increased or decreased in a location?***\t\n\nAnalyze hiring demand trends for any job role in a given geography. These insights help talent teams anticipate attrition risks—if demand is surging, existing employees are more likely to be poached—allowing HR to design retention and workforce planning strategies accordingly.\t\n\nSELECT\nrole_tbl.role AS job_role,\nloc_tbl.country,\nloc_tbl.region,\nDATE_TRUNC('month', jd.date_posted) AS posting_month,\nCOUNT(*) AS total_job_postings\nFROM job_postings_company jd\nLATERAL VIEW explode(jd.draup_job_role) role_tbl AS role\nLATERAL VIEW explode(jd.consolidated_JDs) loc_tbl AS loc\nWHERE role_tbl.role = 'Data Engineer'\nAND loc_tbl.country = 'India'\nAND jd.date_posted >= date_add(current_date, -365)\nGROUP BY role_tbl.role, loc_tbl.country, loc_tbl.region, DATE_TRUNC('month', jd.date_posted)\nORDER BY posting_month;\n\n***Find out what benefits are most commonly offered by product companies in India***\t\n\nBenchmark compensation packages beyond salary by analyzing benefits offered by companies in a target geography. These insights guide HR in refining employer value propositions and designing competitive offers to attract and retain top talent.\t\n\nSELECT\n benefit,\n COUNT(DISTINCT job_descriptions.company_name) AS companies_offering,\n COUNT(*) AS total_job_postings\nFROM job_postings_company\nJOIN company_overview\n ON job_postings_company.company_id = company_overview.company_id\nLATERAL VIEW explode(jd.benefits.categories) benefit_tbl AS benefit\nLATERAL VIEW explode(jd.consolidated_JDs) consoli_jds AS job_descriptions\nWHERE job_descriptions.country = 'India'\n AND LOWER(company_overview.company_type) = 'product' \nGROUP BY benefit\nORDER BY companies_offering DESC, total_job_postings DESC;\n\n***Find what new skills are emerging within a particular job role in a location***\n\nIdentify rising or emerging skills associated with a role in a region, based on the latest job postings. This helps organizations adjust training programs, update role definitions, and proactively build the skills needed to stay competitive in fast-changing markets.\t\n\nWITH recent_skills AS (\n SELECT DISTINCT\n role AS job_role,\n job_descriptions.country,\n skill\n FROM job_postings_company\n LATERAL VIEW explode(jd.draup_job_role) role_tbl AS role\n LATERAL VIEW explode(jd.consolidated_JDs) consolid_jds AS job_description\n LATERAL VIEW explode(jd.core_skills) skill_tbl AS skill\n WHERE role = 'Data Engineer'\n AND job_descriptions.country = 'India'\n AND job_postings_company.date_posted >= date_add(current_date, -180) \n),\npast_skills AS (\n SELECT DISTINCT\n role AS job_role,\n job_descriptions.country,\n skill\n FROM job_postings_company\n LATERAL VIEW explode(jd.draup_job_role) role_tbl AS role\n LATERAL VIEW explode(jd.consolidated_JDs) consoli_jds AS job_descriptions\n LATERAL VIEW explode(jd.core_skills) skill_tbl AS skill\n WHERE role = 'Data Engineer'\n AND job_descriptions.country = 'India'\n AND job_postings_company.date_posted BETWEEN date_add(current_date, -365) AND date_add(current_date, -181) \n)\nSELECT\n recent_skills.job_role,\n recent_skills.country,\n recent_skills.skill AS emerging_skill\nFROM recent_skills\nLEFT JOIN past_skills p\n ON recent_skills.job_role = past_skills.job_role\n AND recent_skills.country = past_skills.country\n AND recent_skills.skill = past_skills.skill\nWHERE past_skills.skill IS NULL;","terms_of_service":"https://draup.com/privacy-policy#:~:text=Unless%20provided%20for%20in%20this,that%20work%20on%20our%20behalf","documentation_link":"","privacy_policy_link":"https://draup.com/privacy-policy","geographical_coverage":"[\"Global\"]","cost":"FREE","update_frequency":{"interval":1,"unit":"MONTHLY"},"collection_granularity":{"interval":1,"unit":"DAILY"},"data_source":"Public Sources","assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"Draup, Inc.","description":"Draup is an enterprise agentic intelligence platform built for HR and Sales leaders navigating transformation. By combining unparalleled real-time global data with agentic AI, Draup enables strategic decision-making across talent and revenue functions.\n \nThrough its Talent Intelligence solution, Draup empowers HR teams to drive workforce planning, precision hiring, predictive skill architecture, and competitive benchmarking. Its Sales Intelligence solution helps go-to-market teams uncover deep account insights, identify buying triggers, and accelerate revenue growth.\n \nTrusted by over 300 global enterprises - including five of the Fortune 10 - Draup supports companies like Microsoft, PepsiCo, PayPal, and Moderna in building future-ready workforces and growth strategies."},"popularity":1251},{"id":"63662989-e12c-4589-9232-20873f557b40","summary":{"name":"BranchIQ: Retail Banking Intelligence","subtitle":"Conversational branch and regional performance analytics powered by Databricks AI/BI Genie.","setting":{"visibility":"PUBLIC"},"created_at":1779876607995,"updated_at":1779877080845,"categories":["FINANCIAL"],"listingType":"PERSONALIZED","provider_id":"69e292c4-7435-4dd8-8306-1f3a5214dc7b"},"detail":{"description":"**Overview**\nBranchIQ: Retail Banking Intelligence is designed to help financial or Banking sector modernize branch and regional performance reporting using conversational analytics powered by Databricks AI/BI Genie.\n\nBy combining Unity Catalog, Delta Lake, certified Metric Views, and natural language querying, the solution enables regional VPs, branch managers, and banking executives to instantly access governed insights on deposits, lending performance, fee income, quota attainment, and operational KPIs — without relying on manual analyst workflows or fragmented reporting systems.\n\nThe accelerator replaces repetitive ad-hoc reporting and disconnected dashboards with a unified banking intelligence platform that improves decision-making speed, standardizes enterprise KPI definitions, and enables scalable self-service analytics across retail banking operations.\n\n**Use cases**\n\n**Branch Performance Analytics** - Monitor deposits, lending activity, fee income, and operational KPIs across banking branches and regions in real time.\n\n**Conversational Executive Analytics** - \nEnable banking leaders and branch managers to query enterprise banking data using natural language interactions.\n\n**Loan Officer Productivity Intelligence** -\nTrack origination quotas, attainment gaps, and productivity performance across officers and regional teams.\n\n**Certified Financial KPI Reporting** -\nStandardize banking metrics and executive reporting definitions using governed Metric Views and semantic models.\n\n**Regional Trend & Operational Analysis** -\nAnalyze customer segments, branch growth trends, operational performance, and regional comparisons interactively.\n\n**Features**\n\n- **Conversational Banking Analytics :** Query banking performance metrics using natural language with AI/BI Genie.\n- **Certified KPI Framework :** Govern financial metrics using Metric Views and enterprise semantic definitions.\n- **Unified Banking Data Platform :** Consolidate deposits, loans, fees, customer, and branch analytics into one governed architecture.\n- **Executive Self-Service Reporting :** Empower non-technical banking leaders with instant access to trusted insights.\n- **Unity Catalog Governance :** Centralized governance, metadata management, and secure banking analytics.\n- **Low-Latency Query Experience :** Deliver fast analytics responses using Databricks Serverless SQL Warehouses.\n- **Role-Based Regional Security :** Support secure region-level access controls and governed visibility policies.\n- **Banking Semantic Intelligence Layer** : Establish consistent enterprise-wide banking KPI definitions and reporting logic.\n\n**Benefits**\n\n- Reduces dependency on manual analyst reporting\n- Accelerates regional and branch-level decision-making\n- Improves consistency of enterprise banking KPIs\n- Increases adoption of self-service analytics\n- Frees analytics teams for strategic forecasting initiatives\n- Enhances operational visibility across banking regions\n- Creates a scalable foundation for AI-driven banking intelligence","terms_of_service":"https://docs.google.com/document/d/1BNvbHG2TDQbJfJ5lJ0D9yfNh1vKQWLOd/edit?pli=1","documentation_link":"","privacy_policy_link":"https://docs.google.com/document/d/1BNvbHG2TDQbJfJ5lJ0D9yfNh1vKQWLOd/edit?pli=1","geographical_coverage":"[\"Global\"]","assets":["ASSET_TYPE_MEDIA","ASSET_TYPE_NOTEBOOK"],"license":"https://docs.google.com/document/d/1BNvbHG2TDQbJfJ5lJ0D9yfNh1vKQWLOd/edit?pli=1"},"provider_summary":{"name":"DataPattern","description":"DataPattern is a consulting and services leader specializing in Data Programs, Generative AI solutions and innovative data products. We collaborate with businesses and startups to unlock innovation, streamline operations, and deliver actionable insights through advanced technologies like IoT, AI/ML and Analytics. In addition to consulting, we design and deliver cutting-edge GenAI and data products, empowering organizations to harness the full potential of their data. Our expertise in data-driven strategies and application development ensures tailored solutions that address unique challenges, driving measurable outcomes. With a focus on excellence and client success, DataPattern enables organizations to thrive in the rapidly evolving digital landscape."},"popularity":1546},{"id":"3b7c8fb7-308c-468f-a9c9-fde385b701f6","summary":{"name":"Substances","subtitle":"This data package contains the entire concept structure (sematic type, concepts and replationship) of UMLS Metathesaur..","share":{"name":"substances","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1682356506540,"updated_at":1695692073393,"categories":["HEALTH","SCIENCE_AND_RESEARCH"],"listingType":"STANDARD","provider_id":"f88849f8-9d77-4de2-b4c7-03d55cc3768e"},"detail":{"description":"\n**Overview**\nThis data package contains the entire concept structure (sematic type, concepts and replationship) of UMLS Metathesaurus for the biomedical domain \"Substances\".\n\n**Description**\nThe UMLS, or Unified Medical Language System, is a set of files and software that brings together many health and biomedical vocabularies and standards to enable interoperability between computer systems. There are three UMLS Knowledge Sources:\n- The Metathesaurus, which contains over one million biomedical concepts from over 100 source vocabularies\n- The Semantic Network, which defines 133 broad categories and fifty-four relationships between categories for labeling the biomedical domain\n- The SPECIALIST Lexicon & Lexical Tools, which provide lexical information and programs for language processing The Metathesaurus is a large, multi-purpose, and multi-lingual vocabulary database that contains information about biomedical and health related concepts, their various names, and the relationships among them. The Metathesaurus is built from the electronic versions of many different thesauri, classifications, code sets, and lists of controlled terms used in patient care, health services billing, public health statistics, indexing and cataloging biomedical literature, and/or basic, clinical, and health services research. The Metathesaurus contains over five million terms, or names, organized by meaning into concepts and assigned a unique identifier. The Metathesaurus is not a vocabulary. It contains many vocabularies that are standards and helps to create mappings between these vocabularies. The Semantic Network consists of:\nSemantic types (high level categories)\n- Semantic relationships (relationships between semantic types): The Semantic Network can be used to categorize any medical vocabulary. There are 133 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types. Semantic types and semantic relationships create a network that represents the biomedical domain. Semantic types and relationships help with interpreting the meaning that has been assigned to the Metathesaurus concept.\n\n**Benefits**\n- the purpose of the umls is to improve the ability of computer programs to \"understand\" the biomedical meaning in user inquiries and to use this understanding to retrieve and integrate relevant machine-readable information for users.\n- one powerful use of the umls is linking health information, medical terms, drug names, and billing codes across different computer systems.\n- linking terms and codes between your doctor, your pharmacy, and your insurance company.\n- patient care coordination among several departments within a hospital.\n- timely access to accurate and up-to-date information improves decision making and ultimately the quality of patient care and research.\n- the umls has many other uses, including search engine retrieval, data mining, public health statistics reporting, and terminology research.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [Amino Acid Peptide or Protein Concepts and Types](https://www.johnsnowlabs.com/marketplace/amino-acid-peptide-or-protein-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Amino Acid Peptide or Protein\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Amino Acid Peptide or Protein Relationships](https://www.johnsnowlabs.com/marketplace/amino-acid-peptide-or-protein-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Amino Acid Peptide or Protein\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Antibiotic Concepts and Types](https://www.johnsnowlabs.com/marketplace/antibiotic-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Antibiotic\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Antibiotic Relationships](https://www.johnsnowlabs.com/marketplace/antibiotic-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Antibiotic\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Biologically Active Substance Concepts and Types](https://www.johnsnowlabs.com/marketplace/biologically-active-substance-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Biologically Active Substance\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Biologically Active Substance Relationships](https://www.johnsnowlabs.com/marketplace/biologically-active-substance-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Biologically Active Substance\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Biomedical or Dental Material Concepts and Types](https://www.johnsnowlabs.com/marketplace/biomedical-or-dental-material-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Biomedical or Dental Material\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Biomedical or Dental Material Relationships](https://www.johnsnowlabs.com/marketplace/biomedical-or-dental-material-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Biomedical or Dental Material\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Body Substance Concepts and Types](https://www.johnsnowlabs.com/marketplace/body-substance-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Body Substance\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Body Substance Relationships](https://www.johnsnowlabs.com/marketplace/body-substance-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Body Substance\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Chemical Concepts and Types](https://www.johnsnowlabs.com/marketplace/chemical-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Chemical\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Chemical Relationships](https://www.johnsnowlabs.com/marketplace/chemical-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Chemical\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Chemical Viewed Functional Concepts and Types](https://www.johnsnowlabs.com/marketplace/chemical-viewed-functional-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Chemical Viewed Functionally\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Chemical Viewed Functional Relationships](https://www.johnsnowlabs.com/marketplace/chemical-viewed-functional-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Chemical Viewed Functionally\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Chemical Viewed Functionally Concepts and Types](https://www.johnsnowlabs.com/marketplace/chemical-viewed-functionally-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Chemical Viewed Functionally\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Chemical Viewed Functionally Relationships](https://www.johnsnowlabs.com/marketplace/chemical-viewed-functionally-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Chemical Viewed Functionally\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Chemical Viewed Structural Concepts and Types](https://www.johnsnowlabs.com/marketplace/chemical-viewed-structural-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Chemical Viewed Structurally\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Chemical Viewed Structural Relationships](https://www.johnsnowlabs.com/marketplace/chemical-viewed-structural-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Chemical Viewed Structurally\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Chemical Viewed Structurally Concepts and Types](https://www.johnsnowlabs.com/marketplace/chemical-viewed-structurally-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Chemical Viewed Structurally\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Chemical Viewed Structurally Relationships](https://www.johnsnowlabs.com/marketplace/chemical-viewed-structurally-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Chemical Viewed Structurally\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Clinical Drug Concepts and Types](https://www.johnsnowlabs.com/marketplace/clinical-drug-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Clinical Drug\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Clinical Drug Relationships](https://www.johnsnowlabs.com/marketplace/clinical-drug-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Clinical Drug\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Drug Delivery Device Concepts and Types](https://www.johnsnowlabs.com/marketplace/drug-delivery-device-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Drug Delivery Device\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Drug Delivery Device Relationships](https://www.johnsnowlabs.com/marketplace/drug-delivery-device-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Drug Delivery Device\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Element Ion or Isotope Concepts and Types](https://www.johnsnowlabs.com/marketplace/element-ion-or-isotope-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Element Ion or Isotope\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Element Ion or Isotope Relationships](https://www.johnsnowlabs.com/marketplace/element-ion-or-isotope-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Element Ion or Isotope\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Enzyme Concepts and Types](https://www.johnsnowlabs.com/marketplace/enzyme-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Enzyme\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Enzyme Relationships](https://www.johnsnowlabs.com/marketplace/enzyme-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Enzyme\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Food Concepts and Types](https://www.johnsnowlabs.com/marketplace/food-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Food\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Food Relationships](https://www.johnsnowlabs.com/marketplace/food-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Food\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Hazardous or Poisonous Substance Concepts and Types](https://www.johnsnowlabs.com/marketplace/hazardous-or-poisonous-substance-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Hazardous or Poisonous Substance\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Hazardous or Poisonous Substance Relationships](https://www.johnsnowlabs.com/marketplace/hazardous-or-poisonous-substance-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Hazardous or Poisonous Substance\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Hormone Concepts and Types](https://www.johnsnowlabs.com/marketplace/hormone-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Hormone\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Hormone Relationships](https://www.johnsnowlabs.com/marketplace/hormone-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Hormone\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Immunologic Factor Concepts and Types](https://www.johnsnowlabs.com/marketplace/immunologic-factor-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Immunologic Factor\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Immunologic Factor Relationships](https://www.johnsnowlabs.com/marketplace/immunologic-factor-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Immunologic Factor\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Indicator Reagent or Diagnostic Aid Concepts and Types](https://www.johnsnowlabs.com/marketplace/indicator-reagent-or-diagnostic-aid-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Indicator Reagent or Diagnostic Aid\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Indicator Reagent or Diagnostic Aid Relationships](https://www.johnsnowlabs.com/marketplace/indicator-reagent-or-diagnostic-aid-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Indicator Reagent or Diagnostic Aid\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Inorganic Chemical Concepts and Types](https://www.johnsnowlabs.com/marketplace/inorganic-chemical-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Inorganic Chemical\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Inorganic Chemical Relationships](https://www.johnsnowlabs.com/marketplace/inorganic-chemical-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Inorganic Chemical\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Manufactured Object Concepts and Types](https://www.johnsnowlabs.com/marketplace/manufactured-object-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Manufactured Object\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Manufactured Object Relationships](https://www.johnsnowlabs.com/marketplace/manufactured-object-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Manufactured Object\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Medical Device Concepts and Types](https://www.johnsnowlabs.com/marketplace/medical-device-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Medical Device\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Medical Device Relationships](https://www.johnsnowlabs.com/marketplace/medical-device-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Medical Device\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Nucleic Acid Nucleoside or Nucleotide Concepts and Types](https://www.johnsnowlabs.com/marketplace/nucleic-acid-nucleoside-or-nucleotide-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Nucleic Acid Nucleoside or Nucleotide\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Nucleic Acid Nucleoside or Nucleotide Relationships](https://www.johnsnowlabs.com/marketplace/nucleic-acid-nucleoside-or-nucleotide-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Nucleic Acid Nucleoside or Nucleotide\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Organic Chemical Concepts and Types](https://www.johnsnowlabs.com/marketplace/organic-chemical-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Organic Chemical\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Organic Chemical Relationships](https://www.johnsnowlabs.com/marketplace/organic-chemical-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Organic Chemical\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Pharmacologic Substance Concepts and Types](https://www.johnsnowlabs.com/marketplace/pharmacologic-substance-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Pharmacologic Substance\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Pharmacologic Substance Relationships](https://www.johnsnowlabs.com/marketplace/pharmacologic-substance-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Pharmacologic Substance\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Receptor Concepts and Types](https://www.johnsnowlabs.com/marketplace/receptor-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Receptor\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Receptor Relationships](https://www.johnsnowlabs.com/marketplace/receptor-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Receptor\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Research Device Concepts and Types](https://www.johnsnowlabs.com/marketplace/research-device-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Research Device\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Research Device Relationships](https://www.johnsnowlabs.com/marketplace/research-device-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Research Device\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Substance Concepts and Types](https://www.johnsnowlabs.com/marketplace/substance-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Substance\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Substance Relationships](https://www.johnsnowlabs.com/marketplace/substance-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Substance\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Vitamin Concepts and Types](https://www.johnsnowlabs.com/marketplace/vitamin-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Vitamin\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Vitamin Relationships](https://www.johnsnowlabs.com/marketplace/vitamin-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Vitamin\". 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Estimates rates and numbers for HIV infection diagnosis and stage 3 infection (AIDS) as collected by the Centers for Disease Control and Prevention. Due to the confidentiality of the HIV diagnosis reporting and the differences in surveillance practices, there is a minimum estimate provided of persons diagnosed with the infection. It also shows the estimated total number of cases multiplied by the population.\n\n**Benefits**\n- Useful for physicians to know the number of cases of aids, hepatitis, infections, etc. and their estimate rates.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [Acute Viral Hepatitis A B and C Total Cases NNDSS 2014](https://www.johnsnowlabs.com/marketplace/acute-viral-hepatitis-a-b-and-c-total-cases-nndss-2014)\n\t- This dataset includes the total number of cases of Acute Viral Hepatitis A, B and C reported in 2014 plus the cumulative cases in 2013 for comparison; in the United States, by region, and by states, in accordance with the current method of displaying MMWR (Morbidity and Mortality Weekly Report) data.\n- [Acute Viral Hepatitis A B and C Total Cases NNDSS 2015](https://www.johnsnowlabs.com/marketplace/acute-viral-hepatitis-a-b-and-c-total-cases-nndss-2015)\n\t- This dataset includes the total number of cases of Acute Viral Hepatitis A, B and C reported in 2015 plus the cumulative cases in 2014 for comparison; in the United States, by region, and by states, in accordance with the current method of displaying MMWR (Morbidity and Mortality Weekly Report) data.\n- [Acute Viral Hepatitis A B and C Total Cases NNDSS 2016](https://www.johnsnowlabs.com/marketplace/acute-viral-hepatitis-a-b-and-c-total-cases-nndss-2016)\n\t- This dataset includes the total number of cases-to-date of Acute Viral Hepatitis A, B and C reported in 2016 plus the cumulative cases in 2015 for comparison; in the United States, by region, and by states, in accordance with the current method of displaying MMWR (Morbidity and Mortality Weekly Report) data.\n- [US State Level HIV Cases](https://www.johnsnowlabs.com/marketplace/us-state-level-hiv-cases)\n\t- This dataset contains surveillance data on diagnoses of HIV for the United States in estimates rates and numbers for Human Immunodeficiency Virus (HIV) infection diagnosis and stage 3 infection Acquired Immunodeficiency Syndrome (AIDS) as collected by the Centers for Disease Control and Prevention (CDC).\n- [US State Level STD Cases](https://www.johnsnowlabs.com/marketplace/us-state-level-std-cases)\n\t- This dataset contains the analysis of surveillance data on Sexually transmitted diseases (STDs) diagnosis for all states in the US. The data was collected from the CDC Division of STD Prevention program from reporting forms and electronic data of the National Electronic Telecommunications System for Surveillance (NETSS). This dataset combines age, sex and races and covers Chlamydia, Gonorrhea and different presentations of Syphilis.\n- [US State Level Tuberculosis Cases](https://www.johnsnowlabs.com/marketplace/us-state-level-tuberculosis-cases)\n\t- This dataset contains the analysis of surveillance data on tuberculosis for all states in the US. The data was collected from the CDC Division of Sexually transmitted diseases (STDs) Prevention program from reporting forms and electronic data of the National Electronic Telecommunications System for Surveillance (NETSS). This dataset combines age, sex and races.\n- [US State Level Viral Hepatitis Cases](https://www.johnsnowlabs.com/marketplace/us-state-level-viral-hepatitis-cases)\n\t- This dataset contains the analysis of surveillance data on viral hepatitis for all states in the US. The data was collected from the CDC Division of Sexually transmitted diseases (STDs) Prevention program from reporting forms and electronic data of the National Electronic Telecommunications System for Surveillance (NETSS). This dataset combines age, sex and races.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated\n- Data Updates\n - Data updates support replace-on-update: outdated foreign keys are deprecated, not deleted\n\n**Our data is curated and enriched by domain experts** \n\n Each dataset is 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Employee counts change, companies move, industries shift. LinkedIn Data gives you weekly-updated firmographics you can push directly into your CRM or data warehouse. Hiring spikes surface as strong signals of active budget and buying intent.\n\n• Append industry, size, headquarters, and employee count to CRM records using LinkedIn Data\n• Flag accounts showing hiring spikes as high-intent prospects via LinkedIn Data signals\n• Refresh stale company records with weekly-updated LinkedIn Data firmographics\n• Segment accounts by company size, industry, and hiring velocity from LinkedIn Data\n• Match LinkedIn Data company profiles to existing accounts to find coverage gaps in your pipeline\nB2B Lead Generation\n\nLinkedIn Data hiring signals tell you which companies are actively expanding, which departments are growing, and what skills they're chasing. A company posting 30 sales roles in a new geography is a better lead than one that hasn't posted in six months. LinkedIn Data gives you that signal at the posting level.\n\n• Build prospect lists of companies expanding specific departments using LinkedIn Data\n• Prioritize outreach by identifying high-growth companies from LinkedIn Data hiring patterns\n• Filter companies by size, industry, and location through LinkedIn Data firmographics\n• Identify new market opportunities by analyzing growth signals in LinkedIn Data\n• Time outreach to companies at the exact moment LinkedIn Data signals active expansion or new department investment\nTalent Acquisition and HR Intelligence\n\nHR teams use LinkedIn Data to track which companies are hiring for specific roles, benchmark compensation, and identify talent hotspots. Combined with LinkedIn Data company profiles, every hiring signal comes with full firmographic context, including company size, industry, and follower count.\n\n• Identify companies actively hiring for specific roles using LinkedIn Data job postings\n• Analyze competitor hiring patterns across functions and geographies with LinkedIn Data\n• Benchmark hiring velocity against industry peers using LinkedIn Data signals\n• Map talent hotspots by tracking LinkedIn Data posting concentrations by location\n• Track in-demand skills by role and region using LinkedIn Data job postings to guide sourcing strategy\nCompany Analysis and Due Diligence\n\nInvestors and analysts use LinkedIn Data hiring trends as a leading indicator of company health. A company tripling headcount in engineering is sending a signal. LinkedIn Data gives you that signal at the posting level, not an aggregated summary from a quarterly report.\n\n• Track hiring velocity of competitors and portfolio companies using LinkedIn Data\n• Conduct due diligence on private companies through LinkedIn Data hiring history\n• Benchmark a company's LinkedIn Data activity against its industry peers\n• Build quantitative models correlating LinkedIn Data hiring trends with performance\n• Size addressable markets by counting companies in LinkedIn Data hiring for specific roles or skills\nHow We Build This LinkedIn Data\n\nOur deduplication model identifies when the same job appears on LinkedIn and elsewhere using semantic matching on title, company, location, and description. It goes beyond exact matching to catch near-duplicates with slightly different wording. Roughly 60% of raw records collapse into unique entries, so LinkedIn Data job postings reflect true hiring intent instead of inflated counts.\n\nThe title taxonomy model normalizes raw LinkedIn Data job titles by stripping location noise, qualifiers, and formatting variations, then maps them into a hierarchical taxonomy of 50,000+ standardized categories from 20M+ raw titles. This makes LinkedIn Data usable for cross-company and cross-region analysis without manual cleanup.\n\nJob category models classify LinkedIn Data postings by seniority level (Entry, Mid, Senior, Lead, Executive) and work modality (Remote, Hybrid, Onsite) using LLM-based analysis of the full job description, not just title keywords.\n\nCompany profiles in our LinkedIn Data are normalized for consistent industry classification, location matching, and employee count standardization across 70M+ global records.\n\nOur annotation team validates every LinkedIn Data model output before delivery.\n\nWhat Makes This LinkedIn Data Different\n\n• Company-to-job match: every LinkedIn Data job posting connects to a verified company profile with firmographics and location data\n• 70M+ global company profiles in one LinkedIn Data feed, matched with US job posting activity\n• Weekly updates: LinkedIn Data company profiles and job postings refresh every week\n• Deduplication: roughly 60% of raw LinkedIn Data job postings collapse into unique records showing true hiring intent\n• Hiring signals: LinkedIn Data shows velocity, department growth, and skill demand at the company level\n• Follower count and company type in LinkedIn Data profiles add context beyond basic firmographics for segmentation and targeting\nWho Uses This LinkedIn Data\n\n• CRM and RevOps Teams use LinkedIn Data to keep account records current and flag high-intent prospects by hiring activity\n• Business Development and Sales Teams use LinkedIn Data hiring signals to prioritize outreach and build targeted prospect lists\n• HR and Talent Acquisition Teams use LinkedIn Data to track competitor hiring and identify talent market trends by region and role\n• Investors, PE Firms, and VC Funds use LinkedIn Data hiring velocity as alternative data for due diligence and portfolio monitoring\n• Site Selection Consultants and EDOs use LinkedIn Data to assess company growth signals and regional hiring concentration\n• Market Research and BI Teams use LinkedIn Data to analyze corporate expansion patterns and industry hiring trends\n• Staffing and Recruiting Agencies use LinkedIn Data to identify market expansion opportunities and build data-driven client advisory reports\n• HR Tech Companies and B2B Platforms use LinkedIn Data to power firmographic enrichment, lead scoring, and talent intelligence tools\nDelivery and Format\n\nLinkedIn Data is delivered in CSV, JSON, or Parquet via AWS S3 or Google Cloud Storage. Custom filters by geography, industry, company size, date range, skill, and seniority. Compatible with Snowflake, Databricks, Power BI, Tableau, Salesforce, and most BI platforms.","terms_of_service":"https://decanaria.com/privacy-policy","contact_email":"support@datarade.ai","privacy_policy_link":"https://decanaria.com/privacy-policy","assets":["ASSET_TYPE_DATA_TABLE"]},"provider_summary":{"name":"Canaria Inc.","description":"Canaria is a leading provider of enriched job posting and labor market data. We collect 1B+ job postings from Indeed, LinkedIn and 200,000+ employer career sites and deduplicate them to 400M+ unique jobs, with deepest coverage in the US plus postings from 243 other countries and territories since 2022.\r\n\r\nData arrives analysis-ready: SOC occupation and NAICS industry codes, normalized titles across 99,799 canonical roles, a 43,000+ entry skills and credentials taxonomy, salary estimates backed by 44,000+ benchmark cells with 95% confidence intervals, worker type (W2 vs 1099), stock tickers for listed employers, and a confidence score on every classified field.\r\n\r\nHedge funds, HR tech platforms, workforce planners and researchers choose Canaria when they want enterprise-grade enrichment without an enterprise contract. Delivery by API or as CSV and Parquet files to S3, GCS, Snowflake or SFTP. Public commercial data only, GDPR and CCPA compliant."},"popularity":1530},{"id":"580ea558-0540-4747-92b9-f6c1e8f4f9c3","summary":{"name":"Geolocet | Points of Interest Data | Petrol Station Locations - Belgium","subtitle":"Complete GIS-ready dataset of 4,461 Belgium petrol station locations (brands & independents).","setting":{"visibility":"PUBLIC"},"created_at":1780643989814,"updated_at":1780643989814,"categories":["GEOSPATIAL","RETAIL"],"listingType":"PERSONALIZED","provider_id":"5d3ddc17-a7e8-4794-a760-13587e1de2c6"},"detail":{"description":"# **All Petrol Station Locations in Belgium: Complete Geographic Dataset**\n---\n### **Premium GIS-Ready Retail Location Intelligence Data for Databricks Analytics**\n\nThis high-fidelity geospatial dataset provides a complete, exhaustive, and standardized overview of **4,461** verified fuel and petrol station locations across Belgium. Updated as of **May 10, 2026**, this comprehensive national dataset captures the entire Belgian forecourt landscape - encompassing major multinational fuel networks, national champions, and prominent independent operators. It offers an analysis-ready foundation for EV charging infrastructure planning, competitor benchmarking, logistics routing, and retail location intelligence.\n\nFully compatible with **Apache Spark**, **Delta Lake**, and **Databricks Unity Catalog** workflows.\n\n---\n\n## **Complete National Coverage: Branded Forecourts & Regional Operators**\nUnlike single-brand extractions, this dataset aggregates all premier fuel networks and independent operators into a single, unified data model. The coverage maps maximum geographic depth across all Belgian provinces:\n\n- **Major Fuel Networks & National Champions:** \n [Total (563 locations)](https://geolocet.com/products/belgium-total-petrol-stations), \n [Q8 (483 locations)](https://geolocet.com/products/belgium-q8-petrol-stations), \n [Esso (469 locations)](https://geolocet.com/products/belgium-esso-petrol-stations), \n [Shell (264 locations)](https://geolocet.com/products/belgium-shell-petrol-stations)\n\n- **Regional & Dynamic Growth Banners:** \n [Gabriels (154 locations)](https://geolocet.com/products/belgium-gabriels-petrol-stations), \n [DATS 24 (152 locations)](https://geolocet.com/products/belgium-dats-24-petrol-stations), \n [Maes (130 locations)](https://geolocet.com/products/belgium-maes-petrol-stations), \n [Gulf (123 locations)](https://geolocet.com/products/belgium-gulf-petrol-stations), \n [Texaco (121 locations)](https://geolocet.com/products/belgium-texaco-petrol-stations), \n [AVIA (94 locations)](https://geolocet.com/products/belgium-avia-petrol-stations), \n [Lukoil (178 locations)](https://geolocet.com/products/belgium-lukoil-petrol-stations), \n [AS 24 (70 locations)](https://geolocet.com/products/belgium-as-24-petrol-stations), \n [Power (53 locations)](https://geolocet.com/products/belgium-power-petrol-stations), \n [G&V (52 locations)](https://geolocet.com/products/belgium-gandv-petrol-stations), \n [tankpool24 (66 locations)](https://geolocet.com/products/belgium-tankpool24-petrol-stations)\n\n**Plus thousands of regional independent forecourts** fully mapped and standardized across all provinces (e.g., Antwerpen: 685 sites; Oost-Vlaanderen: 633 sites; West-Vlaanderen: 619 sites; Hainaut: 560 sites).\n\n---\n\n## **Technical Specifications & Schema Definitions**\nOptimized for zero-friction schema enforcement when reading via `spark.read.format(\"csv\")`. The structured schema below ensures flawless rendering in Databricks notebooks and high accuracy for LLM semantic indexing.\n\n* **`GUID`** (`STRING`): Unique global identifier for deterministic primary key mapping and deduplication.\n* **`Title`** (`STRING`): Official station name or commercial brand banner classification (e.g., Total, Q8, Esso).\n* **`Latitude`** (`DOUBLE`): Precise WGS84 latitude coordinate for point-in-polygon and spatial joins.\n* **`Longitude`** (`DOUBLE`): Precise WGS84 longitude coordinate for spatial indexing (H3 / S2).\n* **`Street_No` / `Street`** (`STRING`): Normalized building numbers and thoroughfare name elements.\n* **`City` / `Postal_Code`** (`STRING`): Core postal addresses and primary municipal settlement names.\n* **`Province`** (`STRING`): Macro-administrative boundary classification (e.g., Antwerpen, Limburg, Hainaut).\n* **`Admin_level_1` / `_2`** (`STRING`): Additional local administrative area and district identifiers.\n* **`Commune`** (`STRING`): Local municipal administrative boundary designation.\n* **`Address`** (`STRING`): Full, standardized street address clean-text output.\n* **`Wheelchair`** (`STRING`): Accessibility flags indicating wheelchair mobility support where available.\n\n---\n\n## **Databricks Workflow & Spatial Analytics Use Cases**\n\n- **EV Charging Whitespace Analysis:** Query the full 4,461 location points against high-voltage grid corridors or traffic volume rasters to rank optimal stations for ultra-fast EV charging infrastructure placement.\n- **H3 Geospatial Indexing:** Convert the precise WGS84 `Latitude` and `Longitude` fields into Uber H3 spatial hexagons (`h3_longlattocell`) to seamlessly cross-reference with granular Belgian statistical sector income datasets.\n- **Logistics & Fleet Benchmarking:** Integrate forecourt coordinates with fleet tracking telemetry to calculate fuel stop density, build optimal corridor routing profiles, and minimize off-route deadhead mileage across the Belgian highway network.\n\n---\n\n## **Data Evaluation & Custom Ingestion**\n\n### **Evaluate with a Free Sample Dataset**\nVerify the structure, column types, and coordinate precision before full implementation. A comprehensive schema sample is available for download in CSV/Parquet-friendly formats.\n\n*Want deeper market insights?* Learn more about the Belgian retail and forecourt landscape in our comprehensive reports:\n* **[View the Belgium Top Brands Petrol Stations Report](https://geolocet.com/pages/belgium-topbrands-petrol-stations-report)**\n* **[View the Belgium Top Brands Grocery Report](https://geolocet.com/pages/belgium-top-brands-grocery-report)**\n\n### **Enterprise Delivery & Tailored Formatting**\nWhile natively available as an analysis-ready CSV for DBFS ingestion, we accommodate enterprise-level data architecture needs. Alternative formats - including **GeoJSON, Esri Shapefiles, PostgreSQL dumps, Excel, or direct Delta sharing profiles** - can be generated. Reach out to our engineering team at **contact@geolocet.com** for integration support or custom European POI data requests.","terms_of_service":"https://geolocet.com/pages/terms-of-use","documentation_link":"","privacy_policy_link":"https://geolocet.com/pages/privacy-policy","assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://geolocet.com/pages/terms-of-use"},"provider_summary":{"name":"Geolocet LTD","description":"Geolocet provides premium geospatial data for retail analysis, GIS, and market planning. We equip businesses with accurate, ready-to-query data to drive spatial analysis, site selection, and customer segmentation.\n\nCore Offerings:\n\nDemographics: Socioeconomic data covering population, age, and employment across diverse geographies.\n\nPoints of Interest (PoI): Datasets spanning retail, healthcare, and transport for catchment analysis.\n\nAdministrative Boundaries: GIS-ready boundaries, including municipalities and postcodes, for territory management.\n\nBrand Locations: Chain location datasets to monitor store networks and market expansion.\n\nWealth Data: Granular economic indicators like purchasing power to identify high-potential markets.\n\nWe source from trusted providers, delivering highly customizable datasets with comprehensive coverage across Europe and worldwide."},"popularity":405},{"id":"3b4ab64e-b176-4c91-a178-3c8985f82abb","summary":{"name":"S&P Global ESG Scores","subtitle":"Provides access to granular sustainability-related data points collected as part of the S&P Global CSA.","setting":{"visibility":"PUBLIC"},"created_at":1785246441141,"updated_at":1785246464539,"categories":["FINANCIAL","CLIMATE_AND_ENVIRONMENT"],"listingType":"PERSONALIZED","provider_id":"bb123ba0-f713-4a39-8c51-2daca088d1de"},"detail":{"description":"The S&P Global ESG Scores package provides access to granular sustainability-related data points collected as part of the S&P Global Corporate Sustainability Assessment (CSA) process from public and additional disclosures.\n\nThe data package combines company disclosures checked against reliable public sources, media and stakeholder analysis, and in-depth company engagement. Leverage the S&P Global ESG Scores to gain insight into a deep range of environmental, social and governance data that's comparable across peers and subindustries and allows for easy aggregation at the portfolio level.\n\nKey features of this dataset include:\n\n- Scores, Media & Stakeholder Analysis, Disclosure Analysis, Criteria Rationale, Aggregates, and Year-On-Year change\n- Coverage of approximately 13,000 companies\n- Historical coverage from 2013\n- Easy linking to other S&P Global Market Intelligence identifiers and data packages\n\nS&P Global ESG Scores data is regularly reviewed and updated in accordance with the relevant S&P Global Sustainable1 data quality practices.","terms_of_service":"https://www.spglobal.com/en/terms-of-use","documentation_link":"https://www.marketplace.spglobal.com/en/datasets/s-p-global-esg-scores-(171)","privacy_policy_link":"https://www.spglobal.com/en/privacy/privacy-policy-english","geographical_coverage":"[\"Global\"]","assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"S&P Global Market Intelligence","description":"At S&P Global Market Intelligence, we understand the importance of accurate, deep and insightful information. Our team of experts delivers unrivaled insights and leading data and technology solutions, partnering with customers to expand their perspective, operate with confidence, and make decisions with conviction."},"popularity":1920},{"id":"9ce95707-35be-4169-bf82-76aa6bc98c7b","summary":{"name":"SAMPLE B2B Contact Data | Global Coverage 100+M Decision Makers | Job Titles, Email, Phone ...","subtitle":"Gain access to a powerful B2B Contact Database. Our comprehensive database provides global coverage, featuring ...","share":{"name":"webautomation_fed487d0_56d6_4373_8222_64f2f6daa330_samples_share","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1699292237543,"updated_at":1747679221829,"categories":["OTHER"],"listingType":"STANDARD","provider_id":"2b5bf7d6-5818-49fd-890f-e0cfaaab9bfe"},"detail":{"description":"Key Features:\r\n\r\nExtensive Global Coverage: Our database spans across multiple countries and industries, offering a diverse and extensive collection of decision makers. Reach out to key professionals worldwide and expand your business horizons.\r\n\r\n\r\nComprehensive Contact Details: Gain access to essential contact information, including names, job titles, email addresses, phone numbers, and company affiliations. Connect with the right individuals and nurture valuable business relationships.\r\n\r\n\r\n\r\nGDPR Compliance: We prioritize data privacy and strictly adhere to the General Data Protection Regulation (GDPR) guidelines. Rest assured that our B2B Contact Database is GDPR compliant, ensuring the protection of personal and sensitive information.","terms_of_service":"https://webautomation.io/4/privacy-policy","contact_email":"marketplace@datarade.ai","privacy_policy_link":"https://webautomation.io/4/privacy-policy","assets":["ASSET_TYPE_DATA_TABLE"]},"provider_summary":{"name":"Webautomation","description":"WebAutomation.io revolutionizes web data collection with its marketplace of pre-built extractors and ready-to-use datasets. In just minutes, users can access hundreds of extractors covering various web sources. Collect millions of data points from e-commerce sites, social media platforms, and more without coding or maintenance. The platform's user-friendly interface simplifies the process, making it accessible to users of all technical backgrounds. WebAutomation.io handles technical complexities like proxies and CAPTCHA challenges, freeing users from infrastructure management. Ready-to-use datasets further enhance the collection process, providing valuable information for market analysis and business intelligence. With WebAutomation.io, users can unlock the power of web data, gaining insights to drive informed decision-making and business growth."},"popularity":998},{"id":"cc446e98-c679-43b1-a949-af8cb1776083","summary":{"name":"Electronic Health Records (EHR) Data : Family Practice","subtitle":"High-quality de-identified EHR data, showing comprehensive patient journeys physician narratives in Family Practice.","share":{"name":"electronichealthrecordsdatafamilypractice","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1723032196405,"updated_at":1723103584704,"categories":["HEALTH","SCIENCE_AND_RESEARCH"],"listingType":"STANDARD","provider_id":"dc00cb61-5b9a-403e-8b4f-71e78dd44d6c"},"detail":{"description":"**Overview**\n\nShaip is a premier provider of data on de-identified electronic health records (EHR). The Shaip EHR data comprises de-identified EHR data that is nationally representative. This data is sourced from practices across the U.S., covering the continuum of care delivery—from independent, single-provider offices to large, integrated delivery systems, including both primary and specialty care. \n\nThis data provides a comprehensive picture of the patient’s journey and physician narrative throughout their hospital stay. With approximately **4 million + unique patient records**, the participating hospitals, offer unique insights into variations in care across healthcare network hospitals and clinics. \n\n**The samples provided and the entire datasets are Gold Standard**\n\n\n**Use cases**\n\n**For Healthcare Organizations**\n\n- Population health management: Analyze patient populations to identify trends and improve overall health.\n- Revenue cycle management: Streamline billing and claims processes. \n- Quality improvement: Track and measure performance to identify areas for improvement. \n- Regulatory compliance: Ensure adherence to healthcare regulations and standards. \n\n**For Researchers and Scientists**\n\n- Drug discovery and development: Identify potential drug targets and patient populations for clinical trials.\n- Disease research: Analyze patient data to understand disease progression and identify risk factors.\n- Public health surveillance: Monitor disease outbreaks and track public health trends.\n\n**For Payers**\n\n- Risk assessment: Evaluate the health risks of insured populations.\n- Fraud detection: Identify potential fraudulent claims.\n- Care management: Manage high-cost patients and chronic conditions.\n\n**Product details**\n\n**Metadata Information:**\n- Includes anonymized MRN\n- Admission and discharge dates\n- Length of stay, \n- Gender \n- Patient class\n- Payer \n- Financial class\n- State \n- Discharge disposition\n- Age \n- DRG\n- DRG description\n- Reimbursement amount\n- AMLOS, GMLOS\n- Risk of mortality \n- Severity of illness\n- Grouper\n- Hospital Zip code, and more.\n\n**Geographical Distribution:** Records from various US regions \n- North East (46.4%)\n- South (8.2%)\n- Midwest (3.1%)\n- West (28.1%)\n- Others (14.2%).\n\n**Patient Classes:** Covers all patient classes—\n- Inpatient\n- Outpatient (Clinical, Rehab, Recurring, Surgical Day Care)\n- Emergency\n\n**Age Groups:** Records for all age groups—\n- <10 years (7.9%)\n- 11-20 years (5.7%)\n- 21-30 years (10.9%)\n- 31-40 years (11.7%)\n- 41-50 years (10.4%)\n- 51-60 years (13.8%)\n- 61-70 years (16.1%)\n- 71-80 years (13.3%)\n- 81-90 years (7.8%)\n- 90+ years (2.4%).\n\n**Gender Ratio:** 46% male and 54% female.\n\n**Language**\n- EnglishOTS\n- French, Spanish, and Portuguese can be acquired very quickly\n- Other Languages - Custom sourcing or translation from English to target language\n\n**Additional Insights**\n\n**Privacy:** Documents have PII redacted according to Safe Harbor Guidelines and HIPAA compliance.","terms_of_service":"https://www.shaip.com/terms-of-service/","documentation_link":"","privacy_policy_link":"https://www.shaip.com/privacy-policy/","geographical_coverage":"[\"United States and Canada\"]","cost":"FREE","data_source":"","assets":["ASSET_TYPE_MEDIA"],"license":""},"provider_summary":{"name":"Shaip","description":"Shaip is a leading AI data platform and services provider specializing in the sourcing, creation, licensing, and transformation of high-quality, ethical, and domain-specific data for AI and machine learning applications. Shaip supports Healthcare AI, Computer Vision, Generative AI, and Conversational AI use cases for leading AI labs and enterprise customers worldwide. Learn more at shaip.com"},"popularity":953},{"id":"4fa1dec3-8918-43a1-b754-528406040cab","summary":{"name":"Simulated Australia Sales and Opportunities Data","subtitle":"This dataset contains simulated data for an Australian sales organization.","share":{"name":"dbacademy_dataset_australia_sales","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1736394961785,"updated_at":1736877504133,"categories":["EDUCATION"],"listingType":"STANDARD","provider_id":"ea1e69ff-0127-4c94-bf39-e841fe1d19d2"},"detail":{"description":"**Overview**\nThis dataset contains simulated data for an Australian sales organization. It is used in the recorded demos for the “AI/BI for Self-service Analytics” course.\n\n**Purpose**\nThe dataset contains information about fictitious Australian customers, sales to them over the years 2021-24, and current opportunities with them, owned by a team of fictitious sales representatives.\n\n**Content**\nThere are three tables: orders, customers, and opportunities.\n\n- The orders table contains sales order data, including order ID, customer ID, product ID, order date, quantity, order amount, and sales representative.\n- The customers table contains customer information, including customer ID, customer name, city, and state.\n- The opportunities table contains sales opportunities data, including opportunity ID, customer ID, state, date, amount, sales representative, and sales phase.\n\nThe tables are connected through the customerid column, which is present in all three tables. This allows for linking orders and opportunities to specific customers.","terms_of_service":"https://cms.databricks.com/sites/default/files/2023-01/marketplace-consumer-terms_0.pdf","documentation_link":"","privacy_policy_link":"https://www.databricks.com/legal/privacynotice","data_source":"https://www.databricks.com/","assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"Databricks","description":"As the world’s first lakehouse platform in the cloud, Databricks combines the best elements of data lakes and data warehouses to deliver the reliability, strong governance, and performance of data warehouses with the openness, flexibility, and machine learning support of data lakes. We are a cross-cloud platform that works across AWS, Azure, and Google.\nThis unified approach simplifies your modern data stack by eliminating the data silos that traditionally separate and complicate data engineering, analytics, BI, data science and machine learning. It’s built on open source and open standards to maximize flexibility. 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For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. 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Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated\n- Data Updates\n - Data updates support replace-on-update: outdated foreign keys are deprecated, not deleted\n\n**Our data is curated and enriched by domain experts** \n\n Each dataset is manually curated by our team of doctors, pharmacists, public health & medical billing experts:\n- Field names, descriptions, and normalized values are chosen by people who actually understand their meaning\n- Healthcare & life science experts add categories, search keywords, descriptions and more to each dataset\n- Both manual and automated data enrichment supported for clinical codes, providers, drugs, and geo-locations\n- The data is always kept up to date – even when the source requires manual effort to get updates\n- Support for data subscribers is provided directly by the domain experts who curated the data sets\n- Every data source’s license is manually verified to allow for royalty-free commercial use and redistribution.\n\n**Need Help?**\nIf you have questions about our products, contact us at [info@johnsnowlabs.com](mailto:info@johnsnowlabs.com). \n 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Report, School Survey, Child Poverty, System International Units, Weight Measures, etc.\n\n**Description**\nThis data package shows information about the different quality performance measures for the different organization such as School Survey, Nursing Home Quality, SI System and weight measures, etc. This data package also contains UK productivity against that of the remaining G7 countries. Output is measured by gross domestic product (GDP). Labor input is measured in two ways – by total hours worked and by the number of workers in employment. These two measures of labor input can yield different results, reflecting differences in working patterns across countries and compositional movements over time, such as changes in the part-time share of employment. It also includes the MTA’s (Metropolitan Transportation Authority) Performance Dashboard and is used to increase transparency at the MTA.\n\nThe Key Performance Indicators (KPI) are used to track performance in areas such as On-Time Performance. The package also includes the Corporate Plan and Performance report. the Council will publish an addendum to the Corporate Plan which sets out updated targets for the year ahead. The 2016-2017 update was agreed at Full Council on 4 April 2016. And it also features detailed information about DCA (Department of Consumer Affairs) inspections of scales, measuring devices, pricing and other equipment.\n\n**Benefits**\n- Value to the agencies who are related to quality performance such as air quality measures, austin airport annual performance, lbb performance, nursing home quality measures, etc.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [AHRQ Patient Safety Indicators](https://www.johnsnowlabs.com/marketplace/ahrq-patient-safety-indicators)\n\t- The Agency for Healthcare Research and Quality (AHRQ) Patient Safety Indicators dataset shows a set of measures that screen for adverse events that patients experience as a result of exposure to the healthcare system. The Patient Safety Indicator is a tool to help identify potentially preventable complications or adverse events for patients in the system level or provider level.\n- [Air Quality Measures on National Environmental Health Tracking Network](https://www.johnsnowlabs.com/marketplace/air-quality-measures-on-national-environmental-health-tracking-network)\n\t- It contains data from approximately 4,000 monitoring stations around the country. This data does not include \"Percent of population in counties exceeding NAAQS (vs. population in counties that either meet the standard or do not monitor PM2.5)\n- [Austin Airport Annual Performance Measures](https://www.johnsnowlabs.com/marketplace/austin-airport-annual-performance-measures)\n\t- This dataset contains the City of Austin's Airport Annual Performance Measures for year 2012, 2013 and 2014.\n- [Availability of Hospital Structural Measures](https://www.johnsnowlabs.com/marketplace/availability-of-hospital-structural-measures)\n\t- This dataset contains the list of hospitals and the availability of structural measures at that hospital. Structure measures evaluate the infrastructure of healthcare settings, such as hospitals or doctor offices, and whether those healthcare settings are able to deliver care.Hospital Structural Measures start date is 01-01-2006 and end date is 12-31-2018.\n- [HEDIS Part C Disease Screening Quality Measures 2014 to 2018](https://www.johnsnowlabs.com/marketplace/hedis-part-c-disease-screening-quality-measures-2014-to-2018)\n\t- The dataset explains different metrics or standard of measurement for Part C (Medicare private health plans) reported at the contract level with less than 500 enrolled. Medicare Part C is the part of Medicare policy that allows private health insurance companies to provide Medicare benefits. These Medicare private health plans, such as HMOs (Health Maintenance Organization) and PPOs (Preferred Provider Organization), are known as Medicare Advantage Plans.\n- [HEDIS Part C and D Quality Measures 2010 to 2017](https://www.johnsnowlabs.com/marketplace/hedis-part-c-and-d-quality-measures-2010-to-2017)\n\t- The dataset explains different metrics or standard of measurement for Part C and D (Medicare private health plans) reported at the contract level with less than 500 enrolled. Medicare Part C and D is the part of Medicare policy that allows private health insurance companies to provide Medicare benefits. These Medicare private health plans, such as HMOs (Health Maintenance Organization) and PPOs (Preferred Provider Organization), are known as Medicare Advantage Plans.\n- [HEDIS Part C and D Quality Measures](https://www.johnsnowlabs.com/marketplace/hedis-part-c-and-d-quality-measures)\n\t- The dataset explains different metrics or standard of measurement for Part C and D (Medicare private health plans) reported at the contract level with less than 500 enrolled. Medicare Part C and D is the part of Medicare policy that allows private health insurance companies to provide Medicare benefits. These Medicare private health plans, such as HMOs (Health Maintenance Organization) and PPOs (Preferred Provider Organization), are known as Medicare Advantage Plans.\n- [International Comparisons of Productivity](https://www.johnsnowlabs.com/marketplace/international-comparisons-of-productivity)\n\t- This dataset shows data underlying comparisons of UK (United Kingdom) productivity against that of the remaining G7 countries.\n- [London Borough of Barnet Performance Reporting 2018-2019](https://www.johnsnowlabs.com/marketplace/london-borough-of-barnet-performance-reporting-2018-2019)\n\t- This dataset contains the list of the second quarter London Borough of Barnet (LBB) performance reporting of 2018-2019. The most up to date information on the Corporate Plan can be found in the 2018/19 Addendum to the Corporate Plan.\n- [MTA Performance Indicators Per Agency](https://www.johnsnowlabs.com/marketplace/mta-performance-indicators-per-agency)\n\t- This information comes from the MTA’s (Metropolitan Transportation Authority) Performance Dashboard and is used to increase transparency at the MTA. The Key Performance Indicators (KPI) are used to track performance in areas such as On-Time Performance.\n- [Multifamily Housing Physical Inspection Scores](https://www.johnsnowlabs.com/marketplace/multifamily-housing-physical-inspection-scores)\n\t- This dataset includes information regarding HUD's (Housing & Urban Development) Real Estate Assessment Center conducts physical property inspections of properties that are owned, insured or subsidized by HUD, including public housing and multifamily assisted housing.\n- [NYC School Survey Preferences](https://www.johnsnowlabs.com/marketplace/nyc-school-survey-preferences)\n\t- This dataset shows the NYC (New York City) School Survey Materials for Survey Coordinators by students, parents and teachers.\n- [NYC Student Discipline Annual Report](https://www.johnsnowlabs.com/marketplace/nyc-student-discipline-annual-report)\n\t- This dataset shows the NYC (New York City) student discipline annual report aggregated by removals, principal and/or superintendent intervention.\n- [Northern Ireland Multiple Deprivation Measures](https://www.johnsnowlabs.com/marketplace/northern-ireland-multiple-deprivation-measures)\n\t- This dataset provides the information for 7 distinct types of deprivation, known as domains along with an overall multiple deprivation measure (MDM). The NIMDM 2017 comprises of 38 indicators in total.\n- [Nursing Home Quality Measures](https://www.johnsnowlabs.com/marketplace/nursing-home-quality-measures)\n\t- This dataset contains the list of the quality measures displayed on Nursing Home Compare, excluding Measures of Rehospitalization, Emergency Room Visit, and Community Discharge. Each row contains a specific quality measure for a specific nursing home and includes the three-quarter score average and the score for each individual quarter. This dataset covers the period from 2013 to 2022.\n- [Precision Values for PSI 90 and Component Measures](https://www.johnsnowlabs.com/marketplace/precision-values-for-psi-90-and-component-measures)\n\t- This dataset includes the Patient Safety and Adverse Events measure (PSI-90) and the individual patient safety indicators. PSI-90 is a composite surgical complication measure composed of ten patient safety indicators. The measure provides an overview of hospital-level quality as it relates to a set of potentially preventable hospital-related events associated with harmful outcomes for patients. This data set includes six digit precision.\n- [Quarterly Corporate Plan and Performance](https://www.johnsnowlabs.com/marketplace/quarterly-corporate-plan-and-performance)\n\t- This dataset contains the list of the quarter Corporate Plan and Performance report of 2016-2017.\n- [Standard Area Measurements for Electoral Areas in UK 2015](https://www.johnsnowlabs.com/marketplace/standard-area-measurements-for-electoral-areas-in-uk-2015)\n\t- This dataset contains the Standard Area Measurements (SAM) for the electoral areas in Great Britain as of 31 December 2015.\n- [UK Adult Social Care Outcomes Framework](https://www.johnsnowlabs.com/marketplace/uk-adult-social-care-outcomes-framework)\n\t- This report provides the findings from the Adult Social Care Outcomes Framework (ASCOF) in England for the period 1 April 2016 to 31 March 2017. The ASCOF draws on data from a number of collections, and measures how well care and support services achieve the outcomes that matter most to people. The measures are grouped into four domains which are typically reviewed in terms of movement over time.\n- [UK Habitats Directive Report](https://www.johnsnowlabs.com/marketplace/uk-habitats-directive-report)\n\t- This dataset lists conservation measures for habitats and species listed under the Habitats Directive. Every six years, all EU Member States are required (under Article 17 of the Directive) to report on the implementation of the EU Habitats Directive.\n- [UK Local Child Poverty Proxy Measure](https://www.johnsnowlabs.com/marketplace/uk-local-child-poverty-proxy-measure)\n\t- This dataset shows the proportion of children living in families receiving out of work benefits for each local authority in England.\n- [UK Measures of Uncertainty With Proportional Contributions](https://www.johnsnowlabs.com/marketplace/uk-measures-of-uncertainty-with-proportional-contributions)\n\t- This dataset shows the Methodology for measuring uncertainty in ONS (Office for National Statistics) local authority mid-year population estimates\n- [Unified Codes For SI Units](https://www.johnsnowlabs.com/marketplace/unified-codes-for-si-units)\n\t- This dataset shows the table of base Units. Any of the fundamental units of length, mass, time, electric current, thermodynamic temperature, amount of substance, or luminous intensity in the International System of Units, consisting respectively of the meter, kilogram, second, ampere, kelvin, mole, and candela.\n- [Units Used In Medicine](https://www.johnsnowlabs.com/marketplace/units-used-in-medicine)\n\t- This dataset lists example unit terms that are commonly used in medicine. Since the space of possible unit terms is infinite in theory and very large in practice, no attempt has been made on a systematic coverage of possible units. All necessary units can be built from the rules of The Unified Code for Units of Measure and there is no need for a particular term to be enumerated in order to be valid.\n- [Water Framework Directive Measures Cycle](https://www.johnsnowlabs.com/marketplace/water-framework-directive-measures-cycle)\n\t- This dataset shows the WFD (Water Framework Directive) Measures Cycle defined as a measure that is required to comply with directives or other legislation.\n- [Weights Measures and Other Tests](https://www.johnsnowlabs.com/marketplace/weights-measures-and-other-tests)\n\t- This dataset features detailed information about DCA (Department of Consumer Affairs) inspections of scales, measuring devices, pricing and other equipment.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated\n- Data Updates\n - Data updates support replace-on-update: outdated foreign keys are deprecated, not deleted\n\n**Our data is curated and enriched by domain experts** \n\n Each dataset is manually curated by our team of doctors, pharmacists, public health & medical billing experts:\n- Field names, descriptions, and normalized values are chosen by people who actually understand their meaning\n- Healthcare & life science experts add categories, search keywords, descriptions and more to each dataset\n- Both manual and automated data enrichment supported for clinical codes, providers, drugs, and geo-locations\n- The data is always kept up to date – even when the source requires manual effort to get updates\n- Support for data subscribers is provided directly by the domain experts who curated the data sets\n- Every data source’s license is manually verified to allow for royalty-free commercial use and redistribution.\n\n**Need Help?**\nIf you have questions about our products, contact us at [info@johnsnowlabs.com](mailto:info@johnsnowlabs.com). \n ","terms_of_service":"https://www.johnsnowlabs.com/healthcare-data-library-eula/","documentation_link":"https://www.johnsnowlabs.com/marketplace/quality-performance-measures-data-package","contact_email":"support@johnsnowlabs.com","privacy_policy_link":"https://www.johnsnowlabs.com/privacy-policy/","embedded_notebook_file_infos":[{"id":"19b42acc-e814-4977-8e97-7a926eedfebc","marketplace_file_type":"EMBEDDED_NOTEBOOK","file_parent":{"parent_id":"7de633d0-f07c-42b9-a871-70df0755a44d","file_parent_type":"LISTING"},"mime_type":"text/html","download_link":"https://marketplace-file-storage-prod.s3-fips.us-east-1.amazonaws.com/EMBEDDED_NOTEBOOK/LISTING_7de633d0-f07c-42b9-a871-70df0755a44d/19b42acc-e814-4977-8e97-7a926eedfebc?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEGgaDnVzLWVhc3QtMS1maXBzIkYwRAIgbC8sWIoYaHR8y7Wq86nPb0pufBl4%2BM4PWpeN%2B5PW9VQCIG7BhAVBDCWTpxFDLeuKYm%2Ff1JLz4iccPMzter0YdhgWKqYECDAQABoMNDE0MzUxNzY3ODI2IgxNAJrHbXaZG20jNfMqgwQyGEb4TsoVOoNvJ%2BaA%2FZ7zeu8hGcHqCeNfblP%2FQo5g6q2aRNtpwtPRKMlhYcIE%2FUQaNT1Lqmyptu32VUQTpzjTTxRVmJrDPkheSCkV06XQ%2FpTMKjU8CC7X1b%2FqYf8CrBwZT7Cv4C5WPjEET38f2A79h0xV6bZgPy28zQWrSeIJ9w6AfZBxiE24%2FVc08zVA0ivCT6lsy7nuU0wcR%2FMg2C9nd1vaMnQ923VpD21CKfVVYcrNWL2b%2FQ%2BNAO%2BxPjPgjiN6UPQbvbvZxkSdhgrGtecIg9NgqGkMcvM5VLTDtdC1kdeZetfOC2obtA%2BT%2F0gy3tIDo6WM5hcQfw743%2FAU9oaGnRBl%2BwoxRRDCZA%2F4jnPdluICSI7NCr6C3HHOe4CSZJjRihczxPA%2FgQczAVjAPs6CrK9N9pIzt0vNJpGUTgg4AoxftTvGfciDREilSctPIcBk6GwB%2Bzt4wbtNCmEPU%2BFD46rc6CWVQmmLwbKOeHw70ipMVFlOc8NlXkEtGO2vu6H44MstSSc6daOVUYUavQSGZ6jGhEjsP2YGz8KDm9XQotB%2FtyVBqiZV01OKqDmmBJA%2FXjUpSJ8M%2FeJXhe5m%2BKcxzNObtoJRI7jci3Tpt08XZyXZ7%2FyIV8H30ya468X3cfkTXrm972uga3z7iXAdubyEHnQIua%2FuvtwOHILi6O5D1Me2MzCPvcnXBjqTAYDoMWBt%2B3AVFuqSs2dmDQNBnvEhXfMgTedKLD5PJHb3aIl15iBkLaNu36GYnVCp9ZU2lSYcTbY5QwNnNeTfJTNy9bKhwzru6Sg%2BslhN%2BTS0zkTn%2BeLMY4tHgPEtEZ2cp%2FCk6GwqswvnYVKHjwO8q%2B1Mx8lc08fVEfarE9dke1GGFqoddx%2Bq%2BZUyHbDlAVfrCmf0yw%3D%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20261110T000135Z&X-Amz-SignedHeaders=host&X-Amz-Expires=86400&X-Amz-Credential=ASIAWA6KKHEJORWBH4N3%2F20261110%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=f67569d91d865a64836882b842e4167424bc76cefa51edfadbfb47cb2ef9698d","created_at":1682537554305,"updated_at":1682537554305,"display_name":"EDA Notebook","status":"FILE_STATUS_PUBLISHED"}]},"provider_summary":{"name":"John Snow Labs","description":"John Snow Labs, an AI and NLP for healthcare company, provides state-of-the-art software, models, and data to help healthcare and life science organizations build, deploy, and operate AI projects."},"popularity":1789},{"id":"4b457315-67db-4c6b-b80a-378c660201e9","summary":{"name":"Cancer Statistics","subtitle":"This data package contains information on cancer its type, its occurrence by age, type and site. It also provides deta..","share":{"name":"cancer_statistics","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1682356093449,"updated_at":1698965635253,"categories":["HEALTH","SCIENCE_AND_RESEARCH"],"listingType":"STANDARD","provider_id":"f88849f8-9d77-4de2-b4c7-03d55cc3768e"},"detail":{"description":"\n**Overview**\nThis data package contains information on cancer its type, its occurrence by age, type and site. It also provides detailed data on adult and childhood cancer survival rates and deaths caused by breast cancer in females.\n\n**Description**\nThis data package consists of multiple datasets on cancer statistics in the United States and England. These datasets include \n- Female breast age adjusted invasive cancer incidence rates in the United States. \n- Rate at which women developed or died from breast cancer. \n- Female breast age adjusted cancer death counts, rates and population in the United States \n- Survival for children (aged 0 to 14 years) diagnosed with cancer. The analyses were carried out using the cohort, period and hybrid approaches. \n- Age-Adjusted Incidence Rates and Confidence Intervals for Cancer types grouped by tumor site. \n- Age-Adjusted Incidence Rates and Confidence Intervals for Cancer types by Age in the United States. \n- Age-Adjusted Incidence Rates and Confidence Intervals for Cancer types by Areas in the United States. \n- Cancer survival in England where adults aged 15 to 99 were diagnosed between 2011 and 2015 and followed up to 2016.\n\n**Benefits**\n- useful for healthcare researchers and students studying cancer.\n- useful to get information about different types of cancer and cancer sites.\n- easy to use, normalized data available on breast cancer prevalence and deaths caused by it.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [Adult Cancer Survival in England 2013 to 2017](https://www.johnsnowlabs.com/marketplace/adult-cancer-survival-in-england-2013-to-2017)\n\t- This dataset includes information regarding Cancer survival in England where adults aged 15 to 99 were diagnosed between 2013 and 2017 and followed up to 2018. When the data for this report were extracted for analysis on 11 August 2019, cancer registrations in 2017 were believed to be at least 98% complete, and the vital status for each patient at 31 December 2017 was known for at least 99% of cancers registered between 2013 and 2017.\n- [Brain Cancer by Tumor Site](https://www.johnsnowlabs.com/marketplace/brain-cancer-by-tumor-site)\n\t- This registry contains data on Age-Adjusted Incidence Rates and 95% Confidence Intervals for Brain and Other Nervous System Tumors by Histologic Grouping , Age, and Behavior. Rates are per 100,000 persons and are age-adjusted to the 2000 U.S. standard population (19 age groups - Census P25-1130). CDC’s National Program of Cancer Registries (NPCR) has funded state cancer registries to collect population-based cancer incidence data under Public Law 102-515, the Cancer Registries Amendment Act.\n- [Cancer Types Grouped by Age](https://www.johnsnowlabs.com/marketplace/cancer-types-grouped-by-age)\n\t- This registry contains data on Age-Adjusted Incidence Rates and Confidence Intervals for Cancer types by Age in the United States. Rates are per 100,000 persons and are age-adjusted to the 2000 U.S. standard population (19 age groups - Census P25-1130). Since 1994, CDC’s National Program of Cancer Registries (NPCR) has funded state cancer registries to collect population-based cancer incidence data under Public Law 102-515, the Cancer Registries Amendment Act.\n- [Cancer Types Grouped by Area](https://www.johnsnowlabs.com/marketplace/cancer-types-grouped-by-area)\n\t- This registry contains data on Age-Adjusted Incidence Rates and Confidence Intervals for Cancer types by Areas in the United States. Rates are per 100,000 persons and are age-adjusted to the 2000 U.S. standard population (19 age groups - Census P25-1130). Since 1994, CDC’s National Program of Cancer Registries (NPCR) has funded state cancer registries to collect population-based cancer incidence data under Public Law 102-515, the Cancer Registries Amendment Act.\n- [Cancer Types Grouped by Site](https://www.johnsnowlabs.com/marketplace/cancer-types-grouped-by-site)\n\t- This registry contains data on Age-Adjusted Incidence Rates and Confidence Intervals for Cancer types grouped by tumor site. Rates are per 100,000 persons and are age-adjusted to the 2000 U.S. standard population (19 age groups - Census P25-1130). Since 1994, CDC’s National Program of Cancer Registries (NPCR) has funded state cancer registries to collect population-based cancer incidence data under Public Law 102-515, the Cancer Registries Amendment Act.\n- [Childhood Cancer Survival in England 1990 to 2018](https://www.johnsnowlabs.com/marketplace/childhood-cancer-survival-in-england-1990-to-2018)\n\t- This dataset includes information regarding survival for children (aged 0 to 14 years) diagnosed with cancer in England from 1990 to 2018, followed up to 31 December 2018. The analyses were carried out using the cohort, period and hybrid approaches.\n- [Female Breast Cancer Death Data 2013](https://www.johnsnowlabs.com/marketplace/female-breast-cancer-death-data-2013)\n\t- This dataset shows the female breast age adjusted cancer death counts, rates and population in United States in the year 2013.\n- [Female Breast Cancer Incidence and Death Rates by State 1999 to 2013](https://www.johnsnowlabs.com/marketplace/female-breast-cancer-incidence-and-death-rates-by-state-1999-to-2013)\n\t- The U.S. states are divided into groups based on the rates at which women developed or died from breast cancer in 2013, which is the most recent year for which incidence data are available.\n- [Female Breast Invasive Cancer Incidence Data 2013](https://www.johnsnowlabs.com/marketplace/female-breast-invasive-cancer-incidence-data-2013)\n\t- This dataset shows the female breast age adjusted invasive cancer incidence rates in United States in the year 2013.\n- [US Cancer Incidence Rates](https://www.johnsnowlabs.com/marketplace/us-cancer-incidence-rates)\n\t- The US Cancer Incidence Rates dataset includes data about cancer occurrence estimates for various cancer sites among men and women under age 18 in the United States by race and Hispanic origin between 1990 and 2014.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 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It includes over 100 attributes related to the sale of a property, covering over 2,740 counties in the United States. The product contains attribution for seller and buyer, property address, recorder and deed information, and sale and mortgage information.\n\nThis is a free sample of Precisely's Property Historical Sales USA product covering the San Francisco, CA area. The full USA product is available as Paid access. Contact Databricks.partnership@precisely.com for full product licensing information.\n\n**Use cases**\n- **Market Analysis**\nEasily create a list of comparable properties\n- **Asset Valuation**\nMore accurately appraise and value property\n\n**Product details**\n\nProperty Historical Sales contains a number of IDs to enhance the usability of the product and provide linkage to other Precisely products. Each record in the product contains a unique product ID (HPAID), a PAID to relate the Property Attributes Assessment product, and the PLINKID to associate property records to the Precisely Parcel Boundaries polygonal dataset. In addition, the product contains the PreciselyID (PBKEY), the unique ID for an addressable location. If a property record represents a secondary address within a building, the parent building PreciselyID will also be provided in a separate field called PARENT.\n\nIt is very common for Assessment and Deed information to be analyzed together, and therefore the Property Historical Sales product is meant to be used along-side the Property Attributes Assessment product. Simply link these two datasets together via the Property Attributes ID (PAID) and get full access to all Assessor and Deed information for a given property. There are scenarios however, where a deed record cannot be linked successfully to an assessment record in product. For example, new property sales that may not have occurred at the time the assessment public roll data was published. 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It is a useful tool for activities such as looking for new safety concerns that might be related to a marketed product, evaluating a manufacturer's compliance with reporting regulations and responding to outside requests for information. The reports in FAERS are evaluated by clinical reviewers, in the Center for Drug Evaluation and Research (CDER) and the Center for Biologics Evaluation and Research (CBER), to monitor the safety of products after they are approved by FDA. These reports are submitted to FDA by healthcare professionals (such as physicians, pharmacists, nurses and others) and consumers (such as patients, family members, lawyers and others). Healthcare professionals and consumers may also report to the products’ manufacturers who then send these reports to the FDA. The datasets in FAERS data package contain much relevant information such as patient demographics, drug name, dose and route of administration, diagnosis and indication for use, therapy starts and ends date and patient outcome.\n\n **Benefits**\n - useful for physicians, pharmacists, pharmaceutical companies, medical students and patients (general public).\n- faers database is available on fda’s website in raw form, whereas a john snow labs (jsl) customer gets it in a clean and normalized form ready to use and create his own custom reports.\n- quarter-wise data merged into one file.\n- easy to comprehend for the customers who are unfamiliar with the medical terminology as all abbreviated terms are replaced with full form, unlike faers original datasets which are full of abbreviations.\n\n **License Information**\n The use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n **Included Datasets**\n - [FDA Adverse Events Reporting System Demographics 2023](https://johnsnowlabs.com/wp-json/datasets-api/v1/fda-adverse-events-reporting-system-demographics-2023)\n\t- The FDA Adverse Event Reporting System (FAERS) contains information on medication errors, quality complaints and drug-related adverse events that were submitted to the FDA (U.S. Food and Drug Administration) in 2023. The Demographics dataset contains patient demographic and administrative information, a single record for each event report.\n- [FDA Adverse Events Reporting System Drug 2023](https://johnsnowlabs.com/wp-json/datasets-api/v1/fda-adverse-events-reporting-system-drug-2023)\n\t- The FDA Adverse Event Reporting System (FAERS) contains information on medication errors, quality complaints and drug-related adverse events that were submitted to FDA (U.S. Food and Drug Administration) in 2023. The Drug dataset contains drug/biologic information for as many medications as were reported for the event (1 or more per event).\n- [FDA Adverse Events Reporting System Drug Indication 2023](https://johnsnowlabs.com/wp-json/datasets-api/v1/fda-adverse-events-reporting-system-drug-indication-2023)\n\t- The FDA Adverse Event Reporting System (FAERS) contains information on medication errors, quality complaints and drug-related adverse events that were submitted to the FDA (U.S. Food and Drug Administration) in 2023. The Indication dataset contains all \"Medical Dictionary for Regulatory Activities\" (MedDRA) terms coded for the indications for use (diagnoses) for the reported drugs (0 or more per drug per event).\n- [FDA Adverse Events Reporting System Drug Reaction 2023](https://johnsnowlabs.com/wp-json/datasets-api/v1/fda-adverse-events-reporting-system-drug-reaction-2023)\n\t- The FDA Adverse Event Reporting System (FAERS) contains information on medication errors, quality complaints and drug-related adverse events that were submitted to FDA (U.S. Food and Drug Administration) in 2023. The drug reaction dataset contains all \"Medical Dictionary for Regulatory Activities\" (MedDRA) terms coded for the adverse event (1 or more).\n- [FDA Adverse Events Reporting System Drug Therapy Dates 2023](https://johnsnowlabs.com/wp-json/datasets-api/v1/fda-adverse-events-reporting-system-drug-therapy-dates-2023)\n\t- The FDA Adverse Event Reporting System (FAERS) contains information on medication errors, quality complaints and drug-related adverse events that were submitted to FDA (U.S. Food and Drug Administration) in 2023. The drug therapy dates dataset contains drug therapy start dates and end dates for the reported drugs.\n- [FDA Adverse Events Reporting System Patient Outcome 2023](https://johnsnowlabs.com/wp-json/datasets-api/v1/fda-adverse-events-reporting-system-patient-outcome-2023)\n\t- The FDA Adverse Event Reporting System (FAERS) contains information on medication errors, quality complaints and drug-related adverse events that were submitted to FDA (U.S. Food and Drug Administration) in 2023. The patient outcome file contains information on patient outcomes for the event (0 or more).\n- [FDA Adverse Events Reporting System Report Source 2023](https://johnsnowlabs.com/wp-json/datasets-api/v1/fda-adverse-events-reporting-system-report-source-2023)\n\t- The FDA Adverse Event Reporting System (FAERS) contains information on medication errors, quality complaints and drug-related adverse events that were submitted to FDA (U.S. Food and Drug Administration) in 2023. The report source dataset contains report sources for event.\n\n\n **Data Engineering Overview**\n **We deliver high-quality data**\n - Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n - Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n - Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated\n - Data Updates\n - Data updates support replace-on-update: outdated foreign keys are deprecated, not deleted\n\n **Our data is curated and enriched by domain experts** \n\n Each dataset is manually curated by our team of doctors, pharmacists, public health & medical billing experts:\n - Field names, descriptions, and normalized values are chosen by people who actually understand their meaning\n - Healthcare & life science experts add categories, search keywords, descriptions and more to each dataset\n - Both manual and automated data enrichment supported for clinical codes, providers, drugs, and geo-locations\n - The data is always kept up to date – even when the source requires manual effort to get updates\n - Support for data subscribers is provided directly by the domain experts who curated the data sets\n - Every data source’s license is manually verified to allow for royalty-free commercial use and redistribution.\n\n **Need Help?**\n If you have questions about our products, contact us at [info@johnsnowlabs.com](mailto:info@johnsnowlabs.com). \n ","terms_of_service":"https://www.johnsnowlabs.com/healthcare-data-library-eula/","documentation_link":"https://johnsnowlabs.com/wp-json/datasets-api/v1/fda-drug-adverse-events-reporting-system-faers-2023-data-package","contact_email":"support@johnsnowlabs.com","privacy_policy_link":"https://www.johnsnowlabs.com/privacy-policy/"},"provider_summary":{"name":"John Snow Labs","description":"John Snow Labs, an AI and NLP for healthcare company, provides state-of-the-art software, models, and data to help healthcare and life science organizations build, deploy, and operate AI projects."},"popularity":68},{"id":"37a86512-1c6a-4f71-8840-b77c469503a0","summary":{"name":"Truck Tonnage | FRED","subtitle":"Truck Tonnage index is a way to measure the gross tonnage of freight which is carried by motor carriers monthly. 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The `TRUCKD11` column represents the truck tonnage index at the time of the corresponding `DATE`.\n\n**Data Sources**\n\nThe data in this product comes from **FRED**.\n\n- **Source Homepage:** [FRED](https://fred.stlouisfed.org/)\n- **Frequency:** \n\nThe data in this product comes from **FRED**.\n\nShort for Federal Reserve Economic Data, FRED is an online database consisting of hundreds of thousands of economic data time series from scores of national, international, public, and private sources. \nFRED, created and maintained by the Research Department at the Federal Reserve Bank of St. Louis, goes far beyond simply providing data: \nIt combines data with a powerful mix of tools that help the user understand, interact with, display, and disseminate the data. \nIn essence, FRED helps users tell their data stories.\n\nThis product uses data from the FRED® API but is not endorsed or certified by the Federal Reserve Bank of St. Louis.\n\n- **Source Homepage:** [FRED](https://fred.stlouisfed.org/)\n- **Source Data:** [Data link](https://fred.stlouisfed.org/series/TRUCKD11)\n- **License:** [License](https://fred.stlouisfed.org/legal/#full-fred-terms)\n- **Frequency:** Monthly\n","terms_of_service":"https://rearc-data-public-assets.s3.amazonaws.com/Rearc_Data_DSA.pdf","documentation_link":"","contact_email":"data@rearc.io","support_link":"https://www.rearc.io/data","privacy_policy_link":"https://rearc-data-public-assets.s3.amazonaws.com/Rearc+Privacy+Policy.pdf","embedded_notebook_file_infos":[{"id":"dc217396-01b7-4e62-a0cb-d2097fe9f55b","marketplace_file_type":"EMBEDDED_NOTEBOOK","file_parent":{"parent_id":"37a86512-1c6a-4f71-8840-b77c469503a0","file_parent_type":"LISTING"},"mime_type":"text/html","download_link":"https://marketplace-file-storage-prod.s3-fips.us-east-1.amazonaws.com/EMBEDDED_NOTEBOOK/LISTING_37a86512-1c6a-4f71-8840-b77c469503a0/dc217396-01b7-4e62-a0cb-d2097fe9f55b?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEGgaDnVzLWVhc3QtMS1maXBzIkYwRAIgbC8sWIoYaHR8y7Wq86nPb0pufBl4%2BM4PWpeN%2B5PW9VQCIG7BhAVBDCWTpxFDLeuKYm%2Ff1JLz4iccPMzter0YdhgWKqYECDAQABoMNDE0MzUxNzY3ODI2IgxNAJrHbXaZG20jNfMqgwQyGEb4TsoVOoNvJ%2BaA%2FZ7zeu8hGcHqCeNfblP%2FQo5g6q2aRNtpwtPRKMlhYcIE%2FUQaNT1Lqmyptu32VUQTpzjTTxRVmJrDPkheSCkV06XQ%2FpTMKjU8CC7X1b%2FqYf8CrBwZT7Cv4C5WPjEET38f2A79h0xV6bZgPy28zQWrSeIJ9w6AfZBxiE24%2FVc08zVA0ivCT6lsy7nuU0wcR%2FMg2C9nd1vaMnQ923VpD21CKfVVYcrNWL2b%2FQ%2BNAO%2BxPjPgjiN6UPQbvbvZxkSdhgrGtecIg9NgqGkMcvM5VLTDtdC1kdeZetfOC2obtA%2BT%2F0gy3tIDo6WM5hcQfw743%2FAU9oaGnRBl%2BwoxRRDCZA%2F4jnPdluICSI7NCr6C3HHOe4CSZJjRihczxPA%2FgQczAVjAPs6CrK9N9pIzt0vNJpGUTgg4AoxftTvGfciDREilSctPIcBk6GwB%2Bzt4wbtNCmEPU%2BFD46rc6CWVQmmLwbKOeHw70ipMVFlOc8NlXkEtGO2vu6H44MstSSc6daOVUYUavQSGZ6jGhEjsP2YGz8KDm9XQotB%2FtyVBqiZV01OKqDmmBJA%2FXjUpSJ8M%2FeJXhe5m%2BKcxzNObtoJRI7jci3Tpt08XZyXZ7%2FyIV8H30ya468X3cfkTXrm972uga3z7iXAdubyEHnQIua%2FuvtwOHILi6O5D1Me2MzCPvcnXBjqTAYDoMWBt%2B3AVFuqSs2dmDQNBnvEhXfMgTedKLD5PJHb3aIl15iBkLaNu36GYnVCp9ZU2lSYcTbY5QwNnNeTfJTNy9bKhwzru6Sg%2BslhN%2BTS0zkTn%2BeLMY4tHgPEtEZ2cp%2FCk6GwqswvnYVKHjwO8q%2B1Mx8lc08fVEfarE9dke1GGFqoddx%2Bq%2BZUyHbDlAVfrCmf0yw%3D%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20261110T000135Z&X-Amz-SignedHeaders=host&X-Amz-Expires=86400&X-Amz-Credential=ASIAWA6KKHEJORWBH4N3%2F20261110%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=f0b17106033e25474e410ebc10f92750ae6f8eee3339925678df7f7a1096219c","created_at":1683045534145,"updated_at":1683045534145,"status":"FILE_STATUS_PUBLISHED"}],"assets":["ASSET_TYPE_DATA_TABLE"]},"provider_summary":{"name":"Rearc","description":"Rearc provides a vast library of data products for Healthcare, Financial Services, and ESG use cases. 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Once you sign up, a token will be emailed to you instantly.**\n\n**MCP Overview:**\nThe US Census ACS MCP Server connects your Databricks AI agents to the U.S. Census Bureau Data API through a native service mirroring the official Census API surface. Agents discover the right dataset, identify exact variables, resolve any U.S. geography to FIPS components, and fetch aggregate data in a single workflow. Coverage includes ACS 1-year and 5-year estimates, Decennial Census, CPS, PEP, and more across all geographies from national to census tract and ZCTA level.\n\n**Use cases:**\n\n- Identify the correct ACS table and variable codes for any SDOH concept, including: income, poverty, insurance coverage, housing burden, disability\n- Resolve any U.S. geography by name or zip code to Census API-compatible FIPS components\n- Retrieve insurance coverage rates by type at zip code and census tract level\n- Fetch aggregate SDOH data across any geography for risk stratification and care desert mapping\n- Compare population health variable\n\n**Sample Prompts:**\n\nPrompt Example 1: Census Tract SDOH Risk Profiling\n\n- \"Find ACS 5-year tables and variable codes for poverty, insurance coverage, and median household income. Resolve zip code 10451 in the Bronx to census tract FIPS codes, fetch aggregate data across those tracts, and rank them by social risk burden.\"\n\nPrompt Example 2: Multi-County Health Equity Benchmarking\n\n- \"Search ACS tables for disability, housing cost burden, and insurance coverage. Look up variable codes, resolve Cook County IL, Wayne County MI, and Orleans Parish LA to FIPS codes, fetch aggregate data, and return a table ranked by uninsured rate and disability prevalence.\"\n\n**These tools are available when installed:**\n\n- census_list_datasets: List all Census datasets including ACS, Decennial, CPS, SIPP, and PEP with available vintages\n- census_fetch_dataset_geography: Return valid geography levels for a specific dataset and vintage\n- census_search_data_tables: Find the correct Census table from a plain-language concept\n- census_lookup_variables: Return specific variable codes within a table that answer the question\n- census_resolve_geography_fips: Resolve any U.S. geography name or zip code to Census API FIPS components\n- census_fetch_aggregate_data: Fetch raw aggregate data by variable, table, geography, and optional predicates\n\n**Additional Information:**\nThis MCP Server is powered by Climb, a leading Consulting and Solutions Integrator Partner of Databricks. Learn more about Climb here: [climb.ai](https://climb.ai) \n\n![Climb logo](https://climb-static-assets.s3.us-west-2.amazonaws.com/assets/PNG/Dark/Climb+Logo+Left-Thirds.png)","terms_of_service":"https://climb.ai/terms-of-service","documentation_link":"https://api.census.gov/data.html","privacy_policy_link":"https://climb.ai/privacy-policy","update_frequency":{"interval":1,"unit":"YEARLY"},"assets":["ASSET_TYPE_MCP"],"license":""},"provider_summary":{"name":"American Community Survey (ACS)","description":"This MCP Server is powered by Climb, a leading Consulting and Solutions Integrator Partner of Databricks. Learn more here: climb.ai | The American Community Survey (ACS), conducted annually by the U.S. Census Bureau, is the nation's most comprehensive ongoing survey of the American population, collecting detailed information on income, housing, education, employment, health insurance coverage, and dozens of other social and economic topics. Unlike the decennial census conducted every ten years, the ACS provides data for over 40 topics on an annual basis, from the national level down to individual neighborhoods and block groups. Its data is used by government agencies, public health organizations, businesses, and researchers to allocate funding, shape policy, and understand the evolving needs of communities across the country. 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Tables, code spans, horizontal rules, blockquotes, and nested formatting are stripped or broken. Here's a version written for that constraint:\n\n# Supplemental Data Sources: India Healthcare Geography & Public Health\nTwo public datasets for enriching India healthcare facility data. Use the PIN code directory for geographic lookup and address enrichment; use the NFHS-5 file to add district-level health and demographic context for demand-side analysis.\nBoth files are published under the Government Open Data License – India via data.gov.in.\n**Data quality note:** These are real-world public-sector datasets. Expect inconsistent place-name casing, ambiguous postal mappings, missing coordinates, and suppressed values. Document how you handle uncertain matches and do not present inferred geography as exact unless verified.\n## India Post PIN Code Directory\n**File:** india_post_pincode_directory.csv\n**Source:** Open Government Data Platform India — All India Pincode Directory till last month\n**Source URL:** https://www.data.gov.in/resource/all-india-pincode-directory-till-last-month\n**License:** Government Open Data License – India (https://www.data.gov.in/Godl)\n165,627 rows covering India's full postal geography. A PIN (Postal Index Number) is a 6-digit code similar to a ZIP or postcode. The file includes 19,586 unique PIN codes across 750 districts and 37 states and union territories.\n**Columns (11):** circlename, regionname, divisionname, officename, pincode, officetype, delivery, district, statename, latitude, longitude\nOffice types: Branch Office (BO, ~140,000 rows), Post Office (PO, ~25,000 rows), Head Office (HO, ~800 rows).\nLatitude and longitude are present but approximately 12,600 rows carry an NA value — do not assume every post office is geocoded.\n**Use cases:**\n- Enrich facility postcodes with district or state context\n- Build geography lookup tables keyed by PIN code, post office, district, or state\n- Explore postal geography ambiguity before joining to other datasets\n**Important — row grain is post office, not PIN code.** A single PIN can appear on multiple rows and may map to more than one district or state. A direct join on pincode will fan out rows unless you deduplicate or aggregate first. Always check cardinality before joining.\n## NFHS-5 District Health Indicators\n**File:** nfhs5_district_health_indicators.csv\n**Source:** National Family Health Survey 2019–21 district fact sheets via data.gov.in\n**Source URL:** https://www.data.gov.in/catalog/national-family-health-survey-5-nfhs-5-india-districts-factsheet-data-provisional\n**Official fact sheets:** https://www.nfhsiips.in/nfhsuser/nfhs5.php\n**License:** Government Open Data License – India (https://www.data.gov.in/Godl)\n706 district rows and 109 columns of indicators from India's National Family Health Survey (field period 2019–2021). This is the most comprehensive district-level public health dataset available for India at this geographic resolution.\n**Indicator groups:** household conditions (electricity, water, sanitation, clean fuel, health insurance), maternal and reproductive health (ANC visits, institutional delivery, C-section rate, family planning), child health and vaccination (BCG, polio, DPT, MCV, rotavirus, vitamin A), nutrition (stunting, wasting, underweight, BMI, breastfeeding), anaemia, non-communicable diseases (blood sugar and blood pressure by sex), cancer screening, tobacco, alcohol.\n**Use cases:**\n- Add district-level health burden context to facility-level analysis\n- Compare facility availability against population health indicators\n- Build district rankings, demand-side maps, or planning dashboards\n- Identify underserved districts where disease burden is high and facility coverage is low\n**Data quality notes:**\n- Column names are long and human-readable — rename to snake_case before loading into a database or Delta table\n- District and state names need normalization before joining to other sources; spelling and casing vary across datasets\n- Asterisk (*) values are suppressed or unavailable — treat as NULL, not zero\n- Parenthesized values such as (29.5) are estimates based on 25–49 unweighted cases and should be used with caution\n- This dataset is NFHS-5 (2019–21). NFHS-6 (2023–24) data is available separately — if you use both, verify that indicator definitions and geographic units are comparable\n## Working with Location Data\nTo map facilities to administrative regions, use facility latitude and longitude with district or state boundary polygons from geoBoundaries (https://www.geoboundaries.org) or DataMeet India Maps (https://datameet.org). A point-in-polygon join assigns each facility to a district, enabling joins to the NFHS-5 file.\nSuggested tools: GeoPandas and Shapely in Python, Databricks geospatial functions (ST_Contains, ST_Point), or QGIS for visual inspection.\nString-matching district names across datasets is unreliable due to inconsistent spelling and transliteration. A spatial join on coordinates is more robust wherever facility coordinates are available.\n\n","terms_of_service":"https://cms.databricks.com/sites/default/files/2023-01/marketplace-consumer-terms_0.pdf","documentation_link":"","privacy_policy_link":"https://www.databricks.com/legal/privacynotice","assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"Databricks","description":"As the world’s first lakehouse platform in the cloud, Databricks combines the best elements of data lakes and data warehouses to deliver the reliability, strong governance, and performance of data warehouses with the openness, flexibility, and machine learning support of data lakes. We are a cross-cloud platform that works across AWS, Azure, and Google.\nThis unified approach simplifies your modern data stack by eliminating the data silos that traditionally separate and complicate data engineering, analytics, BI, data science and machine learning. It’s built on open source and open standards to maximize flexibility. And, its common approach to data management, security and governance helps companies operate more efficiently and innovate faster."},"popularity":1746},{"id":"ee321ce6-5661-4379-9fa4-b283ca18f1ac","summary":{"name":"Electronic Health Records (EHR) Data : Emergency Medicine","subtitle":"High-quality de-identified EHR data, showing comprehensive patient journeys physician narratives in Emergency Medicine.","share":{"name":"electronichealthrecordsdataemergencymedicine","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1723029905835,"updated_at":1723103545575,"categories":["HEALTH","SCIENCE_AND_RESEARCH"],"listingType":"STANDARD","provider_id":"dc00cb61-5b9a-403e-8b4f-71e78dd44d6c"},"detail":{"description":"**Overview**\n\nShaip is a premier provider of data on de-identified electronic health records (EHR). The Shaip EHR data comprises de-identified EHR data that is nationally representative. This data is sourced from practices across the U.S., covering the continuum of care delivery—from independent, single-provider offices to large, integrated delivery systems, including both primary and specialty care. \n\nThis data provides a comprehensive picture of the patient’s journey and physician narrative throughout their hospital stay. With approximately **4 million + unique patient records**, the participating hospitals, offer unique insights into variations in care across healthcare network hospitals and clinics. \n\n**The samples provided and the entire datasets are Gold Standard.**\n\n**Use cases**\n\n**For Healthcare Organizations**\n\n- Population health management: Analyze patient populations to identify trends and improve overall health.\n- Revenue cycle management: Streamline billing and claims processes. \n- Quality improvement: Track and measure performance to identify areas for improvement. \n- Regulatory compliance: Ensure adherence to healthcare regulations and standards. \n\n**For Researchers and Scientists**\n\n- Drug discovery and development: Identify potential drug targets and patient populations for clinical trials.\n- Disease research: Analyze patient data to understand disease progression and identify risk factors.\n- Public health surveillance: Monitor disease outbreaks and track public health trends.\n\n**For Payers**\n\n- Risk assessment: Evaluate the health risks of insured populations.\n- Fraud detection: Identify potential fraudulent claims.\n- Care management: Manage high-cost patients and chronic conditions.\n\n**Product details**\n\n**Medical Records Coverage: Cardiology**\n\n**Metadata Information:**\n- Includes anonymized MRN\n- Admission and discharge dates\n- Length of stay, \n- Gender \n- Patient class\n- Payer \n- Financial class\n- State \n- Discharge disposition\n- Age \n- DRG\n- DRG description\n- Reimbursement amount\n- AMLOS, GMLOS\n- Risk of mortality \n- Severity of illness\n- Grouper\n- Hospital Zip code, and more.\n\n**Geographical Distribution:** Records from various US regions—\n- North East (46.4%)\n- South (8.2%)\n- Midwest (3.1%)\n- West (28.1%)\n- Others (14.2%).\n**Patient Classes:** Covers all patient classes—\n- Inpatient\n- Outpatient (Clinical, Rehab, Recurring, Surgical Day Care)\n- Emergency\n\n**Age Groups:** Records for all age groups—\n- <10 years (7.9%)\n- 11-20 years (5.7%)\n- 21-30 years (10.9%)\n- 31-40 years (11.7%)\n- 41-50 years (10.4%)\n- 51-60 years (13.8%)\n- 61-70 years (16.1%)\n- 71-80 years (13.3%)\n- 81-90 years (7.8%)\n- 90+ years (2.4%).\n\n**Gender Ratio:** 46% male and 54% female.\n\n**Language**\n- EnglishOTS\n- French, Spanish, and Portuguese can be acquired very quickly\n- Other Languages - Custom sourcing or translation from English to target language\n\n**Additional Insights**\n**Privacy:** Documents have PII redacted according to Safe Harbor Guidelines and HIPAA compliance.","terms_of_service":"https://www.shaip.com/terms-of-service/","documentation_link":"","privacy_policy_link":"https://www.shaip.com/privacy-policy/","geographical_coverage":"[\"United States and Canada\"]","cost":"FREE","update_frequency":{"interval":1,"unit":"MONTHLY"},"collection_granularity":{"interval":1,"unit":"DAILY"},"assets":["ASSET_TYPE_MEDIA"],"license":""},"provider_summary":{"name":"Shaip","description":"Shaip is a leading AI data platform and services provider specializing in the sourcing, creation, licensing, and transformation of high-quality, ethical, and domain-specific data for AI and machine learning applications. 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The model evaluates only properties with active mortgages where a mortgage-related foreclosure could occur and explicitly excludes non-mortgage defaults such as HOA or tax liens.\n\nThe model is built using two core data sources: ATTOM’s historical mortgage and pre-foreclosure records and Powerlytics’ proprietary database of anonymized U.S. household tax return data. These datasets are combined and analyzed using machine learning techniques to generate more accurate default risk predictions than either source can provide independently.\n\nEach property record includes both a Propensity Group ranking and a Foreclosure Probability Score ranging from 0–100. Propensity Groups provide relative risk positioning across the dataset, while the probability score enables finer differentiation between properties with similar risk profiles. Higher scores indicate a greater likelihood of mortgage default within the next 12 months.\n\nThe dataset is paired with ATTOM public record data to deliver a comprehensive view of property, ownership, transaction, valuation, and default risk attributes at scale. Model accuracy testing shows the top 30 Propensity Groups capture 95.5% of properties that enter foreclosure within 12 months and are 3.2x more likely to default than a randomly selected property.\n\nUpdated monthly and refreshed through quarterly model validation, Propensity to Default supports risk analytics, investment analysis, and mortgage servicing workflows with nationwide coverage across 2,868 counties and more than 60 million residential properties.","terms_of_service":"https://www.attomdata.com/privacy","contact_email":"support@datarade.ai","privacy_policy_link":"https://www.attomdata.com/privacy","assets":["ASSET_TYPE_DATA_TABLE"]},"provider_summary":{"name":"ATTOM","description":"ATTOM powers innovation across industries with premium property data and analytics covering 160+ million U.S. properties—99% of the population. 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This model has been trained with a dataset that answers general mortgage and loans questions, so potential biases may exist for specific use cases.\n\n**Ethical Considerations**\n\nThis model should be used responsibly, considering ethical implications of automated financial advice. As it is a base model for this financial field, it is crucial to ensure that the model's advice complements human expertise and adheres to relevant financial regulations.\n\n**Acknowledgments**\n\nThis model was developed by the Bitext and trained on infrastructure provided by Bitext.\n\n**License**\n\nThis model, \"Mistral-7B-Mortgage-Loans-v1\", is licensed under the Apache License 2.0 by Bitext Innovations International, Inc. 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Subscribers will have the ability to view SPGE Analytics' latest case, as well as several historical cases to easily compare how SPGE Analytics' outlook has evolved over time.\n\nThis dataset includes:\n\n- Historical coverage from 1995\n\n- Monthly forecast data available through the end of SPGE Analytics' short-term forecast period (between 12 and 18 months) with the first forecast available from January 2014\n\n- Annual forecast data is available through the end of SPGE Analytics' long-term forecast period (between 15 and 20 years)\n\n**Use cases**\n- Fundamental Analysis - Ability to view Energy Analytics' latest case, as well as a number of historical cases to easily compare how S&P Global Energy Analytics' outlook has evolved over time\n- Market Analysis - Get S&P Global Energy’s view on long-term implications on oil production and compare it with alternative scenarios to build your own opinion. Get competitive insights from the company ownership data for crude, condensate and NGL supply elements covering over 500 state and public companies\n- Pricing Analysis - Get a better understanding of the product supply impact on the prices in the global marketplace by using World Oil Supply data in your balancing models, so you can make more informed decisions on upstream investments\n- Risk Analysis - Utilize supply outlook for making decisions for strategic reserves planning, policy making and monitoring. Understand the production costs with consideration of crude quality and use it for your budget and resource planning\n\n**Product details**\n\nSample Tables:\nA ) COST_OF_SUPPLIES\nB ) PRODUCTION\n\nSample Fields:\nA ) COST_OF_SUPPLIES\nREFERENCE_YEAR\nCOST_OF_SUPPLIES_TYPE_ID\nCOST_OF_SUPPLIES_TYPE_NAME\nCOST_PRODUCTION_ELEMENT_ID\nCOST_PRODUCTION_ELEMENT_NAME\nVALUE\n\nB ) PRODUCTION\nSCENARIO_ID\nSCENARIO_TERM_ID\nREGION_ID\nRESERVE_TYPE_ID\nRESERVE_TYPE_NAME\nVALUE\n\nView Descriptions:\n\n- COST_OF_SUPPLIES - Cost of supplies includes Brent equivalent, Brent differential, wellhead costs and annual production in MBD\n- OWNERSHIP - Production ownership by a company\n- PRODUCTION Latest update for historical and forecast oil supply data for individual countries. It returns production by crude grade, capacity and spare capacity, maintenance, disruptions and disruption adjustments. All data is in MBD units. 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This also shows the HCPCS Medicare Outpatient Prospective Payment PartB NDC and Average Sale Price Payment Allowance Limit PartB Drugs by the Centers for Medicare and Medicaid Services. The Medicare Part B payment limits for valid HCPCS codes that are not included in the quarterly ASP pricing files will be determined by the local Medicare contractor.\n\n**Benefits**\n- Useful for providers for medicare cost drug and average sales.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [Average Sale Price Payment Allowance Limit PartB Drugs](https://www.johnsnowlabs.com/marketplace/average-sale-price-payment-allowance-limit-partb-drugs)\n\t- The ASP (Average Sale Price) crosswalks are maintained by the Division of Ambulatory Services to support ASP-based Medicare Part B payments only. The crosswalks are intended to help the public (including entities that submit manufacturer ASP data and providers who bill for drugs) understand which drug products (identified by NDCs - National Drug Code) are assigned to which Healthcare Common Procedure Coding System (HCPCS) billing codes.\n- [HCPCS Crosswalk Medicare Cost AWP Drug Code PartB Drug](https://www.johnsnowlabs.com/marketplace/hcpcs-crosswalk-medicare-cost-awp-drug-code-partb-drug)\n\t- The AWP (Average Wholesale Price) crosswalks are maintained by the Division of Ambulatory Services to support Medicare Part B payments only. The crosswalks are intended to help the public (including entities that submit manufacturer ASP data and providers who bill for drugs) understand which drug products (identified by NDCs- National Drug Code) are assigned to which - National Drug Code billing codes.\n- [HCPCS Crosswalk Medicare Drug Price NOC NDC PartB Drug](https://www.johnsnowlabs.com/marketplace/hcpcs-crosswalk-medicare-drug-price-noc-ndc-partb-drug)\n\t- The ASP (Average Sale Price) crosswalks are maintained by the Division of Ambulatory Services to support ASP-based Medicare Part B payments only. The crosswalks are intended to help the public understand which drug products (identified by NDCs- National Drug Code) are assigned to which Healthcare Common Procedure Coding System (HCPCS) billing codes.\n- [HCPCS Medicare Average Sale Price Drug Code PartB Drug](https://www.johnsnowlabs.com/marketplace/hcpcs-medicare-average-sale-price-drug-code-partb-drug)\n\t- The ASP (Average Sale Price) crosswalks are maintained by the Division of Ambulatory Services to support ASP-based Medicare Part B payments only. The crosswalks are intended to help the public (including entities that submit manufacturer ASP data and providers who bill for drugs) understand which drug products (identified by NDCs- National Drug Code) are assigned to which Healthcare Common Procedure Coding System (HCPCS) billing codes.\n- [HCPCS Medicare Outpatient Prospective Payment PartB NDC](https://www.johnsnowlabs.com/marketplace/hcpcs-medicare-outpatient-prospective-payment-partb-ndc)\n\t- The ASP (Average Sale Price) crosswalks are maintained by the Division of Ambulatory Services to support ASP-based Medicare Part B payments only. The crosswalks are intended to help the public (including entities that submit manufacturer ASP data and providers who bill for drugs) understand which drug products (identified by NDCs- National Drug Code) are assigned to which Healthcare Common Procedure Coding System (HCPCS) billing codes.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - Metadata is provided in the open 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redistribution.\n\n**Need Help?**\nIf you have questions about our products, contact us at [info@johnsnowlabs.com](mailto:info@johnsnowlabs.com). \n 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The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [APM in Quality Payment Program](https://www.johnsnowlabs.com/marketplace/apm-in-quality-payment-program)\n\t- This dataset shows the Alternative Payment Models (APMs) that CMS (Centers for Medicare & Medicaid Services) operates. The dataset identifies which of those APMs CMS has determined to be advanced APMs, unless otherwise noted. The information presented in the dataset applies the Advanced APM criteria adopted in the Quality Payment Program final rule to the current design of the listed APMs.\n- [MIPS Data Validation Criteria](https://www.johnsnowlabs.com/marketplace/mips-data-validation-criteria)\n\t- This dataset includes the MIPS Data Validation Criteria. The Medicare Access and CHIP Reauthorization Act of 2015 (MACRA) streamlines a patchwork collection of programs with a single system where provider can be rewarded for better care. Providers will be able to practice as they always have, but they may receive higher Medicare payments based on their performance.\n- [Patient Facing Encounter Codes](https://www.johnsnowlabs.com/marketplace/patient-facing-encounter-codes)\n\t- CMS defines a patient-facing encounter as an instance in which a MIPS (Merit-based Incentive Payment System) eligible clinician billed for services such as general office visits, outpatient visits, and procedure codes under the Medicare Physician Fee Schedule.\n- [Quality Measure Benchmarks](https://www.johnsnowlabs.com/marketplace/quality-measure-benchmarks)\n\t- This dataset includes an overview of quality measure benchmarks, which is the result of the Medicare Access and CHIP Reauthorization Act (MACRA). The healthcare industry has been introduced to the Merit-Based Payment System (MIPS), which is expected to make changes to overall clinical quality measures. 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The app enables teams to define business-specific quality checks, track results through centralized dashboards, and use AI-powered suggestions to identify risks before they impact reporting, operations, downstream AI systems, or business decisions.\n\n**Use cases** \nUse cases for the listing include:\n- Continuously monitor data quality across Databricks tables.\n- Define and operationalize quality checks for completeness, accuracy, consistency, freshness, and custom business rules.\n- Generate AI-powered data quality check recommendations based on table profiling.\n- Track quality results, trends, and anomalies from a centralized dashboard.\n- Help business and technical users identify data issues before they affect reporting, analytics, or AI initiatives.\n\n**Product details** \nThe Data Quality App supports configurable quality checks and dashboarding directly within Databricks.\n\n- Datasets represented may include customer data, transaction data, finance data, supply chain data, marketing analytics data, and other business-critical tables.\n- Sample fields may include customer_id, transaction_id, order_date, revenue_amount, product_id, region, status, created_timestamp, updated_timestamp, and source_system.\n\nFor more details, refer to the embedded notebook.\n\n**Additional Insights** \nReliable data is foundational to trusted reporting, analytics, and AI outcomes. The Data Quality App helps organizations move from reactive data cleanup to proactive data governance by enabling scalable monitoring, AI-assisted check creation, and business-aligned quality enforcement within Databricks.\n\n**Installation Instructions**\nPlease see the attached README.md file for detailed installation 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By identifying the aircraft type and registration for approximately 98% of all ADS-B-tracked flights, we offer an industry-leading solution for lessors, insurers, airports, OEMs, and analysts seeking precise, reliable, and actionable aviation intelligence.\n\n• High-Resolution ADS-B Integration - Satellite and terrestrial ADS-B flight tracking combined with enriched aircraft and operator data for maximum accuracy and visibility\n• Comprehensive Aircraft Identification - Aircraft type and registration identified for approximately 98% of all ADS-B-tracked flights, using proprietary matching with ch-aviation data and supplementary publicly available authority data sources.\n• Global Flight Coverage - Tracks approximately 160,000–190,000 flights per day across commercial aviation, business jet, and general aviation sectors worldwide.\n• ACMI (Wet-Lease) and Cargo Customer Tracking - Detailed monitoring of ACMI operations, including identification of wet-lease activity between different operators as well as cargo customers identifying flights operated for integrators like DHL Express or FedEx as well as cargo customers such as Amazon.\n• Aircraft Utilisation Tracking - Tracking of flight hours and cycles at both the operator and individual tail number (aircraft) level\n• Matched Operator and Aircraft Data - Every flight is linked to comprehensive ch-aviation datasets, including aircraft ID, history, operator, variant, callsign, and airport details allowing customers to leverage the industry’s most comprehensive integration between ADS-B flight event and fleet/operator/airport data.\n• Fallback Data Enrichment - Where ch-aviation data is unavailable, civil aviation authority and ANSP sources are used to ensure continuity in aircraft identification and data accuracy.\n• Use Case-Driven Insights - Tailored for industry stakeholders like lessors, insurers, OEMs, airports, and analysts seeking operational, commercial, and technical flight data intelligence.\n\nch-aviation integrates its Commercial Aviation Aircraft Data and Business Jet Aircraft Data with Spire Global’s satellite-based ADS-B data that is fused by Spire with terrestrial feeds from two terrestrial ADS-B data providers.\n\nThis data is enriched with mapped callsigns, corrected hexcodes, regional partnership decoding, and identification of wet-leases and cargo customers, enabling detailed insight into each individual flight.\n\nWhere ch-aviation data is unavailable, public data from civil aviation authorities and ANSPs is used to ensure broad and reliable aircraft identification and coverage.\n\nThe data set is available historically going back to January 1, 2018.\n\nThe data set is updated daily.\n\nThe sample data shows flights on 2026-03-30, with Swiss, Alaska Airlines, Horizon Air, ASL Airlines Ireland, Jet Aviation Business Jets, Gestair Private Jets, Wheels Up,\nand RVR Aviation as operators or wet lease customers (as well as 30 random general aviation aircraft).\n\nContact us to get access to ch-aviation's AWS S3 sample data bucket as well allowing you to build proof of concepts with all of our sample data. \n\nThe direct bucket URL for this data set is: https://eu-central-1.console.aws.amazon.com/s3/buckets/dataservices-standardised-samples?region=eu-central-1&bucketType=general&prefix=flights/&showversions=false\n\nFull Technical Data Dictionary: https://about.ch-aviation.com/flights-2/","terms_of_service":"https://www.ch-aviation.com/privacy","contact_email":"support@datarade.ai","privacy_policy_link":"https://www.ch-aviation.com/privacy","assets":["ASSET_TYPE_DATA_TABLE"]},"provider_summary":{"name":"ch-aviation","description":"For more than 20 years, we have been supplying decision-makers around the globe with relevant and up-to-date aviation intelligence and insightful news. 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Inaccurate contact data leads to failed deliveries, wasted spend, and missed opportunities to engage with your customers effectively.\n\nOur Contact Validation solution identifies and fixes errors in your existing customer database whilst appending missing information, including email addresses and telephone numbers. This comprehensive approach allows you to provide excellent customer service, obtain accurate billing information, and achieve high collection rates across all your communications.\n\n**What is it?**\nA combination of cleansing, validation, correction, and appending solutions applied to your customer base, whether residential or commercial. The full process involves the following steps:\n\n- Data Cleansing: Identification and removal of duplicate, outdated, or incorrect contact records\n- Validation: Real-time verification of email addresses and telephone numbers to ensure deliverability\n- Correction: Automatic fixing of common formatting errors and standardisation of contact data\n- Appending: Enhancement of incomplete records with missing email addresses and telephone numbers from our comprehensive database\n\nThis multi-step approach ensures your contact database is not only clean and accurate, but also complete with the most up-to-date information available.\n\n**Use cases?**\n\n- Deliver more messaging to the right customers - Ensure your communications reach their intended recipients by maintaining accurate contact details \n- Less wastage for your messaging and marketing - Reduce bounce rates and failed delivery attempts, maximising your marketing budget efficiency \n- Increase delivery success and engagement propensity - Clean, validated contact data leads to higher open rates, click-through rates, and overall campaign performance \n- Improve customer service delivery - Reach customers through their preferred contact methods with confidence in data accuracy \n- Enhance billing and collection processes - Accurate contact information supports successful payment reminders and collection activities \n- Maintain GDPR compliance - Keep your contact database current and accurate in line with data protection requirements\n\n**Additional Insights**\nThis solution works best when combined with our Customer Enrichment dataset, allowing you to segment your customer base with over 300 variables across individual, household and postcode levels. This enables you to create highly targeted and personalised communications that drive superior engagement and results.\n","terms_of_service":"https://www.sagacitysolutions.co.uk/privacy-policy/","documentation_link":"https://www.sagacitysolutions.co.uk/services/solutions/customer-acquisition-and-engagement/contact-validation","privacy_policy_link":"https://www.sagacitysolutions.co.uk/privacy-policy/","geographical_coverage":"[\"Europe\"]","cost":"PAID","assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"Sagacity","description":"Sagacity are the data intelligence people. We know more about individuals and businesses than any other company. Our advanced analytics and data allow you to identify and generate value quicker. 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The MCP returns structured practitioner profiles with identity resolution, specialty classification, practice location, credentials, demographics, claims activity, panel size, and other provider intelligence signals.\n\n**What Customers Can Do**\n\n* Search for practitioners by name, NPI, specialty, geography, or practice attributes\n* Match providers across name variations and return recommended best-fit results\n* View structured practitioner profiles, including credentials, specialties, practice locations, and affiliations\n* Analyze provider activity using claims volume, recent claim history, panel size, and practice tier\n* Filter results by state, specialty, location, and other practitioner attributes\n* Identify high-relevance providers for targeting, segmentation, outreach, and field planning\n* Support market research, territory planning, referral analysis, KOL identification, and launch execution\n\n**Example Questions**\n\n* \"Find Dr. Sarah Miller, cardiologist in Texas.\"\n* \"Show the best match for Michael Chen, oncology, California.\"\n* \"Find rheumatologists in Florida with high claims activity.\"\n* \"Show NPI, specialty, credentials, and practice address for this provider.\"\n* \"Which providers have the largest patient panels in this specialty and region?\"\n* \"Find practitioners near this location with recent claims activity.\"\n* \"Identify high-volume providers in this market.\"\n\n**Designed for Provider Intelligence Workflows**\n\nThe Practitioner Lookup MCP Server gives agents a trusted way to resolve provider identity and retrieve structured practitioner intelligence.\n\nIt helps teams move from a name, specialty, market, or business question to actionable provider insight. Instead of manually searching across fragmented provider data, users can ask natural language questions and receive structured results that can support downstream planning, segmentation, analytics, and operational workflows.\n\nExample workflow:\n\n**Provider Search → Identity Resolution → Practitioner Profile → Claims and Panel Signals → Segmentation → Targeting and Field Planning**\n\n**Marketplace Positioning**\n\nKythera Practitioner Lookup MCP Server turns provider search into structured, agent-ready practitioner intelligence.\n\nLife sciences and healthcare teams can use natural language to find, match, filter, and analyze practitioners by identity, specialty, location, credentials, practice affiliation, claims activity, and patient panel signals. 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Specialty names are standardized across provinces for cross-registry analysis. Registration statuses are harmonized. All records include lineage metadata for full traceability back to source.\r\n\r\nUPDATE FREQUENCY\r\n\r\nThe dataset is refreshed weekly (every Monday) with automated pipelines. Monthly snapshots are preserved for historical comparison and trend analysis.\r\n\r\nUSE CASES\r\n\r\n- Healthcare Market Intelligence: Size the Canadian physician market by specialty, geography, and registration status\r\n- Provider Outreach & Marketing: Build targeted contact lists of active physicians by specialty and location\r\n- Workforce Analytics: Track physician supply across provinces, identify specialty shortages and distribution patterns\r\n- Network Adequacy: Assess provider coverage for health plan network design and regulatory compliance\r\n- Academic Research: Study physician demographics, specialty distribution, and geographic access to care\r\n- Competitive Intelligence: Monitor physician registration trends and new entrants by province\r\n\r\nDELIVERY OPTIONS\r\n\r\nAvailable via Snowflake Marketplace (shared database), Databricks Unity Catalog, or direct S3 export to your AWS account. Full schema documentation and data dictionary included. Custom data packages available on request.\r\n\r\nCOVERAGE BY PROVINCE (verified July 2026):\r\n- Ontario (CPSO): 55K+ physicians\r\n- Quebec (CMQ): 26K+ physicians\r\n- Alberta (CPSA): 16K+ physicians\r\n\r\nRELATED DATAPLEX PRODUCTS: For US provider coverage see our US NPI Provider Directory (9M+ providers, contact-ready) and US NPI Registry & Taxonomy Data (analytics-ready) — same schema patterns, so cross-border provider studies join cleanly. For validated US provider data (deduplicated, USPS-verified, PECOS-enrolled, OIG-flagged), see our US Validated Provider Golden Record listing.","terms_of_service":"https://dataplex-consulting.com/privacy.html","contact_email":"support@datarade.ai","privacy_policy_link":"https://dataplex-consulting.com/privacy.html","assets":["ASSET_TYPE_DATA_TABLE"]},"provider_summary":{"name":"Dataplex","description":"Dataplex turns public healthcare data into analysis-ready data products. 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One consistent delivery layer."},"popularity":1324},{"id":"789b1ad5-50a2-4340-9ba7-566bfff6d545","summary":{"name":"Manufacturer And User Facility Device Experience","subtitle":"This data package contains the reports of adverse events involving medical devices actually, information on medical de..","share":{"name":"manufacturer_and_user_facility_device_experience","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1682356515289,"updated_at":1695692134401,"categories":["SCIENCE_AND_RESEARCH"],"listingType":"STANDARD","provider_id":"f88849f8-9d77-4de2-b4c7-03d55cc3768e"},"detail":{"description":"\n**Overview**\nThis data package contains the reports of adverse events involving medical devices actually, information on medical devices which may have malfunctioned or caused a death or serious injury during the years 1984 through 1996 and medical product safety network program data.\n\n**Description**\nThis data package contains the lists of manufacturer and user facility device experience database data which represents Reports of adverse Events involving medical Devices the datasets consist of voluntary Reports since June 1993, User facility Reports since 1991, Distributor Reports since 1993, and Manufacturer Reports since August 1996. Its includes also the Center for Devices and Radiological Health CDRH's database information on medical devices which may have malfunctioned or caused a death or serious injury during the years 1984 through 1996 and the reporting of problems with medical products from medical product safety network.\n\n**Benefits**\n- Access to weekly updated fda manufacturer and user facility device experience (maude) data.\n- access to medical device reporting (mdr) data during the years 1984 through 1996.\n- access to the medical product safety network (medsun) data that involves the reporting of problems with medical products from a network of approximately 300 hospitals, nursing homes and home health facilities around the united states.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [MAUDE with Patient Involvement](https://www.johnsnowlabs.com/marketplace/maude-with-patient-involvement)\n\t- MAUDE (Manufacturer and User Facility Device Experience) dataset represents reports of adverse events involving medical devices the data consist of voluntary Reports since June 1993, User facility Reports since 1991, Distributor Reports since 1993, and Manufacturer Reports since August 1996.\n- [Manufacturer And User Facility Device Experience Database](https://www.johnsnowlabs.com/marketplace/manufacturer-and-user-facility-device-experience-database)\n\t- MAUDE data represents Reports of adverse Events involving medical Devices the datasets consist of voluntary Reports since June 1993, User facility Reports since 1991, Distributor Reports since 1993, and Manufacturer Reports since August 1996.\n- [Medical Device Reporting](https://www.johnsnowlabs.com/marketplace/medical-device-reporting)\n\t- The Medical Device Reporting (MDR) dataset lists the Center for Devices and Radiological Health CDRH's database information on medical devices which may have malfunctioned or caused a death or serious injury during the years 1984 through 1996.\n- [Medical Product Safety Network](https://www.johnsnowlabs.com/marketplace/medical-product-safety-network)\n\t- The Medical Product Safety Network (MedSun) is an adverse event reporting program launched by the U.S. Food and Drug Administration’s Center for Devices and Radiological Health (CDRH).\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 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Bioactive Compound & Drug Discovery Intelligence","subtitle":"Connect AI agents to ChEMBL’s 2.4M+ compounds and bioactivity data for discovery workflows in Databricks.","setting":{"visibility":"PUBLIC"},"created_at":1780683272454,"updated_at":1780692382053,"categories":["HEALTH","OPEN_SOURCE","SCIENCE_AND_RESEARCH"],"listingType":"STANDARD","provider_id":"a8f85e7d-3075-4761-81dc-57b6ab58d02f"},"detail":{"description":"**Generate a bearer token by signing up here: https://climb.ai/labs/hls-mcp. Once you sign up, a token will be emailed to you instantly.**\n\n**MCP Overview:**\nThe ChEMBL MCP Server connects your Databricks AI agents to EMBL-EBI's ChEMBL database through a native service following a search-then-lookup pattern. Agents search for molecules by name, synonym, or SMILES, retrieve full compound metadata, discover targets by name or gene symbol, and fetch bioactivity records all within a single orchestrated workflow. Coverage includes 2.4M+ compounds with curated assay data from 90,000+ publications, updated semi-annually.\n\n**Use cases:**\n\n- Search ChEMBL for small molecules by name, synonym, or SMILES and retrieve candidate ChEMBL IDs for follow-up\n- Fetch full compound metadata including structure, drug-likeness properties, and development status by ChEMBL ID\n- Search ChEMBL drug targets by name or gene symbol to identify the correct target for bioactivity queries\n- Retrieve assay bioactivity records for a molecule or target filtered by activity type such as IC50, Ki, or EC50\n- Build compound-target activity tables to support hit identification, lead optimization, and target tractability analysis\n\n**Sample Prompts:**\n\nPrompt Example 1: Target Bioactivity Screening\n\n- \"Search ChEMBL for the BACE1 target by gene symbol and retrieve its ChEMBL ID. Fetch all bioactivity records for that target filtered to IC50 assays only and return the top 10 most potent compounds with their ChEMBL IDs and activity values.\"\n\nPrompt Example 2: Compound Metadata & Activity Lookup\n\n- \"Search ChEMBL for vemurafenib by name and retrieve its ChEMBL ID. Fetch the full compound metadata including structure and drug-likeness properties, then retrieve all bioactivity records for that molecule to summarize its measured activity across assay types.\"\n\n**These tools are available when installed:**\n\n- chembl_search_molecules: Search ChEMBL small molecules by name, synonym, or SMILES and return candidate ChEMBL IDs\n- chembl_get_molecule: Fetch full metadata for a single ChEMBL molecule by ChEMBL ID\n- chembl_search_targets: Search ChEMBL drug targets by name or gene symbol\n- chembl_get_bioactivities: Fetch assay bioactivity records for a molecule or target, optionally filtered by activity type\n\n**Additional Information:**\nThis MCP Server is powered by Climb, a leading Consulting and Solutions Integrator Partner of Databricks. Learn more about Climb here: [climb.ai](https://climb.ai) \n\n![Climb logo](https://climb-static-assets.s3.us-west-2.amazonaws.com/assets/PNG/Dark/Climb+Logo+Left-Thirds.png)","terms_of_service":"https://climb.ai/terms-of-service","documentation_link":"https://www.ebi.ac.uk/chembl","privacy_policy_link":"https://climb.ai/privacy-policy","update_frequency":{"interval":2,"unit":"QUARTERLY"},"assets":["ASSET_TYPE_MCP"],"license":""},"provider_summary":{"name":"ChEMBL","description":"This MCP Server is powered by Climb, a leading Consulting and Solutions Integrator Partner of Databricks. Learn more here: climb.ai | ChEMBL, maintained by the European Bioinformatics Institute (EMBL-EBI), is a manually curated, open-access database of bioactive molecules with drug-like properties, bringing together chemical, bioactivity, and genomic data to help translate scientific discoveries into new medicines. It contains information on more than 2.4 million compounds, including their measured biological activity against specific disease targets, safety and metabolism properties, and connections to known drugs and clinical candidates. Freely accessible via a web interface and REST API, ChEMBL is a globally recognized resource used by researchers across academia and the pharmaceutical industry to guide and accelerate drug discovery. This MCP Server is powered by Climb.ai, a leading Data and Consulting Partner of Databricks."},"popularity":1680},{"id":"60fca794-87a8-4ac8-ad09-442b69df1d77","summary":{"name":"Foundational 1:1 Segments","subtitle":"From Declared Data to Scaled Activation High-Intent Audiences Built for Performance","setting":{"visibility":"PUBLIC"},"created_at":1753909398483,"updated_at":1754003174476,"categories":["ADVERTISING_AND_MARKETING","COMMERCE","DEMOGRAPHICS","EDUCATION","HEALTH","LOOKUP_TABLES","OTHER","RETAIL","TRAVEL_AND_TOURISM","TRANSPORTATION_AND_LOGISTICS","SPORTS","MEDIA","GAMING","FINANCIAL"],"listingType":"PERSONALIZED","provider_id":"ab53f1f8-5656-41d4-85d4-d3dba9125f2a"},"detail":{"description":"**Overview**\nFluent transforms real answers into real results. Our data starts with 1:1 declared insights from over 100 million opted-in U.S. adults across our owned and operated properties. These profiles are PII-backed, permissioned, and refreshed regularly—delivering a level of identity precision and intent signal unmatched by traditional behavioral datasets.\nThis is especially critical in high-sensitivity verticals like Healthcare and Finance, where inferred intent and behavioral data can introduce compliance risks and targeting inaccuracies. Fluent’s declared data approach ensures privacy-safe, high-relevance targeting—built on what individuals explicitly share, not what’s passively tracked.\nThrough integration with TransUnion TAP, Fluent’s declared seed audiences can be modeled to reach up to 96% of the U.S. adult population. Whether you need turnkey audience segments or want to define your own based on real-time survey responses, Fluent provides the tools and reach to act with confidence.\n\n**What Sets Fluent Apart**\n- 100M+ declared 1:1 U.S. adult profiles\n- PII-backed and privacy-safe\n- Modeled expansion to 96% of U.S. adults\n- Activation across social, programmatic, CTV, and CRM\n- Custom audience creation from live survey data\n- Compatible with cleanrooms, LiveRamp, and direct delivery\n- Segments that consistently outperform CPQR benchmarks by 3x or more\n\n**Foundational 1:1 Segments**\nFluent offers turnkey segments built from behavioral, lifestyle, demographic, and media attributes across our owned and operated ecosystem. These audiences are built on known identity and household signals and are ready for immediate use—no survey inputs required.\nSegment Categories: Our foundational audiences are built from licensed demographic and behavioral attributes and are ready for turnkey activation—no survey responses required.\n- Household & Demographics: Homeownership, marital status, age ranges, gender, education level, children by age band, length of residence\n- Financial & Wealth: Household income, net worth bands, estimated assets, credit card segments, deposit behaviors, personal loan users, banking channel usage, financial personas\n- Auto Ownership & Intent: Vehicle type (car, SUV, truck), fuel type (electric, hybrid, PHEV), ownership duration, recent purchase recency, lease consideration, lifestyle-based auto segments\n- Media Consumption & Entertainment: Cord cutters, broadband-only households, paid vs. ad-supported streaming subscribers, political media viewers (Fox, CNN, Spanish-language), music streamers, fantasy sports participants\n- Retail & Shopping Behavior: Grocery spenders and delivery users, online grocery and meal kit subscribers, big box and Amazon shoppers, brand loyalists, impulse buyers, informed consumers\n- Lifestyle & Interests: Pet owners (dogs, cats), music and entertainment fans, frequent travelers, multigenerational households, people with grandchildren, environmentally-conscious shoppers\n- Civic & Political: Party affiliation, battleground and swing counties, congressional district types, civic engagement markers, religious and language-based political media alignment\n- Life Stage & Occupation: Retirees, students, caregivers, and working professionals across healthcare, legal, education, technical, blue-collar, and agricultural roles\n- All segments are privacy-safe, mapped to known identity signals, and available across all major platforms. Audiences are updated regularly to maintain recency and performance.\n\n","terms_of_service":"https://fluentco.com/advertiserterms/","documentation_link":"","privacy_policy_link":"https://fluentco.com/privacy-policy/","geographical_coverage":"[\"United States and Canada\"]","cost":"PAID","data_source":"","assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"Fluent","description":"Fluent (NASDAQ: FLNT) transforms real answers into real results. Our data starts with 1:1 declared insights from over 100 million opted-in U.S. adults across our owned and operated properties. These profiles are PII-backed, permissioned, and refreshed regularly—delivering a level of identity precision and intent signal unmatched by traditional behavioral datasets.\n\nThis is especially critical in high-sensitivity verticals like Healthcare and Finance, where inferred intent and behavioral data can introduce compliance risks and targeting inaccuracies. Fluent’s declared data approach ensures privacy-safe, high-relevance targeting—built on what individuals explicitly share, not what’s passively tracked."},"popularity":782},{"id":"1e80d30f-9e50-4746-9950-0ec25a42a6fd","summary":{"name":"Hospital Compare Inpatient Discharges","subtitle":"This data package contains the New York State level information on discharge details on patient characteristics, diagn..","share":{"name":"hospital_compare_inpatient_discharges","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1682356020338,"updated_at":1695691223474,"categories":["HEALTH","SCIENCE_AND_RESEARCH"],"listingType":"STANDARD","provider_id":"f88849f8-9d77-4de2-b4c7-03d55cc3768e"},"detail":{"description":"\n**Overview**\nThis data package contains the New York State level information on discharge details on patient characteristics, diagnoses, treatments, services, charges and costs from 2009 to 2016.\n\n**Description**\nThis data package contains the Hospital Inpatient Treatment Discharges was initially created to collect information on discharges from hospitals. Statewide Planning and Research Cooperative System (SPARCS) currently collects patient level detail on patient characteristics, diagnoses and treatments, services, and charges for each hospital inpatient stay and outpatient ambulatory surgery, emergency department, and outpatient services. It contains basic record level detail regarding the discharge however the data does not contain protected health information (PHI) under Health Insurance Portability and Accountability Act (HIPAA). The health information is not individually identifiable all data elements considered identifiable have been redacted. The SPARCS is a comprehensive all payer data reporting system established in 1979 as a result of cooperation between the healthcare industry and government. The system was initially created to collect information on discharges from hospital. It currently collects outpatient services visit to a hospital extension clinic and diagnostic and treatment center licensed to provide ambulatory surgery services.\n\n**Benefits**\n- Useful for medical care and ambulatory surgical services diagnoses services, patients, outpatient services and hospitals. hospital compare inpatient discharges database is available on health.data.ny.gov website in raw form, whereas a john snow labs (jsl) customer gets it in a clean and normalized form ready to use in one place and create his own custom reports. easy to comprehend for the customers who are unfamiliar with the medical terminologies as all abbreviated terms are replaced with full form, unlike hospital compare inpatient discharges original datasets which are full of abbreviations.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [Hospital Inpatient Treatment Discharges 2009](https://www.johnsnowlabs.com/marketplace/hospital-inpatient-treatment-discharges-2009)\n\t- This dataset includes New York State level information on discharge details on patient characteristics, diagnoses, treatments, services, charges and costs for the year 2009.\n- [Hospital Inpatient Treatment Discharges 2010](https://www.johnsnowlabs.com/marketplace/hospital-inpatient-treatment-discharges-2010)\n\t- This dataset includes New York State level information on discharge details on patient characteristics, diagnoses, treatments, services, charges and costs for the year 2010.\n- [Hospital Inpatient Treatment Discharges 2011](https://www.johnsnowlabs.com/marketplace/hospital-inpatient-treatment-discharges-2011)\n\t- This dataset includes New York State level information on discharge details on patient characteristics, diagnoses, treatments, services, charges and costs for the year 2011.\n- [Hospital Inpatient Treatment Discharges 2012](https://www.johnsnowlabs.com/marketplace/hospital-inpatient-treatment-discharges-2012)\n\t- This dataset includes New York State level information on discharge details on patient characteristics, diagnoses, treatments, services, charges and costs for the year 2012.\n- [Hospital Inpatient Treatment Discharges 2013](https://www.johnsnowlabs.com/marketplace/hospital-inpatient-treatment-discharges-2013)\n\t- This dataset includes New York State level information on discharge details on patient characteristics, diagnoses, treatments, services, charges and costs for the year 2013.\n- [Hospital Inpatient Treatment Discharges 2014](https://www.johnsnowlabs.com/marketplace/hospital-inpatient-treatment-discharges-2014)\n\t- This dataset includes New York State level information on discharge details on patient characteristics, diagnoses, treatments, services, charges and costs for the year 2014.\n- [Hospital Inpatient Treatment Discharges 2015](https://www.johnsnowlabs.com/marketplace/hospital-inpatient-treatment-discharges-2015)\n\t- This dataset includes New York State level information on discharge details on patient characteristics, diagnoses, treatments, services, charges and costs for the year 2015.\n- [Hospital Inpatient Treatment Discharges 2016](https://www.johnsnowlabs.com/marketplace/hospital-inpatient-treatment-discharges-2016)\n\t- This dataset includes New York State level information on discharge details on patient characteristics, diagnoses, treatments, services, charges and costs for the year 2016.\n- [Hospital Inpatient Treatment Discharges 2017](https://www.johnsnowlabs.com/marketplace/hospital-inpatient-treatment-discharges-2017)\n\t- This dataset includes New York State level information on discharge details on patient characteristics, diagnoses, treatments, services, charges, and costs for the year 2017.\n- [Hospital Inpatient Treatment Discharges 2018](https://www.johnsnowlabs.com/marketplace/hospital-inpatient-treatment-discharges-2018)\n\t- This dataset includes New York State level information on discharge details on patient characteristics, diagnoses, treatments, services, charges, and costs for the year 2018.\n- [Hospital Inpatient Treatment Discharges 2019](https://www.johnsnowlabs.com/marketplace/hospital-inpatient-treatment-discharges-2019)\n\t- This dataset includes New York State level information on discharge details on patient characteristics, diagnoses, treatments, services, charges, and costs for the year 2019.\n- [Hospital Inpatient Treatment Discharges 2020](https://www.johnsnowlabs.com/marketplace/hospital-inpatient-treatment-discharges-2020)\n\t- This dataset includes New York State level information on discharge details on patient characteristics, diagnoses, treatments, services, charges, and costs for the year 2020.\n- [Hospital Inpatient Treatment Discharges 2021](https://www.johnsnowlabs.com/marketplace/hospital-inpatient-treatment-discharges-2021)\n\t- This dataset includes New York State level information on discharge details on patient characteristics, diagnoses, treatments, services, charges, and costs for the year 2021.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 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The goal is to demonstrate how Verticalization/Domain Adaptation for the [media] sector can be easily achieved using our two-step approach to LLM Fine-Tuning. An overview of this approach can be found at: [From General-Purpose LLMs to Verticalized Enterprise Models](https://www.bitext.com/blog/general-purpose-models-verticalized-enterprise-genai/)\n\nThe dataset has the following specifications:\n\n- Use Case: Intent Detection\n- Vertical: Media\n- 25 intents assigned to 8 categories\n- 24627 question/answer pairs, with approximately 1000 per intent\n- 453 entity/slot types\n- 10 different types of language generation tags\n\nThe categories and intents are derived from Bitext's extensive experience across various industry-specific datasets, ensuring the relevance and applicability across diverse contexts.\n\n**Dataset Token Count**\n\nThe dataset contains a total of 2.86 million tokens across 'instruction' and 'response' columns. This extensive corpus is crucial for training sophisticated LLMs that can perform a variety of functions including conversational AI, question answering, and virtual assistant tasks in the media domain.\n\n**Fields of the Dataset**\n\nEach entry in the dataset comprises the following fields:\n\n- tags\n- instruction: a user request from the Media domain\n- category: the high-level semantic category for the intent\n- intent: the specific intent corresponding to the user instruction\n- response: an example of an expected response from the virtual assistant\n\n**Categories and Intents**\n\nThe dataset covers a wide range of media-related categories and intents, which are:\n\n- **CONTACT**: customer_service, human_agent\n- **CONTENT**: report_copyright_infringement, report_inappropiate_content\n- **FUNCTIONING**: devices, general_use, quickstart_guide\n- **PAYMENT**: payment_methods, pay, report_payment_issue\n- **PLATFORM**: fees\n- **PROGRAM_SCHEDULE**: program_schedule, releases\n- **SETTINGS**: change_language, change_subtitle_language, parental_control, recover_password\n- **SUBSCRIPTION**: cancel_subscription, change_subscription, free_trial, premium_subscription, renew_subscription, subscribe, subscription, subscription_prices\n\n**Entities**\n\nThe entities covered by the dataset include:\n\n- **{{WEBSITE_URL}}**, common with all intents.\n- **{{DEVICE_PLATFORM}}**, featured in intents like change_language, change_subtitle_language.\n- **{{PROGRAM_TYPE}}**, associated with intents like program_schedule, releases.\n- **{{LANGUAGE_OPTION}}**, relevant to intents such as change_language, change_subtitle_language.\n- **{{PAYMENT_METHODS_INFORMATION}}**, featured in intents like pay, payment_methods.\n\nThis comprehensive list of entities ensures that the dataset is well-equipped to train models that are highly adept at understanding and processing a wide range of media-related queries and tasks.\n\n**Language Generation Tags**\n\nThe dataset includes tags indicative of various language variations and styles adapted for Media, enhancing the robustness and versatility of models trained on this data. These tags categorize the utterances into different registers such as colloquial, formal, or containing specific media jargon, ensuring that the trained models can understand and generate a range of conversational styles appropriate for different customer interactions in the media sector.\n\n**Language Generation Tags**\n\nThe dataset includes tags that reflect various language variations and styles, crucial for creating adaptable and responsive conversational AI models within the media sector. These tags help in understanding and generating appropriate responses based on the linguistic context and user interaction style.\n\n**Tags for Lexical variation**\n\n- **M - Morphological variation**: Adjusts for inflectional and derivational forms.\n - Example: \"is my account active\", \"is my account activated\"\n- **L - Semantic variations**: Handles synonyms, use of hyphens, and compounding.\n - Example: “what's my balance date\", “what's my billing date”\n\n**Tags for Syntactic structure variation**\n\n- **B - Basic syntactic structure**: Simple, direct commands or statements.\n - Example: \"activate my card\", \"I need to check my balance\"\n- **I - Interrogative structure**: Structuring sentences in the form of questions.\n - Example: “can you show my balance?”, “how do I transfer money?”\n- **C - Coordinated syntactic structure**: Complex sentences coordinating multiple ideas or tasks.\n - Example: “I want to transfer money and check my balance, what should I do?”\n- **N - Negation**: Expressing denial or contradiction.\n - Example: \"I do not wish to proceed with this transaction, how can I stop it?\"\n\n**Tags for language register variations**\n\n- **P - Politeness variation**: Polite forms often used in customer service.\n - Example: “could you please help me check my account balance?”\n- **Q - Colloquial variation**: Informal language that might be used in casual customer interactions.\n - Example: \"can u tell me my balance?\"\n- **W - Offensive language**: Handling potentially offensive language which might occasionally appear in frustrated customer interactions.\n - Example: “I’m upset with these charges, this is ridiculous!”\n\n**Tags for stylistic variations**\n\n- **K - Keyword mode**: Responses focused on keywords.\n - Example: \"balance check\", \"account status\"\n- **E - Use of abbreviations**: Common abbreviations.\n - Example: “acct for account”, “trans for transaction”\n- **Z - Errors and Typos**: Includes common misspellings or typographical errors found in customer inputs.\n - Example: “how can I chek my balance”\n\n**Other tags not in use in this Dataset**\n\n- **D - Indirect speech**: Expressing commands or requests indirectly.\n - Example: “I was wondering if you could show me my last transaction.”\n- **G - Regional variations**: Adjustments for regional language differences.\n - Example: American vs British English: \"checking account\" vs \"current account\"\n- **R - Respect structures - Language-dependent variations**: Formality levels appropriate in different languages.\n - Example: Using “vous” in French for formal addressing instead of “tu.”\n- **Y - Code switching**: Switching between languages or dialects within the same conversation.\n - Example: “Can you help me with my cuenta, please?”\n\nThese tags not only aid in training models for a wide range of customer interactions but also ensure that the models are culturally and linguistically sensitive, enhancing the customer experience in the media environments.\n\n**License**\n\nThe `Bitext-media-llm-chatbot-training-dataset` is released under the **Community Data License Agreement (CDLA) Sharing 1.0**. This license facilitates broad sharing and collaboration while ensuring that the freedom to use, share, modify, and utilize the data remains intact for all users.\n\n**Key Aspects of CDLA-Sharing 1.0**\n\n- **Attribution and ShareAlike**: Users must attribute the dataset and continue to share derivatives under the same license.\n- **Non-Exclusivity**: The license is non-exclusive, allowing multiple users to utilize the data simultaneously.\n- **Irrevocability**: Except in cases of material non-compliance, rights under this license are irrevocable.\n- **No Warranty**: The dataset is provided without warranties regarding its accuracy, completeness, or fitness for a particular purpose.\n- **Limitation of Liability**: Both users and data providers limit their liability for damages arising from the use of the dataset.\n\n**Usage Under CDLA-Sharing 1.0**\n\nBy using the `Bitext-media-llm-chatbot-training-dataset`, you agree to adhere to the terms set forth in the CDLA-Sharing 1.0. It is essential to ensure that any publications or distributions of the data, or derivatives thereof, maintain attribution to the original data providers and are distributed under the same or compatible terms of this agreement.\n\nFor a detailed understanding of the license, refer to the [official CDLA-Sharing 1.0 documentation](https://cdla.dev/sharing-1-0/).\n\nThis license supports the open sharing and collaborative improvement of datasets within the AI and data science community, making it particularly suited for projects aimed at developing and enhancing AI technologies in the media sector.\n\n---\n\n(c) Bitext Innovations, 2024\n","terms_of_service":"https://www.bitext.com/api-terms-of-use/","documentation_link":"","privacy_policy_link":"https://www.bitext.com/privacy-policy/","embedded_notebook_file_infos":[{"id":"22bb8916-08f1-4b20-bd84-bbeede9d8c3d","marketplace_file_type":"EMBEDDED_NOTEBOOK","file_parent":{"parent_id":"43f80d00-479f-4fa7-b538-f78fbf0ef419","file_parent_type":"LISTING"},"mime_type":"text/html","download_link":"https://marketplace-file-storage-prod.s3-fips.us-east-1.amazonaws.com/EMBEDDED_NOTEBOOK/LISTING_43f80d00-479f-4fa7-b538-f78fbf0ef419/22bb8916-08f1-4b20-bd84-bbeede9d8c3d?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEGgaDnVzLWVhc3QtMS1maXBzIkYwRAIgbC8sWIoYaHR8y7Wq86nPb0pufBl4%2BM4PWpeN%2B5PW9VQCIG7BhAVBDCWTpxFDLeuKYm%2Ff1JLz4iccPMzter0YdhgWKqYECDAQABoMNDE0MzUxNzY3ODI2IgxNAJrHbXaZG20jNfMqgwQyGEb4TsoVOoNvJ%2BaA%2FZ7zeu8hGcHqCeNfblP%2FQo5g6q2aRNtpwtPRKMlhYcIE%2FUQaNT1Lqmyptu32VUQTpzjTTxRVmJrDPkheSCkV06XQ%2FpTMKjU8CC7X1b%2FqYf8CrBwZT7Cv4C5WPjEET38f2A79h0xV6bZgPy28zQWrSeIJ9w6AfZBxiE24%2FVc08zVA0ivCT6lsy7nuU0wcR%2FMg2C9nd1vaMnQ923VpD21CKfVVYcrNWL2b%2FQ%2BNAO%2BxPjPgjiN6UPQbvbvZxkSdhgrGtecIg9NgqGkMcvM5VLTDtdC1kdeZetfOC2obtA%2BT%2F0gy3tIDo6WM5hcQfw743%2FAU9oaGnRBl%2BwoxRRDCZA%2F4jnPdluICSI7NCr6C3HHOe4CSZJjRihczxPA%2FgQczAVjAPs6CrK9N9pIzt0vNJpGUTgg4AoxftTvGfciDREilSctPIcBk6GwB%2Bzt4wbtNCmEPU%2BFD46rc6CWVQmmLwbKOeHw70ipMVFlOc8NlXkEtGO2vu6H44MstSSc6daOVUYUavQSGZ6jGhEjsP2YGz8KDm9XQotB%2FtyVBqiZV01OKqDmmBJA%2FXjUpSJ8M%2FeJXhe5m%2BKcxzNObtoJRI7jci3Tpt08XZyXZ7%2FyIV8H30ya468X3cfkTXrm972uga3z7iXAdubyEHnQIua%2FuvtwOHILi6O5D1Me2MzCPvcnXBjqTAYDoMWBt%2B3AVFuqSs2dmDQNBnvEhXfMgTedKLD5PJHb3aIl15iBkLaNu36GYnVCp9ZU2lSYcTbY5QwNnNeTfJTNy9bKhwzru6Sg%2BslhN%2BTS0zkTn%2BeLMY4tHgPEtEZ2cp%2FCk6GwqswvnYVKHjwO8q%2B1Mx8lc08fVEfarE9dke1GGFqoddx%2Bq%2BZUyHbDlAVfrCmf0yw%3D%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20261110T000135Z&X-Amz-SignedHeaders=host&X-Amz-Expires=86400&X-Amz-Credential=ASIAWA6KKHEJORWBH4N3%2F20261110%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=50aa9fa75c3100e9dc4078e57dacb934c3842d80e291212066b8fa55f913e02a","created_at":1727609309306,"updated_at":1727609309306,"display_name":"Media_Dataset_Sample_Notebook","status":"FILE_STATUS_PUBLISHED"}],"geographical_coverage":"[\"Global\"]","cost":"FREE","data_source":"Internally generated by Bitext using proprietary NLP and NLG technologies.","assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"Bitext Innovation International","description":"We understand that one of the critical factors in achieving exceptional performance with LLMs is the quality and relevance of the training data. 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Workflows cover patient support services such as scheduling and 24/7 conversational support, automated adherence reminders, digital health application delivery including connected device data, and a digital patient 360 assembled from every program interaction. Engagement outcomes write back to Databricks as queryable data.\n\n**Use cases**\n- Support patient services with scheduling and 24/7 conversational support\n- Automate adherence reminders across therapies and support programs\n- Deliver digital health applications, including connected device data such as continuous glucose monitoring\n- Implement a digital patient 360 from unified interaction, clinical, and program data\n\n**Additional Insights**\nProgram engagement outcomes are directly measurable as queries on the customer's own lakehouse data. \n\nThe workflows listed are representative; Concurrence builds custom applications for any workflow in this outcome area on the customer's existing Databricks foundation.","terms_of_service":"https://www.concurrence.com/terms-of-service","documentation_link":"https://docs.concurrence.com","privacy_policy_link":"https://www.concurrence.com/privacy","assets":["ASSET_TYPE_DATA_TABLE","ASSET_TYPE_MEDIA","ASSET_TYPE_MODEL","ASSET_TYPE_NOTEBOOK"],"license":"","tags":[{"tag_name":"LISTING_TAG_TYPE_TASK","tag_values":["ASSERTION_STATUS","EMBEDDINGS","ENTITY_RESOLUTION","NAMED_ENTITY_RECOGNITION","QUESTION_ANSWERING","RELATION_EXTRACTION","SUMMARIZATION","TEXT_CLASSIFICATION","TEXT_GENERATION","TRANSLATION","ZERO_SHOT_CLASSIFICATION","CODE_GENERATION","AUTOMATIC_SPEECH_RECOGNITION","TEXT_TO_SPEECH","DOCUMENT_QUESTION_ANSWERING"]}]},"provider_summary":{"name":"Concurrence","description":"Concurrence is the platform for healthcare organizations to deploy custom AI for clinical and operational workflows on a unified data foundation."},"popularity":1175},{"id":"9bd2d077-e035-435c-be74-d0e0154611a4","summary":{"name":"Geolocet | Latest Demographic Data France - Households and Families","subtitle":"Demographic data for the nearly 35,000 communes of France - comprehensive snapshot of their households and families","setting":{"visibility":"PUBLIC"},"created_at":1706789853218,"updated_at":1752139069159,"categories":["DEMOGRAPHICS","ADVERTISING_AND_MARKETING","PUBLIC_SECTOR","RETAIL","GEOSPATIAL"],"listingType":"PERSONALIZED","provider_id":"5d3ddc17-a7e8-4794-a760-13587e1de2c6"},"detail":{"description":"## **Get instant access to the latest Demographics Data for France with [Geolocet](https://geolocet.com)**\n\nExplore the richness of France's demographic landscape with our French Demographics Data. This meticulously compiled dataset containing modeled data, based on official statistical sources, including INSEE, offers a detailed insight into the population landscape of France at the IRIS level. \n\nIf you require data at a less granular level, such as an Uber grid or square grid, please don't hesitate to get in touch with us at *contact@geolocet.com*. We understand that different projects may have varying data needs, and we are here to tailor our data to match your specific requirements.\n\n ## **Key Features:**\n---\n* **Population Data France:** Gain access to the latest and most accurate population statistics for France, meticulously organized by municipality (commune).\n\n* **Demographic Insights:** Dive deep into France's demographic landscape with data on 54 attributes, including: \n* Number of households,\n* Household structure,\n* Number of families,\n* Family structure,\n* Number of households with reserved parking spaces,\n* Number of households with cars.\n\nAll demographic attributes are available at the IRIS level.\n\n* **Geodemographic Insights:** Unlock geodemographic data France-wide to inform location-based decision-making, marketing strategies, and policy formulation.\n* **Seamless Data Access:** Our data is available for instant download in CSV format. No lengthy sign-up processes or NDAs required on our side - just download the data you need.\n* **Data Samples for Evaluation:** To ensure you're getting exactly what you need, we provide free downloadable samples in CSV format for data evaluation.\n* **Data accuracy:** Our datasets are created using the latest demographics data available in national statistics bureaus, guaranteeing accuracy and reliability.\n* **Best-in-Class Quality:** We guarantee best-in-class quality, backed by over two decades of experience in modeling demographics data and an outstanding quality management process.\n* **Competitive Pricing:** We maintain the lowest prices on the market, ensuring that you get the most cost-efficient demographics data acquisition process.\n---\nAdditionally, for those looking to enrich their analysis or project, we offer:\n* **[Administrative Boundaries](https://geolocet.com/products/france-admin-level-8-municipalities):** If you require geographical boundaries (polygons) for the municipalities of France, consider exploring our Administrative Boundaries product, which seamlessly integrates with this dataset.\n\n* **[Point of Interest (PoI) Data](https://geolocet.com/pages/aggregated-points-of-interest-poi-data):** If you're interested in Points of Interest data, please don't hesitate to contact us for customized datasets to enhance your geographical analysis.\n\n* **[Data Customization](https://geolocet.com/pages/data-services-by-geolocet):** We understand that your project may have specific requirements. Feel free to get in touch with us if you need any modifications or specific data formats such as Excel, JSON, GeoJSON, or Shapefile. We are here to tailor the data to your needs.\n\nIf you're looking for data for France, [look here](https://geolocet.com/pages/france) for off-the-shelf or tailor-made datasets to suit your specific requirements.\n\n\n\nMake informed decisions, conduct precise research, and unlock the potential of the French demographic landscape with our comprehensive dataset. Trust in the accuracy and reliability of our official statistical data to drive your projects forward.\nContact us at *contact@geolocet.com* today to access this invaluable resource and explore France census data like never before.\n\n[Geolocet LTD](https://geolocet.com)\n\n","terms_of_service":"https://geolocet.com/pages/terms-of-use","documentation_link":"","privacy_policy_link":"https://geolocet.com/pages/privacy-policy","cost":"PAID","data_source":"","assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://geolocet.com/pages/terms-of-use"},"provider_summary":{"name":"Geolocet LTD","description":"Geolocet provides premium geospatial data for retail analysis, GIS, and market planning. 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Updated every 30-90 days. ...","share":{"name":"x_tract_81e2f4d4_2c60_45d8_aa42_041aebf12a70_samples_share","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1747901114863,"updated_at":1793769855846,"categories":["GEOSPATIAL","RETAIL"],"listingType":"STANDARD","provider_id":"69b0fb78-1374-4520-b01d-55597414c6ad"},"detail":{"description":"Xtract.io’s retail POI data gives you a detailed, accurate map of retail establishments across the United States and Canada, built for retail strategists, market researchers, and business developers who need reliable store location data to back their next move.\n\nPoint of interest data, also known as places data, pinpoints the exact location of buildings, stores, and retail destinations. In a competitive retail landscape where location intelligence drives real decisions, having precise retail store location data means you’re not guessing where the opportunity is; you’re seeing it. This retail dataset covers consumer electronics retail stores, general retail store locations, restaurants, shopping centers and malls, and more, giving you a full picture of the retail environment rather than a narrow slice of it.\n\nDataset Coverage: Retail, Electronics, and More Across 11 Industries:\n\nXtract.io’s POI data platform spans 6 million+ locations across the US, UK, and Canada, covering retail, restaurants, healthcare, automotive, public utilities, and shopping centers and malls. This breadth means you can analyze electronic retail stores alongside adjacent categories whenever your market research calls for wider context.\n\nWhy Teams Choose Xtract.io for Retail POI Data:\n\n- Deliver POI data with 95% accuracy for dependable store location data\n\n- Refresh POIs every 30, 60, or 90 days to keep retail location information current\n\n- Create on-demand POI datasets tailored to your specific retail data requirements\n\n- Handcraft geofences for shopping center locations to sharpen accuracy\n\n- Provide retail POI data and polygon data in multiple file formats\n\nUnlock the Power of Retail Location Intelligence:\n\nWith this point-of-interest data for retail stores in the US, you can perform thorough market analyses using comprehensive store location data, identify the strongest sites for new retail stores, gain insight into consumer behavior and shopping patterns, and sharpen your competitive intelligence across retail markets.\n\nXtract.io has helped businesses turn geospatial insight into real retail strategy. Join the growing list of teams using our retail POI data and shopping center location intelligence to scale smarter and move faster.","terms_of_service":"https://www.xtract.io/privacy-policy","contact_email":"support@datarade.ai","privacy_policy_link":"https://www.xtract.io/privacy-policy","assets":["ASSET_TYPE_DATA_TABLE"]},"provider_summary":{"name":"Xtract","description":"Xtract.io is a POI data provider who help businesses to make their next strategic decision with the help of accurate POI data. Be it business expansion strategies, personalized marketing messages, or understanding competition, you need location intelligence. \r\n\r\nWe empower businesses with POI data that covers several attributes including demographic data, competitor location, positive influencers, negative influencers, and other key attributes like address, city, state, zip code, latitude, and longitude etc.\r\n\r\nWe have a rich repository of 6 million POIs in total across 5000 categories for 3 key regions (US, UK, and Canada)."},"popularity":1618},{"id":"217e62ab-e019-487e-9a62-a41b09633a4b","summary":{"name":"Medicare Claims and Utilization","subtitle":"This data package contains Medicare spending statistics for beneficiaries grouped according to their age, gender, race..","share":{"name":"medicare_claims_and_utilization","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1682355748350,"updated_at":1698965635225,"categories":["HEALTH","SCIENCE_AND_RESEARCH"],"listingType":"STANDARD","provider_id":"f88849f8-9d77-4de2-b4c7-03d55cc3768e"},"detail":{"description":"\n**Overview**\nThis data package contains Medicare spending statistics for beneficiaries grouped according to their age, gender, race/ethnicity and geographical location. At the same time, it provides data about spendings taking into consideration provider specific coordinates like the Hospital Referral Region (HRR) or Hospital Service Area (HSA). The data package contains as well as spending statistics based on the payment system, like the Outpatient Prospective Payment System.\n\n**Description**\nThis data package is mainly based on the public data provided by the Centers for Medicare and Medicaid Services (CMS). It contains also statistics made available by The Dartmouth Atlas of Health Care. It provides access to spending data:\n- by age, gender and race/ethnicity and state/HRR/HRA, based on claims or on the Continuous Medicare History Sample (CMHS)\n- by price adjustment and age, gender and race/ethnicity and state/HRR/HRA\n- by type of claim\n- under Outpatient Prospective Payment System for Ambulatory Payment Classification (APC) Groups\nIn this data package additional data related to providers like clinic group practice reassignments or the Therapy Provider Phase.\n\n**Benefits**\n- Useful to researchers and analysts who seek to compare, understand and find useful information about the costs variations by beneficiaries demographics and localization.\n- useful to public health specialists and specialized institutions to evaluate the type of beneficiaries groups. the fields descriptions along with the dataset description are again useful for the user to quickly understand the data and the dataset.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [Clinic Group Practice Reassignment](https://www.johnsnowlabs.com/marketplace/clinic-group-practice-reassignment)\n\t- This dataset shows the clinic group practice reassignment. Providers and suppliers must apply for enrollment in the Medicare program or make a change in their enrollment information using either the Internet-based Provider Enrollment Chain and Ownership System or the paper enrollment application process.\n- [Hospital Service Area File 2017](https://www.johnsnowlabs.com/marketplace/hospital-service-area-file-2017)\n\t- This dataset contains summarized information of Hospital Service Area (HHA) file by provider number and ZIP code of the Medicare beneficiary.\n- [Hospital Service Area File 2018](https://www.johnsnowlabs.com/marketplace/hospital-service-area-file-2018)\n\t- This dataset contains summarized information of Hospital Service Area (HHA) file by provider number and ZIP code of the Medicare beneficiary.\n- [Hospital Service Area File 2019](https://www.johnsnowlabs.com/marketplace/hospital-service-area-file-2019)\n\t- This dataset contains summarized information of Hospital Service Area (HHA) file by provider number and ZIP code of the Medicare beneficiary.\n- [Hospital Service Area File 2020](https://www.johnsnowlabs.com/marketplace/hospital-service-area-file-2020)\n\t- This dataset contains summarized information of Hospital Service Area (HHA) file by provider number and ZIP code of the Medicare beneficiary.\n- [Hospital Service Area File 2021](https://www.johnsnowlabs.com/marketplace/hospital-service-area-file-2021)\n\t- This dataset contains summarized information of Hospital Service Area (HHA) file by provider number and ZIP code of the Medicare beneficiary.\n- [Medical Review on Therapy Provider Phase Information Claims](https://www.johnsnowlabs.com/marketplace/medical-review-on-therapy-provider-phase-information-claims)\n\t- The Therapy Provider Phase Information dataset is a tool for providers to search for their National Provider Identifier (NPI) number to determine their phase for manual medical review of therapy claims.\n- [Medicare Hospital Spending by Claim](https://www.johnsnowlabs.com/marketplace/medicare-hospital-spending-by-claim)\n\t- This dataset includes a list of all hospitals that have been registered with Medicare and their Medicare Spending per Beneficiary (MSPB). The list includes Hospital Name, Period of hospital admission and Claim Type. It also includes Averages as well as Percentages of Spending per Episode for Hospital, State and Nation.\n- [Medicare Spending CMHS Based by Age Sex and Race HRR Level](https://www.johnsnowlabs.com/marketplace/medicare-spending-cmhs-based-by-age-sex-and-race-hrr-level)\n\t- This dataset includes the Hospital Refferal Region (HRR) level Medicare spending CMHS based data by age, sex and race. The HRRs represent regional healthcare markets for tertiary medical care that generally requires the services of a major referral center. The regions were defined by determining where patients were referred for major cardiovascular surgical procedures and for neurosurgery.\n- [Medicare Spending CMHS Based by Age Sex and Race HSA Level](https://www.johnsnowlabs.com/marketplace/medicare-spending-cmhs-based-by-age-sex-and-race-hsa-level)\n\t- This dataset includes the Hospital Service Area (HSA) level Medicare spending CMHS based data by age, sex and race. Hospital service areas (HSAs) are local healthcare markets for hospital care. An HSA is a collection of ZIP codes whose residents receive most of their hospitalizations from the hospitals in that area. HSAs were defined by assigning ZIP codes to the hospital area where the greatest proportion of their Medicare residents were hospitalized.\n- [Medicare Spending CMHS Based by Age Sex and Race State Level](https://www.johnsnowlabs.com/marketplace/medicare-spending-cmhs-based-by-age-sex-and-race-state-level)\n\t- This dataset includes the State level Medicare spending claims based data by price, age, sex and race. This dataset includes the county level Medicare spending claims based data by price, age, sex and race.\n- [Medicare Spending Claims Based by Price Age Sex and Race County Level](https://www.johnsnowlabs.com/marketplace/medicare-spending-claims-based-by-price-age-sex-and-race-county-level)\n\t- This dataset includes the county level Medicare spending claims based data by price, age, sex and race. The variables included are: state, number of Medicare enrollees, total Medicare reimbursements per enrollee, hospital and skilled nursing facility reimbursements per enrollee, physician reimbursements per enrollee, outpatient facility reimbursements per enrollee, home health agency reimbursements per enrollee, hospice reimbursements per enrollee, and durable medical equipment per enrollee.\n- [Medicare Spending Claims Based by Price Age Sex and Race HRR Level](https://www.johnsnowlabs.com/marketplace/medicare-spending-claims-based-by-price-age-sex-and-race-hrr-level)\n\t- This dataset includes the Hospital Referal Region (HRR) level Medicare spending claims based data by price, age, sex and race. Hospital referral regions (HRRs) represent regional healthcare markets for tertiary medical care that generally requires the services of a major referral center. The regions were defined by determining where patients were referred for major cardiovascular surgical procedures and for neurosurgery.\n- [Medicare Spending Claims Based by Price Age Sex and Race HSA Level](https://www.johnsnowlabs.com/marketplace/medicare-spending-claims-based-by-price-age-sex-and-race-hsa-level)\n\t- This dataset includes the Hospital Service Areas (HSA) level Medicare spending claims based data by price, age, sex and race. Hospital service areas (HSAs) are local healthcare markets for hospital care. An HSA is a collection of ZIP codes whose residents receive most of their hospitalizations from the hospitals in that area. HSAs were defined by assigning ZIP codes to the hospital area where the greatest proportion of their Medicare residents were hospitalized.\n- [Medicare Spending Claims Based by Price Age Sex and Race State Level](https://www.johnsnowlabs.com/marketplace/medicare-spending-claims-based-by-price-age-sex-and-race-state-level)\n\t- This dataset includes the State level Medicare spending claims based data by price, age, sex and race. The variables included are: state, number of Medicare enrollees, total Medicare reimbursements per enrollee, hospital and skilled nursing facility reimbursements per enrollee, physician reimbursements per enrollee, outpatient facility reimbursements per enrollee, home health agency reimbursements per enrollee, hospice reimbursements per enrollee, and durable medical equipment per enrollee.\n- [Provider Summary of Outpatient Prospective Payment System APC 2011](https://www.johnsnowlabs.com/marketplace/provider-summary-of-outpatient-prospective-payment-system-apc-2011)\n\t- This dataset contains estimated hospital specific charges for 30 Ambulatory Payment Classification (APC) Groups paid under the Medicare Outpatient Prospective Payment System (OPPS) for Calendar Year (CY) 2011. The Medicare payment amount includes the APC payment amount, the beneficiary Part B coinsurance amount and the beneficiary deductible amount.\n- [Provider Summary of Outpatient Prospective Payment System APC 2012](https://www.johnsnowlabs.com/marketplace/provider-summary-of-outpatient-prospective-payment-system-apc-2012)\n\t- This dataset contains estimated hospital specific charges for 30 Ambulatory Payment Classification (APC) Groups paid under the Medicare Outpatient Prospective Payment System (OPPS) for Calendar Year (CY) 2012. The Medicare payment amount includes the APC payment amount, the beneficiary Part B coinsurance amount and the beneficiary deductible amount.\n- [Provider Summary of Outpatient Prospective Payment System APC 2013](https://www.johnsnowlabs.com/marketplace/provider-summary-of-outpatient-prospective-payment-system-apc-2013)\n\t- This dataset contains estimated hospital specific charges for 30 Ambulatory Payment Classification (APC) Groups paid under the Medicare Outpatient Prospective Payment System (OPPS) for Calendar Year (CY) 2013. The Medicare payment amount includes the APC payment amount, the beneficiary Part B coinsurance amount and the beneficiary deductible amount.\n- [Provider Summary of Outpatient Prospective Payment System APC 2014](https://www.johnsnowlabs.com/marketplace/provider-summary-of-outpatient-prospective-payment-system-apc-2014)\n\t- This dataset contains estimated hospital specific charges for 32 Ambulatory Payment Classification (APC) Groups paid under the Medicare Outpatient Prospective Payment System (OPPS) for Calendar Year (CY) 2014. The Medicare payment amount includes the APC payment amount, the beneficiary Part B coinsurance amount and the beneficiary deductible amount.\n- [Provider Summary of Outpatient Prospective Payment System APC 2015](https://www.johnsnowlabs.com/marketplace/provider-summary-of-outpatient-prospective-payment-system-apc-2015)\n\t- This dataset contains estimated hospital specific charges for 25 Ambulatory Payment Classification (APC) Groups paid under the Medicare Outpatient Prospective Payment System (OPPS) for Calendar Year (CY) 2015. The Medicare payment amount includes the APC payment amount, the beneficiary Part B coinsurance amount and the beneficiary deductible amount.\n- [Provider Summary of Outpatient Prospective Payment System APC 2016](https://www.johnsnowlabs.com/marketplace/provider-summary-of-outpatient-prospective-payment-system-apc-2016)\n\t- This dataset contains estimated hospital specific charges for 35 Ambulatory Payment Classification (APC) Groups paid under the Medicare Outpatient Prospective Payment System (OPPS) for Calendar Year (CY) 2016. The Medicare payment amount includes the APC payment amount, the beneficiary Part B coinsurance amount and the beneficiary deductible amount.\n- [Provider Summary of Outpatient Prospective Payment System APC 2017](https://www.johnsnowlabs.com/marketplace/provider-summary-of-outpatient-prospective-payment-system-apc-2017)\n\t- This dataset contains estimated hospital specific charges for 60 Ambulatory Payment Classification (APC) Groups paid under the Medicare Outpatient Prospective Payment System (OPPS) for Calendar Year (CY) 2017. The Medicare payment amount includes the APC payment amount, the beneficiary Part B coinsurance amount and the beneficiary deductible amount.\n- [Provider Summary of Outpatient Prospective Payment System APC 2018](https://www.johnsnowlabs.com/marketplace/provider-summary-of-outpatient-prospective-payment-system-apc-2018)\n\t- This dataset contains estimated hospital specific charges for 60 Ambulatory Payment Classification (APC) Groups paid under the Medicare Outpatient Prospective Payment System (OPPS) for Calendar Year (CY) 2018. The Medicare payment amount includes the APC payment amount, the beneficiary Part B coinsurance amount and the beneficiary deductible amount.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated\n- Data Updates\n - Data updates support replace-on-update: outdated foreign keys are deprecated, not deleted\n\n**Our data is curated and enriched by domain experts** \n\n Each dataset is manually curated by our team of doctors, pharmacists, public health & medical billing experts:\n- Field names, descriptions, and normalized values are chosen by people who actually understand their meaning\n- Healthcare & life science experts add categories, search keywords, descriptions and more to each dataset\n- Both manual and automated data enrichment supported for clinical codes, providers, drugs, and geo-locations\n- The data is always kept up to date – even when the source requires manual effort to get updates\n- Support for data subscribers is provided directly by the domain experts who curated the data sets\n- Every data source’s license is manually verified to allow for royalty-free commercial use and redistribution.\n\n**Need Help?**\nIf you have questions about our products, contact us at [info@johnsnowlabs.com](mailto:info@johnsnowlabs.com). \n 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But while it can improve results compared to other mass marketing approaches, the cost in time and effort of developing content has often constrained how truly personalized it can be.\n\n**Why Amperity Customer Data Cloud?**\n\nAmperity’s Customer Data Cloud seamlessly integrates with Databricks via Delta Sharing, allowing direct access to unified and enriched customer data for AI models. By leveraging Amperity's unified profiles, brands can ensure enhanced accuracy and reliability of model outputs. This approach enables businesses to create tailored messages that resonate with specific consumer segments, cutting through the noise and capturing shoppers' attention effectively. By employing Amperity’s unified profiles, companies can streamline data sharing, automate identity resolution, enrich Lakehouse data, and distribute insights effortlessly to numerous destinations, revolutionizing their marketing strategy. \n\nThe increased accuracy and depth of customer data with Amperity directly translate into more precise, impactful, and engaging marketing campaigns, making personalized marketing a scalable and efficient approach for winning over customers during the critical shopping window.\n\nTo access the Solution Accelerator Notebook:\n1. Select \"Get Instant Access\" on the top right of the page\n2. Click \"Open\"\n3. Navigate to the top \"amperity_ai_driven_personalization\" catalog\n4. Click to the subtab \"Other Assets\" in the catalog\n5. Clone the notebook\n6. Navigate to the cloned notebook and click \"Run all\" \n\n**[Sign up for a free trial](https://amperity.com/trial)** to see how your brand can use Amperity to build robust unified datasets and durable customer profiles that support all of your brand’s use cases. You may use trial data provided by Amperity or you may upload samples of your own customer data.\n\n**Amperity Free Trial Includes:** \n- Support: Dedicated support email to talk to experts, public user slack.\n- Demo Data: No need for infosec, use a provided set of trial data\n- Guides and Docs: Step-by-step instructions and videos to guide your trial\n\n**Amperity Customer Data Cloud**\n- [Eliminate data silos](https://www.amperity.com): Seamlessly make your data in Databricks accessible to business users and downstream tools and systems with Amperity's bi-directional data share, while maintaining security & governance with Unity Catalog.\n- [Enrich your lakehouse with unified customer data:](https://docs.amperity.com/reference/stitch_results.html) Automate identity resolution and unify profile data with an AI-powered toolkit, enriching your Lakehouse with customer profile data for further analysis and modeling.\n- [Activate data anywhere:](https://docs.amperity.com/destinations.html) Activate data stored in Databricks with Amperity's reverse ETL to send enriched customer data to 200+ destinations. \n\n","terms_of_service":"https://amperity.com/legal","documentation_link":"https://docs.amperity.com","privacy_policy_link":"https://amperity.com/legal/privacy-notice","embedded_notebook_file_infos":[{"id":"7f8419a5-7341-4558-8d84-30eb4809f473","marketplace_file_type":"EMBEDDED_NOTEBOOK","file_parent":{"parent_id":"03957274-428d-411e-9c6c-6d7cea253d8e","file_parent_type":"LISTING"},"mime_type":"text/html","download_link":"https://marketplace-file-storage-prod.s3-fips.us-east-1.amazonaws.com/EMBEDDED_NOTEBOOK/LISTING_03957274-428d-411e-9c6c-6d7cea253d8e/7f8419a5-7341-4558-8d84-30eb4809f473?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEGgaDnVzLWVhc3QtMS1maXBzIkYwRAIgbC8sWIoYaHR8y7Wq86nPb0pufBl4%2BM4PWpeN%2B5PW9VQCIG7BhAVBDCWTpxFDLeuKYm%2Ff1JLz4iccPMzter0YdhgWKqYECDAQABoMNDE0MzUxNzY3ODI2IgxNAJrHbXaZG20jNfMqgwQyGEb4TsoVOoNvJ%2BaA%2FZ7zeu8hGcHqCeNfblP%2FQo5g6q2aRNtpwtPRKMlhYcIE%2FUQaNT1Lqmyptu32VUQTpzjTTxRVmJrDPkheSCkV06XQ%2FpTMKjU8CC7X1b%2FqYf8CrBwZT7Cv4C5WPjEET38f2A79h0xV6bZgPy28zQWrSeIJ9w6AfZBxiE24%2FVc08zVA0ivCT6lsy7nuU0wcR%2FMg2C9nd1vaMnQ923VpD21CKfVVYcrNWL2b%2FQ%2BNAO%2BxPjPgjiN6UPQbvbvZxkSdhgrGtecIg9NgqGkMcvM5VLTDtdC1kdeZetfOC2obtA%2BT%2F0gy3tIDo6WM5hcQfw743%2FAU9oaGnRBl%2BwoxRRDCZA%2F4jnPdluICSI7NCr6C3HHOe4CSZJjRihczxPA%2FgQczAVjAPs6CrK9N9pIzt0vNJpGUTgg4AoxftTvGfciDREilSctPIcBk6GwB%2Bzt4wbtNCmEPU%2BFD46rc6CWVQmmLwbKOeHw70ipMVFlOc8NlXkEtGO2vu6H44MstSSc6daOVUYUavQSGZ6jGhEjsP2YGz8KDm9XQotB%2FtyVBqiZV01OKqDmmBJA%2FXjUpSJ8M%2FeJXhe5m%2BKcxzNObtoJRI7jci3Tpt08XZyXZ7%2FyIV8H30ya468X3cfkTXrm972uga3z7iXAdubyEHnQIua%2FuvtwOHILi6O5D1Me2MzCPvcnXBjqTAYDoMWBt%2B3AVFuqSs2dmDQNBnvEhXfMgTedKLD5PJHb3aIl15iBkLaNu36GYnVCp9ZU2lSYcTbY5QwNnNeTfJTNy9bKhwzru6Sg%2BslhN%2BTS0zkTn%2BeLMY4tHgPEtEZ2cp%2FCk6GwqswvnYVKHjwO8q%2B1Mx8lc08fVEfarE9dke1GGFqoddx%2Bq%2BZUyHbDlAVfrCmf0yw%3D%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20261110T000135Z&X-Amz-SignedHeaders=host&X-Amz-Expires=86400&X-Amz-Credential=ASIAWA6KKHEJORWBH4N3%2F20261110%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=66dab5cad03a8932aa036974b296bb15ddfe0da766611794b41bbc3ddf8746a9","created_at":1744651631046,"updated_at":1744651631046,"display_name":"Sample: Amperity Customer Data Cloud","status":"FILE_STATUS_PUBLISHED"}],"geographical_coverage":"[\"United States and Canada\"]","cost":"FREE","data_source":"Generated by Amperity","size":6.49,"assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://amperity.com/legal"},"provider_summary":{"name":"Amperity","description":"Amperity is a customer data cloud for turning raw data into unified, comprehensive data model. 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The data package includes a list of the active drugs in the MDRP and specifies the manufacturer status in reporting or not reporting the Average Manufacturer Price. It also contains details of the most recent reports on covered outpatient active drugs as well as a list of National Drug Codes (NDC) that are not listed with the FDA.\n\n**Benefits**\n- Reduces medicaid expenditures for prescription drugs.\n- list of the active drugs in the medicaid drug rebate program can be accessed.\n- provides a list that can be used to check if a certain ndc is listed with the fda or not.\n- manufacturer status in reporting or not can be checked.\n- medicaid expenses on prescription drugs can be analyzed.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [CMS Drug Manufacturer Rebate Drugs](https://www.johnsnowlabs.com/marketplace/cms-drug-manufacturer-rebate-drugs)\n\t- The dataset consists of details of the most recent reports on covered outpatient active drugs by manufacturers participating in the Medicaid Drug Rebate Program of the Centers for Medicare and Medicaid Services (CMS) according to Section 1927 of the Social Security Act. The rebate drug product data file contains the active drugs that have been reported by participating drug manufacturers as of the most recent rebate reporting period under the Medicaid Drug Rebate Program.\n- [CMS Drug Manufacturer Rebate Not FDA](https://www.johnsnowlabs.com/marketplace/cms-drug-manufacturer-rebate-not-fda)\n\t- This dataset contains a list of National Drug Codes (NDC) in the Medicaid Drug Rebate Program (MDRP) that are not listed with the FDA. It is encouraged to drug manufacturers to follow the NDC Structured Product Labeling to ensure their listing with FDA, which is used to verify eligible products for Medicaid coverage, by matching the NDCs in the Medicaid Drug Rebate Program Drugs Product Data dataset against the FDA NSDE file to identify the products not listed with FDA included in the MDRP.\n- [CMS Drug Manufacturer Rebate Report on Average Pricing](https://www.johnsnowlabs.com/marketplace/cms-drug-manufacturer-rebate-report-on-average-pricing)\n\t- This dataset contains a list of the active drugs in the Medicaid Drug Rebate Program and specifies the manufacturer status in reporting or not reporting the Average Manufacturer Price. A report should be issued for each active drug in the program, otherwise, penalties may result. The present dataset contains a list of the drugs included in the MDRP stated as \"Reported\"or \"Not Reported\" according to the submission status of the AMP.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated\n- Data Updates\n - Data updates support replace-on-update: outdated 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It is a useful tool for activities such as looking for new safety concerns that might be related to a marketed product, evaluating a manufacturer's compliance to reporting regulations and responding to outside requests for information. The reports in FAERS are evaluated by clinical reviewers, in the Center for Drug Evaluation and Research (CDER) and the Center for Biologics Evaluation and Research (CBER), to monitor the safety of products after they are approved by FDA. These reports are submitted to FDA by healthcare professionals (such as physicians, pharmacists, nurses and others) and consumers (such as patients, family members, lawyers and others). Healthcare professionals and consumers may also report to the products’ manufacturers who then send these reports to FDA. The datasets in FAERS data package contain many relevant information such as: patient demographics, drug name, dose and route of administration, diagnosis and indication for use, therapy start and end date and patient outcome.\n\n**Benefits**\n- useful for physicians, pharmacists, pharmaceutical companies, medical students and patients (general public).\n- faers database is available on fda’s website in raw form, whereas a jsl customer gets it in clean and normalized form ready to use and create his own custom reports.\n- quarter wise data merged into one file.\n- easy to comprehend for the customers who are unfamiliar with the medical terminology as all abbreviated terms are replaced with full form, unlike faers original datasets which are full of abbreviations.\n- datasets are available for the year 2014, 2015, 2016 and 2017\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [FDA Adverse Events Reporting System Demographics 2015](https://www.johnsnowlabs.com/marketplace/fda-adverse-events-reporting-system-demographics-2015)\n\t- FAERS database is designed to support the FDA’s post-marketing safety surveillance program for drug and therapeutic biologic products. \nThe Demographics data file contains patient demographic and administrative information, a single record for each event report.\n- [FDA Adverse Events Reporting System Drug 2015](https://www.johnsnowlabs.com/marketplace/fda-adverse-events-reporting-system-drug-2015)\n\t- FAERS database is designed to support the FDA’s post-marketing safety surveillance program for drug and therapeutic biologic products. \nThe Drug file contains drug/biologic information for as many medications as were reported for the event (1 or more per event).\n- [FDA Adverse Events Reporting System Drug Indication 2015](https://www.johnsnowlabs.com/marketplace/fda-adverse-events-reporting-system-drug-indication-2015)\n\t- FAERS database is designed to support the FDA’s post-marketing safety surveillance program for drug and therapeutic biologic products. \nThe Indication file contains all \"Medical Dictionary for Regulatory Activities\" (MedDRA) terms coded for the indications for use (diagnoses) for the reported drugs (0 or more per drug per event).\n- [FDA Adverse Events Reporting System Drug Reaction 2015](https://www.johnsnowlabs.com/marketplace/fda-adverse-events-reporting-system-drug-reaction-2015)\n\t- FAERS database is designed to support the FDA’s post-marketing safety surveillance program for drug and therapeutic biologic products. \nThe Reaction file contains all \"Medical Dictionary for Regulatory Activities\" (MedDRA) terms coded for the adverse event (1 or more).\n- [FDA Adverse Events Reporting System Drug Therapy Dates 2015](https://www.johnsnowlabs.com/marketplace/fda-adverse-events-reporting-system-drug-therapy-dates-2015)\n\t- FAERS database is designed to support the FDA’s post-marketing safety surveillance program for drug and therapeutic biologic products. The Drug Therapy Dates file contains drug therapy start dates and end dates for the reported drugs (0 or more per drug per event).\n- [FDA Adverse Events Reporting System Patient Outcome 2015](https://www.johnsnowlabs.com/marketplace/fda-adverse-events-reporting-system-patient-outcome-2015)\n\t- FAERS database is designed to support the FDA’s post-marketing safety surveillance program for drug and therapeutic biologic products. \nThe Outcome file contains patient outcomes for the event (0 or more).\n- [FDA Adverse Events Reporting System Report Source 2015](https://www.johnsnowlabs.com/marketplace/fda-adverse-events-reporting-system-report-source-2015)\n\t- FAERS database is designed to support the FDA’s post-marketing safety surveillance program for drug and therapeutic biologic products. \nThe Report Source file contains report sources for event (0 or more).\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 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The `ANXAVS` column represents value of capital goods in terms of millions of dollars at the time of the corresponding `DATE`.\n\n\n\n**Data Sources**\n\n\nThe data in this product comes from **fs_fred_ANXAVS**.\n\nShort for Federal Reserve Economic Data, FRED is an online database consisting of hundreds of thousands of economic data time series from scores of national, international, public, and private sources. \n\nFRED, created and maintained by the Research Department at the Federal Reserve Bank of St. Louis, goes far beyond simply providing data: It combines data with a powerful mix of tools that help the user understand, interact with, display, and disseminate the data. 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All geospatial data are optimized for seamless integration with popular systems like Esri ArcGIS, Snowflake, QGIS, and more.\n\n\nProduct Features\n\n- Fully and accurately geocoded\n\n- Administrative areas with a level range of 0-4\n\n- Multi-language support including address names in local and foreign languages\n\n- Comprehensive city definitions across countries\n\n- House numbers\n\n\n\nFor additional insights, you can combine the map data with:\n\n- UNLOCODE and IATA codes\n\n- Time zones and Daylight Saving Times\n\n\n\nWhy do companies choose our location databases\n\n- Enterprise-grade service\n\n- Reduce integration time and cost by 30% \n\n- Weekly updates for the highest quality\n\n\nNote: Custom geospatial data packages are available. 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Unlike traditional ESG datasets sourced from corporate disclosures, MarketPsych ESG Analytics provides an external media-based perspective — capturing how companies and countries are actually being perceived and reported on across environmental, social, and governance dimensions.\n\nPowered by MarketPsych Data's proprietary NLP engine, the dataset covers 30,000+ companies across 120+ countries and all 252 global countries and regions, with history dating back to January 1, 1998. Scores are published minutely, hourly, and daily, making this dataset uniquely suited for both real-time risk monitoring and long-horizon quantitative research. 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The source of the data is:\nKCDC (Korea Centers for Disease Control & Prevention) (http://www.cdc.go.kr/)\n\n**Product details**\nKCDC (Korea Centers for Disease Control & Prevention) announces the information of COVID-19 quickly and transparently.\nDS4C (Data Science for COVID-19) made a structured dataset based on the report materials of KCDC and local governments.\n\n**License and/or Citation**\nThis data set is licensed under the following license: CC BY-NC-SA 4.0 (https://creativecommons.org/licenses/by-nc-sa/4.0/)\n\nPlease note that derivative works must be licensed under the same license.\n\nPlease note that this dataset may only be used for NON-COMMERCIAL purposes consistent with the applicable licenses.\n\nDatabricks, Inc. DISCLAIMS ANY WARRANTIES OF ANY KIND, EXPRESS OR IMPLIED. Users of this dataset are solely responsible for their compliance with any copyright, patent or trademark\nrestrictions and are referred to the copyright, patent or trademark notices appearing in the original sources, all of which are hereby incorporated by reference.\n\nIf you believe this mirrored dataset infringes your rights, please contact legal@databricks.com.","terms_of_service":"https://cms.databricks.com/sites/default/files/2023-01/marketplace-consumer-terms_0.pdf","documentation_link":"https://www.kaggle.com/datasets/kimjihoo/coronavirusdataset","privacy_policy_link":"https://www.databricks.com/legal/privacynotice","data_source":"https://www.kaggle.com/datasets/kimjihoo/coronavirusdataset","assets":["ASSET_TYPE_MEDIA"],"license":"https://creativecommons.org/licenses/by-nc-sa/4.0/"},"provider_summary":{"name":"Databricks","description":"As the world’s first lakehouse platform in the cloud, Databricks combines the best elements of data lakes and data warehouses to deliver the reliability, strong governance, and performance of data warehouses with the openness, flexibility, and machine learning support of data lakes. We are a cross-cloud platform that works across AWS, Azure, and Google.\nThis unified approach simplifies your modern data stack by eliminating the data silos that traditionally separate and complicate data engineering, analytics, BI, data science and machine learning. It’s built on open source and open standards to maximize flexibility. And, its common approach to data management, security and governance helps companies operate more efficiently and innovate faster."},"popularity":1443},{"id":"2d87d877-24cd-4300-a3d9-87e65910f30d","summary":{"name":"Geolocet | Points of Interest Data | Petrol Station Locations - Austria","subtitle":"Complete GIS-ready dataset of 3,696 Austria petrol station locations (brands & independents). Optimized for Databricks.","setting":{"visibility":"PUBLIC"},"created_at":1780644093114,"updated_at":1780644093114,"categories":["GEOSPATIAL","RETAIL"],"listingType":"PERSONALIZED","provider_id":"5d3ddc17-a7e8-4794-a760-13587e1de2c6"},"detail":{"description":"# **All Petrol Station Locations in Austria: Complete Geographic Dataset**\n---\n### **Premium GIS-Ready Retail Location Intelligence Data for Databricks Analytics**\n\nThis high-fidelity geospatial dataset provides a complete, exhaustive, and standardized overview of **3,696** verified fuel and petrol station locations across Austria. Updated as of **February 3, 2026**, this comprehensive national dataset captures the entire Austrian forecourt landscape - encompassing premier multinational fuel networks, national champions, and prominent independent operators. It offers an analysis-ready foundation for EV charging infrastructure planning, competitor benchmarking, logistics routing, and retail location intelligence.\n\nFully compatible with **Apache Spark**, **Delta Lake**, and **Databricks Unity Catalog** workflows.\n\n---\n\n## **Complete National Coverage: Branded Forecourts & Regional Operators**\nUnlike single-brand extractions, this dataset aggregates all premier fuel networks and independent operators into a single, unified data model. The coverage maps maximum geographic depth across all 9 Austrian federal states:\n\n- **Major Fuel Networks & National Champions:** \n [Lagerhaus Genol (491 locations)](https://geolocet.com/products/austria-lagerhaus-genol-petrol-stations), \n [Eni (325 locations)](https://geolocet.com/products/austria-eni-petrol-stations), \n [bp (288 locations)](https://geolocet.com/products/austria-bp-petrol-stations), \n [Turmöl (257 locations)](https://geolocet.com/products/austria-turmol-petrol-stations), \n [OMV (231 locations)](https://geolocet.com/products/austria-omv-petrol-stations), \n [Shell (210 locations)](https://geolocet.com/products/austria-shell-petrol-stations), \n [Jet (151 locations)](https://geolocet.com/products/austria-jet-petrol-stations)\n\n- **Regional & Dynamic Growth Banners:** \n [Avanti (137 locations)](https://geolocet.com/products/austria-avanti-petrol-stations), \n [AVIA (109 locations)](https://geolocet.com/products/austria-avia-petrol-stations), \n [Diskont (100 locations)](https://geolocet.com/products/austria-diskont-petrol-stations)\n\n**Plus thousands of regional independent forecourts** fully mapped and standardized across all federal states (e.g., Niederösterreich: 876 sites; Steiermark: 670 sites; Oberösterreich: 632 sites; Tirol: 404 sites).\n\n---\n\n## **Technical Specifications & Schema Definitions**\nOptimized for zero-friction schema enforcement when reading via `spark.read.format(\"csv\")`. The structured schema below ensures flawless rendering in Databricks notebooks and high accuracy for LLM semantic indexing.\n\n* **`GUID`** (`STRING`): Unique global identifier for deterministic primary key mapping and deduplication.\n* **`Title`** (`STRING`): Official station name or commercial brand banner classification (e.g., OMV, bp, Shell).\n* **`Latitude`** (`DOUBLE`): Precise WGS84 latitude coordinate for point-in-polygon and spatial joins.\n* **`Longitude`** (`DOUBLE`): Precise WGS84 longitude coordinate for spatial indexing (H3 / S2).\n* **`Street_No` / `Street`** (`STRING`): Normalized building numbers and thoroughfare name elements.\n* **`City` / `Postal_Code`** (`STRING`): Core municipal settlements and primary postal routing codes.\n* **`Gemeinde`** (`STRING`): Local municipal administrative boundary designation.\n* **`Region`** (`STRING`): Macro-administrative boundary classification (Federal State).\n* **`Admin_level_1` / `_2`** (`STRING`): Additional local administrative area and district identifiers.\n* **`Address`** (`STRING`): Full, standardized street address clean-text output.\n* **`Wheelchair`** (`STRING`): Accessibility flags indicating wheelchair mobility support where available.\n\n---\n\n## **Databricks Workflow & Spatial Analytics Use Cases**\n\n- **EV Charging Whitespace Analysis:** Query the full 3,696 location points against high-voltage grid corridors or traffic volume rasters to rank optimal stations for ultra-fast EV charging infrastructure placement.\n- **H3 Geospatial Indexing:** Convert the precise WGS84 `Latitude` and `Longitude` fields into Uber H3 spatial hexagons (`h3_longlattocell`) to seamlessly cross-reference with granular Austrian administrative boundaries and demographic datasets.\n- **Logistics & Fleet Benchmarking:** Integrate forecourt coordinates with fleet tracking telemetry to calculate fuel stop density, build optimal corridor routing profiles, and minimize off-route deadhead mileage across the Alpine highway network.\n\n---\n\n## **Data Evaluation & Custom Ingestion**\n\n### **Evaluate with a Free Sample Dataset**\nVerify the structure, column types, and coordinate precision before full implementation. A comprehensive schema sample is available for download in CSV/Parquet-friendly formats.\n\n### **Enterprise Delivery & Tailored Formatting**\nWhile natively available as an analysis-ready CSV for DBFS ingestion, we accommodate enterprise-level data architecture needs. Alternative formats - including **GeoJSON, Esri Shapefiles, PostgreSQL dumps, Excel, or direct Delta sharing profiles** - can be generated. Reach out to our engineering team at **contact@geolocet.com** for integration support or custom European POI data requests.","terms_of_service":"https://geolocet.com/pages/terms-of-use","documentation_link":"","privacy_policy_link":"https://geolocet.com/pages/privacy-policy","assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://geolocet.com/pages/terms-of-use"},"provider_summary":{"name":"Geolocet LTD","description":"Geolocet provides premium geospatial data for retail analysis, GIS, and market planning. 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Free, open, and built for compliant data interoperability.**\n\n---\n\nSpindle Token enables the first step of the de-identification process: replacing Personally Identifiable Information (PII) with encrypted tokens that preserve the ability to link records across organizations, without ever exposing the underlying identifiers.\n\nBuilt on **Spindle Health's OPPRL open standard** for Privacy Preserving Record Linkage (PPRL), Spindle Token removes the barriers to tokenization and increases compliant data interoperability across the healthcare ecosystem. This Databricks app is a no-code solution to help extend tokenization capabilities across your entire organization, not just your technical teams.\n\nSpindle Token is HIPAA Compliant and has been independently audited and verified by a third-party cybersecurity firm. [View the security certification report.](https://echeloncyber.com/spindle-health-security-certification-report)\n\n> **Important:** Spindle Token implements tokenization as the first step in a de-identification pipeline. Tokens generated by Spindle Token satisfy HIPAA requirements and are fully compliant. Any additional columns outside of tokens, require re-identification risk assessment and Expert Determination. Organizations requiring full de-identification and Expert Determination Certification for complete data assets should contact Spindle Health directly at [operations@spindlehealth.com](mailto:operations@spindlehealth.com).\n\n---\n\n## Why Spindle Token\n\n- **Free and open** — Free, compliant, open-source software. The OPPRL protocol and its implementations are maintained in the open.\n- **Decentralized** — No costs, no contract. Manage your own encryption keys and collaborate directly with partners. Eliminate the middleman to streamline operations and accelerate results.\n- **Scalable and compatible** — Embarrassingly parallel tokenization built for modern high performance computing infrastructure.\n- **Limitless** — Operate without the confines of a fractured data ecosystem. No need to pay the \"token tax.\"\n\n---\n\n## Use cases\n\n**Real World Data (RWD) analytics**\n\nOrganizations on both sides of the data market rely on Spindle Token. Data providers use it to encrypt PII before sharing, ensuring no identifiable information is disclosed. Data buyers and licensors use it to tokenize and aggregate records across multiple sources, enabling patient journey analysis, market trending, market access strategy, commercial targeting, and a myriad of other use cases that glean insight into the complex healthcare landscape and ecosystem, all without exposing subject identity.\n\n\n**Real World Evidence (RWE)**\n\nClinical teams generating evidence for the FDA or market stakeholders such as payors and ACOs use Spindle Token to link patient records compliantly across data sources, supporting outcomes studies and treatment efficacy analysis without exposing subject identity.\n\n---\n\n## Who uses Spindle Token\n\n- Health systems\n- EHR vendors\n- Pharma & life sciences\n- RWD data vendors\n- Health plans & payors\n- Labs & diagnostics\n- Clearinghouses\n- ePrescribers\n- CROs & research orgs\n- ACOs & provider networks\n- Health tech & analytics companies\n- Healthcare data brokers\n- Consumer data platforms\n- Financial services\n\n---\n\n## Have questions? Learn more:\n\n[Spindle Token FAQs](https://spindlehealth.com/tokenization)\n\n\n","terms_of_service":"https://github.com/Spindle-Health/carduus?tab=Apache-2.0-1-ov-file#readme","documentation_link":"","privacy_policy_link":"https://spindlehealth.com/privacy-policy","embedded_notebook_file_infos":[{"id":"ed77381a-932b-4344-b74d-3e16209a390f","marketplace_file_type":"SCREENSHOT","file_parent":{"parent_id":"fabc3cfb-156c-4153-9d00-82b7720a0aa2","file_parent_type":"LISTING"},"mime_type":"image/png","download_link":"https://marketplace-file-storage-prod.s3-fips.us-east-1.amazonaws.com/SCREENSHOT/LISTING_fabc3cfb-156c-4153-9d00-82b7720a0aa2/ed77381a-932b-4344-b74d-3e16209a390f?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEGgaDnVzLWVhc3QtMS1maXBzIkYwRAIgbC8sWIoYaHR8y7Wq86nPb0pufBl4%2BM4PWpeN%2B5PW9VQCIG7BhAVBDCWTpxFDLeuKYm%2Ff1JLz4iccPMzter0YdhgWKqYECDAQABoMNDE0MzUxNzY3ODI2IgxNAJrHbXaZG20jNfMqgwQyGEb4TsoVOoNvJ%2BaA%2FZ7zeu8hGcHqCeNfblP%2FQo5g6q2aRNtpwtPRKMlhYcIE%2FUQaNT1Lqmyptu32VUQTpzjTTxRVmJrDPkheSCkV06XQ%2FpTMKjU8CC7X1b%2FqYf8CrBwZT7Cv4C5WPjEET38f2A79h0xV6bZgPy28zQWrSeIJ9w6AfZBxiE24%2FVc08zVA0ivCT6lsy7nuU0wcR%2FMg2C9nd1vaMnQ923VpD21CKfVVYcrNWL2b%2FQ%2BNAO%2BxPjPgjiN6UPQbvbvZxkSdhgrGtecIg9NgqGkMcvM5VLTDtdC1kdeZetfOC2obtA%2BT%2F0gy3tIDo6WM5hcQfw743%2FAU9oaGnRBl%2BwoxRRDCZA%2F4jnPdluICSI7NCr6C3HHOe4CSZJjRihczxPA%2FgQczAVjAPs6CrK9N9pIzt0vNJpGUTgg4AoxftTvGfciDREilSctPIcBk6GwB%2Bzt4wbtNCmEPU%2BFD46rc6CWVQmmLwbKOeHw70ipMVFlOc8NlXkEtGO2vu6H44MstSSc6daOVUYUavQSGZ6jGhEjsP2YGz8KDm9XQotB%2FtyVBqiZV01OKqDmmBJA%2FXjUpSJ8M%2FeJXhe5m%2BKcxzNObtoJRI7jci3Tpt08XZyXZ7%2FyIV8H30ya468X3cfkTXrm972uga3z7iXAdubyEHnQIua%2FuvtwOHILi6O5D1Me2MzCPvcnXBjqTAYDoMWBt%2B3AVFuqSs2dmDQNBnvEhXfMgTedKLD5PJHb3aIl15iBkLaNu36GYnVCp9ZU2lSYcTbY5QwNnNeTfJTNy9bKhwzru6Sg%2BslhN%2BTS0zkTn%2BeLMY4tHgPEtEZ2cp%2FCk6GwqswvnYVKHjwO8q%2B1Mx8lc08fVEfarE9dke1GGFqoddx%2Bq%2BZUyHbDlAVfrCmf0yw%3D%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20261110T000135Z&X-Amz-SignedHeaders=host&X-Amz-Expires=86400&X-Amz-Credential=ASIAWA6KKHEJORWBH4N3%2F20261110%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=bfa0f2296e9c8f7316fe4325453d786ee1f992ea06e01c88c9ba62e6513ed5a3","created_at":1781660061418,"updated_at":1781660061418,"display_name":"SpindleTokenApp.png","status":"FILE_STATUS_PUBLISHED"}],"assets":["ASSET_TYPE_APP"],"license":""},"provider_summary":{"name":"Spindle Health","description":"Full-Stack De-Identification for Real World Data\nWe build de-identification technologies for organizations working with Real World Data (RWD) - removing the expensive & inefficient barriers that slow down compliant healthcare data exchange.\nSpindle Token\nFree, HIPAA-compliant tokenization built for scale. Powered by Python on Spark, it handles the largest datasets in minutes through embarrassingly parallel computation - third-party audited and production-ready. Stop paying the 'Token Tax' & limiting your interoperability. This is the new market standard.\nSpindle Portal\nData with context. A secure environment for buyers and sellers to analyze data before acquisition - understand cohort overlap, coverage, and value. No more buying blind.\nSpindle De-ID Workspace\nThe fit-for-purpose de-identification platform. Automates HIPAA Expert Determination workflows so you stay compliant without sacrificing data utility. 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The information provided in this dataset shows the initial view or landing pages for each target family that provides expert-curated overviews of the key properties and selective ligands and tool compounds available. Additional information can be found also in other databases including Ensembl, UniProt, PubChem, ChEMBL, and DrugBank, as well as curated chemical information and literature citations in PubMed.\n\n**Benefits**\n- This data can be useful for further research and for education and training in pharmacological principles and techniques. information from this data package can be useful for researchers and students in pharmacology and drug discovery and provide the general public with accurate information on the basic science underlying drug action.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [Pharmacology Complete List of Endogenous Ligands and Other Peptides](https://www.johnsnowlabs.com/marketplace/pharmacology-complete-list-of-endogenous-ligands-and-other-peptides)\n\t- This dataset shows the complete list of Endogenous Ligands and Other Peptides from the Guide to PHARMACOLOGY; an online, open-access portal to pharmacological information on all the human targets of prescription drugs, which is the product from the collaboration of the International Union of Basic and Clinical Pharmacology (IUPHAR) and the British Pharmacological Society (BPS).\n- [Pharmacology Complete List of Ligand Molecules](https://www.johnsnowlabs.com/marketplace/pharmacology-complete-list-of-ligand-molecules)\n\t- This dataset shows the complete list of ligand molecules from the Guide to PHARMACOLOGY; an online, open-access portal to pharmacological information on all the human targets of prescription drugs, which is the product from the collaboration of the International Union of Basic and Clinical Pharmacology (IUPHAR) and the British Pharmacological Society (BPS).\n- [Pharmacology Complete Target and Family List Identification](https://www.johnsnowlabs.com/marketplace/pharmacology-complete-target-and-family-list-identification)\n\t- This dataset shows the complete target and family list from the Guide to PHARMACOLOGY; an online, open-access portal to pharmacological information on all the human targets of prescription drugs, which is the product from the collaboration of the International Union of Basic and Clinical Pharmacology (IUPHAR) and the British Pharmacological Society (BPS).\n- [Pharmacology Interaction Data for Targets and Ligands](https://www.johnsnowlabs.com/marketplace/pharmacology-interaction-data-for-targets-and-ligands)\n\t- This dataset shows the Interaction Data for Targets and Ligands from the Guide to PHARMACOLOGY; an online, open-access portal to pharmacological information on all the human targets of prescription drugs, which is the product from the collaboration of the International Union of Basic and Clinical Pharmacology (IUPHAR) and the British Pharmacological Society (BPS).\n- [Pharmacology Mapping of Target ID and UniProt Accessions](https://www.johnsnowlabs.com/marketplace/pharmacology-mapping-of-target-id-and-uniprot-accessions)\n\t- This dataset shows the Target Identification and UniProt Accessions from the Guide to PHARMACOLOGY; an online, open-access portal to pharmacological information on all the human targets of prescription drugs, which is the product from the collaboration of the International Union of Basic and Clinical Pharmacology (IUPHAR) and the British Pharmacological Society (BPS).\n- [Pharmacology Mapping of Target and HGNC ID and Symbols](https://www.johnsnowlabs.com/marketplace/pharmacology-mapping-of-target-and-hgnc-id-and-symbols)\n\t- This dataset shows the Target Identification and HGNC ID and Symbols from the Guide to PHARMACOLOGY; an online, open-access portal to pharmacological information on all the human targets of prescription drugs, which is the product from the collaboration of the International Union of Basic and Clinical Pharmacology (IUPHAR) and the British Pharmacological Society (BPS).\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated\n- Data Updates\n - Data updates support replace-on-update: outdated foreign keys are deprecated, not deleted\n\n**Our data is curated and enriched by domain experts** \n\n Each dataset is manually curated by our team of doctors, pharmacists, public health & medical billing experts:\n- Field names, descriptions, and normalized values are chosen by people who actually understand their meaning\n- Healthcare & life science experts add categories, search keywords, descriptions and more to each dataset\n- Both manual and automated data enrichment supported for clinical codes, providers, drugs, and geo-locations\n- The data is always kept up to date – even when the source requires manual effort to get updates\n- Support for data subscribers is provided directly by the domain experts who curated the data sets\n- Every data source’s license is manually verified to allow for royalty-free commercial use and redistribution.\n\n**Need Help?**\nIf you have questions about our products, contact us at [info@johnsnowlabs.com](mailto:info@johnsnowlabs.com). \n 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Notebook","status":"FILE_STATUS_PUBLISHED"}]},"provider_summary":{"name":"John Snow Labs","description":"John Snow Labs, an AI and NLP for healthcare company, provides state-of-the-art software, models, and data to help healthcare and life science organizations build, deploy, and operate AI projects."},"popularity":1020},{"id":"f5ccc1c5-1b56-461e-90f5-043c365113c5","summary":{"name":"Health Insurance Plan Payments","subtitle":"This dataset covers Healthcare coverage status of adults in New York state, Health insurance minorities and low Englis..","share":{"name":"health_insurance_plan_payments","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1682356077828,"updated_at":1695691509135,"categories":["HEALTH","SCIENCE_AND_RESEARCH"],"listingType":"STANDARD","provider_id":"f88849f8-9d77-4de2-b4c7-03d55cc3768e"},"detail":{"description":"\n**Overview**\nThis dataset covers Healthcare coverage status of adults in New York state, Health insurance minorities and low English level by census tract, Health insurance single parent and disability by census tracts, Health insurance and socioeconomic status by census tracts, Medicare Part B, Part D drug cost and utilization data, Medicare Part D plan reconciliation contract, Part D plan reconciliation parent organization, Medicare plan payment Part C county & plan level, Medicare plan payment Part D.\n\n**Description**\nHealth insurance plans Unified Rate Review, Health insurance rate by plan level age tobacco use and location; Information on Medicare FFS claims that underwent Comprehensive Error Rate Testing (CERT) medical review. Estimates of the health care coverage status for the entire population of noninstitutionalized (18+ years old) persons from New York State, broken down by demographic categories and groups. Census tract level and estimated data about the number of uninsured non-institutionalized civilians, the number of persons belonging to minority (from ethnicity point of view, including Hispanic/Latino population) and the number of persons aged 5 and older who speak English less than well. Census tract level estimates for the number of uninsured noninstitutionalized civilians, number of single-parent households with children under 18 and for the number of noninstitutionalized civilians with a disability. Census tract level estimates for the number of uninsured noninstitutionalized civilians, number of persons below the poverty line, unemployed population, number of persons with no high school diploma, which are socioeconomic characteristics with a negative impact on the access to healthcare services.Information on the drug spending and utilization information on Medicare Part B drugs (drugs administered in doctors’ offices and other outpatient settings) and Medicare Part D drugs (drugs patients generally administer themselves).\n\nResults by contract of the audit of the Centers for Medicare & Medicaid Services’ Medicare Part D Prescription Drug Event (PDE) reconciliation process performed by an independent certified public accounting firm. Results by the parent organization of the audit of the Centers for Medicare & Medicaid Services’ Medicare Part D Prescription Drug Event (PDE) reconciliation process performed by an independent certified public accounting firm. Average payment per member per month for Medicare Part C Plans by county, Medicare Part C Plans by plan level, Medicare Part D Plans. The number of people in the U.S. will have an increase in their premium even while qualifying for the Extra Help (Low Income Subsidy) at state and zip code level. Health insurance plans rate filing information on rate increases submitted to the Centers for Medicare & Medicaid Services (CMS). Health Insurance Rate by Plan Level Age Tobacco Use And Location carry plan-level data on individual rates based on an eligible subscriber’s age, tobacco use, and geographic location. Details on variations in insurance plan rates with age ratings, tobacco ratings, geography, family size, small group market ratings. Information on Medicare FFS claims that underwent Comprehensive Error Rate Testing (CERT) medical review.\n\n**Benefits**\n- Useful in assessing the impact of these socioeconomic variables on the access to healthcare services and on the population health status. help inform geographical areas for targeted outreach efforts to find and assist individuals who may see an increase in their cost-sharing while qualifying for the low-income subsidy.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [Active Third Party Administrators in Iowa](https://www.johnsnowlabs.com/marketplace/active-third-party-administrators-in-iowa)\n\t- This dataset shows the active third-party administrator (TPA) in Iowa. A third-party administrator is an entity that performs insurance and claims related tasks for residents of the state of Iowa.\n- [Health Care Coverage Of Adults In New York State](https://www.johnsnowlabs.com/marketplace/health-care-coverage-of-adults-in-new-york-state)\n\t- This dataset contains estimates of the health care coverage status for the entire population of noninstitutionalized (18+ years old) persons from New York State, broken down by and by demographic categories and groups, starting with 2011.\n- [Health Insurance Business Rules PUF](https://www.johnsnowlabs.com/marketplace/health-insurance-business-rules-puf)\n\t- This dataset shows the plan-level data on rating business rules, such as allowed relationships (e.g., spouse, dependents) and tobacco use by the Centers for Medicare & Medicaid Services (CMS).\n- [Health Insurance Minorities and Low English Level by Tracts 2010-2014](https://www.johnsnowlabs.com/marketplace/health-insurance-minorities-and-low-english-level-by-tracts-2010-2014)\n\t- This dataset contains census tract level and estimated data about the number of uninsured non-institutionalized civilians, the number of persons belonging to minority (from ethnicity point of view, including Hispanic/Latino population) and the number of persons aged 5 and older who speak English less than well. In this dataset could be found all US census tracts and the estimates are made using data collected from 2010 to 2014 by the American Community Survey (ACS).\n- [Health Insurance Minorities and Low English Level by Tracts 2014-2018](https://www.johnsnowlabs.com/marketplace/health-insurance-minorities-and-low-english-level-by-tracts-2014-2018)\n\t- This dataset contains census tract level and estimated data about the number of uninsured non-institutionalized civilians, the number of persons belonging to minority (from ethnicity point of view, including Hispanic/Latino population) and the number of persons aged 5 and older who speak English less than well. In this dataset could be found all US census tracts and the estimates are made using data collected from 2014 to 2018 by the American Community Survey (ACS).\n- [Health Insurance Plan ID Crosswalk PUF](https://www.johnsnowlabs.com/marketplace/health-insurance-plan-id-crosswalk-puf)\n\t- This dataset shows the plan-level data mapping plans offered in the previous plan year (2019) to plans offered in the current plan year (2022) by the Centers for Medicare & Medicaid Services (CMS).\n- [Health Insurance Plan Level Individual Rates PUF](https://www.johnsnowlabs.com/marketplace/health-insurance-plan-level-individual-rates-puf)\n\t- This dataset contains the Rate Public Use File (PUF) that makes up the Marketplace Public Use Files. The Rate-PUF contains plan-level data on individual rates based on an eligible subscriber’s age, tobacco use, and geographic location.\n- [Health Insurance Plans Unified Rate Review PUF](https://www.johnsnowlabs.com/marketplace/health-insurance-plans-unified-rate-review-puf)\n\t- The Public Health Service Act (PHSA) and the implementing regulation, establish requirements for health insurance issuers (issuers) offering health insurance coverage for non-grandfathered products in the small group and/or individual markets to submit rate filing information on rate increases to the Centers for Medicare & Medicaid Services (CMS).\n- [Health Insurance Provider Network PUF](https://www.johnsnowlabs.com/marketplace/health-insurance-provider-network-puf)\n\t- This dataset shows the issuer-level data identifying provider network URLs by the Centers for Medicare & Medicaid Services (CMS).\n- [Health Insurance Service Area PUF](https://www.johnsnowlabs.com/marketplace/health-insurance-service-area-puf)\n\t- This dataset shows the Issuer-level data on geographic service areas including state, county, and zip code by the Centers for Medicare & Medicaid Services (CMS).\n- [Health Insurance Single Parent and Disability by Tracts 2010-2014](https://www.johnsnowlabs.com/marketplace/health-insurance-single-parent-and-disability-by-tracts-2010-2014)\n\t- This dataset is focused on offering census tract level estimates for the number of uninsured noninstitutionalized civilians, the number of single-parent households with children under 18 and for the number of noninstitutionalized civilians with a disability. The dataset contains data for all US census tracts; the estimates were made using data collected from 2010 to 2014.\n- [Health Insurance Single Parent and Disability by Tracts 2014-2018](https://www.johnsnowlabs.com/marketplace/health-insurance-single-parent-and-disability-by-tracts-2014-2018)\n\t- This dataset is focused on offering census tract level estimates for the number of uninsured noninstitutionalized civilians, the number of single-parent households with children under 18 and for the number of noninstitutionalized civilians with a disability. The dataset contains data for all US census tracts; the estimates were made using data collected from 2014 to 2018.\n- [Health Insurance Transparency in Coverage PUF](https://www.johnsnowlabs.com/marketplace/health-insurance-transparency-in-coverage-puf)\n\t- This dataset shows the issuer-level claims, appeals, and active URL data. The PY2022 PUF contains data from PY2021 for issuers participating in the Exchange in PY2020.\n- [Health Insurance and Socioeconomic Status by Census Tracts 2010-2014](https://www.johnsnowlabs.com/marketplace/health-insurance-and-socioeconomic-status-by-census-tracts-2010-2014)\n\t- This dataset offers census tract level estimates for the number of uninsured noninstitutionalized civilians, number of persons below poverty line, unemployed population, number of persons with no high school diploma, which are socioeconomic characteristics with a negative impact on the access to healthcare services.\n- [Health Insurance and Socioeconomic Status by Census Tracts 2014-2018](https://www.johnsnowlabs.com/marketplace/health-insurance-and-socioeconomic-status-by-census-tracts-2014-2018)\n\t- This dataset offers census tract level estimates for the number of uninsured noninstitutionalized civilians, number of persons below poverty line, unemployed population, number of persons with no high school diploma, which are socioeconomic characteristics with a negative impact on the access to healthcare services.\n- [Insurance Producers Licensed in Iowa](https://www.johnsnowlabs.com/marketplace/insurance-producers-licensed-in-iowa)\n\t- This dataset provides a listing of resident and non-resident insurance producers licensed to sell to Iowans.\n- [Medicare Comprehensive Error Rate Testing Improper Payment Data](https://www.johnsnowlabs.com/marketplace/medicare-comprehensive-error-rate-testing-improper-payment-data)\n\t- The dataset includes detailed information on Medicare fee-for-service (FFS) claims that underwent Comprehensive Error Rate Testing (CERT) medical review for the years 2011 to 2021 report period. These claims were used to calculate the Medicare fee-for-service improper payment rate.\n- [Medicare Part B Drug Cost and Utilization Data 2011-2015](https://www.johnsnowlabs.com/marketplace/medicare-part-b-drug-cost-and-utilization-data-2011-2015)\n\t- The 2015 Medicare Part B Drug Cost and Utilization Dataset presents drug spending and utilization information on Medicare Part B drugs (drugs administered in doctors’ offices and other outpatient settings) and includes annual data for 2011 to 2015.\n- [Medicare Part B Drug Cost and Utilization Data 2016-2020](https://www.johnsnowlabs.com/marketplace/medicare-part-b-drug-cost-and-utilization-data-2016-2020)\n\t- The Medicare Part B Drug Cost and Utilization Dataset presents drug spending and utilization information on Medicare Part B drugs (drugs administered in doctors’ offices and other outpatient settings) and includes annual data for 2016 to 2020.\n- [Medicare Part B Drug Cost and Utilization Data](https://www.johnsnowlabs.com/marketplace/medicare-part-b-drug-cost-and-utilization-data)\n\t- The 2015 Medicare Part B Drug Cost and Utilization Dataset presents drug spending and utilization information on Medicare Part B drugs (drugs administered in doctors’ offices and other outpatient settings) and includes annual data for 2011 to 2015.\n- [Medicare Part C Plan Payment County Level](https://www.johnsnowlabs.com/marketplace/medicare-part-c-plan-payment-county-level)\n\t- This dataset contains information provided by insurance companies regarding average payment per member per month for Medicare Part C Plans by county.\n- [Medicare Part C Plan Payment](https://www.johnsnowlabs.com/marketplace/medicare-part-c-plan-payment)\n\t- This dataset contains information provided by insurance companies regarding average payment per member per month for Medicare Part C Plans by plan level.\n- [Medicare Part D Drug Cost and Utilization Data 2011-2015](https://www.johnsnowlabs.com/marketplace/medicare-part-d-drug-cost-and-utilization-data-2011-2015)\n\t- The 2015 Medicare Part D Drug Cost and Utilization Dataset presents drug spending and utilization information on Medicare Part D drugs (drugs patients generally administer themselves) and includes annual data for 2011 to 2015.\n- [Medicare Part D Drug Cost and Utilization Data 2016-2020](https://www.johnsnowlabs.com/marketplace/medicare-part-d-drug-cost-and-utilization-data-2016-2020)\n\t- The 2020 Medicare Part D Drug Cost and Utilization Dataset presents drug spending and utilization information on Medicare Part D drugs (drugs patients generally administer themselves) and includes annual data for 2016 to 2020.\n- [Medicare Part D Drug Cost and Utilization Data](https://www.johnsnowlabs.com/marketplace/medicare-part-d-drug-cost-and-utilization-data)\n\t- The 2015 Medicare Part D Drug Cost and Utilization Dataset presents drug spending and utilization information on Medicare Part D drugs (drugs patients generally administer themselves) and includes annual data for 2011 to 2015.\n- [Medicare Part D Plan Payment](https://www.johnsnowlabs.com/marketplace/medicare-part-d-plan-payment)\n\t- This dataset contains information by insurance companies contracts regarding average payment per member per month for Medicare Part D Plans.\n- [Medicare Part D Plan Reconciliation Contract](https://www.johnsnowlabs.com/marketplace/medicare-part-d-plan-reconciliation-contract)\n\t- This dataset contains the results by contract of the audit of the Centers for Medicare & Medicaid Services’ Medicare Part D Prescription Drug Event (PDE) reconciliation process performed by an independent certified public accounting firm.\n- [Medicare Part D Plan Reconciliation Parent Organization](https://www.johnsnowlabs.com/marketplace/medicare-part-d-plan-reconciliation-parent-organization)\n\t- This dataset contains the results by parent organization of the audit of the Centers for Medicare & Medicaid Services’ Medicare Part D Prescription Drug Event (PDE) reconciliation process performed by an independent certified public accounting firm.\n- [Notice List of Beneficiaries Who Chose Premium Plan For 2012](https://www.johnsnowlabs.com/marketplace/notice-list-of-beneficiaries-who-chose-premium-plan-for-2012)\n\t- The dataset reflects the number of people in the U.S. who received the choosers notice sent to those who selected their own plan and will have an increase in their premium even while qualifying for the Extra Help (Low Income Subsidy) in 2012 at the state and zip code level.\n- [Qualified Health Plans in Market](https://www.johnsnowlabs.com/marketplace/qualified-health-plans-in-market)\n\t- This dataset shows 2018 individual and family health plans available in the United States where the federal government is operating the Marketplace. States not represented in the dataset runs their own Marketplaces.\n- [Quality Dental Plans in Market](https://www.johnsnowlabs.com/marketplace/quality-dental-plans-in-market)\n\t- This dataset shows 2023 individual and family dental plans available in states where the federal government is operating the Marketplace. States not represented in the dataset runs their own Marketplaces.\n- [Total Effectuated Enrollment and Financial Assistance by State](https://www.johnsnowlabs.com/marketplace/total-effectuated-enrollment-and-financial-assistance-by-state)\n\t- This dataset shows the total effectuated enrollment and financial assistance yearly from Februay 2016 to February 2021 by the United States.\n- [Washington Dental Loss Ratios](https://www.johnsnowlabs.com/marketplace/washington-dental-loss-ratios)\n\t- This dataset serves as a dental insurance summary database for Performance Year (PY) 2015-2016. This dataset is cumulative at the company level as reported to the National Association of Insurance Commissioners (NAIC).\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated\n- Data Updates\n - Data updates support replace-on-update: outdated foreign keys are deprecated, not deleted\n\n**Our data is curated and enriched by domain experts** \n\n Each dataset is manually curated by our team of doctors, pharmacists, public health & medical billing experts:\n- Field names, descriptions, and normalized values are chosen by people who actually understand their meaning\n- Healthcare & life science experts add categories, search keywords, descriptions and more to each dataset\n- Both manual and automated data enrichment supported for clinical codes, providers, drugs, and geo-locations\n- The data is always kept up to date – even when the source requires manual effort to get updates\n- Support for data subscribers is provided directly by the domain experts who curated the data sets\n- Every data source’s license is manually verified to allow for royalty-free commercial use and redistribution.\n\n**Need Help?**\nIf you have questions about our products, contact us at [info@johnsnowlabs.com](mailto:info@johnsnowlabs.com). \n 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Use the LNG Netbacks dataset to gain insights into import and export dynamics and enable strategic decision-making based on timely data.\n\nThis dataset includes:\n\n- Date, export geography, and import geography, allowing you to tailor netbacks data to your specific needs\n\n- Daily netbacks\n\n- Optimal market identification\n\n- Comprehensive import and export insights\n\n**Use cases**\n- Quantitative Analysis - Leverage LNG netbacks data to design and implement complex and insightful models to support energy and commodities trading\n- Pricing Analysis - Netbacks determine the supplier's price by considering the five-day moving average of the end market price and the five-day moving average of shipping costs, taking into account the specified supply and import geographies. 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All data comes from a single source, ensuring **consistency and comparability** across markets.\n\nThe Sociodemographics-Bundle Basic for North Macedonia includes the following **variables**:\n- [population](https://www.mbi-geodata.com/en/sociodemographic-data/population/)\n- [gender](https://www.mbi-geodata.com/en/sociodemographic-data/sex-gender/)\n- [age structure](https://www.mbi-geodata.com/en/sociodemographic-data/agebands/) (15-year age bands)\n- household figures ([number of households](https://www.mbi-geodata.com/en/sociodemographic-data/households/) and [households by type](https://www.mbi-geodata.com/en/sociodemographic-data/households-by-type/))\n\nOur sociodemographic data is offered for each country on different **levels**:\n- **Postal Code Level:** This fine-grained level of detail makes the data suitable for a wide range of applications – for example, enriching customer databases, which are often organized by postal code.\n- **Administrative Level:** Covers both larger administrative areas (e.g., regions, states, provinces) as well as finer subdivisions such as districts, counties, or municipalities.\n- **Micro Level**: Subdivisions of postal code or administrative boundaries, such as census areas or street segments. This is the most granular level for local analysis.\n\n**Please note:** The dataset available for free download is a **sample dataset** covering a limited number of geographic areas. If you wish to access the full dataset (covering all areas) or if you require additional variables or geographic levels, please contact us. We will be happy to help you find a product that fits your needs.\n \n**Additional Insights**: https://www.mbi-geodata.com/en/sociodemographic-data/\n\n**Data Structure**:\nAGE_F0014: Population (Nighttime) by age: 0 - 14 years, female\nAGE_F1529: Population (Nighttime) by age: 15 - 29 years, female\nAGE_F3044: Population (Nighttime) by age: 30 - 44 years, female\nAGE_F4559: Population (Nighttime) by age: 45 - 59 years, female\nAGE_F60PL: Population (Nighttime) by age: 60 years and above, female\nAGE_M0014: Population (Nighttime) by age: 0 - 14 years, male\nAGE_M1529: Population (Nighttime) by age: 15 - 29 years, male\nAGE_M3044: Population (Nighttime) by age: 30 - 44 years, male\nAGE_M4559: Population (Nighttime) by age: 45 - 59 years, male\nAGE_M60PL: Population (Nighttime) by age: 60 years and above, male\nAGE_T0014: Population (Nighttime) by age: 0 - 14 years, total\nAGE_T1529: Population (Nighttime) by age: 15 - 29 years, male\nAGE_T3044: Population (Nighttime) by age: 30 - 44 years, total\nAGE_T4559: Population (Nighttime) by age: 45 - 59 years, total\nAGE_T60PL: Population (Nighttime) by age: 60 years and above, total\nCTRYCODE: Two digit country code by ISO 3166\nFEMALE: Population (Nighttime): females\nHH_SIZE: Average household size\nHH_T: Households: total number\nMALE: Population (Nighttime): males\nNAME: Name of administrative area / Name of the most populous administrative area / Name of the respective micro-area or the next higher level\nADMINCODE / POSTCODE / MICROCODE Administrative code / Postal code / Micro-code\nP_PRM: Population (Nighttime) year average: per mill of country\nP_T: Population (Nighttime) year average: total number","terms_of_service":"https://service-mbi-geodata.com/s/HNp7Z2tws4WDqWd","documentation_link":"https://www.mbi-geodata.com/en/mbi-supplier-manufacturer-worldwide-geodata/europe/north-macedonia-maps/","privacy_policy_link":"https://www.mbi-geodata.com/en/data-protection/","geographical_coverage":"[\"Europe\"]","cost":"FREE","update_frequency":{"interval":1,"unit":"YEARLY"},"collection_granularity":{"interval":1,"unit":"YEARLY"},"collection_date_start":1704098729222,"collection_date_end":1735634733803,"data_source":"MBI","assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://service-mbi-geodata.com/s/CNryacLrBXqXjJq"},"provider_summary":{"name":"Michael Bauer International GmbH – MBI Geodata","description":"For over 15 years, Michael Bauer International (MBI) has been a leading provider of globally consistent and comparable geographic data, including more than 3 million postcode areas and other detailed datasets like sociodemographics and purchasing power. 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Name and title only.","setting":{"visibility":"PUBLIC"},"created_at":1788236799146,"updated_at":1788350442000,"categories":["ADVERTISING_AND_MARKETING"],"listingType":"PERSONALIZED","provider_id":"cef74d15-013f-4a83-b55c-815ec7a71d40"},"detail":{"description":"**Overview**\n\nWho works at the company, in a professional capacity: full name, role and job title, ordered within each company record.\n\nName and title only. There is no email, phone or social profile at person level. Those channels exist at company level and are a separate listing. We are explicit about this because the distinction is often blurred in this market.\n\nThis listing contains sample data. The full product is delivered under a separate agreement.\n\n**Use cases**\n\n- Identifying the right role to approach at a target company before enrichment.\n- Know-your-business checks that require named directors and officers.\n- Org-shape analysis across a portfolio of companies.\n\n**Product details**\n\n- Dataset represented: people_inside_business_sample, 300 records, 18 columns in this sample and 20 in the full product.\n- Sample fields include CompanyName, Country, City, PersonFullName, PersonFirstName, PersonLastName, PersonPosition, PersonTitle, PersonTitleCode and PersonLangPref.\n- PersonPosition is an ordinal, not a job title: it is the order the person is listed in within the company record. The role is in PersonTitle. This is documented in the column comments.\n- This product joins our other listings on RecordID.\n\nFor more details, refer to the embedded notebook.\n\n**Additional Insights**\n\nThis product contains personal data relating to individuals in a professional capacity: full name, surname, role and job title. It contains no contact details at person level, no identification numbers, no financial account data, no health data, and no data relating to individuals under 16. Gender is carried in the full product and is not included in this sample.\n\nThis is not a beneficial-ownership product and contains no ownership percentages.\n\nCoverage varies sharply by market, and we do not publish a global average because it would hide a range that runs from no coverage in some countries to over 80% in others. Tell us which markets you need and we will measure them.\n\nUpdate cadence: static sample extracted 28 July 2026, refreshed when the listing is refreshed. 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Values are not seasonally adjusted.\n\n**Product Details**\n\nIncludes the following tables:\n\n\n- **fs_fred.fred_FPCPITOTLZGUSA**\n - **Update frequency:** Yearly\n - **Source:** FRED\n - **Description:** This dataset contains the columns: `FPCPITOTLZGUSA` and `DATE`, where `FPCPITOTLZGUSA` represents inflation as a percent, not seasonally adjusted, at the time of the corresponding `DATE`.\n\n\n\n**Data Sources**\n\n\nThe data in this product comes from **FRED**.\n\nShort for Federal Reserve Economic Data, FRED is an online database consisting of hundreds of thousands of economic data time series from scores of national, international, public, and private sources. \n\nFRED, created and maintained by the Research Department at the Federal Reserve Bank of St. Louis, goes far beyond simply providing data: It combines data with a powerful mix of tools that help the user understand, interact with, display, and disseminate the data. 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The datasets contain the highest level of Medicare cost report status.\n\n**Description**\nThis data package contains the Among datasets of the Home Health Agency (HHA) cost reports like the HHA Cost Alphanumeric dataset, HHA Cost Numeric dataset, HHA Cost Report dataset and HHA Cost Rollup dataset, the HHA Cost Report dataset is the parent of the other tables and contains the unique primary key field Report Record Number necessary to match the data from all datasets.\n\n**Benefits**\n- Useful for hha stakeholders and public health specialist or specialized public. the user can find the items descriptions, not only codes corresponding to descriptions. the fields descriptions along with the dataset description are useful for the user to quickly understand the data and the dataset.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [HHA Cost Alphanumeric Data 2017-2018](https://www.johnsnowlabs.com/marketplace/hha-cost-alphanumeric-data-2017-2018)\n\t- This dataset contains all alpha data reported on a cost report such as hospital name, address, dates, and questions that require a yes/no response. It has information on the Home Health Agency (HHA) cost reports received by Healthcare Cost Report Information System (HCRIS).\n- [HHA Cost Alphanumeric Data 2018-2020](https://www.johnsnowlabs.com/marketplace/hha-cost-alphanumeric-data-2018-2020)\n\t- This dataset contains all alpha data reported on a cost report such as hospital name, address, dates, and questions that require a yes/no response. It has information on the Home Health Agency (HHA) cost reports received by Healthcare Cost Report Information System (HCRIS).\n- [HHA Cost Alphanumeric Data 2018-2022](https://www.johnsnowlabs.com/marketplace/hha-cost-alphanumeric-data-2018-2022)\n\t- This dataset contains all alpha data reported on a cost report such as hospital name, address, dates, and questions that require a yes/no response. It has information on the Home Health Agency (HHA) cost reports received by Healthcare Cost Report Information System (HCRIS).\n- [HHA Cost Numeric Data 2017-2018](https://www.johnsnowlabs.com/marketplace/hha-cost-numeric-data-2017-2018)\n\t- This dataset contains all numeric data reported on a cost report such as costs, charges, ratios, number of hours in work week, etc. It has information on the Home Health Agency (HHA) cost reports received by Healthcare Cost Report Information System (HCRIS).\n- [HHA Cost Numeric Data 2018-2020](https://www.johnsnowlabs.com/marketplace/hha-cost-numeric-data-2018-2020)\n\t- This dataset contains all numeric data reported on a cost report such as costs, charges, ratios, number of hours in work week, etc. It has information on the Home Health Agency (HHA) cost reports received by Healthcare Cost Report Information System (HCRIS).\n- [HHA Cost Numeric Data 2018-2022](https://www.johnsnowlabs.com/marketplace/hha-cost-numeric-data-2018-2022)\n\t- This dataset contains all numeric data reported on a cost report such as costs, charges, ratios, number of hours in work week, etc. It has information on the Home Health Agency (HHA) cost reports received by Healthcare Cost Report Information System (HCRIS).\n- [HHA Cost Report Data 2017](https://www.johnsnowlabs.com/marketplace/hha-cost-report-data-2017)\n\t- The dataset, HHA Cost Report Data 2017 has information on the Home Health Agency (HHA) cost reports received by Healthcare Cost Report Information System (HCRIS). 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Built on the Bronze–Silver–Gold Databricks Lakehouse framework, the WFM Accelerator ensures efficient operations, improved employee engagement, and measurable business outcomes.\n\nHere are some articles Dataplatr published on Medium — please check these out:\n[Overview of Contact Center Analytics: Unlock insights with Dataplatr](https://medium.com/@dataplatr/an-overview-of-contact-center-analytics-unlock-insights-with-dataplatr-part-1-5b9428719f2c) \n[Contact Center Analytics : Unlock Call Details KPIs and Dashboard for actionable insights](https://medium.com/@dataplatr/contact-center-analytics-unlock-call-details-kpis-and-dashboard-for-actionable-insights-part-2-b452e0bb0961) \n[Contact Center Analytics: Unveiling the Power of WFM with Strategies, Insights, and Impact](https://medium.com/@dataplatr/contact-center-analytics-unveiling-the-power-of-wfm-with-strategies-insights-and-impact-part-4-68878706c5f2) \n[Contact Center Analytics: A Comprehensive Guide to Quality Assurance (QA ) and Analytics](https://medium.com/@dataplatr/contact-center-analytics-a-comprehensive-guide-to-quality-assurance-qa-and-analytics-part-5-cb7f60f9ed3c)\n\nNote: By clicking '**Get Access**', you agree to our **Terms of Service**. 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[See the full details and get access on MDC](https://mozilladatacollective.com/datasets/cmrmkr3nh01h4nu07gqsvfckc?utm_source=databricks&utm_medium=referral&utm_campaign=databricks_marketplace&utm_content=listing_cta).\n\nThis dataset provides 25 hours of natural, multi-speaker conversational dialogue speech in Gujarati specifically recorded within the high-stakes finance domain. The corpus has been meticulously transcribed into text to ensure premium phonetic quality and structural accuracy for training domain-specific automated speech systems.\n\nThis dataset captures real-world domain interactions in Gujarati, a prominent language spoken in India. The collection focuses on spontaneous conversational turn-taking, technical jargon handling, and colloquial linguistic shifts within the finance setting.\n\nMulti-speaker dialogue setup containing contributions from distinct native speakers, domain professionals (e.g. financial advisors) and local consumers.\n\n**At a glance**\n\n- **Language:** Gujarati\n- **Task:** Automatic speech recognition (ASR)\n- **Format:** WAV, JSON\n\n**Dataset creation**\n\nThe dataset is strictly comprised of natural, spontaneous speech.\n\nSpeakers were explicitly instructed not to follow a script. They were told to pick a topic they felt comfortable with and think: \"How do I talk about this in real life?\"\n\nTo help them sustain natural dialogue for the required 9 to 10 minutes per session, speakers were given a tip to map out 4 to 5 simple bullet points or practice chatting with their partner once before hitting record. They were told to keep it personal, share their own stories, what they have seen around them, and what they think or feel.\n\nThe recordings feature natural pairs of individuals who already have an established real-life relationship. Users were told to record the conversations with someone they know, like a friend, a cousin, or a neighbour.\n\nTo ensure acoustic and conversational diversity, a user could only record with the same partner for a maximum of 3 sessions, and they had to select an entirely different topic each time.\n\nFor every recording, speaker metadata was logged for the conversational partner, including age, gender, phone number and the PIN code of where they are originally from. The files carry only the partner's gender and PIN code, and the gender of the person who made the recording; age and phone number are not included.\n\nSetup and Recording Instructions Given to Speakers:\n\n* Speakers were instructed to talk in a quiet place so their voices remained clear. While minor background noise was acceptable, it could not interfere with clarity, and both individuals had to be equally audible using the phone’s standard built-in microphone.\n* Speakers were given an IMPORTANT safety instruction: they were strictly forbidden from mentioning their real names, their partner's names, where they work, or where they live. All identifying personal details were intentionally kept out of the conversations.\n* Every submission was meant to last 9 to 10 minutes, though some recordings are shorter. Participants were required to complete a minimum of 4 and a maximum of 8 conversation recordings per assigned domain.\n\n**Directory structure**\n\nThe root directory contains pairs of WAV and JSON files that share a prefix: each recording's .wav file has a matching .merged.json file.\n\n**File structure**\n\nThe JSON file has three parts: “audio”, “metadata” and “transcriptions”:\n\n* **audio**: The filename and file format information\n* **metadata**: The metadata of the speaker and the topic\n* **transcriptions**: The time aligned transcriptions, in segments of at most 20 seconds, each marked SPEAKER1 or SPEAKER2\n\nThe transcriptions also mark pauses, speech heard over background noise, and sounds that are not words, with tags such as PAUSE, noise and [inaudible]. English words, which speakers often use for financial terms, are kept in Latin script.\n\nAn example file is in the [full datasheet on MDC](https://mozilladatacollective.com/datasets/cmrmkr3nh01h4nu07gqsvfckc?utm_source=databricks&utm_medium=referral&utm_campaign=databricks_marketplace&utm_content=listing_description).\n\n**Translated Examples**\n\n1. એ એક યોગ્ય બાબત છે. બચત એ આપણા જીવનનું ખૂબ જ મહત્વનું ભાગ છે, અને બચત કરવી એ ખૂબ જ જરૂરી ગણાય છે. (*That is a good thing. Saving is a very important part of our lives, and saving money is considered very necessary.*)\n2. હા અમે પણ અમારી ઘરે બચત કરીએ છીએ. અને હું પણ મારી personal બચત કરું છું. બચત કરવી એ ખૂબ સારી બાબત છે. ઘણા લોકો બચત કરતા નથી જેથી એ લોકોને પૈસાની જરૂર પડે ત્યારે ઓછીના અને ઉધાર માંગે છે અથવા loan લે છે. પરંતુ પૈસા બચાવવા જોઈએ જેથી આપણને ભવિષ્યમાં ખૂબ જ કામ લાગે છે. (*Yes, we also save money at our home. And I also do my personal savings. Saving money is a very good thing. Many people do not save, so when they need money, they ask for it from others and borrow it, or they take a loan. But we should save money so that it is very useful for us in the future.*)\n3. હા આપણને ભવિષ્યમાં કશું કામ કરવું હોય અથવા કોઈ નવા પ્રસંગ કરવો હોય અથવા કઈ નવી વસ્તુ લેવી હોય તો આપણે જે બચત કરેલા હોય તે પૈસા કામ લાગે જ છે. 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Let us know your target audience, and we will provide you with a complimentary quote. To get started, email us at info@techsalerator.com or via https://www.techsalerator.com/contact-us \n\n Once you have selected the data you want, confirm it by replying to our email, and we will deliver the database (in Excel) to you within 24 hours or discuss the annual licensing and subscription cost.\n\nIf you have any other questions or would like to explore data for a different city or country, our team of data experts can assist you with as we do cover firmographic data in every country worldwide.","terms_of_service":"https://www.techsalerator.com/dont-sell-data://","documentation_link":"","privacy_policy_link":"https://www.techsalerator.com/privacy-policy","geographical_coverage":"[\"Asia-Pacific\"]","update_frequency":{"interval":1,"unit":"DAILY"},"assets":["ASSET_TYPE_DATA_TABLE"]},"provider_summary":{"name":"Techsalerator ","description":"Techsalerator is a one-stop source for global data acquisition on Business Data, Consumer Data and Alternative data assets. 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It is fine-tuned to manage questions and provide answers related to retail services.\n\n**Intended Use**\n\n- **Recommended applications**: This model is perfect for use in retail environments. It can be integrated into customer service chatbots or help systems to provide real-time responses to common retail-related inquiries.\n- **Out-of-scope**: This model should not be used for medical, legal, or any serious safety-related purposes.\n\n**Model Architecture**\n\nThe \"Mistral-7B-Retail-v1\" uses the `MistralForCausalLM` structure with a `LlamaTokenizer`. It maintains the setup of the base model but is enhanced to better respond to retail-related questions.\n\n**Training Data**\n\nThis model was trained with a dataset specifically designed for retail-related question and answer interactions. The dataset encompasses a comprehensive range of retail intents, ensuring the model is trained to handle diverse customer inquiries and scenarios. It includes 46 distinct intents such as `add_product`, `availability_in_store`, `cancel_order`, `pay`, `refund_policy`, `track_order`, `use_app`, and many more, reflecting common retail transactions and customer service interactions. Each intent contains 1000 examples, which helps in creating responses across various retail situations.\n\nThis extensive training dataset ensures that the model can understand and respond to a wide array of retail-related queries, providing support in customer service applications. The dataset follows a structured approach, similar to other datasets published on Hugging Face, but is specifically tailored to cater to the customer support sector: [bitext/Bitext-customer-support-llm-chatbot-training-dataset](https://huggingface.co/datasets/bitext/Bitext-customer-support-llm-chatbot-training-dataset)\n\n**Training Procedure**\n\n**Hyperparameters**\n\n- **Optimizer**: AdamW\n- **Learning Rate**: 0.0002\n- **Epochs**: 1\n- **Batch Size**: 8\n- **Gradient Accumulation Steps**: 4\n- **Maximum Sequence Length**: 1024 tokens\n\n**Environment**\n\n- **Transformers Version**: 4.40.0.dev0\n- **Framework**: PyTorch 2.2.1+cu121\n- **Tokenizers**: Tokenizers 0.15.0\n\n**Limitations and Bias**\n\n- The model is fine-tuned on a domain-specific dataset and may not perform well outside the scope of retail advice.\n- Users should be aware of potential biases in the training data, as the model's responses may inadvertently reflect these biases. This model has been trained with a dataset that answers general retail questions, so potential biases may exist for specific use cases.\n\n**Ethical Considerations**\n\nThis model should be used responsibly, considering ethical implications of automated financial advice. As it is a base model for this retail field, it is crucial to ensure that the model's advice complements human expertise and adheres to relevant retail regulations.\n\n**Acknowledgments**\n\nThis model was developed by the Bitext and trained on infrastructure provided by Bitext.\n\n**License**\n\nThis model, \"Mistral-7B-Retail-v1\", is licensed under the Apache License 2.0 by Bitext Innovations International, Inc. This open-source license allows for free use, modification, and distribution of the model but requires that proper credit be given to Bitext.\n\n**Key Points of the Apache 2.0 License**\n\n- **Permissibility**: Users are allowed to use, modify, and distribute this software freely.\n- **Attribution**: You must provide proper credit to Bitext Innovations International, Inc. when using this model, in accordance with the original copyright notices and the license.\n- **Patent Grant**: The license includes a grant of patent rights from the contributors of the model.\n- **No Warranty**: The model is provided \"as is\" without warranties of any kind.\n\nYou may view the full license text at [Apache License 2.0](http://www.apache.org/licenses/LICENSE-2.0).\n\nThis licensing ensures the model can be used widely and freely while respecting the intellectual contributions of Bitext. For more detailed information or specific legal questions about using this license, please refer to the official license documentation linked above.\n","terms_of_service":"https://www.bitext.com/api-terms-of-use/","documentation_link":"","privacy_policy_link":"https://www.bitext.com/privacy-policy/","embedded_notebook_file_infos":[{"id":"027ef0d0-707c-42b3-b02c-5dcc429376ab","marketplace_file_type":"EMBEDDED_NOTEBOOK","file_parent":{"parent_id":"bc7a0b11-9c5b-4eda-9b22-564b5edcc551","file_parent_type":"LISTING"},"mime_type":"text/html","download_link":"https://marketplace-file-storage-prod.s3-fips.us-east-1.amazonaws.com/EMBEDDED_NOTEBOOK/LISTING_bc7a0b11-9c5b-4eda-9b22-564b5edcc551/027ef0d0-707c-42b3-b02c-5dcc429376ab?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEGgaDnVzLWVhc3QtMS1maXBzIkYwRAIgbC8sWIoYaHR8y7Wq86nPb0pufBl4%2BM4PWpeN%2B5PW9VQCIG7BhAVBDCWTpxFDLeuKYm%2Ff1JLz4iccPMzter0YdhgWKqYECDAQABoMNDE0MzUxNzY3ODI2IgxNAJrHbXaZG20jNfMqgwQyGEb4TsoVOoNvJ%2BaA%2FZ7zeu8hGcHqCeNfblP%2FQo5g6q2aRNtpwtPRKMlhYcIE%2FUQaNT1Lqmyptu32VUQTpzjTTxRVmJrDPkheSCkV06XQ%2FpTMKjU8CC7X1b%2FqYf8CrBwZT7Cv4C5WPjEET38f2A79h0xV6bZgPy28zQWrSeIJ9w6AfZBxiE24%2FVc08zVA0ivCT6lsy7nuU0wcR%2FMg2C9nd1vaMnQ923VpD21CKfVVYcrNWL2b%2FQ%2BNAO%2BxPjPgjiN6UPQbvbvZxkSdhgrGtecIg9NgqGkMcvM5VLTDtdC1kdeZetfOC2obtA%2BT%2F0gy3tIDo6WM5hcQfw743%2FAU9oaGnRBl%2BwoxRRDCZA%2F4jnPdluICSI7NCr6C3HHOe4CSZJjRihczxPA%2FgQczAVjAPs6CrK9N9pIzt0vNJpGUTgg4AoxftTvGfciDREilSctPIcBk6GwB%2Bzt4wbtNCmEPU%2BFD46rc6CWVQmmLwbKOeHw70ipMVFlOc8NlXkEtGO2vu6H44MstSSc6daOVUYUavQSGZ6jGhEjsP2YGz8KDm9XQotB%2FtyVBqiZV01OKqDmmBJA%2FXjUpSJ8M%2FeJXhe5m%2BKcxzNObtoJRI7jci3Tpt08XZyXZ7%2FyIV8H30ya468X3cfkTXrm972uga3z7iXAdubyEHnQIua%2FuvtwOHILi6O5D1Me2MzCPvcnXBjqTAYDoMWBt%2B3AVFuqSs2dmDQNBnvEhXfMgTedKLD5PJHb3aIl15iBkLaNu36GYnVCp9ZU2lSYcTbY5QwNnNeTfJTNy9bKhwzru6Sg%2BslhN%2BTS0zkTn%2BeLMY4tHgPEtEZ2cp%2FCk6GwqswvnYVKHjwO8q%2B1Mx8lc08fVEfarE9dke1GGFqoddx%2Bq%2BZUyHbDlAVfrCmf0yw%3D%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20261110T000135Z&X-Amz-SignedHeaders=host&X-Amz-Expires=86400&X-Amz-Credential=ASIAWA6KKHEJORWBH4N3%2F20261110%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=e4d29e15768c55a0fdd3e799784c178752bbdafb7b8467b83e131e55820f908f","created_at":1727477073010,"updated_at":1727477073010,"display_name":"bitext_mistral_7b_models_fine_tune_qlora","status":"FILE_STATUS_PUBLISHED"}],"cost":"FREE","data_source":"Internally generated by Bitext using proprietary NLP and NLG technologies","assets":["ASSET_TYPE_MODEL"],"license":"https://www.apache.org/licenses/LICENSE-2.0","tags":[{"tag_name":"LISTING_TAG_TYPE_TASK","tag_values":["QUESTION_ANSWERING","TEXT2TEXT_GENERATION"]}]},"provider_summary":{"name":"Bitext Innovation International","description":"We understand that one of the critical factors in achieving exceptional performance with LLMs is the quality and relevance of the training data. 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The models provided in this listing are\n\n- [mpt_30b](https://huggingface.co/mosaicml/mpt-30b)\n- [mpt_7b](https://huggingface.co/mosaicml/mpt-7b)\n- [mpt_7b_8k](https://huggingface.co/mosaicml/mpt-7b-8k)\n- [mpt_30b_instruct](https://huggingface.co/mosaicml/mpt-30b-instruct)\n- [mpt_7b_8k_instruct](https://huggingface.co/mosaicml/mpt-7b-8k-instruct)\n- [mpt_7b_instruct](https://huggingface.co/mosaicml/mpt-7b-instruct)\n\nThe models are packaged using MLflow’s transformers flavor.\n- [mpt_30b](https://huggingface.co/mosaicml/mpt-30b), [mpt_7b](https://huggingface.co/mosaicml/mpt-7b), [mpt_7b_8k](https://huggingface.co/mosaicml/mpt-7b-8k): pretrained generative text model that can be used for further fine-tuning on specific applications.\n- [mpt_30b_instruct](https://huggingface.co/mosaicml/mpt-30b-instruct), [mpt_7b_8k_instruct](https://huggingface.co/mosaicml/mpt-7b-8k-instruct), [mpt_7b_instruct](https://huggingface.co/mosaicml/mpt-7b-instruct): instruct fine-tuned for short-form instruction following.\n\nMpt models are licensed under the [Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0). By installing this listing, you acknowledge and agree to the license.\n\nFor example notebooks of using the mpt model in various use cases on Databricks, refer to [the Databricks ML example repository](https://github.com/databricks/databricks-ml-examples/tree/master/llm-models/mpt).\n\n**Use cases**\n\n[mpt_30b](https://huggingface.co/mosaicml/mpt-30b), [mpt_7b](https://huggingface.co/mosaicml/mpt-7b), [mpt_7b_8k](https://huggingface.co/mosaicml/mpt-7b-8k):\n- Fine-tuning on specific applications.\n\n[mpt_30b_instruct](https://huggingface.co/mosaicml/mpt-30b-instruct), [mpt_7b_8k_instruct](https://huggingface.co/mosaicml/mpt-7b-8k-instruct), [mpt_7b_instruct](https://huggingface.co/mosaicml/mpt-7b-instruct):\n- Generating responses to text-based instructions\n\n**Product details**\n\nThe pre-trained model can be used for further fine-tuning on specific applications. The fine-tuned model in this listing can be deployed directly to Databricks Model Serving for immediate use, or loaded for fine-tuning or batch inference use cases. For more details, install the listing and view the provided model cards for each 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Raw JSON streams are ingested without third-party aggregators.\n\n**Data Lineage**\nDirect API ingestion, timestamp normalization to UTC, license mapping to enterprise usage standards, statistical audit validation on the top 1% of assets for parity with live web counters\n\n**Compliance**\nPASSED (no critical nulls detected).\nGDPR clear. No PII. Augmented dataset.\n\n**License**\nCC-4 for SAMPLE\n\n**Acknowledgements**\nCertified by AIDC Data Engineering\nKanchana1990, Kaggle Grandmaster\n\n**Use Cases**\nThis dataset enables high-value analytics and ML workflows on Databricks:\n\n- Predictive Trend Detection: Use Velocity Score signals to identify emerging AI architectures (e.g., quantized models) days before mainstream reporting.\n\n- Enterprise Risk Screening: Filter models by commercial license compliance to reduce IP exposure in production ML pipelines.\n\n- Market Share Intelligence: Analyze adoption shifts between Big Tech, Unicorn, and Indie publishers to guide Edge AI investment strategy.\n\n- Alpha Generation for AI Investing: Build lead–lag and adoption momentum models across daily download dynamics.\n\n- LLM & Task Landscape Mapping: Cluster models by task category to quantify growth in NLP, vision, multimodal, and edge deployments.\n- \n\n**Column Dictionary**\n\n- Model_ID (string): Primary key. 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Available now on Databricks Marketplace."},"popularity":1219},{"id":"93902ab1-eaa8-40ab-ae86-1860f9380af4","summary":{"name":"US Crime Data","subtitle":"This data package contains the number of crimes reported by law enforcement agencies in the year 2015.","share":{"name":"us_crime_data","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1682355689971,"updated_at":1698965635294,"categories":["HEALTH","SCIENCE_AND_RESEARCH"],"listingType":"STANDARD","provider_id":"f88849f8-9d77-4de2-b4c7-03d55cc3768e"},"detail":{"description":"\n**Overview**\nThis data package contains the number of crimes reported by law enforcement agencies in the year 2015.\n\n**Description**\nCrime in the United States has been recorded since colonization. Crime rates have varied over time, with a sharp rise after 1963, reaching a broad peak between the 1970s and early 1990s. Since then, crime has declined significantly in the United States and current crime rates are approximately the same as those of the 1960s.\nViolent crime consists of four criminal offenses: murder and non-negligent manslaughter, forcible rape, robbery, and aggravated assault.\n - Murder and nonnegligent manslaughter, as defined in the UCR program, is the willful (nonnegligent) killing of one human being by another. This offense excludes deaths caused by negligence, suicide, or accident; justifiable homicides; and attempts to murder or assaults to murder.\n - Forcible rape is the carnal knowledge of a female forcibly and against her will. Assaults or attempts to commit rape by force or threat of force are also included; however, statutory rape (without force) and other sex offenses are excluded.\n - Robbery is the taking or attempting to take anything of value from the car, custody, or control of a person or persons by force or threat of force or violence and/or by putting the victim in fear.\n - Aggravated assault is an unlawful attack by one person upon another for the purpose of inflicting severe or aggravated bodily injury. This type of assault is usually accompanied by the use of a weapon or by means likely to produce death or great bodily harm. Attempts are included since an injury does not necessarily have to result when a gun, knife, or other weapon is used, which could and probably would result in a serious personal injury if the crime were successfully completed.\nIn general, property crimes include four crime categories burglary, larceny, motor vehicle theft, and arson.\n - Burglary is the unlawful entry of a structure to commit a felony or theft.\n - Larceny-theft is the unlawful taking, carrying, leading, or riding away of a property from the possession or constructive possession of another. It includes crimes such as shoplifting, pocket picking, purse snatching, thefts from motor vehicles, thefts of motor vehicle parts and accessories, and bicycle thefts, in which no use of force, violence, or fraud occurs. This crime category does not include embezzlement, ‘‘con’’ games, forgery, worthless checks, and motor vehicle theft.\n - Motor vehicle theft is the theft or attempted theft of a motor vehicle. This definition excludes the taking of a motor vehicle for temporary use by those persons having lawful access.\n - Arson is any willful or malicious burning or attempt to burn, with or without intent to defraud, a dwelling house, public building, motor vehicle or aircraft, personal property of another, etc. Only fires determined through investigation to have been willfully or maliciously set are classified as arson. Fires of suspicious or unknown origins are excluded.\n\n**Benefits**\n- Value to the crime reporting agencies for their record and future evaluation. helping to reduce various crimes in united states such crime reported to police, arrests by regions, uniform crime reporting program, number of violent crimes, number of murder and nonnegligent manslaughter crimes etc.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [Chicago Crimes 2017](https://www.johnsnowlabs.com/marketplace/chicago-crimes-2017)\n\t- This dataset reflects reported incidents of crime (with the exception of murders where data exists for each victim) that occurred in the City of Chicago.\n- [Connecticut Pre Trial Inmates Data](https://www.johnsnowlabs.com/marketplace/connecticut-pre-trial-inmates-data)\n\t- This dataset shows the statistical information of Connecticut accused pre-trial in correctional facilities by the Department of Corrections facilities.\n- [Connecticut Sentenced Inmates in Correctional Facilities](https://www.johnsnowlabs.com/marketplace/connecticut-sentenced-inmates-in-correctional-facilities)\n\t- This dataset shows individuals being held in Connecticut Department of Correction (DOC) facilities who are sentenced to a period of incarceration.\n- [Crime Data for NYC Public Schools](https://www.johnsnowlabs.com/marketplace/crime-data-for-nyc-public-schools)\n\t- This dataset contains the collection and maintenance of crime data for incidents that occur in New York City public schools.\n- [Crime in Baltimore 2012 to 2019](https://www.johnsnowlabs.com/marketplace/crime-in-baltimore-2012-to-2019)\n\t- This dataset contains information of crimes in Baltimore from December 2012 to January 2018. All data is geo-coded to the approximate latitude/longitude location of the incident and excludes those records for which an address could not be geo-coded.\n- [Detroit 911 Calls for Service](https://www.johnsnowlabs.com/marketplace/detroit-911-calls-for-service)\n\t- This dataset shows all 911 police emergency response and officer-initiated calls for service in the City of Detroit beginning September 20, 2016.\n- [Detroit All Crime Incidents](https://www.johnsnowlabs.com/marketplace/detroit-all-crime-incidents)\n\t- This dataset reflects reported criminal offenses that have occurred in the City of Detroit since December 6, 2016. Offense data was extracted from the Detroit Police Department's records management system.\n- [Emergency Response Incidents](https://www.johnsnowlabs.com/marketplace/emergency-response-incidents)\n\t- This dataset includes type and address of emergency incident to which OEM (The New York City Office of Emergency Management) responded.\n- [Full Time Law Enforcement Employees by State in 2015](https://www.johnsnowlabs.com/marketplace/full-time-law-enforcement-employees-by-state-in-2015)\n\t- This dataset provides the total number of law enforcement employees, the number of male officers, the number of female officers, the number of male civilian employees, and the number of female civilian employees listed alphabetically by state.\n- [Hartford 911 Calls for Service](https://www.johnsnowlabs.com/marketplace/hartford-911-calls-for-service)\n\t- This dataset shows all 911 police emergency response and officer-initiated calls for service in the City of Hartford beginning May 12, 2021.\n- [Hartford Police Incident Reports](https://www.johnsnowlabs.com/marketplace/hartford-police-incident-reports)\n\t- This dataset reflects reported incidents of crime (with the exception of sexual assaults, which are excluded by statute) that occurred in the City of Hartford from 2005 to the present, minus the most recent ten days. \nData is extracted from the City of Hartford Police Department's CrimeView database on a daily basis.\n- [NJ Active Inmate Data 2018](https://www.johnsnowlabs.com/marketplace/nj-active-inmate-data-2018)\n\t- This dataset shows the statistical information of the New Jersey state inmate population as of January 1, 2018.\n- [Number of Arrests by Age in 2015](https://www.johnsnowlabs.com/marketplace/number-of-arrests-by-age-in-2015)\n\t- This table provides the number of persons arrested nationwide in 2015. These data are broken down by age of the arrestee and include the percent distribution of arrests by offense type. These data represent the number of persons arrested; however, some persons may be arrested more than once during a year. Therefore, the statistics in this table could, in some cases, represent multiple arrests of the same person.\n- [Number of Arrests by State in 2015](https://www.johnsnowlabs.com/marketplace/number-of-arrests-by-state-in-2015)\n\t- This dataset provides arrest data reported for each state for 2015. The table provides both total arrests and arrests of juveniles (persons under the age of 18). These data represent the number of persons arrested; however, some persons may be arrested more than once during a year. Therefore, the statistics in this table could, in some cases, represent multiple arrests of the same person.\n- [Number of Arrests by US Regions 2015](https://www.johnsnowlabs.com/marketplace/number-of-arrests-by-us-regions-2015)\n\t- This dataset shows the estimated rate of violent crime was 372.6 offenses per 100,000 inhabitants, and the property crime rate was 2,487.0 offenses per 100,000 inhabitants. The violent crime rate rose 3.1 percent compared with the 2014 rate, and the property crime rate declined 3.4 percent.\n- [Number of Crimes By Population Group 2015](https://www.johnsnowlabs.com/marketplace/number-of-crimes-by-population-group-2015)\n\t- This dataset provides the rate per 100,000 inhabitants and the number of offenses known to law enforcement for violent crimes (murder and nonnegligent manslaughter, rape, robbery, and aggravated assault) and property crimes (burglary, larceny-theft, and motor vehicle theft) nationally and by city and county groupings for law enforcement agencies submitting 12 months of complete data for 2015.\n- [Offenses Known to Law Enforcement by State and City 2015](https://www.johnsnowlabs.com/marketplace/offenses-known-to-law-enforcement-by-state-and-city-2015)\n\t- This dataset contains the information of offenses known to Law Enforcement by State and City 2015. The FBI collects these data through the Uniform Crime Reporting (UCR) Program.\n- [Offenses Known to Law Enforcement by State and City 2016](https://www.johnsnowlabs.com/marketplace/offenses-known-to-law-enforcement-by-state-and-city-2016)\n\t- This dataset shows the information of offenses known to law enforcement by state and city with population 100,000 and above in the year 2016\n- [Offenses by Metropolitan and Nonmetropolitan Counties 2015](https://www.johnsnowlabs.com/marketplace/offenses-by-metropolitan-and-nonmetropolitan-counties-2015)\n\t- This dataset contains the information of offenses known to Law Enforcement by Metropolitan and Nonmetropolitan Counties 2015. The data shown in this table do not reflect county totals but are the number of offenses reported by the sheriff's office or county police department.\n- [SFPD Incident Report](https://www.johnsnowlabs.com/marketplace/sfpd-incident-report)\n\t- This dataset includes police incident reports filed by officers and by individuals through self-service online reporting for non-emergency cases. Reports included are those for incidents that occurred starting January 1, 2018 onward and have been approved by a supervising officer.\n- [Types of Weapon Used and Number of Murder by State 2015](https://www.johnsnowlabs.com/marketplace/types-of-weapon-used-and-number-of-murder-by-state-2015)\n\t- This table provides the types of weapons used in murder offenses. The data are based on the aggregated data from agencies within each state for which supplemental homicide data (i.e., weapon information) were reported to the FBI. The table also includes a breakdown of the types of firearms used in murders (i.e., handguns, rifles, shotguns, or firearms).\n- [Types of Weapon Used and Number of Murder by State 2019](https://www.johnsnowlabs.com/marketplace/types-of-weapon-used-and-number-of-murder-by-state-2019)\n\t- This table provides the types of weapons used in murder offenses. The data are based on the aggregated data from agencies within each state for which supplemental homicide data (i.e., weapon information) were reported to the FBI. The table also includes a breakdown of the types of firearms used in murders (i.e., handguns, rifles, shotguns, or firearms).\n- [Types of Weapon Used in Aggravated Assault and Robbery by State 2015](https://www.johnsnowlabs.com/marketplace/types-of-weapon-used-in-aggravated-assault-and-robbery-by-state-2015)\n\t- This table provides the types of weapons used in aggravated assault and robbery offenses. The data are based on the aggregated data from agencies within each state for which weapon information was reported to the FBI. The table includes the number of agencies that submitted data by state and the population covered by those agencies. The dataset also includes a breakdown of the types of firearms used in murders (i.e., handguns, rifles, shotguns, or firearms).\n- [Types of Weapon Used in Aggravated Assault and Robbery by State 2019](https://www.johnsnowlabs.com/marketplace/types-of-weapon-used-in-aggravated-assault-and-robbery-by-state-2019)\n\t- This table provides the types of weapons used in aggravated assault and robbery offenses. The data are based on the aggregated data from agencies within each state for which weapon information was reported to the FBI. The table includes the number of agencies that submitted data by state and the population covered by those agencies. The dataset also includes a breakdown of the types of firearms used in murders (i.e., handguns, rifles, shotguns, or firearms).\n- [US Crime Data 1981-2014](https://www.johnsnowlabs.com/marketplace/us-crime-data-1981-2014)\n\t- This dataset contains the information on crimes which were reported to police from 1981 to 2014. Crime categories are divided into two more categories i.e violent & property crimes. Property crimes include burglaries crimes, larceny-thefts crimes, motor vehicle thefts while violent crimes include murder and nonnegligent manslaughter, legacy rape, robbery and aggravated assault.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated\n- Data Updates\n - Data updates support replace-on-update: outdated foreign keys are deprecated, not deleted\n\n**Our data is curated and enriched by domain experts** \n\n Each dataset is manually curated by our team of doctors, pharmacists, public health & medical billing experts:\n- Field names, descriptions, and normalized values are chosen by people who actually understand their meaning\n- Healthcare & life science experts add categories, search keywords, descriptions and more to each dataset\n- Both manual and automated data enrichment supported for clinical codes, providers, drugs, and geo-locations\n- The data is always kept up to date – even when the source requires manual effort to get updates\n- Support for data subscribers is provided directly by the domain experts who curated the data sets\n- Every data source’s license is manually verified to allow for royalty-free commercial use and redistribution.\n\n**Need Help?**\nIf you have questions about our products, contact us at [info@johnsnowlabs.com](mailto:info@johnsnowlabs.com). \n 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Enriched with AI-modeled intent, these segments enable timely, omnichannel engagement with consumers most likely to convert---whether switching brands, making high-value purchases, or exploring new services. \n\nData Axle, formerly Infogroup, has been a trusted provider of data-driven solutions since 1972. With coverage of over 95% of U.S. adults and households, we integrate deterministic offline data---such as property records, auto ownership, utility connects, purchase behaviors, and lifestyle subscriptions---with predictive modeling and digital engagement signals. Our segments are activation-ready and refreshed regularly for consistent precision across all channels. \n\n***What Sets Us Apart:*** \n\n- Combines verified offline behavior with AI-enriched purchase intent scoring \n- Extensive transactional and lifestyle signal coverage across verticals \n- Activation-ready segments with deterministic ID match, HEMs, or identity spine \n- Omnichannel compatibility: CTV, digital, direct mail, social, and mobile \n- Custom segments supported with flexible delivery \n\n***Key Attributes***\n\n- Verified purchase signals across retail, travel, and auto \n- Modeled recency/frequency/urgency of shopping behavior \n- Lifestyle indicators: homeownership, travel frequency, vehicle status \n- Media and platform usage across devices and environments \n- High conversion traits such as promotional responsiveness, category switching, and loyalty patterns \n- Cross-category intent modeling combining deterministic and behavioral inputs \n\n***Segment Categories***\n\nEach category includes multiple AI-scored and behaviorally enriched segments -- below are illustrative examples and business applications. New categories are released regularly. \n\n- Retail Buyers with Strong Purchase Propensity -- Promo-sensitive, category switchers, and brand-loyal consumers across apparel, tech, home goods\\\n Use case: Power personalized promotions, seasonal reactivation, and loyalty offers. \n\n- Automotive In-Market Intenders -- High-scoring prospects for new, hybrid, luxury, or leased vehicles; includes brand converters\\\n Use case: Target dealership visits, conquest campaigns, or lease-to-loan transitions. \n\n- Travel Enthusiasts & Frequent Bookers -- Vacationers, cruise travelers, short-term rental users, and frequent flyers with platform preferences\\\n Use case: Drive destination-specific bookings, upgrades, and loyalty activations. \n\n- Experience Seekers & Lifestyle Spenders -- Buyers in categories such as fitness, wellness, live events, and luxury dining\\\n Use case: Promote curated experiences, wellness subscriptions, or premium memberships. \n\n- Omnichannel Engagement Segments -- Shoppers interacting across mobile, email, social, and CTV with high frequency and intent\\\n Use case: Enhance reach and consistency in full-funnel media planning. \n\n- High Conversion Behavioral Traits -- Modeled urgency, product discovery, and high-frequency buyer signals\\\n Use case: Trigger immediate outreach with time-sensitive offers or dynamic messaging. \n\n***Key Features*** \n\n- AI-scored purchase readiness layered over verified household data \n- Signals derived from digital and offline sources for accuracy and reach \n- 95%+ U.S. household coverage with deterministic PII and HEM compatibility \n- Ready-to-use and customizable segments \n- Quarterly refresh with continuous enrichment \n\n***Use Cases***\n\n- Boost campaign efficiency by prioritizing ready-to-convert audiences \n- Reactivate known customers or acquire high-propensity lookalikes \n- Launch CTV, email, and digital campaigns aligned to seasonal or event-driven demand \n- Promote brand switching incentives, travel packages, or experience bundles \n- Improve ecommerce and omnichannel ROAS using intent-qualified segments \n- Orchestrate cross-sell and upsell campaigns across product categories \n\n***Next Steps*** \n\n- Request listing access \n- Data Axle team will contact you to finalize audience, data cut, and delivery method / frequency \n- Need a Custom Audience? 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No lengthy sign-up processes or NDAs required on our side – just download the data you need.\n* **Data Samples for Evaluation:** To ensure you're getting exactly what you need, we provide free downloadable samples in CSV format for data evaluation.\n* **Data accuracy:** Our datasets are created using the latest demographics data available in national statistics bureaus, guaranteeing accuracy and reliability.\n* **Best-in-Class Quality:** We guarantee best-in-class quality, backed by over two decades of experience in modeling demographics data and an outstanding quality management process.\n* **Competitive Pricing:** We maintain the lowest prices on the market, ensuring that you get the most cost-efficient demographics data acquisition process.\n---\nAdditionally, for those looking to enrich their analysis or project, we offer:\n* **[Administrative Boundaries](https://geolocet.com/products/russia-admin-level-4-federal-subjects):** If you require geographical boundaries (polygons) for the regions of Russia, consider exploring our Administrative Boundaries product, which seamlessly integrates with this dataset.\n\n* **[Point of Interest (PoI) Data](https://geolocet.com/pages/aggregated-points-of-interest-poi-data):** If you're interested in Points of Interest data, please don't hesitate to contact us for customized datasets to enhance your geographical analysis.\n\n* **[Data Customization](https://geolocet.com/pages/data-services-by-geolocet):** We understand that your project may have specific requirements. Feel free to get in touch with us if you need any modifications or specific data formats such as Excel, JSON, GeoJSON, or Shapefile. We are here to tailor the data to your needs.\n\nMake informed decisions, conduct precise research, and unlock the potential of Russia's demographic landscape with our comprehensive dataset. Trust in the accuracy and reliability of our official statistical data to drive your projects forward.\nContact us at *contact@geolocet.com* today to access this invaluable resource and explore Russia census data like never before.\n\n[Geolocet LTD](https://geolocet.com)\n","terms_of_service":"https://geolocet.com/pages/terms-of-use","documentation_link":"","privacy_policy_link":"https://geolocet.com/pages/privacy-policy","geographical_coverage":"[\"Europe\"]","cost":"PAID","update_frequency":{"interval":1,"unit":"YEARLY"},"assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://geolocet.com/pages/terms-of-use"},"provider_summary":{"name":"Geolocet LTD","description":"Geolocet provides premium geospatial data for retail analysis, GIS, and market planning. 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Datasets are cleaned, standardized, and labeled before delivery for immediate use.\r\n\r\n*Note: Our data was previously listed under our sister company and commercial partner, WiserBrand, which you may see mentioned in older reviews."},"popularity":1901},{"id":"c701f87a-4167-4564-9d52-ec22bed45503","summary":{"name":"New Registered Businesses (Monthly)","subtitle":"Track newly registered companies each month to fuel GTM, prospecting, and early sales efforts.","setting":{"visibility":"PUBLIC"},"created_at":1767034420369,"updated_at":1786460105474,"categories":["COMMERCE","ECONOMICS","RETAIL"],"listingType":"PERSONALIZED","provider_id":"d40f7797-9097-408c-aebc-6d8dccd3c0f3"},"detail":{"description":"**Overview**\n\nThis dataset provides authoritative, standardized information on newly registered businesses across the United States, sourced directly from secretary-of-state corporate filing systems. 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Enigma standardizes these records across all jurisdictions, resolving inconsistencies in formats, naming conventions, statuses, and filing schemas to deliver a unified, analytics-ready dataset.\n\nReaching newly formed businesses generated higher responses on marketing efforts before they have selected services such as telecom, financial products and business tools.\n\n**Organizations Use This Dataset to:**\n- Prospect newly formed businesses through direct trigger and event based marketing campaigns\n- Support GTM strategies and early-stage market intelligence on newly formed registered business\n- Enrich internal models for new customer acquisition \n- Inform territory planning, trend analysis, and sales coverage design\n\nDelivered as a single, ready to query Delta table, the dataset is optimized for scalable analytics and integrates seamlessly into downstream workflows such as dashboards, lead generation engines, economic research, and growth forecasting.\n\n**Coverage & Update Frequency:**\n\nCoverage: Prior calendar month only\nUpdate Frequency: Monthly\n\n**Key Features:**\n- Comprehensive coverage of new corporate registrations across the United States\n- Standardized entity names, addresses, filing dates, statuses, and identifiers\n- Includes secretary-of-state identifiers and registration metadata\nIdeal for trend analysis, GTM planning, marketing, and sales intelligence\n- Delivered as analytics-ready Delta tables on Databricks\n\n\n**Segment Details**\n\nVarious Columns we offer: \n\n- Trigger type (Corp Reg)\n- Enigma ID\n- Corp name \n- Full address \n- Address type \n- City\n- State \n- Corp registration issue date \n- Corp registration state \n- Corp registration file number \n- Associated People (Business Owner)\n\n\n**Key Features**\n- Coverage of new business registrations across 50 states and 2 Territories (District of Colombia, Puerto Rico)\n- Standardized entity names, addresses, filing dates, statuses, and identifiers.\n- Historical data from the last month to date.\n- Includes secretary-of-state identifiers and filing metadata.\n- Useful for trend analysis, risk scoring, marketing, and business intelligence.\n- Delivered as a single, ready-to-query Delta tables in Databricks.\n","terms_of_service":"https://www.enigma.com/company/terms-of-service","documentation_link":"","privacy_policy_link":"https://www.enigma.com/company/privacy-policy","geographical_coverage":"[\"United States and Canada\"]","cost":"PAID","update_frequency":{"interval":1,"unit":"MONTHLY"},"collection_granularity":{"interval":1,"unit":"MONTHLY"},"data_source":"Secretary Of State Filings ","size":12.0,"assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"Enigma","description":"Enigma provides comprehensive intelligence about the identity and financial health of small and medium businesses across the United States. 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Metadata Sheet Issues**\n\n**Problem:** Metadata sheet fields (**title, description**) are empty\n**Solution:**\n\n* Manually fill in missing titles and descriptions\n* Check all metadata entries\n* Ensure descriptions are meaningful\n* Verify data types in the schema sheet\n\n**2. DTSi HTML Report Not Loading**\n\n**Problem:** DTSi HTML report opens but shows no data; charts are blank; JavaScript errors\n**Possible Causes:** Incorrect HTML indentation or broken JSON data\n\n**Steps to troubleshoot:**\n\n**Quick Fix**\n\n* Check browser console for errors\n* Try a different browser (**Firefox**, **Safari**)\n\n**Data & Structure Fix**\n\n* Validate **HTML indentation**\n* Check **JSON formatting**, especially `const data`\n\n**Additional Checks**\n\n* Ensure **JavaScript libraries** are loaded\n* Test with a smaller dataset\n* Clear browser cache and reload\n\n**3. Large File Processing Timeouts**\n\n**Problem:** Processing stuck or system unresponsive\n**Troubleshooting Steps:**\n\n* Split dataset into **smaller chunks**\n* Remove unnecessary columns or use **sampled data**\n* Process during **off-peak hours**\n* Use **incremental processing** or **aggregate data**\n\n**4. Memory/Performance Issues**\n\n**Problem:** Browser crashes or calculations incomplete during analysis\n\n**Troubleshooting Steps:**\n\n* Close other tabs and clear cache\n* Use browser with **higher memory limits**\n* Process only required statistical tests or columns in batches\n* Use **sampling** for exploratory analysis\n\n**System Requirements**\n\n**Browsers:** Chrome, Firefox (Recommended)\n**Code Execution Options:**\n**Local Environment:** Use Visual Studio Code (Recommended) or Jupyter Notebook.\n**Databricks Workspace:** Import the notebook directly into your Databricks Workspace to run and explore interactively.","terms_of_service":"https://astreya.com/terms/","documentation_link":"","privacy_policy_link":"https://datapulse.astreya.in/privacy","embedded_notebook_file_infos":[{"id":"be63a910-0968-48c1-bca3-ced71fa87cb6","marketplace_file_type":"EMBEDDED_NOTEBOOK","file_parent":{"parent_id":"f0eb5585-6d21-4dc8-b0b1-04c354d33222","file_parent_type":"LISTING"},"mime_type":"text/html","download_link":"https://marketplace-file-storage-prod.s3-fips.us-east-1.amazonaws.com/EMBEDDED_NOTEBOOK/LISTING_f0eb5585-6d21-4dc8-b0b1-04c354d33222/be63a910-0968-48c1-bca3-ced71fa87cb6?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEGgaDnVzLWVhc3QtMS1maXBzIkYwRAIgbC8sWIoYaHR8y7Wq86nPb0pufBl4%2BM4PWpeN%2B5PW9VQCIG7BhAVBDCWTpxFDLeuKYm%2Ff1JLz4iccPMzter0YdhgWKqYECDAQABoMNDE0MzUxNzY3ODI2IgxNAJrHbXaZG20jNfMqgwQyGEb4TsoVOoNvJ%2BaA%2FZ7zeu8hGcHqCeNfblP%2FQo5g6q2aRNtpwtPRKMlhYcIE%2FUQaNT1Lqmyptu32VUQTpzjTTxRVmJrDPkheSCkV06XQ%2FpTMKjU8CC7X1b%2FqYf8CrBwZT7Cv4C5WPjEET38f2A79h0xV6bZgPy28zQWrSeIJ9w6AfZBxiE24%2FVc08zVA0ivCT6lsy7nuU0wcR%2FMg2C9nd1vaMnQ923VpD21CKfVVYcrNWL2b%2FQ%2BNAO%2BxPjPgjiN6UPQbvbvZxkSdhgrGtecIg9NgqGkMcvM5VLTDtdC1kdeZetfOC2obtA%2BT%2F0gy3tIDo6WM5hcQfw743%2FAU9oaGnRBl%2BwoxRRDCZA%2F4jnPdluICSI7NCr6C3HHOe4CSZJjRihczxPA%2FgQczAVjAPs6CrK9N9pIzt0vNJpGUTgg4AoxftTvGfciDREilSctPIcBk6GwB%2Bzt4wbtNCmEPU%2BFD46rc6CWVQmmLwbKOeHw70ipMVFlOc8NlXkEtGO2vu6H44MstSSc6daOVUYUavQSGZ6jGhEjsP2YGz8KDm9XQotB%2FtyVBqiZV01OKqDmmBJA%2FXjUpSJ8M%2FeJXhe5m%2BKcxzNObtoJRI7jci3Tpt08XZyXZ7%2FyIV8H30ya468X3cfkTXrm972uga3z7iXAdubyEHnQIua%2FuvtwOHILi6O5D1Me2MzCPvcnXBjqTAYDoMWBt%2B3AVFuqSs2dmDQNBnvEhXfMgTedKLD5PJHb3aIl15iBkLaNu36GYnVCp9ZU2lSYcTbY5QwNnNeTfJTNy9bKhwzru6Sg%2BslhN%2BTS0zkTn%2BeLMY4tHgPEtEZ2cp%2FCk6GwqswvnYVKHjwO8q%2B1Mx8lc08fVEfarE9dke1GGFqoddx%2Bq%2BZUyHbDlAVfrCmf0yw%3D%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20261110T000135Z&X-Amz-SignedHeaders=host&X-Amz-Expires=86400&X-Amz-Credential=ASIAWA6KKHEJORWBH4N3%2F20261110%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=ddac0ec52dc720357c8d872b2b2ab9e81e8ab827206da6baaf58843486dc63b3","created_at":1761331291127,"updated_at":1761331291127,"display_name":"DTSi_Sample","status":"FILE_STATUS_PUBLISHED"}],"geographical_coverage":"[\"Global\"]","cost":"FREE","data_source":"","assets":["ASSET_TYPE_GIT_REPO"],"license":""},"provider_summary":{"name":"Astreya Partners LLC","description":"Astreya, a Cognizant company is a global IT managed services provider that powers enter\nprises by designing, deploying, and managing complex technology environments. We deliver end-to-end solutions across hybrid cloud, data centers, network infrastructure, and the digital workplace. Intelligent automation and AI run through everything we build to drive efficiency, accelerate service delivery, and clear barriers to growth for our customers.Learn more at www.astreya.com.\nWe create solutions that tackle everyday issues and complex, high-stakes challenges. Guided by a customer-first mindset, we use technology to sharpen experiences and meet real needs. Our approach builds on curated IT data, giving every solution context and precision. Each of our products is designed to be secure, compliant, scalable, and flexible, so it stays relevant as your business evolves.\nOur ideal buyer personas range from CXOs to operations engineers on the floor."},"popularity":1724},{"id":"8bc1a6fd-9992-4c42-9b26-4acc6495d565","summary":{"name":"BatchData | US Tax Assessor Data | Nationwide Property Characteristics | 155M+ Records | PropTech","subtitle":"Granular US property data on 155M+ parcels. Includes tax, building specs, and valuation. Visit www.batchdata.io.","setting":{"visibility":"PUBLIC"},"created_at":1771433414610,"updated_at":1771433414610,"categories":["OTHER","GEOSPATIAL","TRANSPORTATION_AND_LOGISTICS","DEMOGRAPHICS","ECONOMICS","PUBLIC_SECTOR"],"listingType":"PERSONALIZED","provider_id":"43cf0092-09d4-4dd5-afe7-d9be9a3a8d74"},"detail":{"description":"BatchData provides the industry's most reliable US Tax Assessor and Property Characteristics Data, covering over 3,100 counties nationwide. This dataset offers deep granularity into physical and fiscal property attributes, designed for high-volume PropTech, Insurance, and Real Estate analytics. Visit www.batchdata.io to access ground-truth data on residential and commercial properties.\n\nSchemas, Tables, and Columns\nThis listing contains a normalized schema for property assessment and physical details. Key columns include:\n\nBuilding Characteristics: building.yearBuilt, building.livingAreaSquareFeet, building.bedrooms, building.bathrooms, building.stories, building.foundation, building.roofCover, building.hvacType.\n\nTax & Assessment: assessment.totalAssessedValue, assessment.landMarketValue, assessment.improvementValue, assessment.taxYear, tax.taxAmount.\n\nLot & Zoning: lot.lotSizeAcres, lot.zoningCode, lot.landUseCode, lot.topography, lot.siteInfluence.\n\nLocation: address.fipsCode, address.apn (Parcel ID), address.censusTract, address.carrierRoute.\n\nUse Cases\n\nAutomated Valuation Models (AVMs): Enhance price estimation algorithms with precise square footage, condition, and quality codes.\n\nInsurance Risk: Underwrite policies accurately using roof type, foundation details, and year-built data.\n\nInvestment Analysis: Identify undervalued assets by comparing tax assessments against market listings.\n\nSources\nData is sourced directly from County Assessor offices, Tax Collectors, and supplementary GIS repositories nationwide, updated daily.","terms_of_service":"https://batchdata.io/terms-of-service","documentation_link":"https://developer.batchdata.com/","privacy_policy_link":"https://batchdata.io/privacy-policy","geographical_coverage":"[\"United States and Canada\"]","cost":"PAID","update_frequency":{"interval":1,"unit":"DAILY"},"collection_granularity":{"interval":1,"unit":"DAILY"},"data_source":"","assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"BatchData","description":"BatchData is a real estate technology company providing a comprehensive data platform and APIs for property and owner information, helping businesses with lead generation, skip tracing, data enrichment, and workflow automation in the real estate, proptech, and home services industries."},"popularity":1341},{"id":"ba861df2-e52f-4910-a762-14aa81b25c5d","summary":{"name":"IQVIA's U.S. Managed Care (payer, health plan & PBM) Data Solutions & Services","subtitle":"Healthcare data solutions to support Managed Care entities.","setting":{"visibility":"PUBLIC"},"created_at":1691161586258,"updated_at":1695234497988,"categories":["DEMOGRAPHICS","HEALTH","SCIENCE_AND_RESEARCH"],"listingType":"PERSONALIZED","provider_id":"7fab49b9-819a-4cd8-a7e2-79af51df6f72"},"detail":{"description":"IQVIA's Managed Care Data Solutions and Services supports your ability to unlock the insights you thought were out of reach, until now. Identify cost savings and improve benefit design in a configurable model built on the broadest and deepest non-identified prescription database. With IQVIA’s Managed Care Data Solutions and Services, you can fill the gap in your insights for healthcare claims activity within your market, plus aggregate and analyze vast amounts of data to answer critical business questions.\n\nIQVIA's Managed Care Data Solutions and Services are powered by a multitude of proprietary data assets and offerings covering ~320M anonymized patients in the U.S.:\n\nAll Payer Submitted/Remitted Medical Claims Data: Over 1.6B claims/year sourced from claims clearing houses and practice management companies\n\nAll-Payer Prescription Claims Data: Over 4.3B paid Rx claims/year representing near census coverage sources directly from retail pharmacies, mail order and specialty\n\nPharMetrics Plus Integrated Claims Data: Health Plan sourced adjudicated medical and prescription claims representing over 210M+ enrollee lives since 2006\n\nNational Drug Sales Data: Near census coverage (88% coverage of all US drug sales: 97% of non-retail and 84% of retail) of all drug sales sources directly from drug manufacturer, wholesalers and long-term care facilities.\n\nIQVIA's Managed Care Universe: >3K payers; >12K health plans; >50 unique plan model types; ~200 PBMs & PBM processors\n\nOneKey™ Healthcare Organization and Provider Reference Data: One of the largest provider data solutions in the industry representing over 11M healthcare professionals, over 750K healthcare organizations and almost 6M affiliations.\n\nOther Key Data Assets: Clinical Data (Oncology EMR data and Ambulatory EMR) and Lab Data\n\nIQVIA's Managed Care data solutions can address the following use cases in addition to many others your organization defines as key business needs:\n\n- Impact assessment of pharmacy discount card & manufacturer coupon cards\n- Adherence impact on Medicare Star Ratings\n- Prescription utilization and cost trending\n- Prescribing behavior analysis (focusing on HCP anomalous prescribing patterns)\n\nPLEASE NOTE: Data availability in Databricks is determined by your specific data subscriptions with IQVIA based on details you provide regarding use cases in focus and scope of data required; including specific geographies, conditions, Rx distribution channel, frequency of deliverable and amount of historical data required.","terms_of_service":"https://www.iqvia.com/about-us/terms-of-use","documentation_link":"https://www.iqvia.com/locations/united-states/solutions/healthcare-solutions/payers-segment","privacy_policy_link":"https://www.iqvia.com/about-us/privacy","geographical_coverage":"[\"United States and Canada\"]","update_frequency":{"interval":1,"unit":"MONTHLY"},"collection_date_start":1262361862293},"provider_summary":{"name":"IQVIA","description":"IQVIA (NYSE:IQV) is a leading global provider of advanced analytics, technology solutions and clinical research services to the life sciences industry dedicated to delivering actionable insights. 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This information is invaluable for market intelligence analysts needing a regular pulse of objective data upon which they can rely.\n\nCloud service providers (\"CSP\") devote significant resources to accurate pricing of their services. As such, that information provides value for many other companies about different markets. The prices for services vary on many factors, including CSP, workload, and regions. So, objective pricing data can help companies with their own market intelligence needs by leveraging the work the CSPs have already done.\n\nLiftr Insights has been obtaining data on prices and many other factors for over 5 years. 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Adstra](https://www.adstradata.com/get-started/).\n","terms_of_service":"https://adstradata.com/terms/","documentation_link":"https://www.adstradata.com/wp-content/uploads/2026/10/Conexa-for-Databricks_User-Guide_v1.3.pdf","privacy_policy_link":"https://adstradata.com/privacy-policy/","embedded_notebook_file_infos":[{"id":"d5bce6eb-1c3f-41e7-b1c5-5711a1236c83","marketplace_file_type":"EMBEDDED_MARKDOWN","file_parent":{"parent_id":"6c27940a-f41c-4396-ac73-b5b7eae9b90e","file_parent_type":"LISTING"},"mime_type":"text/markdown","download_link":"https://marketplace-file-storage-prod.s3-fips.us-east-1.amazonaws.com/EMBEDDED_MARKDOWN/LISTING_6c27940a-f41c-4396-ac73-b5b7eae9b90e/d5bce6eb-1c3f-41e7-b1c5-5711a1236c83?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEGgaDnVzLWVhc3QtMS1maXBzIkYwRAIgbC8sWIoYaHR8y7Wq86nPb0pufBl4%2BM4PWpeN%2B5PW9VQCIG7BhAVBDCWTpxFDLeuKYm%2Ff1JLz4iccPMzter0YdhgWKqYECDAQABoMNDE0MzUxNzY3ODI2IgxNAJrHbXaZG20jNfMqgwQyGEb4TsoVOoNvJ%2BaA%2FZ7zeu8hGcHqCeNfblP%2FQo5g6q2aRNtpwtPRKMlhYcIE%2FUQaNT1Lqmyptu32VUQTpzjTTxRVmJrDPkheSCkV06XQ%2FpTMKjU8CC7X1b%2FqYf8CrBwZT7Cv4C5WPjEET38f2A79h0xV6bZgPy28zQWrSeIJ9w6AfZBxiE24%2FVc08zVA0ivCT6lsy7nuU0wcR%2FMg2C9nd1vaMnQ923VpD21CKfVVYcrNWL2b%2FQ%2BNAO%2BxPjPgjiN6UPQbvbvZxkSdhgrGtecIg9NgqGkMcvM5VLTDtdC1kdeZetfOC2obtA%2BT%2F0gy3tIDo6WM5hcQfw743%2FAU9oaGnRBl%2BwoxRRDCZA%2F4jnPdluICSI7NCr6C3HHOe4CSZJjRihczxPA%2FgQczAVjAPs6CrK9N9pIzt0vNJpGUTgg4AoxftTvGfciDREilSctPIcBk6GwB%2Bzt4wbtNCmEPU%2BFD46rc6CWVQmmLwbKOeHw70ipMVFlOc8NlXkEtGO2vu6H44MstSSc6daOVUYUavQSGZ6jGhEjsP2YGz8KDm9XQotB%2FtyVBqiZV01OKqDmmBJA%2FXjUpSJ8M%2FeJXhe5m%2BKcxzNObtoJRI7jci3Tpt08XZyXZ7%2FyIV8H30ya468X3cfkTXrm972uga3z7iXAdubyEHnQIua%2FuvtwOHILi6O5D1Me2MzCPvcnXBjqTAYDoMWBt%2B3AVFuqSs2dmDQNBnvEhXfMgTedKLD5PJHb3aIl15iBkLaNu36GYnVCp9ZU2lSYcTbY5QwNnNeTfJTNy9bKhwzru6Sg%2BslhN%2BTS0zkTn%2BeLMY4tHgPEtEZ2cp%2FCk6GwqswvnYVKHjwO8q%2B1Mx8lc08fVEfarE9dke1GGFqoddx%2Bq%2BZUyHbDlAVfrCmf0yw%3D%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20261110T000135Z&X-Amz-SignedHeaders=host&X-Amz-Expires=86399&X-Amz-Credential=ASIAWA6KKHEJORWBH4N3%2F20261110%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=62958d298c69f85ca553d51002aa5c511f3d2c38c29862f8db1d5a204c2164c4","created_at":1792454292948,"updated_at":1792454292948,"display_name":"App_Preview.md","status":"FILE_STATUS_PUBLISHED"},{"id":"72ea2a25-de6d-4322-8e9d-2015382f9724","marketplace_file_type":"SCREENSHOT","file_parent":{"parent_id":"6c27940a-f41c-4396-ac73-b5b7eae9b90e","file_parent_type":"LISTING"},"mime_type":"image/png","download_link":"https://marketplace-file-storage-prod.s3-fips.us-east-1.amazonaws.com/SCREENSHOT/LISTING_6c27940a-f41c-4396-ac73-b5b7eae9b90e/72ea2a25-de6d-4322-8e9d-2015382f9724?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEGgaDnVzLWVhc3QtMS1maXBzIkYwRAIgbC8sWIoYaHR8y7Wq86nPb0pufBl4%2BM4PWpeN%2B5PW9VQCIG7BhAVBDCWTpxFDLeuKYm%2Ff1JLz4iccPMzter0YdhgWKqYECDAQABoMNDE0MzUxNzY3ODI2IgxNAJrHbXaZG20jNfMqgwQyGEb4TsoVOoNvJ%2BaA%2FZ7zeu8hGcHqCeNfblP%2FQo5g6q2aRNtpwtPRKMlhYcIE%2FUQaNT1Lqmyptu32VUQTpzjTTxRVmJrDPkheSCkV06XQ%2FpTMKjU8CC7X1b%2FqYf8CrBwZT7Cv4C5WPjEET38f2A79h0xV6bZgPy28zQWrSeIJ9w6AfZBxiE24%2FVc08zVA0ivCT6lsy7nuU0wcR%2FMg2C9nd1vaMnQ923VpD21CKfVVYcrNWL2b%2FQ%2BNAO%2BxPjPgjiN6UPQbvbvZxkSdhgrGtecIg9NgqGkMcvM5VLTDtdC1kdeZetfOC2obtA%2BT%2F0gy3tIDo6WM5hcQfw743%2FAU9oaGnRBl%2BwoxRRDCZA%2F4jnPdluICSI7NCr6C3HHOe4CSZJjRihczxPA%2FgQczAVjAPs6CrK9N9pIzt0vNJpGUTgg4AoxftTvGfciDREilSctPIcBk6GwB%2Bzt4wbtNCmEPU%2BFD46rc6CWVQmmLwbKOeHw70ipMVFlOc8NlXkEtGO2vu6H44MstSSc6daOVUYUavQSGZ6jGhEjsP2YGz8KDm9XQotB%2FtyVBqiZV01OKqDmmBJA%2FXjUpSJ8M%2FeJXhe5m%2BKcxzNObtoJRI7jci3Tpt08XZyXZ7%2FyIV8H30ya468X3cfkTXrm972uga3z7iXAdubyEHnQIua%2FuvtwOHILi6O5D1Me2MzCPvcnXBjqTAYDoMWBt%2B3AVFuqSs2dmDQNBnvEhXfMgTedKLD5PJHb3aIl15iBkLaNu36GYnVCp9ZU2lSYcTbY5QwNnNeTfJTNy9bKhwzru6Sg%2BslhN%2BTS0zkTn%2BeLMY4tHgPEtEZ2cp%2FCk6GwqswvnYVKHjwO8q%2B1Mx8lc08fVEfarE9dke1GGFqoddx%2Bq%2BZUyHbDlAVfrCmf0yw%3D%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20261110T000135Z&X-Amz-SignedHeaders=host&X-Amz-Expires=86400&X-Amz-Credential=ASIAWA6KKHEJORWBH4N3%2F20261110%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=a8216fb1f5886cf5fec1a20adbba8e1fbc4c2a89c3f3a7e80e11dfe8a91bf6a3","created_at":1781661522098,"updated_at":1781661522098,"display_name":"profile_report.png","status":"FILE_STATUS_PUBLISHED"},{"id":"3a797910-0caf-4673-8cde-b637bf73c0bf","marketplace_file_type":"SCREENSHOT","file_parent":{"parent_id":"6c27940a-f41c-4396-ac73-b5b7eae9b90e","file_parent_type":"LISTING"},"mime_type":"image/png","download_link":"https://marketplace-file-storage-prod.s3-fips.us-east-1.amazonaws.com/SCREENSHOT/LISTING_6c27940a-f41c-4396-ac73-b5b7eae9b90e/3a797910-0caf-4673-8cde-b637bf73c0bf?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEGgaDnVzLWVhc3QtMS1maXBzIkYwRAIgbC8sWIoYaHR8y7Wq86nPb0pufBl4%2BM4PWpeN%2B5PW9VQCIG7BhAVBDCWTpxFDLeuKYm%2Ff1JLz4iccPMzter0YdhgWKqYECDAQABoMNDE0MzUxNzY3ODI2IgxNAJrHbXaZG20jNfMqgwQyGEb4TsoVOoNvJ%2BaA%2FZ7zeu8hGcHqCeNfblP%2FQo5g6q2aRNtpwtPRKMlhYcIE%2FUQaNT1Lqmyptu32VUQTpzjTTxRVmJrDPkheSCkV06XQ%2FpTMKjU8CC7X1b%2FqYf8CrBwZT7Cv4C5WPjEET38f2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When implemented in software applications, SNOMED CT can be used to represent clinically relevant information consistently, reliably and comprehensively as an integral part of producing electronic health information. SNOMED CT supports the development of comprehensive high-quality clinical content in health records. It provides a standardized way to represent clinical phrases captured by the clinician and enables automatic interpretation of these. SNOMED CT is a clinically validated, semantically rich, controlled vocabulary that facilitates evolutionary growth in expressivity to meet emerging requirements. SNOMED CT based clinical information benefits individual patients and clinicians as well as populations and it supports evidence based care. A 'Delta' release contains only those component versions created, inactivated or changed since the previous release. The 'Delta' release is much smaller than a 'Full' release and is ideal for updating a 'Full' release of the previous version.\n\n**Benefits**\n- there are many benefits of using snomed ct in electronic health records including: \n- health information can be shared consistently within and across healthcare settings.\n- data can be organized, queried, and analyzed for the benefit of the individual and institution.\n- the risk of different interpretations of the record between different healthcare settings can be reduced.\n- updated twice a year to help users keep pace with the advances of healthcare terminology and add to the scope of coverage\n- in addition to adding value to electronic health records, snomed ct supports the following meaningful use objectives:\n- maintain an up-to-date problem list of current and active diagnoses\n- record patient family health history as structure data\n- identify and report cancer cases to state cancer registries\n- record and track changes in patient vital signs\n- record patient smoking status\n- provide a summary record for each transition of care\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [SNOMED CT Delta Association Reference Set](https://www.johnsnowlabs.com/marketplace/snomed-ct-delta-association-reference-set)\n\t- This dataset is a component reference set used to represent a set of unordered associations of a particular type between components of SNOMED CT.\n- [SNOMED CT Delta Attribute Value Reference Set](https://www.johnsnowlabs.com/marketplace/snomed-ct-delta-attribute-value-reference-set)\n\t- This dataset describes the Release File structure of SNOMED CT, referred to as Release Format 2 (RF2). The US Edition of SNOMED CT is the official source of SNOMED CT for use in US healthcare systems. The US Edition is a standalone release that combines the content of both the US Extension and the International release of SNOMED CT.\n\nAttribute value reference set is a component reference set used to apply a tagged value to a SNOMED CT component.\n- [SNOMED CT Delta Concept](https://www.johnsnowlabs.com/marketplace/snomed-ct-delta-concept)\n\t- The Concept file holds the clinical concepts that makeup SNOMED CT. A concept is given meaning by its Fully Specified Name, which is held in the Description file. A concept is distinguished from or refined by association with other concepts using relationships in the Relationship file. This dataset describes the Release File structure of SNOMED CT, referred to as Release Format 2 (RF2). The US Edition of SNOMED CT is the official source of SNOMED CT for use in US healthcare systems.\n- [SNOMED CT Delta Description](https://www.johnsnowlabs.com/marketplace/snomed-ct-delta-description)\n\t- The Description file holds descriptions that describe SNOMED CT concepts. A description is used to give meaning to a concept and provide well-understood and standard ways of referring to a concept. This dataset describes the Release File structure of SNOMED CT, referred to as Release Format 2 (RF2). The US Edition of SNOMED CT is the official source of SNOMED CT for use in US healthcare systems.\n- [SNOMED CT Delta Language Reference Set](https://www.johnsnowlabs.com/marketplace/snomed-ct-delta-language-reference-set)\n\t- A Language reference set is a component reference set that is used to indicate which descriptions contain terms that are acceptable or preferred in a particular language or dialect. This dataset describes the Release File structure of SNOMED CT, referred to as Release Format 2 (RF2). The US Edition of SNOMED CT is the official source of SNOMED CT for use in US healthcare systems.\n- [SNOMED CT Delta MRCM Attribute Domain Reference Set](https://www.johnsnowlabs.com/marketplace/snomed-ct-delta-mrcm-attribute-domain-reference-set)\n\t- MRCM (Machine Readable Concept Model) Attribute Domain Reference Set allows attributes to be associated with the domains in which they may be applied. It also allows grouping and cardinality constraints to be specified for each attribute and domain combination. For each attribute-domain rule, the strength of the rule and the content type over which this rule applies is also specified.\n- [SNOMED CT Delta MRCM Attribute Range Reference Set](https://www.johnsnowlabs.com/marketplace/snomed-ct-delta-mrcm-attribute-range-reference-set)\n\t- An MRCM (Machine Readable Concept Model) Attribute Range Reference Set allows attributes to be associated with a valid value range for a given SNOMED CT content type and rule strength. The range of each attribute is defined using an Expression Constraint. This expression constraint represents the set of concepts, expressions, or concrete values that may be used as the value of the given attribute.\n- [SNOMED CT Delta MRCM Domain Reference Set](https://www.johnsnowlabs.com/marketplace/snomed-ct-delta-mrcm-domain-reference-set)\n\t- This dataset enumerates the concept domains to which SNOMED CT attributes may be applied. Each concept domain is uniquely identified by a SNOMED CT concept. When the scope of a domain covers the concepts in a particular hierarchy (or subhierarchy), the supertype concept of this hierarchy (or subhierarchy) is used to identify the domain. When a domain is defined based on membership in a reference set, the associated reference set concept is used to identify the domain.\n- [SNOMED CT Delta Module Dependency Reference Set](https://www.johnsnowlabs.com/marketplace/snomed-ct-delta-module-dependency-reference-set)\n\t- This dataset describes the Release File structure of SNOMED CT, referred to as Release Format 2 (RF2). The US Edition of SNOMED CT is the official source of SNOMED CT for use in US healthcare systems. The US Edition is a standalone release that combines the content of both the US Extension and the International release of SNOMED CT.\n\nThe Module dependency reference set is used to represent dependencies between modules, taking account of module versioning.\n- [SNOMED CT Delta OWL Expression Reference Set](https://www.johnsnowlabs.com/marketplace/snomed-ct-delta-owl-expression-reference-set)\n\t- This dataset is an OWL (Ontology Web Language) ontology reference set (foundation metadata concept), in which the OWL expressions represent essential information about an ontology. This information includes namespaces, ontology URI (Uniform Resource Identifier), ontology version URI, and import statements. An OWL expression type reference set associates description logic statements with SNOMED CT (Systematized Nomenclature of Medicine Clinical Terms) concept in the OWL functional syntax.\n- [SNOMED CT Delta Refset Descriptor Reference Set](https://www.johnsnowlabs.com/marketplace/snomed-ct-delta-refset-descriptor-reference-set)\n\t- This dataset describes the Delta Refset Descriptor Reference Set of SNOMED CT (SNOMED Clinical Terminology, referred to as Release Format 2 (RF2). The US Edition of SNOMED CT is the official source of SNOMED CT for use in US healthcare systems. The US Edition is a standalone release that combines the content of both the US Extension and the International release of SNOMED CT.\n- [SNOMED CT Delta Relationship](https://www.johnsnowlabs.com/marketplace/snomed-ct-delta-relationship)\n\t- This dataset describes the Release File structure of SNOMED CT, referred to as Release Format 2 (RF2). The US Edition of SNOMED CT is the official source of SNOMED CT for use in US healthcare systems. The US Edition is a standalone release that combines the content of both the US Extension and the International release of SNOMED CT.\n\nThe Relationship file holds one relationship per row. Each relationship is of a particular type, and has a source concept and a destination concept.\n- [SNOMED CT Delta Simple Map Reference Set](https://www.johnsnowlabs.com/marketplace/snomed-ct-delta-simple-map-reference-set)\n\t- This dataset describes the Release File structure of SNOMED CT, referred to as Release Format 2 (RF2). The US Edition of SNOMED CT is the official source of SNOMED CT for use in US healthcare systems. The US Edition is a standalone release that combines the content of both the US Extension and the International release of SNOMED CT.\n\nA Simple Map Reference set is used to represent one-to-one maps between SNOMED CT concepts and codes in another terminology, classification or code system.\n- [SNOMED CT Delta Simple Reference Set](https://www.johnsnowlabs.com/marketplace/snomed-ct-delta-simple-reference-set)\n\t- This dataset describes the Release File structure of SNOMED CT, referred to as Release Format 2 (RF2). The US Edition of SNOMED CT is the official source of SNOMED CT for use in US healthcare systems. The US Edition is a standalone release that combines the content of both the US Extension and the International release of SNOMED CT.\n\nA Simple reference set does not have any additional fields.\n- [SNOMED CT Delta Stated Relationship](https://www.johnsnowlabs.com/marketplace/snomed-ct-delta-stated-relationship)\n\t- This dataset describes the Release File structure of SNOMED CT, referred to as Release Format 2 (RF2). The US Edition of SNOMED CT is the official source of SNOMED CT for use in US healthcare systems. The US Edition is a standalone release that combines the content of both the US Extension and the International release of SNOMED CT.\n\nStated Relationship File is a distribution file containing the stated form of SNOMED CT relationships.\n- [SNOMED CT Delta Text Definition](https://www.johnsnowlabs.com/marketplace/snomed-ct-delta-text-definition)\n\t- This dataset describes the Release File structure of SNOMED CT, referred to as Release Format 2 (RF2). The US Edition of SNOMED CT is the official source of SNOMED CT for use in US healthcare systems. The US Edition is a standalone release that combines the content of both the US Extension and the International release of SNOMED CT.\n- [SNOMED Clinical Terminology to ICD-10-CM Delta Map](https://www.johnsnowlabs.com/marketplace/snomed-clinical-terminology-to-icd-10-cm-delta-map)\n\t- The purpose of the SNOMED CT to ICD-10-CM map is to support semi-automated generation of ICD-10-CM codes from clinical data encoded in SNOMED CT for reimbursement and statistical purposes.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated\n- Data Updates\n - Data updates support replace-on-update: outdated foreign keys are deprecated, not deleted\n\n**Our data is curated and enriched by domain experts** \n\n Each dataset is manually curated by our team of doctors, pharmacists, public health & medical billing experts:\n- Field names, descriptions, and normalized values are chosen by people who actually understand their meaning\n- Healthcare & life science experts add categories, search keywords, descriptions and more to each dataset\n- Both manual and automated data enrichment supported for clinical codes, providers, drugs, and geo-locations\n- The data is always kept up to date – even when the source requires manual effort to get updates\n- Support for data subscribers is provided directly by the domain experts who curated the data sets\n- Every data source’s license is manually verified to allow for royalty-free commercial use and redistribution.\n\n**Need Help?**\nIf you have questions about our products, contact us at [info@johnsnowlabs.com](mailto:info@johnsnowlabs.com). \n ","terms_of_service":"https://www.johnsnowlabs.com/healthcare-data-library-eula/","documentation_link":"https://www.johnsnowlabs.com/marketplace/snomed-clinical-terminology-delta-data-package","contact_email":"support@johnsnowlabs.com","privacy_policy_link":"https://www.johnsnowlabs.com/privacy-policy/","assets":["ASSET_TYPE_DATA_TABLE"]},"provider_summary":{"name":"John Snow Labs","description":"John Snow Labs, an AI and NLP for healthcare company, provides state-of-the-art software, models, and data to help healthcare and life science organizations build, deploy, and operate AI projects."},"popularity":1203},{"id":"86d0e6f5-5957-416d-8de4-531a0284d21b","summary":{"name":"Informatica Cloud Address Verification MCP Server","subtitle":"Informatica MCP server providing data quality assurance tools for AI agents to validate, standardize global addresses","setting":{"visibility":"PUBLIC"},"created_at":1778271137019,"updated_at":1778272715420,"categories":["OTHER"],"listingType":"STANDARD","provider_id":"4683e151-cb8f-4f1a-b07f-5a30a734dbe4"},"detail":{"description":"**Overview**\nInformatica (a Salesforce company)'s MCP server providing data quality assurance tools for AI agents to validate, standardize, and enrich global addresses in real-time,\n\n**Use cases**\nVerify and standardize physical addresses instantly during customer onboarding or data entry workflows\n\nTransform messy or fragmented address inputs into clean, professional postal formats\n\nAutomatically apply correct international postal rules for country-specific formatting (e.g., ZIP codes, provinces)\n\n**Product details**\nInformatica also provides an additional MCP Server in the Databricks Marketplace the **Cloud Data Governance Metadata Search**, enabling you to perform semantic asset search, lineage, and compliance checks for your enterprise data.\n\nThe other notable MCP servers **(currently in preview)** for **Customer identification** through Master Data Management, **Data Integration Job Query, Data Marketplace** for Data querying are available through IDMC\n\n\n**Usage Instructions**\n\n1. Sign up for your IDMC Organization - Learn more [here](https://success.informatica.com/success-accelerators/getting-started-with-idmc-platform.html)\n2. Configure your MCP Server in the Agent AI Engineering Tile\n3. Get your MCP Endpoint and configure your agent in Agent Bricks\n","terms_of_service":"https://www.informatica.com/legal/informatica-license-services-agreement.html","documentation_link":"https://anypoint.mulesoft.com/exchange/68e53915-e89b-4e82-b794-12d37982db4c/cloud-address-verification-asset/minor/1.0/","privacy_policy_link":"https://www.informatica.com/privacy-policy.html","assets":["ASSET_TYPE_MCP"],"license":"https://www.informatica.com/legal/informatica-license-services-agreement.html"},"provider_summary":{"name":"Informatica","description":"Informatica (A Salesforce company) provides Data Management services for Governance, Data Quality, Replication, Ingestion and Integration for Databricks customers"},"popularity":1848},{"id":"bef65504-1f3b-4333-91e2-3419cf32e2bc","summary":{"name":"Order and Referring Medicare Data | CMS","subtitle":"This dataset is based on information gathered from the Provider Enrollment, Chain and Ownership System (PECOS).","share":{"name":"hls_order_referring_cms","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1674242683958,"updated_at":1698965635315,"categories":["HEALTH"],"listingType":"STANDARD","provider_id":"f00071b1-2db4-4f8f-a4bc-4c62c790f25a"},"detail":{"description":"**Overview**\n\nThis release provides information of medical specialists, physicians and other healthcare professionals who can be consulted by those in the Medicare program released by the Centers for Medicare & Medicaid Services (CMS).\n\nCenters for Medicare & Medicaid provide substantial amounts of data to the public about their programs, various health care topics, and care settings. If you are interested in learning more about datasets released by the CMS, please visit the [CMS Data homepage](https://data.cms.gov/).\n\n**Product Details**\n\nIncludes the following tables:\n\n\n- **hls_cms.cms_order_referring_weekly**\n - **Update frequency:** daily\n - **Source:** CMS\n - **Description:** This table contains all physicians and non-physician practitioners who are legally eligible to order and refer in the Medicare program and who have current enrollment records in Medicare.\n\n\n\n**Data Sources**\n\n\nThe data in this product comes from **CMS**.\n\nAll physicians and non-physician practitioners who are legally eligible to order and refer in the Medicare program and who have current enrollment records in Medicare.\n\n\n- **Source Homepage:** [Centers for Medicare & Medicaid Services Data](https://data.cms.gov/)\n- **Source Data:** [Centers for Medicare & Medicaid Services | Order and Referring](https://data.cms.gov/Medicare-Enrollment/Order-and-Referring/qcn7-gc3g)\n- **License:** [Public Domain - U.S. Government Work](https://www.usa.gov/government-works)\n- **Frequency:** weekly\n\n\n\n\n","terms_of_service":"https://rearc-data-public-assets.s3.amazonaws.com/Rearc_Data_DSA.pdf","contact_email":"data@rearc.io","support_link":"https://www.rearc.io/data","privacy_policy_link":"https://rearc-data-public-assets.s3.amazonaws.com/Rearc+Privacy+Policy.pdf","embedded_notebook_file_infos":[{"id":"57521863-ce15-4b9f-9f9a-8880cb027c92","marketplace_file_type":"EMBEDDED_NOTEBOOK","file_parent":{"parent_id":"bef65504-1f3b-4333-91e2-3419cf32e2bc","file_parent_type":"LISTING"},"mime_type":"text/html","download_link":"https://marketplace-file-storage-prod.s3-fips.us-east-1.amazonaws.com/EMBEDDED_NOTEBOOK/LISTING_bef65504-1f3b-4333-91e2-3419cf32e2bc/57521863-ce15-4b9f-9f9a-8880cb027c92?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEGgaDnVzLWVhc3QtMS1maXBzIkYwRAIgbC8sWIoYaHR8y7Wq86nPb0pufBl4%2BM4PWpeN%2B5PW9VQCIG7BhAVBDCWTpxFDLeuKYm%2Ff1JLz4iccPMzter0YdhgWKqYECDAQABoMNDE0MzUxNzY3ODI2IgxNAJrHbXaZG20jNfMqgwQyGEb4TsoVOoNvJ%2BaA%2FZ7zeu8hGcHqCeNfblP%2FQo5g6q2aRNtpwtPRKMlhYcIE%2FUQaNT1Lqmyptu32VUQTpzjTTxRVmJrDPkheSCkV06XQ%2FpTMKjU8CC7X1b%2FqYf8CrBwZT7Cv4C5WPjEET38f2A79h0xV6bZgPy28zQWrSeIJ9w6AfZBxiE24%2FVc08zVA0ivCT6lsy7nuU0wcR%2FMg2C9nd1vaMnQ923VpD21CKfVVYcrNWL2b%2FQ%2BNAO%2BxPjPgjiN6UPQbvbvZxkSdhgrGtecIg9NgqGkMcvM5VLTDtdC1kdeZetfOC2obtA%2BT%2F0gy3tIDo6WM5hcQfw743%2FAU9oaGnRBl%2BwoxRRDCZA%2F4jnPdluICSI7NCr6C3HHOe4CSZJjRihczxPA%2FgQczAVjAPs6CrK9N9pIzt0vNJpGUTgg4AoxftTvGfciDREilSctPIcBk6GwB%2Bzt4wbtNCmEPU%2BFD46rc6CWVQmmLwbKOeHw70ipMVFlOc8NlXkEtGO2vu6H44MstSSc6daOVUYUavQSGZ6jGhEjsP2YGz8KDm9XQotB%2FtyVBqiZV01OKqDmmBJA%2FXjUpSJ8M%2FeJXhe5m%2BKcxzNObtoJRI7jci3Tpt08XZyXZ7%2FyIV8H30ya468X3cfkTXrm972uga3z7iXAdubyEHnQIua%2FuvtwOHILi6O5D1Me2MzCPvcnXBjqTAYDoMWBt%2B3AVFuqSs2dmDQNBnvEhXfMgTedKLD5PJHb3aIl15iBkLaNu36GYnVCp9ZU2lSYcTbY5QwNnNeTfJTNy9bKhwzru6Sg%2BslhN%2BTS0zkTn%2BeLMY4tHgPEtEZ2cp%2FCk6GwqswvnYVKHjwO8q%2B1Mx8lc08fVEfarE9dke1GGFqoddx%2Bq%2BZUyHbDlAVfrCmf0yw%3D%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20261110T000135Z&X-Amz-SignedHeaders=host&X-Amz-Expires=86400&X-Amz-Credential=ASIAWA6KKHEJORWBH4N3%2F20261110%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=31e3289c5de57ff19fb3979000b90d0a0fdf4844b29c35fa1ce73db4ad95e3e6","created_at":1678741487898,"updated_at":1678741487898,"status":"FILE_STATUS_PUBLISHED"}],"assets":["ASSET_TYPE_DATA_TABLE"]},"provider_summary":{"name":"Rearc","description":"Rearc provides a vast library of data products for Healthcare, Financial Services, and ESG use cases. Whether you're looking for genomes or asset prices, there is a Rearc data product to address your team's data needs. If you're looking for something that's not yet available in our library, our data curation team can help you source that data through the Databricks Marketplace as well."},"popularity":181},{"id":"f2e0272a-2daa-4746-be5e-e1252c77785d","summary":{"name":"SAMPLE Anonymous IP Data | 250M+ Global IP Data| Audience Targeting and Attribution | CCPA Compliant","subtitle":"DRAKO's Anonymous IP Data provides a secure and privacy-compliant way to understand consumer behaviors online. ...","share":{"name":"drako_b3f44f54_c576_4131_ac89_4c46fe5ea116_samples_share","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1785767423935,"updated_at":1785767423935,"categories":["OTHER"],"listingType":"STANDARD","provider_id":"b5ff268c-de5a-4d77-b721-185f08e47569"},"detail":{"description":"DRAKO specializes in delivering Anonymous IP Data, focusing on privacy-first approaches to consumer identity and behavior analysis. Our data allows businesses to track user interactions without compromising individual privacy, ensuring compliance with data protection regulations.\r\n\r\nAnonymous IP Data is crucial for effective audience targeting, analyzing traffic sources, and measuring campaign performance. By connecting digital audiences through various IPs, we enable a clearer understanding of user journeys across devices and platforms. Beyond IPs, we’re also able to connect these IDs to broader ID types like Mobile Advertising IDs and CTV Ids.\r\n\r\nKey Features:\r\n- IPV4 and IPV6 in hashed format\r\n- Detailed mapping of Anonymous IPs for secure user behavior analysis\r\n- Integration with Mobile IP Data for insights into mobile user interactions\r\n- Comprehensive Identity Data for enhanced audience profiling\r\n- Digital Audience Data to understand demographics and interests\r\n- Identity Linkage Data for connecting user profiles across different channels\r\n\r\n\r\nUse Cases:\r\n- Audience segmentation and targeting strategies\r\n- Traffic source analysis and optimization\r\n- Digital campaign performance measurement\r\n- User journey mapping across devices\r\n- Compliance-focused marketing solutions\r\n\r\nData Compliance:\r\nOur Anonymous IP Data is fully compliant with industry standards for data privacy and security. We prioritize ethical data collection practices, ensuring that user identities remain anonymous while still providing valuable insights.\r\n\r\n\r\nData Quality:\r\nDRAKO employs rigorous quality assurance protocols to maintain the accuracy and reliability of our Anonymous IP Data. We continuously update our datasets and utilize advanced validation techniques to ensure data integrity.","terms_of_service":"https://drakomediagroup.com/privacy-policy","contact_email":"support@datarade.ai","privacy_policy_link":"https://drakomediagroup.com/privacy-policy","assets":["ASSET_TYPE_DATA_TABLE"]},"provider_summary":{"name":"DRAKO","description":"Drako Data stands at the forefront of geolocation data solutions, offering value-added services designed to empower our clients' analytics capabilities. We understand the complexities of extracting meaningful insights from raw geolocation data. That's why we focus on adding value to the data before it reaches you, streamlining your processes, and enabling you to glean insights more quickly and efficiently.\r\n\r\nOur team works tirelessly to enhance the quality and utility of the geolocation data we provide. We meticulously process and refine the data, ensuring it's ready for immediate use in your analytics and decision-making processes. This not only saves you valuable time and resources but also allows you to focus more on analysis and strategy, rather than data preparation.\r\n\r\nPartner with Drako Data and experience the difference of value-added geolocation data solutions. Let us empower your data science, fuel your growth, and guide you towards a future of insightful, data-driven success."},"popularity":38},{"id":"ce2d0da8-877c-44a4-8768-7ba3fe072da8","summary":{"name":"Restaurants in Egypt (EG) - Places Data Sample","subtitle":"\uD83C\uDDEA\uD83C\uDDEC Restaurant location & metadata table for Egypt, with 5K+ restaurant points-of-interest (PoIs) updated daily.","share":{"name":"eg_restaurant_marketplace","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1727886550643,"updated_at":1746426431235,"categories":["ADVERTISING_AND_MARKETING","GEOSPATIAL","LOOKUP_TABLES","RETAIL"],"listingType":"STANDARD","provider_id":"bcde9d2e-7e2f-4266-8c17-45d2e7bdc27e"},"detail":{"description":"## **Overview**\n\n> Learn more about this dataset on [catalog.dotlas.com](https://catalog.dotlas.com/restaurants/unified_restaurant_establishments/)\n\n> This is a sample of our Egypt Restaurants data product (temporal). 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This sample contains ~190 restaurants across Egypt. \n\nFor pricing and licensing details please [**contact us**](mailto:info@dotlas.com)\n\n**Use cases** \n\n\uD83D\uDD70 Analyze competitor opening hours on each day of the week to help create an optimal operating time frame.\n\uD83D\uDCC8 Identify trending cuisines in different regions by analyzing the frequency and distribution of various cuisine types in different cities.\n\uD83D\uDCB0 Use the distribution data to identify underserved areas with high potential for new restaurant openings based on the lack of certain cuisine types.\n\n### **Potential Sectors**\n* Food & Beverages (F&B)\n* GIS (Geographic Information Systems) and Geocoding Services\n* Marketing and Advertising Leads\n* Competitor Monitoring & Market Analysis\n* Financial tickering of F&B brand performance\n* Site Selection & Suitability Analysis\n* Real Estate Proximity Analysis\n\n**Key features** \n\n▶️ **Restaurant Identification**: Includes unique identifiers and names for each restaurant.\n\n▶️ **Geographical Data**: Detailed information about the `country`, `subregion`, `city`, `area`, and precise geographical coordinates (`latitude` and `longitude`) of each restaurant.\n\n▶️ **Contact Information**: Addresses, phone numbers, email addresses, and website URLs.\n\n▶️ **Social Media Presence**: Links to `Instagram`, `Facebook`, and `Twitter` profiles where available.\n\n▶️ **Amenities**: Information on various amenities such as acceptance of credit cards, availability of Wi-Fi, presence of outdoor seating, and more.\n\n▶️ **Additional Metadata**: Information on entertainment options, executive chef names, dress codes, dining styles, and more","terms_of_service":"https://dotlas.com/terms-of-service","documentation_link":"https://catalog.dotlas.com/restaurants/unified_restaurant_establishments/","privacy_policy_link":"https://dotlas.com/privacy-policy","geographical_coverage":"[\"Middle East\"]","cost":"FREE","update_frequency":{"interval":1,"unit":"DAILY"},"collection_granularity":{"interval":1,"unit":"DAILY"},"data_source":"Dotlas collects and coalesces information from multiple online and offline sources","assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"Dotlas","description":"Dotlas provides high-quality geographical and temporal data on restaurants, real estate and hospitality sectors around the world. 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The original data comes from a specialized data provider: Marktguru. \n\nThe present dataset uniquely combines information from receipts (retailer, product short text, promotional and regular price, promotion type, etc.) with additional data sources (barcode, product details, market-compliant categorization, anonymized user ID). This results in a variety of analysis and utilization possibilities. For example, the product-specific assignment across all distribution channels enables GTIN-based price monitoring. The assignment of receipts to anonymized user IDs allows the actual purchasing behavior to be depicted over time. It should be noted that the provided dataset does not represent the final version, i.e., individual attributes are not fully populated\n\n**Use cases**\n- Evaluate quality of market analysis data\n- Exploratory analysis of Austrian retail behaviour\n\nFor more details, refer to column descriptions in the catalog.\n\n**How to obtain original data**\nPlease contact mostly.ai if you are interested in purchasing the original data. 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Initially developed to serve the needs of a regional electronic health record, CMT has evolved into a core KP asset, serving as the common terminology across all applications. CMT serves as the definitive source of concept definitions for the organization, provides a consistent structure and access method to all codes used by the organization, and is KP's language of interoperability, with cross-mappings to regional ancillary systems and administrative billing codes. The core of CMT is comprised of SNOMED CT, laboratory LOINC, and First DataBank drug terminology. CMT includes datasets from the following categories: Cardiology, Dermatology and respiratory system, gastroenterology, infectious diseases, endocrinology, urology, nephrology, injury, mental health, neurology, gynecology, ophthalmology, orthopedics, pediatrics, radiology and vascular procedures.\n\n**Benefits**\n- cross map sets provide bi-directional translations between cmt and ancillary applications and administrative billing codes.\n- context sets provide subsets of cmt for use in specific contexts.\n- easy usability\n- \"interface\" terminology is differentiated from \"reference\" terminology\n- easy for clinicians to find the concepts they need\n- the immediate value of coded data is apparent to clinician user\n- well defined approach to terminology extensions\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [CMT Common Laboratory Procedures Problem List Subset](https://www.johnsnowlabs.com/marketplace/cmt-common-laboratory-procedures-problem-list-subset)\n\t- This problem list subset includes the most common laboratory procedure concepts in use at KP (Kaiser Permanente) that are mapped to both CPT (Current Procedural Terminology) and LOINC (Logical Observation Identifiers Names and Codes). There are 347 concepts in this file. SNOMED CT (Systematized Nomenclature of Medicine - Clinical Terms) concepts are based on the January 2014 International Release.\n- [CMT Radiology Procedures Problem List Subset](https://www.johnsnowlabs.com/marketplace/cmt-radiology-procedures-problem-list-subset)\n\t- This radiology problem list subset includes 908 concepts that KP (Kaiser Permanente) uses within the KP electronic medical record for radiology orders. This file also includes mappings to CPT (Current Procedural Terminology). This dataset is the compilation of radiology related problem list subsets that KP has released in the year 2015 and 2016.\n- [CMT US Edition Map Set](https://www.johnsnowlabs.com/marketplace/cmt-us-edition-map-set)\n\t- This dataset includes descriptions that are used as display names for Clinicians in Kaiser Permanente's electronic medical record, corresponding patient display names that the members see in their Personal Health Record (PHR) portal and mappings to other terminologies.\n- [CMT Vaccinations Procedure List Subset](https://www.johnsnowlabs.com/marketplace/cmt-vaccinations-procedure-list-subset)\n\t- This procedure list subset includes concepts that Kaiser Permanente clinicians use to place Vaccination orders. There are 92 SNOMED CT (Systematized Nomenclature of Medicine - Clinical Terms) concepts in the file and includes mappings to CPT (Current Procedural Terminology) codes. SNOMED CT concepts are based on the January 2014 International Release.\n- [CMT Vascular Procedures List Subset](https://www.johnsnowlabs.com/marketplace/cmt-vascular-procedures-list-subset)\n\t- CMT Vascular Procedures List Subset inlcudes 80 concepts that Kaiser Permanente uses within the KP electronic medical record. This file also includes mappings to CPT (Current Procedural Terminology) codes. SNOMED CT (Systematized Nomenclature of Medicine - Clinical Terms) concepts are based on the March 2017 United States Edition, which includes the January 2017 International Release.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated\n- Data Updates\n - Data updates support replace-on-update: outdated foreign keys are deprecated, not deleted\n\n**Our data is curated and enriched by domain experts** \n\n Each dataset is manually curated by our team of doctors, pharmacists, public health & medical billing experts:\n- Field names, descriptions, and normalized values are chosen by people who actually understand their meaning\n- Healthcare & life science experts add categories, search keywords, descriptions and more to each dataset\n- Both manual and automated data enrichment supported for clinical codes, providers, drugs, and geo-locations\n- The data is always kept up to date – even when the source requires manual effort to get updates\n- Support for data subscribers is provided directly by the domain experts who curated the data sets\n- Every data source’s license is manually verified to allow for royalty-free commercial use and redistribution.\n\n**Need Help?**\nIf you have questions about our products, contact us at [info@johnsnowlabs.com](mailto:info@johnsnowlabs.com). \n ","terms_of_service":"https://www.johnsnowlabs.com/healthcare-data-library-eula/","documentation_link":"https://www.johnsnowlabs.com/marketplace/convergent-medical-terminology-full-data-package","contact_email":"support@johnsnowlabs.com","privacy_policy_link":"https://www.johnsnowlabs.com/privacy-policy/","embedded_notebook_file_infos":[{"id":"d6560ab4-20f8-4d1e-bab4-f06fbb0b99d5","marketplace_file_type":"EMBEDDED_NOTEBOOK","file_parent":{"parent_id":"f8d23978-f885-4db2-a287-46bfa971d8c8","file_parent_type":"LISTING"},"mime_type":"text/html","download_link":"https://marketplace-file-storage-prod.s3-fips.us-east-1.amazonaws.com/EMBEDDED_NOTEBOOK/LISTING_f8d23978-f885-4db2-a287-46bfa971d8c8/d6560ab4-20f8-4d1e-bab4-f06fbb0b99d5?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEGgaDnVzLWVhc3QtMS1maXBzIkYwRAIgbC8sWIoYaHR8y7Wq86nPb0pufBl4%2BM4PWpeN%2B5PW9VQCIG7BhAVBDCWTpxFDLeuKYm%2Ff1JLz4iccPMzter0YdhgWKqYECDAQABoMNDE0MzUxNzY3ODI2IgxNAJrHbXaZG20jNfMqgwQyGEb4TsoVOoNvJ%2BaA%2FZ7zeu8hGcHqCeNfblP%2FQo5g6q2aRNtpwtPRKMlhYcIE%2FUQaNT1Lqmyptu32VUQTpzjTTxRVmJrDPkheSCkV06XQ%2FpTMKjU8CC7X1b%2FqYf8CrBwZT7Cv4C5WPjEET38f2A79h0xV6bZgPy28zQWrSeIJ9w6AfZBxiE24%2FVc08zVA0ivCT6lsy7nuU0wcR%2FMg2C9nd1vaMnQ923VpD21CKfVVYcrNWL2b%2FQ%2BNAO%2BxPjPgjiN6UPQbvbvZxkSdhgrGtecIg9NgqGkMcvM5VLTDtdC1kdeZetfOC2obtA%2BT%2F0gy3tIDo6WM5hcQfw743%2FAU9oaGnRBl%2BwoxRRDCZA%2F4jnPdluICSI7NCr6C3HHOe4CSZJjRihczxPA%2FgQczAVjAPs6CrK9N9pIzt0vNJpGUTgg4AoxftTvGfciDREilSctPIcBk6GwB%2Bzt4wbtNCmEPU%2BFD46rc6CWVQmmLwbKOeHw70ipMVFlOc8NlXkEtGO2vu6H44MstSSc6daOVUYUavQSGZ6jGhEjsP2YGz8KDm9XQotB%2FtyVBqiZV01OKqDmmBJA%2FXjUpSJ8M%2FeJXhe5m%2BKcxzNObtoJRI7jci3Tpt08XZyXZ7%2FyIV8H30ya468X3cfkTXrm972uga3z7iXAdubyEHnQIua%2FuvtwOHILi6O5D1Me2MzCPvcnXBjqTAYDoMWBt%2B3AVFuqSs2dmDQNBnvEhXfMgTedKLD5PJHb3aIl15iBkLaNu36GYnVCp9ZU2lSYcTbY5QwNnNeTfJTNy9bKhwzru6Sg%2BslhN%2BTS0zkTn%2BeLMY4tHgPEtEZ2cp%2FCk6GwqswvnYVKHjwO8q%2B1Mx8lc08fVEfarE9dke1GGFqoddx%2Bq%2BZUyHbDlAVfrCmf0yw%3D%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20261110T000135Z&X-Amz-SignedHeaders=host&X-Amz-Expires=86400&X-Amz-Credential=ASIAWA6KKHEJORWBH4N3%2F20261110%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=bcdaf2be64504bbb295a107f7f8cb4cb201d51fcf1b4bb769d1d0068af7157d2","created_at":1682500351724,"updated_at":1682500351724,"display_name":"EDA Notebook","status":"FILE_STATUS_PUBLISHED"}],"assets":["ASSET_TYPE_DATA_TABLE"]},"provider_summary":{"name":"John Snow Labs","description":"John Snow Labs, an AI and NLP for healthcare company, provides state-of-the-art software, models, and data to help healthcare and life science organizations build, deploy, and operate AI projects."},"popularity":23},{"id":"9e69c469-394d-4c7f-8f06-38a2139143a5","summary":{"name":"Bright Insights: Actionable AI-driven eCommerce Insights","subtitle":"Unleash the potential of Bright Insights, a game-changer for eCommerce Data, Insights & Analytics. 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It includes following datasets: patients with and without hematoma enlargement, chronic care management differences and patient outcomes in anticoagulant clinics, participant characteristics anticoagulants randomized trial, pulmonary embolism thrombolysis treatment and its complications.\n\n**Description**\nThis data package is a collection of numerous studies related to anticoagulation therapy. The German-wide Multicenter Analysis of Oral Anticoagulation-associated Intracerebral Hemorrhage (RETRACE) investigators performed a study on behalf of IGNITE (Initiative of German Neuro-intensive Trial Engagement). The study investigated the association of anticoagulation reversal and blood pressure (BP) with hematoma enlargement and the effects of OAC resumption. Another study compared outcome for patients using two different mode of reversal treatments: (1) Fresh Frozen Plasma (FFP), and (2) Prothrombin Complex Concentrates (PCC). A research study published in BioMed Central Health Services Research compared the patient outcomes between Dutch Anticoagulant Clinic (AC) regions taking into account the variations in chronic care management (CCM) in order to explore whether these CCM elements could improve the quality of oral anticoagulant therapy. The data were subjected to regression analysis to identify to what extent the differences in patient outcomes are associated with chronic care management activities. Another study was performed to identify the differences in patient outcomes and their relationship with chronic care management of oral anticoagulant. The goal of another study was to perform a systematic review of the literature and determine pre-specified inclusion criteria based on relevant studies, which are as follows: randomized controlled design in patients with PE evaluating thrombolytic therapy as an intervention; a comparator group that included any of the following agents: low-molecular-weight heparin (LMWH), vitamin K antagonist, fondaparinux, or unfractionated heparin; and reporting of mortality outcomes. We did not include trials that compared different thrombolytic agents against one another or different doses of the same thrombolytic drug.\n\n**Benefits**\n- Anticoagulation therapy is used for study purposes.\n- to examine the utilization of anticoagulation services.\n- anticoagulation is an essential lifesaving management practice indicated for arterial, venous and intracardiac thromboembolism.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [Characteristics of OAC Patients With and Without Hematoma Enlargement](https://www.johnsnowlabs.com/marketplace/characteristics-of-oac-patients-with-and-without-hematoma-enlargement)\n\t- The German-wide Multicenter Analysis of Oral Anticoagulation-associated Intracerebral Hemorrhage (RETRACE) investigators performed this study on behalf of IGNITE (Initiative of German Neurointensive Trial Engagement) and was published by The Journal of the American Medical Association (JAMA) on February 24, 2015. The study investigated the association of anticoagulation reversal and blood pressure (BP) with hematoma enlargement and the effects of OAC resumption.\n- [Chronic Care Management Differences in Anticoagulant Clinics](https://www.johnsnowlabs.com/marketplace/chronic-care-management-differences-in-anticoagulant-clinics)\n\t- This research study published in BioMed Central Health Services Research compared the patient outcomes between Dutch Anticoagulant Clinic (AC) regions taking into account the variations in chronic care management (CCM) in order to explore whether these CCM elements could improve the quality of oral anticoagulant therapy. The data were subjected to regression analysis to identify to what extent the differences in patient outcomes are associated with chronic care management activities.\n- [Chronic Care Management Patient Outcomes in Anticoagulant Regions](https://www.johnsnowlabs.com/marketplace/chronic-care-management-patient-outcomes-in-anticoagulant-regions)\n\t- This research study published in BioMed Central Health Services Research compared the patient outcomes between Dutch Anticoagulant Clinic (AC) regions taking into account the variations in chronic care management (CCM) in order to explore whether these CCM elements could improve the quality of oral anticoagulant therapy (OAT). This study was performed to identify the differences in patient outcomes and their relationship with chronic care management of OAT.\n- [Participant Characteristics Anticoagulants Randomized Trial JAMA](https://www.johnsnowlabs.com/marketplace/participant-characteristics-anticoagulants-randomized-trial-jama)\n\t- This clinical investigation that was published in The Journal of the American Medical Association (JAMA) on June 18, 2014, was a collaboration of efforts from PubMed, the Cochrane Library, EMBASE, EBSCO, Web of Science, and CINAHL since its inception from April 10, 2014.\n- [Pulmonary Embolism Thrombolysis Treatment and Its Complications JAMA](https://www.johnsnowlabs.com/marketplace/pulmonary-embolism-thrombolysis-treatment-and-its-complications-jama)\n\t- This clinical investigation that was published in The Journal of the American Medical Association (JAMA) on June 18, 2014, was a collaboration of efforts from PubMed, the Cochrane Library, EMBASE, EBSCO, Web of Science, and CINAHL since its inception from April 10, 2014. The goal of the study was to determine the risks and complications involved with thrombolytic therapy compared with anticoagulation in acute pulmonary embolism.\n- [Subgroup Analysis for Mode of Reversal Treatment FFP vs PCC](https://www.johnsnowlabs.com/marketplace/subgroup-analysis-for-mode-of-reversal-treatment-ffp-vs-pcc)\n\t- The German-wide Multicenter Analysis of Oral Anticoagulation-associated Intracerebral Hemorrhage (RETRACE) investigators performed this study on behalf of IGNITE (Initiative of German Neurointensive Trial Engagement) and was published by The Journal of the American Medical Association (JAMA) on February 24, 2015. The study compared outcome for patients using two different mode of reversal treatments: (1) Fresh Frozen Plasma (FFP), and (2) Prothrombin Complex Concentrates (PCC).\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated\n- Data Updates\n - Data updates support replace-on-update: outdated foreign keys are deprecated, not deleted\n\n**Our data is curated and enriched by domain experts** \n\n Each dataset is manually curated by our team of doctors, pharmacists, public health & medical billing experts:\n- Field names, descriptions, and normalized values are chosen by people who actually understand their meaning\n- Healthcare & life science experts add categories, search keywords, descriptions and more to each dataset\n- Both manual and automated data enrichment supported for clinical codes, providers, drugs, and geo-locations\n- The data is always kept up to date – even when the source requires manual effort to get updates\n- Support for data subscribers is provided directly by the domain experts who curated the data sets\n- Every data source’s license is manually verified to allow for royalty-free commercial use and redistribution.\n\n**Need Help?**\nIf you have questions about our products, contact us at [info@johnsnowlabs.com](mailto:info@johnsnowlabs.com). \n 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The goal is to demonstrate how Verticalization/Domain Adaptation for the [hospitality] sector can be easily achieved using our two-step approach to LLM Fine-Tuning. An overview of this approach can be found at: [From General-Purpose LLMs to Verticalized Enterprise Models](https://www.bitext.com/blog/general-purpose-models-verticalized-enterprise-genai/)\n\nThe dataset has the following specifications:\n\n- Use Case: Intent Detection\n- Vertical: Hospitality\n- 25 intents assigned to 16 categories\n- 25000 question/answer pairs, with approximately 1000 per intent\n- 40 entity/slot types\n- 10 different types of language generation tags\n\nThe categories and intents are derived from Bitext's extensive experience across various industry-specific datasets, ensuring the relevance and applicability across diverse contexts.\n\n**Dataset Token Count**\n\nThe dataset contains a total of 3.15 million tokens across 'instruction' and 'response' columns. This extensive corpus is crucial for training sophisticated LLMs that can perform a variety of functions including conversational AI, question answering, and virtual assistant tasks in the hospitality domain.\n\n**Fields of the Dataset**\n\nEach entry in the dataset comprises the following fields:\n\n- tags\n- instruction: a user request from the Hospitality domain\n- category: the high-level semantic category for the intent\n- intent: the specific intent corresponding to the user instruction\n- response: an example of an expected response from the virtual assistant\n\n**Categories and Intents**\n\nThe dataset covers a wide range of hospitality-related categories and intents, which are:\n\n- **BILLING**: invoices\n- **CANCELLATION_FEES**: cancellation_fees\n- **CHECK_IN**: check_in\n- **CHECK_OUT**: check_out\n- **CONTACT**: customer_service, human_agent\n- **EVENT**: host_event\n- **FEEDBACK**: file_complaint, leave_review\n- **HOTEL**: book_hotel, cancel_hotel_reservation, change_hotel_reservation, check_hotel_facilities, check_hotel_offers, check_hotel_prices, check_hotel_reservation, search_hotel\n- **LUGGAGE**: store_luggage\n- **MENU**: check_menu\n- **NIGHT**: add_night\n- **PARKING_SPACE**: book_parking_space\n- **PETS**: bring_pets\n- **POINTS**: redeem_points\n- **REFUND**: get_refund\n- **SHUTTLE_SERVICE**: shuttle_service\n\n**Entities**\n\nThe entities covered by the dataset include:\n\n- **{{WEBSITE_URL}}**, common with most intents.\n- **{{SPEAK_WITH_HUMAN_AGENT_OPTION}}**, featured in intents like human_agent.\n- **{{CHECK_OUT_OPTION}}**, associated with intents like check_out.\n- **{{CHARGE_TYPE}}**, relevant to intents such as cancellation_fees.\n- **{{DESTINATION_CITY}}**, featured in intents like book_hotel, change_hotel_reservation.\n\nThis comprehensive list of entities ensures that the dataset is well-equipped to train models that are highly adept at understanding and processing a wide range of hospitality-related queries and tasks.\n\n**Language Generation Tags**\n\nThe dataset includes tags indicative of various language variations and styles adapted for Hospitality, enhancing the robustness and versatility of models trained on this data. These tags categorize the utterances into different registers such as colloquial, formal, or containing specific hospitality jargon, ensuring that the trained models can understand and generate a range of conversational styles appropriate for different customer interactions in the hospitality sector.\n\n**Language Generation Tags**\n\nThe dataset includes tags that reflect various language variations and styles, crucial for creating adaptable and responsive conversational AI models within the hospitality sector. These tags help in understanding and generating appropriate responses based on the linguistic context and user interaction style.\n\n**Tags for Lexical variation**\n\n- **M - Morphological variation**: Adjusts for inflectional and derivational forms.\n - Example: \"is my account active\", \"is my account activated\"\n- **L - Semantic variations**: Handles synonyms, use of hyphens, and compounding.\n - Example: “what's my balance date\", “what's my billing date”\n\n**Tags for Syntactic structure variation**\n\n- **B - Basic syntactic structure**: Simple, direct commands or statements.\n - Example: \"activate my card\", \"I need to check my balance\"\n- **I - Interrogative structure**: Structuring sentences in the form of questions.\n - Example: “can you show my balance?”, “how do I transfer money?”\n- **C - Coordinated syntactic structure**: Complex sentences coordinating multiple ideas or tasks.\n - Example: “I want to transfer money and check my balance, what should I do?”\n- **N - Negation**: Expressing denial or contradiction.\n - Example: \"I do not wish to proceed with this transaction, how can I stop it?\"\n\n**Tags for language register variations**\n\n- **P - Politeness variation**: Polite forms often used in customer service.\n - Example: “could you please help me check my account balance?”\n- **Q - Colloquial variation**: Informal language that might be used in casual customer interactions.\n - Example: \"can u tell me my balance?\"\n- **W - Offensive language**: Handling potentially offensive language which might occasionally appear in frustrated customer interactions.\n - Example: “I’m upset with these charges, this is ridiculous!”\n\n**Tags for stylistic variations**\n\n- **K - Keyword mode**: Responses focused on keywords.\n - Example: \"balance check\", \"account status\"\n- **E - Use of abbreviations**: Common abbreviations.\n - Example: “acct for account”, “trans for transaction”\n- **Z - Errors and Typos**: Includes common misspellings or typographical errors found in customer inputs.\n - Example: “how can I chek my balance”\n\n**Other tags not in use in this Dataset**\n\n- **D - Indirect speech**: Expressing commands or requests indirectly.\n - Example: “I was wondering if you could show me my last transaction.”\n- **G - Regional variations**: Adjustments for regional language differences.\n - Example: American vs British English: \"checking account\" vs \"current account\"\n- **R - Respect structures - Language-dependent variations**: Formality levels appropriate in different languages.\n - Example: Using “vous” in French for formal addressing instead of “tu.”\n- **Y - Code switching**: Switching between languages or dialects within the same conversation.\n - Example: “Can you help me with my cuenta, please?”\n\nThese tags not only aid in training models for a wide range of customer interactions but also ensure that the models are culturally and linguistically sensitive, enhancing the customer experience in hospitality environments.\n\n**License**\n\nThe `Bitext-hospitality-llm-chatbot-training-dataset` is released under the **Community Data License Agreement (CDLA) Sharing 1.0**. This license facilitates broad sharing and collaboration while ensuring that the freedom to use, share, modify, and utilize the data remains intact for all users.\n\n**Key Aspects of CDLA-Sharing 1.0**\n\n- **Attribution and ShareAlike**: Users must attribute the dataset and continue to share derivatives under the same license.\n- **Non-Exclusivity**: The license is non-exclusive, allowing multiple users to utilize the data simultaneously.\n- **Irrevocability**: Except in cases of material non-compliance, rights under this license are irrevocable.\n- **No Warranty**: The dataset is provided without warranties regarding its accuracy, completeness, or fitness for a particular purpose.\n- **Limitation of Liability**: Both users and data providers limit their liability for damages arising from the use of the dataset.\n\n**Usage Under CDLA-Sharing 1.0**\n\nBy using the `Bitext-hospitality-llm-chatbot-training-dataset`, you agree to adhere to the terms set forth in the CDLA-Sharing 1.0. It is essential to ensure that any publications or distributions of the data, or derivatives thereof, maintain attribution to the original data providers and are distributed under the same or compatible terms of this agreement.\n\nFor a detailed understanding of the license, refer to the [official CDLA-Sharing 1.0 documentation](https://cdla.dev/sharing-1-0/).\n\nThis license supports the open sharing and collaborative improvement of datasets within the AI and data science community, making it particularly suited for projects aimed at developing and enhancing AI technologies in the hospitality sector.\n\n---\n\n(c) Bitext Innovations, 2024\n","terms_of_service":"https://www.bitext.com/api-terms-of-use/","documentation_link":"","privacy_policy_link":"https://www.bitext.com/privacy-policy/","embedded_notebook_file_infos":[{"id":"3802208b-60a6-4153-ac2e-05f6763a6b48","marketplace_file_type":"EMBEDDED_NOTEBOOK","file_parent":{"parent_id":"e6d1decf-f74f-4c7a-8dd5-62f6dc0cdf21","file_parent_type":"LISTING"},"mime_type":"text/html","download_link":"https://marketplace-file-storage-prod.s3-fips.us-east-1.amazonaws.com/EMBEDDED_NOTEBOOK/LISTING_e6d1decf-f74f-4c7a-8dd5-62f6dc0cdf21/3802208b-60a6-4153-ac2e-05f6763a6b48?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEGgaDnVzLWVhc3QtMS1maXBzIkYwRAIgbC8sWIoYaHR8y7Wq86nPb0pufBl4%2BM4PWpeN%2B5PW9VQCIG7BhAVBDCWTpxFDLeuKYm%2Ff1JLz4iccPMzter0YdhgWKqYECDAQABoMNDE0MzUxNzY3ODI2IgxNAJrHbXaZG20jNfMqgwQyGEb4TsoVOoNvJ%2BaA%2FZ7zeu8hGcHqCeNfblP%2FQo5g6q2aRNtpwtPRKMlhYcIE%2FUQaNT1Lqmyptu32VUQTpzjTTxRVmJrDPkheSCkV06XQ%2FpTMKjU8CC7X1b%2FqYf8CrBwZT7Cv4C5WPjEET38f2A79h0xV6bZgPy28zQWrSeIJ9w6AfZBxiE24%2FVc08zVA0ivCT6lsy7nuU0wcR%2FMg2C9nd1vaMnQ923VpD21CKfVVYcrNWL2b%2FQ%2BNAO%2BxPjPgjiN6UPQbvbvZxkSdhgrGtecIg9NgqGkMcvM5VLTDtdC1kdeZetfOC2obtA%2BT%2F0gy3tIDo6WM5hcQfw743%2FAU9oaGnRBl%2BwoxRRDCZA%2F4jnPdluICSI7NCr6C3HHOe4CSZJjRihczxPA%2FgQczAVjAPs6CrK9N9pIzt0vNJpGUTgg4AoxftTvGfciDREilSctPIcBk6GwB%2Bzt4wbtNCmEPU%2BFD46rc6CWVQmmLwbKOeHw70ipMVFlOc8NlXkEtGO2vu6H44MstSSc6daOVUYUavQSGZ6jGhEjsP2YGz8KDm9XQotB%2FtyVBqiZV01OKqDmmBJA%2FXjUpSJ8M%2FeJXhe5m%2BKcxzNObtoJRI7jci3Tpt08XZyXZ7%2FyIV8H30ya468X3cfkTXrm972uga3z7iXAdubyEHnQIua%2FuvtwOHILi6O5D1Me2MzCPvcnXBjqTAYDoMWBt%2B3AVFuqSs2dmDQNBnvEhXfMgTedKLD5PJHb3aIl15iBkLaNu36GYnVCp9ZU2lSYcTbY5QwNnNeTfJTNy9bKhwzru6Sg%2BslhN%2BTS0zkTn%2BeLMY4tHgPEtEZ2cp%2FCk6GwqswvnYVKHjwO8q%2B1Mx8lc08fVEfarE9dke1GGFqoddx%2Bq%2BZUyHbDlAVfrCmf0yw%3D%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20261110T000135Z&X-Amz-SignedHeaders=host&X-Amz-Expires=86400&X-Amz-Credential=ASIAWA6KKHEJORWBH4N3%2F20261110%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=12ed957e888befb60014514114d811bcbaa0eee50fb8fcc10ab5356aa033b76c","created_at":1727611631638,"updated_at":1727611631638,"display_name":"Hospitality_Dataset_Sample_Notebook","status":"FILE_STATUS_PUBLISHED"}],"geographical_coverage":"[\"Global\"]","cost":"FREE","data_source":"Internally generated by Bitext using proprietary NLP and NLG technologies.","assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"Bitext Innovation International","description":"We understand that one of the critical factors in achieving exceptional performance with LLMs is the quality and relevance of the training data. 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The Hospital Value Based Purchasing (HVBP) Program is a Centers for Medicare & Medicaid Services (CMS) initiative that rewards acute care hospitals with incentive payments for the quality of care they provide to Medicare beneficiaries. CMS rewards hospitals based on the quality of care provided to Medicare patients, closely best clinical practices and well hospitals enhance patients experiences of care during hospital stays.\n\nHospital Compare Clinical Process of Care Efficiency Scores contains a list of hospitals participating in the Hospital Value Based Purchasing Program and their performance ratios and scores for the Efficiency Medicare Spending per Beneficiary (MSPB) measure. Medicare makes incentive payments to hospitals based on how well they perform on each measure and how much they improve their performance on each measure compared to their performance during a baseline period. Hospital Compare Clinical Process of Care Outcome Scores contains a list of hospitals participating in the Hospital Value Based Purchasing Program and their performance rates and scores for the outcome measures.\n\nThis data package also includes a list of hospitals participating in the Hospital Compare and their Clinical Process of Care domain scores, Patient Experience of Care dimension scores, and Total Performance Scores. CMS assesses each hospital’s total performance by comparing its Achievement and Improvement scores for each applicable Hospital Value Based Purchasing measure. CMS uses a threshold and benchmark to determine how many points to award for the achievement and improvement scores. CMS compares the achievement and Improvement scores and uses whichever is greater.\n\n**Benefits**\n- Useful for doctors, medical health planners, patients, hospitals, medical research groups and medical students. hospital compare clinical process of care database is available on centers for medicare & medicaid services (cms) website in raw form, whereas a john snow labs (jsl) customer gets it in a clean and normalized form ready to use in one place and create his own custom reports. it is easy to comprehend for the customers who are unfamiliar with the medical terminologies as all abbreviated terms are replaced with full form,, unlike cms original datasets which are full of abbreviations.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [Clinical Episode Based Payment Measures by Hospital](https://www.johnsnowlabs.com/marketplace/clinical-episode-based-payment-measures-by-hospital)\n\t- This dataset shows 6 Clinical Episode-Based Payment (CEBP) Measures by hospital. It includes Conditions: Cellulitis, Kidney/Urinary Tract Infection, Gastrointestinal Hemorrhage; Procedures: Aortic Aneurysm, Spinal Fusion, Cholecystectomy and Common Duct Exploration. This measure takes into account important factors like patient age, health status (risk adjustment) and geographic payment differences (payment-standardization).\n- [Clinical Episode Based Payment Measures by State](https://www.johnsnowlabs.com/marketplace/clinical-episode-based-payment-measures-by-state)\n\t- This dataset shows 6 Clinical Episode-Based Payment (CEBP) Measures by state. It includes Conditions: Cellulitis, Kidney/Urinary Tract Infection, Gastrointestinal Hemorrhage; Procedures: Aortic Aneurysm, Spinal Fusion, Cholecystectomy and Common Duct Exploration. This measure takes into account important factors like patient age, health status (risk adjustment) and geographic payment differences (payment-standardization).\n- [Hospital Compare Clinical Process of Care Acute MI Scores](https://www.johnsnowlabs.com/marketplace/hospital-compare-clinical-process-of-care-acute-mi-scores)\n\t- This dataset contains a list of hospitals participating in the Hospital Value Based Purchasing Program and their performance rates and scores for the Clinical Process of Care AMI (Acute Myocardial Infarction) measures.\n- [Hospital Compare Clinical Process of Care Efficiency Scores](https://www.johnsnowlabs.com/marketplace/hospital-compare-clinical-process-of-care-efficiency-scores)\n\t- This dataset contains a list of hospitals participating in the Hospital Value Based Purchasing Program and their performance ratios and scores for the Efficiency Medicare Spending per Beneficiary (MSPB) measure.\n- [Hospital Compare Clinical Process of Care Outcome Scores](https://www.johnsnowlabs.com/marketplace/hospital-compare-clinical-process-of-care-outcome-scores)\n\t- This dataset contains a list of hospitals participating in the Hospital Value Based Purchasing Program and their performance rates and scores for the outcome measures.\n- [Hospital Compare Clinical Process of Care Patient Experience Scores](https://www.johnsnowlabs.com/marketplace/hospital-compare-clinical-process-of-care-patient-experience-scores)\n\t- This dataset contains a list of hospitals participating in the Hospital Value Based Purchasing Program and their scores for the Patient Experience of Care HCAHPS dimensions.\n- [Hospital Compare Clinical Process of Care Pneumonia Scores](https://www.johnsnowlabs.com/marketplace/hospital-compare-clinical-process-of-care-pneumonia-scores)\n\t- This dataset contains a list of hospitals participating in the Hospital Value Based Purchasing Program and their performance rates and scores for the Clinical Process of Care Pneumonia measures. 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measures tracked during an episode of care that contains complication rate for hip and knee replacement patients and the Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) Survey information.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated\n- Data Updates\n - Data updates support replace-on-update: outdated foreign keys are deprecated, not deleted\n\n**Our data is curated and enriched by domain experts** \n\n Each dataset is manually curated by our team of doctors, pharmacists, public health & medical billing experts:\n- Field names, descriptions, and normalized values are chosen by people who actually understand their meaning\n- Healthcare & life science experts add categories, search keywords, descriptions and more to each dataset\n- Both manual and automated data enrichment supported for clinical codes, providers, drugs, and geo-locations\n- The data is always kept up to date – even when the source requires manual effort to get updates\n- Support for data subscribers is provided directly by the domain experts who curated the data sets\n- Every data source’s license is manually verified to allow for royalty-free commercial use and redistribution.\n\n**Need Help?**\nIf you have questions about our products, contact us at [info@johnsnowlabs.com](mailto:info@johnsnowlabs.com). \n 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All data comes from a single source, ensuring **consistency and comparability** across markets.\n\nThe Sociodemographics-Bundle Basic for Estonia includes the following **variables**:\n- [population](https://www.mbi-geodata.com/en/sociodemographic-data/population/)\n- [gender](https://www.mbi-geodata.com/en/sociodemographic-data/sex-gender/)\n- [age structure](https://www.mbi-geodata.com/en/sociodemographic-data/agebands/) (15-year age bands)\n- household figures ([number of households](https://www.mbi-geodata.com/en/sociodemographic-data/households/) and [households by type](https://www.mbi-geodata.com/en/sociodemographic-data/households-by-type/))\n\nOur sociodemographic data is offered for each country on different **levels**:\n- **Postal Code Level:** This fine-grained level of detail makes the data suitable for a wide range of applications – for example, enriching customer databases, which are often organized by postal code.\n- **Administrative Level:** Covers both larger administrative areas (e.g., regions, states, provinces) as well as finer subdivisions such as districts, counties, or municipalities.\n- **Micro Level**: Subdivisions of postal code or administrative boundaries, such as census areas or street segments. 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We will be happy to help you find a product that fits your needs.\n \n**Additional Insights**: https://www.mbi-geodata.com/en/sociodemographic-data/\n\n**Data Structure**:\nAGE_F0014: Population (Nighttime) by age: 0 - 14 years, female\nAGE_F1529: Population (Nighttime) by age: 15 - 29 years, female\nAGE_F3044: Population (Nighttime) by age: 30 - 44 years, female\nAGE_F4559: Population (Nighttime) by age: 45 - 59 years, female\nAGE_F60PL: Population (Nighttime) by age: 60 years and above, female\nAGE_M0014: Population (Nighttime) by age: 0 - 14 years, male\nAGE_M1529: Population (Nighttime) by age: 15 - 29 years, male\nAGE_M3044: Population (Nighttime) by age: 30 - 44 years, male\nAGE_M4559: Population (Nighttime) by age: 45 - 59 years, male\nAGE_M60PL: Population (Nighttime) by age: 60 years and above, male\nAGE_T0014: Population (Nighttime) by age: 0 - 14 years, total\nAGE_T1529: Population (Nighttime) by age: 15 - 29 years, male\nAGE_T3044: Population (Nighttime) by age: 30 - 44 years, total\nAGE_T4559: Population (Nighttime) by age: 45 - 59 years, total\nAGE_T60PL: Population (Nighttime) by age: 60 years and above, total\nCTRYCODE: Two digit country code by ISO 3166\nFEMALE: Population (Nighttime): females\nHH_SIZE: Average household size\nHH_T: Households: total number\nMALE: Population (Nighttime): males\nNAME: Name of administrative area / Name of the most populous administrative area / Name of the respective micro-area or the next higher level\nADMINCODE / POSTCODE / MICROCODE Administrative code / Postal code / Micro-code\nP_PRM: Population (Nighttime) year average: per mill of country\nP_T: Population (Nighttime) year average: total number","terms_of_service":"https://service-mbi-geodata.com/s/HNp7Z2tws4WDqWd","documentation_link":"https://www.mbi-geodata.com/en/mbi-supplier-manufacturer-worldwide-geodata/europe/estonia-maps/","privacy_policy_link":"https://www.mbi-geodata.com/en/data-protection/","geographical_coverage":"[\"Europe\"]","cost":"FREE","update_frequency":{"interval":1,"unit":"YEARLY"},"collection_granularity":{"interval":1,"unit":"YEARLY"},"collection_date_start":1704109908538,"collection_date_end":1735645915672,"data_source":"MBI","assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://service-mbi-geodata.com/s/Ma54SMd5wZGtbfW"},"provider_summary":{"name":"Michael Bauer International GmbH – MBI Geodata","description":"For over 15 years, Michael Bauer International (MBI) has been a leading provider of globally consistent and comparable geographic data, including more than 3 million postcode areas and other detailed datasets like sociodemographics and purchasing power. 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Contact Databricks.partnership@precisely.com for full product licensing information.\n\nCanada Address Fabric™ is a current and comprehensive list of all known addresses in Canada - over 18.8 Million addresses across all provinces. Each record is pre-geocoded to provide the most precise latitude and longitude coordinate location, including physical locations that are not deliverable by the postal service. The data is delivered in an easy to use flat file format.\n\n**Use cases**\n\n**360-Degree Customer View**\nUnderstanding, and communicating with your customer has a lot to do with location. Know where your customers live from both an address and geographic location. Use the PreciselyID (a unique and persistent identifier for each address in Canada) to easily enrich your customer data with many additional attributes through Precisely’s data product catalogue. Note: This product does not contain Personally Identifiable Information.\n\n**Location Geocoding**\nAddress Fabric is complete nationwide coverage providing the most comprehensive, current, and accurate location listings. Multi-sourced, pre-geocoded address file that contains parsed street address with the highest precision coordinate available\n\n**Real World Data (RWD)**\nVerify address locations and postal types (residential addresses, business addresses, etc.)\n\n**Personalize Customer Experiences**\nKnowing where your customer lives and having the ability to enrich that location data through PreciselyID (a unique and persistent identifier for each address in Canada) allows you to gain accurate and valuable insights and reach your customers at or near home. 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It serves as a scalable commercial intelligence engine, allowing brands to bridge the gap between marketing campaigns and measurable shifts in treatment behavior with granular precision. \n\n\n**High Impact Use Cases**\n\n- *Commercial Targeting*: \nIdentify the clinical influencers who start patients on a therapy versus those who handle re-fills.\n- *Therapy Switching Analysis*:\nTrack patient movement between competing treatments to reveal real-world brand switching patterns that define market share.\n- *Campaign Effectiveness & Behavioral Attribution*:\nTrack patient movement between competing treatments to reveal real-world brand Provide commercial intelligence to effectively measure how a campaign shifts prescribing habits toward a client’s product.\n\n**Product details**\n\n*Sample Tables:*\n- Yearly Rx event scores by event type and healthcare provider (HCP)\n- Yearly Rx event scores by event type, healthcare provider (HCP), and drug product\n\n*Fields Included:*\n- Claim Year\n- HCP NPI\n- Drug Product Name\n- Outlier Indicator\n- Rx Event Type\n- Rx Event Score\n- Rx Event Score Decile\n- Rx Event Score Quintile\n- Rx Event Score Tercile\n- Rx Event Claims\n- Rx Total Claims\n- Date Updated\n\nReady to transform your strategy?\n\n To unlock the full power of Definitive Healthcare’s data, please engage with our team: databricks@definitivehc.com \n","terms_of_service":"https://www.definitivehc.com/terms-of-use","documentation_link":"https://www.definitivehc.com/data-products","privacy_policy_link":"https://www.definitivehc.com/about","geographical_coverage":"[\"United States and Canada\"]","update_frequency":{"interval":1,"unit":"MONTHLY"},"collection_granularity":{"interval":1,"unit":"YEARLY"},"assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"Definitive Healthcare","description":"Definitive Healthcare is a data and analytics company focused on the business side of healthcare. The healthcare market is complex — our data makes it clearer. We cut through the noise to deliver the insights you need to make smarter, faster, more strategic decisions. Because when you succeed, healthcare gets better for everyone."},"popularity":372},{"id":"1d6caf7a-e3b3-44ad-a2ad-53b6a9f20022","summary":{"name":"Taskmaster OmniVox — Long-Form Conversational Audio — Full Dataset","subtitle":"24+ hours of long-form conversational speech with transcripts and speaker diarization metadata, available by request.","setting":{"visibility":"PUBLIC"},"created_at":1790419558909,"updated_at":1790419558909,"categories":["EDUCATION","MEDIA","OTHER","SCIENCE_AND_RESEARCH"],"listingType":"PERSONALIZED","provider_id":"51e1bafd-2b96-40fb-b5bd-e9d3531279e9"},"detail":{"description":"## Overview\n\nTaskmaster OmniVox — Long-Form Conversational Audio — Full Dataset provides access to a larger licensed collection of long-form conversational speech recordings with corresponding transcript and speaker-diarization metadata.\n\nThe commercial product contains **24+ hours of audio** across the current P01 release candidate and is intended for organizations requiring a larger speech-data resource for research, development, evaluation, and production-oriented workflows.\n\nAccess to the full dataset is provided **by request** and requires provider review and approval.\n\n## Dataset Contents\n\nThe current P01 commercial release contains:\n\n* 24+ hours of long-form conversational audio\n* WAV audio recordings\n* Corresponding transcript metadata in JSON format\n* Speaker-diarization metadata in JSON format\n* Dataset manifest in CSV format\n* Complete episode-level relationships between audio and associated metadata\n* Integrity-validated release artifacts\n\nThe commercial release is substantially larger than the approximately 5-hour free Marketplace sample.\n\n## Use Cases\n\nThe dataset is suitable for workflows including:\n\n* Automatic speech recognition (ASR) research and evaluation\n* Conversational AI development\n* Long-form speech-to-text processing\n* Speech analytics\n* Speaker diarization\n* Speaker-aware conversational processing\n* Speech segmentation\n* Audio preprocessing and pipeline development\n* Evaluation of speech-processing systems\n* Research involving extended conversational speech\n\n## Access Model\n\nThis listing is **By request**.\n\nConsumers submit an access request through Databricks Marketplace. The provider reviews the request and communicates with the consumer regarding applicable commercial terms, licensing, intended use, and fulfillment requirements.\n\nAccess to the commercial dataset is not granted automatically when the request is submitted.\n\nAfter the applicable commercial arrangements are completed and the request is approved, the provider creates or selects the appropriate data share and fulfills the request through Databricks Marketplace.\n\n## Relationship to the Free Sample\n\nA separate free Marketplace listing provides an approximately 5-hour sample of the P01 dataset for evaluation.\n\nThe free sample and this commercial listing are separate products and access paths.\n\nThe free sample does not provide access to the complete commercial dataset.\n\n## Data Organization\n\nThe commercial release contains three primary data components:\n\n* Audio recordings\n* Transcript metadata\n* Speaker-diarization metadata\n\nA release manifest provides the mapping between recordings and their associated metadata.\n\n## Data Validation\n\nThe commercial release has undergone release-candidate validation including:\n\n* File inventory reconciliation\n* Audio integrity validation\n* WAV header validation\n* Duration validation\n* Transcript metadata validation\n* Speaker-diarization metadata validation\n* Manifest reconciliation\n* Physical file mapping\n* SHA-256 integrity verification\n\nThe commercial release currently represents a validated 24+ hour release candidate.\n\n## Licensing and Commercial Access\n\nUse of the dataset is subject to the applicable source-data licenses, commercial agreement, Marketplace terms, and any additional provider terms applicable to the approved request.\n\nConsumers should review the applicable licensing and usage requirements before using or redistributing the data.\n\n## Important Scope Note\n\nThe 24+ hour figure describes the current commercial release candidate and should not be interpreted as the total historical inventory of all P01 source material.\n\nThe approximately 5-hour Marketplace sample is a separate verified subset intended for evaluation before requesting commercial access.\n\n## Support\n\nFor commercial access, licensing questions, intended-use discussions, or fulfillment requirements, submit a request through this Marketplace listing. The provider will review the request and communicate directly regarding the next steps.\n","terms_of_service":"https://taskmastervirtualally.com/Terms-and-Conditions","documentation_link":"https://docs.google.com/document/d/1CdDnn6Q1IIfUjC_T9RMxBobQB13rgRg5fkqeFSG8rZw/edit?usp=sharing","privacy_policy_link":"https://taskmastervirtualally.com/privacy-statement","assets":["ASSET_TYPE_MEDIA"],"license":"https://docs.google.com/document/d/1I64drTpwgIQvRuSvQjPTQPerOvPMEG7FpnXqPvjEKhc/edit?usp=sharing"},"provider_summary":{"name":"Taskmaster Virtual Ally LLC","description":"Taskmaster Virtual Ally solves the AI Data Readiness Gap with commercially cleared speech, vision, and NLP datasets alongside managed Human Infrastructure as a Service (HIaaS) Pods. We provide 8,000+ hours of high-fidelity, multi-speaker Asian language speech assets (Indic TTS, Taglish ASR, Vietnamese), clinical HIPAA-ready transcriptions, and enterprise RLHF annotation. 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A CBSA represents a highly populated core area and adjacent communities that have a high degree of economic and social integration with the core.\n\nThis sample data set covers the state of California. This represents a subset of the full product’s geographic extent. The full commercial offering features geographic coverage of the entire United States. Contact Databricks.partnership@precisely.com for more information on licensing the product.\n**Use cases**\n**Location Data Enrichment**\nNationally consistent geographic boundary definitions for collection, tabulation, and publication of federal statistics and content\n**Location Planning**\nAssess needs and implement business decisions at a local geographic scale\n**Market Analysis**\nLocate customers and assets\n**Fundamental Analysis**\nView and identify patterns and trends\n\n**Product details**\nCBSAs consist of counties and county equivalents, and are defined in two categories:\nMetropolitan Statistical Areas\nMicropolitan Statistical Areas\n\nThe Core Based Statistical Areas (CBSAs) product uses County Boundaries as building blocks for CBSAs. Minor Civil Divisions (MCDs) are used to provide correspondence to New England City and Town Areas (NETCAs).\n\nCombined Statistical Areas (CSAs) are built from Core Based Statistical Area (CBSA) boundaries using information provided in OMB Bulletins.\n\n**Additional Insights**\nhttps://help.precisely.com/r/t/1014165105/2025-09-12/US-Census-Boundaries/pub/Latest/en-US/US-Census-Boundaries-Product-Guide/Core-Based-Statistical-Areas-CBSAs\n\nhttps://www.precisely.com/data-guide/products/core-based-statistical-areas-cbsa","terms_of_service":"https://www.precisely.com/legal/licensing/trial-evaluation-agreement-terms-u-s-may-2021","documentation_link":"https://help.precisely.com/r/t/1014165105/2025-09-12/US-Census-Boundaries/pub/Latest/en-US/US-Census-Boundaries-Product-Guide/Core-Based-Statistical-Areas-CBSAs","privacy_policy_link":"https://www.precisely.com/legal/privacy-policy","embedded_notebook_file_infos":[{"id":"7316216a-ecca-44a7-b57c-1dcf1f524943","marketplace_file_type":"EMBEDDED_NOTEBOOK","file_parent":{"parent_id":"0a2901d5-6145-4f07-88ba-7fe224141205","file_parent_type":"LISTING"},"mime_type":"text/html","download_link":"https://marketplace-file-storage-prod.s3-fips.us-east-1.amazonaws.com/EMBEDDED_NOTEBOOK/LISTING_0a2901d5-6145-4f07-88ba-7fe224141205/7316216a-ecca-44a7-b57c-1dcf1f524943?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEGgaDnVzLWVhc3QtMS1maXBzIkYwRAIgbC8sWIoYaHR8y7Wq86nPb0pufBl4%2BM4PWpeN%2B5PW9VQCIG7BhAVBDCWTpxFDLeuKYm%2Ff1JLz4iccPMzter0YdhgWKqYECDAQABoMNDE0MzUxNzY3ODI2IgxNAJrHbXaZG20jNfMqgwQyGEb4TsoVOoNvJ%2BaA%2FZ7zeu8hGcHqCeNfblP%2FQo5g6q2aRNtpwtPRKMlhYcIE%2FUQaNT1Lqmyptu32VUQTpzjTTxRVmJrDPkheSCkV06XQ%2FpTMKjU8CC7X1b%2FqYf8CrBwZT7Cv4C5WPjEET38f2A79h0xV6bZgPy28zQWrSeIJ9w6AfZBxiE24%2FVc08zVA0ivCT6lsy7nuU0wcR%2FMg2C9nd1vaMnQ923VpD21CKfVVYcrNWL2b%2FQ%2BNAO%2BxPjPgjiN6UPQbvbvZxkSdhgrGtecIg9NgqGkMcvM5VLTDtdC1kdeZetfOC2obtA%2BT%2F0gy3tIDo6WM5hcQfw743%2FAU9oaGnRBl%2BwoxRRDCZA%2F4jnPdluICSI7NCr6C3HHOe4CSZJjRihczxPA%2FgQczAVjAPs6CrK9N9pIzt0vNJpGUTgg4AoxftTvGfciDREilSctPIcBk6GwB%2Bzt4wbtNCmEPU%2BFD46rc6CWVQmmLwbKOeHw70ipMVFlOc8NlXkEtGO2vu6H44MstSSc6daOVUYUavQSGZ6jGhEjsP2YGz8KDm9XQotB%2FtyVBqiZV01OKqDmmBJA%2FXjUpSJ8M%2FeJXhe5m%2BKcxzNObtoJRI7jci3Tpt08XZyXZ7%2FyIV8H30ya468X3cfkTXrm972uga3z7iXAdubyEHnQIua%2FuvtwOHILi6O5D1Me2MzCPvcnXBjqTAYDoMWBt%2B3AVFuqSs2dmDQNBnvEhXfMgTedKLD5PJHb3aIl15iBkLaNu36GYnVCp9ZU2lSYcTbY5QwNnNeTfJTNy9bKhwzru6Sg%2BslhN%2BTS0zkTn%2BeLMY4tHgPEtEZ2cp%2FCk6GwqswvnYVKHjwO8q%2B1Mx8lc08fVEfarE9dke1GGFqoddx%2Bq%2BZUyHbDlAVfrCmf0yw%3D%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20261110T000135Z&X-Amz-SignedHeaders=host&X-Amz-Expires=86400&X-Amz-Credential=ASIAWA6KKHEJORWBH4N3%2F20261110%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=2601440711a169788d0b5483b43d118b8a1d3505ffb987be006d03b77d6b81c0","created_at":1767946778276,"updated_at":1767946778276,"display_name":"Core Based Statistical Areas (CBSA) USA Notebook","status":"FILE_STATUS_PUBLISHED"}],"geographical_coverage":"[\"United States and Canada\"]","cost":"FREE","update_frequency":{"interval":1,"unit":"YEARLY"},"data_source":"","assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://www.precisely.com/legal/licensing/trial-evaluation-agreement-terms-u-s-may-2021"},"provider_summary":{"name":"Precisely","description":"As a global leader in data integrity, Precisely ensures that your data is accurate, consistent, and contextual. 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Learn more at www.precisely.com."},"popularity":1402},{"id":"9d592b38-cc9a-41e2-b4e7-3a378566411c","summary":{"name":"National Address Database | USDOT","subtitle":"Datasets for the National Address Database, covering 65M+ addresses, provided by the US Department of Transportation.","setting":{"visibility":"PUBLIC"},"created_at":1677701898978,"updated_at":1698965635294,"categories":["ADVERTISING_AND_MARKETING","PUBLIC_SECTOR","TRANSPORTATION_AND_LOGISTICS","GEOSPATIAL"],"listingType":"PERSONALIZED","provider_id":"f00071b1-2db4-4f8f-a4bc-4c62c790f25a"},"detail":{"description":"**USDOT - National Address Database**\n\nThis listing contains datasets related to the National Address Database provided by the US Department of Transportation (USDOT).\n\n*What is the National Address Database?*\nThe U.S. Department of Transportation (USDOT) and its partners from all levels of government recognize the need for a National Address Database (NAD). Accurate and up-to-date addresses are critical to transportation safety and are a vital part of Next Generation 9-1-1. They are also essential for a broad range of government services, including mail delivery, permitting, and school siting.\n\nTo meet this need, USDOT partners with address programs from state, local, and tribal governments to compile their authoritative data into the NAD. To date, the NAD contains data from 24 state government partners (with an additional 11 states in the queue), 17 local government partners, and 1 tribal government partner.\n\n*USDOT National Address Database (NAD) Partner Participation by State (as of May 2021)*\n\nTwenty eight (28) states have provided address data including: Alaska, Arizona, Arkansas, Colorado, Connecticut, Delaware, District of Columbia, Indiana, Iowa, Kansas, Maine, Maryland, Massachusetts, Montana, New Jersey, New Mexico, New York, North Carolina, Ohio, Oregon, Rhode Island, Tennessee, Texas, Utah, Vermont, Virginia, West Virginia, and Wisconsin.\n\n*Disclaimer*\nThe National Address Database (NAD) is, with few exceptions, aggregated from state data, which is in turn aggregated from local data. While it is considered to be authoritative and every reasonable effort is made to ensure the accuracy and completeness of the data, the United States Department of Transportation (USDOT) makes no warranties, expressed or implied, concerning the accuracy, completeness or suitability of the data, and it should not be construed or used as a legal description. Read More \n\n**Use Cases**\n1. Inspect Zip Codes at a State Level\n2. Get Residential Addresses in a particular street and zip code\n3. Apply GeoLocation distances to get addresses under a certain distance of a target address: For example, addresses < 1 km away from 3707 Newark St NW, Washington, DC 20016\n\n**Source**\nhttps://www.transportation.gov/gis/national-address-database/national-address-database-0 \n\n**Cadence**\nRevisions will be updated when a new update is published by USDOT.","terms_of_service":"https://rearc-data-public-assets.s3.amazonaws.com/Rearc_Data_DSA.pdf","documentation_link":"","contact_email":"data@rearc.io","support_link":"","privacy_policy_link":"https://rearc-data-public-assets.s3.amazonaws.com/Rearc+Privacy+Policy.pdf","assets":["ASSET_TYPE_DATA_TABLE"]},"provider_summary":{"name":"Rearc","description":"Rearc provides a vast library of data products for Healthcare, Financial Services, and ESG use cases. Whether you're looking for genomes or asset prices, there is a Rearc data product to address your team's data needs. 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This information can be beneficial for businesses that are considering expansion or for start-ups deciding where to establish their operations.\n**Customer Acquisition**\nUnderstanding the employment landscape of a neighborhood can help businesses target potential customers more effectively. For instance, businesses selling professional attire might target neighborhoods with high concentrations of corporate jobs.\n**Location Data Enrichment**\nEmployment data can provide valuable context to location intelligence, offering insights into the socioeconomic factors that influence a neighborhood. This can enhance decision-making processes across various business needs.\n**Risk Analysis**\ncan be useful in risk assessment, particularly for financial institutions. For example, areas with high unemployment rates might indicate higher risk for loan defaults.\n**Economic Impact Analysis**\nBusinesses can use neighborhood employment data to assess the potential economic impact of their operations in a certain area. This could be particularly relevant for businesses with significant local economic footprints, such as manufacturing or retail.\n**Demand Forecasting**\nUnderstanding the employment and income levels in a neighborhood can aid in forecasting demand for a company's products or services. 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This dataset incorporates Hospital Referral Region (HRR) level data that covers demographic, cost utilization and quality data for all ages, ages 18 to 65 and above 65 years by state and national level.\n\n**Description**\nThis data package contains the dataset includes state and county level data that covers demographic, cost utilization and quality data. The data was collected from the CMS Chronic Conditions Data Warehouse, which contains 100 percent of Medicare claims for beneficiaries who are enrolled in the fee-for-service (FFS) program as well as enrollment and eligibility data and facilitates analyzing differences in cost and/or utilization for the settings inpatient hospital, outpatient hospital, multiple post-acute care settings, hospice, physicians, laboratories and suppliers of durable medical equipment. The dataset incorporates measures on Prevention Quality Indicators of the Agency for Healthcare Research and Quality, that includes hospital admission rates for ambulatory care sensitive conditions.\n\n**Benefits**\n- Useful to researchers and analysts who seek to compare, understand and find useful information about the geographical variations of medicare beneficiaries demographics, healthcare services utilization and quality. useful to public health specialists and specialized institutions to evaluate the type of beneficiaries and the demand for and quality of the healthcare services covered by medicare. the fields descriptions along with the dataset description are again useful for the user to quickly understand the data and the dataset.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [Medicare Cost Adults With Utilization And Quality Indicators by HRR](https://www.johnsnowlabs.com/marketplace/medicare-cost-adults-with-utilization-and-quality-indicators-by-hrr)\n\t- This dataset shows the Geographic Variation Public Use File that serves as an evaluation of the utilization and quality of healthcare services according to the geographic area of the population covered by Medicare. This dataset incorporates hospital referral region (HRR) level data that covers demographic, cost utilization and quality data for ages 18 to 65.\n- [Medicare Cost Adults With Utilization And Quality Indicators by State](https://www.johnsnowlabs.com/marketplace/medicare-cost-adults-with-utilization-and-quality-indicators-by-state)\n\t- This dataset contains State data for Medicare beneficiaries of ages 18-65 years old. The dataset includes state and county level data that covers demographic, cost utilization and quality data for all ages. The Geographic Variation Public Use File serve as an evaluation of the utilization and quality of healthcare services according to the geographic area of the population covered by Medicare.\n- [Medicare Cost All Beneficiaries Utilization Quality Indicators State](https://www.johnsnowlabs.com/marketplace/medicare-cost-all-beneficiaries-utilization-quality-indicators-state)\n\t- This dataset contains State data for all Medicare beneficiaries regardless of age. The dataset includes state and county level data that covers demographic, cost utilization and quality data for all ages. The Geographic Variation Public Use File serve as an evaluation of the utilization and quality of healthcare services according to the geographic area of the population covered by Medicare.\n- [Medicare Cost All Beneficiaries Utilization Quality Indicators by HRR](https://www.johnsnowlabs.com/marketplace/medicare-cost-all-beneficiaries-utilization-quality-indicators-by-hrr)\n\t- This dataset shows the Geographic Variation Public Use File that serves as an evaluation of the utilization and quality of healthcare services according to the geographic area of the population covered by Medicare. This dataset incorporates hospital referral region (HRR) level data that covers demographic, cost utilization and quality data for all beneficiaries regardless of age.\n- [Medicare Cost Geriatric With Utilization And Quality Indicators State](https://www.johnsnowlabs.com/marketplace/medicare-cost-geriatric-with-utilization-and-quality-indicators-state)\n\t- This dataset contains State data for Medicare beneficiaries of 65 years or older. The dataset includes state and county level data that covers demographic, cost utilization and quality data for all ages. The Geographic Variation Public Use File serve as an evaluation of the utilization and quality of healthcare services according to the geographic area of the population covered by Medicare.\n- [Medicare Cost Geriatric With Utilization And Quality Indicators by HRR](https://www.johnsnowlabs.com/marketplace/medicare-cost-geriatric-with-utilization-and-quality-indicators-by-hrr)\n\t- This dataset shows the Geographic Variation Public Use File that serves as an evaluation of the utilization and quality of healthcare services according to the geographic area of the population covered by Medicare. This dataset incorporates hospital referral region (HRR) level data that covers demographic, cost utilization and quality data for beneficiaries that are 65 or older.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated\n- Data Updates\n - Data updates support replace-on-update: outdated foreign keys are deprecated, not deleted\n\n**Our data is curated and enriched by domain experts** \n\n Each dataset is manually curated by our team of doctors, pharmacists, public health & medical billing experts:\n- Field names, descriptions, and normalized values are chosen by people who actually understand their meaning\n- Healthcare & life science experts add categories, search keywords, descriptions and more to each dataset\n- Both manual and automated data enrichment supported for clinical codes, providers, drugs, and geo-locations\n- The data is always kept up to date – even when the source requires manual effort to get updates\n- Support for data subscribers is provided directly by the domain experts who curated the data sets\n- Every data source’s license is manually verified to allow for royalty-free commercial use and redistribution.\n\n**Need Help?**\nIf you have questions about our products, contact us at [info@johnsnowlabs.com](mailto:info@johnsnowlabs.com). \n 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This spending includes hospital spending and is the total net revenue that is calculated as gross charges less contractual adjustments, bad debts, and charity care.\n- [US Healthcare Costs by Services and Residence](https://www.johnsnowlabs.com/marketplace/us-healthcare-costs-by-services-and-residence)\n\t- This dataset categorizes healthcare expenditure by service and by state of residence. The columns are different services and the rows are the states. The categories of services include hospital care, physician and clinical services, other professional services, prescription drugs and other medical nondurables, nursing home care, dental services, home healthcare, medical durables and other health, residential and personal care.\n- [US Healthcare Spending by State](https://www.johnsnowlabs.com/marketplace/us-healthcare-spending-by-state)\n\t- This dataset identifies health care spending at medical services such as hospitals, physicians, clinics, and nursing homes etc. as well as for medical products such as medicine, prescription glasses and hearing aids. This dataset pertains to personal health care spending in general. 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This dataset not only covers comprehensive geographic and demographic data but also includes flags for membership in various regional groups like the EU, EZ, and others, making it an indispensable tool for regional analysis. It is a valuable resource for anyone needing comprehensive geospatial data and demographic data on Europe.\n\n## **Key Statistics:**\n\n- **Number of Municipalities:** 79,069\n- **Number of Regions:** 697\n- **Total Population:** 829,251,424\n- **Number of Males:** 401,445,694\n- **Number of Females:** 427,805,730\n- **Number of Children Aged Below 15:** 135,147,180\n- **Number of People Aged 15-64:** 539,294,010\n- **Number of People Aged 65 and Above:** 154,810,234\n- **Area in Sq Km:** 24,288,620.79\n- **Population Density per Sq Km:** 34.14\n\n### **Product Features:**\n\n- **Latest Data Collection**: Featuring data from the year 2023, meticulously compiled and updated as of April 2024 to ensure the highest accuracy and relevance.\n- **Geographic Precision**: Each entry includes detailed polygon shapes of the municipalities in EPSG: 4326 projection, ideal for spatial analysis and mapping.\n- **Rich Demographic Insights**: Features demographic details for each municipality, including:\n - Total Population,\n - Gender Breakdown (Males, Females),\n - Age Groups (0-14, 15-64, 64+), and\n - Population Density.\n- **Regional Membership Flags**: Easily identify municipalities by their regional affiliations with flags for:\n - European Union (EU)\n - Eurozone (EZ)\n - Balkan Countries\n - Baltic Countries\n - Scandinavian Countries\n - BeNeLux Countries\n- **Regional Information**: Data is segmented by Country Code, Country Name, and Region, enhancing regional analysis capabilities.\n- **Multiple Data Formats**: Each dataset is available in both shapefile and CSV format, ensuring compatibility with major GIS software and other data handling tools.\n- **Subset Availability**: For targeted analysis, subsets of the data are available for instant download including regions such as the [European Union](geolocet.com/products/european-union-geodemographics-dataset-with-boundaries), [Eurozone](https://geolocet.com/products/eurozone-geodemographics-dataset-with-boundaries), [Baltic countries](https://geolocet.com/products/baltic-countries-geodemographics-dataset-with-boundaries), [BeNeLux](https://geolocet.com/products/benelux-geodemographics-dataset-with-boundaries), [Scandinavian Countries](https://geolocet.com/products/scandinavian-countries-geodemographics-dataset-with-boundaries), and [Balkan Countries](https://geolocet.com/products/balkan-countries-geodemographics-dataset-with-boundaries). Each country within Europe is also available as a standalone dataset.\n- **Sample Data**: Test the quality and compatibility of our data with a free sample available for download.\n\n### **Access & Usage:**\n\n- Instant download upon purchase for all datasets.\n- Free sample available for instant data evaluation.\n\nElevate your geographical and demographic analysis with the most thorough and accessible dataset available on the European municipalities — Geolocet’s flagship product.\n\n---\n## **Coverage Details:**\n- **[Albania](https://geolocet.com/products/Albania-geodemographics-dataset-with-boundaries)**: 2,762K people\n- **[Andorra](https://geolocet.com/products/Andorra-geodemographics-dataset-with-boundaries)**: 85K people\n- **[Austria](https://geolocet.com/products/Austria-geodemographics-dataset-with-boundaries)**: 9,105K people\n- **[Belarus](https://geolocet.com/products/Belarus-geodemographics-dataset-with-boundaries)**: 9,373K people\n- **[Belgium](https://geolocet.com/products/Belgium-geodemographics-dataset-with-boundaries)**: 11,686K people\n- **[Bosnia](https://geolocet.com/products/Bosnia-geodemographics-dataset-with-boundaries)**: 3,357K people\n- **[Bulgaria](https://geolocet.com/products/Bulgaria-geodemographics-dataset-with-boundaries)**: 6,448K people\n- **[Croatia](https://geolocet.com/products/Croatia-geodemographics-dataset-with-boundaries)**: 4,009K people\n- **[Czechia](https://geolocet.com/products/Czechia-geodemographics-dataset-with-boundaries)**: 10,524K people\n- **[Denmark](https://geolocet.com/products/Denmark-geodemographics-dataset-with-boundaries)**: 5,933K people\n- **[Estonia](https://geolocet.com/products/Estonia-geodemographics-dataset-with-boundaries)**: 1,322K people\n- **[Finland](https://geolocet.com/products/Finland-geodemographics-dataset-with-boundaries)**: 5,548K people\n- **[France](https://geolocet.com/products/France-geodemographics-dataset-with-boundaries)**: 67,733K people\n- **[Germany](https://geolocet.com/products/Germany-geodemographics-dataset-with-boundaries)**: 83,237K people\n- **[Greece](https://geolocet.com/products/Greece-geodemographics-dataset-with-boundaries)**: 10,370K people\n- **[Guernsey](https://geolocet.com/products/Guernsey-geodemographics-dataset-with-boundaries)**: 64K people\n- **[Hungary](https://geolocet.com/products/Hungary-geodemographics-dataset-with-boundaries)**: 9,600K people\n- **[Iceland](https://geolocet.com/products/Iceland-geodemographics-dataset-with-boundaries)**: 388K people\n- **[Ireland](https://geolocet.com/products/Ireland-geodemographics-dataset-with-boundaries)**: 5,057K people\n- **[Isle of Man](https://geolocet.com/products/Isle-of-Man-geodemographics-dataset-with-boundaries)**: 87K people\n- **[Italy](https://geolocet.com/products/Italy-geodemographics-dataset-with-boundaries)**: 58,871K people\n- **[Jersey](https://geolocet.com/products/Jersey-geodemographics-dataset-with-boundaries)**: 103K people\n- **[Kosovo](https://geolocet.com/products/Kosovo-geodemographics-dataset-with-boundaries)**: 1,770K people\n- **[Latvia](https://geolocet.com/products/Latvia-geodemographics-dataset-with-boundaries)**: 1,876K people\n- **[Liechtenstein](https://geolocet.com/products/Liechtenstein-geodemographics-dataset-with-boundaries)**: 40K people\n- **[Lithuania](https://geolocet.com/products/Lithuania-geodemographics-dataset-with-boundaries)**: 2,857K people\n- **[Luxembourg](https://geolocet.com/products/Luxembourg-geodemographics-dataset-with-boundaries)**: 661K people\n- **[Macedonia](https://geolocet.com/products/Macedonia-geodemographics-dataset-with-boundaries)**: 1,837K people\n- **[Malta](https://geolocet.com/products/Malta-geodemographics-dataset-with-boundaries)**: 536K people\n- **[Moldova](https://geolocet.com/products/Moldova-geodemographics-dataset-with-boundaries)**: 2,618K people\n- **[Monaco](https://geolocet.com/products/Monaco-geodemographics-dataset-with-boundaries)**: 36K people\n- **[Montenegro](https://geolocet.com/products/Montenegro-geodemographics-dataset-with-boundaries)**: 642K people\n- **[Netherlands](https://geolocet.com/products/Netherlands-geodemographics-dataset-with-boundaries)**: 17,811K people\n- **[Norway](https://geolocet.com/products/Norway-geodemographics-dataset-with-boundaries)**: 5,489K people\n- **[Poland](https://geolocet.com/products/Poland-geodemographics-dataset-with-boundaries)**: 37,770K people\n- **[Portugal](https://geolocet.com/products/Portugal-geodemographics-dataset-with-boundaries)**: 10,467K people\n- **[Romania](https://geolocet.com/products/Romania-geodemographics-dataset-with-boundaries)**: 19,049K people\n- **[Russia](https://geolocet.com/products/Russia-geodemographics-dataset-with-boundaries)**: 146,400K people\n- **[Serbia](https://geolocet.com/products/Serbia-geodemographics-dataset-with-boundaries)**: 6,651K people\n- **[Slovakia](https://geolocet.com/products/Slovakia-geodemographics-dataset-with-boundaries)**: 5,795K people\n- **[Slovenia](https://geolocet.com/products/Slovenia-geodemographics-dataset-with-boundaries)**: 2,117K people\n- **[Spain](https://geolocet.com/products/Spain-geodemographics-dataset-with-boundaries)**: 45,136K people\n- **[Sweden](https://geolocet.com/products/Sweden-geodemographics-dataset-with-boundaries)**: 10,612K people\n- **[Switzerland](https://geolocet.com/products/Switzerland-geodemographics-dataset-with-boundaries)**: 8,797K people\n- **[Turkey](https://geolocet.com/products/Turkey-geodemographics-dataset-with-boundaries)**: 85,372K people\n- **[UK](https://geolocet.com/products/UK-geodemographics-dataset-with-boundaries)**: 68,254K people\n- **[Ukraine](https://geolocet.com/products/Ukraine-geodemographics-dataset-with-boundaries)**: 40,998K people\n\n\n## **Customize Your Data Experience**\n\nNeed the data in another format? Your requirements drive our solutions. Reach out to us at **contact@geolocet.com** for alternative formats or if you have specific data needs that go beyond the downloadable list of Germany locations available on our platform.\n\n## Explore further with our extensive retail datasets\n\nIf you are exploring other brands and require additional data, we encourage you to **contact@geolocet.com**. Our comprehensive datasets and dedicated team stand ready to assist you.\n","terms_of_service":"https://geolocet.com/pages/terms-of-use","documentation_link":"","privacy_policy_link":"https://geolocet.com/pages/privacy-policy","geographical_coverage":"[\"Europe\"]","cost":"PAID","data_source":"","assets":["ASSET_TYPE_MEDIA"],"license":"https://geolocet.com/pages/terms-of-use"},"provider_summary":{"name":"Geolocet LTD","description":"Geolocet provides premium geospatial data for retail analysis, GIS, and market planning. We equip businesses with accurate, ready-to-query data to drive spatial analysis, site selection, and customer segmentation.\n\nCore Offerings:\n\nDemographics: Socioeconomic data covering population, age, and employment across diverse geographies.\n\nPoints of Interest (PoI): Datasets spanning retail, healthcare, and transport for catchment analysis.\n\nAdministrative Boundaries: GIS-ready boundaries, including municipalities and postcodes, for territory management.\n\nBrand Locations: Chain location datasets to monitor store networks and market expansion.\n\nWealth Data: Granular economic indicators like purchasing power to identify high-potential markets.\n\nWe source from trusted providers, delivering highly customizable datasets with comprehensive coverage across Europe and worldwide."},"popularity":675},{"id":"a25644a4-1935-4aff-b505-592df691dc6d","summary":{"name":"SAMPLE Global Insurance Data | Analyze Insurance Trends, Consumer Behaviors and Demographic ...","subtitle":"This dataset captures insurance behavior, preferences, and satisfaction alongside demographics like location, ...","share":{"name":"rwazi_4eaa3ffd_e078_45bf_99f5_2c5447d68d99_samples_share","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1743439834125,"updated_at":1743521219666,"categories":["ECONOMICS","ADVERTISING_AND_MARKETING"],"listingType":"STANDARD","provider_id":"0296f22e-95af-40d9-b392-ea64d68cec1b"},"detail":{"description":"Consumer Insurance Experience & Demographic Profile\r\n\r\nThis dataset provides a detailed view of how individuals engage with insurance products, paired with demographic and lifestyle attributes to enable powerful segmentation, behavioral analysis, and customer journey mapping. By combining real-world insurance experiences with contextual information about each respondent’s background and preferences, this dataset supports a wide range of data-driven decision-making for insurance providers, policy designers, marketing teams, and product strategists.\r\n\r\nValue of the Dataset\r\nUnderstanding how consumers perceive and interact with insurance offerings is critical to building products that resonate and services that retain. This dataset offers that visibility across multiple dimensions—capturing not only what type of insurance consumers hold and how they purchased it, but also what drives their satisfaction, loyalty, and likelihood to switch. Paired with demographic details like income, education, family status, and lifestyle, this information becomes a foundation for more personalized outreach, better-designed offerings, and improved customer experiences.\r\n\r\nBecause the data reflects lived experiences across diverse markets, it is particularly valuable for benchmarking consumer sentiment in emerging economies, identifying service delivery gaps, or evaluating potential uptake of new policy formats such as digital or personalized insurance.\r\n\r\nExample Use Cases\r\n1. Targeted Product Design\r\nA health insurer looking to launch short-term, digital-first plans could filter this dataset for consumers with low policy tenure, high digital communication preference, and dissatisfaction with current providers. This segment would inform feature design and positioning.\r\n\r\n2. Competitive Analysis\r\nA provider evaluating churn risk can identify patterns among users who have filed claims but report dissatisfaction—indicating operational areas that may be driving customer loss and where improvements could increase retention.\r\n\r\n3. Communication Channel Optimization\r\nBy analyzing preferred communication methods across different demographic segments, insurers can tailor outreach strategies (e.g., SMS vs. in-app chat) to improve engagement and reduce support costs.\r\n\r\n4. Market Expansion & Localization\r\nInternational insurers can explore regional variations in satisfaction drivers, awareness levels, and price sensitivity to refine go-to-market strategies in countries like Senegal, Tanzania, or the UAE.\r\n\r\n5. Personalized Policy Offer Design\r\nUsing data on interest in personalized policies and lifestyle indicators, providers can build customizable offerings for consumers more likely to value flexibility, such as frequent travelers or those with irregular incomes.\r\n\r\nInsurance-Specific Fields & Descriptions\r\nCurrent Insurance Type\r\nCaptures the kind of insurance the individual currently holds, with a focus on health insurance in this dataset.\r\n\r\nPurchase Method\r\nIndicates how the insurance was obtained—through an agent, online, employer, etc.—to understand acquisition channels.\r\n\r\nPolicy Length\r\nDuration of the current policy, categorized (e.g., less than 1 year, 1–3 years, more than 5 years) to analyze tenure-based behaviors.\r\n\r\nSatisfaction\r\nSelf-reported satisfaction with the current insurance provider, useful for benchmarking sentiment.\r\n\r\nTop Factor in Choosing Provider\r\nHighlights what influenced the purchase decision most—such as coverage options, customer service, pricing, or brand reputation.\r\n\r\nPolicy Review Frequency\r\nShows how often individuals revisit their policy details or compare with alternatives, revealing levels of engagement or passive behavior.\r\n\r\nFiled Claim\r\nA yes/no indicator showing whether the consumer has ever filed a claim, useful for analyzing downstream service experiences.\r\n\r\nClaim Satisfaction\r\nMeasures satisfaction with how past claims were handled, providing insight into operational effectiveness.\r\n\r\nPrimary Value Sought\r\nCaptures what consumers value most from their insurance—e.g., peace of mind, financial protection, access to quality care.\r\n\r\nLikelihood to Recommend\r\nActs as a proxy for Net Promoter Score (NPS), indicating brand advocacy and potential referral behavior.\r\n\r\nBiggest Areas for Improvement\r\nOpen-ended or multi-select responses identifying where insurers can do better—lower premiums, faster claims, more digital tools, etc.\r\n\r\nPreferred Method of Communication\r\nIndicates how consumers want to be contacted—via online chat, phone, email, SMS—supporting channel strategy optimization.\r\n\r\nPreferred Services\r\nDetails the types of updates or services consumers want—such as claims status, policy changes, or coverage recommendations.\r\n\r\nInsurance Awareness Score\r\nSelf-reported awareness of how insurance works, including policy options, rights, and terms.\r\n\r\nInterest in Personalized Policies\r\nCaptures whether the individual is open to customized insurance plans, an important indicator for usage-based or modular policy adoption.","terms_of_service":"https://www.rwazi.com/privacy-policy","contact_email":"marketplace@datarade.ai","privacy_policy_link":"https://www.rwazi.com/privacy-policy","assets":["ASSET_TYPE_DATA_TABLE"]},"provider_summary":{"name":"Rwazi","description":"Rwazi is a global market intelligence platform operating across 190+ countries, leveraging AI and a network of 1.5M+ consumers to deliver real-time, zero-party data. With a presence spanning diverse markets worldwide, Rwazi captures granular insights on purchasing behavior, pricing trends, and product availability. Its cutting-edge computer vision technology extracts high-precision data from images and videos, enhancing accuracy and depth in consumer insights. The AI-driven platform transforms raw data into actionable intelligence, helping brands predict demand shifts, refine competitive positioning, and drive growth. By combining advanced AI with direct consumer input, Rwazi provides businesses with consent-based, real-time data to optimize market strategies and navigate an ever-changing global landscape."},"popularity":1863},{"id":"d834ea16-cbc3-47d8-aaab-8fc584c56590","summary":{"name":"MBI Area Boundaries for Bosnia and Herzegovina – Sample","subtitle":"MBI's precise & seamless boundaries for well-founded decisions in Bosnia and Herzegovina. Free trial data.","share":{"name":"share_boundaries_mbi_ba_r_2025","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1764667771109,"updated_at":1784894350523,"categories":["ADVERTISING_AND_MARKETING","GEOSPATIAL","MEDIA","OTHER","PUBLIC_SECTOR","RETAIL","TRANSPORTATION_AND_LOGISTICS"],"listingType":"STANDARD","provider_id":"aa09abd6-ff7f-4452-803d-b8c43b4c07cb"},"detail":{"description":"MBI'S GLOBAL AREA BOUNDARIES COVER MOST COUNTRIES WORLDWIDE AND **CAN BE LICENSED FOR EACH COUNTRY INDIVIDUALLY** AND IN BUNDLES.\n[MBI](https://www.mbi-geodata.com/en/) digital boundaries are available for virtually all countries worldwide.* Created by our experts according to consistent global standards, the polygons are so precise and harmonized that they align seamlessly across borders. The territorial vintages of the worldwide area boundaries are compatible with our globally consistent and comparable [socio-demographic](https://www.mbi-geodata.com/en/sociodemographic-data/) and [economic](https://www.mbi-geodata.com/en/purchasing-power-overview/) data.\nOur area boundaries for Bosnia and Herzegovina are offered at the following levels:\n\n- **Postal Code Level** (Postcode 5-digit): This fine-grained level of detail makes the data suitable for a wide range of applications – for example, enriching customer databases, which are often organized by postal code.\n- **Administrative Level** (Gminy/Miasta): Covers both larger administrative areas (e.g., regions, states, provinces) as well as finer subdivisions such as districts, counties, or municipalities.\n- **Micro Level** (Census Enumeration Areas): Subdivisions of postal code or administrative boundaries, such as census areas or street segments. This is the most granular level for local analysis. | on request\n\n**Use Cases:**\n\n- **Market Analysis**: Use MBI’s boundaries to visualize data on a map, learn about your customers, and uncover untapped market potential.\n- **360-Degree Customer View**: Link boundaries to customer data, sociodemographic, and economic data to gain a holistic understanding of customer needs, behaviors, and potential value.\n- **Location Planning**\n•\tUse MBI’s boundaries to visualize data on a map, learn about your customers\n•\tPlan the provision of services and products across different regions\n•\tDefine sales territories\n•\tVisualize catchment areas and plan actions to reach potential customers\n\n**Additional Insights:**\n[About Area Boundaries](https://www.mbi-geodata.com/en/area-boundaries/)\n\n**Data Structure:**\nThe area boundaries include the following variables:\nCTRYCODE: Two-digit country code by ISO 3166\nNAME: Name of administrative area / Name of the most populous administrative area / Name of the respective micro-area or the next higher level\nADMINCODE / POSTCODE / MICROCODE: Administrative code / Postal code / Micro-code\nWKT: Geography as Well Known Text (lat/long WGS84)\n\n*with only a few specific exceptions","terms_of_service":"https://service-mbi-geodata.com/s/HNp7Z2tws4WDqWd","documentation_link":"https://www.mbi-geodata.com/en/mbi-supplier-manufacturer-worldwide-geodata/europe/bosnia-and-herzegovina-maps/","privacy_policy_link":"https://www.mbi-geodata.com/en/data-protection/","geographical_coverage":"[\"Global\"]","cost":"FREE","update_frequency":{"interval":1,"unit":"YEARLY"},"collection_granularity":{"interval":1,"unit":"YEARLY"},"collection_date_start":1704101314317,"collection_date_end":1735637323381,"data_source":"MBI","assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://service-mbi-geodata.com/s/rfEXex72zgLdnw4"},"provider_summary":{"name":"Michael Bauer International GmbH – MBI Geodata","description":"For over 15 years, Michael Bauer International (MBI) has been a leading provider of globally consistent and comparable geographic data, including more than 3 million postcode areas and other detailed datasets like sociodemographics and purchasing power. MBI's high-quality small-scale data spans 248 countries, supporting businesses across all industries in location planning, branch networks, and distribution strategies. Recognized as a key data source, MBI is the preferred provider for leading GIS software and technology companies worldwide as well as one of the first global distributors for HERE Technologies. MBI is part of the Michael Bauer Group, alongside Michael Bauer Research GmbH and Michael Bauer Micromarketing GmbH."},"popularity":109},{"id":"263dcbf4-92c7-4470-a1bd-acca02f53c50","summary":{"name":"Adobe CX Enterprise Coworker (Beta)","subtitle":"Ask questions, uncover insights, and execute workflows in Adobe CX Enterprise directly from your Databricks Genie space.","setting":{"visibility":"PUBLIC"},"created_at":1793101962632,"updated_at":1793286013157,"categories":["ADVERTISING_AND_MARKETING"],"listingType":"STANDARD","provider_id":"bd17242f-83f4-4699-b4be-548f75ee5c4f"},"detail":{"description":"Adobe CX Enterprise Coworker brings Adobe’s agentic capabilities into your flow of work in Databricks Genie. Using natural language, tell Coworker what you want to do and Coworker understands your intent and takes action in compatible Adobe CX Enterprise applications you have licensed—including Real-Time CDP, Adobe Journey Optimizer, Customer Journey Analytics, Adobe Experience Manager, Adobe Workfront, and more. This helps teams move faster and work smarter across customer experience workflows. To use this offering, your organization must already have access to Coworker in your Adobe environment. If you don’t yet have access, please contact your Adobe representative.\n\n\nLanguage Support: Adobe CX Enterprise Coworker is currently supported in English only. Non-English inputs may produce inconsistent or erroneous results.\n\nPersonal Data: If you include personal data in your prompts, only add what's necessary and only if you have the right to use it.\n\nSign up: Please contact your Adobe admin.\n\nGet Started: Please access Adobe CX Enterprise Coworker in Databricks Genie for guided enablement.\n\nSupport: https://helpx.adobe.com/enterprise.html\n\nBy using Adobe CX Enterprise Coworker, you agree to the privacy policy, terms of use, and permissions.","terms_of_service":"https://www.adobe.com/cc-shared/assets/pdf/legal/licenses-terms/pdf/ama-beta-tou-en.pdf","documentation_link":"https://experienceleague.adobe.com/en/docs/coworker/content/home","privacy_policy_link":"https://www.adobe.com/privacy/policy-linkfree.html","geographical_coverage":"[\"Global\"]","assets":["ASSET_TYPE_MCP"],"license":""},"provider_summary":{"name":"Adobe Inc.","description":"Empowering everyone to create. \n\nAdobe empowers everyone, everywhere to imagine, create, and bring any digital experience to life. From creators and students to small businesses, global enterprises, and nonprofit organizations — customers choose Adobe products to ideate, collaborate, be more productive, drive business growth, and build remarkable experiences."},"popularity":1977},{"id":"fbb67ea0-23cf-4eb4-9f24-250e33003565","summary":{"name":"SAMPLE Global Location Data & Foot Traffic Data for AI, Analytics, and Business Intelligence | ...","subtitle":"Quadrant provides privacy first global location data and foot traffic data for AI, analytics, and business ...","share":{"name":"quadrant_22b36c88_eddb_4798_8596_4e24828b375b_samples_share","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1791278477659,"updated_at":1791278477659,"categories":["GEOSPATIAL"],"listingType":"STANDARD","provider_id":"a8064e88-f58c-4540-97d8-309869635380"},"detail":{"description":"Quadrant provides privacy first, AI Ready global location data that help organizations understand how people interact with places across more than 200 countries and territories. Our enterprise grade datasets enable businesses to measure visitation trends, analyze mobility patterns, support AI models, improve location intelligence, and make confident data driven decisions.\n\nBuilt for analytics and AI, Quadrant's location data helps organizations uncover meaningful geographic patterns across industries including retail, commercial real estate, transportation, urban planning, financial services, market research, tourism, and consumer insights. Whether you are evaluating site performance, measuring market activity, building predictive models, or analyzing movement trends, our data provides a trusted foundation for scalable analysis.\n\nOur location data is privacy first, quality controlled, and designed to meet the needs of modern enterprises. Every dataset undergoes rigorous validation and continuous quality monitoring to ensure consistency, accuracy, and reliability while supporting responsible data practices.\n\nQuadrant offers flexible geographic coverage that adapts to your business requirements. Unlike providers limited to predefined geographic boundaries, we support reporting and analysis at the city, county, state, regional, and national levels, making it easier to analyze Points of Interest, compare markets, evaluate trade areas, and understand mobility across locations.\n\nKey benefits include:\n\nGlobal coverage across more than 200 countries and territories\nPrivacy first and quality controlled location data\nAI ready and analytics friendly datasets\nFlexible geographic reporting at the city, county, state, regional, and national levels\nHistorical data available from 2022 onwards\nTailored datasets aligned with your business requirements\nExpert technical support from our in house data specialists\n\n## Use Cases\n\n**Mobility Data and Movement Analysis**\nUse mobility data and movement data to understand how people move between locations, identify mobility patterns, analyze travel behavior, and measure changes in movement across cities, regions, and markets.\n\n**Footfall Analytics and Footfall Traffic**\nUse footfall analytics and footfall traffic data to measure visitation trends, understand visitor volumes, compare locations, analyze peak periods, and evaluate changes in physical activity around Points of Interest.\n\n**Site Selection and Location Analysis**\nUse location data to evaluate potential sites, compare markets, analyze trade areas, understand catchment areas, and identify geographic patterns that support site selection and expansion decisions.\n\n**Retail and Consumer Insights**\nAnalyze foot traffic, visitation trends, mobility data, and movement data to understand consumer behavior, compare store performance, measure market activity, and identify changes in customer visitation patterns.\n\n**Commercial Real Estate**\nUse location data and footfall analytics to understand property activity, evaluate trade areas, analyze surrounding movement patterns, measure visitation, and support commercial real estate analysis.\n\n**Transportation and Mobility Planning**\nUse mobility data and movement data to analyze travel patterns, understand transportation flows, evaluate mobility trends, and support transportation and urban planning initiatives.\n\n**Urban Planning**\nAnalyze location data, mobility data, and foot traffic data to understand movement across cities, evaluate activity patterns, support infrastructure planning, and develop data driven insights for urban environments.\n\n**Market Intelligence and Competitive Benchmarking**\nUse location data and footfall traffic data to compare markets, benchmark locations, analyze competitor activity, measure visitation trends, and identify geographic opportunities.\n\n**Catchment and Trade Area Analysis**\nAnalyze movement data and location data to understand where visitors come from, define catchment areas, evaluate trade areas, and measure the geographic reach of physical locations.\n\n**AI and Predictive Analytics**\nUse high quality location data and mobility data to support AI models, predictive analytics, machine learning applications, and data driven business intelligence.\n\n**Tourism and Destination Analysis**\nUse footfall analytics, mobility data, and movement data to understand visitation patterns, analyze destination activity, measure visitor flows, and evaluate changes in tourism activity.\n\n**Financial Services and Market Research**\nUse location data, foot traffic data, and movement data to support market research, geographic analysis, financial modeling, consumer insights, and location based business intelligence.\n\nQuadrant's location data and foot traffic data power a wide range of applications including market intelligence, consumer behavior analysis, site selection, competitive benchmarking, catchment analysis, mobility analytics, urban planning, location based services, and AI driven business intelligence. By combining global scale with flexible delivery and a strong commitment to data quality and privacy, we help organizations transform location intelligence into better business outcomes.","terms_of_service":"https://www.quadrant.io/privacy-policy","contact_email":"support@datarade.ai","privacy_policy_link":"https://www.quadrant.io/privacy-policy","assets":["ASSET_TYPE_DATA_TABLE"]},"provider_summary":{"name":"Quadrant","description":"Quadrant, an Appen company, is a global provider of privacy first mobile location data and Point of Interest (POI) data. Our data that helps enterprises, organizations, and innovators understand how people and places interact.\r\n\r\nWith coverage across 200+ countries and territories, 1+ billion opted in unique devices, and 50+ billion daily location events, our mobile location data delivers global scale for location intelligence. Our POI data spans 200+ countries and includes 300+ million places with 50+ verified attributes, enabling organizations to build better maps, search experiences, market intelligence, and location based products. We also offer POI validation, enrichment, and enhancement services to improve existing datasets.\r\n\r\nAs part of Appen, a global leader in AI training data, Quadrant combines global scale, enterprise grade data quality, and privacy first governance to help organizations build the next generation of location enabled products and services."},"popularity":12},{"id":"c76d15d0-af4b-4bfd-9a65-3ae6864a687d","summary":{"name":"For Providers: Acquire & Retain Patients","subtitle":"Applications spanning intake, reactivation, retention, and network leakage, with analytics on lakehouse data.","setting":{"visibility":"PUBLIC"},"created_at":1787601370780,"updated_at":1790101503373,"categories":["HEALTH","PUBLIC_SECTOR","SCIENCE_AND_RESEARCH"],"listingType":"PERSONALIZED","provider_id":"8a664186-91cb-4cc1-aa1f-080da273763d"},"detail":{"description":"**Overview**\nBuild patient acquisition and retention applications on your Databricks lakehouse. Coverage spans the full patient lifecycle: 24/7 intake and triage that converts inbound demand into booked appointments, proactive outreach that reactivates lapsed patients, engagement workflows that reduce no-shows and keep patients supported between visits, and referral loop management that keeps care in network. Every interaction writes back to Databricks, where call center analytics and propensity-to-engage models run on the unified record.\n\n**Use cases**\n- Convert inbound demand into booked appointments with 24/7 intake and triage\n- Reactivate lapsed patients through proactive, personalized outreach\n- Reduce no-shows and churn with automated reminders and rebooking\n- Reduce network leakage by closing referral loops end to end\n- Support conversation analytics on interaction data\n- Predict propensity to engage, including social determinants data where available\n\n**Additional Insights**\nBecause every intake, outreach, and referral interaction lands in the lakehouse, acquisition and retention outcomes are directly measurable: conversion rates, reactivation rates, no-show reduction, and leakage trends all run as queries on your data. \n\nThe workflows listed are representative; Concurrence builds custom applications for any workflow in this outcome area on an existing Databricks foundation.","terms_of_service":"https://www.concurrence.com/terms-of-service","documentation_link":"https://docs.concurrence.com","privacy_policy_link":"https://www.concurrence.com/privacy","assets":["ASSET_TYPE_DATA_TABLE","ASSET_TYPE_MODEL","ASSET_TYPE_MEDIA","ASSET_TYPE_NOTEBOOK"],"license":"","tags":[{"tag_name":"LISTING_TAG_TYPE_TASK","tag_values":["ASSERTION_STATUS","EMBEDDINGS","ENTITY_RESOLUTION","QUESTION_ANSWERING","NAMED_ENTITY_RECOGNITION","RELATION_EXTRACTION","TEXT_CLASSIFICATION","SUMMARIZATION","TEXT_GENERATION","TRANSLATION","ZERO_SHOT_CLASSIFICATION","CODE_GENERATION","AUTOMATIC_SPEECH_RECOGNITION","TEXT_TO_SPEECH","DOCUMENT_QUESTION_ANSWERING"]}]},"provider_summary":{"name":"Concurrence","description":"Concurrence is the platform for healthcare organizations to deploy custom AI for clinical and operational workflows on a unified data foundation."},"popularity":642},{"id":"c95f03f2-963d-41b7-9d23-7a104ef32d69","summary":{"name":"Public Companies Supply Chain Data Feed","subtitle":"Global trade data on public businesses for market research, risk analysis, and insights into goods, HS codes, trade lane","setting":{"visibility":"PUBLIC"},"created_at":1763632302611,"updated_at":1764660370670,"categories":["TRANSPORTATION_AND_LOGISTICS","PUBLIC_SECTOR","OTHER","MANUFACTURING","CLIMATE_AND_ENVIRONMENT","EDUCATION","ENERGY","FINANCIAL","GAMING","HEALTH","MEDIA","RETAIL","SCIENCE_AND_RESEARCH","SECURITY"],"listingType":"PERSONALIZED","provider_id":"695cd75c-a62d-4caa-b9ae-c3333b03d8af"},"detail":{"description":"\nThe Public Companies Supply Chain Data Feed provides deep visibility into the global trade activities of publicly listed companies and their subsidiaries. Designed for financial institutions, compliance teams, supply chain analysts, and risk professionals, this dataset brings together verified, structured, and enriched global shipment records to help users analyze sourcing patterns, customer dependencies, geopolitical exposure, compliance risks, and market trends.\n\nThis data feed includes billions of trade records sourced from customs filings, port data, and shipping manifests. Each record is standardized, entity-resolved, and enriched with company identifiers, parent–child hierarchies, transportation modes, ports, HS codes, and shipment-level commercial details.\n\nBy focusing specifically on public companies and their corporate families, the dataset becomes uniquely valuable for investment research, supply chain risk modeling, competitive intelligence, ESG due diligence, and bank-level trade finance assessments of listed entities.\n\n**Users & Key Use Cases**\nSuppliers, buyers, trade finance companies, logistics firms, and investment groups utilize this data to monitor investments, analyze market trends, discover new business opportunities, enhance or diversify supply chains and operations, mitigate risks, and keep track of competitors.\n\n**Information About the Sample Dataset**\nYour listing includes three sample formats, each representing a level of detail available in the full dataset.\n\n**- Format 3: Full Enriched Supply Chain View**\nThis is the most comprehensive sample structure, containing:\n- Shipper & consignee identifiers, addresses, and parent company links\n- Public company stock tickers & exchange metadata\n- Complete shipment attributes (value, weight, product, HS code)\n- Transportation & port details\n\nFields Included (abbreviated):\nShipment Date, Declaration Type, Trademo Shipment ID, HS Code, Product Description, Ports, Shipper/Consignee Standardized Name, Company IDs, Stock Tickers, Parent Company IDs, Quantity, Value, Weight.\n\n- **Format 2 : Standard Company-Level Shipment View**\nA simplified version useful for:\n- Trade lane analysis\n- Shipment value and volume modeling\n- Shipper–consignee mapping\n\nIncludes: company IDs, addresses, ports, HS codes, shipment value/weight, quantity, and standardized names.\n\n- **Format 1: Minimal Shipment Data**\nBest for:\n- Quick ingestion\n- Lightweight ETL\n- High-level shipment modeling and aggregation\n\nIncludes: Shipper & consignee names, addresses, shipment details, value, weight, and HS code.\n\n","terms_of_service":"https://www.trademo.com/terms","documentation_link":"","privacy_policy_link":"https://www.trademo.com/privacy-policy","geographical_coverage":"[\"Global\"]","cost":"FREE","data_source":"","assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"Trademo","description":"Trademo's vision is to build a single source of truth on global supply chains to help stakeholders discover new business opportunities, ensure compliance with trade regulations, build operational resilience, and ensure border security."},"popularity":1521},{"id":"18d69810-206f-421e-bf99-9d59c9b6b1ff","summary":{"name":"ESG Corporate Sustainability Intelligence Dataset","subtitle":"Enterprise-grade ESG intelligence covering 521 public companies with verified and estimated carbon data.","share":{"name":"sample_2023_2025_esg_corporate_sustainability_intelligence_dataset","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1768430066226,"updated_at":1768430066226,"categories":["CLIMATE_AND_ENVIRONMENT","COMMERCE","DEMOGRAPHICS","ECONOMICS","ENERGY","FINANCIAL","SCIENCE_AND_RESEARCH"],"listingType":"STANDARD","provider_id":"2d97eccd-0fcb-4935-8bdf-5c34065e0349"},"detail":{"description":"Enterprise-grade ESG intelligence covering 521 public companies with verified and estimated carbon data.\nThe ESG Corporate Sustainability Intelligence Dataset delivers a comprehensive, analysis-ready view of Environmental, Social, and Governance performance across 521 publicly traded companies spanning 11 industry sectors. Built using a proprietary scoring methodology, the dataset consolidates ESG metrics into a standardized, comparable structure optimized for large-scale analytics and machine learning workflows.\nThe dataset includes deep coverage across Environmental (carbon emissions, energy usage, waste), Social (diversity, workplace safety, labor practices), and Governance (board composition, executive compensation, ethics) dimensions. Carbon emissions data is 100% complete, with 36 companies verified directly from published corporate reports (e.g., Google, Apple, Amazon) and the remaining companies modeled using sector-calibrated estimates derived from EPA and IEA benchmarks. This balance of verified disclosures and transparent modeling enables robust climate risk analysis, portfolio construction, and regulatory reporting at scale.\nProvenance\nThis dataset is compiled exclusively from 100% publicly available sources, ensuring transparency, auditability, and regulatory alignment. Source materials include corporate sustainability disclosures, mandatory financial filings, and authoritative government datasets. No proprietary, scraped, or personal data is included.\nData Sources: - Corporate sustainability reports (publicly disclosed)\n- SEC filings (10-K, DEF 14A proxy statements)\n- EPA environmental databases (public records)\n- OSHA workplace safety records (public records)\n- Industry averages from EPA/IEA publications\nAll data points are tagged with their origin, clearly distinguishing verified disclosures from sector-calibrated estimates.\nAcknowledgements\nThe full production dataset is licensed by AIDC Data LLC under contract with Maths with Kanchana, a Kaggle Grandmaster in datasets.\nUse Cases\nThis dataset supports high-value analytics and machine learning workflows across finance, sustainability, and risk domains:\n•\tESG Portfolio Screening & Construction: Rank, filter, and weight equities using standardized ESG and carbon metrics.\n•\tClimate Risk & Carbon Exposure Analysis: Quantify Scope 1–3 emissions exposure across sectors and portfolios.\n•\tRegulatory Compliance Analytics: Support SEC Climate Disclosure and EU CSRD reporting with traceable ESG data.\n•\tPeer Benchmarking & Competitive Intelligence: Compare ESG performance across companies and industries.\n•\tCredit & Risk Modeling with ESG Factors: Enhance credit risk, default probability, and stress-testing models.\n•\tML-Based ESG Risk Scoring: Train predictive models for ESG risk classification and sustainability forecasting.\nColumn Dictionary\n(Representative examples; full schema includes 148 columns)\n•\tcompany_name (string): Legal name of the publicly traded company\n\n•\tticker (string): Stock ticker symbol\n\n•\tsector (string): Industry sector classification\n\n•\tesg_score_total (decimal): Composite ESG score (0–100)\n\n•\tenvironment_score (decimal): Environmental pillar score\n\n•\tsocial_score (decimal): Social pillar score\n\n•\tgovernance_score (decimal): Governance pillar score\n\n•\tscope1_emissions (decimal): Direct carbon emissions\n\n•\tscope2_emissions (decimal): Indirect energy emissions\n\n•\tscope3_emissions (decimal): Value-chain emissions\n\n•\tcarbon_data_source (string): Verified vs. estimated indicator\n\n•\tcarbon_data_year (integer): Reporting year for emissions data\n\n•\tdata_collection_timestamp (timestamp): Dataset build timestamp\nTechnical Metadata\n•\tGeographic Coverage: Global\n\n•\tUpdate Frequency: Quarterly\n\n•\tCollection Granularity: Annual (company-level ESG metrics)\n\n•\tKeywords: ESG, sustainability, carbon emissions, climate risk, SEC disclosure, EU CSRD, sustainable finance, ESG scoring","terms_of_service":"https://www.aidc.app/sample-terms-of-service","documentation_link":"https://app.aidatacoop.com/datasets/d5dfa57c-7f38-4ee0-85e0-24e65fdd30e0","privacy_policy_link":"https://www.aidc.app/faq","geographical_coverage":"[\"Global\"]","cost":"FREE","update_frequency":{"interval":1,"unit":"QUARTERLY"},"collection_granularity":{"interval":1,"unit":"YEARLY"},"collection_date_start":1672612220433,"collection_date_end":1735684239955,"data_source":"This dataset is compiled exclusively from 100% publicly available sources, ensuring transparency, auditability, and regulatory alignment. Source materials include corporate sustainability disclosures, mandatory financial filings, and authoritative government datasets. No proprietary, scraped, or personal data is included.","size":0.02,"assets":["ASSET_TYPE_MEDIA"],"license":"https://spdx.org/licenses/CC-BY-4.0.html"},"provider_summary":{"name":"AIDC, Inc.","description":"Great AI starts with great data. We have it. Ship data like software. AIDC packages external data as governed Delta tables with Unity Catalog, complete with schema, metadata, lineage, and clear license terms. Pre‑vetted contracts minimize back‑and‑forth, so squads move from browse to pipeline in days. Quality control is operated by Kaggle Grandmasters and senior data engineers at that standard, so datasets behave in production and pass audits. Coverage spans luxury item pricing (valuation, comp, elasticity studies) to retail property analytics (site selection, underwriting, performance prediction). Instrument adoption and prove lift: reduced ETL, faster training, higher dataset usability rates. Repeatable motion across teams: import → govern → build → measure → scale. Available now on Databricks Marketplace."},"popularity":1928},{"id":"bfb8fe9f-3e1c-4e69-ac94-9eecb70cf69a","summary":{"name":"Consumer Segmentation","subtitle":"Choose from thousands of Syndicated Audiences across industry leading Segmentation Systems such as PRIZM and P$YCLE.","setting":{"visibility":"PUBLIC"},"created_at":1722454166193,"updated_at":1724363011460,"categories":["ADVERTISING_AND_MARKETING","MEDIA","DEMOGRAPHICS"],"listingType":"PERSONALIZED","provider_id":"8d718787-c1f0-462e-8265-d1d8605c2fd2"},"detail":{"description":"Based on the most robust sets of demographic, geographic and proprietary behavioral data, Claritas offers over 2,500 Syndicated Audiences built from over 10,000 demographic and behavioral attributes including four of the industry’s most widely used segmentations to know more about each audience, so you can more precisely determine the ones with the most potential for your business.\n\n- **PRIZM® Premier**: The industry’s most widely adopted segmentation solution, providing rich insights for every household in the U.S. including demographics, lifestyle and media preferences, shopping behaviors, and technology usage.\n\n- **P$YCLE® Premier**: Provides comprehensive consumer insights concerning financial wealth, insurance and investment products held, home value, technology behaviors, and media preferences, specifically for the financial services industry.\n\n- **ConneXions®**: Designed for the technology and telecommunications industry to help you know more about the technology behaviors of today’s connected consumers and predict adoption rates for all major tech categories, including smart tech.\n\n- **CultureCode®**: Gain insight into over 130 million multicultural consumers by acculturation, country of origin, preferred language, lifestyle preferences, and more.\n\n\n**Need to Build a Custom Audience?**\nMeeting your objectives sometimes requires addressing a very specific audience. Claritas excels at creating high-performing audiences that address the unique needs of businesses and then activating them in-market in near-real time, resulting in increased precision.\n\n**- Custom Segmentation**: Unleash the potential of your 1st-party data by combining it with our robust demographic and behavioral insights. Using our proprietary modeling process, these custom audiences include extensive deliverables like audience profiles, storyboards, geographic distributions and directories.\n\n**- Look Alike Audiences**: Already know what your best customer looks like? Based on their known attributes and behaviors, Claritas will find more customers that look exactly like them using the most adopted segmentation in the market along with our unmatched breadth of data.\n\n\n**Find out more here:** https://claritas.com/syndicated-audiences/ or **Get in touch here:** databricks-marketplace@claritas.com\n","terms_of_service":"https://claritas.com/wp-content/uploads/2024/06/Website-Terms-of-Use_June2024.pdf","documentation_link":"https://www2.claritas.com/ClaritasIdentifyResourcePortal","privacy_policy_link":"https://claritas.com/privacy-legal/","geographical_coverage":"[\"United States and Canada\"]","cost":"PAID","assets":["ASSET_TYPE_DATA_TABLE","ASSET_TYPE_MEDIA"],"license":""},"provider_summary":{"name":"Claritas","description":"Unleash AI-Powered Marketing with Claritas. Our platform delivers unmatched audience insights, segmentation, precise targeting, and real-time optimization.\nFor more than 50 years, Claritas has been a trailblazer in understanding the American consumer, resulting in the industry's most embraced audience segmentation. Leveraging strategic acquisitions & a distinctive Identity Graph, the company has evolved into a marketing powerhouse. Our platform enables marketers to enhance ROI by pinpointing ideal audiences, execute precise multichannel marketing engagements, and optimize media spend across online & offline channels. Our robust ID Graph serves as the engine that drives these powerful solutions, encompassing a proprietary data set including 100% of U.S. adult consumers, over 2.5 billion devices and 10,000+ demographic & behavioral insights. The integration of patented and advanced AI technology as an underpin enhances the accuracy, speed, ease of use and scale of the Claritas solutions."},"popularity":1582},{"id":"9597089f-209f-4c1e-a84f-0e521b54db0a","summary":{"name":"SAMPLE M-ART: AI Training Dataset - 4K/6K RAW Video Content | Commercially Cleared with Model, ...","subtitle":"High-quality AI datasets with 20,000+ 4K/6K RAW video assets. 100% rights-cleared with model and property releases, ...","share":{"name":"m_art_170aa23a_4647_47c6_aef9_8ddd625394c7_samples_share","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1789465486515,"updated_at":1789560321331,"categories":["OTHER"],"listingType":"STANDARD","provider_id":"37c05363-a973-4f03-ab0f-0785c32e582a"},"detail":{"description":"M-ART delivers diverse computer vision and AI training datasets with over 20,000 assets in 4K/6K RAW video. All content is filmed on RED cinema cameras, commercially cleared with full releases, and structured with detailed metadata for computer vision applications. Key areas of the catalog include drone and aerial footage, people and lifestyle, healthcare and medical, food and cooking, business and finance, construction and tools, education, and nature landscapes. In addition, \n\nM-ART offers the ability to create custom datasets for clients, providing unique, high-quality video collections that help companies stand out and accelerate AI model training.","terms_of_service":"https://datarade.ai/company/legal/terms-of-service","contact_email":"support@datarade.ai","privacy_policy_link":"https://datarade.ai/company/legal/privacy-policy","assets":["ASSET_TYPE_DATA_TABLE"]},"provider_summary":{"name":"M-ART","description":"M-ART is a creative data provider based in Poland.\r\nWe specialize in producing and delivering large-scale, high-quality video and photo datasets for training AI models. M-ART combines cinematic production expertise with structured dataset solutions trusted by leading global companies.\r\n\r\nOur mission is to deliver authentic, diverse, and scalable content that empowers partners to build the next generation of AI technologies."},"popularity":1006},{"id":"31440447-3894-4737-84e7-1b280ab59466","summary":{"name":"Equilar Executive & Board Profiles: Reference Data for CRM, CDP and MDM","subtitle":"Verified profiles of 4M+ executives and directors at 1M+ orgs. Enrich CRM, CDP and MDM daily.","setting":{"visibility":"PUBLIC"},"created_at":1791590593410,"updated_at":1793827166296,"categories":["ADVERTISING_AND_MARKETING","FINANCIAL","LOOKUP_TABLES"],"listingType":"PERSONALIZED","provider_id":"e4fc2d5b-862e-40a5-8b8e-3f55c9d1dae3"},"detail":{"description":"**Overview**\n\nEquilar maintains verified profiles of 4M+ executives and board members at 1M+ organizations, built from SEC filings, board rosters, company leadership pages and press, with 25+ years of history. Coverage is global and deepest in US public companies. Every person and organization carries a persistent Equilar ID, so you can use this dataset as the reference master for executive and company records across your CRM, CDP, MDM hub and lakehouse. Organizations include CIK, ticker and exchange (MIC), website, parent organization, industry, revenue, market cap and employee count, so you can crosswalk to your existing company master, roll subsidiaries up to parents and segment accounts. Appointments, role changes and departures arrive daily in the transition table with before and after titles, the filing date and a source URL. Delivered through Delta Sharing, so you query it in place in Unity Catalog.\n\n\n**Use cases**\n\n- **CRM enrichment and hygiene:** Fill in titles, bios and board seats. Flag contacts who have left (is_current) and add executives your CRM is missing. Works with Salesforce, Microsoft Dynamics and any CRM you load from Databricks.\n- **Master data management and entity resolution:** Use person_id and organization_id as persistent reference keys. Match companies on CIK, ticker + MIC or website, build hierarchies from parent_organization_id, and use divisional and regional role flags to keep the CFO of one division from being merged with the CFO of another.\n- **CDP and account-based marketing:** Enrich matched B2B profiles with seniority, function, board seats and committee roles, and segment accounts by industry, revenue and employee count. Start journeys when a target account names a new CFO or adds a director.\n- **Buying signals:** Use executive appointments, role changes and departures as triggers for sales and marketing plays.\n- **Conflicts and KYC checks:** Confirm an executive's current and past corporate affiliations and board seats.\n- **Meeting prep and AI assistants:** Use bios, career history and affiliations as grounded context for copilots and briefing tools.\n- **Board composition and governance analytics:** Analyze board independence, financial experts, committee makeup, tenure, director election support, age, gender and ethnicity.\n- **Audit market intelligence:** See each company's audit firm, engagement partner and audit fees.\n\n**Tables**\n\n- **person:** One row per executive or director. Key columns: person_id, full_name, linkedin_url, age, gender, image, update_date\n- **organization:** One row per organization. Key columns: organization_id, name, cik, ticker, mic, website, parent_organization_id, industry, revenues, market_cap, employee_count, country_code, is_active, update_date\n- **position:** Every role a person has held. Key columns: person_id, organization_id, title, start_date, end_date, is_current\n- **position_role:** Standardized role level and function. A CFO is role Chief, function Finance. Key columns: position_id, role, function, is_divisional, is_regional\n- **person_board_detail:** Board seats. Key columns: person_id, organization_id, start_date, end_date, is_current, independence, is_financial_expert, approval_percentage\n- **committee:** Board committee membership. Key columns: person_id, organization_id, committee_type, role, is_current\n- **transition:** Appointments, role changes, departures. Key columns: person_id, organization_id, transition_type, transition_date, pre_transition_title, post_transition_title, url\n- **bio, ethnicity:** Biography, ethnicity. Key columns: person_id\n- **auditor, audit_fee:** Audit firm, engagement partner, fees. Key columns: organization_id, audit_firm_organization_id, total_fees\nUse Delta Change Data Feed to pick up inserts, updates and deletes since your last load. Joins to Equilar's Relationship and Equity & Compensation listings on person_id.\n\nUse Delta Change Data Feed to pick up inserts, updates and deletes since your last load. These tables join to Equilar's Relationship and Equity & Compensation listings on person_id and organization_id.\n\n**Example query**\n\n-- New CFO appointments in the last 30 days at companies with $1B+ revenue\nSELECT o.name, o.ticker, p.full_name,\nt.pre_transition_title, t.post_transition_title,\nt.transition_date, t.url\nFROM transition t\nJOIN person p ON p.person_id = t.person_id\nJOIN organization o ON o.organization_id = t.organization_id\nWHERE t.transition_type = 'Appointment'\nAND t.post_transition_title ILIKE '%chief financial officer%'\nAND t.transition_date >= date_sub(current_date(), 30)\nAND o.revenues >= 1000000000;\n","terms_of_service":"https://www.equilar.com/terms-and-conditions","documentation_link":"https://static.equilar.com/Equilar+Data+Feed+Documentation.pdf","privacy_policy_link":"https://www.equilar.com/privacy-policy.html","geographical_coverage":"[\"Global\"]","update_frequency":{"interval":1,"unit":"DAILY"},"assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"Equilar ExecAtlas","description":"Equilar is the leading provider of business intelligence and corporate leadership data solutions. Companies of all sizes, including 70% of the Fortune 500, rely on Equilar's data for insights around executive talent and relationships. Equilar's data-driven solutions help corporate leaders and advisors accelerate sales and business development activities by identifying paths to new opportunities and tracking real-time executive appointments and departures."},"popularity":844},{"id":"af2c2027-72b3-4ccb-913f-72e4e79f9ca3","summary":{"name":"Census Data by Zip Code 2010-2014","subtitle":"This data package has the purpose to offer data for demographic indicators, part of 5-years American Community Census,..","share":{"name":"census_data_by_zip_code_2010_2014","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1682355681519,"updated_at":1698965635294,"categories":["HEALTH","SCIENCE_AND_RESEARCH"],"listingType":"STANDARD","provider_id":"f88849f8-9d77-4de2-b4c7-03d55cc3768e"},"detail":{"description":"\n**Overview**\nThis data package has the purpose to offer data for demographic indicators, part of 5-years American Community Census, that could be needed in the analysis made along with health-related data or alone. The American Community Survey based on 5-years estimates is, according to U.S Census Bureau, the most reliable, because the samples used are the largest and the data collected cover all country areas, regardless of the population number.\n\n**Description**\nThe U.S. Census Bureau, American Community Survey (ACS) is a nationwide survey designed to provide communities a fresh look at how they are changing. The ACS replaced the decennial census long form in 2010 and thereafter by collecting long form type information throughout the decade rather than only once every 10 years. The American Community Survey produces demographic, social, housing and economic estimates in the form of 1-year, 3-year and 5-year estimates based on population thresholds. The strength of the ACS is in estimating population and housing characteristics. It produces estimates for small areas, including census tracts and population subgroups.\n\nThe main indicators categories and subcategories that can be found in the content of this data package are as follows:\n - General demographics: age, gender (sex) and race/ethnicity\n - Language: language spoken at home and English ability\n - Origins: nativity, place of birth and ancestry\n - Socio-economic demographics: general social and economic characteristics, poverty status\nEvery dataset in this data package has over 100 demographic indicators, each accompanied by the margin of error of the estimated value of the indicator.\n\nDemography has an important role in public health planning and assessment. At the same time demographics plays an important role in the field of health-related research and development. Socio-economic factors are health influencers that are assessed in order to understand better the root causes of health differences among population groups. Personal factors like age, gender and race/ethnicity are directly influencing the risk levels for various diseases. The English ability has an important in relation to health-related communication and health literacy. Place of birth is important because of the different exposures to health risk factors and of differences related to health services (for example immunizations).\n\n**Benefits**\n- Useful for public health specialists and specialized institutions to plan and assess the health services and projects. useful for researchers seeking to improve the methods, regulations, health policies and strategy. the raw version can be found on the author's website, but jsl is offering a clean, standardized and easier to use version, alone or in combination with other datasets found in jsl dataset library. the jsl version was enriched by adding for each zip code tabulation area the geographic coordinates and the land and water surfaces size. the fields descriptions along with the dataset description are again useful for the user to quickly understand the data and the dataset.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [Age and Sex by Zip Code Tabulation Area 2010-2014](https://www.johnsnowlabs.com/marketplace/age-and-sex-by-zip-code-tabulation-area-2010-2014)\n\t- This dataset identifies age and sex for the population by zip code tabulation areas within the United States. This dataset resulted from the American Community Survey (ACS) conducted from 2010 through 2014. This dataset also identifies median age, sex ratio, old-age and child dependency and estimates of population by gender by age category, total estimates as well as these category’s margin of error and margin of error ratio.\n- [Ancestry by Zip Code Tabulation Area 2010-2014](https://www.johnsnowlabs.com/marketplace/ancestry-by-zip-code-tabulation-area-2010-2014)\n\t- This dataset identifies ancestry by zip code tabulation areas within the United States. “Ancestry refers to a person’s ethnic origin or descent, \"roots,\" or heritage, or the place of birth of the person or the person’s parents or ancestors before their arrival in the United States.” This dataset resulted from the American Community Survey (ACS) conducted from 2009 through 2013.\n- [Characteristics Language Zip Code Tabulation Area 2010-2014](https://www.johnsnowlabs.com/marketplace/characteristics-language-zip-code-tabulation-area-2010-2014)\n\t- This dataset looks at certain characteristics of people by the language spoken at home by zip code tabulation areas within the United States. This dataset resulted from the American Community Survey (ACS) conducted from 2010 through 2014. The characteristics of the people include age, nativity and citizenship status, poverty status, educational attainment, and percent imputed etc.\n- [Characteristics by Nativity Zip Code Tabulation Area 2010-2014](https://www.johnsnowlabs.com/marketplace/characteristics-by-nativity-zip-code-tabulation-area-2010-2014)\n\t- This dataset identifies selected characteristics of the total and native population by zip code tabulation areas within the United States. This dataset resulted from the American Community Survey (ACS) conducted from 2010 through 2014. The dataset identifies population by native and foreign-born, including age, sex, language spoken at home, ability to speak English, marital status, educational attainment, income, poverty and citizenship status by zip code tabulation area.\n- [Characteristics by Poverty by Zip Code Tabulation Area 2010-2014](https://www.johnsnowlabs.com/marketplace/characteristics-by-poverty-by-zip-code-tabulation-area-2010-2014)\n\t- This dataset identifies selected characteristics of people at specified levels of poverty by zip code tabulation areas within the United States. This dataset resulted from the American Community Survey (ACS) conducted from 2010 through 2014. The selected characteristics include sex, age, race, living arrangement, educational attainment, nativity and citizenship status, disability status, and work status.\n- [Demographic And Housing Estimates ACS 2011-2015](https://www.johnsnowlabs.com/marketplace/demographic-and-housing-estimates-acs-2011-2015)\n\t- This dataset identifies demographic and housing estimates including sex and age, race and housing units by zip code tabulation areas within the United States. This dataset resulted from the American Community Survey (ACS) conducted from 2011 through 2015. JSL enriched this dataset with Latitude and Longitude information and with the map information about the land and water area of zip code tabulation areas.\n- [Demographic Housing Estimates Zip Code Tabulation Area 2010-2014](https://www.johnsnowlabs.com/marketplace/demographic-housing-estimates-zip-code-tabulation-area-2010-2014)\n\t- This dataset identifies demographic and housing estimates including sex and age, race and housing units by zip code tabulation areas within the United States. This dataset resulted from the American Community Survey (ACS) conducted from 2010 through 2014. JSL enriched this dataset with Latitude and Longitude information and with the map information about the land and water area of zip code tabulation areas.\n- [Detailed Race by Zip Code Tabulation Area 2010-2014](https://www.johnsnowlabs.com/marketplace/detailed-race-by-zip-code-tabulation-area-2010-2014)\n\t- This dataset identifies race in detail by zip code tabulation areas within the United States. The dataset resulted from the American Community Survey (ACS) conducted from 2010 through 2014. The races included in this dataset are White, Black or African American, American Indian and Alaskan Native, Asian, Native Hawaiian and other Pacific Islander, and others. The ACS also looks at races as stand-alone, and two or more races combined.\n- [Economic Characteristics by Zip Code Tabulation Area Geographic Data](https://www.johnsnowlabs.com/marketplace/economic-characteristics-by-zip-code-tabulation-area-geographic-data)\n\t- This dataset identifies selected economic characteristics by zip code tabulation areas within the United States. This dataset resulted from the American Community Survey (ACS) conducted from 2010 through 2014. The economic characteristics include employment status, commuting to work, occupation, class of worker, income and benefits, health insurance coverage, and percentage of families and people whose income in the past 12 months is below the poverty level.\n- [English Language Ability by Zip Code Tabulation Area Geographic Data](https://www.johnsnowlabs.com/marketplace/english-language-ability-by-zip-code-tabulation-area-geographic-data)\n\t- This dataset identifies the language spoken at home by zip code tabulation areas within the United States. This dataset resulted from the American Community Survey (ACS) conducted from 2010 through 2014. The dataset also shows how well the language is spoken (well, very well, less than very well). JSL enriched this dataset with Latitude and Longitude information and with the map information about the land and water area of zip code tabulation areas.\n- [Language Spoken at Home by Zip Code Tabulation Area 2010-2014](https://www.johnsnowlabs.com/marketplace/language-spoken-at-home-by-zip-code-tabulation-area-2010-2014)\n\t- This dataset identifies the language spoken at home and how well English is spoken by zip code tabulation areas within the United States. This dataset resulted from the American Community Survey (ACS) conducted from 2010 through 2014. JSL enriched this dataset with Latitude and Longitude information and with the map information about the land and water area of zip code tabulation areas.\n- [Nativity Zip Code Tabulation Area Geographic Data](https://www.johnsnowlabs.com/marketplace/nativity-zip-code-tabulation-area-geographic-data)\n\t- This dataset identifies nativity by zip code tabulation areas within the United States. This dataset resulted from the American Community Survey (ACS) conducted from 2010 through 2014. The data set shows nativity at place of birth, native versus foreign-born. JSL enriched this dataset with Latitude and Longitude information and with the map information about the land and water area of zip code tabulation areas.\n- [Place of Birth by Zip Code Tabulation Area 2010-2014](https://www.johnsnowlabs.com/marketplace/place-of-birth-by-zip-code-tabulation-area-2010-2014)\n\t- This dataset identifies the place of birth for the foreign-born population by zip code tabulation areas within the United States. This dataset resulted from the American Community Survey (ACS) conducted from 2010 through 2014. JSL enriched this dataset with Latitude and Longitude information and with the map information about the land and water area of zip code tabulation areas.\n- [Poverty Status by Age and Zip Code Tabulation Area 2010-2014](https://www.johnsnowlabs.com/marketplace/poverty-status-by-age-and-zip-code-tabulation-area-2010-2014)\n\t- This dataset identifies poverty status by age and zip code tabulation areas within the United States. This dataset resulted from the American Community Survey (ACS) conducted from 2010 through 2014. JSL enriched this dataset with Latitude and Longitude information and with the map information about the land and water area of zip code tabulation areas.\n- [Poverty Status by Sex Age and Zip Code Tabulation Area 2010-2014](https://www.johnsnowlabs.com/marketplace/poverty-status-by-sex-age-and-zip-code-tabulation-area-2010-2014)\n\t- This dataset identifies poverty status by sex, age and zip code tabulation areas within the United States. This dataset resulted from the American Community Survey (ACS) conducted from 2010 through 2014. JSL enriched this dataset with Latitude and Longitude information and with the map information about the land and water area of zip code tabulation areas.\n- [Poverty Status by Zip Code Tabulation Area 2010-2014](https://www.johnsnowlabs.com/marketplace/poverty-status-by-zip-code-tabulation-area-2010-2014)\n\t- This dataset identifies poverty status by zip code tabulation areas within the United States. This dataset resulted from the American Community Survey (ACS) conducted from 2010 through 2014. The poverty characteristics include race, sex, age, educational attainment, employment status, work experience, and poverty level. JSL enriched it with Latitude and Longitude information and with the map information about the land and water area of zip code tabulation areas.\n- [Poverty Status of Families by Zip Code Tabulation Area 2010-2014](https://www.johnsnowlabs.com/marketplace/poverty-status-of-families-by-zip-code-tabulation-area-2010-2014)\n\t- This dataset identifies the poverty status of families by zip code tabulation areas within the United States. This dataset resulted from the American Community Survey (ACS) conducted from 2010 through 2014. The family characteristics include race, age, householder work status, supplemental income, educational attainment, number of related children, number of people, and number of workers in the household.\n- [Race by Zip Code Tabulation Area 2010-2014](https://www.johnsnowlabs.com/marketplace/race-by-zip-code-tabulation-area-2010-2014)\n\t- This dataset identifies race by zip code tabulation areas within the United States. This dataset resulted from the American Community Survey (ACS) conducted from 2010 through 2014. The races included are White, Black or African American, American Indian and Alaskan Native, Asian, Native Hawaiian and other Pacific Islander, and other.\n- [Social Characteristics Zip Code Tabulation Area 2010-2014](https://www.johnsnowlabs.com/marketplace/social-characteristics-zip-code-tabulation-area-2010-2014)\n\t- This dataset identifies selected social characteristics by zip code tabulation area within the United States. This dataset resulted from the American Community Survey (ACS) conducted from 2010 through 2014. The dataset identifies households by type, relationships within household, marital status, fertility, grandparents, school enrollment, veteran status, year of entry, place of birth, language spoken, ancestry, and access to computer and internet service within in the home.\n- [US States and Counties FIPS Codes 2010](https://www.johnsnowlabs.com/marketplace/us-states-and-counties-fips-codes-2010)\n\t- This dataset contains data about FIPS (Federal Information Processing Standards) codes for US states and counties. This dataset provides demographic data that could be necessary for evaluation and organization of medical services and population health evaluation.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated\n- Data Updates\n - Data updates support replace-on-update: outdated foreign keys are deprecated, not deleted\n\n**Our data is curated and enriched by domain experts** \n\n Each dataset is manually curated by our team of doctors, pharmacists, public health & medical billing experts:\n- Field names, descriptions, and normalized values are chosen by people who actually understand their meaning\n- Healthcare & life science experts add categories, search keywords, descriptions and more to each dataset\n- Both manual and automated data enrichment supported for clinical codes, providers, drugs, and geo-locations\n- The data is always kept up to date – even when the source requires manual effort to get updates\n- Support for data subscribers is provided directly by the domain experts who curated the data sets\n- Every data source’s license is manually verified to allow for royalty-free commercial use and redistribution.\n\n**Need Help?**\nIf you have questions about our products, contact us at [info@johnsnowlabs.com](mailto:info@johnsnowlabs.com). \n ","terms_of_service":"https://www.johnsnowlabs.com/healthcare-data-library-eula/","documentation_link":"https://www.johnsnowlabs.com/marketplace/census-data-by-zip-code-2010-2014-data-package","contact_email":"support@johnsnowlabs.com","privacy_policy_link":"https://www.johnsnowlabs.com/privacy-policy/","assets":["ASSET_TYPE_DATA_TABLE"]},"provider_summary":{"name":"John Snow Labs","description":"John Snow Labs, an AI and NLP for healthcare company, provides state-of-the-art software, models, and data to help healthcare and life science organizations build, deploy, and operate AI projects."},"popularity":980},{"id":"970ca404-bec9-43f7-8c6d-6a473aa45239","summary":{"name":"Company Data License","subtitle":"Over 60 million companies to power your business, updated monthly.","setting":{"visibility":"PUBLIC"},"created_at":1700503643498,"updated_at":1700503740379,"categories":["ADVERTISING_AND_MARKETING","FINANCIAL","LOOKUP_TABLES"],"listingType":"PERSONALIZED","provider_id":"066869f4-06d9-4b50-b56d-e05bebcfddcd"},"detail":{"description":"**About People Data Labs**\n\n- People Data Labs is your engineering partner for all things b2b data. Our company has enabled thousands of engineering, data science, product, and other technical teams to build compliant, innovative, software solutions using b2b data.\n- PDL provides company and person data via flat files and APIs\n\n**About this Dataset**\n\n- This is a paid dataset that requires a signed order form with PDL to access\n- Our company dataset contains over 60 million profiles of companies in our identity graph.\n- This data covers global profiles with a linkedin url as the primary key.\n\n**Tables Included**:\n\n1. ROOT_COMPANY\n\nid | The ID value is currently non-persistent and generated from the company's primary LinkedIn username.\naverage_employee_tenure | Average employee tenure is the average number of years employees work for the company. It is represented by a floating number greater than zero and rounded to the nearest thousandth.\nemployee_count | employee_count is an integer greater than zero. We calculate it by finding the number of our profiles whose job_company_id matches the company.\nfacebook_url | The main Facebook profile URL for the company based on source agreement.\nfounded | The founding year of the company.\ngics_sector | GICS standard sector classification for public companies.\nheadline | The company’s headline summary.\nindustry | The self-reported industry of the company.\nlinkedin_id | The main LinkedIn profile ID for the company based on source agreement.\nlinkedin_url | The main LinkedIn profile URL for the company based on source agreement.\nlocation_address_line_2 | HQ location information.\nlocation_continent | HQ location information.\nlocation_country | HQ location information.\nlocation_geo | City-center geo code of the company’s current HQ.\nlocation_locality | HQ location information.\nlocation_metro | US ONLY MSA of the company’s current HQ.\nlocation_name | HQ location information in the format \"locality, region, country\".\nlocation_postal_code | HQ location information.\nlocation_region | HQ location information.\nlocation_street_address | HQ location information.\ninferred_revenue | Company's estimated annual revenue in US dollars.\nmic_exchange | The MIC code for the stock exchange that the company's ticker is listed on.\nname | The company's main common name.\nsize | A self-reported range representing the number of people working at the company.\nsummary | A description of the company. \nticker | The company ticker (only for public companies).\ntwitter_url | The main Twitter profile URL for the company based on source agreement.\ntype | type will be one of the Canonical Company Types in the PDL company data. If a company has a known ticker, then its type is public.\nwebsite | The primary company website.\n\n2. TAG\n\ncompany_id | PDL company ID.\ntag | Tag associated with the company.\n\n3. NAICS\n\ncompany_id | PDL company ID.\nnaics_code | The NAICS code associated with a company’s industry classification.\nsector | The industry classification according to the first two digits in the NAICS code.\nsub_sector | The industry classification according to the first three digits in the NAICS code.\nindustry_group | The industry classification according to the first four digits in the NAICS code.\nindustry | The industry classification according to the first five digits in the NAICS code.\nnational_industry | The industry classification according to all six digits in the NAICS code.\n\n4. SIC\n\ncompany_id | PDL company ID.\nsic_code | The SIC code associated with a company’s industry classification.\nmajor_group | The industry classification according to the first two digits in the SIC code.\nindustry_group | The industry classification according to the first three digits in the SIC code.\nindustry_sector | The industry classification according to all four digits in the SIC code.\n\n5. AFFILIATED_PROFILE\n\ncompany_id | PDL company ID.\naffiliated_profile | Company ID that is affiliated with the queried company.\n\n6. ALTERNATIVE_DOMAIN\n\ncompany_id | PDL company ID.\nalternative_domain | An alternate domain associated with this company.\n\n7. ALTERNATIVE_NAME\n\ncompany_id | PDL company ID.\nalternative_name | A name associated with this company.\n\n8. PROFILE\n\ncompany_id | PDL company ID.\nprofile | A known social profile URL for the company.\n\n9. HEADCOUNT_CHANGE_BY_MONTH\n\ncompany_id | PDL company ID.\nmonth | Month of event.\ngross_additions | The total number of people that joined the company in this month.\ngross_departures | The total number of people that departed the company each month.\n\n10. EMPLOYEE_CHANGE_RATES\nChurn rates represent the rate of decrease in employee headcount from N months prior rounded to the nearest ten-thousandth.\nGrowth rates represent The percentage increase in total headcount from 3 months prior rounded to the nearest ten-thousandth.\n\ncompany_id | PDL company ID.\nchurn_rate_3_month\nchurn_rate_6_month\nchurn_rate_12_month\nchurn_rate_24_month\ngrowth_rate_3_month\ngrowth_rate_6_month\ngrowth_rate_12_month\ngrowth_rate_24_month\n\n11. HEADCOUNT_BY_MONTH\n\ncompany_id | PDL company ID.\nmonth | Month of event.\nvalue | The number of employees at the end of each month.\n\n12. HEADCOUNT_BY_COUNTRY\n\ncompany_id | PDL company ID.\ncountry | Canonical country in the PDL data.\nvalue | The number of current employees in this country.\n\n13. RECENT_EXEC_CHANGES\n\nprevious_company_id | PDL company ID.\nevent_date | Month of executive changing roles. Formatted as YYYY-MM-01.\nperson_id | PDL persistent person ID of the exec\nprevious_title | The executive's previous job title at the old company.\nprevious_role | The executive's previous job role at the old company. This will be one of the Canonical Title Roles.\nprevious_sub_role | The executive's previous job sub-role at the old company. This will be one of the Canonical Title Sub-Roles.\nprevious_levels | The executive's previous job level at the old company. This will be one of the Canonical Title Levels.\nnext_company_id | PDL company ID. \nnext_title | The executive's job title at their next company.\nnext_role | The executive's previous job title role at their next company. This will be one of the Canonical Title Roles.\nnext_sub_role | The executive's previous job title sub-role at their next company. This will be one of the Canonical Title Sub-Roles.\nnext_levels | The executive's previous job title level at their next company. This will be one of the Canonical Title Levels.\n\n14. EMPLOYEE_MOVEMENT\n\nprevious_company_id | PDL company ID.\nnext_company_id | PDL company ID.\nrole | PDL canonical role generated from an employee's title in their experience history. This value can also be \"all\" for a sum of all employees across the company.\nvalue | Integer value representing the number of employees who moved from previous company to next company.\n\n15. TOP_US_EMPLOYEE_METRO\n\ncompany_id | PDL company ID.\nmetro | Each metro listed is one of our Canonical Metros.\ncurrent_headcount | The number of employees in the metro.\ngrowth_rate_12_month | The growth rate in the metro over the last twelve months, rounded to the nearest ten-thousandth.\n\n16. TENURE_BY_LEVEL\n\ncompany_id | PDL company ID.\nrole | PDL canonical level generated from an employee's title in their experience history.\nvalue | Average tenure by role, rounded to the nearest thousandth.\n\n17. TENURE_BY_ROLE\n\ncompany_id | PDL company ID.\nrole | PDL canonical role generated from an employee's title in their experience history.\nvalue | Average tenure by role, rounded to the nearest thousandth.\n\n**Sourcing**\n\nPeople Data Labs has thousands of contributing data sources that enhance our person and company data as well as our ancillary location and school datasets. These sources fall into two categories, our \"Data Union\" sources and our public data sources. The \"Data Union\" is our proprietary data sharing co-op. Customers opt-in to sharing their data and warrant that their data is fully compliant with global data privacy regulations. Our public data sources help us shore up areas where our data union sources are not providing enough information. These sources include open-sourced datasets, publicly available and free-use government filings, and others. This data is also globally compliant and fair use.","terms_of_service":"https://www.peopledatalabs.com/privacy/services-subscription-agreement","documentation_link":"https://docs.peopledatalabs.com/docs/company-data-overview","privacy_policy_link":"https://privacy.peopledatalabs.com/policies?name=privacy-policy","geographical_coverage":"[\"Global\"]","cost":"PAID","update_frequency":{"interval":1,"unit":"MONTHLY"},"collection_granularity":{"interval":1,"unit":"MONTHLY"},"collection_date_start":1262368451645,"collection_date_end":1700502862160,"data_source":"https://docs.peopledatalabs.com/docs/data-sources","size":300.0,"assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"People Data Labs","description":"People Data Labs is focused on democratizing access to world-class B2B information. We empower developers to create innovative and compliant data products, at scale."},"popularity":1444},{"id":"7e22dd53-808e-41a9-9449-bd4a633d7c98","summary":{"name":"Platts Market Data - Oil Fundamentals","subtitle":"The dataset provides crucial data for informed trading decisions and risk reduction","setting":{"visibility":"PUBLIC"},"created_at":1713951366091,"updated_at":1774525339811,"categories":["ENERGY","FINANCIAL"],"listingType":"PERSONALIZED","provider_id":"eff4b18d-fe1b-4884-9591-eab9d16d2e2a"},"detail":{"description":"**Overview**\nThe Platts Market Data - Oil Fundamentals dataset provides fundamental data needed to understand the future and reduce risk when making important trading decisions or valuing positions.\n\nFollowing clearly defined guidelines and specifications, we publish highly sought-after fundamentals. With baseload data, you can monitor growing trading volumes, construct new oil supply and demand models, supplement or complement existing data sets and models to make confident trading decisions, keep track of inventory and support your trading decisions.\n\nThis dataset includes:\n\n- ARA stocks: Weekly oil stock stats for ARA regions. This includes statistics for Fuel oil, Gasoil, Gasoline, Jet Kero, Light-Ends, Naphtha, Residuals, and Middle Distillates\n\n- Baker Hughes: Global rig count by region/country on monthly basis along with rig count for U.S. on weekly basis. It includes data on oil and oil and gas.\n\n- EIA Weekly Petroleum: Weekly Petroleum statistics for stocks provided by the Energy Information Administration (EIA) along with stocks for individual PADD regions.\n\n**Use cases**\n- Quantitative Analysis - Leverage inventory data to build and support internal models for trading decisions\n- Inventory Management - Follow changes in stock level data and rig count data. These data serve as indicators of demand, thereby supporting capacity expansion decisions\n- Fundamental Analysis - Keep track of oil stock levels and changes in stock levels. Supporting supply/demand/price estimates can be an essential part of trading strategy and analysis\n- Demand Forecasting - Inventory change and rig count data can serve as indicators of demand, providing insight into the direction in which demand may be moving\n\n**Product Details**\n\nSample Tables:\nA ) PRICEDATA\nB ) REF_DATA_MARKETDATASample Fields:\n\nA ) PRICEDATA\n- SYMBOL\n- MDC\n- DESCRIPTION\n- BATE\n- ASSESSDATE\n- UOM\n- CURRENCY\n\nB ) REF_DATA_MARKETDATA\n- COMMODITY\n- DELIVERY_REGION\n- DESCRIPTIONView\n\nView Descriptions:\n- PRICEDATA - Get the latest and historical assessments by Symbol\n- CORRECTIONS - Get all corrections to the assessments by Symbol\n- REF_DATA_MARKETDATA - Get additional reference data for Symbols\n- MARKETDATA_DICTIONARY - Contains descriptions and examples of each of the columns in the above views for a better understanding of the dataset","terms_of_service":"https://www.spglobal.com/en/privacy/privacy-policy-english","documentation_link":"https://www.marketplace.spglobal.com/en/datasets/platts-market-data-oil-fundamentals-(1698859371)","privacy_policy_link":"https://www.spglobal.com/en/privacy/privacy-policy-english","geographical_coverage":"[\"Global\"]","cost":"PAID","update_frequency":{"interval":1,"unit":"SECOND"},"collection_granularity":{"interval":1,"unit":"SECOND"},"collection_date_start":441801297632,"data_source":"","assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"S&P Global Energy","description":"At S&P Energy, our complete view of global energy and commodities markets enables our customers to make decisions with conviction and create long-term, sustainable value. For more than 100 years, we've been a trusted connector that brings together thought leaders, market participants, governments, and regulators to co-create solutions that lead to progress. Vital to navigating Energy Transition, S&P Global Energy's coverage includes oil and gas, power, chemicals, metals, agriculture, and shipping. S&P Global Energy is a division of S&P Global (NYSE: SPGI)."},"popularity":1596},{"id":"a685f81f-3afc-4f22-aa99-5386b0d12ab5","summary":{"name":"Asana MCP","subtitle":"Brings your Asana Work Graph directly into Databricks, allowing you to turn AI-powered discussions into actionable work.","setting":{"visibility":"PUBLIC"},"created_at":1775821603112,"updated_at":1775856173630,"categories":["OTHER"],"listingType":"STANDARD","provider_id":"b9448628-ea98-4c3f-bc6c-379ea68a133f"},"detail":{"description":"**Overview**\nAsana offers a Model Context Protocol (MCP) server that allows AI assistants and other applications to access the Asana Work Graph. This server provides a way to interact with your Asana workspace through various AI platforms and tools that support MCP.\n\n**Use cases**\n- Access Asana data from compatible AI applications\n- Create and manage tasks and projects through natural language\n- Generate reports and summaries based on Asana data\n- Analyze project data and get AI-powered insights\n\n**Example Requests**\n- \"Find all my incomplete tasks due this week\"\n- \"Create a new task in the Marketing project assigned to me\"\n- \"List all sections in the Product Launch project\"\n- \"Show me the status of the Q2 Planning project\"\n\n**Configuration**\n\nTo connect to the Asana MCP, follow the [Integration Guide](https://developers.asana.com/docs/integrating-with-asanas-mcp-server) to configure an Asana OAuth App. \n- Add `https://.cloud.databricks.com/login/oauth/http.html` to the Authorized Redirects for your Asana app.\n- Save the `client_secret` and `client_id` to use when configuring your MCP connection in Databricks.","terms_of_service":"https://asana.com/terms","documentation_link":"https://developers.asana.com/docs/mcp-server","privacy_policy_link":"https://asana.com/terms/privacy-statement","data_source":"","assets":["ASSET_TYPE_MCP"],"license":""},"provider_summary":{"name":"Asana","description":"Asana is the System of Action for Work, where humans and AI collaborate to help individuals work smarter, teams move faster, and organizations deliver results. Powered by the Work Graph®️, Asana provides the context and governance for AI to operate inside real workflows, helping 180,000+ organizations build the Agentic Enterprise."},"popularity":1996},{"id":"944c40a1-25c2-4088-997d-ebb262179489","summary":{"name":"Data Publish","subtitle":"Connect supply chain and enterprise data to accelerate analytics, AI, and better decisions. ","setting":{"visibility":"PUBLIC"},"created_at":1790974655175,"updated_at":1790974655175,"categories":["MANUFACTURING","RETAIL","TRANSPORTATION_AND_LOGISTICS","HEALTH"],"listingType":"PERSONALIZED","provider_id":"5d5378db-7c78-4afb-830b-28f1e004e83e"},"detail":{"description":"**Overview**\nKinaxis helps organizations plan, respond, and act across complex supply chains. Its AI-powered Maestro solution brings together supply chain data, planning and decision intelligence, scenario analysis, and agentic AI so teams can understand change, evaluate trade-offs, and make better decisions faster.\nKinaxis is also extending these capabilities toward **operational orchestration** — connecting data, context, decisions, people, AI agents, and actions across functions and enterprise systems. This helps organizations move beyond optimizing individual planning processes toward coordinating the broader operational outcomes that depend on them.\nWith capabilities such as Kinaxis Data Publish, organizations can also make trusted Kinaxis planning and operational data available within their enterprise data environment, combining supply chain context with information from finance, manufacturing, procurement, commercial, logistics, and other domains.\n\n**Use cases**\n\n- Supply Chain Planning and Decision-Making: Connect demand, supply, inventory, capacity, and other planning processes so teams can continuously evaluate changes, understand trade-offs, model scenarios, and coordinate decisions.\n- Enterprise Reporting and Analytics: Combine Kinaxis planning and operational data with financial, manufacturing, procurement, commercial, and other enterprise data to create a more complete view of business performance.\n- Operational Business Visibility: Give business and analytics teams timely access to trusted supply chain information without relying on file exports, intermediate storage layers, or custom downstream ingestion pipelines.\n- Cross-Functional Operational Orchestration: Connect supply chain decisions with the processes and actions that follow across functions and systems, helping teams coordinate around outcomes such as responding to disruption, protecting customer service, managing inventory, or improving working capital.\n- AI and Advanced Analytics: Make trusted, contextualized supply chain data available for enterprise analytics, AI, and agentic applications while allowing organizations to build on their existing data platform investments.\n\n**Product details**\nKinaxis Maestro provides a foundation for supply chain planning and decision-making, bringing together data, supply chain context, analytics, optimization, simulation, and AI.\nKinaxis Data Publish enables organizations to make trusted supply chain planning and operational data available within their enterprise data platform without relying on file-based exports, intermediate storage layers, or custom ingestion processes.\nThis can help organizations:\n- Reduce the cost and complexity of maintaining analytics pipelines\n- Improve data quality with analytics-ready business data\n- Accelerate new reporting and analytics initiatives\n- Combine supply chain information with data from other enterprise domains\n- Extend the value of existing enterprise data platform investments\n\nPublished data can support use cases spanning enterprise reporting, business intelligence, advanced analytics, AI, and broader operational applications.\n \n**Additional insights**\nSupply chain decisions rarely exist in isolation. A change in demand, supply, inventory, capacity, or risk can affect procurement, manufacturing, logistics, finance, commercial teams, and customer commitments.\nKinaxis' approach to operational orchestration builds on its supply chain planning and decision intelligence foundation to help organizations connect these decisions with the wider enterprise context and actions required to achieve a business outcome. Trusted data sharing capabilities such as Kinaxis Data Publish help make that supply chain context available beyond the planning environment, supporting more connected analytics, AI, and operational processes.","terms_of_service":"https://www.kinaxis.com","documentation_link":"","privacy_policy_link":"https://www.kinaxis.com/en/privacy-policy","assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"Kinaxis","description":"Kinaxis is a leader in modern supply chain planning and orchestration, powering complex global supply chains, and supporting the people who manage them. Our powerful, AI-infused supply chain orchestration platform, Maestro, combines proprietary technologies and techniques that provide full transparency and agility across the entire supply chain — from multi-year strategic planning to last-mile delivery. We are trusted by renowned global brands to provide the agility and predictability needed to navigate today’s volatility and disruption."},"popularity":1940},{"id":"4d9f22b0-20ef-440e-be6d-eedb6f1c7fe6","summary":{"name":"FactSet AI-Ready Data MCP","subtitle":"Access FactSet's trusted data delivered seamlessly for AI-driven insights.\n","setting":{"visibility":"PUBLIC"},"created_at":1769033487928,"updated_at":1769033653184,"categories":["FINANCIAL"],"listingType":"STANDARD","provider_id":"48d55bd7-064c-4665-844d-dff8a6def12c"},"detail":{"description":"**Overview**\nAccess FactSet's trusted equity prices, fundamentals, ownership, M&A, people, events, supply chain, geographic revenue exposure, pricing and consensus estimates - delivered seamlessly for AI-driven insights. With comprehensive coverage and reliable data, FactSet empowers smarter financial analysis, decision-making, and innovation.\n\n\n**Product Details**\n\n**Global Prices** - End-of-day pricing data, returns, corporate actions, dividends, and shares outstanding for global equities\n\n**Fundamentals** - Company financial statements and ratios from regulatory filings\n\n**Consensus Estimates** - Earnings estimates, surprise data, and buy/hold/sell ratings consensus\n\n**Supply Chain Relationships** - Business network connections including competitors, customers, suppliers, and partners\n\n**Geographic Revenue Exposure** - Country-level revenue exposures\n\n**Mergers and Acquisitions** - M&A deal data including transaction details, parties involved, and deal terms\n\n**Company Events** - Corporate calendar events including earnings calls, investor meetings, and conferences\n\n**Ownership** - Fund holdings, institutional ownership, insider transactions, and position changes from regulatory filings\n\n**People** - Executive profiles, employment history, compensation data, and leadership statistics for companies and individuals","terms_of_service":"https://www.factset.com/legal","documentation_link":"https://developer.factset.com/mcp/factset-ai-ready-data-mcp","privacy_policy_link":"https://www.factset.com/legal","data_source":"","assets":["ASSET_TYPE_MCP"],"license":"https://www.factset.com/legal"},"provider_summary":{"name":"FactSet","description":"FactSet is an industry leader in acquiring, integrating, and managing financial data. Our content gives you the power to monitor the global markets, research public and private companies, and gain industry-level insight through. Our proprietary symbology links and aggregates a variety of content sources to ensure consistency, transparency, and data integrity across your business."},"popularity":2082},{"id":"829157ee-dbf2-4e54-ab65-d7407037c272","summary":{"name":"Draup Account Intelligence - Sample","subtitle":"Multi-industry profiles with roles, skills, education, diversity, projects & career histories","share":{"name":"account_intelligence","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1755718033594,"updated_at":1788933258772,"categories":["ADVERTISING_AND_MARKETING","COMMERCE","ECONOMICS","EDUCATION","OTHER","SCIENCE_AND_RESEARCH"],"listingType":"STANDARD","provider_id":"9900a255-6aa9-496f-9b6a-421a3568af3b"},"detail":{"description":"**Overview**\nDraup Account Intelligence provides a holistic and dynamic view of global companies by aggregating critical business, financial, operational, and leadership data into one unified dataset. It empowers sales, business development, and market intelligence teams with detailed company profiles that span foundational information (headquarters, industry verticals, employee counts, R&D workforce size) through to deeper insights on financial performance, IT spend, and strategic priorities.\nBeyond static company profiles, the dataset delivers dynamic intelligence — including leadership movement tracking, competitor landscapes, outsourcing relationships, and curated signal alerts that highlight key developments and market shifts. Supplemented with regulatory filings, earnings transcripts, and shareholder data, Draup Account Intelligence offers a comprehensive lens into target accounts, connecting granular operational signals with broader corporate strategy.\n\n**Methodology & Quality**\nDraup aggregates data from diverse proprietary and public sources, then standardizes it into consistent company, profile, and workload taxonomies. Our processes normalize financial structures, align technology stacks to vendor hierarchies, and tag usage intensity to reduce noise. Leadership movements, job postings, and company signals are refreshed frequently to ensure timeliness. Filings and transcripts are mapped systematically to company entities, creating a reliable and auditable data trail.\n\n**Key Tables Include**\n•\tCompany Overview: firmographics, headquarters, employee count, R&D workforce\n•\tCompany Financials: overall, by business unit, and by region\n•\tIT Spend & Strategic Priorities: category-wise spend, initiatives, mapped use cases\n•\tTechnology Stack: vendor–product adoption, workloads, usage intensity\n•\tCompany Locations: workforce distribution, site types, workloads\n•\tDeals & Service Providers: outsourcing contracts, project scope, vendor relationships\n•\tLeadership Movements: executive transitions across roles, functions, and geographies\n•\tJob Postings (Company-Level): standardized job roles, skills, and hiring trends\n•\tCompany Signals & Competitors: curated events, partnerships, competitive landscapes\n•\tFilings & Transcripts: SEC filings, international filings, earnings transcripts & attendees\n•\tShareholdings: ownership details, filing dates, shareholding percentages\n\n**Use Cases:**\n***Analyze the availability of talent for a given role in a specific location.***\n\nMeasure the talent pool size and distribution for target roles within chosen geographies to support workforce planning and sourcing.\n\nSELECT\n profile_info.company_name,\n profile_info.business_function,\n COUNT(DISTINCT profile_info.profile_id) AS talent_count\nFROM profile_info\nWHERE profile_info.job_role ILIKE '%Software Engineer%'\n AND profile_info.location ILIKE '%San Francisco%'\nGROUP BY profile_info.company_name, profile_info.business_function\nORDER BY talent_count DESC;\n\n***Identify the most common certifications for a DevOps Engineer role in the USA.***\n\nIdentify widely held certifications among professionals in a role and geography to align upskilling, hiring, and credentialing strategies.\n\nSELECT\n profile_certifications.certification_name,\n COUNT(DISTINCT profile_certifications.profile_id) AS holders_count\nFROM profile_certifications\nJOIN profile_info\n ON profile_certifications.profile_id = profile_info.profile_id\nWHERE profile_info.job_role = 'Devops Engineer'\n AND profile_info.location ILIKE 'United States'\n AND profile_certifications.certification_name IS NOT NULL\nGROUP BY profile_certifications.certification_name\nORDER BY holders_count DESC\nLIMIT 10;\n\n***Which competitors is Snowflake hiring talent from and who are they losing their talent to?***\n\nDraup’s talent movement capabilities reveal a company's inbound and outbound talent flows across roles, functions, and geographies. Identify key feeder and destination companies to inform talent strategy, poaching risk, or market positioning.\n\nSELECT\n company_name AS competitor_company,\n executive_name,\n from_title AS previous_title,\n to_title AS current_title,\n movement_date\nFROM\n company_leadership_movement\nWHERE\n affiliated_entity_name = 'Snowflake Inc.'\n AND company_name IS NOT NULL\n AND movement_date >= DATE_SUB(CURRENT_TIMESTAMP(), INTERVAL '1' YEAR)\nORDER BY\n movement_date DESC;\n\n***List VP-and-above R&D leaders who left Pfizer in the past 12 months and their new employers***\n\nTrack high-level executive movements from any company using Draup’s leadership change data. Filter by function, designation, and time period to uncover exits and transitions; useful for competitive intelligence, recruitment targeting, or succession mapping.\n\nSELECT\n executive_name,\n from_title AS title_at_pfizer,\n company_name AS new_employer,\n to_title AS new_title,\n movement_date\nFROM\n company_leadership_movement\nWHERE\n affiliated_entity_name = 'Pfizer'\n AND business_function = 'R&D'\n AND (from_title LIKE '%VP%' OR from_title LIKE '%President%' OR from_title LIKE '%Chief%')\n AND movement_date >= current_timestamp - INTERVAL '1' YEAR\nORDER BY\n movement_date DESC;\n\n***See how does your peers’ workforce split by function or location compare with you.***\n\nBenchmark workforce distribution across functions and geographies against competitors to identify over- or under-invested areas in your organization.\n\nWITH workforce_distribution AS (\n SELECT\n profile_info.company_name,\n profile_info.business_function,\n profile_info.location,\n COUNT(*) AS headcount\n FROM profile_info\n WHERE profile_info.business_function IS NOT NULL\n AND profile_info.location IS NOT NULL\n GROUP BY profile_info.company_name, profile_info.business_function, profile_info.location\n),\ntotal_workforce AS (\n SELECT\n workforce_distribution.company_name,\n SUM(workforce_distribution.headcount) AS total_headcount\n FROM workforce_distribution\n GROUP BY workforce_distribution.company_name\n),\nworkforce_percentage AS (\n SELECT\n workforce_distribution.company_name,\n workforce_distribution.business_function,\n workforce_distribution.location,\n workforce_distribution.headcount,\n ROUND((workforce_distribution.headcount * 100.0) / total_workforce.total_headcount, 2) AS percentage_split\n FROM workforce_distribution\n JOIN total_workforce\n ON workforce_distribution.company_name = total_workforce.company_name\n)\nSELECT\n CASE \n WHEN workforce_percentage.company_name = 'Microsoft' THEN 'Microsoft'\n ELSE 'Peer Companies'\n END AS company_group,\n workforce_percentage.business_function,\n workforce_percentage.location,\n SUM(workforce_percentage.headcount) AS total_headcount,\n ROUND(AVG(workforce_percentage.percentage_split), 2) AS avg_percentage_split\nFROM workforce_percentage\nGROUP BY\n CASE \n WHEN workforce_percentage.company_name = 'Microsoft' THEN 'Microsoft'\n ELSE 'Peer Companies'\n END,\n workforce_percentage.business_function,\n workforce_percentage.location\nORDER BY\n workforce_percentage.business_function,\n workforce_percentage.location;\n\n***Analyze the historical talent movement and attrition patterns in a location***\n\nAnalyze career transitions, exits, and movements over time within a geography to anticipate attrition risks and strengthen retention strategies.\n\nWITH profile_experience_expanded AS (\n SELECT\n profile_id,\n company_name,\n UNNEST(msa) AS location,\n start_date,\n end_date\n FROM profile_experiences\n WHERE start_date IS NOT NULL\n),\nlocation_joins AS (\n SELECT\n location,\n EXTRACT(YEAR FROM start_date) AS start_year,\n COUNT(DISTINCT profile_id) AS joins\n FROM profile_experience_expanded\n GROUP BY location, EXTRACT(YEAR FROM start_date)\n),\nlocation_exits AS (\n SELECT\n location,\n EXTRACT(YEAR FROM end_date) AS end_year,\n COUNT(DISTINCT profile_id) AS exits\n FROM profile_experience_expanded\n WHERE end_date IS NOT NULL\n GROUP BY location, EXTRACT(YEAR FROM end_date)\n)\nSELECT\n COALESCE(location_joins.location, location_exits.location) AS location,\n COALESCE(location_joins.start_year, location_exits.end_year) AS year,\n COALESCE(location_joins.joins, 0) AS total_joins,\n COALESCE(location_exits.exits, 0) AS total_exits,\n COALESCE(location_exits.exits, 0) - COALESCE(location_joins.joins, 0) AS net_attrition\nFROM location_joins\nFULL OUTER JOIN location_exits\n ON location_joins.location = location_exits.location\n AND location_joins.start_year = location_exits.end_year\nORDER BY location, year;","terms_of_service":"https://draup.com/privacy-policy#:~:text=Unless%20provided%20for%20in%20this,that%20work%20on%20our%20behalf","documentation_link":"","privacy_policy_link":"https://draup.com/privacy-policy","geographical_coverage":"[\"Global\"]","cost":"FREE","update_frequency":{"interval":1,"unit":"WEEKLY"},"collection_granularity":{"interval":1,"unit":"DAILY"},"data_source":"Public Sources","assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"Draup, Inc.","description":"Draup is an enterprise agentic intelligence platform built for HR and Sales leaders navigating transformation. By combining unparalleled real-time global data with agentic AI, Draup enables strategic decision-making across talent and revenue functions.\n \nThrough its Talent Intelligence solution, Draup empowers HR teams to drive workforce planning, precision hiring, predictive skill architecture, and competitive benchmarking. Its Sales Intelligence solution helps go-to-market teams uncover deep account insights, identify buying triggers, and accelerate revenue growth.\n \nTrusted by over 300 global enterprises - including five of the Fortune 10 - Draup supports companies like Microsoft, PepsiCo, PayPal, and Moderna in building future-ready workforces and growth strategies."},"popularity":1019},{"id":"b8b6efe7-bde6-49a5-892b-ca87090100a9","summary":{"name":"Neighborhood Real Estate","subtitle":"Housing market details for every neighborhood in America, compare against state and national averages.","setting":{"visibility":"PUBLIC"},"created_at":1789679775681,"updated_at":1789679775681,"categories":["FINANCIAL"],"listingType":"PERSONALIZED","provider_id":"26040e80-2e39-4e01-8fea-0667064d9fce"},"detail":{"description":"Neighborhood Real Estate delivers detailed housing market data for every neighborhood in the United States, enabling clear comparisons to state and national averages. Refreshed quarterly, this dataset provides details into local home values, trends, housing characteristics, rental data, and more. \n\nProfessionals rely on Neighborhood Real Estate data to analyze local market trends, price and value properties accurately, assess investment and lending risk, enrich location intelligence, and identify new opportunities to attract and serve customers. \n\n**Market Analysis**\nDetailed insights into housing market trends, which can be critical for real estate developers, investors, and brokers in assessing market opportunities and risks, understanding price trends, and identifying potential investment areas.\n**Asset Valuation**\nFor financial institutions like banks and mortgage lenders, this data can be used to verify the value of real estate assets for loan underwriting, securitization, and risk management purposes. Accurate and current property valuations are essential for asset-backed lending and investment activities.\n**Pricing Analysis**\nReal estate professionals can leverage this data to conduct comparative market analysis, helping to set competitive listing prices for sellers and advise buyers on fair offer prices.\n**Customer Acquisition**\nUnderstanding the real estate landscape of a neighborhood can help businesses, especially those in the home services industry, to target potential customers more effectively.\n**Location Data Enrichment**\nReal estate data can provide crucial context to location intelligence, offering insights into the housing market conditions and property values in a neighborhood. This can enhance decision-making across various business functions.\n**Risk Analysis**\nFinancial institutions and investors can use this data to evaluate the risk associated with real estate investments in a particular neighborhood. For instance, areas with declining home values might indicate higher risk.","terms_of_service":"https://www.cotality.com/","documentation_link":"https://www.cotality.com/products/neighborhood-real-estate?utm_campaign=412443580-EDSG-ALL-CloudMarketplace-Databricks-2026&utm_source=databricks&utm_medium=marketplace&utm_term=neighborhoodrealestate","privacy_policy_link":"https://www.cotality.com/","assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"Cotality","description":"We illuminate data, insights, and workflows across the property ecosystem, analyzing the largest objective dataset in the U.S. for real-time insights in minutes, not months. Together, we’ll identify hidden risks and powerful opportunities for transforming your business and solving society’s greatest challenges."},"popularity":664},{"id":"c32146a4-72f9-4692-b59c-8189ea4b4f8f","summary":{"name":"California Medical Intelligence Dataset - SAMPLE","subtitle":"Verified profiles of 997 California medical and cosmetic providers across five high-demand specialties. 100 rows.","share":{"name":"california_medical_intelligence_dataset_sample","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1768511210645,"updated_at":1768607715606,"categories":["ADVERTISING_AND_MARKETING","COMMERCE","DEMOGRAPHICS","ECONOMICS","EDUCATION","HEALTH","RETAIL","SCIENCE_AND_RESEARCH"],"listingType":"STANDARD","provider_id":"2d97eccd-0fcb-4935-8bdf-5c34065e0349"},"detail":{"description":"## **Overview**\n\nThis dataset delivers a verified, analysis-ready view of 997 licensed medical and cosmetic providers across California’s major metropolitan regions. Covering five commercially valuable specialties—Plastic Surgery, Dermatology, Pain Management, Medical Spas, and Cosmetic Dentistry—the dataset enables organizations to understand provider distribution, service availability, and competitive positioning in one of the nation’s most economically significant healthcare markets.\n\nWith exceptionally high cell completeness (99.4%), curated business details, and geographic depth, this dataset is especially attractive to insurers optimizing network coverage, healthcare organizations conducting market expansion analysis, and technology partners building provider intelligence models. Its structured format supports seamless ingestion into Databricks for analytics, enrichment, and machine learning workflows.\n\n## **Provenance**\n\nThe dataset originates from publicly available Google Maps listings. It was compiled by **Maths with Kanchana LLC**, following ethical data practices that rely exclusively on public business information without collecting personal or sensitive data. Provider entries were verified during compilation to ensure accuracy and consistency. The data is publicly available and no proprietary or restricted sources were used.\n\n## **Use Cases**\n\nThis dataset enables high-value analytics and ML workflows on Databricks:\n\n* **Provider Network Optimization:** Identify coverage gaps, saturation zones, and high-value expansion areas for insurers and health systems.\n* **Market Intelligence & Competitive Analysis:** Benchmark specialties and provider density across California metros to support strategic planning.\n* **Healthcare Lead Scoring & Segmentation:** Power ML models that classify and prioritize providers for outreach, partnerships, or sales.\n* **Geospatial Analysis:** Map provider concentration and regional demand signals using Databricks geospatial libraries.\n* **Business Operations Automation:** Feed into CRM, territory planning, or revenue intelligence dashboards for healthcare organizations.\n\n## **Column Dictionary**\n\n* **title** (string): Business or provider practice name.\n* **totalScore** (decimal): Average Google Maps rating.\n* **reviewsCount** (integer): Total number of public reviews.\n* **street** (string): Street address of the provider location.\n* **city** (string): City where the business is located.\n* **state** (string): State (California).\n* **countryCode** (string): Country code (US).\n* **website** (string): Official website link if available.\n* **phone** (string): Business phone number.\n* **categoryName** (string): Primary Google Maps business category.\n* **url** (string): Direct Google Maps listing URL.\n\n## **Keywords:** \n\nCalifornia healthcare, provider intelligence, medical providers, geospatial dataset, cosmetic providers, dermatology, plastic surgery, insurance analytics, healthcare market data","terms_of_service":"https://www.aidc.app/sample-terms-of-service","documentation_link":"https://app.aidatacoop.com/datasets/63fdd426-0af7-4f57-93f4-41b02ecfe7b5#5cc1b219-ab3c-41f0-ac6d-0a192fccaf76","privacy_policy_link":"https://www.aidc.app/faq","embedded_notebook_file_infos":[{"id":"d536aed2-d71e-47c4-864c-c7d712f444c7","marketplace_file_type":"EMBEDDED_NOTEBOOK","file_parent":{"parent_id":"c32146a4-72f9-4692-b59c-8189ea4b4f8f","file_parent_type":"LISTING"},"mime_type":"text/html","download_link":"https://marketplace-file-storage-prod.s3-fips.us-east-1.amazonaws.com/EMBEDDED_NOTEBOOK/LISTING_c32146a4-72f9-4692-b59c-8189ea4b4f8f/d536aed2-d71e-47c4-864c-c7d712f444c7?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEGgaDnVzLWVhc3QtMS1maXBzIkYwRAIgbC8sWIoYaHR8y7Wq86nPb0pufBl4%2BM4PWpeN%2B5PW9VQCIG7BhAVBDCWTpxFDLeuKYm%2Ff1JLz4iccPMzter0YdhgWKqYECDAQABoMNDE0MzUxNzY3ODI2IgxNAJrHbXaZG20jNfMqgwQyGEb4TsoVOoNvJ%2BaA%2FZ7zeu8hGcHqCeNfblP%2FQo5g6q2aRNtpwtPRKMlhYcIE%2FUQaNT1Lqmyptu32VUQTpzjTTxRVmJrDPkheSCkV06XQ%2FpTMKjU8CC7X1b%2FqYf8CrBwZT7Cv4C5WPjEET38f2A79h0xV6bZgPy28zQWrSeIJ9w6AfZBxiE24%2FVc08zVA0ivCT6lsy7nuU0wcR%2FMg2C9nd1vaMnQ923VpD21CKfVVYcrNWL2b%2FQ%2BNAO%2BxPjPgjiN6UPQbvbvZxkSdhgrGtecIg9NgqGkMcvM5VLTDtdC1kdeZetfOC2obtA%2BT%2F0gy3tIDo6WM5hcQfw743%2FAU9oaGnRBl%2BwoxRRDCZA%2F4jnPdluICSI7NCr6C3HHOe4CSZJjRihczxPA%2FgQczAVjAPs6CrK9N9pIzt0vNJpGUTgg4AoxftTvGfciDREilSctPIcBk6GwB%2Bzt4wbtNCmEPU%2BFD46rc6CWVQmmLwbKOeHw70ipMVFlOc8NlXkEtGO2vu6H44MstSSc6daOVUYUavQSGZ6jGhEjsP2YGz8KDm9XQotB%2FtyVBqiZV01OKqDmmBJA%2FXjUpSJ8M%2FeJXhe5m%2BKcxzNObtoJRI7jci3Tpt08XZyXZ7%2FyIV8H30ya468X3cfkTXrm972uga3z7iXAdubyEHnQIua%2FuvtwOHILi6O5D1Me2MzCPvcnXBjqTAYDoMWBt%2B3AVFuqSs2dmDQNBnvEhXfMgTedKLD5PJHb3aIl15iBkLaNu36GYnVCp9ZU2lSYcTbY5QwNnNeTfJTNy9bKhwzru6Sg%2BslhN%2BTS0zkTn%2BeLMY4tHgPEtEZ2cp%2FCk6GwqswvnYVKHjwO8q%2B1Mx8lc08fVEfarE9dke1GGFqoddx%2Bq%2BZUyHbDlAVfrCmf0yw%3D%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20261110T000135Z&X-Amz-SignedHeaders=host&X-Amz-Expires=86400&X-Amz-Credential=ASIAWA6KKHEJORWBH4N3%2F20261110%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=59d29c687b9fc997ced0e4f19c7deebf811153e663561868ad6689957869435c","created_at":1768607633942,"updated_at":1768607633942,"display_name":"california_medical_intelligence_sample_notebook_20260116","status":"FILE_STATUS_PUBLISHED"}],"geographical_coverage":"[\"United States and Canada\"]","cost":"FREE","update_frequency":{"interval":1,"unit":"MONTHLY"},"collection_granularity":{"interval":1,"unit":"DAILY"},"collection_date_start":1764709355872,"collection_date_end":1764795761476,"data_source":"The dataset originates from publicly available Google Maps listings. It was compiled by a Kaggle Datasets Grandmaster, following ethical data practices that rely exclusively on public business information without collecting personal or sensitive data.","size":1.0,"assets":["ASSET_TYPE_MEDIA"],"license":"https://spdx.org/licenses/CC-BY-4.0.html"},"provider_summary":{"name":"AIDC, Inc.","description":"Great AI starts with great data. We have it. Ship data like software. AIDC packages external data as governed Delta tables with Unity Catalog, complete with schema, metadata, lineage, and clear license terms. Pre‑vetted contracts minimize back‑and‑forth, so squads move from browse to pipeline in days. Quality control is operated by Kaggle Grandmasters and senior data engineers at that standard, so datasets behave in production and pass audits. Coverage spans luxury item pricing (valuation, comp, elasticity studies) to retail property analytics (site selection, underwriting, performance prediction). Instrument adoption and prove lift: reduced ETL, faster training, higher dataset usability rates. Repeatable motion across teams: import → govern → build → measure → scale. Available now on Databricks Marketplace."},"popularity":1318},{"id":"3a5cd5e3-b4b3-4fb0-8a3b-072e57268bc8","summary":{"name":"Improving Surgical Outcomes for Elective Procedures","subtitle":"Systematically identify and address modifiable preoperative risk factors for elective procedures","share":{"name":"surgery","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1751255289822,"updated_at":1751315695716,"categories":["HEALTH"],"listingType":"STANDARD","provider_id":"6c6166cf-26dc-4aec-a16d-6d1cc8dde719"},"detail":{"description":"**Overview**\nHip and knee replacements are among the most common elective surgical procedures, with over 1 million performed annually in the United States. These procedures significantly improve quality of life for patients with severe arthritis and joint degeneration, but they also carry risks of complications, extended hospital stays, and readmissions.\n\n**Objective**: This demonstration analysis identifies modifiable risk factors in elective surgery patients and develops targeted preoperative intervention strategies to reduce surgical complications and improve outcomes. By combining risk factor analysis with care gap identification and predictive modeling, we show how to create actionable frameworks that transform preoperative care from standardized protocols to personalized, risk-stratified intervention programs.\n\n**Key Focus Areas**:\n- Identify which preoperative modifiable risk factors have the greatest impact on surgical complications\n- Determine how targeted interventions reduce complication rates\n- Care delivery gap analysis to identify missed opportunities for preoperative risk mitigation\n- Calculate the potential value of implementing a comprehensive preoperative optimization program\n\n**Product Details**\n**Dataset Overview**\nThe analysis utilizes synthetic data from the `elective_surgery.surgery_ortho_synthetic_data` table, representing 5,000 elective orthopedic surgery patients from 2022-2024. The dataset includes detailed patient demographics, clinical characteristics, surgical outcomes, and cost information, capturing real-world patterns in surgical risk factors and their relationship to postoperative complications.\n\n**Key data elements**\n\n- **Patient Demographics**: Age, gender, race, ethnicity, insurance type\n- **Clinical Data**: ASA physical status, BMI, comorbidities (diabetes, hypertension, anemia)\n- **Surgical Information**: Procedure type (hip vs knee), surgery date, surgeon\n- **Risk Factors**: Frailty score, tobacco use, substance use disorder\n- **Social Determinants**: Social vulnerability index, transportation barriers, no-show history\n- **Outcomes**: Complications, readmissions, length of stay, utilization metrics\n\nFor more details, refer to the embedded notebook.\n\n*This product uses synthetic data and simplified models for illustration purposes. Results shown are not indicative of actual performance. 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It is web commerce only: not a general technology stack, not infrastructure, not hosting, not internal tooling. If you need to know whether a company sells online and through what, this answers it. If you need their cloud provider or their CRM, it does not.\n\nThis listing contains sample data. The full product is delivered under a separate agreement.\n\n**Use cases**\n\n- Finding companies running a specific e-commerce platform or payment gateway.\n- Competitive displacement, by identifying who runs an incumbent tool.\n- Qualifying whether a company sells online at all before an outbound programme.\n\n**Product details**\n\n- Dataset represented: technographic_sample, 300 records across 43 countries, 17 columns.\n- Sample fields include CompanyName, Country, Website, WebDomain, EcommercePlatforms, PaymentProviders, PaymentMethods, MarketingAutomation and a count column for each.\n- Values are pipe-separated lists and are not case-normalised: Woocommerce, shopify and openCart all occur, so lowercase before matching.\n- Counted case-insensitively, the sample vocabulary runs to 50 distinct platforms, 43 gateways, 12 payment methods and 22 marketing-automation tools.\n- This product joins our other listings on RecordID.\n\nFor more details, refer to the embedded notebook.\n\n**Additional Insights**\n\nWe do not publish a coverage figure for this product. The figure we have was measured on a population that does not represent the base, so quoting it would be inaccurate in either direction, and we would rather say that than publish a number we cannot stand behind. Ask us for coverage on the market and segment you care about and we will measure it against a stated denominator.\n\nDetection is signal-based: an absent value means no signal was detected, not a confirmed absence.\n\nUpdate cadence: static sample extracted 28 July 2026, refreshed when the listing is refreshed. 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It provides data over CAHPS Practice Site Respondents by Region Ownership Mode and Provider, Health Plan Samples by State and HCAHPS of National and State Averages. It also contains dataset for Hospital Consumer Assessment of Providers and Patient Survey and Maryland’s Quality-Based Reimbursement (QBR) program for the fiscal year 2014.\n\n**Description**\nThis data package contains the Top box scores display the percentage of respondents reporting the most positive response for a composite, rating, or question item at the respondent level. Top box scores are presented for several practice characteristics, such as region, physician specialty, provider type, practice ownership, survey completion mode and physicians working each week. Consumer Assessment of Healthcare Providers and Systems (CAHPS) surveys ask consumers about their experiences with health care. The CAHPS program at the U.S. Agency for Healthcare Research and Quality (AHRQ) supports the development and promotion of CAHPS surveys, toolkit materials, comparative databases, and provides technical assistance to users. It presents summary-level results for the CAHPS Health Plan Survey 5.0 version for the following health plan enrollee populations: Adult Medicaid, Child Medicaid, CHIP Child, and Medicare Managed Care.\n\nThe Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) Patient Survey is a survey instrument and data collection methodology for measuring patients’ perception of their hospital experience. This dataset reports the results for the HCAHPS Patient Survey by the hospital, state as well as national level. The HCAHPS Patient Survey is part of a national initiative by the United States Department of Health and Human Services to measure the quality of care in hospitals. Maryland’s Quality-Based Reimbursement (QBR) program is in place since July 2009 and uses similar measures to the federal Medicare Value-Based Purchasing (VBP) program, which is in place since October 2012. The response percentage and the number of completed surveys for this dataset are included for year 2014. Due to Maryland’s long-standing Medicare waiver for its all-payer hospital rate-setting system and the implementation of the Quality-Based Reimbursement (QBR) program, the Centers for Medicare & Medicaid Services (CMS) has given Maryland various special considerations, including annual exemption from the Medicare VBP program.\n\n**Benefits**\n- Useful for medical care and providers, patients, consumers and hospitals. cahps survey database and chartbook database is available on the maryland health services cost review commission, agency for healthcare research and quality (ahrq), consumer assessment of healthcare providers and systems (cahps) website in raw form, whereas a jsl customer gets it in clean and normalized form ready to use in one place and create his own custom reports. easy to comprehend for the customers who are unfamiliar with the medical terminologies as all abbreviated terms are replaced with full form, unlike cahps survey database and chartbook original datasets which are full of abbreviations.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [CAHPS Comparison Top Box Scores by Population](https://www.johnsnowlabs.com/marketplace/cahps-comparison-top-box-scores-by-population)\n\t- This dataset is from the 2016 CAHPS Health Plan Survey Database and 2016 Chartbook: What Consumers Say About Their Experiences with Their Health Plans and Medical Care. It presents summary-level results submitted in 2016 for the CAHPS Health Plan Survey 5.0 version for the following health plan enrollee populations: Adult Medicaid, Child Medicaid, CHIP (Children's Health Insurance Program), and Medicare Managed Care.\n- [CAHPS Comparison of Adult Survey Top Box Scores by Specialty](https://www.johnsnowlabs.com/marketplace/cahps-comparison-of-adult-survey-top-box-scores-by-specialty)\n\t- This dataset is from the CAHPS Clinician & Group Survey Database and Chartbook: What Patients Say About Their Health Care Providers and Medical Practices. It presents comparative results from the Comparison of Adult Survey 2.0, Adult Survey 3.0 and Child Survey 3.0 Combined Top-Box Scores that display the percent of survey respondents who chose the most positive score for a given item response scale to a composite item by Specialty from 2015 to 2018.\n- [CAHPS Comparison of Adult Survey Top Box Scores by State](https://www.johnsnowlabs.com/marketplace/cahps-comparison-of-adult-survey-top-box-scores-by-state)\n\t- This dataset is from the CAHPS Clinician & Group Survey Database and Chartbook: What Patients Say About Their Health Care Providers and Medical Practices. It presents comparative results from the Comparison of Adult Survey 2.0, Adult Survey 3.0 and Child Survey 3.0 Combined Top-Box Scores that display the percent of survey respondents who chose the most positive score for a given item response scale to a composite item by State from 2015 to 2018.\n- [CAHPS Practice Site Respondents by Region Ownership Mode and Provider](https://www.johnsnowlabs.com/marketplace/cahps-practice-site-respondents-by-region-ownership-mode-and-provider)\n\t- This dataset shows the distribution of practice sites and respondents by region, practice ownership and affiliation, survey completion mode and number of providers working per week that make up the comparative results for all versions of the CAHPS survey including Adult 2.0 Combined, Child 2.0 Combined, Adult Visit 2.0 and Adult 3.0 for 2015. Latest inclusion for 2016 data include Adult 3.0 Combined and Child 3.0 Combined\n- [CAHPS Survey Respondents and Health Plan Samples by State](https://www.johnsnowlabs.com/marketplace/cahps-survey-respondents-and-health-plan-samples-by-state)\n\t- This dataset shows data submissions to the CAHPS Health Plan Survey Database by State from 2014 to 2016. The total number of respondents and plans by State are presented by population and health plan samples. It presents comparative results from the 2014 to 2016 Survey Respondents and Health Plan Samples by State from Adult Medicaid, Child Medicaid, Children's Health Insurance Program (CHIP) Child and Medicare.\n- [HCAHPS National and State Averages](https://www.johnsnowlabs.com/marketplace/hcahps-national-and-state-averages)\n\t- The Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) Patient Survey is a survey instrument and data collection methodology for measuring patients’ perception of their hospital experience. This dataset reports the results for the HCAHPS Patient Survey by state-level and also national-level.\n- [Hospital Consumer Assessment of Providers and Patient Survey](https://www.johnsnowlabs.com/marketplace/hospital-consumer-assessment-of-providers-and-patient-survey)\n\t- The Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) Patient Survey is a survey instrument and data collection methodology for measuring patients’ perception of their hospital experience.\n- [Maryland HCAHPS Baseline Period Results FY2014](https://www.johnsnowlabs.com/marketplace/maryland-hcahps-baseline-period-results-fy2014)\n\t- This dataset contains Maryland’s Quality-Based Reimbursement (QBR) program, which is in place since July 2009 and uses similar measures to the federal Medicare Value-Based Purchasing (VBP) program, in place since October 2012. The response percentage and the number of completed surveys for this dataset are included for year 2014.\n- [OAS CAHPS Survey for Ambulatory Surgical Centers by Facility](https://www.johnsnowlabs.com/marketplace/oas-cahps-survey-for-ambulatory-surgical-centers-by-facility)\n\t- This dataset contains a list of ambulatory surgical center ratings for the Outpatient and Ambulatory Surgery Consumer Assessment of Healthcare Providers and Systems (OAS CAHPS) survey. The data values are updated and reported each quarter with data from the most recently completed quarter replacing the oldest quarter.\n- [OAS CAHPS Survey for Ambulatory Surgical Centers by State](https://www.johnsnowlabs.com/marketplace/oas-cahps-survey-for-ambulatory-surgical-centers-by-state)\n\t- This dataset contains a list of the state averages for the ambulatory surgical center ratings for the Outpatient and Ambulatory Surgery Consumer Assessment of Healthcare Providers and Systems (OAS CAHPS) survey responses. The OAS CAHPS survey collects information about patient's experiences of care in Hospital Outpatient Departments (HOPDs) and Ambulatory Surgical Centers (ASCs).\n- [OAS CAHPS Survey for Hospital Outpatient Departments by Facility](https://www.johnsnowlabs.com/marketplace/oas-cahps-survey-for-hospital-outpatient-departments-by-facility)\n\t- This dataset contains a list of hospital outpatient department ratings for the Outpatient and Ambulatory Surgery Consumer Assessment of Healthcare Providers and Systems (OAS CAHPS) survey. The dataset is updated and reported each quarter with data from the most recently completed quarter replacing the oldest quarter of data.\n- [OAS CAHPS Survey for Hospital Outpatient Departments by State](https://www.johnsnowlabs.com/marketplace/oas-cahps-survey-for-hospital-outpatient-departments-by-state)\n\t- This dataset contains a list of the state averages for the Outpatient and Ambulatory Surgery Consumer Assessment of Healthcare Providers and Systems (OAS CAHPS) survey responses. The OAS CAHPS survey collects information about patient's experiences of care in Hospital Outpatient Departments (HOPDs) and Ambulatory Surgical Centers (ASCs).\n- [PCH HCAHPS Patient Survey by Hospital](https://www.johnsnowlabs.com/marketplace/pch-hcahps-patient-survey-by-hospital)\n\t- This dataset contains a list of hospital ratings for the Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS). HCAHPS is a national, standardized survey of hospital patients about their experiences during a recent inpatient hospital stay.\n- [PCH HCAHPS Patient Survey by State](https://www.johnsnowlabs.com/marketplace/pch-hcahps-patient-survey-by-state)\n\t- This dataset contains a list of the state averages for the Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) survey responses. HCAHPS is a national, standardized survey of hospital patients about their experiences during a recent inpatient hospital stay.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated\n- Data Updates\n - Data updates support replace-on-update: outdated foreign keys are deprecated, not deleted\n\n**Our data is curated and enriched by domain experts** \n\n Each dataset is manually curated by our team of doctors, pharmacists, public health & medical billing experts:\n- Field names, descriptions, and normalized values are chosen by people who actually understand their meaning\n- Healthcare & life science experts add categories, search keywords, descriptions and more to each dataset\n- Both manual and automated data enrichment supported for clinical codes, providers, drugs, and geo-locations\n- The data is always kept up to date – even when the source requires manual effort to get updates\n- Support for data subscribers is provided directly by the domain experts who curated the data sets\n- Every data source’s license is manually verified to allow for royalty-free commercial use and redistribution.\n\n**Need Help?**\nIf you have questions about our products, contact us at [info@johnsnowlabs.com](mailto:info@johnsnowlabs.com). \n 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It is optimized to answer questions and assist users with various Restaurants-related procedures. It has been trained using hybrid synthetic data generated using our NLP/NLG technology and our automated Data Labeling (DAL) tools.\n \nThe goal of this model is to show that a generic verticalized model makes customization for a final use case much easier. An overview of this approach can be found at: [From General-Purpose LLMs to Verticalized Enterprise Models](https://www.bitext.com/blog/general-purpose-models-verticalized-enterprise-genai/)\n\n**Intended Use**\n\n- **Recommended applications**: This model is designed to be used as the first step in Bitext’s two-step approach to LLM fine-tuning for the creation of chatbots, virtual assistants and copilots for the Restaurants domain, providing customers with fast and accurate answers about their needs.\n- **Out-of-scope**: This model is not suited for non-restaurants related questions and should not be used for providing health, legal, or critical safety advice.\n\n**Model Architecture**\n\nThis model utilizes the `MistralForCausalLM` architecture with a `LlamaTokenizer`, ensuring it retains the foundational capabilities of the base model while being specifically enhanced for restaurants-related interactions.\n\n**Training Data**\n\nThe model was fine-tuned on the [Bitext Restaurants Dataset](https://huggingface.co/datasets/bitext/Bitext-restaurants-llm-chatbot-training-dataset) comprising various restaurants-related intents, including: find_restaurant, check_menu, make_reservation, order_food_online, events, and more. Totaling 30 intents, and each intent is represented by approximately 1000 examples.\n\nThis comprehensive training helps the model address a broad spectrum of restaurants-related questions effectively. The dataset follows the same structured approach as our dataset published on Hugging Face as [bitext/Bitext-customer-support-llm-chatbot-training-dataset](https://huggingface.co/datasets/bitext/Bitext-customer-support-llm-chatbot-training-dataset), but with a focus on the restaurants domain.\n\n**Training Procedure**\n\n**Hyperparameters**\n\n- **Optimizer**: AdamW\n- **Learning Rate**: 0.0002 with a cosine learning rate scheduler\n- **Epochs**: 3\n- **Batch Size**: 4\n- **Gradient Accumulation Steps**: 4\n- **Maximum Sequence Length**: 8192 tokens\n\n**Environment**\n\n- **Transformers Version**: 4.43.4\n- **Framework**: PyTorch 2.3.1+cu121\n- **Tokenizers**: Tokenizers 0.19.1\n\n**Limitations and Bias**\n\n- The model is trained for restaurants-specific contexts but may underperform in unrelated areas.\n- Potential biases in the training data could affect the neutrality of the responses; users are encouraged to evaluate responses critically.\n\n**Ethical Considerations**\n\nIt is important to use this technology thoughtfully, ensuring it does not substitute for human judgment where necessary, especially in sensitive situations.\n\n**Acknowledgments**\n\nThis model was developed and trained by Bitext using proprietary data and technology.\n\n**License**\n\nThis model, \"Mistral-7B-Restaurants\", is licensed under the Apache License 2.0 by Bitext Innovations International, Inc. This open-source license allows for free use, modification, and distribution of the model but requires that proper credit be given to Bitext.\n\n**Key Points of the Apache 2.0 License**\n\n- **Permissibility**: Users are allowed to use, modify, and distribute this software freely.\n- **Attribution**: You must provide proper credit to Bitext Innovations International, Inc. when using this model, in accordance with the original copyright notices and the license.\n- **Patent Grant**: The license includes a grant of patent rights from the contributors of the model.\n- **No Warranty**: The model is provided \"as is\" without warranties of any kind.\n\nYou may view the full license text at [Apache License 2.0](http://www.apache.org/licenses/LICENSE-2.0).\n\nThis licensing ensures the model can be used widely and freely while respecting the intellectual contributions of Bitext. For more detailed information or specific legal questions about using this license, please refer to the official license documentation linked above.\n","terms_of_service":"https://www.bitext.com/api-terms-of-use/","documentation_link":"","privacy_policy_link":"https://www.bitext.com/privacy-policy/","embedded_notebook_file_infos":[{"id":"fd164c9c-8d71-4a92-8751-7c723c62ef58","marketplace_file_type":"EMBEDDED_NOTEBOOK","file_parent":{"parent_id":"2f27e394-01d5-470b-90b8-70c4af946b98","file_parent_type":"LISTING"},"mime_type":"text/html","download_link":"https://marketplace-file-storage-prod.s3-fips.us-east-1.amazonaws.com/EMBEDDED_NOTEBOOK/LISTING_2f27e394-01d5-470b-90b8-70c4af946b98/fd164c9c-8d71-4a92-8751-7c723c62ef58?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEGgaDnVzLWVhc3QtMS1maXBzIkYwRAIgbC8sWIoYaHR8y7Wq86nPb0pufBl4%2BM4PWpeN%2B5PW9VQCIG7BhAVBDCWTpxFDLeuKYm%2Ff1JLz4iccPMzter0YdhgWKqYECDAQABoMNDE0MzUxNzY3ODI2IgxNAJrHbXaZG20jNfMqgwQyGEb4TsoVOoNvJ%2BaA%2FZ7zeu8hGcHqCeNfblP%2FQo5g6q2aRNtpwtPRKMlhYcIE%2FUQaNT1Lqmyptu32VUQTpzjTTxRVmJrDPkheSCkV06XQ%2FpTMKjU8CC7X1b%2FqYf8CrBwZT7Cv4C5WPjEET38f2A79h0xV6bZgPy28zQWrSeIJ9w6AfZBxiE24%2FVc08zVA0ivCT6lsy7nuU0wcR%2FMg2C9nd1vaMnQ923VpD21CKfVVYcrNWL2b%2FQ%2BNAO%2BxPjPgjiN6UPQbvbvZxkSdhgrGtecIg9NgqGkMcvM5VLTDtdC1kdeZetfOC2obtA%2BT%2F0gy3tIDo6WM5hcQfw743%2FAU9oaGnRBl%2BwoxRRDCZA%2F4jnPdluICSI7NCr6C3HHOe4CSZJjRihczxPA%2FgQczAVjAPs6CrK9N9pIzt0vNJpGUTgg4AoxftTvGfciDREilSctPIcBk6GwB%2Bzt4wbtNCmEPU%2BFD46rc6CWVQmmLwbKOeHw70ipMVFlOc8NlXkEtGO2vu6H44MstSSc6daOVUYUavQSGZ6jGhEjsP2YGz8KDm9XQotB%2FtyVBqiZV01OKqDmmBJA%2FXjUpSJ8M%2FeJXhe5m%2BKcxzNObtoJRI7jci3Tpt08XZyXZ7%2FyIV8H30ya468X3cfkTXrm972uga3z7iXAdubyEHnQIua%2FuvtwOHILi6O5D1Me2MzCPvcnXBjqTAYDoMWBt%2B3AVFuqSs2dmDQNBnvEhXfMgTedKLD5PJHb3aIl15iBkLaNu36GYnVCp9ZU2lSYcTbY5QwNnNeTfJTNy9bKhwzru6Sg%2BslhN%2BTS0zkTn%2BeLMY4tHgPEtEZ2cp%2FCk6GwqswvnYVKHjwO8q%2B1Mx8lc08fVEfarE9dke1GGFqoddx%2Bq%2BZUyHbDlAVfrCmf0yw%3D%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20261110T000135Z&X-Amz-SignedHeaders=host&X-Amz-Expires=86400&X-Amz-Credential=ASIAWA6KKHEJORWBH4N3%2F20261110%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=759d565debb249df932d461aa2fe498d46d74579c1422edef4a1bb31538d280f","created_at":1727476356254,"updated_at":1727476356254,"display_name":"bitext_mistral_7b_models_fine_tune_qlora","status":"FILE_STATUS_PUBLISHED"}],"cost":"FREE","data_source":"Internally generated by Bitext using proprietary NLP and NLG technologies","assets":["ASSET_TYPE_MODEL"],"license":"https://www.apache.org/licenses/LICENSE-2.0","tags":[{"tag_name":"LISTING_TAG_TYPE_TASK","tag_values":["QUESTION_ANSWERING","TEXT2TEXT_GENERATION"]}]},"provider_summary":{"name":"Bitext Innovation International","description":"We understand that one of the critical factors in achieving exceptional performance with LLMs is the quality and relevance of the training data. That’s why we offer a comprehensive suite of tools and services specifically designed to automate and streamline the datasets for fine-tuning LLMs."},"popularity":851},{"id":"15ce44fd-f643-475c-a11f-4b3093fd5056","summary":{"name":"Geolocet | Points of Interest Data | Locations of Top UK Grocery Brand Stores - ALDI, ASDA, Coop, Li","subtitle":"This dataset provides an up-to-date detailed overview of 28,000+ top grocery retailer stores across the UK ","setting":{"visibility":"PUBLIC"},"created_at":1711455223306,"updated_at":1780388538622,"categories":["RETAIL"],"listingType":"PERSONALIZED","provider_id":"5d3ddc17-a7e8-4794-a760-13587e1de2c6"},"detail":{"description":"# UK Grocery Locations Dataset: Top 17 Supermarket Brands\n\n### GIS-Ready Branded Retail Location Intelligence -- 28,534 Verified Sites, Databricks-Native\n\n---\n\nBenchmarking UK grocery chains means having every branded location in one clean, analysis-ready file. This dataset consolidates 28,534 verified sites across the 17 leading supermarket brands in the UK -- from Premier and Coop to Tesco, Lidl, and Waitrose -- giving competitive analysts, territory managers, and logistics teams a single source of truth for branded retail coverage.\n\nSourced from multiple public registries, cross-validated, and refreshed quarterly. Fully compatible with Apache Spark, Delta Lake, and Databricks Unity Catalog.\n\n---\n\n## Who this is for\n\n- **Retail site selectors & expansion teams** benchmarking branded competitor saturation before committing to new locations\n- **CPG & FMCG territory managers** mapping distribution coverage against the branded store network\n- **Logistics & last-mile delivery planners** building drive-time models and optimizing fulfilment zones around major chains\n- **Academic & municipal researchers** studying branded retail accessibility, food deserts, and urban planning\n\n---\n\n## Coverage: 28,534 branded locations across all four UK nations\n\nThis dataset focuses exclusively on the top 17 supermarket brands, making it the right choice when you need clean, comparable branded data without independent or unbranded noise.\n\n**Breakdown by country:** England: 23,676 sites · Scotland: 2,297 · Wales: 1,701 · Northern Ireland: 796\n\n### Major chains & convenience leaders\n\n- [Premier](https://geolocet.com/products/uk-premier-grocery) -- 5,113 locations\n- [Coop](https://geolocet.com/products/uk-coop-grocery) -- 3,808 locations\n- [Tesco](https://geolocet.com/products/uk-tesco-grocery) -- 3,124 locations\n- [SPAR](https://geolocet.com/products/uk-spar-grocery) -- 1,959 locations\n- [Londis](https://geolocet.com/products/uk-londis-grocery) -- 2,138 locations\n- [Morrisons](https://geolocet.com/products/uk-morrisons-grocery) -- 1,719 locations\n- [Costcutter](https://geolocet.com/products/uk-costcutter-grocery) -- 1,531 locations\n- [Sainsbury's](https://geolocet.com/products/uk-sainsburys-grocery) -- 1,504 locations\n- [One Stop](https://geolocet.com/products/uk-one-stop-grocery) -- 1,143 locations\n- [ASDA](https://geolocet.com/products/uk-asda-grocery) -- 1,117 locations\n- [M&S](https://geolocet.com/products/uk-marks-and-spencer-grocery) -- 1,117 locations\n\n### Discounters\n\n- [ALDI](https://geolocet.com/products/uk-aldi-grocery) -- 1,098 locations\n- [Lidl](https://geolocet.com/products/uk-lidl-grocery) -- 1,075 locations\n- [Iceland](https://geolocet.com/products/uk-iceland-grocery) -- 952 locations\n\n### Specialty & regional\n\n- [Waitrose](https://geolocet.com/products/uk-waitrose-grocery) -- 445 locations\n- [Budgens](https://geolocet.com/products/uk-budgens-grocery) -- 349 locations\n- [Heron Foods](https://geolocet.com/products/uk-heron-foods-grocery) -- 342 locations\n\n**Need full national coverage including independents?** The [complete UK dataset](https://geolocet.com/products/uk-grocery-poi-data) covers 71,383 locations across all branded and unbranded grocers.\n\n---\n\n## Schema\n\nEach record includes 17 standardized fields, optimized for zero-friction ingestion via `spark.read.format(\"csv\")`.\n\n- **GUID** (STRING) -- Unique identifier for deterministic primary key mapping\n- **Title** (STRING) -- Official store name or brand title\n- **Latitude** (DOUBLE) -- WGS84 latitude for point-in-polygon & spatial joins\n- **Longitude** (DOUBLE) -- WGS84 longitude for H3 / S2 spatial indexing\n- **Street_No / Street** (STRING) -- Normalized building number and street name\n- **City** (STRING) -- City, municipality, or primary settlement area\n- **Town** (STRING) -- Town, village, or other settlement area\n- **Local Authority** (STRING) -- Local authority administrative area\n- **District** (STRING) -- Administrative district\n- **Postal_Code** (STRING) -- Postal routing code for the region\n- **Address** (STRING) -- Full, standardized street address\n- **Wheelchair** (STRING) -- Accessibility flags for wheelchair mobility\n- **Popularity_Score** (INTEGER) -- Foot-traffic / rating metric, standardized 1-10 scale\n- **Opening_Hours** (STRING) -- Structured daily operating schedules\n- **Phone / Website** (STRING) -- Branch contact details and landing URLs\n\n**Sample record:**\n\n- GUID: c4a1-7f83-...\n- Title: Tesco Express Camden\n- Latitude: 51.5390 / Longitude: -0.1426\n- City: London · Postal_Code: NW1 8AH\n- Local Authority: London Borough of Camden\n- Popularity_Score: 8\n- Opening_Hours: Mo-Su 06:00-23:00\n- Wheelchair: yes\n\n---\n\n## What you can build with it\n\n**Competitor benchmarking & market share analysis**\nCompare store counts, regional density, and network footprint across all 17 brands in a single query. Identify which chains dominate specific local authorities or postal districts and where competitive gaps exist.\n\n**Proximity & catchment modelling**\nUse the geocoded coordinates for drive-time analysis, catchment zone modelling, and nearest-neighbour queries. Understand which branded stores serve overlapping catchments and where underserved pockets remain.\n\n**Network optimisation & territory planning**\nJoin store locations against sales territory boundaries, delivery zones, or field rep assignments. Score each branded cluster for density and accessibility to optimise routing and coverage.\n\n**Demographics & income enrichment**\nCombine with the [UK Demographics Dataset](https://geolocet.com/products/united-kingdom-geodemographics-dataset-with-boundaries) or the [UK Income Indicators Dataset](https://geolocet.com/products/uk-income-indicators-small-area) to layer population structure, household earnings, and socio-economic segmentation onto each branded location.\n\n**Machine learning & predictive analytics**\nThe structured tabular format and standardized coordinates make this dataset ready for ML pipelines -- demand forecasting, site scoring models, and spatial clustering workflows.\n\n---\n\n## Data methodology & freshness\n\nLocations are aggregated from multiple public sources, then cross-validated for coordinate accuracy and schema consistency. The dataset is refreshed **quarterly** -- the current release reflects verified data as of **30 May 2026**.\n\nA free sample dataset is available for download in CSV format. Verify coordinate precision, column types, and record completeness before purchase.\n\n---\n\n## Frequently asked questions\n\n**Can I use this dataset for competitor benchmarking?**\nYes. The dataset is frequently used to compare retail footprints, market density, and regional presence across competing brands in a single unified file.\n\n**Can I combine this dataset with administrative boundaries?**\nYes. The coordinates can be spatially joined with municipalities, census units, postal areas, and other administrative polygons for granular territorial analysis.\n\n**Can I analyze this dataset with AI tools?**\nYes. The dataset is structured for compatibility with AI workflows including Claude, ChatGPT, Gemini, RAG pipelines, and Python-based analytics.\n\n**Is this dataset suitable for machine learning workflows?**\nYes. The structured tabular format and standardized coordinates make it ready for ML and predictive analytics applications, including site scoring and demand forecasting.\n\n**Can I use this dataset for proximity analysis?**\nYes. The geocoded coordinates support drive-time analysis, catchment modelling, nearest-neighbour analysis, and accessibility studies out of the box.\n\n**Can I preview the dataset before purchasing?**\nYes. A free sample is available so you can evaluate the structure, fields, and geospatial quality before purchase.\n\n---\n\n## Formats & delivery\n\nNative format is CSV, ready for DBFS ingestion. Enterprise formats available on request:\n\n- GeoJSON\n- Esri Shapefiles\n- PostgreSQL dumps\n- Delta sharing profiles\n- Excel\n\nContact **contact@geolocet.com** to request a custom format, discuss European POI data needs, or get integration support from our engineering team.\n","terms_of_service":"https://geolocet.com/pages/terms-of-use","documentation_link":"","privacy_policy_link":"https://geolocet.com/pages/privacy-policy","geographical_coverage":"[\"Europe\"]","cost":"PAID","assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://geolocet.com/pages/terms-of-use"},"provider_summary":{"name":"Geolocet LTD","description":"Geolocet provides premium geospatial data for retail analysis, GIS, and market planning. 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Indeed Data employee reviews and company profiles add culture scores, management ratings, and firmographic context to every hiring signal.\n\n• Track hiring trends by company, role, and region with Indeed Data job postings\n• Benchmark salaries using AI-predicted pay ranges from Indeed Data listings\n• Monitor competitor hiring velocity and skill demand via Indeed Data signals\n• Use Indeed Data reviews to track employer reputation in your recruiting market\n• Identify company size and growth stage through Indeed Data company profiles\nBusiness Development and CRM Enrichment\n\nSales teams use Indeed Data hiring signals to find companies in growth mode. A spike in job postings means new budget, expansion, or a strategic shift. 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All three datasets in one Indeed Data feed means no joining of separate sources.\n\n• Analyze hiring demand by industry, region, and occupation with Indeed Data\n• Correlate hiring and salary trends with employee sentiment from Indeed Data\n• Build predictive workforce models powered by AI-enriched Indeed Data job postings\n• Support site selection, skills gap analysis, and planning using Indeed Data signals\nInvestment and Alternative Data\n\nInvestors use Indeed Data as a leading indicator before earnings reports. 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Understand how participant list and program changes affect benchmarks, risk scores, and shared savings. Excel-based model with traceable logic, complete ACO and regional benchmark calculations, CMS-HCC v24 and v28 risk scores, provider-level trend forecasts, and all other information need to accurately forecast shared savings.\n\n**ACO Builder Explorer**: Identify the best providers to target for network expansion using participant-level performance metrics with unique provider identifiers. Flat files optimized for business intelligence platforms, cross-ACO comparison capabilities\n\n**ACO Builder Opportunity**: See how providers in any market perform relative to peers using provider performance benchmarks with quality and cost metrics. Power BI visualizations with interactive dashboards, regional average and well managed benchmarks, percentile rankings, and savings opportunity metrics.\n\n**Medicare Advantage Network Intelligence (MANI)**: Evaluate Medicare Advantage (MA) network cost, revenue, and quality performance by payer and provider. Quantify the impact of network changes on ACO and MA plan performance.\n\n\n**Data Sources & Methodology**\n\n* **Primary Source**: CMS Research Identifiable Files (RIFs) - 100% Medicare claims data\n* **Provider Attribution**: CMS attribution models refined by Milliman actuaries\n* **Risk Adjustment**: AD and ESRD CMS HCC models, including v28 models, with all CMS normalization and coding adjustment applied.\n* **Validation**: Accuracy validated against published CMS ACO financial results and other CMS sources\n* **Standards Compliance**: Fully replicates CMS shared risk model methodologies. 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The information is classified by health information, regulatory information and advanced search. This data package contains the necessary technical information (excluding programming scripts) to reproduce the online version of Drugs@FDA. The data package is best to use with a database program.\n\n **Description**\n Drugs at FDA is a database of FDA Approved Drug Products available in the FDA official website for search by drug name, active ingredient or application number. It provides information of drug (generic) name, active ingredient, form and strength available, FDA application number, label info, dosage form or route, marketing status and pharmaceutical company as well as patient information, approval letters, review and other facts for drugs approved after 1997. Drugs@FDA contains prescription and over-the-counter human drugs and therapeutic biologicals currently approved for sale in the United States. Drugs@FDA includes discontinued drugs and \"Chemical Type 6\" approvals. Drugs@FDA contains the following therapeutic biological products:\n- monoclonal antibodies\n- cytokines, growth factors, enzymes, immunomodulators; and thrombolytics\n- proteins intended for therapeutic use that are extracted from animals or microorganisms, including recombinant versions of these products (except clotting factors)\n- other non-vaccine therapeutic immunotherapies\n\n **Benefits**\n - The data package is useful for finding labels for approved drug products, generic drug products for an innovator drug product, therapeutically equivalent drug products for an innovator or generic drug product, consumer information for drugs, all drugs with a specific active ingredient, viewing the approval history of a drug.\n\n **License Information**\n The use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n **Included Datasets**\n - [Approved Risk Evaluation and Mitigation Strategies Materials Data](https://johnsnowlabs.com/wp-json/datasets-api/v1/approved-risk-evaluation-and-mitigation-strategies-materials-data)\n\t- The Approved Risk Evaluation and Mitigation Strategies Materials (REMS) dataset includes data on all of the materials that have ever been part of a REMS program, including information on materials that are no longer subject to a Risk Evaluation and Mitigation Strategies (REMS).\n- [Approved Risk Evaluation and Mitigation Strategies Products Data](https://johnsnowlabs.com/wp-json/datasets-api/v1/approved-risk-evaluation-and-mitigation-strategies-products-data)\n\t- The Approved Risk Evaluation and Mitigation Strategies Products dataset includes data on all of the drugs that have ever been part of a REMS program, including information on products that are no longer marketed and/or no longer subject to a Risk Evaluation and Mitigation Strategies (REMS).\n- [Clinical Investigator Inspection List](https://johnsnowlabs.com/wp-json/datasets-api/v1/clinical-investigator-inspection-list)\n\t- The Clinical Investigator Inspection List (CLIIL) contains names, addresses, and other pertinent information gathered from inspections of clinical investigators who have performed studies with investigational new drugs.\n- [Clinical Investigators Disqualification Proceedings](https://johnsnowlabs.com/wp-json/datasets-api/v1/clinical-investigators-disqualification-proceedings)\n\t- The Clinical Investigators Disqualification Proceedings dataset lists clinical investigators who are or have been subject to an administrative clinical investigator disqualification action and indicates the current status of that action.\n- [Drugs Application Number and Sponsor](https://johnsnowlabs.com/wp-json/datasets-api/v1/drugs-application-number-and-sponsor)\n\t- This dataset contains the Applications part to build the Drugs@FDA database. Drugs at FDA is a database of FDA Approved Drug Products available on the FDA official website. It provides information of drug (generic) name, active ingredient, form and strength available, FDA application number, label info, dosage form or route, marketing status and pharmaceutical company as well as patient information, approval letters, review and other facts for drugs approved after 1997.\n- [Drugs Application Submission Property Type](https://johnsnowlabs.com/wp-json/datasets-api/v1/drugs-application-submission-property-type)\n\t- This dataset contains the Application Submission Property Type part to build the Drugs@FDA database. Drugs at FDA is a database of FDA Approved Drug Products available on the FDA official website. It provides information of drug (generic) name, active ingredient, form and strength available, FDA application number, label info, dosage form or route, marketing status and pharmaceutical company as well as patient information, approval letters, review and other facts for drugs approved after 1997.\n- [Drugs Data File Document Addresses](https://johnsnowlabs.com/wp-json/datasets-api/v1/drugs-data-file-document-addresses)\n\t- This dataset contains the Application Documents part to build the Drugs@FDA database. Application Documents refers to \"document addresses or URLs to letters, labels, reviews, Consumer Information Sheets, FDA Talk Papers, and other types. Drugs at FDA provides information of drug name, active ingredient, strength, application number, label info, dosage form or route, marketing status as well as patient information, approval letters, review and other facts for drugs approved after 1997.\n- [Drugs Database Marketing Status](https://johnsnowlabs.com/wp-json/datasets-api/v1/drugs-database-marketing-status)\n\t- This dataset contains the Marketing Status part to build the Drugs@FDA database. Drugs at FDA is a database that provides information of drug (generic) name, active ingredient, form and strength available, FDA application number, label info, dosage form or route, marketing status and pharmaceutical company as well as patient information, approval letters, review and other facts for drugs approved after 1997.\n- [Drugs Database Products Included](https://johnsnowlabs.com/wp-json/datasets-api/v1/drugs-database-products-included)\n\t- This dataset contains the Product part to build the Drugs@FDA database. Drugs at FDA is a database of FDA Approved Drug Products available on the FDA official website. It provides information of drug (generic) name, active ingredient, form and strength available, FDA application number, label info, dosage form or route, marketing status and pharmaceutical company as well as patient information, approval letters, review and other facts for drugs approved after 1997.\n- [Drugs Database Submissions Included](https://johnsnowlabs.com/wp-json/datasets-api/v1/drugs-database-submissions-included)\n\t- This dataset contains the Application Submission part to build the Drugs@FDA database from the Food and Drug Administration (FDA) Approved Drug Products available on their official website. It provides information on drug (generic) name, active ingredient, form and strength available, FDA application number, label info, dosage form or route, marketing status, and pharmaceutical company as well as patient information, approval letters, review and other facts for drugs approved after 1997.\n- [Drugs Therapeutic Equivalence Code](https://johnsnowlabs.com/wp-json/datasets-api/v1/drugs-therapeutic-equivalence-code)\n\t- This dataset contains the Therapeutic Equivalence Code part to build the Drugs@FDA database. Application Documents refers to \"document addresses or URLs to letters, labels, reviews, Consumer Information Sheets, FDA Talk Papers, and other types. Drugs at FDA is a database of FDA Approved Drug Products. It provides information of drug (generic) name, active ingredient, form and strength available, FDA application number, label info, dosage form or route, marketing status and other facts.\n- [FDA Drug Discontinuation](https://johnsnowlabs.com/wp-json/datasets-api/v1/fda-drug-discontinuation)\n\t- The Food and Drug Administration (FDA) Drug discontinuation database includes information about current drugs in discontinuations of specific drug products, resource information, and relevant links.\n- [FDA Drug Master Files Directory](https://johnsnowlabs.com/wp-json/datasets-api/v1/fda-drug-master-files-directory)\n\t- This dataset contains a list of the Drug Master Files (DMF) submitted to the Food and Drug Administration (FDA) and the Center for Drug Evaluation and Research (CDER) since 1900 to the current date. 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It offers an analysis-ready foundation for full-scale retail site selection, competitive intelligence, logistics optimization, and spatial demographics mapping.\n\nFully compatible with **Apache Spark**, **Delta Lake**, and **Databricks Unity Catalog** workflows.\n\n---\n\n## **Complete National Coverage: Branded & Independent Grocers**\nUnlike top-tier only datasets, this exhaustive collection covers maximum geographic depth across all Norwegian regions (including highly dense areas like Oslo og Viken, Vestlandet, and Agder og Sør-Østlandet). It includes independent local grocers alongside the top national retail groups:\n\n- **NorgesGruppen Banners:** [Kiwi (730 locations)](https://geolocet.com/products/norway-kiwi-grocery), \n [Joker (459 locations)](https://geolocet.com/products/norway-joker-grocery), \n [SPAR (290 locations)](https://geolocet.com/products/norway-spar-grocery), \n [MENY (178 locations)](https://geolocet.com/products/norway-meny-grocery)\n\n- **Coop Norge Banners:** [Extra (579 locations)](https://geolocet.com/products/norway-extra-grocery), \n [Coop (563 locations)](https://geolocet.com/products/norway-coop-grocery)\n\n- **Reitangruppen & Discount Chains:** [REMA 1000 (699 locations)](https://geolocet.com/products/norway-rema-1000-grocery), \n [Europris (289 locations)](https://geolocet.com/products/norway-europris-grocery), \n [Bunnpris (250 locations)](https://geolocet.com/products/norway-bunnpris-grocery)\n\n**Plus over 2,500 independent, regional, and unbranded grocers** fully mapped and standardized across all macro-regions (e.g., Oslo og Viken: 2,019 sites; Vestlandet: 1,797 sites; Agder og Sør-Østlandet: 940 sites).\n\n---\n\n## **Technical Specifications & Schema Definitions**\nOptimized for zero-friction schema enforcement when reading via `spark.read.format(\"csv\")`. 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A comprehensive schema sample is available for download in CSV/Parquet-friendly formats.\n\n### **Enterprise Delivery & Tailored Formatting**\nWhile natively available as an analysis-ready CSV for DBFS ingestion, we accommodate enterprise-level data architecture needs. Alternative formats - including **GeoJSON, Esri Shapefiles, PostgreSQL dumps, Excel, or direct Delta sharing profiles** - can be generated. Reach out to our engineering team at **contact@geolocet.com** for integration support or custom European POI data requests.","terms_of_service":"https://geolocet.com/pages/terms-of-use","documentation_link":"","privacy_policy_link":"https://geolocet.com/pages/privacy-policy","assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://geolocet.com/pages/terms-of-use"},"provider_summary":{"name":"Geolocet LTD","description":"Geolocet provides premium geospatial data for retail analysis, GIS, and market planning. 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The territorial vintages of the worldwide area boundaries are compatible with our globally consistent and comparable [socio-demographic](https://www.mbi-geodata.com/en/sociodemographic-data/) and [economic](https://www.mbi-geodata.com/en/purchasing-power-overview/) data.\nOur area boundaries for Spain are offered at the following levels:\n\n- **Postal Code Level** (Postcode 5-digit): This fine-grained level of detail makes the data suitable for a wide range of applications – for example, enriching customer databases, which are often organized by postal code.\n- **Administrative Level** (Municipios): Covers both larger administrative areas (e.g., regions, states, provinces) as well as finer subdivisions such as districts, counties, or municipalities.\n- **Micro Level** (Secciones Censales): Subdivisions of postal code or administrative boundaries, such as census areas or street segments. 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All Rights Reserved](https://ai.meta.com/resources/models-and-libraries/llama-downloads/). 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MBI is part of the Michael Bauer Group, alongside Michael Bauer Research GmbH and Michael Bauer Micromarketing GmbH."},"popularity":1221},{"id":"b1cb5fc1-bd41-455a-bcc7-b8fa475aba84","summary":{"name":"Geolocet | Points of Interest Data | Grocery Stores Locations - France","subtitle":"Updated Quarterly\nThe dataset offers over 93,000 locations of grocery stores and supermarkets across France","setting":{"visibility":"PUBLIC"},"created_at":1718174806976,"updated_at":1780388221572,"categories":["RETAIL"],"listingType":"PERSONALIZED","provider_id":"5d3ddc17-a7e8-4794-a760-13587e1de2c6"},"detail":{"description":"# All Supermarket Locations in France: Complete Geographic Dataset\n\n### GIS-Ready Retail Location Intelligence - 93,734 Verified Sites, Databricks-Native\n\n---\n\nFinding retail white space in France means knowing where 93,000+ grocers already are. This dataset maps every verified supermarket, hypermarket, discounter, and independent grocer across France - from Carrefour flagships to rural unbranded markets - giving site selectors, logistics planners, and territory managers a single, analysis-ready source of truth.\n\nSourced from multiple public registries, cross-validated, and refreshed quarterly. Fully compatible with Apache Spark, Delta Lake, and Databricks Unity Catalog.\n\n---\n\n## Who this is for\n\n- **Retail site selectors & expansion teams** identifying whitespace and competitor saturation before opening new locations\n- **CPG & FMCG territory managers** mapping distribution coverage and prioritizing field rep routing\n- **Logistics & last-mile delivery planners** building drive-time models and optimizing fulfilment zones\n- **Academic & municipal researchers** studying food accessibility, transit proximity, and urban planning\n\n---\n\n## Coverage: 93,734 locations across all French regions\n\nUnlike top-tier-only datasets, this collection captures the full French grocery landscape - major chains and the tens of thousands of independent and regional grocers that rarely appear in commercial data products.\n\n**Regional breakdown (selected):** Île-de-France: 16,544 sites · Auvergne-Rhône-Alpes: 12,311 · Occitanie: 10,263\n\n### Hypermarket & mainstream leaders\n\n- [Carrefour](https://geolocet.com/products/france-carrefour-grocery) - 6,632 locations\n- [Casino Group](https://geolocet.com/products/france-casino-grocery) - 4,175 locations\n- [Intermarché](https://geolocet.com/products/france-intermarche-grocery) - 2,301 locations\n- [E.Leclerc](https://geolocet.com/products/france-e-leclerc-grocery) - 1,890 locations\n- [System U](https://geolocet.com/products/france-system-u-grocery) - 1,886 locations\n- [Auchan](https://geolocet.com/products/france-auchan-grocery) - 889 locations\n- [Supermarché Match](https://geolocet.com/products/france-supermarche-match-grocery) - 115 locations\n\n### Discounters\n\n- [Lidl](https://geolocet.com/products/france-lidl-grocery) - 1,694 locations\n- [ALDI](https://geolocet.com/products/france-aldi-grocery) - 1,386 locations\n- [Netto](https://geolocet.com/products/france-netto-grocery) - 449 locations\n- [Colruyt](https://geolocet.com/products/france-colruyt-grocery) - 109 locations\n\n### Convenience & proximity formats\n\n- [Vival](https://geolocet.com/products/france-vival-grocery) - 1,521 locations\n- [Codifrance Group](https://geolocet.com/products/france-codifrance-grocery) - 1,489 locations\n- [Franprix](https://geolocet.com/products/france-franprix-grocery) - 1,036 locations\n- [SPAR](https://geolocet.com/products/france-spar-grocery) - 783 locations\n- [Monoprix](https://geolocet.com/products/france-monoprix-grocery) - 407 locations\n\n### Organic & fresh specialty\n\n- [Biocoop](https://geolocet.com/products/france-biocoop-grocery) - 712 locations\n- [Grand Frais](https://geolocet.com/products/france-grand-frais-grocery) - 362 locations\n\n**Plus 66,000+ independent, regional, and unbranded grocers** fully mapped and standardized across all French regions.\n\n---\n\n## Schema\n\nEach record includes 17 standardized fields, optimized for zero-friction ingestion via `spark.read.format(\"csv\")`.\n\n- **GUID** (STRING) - Unique identifier for deterministic primary key mapping\n- **Title** (STRING) - Official store name or independent title\n- **Group** (STRING) - Parent company or retail group (e.g. Groupe Casino)\n- **Latitude** (DOUBLE) - WGS84 latitude for point-in-polygon & spatial joins\n- **Longitude** (DOUBLE) - WGS84 longitude for H3 / S2 spatial indexing\n- **Street_No / Street** (STRING) - Normalized building number and street name\n- **City / Postal_Code** (STRING) - Municipal name and postal address\n- **Commune** (STRING) - French municipal administrative boundary\n- **Region** (STRING) - Macro-administrative region (e.g. Bretagne, Normandie)\n- **Admin_level_1 / _2** (STRING) - Local area and departmental identifiers\n- **Population** (INTEGER) - Demographic population attributed to the location\n- **Wheelchair** (STRING) - Accessibility flags for wheelchair mobility\n- **Popularity_Score** (INTEGER) - Foot-traffic / rating metric, standardized 1–10 scale\n- **Opening_Hours** (STRING) - Structured daily operating schedules\n- **Phone / Website** (STRING) - Branch contact details and landing URLs\n\n**Sample record:**\n\n- GUID: a3f7-9c21-...\n- Title: Carrefour Market Saint-Denis\n- Group: Carrefour\n- Latitude: 48.9362 / Longitude: 2.3574\n- City: Saint-Denis · Postal_Code: 93200\n- Region: Île-de-France\n- Popularity_Score: 7\n- Opening_Hours: Mo-Sa 08:00-21:00; Su 09:00-13:00\n- Wheelchair: yes\n\n---\n\n## What you can build with it\n\n**Whitespace & underserved market analysis**\nQuery all 93,734 locations against population density tables to identify true retail deserts - especially in rapidly developing periurban communes and rural departments. Pinpoint expansion opportunities before competitors do.\n\n**Urban accessibility & transit scoring**\nCross-reference store locations with GTFS transit feeds using GeoPandas or Spark spatial joins. Score each site's walkability and public transport accessibility - essential for non-driving demographics in dense urban areas like Île-de-France.\n\n**H3 geospatial indexing & demographic overlay**\nConvert WGS84 coordinates into Uber H3 hexagons and join against IRIS socio-economic or income raster data for granular market characterisation at sub-commune resolution.\n\n---\n\n## Data methodology & freshness\n\nLocations are aggregated from multiple public sources, then cross-validated for coordinate accuracy and schema consistency. The dataset is refreshed **quarterly** - the current release reflects verified data as of **May 27, 2026**.\n\nA free sample dataset is available for download in CSV and Parquet formats. Verify coordinate precision, column types, and record completeness before purchase.\n\n---\n\n## Formats & delivery\n\nNative format is CSV, ready for DBFS ingestion. Enterprise formats available on request:\n\n- GeoJSON\n- Esri Shapefiles\n- PostgreSQL dumps\n- Delta sharing profiles\n\nContact **contact@geolocet.com** to request a custom format, discuss European POI data needs, or get integration support from our engineering team.\n","terms_of_service":"https://geolocet.com/pages/terms-of-use","documentation_link":"","privacy_policy_link":"https://geolocet.com/pages/privacy-policy","geographical_coverage":"[\"Europe\"]","cost":"PAID","assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://geolocet.com/pages/terms-of-use"},"provider_summary":{"name":"Geolocet LTD","description":"Geolocet provides premium geospatial data for retail analysis, GIS, and market planning. 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The MUEs Outpatient Hospital Services which are designed to limit fraud and/or coding errors. They represent an upper limit that unquestionably requires further documentation to support. The ideal MUE is the maximum unit of service for a code on the majority of medical claims. MUE is part of the National Correct Coding Initiative (NCCI) to address coding methodologies. The MUEs Practitioner Services are also a part of this data package. The edits were developed based on anatomic considerations, HCPCS/CPT code descriptors, CPT instructions, Centers for Medicare and Medicaid Services (CMS) policies, nature of service/procedure, nature of the analyte, nature of the equipment, and clinical judgment. Prior to implementation, all edits were reviewed by national healthcare organizations, and their alternative recommendations were taken into consideration.\n\n**Benefits**\n- Useful for medical health planners, policymakers and medical research groups and medical students. dme supplier hospital and practitioner services report codes database is available on centers for medicare and medicaid services website in raw form, whereas a john snow labs (jsl) customer gets it in a clean and normalized form ready to use and create his own custom reports.\n- easy to comprehend for the customers who are unfamiliar with the medical terminologies as all abbreviated terms are replaced with full form, unlike centers for medicare & medicaid services (cms) original datasets which are full of abbreviations.\n- recent datasets for 2018 is available for use.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [Medically Unlikely Edits Durable Medical Equipment Supplier Services](https://www.johnsnowlabs.com/marketplace/medically-unlikely-edits-durable-medical-equipment-supplier-services)\n\t- This dataset showcases the Medically Unlikely Edits (MUEs) for Durable Medical Equipment Supplier Services as defined for many Healthcare Common Procedure Coding System (HCPCS) / Current Procedural Terminology (CPT) codes, the maximum allowable number of units of service by the same provider, for the same beneficiary, the same date of service, on the same claim line. Reported units of service greater than the MUE value are unlikely to be correct.\n- [Medically Unlikely Edits Outpatient Hospital Services](https://www.johnsnowlabs.com/marketplace/medically-unlikely-edits-outpatient-hospital-services)\n\t- This dataset showcases the Medically Unlikely Edits (MUEs) for outpatient hospital services as defined for many Healthcare Common Procedure Coding System (HCPCS) / Current Procedural Terminology (CPT) codes, the maximum allowable number of units of service by the same provider, for the same beneficiary, the same date of service, on the same claim line. Reported units of service greater than the MUE value are unlikely to be correct.\n- [Medically Unlikely Edits Practitioner Services](https://www.johnsnowlabs.com/marketplace/medically-unlikely-edits-practitioner-services)\n\t- This dataset showcases the Medically Unlikely Edits (MUEs) for Practitioner Services as defined for many Healthcare Common Procedure Coding System (HCPCS) / Current Procedural Terminology (CPT) codes, the maximum allowable number of units of service by the same provider, for the same beneficiary, the same date of service, on the same claim line. Reported units of service greater than the MUE value are unlikely to be correct.\n- [Physician Supplier Procedure Summary 2018](https://www.johnsnowlabs.com/marketplace/physician-supplier-procedure-summary-2018)\n\t- The Physician/Supplier Procedure Summary (PSPS) dataset is a summary of the calendar year Medicare Part B carrier and durable medical equipment fee-for-service claims. The dataset is organized by carrier, pricing locality, Healthcare Common Procedure Coding System (HCPCS) code, HCPCS modifier, provider specialty, type of service, and place of service.\n- [Physician Supplier Procedure Summary 2019](https://www.johnsnowlabs.com/marketplace/physician-supplier-procedure-summary-2019)\n\t- The Physician/Supplier Procedure Summary (PSPS) dataset is a summary of the calendar year Medicare Part B carrier and durable medical equipment fee-for-service claims. The dataset is organized by carrier, pricing locality, Healthcare Common Procedure Coding System (HCPCS) code, HCPCS modifier, provider specialty, type of service, and place of service.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated\n- Data Updates\n - Data updates support replace-on-update: outdated foreign keys are deprecated, not deleted\n\n**Our data is curated and enriched by domain experts** \n\n Each dataset is manually curated by our team of doctors, pharmacists, public health & medical billing experts:\n- Field names, descriptions, and normalized values are chosen by people who actually understand their meaning\n- Healthcare & life science experts add categories, search keywords, descriptions and more to each dataset\n- Both manual and automated data enrichment supported for clinical codes, providers, drugs, and geo-locations\n- The data is always kept up to date – even when the source requires manual effort to get updates\n- Support for data subscribers is provided directly by the domain experts who curated the data sets\n- Every data source’s license is manually verified to allow for royalty-free commercial use and redistribution.\n\n**Need Help?**\nIf you have questions about our products, contact us at [info@johnsnowlabs.com](mailto:info@johnsnowlabs.com). \n 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It offers an analysis-ready foundation for full-scale retail site selection, competitive intelligence, logistics optimization, and spatial demographics mapping.\n\nFully compatible with **Apache Spark**, **Delta Lake**, and **Databricks Unity Catalog** workflows.\n\n---\n\n## **Complete National Coverage: Branded & Independent Grocers**\nUnlike top-tier only datasets, this exhaustive collection covers maximum geographic depth across all Hungarian regions (including highly dense areas like Közép-Magyarország, Észak-Alföld, and Dél-Alföld). It includes thousands of independent local grocers alongside the top national and regional supermarket chains:\n\n- **Major National Supermarkets & Hypermarkets:** \n [Tesco (253 locations)](https://geolocet.com/products/hungary-tesco-grocery), \n [SPAR (658 locations)](https://geolocet.com/products/hungary-spar-grocery), \n [Auchan (27 locations)](https://geolocet.com/products/hungary-auchan-grocery)\n\n- **Leading Discounters (Hard & Soft Discount):** \n [PENNY (251 locations)](https://geolocet.com/products/hungary-penny-grocery), \n [Lidl (226 locations)](https://geolocet.com/products/hungary-lidl-grocery), \n [ALDI (190 locations)](https://geolocet.com/products/hungary-aldi-grocery)\n\n- **Cooperative, Proximity & Local Formats:** \n [Coop (1,648 locations)](https://geolocet.com/products/hungary-coop-grocery), \n [Tom Market (102 locations)](https://geolocet.com/products/hungary-tom-market-grocery)\n\n**Plus over 8,800 independent, regional, and unbranded grocers** fully mapped and standardized across all macro-regions (e.g., Közép-Magyarország: 2,813 sites; Észak-Alföld: 1,942 sites; Dél-Alföld: 1,736 sites).\n\n---\n\n## **Technical Specifications & Schema Definitions**\nOptimized for zero-friction schema enforcement when reading via `spark.read.format(\"csv\")`. The structured schema below ensures flawless rendering in Databricks notebooks and high accuracy for LLM semantic indexing.\n\n* **`GUID`** (`STRING`): Unique global identifier for deterministic primary key mapping.\n* **`Title`** (`STRING`): Official store name, brand banner, or independent title of the location.\n* **`Latitude`** (`DOUBLE`): Precise WGS84 latitude coordinate for point-in-polygon & spatial joins.\n* **`Longitude`** (`DOUBLE`): Precise WGS84 longitude coordinate for spatial indexing (H3 / S2).\n* **`Street_No` / `Street`** (`STRING`): Normalized building/house number and street name elements.\n* **`City` / `Postal_Code`** (`STRING`): Core postal addresses and primary municipal settlement names.\n* **`Municipality`** (`STRING`): Hungarian municipal administrative boundary designation.\n* **`Region`** (`STRING`): Macro-administrative boundary classification (e.g., Közép-Magyarország, Nyugat-Dunántúl).\n* **`Population`** (`INTEGER`): Contextual demographic population metric attributed to the location.\n* **`Address`** (`STRING`): Full, standardized street address of the location.\n* **`Wheelchair`** (`STRING`): Accessibility flags indicating wheelchair mobility support.\n* **`Popularity_Score`** (`INTEGER`): Relative foot-traffic/rating metric graded on a standardized 1-10 scale.\n* **`Opening_Hours`** (`STRING`): Standard daily operational schedules and structured operating text.\n* **`Phone` / `Website`** (`STRING`): Direct communication data, branch-specific landing URLs, and domain vectors.\n\n---\n\n## **Databricks Workflow & Spatial Analytics Use Cases**\n\n- **Whitespace & Underserved Market Analysis:** Query the full 12,179 locations against population density tables to identify true retail deserts and expansion opportunities untouched by major national networks.\n- **H3 Geospatial Indexing:** Convert the precise WGS84 `Latitude` and `Longitude` fields into Uber H3 spatial hexagons (`h3_longlattocell`) to cross-reference with granular Hungarian municipal income raster files or demographic datasets.\n- **Supply Chain & Last-Mile Logistics Optimization:** Combine store location points with regional administrative boundary layers to optimize delivery routes, distribution warehouse placements, and network expansion footprints.\n\n---\n\n## **Data Evaluation & Custom Ingestion**\n\n### **Evaluate with a Free Sample Dataset**\nVerify the structure, column types, and coordinate precision before full implementation. A comprehensive schema sample is available for download in CSV/Parquet-friendly formats.\n\n*Want deeper market insights?* Explore how demographic mapping supports location intelligence workflows in our spatial analysis: **[The Hungary Income Map: Suburban Socio-Economic Patterns](https://geolocet.com/blogs/news/the-hungary-income-map-why-suburban-is-the-new-rich-in-2024)**\n\n### **Enterprise Delivery & Tailored Formatting**\nWhile natively available as an analysis-ready CSV for DBFS ingestion, we accommodate enterprise-level data architecture needs. Alternative formats - including **GeoJSON, Esri Shapefiles, PostgreSQL dumps, Excel, or direct Delta sharing profiles** - can be generated. Reach out to our engineering team at **contact@geolocet.com** for integration support or custom European POI data requests.","terms_of_service":"https://geolocet.com/pages/terms-of-use","documentation_link":"","privacy_policy_link":"https://geolocet.com/pages/privacy-policy","assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://geolocet.com/pages/terms-of-use"},"provider_summary":{"name":"Geolocet LTD","description":"Geolocet provides premium geospatial data for retail analysis, GIS, and market planning. We equip businesses with accurate, ready-to-query data to drive spatial analysis, site selection, and customer segmentation.\n\nCore Offerings:\n\nDemographics: Socioeconomic data covering population, age, and employment across diverse geographies.\n\nPoints of Interest (PoI): Datasets spanning retail, healthcare, and transport for catchment analysis.\n\nAdministrative Boundaries: GIS-ready boundaries, including municipalities and postcodes, for territory management.\n\nBrand Locations: Chain location datasets to monitor store networks and market expansion.\n\nWealth Data: Granular economic indicators like purchasing power to identify high-potential markets.\n\nWe source from trusted providers, delivering highly customizable datasets with comprehensive coverage across Europe and worldwide."},"popularity":871},{"id":"66dfda57-7066-44d7-81cd-cd03cb628ca0","summary":{"name":"Oral Health Surveillance","subtitle":"This data package includes the Behavioral Risk Factor Surveillance System for indicators of adult oral health from 201..","share":{"name":"oral_health_surveillance","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1682356064049,"updated_at":1695691452176,"categories":["HEALTH","SCIENCE_AND_RESEARCH"],"listingType":"STANDARD","provider_id":"f88849f8-9d77-4de2-b4c7-03d55cc3768e"},"detail":{"description":"\n**Overview**\nThis data package includes the Behavioral Risk Factor Surveillance System for indicators of adult oral health from 2012-2014 and child oral health from 1993-2015. It comprises information in tracking state efforts to improve oral health and contributions to progress toward the national targets for Healthy People program objectives. It also consists of annual data regarding Water Fluoridation Reporting System (WFRS), state and national Water Fluoridation Systems and Public Water Systems (PWS).\n\n**Description**\nThis data package contains the National Oral Health Surveillance System (NOHSS) collaborative effort between Centers for Disease Control and Prevention's (CDC's) Division of Oral Health and the Association of State and Territorial Dental Directors (ASTDD). NOHSS Adult Indicators estimates are prepared from the Behavioral Risk Factor Surveillance System (BRFSS) and it includes indicators of adult oral health. NOHSS is designed to monitor the burden of oral disease, use of the oral health care delivery system, and the status of community water fluoridation on both a national and state level. It is also designed to track oral health surveillance indicators based on data sources and surveillance capacity available to most states.\n\nThe Council of State and Territorial Epidemiologists (CSTE) and the National Association of Chronic Disease Directors (NACDD) were instrumental in developing the framework for chronic disease surveillance indicators, including these oral health indicators. Oral Health Data includes the following indicators of adult oral health from the NOHSS: i). Dental Visit, ii). Teeth Cleaning, iii). Complete Tooth Loss, and iv). Lost 6 or More Teeth. NOHSS Child Indicators includes the following indicators of children’s oral health: i). Percentage of 3rd-grade students with caries experience, including treated and untreated tooth decay, ii). Percentage of 3rd-grade students with untreated tooth decay, and iii). Percentage of 3rd-grade students with dental sealants on at least one permanent molar tooth. Water Fluoridation Reporting System (WFRS) is designed to track all community water systems including 1) fluoridated water systems; 2) water systems that add fluoride; 3) water systems that are naturally fluoridated; 4) water systems that are not fluoridated.\n\nNational- and state-based surveys monitor the oral health status of the U.S. population and the WFRS compiles data useful for monitoring access to and evaluating the quality of water fluoridation programs at the state and water system levels. The number of communities and people who benefit from water fluoridation is continuing to grow. In 2014, 74.4% of the U.S. population on public water systems, or a total of 211,393,167 people, had access to fluoridated water. CDC monitors the progress of the nation and individual states toward meeting the Healthy People 2020 objective on community water fluoridation — that 79.6% of people on public water systems will receive water that has the optimum level of fluoride recommended for preventing tooth decay.\n\n**Benefits**\n- Useful for doctors, medical health planners, dentists, medical research groups and medical students. oral health surveillance database is available on the centers for disease control and prevention website in raw form, whereas a john snow labs (jsl) customer gets it in clean and normalized form ready to use and create his own custom reports. easy to comprehend for the customers who are unfamiliar with the medical terminologies as all abbreviated terms are replaced with full form, centers for disease control and prevention original datasets which are full of abbreviations.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [NOHSS Adult Indicators BRFSS Data](https://www.johnsnowlabs.com/marketplace/nohss-adult-indicators-brfss-data)\n\t- This dataset includes information from Behavioral Risk Factor Surveillance System (BRFSS) for indicators of adult oral health for 2012 to 2016. National estimates are represented by the median prevalence among 50 states and the District of Columbia data.\n- [National Oral Health Surveillance System Adult Indicators](https://www.johnsnowlabs.com/marketplace/national-oral-health-surveillance-system-adult-indicators)\n\t- This dataset includes information from Behavioral Risk Factor Surveillance System (BRFSS) for indicators of adult oral health for even years from 2012 through 2014. National estimates are represented by the median prevalence among 50 states and the District of Columbia data. Estimates are prepared from the BRFSS public use data sets.\n- [National Oral Health Surveillance System Child Indicators](https://www.johnsnowlabs.com/marketplace/national-oral-health-surveillance-system-child-indicators)\n\t- This dataset includes information from data for School year-end 1994 through year-end 2017. State oral health surveys are the data sources for these indicators. States periodically conduct independent screening surveys of a probability sample designed to be representative of all third grade students in the state.\n- [Selected Indicators for ASTDD Synopses of SOH Programs](https://www.johnsnowlabs.com/marketplace/selected-indicators-for-astdd-synopses-of-soh-programs)\n\t- This dataset includes information useful in tracking states’ efforts to improve oral health and contributions to progress toward the national targets for Healthy People objectives for oral health.\n- [State and National Water Fluoridation System and Public Water Systems](https://www.johnsnowlabs.com/marketplace/state-and-national-water-fluoridation-system-and-public-water-systems)\n\t- This dataset comprises of the Water Fluoridation Reporting System (WFRS) which has been developed to provide tools to assist states in managing fluoridation programs for the years 2016 and 2017. WFRS is designed to track all community water systems including: 1) fluoridated water systems; 2) water systems that add fluoride; 3) water systems that are naturally fluoridated; 4) water systems that are not fluoridated.\n- [Water Fluoridation Reporting System Annual Data](https://www.johnsnowlabs.com/marketplace/water-fluoridation-reporting-system-annual-data)\n\t- This dataset comprises of Water Fluoridation Statistics which is an annual report of the percentage and number of people receiving fluoridated water from 2012 through 2016.\n- [Water Fluoridation Reporting System and Public Water Systems](https://www.johnsnowlabs.com/marketplace/water-fluoridation-reporting-system-and-public-water-systems)\n\t- This dataset comprises of the Water Fluoridation Reporting System (WFRS) which has been developed to provide tools to assist states in managing fluoridation programs during 2017. WFRS is designed to track all community water systems including: 1) fluoridated water systems; 2) water systems that add fluoride; 3) water systems that are naturally fluoridated; 4) water systems that are not fluoridated.\n- [Water Fluoridation Statistics About People Receiving Fluoridated Water](https://www.johnsnowlabs.com/marketplace/water-fluoridation-statistics-about-people-receiving-fluoridated-water)\n\t- This dataset comprises of Water Fluoridation Statistics which is a biennial report of the percentage and number of people receiving fluoridated water from 2000 through 2014.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated\n- Data Updates\n - Data updates support replace-on-update: outdated foreign keys are deprecated, not deleted\n\n**Our data is curated and enriched by domain experts** \n\n Each dataset is manually curated by our team of doctors, pharmacists, public health & medical billing experts:\n- Field names, descriptions, and normalized values are chosen by people who actually understand their meaning\n- Healthcare & life science experts add categories, search keywords, descriptions and more to each dataset\n- Both manual and automated data enrichment supported for clinical codes, providers, drugs, and geo-locations\n- The data is always kept up to date – even when the source requires manual effort to get updates\n- Support for data subscribers is provided directly by the domain experts who curated the data sets\n- Every data source’s license is manually verified to allow for royalty-free commercial use and redistribution.\n\n**Need Help?**\nIf you have questions about our products, contact us at [info@johnsnowlabs.com](mailto:info@johnsnowlabs.com). \n ","terms_of_service":"https://www.johnsnowlabs.com/healthcare-data-library-eula/","documentation_link":"https://www.johnsnowlabs.com/marketplace/oral-health-surveillance-data-package","contact_email":"support@johnsnowlabs.com","privacy_policy_link":"https://www.johnsnowlabs.com/privacy-policy/","embedded_notebook_file_infos":[{"id":"67d826b8-6ff7-4c29-ba55-678b57f7359a","marketplace_file_type":"EMBEDDED_NOTEBOOK","file_parent":{"parent_id":"66dfda57-7066-44d7-81cd-cd03cb628ca0","file_parent_type":"LISTING"},"mime_type":"text/html","download_link":"https://marketplace-file-storage-prod.s3-fips.us-east-1.amazonaws.com/EMBEDDED_NOTEBOOK/LISTING_66dfda57-7066-44d7-81cd-cd03cb628ca0/67d826b8-6ff7-4c29-ba55-678b57f7359a?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEGgaDnVzLWVhc3QtMS1maXBzIkYwRAIgbC8sWIoYaHR8y7Wq86nPb0pufBl4%2BM4PWpeN%2B5PW9VQCIG7BhAVBDCWTpxFDLeuKYm%2Ff1JLz4iccPMzter0YdhgWKqYECDAQABoMNDE0MzUxNzY3ODI2IgxNAJrHbXaZG20jNfMqgwQyGEb4TsoVOoNvJ%2BaA%2FZ7zeu8hGcHqCeNfblP%2FQo5g6q2aRNtpwtPRKMlhYcIE%2FUQaNT1Lqmyptu32VUQTpzjTTxRVmJrDPkheSCkV06XQ%2FpTMKjU8CC7X1b%2FqYf8CrBwZT7Cv4C5WPjEET38f2A79h0xV6bZgPy28zQWrSeIJ9w6AfZBxiE24%2FVc08zVA0ivCT6lsy7nuU0wcR%2FMg2C9nd1vaMnQ923VpD21CKfVVYcrNWL2b%2FQ%2BNAO%2BxPjPgjiN6UPQbvbvZxkSdhgrGtecIg9NgqGkMcvM5VLTDtdC1kdeZetfOC2obtA%2BT%2F0gy3tIDo6WM5hcQfw743%2FAU9oaGnRBl%2BwoxRRDCZA%2F4jnPdluICSI7NCr6C3HHOe4CSZJjRihczxPA%2FgQczAVjAPs6CrK9N9pIzt0vNJpGUTgg4AoxftTvGfciDREilSctPIcBk6GwB%2Bzt4wbtNCmEPU%2BFD46rc6CWVQmmLwbKOeHw70ipMVFlOc8NlXkEtGO2vu6H44MstSSc6daOVUYUavQSGZ6jGhEjsP2YGz8KDm9XQotB%2FtyVBqiZV01OKqDmmBJA%2FXjUpSJ8M%2FeJXhe5m%2BKcxzNObtoJRI7jci3Tpt08XZyXZ7%2FyIV8H30ya468X3cfkTXrm972uga3z7iXAdubyEHnQIua%2FuvtwOHILi6O5D1Me2MzCPvcnXBjqTAYDoMWBt%2B3AVFuqSs2dmDQNBnvEhXfMgTedKLD5PJHb3aIl15iBkLaNu36GYnVCp9ZU2lSYcTbY5QwNnNeTfJTNy9bKhwzru6Sg%2BslhN%2BTS0zkTn%2BeLMY4tHgPEtEZ2cp%2FCk6GwqswvnYVKHjwO8q%2B1Mx8lc08fVEfarE9dke1GGFqoddx%2Bq%2BZUyHbDlAVfrCmf0yw%3D%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20261110T000135Z&X-Amz-SignedHeaders=host&X-Amz-Expires=86400&X-Amz-Credential=ASIAWA6KKHEJORWBH4N3%2F20261110%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=6d863a5f0dafdfed6c5fc429df62eb9cfee832caaf9aaac7973353e3d91f5a0c","created_at":1682537502918,"updated_at":1682537502918,"display_name":"EDA Notebook","status":"FILE_STATUS_PUBLISHED"}]},"provider_summary":{"name":"John Snow Labs","description":"John Snow Labs, an AI and NLP for healthcare company, provides state-of-the-art software, models, and data to help healthcare and life science organizations build, deploy, and operate AI projects."},"popularity":773},{"id":"9f46c97c-3a70-456a-8ea5-c2fe5bcbc893","summary":{"name":"Netherlands Income Indicators 2024 – Buurt Level","subtitle":"2024 income data at buurt level in the Netherlands — detailed, high-resolution data. ","setting":{"visibility":"PUBLIC"},"created_at":1762430563801,"updated_at":1762430889829,"categories":["GEOSPATIAL","DEMOGRAPHICS","RETAIL","ADVERTISING_AND_MARKETING","COMMERCE"],"listingType":"PERSONALIZED","provider_id":"5d3ddc17-a7e8-4794-a760-13587e1de2c6"},"detail":{"description":"# Netherlands Income Indicators 2024 – Buurt Level\n\nUnlock granular **2024 income and household indicators** for all statistical sectors (**buurten**) in the Netherlands. Ideal for location intelligence, market planning, and socio-economic modelling.\n\n## **Why this dataset matters** \n- **Reference year:** 2024 ([official product page](https://geolocet.com/products/netherlands-income-indicators-buurt)) \n- Coverage: All ~14,574 buurten across the Netherlands \n- Granularity: Average ~1,200 persons per buurt — perfect for fine-grained market or policy analysis \n- Indicators include:\n - Total population (2024) \n - Employed population (2024) \n - Number of households (2024) \n - Average household income (EUR/year, 2024) split into 15 income bands \n- Formats: CSV and Shapefile, spatial reference EPSG:4326 \n- Use-cases: Retail network optimisation, catchment area profiling, socio-economic segmentation, public policy design \n\n## **Data provenance & quality** \nThis 2024 dataset is modelled using authoritative data from **CBS (Centraal Bureau voor de Statistiek)** and enhanced with Geolocet’s proprietary spatial-modelling techniques to deliver consistent, reliable income estimates across all neighbourhoods.\n\n## **Additional resources** \n- See how 2024 household income varies by neighbourhood in our blog post: \n [Netherlands Average Income Map – Neighbourhoods](https://geolocet.com/blogs/news/netherlands-average-income-map-neighbourhoods) \n- Explore the full product (sample available, full dataset on request): \n [Netherlands: Income Indicators at a fine-grained statistical sector (buurten) level](https://geolocet.com/products/netherlands-income-indicators-buurt) \n- Need the full dataset or a custom request? [Contact us](https://geolocet.com/pages/contact-us) \n\n## **Dataset availability** \nA **sample for 2024** is available for immediate download on the product page. \nThe **full 2024 dataset** is **available upon request only** — simply reach out if you require full access or have specific parameters.\n\n## **Integration & compatibility** \n- Seamlessly integrates with 2024 Geolocet demographics and administrative boundaries datasets \n- Ready for GIS, BI tools, and machine-learning pipelines \n- Delivered in standard formats with global spatial reference (EPSG:4326)\n\n---\n\nSupercharge your 2024 location-based insights with the most detailed neighbourhood-level income data for the Netherlands — trusted by marketers and data scientists alike.\n","terms_of_service":"https://geolocet.com/pages/terms-of-use","documentation_link":"","privacy_policy_link":"https://geolocet.com/pages/privacy-policy","cost":"PAID","data_source":"","assets":["ASSET_TYPE_DATA_TABLE","ASSET_TYPE_MEDIA"],"license":"https://geolocet.com/pages/terms-of-use"},"provider_summary":{"name":"Geolocet LTD","description":"Geolocet provides premium geospatial data for retail analysis, GIS, and market planning. We equip businesses with accurate, ready-to-query data to drive spatial analysis, site selection, and customer segmentation.\n\nCore Offerings:\n\nDemographics: Socioeconomic data covering population, age, and employment across diverse geographies.\n\nPoints of Interest (PoI): Datasets spanning retail, healthcare, and transport for catchment analysis.\n\nAdministrative Boundaries: GIS-ready boundaries, including municipalities and postcodes, for territory management.\n\nBrand Locations: Chain location datasets to monitor store networks and market expansion.\n\nWealth Data: Granular economic indicators like purchasing power to identify high-potential markets.\n\nWe source from trusted providers, delivering highly customizable datasets with comprehensive coverage across Europe and worldwide."},"popularity":262},{"id":"a747d634-087e-4a0b-a61b-45808f57f16f","summary":{"name":"NAICS 2022 (North American Industry Classification System)","subtitle":"NAICS 2022 with all the code's information in English, French and Spanish.","share":{"name":"naics_2022","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1729530240295,"updated_at":1729898782533,"categories":["ECONOMICS","FINANCIAL"],"listingType":"STANDARD","provider_id":"c63c8b2a-a8d8-4026-859a-03574f5693b0"},"detail":{"description":"**NAICS 2022 (North American Industry Classification System)**\n\n**Overview**\nThis is a free dataset of Data LV's North American Industry Classification System (NAICS). This dataset contains the full information about NAICS on USA, Canada and Mexico. \nThe North American Industry Classification System (NAICS) is an industry classification system developed by the statistical agencies of the United States, Canada and Mexico. Created against the background of the North American Free Trade Agreement, it is designed to provide common definitions of the industrial structure of the three countries and a common statistical framework to facilitate the analysis of the three economies. NAICS is based on supply-side or production-oriented principles, to ensure that industrial data, classified to NAICS, are suitable for the analysis of production-related issues such as industrial performance.\nThe great advantage of this dataset compared to other available is that it has all the codes, even if the code is defined by the statistical agency of only one country, and it has the information in **English, French and Spanish**.\n\n**Use cases**\n- Market Segmentation and Analysis\n- Risk Assessment and Credit Scoring\n- Customer Profiling and Personalization\n- Competitor Analysis\n- Economic Impact and Industry Analysis\n- Supply Chain Optimization\n- Regulatory and Compliance Analysis\n- Data Enrichment for Machine Learning Models\n- Investment and Portfolio Management\n\n**Product details**\nThis share contains the following datasets:\n- **NAICS_AllCodes**: All the codes defined by any of the national statistical agencies with the title and description in English, French and Spanish.\n- **CanadianIndustry_Element_EN**: Table containing the elements included or excluded in the different Canadian Sectors, Subsectors, Industry groups and Industries, in English.\n- **CanadianIndustry_Element_FR**: Table containing the elements included or excluded in the different Canadian Sectors, Subsectors, Industry groups and Industries, in French.\n- **MexicanIndustry_Element_ES**: Table containing the elements included in the different Mexican Industries, in Spanish.\n- **Mexico_Included_Excluded_ES**: Table containing the elements included and excluded in the different Mexican Sectors, Subsectors, Industry groups and Industries, in Spanish.\n\n**Additional information:**\nFor more details, refer to the embedded notebook.\n\n**Additional Insights**\nFor support, please contact support@datalv.io for full product licensing information.\nFor full product licensing information, please contact databricks@datalv.io \n","terms_of_service":"https://datalv.io/terms-of-use","documentation_link":"","privacy_policy_link":"https://datalv.io/privacy-policy","embedded_notebook_file_infos":[{"id":"7a94ed64-f921-4480-8322-a8e019d214d4","marketplace_file_type":"EMBEDDED_NOTEBOOK","file_parent":{"parent_id":"a747d634-087e-4a0b-a61b-45808f57f16f","file_parent_type":"LISTING"},"mime_type":"text/html","download_link":"https://marketplace-file-storage-prod.s3-fips.us-east-1.amazonaws.com/EMBEDDED_NOTEBOOK/LISTING_a747d634-087e-4a0b-a61b-45808f57f16f/7a94ed64-f921-4480-8322-a8e019d214d4?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEGgaDnVzLWVhc3QtMS1maXBzIkYwRAIgbC8sWIoYaHR8y7Wq86nPb0pufBl4%2BM4PWpeN%2B5PW9VQCIG7BhAVBDCWTpxFDLeuKYm%2Ff1JLz4iccPMzter0YdhgWKqYECDAQABoMNDE0MzUxNzY3ODI2IgxNAJrHbXaZG20jNfMqgwQyGEb4TsoVOoNvJ%2BaA%2FZ7zeu8hGcHqCeNfblP%2FQo5g6q2aRNtpwtPRKMlhYcIE%2FUQaNT1Lqmyptu32VUQTpzjTTxRVmJrDPkheSCkV06XQ%2FpTMKjU8CC7X1b%2FqYf8CrBwZT7Cv4C5WPjEET38f2A79h0xV6bZgPy28zQWrSeIJ9w6AfZBxiE24%2FVc08zVA0ivCT6lsy7nuU0wcR%2FMg2C9nd1vaMnQ923VpD21CKfVVYcrNWL2b%2FQ%2BNAO%2BxPjPgjiN6UPQbvbvZxkSdhgrGtecIg9NgqGkMcvM5VLTDtdC1kdeZetfOC2obtA%2BT%2F0gy3tIDo6WM5hcQfw743%2FAU9oaGnRBl%2BwoxRRDCZA%2F4jnPdluICSI7NCr6C3HHOe4CSZJjRihczxPA%2FgQczAVjAPs6CrK9N9pIzt0vNJpGUTgg4AoxftTvGfciDREilSctPIcBk6GwB%2Bzt4wbtNCmEPU%2BFD46rc6CWVQmmLwbKOeHw70ipMVFlOc8NlXkEtGO2vu6H44MstSSc6daOVUYUavQSGZ6jGhEjsP2YGz8KDm9XQotB%2FtyVBqiZV01OKqDmmBJA%2FXjUpSJ8M%2FeJXhe5m%2BKcxzNObtoJRI7jci3Tpt08XZyXZ7%2FyIV8H30ya468X3cfkTXrm972uga3z7iXAdubyEHnQIua%2FuvtwOHILi6O5D1Me2MzCPvcnXBjqTAYDoMWBt%2B3AVFuqSs2dmDQNBnvEhXfMgTedKLD5PJHb3aIl15iBkLaNu36GYnVCp9ZU2lSYcTbY5QwNnNeTfJTNy9bKhwzru6Sg%2BslhN%2BTS0zkTn%2BeLMY4tHgPEtEZ2cp%2FCk6GwqswvnYVKHjwO8q%2B1Mx8lc08fVEfarE9dke1GGFqoddx%2Bq%2BZUyHbDlAVfrCmf0yw%3D%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20261110T000135Z&X-Amz-SignedHeaders=host&X-Amz-Expires=86400&X-Amz-Credential=ASIAWA6KKHEJORWBH4N3%2F20261110%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=377be1dab3c2c6e0f06ff3b706a9c33ff020d8ec707578702276070f30c2072e","created_at":1729530266121,"updated_at":1729530266121,"display_name":"marketplace-notebook-for-NAICS","status":"FILE_STATUS_PUBLISHED"}],"geographical_coverage":"[\"United States and Canada\",\"Latin America and Caribbean\"]","cost":"FREE","update_frequency":{"interval":5,"unit":"YEARLY"},"collection_granularity":{"interval":1,"unit":"YEARLY"},"collection_date_start":1641060026803,"collection_date_end":1672509638998,"data_source":"United States Census Bureau, Statistics Canada and Instituto Nacional de Estadística y Geografía (INEGI) de México","size":0.1,"assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"Data LV","description":"DataLV is dedicated to simplifying the data acquisition process for businesses. We collect, clean, and organize datasets, allowing clients to focus on leveraging insights and driving results without the hassle of managing data pipelines."},"popularity":1729},{"id":"1b037a27-ee76-4673-918b-68fe7e85e853","summary":{"name":"LEBA European Gas Prompt Indices","subtitle":"Collection of UK energy indices including Power, Natural Gas, Emissions and Coal.","setting":{"visibility":"PUBLIC"},"created_at":1710512802597,"updated_at":1710538781838,"categories":["FINANCIAL"],"listingType":"PERSONALIZED","provider_id":"b78987ea-8d21-4f8b-9fed-8db9868d8297"},"detail":{"description":"**Overview**\nEuropean Gas Prompt Indices: The UK market can be viewed on a standalone basis (see LEBA NBP UK Gas indices) but the European gas market is a highly interconnected set of hubs across a complex physical infrastructure.\n\n\n**Delivery Attributes**\n\n- Day Ahead\n- Week-End\n- Balance of week\n- Saturday\n- Sunday\n- Working days next week\n- Balance of month\n- Month ahead indices\n\n**Coverage**\n\n- AVTP\n- PEG\n- PSV\n- THE\n- TTF\n- ZEE\n- ZTP\n","terms_of_service":"https://www.lebaltd.com/legal/","documentation_link":"https://eose.datawebstore.net/#/products/LEBA_EUROPE_GASPRMT_*_*/documentation/details_0","privacy_policy_link":"https://www.lebaltd.com/privacy/","geographical_coverage":"[\"Europe\"]","cost":"PAID","assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"London Energy Brokers Association (LEBA)","description":"London Energy Brokers Association (LEBA) providea daily reference data indices for UK and European Gas, Power, Coal and Carbon.\n\nProfile managed by EOSE Data."},"popularity":879},{"id":"17710366-adf9-4475-afdf-84a19733f0fc","summary":{"name":"SAMPLE Enriched Map Data for Location Intelligence & Lead Generation | Map Data - Millions of US ...","subtitle":"Map Data covering millions of US business records with verified addresses, geocoordinates, ratings, and diversity ...","share":{"name":"canaria_inc_e31b4234_80be_443b_aa93_2d9bfe61e782_samples_share","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1768711453325,"updated_at":1783433300451,"categories":["GEOSPATIAL","OTHER"],"listingType":"STANDARD","provider_id":"24eb2934-4a3f-4aec-b02c-4465c271a025"},"detail":{"description":"Most business data is stale. 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Every record includes:\n\n• Business name and standardized address\n• City, state, ZIP code, and latitude/longitude coordinates\n• Google Plus Code for precise spatial mapping\n• Phone number and website\n• Business categories and subcategories\n• Open hours and operational status\n• Star rating and review count\n• Review distribution by rating tier\n• Diversity and ownership tags (women-owned, minority-owned, veteran-owned, and more)\n• Branch and multi-location information\nThis Map Data covers the United States, updated daily to weekly, rolling current snapshot.\n\nWhat You Can Do With This Map Data\n\nLead Scoring and B2B Prospecting\n\nSales and GTM teams use Map Data to build targeted prospect lists, score leads by quality signals, and prioritize outreach by geography and business attributes. Map Data with ratings, review counts, and category filters lets you reach the right businesses before competitors do.\n\n• Build prospect lists by filtering Map Data on business category, geography, and review score\n• Score leads using Map Data star ratings and review counts as quality and engagement proxies\n• Identify businesses with specific ownership attributes using Map Data diversity and ownership tags\n• Enrich lead records with Map Data phone numbers, websites, and verified addresses for direct outreach\nLocation Intelligence and Site Selection\n\nSite selection consultants and economic development organizations use Map Data to map commercial activity, define labor sheds, and compare business density across competing geographies. Map Data updated daily to weekly gives analysts a current view of local economic conditions without the 6-18 month lag of government sources.\n\n• Map commercial activity and business density across geographies using Map Data records\n• Compare business concentration across metro areas for economic development proposals with Map Data\n• Visualize the competitive landscape around a specific address or point of interest using Map Data\n• Build site selection reports backed by Map Data current business counts and category distributions\nMarket Research and BI\n\nBI and market research teams use Map Data to size local markets, track business openings and closings, benchmark industry composition, and analyze consumer sentiment through review distributions. Map Data with millions of records gives analysts a granular view of the US commercial landscape no government dataset can match.\n\n• Size addressable markets by counting Map Data records matching your target category and geography\n• Track business formation and closure trends using Map Data snapshots over time\n• Benchmark industry composition and health metrics using Map Data category and rating distributions\n• Analyze consumer sentiment through Map Data review counts and star rating distributions\nCRM Enrichment and Business Validation\n\nRevOps and CRM teams use Map Data to append verified addresses, geocoordinates, and contact details to existing records, keeping databases clean and current. Map Data with daily to weekly updates means your CRM reflects the real-world state of the businesses you sell to.\n\n• Append verified addresses and geocoordinates to customer records using Map Data\n• Validate and refresh business names, phone numbers, and websites with Map Data\n• Add operational details including categories, open hours, and branch information from Map Data\n• Segment existing accounts using Map Data diversity and ownership tags for targeted campaigns\nDEI and Ownership Intelligence\n\nCompliance, ESG, and procurement teams use Map Data ownership tags to identify and analyze minority-owned, women-owned, and veteran-owned businesses across the US. Map Data diversity attributes are rare among business datasets and provide a direct input for supplier diversity programs, ESG reporting, and DEI-focused market analysis.\n\n• Identify minority-owned, women-owned, and veteran-owned businesses using Map Data ownership tags\n• Build supplier diversity pipelines from Map Data filtered by category, geography, and ownership type\n• Support DEI-focused market analysis and ESG reporting with Map Data diversity signals\n• Quantify ownership representation by industry and region using Map Data records\n• Target and engage underrepresented business segments using Map Data ownership attributes\nHow We Source This Map Data\n\nMap Data is sourced directly from Google Maps and verified across all US geographies and business categories. Records are updated daily to weekly to capture openings, closures, and changes in hours, phone numbers, ratings, and ownership classifications.\n\nThis Map Data joins directly with Canaria's job postings and company profile datasets, connecting a physical business location to its hiring activity, required skills, and corporate firmographics for deeper multi-dimensional intelligence.\n\nWhat Makes This Map Data Different\n\n• Diversity tags: Map Data includes women-owned, minority-owned, and veteran-owned classifications that are rare in standard business directories\n• Daily to weekly updates: Map Data is refreshed far more frequently than government sources, which lag by 6-18 months\n• Geocoordinates: Map Data includes latitude, longitude, and Google Plus Codes for precise spatial analysis and mapping workflows\n• Ratings and reviews: Map Data star ratings and review distributions serve as business quality and consumer sentiment signals\n• Matchable: Map Data joins directly with job postings and company profiles to connect location to hiring activity and firmographics\n• Granular filters: Map Data is customizable by geography, category, rating, hours, and ownership type to fit any workflow\nWho Uses This Map Data\n\n• Sales and GTM Teams use Map Data to build targeted prospect lists, score leads, and prioritize outreach by geography and business quality\n• CRM and RevOps Teams use Map Data to enrich and validate customer records with verified addresses, geocoordinates, and contact details\n• Market Research and BI Teams use Map Data to size local markets, benchmark industry composition, and analyze consumer sentiment through review data\n• Site Selection Consultants use Map Data to map commercial activity, compare business density, and build defensible location analysis for corporate site decisions\n• Economic Development Organizations use Map Data to respond to RFIs with current business data and define competitive economic landscapes\n• Compliance and KYC Teams use Map Data to verify business existence, confirm physical addresses, and enrich third-party compliance records\n• DEI, ESG, and Procurement Teams use Map Data ownership tags to identify underrepresented businesses and build supplier diversity pipelines\nDelivery and Format\n\nMap Data is delivered in CSV, JSON, or Parquet via AWS S3 or Google Cloud Storage. Custom filters by geography (ZIP, city, state), business category, star rating, review count, open hours, and ownership type. Compatible with Snowflake, Databricks, Power BI, Tableau, Salesforce, and most BI platforms.","terms_of_service":"https://decanaria.com/privacy-policy","contact_email":"support@datarade.ai","privacy_policy_link":"https://decanaria.com/privacy-policy","assets":["ASSET_TYPE_DATA_TABLE"]},"provider_summary":{"name":"Canaria Inc.","description":"Canaria is a leading provider of enriched job posting and labor market data. We collect 1B+ job postings from Indeed, LinkedIn and 200,000+ employer career sites and deduplicate them to 400M+ unique jobs, with deepest coverage in the US plus postings from 243 other countries and territories since 2022.\r\n\r\nData arrives analysis-ready: SOC occupation and NAICS industry codes, normalized titles across 99,799 canonical roles, a 43,000+ entry skills and credentials taxonomy, salary estimates backed by 44,000+ benchmark cells with 95% confidence intervals, worker type (W2 vs 1099), stock tickers for listed employers, and a confidence score on every classified field.\r\n\r\nHedge funds, HR tech platforms, workforce planners and researchers choose Canaria when they want enterprise-grade enrichment without an enterprise contract. Delivery by API or as CSV and Parquet files to S3, GCS, Snowflake or SFTP. 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This makes it easier to connect facility intelligence to provider analytics, market planning, network strategy, field operations, and real-world data workflows.\n\nExample workflow:\n\n**Facility Search → Identity Resolution → Official Directory Record → Geography and Organization Context → Provider / Market Analysis → Operational Planning**","terms_of_service":"https://www.kytheralabs.com/eula","documentation_link":"","privacy_policy_link":"https://www.kytheralabs.com/privacy","assets":["ASSET_TYPE_MCP"],"license":""},"provider_summary":{"name":"Kythera Labs Inc","description":"Kythera Labs is a healthcare data technology company that helps healthcare and life sciences organizations reduce uncertainty and make decisions with greater confidence. 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With coverage of over 260 million validated US individuals and 300+ insightful data elements, you’ll have the depth and precision to drive results across every channel. \n\nBrands that own and control their identity assets are best positioned to build trust, maximize customer loyalty, and drive long-term value.\n\n\n**CONSUMER ESSENTIALS ATTRIBUTES**\n\n- **Individual: Build Your ICP with Foundational Insights.** Individual attributes—including age, gender, and education—allow you to segment audiences with precision, personalize messaging, and ensure your products connect with the right people, every time.\n- **Household: Understand the Dynamics of the Entire Household.** With household composition, income levels, and homeownership, you can tailor offers, predict needs, and craft marketing strategies that resonate with the collective purchasing power of the family unit.\n- **Interests: Connect with Customers Through Their Passions.** Forge authentic connections by understanding what drives your audience outside of their purchases. 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Get critical insight on payment preferences, product affinities, and channel usage, to refine channel strategy, personalize promotions, and increase conversion rates.\n\n**PREMIUM ATTRIBUTES**\n\n- **Adstra Auto+: Shift Your Automotive Marketing into Overdrive.** The industry's most comprehensive automotive data, Adstra Auto+ delivers granular vehicle details, empowering dealerships, insurers, and aftermarket retailers to execute hyper-targeted campaigns.\n- **Adstra Caregivers: Reach Influential Healthcare Decision Makers.** Connect with the dedicated individuals making critical healthcare and household decisions. Adstra Caregivers enables you to deliver timely, supportive, and relevant products and services to those caring for loved ones.\n- **Adstra HCP: Engage Directly with Medical Professionals.** Reach physicians, nurses, dentists, and other practitioners – and essential dataset for pharmaceutical companies, medical device manufacturers, and marketers looking to connect with key decision-makers and influencers.\n- **Adstra Health: Power Your Wellness Marketing.** Adstra Health provides privacy-compliant insights into consumer health lifestyles, ailments, and interests in prevention and therapies. It's the definitive dataset for crafting resonant messaging for CPG, fitness, and health service brands.\n- **Adstra Political: Navigate the Political and Advocacy Landscape.** Adstra Political delivers insights into political affiliation, donation history, and support for key issues. 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There are three UMLS Knowledge Sources: \n- The Metathesaurus, which contains over one million biomedical concepts from over 100 source vocabularies \n- The Semantic Network, which defines 133 broad categories and fifty-four relationships between categories for labeling the biomedical domain \n- The SPECIALIST Lexicon & Lexical Tools, which provide lexical information and programs for language processing The Metathesaurus is a large, multi-purpose, and multi-lingual vocabulary database that contains information about biomedical and health related concepts, their various names, and the relationships among them. The Metathesaurus is built from the electronic versions of many different thesauri, classifications, code sets, and lists of controlled terms used in patient care, health services billing, public health statistics, indexing and cataloging biomedical literature, and/or basic, clinical, and health services research. The Metathesaurus contains over five million terms, or names, organized by meaning into concepts and assigned a unique identifier. The Metathesaurus is not a vocabulary. It contains many vocabularies that are standards and helps to create mappings between these vocabularies. The Semantic Network consists of: \n- Semantic types (high level categories) \n- Semantic relationships (relationships between semantic types) The Semantic Network can be used to categorize any medical vocabulary. There are 133 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types. Semantic types and semantic relationships create a network that represents the biomedical domain. Semantic types and relationships help with interpreting the meaning that has been assigned to the Metathesaurus concept.\n\n**Benefits**\n- The purpose of the umls is to improve the ability of computer programs to \"understand\" the biomedical meaning in user inquiries and to use this understanding to retrieve and integrate relevant machine-readable information for users.\n- one powerful use of the umls is linking health information, medical terms, drug names, and billing codes across different computer systems.\n- • linking terms and codes between your doctor, your pharmacy, and your insurance company.\n-Patient care coordination among several departments within a hospital.\n-Timely access to accurate and up-to-date information improves decision making and ultimately the quality of patient care and research.\n- the umls has many other uses, including search engine retrieval, data mining, public health statistics reporting, and terminology research.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [Biologic Function Concepts and Types](https://www.johnsnowlabs.com/marketplace/biologic-function-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Biologic Function\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Biologic Function Relationships](https://www.johnsnowlabs.com/marketplace/biologic-function-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Biologic Function\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Cell Function Concepts and Types](https://www.johnsnowlabs.com/marketplace/cell-function-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Cell Function\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Cell Function Relationships](https://www.johnsnowlabs.com/marketplace/cell-function-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Cell Function\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Cell or Molecular Dysfunction Concepts and Types](https://www.johnsnowlabs.com/marketplace/cell-or-molecular-dysfunction-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Cell or Molecular Dysfunction\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Cell or Molecular Dysfunction Relationships](https://www.johnsnowlabs.com/marketplace/cell-or-molecular-dysfunction-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Cell or Molecular Dysfunction\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Disease or Syndrome Concepts and Types](https://www.johnsnowlabs.com/marketplace/disease-or-syndrome-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Disease or Syndrome\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Disease or Syndrome Relationships](https://www.johnsnowlabs.com/marketplace/disease-or-syndrome-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Disease or Syndrome\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Environmental Effect of Humans Concepts and Types](https://www.johnsnowlabs.com/marketplace/environmental-effect-of-humans-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Environmental Effect of Humans\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Environmental Effect of Humans Relationships](https://www.johnsnowlabs.com/marketplace/environmental-effect-of-humans-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Environmental Effect of Humans\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Experimental Model of Disease Concepts and Types](https://www.johnsnowlabs.com/marketplace/experimental-model-of-disease-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Experimental Model of Disease\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Experimental Model of Disease Relationships](https://www.johnsnowlabs.com/marketplace/experimental-model-of-disease-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Experimental Model of Disease\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Genetic Function Concepts and Types](https://www.johnsnowlabs.com/marketplace/genetic-function-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Genetic Function\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Genetic Function Relationships](https://www.johnsnowlabs.com/marketplace/genetic-function-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Genetic Function\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Human-caused Phenomenon or Process Concepts and Types](https://www.johnsnowlabs.com/marketplace/human-caused-phenomenon-or-process-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Human-caused Phenomenon or Process\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Human-caused Phenomenon or Process Relationships](https://www.johnsnowlabs.com/marketplace/human-caused-phenomenon-or-process-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Human-caused Phenomenon or Process\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Injury or Poisoning Concepts and Types](https://www.johnsnowlabs.com/marketplace/injury-or-poisoning-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Injury or Poisoning\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Injury or Poisoning Relationships](https://www.johnsnowlabs.com/marketplace/injury-or-poisoning-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Injury or Poisoning\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Mental Process Concepts and Types](https://www.johnsnowlabs.com/marketplace/mental-process-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Mental Process\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Mental Process Relationships](https://www.johnsnowlabs.com/marketplace/mental-process-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Mental Process\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Mental or Behavioral Dysfunction Concepts and Types](https://www.johnsnowlabs.com/marketplace/mental-or-behavioral-dysfunction-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Mental or Behavioral Dysfunction\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Mental or Behavioral Dysfunction Relationships](https://www.johnsnowlabs.com/marketplace/mental-or-behavioral-dysfunction-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Mental or Behavioral Dysfunction\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Molecular Function Concepts and Types](https://www.johnsnowlabs.com/marketplace/molecular-function-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Molecular Function\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Molecular Function Relationships](https://www.johnsnowlabs.com/marketplace/molecular-function-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Molecular Function\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Natural Phenomenon or Process Concepts and Types](https://www.johnsnowlabs.com/marketplace/natural-phenomenon-or-process-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Natural Phenomenon or Process\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Natural Phenomenon or Process Relationships](https://www.johnsnowlabs.com/marketplace/natural-phenomenon-or-process-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Natural Phenomenon or Process\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Neoplastic Process Concepts and Types](https://www.johnsnowlabs.com/marketplace/neoplastic-process-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Neoplastic Process\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Neoplastic Process Relationships](https://www.johnsnowlabs.com/marketplace/neoplastic-process-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Neoplastic Process\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Organ or Tissue Function Concepts and Types](https://www.johnsnowlabs.com/marketplace/organ-or-tissue-function-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Organ or Tissue Function\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Organ or Tissue Function Relationships](https://www.johnsnowlabs.com/marketplace/organ-or-tissue-function-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Organ or Tissue Function\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Organism Function Concepts and Types](https://www.johnsnowlabs.com/marketplace/organism-function-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Organism Function\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Organism Function Relationships](https://www.johnsnowlabs.com/marketplace/organism-function-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Organism Function\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Pathologic Function Concepts and Types](https://www.johnsnowlabs.com/marketplace/pathologic-function-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Pathologic Function\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Pathologic Function Relationships](https://www.johnsnowlabs.com/marketplace/pathologic-function-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Pathologic Function\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Phenomenon or Process Concepts and Types](https://www.johnsnowlabs.com/marketplace/phenomenon-or-process-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Phenomenon or Process\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Phenomenon or Process Relationships](https://www.johnsnowlabs.com/marketplace/phenomenon-or-process-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Phenomenon or Process\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Physiologic Function Concepts and Types](https://www.johnsnowlabs.com/marketplace/physiologic-function-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Physiologic Function\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Physiologic Function Relationships](https://www.johnsnowlabs.com/marketplace/physiologic-function-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Physiologic Function\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated\n- Data Updates\n - Data updates support replace-on-update: outdated foreign keys are deprecated, not deleted\n\n**Our data is curated and enriched by domain experts** \n\n Each dataset is manually curated by our team of doctors, pharmacists, public health & medical billing experts:\n- Field names, descriptions, and normalized values are chosen by people who actually understand their meaning\n- Healthcare & life science experts add categories, search keywords, descriptions and more to each dataset\n- Both manual and automated data enrichment supported for clinical codes, providers, drugs, and geo-locations\n- The data is always kept up to date – even when the source requires manual effort to get updates\n- Support for data subscribers is provided directly by the domain experts who curated the data sets\n- Every data source’s license is manually verified to allow for royalty-free commercial use and redistribution.\n\n**Need Help?**\nIf you have questions about our products, contact us at [info@johnsnowlabs.com](mailto:info@johnsnowlabs.com). \n 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DrugBank is a richly annotated resource that combines detailed drug data with comprehensive drug target and drug action information. DrugBank is widely used to facilitate in silico drug target discovery, drug design, drug docking or screening, drug metabolism prediction, drug interaction prediction and general pharmaceutical education.\n\n**Description**\nThe DrugBank database is a unique bioinformatics and cheminformatics resource that combines detailed drug (i.e. chemical, pharmacological and pharmaceutical) data with comprehensive drug target (i.e. sequence, structure, and pathway) information. The database contains 9591 drug entries including 2037 FDA-approved small molecule drugs, 241 FDA-approved biotech (protein/peptide) drugs, 96 nutraceuticals and over 6000 experimental drugs. Additionally, 4661 non-redundant protein (i.e. drug target/enzyme/transporter/carrier) sequences are linked to these drug entries.\n\n**Benefits**\n- drugbank is widely used by the drug industry, medicinal chemists, pharmacists, physicians, students and the general public.\n- extensive drug and drug-target data\n- enables the discovery and repurposing of a number of existing drugs to treat rare and newly identified illnesses.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [DrugBank Vocabulary](https://www.johnsnowlabs.com/marketplace/drugbank-vocabulary)\n\t- DrugBank is a richly annotated resource that combines drug data with drug target and drug action information. Released in 2006, DrugBank has been widely used to facilitate in silico drug target discovery, drug design, drug docking or screening, drug metabolism prediction, drug interaction prediction and general pharmaceutical education. \"Drug Bank Vocabulary\" contains information on DrugBank identifiers, names, and synonyms to permit easy linking and integration into any type of project.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - 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The two current sources of information are the International Institute for Applied Systems Analysis (IIASA), from Austria and the U.K. Office for National Statistics.\n\n**Description**\nAccording to its self-description, the International Institute for Applied Systems Analysis (IIASA) is an international scientific institute that conducts research into the critical issues of global environmental, economic, technological, and social change that we face in the twenty-first century. The U.K. Office for National Statistics, being the largest independent producer of official statistics and recognized as the national statistical institute, has as responsibilities to collect and publish statistics related to the economy, population and society at national, regional and local levels. This data package contains indicators at European country and region levels for the population dynamics, related to population growth and decrease processes: births, fertility, migration, deaths, life expectancy and population projections for the year 2050. At the same time, it includes data about population size, age and elders dependency ratio. The U.K. population statistics datasets contain estimates for age groups at country level and for the number and density of population at town/city, county, region and country level.\n\n**Benefits**\n- Useful for public health specialists and specialized institutions that plan and assess health services. useful for researchers seeking to improve the methods, regulations, health policies and strategy. the raw version can be found on the author's website, but jsl is offering a clean, standardized and easier to use version, alone or in combination with other datasets found in jsl dataset library. the fields descriptions along with the dataset description are again useful for the user to quickly understand the data and the dataset.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [European Census Population Fertility Life Expectancy By Country](https://www.johnsnowlabs.com/marketplace/european-census-population-fertility-life-expectancy-by-country)\n\t- This European Census dataset covers data on population, fertility, migration, birth and death rates as well as life expectancy in the recent years or projected to 2050.\n- [European Census Population Fertility Life Expectancy By Region](https://www.johnsnowlabs.com/marketplace/european-census-population-fertility-life-expectancy-by-region)\n\t- This European Census dataset covers data on population, fertility, as well as life expectancy in the recent years or projected to 2050. It arranges Europe into 11 types of regions according to different ways of classification.\n- [Population Estimates By Age UK Mid 2016](https://www.johnsnowlabs.com/marketplace/population-estimates-by-age-uk-mid-2016)\n\t- This dataset contains information on estimates of the usual resident population in the UK as of 30 June 2016 by age and gender; the data is from the local authorities.\n- [Population Estimates In The UK Mid 2001 To Mid 2016](https://www.johnsnowlabs.com/marketplace/population-estimates-in-the-uk-mid-2001-to-mid-2016)\n\t- This dataset contains information on population density for the local authorities in the UK, population estimates from 2001 to 2016.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - 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It also provides data on monthly figures on deaths registered in England and Wales.\n\n**Description**\nThis data package consists of 6 datasets containing data on mortality in England and Wales. Datasets include\n- Age-standardized and age-specific alcohol-related death rates in the UK, its constituent countries and regions of England, deaths registered from 1994 to 2016.\n- Deaths from breast cancer in females registered in the calendar year, directly standardized by age group, given as a rate per 100,000 registered female patients.\n- Number of babies dying before the age of one for every 1,000 that were born alive in England and Wales.\n- Monthly death figures, provided counts of the number of deaths registered in a calendar month, by area of usual residence in England and Wales.\n- Number of stillbirths and deaths within 28 days of birth per 1,000 live births and stillbirths, in England and Wales.\n- Rate at which people under the age of 75 years have died from disease of the liver, respiratory system, cardiovascular system and/or cancer.\n\n**Benefits**\n- provides useful information on mortality statistics.\n- can be used for analytical purposes.\n- uk’s death rate can be calculated and analyzed.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [Alcohol Related Deaths in the UK 1994 To 2016](https://www.johnsnowlabs.com/marketplace/alcohol-related-deaths-in-the-uk-1994-to-2016)\n\t- This dataset includes information on age-standardized and age-specific alcohol-related death rates in the UK, its constituent countries and regions of England, deaths registered from 1994 to 2016.\n- [Alcohol Related Hospital Admissions Rate By England Area](https://www.johnsnowlabs.com/marketplace/alcohol-related-hospital-admissions-rate-by-england-area)\n\t- This dataset contains estimated age-specific standardized hospitalization rates due to alcohol-specific (wholly attributable) conditions by England regions, counties and unitary authorities, and the level of multiple deprivations. Data about rates level compared to England and region-level are also available in the dataset.\n- [Breast Cancer Mortality Statistics](https://www.johnsnowlabs.com/marketplace/breast-cancer-mortality-statistics)\n\t- Deaths from breast cancer in females registered in the calendar year, directly standardized by age group, given as a rate per 100,000 registered female patients.\n- [Cardio-Vascular Disease Cases By Year And England Area](https://www.johnsnowlabs.com/marketplace/cardio-vascular-disease-cases-by-year-and-england-area)\n\t- This dataset contains the estimated percentages of patients in the general practitioners (GP) disease registers with cardiovascular disease (hypertension, ischemic heart disease or stroke/transient ischemic attack) by England regions, counties and unitary authorities, and the deprivation level. Comparison to England and region levels are also available in the dataset.\n- [Diabetes Cases By Deprivation Level Year And England Area](https://www.johnsnowlabs.com/marketplace/diabetes-cases-by-deprivation-level-year-and-england-area)\n\t- This dataset contains the estimated percentages of patients, 17 years old and over, with diabetes existent in the general practitioners (GP) disease registers by England regions, counties and unitary authorities, and the deprivation level. Comparisons to England and region levels, are also available in the dataset.\n- [Infant Mortality Statistics](https://www.johnsnowlabs.com/marketplace/infant-mortality-statistics)\n\t- This dataset provides details about the number of babies dying before the age of one for every 1,000 that were born alive in England and Wales.\n- [Liver Disease Hospital Admission By Deprivation Level And England Area](https://www.johnsnowlabs.com/marketplace/liver-disease-hospital-admission-by-deprivation-level-and-england-area)\n\t- This dataset contains estimated age-specific standardized hospitalization rates due to liver disease, by type of disease, England regions, counties and unitary authorities, and the level of multiple deprivations. Data about rates level compared to England and region-level, are also available in the dataset.\n- [Liver Mortality By Disease Location And Deprivation In England](https://www.johnsnowlabs.com/marketplace/liver-mortality-by-disease-location-and-deprivation-in-england)\n\t- This dataset contains the estimated age-specific standardized rates of mortality due to by liver diseases, among persons under 75 years old, by type of liver disease, England regions, counties and unitary authorities, and the level of multiple deprivations. Data about rates level compared to England and region-level, are also available in the dataset.\n- [Monthly Figures On Deaths Registered In England And Wales 2013 To 2020](https://www.johnsnowlabs.com/marketplace/monthly-figures-on-deaths-registered-in-england-and-wales-2013-to-2020)\n\t- This dataset contains information of monthly death figures, provided counts of the number of deaths registered in a calendar month, by area of usual residence in England and Wales for 2013 to 2018. In this dataset, the figures are presented by regions (within England), counties, and London boroughs.\n- [NHS Health Check Participation Data By Quarter Year And England Area](https://www.johnsnowlabs.com/marketplace/nhs-health-check-participation-data-by-quarter-year-and-england-area)\n\t- This dataset contains the estimated percentages of eligible England resident persons (age 40-74 years old) who were invited and received the National Health Service (NHS) Health Check, by type of liver disease England regions, counties and unitary authorities, and the level of multiple deprivations. Comparisons to England and region level, are also available in the dataset.\n- [Neonatal Mortality and Stillbirth Statistics](https://www.johnsnowlabs.com/marketplace/neonatal-mortality-and-stillbirth-statistics)\n\t- This dataset provides details about the number of stillbirths and deaths within 28 days of birth per 1,000 live births and stillbirths, in England and Wales reported at CCG (Clinical Commissioning Groups) of residence level by calendar year.\n- [Smokers Overweight And Physical Inactive Adults By England Area](https://www.johnsnowlabs.com/marketplace/smokers-overweight-and-physical-inactive-adults-by-england-area)\n\t- This dataset contains the estimated percentages of adults smokers (18+ years), overweight or obese adults (18+ years) and physically inactive adults (19+ years) by England regions, counties and unitary authorities, and by socioeconomic variables. Data for demographic groups (age, gender, race/ethincity, religion or sexual orientation-based) and comparisons to England and region levels, are also available in the dataset.\n- [Under 75 Mortality From Various Diseases](https://www.johnsnowlabs.com/marketplace/under-75-mortality-from-various-diseases)\n\t- This dataset reports the rate at which people under the age of 75 years have died from disease of the liver, respiratory system, cardiovascular system and/or cancer. The mortality rates are directly standardized by age and sex to the England population.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated\n- Data Updates\n - Data updates support replace-on-update: outdated foreign keys are deprecated, not deleted\n\n**Our data is curated and enriched by domain experts** \n\n Each dataset is manually curated by our team of doctors, pharmacists, public health & medical billing experts:\n- Field names, descriptions, and normalized values are chosen by people who actually understand their meaning\n- Healthcare & life science experts add categories, search keywords, descriptions and more to each dataset\n- Both manual and automated data enrichment supported for clinical codes, providers, drugs, and geo-locations\n- The data is always kept up to date – even when the source requires manual effort to get updates\n- Support for data subscribers is provided directly by the domain experts who curated the data sets\n- Every data source’s license is manually verified to allow for royalty-free commercial use and redistribution.\n\n**Need Help?**\nIf you have questions about our products, contact us at [info@johnsnowlabs.com](mailto:info@johnsnowlabs.com). \n 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Our data enables businesses to achieve faster time to value, reduce costs, and drive growth. \n\n- Integrate data and analytics on 570 million+ business records globally. \n- Enable near real-time native updates by tapping into unique entity identifiers. \n- Accelerate time to value by reducing set up and activation time, as well as extract, transform, and load (ETL) processes. \n- Access the Databricks Lakehouse Platform for data, analytics and AI.  \n- Access cost-effective global, verified, accurate, and structured data sets.\n \n**Use cases**\n- Master Data Management (MDM), including Match and Append\n- Improve Operational Efficiency and Automation \n- Predict Customer Lifetime Value \n- Enable Credit Risk Modeling \n- Activate Customer Segmentation & Personalization \n- Enhance Regulatory Compliance & Reporting \n- Optimize Supply Chain Management \n- Inform ESG & Sustainability Efforts \n- Drive Innovation with Artificial Intelligence \n\n**Available Dun & Bradstreet Data**\n\n- Company Financials \n- Company Information \n- Diversity Insights \n- Educational Data \n- ESG Insights \n- External Disruption Insights \n- Filings and Events \n- Financial Strength Insights \n- Global Business Ranking \n- Global Financials \n- Hierarchies and Connections \n- Ownership Insights \n- Payment Insights \n- Principals and Contacts \n- Sales and Marketing Insights \n- Shipping Insights \n- Supply Chain Risk Index \n- Third Party Risk Insights\n\n\n**For more information**\nPlease reach out to Databricks@dnb.com or your Dun & Bradstreet representative.","terms_of_service":"https://www.dnb.com/utility-pages/terms-of-use.html","documentation_link":"","privacy_policy_link":"https://www.dnb.com/about-us/data-transparency.html","geographical_coverage":"[\"Global\"]","cost":"PAID","update_frequency":{"interval":1,"unit":"DAILY"},"data_source":"","assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"Dun & Bradstreet","description":"Dun & Bradstreet delivers the world’s most comprehensive business data and analytical insights to power today’s crucial business needs. That’s why 90% of the Fortune 500, and companies of all sizes around the world, rely on Dun & Bradstreet to help grow and protect their businesses. "},"popularity":1969},{"id":"5f695a9d-3eca-4e37-b9a0-94c9a1e98fa1","summary":{"name":"RxNorm Drug Terminologies","subtitle":"This data package contains information on RxNorm concepts, atoms, attributes, retired concepts, relationships that exi..","share":{"name":"rxnorm_drug_terminologies","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1682356486852,"updated_at":1695691975644,"categories":["HEALTH","SCIENCE_AND_RESEARCH"],"listingType":"STANDARD","provider_id":"f88849f8-9d77-4de2-b4c7-03d55cc3768e"},"detail":{"description":"\n**Overview**\nThis data package contains information on RxNorm concepts, atoms, attributes, retired concepts, relationships that exist between atoms and concepts, semantic types, or broad subject categories assigned to each concept, relationships that exist between atoms and concepts for currently prescribed drugs, RxNorm drug and ingredient names and codes as well as source-provided drug and ingredient names.\n\n**Description**\nThis data package contains complete database of RxNorm. RxNorm provides normalized names for clinical drugs and links its names to many of the drug vocabularies commonly used in pharmacy management and drug interaction software, including those of First Databank, Micromedex, MediSpan, Gold Standard Drug Database, and Multum. By providing links between these vocabularies, RxNorm can mediate messages between systems not using the same software and vocabulary.\n\n**Benefits**\n- Hospitals, pharmacies, and other organizations use computer systems to record and process drug information. because these systems use many different sets of drug names, it can be difficult for one system to communicate with another. to address this challenge, rxnorm data package provides normalized names and unique identifiers for medicines and drugs. the goal this data package is to allow computer systems to communicate drug-related information efficiently and unambiguously.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [RxNorm Archived Atoms Information](https://www.johnsnowlabs.com/marketplace/rxnorm-archived-atoms-information)\n\t- This dataset includes history information about RxNorm atoms. It contains information about SAB=RXNORM atoms that are no longer in “RxNorm Drugs Names and Identifiers”.\n- [RxNorm Attributes Data for Concepts and Atoms](https://www.johnsnowlabs.com/marketplace/rxnorm-attributes-data-for-concepts-and-atoms)\n\t- This dataset contains all of the attribute data. This includes RXNORM provided attributes, such as normalized 11-digit National Drug Codes (NDCs), UNII codes, and human or veterinary usage markers, and source-provided attributes, such as labeler, definition, and imprint information. Each attribute has an 'Attribute Name' (ATN) and 'Attribute Value' (ATV) combination. For example, NDCs have an ATN of 'NDC' and an ATV of the actual NDC value.\n- [RxNorm Changed Atoms and Concepts from Last Release](https://www.johnsnowlabs.com/marketplace/rxnorm-changed-atoms-and-concepts-from-last-release)\n\t- This dataset contains information on atoms that have changed concepts from the previous release to the current release. It only provides data about changes since the last release of RxNorm, it is not cumulative.\n- [RxNorm Data Elements and Attributes Names](https://www.johnsnowlabs.com/marketplace/rxnorm-data-elements-and-attributes-names)\n\t- This dataset provides a brief explanation of the names and values for the data elements and attributes in RxNorm and the UMLS.\n- [RxNorm Drugs Names and Identifiers](https://www.johnsnowlabs.com/marketplace/rxnorm-drugs-names-and-identifiers)\n\t- This dataset contains all of the source-provided drug and ingredient names, as well as the normalized SAB=RXNORM names. Every string or concept name in RxNorm appears in this file, connected to its language, source vocabularies, and its concept identifier.\n- [RxNorm Prescription Drugs Attributes Data for Concepts and Atoms](https://www.johnsnowlabs.com/marketplace/rxnorm-prescription-drugs-attributes-data-for-concepts-and-atoms)\n\t- This dataset contains all of the attribute data. This includes RXNORM provided attributes for currently prescribable drugs. The attributes such as normalized 11-digit National Drug Codes (NDCs), UNII codes, and human or veterinary usage markers, and source-provided attributes, such as labeler, definition, and imprint information. Each attribute has an 'Attribute Name' (ATN) and 'Attribute Value' (ATV) combination. For example, NDCs have an ATN of 'NDC' and an ATV of the actual NDC value.\n- [RxNorm Prescription Drugs Names and Identifiers](https://www.johnsnowlabs.com/marketplace/rxnorm-prescription-drugs-names-and-identifiers)\n\t- This dataset contains all Currently Prescribable drug and ingredient names and codes. Every string or concept name in RxNorm appears in this file, connected to its language, source vocabularies, and its concept identifier.\n- [RxNorm Prescription Drugs Relationships between Concepts and Atoms](https://www.johnsnowlabs.com/marketplace/rxnorm-prescription-drugs-relationships-between-concepts-and-atoms)\n\t- This dataset contains all of the relationships that exist between atoms and between concepts for currently prescribable drugs. This includes source-provided relationships and relationships between RXNORM source-provided drug names.\n- [RxNorm Relationships between Concepts and Atoms](https://www.johnsnowlabs.com/marketplace/rxnorm-relationships-between-concepts-and-atoms)\n\t- This dataset contains all of the relationships that exist between atoms and between concepts. This includes source-provided relationships and relationships between RXNORM source-provided drug names.\n- [RxNorm Retired Concept CUIs](https://www.johnsnowlabs.com/marketplace/rxnorm-retired-concept-cuis)\n\t- This dataset contains information on retired concepts. A concept is retired when it merges into another concept, has been renamed as a result of an error or no longer appears in the source vocabulary.\n- [RxNorm Semantic Types for Concepts](https://www.johnsnowlabs.com/marketplace/rxnorm-semantic-types-for-concepts)\n\t- This dataset contains the semantic types, or broad subject categories, assigned to each concept. Every concept receives at least one semantic type.\n- [RxNorm Source Provider Information](https://www.johnsnowlabs.com/marketplace/rxnorm-source-provider-information)\n\t- This dataset contains contact information for source providers, available term types and attributes, and other information related to each RxNorm source.\n- [RxNorm Term Types for Normalized Names](https://www.johnsnowlabs.com/marketplace/rxnorm-term-types-for-normalized-names)\n\t- RXNORM source uses term types (TTYs) to indicate generic and branded drug names at different levels of specificity. This dataset includes the full list of term types, where source provider is RXNORM.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated\n- Data Updates\n - Data updates support replace-on-update: outdated foreign keys are deprecated, not deleted\n\n**Our data is curated and enriched by domain experts** \n\n Each dataset is manually curated by our team of doctors, pharmacists, public health & medical billing experts:\n- Field names, descriptions, and normalized values are chosen by people who actually understand their meaning\n- Healthcare & life science experts add categories, search keywords, descriptions and more to each dataset\n- Both manual and automated data enrichment supported for clinical codes, providers, drugs, and geo-locations\n- The data is always kept up to date – even when the source requires manual effort to get updates\n- Support for data subscribers is provided directly by the domain experts who curated the data sets\n- Every data source’s license is manually verified to allow for royalty-free commercial use and redistribution.\n\n**Need Help?**\nIf you have questions about our products, contact us at [info@johnsnowlabs.com](mailto:info@johnsnowlabs.com). \n 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marketplaces (including the US, UK, Germany, and Canada). It contains structured data across three dimensions: products, offers, and customer reviews — supporting analysis of stock levels, pricing strategies, seller dynamics, and buyer sentiment.\nThe amazon_offers table includes offer-specific details such as price, condition, inventory count, Buy Box status, shipping information, and offer rank. The inventory field is particularly valuable for tracking stock availability per seller and offer over time.\nThe amazon_products table provides product-level attributes, including title, description, images, monthly sales estimates, technical specifications, and best sellers rank. The reviews table contains verified user feedback, complete with ratings, full text, timestamps, and verification status.\nTo meet specific analytical or integration needs, we’re happy to collaborate and publish a new listing aligned with your preferred schema and schedule.\n\n**Sample Use Cases**\n\n- \uD83D\uDCCA Dynamic Pricing Models:\n- Use offer-level data to power machine learning pricing engines.\n- \uD83D\uDED2 Buy Box Tracking:\n- Identify which sellers dominate the buy box across time and categories.\n- \uD83D\uDCAC Sentiment Modeling:\n- Train LLMs or NLP classifiers on verified review text.\n- \uD83D\uDCC8 Sales Trend Forecasting:\n- Predict demand by analyzing the interplay between BSR, review velocity, and inventory fluctuations.\n- \uD83E\uDDE0 Brand & Product Performance Intelligence:\n- Track product-market fit and competitive pricing effectiveness.","terms_of_service":"https://groupbwt.com/terms-of-use/","documentation_link":"https://drive.google.com/file/d/1pomPugXjvl_GqNlc527RnIoUordQZ7A6","privacy_policy_link":"https://groupbwt.com/privacy-policy/","geographical_coverage":"[\"Europe\",\"United States and Canada\"]","cost":"FREE","collection_granularity":{"interval":1,"unit":"DAILY"},"collection_date_start":1746520534974,"collection_date_end":1746693337083,"data_source":"Amazon US & EU marketplaces","assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"GroupBWT","description":"GroupBWT is a data provider specializing in large-scale web data aggregation for enterprise analytics, forecasting, and machine learning.\nWe serve industries such as retail, e-commerce, beauty & personal care, micromobility, logistics, and public procurement.\nOur typical data assets include product and pricing intelligence, store and location data, availability signals, and public tenders — all collected from complex online sources and delivered in clean, analysis-ready formats."},"popularity":1741},{"id":"860d0a18-9426-477b-8f6a-af15c0d7ecec","summary":{"name":"Human Gene Expression Database","subtitle":"This data package contains expression profiles for proteins in normal and cancer tissues. It also contains data on seq..","share":{"name":"human_gene_expression_database","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1682356589172,"updated_at":1698965635273,"categories":["SCIENCE_AND_RESEARCH"],"listingType":"STANDARD","provider_id":"f88849f8-9d77-4de2-b4c7-03d55cc3768e"},"detail":{"description":"\n**Overview**\nThis data package contains expression profiles for proteins in normal and cancer tissues. It also contains data on sequence based RNA levels in human tissue and cell line.\n\n**Description**\nThe Expression Profiles for Proteins in Normal Human Tissues contains quantitative data and images describing the expression and distribution of human proteins across tissues and organs, both on the mRNA and protein level. The protein expression data is derived from annotation of immune-histochemical staining of cell populations in all major human tissues and organs, including the brain, liver, kidney, lymphoid tissues, heart, lung, skin, gastrointestinal tract, pancreas, endocrine tissues and the reproductive organs. In total, 44 different human tissues are included and contain annotation data for altogether 76 different cell types. The antibody-based protein profiles are qualitative and describe the spatial distribution, cell type specificity and the rough relative abundance of proteins in these tissues, whereas the mRNA data provide quantitative data on the average gene expression within an entire tissue. For each gene, the immune-histochemical staining profile, based on a single or multiple antibodies, is matched with mRNA data and gene/protein characterization data to yield an \"annotated protein expression\" profile. The datasets in this data package cover the Expression profiles for proteins in human normal tissues and tumor tissues based on immunohistochemistry using tissue microarrays. Another dataset covers the RNA (Ribonucleic acid) levels in 56 cell lines and 37 tissues based on RNA-sequence. The data is based on The Human Protein Atlas version 16 and Ensembl version 83.38.\n\n**Benefits**\n- sequence based rna levels in human tissue and cell line dataset helps to analyze the continually changing cellular transcriptome.\n- facilitates the ability to look at alternative gene spliced transcripts, post-transcriptional modifications, gene fusion, mutations/snps and changes in gene expression.\n- the data package also helps to look at and analyze different populations of rna to include total rna, small rna, such as mirna, trna, and ribosomal profiling.\n- expression profile dataset helps in the identification of a potential protein signature for each given type of cancer and provides a starting point for further analyses of cancer type-specific proteins.\n- because the cancer atlas contains a large number of cancer samples, the available protein profiles provide an excellent starting point for identifying new potential cancer biomarkers.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [Expression Profiles For Proteins in Human Cancer Tissues](https://www.johnsnowlabs.com/marketplace/expression-profiles-for-proteins-in-human-cancer-tissues)\n\t- This dataset covers the staining profiles for proteins in human tumor tissue based on immunohistochemistry using tissue microarrays. The data is based on The Human Protein Atlas version 21.0 and Ensembl version 103.38.\n- [Expression Profiles For Proteins in Normal Human Tissues](https://www.johnsnowlabs.com/marketplace/expression-profiles-for-proteins-in-normal-human-tissues)\n\t- This dataset covers the Expression profiles for proteins in human tissues based on immunohistochemistry using tissue microarrays. The data is based on The Human Protein Atlas version 21.1 and Ensembl version 103.38.\n- [Sequence Based RNA Levels in Human Tissue and Cell Line](https://www.johnsnowlabs.com/marketplace/sequence-based-rna-levels-in-human-tissue-and-cell-line)\n\t- This dataset covers the RNA (Ribonucleic acid) levels in 56 cell lines and 37 tissues based on RNA-sequence. The data is based on The Human Protein Atlas version 21.0 and Ensembl version 103.38.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated\n- Data Updates\n - Data updates support replace-on-update: outdated foreign keys are deprecated, not deleted\n\n**Our data is curated and enriched by domain experts** \n\n Each dataset is manually curated by our team of doctors, pharmacists, public health & medical billing experts:\n- Field names, descriptions, and normalized values are chosen by people who actually understand their meaning\n- Healthcare & life science experts add categories, search keywords, descriptions and more to each dataset\n- Both manual and automated data enrichment supported for clinical codes, providers, drugs, and geo-locations\n- The data is always kept up to date – even when the source requires manual effort to get updates\n- Support for data subscribers is provided directly by the domain experts who curated the data sets\n- Every data source’s license is manually verified to allow for royalty-free commercial use and redistribution.\n\n**Need Help?**\nIf you have questions about our products, contact us at [info@johnsnowlabs.com](mailto:info@johnsnowlabs.com). \n 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Datavant works to reduce the friction of data sharing across the healthcare industry by building technology that protects the privacy of patients while supporting the linkage of patient health records across datasets. \n\nDatavant enables the ability to monitor requests for access to data, tightly control permissions, and leverage rapid, trusted HIPAA compliance offerings to accelerate innovations that benefit patients. \n\nWork with Datavant for an end-to-end health data connectivity solution to help you connect health data, control access to it and comply with privacy regulations. \n\nLearn more about Datavant at www.datavant.com."},"popularity":1366},{"id":"7f0ea6c6-90b5-4277-bb0c-dd67c51bd81f","summary":{"name":"Restaurants in the United Arab Emirates (AE) - Places Data Sample","subtitle":"\uD83C\uDDE6\uD83C\uDDEA Restaurant location & metadata table for the UAE, with 40K+ restaurant points-of-interest (PoIs) updated daily.","share":{"name":"ae_restaurants_marketplace","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1721970308381,"updated_at":1746426451629,"categories":["ADVERTISING_AND_MARKETING","GEOSPATIAL","LOOKUP_TABLES","RETAIL","COMMERCE"],"listingType":"STANDARD","provider_id":"bcde9d2e-7e2f-4266-8c17-45d2e7bdc27e"},"detail":{"description":"## **Overview**\n\n> Learn more about this dataset on [catalog.dotlas.com](https://catalog.dotlas.com/restaurants/unified_restaurant_establishments/)\n\n> This is a sample of our UAE Restaurants data product (temporal). The product contains **`40K+ restaurants`** updated frequently, and additional columns catered for leads and segmentation. \n\n> Additionally, we also curate restaurant menu data including dishes, prices, price history, restaurant promotions and social insights. This sample contains < 500 restaurants across Dubai, Sharjah, Abu Dhabi, Ajman and more. \n\nFor pricing and licensing details please [**contact us**](mailto:info@dotlas.com)\n\n**Use cases** \n\n\uD83D\uDD70 Analyze competitor opening hours on each day of the week to help create an optimal operating time frame.\n\uD83D\uDCC8 Identify trending cuisines in different regions by analyzing the frequency and distribution of various cuisine types in different cities.\n\uD83D\uDCB0 Use the distribution data to identify underserved areas with high potential for new restaurant openings based on the lack of certain cuisine types.\n\n### **Potential Sectors**\n* Food & Beverages (F&B)\n* GIS (Geographic Information Systems) and Geocoding Services\n* Marketing and Advertising Leads\n* Competitor Monitoring & Market Analysis\n* Financial tickering of F&B brand performance\n* Site Selection & Suitability Analysis\n* Real Estate Proximity Analysis\n\n**Key features** \n\n▶️ **Restaurant Identification**: Includes unique identifiers and names for each restaurant.\n\n▶️ **Geographical Data**: Detailed information about the `country`, `subregion`, `city`, `area`, and precise geographical coordinates (`latitude` and `longitude`) of each restaurant.\n\n▶️ **Contact Information**: Addresses, phone numbers, email addresses, and website URLs.\n\n▶️ **Social Media Presence**: Links to `Instagram`, `Facebook`, and `Twitter` profiles where available.\n\n▶️ **Amenities**: Information on various amenities such as acceptance of credit cards, availability of Wi-Fi, presence of outdoor seating, and more.\n\n▶️ **Additional Metadata**: Information on entertainment options, executive chef names, dress codes, dining styles, and more","terms_of_service":"https://dotlas.com/terms-of-service/","documentation_link":"https://catalog.dotlas.com/restaurants/unified_restaurant_establishments/","privacy_policy_link":"https://dotlas.com/privacy-policy","geographical_coverage":"[\"Middle East\"]","cost":"FREE","update_frequency":{"interval":1,"unit":"DAILY"},"collection_granularity":{"interval":1,"unit":"DAILY"},"data_source":"Dotlas collects and coalesces information from multiple online and offline sources","assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"Dotlas","description":"Dotlas provides high-quality geographical and temporal data on restaurants, real estate and hospitality sectors around the world. 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It offers an analysis-ready foundation for EV charging infrastructure planning, competitor benchmarking, logistics routing, and retail location intelligence.\n\nFully compatible with **Apache Spark**, **Delta Lake**, and **Databricks Unity Catalog** workflows.\n\n---\n\n## **Complete National Coverage: Branded Forecourts & Regional Banners**\nUnlike single-brand extractions, this dataset aggregates all premier fuel networks and independent operators into a single, unified data model. The coverage maps maximum geographic depth across all districts, the Azores, and Madeira:\n\n- **Major Fuel Networks & Multinational Banners:** \n [Galp (691 locations)](https://geolocet.com/products/Portugal-Galp-Petrol-Stations), \n [bp (486 locations)](https://geolocet.com/products/Portugal-bp-Petrol-Stations), \n [Repsol (476 locations)](https://geolocet.com/products/Portugal-Repsol-Petrol-Stations), \n [Moeve (248 locations)](https://geolocet.com/products/Portugal-Moeve-Petrol-Stations), \n [PRIO (227 locations)](https://geolocet.com/products/Portugal-PRIO-Petrol-Stations), \n [Shell (39 locations)](https://geolocet.com/products/Portugal-Shell-Petrol-Stations)\n\n**Plus thousands of regional independent forecourts** fully mapped and standardized across all administrative regions, including high-density districts:\n- **Porto / Lisboa:** Over 1,000 combined locations (Porto: 540; Lisboa: 487)\n- **Core Interior & Coastal Districts:** Comprehensive mapping across Braga (315), Aveiro (301), Santarém (294), Viseu (268), Leiria (250), Setúbal (232), and Faro (205)\n- **Insular Regions:** Complete coverage for the Azores (103) and Madeira (62)\n\n---\n\n## **Technical Specifications & Schema Definitions**\nOptimized for zero-friction schema enforcement when reading via `spark.read.format(\"csv\")`. The structured schema below ensures flawless rendering in Databricks notebooks and high accuracy for LLM semantic indexing.\n\n* **`GUID`** (`STRING`): Unique global identifier for deterministic primary key mapping and deduplication.\n* **`Title`** (`STRING`): Official station name or commercial brand banner classification (e.g., Galp, bp, Repsol).\n* **`Latitude`** (`DOUBLE`): Precise WGS84 latitude coordinate for point-in-polygon and spatial joins.\n* **`Longitude`** (`DOUBLE`): Precise WGS84 longitude coordinate for spatial indexing (H3 / S2).\n* **`Street_No` / `Street`** (`STRING`): Normalized building numbers and thoroughfare name elements.\n* **`City` / `Locality`** (`STRING`): Core municipal settlements and primary local town designations.\n* **`District`** (`STRING`): High-level administrative district classification (e.g., Lisboa, Porto, Aveiro).\n* **`Postal_Code`** (`STRING`): Standardized Portuguese postal routing code string.\n* **`Address`** (`STRING`): Full, standardized street address clean-text output.\n* **`Wheelchair`** (`STRING`): Accessibility flags indicating wheelchair mobility support where available.\n* **`Opening_Hours`** (`STRING`): Standard daily operational schedules and structured operating text (Optional enrichment field).\n* **`Popularity_Score` / `Ratings`** (`INTEGER`): Relative foot-traffic/rating metric graded on a standardized scale (Optional enrichment field).\n\n---\n\n## **Databricks Workflow & Spatial Analytics Use Cases**\n\n- **EV Charging Whitespace Analysis:** Query the full station points network against high-voltage transmission lines or national highway traffic volumes to rank optimal sites for ultra-fast EV charging infrastructure placement.\n- **H3 Geospatial Indexing:** Convert the precise WGS84 `Latitude` and `Longitude` fields into Uber H3 spatial hexagons (`h3_longlattocell`) to seamlessly cross-reference with granular local civil parish ([Freguesia-Level Income Datasets](https://geolocet.com/products/portugal-income-indicators-freguesias)) to gauge socioeconomic potential and purchasing power.\n- **Logistics & Fleet Benchmarking:** Integrate forecourt coordinates with fleet tracking telemetry to calculate fuel stop density, build optimal corridor routing profiles, and minimize off-route deadhead mileage across the national highway network.\n\n---\n\n## **Data Evaluation & Custom Ingestion**\n\n### **Evaluate with a Free Sample Dataset**\nVerify the structure, column types, and coordinate precision before full implementation. A comprehensive schema sample is available for download in CSV/Parquet-friendly formats.\n\n*Want deeper market insights?* Explore how demographic mapping supports location intelligence workflows on our dedicated country library: **[Explore Data and Demographics for Portugal](https://geolocet.com/pages/portugal)**\n\n### **Enterprise Delivery & Tailored Formatting**\nWhile natively available as an analysis-ready CSV for DBFS ingestion, we accommodate enterprise-level data architecture needs. Alternative formats - including **GeoJSON, Esri Shapefiles, PostgreSQL dumps, Excel, or direct Delta sharing profiles** - can be generated. Reach out to our engineering team at **contact@geolocet.com** for integration support or custom European POI data requests.","terms_of_service":"https://geolocet.com/pages/terms-of-use","documentation_link":"","privacy_policy_link":"https://geolocet.com/pages/privacy-policy","assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://geolocet.com/pages/terms-of-use"},"provider_summary":{"name":"Geolocet LTD","description":"Geolocet provides premium geospatial data for retail analysis, GIS, and market planning. We equip businesses with accurate, ready-to-query data to drive spatial analysis, site selection, and customer segmentation.\n\nCore Offerings:\n\nDemographics: Socioeconomic data covering population, age, and employment across diverse geographies.\n\nPoints of Interest (PoI): Datasets spanning retail, healthcare, and transport for catchment analysis.\n\nAdministrative Boundaries: GIS-ready boundaries, including municipalities and postcodes, for territory management.\n\nBrand Locations: Chain location datasets to monitor store networks and market expansion.\n\nWealth Data: Granular economic indicators like purchasing power to identify high-potential markets.\n\nWe source from trusted providers, delivering highly customizable datasets with comprehensive coverage across Europe and worldwide."},"popularity":35},{"id":"b47e9795-ddd2-4480-9ca0-5fdfd8873dc9","summary":{"name":"Electronic Health Records (EHR) Data : Pulmonology","subtitle":"High-quality de-identified EHR data, showing comprehensive patient journeys physician narratives in Pulmonology.","share":{"name":"electronichealthrecordsdatapulmonology","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1723033045731,"updated_at":1723104475747,"categories":["HEALTH","SCIENCE_AND_RESEARCH"],"listingType":"STANDARD","provider_id":"dc00cb61-5b9a-403e-8b4f-71e78dd44d6c"},"detail":{"description":"**Overview**\n\nShaip is a premier provider of data on de-identified electronic health records (EHR). The Shaip EHR data comprises de-identified EHR data that is nationally representative. This data is sourced from practices across the U.S., covering the continuum of care delivery—from independent, single-provider offices to large, integrated delivery systems, including both primary and specialty care. \n\nThis data provides a comprehensive picture of the patient’s journey and physician narrative throughout their hospital stay. With approximately **4 million + unique patient records**, the participating hospitals, offer unique insights into variations in care across healthcare network hospitals and clinics. \n\n**The samples provided and the entire datasets are Gold Standard**\n\n\n**Use cases**\n\n**For Healthcare Organizations**\n\n- Population health management: Analyze patient populations to identify trends and improve overall health.\n- Revenue cycle management: Streamline billing and claims processes. \n- Quality improvement: Track and measure performance to identify areas for improvement. \n- Regulatory compliance: Ensure adherence to healthcare regulations and standards. \n\n**For Researchers and Scientists**\n\n- Drug discovery and development: Identify potential drug targets and patient populations for clinical trials.\n- Disease research: Analyze patient data to understand disease progression and identify risk factors.\n- Public health surveillance: Monitor disease outbreaks and track public health trends.\n\n**For Payers**\n\n- Risk assessment: Evaluate the health risks of insured populations.\n- Fraud detection: Identify potential fraudulent claims.\n- Care management: Manage high-cost patients and chronic conditions.\n\n**Product details**\n\n**Metadata Information:**\n- Includes anonymized MRN\n- Admission and discharge dates\n- Length of stay, \n- Gender \n- Patient class\n- Payer \n- Financial class\n- State \n- Discharge disposition\n- Age \n- DRG\n- DRG description\n- Reimbursement amount\n- AMLOS, GMLOS\n- Risk of mortality \n- Severity of illness\n- Grouper\n- Hospital Zip code, and more.\n\n**Geographical Distribution:** Records from various US regions \n- North East (46.4%)\n- South (8.2%)\n- Midwest (3.1%)\n- West (28.1%)\n- Others (14.2%).\n\n**Patient Classes:** Covers all patient classes—\n- Inpatient\n- Outpatient (Clinical, Rehab, Recurring, Surgical Day Care)\n- Emergency\n\n**Age Groups:** Records for all age groups—\n- <10 years (7.9%)\n- 11-20 years (5.7%)\n- 21-30 years (10.9%)\n- 31-40 years (11.7%)\n- 41-50 years (10.4%)\n- 51-60 years (13.8%)\n- 61-70 years (16.1%)\n- 71-80 years (13.3%)\n- 81-90 years (7.8%)\n- 90+ years (2.4%).\n\n**Gender Ratio:** 46% male and 54% female.\n\n**Language**\n- EnglishOTS\n- French, Spanish, and Portuguese can be acquired very quickly\n- Other Languages - Custom sourcing or translation from English to target language\n\n**Additional Insights**\n\n**Privacy:** Documents have PII redacted according to Safe Harbor Guidelines and HIPAA compliance.","terms_of_service":"https://www.shaip.com/terms-of-service/","documentation_link":"","privacy_policy_link":"https://www.shaip.com/privacy-policy/","geographical_coverage":"[\"United States and Canada\"]","cost":"FREE","data_source":"","assets":["ASSET_TYPE_MEDIA"],"license":""},"provider_summary":{"name":"Shaip","description":"Shaip is a leading AI data platform and services provider specializing in the sourcing, creation, licensing, and transformation of high-quality, ethical, and domain-specific data for AI and machine learning applications. 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It centers on five liquid AI “anchor” equities (AMD, GOOGL, META, MSFT, NVDA) and 17 “satellite” names spanning Cooling_Power, Materials, Semicon_Eq, and Testing sectors. For each anchor–satellite pair and date, the atlas provides ready-to-use quantitative signals rather than just raw prices.\n\nEach row encodes a complete lead–lag trading view: correlation over a 30-day window, a predictive lead signal, dynamic hedge ratios, spread Z-scores, and Ornstein–Uhlenbeck (OU) half-life estimates. This design allows quants, multi-asset PMs, and risk teams on Databricks to go straight into backtesting long/short strategies, scenario analysis, and supply-chain stress testing without spending weeks on data engineering and signal construction.\n\n**Provenance**\n\nAll underlying data for this atlas is sourced from EDGAR, using public disclosures to identify AI “anchor” companies and their key upstream suppliers, infrastructure enablers, and adjacent beneficiaries. The publisher has then engineered a lead–lag framework on top of this mapping to quantify how shocks to the core AI names transmit through related equities over time. This dataset has been augmented - analysis added to trace supply chain impact of AI companies and associated buildout.\n\nThe premium file (Global_AI_Hardware_LeadLag_Atlas_PREMIUM.csv) contains 37,570 observations spanning 2024-04-16 to 2026-01-20, with five anchors, 17 satellites, and four clearly labeled sectors. All transformations (correlations, hedge ratios, Z-scores, OU half-lives) are derived from these public-market relationships and structured for direct institutional use on Databricks.\n\nLicensing for the sample is explicitly stated as: “This free sample is published under CC-4. Valid for commercial use with attribution.” You can use the free sample commercially with attribution; full premium access can be layered under separate commercial terms.\n\n**Use Cases**\n\nThis dataset enables high-value analytics and ML workflows on Databricks:\n\nLead–Lag Pair-Trading Research: Use Lead_Signal_Predictive, Spread_Z_Score, and Hedge_Ratio_Beta to discover and backtest long/short opportunities between AI anchors and their suppliers directly in Spark and MLflow.\n\nMean-Reversion & Holding-Period Optimization: Leverage OU_Half_Life_Days to design capital-efficient, mean-reversion strategies with statistically grounded holding periods and rebalancing rules.\n\nAI Supply-Chain Risk & Concentration Analytics: Quantify how shocks to AMD, NVDA, or other anchors propagate to Cooling_Power, Materials, Semicon_Eq, and Testing names, and surface hidden second-order exposures in portfolios.\n\nThematic Basket & Index Construction: Build AI-infrastructure long/short baskets using sector tags plus lead–lag metrics to construct smarter thematic indices on top of Delta tables.\n\nScenario & Stress Testing in the Lakehouse: Run what-if simulations (e.g., AI slowdown or GPU capex surge) by shocking anchor returns and propagating impacts through correlated satellites at scale using Databricks notebooks and jobs.\n\n**Column Dictionary**\n\nDate (date): Calendar date for each observation in YYYY-MM-DD format.\n\nAnchor_Ticker (string): Ticker symbol of the AI “anchor” equity (e.g., AMD, GOOGL, META, MSFT, NVDA).\n\nSatellite_Ticker (string): Ticker symbol of the “satellite” stock linked to the anchor via supply-chain or thematic exposure.\n\nSector (string): Sector bucket for the satellite, such as Cooling_Power, Materials, Semicon_Eq, or Testing.\n\nAnchor_Close (decimal): End-of-day closing price of the anchor ticker on the given date.\n\nSat_Close (decimal): End-of-day closing price of the satellite ticker on the given date.\n\nCorrelation_30d (decimal): 30-day rolling correlation between the anchor and satellite returns, used to characterize pair co-movement.\n\nLead_Signal_Predictive (decimal): Engineered predictive lead–lag signal indicating whether the anchor is statistically leading the satellite (or vice versa).\n\nHedge_Ratio_Beta (decimal): Dynamic hedge ratio (beta) for constructing risk-neutral, dollar- or beta-balanced pairs between anchor and satellite.\n\nSpread_Z_Score (decimal): Standardized Z-score of the price spread between the anchor and hedged satellite, used as a buy/sell trigger in mean-reversion setups.\n\nOU_Half_Life_Days (decimal): Ornstein–Uhlenbeck half-life of the spread, in days, providing a statistically grounded estimate of how long reversion typically takes (capital efficiency and holding-period guidance).\n\n**Technical Metadata**\n\nGeographic Coverage: Global\n\nUpdate Frequency: Monthly\n\nCollection Granularity: Daily (calendar date per anchor–satellite pair; no intraday timestamps)\n\nData Source / Quality Evaluation:\n\nInstitutional QC report indicates 37,570 observations from 2024-04-16 to 2026-01-20.\n\nSpread Z-score validation: mean ≈ 0.0119 (target ~0.0) and standard deviation ≈ 0.9919 (target ~1.0), confirming centered, unit-variance signals.\n\nCorrelation bounds are within [-1.0, 1.0].\n\nMissing values: 0, with strict timezone alignment, stripped timezones on the Date field, and verified key columns: Hedge_Ratio_Beta, Spread_Z_Score, and OU_Half_Life_Days.\n\n**Approved for institutional distribution.**\n\nCompliance Notes: This dataset has been augmented - analysis added to trace supply chain impact of AI companies and associated buildout.\n\nTime Range: 2024-04-16 – 2026-01-20\n\nSize (GB): 0.0029\n\nLicense (sample): This free sample is published under CC-4. 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Coverage spans luxury item pricing (valuation, comp, elasticity studies) to retail property analytics (site selection, underwriting, performance prediction). Instrument adoption and prove lift: reduced ETL, faster training, higher dataset usability rates. Repeatable motion across teams: import → govern → build → measure → scale. Available now on Databricks Marketplace."},"popularity":1645},{"id":"7c74755b-e3d0-45e5-bd6b-c672678159ed","summary":{"name":"SAMPLE Knowtrex Tire Product Master | Product Data | Global Coverage | 10 Years History | 300k+ ...","subtitle":"The Tire Product Master offers a comprehensive tire catalogue with 50+ attributes per tire, including ...","share":{"name":"knowtrex_f4657bc7_cb05_4dc9_9eda_dbc2e60dcdfc_samples_share","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1750683137501,"updated_at":1751883734055,"categories":["RETAIL","ECONOMICS","OTHER"],"listingType":"STANDARD","provider_id":"185c4c8d-6a29-4c60-b23c-644bae9eca6f"},"detail":{"description":"Empower Your Decisions with Our Comprehensive Tire Catalog
\nNavigate the complex world of tires with confidence using our comprehensive tire catalog. Access a wealth of information and insights to empower your product analysis, marketing initiatives, and strategic decision-making. Benefit from exceptional data quality, up-to-date marketing materials, and high-resolution images, all designed to streamline your workflow and enhance your understanding of the tire market.
\n\nKey Features & Benefits:
\n• Extensive Tire Coverage: Dive into a vast repository of information covering over 300,000 tires across all vehicle segments. Whether you’re focused on passenger cars, commercial trucks, SUVs, Light Trucks, Bicycle, Motorcycle or specialty vehicles, our catalog provides the data you need.
\n• Rich Attribute Data: Gain access to over 50 tire attributes, meticulously collected and verified to ensure accuracy and completeness. From technical specifications to performance metrics, our catalog covers all the essential details, including:
\n ▪ Brand: Tire manufacturer information incl. segmentation\n ▪ Season Type: Seasonal suitability\n ▪ GTIN: Global Trade Item Number for unique product identification.
\n ▪ Pattern: Detailed tread pattern information for visual analysis and performance assessment.
\n ▪ Dimension: Precise tire dimensions (width, height, diameter, load- and speed-indices) for compatibility checks.
\n ▪ Fuel Efficiency: Rolling resistance data\n ▪ Rolling Noise: Noise emission levels\n ▪ Intention: Intended use case of the tire (e.g., regional, construction site, performance, ..)\n ▪ Acoustic: Acoustic properties for noise reduction
\n ▪ 3PMSF: Three-Peak Mountain Snowflake indication
\n ▪ Spikes: Indication of whether the tire is studded\n ▪ Ply Rating: Indication of tire strength and durability.\n ▪ Wet Grip: Wet traction performance rating\n ▪ Noise Class: Noise emission classification\n ▪ RFT: Run-Flat Tire designation for extended mobility
\n ▪ Seal: Sealant technology\n ▪ OE: Original Equipment designation\n ▪ Studdable: Indicates whether the tire can be fitted with studs\n• Marketing Descriptions & Images: Complement technical data with compelling marketing descriptions and high-resolution images. Showcase your products with professional-grade visuals and persuasive copy.
\n\nUse Cases\n• Competitive Intelligence: Spot competitors and market opportunities with our flexible like-for-like analyses. Effortlessly compare similar tires to your own portfolio and identify key differentiators.
\n• Gap Analysis: Identify gaps in distribution channels, markets, or sizes.\nReceive customized reports tailored to your specific needs and make informed decisions about market entry and expansion.\n• Competitor Monitoring: Monitor your competitors’ marketing information, including product descriptions, profile pictures, and labels. Stay ahead of the curve with comprehensive competitor insights, available in multiple formats.
\n• Exceptional Data Quality: Trust in the accuracy and reliability of our data. Our rigorous quality control processes ensure that you’re working with the most up-to-date and comprehensive information available.
\n• Make informed decisions about product development and sourcing.
\n• Optimize your marketing campaigns for maximum impact.
\n• Identify new market opportunities and expand your reach.
\n• Gain a competitive edge in the dynamic tire industry.
\n• Improve cost efficiency by comparing and matching tires
\n• Ensure regulatory compliance
\n\nReady to experience the difference? 
Contact us today to learn more about our comprehensive tire catalog and how it can empower your business.
 Get in touch to schedule a demo and discover the power of informed decision-making.","terms_of_service":"https://www.knowtrex.com/privacy-policy","contact_email":"marketplace@datarade.ai","privacy_policy_link":"https://www.knowtrex.com/privacy-policy","assets":["ASSET_TYPE_DATA_TABLE"]},"provider_summary":{"name":"Knowtrex","description":"Knowtrex is a data provider offering Product Pricing Data, Ecommerce Market Data, Seller Data, Competitor Pricing Data, and Price Intelligence Data. 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The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [Medical Transcription Samples](https://www.johnsnowlabs.com/marketplace/medical-transcription-samples)\n\t- This dataset contains sample medical transcriptions for various medical specialties.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated\n- Data Updates\n - Data updates support replace-on-update: outdated foreign keys are deprecated, not deleted\n\n**Our data is curated and enriched by domain experts** \n\n Each dataset is manually curated by our team of doctors, pharmacists, public health & medical billing experts:\n- Field names, descriptions, and normalized values are chosen by people who actually understand their meaning\n- Healthcare & life science experts add categories, search keywords, descriptions and more to each dataset\n- Both manual and automated data enrichment supported for clinical codes, providers, drugs, and geo-locations\n- The data is always kept up to date – even when the source requires manual effort to get updates\n- Support for data subscribers is provided directly by the domain experts who curated the data sets\n- Every data source’s license is manually verified to allow for royalty-free commercial use and redistribution.\n\n**Need Help?**\nIf you have questions about our products, contact us at [info@johnsnowlabs.com](mailto:info@johnsnowlabs.com). \n ","terms_of_service":"https://www.johnsnowlabs.com/healthcare-data-library-eula/","documentation_link":"https://www.johnsnowlabs.com/marketplace/medical-transcription-samples-data-package","contact_email":"support@johnsnowlabs.com","privacy_policy_link":"https://www.johnsnowlabs.com/privacy-policy/","embedded_notebook_file_infos":[{"id":"6539c9cd-a1dd-42c6-9266-24384ae87cd5","marketplace_file_type":"EMBEDDED_NOTEBOOK","file_parent":{"parent_id":"cd0b8356-8ae8-4178-a55b-7f69f040c0b8","file_parent_type":"LISTING"},"mime_type":"text/html","download_link":"https://marketplace-file-storage-prod.s3-fips.us-east-1.amazonaws.com/EMBEDDED_NOTEBOOK/LISTING_cd0b8356-8ae8-4178-a55b-7f69f040c0b8/6539c9cd-a1dd-42c6-9266-24384ae87cd5?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEGgaDnVzLWVhc3QtMS1maXBzIkYwRAIgbC8sWIoYaHR8y7Wq86nPb0pufBl4%2BM4PWpeN%2B5PW9VQCIG7BhAVBDCWTpxFDLeuKYm%2Ff1JLz4iccPMzter0YdhgWKqYECDAQABoMNDE0MzUxNzY3ODI2IgxNAJrHbXaZG20jNfMqgwQyGEb4TsoVOoNvJ%2BaA%2FZ7zeu8hGcHqCeNfblP%2FQo5g6q2aRNtpwtPRKMlhYcIE%2FUQaNT1Lqmyptu32VUQTpzjTTxRVmJrDPkheSCkV06XQ%2FpTMKjU8CC7X1b%2FqYf8CrBwZT7Cv4C5WPjEET38f2A79h0xV6bZgPy28zQWrSeIJ9w6AfZBxiE24%2FVc08zVA0ivCT6lsy7nuU0wcR%2FMg2C9nd1vaMnQ923VpD21CKfVVYcrNWL2b%2FQ%2BNAO%2BxPjPgjiN6UPQbvbvZxkSdhgrGtecIg9NgqGkMcvM5VLTDtdC1kdeZetfOC2obtA%2BT%2F0gy3tIDo6WM5hcQfw743%2FAU9oaGnRBl%2BwoxRRDCZA%2F4jnPdluICSI7NCr6C3HHOe4CSZJjRihczxPA%2FgQczAVjAPs6CrK9N9pIzt0vNJpGUTgg4AoxftTvGfciDREilSctPIcBk6GwB%2Bzt4wbtNCmEPU%2BFD46rc6CWVQmmLwbKOeHw70ipMVFlOc8NlXkEtGO2vu6H44MstSSc6daOVUYUavQSGZ6jGhEjsP2YGz8KDm9XQotB%2FtyVBqiZV01OKqDmmBJA%2FXjUpSJ8M%2FeJXhe5m%2BKcxzNObtoJRI7jci3Tpt08XZyXZ7%2FyIV8H30ya468X3cfkTXrm972uga3z7iXAdubyEHnQIua%2FuvtwOHILi6O5D1Me2MzCPvcnXBjqTAYDoMWBt%2B3AVFuqSs2dmDQNBnvEhXfMgTedKLD5PJHb3aIl15iBkLaNu36GYnVCp9ZU2lSYcTbY5QwNnNeTfJTNy9bKhwzru6Sg%2BslhN%2BTS0zkTn%2BeLMY4tHgPEtEZ2cp%2FCk6GwqswvnYVKHjwO8q%2B1Mx8lc08fVEfarE9dke1GGFqoddx%2Bq%2BZUyHbDlAVfrCmf0yw%3D%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20261110T000135Z&X-Amz-SignedHeaders=host&X-Amz-Expires=86400&X-Amz-Credential=ASIAWA6KKHEJORWBH4N3%2F20261110%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=0e282757e4602c728bdeaab42a8ecf019bbaca372649da281001b7c99c18f3ea","created_at":1682537706782,"updated_at":1682537706782,"display_name":"EDA Notebook","status":"FILE_STATUS_PUBLISHED"}]},"provider_summary":{"name":"John Snow Labs","description":"John Snow Labs, an AI and NLP for healthcare company, provides state-of-the-art software, models, and data to help healthcare and life science organizations build, deploy, and operate AI projects."},"popularity":142},{"id":"5548bb6e-3883-42ce-95e2-96bf4ba57353","summary":{"name":"Occupational Health And Safety","subtitle":"Information on utilization and payment data for Home health agency, Hospice, skilled nursing facitlity. Information on..","share":{"name":"occupational_health_and_safety","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1682356121888,"updated_at":1698965635253,"categories":["HEALTH","SCIENCE_AND_RESEARCH"],"listingType":"STANDARD","provider_id":"f88849f8-9d77-4de2-b4c7-03d55cc3768e"},"detail":{"description":"\n**Overview**\nInformation on utilization and payment data for Home health agency, Hospice, skilled nursing facitlity. Information on Inpatient Prospective Payment System (IPPS) payments, Inpatient Rehabilitation Facilities (IRFs)\n\n**Description**\nInformation on Home Health Agency and Hospice providers Utilization and Payment data, Inpatient Prospective Payment System (IPPS) payments for the most frequently billed discharges, paid under Medicare based on a rate per discharge using the Medicare Severity Diagnosis Related Group (MS-DRG), list of inpatient rehabilitation facilities with data on the number of times people with Medicare who had certain medical conditions were treated in the last year. General information on the Inpatient Rehabilitation Facilities (IRFs) and information about the Inpatient Rehabilitation Facilities (IRFs) Prospective Payment System (PPS) Rate Settings and data regarding different measure codes and their corresponding scores for majority of the IRFs. Data on the spending and utilization data in the Home Health Agency PUF are aggregated to the identification number for the agency, the Home Health Resource Group (HHRG) and by state. Information on the annual most frequent hospice diagnoses, the number of patients, and average length of stay. The Medicare Part D Prescriber Public Use File provides information on prescription drug events (PDEs) incurred by Medicare beneficiaries with a Part D prescription drug plan by National Provider Identifier (NPI) and drug name and contains information on drug utilization (claim counts and day supply) and total drug costs. The Skilled Nursing Facility Public Use File contains information on utilization, payment (allowed amount, Medicare payment and standard payment), submitted charges, and beneficiary demographic and chronic condition indicators organized by Centres for Medicare and Medicaid Services Certification Number (6-digit provider identification number), Resource Utilization Group (RUG), and state of service.\n\n**Benefits**\n- Provider payment information, cost of healthcare, medical insurance cost\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [Days Lost Due To Work Related Illness By Gender And Age In UK](https://www.johnsnowlabs.com/marketplace/days-lost-due-to-work-related-illness-by-gender-and-age-in-uk)\n\t- This dataset contains estimates for the number of working days lost due to work-related illness and the rate of working days lost per worker and the rate of working days lost per case of work-related illness, by type of illness, gender and occupation, in the United Kingdom. The statistics are calculated by Health and Safety Executive (HSE) based on the Labor Force Survey (LFS) data provided by the Office for National Statistics.\n- [Days Lost Due To Work Related Illness By Industry In UK](https://www.johnsnowlabs.com/marketplace/days-lost-due-to-work-related-illness-by-industry-in-uk)\n\t- This dataset contains estimates for the number of working days lost due to work-related illness and the rate of working days lost per worker, by type of illness and industry, in the United Kingdom. The statistics are calculated by Health and Safety Executive (HSE) based on the Labor Force Survey (LFS) data provided by the Office for National Statistics.\n- [Days Lost Due To Work Related Illness By Occupation In UK](https://www.johnsnowlabs.com/marketplace/days-lost-due-to-work-related-illness-by-occupation-in-uk)\n\t- This dataset contains estimates for the number of working days lost due to work-related illness and the rate of working days lost per worker, by type of illness and occupation, in the United Kingdom. The statistics are calculated by Health and Safety Executive (HSE) based on the Labor Force Survey (LFS) data provided by the Office for National Statistics.\n- [Days Lost Due To Workplace Injuries By Gender And Age In UK](https://www.johnsnowlabs.com/marketplace/days-lost-due-to-workplace-injuries-by-gender-and-age-in-uk)\n\t- This dataset contains estimates for the number of working days lost due to workplace non-fatal injuries and the rates of working days lost per worker and per case of injury, by the gender and age of workers, in the United Kingdom. The statistics are calculated by Health and Safety Executive (HSE) based on the Labor Force Survey (LFS) data provided by the Office for National Statistics.\n- [Days Lost Due To Workplace Injuries By Industry In UK](https://www.johnsnowlabs.com/marketplace/days-lost-due-to-workplace-injuries-by-industry-in-uk)\n\t- This dataset contains estimates for the number of working days lost due to workplace non-fatal injuries and the rate of working days lost per worker, by industry, in the United Kingdom. The statistics are calculated by Health and Safety Executive (HSE) based on the Labor Force Survey (LFS) data provided by the Office for National Statistics.\n- [Days Lost Due To Workplace Injuries By Occupation In UK](https://www.johnsnowlabs.com/marketplace/days-lost-due-to-workplace-injuries-by-occupation-in-uk)\n\t- This dataset contains estimates for the number of working days lost due to workplace non-fatal injuries and the rate of working days lost per worker, by occupation, in the United Kingdom. The statistics are calculated by Health and Safety Executive (HSE) based on the Labor Force Survey (LFS) data provided by the Office for National Statistics.\n- [Lost Time Injuries Frequency By Gender And Age In UK](https://www.johnsnowlabs.com/marketplace/lost-time-injuries-frequency-by-gender-and-age-in-uk)\n\t- This dataset contains the estimated number of new cases of non-fatal injuries, along with the incidence rates of non-fatal injuries, by the gender and age of workers, made available by the Health and Safety Executive (HSE) for the employees working in the United Kingdom. The statistics are calculated by HSE based on the Labor Force Survey (LFS) data provided by the Office for National Statistics.\n- [Lost Time Injuries Frequency By Industry In UK](https://www.johnsnowlabs.com/marketplace/lost-time-injuries-frequency-by-industry-in-uk)\n\t- This dataset contains the estimated number of new cases of non-fatal injuries, along with the incidence rates of non-fatal injuries, by industry, made available by the Health and Safety Executive (HSE) for the employees working in the United Kingdom. The statistics are calculated by HSE based on the Labor Force Survey (LFS) data provided by the Office for National Statistics.\n- [Lost Time Injuries Frequency By Occupation In UK](https://www.johnsnowlabs.com/marketplace/lost-time-injuries-frequency-by-occupation-in-uk)\n\t- This dataset contains the estimated number of new cases of non-fatal injuries, along with the incidence rates of non-fatal injuries, by occupation, made available by the Health and Safety Executive (HSE) for the employees working in the United Kingdom. The statistics are calculated by HSE based on the Labor Force Survey (LFS) data provided by the Office for National Statistics.\n- [NIOSH Cancer Mortality Among US Workers By Industry](https://www.johnsnowlabs.com/marketplace/niosh-cancer-mortality-among-us-workers-by-industry)\n\t- The dataset contains data for US workers who resided and died due to cancer during 2007-2010, in one of 25 US States. Mortality described through proportionate mortality ratios (PMRs) along with the number of deaths is described by the type of cancer, gender and age-group of workers, as well as by the industry they worked in.\n- [NIOSH Cardiovascular Mortality Among US Workers By Industry](https://www.johnsnowlabs.com/marketplace/niosh-cardiovascular-mortality-among-us-workers-by-industry)\n\t- The dataset contains data for US workers who resided and died due to a cardiovascular disease during the periods 2007-2010, in one of 25 US States. Mortality described through proportionate mortality ratios (PMRs) along with the number of deaths is described by the type of cardiovascular disease, gender and age-group of workers, as well as by the industry they worked in.\n- [NIOSH Dust Exposure And Pneumoconiosis Among US Coal Miners](https://www.johnsnowlabs.com/marketplace/niosh-dust-exposure-and-pneumoconiosis-among-us-coal-miners)\n\t- The dataset contains data based on radiographic readings and dust samples assessments performed during the first round of The National Study of Coal Workers' Pneumoconiosis. Data about the presence or absence of massive progressive lung fibrosis is presented along with miners age, radiological opacities profusion category, data about respirable dust exposures and coal rank regions.\n- [NIOSH Employees Hearing Loss Data By Industry In US](https://www.johnsnowlabs.com/marketplace/niosh-employees-hearing-loss-data-by-industry-in-us)\n\t- The dataset contains audiometric data collected from audiograms performed on workers who were exposed to hazardous noise levels on the job (≥85 dBA) and were tested to comply with government regulations or safety recommendations. The audiometric data from this dataset is based on audiograms performed from 2000 to 2008.\n- [NIOSH Lead Exposure Data By Type Of Job](https://www.johnsnowlabs.com/marketplace/niosh-lead-exposure-data-by-type-of-job)\n\t- The dataset contains workplace lead measurement results collected during health hazards evaluation surveys from 1991 to 2015 for over 1200 personal lead exposure assessment. The data about exposure are estimates of lead concentration in air and on working area surfaces and are accompanied by description of location, industry, work type, working area, the activity that generates exposure, use of specific personal protective equipment as well as other variables.\n- [NIOSH Lead Exposure Data By Work Area](https://www.johnsnowlabs.com/marketplace/niosh-lead-exposure-data-by-work-area)\n\t- The dataset contains workplace lead measurement results collected during health hazards evaluation surveys from 1991 to 2015 for over 1,900 area lead exposure assessment. The data about exposure are estimates of lead concentration in air and on working area surfaces and are accompanied by description of location, industry, working area, the activity that generates exposure, as well as other variables.\n- [NIOSH Noise Exposure Data By Type Of Job](https://www.johnsnowlabs.com/marketplace/niosh-noise-exposure-data-by-type-of-job)\n\t- The dataset contains workplace noise measurement results collected during health hazards evaluation surveys from 1997 to 2013 for over 800 personal noise exposure assessments. The collected data about exposure are based on OSHA and NIOSH assessment criteria and are accompanied by description of location, industry, work type, working area, the activity that generates exposure, use of specific personal protective equipment as well as other variables.\n- [NIOSH Noise Exposure Data By Work Area](https://www.johnsnowlabs.com/marketplace/niosh-noise-exposure-data-by-work-area)\n\t- The dataset contains workplace noise measurement results collected during health hazards evaluation surveys from 1996 to 2013 for over 580 area noise level assessments. The collected data about exposure are based on OSHA and NIOSH assessment criteria and are accompanied by description of location, industry, working area, the activity that generates exposure, as well as other variables.\n- [NIOSH Prevalence Of Pneumoconiosis In A Cohort Of Coal Miners](https://www.johnsnowlabs.com/marketplace/niosh-prevalence-of-pneumoconiosis-in-a-cohort-of-coal-miners)\n\t- The dataset contains data for pneumoconiosis prevalence among a cohort of 3,194 bituminous coal miners and ex-miners examined between 1985 and 1988. Data about the presence or absence of massive progressive lung fibrosis is presented along with miners age, radiological opacities profusion category, data about respirable dust exposures and ranks of coal mines.\n- [NIOSH US Firefighters Anthropometric Measurements Data](https://www.johnsnowlabs.com/marketplace/niosh-us-firefighters-anthropometric-measurements-data)\n\t- The dataset contains recent anthropometric measurements data useful for updating ergonomic and safety specifications for fire apparatus and firefighter personal protective equipment (PPE). Data collected during the anthropometric measurements performed and from the consecutive analysis done on primary data is segregated by gender of individuals, posture and wearing or not of the gear during measurements and as well by the type of units (metric or imperial).\n- [OSHA Work Related Fatalities And Catastrophes Inspection Report Data](https://www.johnsnowlabs.com/marketplace/osha-work-related-fatalities-and-catastrophes-inspection-report-data)\n\t- The dataset contains data about work-related fatalities and catastrophes based on the incident investigations done by The Occupational Safety and Health Administration (OSHA) and State Plan jurisdiction for cases that have been closed on or after January 1, 2017. Work-related fatalities and catastrophes are described by date, city or place, state, ZIP Code, number along with a short description of the incident.\n- [US Bureau Of Labor Statistics Injury And Illness Data By Industry](https://www.johnsnowlabs.com/marketplace/us-bureau-of-labor-statistics-injury-and-illness-data-by-industry)\n\t- This dataset contains non-fatal injury and illness data by industry from US Bureau of Labor Statistics for 2016. The industries are classified according to the North American Industry Classification System (NAICS).\n- [Work Related Illness Frequency By Gender And Age In UK](https://www.johnsnowlabs.com/marketplace/work-related-illness-frequency-by-gender-and-age-in-uk)\n\t- This dataset contains the estimated number of incident and prevalent cases of illness, along with the incidence and prevalence rates of illnesses, by the gender and age of workers, made available by the Health and Safety Executive (HSE) for the employees working in the United Kingdom. The statistics are calculated by HSE based on the Labor Force Survey (LFS) data provided by the Office for National Statistics.\n- [Work Related Illness Frequency By Industry In UK](https://www.johnsnowlabs.com/marketplace/work-related-illness-frequency-by-industry-in-uk)\n\t- This dataset contains the estimated number of incident and prevalent cases of illness, along with the incidence and prevalence rates of illnesses, by industry, made available by the Health and Safety Executive (HSE) for the employees working in the United Kingdom. The statistics are calculated by HSE based on the Labor Force Survey (LFS) data provided by the Office for National Statistics.\n- [Work Related Illness Frequency By Occupation In UK](https://www.johnsnowlabs.com/marketplace/work-related-illness-frequency-by-occupation-in-uk)\n\t- This dataset contains the estimated number of incident and prevalent cases of illness, along with the incidence and prevalence rates of illnesses, by occupation made available by the Health and Safety Executive (HSE) for the employees working in the United Kingdom. The statistics are calculated by HSE based on the Labor Force Survey (LFS) data provided by the Office for National Statistics.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated\n- Data Updates\n - Data updates support replace-on-update: outdated foreign keys are deprecated, not deleted\n\n**Our data is curated and enriched by domain experts** \n\n Each dataset is manually curated by our team of doctors, pharmacists, public health & medical billing experts:\n- Field names, descriptions, and normalized values are chosen by people who actually understand their meaning\n- Healthcare & life science experts add categories, search keywords, descriptions and more to each dataset\n- Both manual and automated data enrichment supported for clinical codes, providers, drugs, and geo-locations\n- The data is always kept up to date – even when the source requires manual effort to get updates\n- Support for data subscribers is provided directly by the domain experts who curated the data sets\n- Every data source’s license is manually verified to allow for royalty-free commercial use and redistribution.\n\n**Need Help?**\nIf you have questions about our products, contact us at [info@johnsnowlabs.com](mailto:info@johnsnowlabs.com). \n ","terms_of_service":"https://www.johnsnowlabs.com/healthcare-data-library-eula/","documentation_link":"https://www.johnsnowlabs.com/marketplace/occupational-health-and-safety-data-package","contact_email":"support@johnsnowlabs.com","privacy_policy_link":"https://www.johnsnowlabs.com/privacy-policy/","embedded_notebook_file_infos":[{"id":"0ea2f4fa-51fe-49cd-bfc3-069cc828fd78","marketplace_file_type":"EMBEDDED_NOTEBOOK","file_parent":{"parent_id":"5548bb6e-3883-42ce-95e2-96bf4ba57353","file_parent_type":"LISTING"},"mime_type":"text/html","download_link":"https://marketplace-file-storage-prod.s3-fips.us-east-1.amazonaws.com/EMBEDDED_NOTEBOOK/LISTING_5548bb6e-3883-42ce-95e2-96bf4ba57353/0ea2f4fa-51fe-49cd-bfc3-069cc828fd78?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEGgaDnVzLWVhc3QtMS1maXBzIkYwRAIgbC8sWIoYaHR8y7Wq86nPb0pufBl4%2BM4PWpeN%2B5PW9VQCIG7BhAVBDCWTpxFDLeuKYm%2Ff1JLz4iccPMzter0YdhgWKqYECDAQABoMNDE0MzUxNzY3ODI2IgxNAJrHbXaZG20jNfMqgwQyGEb4TsoVOoNvJ%2BaA%2FZ7zeu8hGcHqCeNfblP%2FQo5g6q2aRNtpwtPRKMlhYcIE%2FUQaNT1Lqmyptu32VUQTpzjTTxRVmJrDPkheSCkV06XQ%2FpTMKjU8CC7X1b%2FqYf8CrBwZT7Cv4C5WPjEET38f2A79h0xV6bZgPy28zQWrSeIJ9w6AfZBxiE24%2FVc08zVA0ivCT6lsy7nuU0wcR%2FMg2C9nd1vaMnQ923VpD21CKfVVYcrNWL2b%2FQ%2BNAO%2BxPjPgjiN6UPQbvbvZxkSdhgrGtecIg9NgqGkMcvM5VLTDtdC1kdeZetfOC2obtA%2BT%2F0gy3tIDo6WM5hcQfw743%2FAU9oaGnRBl%2BwoxRRDCZA%2F4jnPdluICSI7NCr6C3HHOe4CSZJjRihczxPA%2FgQczAVjAPs6CrK9N9pIzt0vNJpGUTgg4AoxftTvGfciDREilSctPIcBk6GwB%2Bzt4wbtNCmEPU%2BFD46rc6CWVQmmLwbKOeHw70ipMVFlOc8NlXkEtGO2vu6H44MstSSc6daOVUYUavQSGZ6jGhEjsP2YGz8KDm9XQotB%2FtyVBqiZV01OKqDmmBJA%2FXjUpSJ8M%2FeJXhe5m%2BKcxzNObtoJRI7jci3Tpt08XZyXZ7%2FyIV8H30ya468X3cfkTXrm972uga3z7iXAdubyEHnQIua%2FuvtwOHILi6O5D1Me2MzCPvcnXBjqTAYDoMWBt%2B3AVFuqSs2dmDQNBnvEhXfMgTedKLD5PJHb3aIl15iBkLaNu36GYnVCp9ZU2lSYcTbY5QwNnNeTfJTNy9bKhwzru6Sg%2BslhN%2BTS0zkTn%2BeLMY4tHgPEtEZ2cp%2FCk6GwqswvnYVKHjwO8q%2B1Mx8lc08fVEfarE9dke1GGFqoddx%2Bq%2BZUyHbDlAVfrCmf0yw%3D%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20261110T000135Z&X-Amz-SignedHeaders=host&X-Amz-Expires=86400&X-Amz-Credential=ASIAWA6KKHEJORWBH4N3%2F20261110%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=a2e736a5ea8266d45a1acfc0da32645ca682fc4ce3412bb143762354d592fd1b","created_at":1682537462178,"updated_at":1682537462178,"display_name":"EDA Notebook","status":"FILE_STATUS_PUBLISHED"}],"assets":["ASSET_TYPE_DATA_TABLE"]},"provider_summary":{"name":"John Snow Labs","description":"John Snow Labs, an AI and NLP for healthcare company, provides state-of-the-art software, models, and data to help healthcare and life science organizations build, deploy, and operate AI projects."},"popularity":1358},{"id":"c7b48609-45c5-459b-8d1d-904d374c9030","summary":{"name":"Ambee Global Pollen Data - Historical | Present | Forecast","subtitle":"Hyperlocal pollen data for 30+ species, hourly updates. 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The dataset is powered by a blend of environmental signals and proprietary modeling techniques, designed to reflect real-world conditions with some of the highest accuracy benchmarks in the industry.\n\n**Use cases**\n\n- Personalized digital health recommendations and allergy management \n- Pharmaceutical demand forecasting for allergy medications\n- Clinical workflow integration and patient care optimization \n- Digital wellness platform development \n- Allergy-aware routing and outdoor navigation\n- Consumer health app integration \n- Clinical trials efficiency and planning\n- Climate-aware planning for outdoor events and activities\n\n**Sample fields:**\n\n- grass_pollen_count – Grass pollen count (particles/m³)\n- grass_pollen – Risk level for grass pollen\n- tree_pollen_count – Tree pollen count (particles/m³)\n- tree_pollen – Risk level for tree pollen\n- weed_pollen_count – Weed pollen count (particles/m³)\n- weed_pollen – Risk level for weed pollen\n- Species – Pollen count by individual subspecies (particles/m³) varies by region\n- SpeciesRisk – Pollen risk level by individual subspecies (Low, Moderate, High, Very High), varies by region\n\n**Subspecies covered:** \nAcacia, Alder, Ash, Aster, Birch, Casuarina, Cedar, Chenopod, Cypress, Elm, Grass, Juniper, Maple, Mugwort, Mulberry, Myrtaceae, Nettle, Oak, Olive, Pine, Plane, Plantago, Poplar/Cottonwood, Ragweed, Rumex, Sedges, and Willow\n\n**Tables included**\n\n- Pollen_1hr_current_data \n- Pollen_historical_data \n- Pollen_historical_daily \n- Pollen_forecast_120_hr_data \n- Pollen_forecast_daily_30_days\n\n**Benefits to Databricks Marketplace users**\n\n- Instantly available: Delivered directly for immediate integration into any analytics, machine learning, or Databricks workflow.\n- NAB-compliant risk scoring: Risk levels (Low, Moderate, High, Very High) following National Allergy Bureau guidelines for standardized pollen species assessment\n- 30+ species and subspecies coverage: Includes category-level species such as Tree, Grass, and Weed, along with subspecies like Hazel, Elm, Pine, Alder, Oak, Birch, Cypress, Mugwort, Ragweed, Maple, Ash, and more.\n- Historical depth + forecast horizon: Over a decade of pristine historical archives combined with industry-leading forecast data, enabling both historical analysis and forward-looking precision at scale.\n- Hyperlocal precision: Data is provided on a scientifically consistent grid that can be mapped to any user-defined location or geography.","terms_of_service":"https://www.getambee.com/terms-and-conditions","documentation_link":"https://docs.ambeedata.com/apis/pollen","privacy_policy_link":"https://www.getambee.com/privacy-policy","geographical_coverage":"[\"Global\"]","cost":"FREE","update_frequency":{"interval":1,"unit":"HOURLY"},"collection_granularity":{"interval":1,"unit":"HOURLY"},"data_source":"","assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://www.getambee.com/servicesagreement"},"provider_summary":{"name":"Ambee","description":"Ambee is a global climate tech company delivering high-resolution, real-time, historical, and forecast environmental intelligence. 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Wildfire Risk Extreme provides an up-to-date, scientific model that accurately captures wildfire risk under severe conditions, for every location in the United States. This sample data set is limited to the Marin County, CA area.\n\nWildfire Risk Extreme was designed as a companion product to Precisely's existing Wildfire Risk. Wildfire Risk Extreme includes temperature, relative humidity, and maximum wind speeds under fire conditions that occur only 1 percent of the time. Additionally, the direction of predominant winds in an area provides insight provides insight into where communities may be vulnerable. Also, for the first time, the threat from urban conflagration (fires that are no longer carried by wildland fuel but by the ignition of structures by other structures) is modeled. This new product offers a nationwide wildfire hazard and risk assessment tool that incorporates the predicted severity (hazard) and predicted frequency (risk) or wildfire at a given location.\n\nMany wildfire-related datasets are delivered in a format that divides the landscape into square grids. A square has very little relation to how a fire burns and the variation from square to square can be difficult to interpret. Wildfire Risk Extreme takes a different approach: All the datasets Wildfire Risk Extreme uses to create a final analysis – many of which come in a grid format – are integrated into polygons that reflect the landscape and how wildfires would actually burn within that landscape. Wildfire Risk Extreme divides the data into firesheds that are based on the topography (hills and valleys) of the landscape. These firesheds correlate to vegetation or wildland fuels and determine how a fire might burn and behave. This means that firesheds are more likely to divide geography into landscape-based units that exhibit similarities in vegetation, slope, and resultant fire behavior. The [Wildland](https://help.precisely.com/r/OhmEbpQTUxYR9oe0m9v7vA/IWx8BZabTu5rZylF7Ss00A) and [Intermix](https://help.precisely.com/r/OhmEbpQTUxYR9oe0m9v7vA/Kmal0pyr4ggiuOfeqF7ALw) modules in Wildfire Risk Extreme use the concept of firesheds to aggregate the landscape.\n\nWildfire Risk Extreme also accounts for the fact that firesheds are impacted by wildfire effects from outside of their boundaries. A fireshed may be mostly covered by a meadow but surrounded by volatile timber or chaparral (a California reference). If a structure exists in the meadow, it is not only subject from the grass fuel in the fireshed, but also to fire embers in the surrounding firesheds' vegetation. To account for this, Wildfire Risk Extreme incorporates adjacent fireshed values into the threat profile for every fireshed, providing a more realistic definition of the fire environment.\n\nThis is a free sample of Precisely's Wildfire Risk Extreme USA product covering the Marin County, CA area. The full USA product is available as Paid access. Contact Databricks.partnership@precisely.com for full product licensing information.\n\n**Use cases**\n **Asset Valuation**\n\nUnderstanding the potential risk posed to property and possessions allows for the appropriate valuation of those assets. For Insurance use cases, understanding fire risk to a specific property is critical to both a properties value and the potential costs associated with fire damage.\n\n **Economic Impact Analysis**\n\nWildfire damage can have a devastating effect on economies of various scales. Understand the potential impact on economies from the local to national level resulting from wildfire damage.\n\n **Location Data Enrichment**\n\nThe Precisely Wildfire Risk Extreme dataset can uniquely deliver wildfire information to the individual address level. Enrich existing address lists using Precisely's Wildfire Risk Extreme in conjunction with the Precisely Address Fabric USA product or Precisely geocoding solutions.\n\n **Risk Analysis**\n\nWildfire Risk Extreme enables companies to understand potential fire risk down to the individual address level. Used in conjunction with Precisely's Address Fabric product allows for the enrichment of in house addresses with specific wildfire risk attribution.\n\nInsurance Risk: Incorporate Wildfire data when modeling risk levels for various insurance purposes.\n\nWildfire Protection Plans: Incorporate risk levels based on real world data in the creation of wildfire protection plans.\n\nEmergency Response: Understand relative risk by location with respect to emergency response planning.\n\n **Real World Data (RWD)**\n\nUnderstand the relative threat to a location from wildfire based on real world data. This product contains data including: historical fire occurrence, topography, vegetation cover, moisture drying relative to sun exposure, forest cover details and more.\n\n**Product details**\nWildfire Risk Extreme uses the concept of dividing the landscape into relative amounts of built environment (structures, roads, and other infrastructure) versus pure wildland fuels. 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The territorial vintages of the worldwide area boundaries are compatible with our globally consistent and comparable [socio-demographic](https://www.mbi-geodata.com/en/sociodemographic-data/) and [economic](https://www.mbi-geodata.com/en/purchasing-power-overview/) data.\nOur area boundaries for Slovenia are offered at the following levels:\n\n- **Postal Code Level** (Postcode 4-digit): Suitable for a wide range of applications – for example, enriching customer databases, which are often organized by postal code.\n- **Administrative Level** (Občine): Covers both larger administrative areas (e.g., regions, states, provinces) as well as finer subdivisions such as districts, counties, or municipalities.\n- **Micro Level** (Naselja, Prostorski Okoliši): Subdivisions of postal code or administrative boundaries, such as census areas or street segments. 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The crime statistics can help in understanding the safety of the area, informing decisions about property investment, relocation, or business establishment.\n**Asset Valuation**\nCrime data can be instrumental in validating the value of a property. High crime rates may negatively impact property values. Thus, this data can be crucial during property valuation or asset assessment.\n**Location Data Enrichment**\nEnrich location-based datasets. By integrating crime data, businesses can have a more comprehensive view of a neighborhood, improving their location intelligence.\n**Market Analysis**\nUnderstanding the crime rates in various neighborhoods can help businesses analyze the market better. It can provide valuable insights into potential business locations, customer behavior patterns, and more.\n**Customer Acquisition**\nFor businesses like insurance providers, real estate agents, or security firms, crime data can inform customer acquisition strategies. 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Indicators of opportunities for improving patient flow include\n- Increased lengths of stay\n- Reduced patient satisfaction scores\n- Increased operational costs\n- Reduced access to critical care resources in a timely manner\n\n**Objective:** \nThis demonstration notebook provides a preview of how Health Catalyst's advanced analytics platform approaches patient flow optimization. \nFor this analysis, we will focus specifically on ED boarding solutions, showcasing our predictive analytics capabilities that forecast patient discharges and optimize real-time bed allocation to reduce boarding times and improve patient flow.\n\n**Key Focus Areas**:\n- Identify bottlenecks or constraints\n- Eliminate or reduce bottlenecks or constraints\n- Maximize hospital capacity utilization\n \n\n**Product details**\n\n**Demo Schema:** *patient_flow_forecasting*\n- *patient_throughput_hourly_flow* - Patient hourly flow\n- *patient_throughput_encounters* - Patient encounters\n- *patient_throughput_bed_metrics* - Bed management metrics\n\n**Hourly Patient Flow (Sample Fields)**\n- `timestamp`: Hour timestamp\n- `hospital_id`: Hospital identifier\n- `unit_code`: Care unit code (ED, ICU, etc.)\n- `occupancy_rate`: Current bed occupancy percentage\n- `avg_wait_minutes`: Average wait time for the unit\n\n**Patient Encounters (Sample Fields)**\n- `encounter_id`: Unique encounter identifier\n- `patient_id`: Patient identifier\n- `admission_datetime`: Admission timestamp\n- `discharge_datetime`: Discharge timestamp\n- `unit_sequence`: Path through hospital units\n- `ed_boarding_hours`: Time spent boarding in ED\n\nFor more details, refer to the embedded notebook.\n\n\n*This product uses synthetic data and simplified models for illustration purposes. Results shown are not indicative of actual performance. Health Catalyst's production platform includes comprehensive data security, HIPAA compliance, clinical validation, and continuous monitoring that cannot be fully represented in this demonstration environment.*","terms_of_service":"https://www.healthcatalyst.com/evaluation-agreement","documentation_link":"","privacy_policy_link":"https://www.healthcatalyst.com/privacy-policy","embedded_notebook_file_infos":[{"id":"1bf065d0-5729-45e9-898e-d4fe852c126e","marketplace_file_type":"EMBEDDED_NOTEBOOK","file_parent":{"parent_id":"6316ef52-1b49-4024-82a6-c0937f6b8b87","file_parent_type":"LISTING"},"mime_type":"text/html","download_link":"https://marketplace-file-storage-prod.s3-fips.us-east-1.amazonaws.com/EMBEDDED_NOTEBOOK/LISTING_6316ef52-1b49-4024-82a6-c0937f6b8b87/1bf065d0-5729-45e9-898e-d4fe852c126e?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEGgaDnVzLWVhc3QtMS1maXBzIkYwRAIgbC8sWIoYaHR8y7Wq86nPb0pufBl4%2BM4PWpeN%2B5PW9VQCIG7BhAVBDCWTpxFDLeuKYm%2Ff1JLz4iccPMzter0YdhgWKqYECDAQABoMNDE0MzUxNzY3ODI2IgxNAJrHbXaZG20jNfMqgwQyGEb4TsoVOoNvJ%2BaA%2FZ7zeu8hGcHqCeNfblP%2FQo5g6q2aRNtpwtPRKMlhYcIE%2FUQaNT1Lqmyptu32VUQTpzjTTxRVmJrDPkheSCkV06XQ%2FpTMKjU8CC7X1b%2FqYf8CrBwZT7Cv4C5WPjEET38f2A79h0xV6bZgPy28zQWrSeIJ9w6AfZBxiE24%2FVc08zVA0ivCT6lsy7nuU0wcR%2FMg2C9nd1vaMnQ923VpD21CKfVVYcrNWL2b%2FQ%2BNAO%2BxPjPgjiN6UPQbvbvZxkSdhgrGtecIg9NgqGkMcvM5VLTDtdC1kdeZetfOC2obtA%2BT%2F0gy3tIDo6WM5hcQfw743%2FAU9oaGnRBl%2BwoxRRDCZA%2F4jnPdluICSI7NCr6C3HHOe4CSZJjRihczxPA%2FgQczAVjAPs6CrK9N9pIzt0vNJpGUTgg4AoxftTvGfciDREilSctPIcBk6GwB%2Bzt4wbtNCmEPU%2BFD46rc6CWVQmmLwbKOeHw70ipMVFlOc8NlXkEtGO2vu6H44MstSSc6daOVUYUavQSGZ6jGhEjsP2YGz8KDm9XQotB%2FtyVBqiZV01OKqDmmBJA%2FXjUpSJ8M%2FeJXhe5m%2BKcxzNObtoJRI7jci3Tpt08XZyXZ7%2FyIV8H30ya468X3cfkTXrm972uga3z7iXAdubyEHnQIua%2FuvtwOHILi6O5D1Me2MzCPvcnXBjqTAYDoMWBt%2B3AVFuqSs2dmDQNBnvEhXfMgTedKLD5PJHb3aIl15iBkLaNu36GYnVCp9ZU2lSYcTbY5QwNnNeTfJTNy9bKhwzru6Sg%2BslhN%2BTS0zkTn%2BeLMY4tHgPEtEZ2cp%2FCk6GwqswvnYVKHjwO8q%2B1Mx8lc08fVEfarE9dke1GGFqoddx%2Bq%2BZUyHbDlAVfrCmf0yw%3D%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20261110T000135Z&X-Amz-SignedHeaders=host&X-Amz-Expires=86400&X-Amz-Credential=ASIAWA6KKHEJORWBH4N3%2F20261110%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=eb05822c9fe5fbdc576e31f50bc86156cd6582375db3522a8ed7889a77722971","created_at":1751312928484,"updated_at":1751312928484,"display_name":"Health Catalyst: Patient Throughput Analysis","status":"FILE_STATUS_PUBLISHED"}],"geographical_coverage":"[\"Global\"]","cost":"FREE","data_source":"","assets":["ASSET_TYPE_DATA_TABLE","ASSET_TYPE_GIT_REPO"],"license":"https://www.healthcatalyst.com/evaluation-agreement"},"provider_summary":{"name":"Health Catalyst","description":"Health Catalyst is a leading provider of data and analytics technology and services to healthcare organizations, committed to being the catalyst for massive, measurable, data-informed healthcare improvement."},"popularity":780},{"id":"130ead15-8051-4157-8b67-229529d3e072","summary":{"name":"Custom Audience Data- Influencer, Media, Brand Preferences","subtitle":"Detailed insights into consumers' social engagement across various topics, influencers, media, and brands.","setting":{"visibility":"PUBLIC"},"created_at":1722887565506,"updated_at":1724693798631,"categories":["ADVERTISING_AND_MARKETING"],"listingType":"PERSONALIZED","provider_id":"0ca2c7e6-e822-4df8-8fbf-0b2ddc27e0ca"},"detail":{"description":"StatSocial offers detailed insights into any consumers, brand, or social audience's engagement with various topics, influencers, media channels, brands and more. \n\n**Across any specific audience segment, discover those engaging with specific**:\n\n- Topics of interest (e.g. Health & Wellness, Parenting, Politics, B2B)\n- Influencers & content creators (e.g. Celebrity to Nano, Topic-Based)\n- Media channels (e.g. Podcasts, TV Networks, Digital Publications)\n- Brands (e.g. Retail, CPG, Technology, Auto) \n\n**Use Cases**\nBy leveraging these segments, both B2C and B2B organizations can:\n- Fill critical gaps in their datasets by integrating data from social platforms at scale\n- Analyze an audience’s interests, media preferences, trusted influencers, and brand affinities\n- Inform highly targeted campaigns based on media and influencer preferences\n- Take action on custom segments for measurement of organically exposed audiences\n\nThis data allows for precise planning and execution of marketing strategies, ensuring that communication resonates with highly engaged and relevant audiences. \n\n**About StatSocial**\nStatSocial is the industry’s leading provider of cross-platform social audience insights, empowering more informed marketing decisions with advanced solutions for consumer insights, media and brand planning, influencer marketing and more. For more information, visit [StatSocial.com](url). \n\n","terms_of_service":"https://www.statsocial.com/terms-of-service","documentation_link":"","privacy_policy_link":"https://www.statsocial.com/privacy-policy","geographical_coverage":"[\"Global\"]","cost":"PAID","data_source":"Major social media platforms","assets":["ASSET_TYPE_MEDIA"],"license":""},"provider_summary":{"name":"StatSocial","description":"StatSocial is the industry's exclusive provider of cross-platform social audience insights, offering unparalleled data on any audience's self-declared interests, media preferences, trusted influencers, and brand affinities, and personality traits. By leveraging our patented Identity Graph, we connect individuals' social profiles to offline identities, delivering comprehensive and actionable insights. Brands can access vertical-specific segments or create custom consumer, brand, or social audiences within their Databricks account or other preferred environments. 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This meticulously compiled dataset, sourced from official statistical sources, including Centraal Bureau voor de Statistiek, offers a detailed insight into the population landscape of the Netherlands at the buurt level. \n\nIf you require data at a less granular level, such as an Uber grid or square grid, please don't hesitate to get in touch with us at *contact@geolocet.com*. We understand that different projects may have varying data needs, and we are here to tailor our data to match your specific requirements.\n\n ## Key Features:\n---\n* **Population Data Netherlands:** Gain access to the latest and most accurate population statistics for the Netherlands, meticulously organized by municipality (gemeenten).\n\n* **Demographic Insights:** Dive deep into the Netherlands demographic landscape with data on 107 attributes, including: \n* Total population,\n* Gender,\n* Marital Status,\n* Gender split by Marital Status,\n* Age bands,\n* Gender split by Age,\n* Nationality,\n* Gender split by Nationality,\n* Total number of households,\n* Household composition,\n* Household size,\n* Households with children,\n* Employment status,\n* Income, \nall available at the municipal (gemeenten) level.\n\n* **Geodemographic Insights:** Unlock geodemographic data Netherlands-wide to inform location-based decision-making, marketing strategies, and policy formulation.\n\n* **Ready to use:** Our dataset is currently available in CSV format, providing ease of use and compatibility with a wide range of data analysis tools. However, if you prefer a different format, our team is readily available to assist you in obtaining the data in your desired format.\n---\nAdditionally, for those looking to enrich their analysis or project, we offer:\n* **[Administrative Boundaries](https://geolocet.com/products/netherlands-all-available-administrative-boundaries):** If you require geographical boundaries (polygons) for the municipalities of the Netherlands, consider exploring our Admin Boundaries product, which seamlessly integrates with this dataset.\n\n* **[Point of Interest (PoI) Data](https://geolocet.com/pages/aggregated-points-of-interest-poi-data):** If you're interested in Points of Interest data, please don't hesitate to contact us for customized datasets to enhance your geographical analysis.\n\n* **[Data Customization](https://geolocet.com/pages/data-services-by-geolocet):** We understand that your project may have specific requirements. Feel free to get in touch with us if you need any modifications or specific data formats such as Excel, JSON, GeoJSON, or Shapefile. We are here to tailor the data to your needs.\n\nIf you're looking for data for the Netherlands, [look here](https://geolocet.com/pages/netherlands) for off-the-shelf or tailor-made datasets to suit your specific requirements.\n\n\nMake informed decisions, conduct precise research, and unlock the potential of the Netherlands demographic landscape with our comprehensive dataset. Trust in the accuracy and reliability of our official statistical data to drive your projects forward.\nContact us at *contact@geolocet.com* today to access this invaluable resource and explore the Netherlands census data like never before.\n\n[Geolocet LTD](https://geolocet.com)\n","terms_of_service":"https://geolocet.com/pages/terms-of-use","documentation_link":"","privacy_policy_link":"https://geolocet.com/pages/privacy-policy","geographical_coverage":"[\"Europe\"]","cost":"PAID","data_source":"","assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://geolocet.com/pages/terms-of-use"},"provider_summary":{"name":"Geolocet LTD","description":"Geolocet provides premium geospatial data for retail analysis, GIS, and market planning. 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These pre-annotated datasets are instrumental in developing Parts of Speech (POS) elements for Clinical NLP models. By providing a robust foundation for advanced linguistic analysis and natural language processing in the medical field, Shaip's chunking datasets enable precise parsing and understanding of clinical text. This, in turn, facilitates the extraction of relevant medical information, improving the accuracy and efficiency of healthcare applications such as automated medical coding, clinical documentation, and patient data analysis.\n\n**Use cases**\n\n**Clinical Document Analysis**\n\n- **Information Extraction**: Chunking can help identify specific sections within clinical notes (e.g., patient history, physical exam, diagnosis, treatment plan) to extract relevant information efficiently.\n- **Entity Recognition**: By segmenting text into smaller chunks, it becomes easier to apply Named Entity Recognition (NER) models to identify medical terms, medications, and other entities.\n- **Relationship Extraction**: Chunking can help identify relationships between medical entities within specific contexts, aiding in knowledge graph construction.\n\n**Patient Phenotyping**\n\n- **Cohort Identification**: By chunking patient records, it's possible to identify specific patient populations based on shared characteristics (e.g., disease, treatment, demographics) for cohort studies.\n- **Outcome Prediction**: Analyzing segmented patient data can help identify patterns and predictors of patient outcomes.\n\n**Drug Safety and Adverse Event Detection**\n\n- **Signal Detection**: Chunking can help identify potential adverse drug reactions by examining specific sections of patient records related to medication use and adverse events.\n- **Risk Factor Analysis**: Analyzing segmented data can help identify risk factors associated with adverse events.\n\n**Natural Language Processing (NLP) Model Training**\n\n- **Data Preparation**: Chunking can help prepare large datasets for NLP model training by creating smaller, more manageable training examples.\n- **Model Evaluation**: By dividing data into training, validation, and test sets, chunking aids in model evaluation and performance assessment.\n\n**Product details**\n\nOur datasets include comprehensive chunking elements, covering\n\n- Noun phrases\n- Pronoun phrases\n- Verb phrases\n- Adverb phrases\n- Prepositional phrases and more. \n\nThese pre-annotated datasets are designed to facilitate the development of Parts of Speech (POS) elements for Clinical NLP models, providing a robust foundation for advanced linguistic analysis and natural language processing in the medical field.","terms_of_service":"https://www.shaip.com/terms-of-service/","documentation_link":"","privacy_policy_link":"https://www.shaip.com/privacy-policy/","geographical_coverage":"[\"United States and Canada\"]","cost":"FREE","assets":["ASSET_TYPE_MEDIA"],"license":""},"provider_summary":{"name":"Shaip","description":"Shaip is a leading AI data platform and services provider specializing in the sourcing, creation, licensing, and transformation of high-quality, ethical, and domain-specific data for AI and machine learning applications. 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Luma is the scientific operating system built to deliver: a platform that fuses the digital, logical, and physical layers of the lab into one autonomous, orchestrated system. \n\nInstruments, workflows, data, and decisions stop living in silos and start running as a single continuous loop, every result captured with full scientific context, every process orchestrated end to end, every insight feeding the next experiment without a human bottleneck in between. This is the foundation of the modern lab: instrumentation and automation choreographed by intelligence, not just wired together by integration. Built on Databricks, Luma pairs this autonomous scientific core with enterprise-scale data and AI, so scientists, automation engineers, and IT teams can move from lab notebooks to lab intelligence, and from manual science to self-driving discovery. \n\nThat same operating system carries scientific intelligence across every modality — biology, chemistry, and formulations — so no result sits stranded in a format only one team can read. Luma keeps the digital thread intact as a program moves from research through development and into tech transfer, closing the handoff gaps, format conversions, and re-work that have historically stalled programs at every stage. The result is fewer bottlenecks and a faster path from discovery to development to production, not only in pharma, where Luma helps compress the path from candidate to commercial-scale process, but across chemicals, materials, CPG, energy, and any industry built on formulation and process science. \n\n**Product capabilities**\n\n- **AI-Ready Orchestration**: Unify data across instruments, systems, and workflows into a single connected layer, so AI can reason over real-time lab data and support decisions as experiments unfold. No manual data wrangling. \n\n- **Open Ecosystem & App Design**: Luma meets your lab where it is. Connect what you already run, then build AI powered apps that put your scientists in control of the lab. Orchestrate everything into automated workflows. No rip and replace. \n\n- **Registration**: Give every sample, compound, and material a single source of truth from the moment it enters the system, providing the ledger an autonomous lab runs on. \n\n- **Data Management & Visualization**: Turn raw signal into structured, contextualized data the instant it’s captured, so it’s ready for models and agents to act on. \n\n- **AI & Agents**: Agents that act, executing the next step in plain language while keeping every decision traceable back to its source. \n\n- **Digital Thread & Tech Transfer**: Carry scientific intelligence across biology, chemistry, and formulations as a program moves from research to development to tech transfer, closing handoff gaps that slow discovery, development, and production. \n\n \n**Supported Industries**\n\n - Advanced Materials \n- Agriculture\n- Biotechnology & Pharmaceuticals \n- Consumer Products \n- Clinical & Diagnostics \n- Energy & Fuel \n- Environmental \n- Flavors & Fragrances \n- Paints & Coatings \n\n**Supported Scientific Workflows**\n\n- Analytical & Bioanalytical Chemistry \n- Cell Biology \n- Drug Development \n- Genomics \n- Protein Science \n- Synthetic Biology \n- Therapeutics \n- Formulations ","terms_of_service":"https://www.dotmatics.com/terms-and-conditions","documentation_link":"","privacy_policy_link":"https://www.dotmatics.com/privacy-policy","assets":["ASSET_TYPE_MEDIA"],"license":""},"provider_summary":{"name":"Dotmatics","description":"Dotmatics is a leader in R&D scientific software connecting science, data, and decision-making. 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It also provides a c..","share":{"name":"suicide_data","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1682356125571,"updated_at":1698965635273,"categories":["HEALTH","SCIENCE_AND_RESEARCH"],"listingType":"STANDARD","provider_id":"f88849f8-9d77-4de2-b4c7-03d55cc3768e"},"detail":{"description":"\n**Overview**\nThis accelerator contains information about reasons and methods involved in attempting a suicide. It also provides a comprehensive list of prescription drugs which might cause depression or other psychiatric disorders as a side effect that lead to suicidal ideation and failed or successful suicidal attempts.\n\n**Description**\nSuicide is the act of intentionally causing one's own death. Risk factors include mental disorders such as depression, bipolar disorder, schizophrenia, personality disorders, and substance abuse, including alcoholism and use of benzodiazepines. Other suicides are impulsive acts due to stress such as from financial difficulties, troubles with relationships, or from bullying. Those who have previously attempted suicide are at higher risk for future attempts. Suicide prevention efforts include limiting access to methods of suicide, such as firearms, drugs, and poisons, treating mental disorders and substance misuse, proper media reporting of suicide, and improving economic conditions. The datasets included in this accelerator consists of following information: - A list of reasons and methods used for suicidal attempts and their relevant ICD-10-CM codes and UMLS concept IDs. Reasons like depression and various mental disorders that lead to suicide can be treated with medication and counseling. Although the treatment plans vary from patient to patient, general treatment options available have been mentioned along with their NDC Product Codes. - A comprehensive list of prescription drugs which might cause depression or other psychiatric disorders as a side effect. If not treated, these side effects may lead to suicidal ideations and even attempts of failed or successful suicides. The side effects causing psychiatric disorders have further been categorized into very common, common, uncommon, rare and very rare based on their frequency of occurrence. Any psychiatric side effects reported to the FDA as a result of post-marketing surveillance have also been included in this dataset.\n\n**Benefits**\n- useful for physicians, pharmacists, pharmaceutical companies, medical students and patients (general public)\n- enhance patients awareness regarding the risks involved in taking drug treatments that are associated with suicidal behavior\n- preventive care\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [Medication Correlated Suicide Risks](https://www.johnsnowlabs.com/marketplace/medication-correlated-suicide-risks)\n\t- This dataset consists of a comprehensive list of prescription drugs which might cause depression or other psychiatric disorders as a side effect. If not treated, these side effects may lead to suicidal ideations and even attempts of failed or successful suicides. The side effects causing psychiatric disorders have further been categorized into very common, common, uncommon, rare and very rare based on their frequency of occurrence.\n- [Suicide Related Diagnoses Symptoms and Drugs](https://www.johnsnowlabs.com/marketplace/suicide-related-diagnoses-symptoms-and-drugs)\n\t- This dataset consists of information available over the internet for the reasons and methods used for suicidal attempts and their relevant ICD-10-CM codes and UMLS concept IDs. Reasons like depression and various mental disorders that lead to suicide can be treated with medication and counseling. Although the treatment plans vary from patient to patient, general treatment options available have been mentioned in this dataset along with their NDC Product Codes.\n- [Suicides in England By Upper Tier Local Authority](https://www.johnsnowlabs.com/marketplace/suicides-in-england-by-upper-tier-local-authority)\n\t- This dataset includes information regarding number of deaths registered and age-standardized suicide rates for upper tier local authorities, rolling three-year aggregates, deaths registered 2002 to 2016.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - 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Consumer Brand (3X increase in retail sales): A leading consumer brand expanded market share by increasing physical store sales by 3X for a 3-month period, resulting in $500k in increased sales, by adding Yobi Signal to their ad targeting model.\n3. Retail (81% decrease in marketing fees): A US retailer reduced their marketing fees by 81% (while still improving their performance), by using Yobi Signal for their candidate selection and segmentation modeling, and distributing through lower cost channels.\n\n**Dataset Details:**\n$5.5B Daily Spend\n70B Daily Ad Events\n40B Daily GPS Pings\n5B Daily Search Queries\n50M Connected TVs\n\n**Robust Identity Matching:**\nConsent string and time stamp maintained across entire dataset.\n1. Email\n2. IP Address\n3. Mailing Address\n4. MAID\n5. UID2\n6. CTV ID\n\n**Our Commitment to Privacy & Opt In:**\n1. Yobi will honor all opt-out, do-not-process, and deletion requests.\n2. Yobi will encrypt all data storage at rest.\n3. 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Receive independent valuation of target assets, projects, and companies or assets targeted for divestment.\n\n**Use cases**\n- Fundamental Analysis - Understand drivers of financial and operational performance and company's ability to grow shareholder value\n- Benchmarking & Competitor Intelligence - Compare overall financial performance among peer groups and individual companies; growth, profitability, financial strength, capital efficiency,\nand capital allocation","terms_of_service":"https://www.spglobal.com/en/privacy/privacy-policy-english","documentation_link":"https://onlinehelp.ihs.com/Energy/NA_Application_Data_Model_(ADM)/Webhelp/Default.htm#resources/pdf/na_adm_user_documentation.pdf?TocPath=_____13","privacy_policy_link":"https://www.spglobal.com/en/privacy/privacy-policy-english","geographical_coverage":"[\"Global\"]","cost":"PAID","update_frequency":{"interval":1,"unit":"DAILY"},"assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"S&P Global Energy","description":"At S&P Energy, our complete view of global energy and commodities markets enables our customers to make decisions with conviction and create long-term, sustainable value. 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Data Sources DEI-food-access-research-atlas DEI-food-nutrition-service-programs DEI-food-security-international DEI-food-security-US\n\n**Product Details**\n\nIncludes the following tables:\n\n\n- **esg_dei_food.dei_food_global_security_by_country_yearly_usda**\n - **Update frequency:** yearly\n - **Source:** DEI-food-security-international - USDA\n - **Description:** annual country-level data on area, yield, production, nonfood use, trade, and consumption for grains and root and tuber crops\n\n- **esg_dei_food.dei_food_security_in_us_household_yearly_uscb**\n - **Update frequency:** yearly\n - **Source:** DEI-food-security-US - US Census Bureau\n - **Description:** This table provides the supplement information about the full range of severity of food insecurity as experienced in U.S. households.\n\n- **esg_dei_food.dei_food_access_research_atlas_yearly_usda**\n - **Update frequency:** yearly\n - **Source:** DEI-food-access-research-atlas - US Department of Agriculture Economic Research Service\n - **Description:** This table provides an overview of food access indicators for low-income and other census tracts using different measures of supermarket accessibility\n\n- **esg_dei_food.dei_food_access_research_atlas_variable_lookup_yearly_usda**\n - **Update frequency:** yearly\n - **Source:** DEI-food-access-research-atlas - US Department of Agriculture Economic Research Service\n - **Description:** This table provides details of lookup info for an overview of food access indicators for low-income and other census tracts using different measures of supermarket accessibility\n\n- **esg_dei_food.dei_food_access_research_atlas_variable_lookup_yearly_usda**\n - **Update frequency:** yearly\n - **Source:** DEI-food-nutrition-service-programs - Food And Nutrition Service - US Department of Agriculture\n - **Description:** This table provides details of selected statistical information on activity in all major Food and Nutrition Service Programs (FNS)\n\n\n\n**Data Sources**\n\nThe data in this product comes from the following data sources:\n\n\n**USDA ERS - Food Access Research Atlas**\n\nPresents an overview of food access indicators for low-income and other census tracts using different measures of supermarket accessibility; \nProvides food access data for populations within census tracts; and offers census-tract-level data on food access that can be downloaded for \ncommunity planning or research purposes.\n\n\n- **Source Homepage:** [US Department of Agriculture Economic Research Service](https://www.ers.usda.gov/)\n- **Source Data:** [USDA | Economic Research Service - Food Access Research Atlas](https://www.ers.usda.gov/data-products/food-access-research-atlas/)\n- **License:** [Public Domain U.S. Government](https://www.usa.gov/government-works/)\n- **Frequency:** yearly\n\n\n\n**USDA | Ag Data Commons | Food availability and access for several low- and middle-income countries**\n\nThe dataset includes annual country-level data on area, yield, production, nonfood use, trade, and consumption for grains and root and tuber \ncrops (combined as R&T in the documentation tables),food aid, total value of imports and exports, gross domestic product, and population\ncompiled from a variety of sources.\n\nThis dataset measures food availability and access for 76 low- and middle-income countries. \n\n\n\n- **Source Homepage:** [U.S. Department of Agriculture](https://www.usda.gov/)\n- **Source Data:** [U.S. Department of Agriculture - Ag Data Commons](https://data.nal.usda.gov/dataset/international-food-security-0)\n- **License:** [Public Domain U.S. Government](https://www.usa.gov/government-works/)\n- **Frequency:** yearly\n\n\n\n**USCB | Supplement questions for full range of severity of food insecurity as experienced in U.S.**\n\nThe CPS Food Security Supplement was conducted by the U.S. Census Bureau for the Economic Research Service (ERS) of the \nU.S. Department of Agriculture (USDA). Supplement questions were asked of all interviewed households, as appropriate.\nThe supplement was intended to research the full range of severity of food insecurity as experienced in U.S. households.\n\n\n- **Source Homepage:** [U.S. Census Bureau](https://www.census.gov/)\n- **Source Data:** [U.S. Census Bureau](https://www.census.gov/data/datasets/time-series/demo/cps/cps-supp_cps-repwgt/cps-food-security.html#cpssupps)\n- **License:** [Public Domain U.S. Government](https://www.usa.gov/government-works/)\n- **Frequency:** yearly\n\n\n\n**USDA | FNS | Statistical information on activity in all major Food and Nutrition Service Programs**\n\nProvides selected statistical information on activity in all major Food and Nutrition Service Programs (FNS). \nThese include the Supplemental Nutrition Assistance Program (SNAP); the Special Supplemental Nutrition Program\nfor Women, Infants and Children (WIC); child nutrition programs (National School Lunch, School Breakfast, Child \nand Adult Care, Summer Food Service and Special Milk); and food distribution programs (Schools, Emergency Food \nAssistance, Indian Reservations, Commodity Supplemental, Nutrition for the Elderly, and Charitable Institutions).\n\n\n- **Source Homepage:** [USDA Program Data Overview](https://www.fns.usda.gov/)\n- **Source Data:** [Food And Nutrition Service - US Department of Agriculture](https://www.fns.usda.gov/pd/overview)\n- **License:** [Public Domain U.S. Government](https://www.usa.gov/government-works/)\n- **Frequency:** yearly\n\n\n\n","terms_of_service":"https://rearc-data-public-assets.s3.amazonaws.com/Rearc_Data_DSA.pdf","documentation_link":"","privacy_policy_link":"https://rearc-data-public-assets.s3.amazonaws.com/Rearc+Privacy+Policy.pdf","embedded_notebook_file_infos":[{"id":"218480b7-e3f8-4f47-85d7-f1f7ea59e2f0","marketplace_file_type":"EMBEDDED_NOTEBOOK","file_parent":{"parent_id":"75fd204a-858b-4587-8c19-92b2fed8ac92","file_parent_type":"LISTING"},"mime_type":"text/html","download_link":"https://marketplace-file-storage-prod.s3-fips.us-east-1.amazonaws.com/EMBEDDED_NOTEBOOK/LISTING_75fd204a-858b-4587-8c19-92b2fed8ac92/218480b7-e3f8-4f47-85d7-f1f7ea59e2f0?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEGgaDnVzLWVhc3QtMS1maXBzIkYwRAIgbC8sWIoYaHR8y7Wq86nPb0pufBl4%2BM4PWpeN%2B5PW9VQCIG7BhAVBDCWTpxFDLeuKYm%2Ff1JLz4iccPMzter0YdhgWKqYECDAQABoMNDE0MzUxNzY3ODI2IgxNAJrHbXaZG20jNfMqgwQyGEb4TsoVOoNvJ%2BaA%2FZ7zeu8hGcHqCeNfblP%2FQo5g6q2aRNtpwtPRKMlhYcIE%2FUQaNT1Lqmyptu32VUQTpzjTTxRVmJrDPkheSCkV06XQ%2FpTMKjU8CC7X1b%2FqYf8CrBwZT7Cv4C5WPjEET38f2A79h0xV6bZgPy28zQWrSeIJ9w6AfZBxiE24%2FVc08zVA0ivCT6lsy7nuU0wcR%2FMg2C9nd1vaMnQ923VpD21CKfVVYcrNWL2b%2FQ%2BNAO%2BxPjPgjiN6UPQbvbvZxkSdhgrGtecIg9NgqGkMcvM5VLTDtdC1kdeZetfOC2obtA%2BT%2F0gy3tIDo6WM5hcQfw743%2FAU9oaGnRBl%2BwoxRRDCZA%2F4jnPdluICSI7NCr6C3HHOe4CSZJjRihczxPA%2FgQczAVjAPs6CrK9N9pIzt0vNJpGUTgg4AoxftTvGfciDREilSctPIcBk6GwB%2Bzt4wbtNCmEPU%2BFD46rc6CWVQmmLwbKOeHw70ipMVFlOc8NlXkEtGO2vu6H44MstSSc6daOVUYUavQSGZ6jGhEjsP2YGz8KDm9XQotB%2FtyVBqiZV01OKqDmmBJA%2FXjUpSJ8M%2FeJXhe5m%2BKcxzNObtoJRI7jci3Tpt08XZyXZ7%2FyIV8H30ya468X3cfkTXrm972uga3z7iXAdubyEHnQIua%2FuvtwOHILi6O5D1Me2MzCPvcnXBjqTAYDoMWBt%2B3AVFuqSs2dmDQNBnvEhXfMgTedKLD5PJHb3aIl15iBkLaNu36GYnVCp9ZU2lSYcTbY5QwNnNeTfJTNy9bKhwzru6Sg%2BslhN%2BTS0zkTn%2BeLMY4tHgPEtEZ2cp%2FCk6GwqswvnYVKHjwO8q%2B1Mx8lc08fVEfarE9dke1GGFqoddx%2Bq%2BZUyHbDlAVfrCmf0yw%3D%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20261110T000135Z&X-Amz-SignedHeaders=host&X-Amz-Expires=86400&X-Amz-Credential=ASIAWA6KKHEJORWBH4N3%2F20261110%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=50ef4658b6564e758a3efda7f8cdb5d3de6336d80d5704594c26cf10d697c235","created_at":1682531602080,"updated_at":1682531602080,"status":"FILE_STATUS_PUBLISHED"}],"assets":["ASSET_TYPE_DATA_TABLE"]},"provider_summary":{"name":"Rearc","description":"Rearc provides a vast library of data products for Healthcare, Financial Services, and ESG use cases. 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The quarterly additions include information about the changes/additions in the PTP edit published files for the current quarter.\n\n**Description**\nThis data package contains the Centers for Medicare and Medicaid Services (CMS) developed the National Correct Coding Initiative (NCCI) to promote national correct coding methodologies and to control improper coding that leads to inappropriate payment of Part B claims. The coding policies are based on coding conventions defined in the American Medical Association's Current Procedural Terminology (CPT) Manual, national and local Medicare policies and edits, coding guidelines developed by national societies, standard medical and surgical practice, and/or current coding practice. NCCI includes three types of edits: NCCI Procedure-to-Procedure (PTP) edits, Medically Unlikely Edits (MUE), and Add-on Code Edits. NCCI PTP edits prevent inappropriate payment of services that should not be reported together. Each edit has a column one and column two HCPCS/CPT code. If a provider reports the two codes of an edit pair for the same beneficiary on the same date of service, the column one code is eligible for payment but the column two code is denied unless a clinically appropriate NCCI-associated modifier is also reported. The additions, deletions, and modifier indicator quarterly changes to PTP column 1/ column 2 correct coding edits and the PTP mutually exclusive code edits for Hospital Outpatient Prospective Payment System (PPS) in the Outpatient Code Editor as well as physicians/practitioners are updated on CMS website.\n\n**Benefits**\n- Useful for individuals operating within and working according to prospective payment system, healthcare professionals, healthcare providers, suppliers and medical research groups. national correct coding initiative database is available on centers for medicare & medicaid services website in raw form, whereas a john snow labs (jsl) customer gets it in a clean and normalized form ready to use and create his own custom reports. easy to comprehend for the customers who are unfamiliar with the medical terminologies as all abbreviated terms are replaced with full form, unlike centers for medicare & medicaid services (cms) original datasets which have many abbreviations. recent dataset for 2018 is available for use.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [Quarterly NCCI PTP Edits for Outpatient Hospitals](https://www.johnsnowlabs.com/marketplace/quarterly-ncci-ptp-edits-for-outpatient-hospitals)\n\t- The National Correct Coding Initiative, the procedure to procedure (PTP) edits, for outpatient hospitals, quarterly additions contain the information about the changes/additions in the PTP edit published files for the current quarter.\n- [Quarterly NCCI PTP Edits for Physicians or Practitioners](https://www.johnsnowlabs.com/marketplace/quarterly-ncci-ptp-edits-for-physicians-or-practitioners)\n\t- The National Correct Coding Initiative, the procedure to procedure (PTP) edits, for physicians/practitioners, quarterly additions contain the information about the changes/additions or deletions in the PTP edit published files for the current quarter.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 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Datasets are cleaned, standardized, and labeled before delivery for immediate use.\r\n\r\n*Note: Our data was previously listed under our sister company and commercial partner, WiserBrand, which you may see mentioned in older reviews."},"popularity":1003},{"id":"b3ef93f3-10dd-4456-9f66-cda1c9e00ae5","summary":{"name":"SAMPLE B2B Marketing Data | B2B Leads Data | Global Coverage | 175M+ Records | Decision Makers, ...","subtitle":"Access 175M+ verified B2B decision-maker contacts with direct dials, work emails, and 20+ firmographic data points. ...","share":{"name":"exellius_systems_49d5a991_8d7b_4b49_8a59_95c27cf72532_samples_share","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1706552889570,"updated_at":1775224587530,"categories":["OTHER"],"listingType":"STANDARD","provider_id":"3c844684-0836-4f38-bbd9-35304ff66675"},"detail":{"description":"Global B2B Marketing Data | Verified Business Contact Database | Decision-Maker Emails & Company Intelligence\n\n- High-Performance B2B Marketing Data for Sales, ABM & Revenue Teams\n\nOur B2B Marketing Data delivers structured, segmentation-ready business intelligence designed for outbound sales, account-based marketing (ABM), demand generation, and revenue operations. 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Buyers are responsible for ensuring usage aligns with applicable regulations including GDPR, CCPA, CAN-SPAM, CASL, and related data protection frameworks.\n\nIntended strictly for legitimate B2B outreach and professional use.\n\n---------------------------------------------------------------------------------------------\n\n- Why Teams Choose This B2B Marketing Data\n\n* Segmentation-ready structure\n* Decision-maker contact access\n* Global coverage across industries\n* Multi-attribute filtering capabilities\n* Compatible with modern sales and marketing stacks\n* Designed for scalable revenue operations","terms_of_service":"https://www.exellius.com/privacy-policy","contact_email":"marketplace@datarade.ai","privacy_policy_link":"https://www.exellius.com/privacy-policy","assets":["ASSET_TYPE_DATA_TABLE"]},"provider_summary":{"name":"Exellius Systems","description":"Exellius Systems is a global data solutions provider helping businesses scale their outreach with high-quality, targeted datasets.\r\n\r\nWe offer specialized data solutions, including Targeted, Installbase, Role-based, Account-based, and Partner-based datasets—designed to support precise go-to-market strategies.\r\n\r\nOur database spans 160M+ global records, with verified email addresses and direct dial numbers, enabling teams to connect with the right decision-makers efficiently.\r\n\r\nWe prioritize data accuracy and compliance, following strict sourcing and validation processes aligned with GDPR standards.\r\n\r\nTo ensure reliability, we also offer a 100% replacement guarantee on undelivered or invalid data.\r\n\r\nAt Exellius Systems, our focus is simple: deliver accurate, actionable data that drives measurable business outcomes."},"popularity":208},{"id":"65d890bb-7d1f-4038-a588-fbf3cefa7dcc","summary":{"name":"Dallas Residential Property Listing SAMPLE","subtitle":"Dallas property dataset for AI & analytics — 5,430 listings for price, market, and housing trend insights. 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This facilitates better customer retention, customer acquisition, cross-sell or up-sell, and engagement rates.\n\nHealth & Wellness Attributes – Offline can be used to enrich CRM data by appending attributes to existing individuals in order to learn more about your customers.\n\nHealth & Wellness Attributes – Offline can be used for your own modeling processes.\n\nHealth & Wellness Attributes – Offline can be used to analyze health trends and social determinants of health across different geographies and time periods.\n\n\nGeographic coverage | United States of America\n\nData Population Level | Individual or Household\n\nNumber of individuals/households covered | 257+ million \nindividuals & 120+ million households\n\nRaw or scraped data | Raw Data\nKey Fields | BMI, Exercise Level & Types, Alternative Medicine Interests\nData Source(s) | Our data is created in an offline process but leverage offline and online data and behaviors. The vast majority of our database is proprietary although a few publicly available data sources are leveraged in its development AnalyticsIQ sources data from over 100 sources. These are predominantly public sources including: *Core Demographic data from multiple sources; Census Block and Block Group level data; Econometric data from the US government; Summarized credit data from multiple credit bureaus; Property and mortgage information from county courthouses; Occupation information from state licensing boards; Past purchase behavior from catalogers and retailers that contribute their data at a category level.* AnalyticsIQ is not an original compiler as the data above is readily available for purchase out in the market. However, AnalyticsIQ uses superior analytics to make our data best-in-class. One tool that is completely unique to AnalyticsIQ’s product development process is our proprietary survey data. 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This meticulously compiled dataset, sourced from official statistical sources, including [Federal Statistical Office](https://www.bfs.admin.ch/bfs/en/home.html), offers a detailed insight into the population landscape of Switzerland at the municipality (communes) level. Whether you are a researcher, business analyst, or policymaker, this dataset provides invaluable insights for various applications.\nIf you require data at a less granular level, such as an Uber grid or square grid, please don't hesitate to get in touch with us. 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No lengthy sign-up processes or NDA agreements required on our side – just download the data you need.\n* **Data Samples for Evaluation:** To ensure you're getting exactly what you need, we provide free downloadable samples in CSV format for data evaluation.\n* **Data accuracy:** Rest assured, our datasets are created using the latest demographics data available in national statistics bureaus, guaranteeing accuracy and reliability.\n* **Best-in-Class Quality:** We guarantee best-in-class quality, backed by over two decades of experience in modeling demographics data and an outstanding quality management process.\n* **Competitive Pricing:** We maintain the lowest prices on the market, ensuring that you get the most cost-effective demographics data acquisition process.\n---\nAdditionally, for those looking to enrich their analysis or project, we offer:\n* **[Administrative Boundaries](https://geolocet.com/products/switzerland-all-available-administrative-boundaries):** If you require geographical boundaries (polygons) for the municipalities of Switzerland, consider exploring our Admin Boundaries product, which seamlessly integrates with this dataset.\n\n* **[Point of Interest (PoI) Data](https://geolocet.com/pages/aggregated-points-of-interest-poi-data):** If you're interested in Points of Interest data, please don't hesitate to contact us for customized datasets to enhance your geographical analysis.\n\n* **[Data Customization](https://geolocet.com/pages/data-services-by-geolocet):** We understand that your project may have specific requirements. Feel free to get in touch with us if you need any modifications or specific data formats such as Excel, JSON, GeoJSON, or Shapefile. We are here to tailor the data to your needs.\n\nIf you're looking for data for Switzerland, [look here](https://geolocet.com/pages/switzerland) for off-the-shelf or tailor-made datasets to suit your specific requirements.\n\nMake informed decisions, conduct precise research, and unlock the potential of Switzerland's demographic landscape with our comprehensive dataset. Trust in the accuracy and reliability of our official statistical data to drive your projects forward.\nContact us at *contact@geolocet.com* today to access this invaluable resource and explore Switzerland census data like never before.\n\n[Geolocet LTD](https://geolocet.com)\n","terms_of_service":"https://geolocet.com/pages/terms-of-use","documentation_link":"","privacy_policy_link":"https://geolocet.com/pages/privacy-policy","geographical_coverage":"[\"Europe\"]","cost":"PAID","data_source":"","assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://geolocet.com/pages/terms-of-use"},"provider_summary":{"name":"Geolocet LTD","description":"Geolocet provides premium geospatial data for retail analysis, GIS, and market planning. We equip businesses with accurate, ready-to-query data to drive spatial analysis, site selection, and customer segmentation.\n\nCore Offerings:\n\nDemographics: Socioeconomic data covering population, age, and employment across diverse geographies.\n\nPoints of Interest (PoI): Datasets spanning retail, healthcare, and transport for catchment analysis.\n\nAdministrative Boundaries: GIS-ready boundaries, including municipalities and postcodes, for territory management.\n\nBrand Locations: Chain location datasets to monitor store networks and market expansion.\n\nWealth Data: Granular economic indicators like purchasing power to identify high-potential markets.\n\nWe source from trusted providers, delivering highly customizable datasets with comprehensive coverage across Europe and worldwide."},"popularity":619},{"id":"e30b927e-7873-4a4e-8a0b-af2db1810f1d","summary":{"name":"Geolocet | Latest Demographic Data Jersey - 36 attributes ","subtitle":"Demographics data for the 12 municipalities (parishes) of Jersey - age, gender, education, and household size. ","setting":{"visibility":"PUBLIC"},"created_at":1708500443399,"updated_at":1708500470667,"categories":["DEMOGRAPHICS","ADVERTISING_AND_MARKETING","PUBLIC_SECTOR","RETAIL","GEOSPATIAL"],"listingType":"PERSONALIZED","provider_id":"5d3ddc17-a7e8-4794-a760-13587e1de2c6"},"detail":{"description":"## **Get instant access to the latest Demographics Data for Jersey with [Geolocet](https://geolocet.com)**\n\nExplore the richness of Jersey’s demographic landscape with our Jersey Demographics Data. This meticulously compiled dataset, sourced from official statistical sources, offers a detailed insight into the population landscape of Jersey at the municipality (parish) level. \n\nIf you require data at a less granular level, such as an Uber grid or square grid, please don't hesitate to get in touch with us at *contact@geolocet.com*. We understand that different projects may have varying data needs, and we are here to tailor our data to match your specific requirements.\n\n ## **Key Features:**\n---\n* **Population Data Jersey:** Gain access to the latest and most accurate population statistics for Jersey, meticulously organized by municipality (parish).\n\n* **Demographic Insights:** Dive deep into Jersey’s demographic landscape with data on 36 attributes, including: \n* Total population,\n* Gender,\n* Age bands,\n* Education,\n* Household size.\n\nAll demographic attributes are available at the municipal (parish) level.\n\n* **Geodemographic Insights:** Unlock geodemographic data Jersey-wide to inform location-based decision-making, marketing strategies, and policy formulation.\n* **Seamless Data Access:** Our data is available for instant download in CSV format. No lengthy sign-up processes or NDAs required on our side – just download the data you need.\n* **Data Samples for Evaluation:** To ensure you're getting exactly what you need, we provide free downloadable samples in CSV format for data evaluation.\n* **Data accuracy:** Our datasets are created using the latest demographics data available in national statistics bureaus, guaranteeing accuracy and reliability.\n* **Best-in-Class Quality:** We guarantee best-in-class quality, backed by over two decades of experience in modeling demographics data and an outstanding quality management process.\n* **Competitive Pricing:** We maintain the lowest prices on the market, ensuring that you get the most cost-efficient demographics data acquisition process.\n---\nAdditionally, for those looking to enrich their analysis or project, we offer:\n* **[Administrative Boundaries](https://geolocet.com/pages/administrative-boundaries-data-for-european-countries):** If you require geographical boundaries (polygons) for the municipalities of Jersey, consider exploring our Administrative Boundaries product, which seamlessly integrates with this dataset.\n\n* **[Point of Interest (PoI) Data](https://geolocet.com/pages/aggregated-points-of-interest-poi-data):** If you're interested in Points of Interest data, please don't hesitate to contact us for customized datasets to enhance your geographical analysis.\n\n* **[Data Customization](https://geolocet.com/pages/data-services-by-geolocet):** We understand that your project may have specific requirements. Feel free to get in touch with us if you need any modifications or specific data formats such as Excel, JSON, GeoJSON, or Shapefile. We are here to tailor the data to your needs.\n\nMake informed decisions, conduct precise research, and unlock the potential of the Jersey demographic landscape with our comprehensive dataset. Trust in the accuracy and reliability of our official statistical data to drive your projects forward.\nContact us at *contact@geolocet.com* today to access this invaluable resource and explore Jersey census data like never before.\n\n[Geolocet LTD](https://geolocet.com)\n\n","terms_of_service":"https://geolocet.com/pages/terms-of-use","documentation_link":"","privacy_policy_link":"https://geolocet.com/pages/privacy-policy","cost":"PAID","assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://geolocet.com/pages/terms-of-use"},"provider_summary":{"name":"Geolocet LTD","description":"Geolocet provides premium geospatial data for retail analysis, GIS, and market planning. We equip businesses with accurate, ready-to-query data to drive spatial analysis, site selection, and customer segmentation.\n\nCore Offerings:\n\nDemographics: Socioeconomic data covering population, age, and employment across diverse geographies.\n\nPoints of Interest (PoI): Datasets spanning retail, healthcare, and transport for catchment analysis.\n\nAdministrative Boundaries: GIS-ready boundaries, including municipalities and postcodes, for territory management.\n\nBrand Locations: Chain location datasets to monitor store networks and market expansion.\n\nWealth Data: Granular economic indicators like purchasing power to identify high-potential markets.\n\nWe source from trusted providers, delivering highly customizable datasets with comprehensive coverage across Europe and worldwide."},"popularity":73},{"id":"38535b50-6c4d-4c6a-8fe4-e4f39b9fc438","summary":{"name":"SAMPLE Path IQ App Session Data | 34 Geos | App Usage, Open activity, App analytics | Mobile ...","subtitle":"Daily-refreshed MAID-based app activity data covering installs, in-app events, and IAP spend — tied to device ID ...","share":{"name":"path_iq_fa390246_d118_4f75_bfce_e508330b8709_samples_share","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1780031553759,"updated_at":1780031553759,"categories":["OTHER"],"listingType":"STANDARD","provider_id":"4aaea6e0-2356-468e-aa3c-cca01d5fda3f"},"detail":{"description":"Path IQ's App Activity SDK data delivers daily-refreshed, MAID-based mobile signals captured at the app event level — giving you a deterministic view of how real users install, engage with, and spend within apps across 20 markets.\n\nEach record is anchored to a persistent device identifier (IDFA or ADID) and enriched with a full device and network profile, making it immediately actionable for audience targeting, attribution validation, and behavioural analytics.\nWhat's included per event:\n\nApp identity — App name and bundle ID (iTunes App ID for iOS; com.* Bundle ID for Android)\nActivity type — Install events and post-install in-app event names, so you can distinguish acquisition from engagement\nIAP spend — USD spend amount per event, available for in-app purchase actions\nDevice fingerprint — Device make, model, marketing name, OS, OS version, hardware type, and user agent\nIdentity & location — IDFA/ADID, IP address, and country code\nConsent signal — IAB US Privacy String included per record\n\nUse cases:\n\nMobile audience targeting — Build and enrich MAID-based segments from real app behaviour\nApp analytics & competitive intelligence — Understand install trends and engagement across your category\nAttribution & fraud prevention — Cross-reference device signals to validate installs and flag anomalies\nCredit & risk scoring — App usage patterns as a behavioural proxy for financial modelling\n\nAvailable in 20 markets: AE, AU, BR, CO, HK, TR, ID, IN, JP, KR, LK, SA, MX, MY, NZ, PH, SG, TH, TW, VN","terms_of_service":"https://event.kindred.co/privacy-policy#:~:text=Kindred%20does%20not%20collect%20data,insights%20and%20audience%20curation%20services.","contact_email":"marketplace@datarade.ai","privacy_policy_link":"https://event.kindred.co/privacy-policy#:~:text=Kindred%20does%20not%20collect%20data,insights%20and%20audience%20curation%20services.","assets":["ASSET_TYPE_DATA_TABLE"]},"provider_summary":{"name":"Path IQ","description":"PathIQ delivers 2B+ verified audience profiles across 35 markets — purchase intent, location, telco, demographic, and behavioural signals in a single stack."},"popularity":1084},{"id":"aa4e4dfc-b836-428b-a5a9-e4147500bfcd","summary":{"name":"Trucost ESG Financial and Market Data","subtitle":"Provides key metrics for public entities in the Trucost universe with environmental emissions data.","setting":{"visibility":"PUBLIC"},"created_at":1761129283921,"updated_at":1761129379839,"categories":["CLIMATE_AND_ENVIRONMENT","FINANCIAL"],"listingType":"PERSONALIZED","provider_id":"bb123ba0-f713-4a39-8c51-2daca088d1de"},"detail":{"description":"The Trucost ESG Financial and Market data offering provides key standardized financial and market data metrics for public entities in the Trucost universe with environmental emissions data. The data is available for 15,000+ companies with historical annual data available as far back as 2002.\n\nThis dataset can help ESG data users achieve multiple objectives, including:\n\n- Creating transparency for stakeholders\n- Managing financial risks associated with climate policies and regulations\n- Helping with reporting frameworks like PCAF\n- Aligning financial flows with the goals of the Paris Agreement\n- Measuring the financed emissions associated with companies' loans and investments","terms_of_service":"https://www.spglobal.com/en/terms-of-use","documentation_link":"https://www.marketplace.spglobal.com/en/datasets/trucost-esg-financial-market-data-(265)","privacy_policy_link":"https://www.spglobal.com/en/privacy/privacy-policy-english","geographical_coverage":"[\"Global\"]","cost":"PAID","data_source":"Publicly available documents, company filings, annual reports, press releases","assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"S&P Global Market Intelligence","description":"At S&P Global Market Intelligence, we understand the importance of accurate, deep and insightful information. Our team of experts delivers unrivaled insights and leading data and technology solutions, partnering with customers to expand their perspective, operate with confidence, and make decisions with conviction."},"popularity":236},{"id":"b7bc63d6-9beb-4812-9c2a-edd30acdacda","summary":{"name":"BatchData | US Real Estate Listings | Active, Pending & Sold | Nationwide MLS Data | Agent Details","subtitle":"Real-time US listing data: Active/Sold status, price history, and agent info. Visit www.batchdata.io for market data.","setting":{"visibility":"PUBLIC"},"created_at":1771433869104,"updated_at":1771433869104,"categories":["ADVERTISING_AND_MARKETING","DEMOGRAPHICS","ECONOMICS","FINANCIAL","TRANSPORTATION_AND_LOGISTICS","OTHER"],"listingType":"PERSONALIZED","provider_id":"43cf0092-09d4-4dd5-afe7-d9be9a3a8d74"},"detail":{"description":"Gain a competitive edge with BatchData’s US Real Estate Listings Data. Visit www.batchdata.io for real-time insights on Active, Pending, and Sold properties. This dataset aggregates MLS signals nationwide to provide a live view of market supply, demand, and pricing dynamics.\n\nSchemas, Tables, and Columns\nA standardized view of the active market. Key columns include:\n\nListing Status: listing.status (Active, Sold, Pending), listing.listPrice, listing.soldPrice, listing.daysOnMarket (DOM).\n\nAgent & Broker: listing.agentName, listing.agentEmail, listing.brokerageName, listing.officePhone.\n\nTimeline: listing.listDate, listing.soldDate, listing.priceChangeHistory, listing.contractDate.\n\nProperty Details: listing.description, listing.hoaFee, listing.appliances, listing.photos (URLs).\n\nUse Cases\n\nComparative Market Analysis (CMA): Price properties accurately using recent sales and active competition.\n\nInvestment Scouting: Spot price drops and \"motivated seller\" signals instantly.\n\nAgent Recruitment: Identify high-volume agents and top-performing brokerages for recruitment.\n\nSources\nAggregated from Multiple Listing Services (MLS), broker syndication feeds, and public real estate portals.","terms_of_service":"https://batchdata.io/terms-of-service","documentation_link":"https://developer.batchdata.com/","privacy_policy_link":"https://batchdata.io/privacy-policy","geographical_coverage":"[\"United States and Canada\"]","cost":"PAID","update_frequency":{"interval":1,"unit":"DAILY"},"collection_granularity":{"interval":1,"unit":"DAILY"},"data_source":"","assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"BatchData","description":"BatchData is a real estate technology company providing a comprehensive data platform and APIs for property and owner information, helping businesses with lead generation, skip tracing, data enrichment, and workflow automation in the real estate, proptech, and home services industries."},"popularity":970},{"id":"f07554aa-8487-45f9-a726-a0c76660650e","summary":{"name":"USA Purchase Intent Data | KeyWord Search | 150M Daily Hashed Emails","subtitle":"Daily Signal Volume - 150+ million distinct HEMs per day with 65-70 million successfully matched to our Universal ...","share":{"name":"bigdbm_889ae783_1bcb_45cb_95db_3bb04211729f_samples_share","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1764259148988,"updated_at":1780306275398,"categories":["ADVERTISING_AND_MARKETING","OTHER"],"listingType":"PERSONALIZED","provider_id":"7624320d-dddb-4b09-abe2-92d0a7630860"},"detail":{"description":"BigDBM's purchase Intent Data transforms how businesses understand and engage with their customers by providing a comprehensive, real-time view of buyer purchase intent data across both US consumer and B2B markets. With over 20 years of expertise in building identity graphs, our platform processes more than 110 million distinct hashed email addresses daily, delivering actionable insights that drive measurable ROI. \n\nOur proprietary methodology combines data from multiple live streams and maps website domains and emails to IAB classification codes, giving you structured insights into market interests and purchase intent. Through advanced natural language processing and a custom five-tiered taxonomy, we extract granular keywords while maintaining broad category classification for flexible targeting. \n\nOur unique intent intensity scoring quantifies purchase likelihood based on frequency and consistency of interest, while timestamp tracking reveals behavioral shifts and trends over time. With robust privacy compliance and ethical sourcing practices, BigDBM enables organizations to make smarter, faster decisions across customer acquisition, retention, and engagement through applications in account-based marketing, audience expansion, data enrichment, and programmatic advertising.\n\n** We do not provide any phone details from Colorado residents.","terms_of_service":"https://bigdbm.com/privacy.html","contact_email":"support@datarade.ai","privacy_policy_link":"https://bigdbm.com/privacy.html","assets":["ASSET_TYPE_DATA_TABLE"]},"provider_summary":{"name":"BIGDBM","description":"BigDBM is a leading US data provider with 7+ years of experience building identity graphs with a focus on ROI, privacy, and quality.\r\n\r\nUnlock significant value in your marketing campaigns, lead generation strategies, and identity verification workflows with our US consumer and B2B datasets.\r\n\r\nUtilize the self-service BigDBM Data Market for easy and affordable audience/list generation and custom appends.\r\n\r\nIdentify website visitor traffic using our WeVi product suite of real-time data collection via pixels and real-time identity resolution APIs.\r\n\r\nPopular products:\r\n- Telecom-verified phone numbers>consumers\r\n- IP>consumer and IP>company domain linkages\r\n- Verified consumer emails\r\n- Consumer and B2B intent\r\n- Consumer demographics and behavioral affinities\r\n- Residential and commercial property owners and contact information\r\n- MAID>consumer linkages"},"popularity":923},{"id":"9cbecac9-d744-4b93-86b7-1d2a3fc9b31d","summary":{"name":"SAP BDC Data Share App","subtitle":"Use a Databricks app to manage the full lifecycle of Databricks → SAP BDC shares: publish, monitor, and unpublish!","setting":{"visibility":"PUBLIC"},"created_at":1778947731423,"updated_at":1781001127319,"categories":["OTHER","OPEN_SOURCE"],"listingType":"STANDARD","provider_id":"ea1e69ff-0127-4c94-bf39-e841fe1d19d2"},"detail":{"description":"**Overview**\n**The SAP BDC Data Share App** automates the full lifecycle of publishing a Unity Catalog share to SAP Business Data Cloud (BDC Connect) — both the initial publish (GRANT SELECT ON SHARE → BDC SDK calls → CSN schema upsert → data product publish) and the eventual unpublish (BDC catalog cleanup → recipient access removal → REVOKE SELECT ON SHARE).\n\nThe traditional notebook-based workflow is collapsed into a guided UI: \n\n1. pick a share\n2. pick a recipient\n3. click publish\n4. or click delete on a previously published pair to retire it.\n\nThe app handles the operational details **a notebook user typically gets wrong**: \n- it auto-detects shares that have already been published to a recipient\n- blocks object types BDC doesn't support (views, materialized views)\n- prompts for primary keys on tables that don't define one in Unity Catalog\n- runs the privilege checks against the calling user's identity. \n\n\nEvery publish, retry, and delete is recorded in a configurable **Delta-table audit log** with the user, share, recipient, run ID, and any error message — surfaced in an in-app Activity tab and queryable from any DBSQL editor. \n**Workspace administrators don't need to set anything up**: a one-time in-app setup wizard provisions the audit table and grants on first use.\n\n**Use cases**\n\n- **Self-serve cross-platform sharing.** A data owner in a hybrid SAP + Databricks org publishes a Lakehouse share into BDC without filing a ticket, opening a notebook, or asking for workspace-admin rights.\n\n- **Retiring a share cleanly.** When a data product is superseded or a recipient relationship ends, one-click unpublish coordinates the BDC catalog removal and the Delta Sharing revoke in the right order. The Databricks share itself is preserved — only the BDC-side publication and the per-recipient grant are reversed.\n\n- **Compliance and audit.** Centralizes \"who shared (and unshared) what with whom\" into a Delta table you can query, dashboard, or feed into your existing access-review process.\n\n- **Onboarding shares to BDC at scale.** Cross-filters shares vs. recipients live, marks already-published pairs with a checkmark, and disables already-shared recipients inline.\n\n**Product details**\nThe listing deploys two components into your Databricks workspace via an Asset Bundle:\n\n- **Databricks App `bdc-sharing`**— Node/Express backend + static-HTML UI. Surfaces four pages: a publish/unpublish wizard, an activity log, an in-product FAQ, and a docs page.\n\n- **Two parameterized notebooks** invoked by the app at runtime via Jobs 2.2:\n\n - *bdc_publish* — create_or_update_share + CSN schema generation + primary-key annotation + publish_data_product.\n - *bdc_unpublish* — delete_share (removes BDC catalog entry, ORD descriptor, CSN, and BDC-side recipient access).\n\n**Capabilities included:**\n- Hybrid authentication: SQL/UC operations (SHOW SHARES, GRANT SELECT ON SHARE, REVOKE SELECT ON SHARE, DESCRIBE TABLE) execute as the calling user via OBO; warehouse and Jobs control run as the app's service principal.\n- Fail-safe delete ordering: BDC-side cleanup completes before the Databricks REVOKE runs, so consumers don't lose access mid-read if the BDC API fails.\n- Delta-backed activity log with event types covering both flows: publish_submitted, publish_grant_failed, publish_job_succeeded, publish_job_failed, delete_submitted, delete_job_succeeded, delete_job_failed, delete_finalized, delete_finalize_failed.\n- Optional admin-group filter (BDC_ACTIVITY_ADMIN_GROUP) — admins see all users' activity, others see only their own.\n- In-app setup wizard for first-run audit-log provisioning (no databricks.yml editing needed).\n- Self-introspection: discovers its own deployment path via the Apps API, so the same source works under any Databricks Asset Bundle target.\n\nFor full architecture details, refer to the embedded notebooks and the in-app FAQ page.\n\n**Additional Insights**\n\n- **[SAP Business Data Cloud (BDC Connect)](https://help.sap.com/docs/business-data-cloud)** — official SAP documentation\n- **[Databricks Delta Sharing](https://docs.databricks.com/en/delta-sharing)** — open protocol underlying the share/recipient model\n\n- **[Databricks Apps](https://docs.databricks.com/en/dev-tools/databricks-apps)**— runtime that hosts this accelerator\n- **[`sap-bdc-connect-sdk`](https://pypi.org/project/sap-bdc-connect-sdk)** — the Python client used by the publish notebook, distributed by SAP via PyPI\n- **[Source repository](https://github.com/databricks-industry-solutions/sap-bdc-publisher)**\n- **Recipient auth-type prerequisite** — the accelerator works out-of-the-box with OIDC_FEDERATION-type recipients (the default produced by SAP's BDC onboarding flow). TOKEN-type recipients require a one-time workspace bootstrap; details are in the in-app FAQ.","terms_of_service":"https://cms.databricks.com/sites/default/files/2023-01/marketplace-consumer-terms_0.pdf","documentation_link":"https://github.com/databricks-industry-solutions/sap-bdc-publisher/blob/main/README.md","privacy_policy_link":"https://www.databricks.com/legal/privacynotice","assets":["ASSET_TYPE_APP"],"license":"https://github.com/databricks-industry-solutions/sap-bdc-publisher/blob/main/LICENSE.md"},"provider_summary":{"name":"Databricks","description":"As the world’s first lakehouse platform in the cloud, Databricks combines the best elements of data lakes and data warehouses to deliver the reliability, strong governance, and performance of data warehouses with the openness, flexibility, and machine learning support of data lakes. We are a cross-cloud platform that works across AWS, Azure, and Google.\nThis unified approach simplifies your modern data stack by eliminating the data silos that traditionally separate and complicate data engineering, analytics, BI, data science and machine learning. It’s built on open source and open standards to maximize flexibility. And, its common approach to data management, security and governance helps companies operate more efficiently and innovate faster."},"popularity":2126},{"id":"11f80117-9cc6-46b2-bc36-c4ccca321b00","summary":{"name":"Accelerate Procurement Efficiency with SAP Procure-to-Pay Modernization","subtitle":"Enterprise research on integrating SAP ERP data with Databricks for secure, scalable P2P analytics and AI-driven insight","setting":{"visibility":"PUBLIC"},"created_at":1772515715275,"updated_at":1779858087560,"categories":["FINANCIAL","RETAIL","MANUFACTURING","ENERGY"],"listingType":"PERSONALIZED","provider_id":"3e1fd420-8722-4ebc-abaa-79f86ceffda0"},"detail":{"description":"Contact us at: chandra.reddy@dataplatr.com\nFor consultations or custom inquiries: https://dataplatr.com/contact-us\n\n**SAP to Databricks Integration Accelerator**\n\n**High-Performance SAP-to-Databricks Integration with BryteFlow, CData Sync & Fivetran\nConverting Raw ERP Transactions into Business-Ready Gold Tables for Advanced Analytics**\n\nModern enterprises rely on SAP ERP systems (ECC, S/4HANA, BW) to manage mission-critical operations across Procure-to-Pay (P2P), Accounts Payable (AP), Materials Management (MM), Purchase Orders (PO), Goods Receipts and Supplier Master data. However, unlocking this data for scalable analytics, AI and real-time reporting requires a robust and compliant integration strategy. **The Dataplatr ELT Accelerator provides a structured, enterprise-ready framework for securely ingesting, transforming and optimizing SAP data within the Databricks Lakehouse**. This solution ensures high-performance replication, governance compliance, cost optimization and analytics-ready transformation at scale.\n\n**Business Challenges**\n\n- **Extraction & Compliance Restrictions** : Recent SAP policy changes (e.g., ODP RFC limitations) restrict traditional extraction mechanisms, requiring compliant OData-based or approved integration methods.\n- **Performance & Volume Constraints** : OData extraction is slower than log-based replication and High-frequency CDC can overload SAP systems\n- **Complex SAP Data Structures** : Deeply nested hierarchies, Mix of technical codes and business descriptions and Strong inter-table dependencies across P2P modules\n- **Fragmented Operational Data** : Procurement, finance, inventory and supplier data often operate in silos, making end-to-end analytics difficult without centralized transformation logic.\n- **Governance & Security Requirements** : Secure authentication, access control, lineage tracking and compliance monitoring must be enforced across both SAP and Databricks environments.\n\n**Dataplatr ELT Solution**\nThe Dataplatr ELT Accelerator provides a scalable integration framework leveraging:\n- **BryteFlow** : High-throughput, enterprise-grade CDC and real-time SAP data replication at scale\n- **CData Sync** : Secure, policy-compliant API/OData-based extraction with automated schema mapping capabilities\n- **Fivetran** : Fully managed cloud connectors for reliable, scalable SAP-to-cloud data ingestion\nThese tools enable compliant extraction while Databricks manages transformation and analytics through a structured Medallion architecture.\n\n**Medallion Architecture Implementation\nBronze Layer – Raw Ingestion**\n- **Ingest SAP transactional and master** data for secure and scalable replication\n- **Capture both full data loads and incremental Change Data Capture (CDC)** streams for continuous synchronization\n- **Maintain detailed source metadata, ingestion timestamps, lineage tracking and audit columns** for governance and traceability\n- **Store raw, immutable datasets in Delta Lake** to ensure reliability, scalability and version control\n\n**Silver Layer – Standardized & Cleansed**\n- **Normalize complex SAP data structures** into analytics-ready relational formats\n- **Harmonize schema across ECC and S/4HANA modules** to ensure consistency and cross-functional reporting\n- **Apply incremental merge logic** using optimized Delta Lake strategies to improve performance and reduce compute costs\n- **Enforce robust data quality checks** and **validation rules using Delta Live Tables (DLT)** for reliability and governance\n\n**Gold Layer – Business-Ready Insights**\n- **Curate complete P2P analytics tables** including PO cycle time, invoice aging, payment performance and supplier scorecards\n- **Deliver standardized inventory and procurement KPIs** for operational visibility and performance tracking\n- **Provide AI-ready, feature-engineered datasets** optimized for forecasting, anomaly detection and predictive analytics\n- **Enable seamless integration with BI dashboards, advanced analytics tools** and **machine learning** workloads on the Databricks Lakehouse\n\n**Key Features & Deliverables\nCore Capabilities**\n\n- **Delta Live Tables (DLT)** pipeline orchestration for automated transformation, validation, and monitoring\n- **Optimized Delta MERGE strategies** designed to improve performance and reduce compute costs\n- **Unity Catalog-based governance and access control** enabling fine-grained security, lineage tracking, and enterprise compliance\n\n**Deliverables**\n\n- **Production-ready Bronze, Silver and Gold notebooks** for end-to-end SAP ELT implementation on Databricks\n- **Pre-built SAP P2P object mappings** covering PO, AP, MM, Goods Receipt and Supplier Master data\n- **DLT SQL files for incremental transformations** with optimized merge and CDC handling logic\n- **Complete CDC orchestration framework** for reliable, real-time and 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integrity.\n\n* Regular refresh cycles\n* Email verification processes applied\n* Ongoing cleansing and deduplication\n* Replacement support for invalid records (subject to agreement)\n\nOur objective is operational usability — data that converts into conversations.\n\n---------------------------------------------------------------------------------------------\n\n- Compliance & Privacy Framework\n\nWe prioritize responsible B2B data usage.\n\n* Sourced from publicly available and business-related data sources\n* Structured for professional B2B communication purposes\n* Designed to support GDPR, PDPA, CAN-SPAM, and relevant APAC regulations\n* Opt-out and suppression workflows available upon request\n\nWe encourage compliant outreach practices aligned with regional regulations.\n\n---------------------------------------------------------------------------------------------\n\n- Delivery & Commercial Model\n\n* Delivered in CSV or Excel format\n* Custom volume packages available\n* 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Success in this market requires accurate targeting, verified contact channels, and scalable outreach infrastructure.\n\nExellius Systems equips your sales and growth teams with the contact intelligence required to accelerate expansion across APAC.\n\nContact us to request a sample dataset or discuss your targeting criteria.","terms_of_service":"https://www.exellius.com/privacy-policy","contact_email":"marketplace@datarade.ai","privacy_policy_link":"https://www.exellius.com/privacy-policy","assets":["ASSET_TYPE_DATA_TABLE"]},"provider_summary":{"name":"Exellius Systems","description":"Exellius Systems is a global data solutions provider helping businesses scale their outreach with high-quality, targeted datasets.\r\n\r\nWe offer specialized data solutions, including Targeted, Installbase, Role-based, Account-based, and Partner-based datasets—designed to support precise go-to-market strategies.\r\n\r\nOur database spans 160M+ global records, with verified email addresses and direct dial numbers, enabling teams to connect with the right decision-makers efficiently.\r\n\r\nWe prioritize data accuracy and compliance, following strict sourcing and validation processes aligned with GDPR standards.\r\n\r\nTo ensure reliability, we also offer a 100% replacement guarantee on undelivered or invalid data.\r\n\r\nAt Exellius Systems, our focus is simple: deliver accurate, actionable data that drives measurable business outcomes."},"popularity":975},{"id":"224f4e48-8a02-43fb-8c6b-7065a08097a1","summary":{"name":"Genie Benchmarking Accelerator","subtitle":"Generate, execute, and analyze comprehensive benchmark test cases to stress test Databricks Genie spaces before business","setting":{"visibility":"PUBLIC"},"created_at":1780471036499,"updated_at":1780587887539,"categories":["FINANCIAL","ENERGY","HEALTH","MANUFACTURING","MEDIA","RETAIL","TRANSPORTATION_AND_LOGISTICS","TRAVEL_AND_TOURISM","EDUCATION"],"listingType":"PERSONALIZED","provider_id":"6ff41570-2140-4c32-9b1b-ffc962b2b744"},"detail":{"description":" **Overview**\n\n**Genie Benchmark Accelerator** helps Databricks implementation and QA teams move Genie testing from ad-hoc user prompts to structured, repeatable benchmark-based validation.\nDatabricks provides native benchmarking capabilities, but the quality of benchmarking still depends on the test cases created by the implementation team. If the benchmark set is narrow, the Genie space may pass basic tests while still failing in real business usage.\nThis accelerator addresses that gap by generating a representative benchmark set for the use case. It creates natural language questions and corresponding golden SQL queries, executes the test cases, compares Genie-generated SQL with the reviewed golden SQL, and identifies where the Genie space is failing.\nThe goal is to help teams stress test the agent during QA, uncover semantic gaps early, and improve confidence before wider adoption.\n \n**Use Cases**\n \n- Generate comprehensive benchmark test cases for Genie spaces\n- Stress test Genie across measures, filters, entities, and business scenarios\n- Validate Genie-generated SQL against golden SQL\n- Identify failures caused by wrong filters, wrong measures, date logic, joins, or entity interpretation\n- Track benchmark accuracy across multiple evaluation runs\n- Support QA teams with repeatable test execution and failure diagnosis\n- Create benchmark coverage beyond manually written sample questions\n \n**Product Details**\n \nThe accelerator starts with the use case definition and sample business questions. Based on this input, it generates a broader benchmark set designed to test the strength of the Genie space.\n \nEach benchmark test case includes:\n \n- Natural language business question\n- Golden SQL query\n- Genie-generated SQL\n- Execution result\n- Pass/fail outcome\n- Failure reason, where applicable\n- Recommendation for correction\n \nThe golden SQL is generated through an automated process and reviewed by the implementation team before being used as the reference for validation.\n \n**Benchmark Coverage**\n\n1. Simple SQL-Style Questions\n\n - Tests basic data retrieval.\n - Validates aggregations, grouping, and filtering behavior.\n\n2. Measure-Specific Questions\n\n - Tests whether key business metrics are calculated correctly.\n - Verifies measure definitions and business logic.\n\n3. Filter-Specific Questions\n\n - Tests the application of default filters.\n - Verifies user-selected filters are handled correctly.\n\n4. Entity Ambiguity Questions\n\n - Tests whether business terms are mapped to the correct columns and values.\n - Validates handling of synonyms and ambiguous terminology.\n\n5. Multi-Topic Questions\n\n - Tests Genie’s ability to answer questions involving multiple measures, filters, or business concepts.\n - Verifies cross-domain reasoning and query generation.\n\n6. Strategic / Analytical Questions\n\n - Tests whether broader business questions can be translated into the appropriate data queries.\n - Evaluates analytical reasoning capabilities.\n\n7. Negative / Unsupported Questions\n\n - Tests how Genie responds to unsupported, invalid, or out-of-scope requests.\n - Verifies safe and appropriate handling of such scenarios.\n \n**Output**\n \nThe accelerator provides both a UI and a structured report.\n\nThe output includes:\n \n- Benchmark accuracy score\n- Test case execution summary\n- Passed and failed question list\n- Failure classification\n- SQL comparison between golden SQL and Genie-generated SQL\n- Root-cause diagnosis\n- Recommended corrective actions\n- Run history across benchmark cycles\n \nFailures are classified into practical categories such as wrong measure, wrong filter, wrong date logic, wrong entity mapping, incorrect join, missing condition, or unsupported question handling.\n \n**What Makes It Different**\n \nThe accelerator is not just a place to store benchmark questions. It helps create the right benchmark set.\n\nIt is designed to break the Genie space during QA by testing combinations of measures, filters, entities, and business scenarios that users are likely to ask in real usage. This makes the benchmark process more comprehensive than manually trying a few known questions.\n \nThe accelerator also supports continuous benchmark tracking, allowing teams to see whether changes to the Genie space are improving or reducing accuracy over time.\n \n**Best Fit**\n \nGenie Benchmark Accelerator is best suited for Databricks implementation and QA teams working on enterprise Genie spaces.\nIt is especially useful when teams need to prove that a Genie space can handle more than simple demo questions and must be validated against a broader, repeatable benchmark set before business rollout.\n \n**Important Note**\n \nThe accelerator does not replace implementation team review. Golden SQL queries should be reviewed and approved before being used as the benchmark reference.\nThe accelerator helps automate benchmark generation, execution, comparison, failure classification, and reporting, while keeping final validation under the control of the implementation team.","terms_of_service":"https://www.smartbots.ai/acceptable-usage-policy","documentation_link":"","privacy_policy_link":"https://www.smartbots.ai/privacy-policy","embedded_notebook_file_infos":[{"id":"3e570023-f9b3-45f7-a1e0-9918737cbd00","marketplace_file_type":"EMBEDDED_NOTEBOOK","file_parent":{"parent_id":"224f4e48-8a02-43fb-8c6b-7065a08097a1","file_parent_type":"LISTING"},"mime_type":"text/html","download_link":"https://marketplace-file-storage-prod.s3-fips.us-east-1.amazonaws.com/EMBEDDED_NOTEBOOK/LISTING_224f4e48-8a02-43fb-8c6b-7065a08097a1/3e570023-f9b3-45f7-a1e0-9918737cbd00?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEGgaDnVzLWVhc3QtMS1maXBzIkYwRAIgbC8sWIoYaHR8y7Wq86nPb0pufBl4%2BM4PWpeN%2B5PW9VQCIG7BhAVBDCWTpxFDLeuKYm%2Ff1JLz4iccPMzter0YdhgWKqYECDAQABoMNDE0MzUxNzY3ODI2IgxNAJrHbXaZG20jNfMqgwQyGEb4TsoVOoNvJ%2BaA%2FZ7zeu8hGcHqCeNfblP%2FQo5g6q2aRNtpwtPRKMlhYcIE%2FUQaNT1Lqmyptu32VUQTpzjTTxRVmJrDPkheSCkV06XQ%2FpTMKjU8CC7X1b%2FqYf8CrBwZT7Cv4C5WPjEET38f2A79h0xV6bZgPy28zQWrSeIJ9w6AfZBxiE24%2FVc08zVA0ivCT6lsy7nuU0wcR%2FMg2C9nd1vaMnQ923VpD21CKfVVYcrNWL2b%2FQ%2BNAO%2BxPjPgjiN6UPQbvbvZxkSdhgrGtecIg9NgqGkMcvM5VLTDtdC1kdeZetfOC2obtA%2BT%2F0gy3tIDo6WM5hcQfw743%2FAU9oaGnRBl%2BwoxRRDCZA%2F4jnPdluICSI7NCr6C3HHOe4CSZJjRihczxPA%2FgQczAVjAPs6CrK9N9pIzt0vNJpGUTgg4AoxftTvGfciDREilSctPIcBk6GwB%2Bzt4wbtNCmEPU%2BFD46rc6CWVQmmLwbKOeHw70ipMVFlOc8NlXkEtGO2vu6H44MstSSc6daOVUYUavQSGZ6jGhEjsP2YGz8KDm9XQotB%2FtyVBqiZV01OKqDmmBJA%2FXjUpSJ8M%2FeJXhe5m%2BKcxzNObtoJRI7jci3Tpt08XZyXZ7%2FyIV8H30ya468X3cfkTXrm972uga3z7iXAdubyEHnQIua%2FuvtwOHILi6O5D1Me2MzCPvcnXBjqTAYDoMWBt%2B3AVFuqSs2dmDQNBnvEhXfMgTedKLD5PJHb3aIl15iBkLaNu36GYnVCp9ZU2lSYcTbY5QwNnNeTfJTNy9bKhwzru6Sg%2BslhN%2BTS0zkTn%2BeLMY4tHgPEtEZ2cp%2FCk6GwqswvnYVKHjwO8q%2B1Mx8lc08fVEfarE9dke1GGFqoddx%2Bq%2BZUyHbDlAVfrCmf0yw%3D%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20261110T000135Z&X-Amz-SignedHeaders=host&X-Amz-Expires=86400&X-Amz-Credential=ASIAWA6KKHEJORWBH4N3%2F20261110%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=a1b3da2012e0bce4b3851f922aa785ba09da4a24e9c29fda3d76096855e1418d","created_at":1780573599195,"updated_at":1780573599195,"display_name":"Genie_benchmarking","status":"FILE_STATUS_PUBLISHED"}],"update_frequency":{"interval":90,"unit":"DAILY"},"assets":["ASSET_TYPE_NOTEBOOK"],"license":""},"provider_summary":{"name":"SmartBots AI","description":"Smartbots is an AI-driven automation and data solutions provider focused on accelerating enterprise data transformation on Databricks. We offer a suite of ready-to-deploy accelerators that simplify data migration, security migration, ELT processes, and AI-powered analytics.\n\nOur solutions include Data Migration, AI-based Data Migration, Security Migration, ELT Framework, Genie Benchmarking, and Genie Health Score, enabling organizations to modernize data platforms efficiently while reducing time, cost, and complexity.\n\nWe support industries such as banking, financial services, and enterprise IT, helping teams achieve faster insights, improved governance, and scalable data architectures through automation and intelligent workflows."},"popularity":1892},{"id":"29179ee9-bf63-4102-a403-16cc1f50dfc5","summary":{"name":"Taskmaster OmniVox — Indic TTS Corpus — Full Dataset","subtitle":"Full 1,656.55-hour Indic TTS corpus spanning 22 languages for speech AI, TTS research, and model development.\n","setting":{"visibility":"PUBLIC"},"created_at":1790770303156,"updated_at":1790770303156,"categories":["EDUCATION","OTHER","MEDIA","SCIENCE_AND_RESEARCH"],"listingType":"PERSONALIZED","provider_id":"51e1bafd-2b96-40fb-b5bd-e9d3531279e9"},"detail":{"description":"**Overview**\n\nTaskmaster OmniVox — Indic TTS Corpus — Full Dataset is a large-scale multilingual speech corpus designed for organizations and AI teams developing text-to-speech (TTS), speech synthesis, conversational AI, and multilingual speech technologies.\n\nThe validated corpus contains approximately **1,656.545069 hours of speech across 22 Indic languages**, providing a substantial foundation for model training, fine-tuning, evaluation, speech research, and production-oriented AI development.\n\nThe full dataset is available through a **request-based access process**, allowing organizations to evaluate their requirements and obtain access subject to applicable licensing, commercial, and access conditions.\n\n**Use cases**\n\n* Train and fine-tune multilingual text-to-speech models\n* Develop Indic-language speech synthesis systems\n* Build multilingual conversational AI and voice assistants\n* Conduct large-scale speech and acoustic research\n* Develop and evaluate multilingual speech-processing pipelines\n* Benchmark TTS and speech-generation systems across Indic languages\n* Improve language coverage in speech and voice AI applications\n* Support research into pronunciation, acoustic modeling, and speech generation\n* Develop production-oriented multilingual voice applications\n* Create language-specific and multilingual speech technology workflows\n\n**Product details**\n\nThe full dataset is delivered as Parquet files through a Databricks Files asset and contains speech data and associated metadata suitable for data engineering, analytics, machine-learning, and speech-AI workflows.\n\n* Datasets represented include speech data covering **22 Indic languages**, with language-specific records organized within the corpus.\n* Sample fields include `lang`, `duration`, transcript/text information, and additional dataset metadata fields.\n* The validated corpus contains approximately **1,656.545069 hours** of speech.\n* The dataset uses a consistent **24-column Parquet schema**.\n* The data is provided in Apache Parquet format for efficient columnar access and integration with Databricks-based workflows.\n* The dataset includes transcript/text information within the Parquet records rather than relying on separate transcript files.\n* Language and duration metadata support filtering, aggregation, quality analysis, and language-specific dataset exploration.\n\nFor more details, refer to the embedded notebook.\n\n**Additional Insights**\n\nThe full dataset is intended for organizations that require substantially greater data volume and language coverage than the Marketplace evaluation sample. It provides access to the broader validated corpus across 22 Indic languages and approximately 1,656.545069 hours of speech.\n\nA smaller Marketplace sample is available separately to help organizations evaluate the dataset structure, schema, language coverage, metadata, and suitability for their intended workflows before requesting full access.\n\nThe full dataset is provided through a request-based fulfillment process. Access is subject to the applicable dataset license, commercial terms, and access conditions.\n\nUsers should review the applicable licensing and usage terms before using the dataset for commercial applications, redistribution, derivative works, or model development.\n","terms_of_service":"https://taskmastervirtualally.com/Terms-and-Conditions","documentation_link":"https://docs.google.com/document/d/1YQhhvsnv1KiWHO_uQe6-IIdbFb840EpHKYtFgSggFmM/edit?usp=sharing","privacy_policy_link":"https://taskmastervirtualally.com/privacy-statement","assets":["ASSET_TYPE_MEDIA"],"license":"https://docs.google.com/document/d/11fR2ysT5l6c9lDsuMgFVQTo7S-BsGqRYamW5pkadLBY/edit?usp=sharing"},"provider_summary":{"name":"Taskmaster Virtual Ally LLC","description":"Taskmaster Virtual Ally solves the AI Data Readiness Gap with commercially cleared speech, vision, and NLP datasets alongside managed Human Infrastructure as a Service (HIaaS) Pods. We provide 8,000+ hours of high-fidelity, multi-speaker Asian language speech assets (Indic TTS, Taglish ASR, Vietnamese), clinical HIPAA-ready transcriptions, and enterprise RLHF annotation. Operating on a strict zero-data-retention model within your Databricks governance boundary, our dedicated 2-2-1 Data Pods integrate directly via Delta Sharing to curate, clean, and benchmark enterprise AI training data with 99%+ data fidelity."},"popularity":1098},{"id":"730eaebc-a7eb-417b-80e1-e2d1cb92cb87","summary":{"name":"Data Link MCP Server","subtitle":"The Data Link MCP server connects your agentic workflows with data provided through Data Link.","setting":{"visibility":"PUBLIC"},"created_at":1762394847232,"updated_at":1776518580690,"categories":["FINANCIAL"],"listingType":"PERSONALIZED","provider_id":"f689fc4b-c6a1-4078-9fe2-36da2a1d445e"},"detail":{"description":"**Overview**\nConnect AI agentic workflows with your access to data products provided by Nasdaq Data Link. \n\n**Subscription Requirement**\nSubscription to data product(s) provided by Nasdaq Data Link is required.\nFor details on available data products and subscription options, please explore [our catalog](https://data.nasdaq.com/search).\n\n**Use cases**\n\n- Ask questions related to financial data and analysis.\n\n**Product details**\n\n[See product pages for more details](https://data.nasdaq.com/search).\n\n","terms_of_service":"https://data.nasdaq.com/terms","documentation_link":"https://docs.data.nasdaq.com/","privacy_policy_link":"https://www.nasdaq.com/privacy-statement","geographical_coverage":"[\"United States and Canada\"]","cost":"FREE","data_source":"https://data.nasdaq.com","assets":["ASSET_TYPE_MCP"],"license":"https://www.nasdaq.com/legal"},"provider_summary":{"name":"Nasdaq","description":"Positioned at the nexus of technology and the capital markets, Nasdaq provides premier platforms and services for global capital markets and beyond with unmatched technology, insights and markets expertise. \n \nFrom pioneering the world's first electronic exchange and building innovative indexes to providing industry-leading technology that enables more than 130 markets around the world, we are driven by a relentless pursuit of reimagining a better tomorrow. \n\nNasdaq's purpose is to power stronger economies, create more equitable opportunities and contribute to a more sustainable world to help communities, clients, employees and people of all backgrounds reach their full potential."},"popularity":2033},{"id":"975938a6-9642-418a-ab12-0da120649a0a","summary":{"name":"CMBS Servicer Commentary","subtitle":"Special servicer commentary history for 357 CMBS conduit deals from SEC EDGAR ABS-EE filings.","share":{"name":"cmdrvl_cmbs_commentary","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1774481870832,"updated_at":1774483972126,"categories":["FINANCIAL"],"listingType":"PERSONALIZED","provider_id":"8223b0dd-053b-4451-854e-93fb4aa588ac"},"detail":{"description":"**Overview**\n\n40,021 special servicer commentary entries capturing workout narratives, modification details, and resolution timelines across 357 CMBS conduit deals. This is the unstructured intelligence layer of CMBS surveillance — the text that tells you what the numbers cannot.\n\n**Key Fields**\n\n- SPECIAL_SERVICING_COMMENTS — free-text narrative from the special servicer\n- PROSPECTUS_ID — loan identifier within the deal\n- CIK, FILING_ID, FILING_DATE — full SEC filing provenance\n- ACTUAL_BALANCE — loan balance at time of comment\n- APPRAISAL_VALUE, APPRAISAL_DATE — most recent appraisal\n- DSCR, NET_OPERATING_INCOME — financial context at time of comment\n- MATURITY_DATE — loan maturity\n- RESOLUTION_STRATEGY_CODE — servicer's stated workout strategy\n- PROPERTY_TYPE, STATE — property classification and location\n\n**Use Cases**\n\n- Distressed loan tracking: follow workout progress through servicer narratives over time\n- Early warning signals: detect deterioration language before it shows up in financial metrics\n- NLP/ML training: build classifiers for resolution strategy, sentiment, or outcome prediction\n- Audit trail: trace the full servicing history of any loan back to SEC filings\n\n**Coverage**\n\n40,021 commentary entries across 357 CMBS conduit deals. Updated daily as new filings are published.\n\n**Pricing**\n\n$750/month. Contact cairn@cmdrvl.com for enterprise licensing or to request a trial.\n\n**Related Listings**\n\nPairs with **CMBS Loan Data** for balance and delinquency context, and **CMBS Property Collateral** for the underlying asset details. **CMBS Tenant Names** is available free.","terms_of_service":"https://cmdrvl.com/terms/","documentation_link":"https://dealcharts.org/capitalmarkets/abs/cmbs/","privacy_policy_link":"https://cmdrvl.com/privacy/","cost":"PAID","update_frequency":{"interval":1,"unit":"DAILY"},"collection_date_start":1480512030642,"data_source":"SEC.gov EDGAR ABS-EE Filings","assets":["ASSET_TYPE_DATA_TABLE"]},"provider_summary":{"name":"CMD+RVL","description":"We believe that every person should have access to the resources necessary to improve their mastery of data analytics. 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The data contains the number of Medicare beneficiaries (patients) with creditable prescription drug coverage by type of Medicare Part D plan. The data is displayed by state. Another dataset displays the number of prescription drug plans available per region and indicates the number of plans per contract for Prescription Drug Plan organizations offered by region. Another dataset contains the number of prescription drug plans that are eligible for low income subsidy. Low income eligible plans or benefit packages do not include premium and are eligible for auto-enrollment, dual eligible or facilitated enrollment. The data package also contains the percentage of Medicare Part D Beneficiaries that are eligible and with known creditable prescription drug coverage and percentage of Medicare population enrolled in the Stand Alone Prescription Drug Plan.\n\n**Benefits**\n- Information in medicare prescription drug plans is used for the purpose of research, analysis, reporting, and public health functions.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [Medicare Beneficiaries by Type](https://www.johnsnowlabs.com/marketplace/medicare-beneficiaries-by-type)\n\t- This dataset contains the number of beneficiaries of the different types of Medicare Part D Plan with prescription drug coverage. The data contains the number of Medicare beneficiaries (patients) with creditable prescription drug coverage by type of Medicare Part D plan. The data is displayed by state.\n- [Medicare Part D Eligible Beneficiaries](https://www.johnsnowlabs.com/marketplace/medicare-part-d-eligible-beneficiaries)\n\t- This dataset contains the percent of Medicare Part D Beneficiaries that are eligible and with known creditable prescription drug coverage. Prescription drug coverage to the Medicare Part D is according to The Medicare Modernization act of 2003. The beneficiaries are thus enrolled in a stand-alone prescription drug plan (PDP) or Medicare Advantage (MA) plan that covers prescription drugs.\n- [Medicare Prescription Drug Plans Enrollment](https://www.johnsnowlabs.com/marketplace/medicare-prescription-drug-plans-enrollment)\n\t- This dataset contains the number and percent of Medicare population enrolled in the Stand Alone Prescription Drug Plan (PDP) and average premium for prescription drug plan charged by state. It also contains information on the number of plans per contract for Prescription Drug Plan organizations offered by region and number of prescription drug plans that are eligible for low income subsidy.\n- [Medicare Prescription Drugs Claims](https://www.johnsnowlabs.com/marketplace/medicare-prescription-drugs-claims)\n\t- This de-identified dataset contains details 100% of the prescription drug claims made by Medicare beneficiaries during the year of release. This dataset is part of the Public Use Files (PUFs) released by the Centers for Medicare & Medicaid Services (CMS), which are public domain de-identified data files available for research with claim-specific information. The purpose of these files is to provide information while protecting confidentiality.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated\n- Data Updates\n - Data updates support replace-on-update: outdated foreign keys are deprecated, not deleted\n\n**Our data is curated and enriched by domain experts** \n\n Each dataset is manually curated by our team of doctors, pharmacists, public health & medical billing experts:\n- Field names, descriptions, and normalized values are chosen by people who actually understand their meaning\n- Healthcare & life science experts add categories, search keywords, descriptions and more to each dataset\n- Both manual and automated data enrichment supported for clinical codes, providers, drugs, and geo-locations\n- The data is always kept up to date – even when the source requires manual effort to get updates\n- Support for data subscribers is provided directly by the domain experts who curated the data sets\n- Every data source’s license is manually verified to allow for royalty-free commercial use and redistribution.\n\n**Need Help?**\nIf you have questions about our products, contact us at [info@johnsnowlabs.com](mailto:info@johnsnowlabs.com). \n 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Our Marketplace offers hundreds of data sources across medical, pharmacy, lab, biomarker, EHR, consumer, and others. By integrating patient identity resolution, data privacy, and governance, we ensure HIPAA-compliant, interoperable data that accelerates research, improves clinical trials, and enhances health outcomes. Learn more at www.healthverity.com."},"popularity":2046},{"id":"990f1351-ead3-44b6-b908-134acc707d19","summary":{"name":"Geolocet | Latest Demographic Data Sweden - Households and Dwellings","subtitle":"Demographic data for the municipalities (kommun) of Sweden and their households and dwellings ","setting":{"visibility":"PUBLIC"},"created_at":1705995001438,"updated_at":1752136487069,"categories":["DEMOGRAPHICS","ADVERTISING_AND_MARKETING","GEOSPATIAL","PUBLIC_SECTOR","RETAIL"],"listingType":"PERSONALIZED","provider_id":"5d3ddc17-a7e8-4794-a760-13587e1de2c6"},"detail":{"description":"## **Get instant access to the latest Demographics Data for Sweden with [Geolocet](https://geolocet.com)**\n\nExplore the richness of Sweden’s demographic landscape with our Sweden Census Data. This meticulously compiled dataset, sourced from official statistical sources, including Statistics Sweden, offers a detailed insight into the population landscape of Sweden at the municipality (kommun) level. \n\nIf you require data at a less granular level, such as an Uber grid or square grid, please don't hesitate to get in touch with us at *contact@geolocet.com*. We understand that different projects may have varying data needs, and we are here to tailor our data to match your specific requirements.\n\n ## **Key Features:**\n---\n* **Population Data Sweden:** Gain access to the latest and most accurate population statistics for Sweden, meticulously organized by municipality (kommun).\n\n* **Demographic Insights:** Dive deep into Sweden’s demographic landscape with data on 50+ attributes, including: \n* Number of households,\n* Household composition,\n* Dwellings,\n* Dwellings type and occupancy.\n\nAll demographic attributes are available at the municipal (kommun) level.\n\n* **Geodemographic Insights:** Unlock geodemographic data Sweden-wide to inform location-based decision-making, marketing strategies, and policy formulation.\n* **Seamless Data Access:** Our data is available for instant download in CSV format. No lengthy sign-up processes or NDAs required on our side – just download the data you need.\n* **Data Samples for Evaluation:** To ensure you're getting exactly what you need, we provide free downloadable samples in CSV format for data evaluation.\n* **Data accuracy:** Our datasets are created using the latest demographics data available in national statistics bureaus, guaranteeing accuracy and reliability.\n* **Best-in-Class Quality:** We guarantee best-in-class quality, backed by over two decades of experience in modeling demographics data and an outstanding quality management process.\n* **Competitive Pricing:** We maintain the lowest prices on the market, ensuring that you get the most cost-efficient demographics data acquisition process.\n---\nAdditionally, for those looking to enrich their analysis or project, we offer:\n* **[Administrative Boundaries](https://geolocet.com/products/sweden-all-available-administrative-boundaries):** If you require geographical boundaries (polygons) for the municipalities of Sweden, consider exploring our Administrative Boundaries product, which seamlessly integrates with this dataset.\n\n* **[Point of Interest (PoI) Data](https://geolocet.com/pages/aggregated-points-of-interest-poi-data):** If you're interested in Points of Interest data, please don't hesitate to contact us for customized datasets to enhance your geographical analysis.\n\n* **[Data Customization](https://geolocet.com/pages/data-services-by-geolocet):** We understand that your project may have specific requirements. Feel free to get in touch with us if you need any modifications or specific data formats such as Excel, JSON, GeoJSON, or Shapefile. We are here to tailor the data to your needs.\n\nIf you're looking for data for Sweden, [look here](https://geolocet.com/pages/sweden) for off-the-shelf or tailor-made datasets to suit your specific requirements.\n\nMake informed decisions, conduct precise research, and unlock the potential of Sweden's demographic landscape with our comprehensive dataset. Trust in the accuracy and reliability of our official statistical data to drive your projects forward.\nContact us at *contact@geolocet.com* today to access this invaluable resource and explore Sweden census data like never before.\n\n[Geolocet LTD](https://geolocet.com)\n","terms_of_service":"https://geolocet.com/pages/terms-of-use","documentation_link":"","privacy_policy_link":"https://geolocet.com/pages/privacy-policy","geographical_coverage":"[\"Europe\"]","cost":"PAID","update_frequency":{"interval":1,"unit":"YEARLY"},"data_source":"","assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://geolocet.com/pages/terms-of-use"},"provider_summary":{"name":"Geolocet LTD","description":"Geolocet provides premium geospatial data for retail analysis, GIS, and market planning. We equip businesses with accurate, ready-to-query data to drive spatial analysis, site selection, and customer segmentation.\n\nCore Offerings:\n\nDemographics: Socioeconomic data covering population, age, and employment across diverse geographies.\n\nPoints of Interest (PoI): Datasets spanning retail, healthcare, and transport for catchment analysis.\n\nAdministrative Boundaries: GIS-ready boundaries, including municipalities and postcodes, for territory management.\n\nBrand Locations: Chain location datasets to monitor store networks and market expansion.\n\nWealth Data: Granular economic indicators like purchasing power to identify high-potential markets.\n\nWe source from trusted providers, delivering highly customizable datasets with comprehensive coverage across Europe and worldwide."},"popularity":281},{"id":"6eec2cb5-2bbe-4005-9feb-aabaede0d3fc","summary":{"name":"Geolocet | Points of Interest Data | Grocery Store Locations - Denmark","subtitle":"Complete GIS-ready dataset of 5,264 Denmark grocery locations (brands & independents). Optimized for Databricks analytic","setting":{"visibility":"PUBLIC"},"created_at":1780638192330,"updated_at":1780638209839,"categories":["GEOSPATIAL","RETAIL"],"listingType":"PERSONALIZED","provider_id":"5d3ddc17-a7e8-4794-a760-13587e1de2c6"},"detail":{"description":"# **All Supermarket Locations in Denmark: Complete Geographic Dataset**\n---\n### **Premium GIS-Ready Retail Location Intelligence Data for Databricks Analytics**\n\nThis high-fidelity geospatial dataset provides a complete, exhaustive, and standardized overview of **5,264** verified grocery store, supermarket, and proximity retail locations across Denmark. Updated as of **May 28, 2026**, this comprehensive national dataset captures the entire Danish grocery ecosystem - encompassing both major branded retail chains and independent local grocers. It offers an analysis-ready foundation for full-scale retail site selection, competitor benchmarking, logistics optimization, and spatial demographics mapping.\n\nFully compatible with **Apache Spark**, **Delta Lake**, and **Databricks Unity Catalog** workflows.\n\n---\n\n## **Complete National Coverage: Branded & Independent Grocers**\nUnlike top-tier only datasets, this exhaustive collection covers maximum geographic depth across all 5 Danish regions (including highly dense areas like Region Hovedstaden, Region Syddanmark, and Region Midtjylland). It includes thousands of independent markets and local grocers alongside the top national supermarket chains:\n\n- **Discount & Value Supermarkets:** \n [Netto (571 locations)](https://geolocet.com/products/denmark-netto-grocery), \n [REMA 1000 (431 locations)](https://geolocet.com/products/denmark-rema-1000-grocery), \n [Lidl (172 locations)](https://geolocet.com/products/denmark-lidl-grocery)\n\n- **Coop Denmark Banners:** \n [Coop SuperBrugsen (216 locations)](https://geolocet.com/products/denmark-coop-superbrugsen-grocery), \n [Coop Brugsen (135 locations)](https://geolocet.com/products/denmark-coop-brugsen-grocery), \n [Coop DagliBrugsen (130 locations)](https://geolocet.com/products/denmark-coop-daglibrugsen-grocery), \n [Kvickly (61 locations)](https://geolocet.com/products/denmark-kvickly-grocery)\n\n- **Dagrofa & Salling Group Supermarkets:** \n [Min Købmand (134 locations)](https://geolocet.com/products/denmark-min-kobmand-grocery), \n [Meny (118 locations)](https://geolocet.com/products/denmark-meny-grocery), \n [føtex (116 locations)](https://geolocet.com/products/denmark-fotex-grocery), \n [SPAR (149 locations)](https://geolocet.com/products/denmark-spar-grocery)\n\n- **Convenience Formats:** \n [7-Eleven (152 locations)](https://geolocet.com/products/denmark-7-eleven-grocery)\n\n**Plus thousands of independent, regional, and unbranded grocers** fully mapped and standardized across all regions (e.g., Region Hovedstaden: 1,455 sites; Region Syddanmark: 1,198 sites; Region Midtjylland: 1,164 sites).\n\n---\n\n## **Technical Specifications & Schema Definitions**\nOptimized for zero-friction schema enforcement when reading via `spark.read.format(\"csv\")`. The structured schema below ensures flawless rendering in Databricks notebooks and high accuracy for LLM semantic indexing.\n\n* **`GUID`** (`STRING`): Unique global identifier for deterministic primary key mapping.\n* **`Title`** (`STRING`): Official store name, brand banner, or independent title of the location.\n* **`Latitude`** (`DOUBLE`): Precise WGS84 latitude coordinate for point-in-polygon & spatial joins.\n* **`Longitude`** (`DOUBLE`): Precise WGS84 longitude coordinate for spatial indexing (H3 / S2).\n* **`Street_No` / `Street`** (`STRING`): Normalized building/house number and street name elements.\n* **`City` / `Postal_Code`** (`STRING`): Core postal addresses and primary municipal settlement names.\n* **`Kommune`** (`STRING`): Danish municipal administrative boundary designation (Kommune).\n* **`Region`** (`STRING`): Macro-administrative boundary classification (e.g., Region Hovedstaden, Region Sjælland).\n* **`Population`** (`INTEGER`): Contextual demographic population metric attributed to the location.\n* **`Address`** (`STRING`): Full, standardized street address of the location.\n* **`Wheelchair`** (`STRING`): Accessibility flags indicating wheelchair mobility support.\n\n---\n\n## **Databricks Workflow & Spatial Analytics Use Cases**\n\n- **Whitespace & Underserved Market Analysis:** Query the full 5,264 locations against regional population density tables to identify true retail deserts and expansion opportunities untouched by major national networks.\n- **H3 Geospatial Indexing:** Convert the precise WGS84 `Latitude` and `Longitude` fields into Uber H3 spatial hexagons (`h3_longlattocell`) to cross-reference with granular Danish small-area parish (Sogn) income indicators or demographic data.\n- **Supply Chain & Supply Footprint Optimization:** Combine store location points with regional administrative boundary layers to optimize urban delivery routes, distribution warehouse placements, and last-mile logistics footprints.\n\n---\n\n## **Data Evaluation & Custom Ingestion**\n\n### **Evaluate with a Free Sample Dataset**\nVerify the structure, column types, and coordinate precision before full implementation. A comprehensive schema sample is available for download in CSV/Parquet-friendly formats.\n\n### **Enterprise Delivery & Tailored Formatting**\nWhile natively available as an analysis-ready CSV for DBFS ingestion, we accommodate enterprise-level data architecture needs. Alternative formats - including **GeoJSON, Esri Shapefiles, PostgreSQL dumps, Excel, or direct Delta sharing profiles** - can be generated. Reach out to our engineering team at **contact@geolocet.com** for integration support or custom European POI data requests.","terms_of_service":"https://geolocet.com/pages/terms-of-use","documentation_link":"","privacy_policy_link":"https://geolocet.com/pages/privacy-policy","assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://geolocet.com/pages/terms-of-use"},"provider_summary":{"name":"Geolocet LTD","description":"Geolocet provides premium geospatial data for retail analysis, GIS, and market planning. We equip businesses with accurate, ready-to-query data to drive spatial analysis, site selection, and customer segmentation.\n\nCore Offerings:\n\nDemographics: Socioeconomic data covering population, age, and employment across diverse geographies.\n\nPoints of Interest (PoI): Datasets spanning retail, healthcare, and transport for catchment analysis.\n\nAdministrative Boundaries: GIS-ready boundaries, including municipalities and postcodes, for territory management.\n\nBrand Locations: Chain location datasets to monitor store networks and market expansion.\n\nWealth Data: Granular economic indicators like purchasing power to identify high-potential markets.\n\nWe source from trusted providers, delivering highly customizable datasets with comprehensive coverage across Europe and worldwide."},"popularity":359},{"id":"322095a8-bccf-4a92-b7f1-1f8217564e7f","summary":{"name":"Vantage-MCP: Automated Cloud Cost Intelligence for FinOps Teams","subtitle":"Gain automated visibility into Databricks and cloud costs from 25+ providers for unified reporting and FinOps insights.","setting":{"visibility":"PUBLIC"},"created_at":1761327701751,"updated_at":1761694055855,"categories":["FINANCIAL","ECONOMICS","OTHER"],"listingType":"STANDARD","provider_id":"f635b60e-d1cc-445f-be6a-599a9a720e1a"},"detail":{"description":"The Vantage MCP provides a single, automated interface to explore and analyze your cloud and AI costs, including Databricks-specific spend, alongside data from 25+ other providers. 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Through the Vantage Model Context Protocol (MCP) and AI integrations, customers can access standardized cost datasets, automate reporting, and connect cost data directly into their internal systems and tools with smart insights and proactive cost recommendations."},"popularity":1905},{"id":"1349978b-9eb9-4d44-aaca-a06238e58914","summary":{"name":"Mortgage Data","subtitle":"Borrower, Lender, and Mortgage Details with up to 50 Years History","setting":{"visibility":"PUBLIC"},"created_at":1789679179265,"updated_at":1789679179265,"categories":["FINANCIAL"],"listingType":"PERSONALIZED","provider_id":"26040e80-2e39-4e01-8fea-0667064d9fce"},"detail":{"description":"Mortgage delivers property-level mortgage records, including borrower, lender, and loan details covering up to 50 years of transaction history. It is used by mortgage lenders, researchers, and other industries to evaluate homeowner mortgage status, lender activity, and historical financing trends. \n\nEach delivery includes a chronological, incremental view of mortgage financing recorded against a property. This enables clients to identify distinct mortgages and analyze origination volume over time, transaction by transaction.\n\n**Customer Acquisition**\nMortgage data can be used by lenders to identify potential borrowers who may be seeking to refinance, purchase a new home, or secure a home equity line of credit. This can help lenders target their marketing and sales efforts more effectively.\n**Risk Analysis**\nBy analyzing a borrower's mortgage history, lenders can assess the level of risk associated with lending to that individual. This can include looking at the borrower's past repayment behavior, the types of mortgages they've had, and the amounts they've borrowed.\n**Fraud Remediation**\nMortgage data can be used to identify patterns or anomalies that may indicate fraudulent activity. For example, if a borrower appears to have taken out multiple mortgages on the same property within a short time period, this could be a sign of fraud.\n**Regulatory Reporting**\nMortgage lenders are subject to a number of regulatory requirements, including reporting on their lending activity. The mortgage data provided can assist lenders in compiling and submitting these reports.\n**Market Analysis**\nMortgage data can be used to gain insight into broader market trends, such as shifts in the popularity of different types of loans (e.g., conventional, FHA, VA), changes in the average size of loans, or trends in refinance activity. This can inform strategic planning and decision-making.\n**Customer Onboarding**\nWhen a new customer is onboarded, understanding their mortgage history can help a lender tailor their services to the customer's needs. For example, if a customer has a history of refinancing, a lender might offer information about their refinancing options.","terms_of_service":"https://www.cotality.com/","documentation_link":"https://www.cotality.com/products/mortgage-data?utm_campaign=412443580-EDSG-ALL-CloudMarketplace-Databricks-2026&utm_source=databricks&utm_medium=marketplace&utm_term=mortgagedata","privacy_policy_link":"https://www.cotality.com/","assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"Cotality","description":"We illuminate data, insights, and workflows across the property ecosystem, analyzing the largest objective dataset in the U.S. for real-time insights in minutes, not months. 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German Monolingual Dictionary Data \n\nOur German monolingual features clear definitions, headwords, examples, and comprehensive coverage of the German language spoken today. \n\n- Words: 85,500 \n- Senses: 78,000 \n- Example sentences: 55,000 \n- Format: XML format \n- Delivery: Email (link-based file sharing) \n\n\n2. German Bilingual Dictionary Data \n\nThe bilingual data provides translations in both directions, from English to German and from German to English. It is annually reviewed and updated by our in-house team of language experts. Offers comprehensive coverage of the language, providing a substantial volume of translated words of excellent quality. \n\n- Translations: 393,000 \n- Senses: 207,500 \n- Example translations: 129,500 \n- Format: XML and JSON formats \n- Delivery: Email (link-based file sharing) and REST API \n- Updated frequency: annually \n\n\n3. German Word List Data \n\nThis language data contains a carefully curated and comprehensive list of 338,000 German words. \n\n- Wordforms: 338,000 \n- Format: CSV and TXT formats \n- Delivery: Email (link-based file sharing) \n\n\nUse Cases: \n\nWe consistently work with our clients on new use cases as language technology continues to evolve. These include Natural Language Processing (NLP) applications, TTS, dictionary display tools, games, translations, word embedding, and word sense disambiguation (WSD). \n\nIf you have a specific use case in mind that isn't listed here, we’d be happy to explore it with you. Don’t hesitate to get in touch with us at Growth.OL@oup.com to start the conversation. \n \n\nPricing: \n\nOxford Languages offers flexible pricing based on use case and delivery format. Our datasets are licensed via term-based IP agreements and tiered pricing for API-delivered data. Whether you’re integrating into a product, training an LLM, or building custom NLP solutions, we tailor licensing to your specific needs. \n\nContact our team or email us at Growth.OL@oup.com to explore pricing options and discover how our language data can support your goals.\n\n\nAbout the sample: \n\nThe samples offer a brief overview of one or two language datasets (monolingual or/and bilingual dictionary data). To help you explore the structure and features of our dataset, we provide a sample in CSV format for preview purposes only. \n\nIf you need the complete original sample or more details about any dataset, please contact us (Growth.OL@oup.com) to request access or further information.","terms_of_service":"https://corp.oup.com/privacy-policy","contact_email":"support@datarade.ai","privacy_policy_link":"https://corp.oup.com/privacy-policy","assets":["ASSET_TYPE_DATA_TABLE"]},"provider_summary":{"name":"Oxford Languages","description":"Oxford Languages delivers multilingual language datasets designed to power the next generation of language technologies. Built through decades of research and curated by expert lexicographers, our data fuels diverse applications – from text-to-speech (TTS) and predictive text to language models dictionary display tools, assistive tech, chatbots, games, and more. 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The territorial vintages of the worldwide area boundaries are compatible with our globally consistent and comparable [socio-demographic](https://www.mbi-geodata.com/en/sociodemographic-data/) and [economic](https://www.mbi-geodata.com/en/purchasing-power-overview/) data.\nOur area boundaries for Moldova are offered at the following levels:\n\n- **Postal Code Level** (Postcode 2-digit, 5-digit on request): This fine-grained level of detail makes the data suitable for a wide range of applications – for example, enriching customer databases, which are often organized by postal code.\n- **Administrative Level** (Raioane): Covers both larger administrative areas (e.g., regions, states, provinces) as well as finer subdivisions such as districts, counties, or municipalities.\n\n**Use Cases:**\n\n- **Market Analysis**: Use MBI’s boundaries to visualize data on a map, learn about your customers, and uncover untapped market potential.\n- **360-Degree Customer View**: Link boundaries to customer data, sociodemographic, and economic data to gain a holistic understanding of customer needs, behaviors, and potential value.\n- **Location Planning**\n•\tUse MBI’s boundaries to visualize data on a map, learn about your customers\n•\tPlan the provision of services and products across different regions\n•\tDefine sales territories\n•\tVisualize catchment areas and plan actions to reach potential customers\n\n**Additional Insights:**\n[About Area Boundaries](https://www.mbi-geodata.com/en/area-boundaries/)\n\n**Data Structure:**\nThe area boundaries include the following variables:\nCTRYCODE: Two-digit country code by ISO 3166\nNAME: Name of administrative area / Name of the most populous administrative area / Name of the respective micro-area or the next higher level\nADMINCODE / POSTCODE / MICROCODE: Administrative code / Postal code / Micro-code\nWKT: Geography as Well Known Text (lat/long WGS84)\n\n*with only a few specific exceptions","terms_of_service":"https://service-mbi-geodata.com/s/HNp7Z2tws4WDqWd","documentation_link":"https://www.mbi-geodata.com/en/mbi-supplier-manufacturer-worldwide-geodata/europe/moldova-maps/","privacy_policy_link":"https://www.mbi-geodata.com/en/data-protection/","update_frequency":{"interval":1,"unit":"YEARLY"},"assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://service-mbi-geodata.com/s/jRRRY3mbwJSc9tB"},"provider_summary":{"name":"Michael Bauer International GmbH – MBI Geodata","description":"For over 15 years, Michael Bauer International (MBI) has been a leading provider of globally consistent and comparable geographic data, including more than 3 million postcode areas and other detailed datasets like sociodemographics and purchasing power. 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Covers locations for major retail chains, local businesses, convenience stores, hotels, airports, schools, hospitals & more. Dataset contains millions of POIs globally across all categories and thousands of recognized brands. This dataset is also available for download in all regions.\n\n**Samples/Tables Included:**\n\n- Location name\n- Address\n- Category\n- Brand association for points of interest (POIs) where people spend time or money\n\n**Fields Included:**\n\n- Latitude/Longitude\n- Hours Open\n- Postal Code\n- Phone Number\n\n**Use Cases:**\n- Site selection\n- Investment research\n- Trade area analysis\n- Competitive intelligence\n- Mapping\n- Network planning\n- Risk assessment ","terms_of_service":"https://www.safegraph.com/terms-of-service","documentation_link":"https://docs.safegraph.com/docs/places","privacy_policy_link":"https://www.safegraph.com/privacy-policy","assets":["ASSET_TYPE_DATA_TABLE"]},"provider_summary":{"name":"SafeGraph","description":"SafeGraph is a geospatial data company that curates high-precision data on millions of places around the globe. 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"},"popularity":1405},{"id":"d70f552d-c1a4-482a-a248-c4472e40b56a","summary":{"name":"SAMPLE UniCourt Legal Analytics & Litigation Data AP - USA Legal Data (AI Normalized)","subtitle":"Get easy access to legal analytics and the structured state and federal court data you need to build analytics for ...","share":{"name":"unicourt_66713254_5e52_4a39_a700_a3179e821001_samples_share","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1777294808645,"updated_at":1789995191327,"categories":["OTHER"],"listingType":"STANDARD","provider_id":"577c526f-dbd0-425a-b5cf-850da41daf30"},"detail":{"description":"UniCourt provides easy access to normalized litigation data and legal analytics via our Attorney Analytics API, Law Firm Analytics API, Judge Analytics API, Party Analytics API, and Court Analytics API, giving you the flexibility you need to intuitively move between interconnected data points. 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This will provide quick and efficient access to Platts eWindow Market Data as it’s delivered seamlessly into workflows, giving you timely updates to interpret instantly.\n\nThe following trade information is included in the dataset:\n- Transactional details including prices of products and locations traded\n\n- Values of bids and offers including the trade status\n\n- Date and counterparty information of all participants\n\n- All historic activity of the traded product\n\n- Traded volumes\n\n**Use cases**\n- Asset Valuation - Evaluate ROI for energy projects and assets\n- Fundamental Analysis - Connect Platts eWindow market data with financial and operations data, to optimize decision-making on spending, productivity and cycle time\n- Quantitative Analysis - Leverage Platts eWindow market data to design and implement complex and insightful models to support energy and commodities trading\n- Risk Analysis - Utilize Platts eWindow market data to support risk management functions including validation of internal valuations and alignment of risk tolerance with business strategy\n- Pricing Analysis - Improve intraday pricing benchmarking, bid/offer and window-based price monitoring, and rapid variance analysis by integrating Platts eWindow Market Data pricing signals into internal analytics to support trading execution, contract governance, and risk decision-making.\n\n**Product details**\n\nSample Tables:\nA ) EWINDOW_METADATA\nB ) EWINDOW_TRADEDATA\n\nSample Fields:\nA ) EWINDOW_METADATA\nIDENTIFIER\nDESCRIPTION\nEXAMPLE\n\nB ) EWINDOW_TRADEDATA\nMARKET\nPRODUCT\nHUB\nSTRIP\nORDER_TYPE\nORDER_STATE\nBUYER\nSELLER\n\nView Descriptions:\n\n- EWINDOW_TRADEDATA - Get eWindow Trade Data activity\n- EWINDOW_METADATA - Contains descriptions and examples of each of the columns in the above views for a better understanding of the dataset","terms_of_service":"https://www.spglobal.com/en/privacy/privacy-policy-english","documentation_link":"https://www.marketplace.spglobal.com/en/datasets/platts-ewindow-market-data-(25)","privacy_policy_link":"https://www.spglobal.com/en/privacy/privacy-policy-english","geographical_coverage":"[\"Global\"]","cost":"PAID","update_frequency":{"interval":1,"unit":"SECOND"},"collection_granularity":{"interval":1,"unit":"SECOND"},"collection_date_start":1167647044669,"assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"S&P Global Energy","description":"At S&P Energy, our complete view of global energy and commodities markets enables our customers to make decisions with conviction and create long-term, sustainable value. 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Free trial data.","share":{"name":"share_boundaries_mbi_gb_r_2025","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1785221151482,"updated_at":1785221151482,"categories":["ADVERTISING_AND_MARKETING","CLIMATE_AND_ENVIRONMENT","ENERGY","GEOSPATIAL","MEDIA","OTHER","PUBLIC_SECTOR","RETAIL","TRANSPORTATION_AND_LOGISTICS","TRAVEL_AND_TOURISM"],"listingType":"STANDARD","provider_id":"aa09abd6-ff7f-4452-803d-b8c43b4c07cb"},"detail":{"description":"MBI'S GLOBAL AREA BOUNDARIES COVER MOST COUNTRIES WORLDWIDE AND **CAN BE LICENSED FOR EACH COUNTRY INDIVIDUALLY** AND IN BUNDLES.\n[MBI](https://www.mbi-geodata.com/en/) digital boundaries are available for virtually all countries worldwide.* Created by our experts according to consistent global standards, the polygons are so precise and harmonized that they align seamlessly across borders. The territorial vintages of the worldwide area boundaries are compatible with our globally consistent and comparable [socio-demographic](https://www.mbi-geodata.com/en/sociodemographic-data/) and [economic](https://www.mbi-geodata.com/en/purchasing-power-overview/) data.\nOur area boundaries for the United Kingdom are offered at the following levels:\n\n- **Postal Code Level** (Postcode Sectors, Postcode Units on request): This fine-grained level of detail makes the data suitable for a wide range of applications – for example, enriching customer databases, which are often organized by postal code.\n- **Administrative Level** (Districts): Covers both larger administrative areas (e.g., regions, states, provinces) as well as finer subdivisions such as districts, counties, or municipalities.\n- **Micro Level** (Census Output Areas): Subdivisions of postal code or administrative boundaries, such as census areas or street segments. This is the most granular level for local analysis. | on request\n\n**Use Cases:**\n\n- **Market Analysis**: Use MBI’s boundaries to visualize data on a map, learn about your customers, and uncover untapped market potential.\n- **360-Degree Customer View**: Link boundaries to customer data, sociodemographic, and economic data to gain a holistic understanding of customer needs, behaviors, and potential value.\n- **Location Planning**\n•\tUse MBI’s boundaries to visualize data on a map, learn about your customers\n•\tPlan the provision of services and products across different regions\n•\tDefine sales territories\n•\tVisualize catchment areas and plan actions to reach potential customers\n\n**Additional Insights:**\n[About Area Boundaries](https://www.mbi-geodata.com/en/area-boundaries/)\n\n**Data Structure:**\nThe area boundaries include the following variables:\nCTRYCODE: Two-digit country code by ISO 3166\nNAME: Name of administrative area / Name of the most populous administrative area / Name of the respective micro-area or the next higher level\nADMINCODE / POSTCODE / MICROCODE: Administrative code / Postal code / Micro-code\nWKT: Geography as Well Known Text (lat/long WGS84)\n\n*with only a few specific exceptions","terms_of_service":"https://service-mbi-geodata.com/s/HNp7Z2tws4WDqWd","documentation_link":"https://www.mbi-geodata.com/en/mbi-supplier-manufacturer-worldwide-geodata/europe/united-kingdom-maps/","privacy_policy_link":"https://www.mbi-geodata.com/en/data-protection/","update_frequency":{"interval":1,"unit":"YEARLY"},"assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://service-mbi-geodata.com/s/5MQJ5GXzp5S3NYt"},"provider_summary":{"name":"Michael Bauer International GmbH – MBI Geodata","description":"For over 15 years, Michael Bauer International (MBI) has been a leading provider of globally consistent and comparable geographic data, including more than 3 million postcode areas and other detailed datasets like sociodemographics and purchasing power. MBI's high-quality small-scale data spans 248 countries, supporting businesses across all industries in location planning, branch networks, and distribution strategies. Recognized as a key data source, MBI is the preferred provider for leading GIS software and technology companies worldwide as well as one of the first global distributors for HERE Technologies. MBI is part of the Michael Bauer Group, alongside Michael Bauer Research GmbH and Michael Bauer Micromarketing GmbH."},"popularity":892},{"id":"35b4d1de-4353-4754-80fe-d8c4e93694b6","summary":{"name":"Convergent Medical Terminology Subsets","subtitle":"Convergent Medical Terminology (CMT) Release files include descriptions that are used as display names for Clinicians ..","share":{"name":"convergent_medical_terminology_subsets","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1682356171985,"updated_at":1698965635253,"categories":["HEALTH","SCIENCE_AND_RESEARCH"],"listingType":"STANDARD","provider_id":"f88849f8-9d77-4de2-b4c7-03d55cc3768e"},"detail":{"description":"\n**Overview**\nConvergent Medical Terminology (CMT) Release files include descriptions that are used as display names for Clinicians in Kaiser Permanente's electronic medical record, corresponding patient display names that the members see in their PHR portal, and mappings to other terminologies.\n\n**Description**\nThis data package is Kaiser Permanente's (KP) enterprise-wide medical terminology solution, referred to as our Convergent Medical Terminology (CMT). Initially developed to serve the needs of a regional electronic health record, CMT has evolved into a core KP asset, serving as the common terminology across all applications. CMT serves as the definitive source of concept definitions for the organization, provides a consistent structure and access method to all codes used by the organization, and is KP's language of interoperability, with cross-mappings to regional ancillary systems and administrative billing codes. The core of CMT is comprised of SNOMED CT, laboratory LOINC, and First DataBank drug terminology. CMT includes datasets from the following categories: Cardiology, Dermatology and respiratory system, gastroenterology, infectious diseases, endocrinology, urology, nephrology, injury, mental health, neurology, gynecology, ophthalmology, orthopedics, pediatrics, radiology and vascular procedures.\n\n**Benefits**\n- cross map sets provide bi-directional translations between cmt and ancillary applications and administrative billing codes.\n- context sets provide subsets of cmt for use in specific contexts.\n- easy usability\n- \"interface\" terminology is differentiated from \"reference\" terminology\n- easy for clinicians to find the concepts they need\n- the immediate value of coded data is apparent to clinician user\n- well defined approach to terminology extensions\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [CMT Cardiology Problem List Subset](https://www.johnsnowlabs.com/marketplace/cmt-cardiology-problem-list-subset)\n\t- This problem list subset includes concepts that KP (Kaiser Permanente) uses within the Cardiology problem list. There are 2,741 SNOMED CT (Systematized Nomenclature of Medicine - Clinical Terms) concepts in the file and includes mappings to ICD-9-CM and ICD-10-CM. This dataset is the compilation of cardiology related problem list subsets that KP has released in the year 2011, 2014 and 2016.\n- [CMT Dermatology and Respiratory Problem List Subset](https://www.johnsnowlabs.com/marketplace/cmt-dermatology-and-respiratory-problem-list-subset)\n\t- This problem list subset includes concepts that KP (Kaiser Permanente) uses within the Skin/Dermatology and Respiratory problem list. There are 3,733 SNOMED CT (Systematized Nomenclature of Medicine - Clinical Terms) concepts in the file and includes mappings to ICD-9-CM and ICD-10-CM. This dataset is the compilation of Skin/Dermatology and Respiratory related problem list subsets that KP has released in the year 2012 and 2015.\n- [CMT ENT Gastroenterology and Infectious Diseases Problem List Subset](https://www.johnsnowlabs.com/marketplace/cmt-ent-gastroenterology-and-infectious-diseases-problem-list-subset)\n\t- This problem list subset includes concepts that KP (Kaiser Permanente) uses within the ENT, Gastroenterology, and Infectious Diseases Problem Lists. There are 4,445 SNOMED CT (Systematized Nomenclature of Medicine - Clinical Terms) concepts in the file and includes mappings to ICD-9-CM and ICD-10-CM. This dataset is the compilation of ENT, Gastroenterology, and Infectious Diseases related problem list subsets that KP has released in the year 2012 and 2015.\n- [CMT Emergency Department Problem List Subset](https://www.johnsnowlabs.com/marketplace/cmt-emergency-department-problem-list-subset)\n\t- This problem list subset includes concepts that KP (Kaiser Permanente) uses within the Emergency Department Problem List. There are 2189 concepts in this file. SNOMED CT (Systematized Nomenclature of Medicine - Clinical Terms) concepts are based on the January 2012 International Release.\n- [CMT Endocrine Nephrology And Urology Problem List Subset](https://www.johnsnowlabs.com/marketplace/cmt-endocrine-nephrology-and-urology-problem-list-subset)\n\t- This problem list subset update includes concepts that Kaiser Permanente uses within the Endocrine, Nephrology, and Urology Problem Lists. There are 3,390 SNOMED CT (Systematized Nomenclature of Medicine - Clinical Terms) concepts in the file and includes mappings to ICD-9-CM and ICD-10-CM. This dataset is the compilation of endocrine, nephrology And urology related problem list subsets that KP has released in the year 2012 and 2015.\n- [CMT Hematology And Oncology Problem List Subset](https://www.johnsnowlabs.com/marketplace/cmt-hematology-and-oncology-problem-list-subset)\n\t- This updated problem list subset includes concepts that KP (Kaiser Permanente) uses within Hematology and Onoclogy Problem Lists. There are 6,934 SNOMED CT (Systematized Nomenclature of Medicine - Clinical Terms) concepts in the file and includes mappings to ICD-9-CM and ICD-10-CM. This dataset is the compilation of hematology and onoclogy related problem list subsets that KP has released in the year 2011 and 2015.\n- [CMT History and Family History Problem List Subset](https://www.johnsnowlabs.com/marketplace/cmt-history-and-family-history-problem-list-subset)\n\t- This problem list subset includes concepts that KP (Kaiser Permanente) uses for documenting History and Family History. There are 1,856 SNOMED CT (Systematized Nomenclature of Medicine - Clinical Terms) concepts in the file and includes mappings to ICD-9-CM and ICD-10-CM. This dataset is the compilation of history and family history related problem list subsets that KP has released in the year 2013 and 2015.\n- [CMT Injury Problem List Subset](https://www.johnsnowlabs.com/marketplace/cmt-injury-problem-list-subset)\n\t- This problem list subset includes concepts that KP (Kaiser Permanente) uses within the Injury problem list. There are 6,526 SNOMED CT (Systematized Nomenclature of Medicine - Clinical Terms) concepts in the file and includes mappings to ICD-9-CM and ICD-10-CM. This dataset is the compilation of Injury related problem list subsets that KP has released in the year 2013 and 2014.\n- [CMT Mental Health Problem List Subset](https://www.johnsnowlabs.com/marketplace/cmt-mental-health-problem-list-subset)\n\t- This problem list subset includes concepts that KP (Kaiser Permanente) uses within the Mental Health Problem Lists. There are 2,365 SNOMED CT (Systematized Nomenclature of Medicine - Clinical Terms) concepts in the file and includes mappings to ICD-9-CM and ICD-10-CM. This dataset is the compilation of mental health related problem list subsets that KP has released in the year 2011 and 2016.\n- [CMT Miscellaneous Problem List Subset](https://www.johnsnowlabs.com/marketplace/cmt-miscellaneous-problem-list-subset)\n\t- This miscellaneous problem list subset includes 737 concepts that KP (Kaiser Permanente) uses within the KP electronic medical record. This file also includes mappings to ICD-9-CM and ICD-10-CM. SNOMED CT (Systematized Nomenclature of Medicine - Clinical Terms) concepts are based on the September 2016 United States Edition, which includes the July 2016 International Release.\n- [CMT Musculoskeletal Problem List Subset](https://www.johnsnowlabs.com/marketplace/cmt-musculoskeletal-problem-list-subset)\n\t- This problem list subset includes concepts that KP (Kaiser Permanente) uses within the musculoskeletal problem list. There are 10,165 SNOMED CT (Systematized Nomenclature of Medicine - Clinical Terms) concepts in the file and includes mappings to ICD-9-CM and ICD-10-CM. This dataset is the compilation of musculoskeletal related problem list subsets that KP has released in the year 2011, 2014 and 2016.\n- [CMT Neurology Problem List Subset](https://www.johnsnowlabs.com/marketplace/cmt-neurology-problem-list-subset)\n\t- This problem list subset includes concepts that KP (Kaiser Permanente) uses within the Neurology problem list. There are 3,288 SNOMED CT (Systematized Nomenclature of Medicine - Clinical Terms) concepts in the file and includes mappings to ICD-9-CM and ICD-10-CM. This dataset is the compilation of Neurology related problem list subsets that KP has released in the year 2011 and 2015.\n- [CMT Obstetrics and Gynecology Problem List Subset](https://www.johnsnowlabs.com/marketplace/cmt-obstetrics-and-gynecology-problem-list-subset)\n\t- This problem list subset includes concepts that KP Kaiser Permanente uses within the Obstetrics and Gynecology Problem Lists. There are 2,199 SNOMED CT (Systematized Nomenclature of Medicine - Clinical Terms) concepts in the file and includes mappings to ICD-9-CM and ICD-10-CM. This dataset is the compilation of obstetrics and gynecology related problem list subsets that KP has released in the year 2013and 2015.\n- [CMT Ophthalmology Problem List Subset](https://www.johnsnowlabs.com/marketplace/cmt-ophthalmology-problem-list-subset)\n\t- This problem list subset includes concepts that KP (Kaiser Permanente) uses within the Ophthalmology problem list. There are 6,286 SNOMED CT (Systematized Nomenclature of Medicine - Clinical Terms) concepts in the file and includes mappings to ICD-9-CM and ICD-10-CM. his dataset is the compilation of ophthalmology related problem list subsets that KP has released in the year 2011, 2014 and 2016.\n- [CMT Orthopedics Problem List Subset](https://www.johnsnowlabs.com/marketplace/cmt-orthopedics-problem-list-subset)\n\t- This problem list subset includes concepts that KP (Kaiser Permanente) uses within the Orthopedics problem list. There are 1,137 SNOMED CT (Systematized Nomenclature of Medicine - Clinical Terms) concepts in the file and includes mappings to ICD-9-CM and ICD-10-CM. This dataset is the compilation of Orthopedics related problem list subsets that KP has released in the year 2013 and 2014.\n- [CMT Pediatrics Problem List Subset](https://www.johnsnowlabs.com/marketplace/cmt-pediatrics-problem-list-subset)\n\t- This problem list subset includes concepts that KP (Kaiser Permanente) uses within the Pediatrics Problem List. There are 3,792 SNOMED CT concepts in this file and includes mappings to ICD-9-CM and ICD-10-CM. SNOMED CT (Systematized Nomenclature of Medicine - Clinical Terms) Concepts are based on the January 2012 International Release.\n- [CMT Primary Care Problem List Subset](https://www.johnsnowlabs.com/marketplace/cmt-primary-care-problem-list-subset)\n\t- This updated problem list subset includes concepts that KP (Kaiser Permanente) uses for problem lists for Primary Care. There are 2830 SNOMED CT (Systematized Nomenclature of Medicine - Clinical Terms) concepts in the file and includes mappings to ICD-9-CM and ICD-10-CM.\n- [CMT Specimen Source Problem List Subset](https://www.johnsnowlabs.com/marketplace/cmt-specimen-source-problem-list-subset)\n\t- This problem list subset includes concepts that Kaiser Permanente clinicians use in KP electronic medical records to describe Specimen Source. There are 556 SNOMED CT (Systematized Nomenclature of Medicine - Clinical Terms) concepts in the Specimen Source file. SNOMED CT concepts are based on the January 2014 International Release.\n- [CMT Specimen Type Problem List Subset](https://www.johnsnowlabs.com/marketplace/cmt-specimen-type-problem-list-subset)\n\t- This problem list subset includes concepts that Kaiser Permanente clinicians use in KP electronic medical records to describe Specimen Type. There are 621 SNOMED CT (Systematized Nomenclature of Medicine - Clinical Terms) concepts in the Specimen Type file. SNOMED CT concepts are based on the January 2014 International Release.\n- [CMT Top Concepts Problem List Subset](https://www.johnsnowlabs.com/marketplace/cmt-top-concepts-problem-list-subset)\n\t- This problem list subset includes the top concepts that KP (Kaiser Permanente) uses within the KP electronic medical record. There are 6,264 SNOMED CT (Systematized Nomenclature of Medicine - Clinical Terms) concepts in the file and includes mappings to ICD-9-CM and ICD-10-CM. This dataset is the compilation of top concepts problem list subsets that KP has released in the year 2011, 2014 and 2016.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated\n- Data Updates\n - Data updates support replace-on-update: outdated foreign keys are deprecated, not deleted\n\n**Our data is curated and enriched by domain experts** \n\n Each dataset is manually curated by our team of doctors, pharmacists, public health & medical billing experts:\n- Field names, descriptions, and normalized values are chosen by people who actually understand their meaning\n- Healthcare & life science experts add categories, search keywords, descriptions and more to each dataset\n- Both manual and automated data enrichment supported for clinical codes, providers, drugs, and geo-locations\n- The data is always kept up to date – even when the source requires manual effort to get updates\n- Support for data subscribers is provided directly by the domain experts who curated the data sets\n- Every data source’s license is manually verified to allow for royalty-free commercial use and redistribution.\n\n**Need Help?**\nIf you have questions about our products, contact us at [info@johnsnowlabs.com](mailto:info@johnsnowlabs.com). \n 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monetization. With a single integration, you can use this data for your own campaigns, power a managed-service offering for your clients, or make it available to other advertisers as a third-party data provider.\n\nYou can send audience segments, conversion events, and opt-out/deletion requests. Conversion data can be organized at the product level, enabling granular segmentation and measurement for retail use cases.\n\nSupported identifier types include TDIDs, device IDs, CTV IDs, Ramp IDs, UID2s, and EUIDs. UID2 is a privacy-conscious identity framework for hashing and encrypting email addresses or mobile phone numbers. For details, see the UID2 Marketplace Listing.\n\nTo monetize your data as a third-party data provider on The Trade Desk, reach out to your contact at The Trade Desk for more information.\n\nNOTE: Automatic UID2 generation from email addresses or phone numbers is not yet supported by this SDK. You can generate UID2 with the [UID2 clean room solution](https://unifiedid.com/docs/guides/integration-databricks).\n\n**Capabilities**\n\nOnboard, manage and fully leverage your first-party or third-party data with The Trade Desk DSP.\n\n- Send, update and manage first-party targeting data for both advertisers and onboarders\n\n- Send, update and manage third-party targeting data for data providers and onboarders\n\n- Send offline conversion events data for advertisers, providers, and retailers\n\n- Conversion events support an items collection which can include retail information\n\n- Fully manage deletion and opt-out for advertising identifiers throughout The Trade Desk DSP\n\n- Accepts leading advertising IDs: TDIDs, device IDs, CTV IDs, Ramp IDs, UID2s, and EUIDs. Subject to change.\n\n> **Note**: UID2s are supported in the payload for these capabilities, but the solutions does not currently offer automatic creation of UID2 from email, phone inputs. \n\n**Use cases**\n\n*Audience Activation*\n\nActivate first-party or third-party segments across all programmatic channels in The Trade Desk DSP. Build look-alike models to find new prospects similar to your existing customers.\n\n*Conversion Measurement*\n\nSend conversion events tied to specific products for campaign attribution, reporting, and optimization. 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These datasets enable customers to analyze school quality metrics, track educational outcomes over time, and integrate authoritative K-12 data into real estate platforms, academic research, and business applications."},"popularity":560},{"id":"8ed6c71b-f3d9-4b72-a479-99250a7720c6","summary":{"name":"Geolocet | Latest Demographic Data Liechtenstein - 46 attributes","subtitle":"Demographic data for the 11 municipalities of Liechtenstein and their 2023 population - age, gender, marital status,...","share":{"name":"geolocet_ltd_liechtenstein_demographics_data_2023","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1706002821850,"updated_at":1706002839749,"categories":["DEMOGRAPHICS","ADVERTISING_AND_MARKETING","GEOSPATIAL","PUBLIC_SECTOR","RETAIL"],"listingType":"STANDARD","provider_id":"5d3ddc17-a7e8-4794-a760-13587e1de2c6"},"detail":{"description":"## **Get instant access to the latest Demographics Data for Liechtenstein with [Geolocet](https://geolocet.com)**\n\nExplore the richness of Liechtenstein’s demographic landscape with our Liechtenstein Dataset for 2023. This meticulously compiled dataset, sourced from official statistical sources, including Institute of Liechtenstein’s Office of Statistics, offers a detailed insight into the population landscape of Liechtenstein at the municipality level. \n\n ## **Key Features:**\n---\n* **Population Data Liechtenstein:** Gain access to the latest and most accurate population statistics for Liechtenstein, meticulously organized by municipality.\n\n* **Demographic Insights:** Dive deep into Liechtenstein’s demographic landscape with data on 46 attributes, including: \n* gender,\n* age bands, \n* marital status,\n* religion,\n* main language,\n* country of birth.\n\nAll demographic attributes are available at the municipal level.\n\n* **Geodemographic Insights:** Unlock geodemographic data Liechtenstein-wide to inform location-based decision-making, marketing strategies, and policy formulation.\n* **Seamless Data Access:** Our data is available for instant download in CSV format. No lengthy sign-up processes or NDAs required on our side – just download the data you need.\n---\nAdditionally, for those looking to enrich their analysis or project, we offer:\n* **[Administrative Boundaries](https://geolocet.com/products/liechtenstein-all-available-administrative-boundaries):** If you require geographical boundaries (polygons) for the municipalities of Liechtenstein, consider exploring our Administrative Boundaries product, which seamlessly integrates with this dataset.\n\n* **[Point of Interest (PoI) Data](https://geolocet.com/pages/aggregated-points-of-interest-poi-data):** If you're interested in Points of Interest data, please don't hesitate to contact us for customized datasets to enhance your geographical analysis.\n\n* **[Data Customization](https://geolocet.com/pages/data-services-by-geolocet):** We understand that your project may have specific requirements. Feel free to get in touch with us if you need any modifications or specific data formats such as Excel, JSON, GeoJSON, or Shapefile. We are here to tailor the data to your needs.\n\n\n[Geolocet LTD](https://geolocet.com)\n\n","terms_of_service":"https://geolocet.com/pages/terms-of-use","documentation_link":"","privacy_policy_link":"https://geolocet.com/pages/privacy-policy","geographical_coverage":"[\"Europe\"]","cost":"FREE","update_frequency":{"interval":1,"unit":"YEARLY"},"assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://geolocet.com/pages/terms-of-use"},"provider_summary":{"name":"Geolocet LTD","description":"Geolocet provides premium geospatial data for retail analysis, GIS, and market planning. We equip businesses with accurate, ready-to-query data to drive spatial analysis, site selection, and customer segmentation.\n\nCore Offerings:\n\nDemographics: Socioeconomic data covering population, age, and employment across diverse geographies.\n\nPoints of Interest (PoI): Datasets spanning retail, healthcare, and transport for catchment analysis.\n\nAdministrative Boundaries: GIS-ready boundaries, including municipalities and postcodes, for territory management.\n\nBrand Locations: Chain location datasets to monitor store networks and market expansion.\n\nWealth Data: Granular economic indicators like purchasing power to identify high-potential markets.\n\nWe source from trusted providers, delivering highly customizable datasets with comprehensive coverage across Europe and worldwide."},"popularity":299},{"id":"0edcdcb2-0825-4b73-8149-e5254e5fb8ff","summary":{"name":"Geolocet | Points of Interest Data | Grocery Store Locations - Belgium","subtitle":"Complete GIS-ready dataset of 17,734 Belgium grocery locations (brands & independents). Optimized for Spark/Databricks.","setting":{"visibility":"PUBLIC"},"created_at":1780637569780,"updated_at":1780637593490,"categories":["GEOSPATIAL","RETAIL"],"listingType":"PERSONALIZED","provider_id":"5d3ddc17-a7e8-4794-a760-13587e1de2c6"},"detail":{"description":"# **All Supermarket Locations in Belgium: Complete Geographic Dataset**\n---\n### **Premium GIS-Ready Retail Location Intelligence Data for Databricks Analytics**\n\nThis high-fidelity geospatial dataset provides a complete, exhaustive, and standardized overview of **17,734** verified grocery store and supermarket locations across Belgium. Updated as of **May 26, 2026**, this comprehensive national dataset captures the entire Belgian retail landscape - encompassing both major branded retail chains and independent local markets. It offers an analysis-ready foundation for full-scale retail site selection, competitive intelligence, logistics optimization, and spatial demographics mapping.\n\nFully compatible with **Apache Spark**, **Delta Lake**, and **Databricks Unity Catalog** workflows.\n\n---\n\n## **Complete National Coverage: Branded & Independent Grocers**\nUnlike top-tier only datasets, this exhaustive collection covers maximum geographic depth across all Belgian provinces and the Brussels-Capital Region. It includes thousands of independent markets and local grocers alongside the top national supermarket chains:\n\n- **Major National Supermarkets & Groups:** \n [Delhaize Group (875 locations)](https://geolocet.com/products/belgium-delhaizegroup-grocery), \n [Carrefour (726 locations)](https://geolocet.com/products/belgium-carrefour-grocery), \n [Colruyt (273 locations)](https://geolocet.com/products/belgium-colruyt-grocery), \n [Louis Delhaize (177 locations)](https://geolocet.com/products/belgium-louis-delhaize-grocery), \n [Intermarché (163 locations)](https://geolocet.com/products/belgium-intermarche-grocery)\n\n- **Leading Discounters & Value Brands:** \n [ALDI (459 locations)](https://geolocet.com/products/belgium-aldi-grocery), \n [Lidl (308 locations)](https://geolocet.com/products/belgium-lidl-grocery), \n [Jumbo (46 locations)](https://geolocet.com/products/belgium-jumbo-grocery)\n\n- **Proximity, Organic & Digital Formats:** \n [SPAR (347 locations)](https://geolocet.com/products/belgium-spar-grocery), \n [Collect&Go (281 locations)](https://geolocet.com/products/belgium-collectandgo-grocery), \n [OKay (185 locations)](https://geolocet.com/products/belgium-okay-grocery), \n [Bio-Planet (40 locations)](https://geolocet.com/products/belgium-bioplanet-grocery)\n\n**Plus over 14,000 independent, regional, and unbranded grocers** fully mapped and standardized across all provinces (e.g., Région de Bruxelles-Capitale: 2,518 sites; Province d'Anvers: 2,427 sites; Province de Flandre Orientale: 2,326 sites).\n\n---\n\n## **Technical Specifications & Schema Definitions**\nOptimized for zero-friction schema enforcement when reading via `spark.read.format(\"csv\")`. The structured schema below ensures flawless rendering in Databricks notebooks and high accuracy for LLM semantic indexing.\n\n* **`GUID`** (`STRING`): Unique global identifier for deterministic primary key mapping.\n* **`Title`** (`STRING`): Official store name, brand banner, or independent title of the location.\n* **`Group`** (`STRING`): Parent retail group or corporate entity classification (e.g., Colruyt Group, Carrefour).\n* **`Latitude`** (`DOUBLE`): Precise WGS84 latitude coordinate for point-in-polygon & spatial joins.\n* **`Longitude`** (`DOUBLE`): Precise WGS84 longitude coordinate for spatial indexing (H3 / S2).\n* **`Street_No` / `Street`** (`STRING`): Normalized building/house number and street name elements.\n* **`City` / `Postal_Code`** (`STRING`): Core postal addresses and primary municipal settlement names.\n* **`Area`** (`STRING`): Local neighborhood or borough associated with the retail point.\n* **`Commune`** (`STRING`): Belgian municipal administrative boundary designation (Commune / Gemeente).\n* **`Province`** (`STRING`): High-level provincial classification (e.g., Anvers, Liège, Hainaut).\n* **`Admin_level_1` / `_2`** (`STRING`): Additional macro-regional and district boundary identifiers.\n* **`Population`** (`INTEGER`): Contextual demographic population metric attributed to the location.\n* **`Wheelchair`** (`STRING`): Accessibility flags indicating wheelchair mobility support.\n* **`Popularity_Score`** (`INTEGER`): Relative foot-traffic/rating metric graded on a standardized 1-10 scale.\n* **`Opening_Hours`** (`STRING`): Standard daily operational schedules and structured operating text.\n* **`Phone` / `Website`** (`STRING`): Direct communication data, branch-specific landing URLs, and domain vectors.\n\n---\n\n## **Databricks Workflow & Spatial Analytics Use Cases**\n\n- **Whitespace & Underserved Market Analysis:** Query the full 17,734 locations against local population density data to identify true retail deserts and expansion opportunities untouched by major national networks.\n- **H3 Geospatial Indexing:** Convert the precise WGS84 `Latitude` and `Longitude` fields into Uber H3 spatial hexagons (`h3_longlattocell`) to cross-reference with granular statistical sector income datasets or demographic raster layers.\n- **Logistics & Transit Accessibility:** Cross-reference store locations with urban transit hubs to evaluate accessibility for non-driving customers, or model optimized supply routes across the Belgian highway network.\n\n---\n\n## **Data Evaluation & Custom Ingestion**\n\n### **Evaluate with a Free Sample Dataset**\nVerify the structure, column types, and coordinate precision before full implementation. A comprehensive schema sample is available for download in CSV/Parquet-friendly formats.\n\n*Want deeper market insights?* Learn more about the brand network in our comprehensive report: **[View the Belgium Top Brands Grocery Report](https://geolocet.com/pages/belgium-top-brands-grocery-report)**\n\n### **Enterprise Delivery & Tailored Formatting**\nWhile natively available as an analysis-ready CSV for DBFS ingestion, we accommodate enterprise-level data architecture needs. Alternative formats - including **GeoJSON, Esri Shapefiles, PostgreSQL dumps, Excel, or direct Delta sharing profiles** - can be generated. Reach out to our engineering team at **contact@geolocet.com** for integration support or custom European POI data requests.","terms_of_service":"https://geolocet.com/pages/terms-of-use","documentation_link":"","privacy_policy_link":"https://geolocet.com/pages/privacy-policy","assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://geolocet.com/pages/terms-of-use"},"provider_summary":{"name":"Geolocet LTD","description":"Geolocet provides premium geospatial data for retail analysis, GIS, and market planning. 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Data Axle combines deterministic offline and online sources—voter files, utility connects, real estate, and more—to create enriched, actionable consumer profiles which can be consumed directly within your Databricks environment. \n\n**What Sets Us Apart:**\nUnlike traditional forecasting models built on static rules or demographics, our AI engine continuously learns and adapts, using thousands of behavioral, attitudinal, and transactional signals to generate nuanced audience definitions \n\n- Trained on expansive offline PII data, utilizing all signals (not just age/income) \n- Scores every individual (0–1) by propensity to act \n- Optimized for personalized targeting, next-best action, and cross-sell models \n- Built for scalable segmentation, enrichment, and omnichannel activation \n\n**Key Attributes**\n- Verified lifestyle behavior: gym visits, fitness classes, fitness app usage \n- Purchase behavior for supplements, vitamins, health foods, and wellness products \n- Engagement with at-home fitness, connected devices, and streaming workouts \n- Clean-living indicators: organic buying, food label awareness, and avoidance of processed items \n- Digital engagement: subscription wellness programs, guided meditation, wearables \n- Motivational drivers for health-related routines (weight management, mental well-being, prevention) \n\n**Segment Categories**\nEach category includes multiple AI-scored segments – below are illustrative examples and business applications. 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We deliver tailored segments within 5–10 business days \n\n**Business Needs**\n- *360-Degree Customer View*\n - Build comprehensive profiles of health-conscious consumers by enriching your data with verified behavior across physical activity, nutrition, and wellness engagement. \n- *Customer Acquisition*\n - Drive efficient acquisition of consumers highly motivated by health, longevity, and physical performance. 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It is optimized to answer questions and assist users with various Travel-related procedures. It has been trained using hybrid synthetic data generated using our NLP/NLG technology and our automated Data Labeling (DAL) tools.\n \nThe goal of this model is to show that a generic verticalized model makes customization for a final use case much easier. An overview of this approach can be found at: [From General-Purpose LLMs to Verticalized Enterprise Models](https://www.bitext.com/blog/general-purpose-models-verticalized-enterprise-genai/)\n\n**Intended Use**\n\n- **Recommended applications**: This model is designed to be used as the first step in Bitext’s two-step approach to LLM fine-tuning for the creation of chatbots, virtual assistants and copilots for the Travel domain, providing customers with fast and accurate answers about their travel needs.\n- **Out-of-scope**: This model is not suited for non-travel related questions and should not be used for providing health, legal, or critical safety advice.\n\n**Model Architecture**\n\nThis model utilizes the `MistralForCausalLM` architecture with a `LlamaTokenizer`, ensuring it retains the foundational capabilities of the base model while being specifically enhanced for travel-related interactions.\n\n**Training Data**\n\nThe model was fine-tuned on the [Bitext Travel Dataset](https://huggingface.co/datasets/bitext/Bitext-travel-llm-chatbot-training-dataset) comprising various travel-related intents, including: book_flight, choose_seat, check_arrival_time, book_trip, purchase_flight_insurance, check_cancellation_fee, check_baggage_allowance, and more. Totaling 33 intents, and each intent is represented by approximately 1000 examples.\n\nThis comprehensive training helps the model address a broad spectrum of travel-related questions effectively. The dataset follows the same structured approach as our dataset published on Hugging Face as [bitext/Bitext-customer-support-llm-chatbot-training-dataset](https://huggingface.co/datasets/bitext/Bitext-customer-support-llm-chatbot-training-dataset), but with a focus on travel.\n\n**Training Procedure**\n\n**Hyperparameters**\n\n- **Optimizer**: AdamW\n- **Learning Rate**: 0.0002 with a cosine learning rate scheduler\n- **Epochs**: 1\n- **Batch Size**: 4\n- **Gradient Accumulation Steps**: 8\n- **Maximum Sequence Length**: 8192 tokens\n\n**Environment**\n\n- **Transformers Version**: 4.43.4\n- **Framework**: PyTorch 2.3.1+cu121\n- **Tokenizers**: Tokenizers 0.19.1\n\n**Limitations and Bias**\n\n- The model is trained for travel-specific contexts but may underperform in unrelated areas.\n- Potential biases in the training data could affect the neutrality of the responses; users are encouraged to evaluate responses critically.\n\n**Ethical Considerations**\n\nIt is important to use this technology thoughtfully, ensuring it does not substitute for human judgment where necessary, especially in sensitive situations.\n\n**Acknowledgments**\n\nThis model was developed and trained by Bitext using proprietary data and technology.\n\n**License**\n\nThis model, \"Mistral-7B-Travel\", is licensed under the Apache License 2.0 by Bitext Innovations International, Inc. This open-source license allows for free use, modification, and distribution of the model but requires that proper credit be given to Bitext.\n\n**Key Points of the Apache 2.0 License**\n\n- **Permissibility**: Users are allowed to use, modify, and distribute this software freely.\n- **Attribution**: You must provide proper credit to Bitext Innovations International, Inc. when using this model, in accordance with the original copyright notices and the license.\n- **Patent Grant**: The license includes a grant of patent rights from the contributors of the model.\n- **No Warranty**: The model is provided \"as is\" without warranties of any kind.\n\nYou may view the full license text at [Apache License 2.0](http://www.apache.org/licenses/LICENSE-2.0).\n\nThis licensing ensures the model can be used widely and freely while respecting the intellectual contributions of Bitext. 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This listing contains a sample for Cigna's rates in Illinois for the top 25 billed CPT procedures. The data includes specific providers' negotiated rates. \n\n**Use cases**\n\nFor Providers:\n- Competitor benchmarking -- how does your reimbursement schedule compare to peers \n- Contracting and payer strategy -- negotiating higher reimbursements with commercial payers\n- Roll-ups / acquisitions / M&A -- identifying targets such as highest reimbursed practices in a region\n- Provider recruiting and partnerships -- prospecting health systems or individual practitioners to add to your practice\n- Margin expansion / improvement -- forecasting revenue impact from changing operations (reducing time per visit from ~45 min. to ~20 min., performing procedures using nurse practitioners vs. physicians, alternative coding, ...)\n\nFor Payers:\n\n- Rate and network benchmarking -- size, cost, and scope of networks across competitors and their products (HMO, PPO, ACO, self-insured)\n- Network expansion, optimization, and management -- scan market to understand relative cost of care, add providers in-network based on adequacy, create narrow high-performance networks, evaluate provider integration to become a \"pay-vider\" \n- Plan design -- right-size premiums / deductibles based on reimbursement fees, innovate on fee-for-service and create bundles / capitated arrangements\n\nFor Employers, TPAs, Brokers\n\n- Network comparison -- comparing cost of care across various networks and products \n- Direct contracting -- determining which providers to add to your network\n- Payment integrity, claims verification, claims re-pricing -- assess over/under-billing against published fee schedules\n- Network tiering and steering -- identify sites with lower cost of care and create dynamic co-pay programs to direct patients \n\nFor Pharmaceuticals, Life Sciences, Medical Devices:\n\n- Pricing analysis -- see what 3rd party commercial payers reimburse providers for injectables, medical supplies to capture their margin\n\n**Product details**\nColumns included in the sample are: \n- Payer: Cigna\n- Region: Either IL or \"Blank\" if not provided\n- Network: Localplus PPO\n- EIN: A 9 digit taxable identification number without hyphen \n- NPI List: Comma separated NPI values\n- NPI List Length: Count of NPIs in NPI list\n- Taxonomy: Most common NUCC Taxonomy code for the NPIs in the NPI List\n- Taxonomy Name: NUCC description associated with NUCC Taxonomy Code in \"Taxonomy\" field\n- Code: 5 character CPT or HCPCS\n- Code Type: CPT or HCPCS\n- Code Type Version: Year associated with published Code Type\n- Modifier List: Any billing code modifiers present on this code and rate, comma separated. If not present, will be an empty string \n- Bundled Code List: For ‘bundled’ or ‘capitation’ arrangements, comma separated list of codes included in the bundled rate, otherwise empty string.\n- Billing Class: Allowed values: ‘professional’ or ‘institutional’\n- Place Of Service List: Comma separated list of CMS place of service codes that apply to the stated rate\n- Negotiation Type: Allowed values: \"negotiated\", \"derived\", \"fee schedule\", \"percentage\", and \"per diem\"\n- Arrangement: Allowed values: ‘ffs’, ‘bundled’, ‘capitation’\n- Rate: Decimal with two digits after a period, no commas or dollar signs. e.g. 10125.85\n- Additional Information: Any commentary published by payor in MRF, e.g., why 2 different rates exist for the same provider and code\n- Is Billable: Allowed values: 'true' if CMS accepts claim from that taxonomy for given code, 'false' if CMS does not accept, 'null\" if un-determined\n- Matched On: Field explaining data extension source. Allowed values: “Type1NPI”, “Type2NPI”, “TIN”, “ImputedType2NPI”\n- Matched ID: The ID (matched or imputed) used to tag this data row\n- Entity Name: Organization or Entity Name, from TIN or NPI data sources\n- Entity Address: Organization or Entity Address, from TIN or NPI data sources\n","terms_of_service":"https://www.serifhealth.com/terms-of-use","documentation_link":"","privacy_policy_link":"https://www.serifhealth.com/privacy-policy","geographical_coverage":"[\"United States and Canada\"]","cost":"FREE","update_frequency":{"interval":1,"unit":"MONTHLY"},"data_source":"https://www.cigna.com/legal/compliance/machine-readable-files"},"provider_summary":{"name":"Serif Health","description":"Serif Health provides access to comprehensive price transparency data from payor machine readable file (MRF) fee schedules including specific negotiated reimbursement rates by NPI, EIN, specialty, and location for available CPT, DRG, HCPCS, and RC codes. 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We commonly make our data available in csv, parquet extracts but can work with any configuration."},"popularity":541},{"id":"9677800f-1330-4117-b7cd-7d3149cff6db","summary":{"name":"Neighborhood Demographics","subtitle":"neighborhood demographic analytics for every state, city and neighborhood in the U.S.","setting":{"visibility":"PUBLIC"},"created_at":1789679528880,"updated_at":1789679528880,"categories":["FINANCIAL"],"listingType":"PERSONALIZED","provider_id":"26040e80-2e39-4e01-8fea-0667064d9fce"},"detail":{"description":"Neighborhood Demographics delivers demographic analytics for every state, city, and neighborhood across the U.S, refreshed annually to ensure consistency and accuracy. 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This can help in defining audience segments and tailoring marketing strategies accordingly.\n**Customer Acquisition**\nBy understanding the demographics of a neighborhood, businesses can identify potential customers and develop targeted acquisition strategies. For example, a business selling high-end products might target neighborhoods with higher income levels.\n**Market Analysis**\nThe demographic data of a neighborhood can be used for in-depth market analysis. Businesses can use this data to identify potential markets, understand market trends, and make informed decisions.\n**Location Data Enrichment**\nEnrich the datasets related to location intelligence. It can provide a comprehensive view of a neighborhood, improving the accuracy of business insights based on location data.\n**Personalize Customer Experiences**\nBusinesses can use demographic data to tailor their services and products to the needs and preferences of the customers in a particular neighborhood, thereby enhancing the customer experience.\n**Risk Analysis**\nFor businesses in sectors like insurance or real estate, demographic data can be used to assess risk. For instance, a higher percentage of young drivers in a neighborhood might indicate a higher risk of auto insurance claims.","terms_of_service":"https://www.cotality.com/","documentation_link":"https://www.cotality.com/products/neighborhood-demographics?utm_campaign=412443580-EDSG-ALL-CloudMarketplace-Databricks-2026&utm_source=databricks&utm_medium=marketplace&utm_term=neighborhooddemographics","privacy_policy_link":"https://www.cotality.com/","assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"Cotality","description":"We illuminate data, insights, and workflows across the property ecosystem, analyzing the largest objective dataset in the U.S. for real-time insights in minutes, not months. 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Our database currently consists of 171MM+ highly curated B2B Contacts ( US only), along with over 4M+ companies, and is updated regularly to ensure we have the most up-to-date information. \n\nWe can enrich your in-house data ( CRM Enrichment, Lead Enrichment, etc.) and provide you with a custom dataset ( such as a lead list) tailored to your target audience specifications and data use-case. We also support large-scale data licensing to software providers and agencies that intend to redistribute our data to their customers and end-users. \n\nWhat makes Salutary unique?\n- We offer our clients a truly unique, one-stop aggregation of the best-of-breed quality data sources. Our supplier network consists of numerous, established high quality suppliers that are rigorously vetted.\n- We leverage third party verification vendors to ensure phone numbers and emails are accurate and connect to the right person. Additionally, we deploy automated and manual verification techniques to ensure we have the latest job information for contacts.\n- We're reasonably priced and easy to work with.\n\nProducts:\nAPI Suite\nWeb UI\nFull and Custom Data Feeds\n\nServices:\nData Enrichment - We assess the fill rate gaps and profile your customer file for the purpose of appending fields, updating information, and/or rendering net new “look alike” prospects for your campaigns.\nABM Match & Append - Send us your domain or other company related files, and we’ll match your Account Based Marketing targets and provide you with B2B contacts to campaign. Optionally throw in your suppression file to avoid any redundant records.\nVerification (“Cleaning/Hygiene”) Services - Address the 2% per month aging issue on contact records! We will identify duplicate records, contacts no longer at the company, rid your email hard bounces, and update/replace titles or phones. This is right up our alley and levers our existing internal and external processes and systems.","terms_of_service":"https://salutarydata.com/privacy-policy","contact_email":"support@datarade.ai","privacy_policy_link":"https://salutarydata.com/privacy-policy","assets":["ASSET_TYPE_DATA_TABLE"]},"provider_summary":{"name":"Salutary Data","description":"In 2016, Jeff Clewley and Scott Gordon co-founded Salutary Data. Previously, Jeff and Scott worked together for 6 years at NetProspex. As Technical Co-Founder and SVP of Partnerships, respectively, Jeff and Scott contributed to NetProspex's growth until it was sold to Dun & Bradstreet in 2015.\r\n\r\nJeff and Scott founded Salutary Data on several principles. Data accuracy is essential; our north star, our reason to be in business. The data industry is a vital sector and there are good people in it. We wanted to continue to participate in it, find a way to give back to it, and serve our customers well."},"popularity":1274},{"id":"3fcd1d2d-cbec-489b-895a-0be071383efa","summary":{"name":"Epsilon CDP","subtitle":"Marketing solution with connected identity for best view of customers to create intelligent engagement & improved CLV","setting":{"visibility":"PUBLIC"},"created_at":1729866385132,"updated_at":1736281305107,"categories":["ADVERTISING_AND_MARKETING","DEMOGRAPHICS","EDUCATION","CLIMATE_AND_ENVIRONMENT","FINANCIAL","GAMING","HEALTH","RETAIL","SECURITY","SPORTS","TRAVEL_AND_TOURISM","TRANSPORTATION_AND_LOGISTICS","MEDIA"],"listingType":"PERSONALIZED","provider_id":"158429b8-233e-4e4c-9f60-54eae0ad4821"},"detail":{"description":"**Overview**\nEpsilon CDP is an extended first party data marketing solution, built on connected identity, that delivers a unified view of individual customers across owned & paid channels so marketers can natively execute the highest performing activation through a simple and powerful self-service environment. Its solution scales up and down to make sure mid-market and enterprise clients can drive success with first party data marketing.\n\n**Use cases**\nImproving customer lifetime value, customer nurturing, increasing spend during conversion interactions, reducing costs to drive marketing outcomes\n\n**Product details**\nSample System Data Objects Available: \n\n•\tGolden Profile: the best, single view of a customer\n•\tBest ContactPointPhoneVoice: the most effective phone number\n•\tBest ContactPointPhoneSMS: the most effective mobile number for SMS\n•\tBest ContactPointEmail: the most effective email address \n•\tBest ContactPointAddress: the most effective physical address \n•\tWebsite Activity: Site visitation data at the Golden Profile \n•\tAnalytic Model: The reference data for AI scores\n•\tAnalytic Model Profile Output: The AI output scores for a Profile\n•\tAnalytic Model Product Output: The AI output scores related to a product or product category\n•\tTotal Source Plus: A view of a the Total Source Plus attributes for a Golden Profile\n•\tCore ID Distinct Count: The count of individuals with a Core ID\n\n**Securely Access Your Data via Delta Sharing**\n•\tWith Epsilon CDP, customers can effortlessly unify customer data and activate it across platforms, leveraging \nDelta Sharing.\n•\tEpsilon utilizes Databricks' Delta Sharing with Epsilon CDP to securely receive and return data. 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Client would be able to activate Sports Innovation Lab audiences to reach a highly targeted women's sports fan audience at scale based on transaction data and behavioral data.\n\n**Product details**\n- Deterministic at scale\n- **Fluid Fan communities:** Values Driven, Fans of Women’s Sports, Gen Z, High Value Bettors, Sports Streamers + 100 more…\n- **Available on:** LiveRamp, The Trade Desk, DV360, AdTheorent, Xandr, Viant, Yahoo!, and more\n\n_**Example audiences:**_\n\n*Teams*\n- Sports Innovation Lab > Purchase Based Fans > NBA > New York Knicks\n- Sports Innovation Lab > Purchase Based Fans > MLS > Columbus Crew\n\n*Leagues/Sports*\n- Sports Innovation Lab > Purchase Based Fans > MLB\n- Sports Innovation Lab > Purchase Based Fans > Tennis\n\n*Youth Sports*\n- Sports Innovation Lab> Sports Parents>Soccer Fans>Apparel & Accessories, Hobbies & Toys, Gear, Memberships\n- Sports Innovation Lab> Youth Sports Fans>Golf>Gear, Merchandise, Subscriptions, Entertainment\n\n*Betting*\n- Sports Innovation Lab> Fan Gamblers & Bettors>NBA & Basketball >Online Properties & Resorts, Casinos\n\n*Interests*\n- Sports Innovation Lab > Sports Gamer Fans>All Sports Entertainment>Console, Mobile, Online Gaming\n- Sports Innovation Lab> Outdoor Enthusiast Fans>Entertainment Services, Memberships, Gear\n- Sports Innovation Lab> Charitable & Sustainable Giving Sports Fans>NHL\n\n*Women's Sports*\n- Sports Innovation Lab> Sports Fans>High Value Fans>Women's Sports\n- Sports Innovation Lab> Gen Z Sports Fans>Women's Sports\n\n*Apparel*\n- Sports Innovation Lab> Fan Basketball>NBA, Basketball Entertainment, Apparel, Subscriptions>Sneakerhead and Apparel\n\n*Streaming Services*\n- Sports Innovation Lab> Home Entertainment Sports Fan>OTT Subscriber>MLB & Baseball Fans>Video Streaming\n\n**Data Assets:**\n- Individual and Household Attributes:\n - 140M+ Individuals’ passively collected transactional data\n - 240M+ Additional individual's targeting attributed (name-based or pseudonymous)\n - 8.5B+ Online behaviors\n - 700M+ Individuals’ emails\n - 7000+ Sports & Entertainment proprietary IP identified Venues, Merchants, Brands\n - 80+ Custom Sports & Entertainment communities\n\n**Additional Insights**\nCustom audiences are available upon request to address specific client targeting needs above and beyond syndicated audience offerings. Audience segments can also be sent to your CRM first-party data systems to improve owned assets. Sports Innovation Lab can push these prebuilt audiences on a unique identifier of your choice.\n\nData is for US consumers only.","terms_of_service":"https://www.sportsilab.com/termsofservice","documentation_link":"","privacy_policy_link":"https://www.sportsilab.com/privacy-policy","geographical_coverage":"[\"United States and Canada\"]","cost":"PAID","assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"Sports Innovation Lab","description":"Sports Innovation Lab is an Insight, Data and Analytics company that enables brands and sports properties to build more effective sponsorships, improve targeted advertising, and enhance fan experiences. 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Learn how global brands including the NFL, Nike, NHL, Ally Financial, WNBA, and NASCAR use Sports Innovation Lab's data, audiences, and strategic advisory to drive revenue by visiting sportsilab.com or following on LinkedIn, Instagram, and Twitter."},"popularity":705},{"id":"abc3c3ea-b8dc-4a8f-87ba-1067a86f115c","summary":{"name":"Mobile Food","subtitle":"This data package contains datasets related to mobile food including Calderdale public food register, prices of mobile..","share":{"name":"mobile_food","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1682356567248,"updated_at":1695692364954,"categories":["SCIENCE_AND_RESEARCH"],"listingType":"STANDARD","provider_id":"f88849f8-9d77-4de2-b4c7-03d55cc3768e"},"detail":{"description":"\n**Overview**\nThis data package contains datasets related to mobile food including Calderdale public food register, prices of mobile food, food nutrition value database, mobile food schedule and facility permit information.\n\n**Description**\nCalderdale Public Food Register dataset contains details of all Calderdale establishments registered to sell food including name, address, ward and type. Food-Away-From-Home Prices dataset provides average prices for food away from home (FAFH) and alcohol, both at home and away from home. Food away from home is an integral component of the typical American diet and food budget; it also plays a key role in the nutrition and health of Americans. Data on variation in food prices over time and across regions allows estimating how price changes affect the demand for different products such as through changes in quantities purchased or expenditures and to examine how changes in demand, in turn, affect nutritional and health outcomes The USDA (United States Department of Agriculture) National Nutrient Database for Standard Reference (SR) is the major source of food composition data in the United States. It provides the foundation for most food composition databases in the public and private sectors. Mobile Food Facility Permit dataset consists of data related to mobile food facility permits including name of vendor, location, type of food sold and status of the permit. Mobile Food Schedule dataset consists of food schedule of mobile food vendors including the day of the week, start and end time, location and a description of the type of food sold by the vendor. San Francisco (SF) OpenData is the central clearing house for data published by the City and County of San Francisco.\n\n**Benefits**\n- This data package contains complete information related to mobile food services in the united states including name, address, and type of mobile food, its prices, facility permit information and schedule of mobile food vendors including the day of the week, start and end time, location and a description of the type of food sold by the vendor.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [Calderdale Public Food Register](https://www.johnsnowlabs.com/marketplace/calderdale-public-food-register)\n\t- This dataset contains details of all Calderdale establishments registered to sell food including name, address, ward and type.\n- [Food Away From Home Prices](https://www.johnsnowlabs.com/marketplace/food-away-from-home-prices)\n\t- This dataset consists of average quarterly, weighted prices for 20 food-away-from-home (FAFH) and alcohol products for the United States, the four census regions and nine census divisions, as well as for four establishment types—full-service restaurants, limited-service restaurants, vending machines, and school lunches.\n- [Food Nutrition Value Database](https://www.johnsnowlabs.com/marketplace/food-nutrition-value-database)\n\t- The USDA (United States Department of Agriculture) National Nutrient Database for Standard Reference (SR) is the major source of food composition data in the United States. It provides the foundation for most food composition databases in the public and private sectors.\n- [Mobile Food Facility Permit](https://www.johnsnowlabs.com/marketplace/mobile-food-facility-permit)\n\t- This dataset consists of data related to mobile food facility permits including name of vendor, location, type of food sold and status of the permit.\n- [Mobile Food Schedule](https://www.johnsnowlabs.com/marketplace/mobile-food-schedule)\n\t- This dataset consists of food schedule of mobile food vendors including the day of the week, start and end time, location and a description of the type of food sold by the vendor.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - 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The update includ..","share":{"name":"clinical_diagnostic_carrier_and_laboratory_fee_schedule","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1682355730774,"updated_at":1695690575185,"categories":["HEALTH","SCIENCE_AND_RESEARCH"],"listingType":"STANDARD","provider_id":"f88849f8-9d77-4de2-b4c7-03d55cc3768e"},"detail":{"description":"\n**Overview**\nThis data package contains the clinical diagnostic laboratory fee schedule revised for January 2016. The update includes all changes identified in CR 9465. It also contains list of Medicare fee locality, which is a specific geographical area designated by Centers for Medicare and Medicaid Services (CMS) to receive the same fee schedule amount.\n\n**Description**\nThis data package contains the Outpatient clinical laboratory services are paid based on a fee schedule in accordance with Section 1833(h) of the Social Security Act. Payment is the lesser of the amount billed, the local fee for a geographic area, or a national limit. In accordance with the statute, the national limits are set at a percent of the median of all local fee schedule amounts for each laboratory test code. Each year, fees are updated for inflation based on the percentage change in the Consumer Price Index. However, legislation by Congress can modify the update to the fees. Co-payments and deductibles do not apply to services paid under the Medicare clinical laboratory fee schedule. Each year, new laboratory test codes are added to the clinical laboratory fee schedule and corresponding fees are developed in response to a public comment process. There are two main types of fee localities: Medicare Physicians Fee Schedule (MPFS) Localities and Reasonable Charge Localities. MPFS Localities are used to determine payments for codes that are carried on the Medicare Physician's Fee Schedule. Reasonable Charge localities are used to determine pricing for the types of procedures.\n\n**Benefits**\n- Medicare physician fee schedule (mpfs) is used by cms to reimburse physician services and it includes resource costs associated with physician work, practice expense and professional liability insurance.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [Clinical Diagnostic Carrier and Locality Codes](https://www.johnsnowlabs.com/marketplace/clinical-diagnostic-carrier-and-locality-codes)\n\t- A Medicare fee locality is a specific geographical area designated by CMS (Centers for Medicare and Medicaid Services) to receive the same fee schedule amount. Fee localities are typically divided along county lines, and may consist of a single county, several counties or maybe a statewide designation.\n- [Clinical Diagnostic Laboratory Fee Schedule](https://www.johnsnowlabs.com/marketplace/clinical-diagnostic-laboratory-fee-schedule)\n\t- This dataset contains the clinical diagnostic laboratory fee schedule revised for April 2023. 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Two hardware contexts are covered:\n- Phone-based replay attacks — spoofing attempts captured and replayed via mobile devices, reflecting the most common attack vector in mobile biometric authentication\n- PC-based replay attacks — scenarios involving desktop or laptop cameras, relevant for web-based identity verification and enterprise access control\nThis dual-device coverage allows anti-spoofing models to generalize across different attack types and hardware profiles encountered in real-world deployments.\n\nUse Cases\n- Financial Services & FinTech. Banks and digital financial platforms use this dataset to strengthen biometric authentication pipelines, training detection systems to recognize replay attacks during remote onboarding, KYC procedures, and identity verification workflows. FinTech companies face up to 100,000 biometric spoofing attempts monthly — this data directly supports fraud prevention.\n- Mobile Identity & Access Management. 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The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [Approved Risk Evaluation and Mitigation Strategies Materials Data](https://www.johnsnowlabs.com/marketplace/approved-risk-evaluation-and-mitigation-strategies-materials-data)\n\t- The Approved Risk Evaluation and Mitigation Strategies Materials (REMS) dataset includes data on all of the materials that have ever been part of a REMS program, including information on materials that are no longer subject to a Risk Evaluation and Mitigation Strategies (REMS).\n- [Approved Risk Evaluation and Mitigation Strategies Products Data](https://www.johnsnowlabs.com/marketplace/approved-risk-evaluation-and-mitigation-strategies-products-data)\n\t- The Approved Risk Evaluation and Mitigation Strategies Products dataset includes data on all of the drugs that have ever been part of a REMS program, including information on products that are no longer marketed and/or no longer subject to a Risk Evaluation and Mitigation Strategies (REMS).\n- [Clinical Investigator Inspection List](https://www.johnsnowlabs.com/marketplace/clinical-investigator-inspection-list)\n\t- The Clinical Investigator Inspection List (CLIIL) contains names, addresses, and other pertinent information gathered from inspections of clinical investigators who have performed studies with investigational new drugs.\n- [Clinical Investigators Disqualification Proceedings](https://www.johnsnowlabs.com/marketplace/clinical-investigators-disqualification-proceedings)\n\t- The Clinical Investigators Disqualification Proceedings dataset lists clinical investigators who are or have been subject to an administrative clinical investigator disqualification action and indicates the current status of that action.\n- [Drugs Application Number and Sponsor](https://www.johnsnowlabs.com/marketplace/drugs-application-number-and-sponsor)\n\t- This dataset contains the Applications part to build the Drugs@FDA database. Drugs at FDA is a database of FDA Approved Drug Products available on the FDA official website. It provides information of drug (generic) name, active ingredient, form and strength available, FDA application number, label info, dosage form or route, marketing status and pharmaceutical company as well as patient information, approval letters, review and other facts for drugs approved after 1997.\n- [Drugs Application Submission Property Type](https://www.johnsnowlabs.com/marketplace/drugs-application-submission-property-type)\n\t- This dataset contains the Application Submission Property Type part to build the Drugs@FDA database. Drugs at FDA is a database of FDA Approved Drug Products available on the FDA official website. It provides information of drug (generic) name, active ingredient, form and strength available, FDA application number, label info, dosage form or route, marketing status and pharmaceutical company as well as patient information, approval letters, review and other facts for drugs approved after 1997.\n- [Drugs Data File Document Addresses](https://www.johnsnowlabs.com/marketplace/drugs-data-file-document-addresses)\n\t- This dataset contains the Application Documents part to build the Drugs@FDA database. Application Documents refers to \"document addresses or URLs to letters, labels, reviews, Consumer Information Sheets, FDA Talk Papers, and other types. Drugs at FDA provides information of drug name, active ingredient, strength, application number, label info, dosage form or route, marketing status as well as patient information, approval letters, review and other facts for drugs approved after 1997.\n- [Drugs Database Marketing Status](https://www.johnsnowlabs.com/marketplace/drugs-database-marketing-status)\n\t- This dataset contains the Marketing Status part to build the Drugs@FDA database. Drugs at FDA is a database that provides information of drug (generic) name, active ingredient, form and strength available, FDA application number, label info, dosage form or route, marketing status and pharmaceutical company as well as patient information, approval letters, review and other facts for drugs approved after 1997.\n- [Drugs Database Products Included](https://www.johnsnowlabs.com/marketplace/drugs-database-products-included)\n\t- This dataset contains the Product part to build the Drugs@FDA database. Drugs at FDA is a database of FDA Approved Drug Products available on the FDA official website. It provides information of drug (generic) name, active ingredient, form and strength available, FDA application number, label info, dosage form or route, marketing status and pharmaceutical company as well as patient information, approval letters, review and other facts for drugs approved after 1997.\n- [Drugs Database Submissions Included](https://www.johnsnowlabs.com/marketplace/drugs-database-submissions-included)\n\t- This dataset contains the Application Submission part to build the Drugs@FDA database from the Food and Drug Administration (FDA) Approved Drug Products available on their official website. It provides information on drug (generic) name, active ingredient, form and strength available, FDA application number, label info, dosage form or route, marketing status, and pharmaceutical company as well as patient information, approval letters, review and other facts for drugs approved after 1997.\n- [Drugs Therapeutic Equivalence Code](https://www.johnsnowlabs.com/marketplace/drugs-therapeutic-equivalence-code)\n\t- This dataset contains the Therapeutic Equivalence Code part to build the Drugs@FDA database. Application Documents refers to \"document addresses or URLs to letters, labels, reviews, Consumer Information Sheets, FDA Talk Papers, and other types. Drugs at FDA is a database of FDA Approved Drug Products. 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This dataset not only covers comprehensive geographic and demographic data but also includes flags for membership in various regional groups like the EZ, BeNeLux, and others, making it an indispensable tool for regional analysis. It is a valuable resource for anyone needing comprehensive geospatial data and demographic data on the European Union.\n\n## **Key Statistics:**\n\n- **Number of Countries:** 27\n- **Number of Municipalities:** 71,318\n- **Total Population:** 441,024,533\n- **Number of Males:** 215,492,705\n- **Number of Females:** 225,531,828\n- **Number of Children Aged Below 15:** 65,244,368\n- **Number of People Aged 15-64:** 282,032,125\n- **Number of People Aged 65 and Above:** 93,748,040\n- **Area in Sq Km:** 4,265,632.46\n- **Population Density per Sq Km:** 103.39\n\n### **Product Features:**\n\n- **Latest Data Collection**: Featuring data from the year 2023, meticulously compiled and updated as of April 2024 to ensure the highest accuracy and relevance.\n- **Geographic Precision**: Each entry includes detailed polygon shapes of the municipalities in EPSG: 4326 projection, ideal for spatial analysis and mapping.\n- **Rich Demographic Insights**: Features demographic details for each municipality, including:\n - Total Population\n - Gender Breakdown (Males, Females)\n - Age Groups (0-14, 15-64, 64+)\n - Population Density\n- **Regional Membership Flags**: Easily identify municipalities by their regional affiliations with flags for:\n - Eurozone (EZ)\n - Balkan Countries\n - Baltic Countries\n - Scandinavian Countries\n - BeNeLux Countries\n- **Regional Information**: Data is segmented by Country Code, Country Name, and Region, enhancing regional analysis capabilities.\n- **Multiple Data Formats**: Each dataset is available in both shapefile and CSV format, ensuring compatibility with major GIS software and other data handling tools.\n- **Sample Data**: Test the quality and compatibility of our data with a free sample available for download.\n- **Related offerings**: For targeted analysis, the following datasets are available for instant download including regions such as the [Europe](https://geolocet.com/products/europe-geodemographics-dataset-with-boundaries), [Eurozone](https://geolocet.com/products/eurozone-geodemographics-dataset-with-boundaries), [Baltic countries](https://geolocet.com/products/baltic-countries-geodemographics-dataset-with-boundaries), [BeNeLux](https://geolocet.com/products/benelux-geodemographics-dataset-with-boundaries), [Scandinavian Countries](https://geolocet.com/products/scandinavian-countries-geodemographics-dataset-with-boundaries), and [Balkan Countries](https://geolocet.com/products/balkan-countries-geodemographics-dataset-with-boundaries). Each country within Europe is also available as a standalone dataset.\n\n### **Access & Usage:**\n\n- Instant download upon purchase for all datasets.\n- Free sample available for instant data evaluation.\n\nElevate your geographical and demographic analysis with the most thorough and accessible dataset available on the European Union municipalities — Geolocet’s flagship product.\n\n---\n\n### **Coverage Details:**\n- **[Austria](https://geolocet.com/products/Austria-geodemographics-dataset-with-boundaries)**: 9,105K people\n- **[Belgium](https://geolocet.com/products/Belgium-geodemographics-dataset-with-boundaries)**: 11,686K people\n- **[Bulgaria](https://geolocet.com/products/Bulgaria-geodemographics-dataset-with-boundaries)**: 6,448K people\n- **[Croatia](https://geolocet.com/products/Croatia-geodemographics-dataset-with-boundaries)**: 4,009K people\n- **[Czechia](https://geolocet.com/products/Czechia-geodemographics-dataset-with-boundaries)**: 10,524K people\n- **[Denmark](https://geolocet.com/products/Denmark-geodemographics-dataset-with-boundaries)**: 5,933K people\n- **[Estonia](https://geolocet.com/products/Estonia-geodemographics-dataset-with-boundaries)**: 1,322K people\n- **[Finland](https://geolocet.com/products/Finland-geodemographics-dataset-with-boundaries)**: 5,548K people\n- **[France](https://geolocet.com/products/France-geodemographics-dataset-with-boundaries)**: 67,733K people\n- **[Germany](https://geolocet.com/products/Germany-geodemographics-dataset-with-boundaries)**: 83,237K people\n- **[Greece](https://geolocet.com/products/Greece-geodemographics-dataset-with-boundaries)**: 10,370K people\n- **[Hungary](https://geolocet.com/products/Hungary-geodemographics-dataset-with-boundaries)**: 9,600K people\n- **[Ireland](https://geolocet.com/products/Ireland-geodemographics-dataset-with-boundaries)**: 5,057K people\n- **[Italy](https://geolocet.com/products/Italy-geodemographics-dataset-with-boundaries)**: 58,871K people\n- **[Latvia](https://geolocet.com/products/Latvia-geodemographics-dataset-with-boundaries)**: 1,876K people\n- **[Lithuania](https://geolocet.com/products/Lithuania-geodemographics-dataset-with-boundaries)**: 2,857K people\n- **[Luxembourg](https://geolocet.com/products/Luxembourg-geodemographics-dataset-with-boundaries)**: 661K people\n- **[Malta](https://geolocet.com/products/Malta-geodemographics-dataset-with-boundaries)**: 536K people\n- **[Netherlands](https://geolocet.com/products/Netherlands-geodemographics-dataset-with-boundaries)**: 17,811K people\n- **[Poland](https://geolocet.com/products/Poland-geodemographics-dataset-with-boundaries)**: 37,770K people\n- **[Portugal](https://geolocet.com/products/Portugal-geodemographics-dataset-with-boundaries)**: 10,467K people\n- **[Romania](https://geolocet.com/products/Romania-geodemographics-dataset-with-boundaries)**: 19,049K people\n- **[Slovakia](https://geolocet.com/products/Slovakia-geodemographics-dataset-with-boundaries)**: 5,795K people\n- **[Slovenia](https://geolocet.com/products/Slovenia-geodemographics-dataset-with-boundaries)**: 2,117K people\n- **[Spain](https://geolocet.com/products/Spain-geodemographics-dataset-with-boundaries)**: 45,136K people\n- **[Sweden](https://geolocet.com/products/Sweden-geodemographics-dataset-with-boundaries)**: 10,612K people\n\n\n## **Customize Your Data Experience**\n\nNeed the data in another format? Your requirements drive our solutions. Reach out to us at **contact@geolocet.com** for alternative formats or if you have specific data needs that go beyond the downloadable list of Germany locations available on our platform.\n\n## **Explore further with our extensive retail datasets**\n\nIf you are exploring other brands and require additional data, we encourage you to **contact@geolocet.com**. Our comprehensive datasets and dedicated team stand ready to assist you.\n","terms_of_service":"https://geolocet.com/pages/terms-of-use","documentation_link":"","privacy_policy_link":"https://geolocet.com/pages/privacy-policy","geographical_coverage":"[\"Europe\"]","cost":"PAID","data_source":"","assets":["ASSET_TYPE_MEDIA"],"license":"https://geolocet.com/pages/terms-of-use"},"provider_summary":{"name":"Geolocet LTD","description":"Geolocet provides premium geospatial data for retail analysis, GIS, and market planning. 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With validated datasets that reduce time spent sourcing and integrating data, you can rapidly find and use the data you need to make more informed decisions, unlock new opportunities, and power your next breakthrough.\n- Unlock new opportunities faster by finding high-quality, trusted datasets through a single ecosystem, accelerating the processes of sourcing and validating external data.\n- Surface key insights and assess relationships between data more quickly with an expansive selection of best-in-class datasets.\n\n**Scale**\nTake your data strategy to new heights. Scale the amount of external data you use as your business needs grow, enabling you to streamline operations, enhance decision-making, and better support key use cases and applications.\n- Grow your data capabilities with access to an ever-expanding ecosystem of pre-linked datasets from Precisely and trusted partners.\n- Streamline data enrichment without spending additional time and resources on ongoing data management.\n\n**Product details**\nPower your priority use cases with pre-linked data from GeoX and Precisely\nData Link for GeoX connects Precisely Building ID to GeoX Footprint ID with data attributions.\n\n- Commercial and residential property risk\n- Co-tenancy risk\n- Adjacency risk\n- Geographic exposure aggregation\n\nHarness data attributes that provide property, land, vegetation, and location intelligence, including:\n- Roof type and condition\n- Number of buildings and stories\n- Distance to trees/shrubs\n- Vegetation density and overhang\n- Distance to fire station\n- Pool and yard details\n\nImprove your risk assessments and underwriting processes with detailed property attribute data from GeoX and Precisely location, business, and risk data. 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The goal is to demonstrate how Verticalization/Domain Adaptation for the [Retail (eCommerce)] sector can be easily achieved using our two-step approach to LLM Fine-Tuning. An overview of this approach can be found at: [From General-Purpose LLMs to Verticalized Enterprise Models](https://www.bitext.com/blog/general-purpose-models-verticalized-enterprise-genai/)\n\nThe dataset has the following specifications:\n\n- Use Case: Intent Detection\n- Vertical: Retail (eCommerce)\n- 46 intents assigned to 13 categories\n- 44884 question/answer pairs, with approximately 1000 per intent\n- 2970 entity/slot types\n- 10 different types of language generation tags\n\nThe categories and intents are derived from Bitext's extensive experience across various industry-specific datasets, ensuring the relevance and applicability across diverse contexts.\n\n**Dataset Token Count**\n\nThe dataset contains a total of 8.47 million tokens across 'instruction' and 'response' columns. This extensive corpus is crucial for training sophisticated LLMs that can perform a variety of functions including conversational AI, question answering, and virtual assistant tasks in the retail (eCommerce) domain.\n\n**Fields of the Dataset**\n\nEach entry in the dataset comprises the following fields:\n\n- tags\n- instruction: a user request from the Retail (eCommerce) domain\n- category: the high-level semantic category for the intent\n- intent: the specific intent corresponding to the user instruction\n- response: an example of an expected response from the virtual assistant\n\n**Categories and Intents**\n\nThe dataset covers a wide range of retail-related categories and intents, which are:\n\n- **CONTACT**: customer_service, human_agent\n- **ACCOUNT**: change_account, close_account, open_account, order_history, recover_password\n- **APP_WEBSITE**: technical_issue, use_app\n- **CART**: add_product, remove_product\n- **DELIVERY**: damaged_delivery, delivery_issue, delivery_time, missing_item, shipping_costs, track_delivery, wrong_item\n- **FEEDBACK**: submit_feedback, submit_product_feedback, submit_product_idea\n- **ORDER**: cancel_order, change_order, request_invoice, track_order\n- **PAYMENT**: pay, payment_issue, payment_methods\n- **PRODUCT**: availability, availability_in_store, availability_online, exchange_product, exchange_product_in_store, product_information, product_issue\n- **RETURNS**: refund_policy, refund_status, request_refund, return_policy, return_product, return_product_in_store, return_product_online\n- **SALES**: sales_period\n- **STORE**: store_location, store_opening_hours\n- **USER**: request_right_to_rectification\n\n**Entities**\n\nThe entities covered by the dataset include:\n\n- **{{Customer Support Phone Number}}**, **{{Company Website URL}}**, common with all intents.\n- **{{Email Address}}**, **{{Name}}**, common with most intents.\n- **{{Order History}}**, featured in intents like cancel_order, change_order.\n- **{{My Orders}}**, featured in intents like exchange_product, order_history, request_invoice.\n- **{{Store Location}}**, relevant to intents such as availability_in_store, exchange_product_in_store.\n- **{{Returns}}**, associated with intents like exchange_product, refund_policy, refund_status.\n- **{{Account Settings}}**, important for intents including change_account, close_account.\n- **{{Return Policy}}**, typically present in intents such as refund_policy.\n- **{{Tracking Number}}**, featured in intents like delivery_issue, delivery_time.\n- **{{Refunds}}**, relevant to intents such as exchange_product, return_product.\n- **{{Edit Order}}**, associated with intents like change_order, remove_product.\n- **{{Purchase History}}**, important for intents including order_history, request_refund.\n\nThis comprehensive list of entities ensures that the dataset is well-equipped to train models that are highly adept at understanding and processing a wide range of retail-related queries and tasks.\n\n**Language Generation Tags**\n\nThe dataset includes tags indicative of various language variations and styles adapted for Retail (eCommerce), enhancing the robustness and versatility of models trained on this data. These tags categorize the utterances into different registers such as colloquial, formal, or containing specific retail jargon, ensuring that the trained models can understand and generate a range of conversational styles appropriate for different customer interactions in the retail sector.\n\n**Language Generation Tags**\n\nThe dataset includes tags that reflect various language variations and styles, crucial for creating adaptable and responsive conversational AI models within the retail sector. These tags help in understanding and generating appropriate responses based on the linguistic context and user interaction style.\n\n**Tags for Lexical variation**\n\n- **M - Morphological variation**: Adjusts for inflectional and derivational forms.\n - Example: \"is my account active\", \"is my account activated\"\n- **L - Semantic variations**: Handles synonyms, use of hyphens, and compounding.\n - Example: “what's my balance date\", “what's my billing date”\n\n**Tags for Syntactic structure variation**\n\n- **B - Basic syntactic structure**: Simple, direct commands or statements.\n - Example: \"activate my card\", \"I need to check my balance\"\n- **I - Interrogative structure**: Structuring sentences in the form of questions.\n - Example: “can you show my balance?”, “how do I transfer money?”\n- **C - Coordinated syntactic structure**: Complex sentences coordinating multiple ideas or tasks.\n - Example: “I want to transfer money and check my balance, what should I do?”\n- **N - Negation**: Expressing denial or contradiction.\n - Example: \"I do not wish to proceed with this transaction, how can I stop it?\"\n\n**Tags for language register variations**\n\n- **P - Politeness variation**: Polite forms often used in customer service.\n - Example: “could you please help me check my account balance?”\n- **Q - Colloquial variation**: Informal language that might be used in casual customer interactions.\n - Example: \"can u tell me my balance?\"\n- **W - Offensive language**: Handling potentially offensive language which might occasionally appear in frustrated customer interactions.\n - Example: “I’m upset with these charges, this is ridiculous!”\n\n**Tags for stylistic variations**\n\n- **K - Keyword mode**: Responses focused on keywords.\n - Example: \"balance check\", \"account status\"\n- **E - Use of abbreviations**: Common abbreviations.\n - Example: “acct for account”, “trans for transaction”\n- **Z - Errors and Typos**: Includes common misspellings or typographical errors found in customer inputs.\n - Example: “how can I chek my balance”\n\n**Other tags not in use in this Dataset**\n\n- **D - Indirect speech**: Expressing commands or requests indirectly.\n - Example: “I was wondering if you could show me my last transaction.”\n- **G - Regional variations**: Adjustments for regional language differences.\n - Example: American vs British English: \"checking account\" vs \"current account\"\n- **R - Respect structures - Language-dependent variations**: Formality levels appropriate in different languages.\n - Example: Using “vous” in French for formal addressing instead of “tu.”\n- **Y - Code switching**: Switching between languages or dialects within the same conversation.\n - Example: “Can you help me with my cuenta, please?”\n\nThese tags not only aid in training models for a wide range of customer interactions but also ensure that the models are culturally and linguistically sensitive, enhancing the customer experience in retail environments.\n\n**License**\n\nThe `Bitext-retail-ecommerce-llm-chatbot-training-dataset` is released under the **Community Data License Agreement (CDLA) Sharing 1.0**. This license facilitates broad sharing and collaboration while ensuring that the freedom to use, share, modify, and utilize the data remains intact for all users.\n\n**Key Aspects of CDLA-Sharing 1.0**\n\n- **Attribution and ShareAlike**: Users must attribute the dataset and continue to share derivatives under the same license.\n- **Non-Exclusivity**: The license is non-exclusive, allowing multiple users to utilize the data simultaneously.\n- **Irrevocability**: Except in cases of material non-compliance, rights under this license are irrevocable.\n- **No Warranty**: The dataset is provided without warranties regarding its accuracy, completeness, or fitness for a particular purpose.\n- **Limitation of Liability**: Both users and data providers limit their liability for damages arising from the use of the dataset.\n\n**Usage Under CDLA-Sharing 1.0**\n\nBy using the `Bitext-retail-ecommerce-llm-chatbot-training-dataset`, you agree to adhere to the terms set forth in the CDLA-Sharing 1.0. 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No lengthy sign-up processes or NDAs required on our side – just download the data you need.\n* **Data Samples for Evaluation:** To ensure you're getting exactly what you need, we provide free downloadable samples in CSV format for data evaluation.\n* **Data accuracy:** Our datasets are created using the latest demographics data available in national statistics bureaus, guaranteeing accuracy and reliability.\n* **Best-in-Class Quality:** We guarantee best-in-class quality, backed by over two decades of experience in modeling demographics data and an outstanding quality management process.\n* **Competitive Pricing:** We maintain the lowest prices on the market, ensuring that you get the most cost-efficient demographics data acquisition process.\n---\nAdditionally, for those looking to enrich their analysis or project, we offer:\n* **[Administrative Boundaries](https://geolocet.com/products/sweden-all-available-administrative-boundaries):** If you require geographical boundaries (polygons) for the municipalities of Sweden, consider exploring our Administrative Boundaries product, which seamlessly integrates with this dataset.\n\n* **[Point of Interest (PoI) Data](https://geolocet.com/pages/aggregated-points-of-interest-poi-data):** If you're interested in Points of Interest data, please don't hesitate to contact us for customized datasets to enhance your geographical analysis.\n\n* **[Data Customization](https://geolocet.com/pages/data-services-by-geolocet):** We understand that your project may have specific requirements. 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Trust in the accuracy and reliability of our official statistical data to drive your projects forward.\nContact us at *contact@geolocet.com* today to access this invaluable resource and explore Sweden census data like never before.\n\n[Geolocet LTD](https://geolocet.com)\n","terms_of_service":"https://geolocet.com/pages/terms-of-use","documentation_link":"","privacy_policy_link":"https://geolocet.com/pages/privacy-policy","geographical_coverage":"[\"Europe\"]","cost":"PAID","update_frequency":{"interval":1,"unit":"YEARLY"},"data_source":"","assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://geolocet.com/pages/terms-of-use"},"provider_summary":{"name":"Geolocet LTD","description":"Geolocet provides premium geospatial data for retail analysis, GIS, and market planning. 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We deliver tailored segments within 5–10 business days \n\n***Business Needs***\n\n 360-Degree Customer View \n- Integrate credit activity, spending behavior, and investment signals to build a unified, data-rich profile for each consumer. This enables financial institutions and fintech platforms to personalize offers, optimize cross-sell strategies, and deliver tailored messaging across customer journeys. \n\nCustomer Acquisition \n- Identify and prioritize consumers who are actively seeking credit, exploring new financial tools, or considering investment opportunities. Use AI propensities to target the right individuals at the right time—maximizing marketing efficiency and boosting qualified lead volume across lending, cards, and fintech verticals. \n\nDemand Forecasting \n- Leverage real-time and historical behavioral signals to anticipate demand shifts in financial products—such as new credit line requests, refinancing intent, or growing interest in digital investments. 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Users do not need any coding knowledge - SQL or other.\n\nUsers are guided by the UI through the process of entering required parameters (either discreet values or cell references) for the Model. Once entered, the user can copy the resulting Excel Function and paste wherever desired, or click “Apply” and the formula will be entered into the active Excel cell. \n\nOnce entered, the custom function will immediately call the Databricks Model Serving Endpoint and will compute the output. \n\nUsers can bypass the UI formula builder and enter custom Excel functions directly into cells by following the model function’s template.\n\n\n**Using Exponam.Connect to execute ML Model**\nhttps://youtu.be/7bBnBW7ibbw\n\n\n**Additional Exponam.Connect Functionality**\nExponam.Connect can also be used to access Databricks Delta Lake and Marketplace data directly in Excel without any technical configuration or setup beyond the scope of ordinary Excel and business-focused users. If a user can create a column filter in Excel, they can comfortably interact with billions of rows of data. They can work with millions of rows in Excel (spread over multiple sheets, subject to machine limitations). \n\n- The Exponam.Connect Excel Add-in is a no-code, highly intuitive application designed for use without prior instruction or user manual. \n- Users are guided via clear instructional hints to add their connection information for Delta Share server(s). Connection information is retained for easy re-use every time the application is opened.\n- Familiar tool symbols and buttons guide them through filtering data, including or excluding columns, and saving filtered views. \n- Users import data and extensive meta-data to the worksheet(s) of their choosing. \n- Users may elect to save data directly to a file for sharing, storing and distribution. \n\n*Licensing for Data Access* - Users do NOT require a license to start using Exponam.Connect for Data Access. However, data results are limited and some functionality is not available without a license. Users may request a trial license to remove restrictions by contacting info@exponam.com or visiting www.exponam.com/connect\n\n**Using Exponam.Connect to import data**\nhttps://youtu.be/uobZCJugHM0\n\n\n**Enterprise deployment**\nEnterprises deployments including white-labeled options are available via custom installers to work with an enterprise's standard software deployment methodology. Contact info@exponam.com for additional details.\n\n**Installation**\n- Prerequisite: Excel must be installed\n- Download and Install Excel Add-in:\n - FOR WINDOWS: Install Exponam.Connect via our publicly available click-once installer. https://exponam.zendesk.com/hc/en-us/articles/11778422961687-How-do-I-download-install-and-use-the-Windows-Excel-Add-in-\n - FOR MAC: (ONLY FOR DATA ACCESS - NOT MODEL EXECUTION) Install Exponam.Connect: https://exponam.zendesk.com/hc/en-us/articles/14367099543831-How-do-I-download-install-and-use-the-Mac-version-\n- Optional: Add license key by clicking the License button and pasting provided key\n\n\n**Configure ML Model Execution**\n- (Note) Model Execution is a premium feature which requires a paid or trial license\n- Click Model Serving; click the (+) button to add your Model Serving Endpoint and a bearer token (PAT). 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For unrestricted use, contact info@exponam.com for a trial license and licensing information.\n\nData import is via Delta Sharing (.share file for access) and governed via Unity Catalog. 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For more information, visit [StatSocial.com](url). \n.","terms_of_service":"https://www.statsocial.com/terms-of-service","documentation_link":"","privacy_policy_link":"https://www.statsocial.com/privacy-policy","geographical_coverage":"[\"Global\"]","cost":"PAID","data_source":"Major social media platforms","assets":["ASSET_TYPE_MEDIA"],"license":""},"provider_summary":{"name":"StatSocial","description":"StatSocial is the industry's exclusive provider of cross-platform social audience insights, offering unparalleled data on any audience's self-declared interests, media preferences, trusted influencers, and brand affinities, and personality traits. By leveraging our patented Identity Graph, we connect individuals' social profiles to offline identities, delivering comprehensive and actionable insights. Brands can access vertical-specific segments or create custom consumer, brand, or social audiences within their Databricks account or other preferred environments. 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In addition, this dataset provides metadata on the commercial participants involved, distinguishing between short-term and long-term engagements, as well as volumes traded and geographic information.\n\nLeverage this dataset to dissect each component of an LNG trip, scrutinizing the intricacies of diversions, idling periods, and evolving trade routes. LNG Cargo allows users to aggregate data across various dimensions to craft nuanced trade flow analytics. 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Built for marketers, insurance companies, supplier networks, and SaaS platforms targeting the skilled trades segment. \n\nData Axle, formerly Infogroup, has been a trusted provider of data-driven solutions since 1972. With verified coverage of over 95% of U.S. businesses and consumers, Data Axle delivers accurate, privacy-compliant data to power analytics, targeting, and activation. Our B2B and B2C datasets are built from deterministic sources and continuously updated. \n\n***Segment Categories:*** \n\n- Service Types -- Handymen, Carpenters, Electricians, Plumbers, Lawn Care, Roofing, Painting, HVAC \n- Business Structure -- Sole Proprietors, LLCs, Independent Contractors, Local Service Businesses \n- Annual Revenue -- <$250K to $5M \n- Employee Size -- 1 to 10 employees \n- Geographic Reach -- ZIP code, County, Metro Area \n- Contact Information -- Owner/operator emails, phone numbers, mailing addresses \n- Marketing Engagement -- Web presence, recent ad activity, digital responsiveness \n- Certifications & Licenses -- Trade licenses, insurance status, years in service \n\n***Key Features:***\n\n- 200+ segments of verified home service professionals across multiple trades \n- Strong coverage of hyperlocal businesses often missed by traditional firmographic datasets \n- Proprietor-level contact data including email, phone, and address \n- Enriched with business size, legal structure, and trade classification \n- Data privacy-compliant and refreshed regularly for accuracy \n- Compatible with CRM systems, field sales platforms, and marketing automation tools \n\n***Use Cases:*** \n\n- Supplier & Distributor Targeting: Identify active tradespeople and contractors for tools, materials, and equipment outreach \n- Home Services SaaS Sales: Sell invoicing, scheduling, or CRM tools to owner-operated service businesses \n- Insurance & Financial Services: Engage licensed operators for contractor liability, business insurance, or credit offers \n- Local Market Development: Build regional campaigns and service networks based on ZIP or metro-level service density \n- Consumer Marketplace Onboarding: Recruit providers for platforms in home improvement, landscaping, or repair services \n\n***Next Steps:*** \n\n- Request listing access \n- Data Axle team will contact you to finalize audience, data cut, and delivery method / frequency \n- Need a Custom Audience? 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Using LLM-based data agents and human-in-the-loop validation, the accelerator applies supply-chain domain logic to create a unified Supply Chain Control Hub that powers inventory visibility, supplier performance insights, logistics tracking, and fulfillment analytics.\nWith AELT for Supply Chain, teams benefit from intelligent metadata enrichment, automated schema harmonization, event-to-event stitching (PO → Shipment → Receipt → Fulfillment), incremental pipeline generation, and conversational SQL—reducing manual engineering effort by 60% while ensuring full transparency, traceability, and governance across the entire supply chain lifecycle.\n\n**Business Challenge**\nSupply chain teams struggle with two major issues:\n**1. Data fragmentation and integration challenges:**\nPOs, shipments, ASNs, warehouse events, and carrier updates live in fragmented, high-volume systems. Integrating these into a single, end-to-end order-to-delivery view is difficult and error-prone.\n**2. Operational silos across supply chain functions:**\nProcurement, logistics, warehousing, and finance operate in silos, making it hard to track delays, supplier performance, inventory risks, and fulfillment bottlenecks.\n\n**Architecture Overview – Medallion + Agentic Orchestration**\n- Bronze (L0): Raw ingestion of Supply Chain datasets:\n- Silver (L1–L2): AI agents automatically generate standardized SQL, enforce incremental load logic, and validate primary keys using Delta Live Tables (DLT).\n- Gold (L3): Conversational agent assists users in defining business tables—such as Onhand Inventory —through natural language, validating and deploying DLT SQL automatically.\n\n**Pipeline Flow**\n- Metadata Enricher Agent (Bronze): Automatically generates semantic descriptions for POs, shipments, ASNs, receipts, inventory movements, and carrier events with HITL validation.\n- Silver SQL Generator Agent: Builds L1 standardized views for calls, agents, queues, and routing\n- Conversational Gold Agent: Enables Supply Chain teams to create advanced analytics tables simply by describing them (“Build a PO-to-Delivery cycle time summary” or “Generate a Supplier On-Time Performance table”). The agent validates logic, resolves SQL errors, and enforces consistency across supply chain KPIs.\n- DLT Execution: A single Databricks Delta Live Tables pipeline constructs the full Supply Chain lifecycle, stitching POs → Shipments → Receipts → Deliveries while providing complete data lineage, CDC handling, quality checks, and auditability\n\n**Supply Chain Hub Functional Insights (Gold Layer)**\nOnce the agentic pipeline is deployed, the accelerator delivers ready-to-use insights:\n\n**1. End-to-End Order & Shipment Visibility**\nProvides complete traceability from Purchase Order → Supplier Dispatch → Shipment → Warehouse Receipt → Customer Delivery.\nAutomatically stitches multi-system events (ERP, WMS, TMS) to create a unified, timeline-ready supply chain journey.\n**2. Supplier & Fulfillment Performance Intelligence**\nTracks supplier behaviour across On-Time Delivery, Fill Rate, Lead Time Variance, ASN accuracy, and quality issues.\nIntegrates carrier and warehouse data to offer a holistic view of fulfilment performance and bottlenecks.\n**3. 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Sign up for your IDMC Organization - Learn more [here](https://success.informatica.com/success-accelerators/getting-started-with-idmc-platform.html)\n2. Configure your MCP Server in the Agent AI Engineering Tile\n3. Get your MCP Endpoint and configure your agent in Agent Bricks\n","terms_of_service":"https://www.informatica.com/terms-of-use.html","documentation_link":"https://onlinehelp.informatica.com/IICS/prod/agentic-ui/en/index.htm#page/uu-aiae-mcp-servers/Available_MCP_servers.html","privacy_policy_link":"https://www.informatica.com/privacy-policy.html","assets":["ASSET_TYPE_MCP"],"license":"https://www.informatica.com/legal/informatica-license-services-agreement.html"},"provider_summary":{"name":"Informatica","description":"Informatica (A Salesforce company) provides Data Management services for Governance, Data Quality, Replication, Ingestion and Integration for Databricks customers"},"popularity":1707},{"id":"743fe6c2-8978-48c3-8b6c-4f2793245ea0","summary":{"name":"Geolocet | Points of Interest Data | Grocery Store Locations - Poland","subtitle":"Complete GIS-ready dataset of 58,586 Poland grocery locations (brands & independents). 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It offers an analysis-ready foundation for full-scale retail site selection, competitive intelligence, logistics optimization, and spatial demographics mapping.\n\nFully compatible with **Apache Spark**, **Delta Lake**, and **Databricks Unity Catalog** workflows.\n\n---\n\n## **Complete National Coverage: Branded & Independent Grocers**\nUnlike top-tier only datasets, this exhaustive collection covers maximum geographic depth across all 16 Polish voivodeships (including highly dense areas like Mazowieckie, Śląskie, and Wielkopolskie). It includes tens of thousands of independent local grocers alongside the top national and regional supermarket chains:\n\n- **Major National Supermarkets & Hypermarkets:** \n [Biedronka (3,900 locations)](https://geolocet.com/products/poland-biedronka-grocery), \n [Carrefour (730 locations)](https://geolocet.com/products/poland-carrefour-grocery), \n [Kaufland (264 locations)](https://geolocet.com/products/poland-kaufland-grocery), \n [Auchan (245 locations)](https://geolocet.com/products/poland-auchan-grocery), \n [E.Leclerc (74 locations)](https://geolocet.com/products/poland-eleclerc-grocery)\n\n- **Leading Discounters & Value Brands:** \n [Lidl (1,002 locations)](https://geolocet.com/products/poland-lidl-grocery), \n [Netto (715 locations)](https://geolocet.com/products/poland-netto-grocery), \n [ALDI (423 locations)](https://geolocet.com/products/poland-aldi-grocery)\n\n- **Proximity, Convenience & Regional Formats:** \n [Lewiatan (3,315 locations)](https://geolocet.com/products/poland-lewiatan-grocery), \n [Dino (3,113 locations)](https://geolocet.com/products/poland-dino-grocery), \n [Delikatesy Centrum (1,347 locations)](https://geolocet.com/products/poland-delikatesy-centrum-grocery), \n [Stokrotka (986 locations)](https://geolocet.com/products/poland-stokrotka-grocery), \n [Nasz Sklep (288 locations)](https://geolocet.com/products/poland-nasz-sklep-grocery), \n [POLOmarket (258 locations)](https://geolocet.com/products/poland-polomarket-grocery), \n [Euro Sklep (232 locations)](https://geolocet.com/products/poland-euro-sklep-grocery), \n [SPAR (223 locations)](https://geolocet.com/products/poland-spar-grocery), \n [Intermarche (196 locations)](https://geolocet.com/products/poland-intermarche-grocery), \n [Polski Sklep (182 locations)](https://geolocet.com/products/poland-polski-sklep-grocery), \n [Hitpol (126 locations)](https://geolocet.com/products/poland-hitpol-grocery), \n [Arhelan (100 locations)](https://geolocet.com/products/poland-arhelan-grocery), \n [Gzella (63 locations)](https://geolocet.com/products/poland-gzella-grocery)\n\n**Plus over 40,000 independent, regional, and unbranded grocers** fully mapped and standardized across all macro-regions (e.g., Mazowieckie: 7,203 sites; Śląskie: 6,366 sites; Wielkopolskie: 6,045 sites).\n\n---\n\n## **Technical Specifications & Schema Definitions**\nOptimized for zero-friction schema enforcement when reading via `spark.read.format(\"csv\")`. The structured schema below ensures flawless rendering in Databricks notebooks and high accuracy for LLM semantic indexing.\n\n* **`GUID`** (`STRING`): Unique global identifier for deterministic primary key mapping.\n* **`Title`** (`STRING`): Official store name, brand banner, or independent title of the location.\n* **`Latitude`** (`DOUBLE`): Precise WGS84 latitude coordinate for point-in-polygon & spatial joins.\n* **`Longitude`** (`DOUBLE`): Precise WGS84 longitude coordinate for spatial indexing (H3 / S2).\n* **`Street_No` / `Street`** (`STRING`): Normalized building/house number and street name elements.\n* **`City` / `Postal_Code`** (`STRING`): Core postal addresses and primary municipal settlement names.\n* **`Municipality`** (`STRING`): Polish municipal administrative boundary designation (Gmina).\n* **`Region`** (`STRING`): Macro-administrative boundary classification (Voivodeship / Województwo).\n* **`Admin_level_1` / `_2`** (`STRING`): Additional local administrative area and district (Powiat) identifiers.\n* **`Population`** (`INTEGER`): Contextual demographic population metric attributed to the location.\n* **`Address`** (`STRING`): Full, standardized street address of the location.\n* **`Wheelchair`** (`STRING`): Accessibility flags indicating wheelchair mobility support.\n* **`Popularity_Score`** (`INTEGER`): Relative foot-traffic/rating metric graded on a standardized 1-10 scale.\n* **`Opening_Hours`** (`STRING`): Standard daily operational schedules and structured operating text.\n* **`Phone` / `Website`** (`STRING`): Direct communication data, branch-specific landing URLs, and domain vectors.\n\n---\n\n## **Databricks Workflow & Spatial Analytics Use Cases**\n\n- **Whitespace & Underserved Market Analysis:** Query the full 58,586 locations against population density tables to identify true retail deserts and expansion opportunities untouched by major national networks like Biedronka or Dino.\n- **H3 Geospatial Indexing:** Convert the precise WGS84 `Latitude` and `Longitude` fields into Uber H3 spatial hexagons (`h3_longlattocell`) to cross-reference with granular Polish municipal (Gmina) income raster files or demographic datasets.\n- **Supply Chain & Last-Mile Logistics Optimization:** Combine store location points with regional administrative boundary layers to optimize delivery routes, distribution warehouse placements, and network expansion footprints.\n\n---\n\n## **Data Evaluation & Custom Ingestion**\n\n### **Evaluate with a Free Sample Dataset**\nVerify the structure, column types, and coordinate precision before full implementation. A comprehensive schema sample is available for download in CSV/Parquet-friendly formats.\n\n### **Enterprise Delivery & Tailored Formatting**\nWhile natively available as an analysis-ready CSV for DBFS ingestion, we accommodate enterprise-level data architecture needs. Alternative formats - including **GeoJSON, Esri Shapefiles, PostgreSQL dumps, Excel, or direct Delta sharing profiles** - can be generated. Reach out to our engineering team at **contact@geolocet.com** for integration support or custom European POI data requests.","terms_of_service":"https://geolocet.com/pages/terms-of-use","documentation_link":"","privacy_policy_link":"https://geolocet.com/pages/privacy-policy","assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://geolocet.com/pages/terms-of-use"},"provider_summary":{"name":"Geolocet LTD","description":"Geolocet provides premium geospatial data for retail analysis, GIS, and market planning. 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market indicator reports synthesize refined-product and crack spread drivers, refinery utilization trends, inventory movements, and demand signals across major consuming regions.\n- In-depth market profiles and project reports provide deeper context on refinery assets, process configurations, capacity changes, ownership and operator history, and the longer-term implications of expansions and rationalization.\n- Event-driven research reports deliver concise, chart-driven analytics on themes such as refinery outages and expected throughput impacts, turnaround schedules, crack-spread regime shifts, and changes to product arbitrage conditions.\n\n**Structured data layer**\n\n- The structured data layer of the Fuels and Refining MCP is organized into clearly defined table groups that mirror the operational and economic structure of the refining value chain.\n- Refinery capacity tables cover capacity change events by refinery, including process-unit movements, expansions, and announced closures, supporting visibility into how supply capacity evolves over time.\n- Refinery runs and yields tables capture refinery throughput and product yields at the refinery level, enabling analysis of production performance, product mix, and operational drivers.\n- Refinery ownership and operator’s tables provide ownership stakes and operatorship over time, supporting governance, strategy, and asset-tracking use cases.\n- Refinery outages tables (grouped) include planned and unplanned outages with affected volume and process units, supporting impact analysis on product supply and utilization.\n- Refined Product demand, Oil inventories, production, and refinery utilization tables provide product and refinery activity coverage by geography, enabling balance analysis across gasoline, diesel/gasoil, jet, fuel oil, and naphtha.\n- Refined product price forecasts informing trading decisions and performing risk management assessments across gasoline, diesel/gasoil, jet, fuel oil, naphtha and 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Our flagship product, UniCourt DEEP, surfaces data and insights buried deep in dockets and documents, and enables rapid API integration and data shares connecting to any data warehouse, data lake, or existing application using pre-built integrations."},"popularity":679},{"id":"662aec61-72e3-4dcc-b6f5-125e59234e8e","summary":{"name":"Anonymized Driving Events Data","subtitle":"Anonymized insights into harsh driving behaviors","setting":{"visibility":"PUBLIC"},"created_at":1741259825049,"updated_at":1760440938311,"categories":["GEOSPATIAL","TRANSPORTATION_AND_LOGISTICS"],"listingType":"PERSONALIZED","provider_id":"534843a5-204d-4f28-9f5f-a6331173bba6"},"detail":{"description":"**Description**\nThe Mobito driving events data provides anonymized insights into harsh driving behaviors, including harsh braking, acceleration/deceleration, sharp cornering (left/right), and crash events.\n\n**Data Source**\nThe data is sourced by millions of vehicles globally from our vetted partners including:\n\n- Automotive OEMs; vehicles with embedded connectivity from the car manufacturer\n- Fleets with after-sales in-vehicle hardware telematics devices \n\n**Driving event types**\nCommon event types used to characterize driving behaviour include:\n\n- Braking\n- Cornering (left/right)\n- Speeding up/ acceleration\n- Crash (while driving or parked)\n\n**Use Cases**\nThis data enables road safety analysis, infrastructure planning, and driver behavior profiling.\n\n- Road safety: Helps road operators and municipalities identify high-risk road segments based on driving behavior and near-miss incidents, allowing for better maintenance planning and prioritization\n- Road infrastructure: Assists in planning and assessing the impact of infrastructure improvements on traffic flow and safety\n- Driving behaviour analysis: Supports automotive engineers in developing driver personas based on behavior patterns to inform the design of next-generation vehicles\n\n**Driving Events Data Schema**\nThe following list provides the main data attributes that are available via the Mobito Driving Events Data Product:\n\n- unique_id\n- start_time\n- end_time\n- latitude\n- longitude\n- start_speed\n- end_speed\n- acceleration_direction\n- vehicle_type\n- event_type\n\n**Driving Events Data Specs**\n**Historicity**\nHistorical data availability as of 2021. 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However, vehicle make and model details are available for only a limited portion of our total car park.\n\nFor more details, visit the Mobito [Driving Events Data](https://support.mobito.io/en/articles/10640728-driving-events-data-product) page.","terms_of_service":"https://support.mobito.io/en/articles/10627684-data-license-terms","documentation_link":"https://support.mobito.io/en/articles/10640728-driving-events-data-product","privacy_policy_link":"https://www.mobito.io/privacy-policy","geographical_coverage":"[\"Global\"]","cost":"PAID","data_source":" Car manufacturers and fleet service providers","assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://support.mobito.io/en/articles/10627684-data-license-terms"},"provider_summary":{"name":"Mobito","description":"Mobito is a mobility data solutions provider that enables organizations to monetize and utilize mobility data. We offer access to anonymized vehicle data from millions of vehicles globally, sourced from vetted car manufacturers and fleet service providers. \n\nOur data products include timestamped vehicle location data (probe data), anonymized harsh driving behavior events (such as harsh braking, acceleration, cornering, crashes), standstill (stop location) events, and origin-destination insights. \n\nThese data sets are used in various use cases, including traffic management, transportation analytics, site selection (e.g. EV charging station locations), and road safety."},"popularity":1050},{"id":"d6107801-149e-43cf-837f-3b8d0059bd97","summary":{"name":"INDIC_OTS — Indic Speech Audio Dataset 5HR sample","subtitle":"5-hour lossless Indic speech dataset with 33,603 FLAC recordings and native transcriptions across 12 Indian languages.","setting":{"visibility":"PUBLIC"},"created_at":1788919692234,"updated_at":1788919767438,"categories":["EDUCATION","SCIENCE_AND_RESEARCH"],"listingType":"PERSONALIZED","provider_id":"51e1bafd-2b96-40fb-b5bd-e9d3531279e9"},"detail":{"description":"**Overview**\nINDIC_OTS is a 5-hour lossless Indic speech dataset containing 33,603 FLAC audio recordings across 12 Indian languages: Bengali, Gujarati, Hindi, Kannada, Malayalam, Marathi, Odia, Punjabi, Sanskrit, Tamil, Telugu, and Urdu.\n\nThe dataset combines high-quality lossless speech recordings with native-script transcriptions and structured metadata, providing a ready-to-use resource for speech AI, automatic speech recognition (ASR), multilingual machine learning, and language technology research.\n\nThe Marketplace product includes:\n\n33,603 lossless FLAC audio recordings delivered through a Databricks Unity Catalog Volume.\n33,603 corresponding metadata and transcription records available through a Unity Catalog table.\nNative Indic-script transcriptions preserved in Unicode.\nSpeaker, language, duration, audio format, sampling, channel, and file-size metadata.\n**Use cases**\nINDIC_OTS can support:\n\n- Automatic speech recognition (ASR) model development and evaluation.\n- Multilingual and low-resource speech research.\n- Indic language speech technology development.\n- Speech-to-text model benchmarking.\n- Audio data exploration and quality analysis.\n- Language identification and speech classification.\n- Training and evaluating machine learning models for Indian-language speech.\n- Research into multilingual and cross-lingual speech systems.\n- Development of speech datasets and preprocessing pipelines.\n\n**Product details**\n**Audio asset**\n\nThe indic_ots.product.audio Unity Catalog Volume contains 33,603 FLAC recordings organized by language.\n\nThe audio files are provided in lossless FLAC format and retain the original product structure by language.\n\n**Manifest table**\n\nThe indic_ots.product.product_manifest_v1_0 table contains 33,603 records, with one record corresponding to each audio file.\n\n**Key fields include:**\n\ndataset_id — Dataset identifier.\ndataset_name — Dataset name.\ndataset_version — Dataset version.\nfilename — Audio filename.\nrelative_path — Relative location of the audio file.\ntext — Native-script transcription.\nlang — Language identifier.\nduration — Recording duration.\ngender — Speaker gender metadata.\nspeaker_id — Speaker identifier.\nformat_name — Audio container format.\ncodec_name — Audio codec.\nsample_rate — Audio sampling rate.\nchannels — Number of audio channels.\nchannel_layout — Channel layout.\nbits_per_sample — Bits per sample.\naudio_duration_sec — Audio duration in seconds.\nfile_size_bytes — Audio file size.\nprobe_status — Technical media validation status.\nqa_status — Quality assurance status.\nmanifest_generated_utc — Manifest generation timestamp.\n\nAll 33,603 records have passed the product's technical and QA validation checks.\n\n**Data organization**\n\nThe product is organized as a Databricks Marketplace share containing:\n\nindic_ots.product.product_manifest_v1_0 — structured metadata and transcription table.\nindic_ots.product.audio — audio Volume containing the corresponding FLAC recordings.\n\nThe manifest provides the metadata needed to discover and analyze recordings, while the Volume provides access to the underlying audio files.\n\n**Additional Insights**\nINDIC_OTS is designed as a practical dataset for researchers, data scientists, ML engineers, and organizations working with Indic speech technologies.\n\nThe dataset covers 12 languages and preserves native Indic scripts rather than transliterating or converting transcriptions into Latin characters. 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Contact [Sales](mailto:Databricks-Marketplace-listing@johnsnowlabs.com) at John Snow Labs for a free 15 day trial license key or purchase our most popular annual subscription license. \n2. Import this listing.\n3. 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For more than 100 years, we've been a trusted connector that brings together thought leaders, market participants, governments, and regulators to co-create solutions that lead to progress. Vital to navigating Energy Transition, S&P Global Energy's coverage includes oil and gas, power, chemicals, metals, agriculture, and shipping. S&P Global Energy is a division of S&P Global (NYSE: SPGI)."},"popularity":863},{"id":"14bfdec9-7f72-4ed0-a29b-1608d85ab8e3","summary":{"name":"Phone Number Data | USA Coverage | 160 Mil+ Properties | Homeowner Contact Data | 97% Accuracy","subtitle":"Nationwide U.S. consumer phone and email contact data linked to assessor and recorder property records across all 50 ...","share":{"name":"kinder_data_c69dc81c_28fc_4ce8_84d8_57bcfe76e65f_samples_share","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1768177959924,"updated_at":1768177959924,"categories":["OTHER","ECONOMICS"],"listingType":"PERSONALIZED","provider_id":"5d7c419c-8c2a-49a5-b4e7-1043dfe76473"},"detail":{"description":"Nationwide U.S. Consumer Phone & Contact Dataset with Property Intelligence\n\nThe KinderData U.S. Consumer Phone & Contact Dataset delivers verified, property-linked phone and email contact data for homeowners nationwide. Built on the same daily-updated assessor, recorder, and contact pipelines as KinderData’s core property intelligence products, this dataset is designed to help users reach the right property owners efficiently and stop wasting time and money on bad data.\n\nCovering all 50 states, the dataset links one or multiple verified phone numbers and email addresses to individual homeowners and parcels, enriched with ownership and property context to support real estate, financial services, marketing, and analytics use cases.\n\nWhat Makes KinderData Consumer Contact Data Different\n\nProperty-Linked Consumer Phone & Email Data\nVerified Tier 1 right-party phone numbers and email addresses directly linked to residential and commercial parcels and their current owners.\n\nMultiple Contact Points per Owner\nIncludes one or multiple 10-digit phone numbers and email addresses per individual, improving reachability and enabling multi-channel outreach strategies.\n\nNationwide 50-State Coverage\nComprehensive U.S. coverage across millions of residential and commercial properties sourced from county assessor, recorder, and verified contact inputs.\n\nContinuously Updated & Validated\nDaily refresh cycles ensure current ownership, transaction, and contact linkages, reducing list decay and improving outreach performance over time.\n\nIntegrated Property & Ownership Context\nContact data is enriched with key property and ownership attributes, including owner-occupied status, corporate ownership indicators, acquisition history, and valuation signals.\n\nAI-Driven Confidence & Prioritization Signals\nAI-based scores help users identify and prioritize the most accurate and relevant records for outreach, analytics, and decision-making.\n\nEnterprise-Ready Standardization\nNormalized schemas and standardized formats enable seamless integration into CRMs, analytics platforms, and internal data systems.\n\nFlexible Access Options\nAvailable via bulk delivery, API access, or customized extracts aligned to specific geographic, ownership, or property criteria.\n\nData Elements Included\n\nConsumer Contact Data: Tier 1 verified phone numbers (one or multiple per owner), email addresses, mailing addresses\n\nOwnership & Property Linkage: Current owners, owner-occupied flags, corporate ownership indicators, cash buyer signals\n\nProperty Context: Parcel/APN identifiers, property type, year built, lot and building characteristics\n\nValuation & Transaction Signals: Assessed values, market value estimates, historical and recent sales\n\nAI Scores & Confidence Indicators: Record relevance and prioritization signals\n\nDesigned For\n\nReal Estate & PropTech: investor outreach, portfolio analysis, acquisitions, disposition and listing platforms\n\nFinancial Services: mortgage, insurance, underwriting, and investment due diligence\n\nMarketing & Lead Generation: property-linked phone, SMS, email, and direct outreach campaigns\n\nData & Analytics Teams: contact enrichment, segmentation, predictive modeling, and audience building\n\nQuality & Delivery\n\nAll consumer contact and property records undergo continuous validation and normalization, including source confidence weighting, duplicate resolution, geocoding checks, and schema standardization to ensure accuracy, consistency, and usability at scale.","terms_of_service":"https://datarade.ai/company/legal/terms-of-service","contact_email":"marketplace@datarade.ai","privacy_policy_link":"https://datarade.ai/company/legal/privacy-policy","assets":["ASSET_TYPE_DATA_TABLE"]},"provider_summary":{"name":"Kinder Data","description":"Kinder Data is a data provider offering Mortgage Data, Home Ownership Data, B2C Contact Data, Commercial Real Estate Data, and Residential Real Estate Data. They are headquartered in United States of America."},"popularity":533},{"id":"a451912b-8cd3-4c9e-9757-15fa14a84395","summary":{"name":"Health","subtitle":"This data package is a combination of different health-related datasets. These include data on vaccination, causes of ..","share":{"name":"health","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1682355718963,"updated_at":1695690525187,"categories":["HEALTH","SCIENCE_AND_RESEARCH"],"listingType":"STANDARD","provider_id":"f88849f8-9d77-4de2-b4c7-03d55cc3768e"},"detail":{"description":"\n**Overview**\nThis data package is a combination of different health-related datasets. These include data on vaccination, causes of death and death rate, diabetes, water consumption. Water disposal, mammography and health codes list.\n\n**Description**\nThis data package is a combination of different health related datasets. These include data on vaccination, causes of death and death rate, diabetes, water consumption. The dataset Assertive Community Treatment (ACT) is an Evidence-Based Practice Model designed to provide treatment, rehabilitation and support services to individuals who are diagnosed with a severe mental illness and whose needs have not been well met by more traditional mental health services. The ACT team provides services directly to an individual that are tailored to meet his or her specific needs. The dataset Austin Adult and Children Vaccinations contains information on the number of Vaccine for Children (VFC) and adult safety net (ASN) eligible clients that are immunized, on a monthly basis, through the Austin Public Health ‘Shots for Tots’ and ‘Big Shots’ programs at the St. John and Far South clinics. The dataset Bloomington Animal Care and Control Adopted Animals shows the animal care and control for adopted animals in the city of Bloomington. Bloomington Animal Care and Control's mission is to address and respond to all animal needs in the community through education, enforcement and support in order to build a community where animals are valued and treated with kindness and respect. The dataset Community Emergency Response Team (CERT) program helps train people to be better prepared to respond to emergency situations in their communities. CERT members can also help with non-emergency projects that help improve the safety of the community.\n\nThe dataset Diagnosed Diabetes Prevalence contains the number and percentage of diabetes patients in the US during 2013. The prevalence and incidence of diabetes have increased in the United States in recent decades, no studies have systematically examined long-term, national trends in the prevalence and incidence of diagnosed diabetes. The National Healthcare Safety Network (NHSN) Organisms Lists dataset includes provides the NHSN code for each organism and correspondent SNOMED Concept code with the SNOMED Fully Specified Name. The dataset shows the searchable listing of all NJ (New Jersey) licensed child care centers and inspection reports. In New Jersey, child care centers serving six or more children under the age of 13 must be licensed by DCF (Department of Children and Families). The dataset shows the New Jersey deaths due to underlying causes of deaths eligible to be ranked as leading causes of death based on the National Center for Health Statistics standards. Unintentional injury, stroke, chronic lower respiratory diseases (CLRD), Alzheimer's disease, diabetes, septicemia (sepsis), kidney disease, and influenza/pneumonia round out the remainder of the ten leading causes of death for New Jersey. The dataset on Health Codes is from the New York Department of Health and Mental Hygiene. The New York City Department, through its Office of Radiological Health (ORH), regulates radioactive material for medical, research and academic purposes within the five boroughs of New York City. The dataset shows the searchable listing of all NJ (New Jersey) licensed child care centers and inspection reports. Centers are licensed for a three-year period. A copy of the centers’ license is displayed in a prominent location at the center.\n\nThe dataset OMHA Receipts provides information about the appeals and claims received by the Office of Medicare and Hearings by Appeal (OMHA) category for Fiscal Year 2011-2013. OMHA administers the nationwide Administrative Law Judge (ALJ) hearing program for appeals arising from individual claims for Medicare coverage and payment for items and services furnished to beneficiaries (or enrollees) under Medicare Parts A, B, C and D. The dataset Studies On Safe Drugs During Pregnancy contains a list of the current Pregnancy Registries in research in the United States, their status and contact information. Pregnancy exposure registries are observational studies that focus on the outcome of pregnancies of women with medical conditions that require taking medications. The dataset West Nile virus (WNV) is most commonly transmitted to humans by mosquitoes. There are no medications to treat or vaccines to prevent WNV infection. Fortunately, most people infected with WNV will have no symptoms. The dataset eICU Collaborative Research Admissions Summary Statistics contains demographic, diagnosis and hospital level summary statistics for 200,859 patient unit encounters corresponding to 139,367 unique patients admitted between 2014 and 2015. The Philips eICU program is a transformational critical care telehealth program that delivers need-to-know information to caregivers, empowering them to care for the patients. The dataset eICU Collaborative Research Database (eICU-CRD) is a multi-center intensive care unit (ICU) database with high granularity data for admissions monitored by eICU Programs across the United States. The eICU Collaborative Research Database is a subset of a research data repository maintained by eRI. A stratified random sample of patients was used to select patients for inclusion in the public dataset.\n\n**Benefits**\n- Useful for healthcare researchers. useful for predictions and analysis related to healthcare. normalized form of datasets.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [Assertive Community Treatment Outcomes](https://www.johnsnowlabs.com/marketplace/assertive-community-treatment-outcomes)\n\t- This dataset contains the number of ACT consumers within particular psychosocial and service utilization indicators at baseline and most recent follow-up by ACT team/program, county, region, and statewide.\n- [Austin Adult and Children Vaccinations](https://www.johnsnowlabs.com/marketplace/austin-adult-and-children-vaccinations)\n\t- This dataset contains information on the number of Vaccine for Children (VFC) and adult safety net (ASN) eligible clients that are immunized, on a monthly basis, through the Austin Public Health ‘Shots for Tots’ and ‘Big Shots’ programs at the St. John and Far South clinics.\n- [Bloomington Animal Care and Control Adopted Animals](https://www.johnsnowlabs.com/marketplace/bloomington-animal-care-and-control-adopted-animals)\n\t- This dataset shows the animal care and control for adopted animals in the city of Bloomington. In early 2017, the Bloomington Animal Shelter migrated management software from AnimalShelterNet to Shelter Manager.\n- [COVID-19 Active Testing Locations](https://www.johnsnowlabs.com/marketplace/covid-19-active-testing-locations)\n\t- This dataset contains a list of school locations across New York City offering COVID-19 testing locations. The list is sorted by district, school borough and three digit school number.\n- [COVID-19 Case Surveillance Public Use Data](https://www.johnsnowlabs.com/marketplace/covid-19-case-surveillance-public-use-data)\n\t- This dataset shows the COVID-19 case surveillancepPublic use data with geography. This dataset has been prepared by the CDC Case Surveillance Task Force.\n- [COVID-19 Data from National Hospital Survey](https://www.johnsnowlabs.com/marketplace/covid-19-data-from-national-hospital-survey)\n\t- This dataset shows the COVID-19 Hospital Data from the National Hospital Care Survey. The data is provided by the National Hospital Care Survey (NHCS). The data are from 63 hospitals submitting inpatient and 60 hospitals submitting ED (Emergency Department) Uniform Bill (UB)-04 administrative claims.\n- [COVID-19 Death Registrations and Occurrences in UK](https://www.johnsnowlabs.com/marketplace/covid-19-death-registrations-and-occurrences-in-uk)\n\t- This dataset shows the provisional COVID-19 Death registrations and occurrences by local authority and health board in England and Wales. This dataset has been prepared by Office for National Statistics.\n- [COVID-19 National Vaccination Demographics](https://www.johnsnowlabs.com/marketplace/covid-19-national-vaccination-demographics)\n\t- This dataset shows the COVID-19 national vaccination demogrpahics in the Unites States. The data is provided by the Centers for Disease Control and Prevention (CDC).\n- [COVID-19 Outcomes by Testing Cohorts](https://www.johnsnowlabs.com/marketplace/covid-19-outcomes-by-testing-cohorts)\n\t- This dataset shows the COVID-19 outcomes by testing cohorts. It shows the cases, hospitalizations and Deaths in the NYC (New York City). The data is provided by the Department of Health and Mental Hygiene (DOHMH).\n- [COVID-19 Vaccinations in England](https://www.johnsnowlabs.com/marketplace/covid-19-vaccinations-in-england)\n\t- This dataset contains information on the number of COVID-19 vaccinations provided by the NHS in England, both in total since vaccinations began and to the current eligible population.\n- [COVID-19 Weekly Deaths by State and Select Causes](https://www.johnsnowlabs.com/marketplace/covid-19-weekly-deaths-by-state-and-select-causes)\n\t- This dataset shows the weekly provisional counts of deaths by state and select causes for year 2020-2022. The data is provided by the National Center for Health Statistics.\n- [Community Emergency Response Teams](https://www.johnsnowlabs.com/marketplace/community-emergency-response-teams)\n\t- The Community Emergency Response Team (CERT) program helps train people to be better prepared to respond to emergency situations in their communities. When emergencies happen, CERT members can give critical support to first responders, provide immediate assistance to victims, and organize spontaneous volunteers at a disaster site. CERT members can also help with non-emergency projects that help improve the safety of the community.\n- [Conditions Contributing to COVID 19 Deaths by State and Age](https://www.johnsnowlabs.com/marketplace/conditions-contributing-to-covid-19-deaths-by-state-and-age)\n\t- This dataset shows the conditions contributing to COVID-19 death by state and age. The data is provided by the National Center for Health Statistics.\n- [Current States and DC with Health IT Partners](https://www.johnsnowlabs.com/marketplace/current-states-and-dc-with-health-it-partners)\n\t- This dataset provides monthly data about the current States (and District of Columbia) with health IT partners. The information is for fiscal years 2008 - 2017.\n- [Diagnosed Diabetes Prevalence 2004-2013](https://www.johnsnowlabs.com/marketplace/diagnosed-diabetes-prevalence-2004-2013)\n\t- This dataset contains number and percentage of diabetes patients in the US during 2013 grouped by ZIP code. The prevalence and incidence of diabetes have increased in the United States in recent decades, no studies have systematically examined long-term, national trends in the prevalence and incidence of diagnosed diabetes.\n- [Mammography Data from Breast Cancer Surveillance Consortium](https://www.johnsnowlabs.com/marketplace/mammography-data-from-breast-cancer-surveillance-consortium)\n\t- This dataset includes data from a random sample of 20,000 digital and 20,000 film-screen mammograms received by women age 60-89 years within the Breast Cancer Surveillance Consortium (BCSC) between January 2005 and December 2008. Some women contribute multiple examinations to the dataset. Data is useful in teaching about data analysis, epidemiological study designs, or statistical methods for binary outcomes or correlated data.\n- [NHSN Organisms Lists](https://www.johnsnowlabs.com/marketplace/nhsn-organisms-lists)\n\t- The National Healthcare Safety Network (NHSN) Organisms Lists dataset includes organisms names and correspondent NHSN organism codes and SNOMED concept codes.\n- [NJ Licensed Child Care Centers](https://www.johnsnowlabs.com/marketplace/nj-licensed-child-care-centers)\n\t- This dataset shows the searchable listing of all NJ (New Jersey) licensed child care centers and inspection reports.\n- [NJ Residents Leading Causes of Death](https://www.johnsnowlabs.com/marketplace/nj-residents-leading-causes-of-death)\n\t- This dataset shows the New Jersey deaths due to underlying causes of deaths eligible to be ranked as leading causes of death based on the National Center for Health Statistics standards.\n- [NYC Health Code](https://www.johnsnowlabs.com/marketplace/nyc-health-code)\n\t- This dataset on Health Codes is from the New York Department of Health and Mental Hygiene. The data included is intended for informational purposes only and is not the official version of the New York City Administrative Code and Rules of the City of New York.\n- [NYC Parks Closure Status Due to COVID-19](https://www.johnsnowlabs.com/marketplace/nyc-parks-closure-status-due-to-covid-19)\n\t- This dataset shows the status of parks and playgrounds closure due to COVID-19 in the New York City (NYC). The data is provided by the National Hospital Care Survey (NHCS). The dataset has been provided by the Department of Parks and Recreation (DPR).\n- [NYC Services Volunteer Opportunities](https://www.johnsnowlabs.com/marketplace/nyc-services-volunteer-opportunities)\n\t- This dataset shows the list of volunteer opportunities, organized by event, category of event type, organization and location.\n- [New York City Leading Causes of Death](https://www.johnsnowlabs.com/marketplace/new-york-city-leading-causes-of-death)\n\t- The causes of death reported in these pages are the underlying causes classified according to the tenth revision of the International Classification of Diseases (ICD, 10th revision) adopted by New York State in 1999. Historically, several revisions of the ICD have been used, therefore, it is necessary to employ a comparability ratio when comparing cause of death statistics across revisions. Comparability ratios have been published by the National Center for Health Statistics (NCHS).\n- [OMHA Receipts for Fiscal Year 2011-2013](https://www.johnsnowlabs.com/marketplace/omha-receipts-for-fiscal-year-2011-2013)\n\t- This dataset provides information about the appeals and claims received by the Office of Medicare and Hearings by appeal category for Fiscal Year 2011-2013. This dataset is provided by the U.S. Department of Health & Human Services. It includes the information regarding appeals and claims by the Office of Medicare Hearings and Appeals (OMHA).\n- [Order and Referring Physicians and Non Physicians](https://www.johnsnowlabs.com/marketplace/order-and-referring-physicians-and-non-physicians)\n\t- Order and Referring data file has National Provider Identifier (NPI) and legal name (last name, first name) of all physicians and non-physician practitioners who are of a type/specialty that is legally eligible to order and refer in the Medicare program and who have current enrollment records in Medicare.\n- [Provisional COVID-19 Deaths by Sex and Age](https://www.johnsnowlabs.com/marketplace/provisional-covid-19-deaths-by-sex-and-age)\n\t- This dataset shows the provisional COVID-19 deaths by sex and age. The data is about deaths involving coronavirus disease (COVID-19), pneumonia, and influenza reported to NCHS by sex, age group, and jurisdiction of occurrence.\n- [Studies On Safe Drugs During Pregnancy](https://www.johnsnowlabs.com/marketplace/studies-on-safe-drugs-during-pregnancy)\n\t- This dataset contains a list of the current Pregnancy Registries in research in the United States, their status and contact information. Pregnancy exposure registries are observational studies that focus on the outcome of pregnancies of women with medical conditions that require taking medications, they observe if birth defects are shown, acting as postmarketing surveillance, although women are enrolled in the study before the outcome is known.\n- [US COVID-19 Cases and Deaths by State Over Time](https://www.johnsnowlabs.com/marketplace/us-covid-19-cases-and-deaths-by-state-over-time)\n\t- This dataset shows the United States COVID-19 cases and deaths by state over time. This dataset is provided by the CDC Case Task Force.\n- [West Nile Virus Cases By County CA](https://www.johnsnowlabs.com/marketplace/west-nile-virus-cases-by-county-ca)\n\t- This dataset contains positive cases of West Nile Virus found in humans by county of residence, 2006 to present. Positive cases in this dataset include both West Nile fever and West Nile neuroinvasive disease.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated\n- Data Updates\n - Data updates support replace-on-update: outdated foreign keys are deprecated, not deleted\n\n**Our data is curated and enriched by domain experts** \n\n Each dataset is manually curated by our team of doctors, pharmacists, public health & medical billing experts:\n- Field names, descriptions, and normalized values are chosen by people who actually understand their meaning\n- Healthcare & life science experts add categories, search keywords, descriptions and more to each dataset\n- Both manual and automated data enrichment supported for clinical codes, providers, drugs, and geo-locations\n- The data is always kept up to date – even when the source requires manual effort to get updates\n- Support for data subscribers is provided directly by the domain experts who curated the data sets\n- Every data source’s license is manually verified to allow for royalty-free commercial use and redistribution.\n\n**Need Help?**\nIf you have questions about our products, contact us at [info@johnsnowlabs.com](mailto:info@johnsnowlabs.com). \n ","terms_of_service":"https://www.johnsnowlabs.com/healthcare-data-library-eula/","documentation_link":"https://www.johnsnowlabs.com/marketplace/health-data-package","contact_email":"support@johnsnowlabs.com","privacy_policy_link":"https://www.johnsnowlabs.com/privacy-policy/","embedded_notebook_file_infos":[{"id":"8b0f8f37-b06f-4d25-9fa6-1e8574eb70e3","marketplace_file_type":"EMBEDDED_NOTEBOOK","file_parent":{"parent_id":"a451912b-8cd3-4c9e-9757-15fa14a84395","file_parent_type":"LISTING"},"mime_type":"text/html","download_link":"https://marketplace-file-storage-prod.s3-fips.us-east-1.amazonaws.com/EMBEDDED_NOTEBOOK/LISTING_a451912b-8cd3-4c9e-9757-15fa14a84395/8b0f8f37-b06f-4d25-9fa6-1e8574eb70e3?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEGgaDnVzLWVhc3QtMS1maXBzIkYwRAIgbC8sWIoYaHR8y7Wq86nPb0pufBl4%2BM4PWpeN%2B5PW9VQCIG7BhAVBDCWTpxFDLeuKYm%2Ff1JLz4iccPMzter0YdhgWKqYECDAQABoMNDE0MzUxNzY3ODI2IgxNAJrHbXaZG20jNfMqgwQyGEb4TsoVOoNvJ%2BaA%2FZ7zeu8hGcHqCeNfblP%2FQo5g6q2aRNtpwtPRKMlhYcIE%2FUQaNT1Lqmyptu32VUQTpzjTTxRVmJrDPkheSCkV06XQ%2FpTMKjU8CC7X1b%2FqYf8CrBwZT7Cv4C5WPjEET38f2A79h0xV6bZgPy28zQWrSeIJ9w6AfZBxiE24%2FVc08zVA0ivCT6lsy7nuU0wcR%2FMg2C9nd1vaMnQ923VpD21CKfVVYcrNWL2b%2FQ%2BNAO%2BxPjPgjiN6UPQbvbvZxkSdhgrGtecIg9NgqGkMcvM5VLTDtdC1kdeZetfOC2obtA%2BT%2F0gy3tIDo6WM5hcQfw743%2FAU9oaGnRBl%2BwoxRRDCZA%2F4jnPdluICSI7NCr6C3HHOe4CSZJjRihczxPA%2FgQczAVjAPs6CrK9N9pIzt0vNJpGUTgg4AoxftTvGfciDREilSctPIcBk6GwB%2Bzt4wbtNCmEPU%2BFD46rc6CWVQmmLwbKOeHw70ipMVFlOc8NlXkEtGO2vu6H44MstSSc6daOVUYUavQSGZ6jGhEjsP2YGz8KDm9XQotB%2FtyVBqiZV01OKqDmmBJA%2FXjUpSJ8M%2FeJXhe5m%2BKcxzNObtoJRI7jci3Tpt08XZyXZ7%2FyIV8H30ya468X3cfkTXrm972uga3z7iXAdubyEHnQIua%2FuvtwOHILi6O5D1Me2MzCPvcnXBjqTAYDoMWBt%2B3AVFuqSs2dmDQNBnvEhXfMgTedKLD5PJHb3aIl15iBkLaNu36GYnVCp9ZU2lSYcTbY5QwNnNeTfJTNy9bKhwzru6Sg%2BslhN%2BTS0zkTn%2BeLMY4tHgPEtEZ2cp%2FCk6GwqswvnYVKHjwO8q%2B1Mx8lc08fVEfarE9dke1GGFqoddx%2Bq%2BZUyHbDlAVfrCmf0yw%3D%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20261110T000135Z&X-Amz-SignedHeaders=host&X-Amz-Expires=86400&X-Amz-Credential=ASIAWA6KKHEJORWBH4N3%2F20261110%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=8070aa18d63844ec960dbf43b24673e86b9ed1f129457ebbff09a6e1ab0f8b92","created_at":1682537626399,"updated_at":1682537626399,"display_name":"EDA Notebook","status":"FILE_STATUS_PUBLISHED"}]},"provider_summary":{"name":"John Snow Labs","description":"John Snow Labs, an AI and NLP for healthcare company, provides state-of-the-art software, models, and data to help healthcare and life science organizations build, deploy, and operate AI projects."},"popularity":1946},{"id":"9c58a00d-4802-4fc2-8d22-45c78cfc29d9","summary":{"name":"AI Training Data | US Transcription Data| Unique Consumer Sentiment Data: Transcription of the ...","subtitle":"Сollection of transcribed customer call records. Ideal for in-depth analysis of customer interactions and behavior ...","share":{"name":"wiserbrand_com_39983898_e780_48da_a597_a4dda5ce37aa_samples_share","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1756119865250,"updated_at":1794237011080,"categories":["OTHER"],"listingType":"PERSONALIZED","provider_id":"2eed6057-c654-4c40-b66d-13a25153cb95"},"detail":{"description":"Comprehensive Customer Call Transcription Dataset: Tailored Insights\n\nWe offer a customizable dataset comprising transcribed customer call records, meticulously tailored to your specific requirements. This extensive dataset includes:\n\n- User ID and Firm Name: Identify and categorize calls by unique user IDs and company names.\n- Call Duration: Analyze engagement levels through call lengths.\n- Geographical Information: Detailed data on city, state, and country for regional analysis.\n- Call Timing: Track peak interaction times with precise timestamps.\n- Call Reason and Group: Categorised reasons for calls, helping to identify common customer issues.\n- Device and OS Types: Information on the devices and operating systems used for technical support analysis.\nTranscriptions: Full-text transcriptions of each call, enabling sentiment analysis, keyword extraction, and detailed interaction reviews.\n\nThis dataset is essential for companies looking to leverage Consumer Data and B2B Marketing Data to drive their strategic initiatives in the English-speaking markets of the USA, UK, and Australia. By accessing this rich dataset, businesses can uncover trends and insights critical for improving customer engagement and satisfaction.\n\nCases:\n\n1. Training Speech Recognition (Speech-to-Text) and Speech Synthesis (Text-to-Speech) Models\n\nComprehensive Customer Call Transcription Dataset is an excellent resource for training and improving speech recognition models (Speech-to-Text, STT) and speech synthesis systems (Text-to-Speech, TTS). Here’s how this dataset can contribute to these tasks:\n\nEnriching STT Models: The dataset comprises a diverse range of real-world customer service calls, featuring various accents, tones, and terminologies. This makes it highly valuable for training speech-to-text models to better recognize different dialects, regional speech patterns, and industry-specific jargon. It could help improve accuracy in transcribing conversations in customer service, sales, or technical support.\n\nContextualized Speech Recognition: Given the contextual information (e.g., reasons for calls, call categories, etc.), it can help models differentiate between various types of conversations (technical support vs. sales queries), which would improve the model’s ability to transcribe in a more contextually relevant manner.\n\nImproving TTS Systems: The transcriptions, along with their associated metadata (such as call duration, timing, and call reason), can aid in training Text-to-Speech models that mimic natural conversation patterns, including pauses, tone variation, and proper intonation. This is especially beneficial for developing conversational agents that sound more natural and human-like in their responses.\n\n Noise and Speech Quality Handling: Real-world customer service calls often contain background noise, overlapping speech, and interruptions, which are crucial elements for training speech models to handle real-life scenarios more effectively.\n\n2. Training AI Agents for Replacing Customer Service Representatives\nOur dataset can be incredibly valuable for businesses looking to develop AI-powered customer support agents that can replace or augment human customer service representatives. Here’s how this dataset supports AI agent training:\n\nCustomer Interaction Simulation: The transcriptions provide a comprehensive view of real customer interactions, including common queries, complaints, and support requests. By training AI models on this data, businesses can equip their virtual agents with the ability to understand customer concerns, follow up on issues, and provide meaningful solutions, all while mimicking human-like conversational flow.\n\nSentiment Analysis and Emotional Intelligence: The full-text transcriptions, along with associated call metadata (e.g., reason for the call, call duration, and geographical data), allow for sentiment analysis, enabling AI agents to gauge the emotional tone of customers. This helps the agents respond appropriately, whether it’s providing reassurance during frustrating technical issues or offering solutions in a polite, empathetic manner. Such capabilities are essential for improving customer satisfaction in automated systems.\n\nCustomizable Dialogue Systems: The dataset allows for categorizing and identifying recurring call patterns and issues. 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Over 250 peer reviewed publications have used PharMetrics Plus data.\n\nSample Tables:\n-Medical and Pharmacy Claims with history going back to 2006\n-Enrollee Demographics including birth year, gender, and zip3\n-Monthly Enrollment for medical and pharmacy benefit by plantype and paytype\n-Up to 7 additional tables include various lookups, labs, and vitals\n\nFields Included:\n-Patient Id\n-Birth Year\n-Gender\n-Claim Id\n-Service Date\n-Place of Service\n-Diagnosis Code\n-Procedure Code\n-National Drug Code (NDC)\n-Provider Id\n-Allowed Amount\n-Paid Amount\n-Copay\n-Plan type\n-Enrolled Month\n-100+ additional fields available including (NPI, Zip3, and Linkable Patient Id which are premium variables)\n\nInterested data consumers should reach out to listed IQVIA contacts for specific pricing and delivery options.\n","terms_of_service":"https://www.iqvia.com/about-us/privacy","documentation_link":"https://www.iqvia.com/library/fact-sheets/iqvia-pharmetrics-plus","privacy_policy_link":"https://www.iqvia.com/about-us/privacy","geographical_coverage":"[\"United States and Canada\"]","update_frequency":{"interval":1,"unit":"MONTHLY"},"assets":["ASSET_TYPE_DATA_TABLE"]},"provider_summary":{"name":"IQVIA","description":"IQVIA (NYSE:IQV) is a leading global provider of advanced analytics, technology solutions and clinical research services to the life sciences industry dedicated to delivering actionable insights. 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The Department of Health and Mental Hygiene (DOHMH) inspects about 24,000 restaurants a year to monitor compliance with city and state food safety regulations in US. The Health Department conducts unannounced inspections of restaurants at least once a year. Inspectors check for compliance in food handling, food temperature, personal hygiene and vermin control. Each violation of a regulation gets a certain number of points. At the end of the inspection, the inspector totals the points, and this number is the restaurant's inspection score—the lower the score, the better the Grade. Restaurants with a score between 0 and 13 points earn an A, those with 14 to 27 points receive a B and those with 28 or more a C. A restaurant has two chances to earn an A in every inspection cycle. If it doesn’t earn an A on the first inspection, it’s scored but ungraded. An inspector goes back to the restaurant unannounced, typically within a month, to inspect it again and the re-inspection is graded. If the grade is a B or C, the restaurant will receive a grade card and a grade pending card. It can post either card until it has an opportunity to be heard at the Office of Administrative Trials and Hearings Health Tribunal. The datasets in this data package contain\n\n**Benefits**\n- before visiting a new restaurant, the diners can check the restaurant's score in order to decide if it's worth spending money.\n- useful for new restauranteurs to check the scores of existing restaurants\n- score are available for many cities across us\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [Approved Food Establishments in UK](https://www.johnsnowlabs.com/marketplace/approved-food-establishments-in-uk)\n\t- This dataset contains a list of establishments in United Kingdom which are approved to handle, prepare or produce products of animal origin for which requirements are laid down in Regulation (EC) No 853/2004.\n- [Food Hygiene Rating Data in Oxford City Council](https://www.johnsnowlabs.com/marketplace/food-hygiene-rating-data-in-oxford-city-council)\n\t- This dataset provides the food hygiene rating or inspection result given to a business and reflects the standards of food hygiene found on the date of inspection or visit by the local authority. Businesses include restaurants, pubs, cafés, takeaways, hotels and other places consumers eat, as well as supermarkets. The data is held on behalf of local authorities participating in the Food Standards Agency's National Food Hygiene Rating Scheme (FHRS) in England, Northern Ireland and Wales.\n- [Restaurant Inspection Scores in Austin Texas](https://www.johnsnowlabs.com/marketplace/restaurant-inspection-scores-in-austin-texas)\n\t- This dataset provides restaurant scores for inspections performed within the last three years. It also provides the name of the restaurant as well as the location and address of the restaurants.\n- [Restaurant Inspection Scores in King County](https://www.johnsnowlabs.com/marketplace/restaurant-inspection-scores-in-king-county)\n\t- This dataset provides restaurant scores for inspections performed by the Health Department in King County, from 2006 to the present. It includes the name, location and address of the restaurants. This dataset is organized by business / inspection date / violation. Each row in this dataset is an inspection, and if an inspection at a particular business results in multiple violations there will be multiple rows for that business with the same inspection serial number.\n- [Restaurant Inspection Scores in New York City](https://www.johnsnowlabs.com/marketplace/restaurant-inspection-scores-in-new-york-city)\n\t- This dataset provides restaurant inspection scores, violations, grades and adjudication information for restaurants in New York City. It also provides the name as well as the location and address of the restaurants.\n- [Restaurant Inspection Scores in San Francisco](https://www.johnsnowlabs.com/marketplace/restaurant-inspection-scores-in-san-francisco)\n\t- This dataset provides restaurant scores for inspections performed by LIVES (Local Inspector Value-Entry Specification). It also provides the name of the restaurant as well as the location and address of the restaurants.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated\n- Data Updates\n - Data updates support replace-on-update: outdated foreign keys are deprecated, not deleted\n\n**Our data is 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Every insight traces back to its source.\n\n**Data Sources**\n\n- Social: Meta, TikTok, X, BlueSky, LinkedIn, Meta (Facebook, Instagram, Threads), YouTube, forums\n- Media: News, TV/broadcast transcripts, podcasts\n- Reviews & Consumer: product reviews, consumer forums\n- Company, Market, and Investment: Crunchbase, Capital IQ (Cap IQ), Earnings Calls Scripts\n- Patents & Innovation: Clarivate\n- Search & Trends: category trend indices, TikTok trend data\n\n**Tools Available via MCP**\n\n- Ask Q — plain-language prompts against any Quid dataset\n- Q Agents — automated briefs, brand snapshots, and product analyses (40+ agents)\n- Trend Leaderboards — momentum scores, audience demographics, and source posts by category\n- Dataset Browser — list and query available datasets\n\n**Use Cases**\n\n- Marketing campaign performance along side ad and revenue metrics\n- Track brand health, brand monitoring, and share of voice against competitors\n- Spot emerging consumer trends among targeted audiences and the conversation driving them\n- Blend consumer sentiment and conversation metrics with sales, CRM, or product data\n- Ground Databricks agents in live consumer and market signals\n- Finding valuable trends from investment, company, patent, and consumer data that are opportunities for M&A or R&D / Innovation pipeline\n- Monitor posts, images, and videos for threats towards offices, employees, and executives\n\n\n**Example Questions**\n\n- \"What's driving negative sentiment about [brand] this quarter?\"\n- \"Which skincare trends gained the most momentum in the past month?\"\n- \"Show me the top 10 trending topics in [industry] on TikTok this week\"\n\n**Requirements**\nRequires a Databricks workspace with Unity Catalog enabled. 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Delivered as a single, ready to query Delta table. \n\n**Coverage & Update Frequency:**\n\nCoverage: Prior calendar month only\nUpdate Frequency: Monthly\n\n\n**Segment Details:**\n- Brand ID\n- Name\n- Website \n- Business Phone Number \n- Street address \n- City \n- State \n- Zipcode\n- Enigma description \n- Industry NAICS type \n- Industry NAICS code \n- Industry NAICS description \n- Card revenue amount 12m \n- Reviews total count \n- Review avg rating \n- First review date \n- First transaction date\n- Operating status \n- Operating status last observed \n\n**Key Features:**\n- Coverage of new brands across 50 states and 2 territories (District of Colombia, Puerto Rico)\n- Detects when new businesses first appear online\n- Includes operating indicator, and active digital presence signals\n- Captures first customer review dates\n- Helps distinguish active businesses from inactive registered business filings\n- Significant GTM value for prioritization, segmentation, and outreach\n- Contains information from the last months to date. \n- Delivered as a single, ready-to-query table\n\n\n\n\n\n","terms_of_service":"https://www.enigma.com/company/terms-of-service","documentation_link":"","privacy_policy_link":"https://www.enigma.com/company/privacy-policy","geographical_coverage":"[\"United States and Canada\"]","cost":"PAID","update_frequency":{"interval":1,"unit":"MONTHLY"},"collection_granularity":{"interval":1,"unit":"MONTHLY"},"data_source":"Hundreds of Public & Third-Party Sources ","size":12.0,"assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"Enigma","description":"Enigma provides comprehensive intelligence about the identity and financial health of small and medium businesses across the United States. 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Transportation's safety record, giving data on accidents, crashes, fatalities, and injuries for each transportation mode and hazardous materials. Compiled and published by the U.S. Department of Transportation's Bureau of Transportation Statistics (BTS), National Transportation Statistics presents information on the U.S. transportation system, including its physical components, safety record, economic performance, energy use, and environmental impacts. The data package gives information on the following areas:\n\n- Total energy consumed and distance traveled in National Passenger Railroad Corporation (Amtrak), air carriers, class I rail freight and transit modes of transportation\n- Euro 2-6 car emissions traded on UK markets \n- Injuries and fatalities on roads and air carriers\n- Vehicle, air taxi and air carrier safety data\n\n**Benefits**\n- Useful for advocating for road safety, identifying specific problems and risks, setting targets, formulating appropriate strategies and monitoring impact. improves measurement of a country's road traffic injury problem. facilitates the selection of evidence-based interventions. allows for better evaluation of progress. useful to develop policies and interventions and to assess prevention measures.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [Amtrak Passenger Rail Fuel Consumption and Travel](https://www.johnsnowlabs.com/marketplace/amtrak-passenger-rail-fuel-consumption-and-travel)\n\t- The dataset gives information on the total energy consumed and distance traveled in National Passenger Railroad Corporation (Amtrak) modes of transportation.\n- [CTA Ridership Bus Routes](https://www.johnsnowlabs.com/marketplace/cta-ridership-bus-routes)\n\t- This dataset shows the total daily ridership on a per-route basis dating back to 2001. Day types are W=Weekday, A=Saturday, U=Sunday/Holiday.\n- [Certificated Air Carrier Fuel Consumption And Travel](https://www.johnsnowlabs.com/marketplace/certificated-air-carrier-fuel-consumption-and-travel)\n\t- The dataset gives information on the fuel consumption and travel miles by certified air carrier transportation in the United States. The different types of energy sources or fuels include petroleum products, which include crude oil and petroleum liquids that result from natural gas processing, including gasoline, diesel fuel, jet fuel, residual fuel oil, and propane.\n- [Chicago Alternative Fuel Locations](https://www.johnsnowlabs.com/marketplace/chicago-alternative-fuel-locations)\n\t- This dataset shows the list of locations in North East Illinois, North West Indiana, and South East Wisconsin where alternative vehicle fuels are available.\n- [Chicago Bike Racks Map](https://www.johnsnowlabs.com/marketplace/chicago-bike-racks-map)\n\t- This dataset shows the bike's rack (map) in the City of Chicago. These maps required a special GIS (Geographic Information System) software such as ESRI (Environmental Systems Research Institute) ArcGIS map.\n- [Chicago Public Chauffeurs](https://www.johnsnowlabs.com/marketplace/chicago-public-chauffeurs)\n\t- This dataset shows the list of the City of Chicago licensed public chauffeurs, who may operate a licensed Taxicab, Livery, or Horse-Drawn Carriage.\n- [Chicago Red Light and Speed Camera Violations](https://www.johnsnowlabs.com/marketplace/chicago-red-light-and-speed-camera-violations)\n\t- This dataset reflects the daily volume of violations created by the City of Chicago Red Light Program and Speed Camera (Children's Safety Zones) for each camera. The data reflects violations that occurred from July 1, 2014 until the present, minus the most recent 14 days. This data may change due to occasional time lags between the capturing of a potential violation and the processing and determination of a violation.\n- [Chicago Roadway Construction Moratoriums](https://www.johnsnowlabs.com/marketplace/chicago-roadway-construction-moratoriums)\n\t- This dataset shows the Chicago roadway construction moratoriums. Moratoriums are established by the Department of Transportation as a method of protecting reconstructed or repaved roadways within the boundaries of the city.\n- [Chicago Taxi Trips](https://www.johnsnowlabs.com/marketplace/chicago-taxi-trips)\n\t- This dataset contains the taxi trips reported to the City of Chicago in its role as a regulatory agency. To protect privacy but allow for aggregate analyses, the Taxi ID is consistent for any given taxi medallion number but does not show the number, Census Tracts are suppressed in some cases, and times are rounded to the nearest 15 minutes. Due to the data reporting process, not all trips are reported but the City believes that most are.\n- [Chicago Traffic Tracker](https://www.johnsnowlabs.com/marketplace/chicago-traffic-tracker)\n\t- This dataset contains the current estimated speed for about 1250 segments covering 300 miles of arterial roads. The Chicago Traffic Tracker estimates traffic congestion on Chicago’s arterial streets (nonfreeway streets) in real-time by continuously monitoring and analyzing GPS traces received from Chicago Transit Authority (CTA) buses.\n- [Class I Rail Freight Fuel Consumption and Travel](https://www.johnsnowlabs.com/marketplace/class-i-rail-freight-fuel-consumption-and-travel)\n\t- The dataset gives information on the total energy consumed and distance traveled in Class I rail freight modes of transportation.\n- [Domestic Demand For Gasoline By Mode Of Transportation](https://www.johnsnowlabs.com/marketplace/domestic-demand-for-gasoline-by-mode-of-transportation)\n\t- The dataset gives information on the demand for gasoline by different modes of transportation in the United States. The different types of energy sources or fuels include petroleum products, which include crude oil and petroleum liquids that result from natural gas processing, including gasoline, diesel fuel, jet fuel, residual fuel oil, and propane.\n- [Electric Vehicle Charging Network](https://www.johnsnowlabs.com/marketplace/electric-vehicle-charging-network)\n\t- This dataset includes Austin Energy that operates a network of electric vehicle charging stations powered by renewable energy through our Green Choice program.\n- [Energy Consumed In Transit Modes Of Transportation](https://www.johnsnowlabs.com/marketplace/energy-consumed-in-transit-modes-of-transportation)\n\t- The dataset gives information on the total energy consumed in different transit modes of transportation. The U.S. Energy Information Administration estimates that cars, light trucks, and motorcycles use the largest share of the total energy consumed for transportation in the United States. Cars, vans, and buses are commonly used to transport people.\n- [Energy Consumption By Mode Of Transportation And Type Of Energy](https://www.johnsnowlabs.com/marketplace/energy-consumption-by-mode-of-transportation-and-type-of-energy)\n\t- The dataset gives information on the total energy consumption in different modes of transportation by type of energy. It is important to mention that the data for 2007-2013 are based on new categories and are not comparable to previous years.\n- [Euro 2 Cars Emissions Traded On UK Market 2000-2002](https://www.johnsnowlabs.com/marketplace/euro-2-cars-emissions-traded-on-uk-market-2000-2002)\n\t- This dataset contains car emissions and estimated data about new cars (other than vans, 4x4s and pickups) available for sale in the UK market from EU Emission Standard Euro 2 from 2000 to 2002. The estimated data for emissions, consumption and cost of fuel are presented along with car identification data, as well as data on fuel type, engine capacity and transmission type.\n- [Euro 3 Cars Emissions Traded On UK Market 2000-2007](https://www.johnsnowlabs.com/marketplace/euro-3-cars-emissions-traded-on-uk-market-2000-2007)\n\t- This dataset contains car emissions and estimated data about new cars (other than vans, 4x4s and pickups) available for sale in the UK market from EU Emission Standard Euro 3 from 2000 to 2007. The estimated data for emissions, consumption and cost of fuel are presented along with car identification data, as well as data on fuel type, engine capacity and transmission type.\n- [Euro 4 Cars Emissions Traded On UK Market 2000-2012](https://www.johnsnowlabs.com/marketplace/euro-4-cars-emissions-traded-on-uk-market-2000-2012)\n\t- This dataset contains car emissions and estimated data about new cars (other than vans, 4x4s and pickups) available for sale in the UK market from EU Emission Standard Euro 4 from 2000 to 2012. The estimated data for emissions, consumption and cost of fuel are presented along with car identification data, as well as data on fuel type, engine capacity and transmission type.\n- [Euro 5 Cars Emissions Traded On UK Market 2009-2015](https://www.johnsnowlabs.com/marketplace/euro-5-cars-emissions-traded-on-uk-market-2009-2015)\n\t- This dataset contains car emissions and estimated data about new cars (other than vans, 4x4s and pickups) available for sale in the UK market from EU Emission Standard Euro 5 from 2009 to 2015. The estimated data for emissions, consumption and cost of fuel are presented along with car identification data, as well as data on fuel type, engine capacity and transmission type.\n- [Euro 6 Cars Emissions Traded On UK Market 2011-2017](https://www.johnsnowlabs.com/marketplace/euro-6-cars-emissions-traded-on-uk-market-2011-2017)\n\t- This dataset contains car emissions and estimated data about new cars (other than vans, 4x4s and pickups) available for sale in the UK market from EU Emission Standard Euro 6 from 2011 to 2017. The estimated data for emissions, consumption and cost of fuel are presented along with car identification data, as well as data on fuel type, engine capacity and transmission type.\n- [Fatalities In Highway Crashes By Driver Blood Alcohol Concentration](https://www.johnsnowlabs.com/marketplace/fatalities-in-highway-crashes-by-driver-blood-alcohol-concentration)\n\t- The dataset contains information on the motor vehicle fatalities on U.S. roads and the Blood alcohol concentration (BAC) of the driver from 1985 to 2015. National Transportation Statistics presents statistics on the U.S. transportation system, including its physical components, safety record, economic performance, the human and natural environment, and national security.\n- [Fuel Consumption And Travel Different Types Of Vehicles](https://www.johnsnowlabs.com/marketplace/fuel-consumption-and-travel-different-types-of-vehicles)\n\t- The dataset gives information on the total energy consumed and distance traveled in different types of vehicles.\n- [Hawaii Public Electric Vehicle Charging Stations](https://www.johnsnowlabs.com/marketplace/hawaii-public-electric-vehicle-charging-stations)\n\t- This Dataset Includes Hawaii’s network of electric vehicle charging stations that help to reduce any range anxiety Electric vehicle drivers may have.\n- [Injured Persons by Transportation Mode](https://www.johnsnowlabs.com/marketplace/injured-persons-by-transportation-mode)\n\t- The dataset contains information on the injured persons with different modes of transportation in U.S. It contains the number of injured persons on highway, rails, aviation, marine and pipeline. Highway transportation includes passenger cars, light trucks and vans, pedestrians, motorcycles, pedalcycles, medium and heavy trucks, buses and Others. Rail includes freight, passenger, and commuter rail (Trespassing), rail transit.\n- [Motor Vehicle Fatal Crashes by Posted Speed Limit](https://www.johnsnowlabs.com/marketplace/motor-vehicle-fatal-crashes-by-posted-speed-limit)\n\t- The dataset contains information on the motor vehicle fatalities on U.S. roads with various conditions of speed limits. National Transportation Statistics presents statistics on the U.S. transportation system, including its physical components, safety record, economic performance, the human and natural environment, and national security.\n- [Motor Vehicle Fatal Crashes by Time and Day and Weather Conditions](https://www.johnsnowlabs.com/marketplace/motor-vehicle-fatal-crashes-by-time-and-day-and-weather-conditions)\n\t- The dataset contains information on the Motor vehicle fatalities on U.S. roads with time of the accidents, Day of the accidents and weather conditions. National Transportation Statistics presents statistics on the U.S. transportation system, including its physical components, safety record, economic performance, the human and natural environment, and national security.\n- [Motor Vehicle Fatalities And Miles And Associated Rates](https://www.johnsnowlabs.com/marketplace/motor-vehicle-fatalities-and-miles-and-associated-rates)\n\t- The dataset contains information on the motor vehicle fatalities on U.S. roads and miles of distance and rate of fatalities in various condition. National Transportation Statistics presents statistics on the U.S. transportation system, including its physical components, safety record, economic performance, the human and natural environment, and national security.\n- [Motor Vehicle Fuel Consumption And Travel](https://www.johnsnowlabs.com/marketplace/motor-vehicle-fuel-consumption-and-travel)\n\t- The dataset gives information on the fuel consumption and travel miles of motor vehicles in the United States. The different types of energy sources or fuels include petroleum products, which include crude oil and petroleum liquids that result from natural gas processing, including gasoline, diesel fuel, jet fuel, residual fuel oil, and propane.\n- [Motor Vehicle Safety Data](https://www.johnsnowlabs.com/marketplace/motor-vehicle-safety-data)\n\t- The dataset contains information on the motor vehicle fatalities on U.S. roads with number of fatalities, crashes, number of injured persons and miles covered by vehicles. National Transportation Statistics presents statistics on the U.S. transportation system, including its physical components, safety record, economic performance, the human and natural environment, and national security.\n- [Motorist Non Motorist Fatalities by No of Vehicles Alcohol Involvement](https://www.johnsnowlabs.com/marketplace/motorist-non-motorist-fatalities-by-no-of-vehicles-alcohol-involvement)\n\t- The dataset contains statistics on the motorist and non-motorist fatalities involving any driver, pedestrian or pedalcyclist in the accident. It includes motor vehicle fatalities on the U.S. roads, influence of alcohol on fatalities and the number of vehicles involved in various conditions. National Transportation Statistics presents data on physical components of the U.S. transportation system, its safety record, economic performance, the human and natural environment, and national security.\n- [NJ Traffic Counts Data](https://www.johnsnowlabs.com/marketplace/nj-traffic-counts-data)\n\t- This dataset shows the traffic counts data by NJDOT (New Jersey Department of Transportation) for State of New Jersey.\n- [NYPD Motor Vehicle Collisions](https://www.johnsnowlabs.com/marketplace/nypd-motor-vehicle-collisions)\n\t- In New York City (NYC), more than 200,000 motor vehicle collisions happen every year. This means about every 3 minute, a collision happens somewhere in NYC. To reduce collisions, there is a need to discover the key factors to improve. The use of NYC collision data and historical weather data to identify the worst location (total number of collisions) and worst weather condition (frequency of collision)\n- [New Driver TLC License Application Status](https://www.johnsnowlabs.com/marketplace/new-driver-tlc-license-application-status)\n\t- This dataset displays the status of TLC (Taxi and Limousine Commission) driver applications for drivers who applied for new TLC driver’s licenses.\n- [Number Of Pilot Reported Near Midair Collisions By Degree Of Hazard](https://www.johnsnowlabs.com/marketplace/number-of-pilot-reported-near-midair-collisions-by-degree-of-hazard)\n\t- This dataset gives information on the number of incidence of near mid air collisions (NMAC) reported with different degree of collision hazards.\n- [Occupant and Nonoccupant Fatalities by Vehicle Type](https://www.johnsnowlabs.com/marketplace/occupant-and-nonoccupant-fatalities-by-vehicle-type)\n\t- The dataset contains information on the occupant and non-occupant fatalities with different motor vehicle type on U.S. roads. National Transportation Statistics presents statistics on the U.S. transportation system, including its physical components, safety record, economic performance, the human and natural environment, and national security.\n- [Pot Holes Reported by 311 Service](https://www.johnsnowlabs.com/marketplace/pot-holes-reported-by-311-service)\n\t- This dataset shows the pot holes reported by the 311 Service. It includes all open requests and all completed requests since January 1, 2011.\n- [Public Passenger Vehicle Licenses](https://www.johnsnowlabs.com/marketplace/public-passenger-vehicle-licenses)\n\t- This dataset shows the public passenger vehicle licenses and records which are used for the transportation of passengers in Chicago State hired by a public chauffeur.\n- [Relocated Vehicles in Chicago Last 90 Days](https://www.johnsnowlabs.com/marketplace/relocated-vehicles-in-chicago-last-90-days)\n\t- This dataset displays the location of vehicles that have been relocated by the City of Chicago within the last 90 days.\n- [Schools for Life Safety Evaluations](https://www.johnsnowlabs.com/marketplace/schools-for-life-safety-evaluations)\n\t- This dataset contains NYC (New York City) Safe Routes to Schools (Priority Schools). DOT (Department of Transportation) analyzed citywide crash data and school data in order to identify a new group of 135 public, private and parochial elementary and middle schools Priority Schools. Each of DOT's Priority Schools receives an individualized planning study which determines both short-term and long-term measures to improve safety.\n- [Towed Vehicles in Chicago Last 90 Days](https://www.johnsnowlabs.com/marketplace/towed-vehicles-in-chicago-last-90-days)\n\t- This dataset displays location of vehicles that have been towed and impounded by the City of Chicago within the last 90 days.\n- [Truck Occupant Safety Data](https://www.johnsnowlabs.com/marketplace/truck-occupant-safety-data)\n\t- The dataset contains information on the truck occupant fatalities on U.S. roads with number of fatalities, crashes, number of injured persons and miles covered by vehicles.\n- [United States Air Carrier Fatal Accidents By First Phase Of Operation](https://www.johnsnowlabs.com/marketplace/united-states-air-carrier-fatal-accidents-by-first-phase-of-operation)\n\t- This dataset gives information on the number of United States air carrier fatal accidents reported by phase of operation. The phase of operations are classified to approach, descent or landing, taxi, takeoff or climb, cruise in flight, standing static, maneuvering and others.\n- [United States Commuter Air Carrier Fatal Accidents Phase Of Operation](https://www.johnsnowlabs.com/marketplace/united-states-commuter-air-carrier-fatal-accidents-phase-of-operation)\n\t- This dataset gives information on the number of United States commuter air carrier fatal accidents reported by phase of operation. The phase of operations are classified to approach, descent or landing, taxi, takeoff or climb, cruise in flight, standing static, maneuvering and Others.\n- [United States General Aviation Safety Data](https://www.johnsnowlabs.com/marketplace/united-states-general-aviation-safety-data)\n\t- This dataset gives information on the number of United States general aviation safety data. It gives information on the accident rates, seriously injured incidences, fatality rates and accidents with fatality rates on flight hours.\n- [United States On-Demand Air Taxi Safety Data](https://www.johnsnowlabs.com/marketplace/united-states-on-demand-air-taxi-safety-data)\n\t- This dataset gives information on the number of United States on demand air taxi safety data. It gives information on the accident rates, seriously injured incidences, fatality rates and accidents with fatality rates on flight hours.\n- [Vehicle Occupant Safety Data](https://www.johnsnowlabs.com/marketplace/vehicle-occupant-safety-data)\n\t- The dataset contains information on the passenger car fatalities data, bus occupant fatalities data and motorcycle rider fatalities data on U.S. roads with number of fatalities, crashes, number of injured persons and miles covered by vehicles.\n- [Windsor Transit Bus Stops](https://www.johnsnowlabs.com/marketplace/windsor-transit-bus-stops)\n\t- This dataset contains the coordinates of the bus stops, route names, route number, bus direction, bus orientation, bus stop ID number and various information regarding bus routing street locations.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - 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The dataset focuses on diverse human activities, interactions, and motion patterns across varied environments, and supports both ready-made and custom dataset creation.\n\n**Use cases**\n- Training and evaluating action recognition and activity classification models\n- Human behavior analysis and interaction understanding\n- Video-based pose estimation and motion tracking\n- Sports analytics, fitness, and entertainment applications\n- Benchmarking video processing pipelines for human-centric video data\n\n**Product details**\n- Datasets include large-scale collections of human action videos with rich visual, temporal, technical, and semantic metadata captured from real-world environments.\n- Sample fields include video ID, video URL, duration, frame rate, resolution, codec, timestamps, scene context, action labels (e.g., walking, running, sitting, lifting, interacting), temporal action segments, and confidence and ranking scores.","terms_of_service":"https://gurushots.com/terms","documentation_link":"","privacy_policy_link":"https://gurushots.com/privacy","geographical_coverage":"[\"Global\"]","cost":"PAID","update_frequency":{"interval":30,"unit":"DAILY"},"collection_granularity":{"interval":1,"unit":"DAILY"},"data_source":"Proprietary and curated datasets collected, licensed, and generated by DataSeeds.ai","assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"DataSeeds.ai","description":"DataSeeds.ai provides high-quality, large-scale multimodal datasets designed for modern AI and machine learning workflows. 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The integrated city profiles support location intelligence, market analysis, regional benchmarking, cultural planning, business intelligence, and AI-driven analytics.\nUnlike traditional municipality reference datasets, this dataset enriches administrative and demographic information with real cultural activity, venue presence, live-event statistics, and music-related indicators, providing a unique perspective on Germany's local music ecosystems.\nThe dataset supports analytics teams, public-sector organizations, tourism agencies, cultural institutions, researchers, consulting firms, music industry stakeholders, and data scientists seeking comprehensive municipality-level intelligence.\nThis listing contains a representative sample of 1,000 municipalities. Access to the complete dataset is available upon request.\n\n**Use Cases**\n* Analyze demographic and geographic differences across German municipalities.\n* Benchmark municipalities using population, density, area, and administrative characteristics.\n* Assess cultural infrastructure and venue availability.\n* Identify municipalities with strong live-event and music activity.\n* Support location intelligence and territory planning.\n* Enhance customer segmentation and regional market analysis.\n* Build dashboards for municipal benchmarking and regional development.\n* Support geospatial analytics using municipality identifiers and coordinates.\n* Enrich forecasting, recommendation, and market-potential models.\n* Explore relationships between demographics, cultural infrastructure, and music ecosystems.\n\n**Product Details**\nThe sample dataset includes:\n* Municipality identifiers\n* Municipality and administrative names\n* Geographic and regional classifications\n* Population and 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provides real, usage-based data on music licensing and public music usage in Germany. 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These datasets support analytics for the music, media, entertainment, event, advertising, and location intelligence sectors."},"popularity":138},{"id":"a72efea7-8886-468b-84cb-30cce30d1511","summary":{"name":"Draup Global Professional Profiles Data","subtitle":"Multi-industry profiles with roles, skills, education, diversity, projects & career histories","setting":{"visibility":"PUBLIC"},"created_at":1756522986100,"updated_at":1788940856144,"categories":["ADVERTISING_AND_MARKETING","COMMERCE","ECONOMICS","EDUCATION","OTHER","SCIENCE_AND_RESEARCH"],"listingType":"PERSONALIZED","provider_id":"9900a255-6aa9-496f-9b6a-421a3568af3b"},"detail":{"description":"**Overview**\nDraup’s Global Professional Profiles dataset provides a comprehensive and multidimensional view of professionals across industries, roles, and geographies. It captures detailed career journeys, covering current and past roles, responsibilities, skills (core and soft), education, certifications, projects, awards, and publications—enabling organizations to understand professional capabilities and career trajectories at scale. By enriching profiles with demographic attributes, languages, and diversity indicators, the dataset goes beyond simple contact records, offering holistic insights into the workforce.\nThis dataset is designed to support HR leaders, talent acquisition teams, workforce planners, and market intelligence professionals. It enables use cases such as identifying talent pools for specialized roles, benchmarking professional experience across industries, mapping career pathways, and analyzing diversity representation. Organizations can leverage this intelligence for sourcing strategies, succession planning, skill-gap analysis, and broader workforce research—strengthening their ability to attract, retain, and develop talent in alignment with business goals.\n\n**Methodology & Quality**\nThe dataset aggregates professional information from public sources, labor market data, and validated third-party feeds. Draup applies proprietary normalization processes to align job titles, roles, and skills with standardized taxonomies. Data is refreshed regularly to ensure accuracy, with updates to profile attributes, role histories, and credentials. Advanced natural language processing is used to extract and structure skills, experiences, and workloads, while multi-source verification reduces noise and bias. Profiles include indicators such as verified email presence and military veteran status where available, ensuring reliability for talent strategy use cases.\n\n**Key Tables Include**\n•\tProfessional Profiles – Core profile details including role, experience, skills, demographics, contact attributes, and organizational context.\n•\tCareer Experiences – Historical role data by company, designation, location, and timelines.\n•\tEducation Background – University, degree, majors, and course information across career stages.\n•\tCertifications – Professional certifications, issuing authorities, and validity timelines.\n•\tProjects & Contributions – Professional projects with descriptions, collaborators, and outcomes.\n•\tAwards & Honors – Recognition, awards, sponsorships, and associated details.\n•\tInvestments & Affiliations – Records of professional investments and organizational affiliations.\n•\tPublications – Authored publications, publishers, dates, and links.\n•\tPatents – Registered patents including title, description, and grant date.\n•\tVolunteering Experience – Roles, causes, and contributions in nonprofit and community settings.\n\n**Use Cases:**\n***Analyze the availability of talent for a given role in a specific location.***\t\n\nMeasure the talent pool size and distribution for target roles within chosen geographies to support workforce planning and sourcing.\t\n\nSELECT\n profile_info.company_name,\n profile_info.business_function,\n COUNT(DISTINCT profile_info.profile_id) AS talent_count\nFROM profile_info\nWHERE profile_info.job_role ILIKE '%Software Engineer%'\n AND profile_info.location ILIKE '%San Francisco%'\nGROUP BY profile_info.company_name, profile_info.business_function\nORDER BY talent_count DESC;\n\n***Identify the most common certifications for a DevOps Engineer role in the USA.***\t\n\nIdentify widely held certifications among professionals in a role and geography to align upskilling, hiring, and credentialing strategies.\t\n\nSELECT\n profile_certifications.certification_name,\n COUNT(DISTINCT profile_certifications.profile_id) AS holders_count\nFROM profile_certifications\nJOIN profile_info\n ON profile_certifications.profile_id = profile_info.profile_id\nWHERE profile_info.job_role = 'Devops Engineer'\n AND profile_info.location ILIKE 'United States'\n AND profile_certifications.certification_name IS NOT NULL\nGROUP BY profile_certifications.certification_name\nORDER BY holders_count DESC\nLIMIT 10;\n\n***Which competitors is Snowflake hiring talent from and who are they losing their talent to?***\t\n\nDraup’s talent movement capabilities reveal a company's inbound and outbound talent flows across roles, functions, and geographies. Identify key feeder and destination companies to inform talent strategy, poaching risk, or market positioning.\t\n\nSELECT\n company_name AS competitor_company,\n executive_name,\n from_title AS previous_title,\n to_title AS current_title,\n movement_date\nFROM\n company_leadership_movement\nWHERE\n affiliated_entity_name = 'Snowflake Inc.'\n AND company_name IS NOT NULL\n AND movement_date >= DATE_SUB(CURRENT_TIMESTAMP(), INTERVAL '1' YEAR)\nORDER BY\n movement_date DESC;\n\n***List VP-and-above R&D leaders who left Pfizer in the past 12 months and their new employers***\t\n\nTrack high-level executive movements from any company using Draup’s leadership change data. Filter by function, designation, and time period to uncover exits and transitions; useful for competitive intelligence, recruitment targeting, or succession mapping.\t\n\nSELECT\n executive_name,\n from_title AS title_at_pfizer,\n company_name AS new_employer,\n to_title AS new_title,\n movement_date\nFROM\n company_leadership_movement\nWHERE\n affiliated_entity_name = 'Pfizer'\n AND business_function = 'R&D'\n AND (from_title LIKE '%VP%' OR from_title LIKE '%President%' OR from_title LIKE '%Chief%')\n AND movement_date >= current_timestamp - INTERVAL '1' YEAR\nORDER BY\n movement_date DESC;\n\n***See how does your peers’ workforce split by function or location compare with you.***\t\n\nBenchmark workforce distribution across functions and geographies against competitors to identify over- or under-invested areas in your organization.\t\n\nWITH workforce_distribution AS (\n SELECT\n profile_info.company_name,\n profile_info.business_function,\n profile_info.location,\n COUNT(*) AS headcount\n FROM profile_info\n WHERE profile_info.business_function IS NOT NULL\n AND profile_info.location IS NOT NULL\n GROUP BY profile_info.company_name, profile_info.business_function, profile_info.location\n),\ntotal_workforce AS (\n SELECT\n workforce_distribution.company_name,\n SUM(workforce_distribution.headcount) AS total_headcount\n FROM workforce_distribution\n GROUP BY workforce_distribution.company_name\n),\nworkforce_percentage AS (\n SELECT\n workforce_distribution.company_name,\n workforce_distribution.business_function,\n workforce_distribution.location,\n workforce_distribution.headcount,\n ROUND((workforce_distribution.headcount * 100.0) / total_workforce.total_headcount, 2) AS percentage_split\n FROM workforce_distribution\n JOIN total_workforce\n ON workforce_distribution.company_name = total_workforce.company_name\n)\nSELECT\n CASE \n WHEN workforce_percentage.company_name = 'Microsoft' THEN 'Microsoft'\n ELSE 'Peer Companies'\n END AS company_group,\n workforce_percentage.business_function,\n workforce_percentage.location,\n SUM(workforce_percentage.headcount) AS total_headcount,\n ROUND(AVG(workforce_percentage.percentage_split), 2) AS avg_percentage_split\nFROM workforce_percentage\nGROUP BY\n CASE \n WHEN workforce_percentage.company_name = 'Microsoft' THEN 'Microsoft'\n ELSE 'Peer Companies'\n END,\n workforce_percentage.business_function,\n workforce_percentage.location\nORDER BY\n workforce_percentage.business_function,\n workforce_percentage.location;\n\n***Analyze the historical talent movement and attrition patterns in a location.***\t\n\nAnalyze career transitions, exits, and movements over time within a geography to anticipate attrition risks and strengthen retention strategies.\t\n\nWITH profile_experience_expanded AS (\n SELECT\n profile_id,\n company_name,\n UNNEST(msa) AS location,\n start_date,\n end_date\n FROM profile_experiences\n WHERE start_date IS NOT NULL\n),\nlocation_joins AS (\n SELECT\n location,\n EXTRACT(YEAR FROM start_date) AS start_year,\n COUNT(DISTINCT profile_id) AS joins\n FROM profile_experience_expanded\n GROUP BY location, EXTRACT(YEAR FROM start_date)\n),\nlocation_exits AS (\n SELECT\n location,\n EXTRACT(YEAR FROM end_date) AS end_year,\n COUNT(DISTINCT profile_id) AS exits\n FROM profile_experience_expanded\n WHERE end_date IS NOT NULL\n GROUP BY location, EXTRACT(YEAR FROM end_date)\n)\nSELECT\n COALESCE(location_joins.location, location_exits.location) AS location,\n COALESCE(location_joins.start_year, location_exits.end_year) AS year,\n COALESCE(location_joins.joins, 0) AS total_joins,\n COALESCE(location_exits.exits, 0) AS total_exits,\n COALESCE(location_exits.exits, 0) - COALESCE(location_joins.joins, 0) AS net_attrition\nFROM location_joins\nFULL OUTER JOIN location_exits\n ON location_joins.location = location_exits.location\n AND location_joins.start_year = location_exits.end_year\nORDER BY location, year;","terms_of_service":"https://draup.com/privacy-policy#:~:text=Unless%20provided%20for%20in%20this,that%20work%20on%20our%20behalf","documentation_link":"","privacy_policy_link":"https://draup.com/privacy-policy","geographical_coverage":"[\"Global\"]","cost":"PAID","update_frequency":{"interval":1,"unit":"WEEKLY"},"collection_granularity":{"interval":1,"unit":"DAILY"},"data_source":"Public Sources","assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"Draup, Inc.","description":"Draup is an enterprise agentic intelligence platform built for HR and Sales leaders navigating transformation. By combining unparalleled real-time global data with agentic AI, Draup enables strategic decision-making across talent and revenue functions.\n \nThrough its Talent Intelligence solution, Draup empowers HR teams to drive workforce planning, precision hiring, predictive skill architecture, and competitive benchmarking. Its Sales Intelligence solution helps go-to-market teams uncover deep account insights, identify buying triggers, and accelerate revenue growth.\n \nTrusted by over 300 global enterprises - including five of the Fortune 10 - Draup supports companies like Microsoft, PepsiCo, PayPal, and Moderna in building future-ready workforces and growth strategies."},"popularity":243},{"id":"84d77c16-f840-486f-b9b7-e798aca0f772","summary":{"name":"OnPoint Climatology: ECMWF | Statistical Weather Norms & Anomalies | 20-Year Baseline | Global ...","subtitle":"Long-term weather averages and patterns to reveal typical climate trends by location","share":{"name":"pelmorex_weather_source_6be6e832_40f9_4054_83f4_85e000c5df25_samples_share","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1768489107279,"updated_at":1792680734440,"categories":["OTHER"],"listingType":"PERSONALIZED","provider_id":"871f3467-760a-49de-ada4-1556857ee355"},"detail":{"description":"OnPoint Climatology provides a comprehensive statistical baseline for typical weather conditions, delivering 20-year averages, standard deviations, and event frequencies such as rainfall and snowfall for any location and time of year. Built on Pelmorex Weather Source’s enhanced historical dataset, this product goes beyond simple averages by incorporating both historical and forecast data to expand the sample and improve reliability.\nOnPoint Climatology is an objective tool for quantifying expected weather, identifying and contextualizing anomalies, and determining how rare or unusual specific weather events are—empowering businesses, researchers, and planners with actionable climate intelligence.\n\nIs OnPoint Climatology Right for You?\n\nOnPoint Climatology is best for organizations and researchers who need to quantify typical weather, identify and contextualize anomalies, and make data-driven decisions for planning, risk assessment, and adaptation. If your focus is on understanding what’s “normal” for a location—or how unusual current conditions are—this product is the right fit.\n\n- Ideal for Data Scientists & Analysts who require a scientifically validated baseline to train machine learning models, normalize datasets, or calculate \"weather-alpha\" in financial and retail modeling.\n\n- Essential for Risk & Insurance Professionals needing to identify the probability of rare meteorological events and contextualize anomalies against 20 years of standard deviation data.\n\n- Strategic for Energy & Agriculture planners who rely on historical normals for load forecasting, crop suitability mapping, and site selection based on long-term solar radiation, wind, and precipitation trends.\n\nAt The Weather Network for Business, we turn weather volatility into a predictable business advantage for enterprises globally. Backed by over 35 years of meteorological expertise, with over a decade of dedicated enterprise experience, we deliver hyper-local weather data, real-time alerts, and predictive analytics that protect assets, optimize operations, and drive growth. From retail and energy to enterprise logistics and public safety, our specialized commercial solutions help businesses navigate climate complexity with confidence.\n\nDiscover how we can help your business stay ahead of the storm at business.theweathernetwork.com/","terms_of_service":"https://business.theweathernetwork.com/privacy-policy","contact_email":"support@datarade.ai","privacy_policy_link":"https://business.theweathernetwork.com/privacy-policy","assets":["ASSET_TYPE_DATA_TABLE"]},"provider_summary":{"name":"The Weather Network For Business","description":"At The Weather Network for Business, we turn weather volatility into a predictable business advantage for enterprises globally. Backed by over 35 years of meteorological expertise, with over a decade of dedicated enterprise experience, we deliver hyper-local weather data, real-time alerts, and predictive analytics that protect assets, optimize operations, and drive growth. From retail and energy to enterprise logistics and public safety, our specialized commercial solutions help businesses navigate climate complexity with confidence.\r\n\r\nDiscover how we can help your business stay ahead of the storm at business.theweathernetwork.com/"},"popularity":734},{"id":"b1479b0a-1fd7-4460-bd9e-b7570ab47da3","summary":{"name":"SAMPLE Audience Targeting Data (Webbula Demo) | 382M+ Individuals, 500+ Syndicated Segments | ...","subtitle":"Webbula Demo encapsulates a 360 degree view of an individual, beyond their basic information.","share":{"name":"webbula_4c81859f_d844_4e41_b422_34e38d3b9cf9_samples_share","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1766171653312,"updated_at":1780306258270,"categories":["ADVERTISING_AND_MARKETING","OTHER"],"listingType":"STANDARD","provider_id":"686e23ab-8fd6-4c66-b1e0-f4ef1cab08dc"},"detail":{"description":"526 syndicated segments\n382M+ individuals\n\nExamples of segments available:\n- Birth Month/Year, Gender\n- Household comp\n- Marital Status, Number of adults/children, HH Rank \n- Ethnicity, Religion\n- Primary Spoken Language\n- Own/rent, residence length\n- Type of A/C, Fuel type, Pool, Water type \n- Income range, House value range, Net Worth\n- Credit Card type","terms_of_service":"https://webbula.com/privacy-center","contact_email":"support@datarade.ai","privacy_policy_link":"https://webbula.com/privacy-center","assets":["ASSET_TYPE_DATA_TABLE"]},"provider_summary":{"name":"Webbula","description":"As a derivative of our lengthy, successful email and data hygiene tenure, Webbula has seven audience verticals based solely on PII that can be used for marketing across multiple channels, with a STRICT focus on accuracy: \r\n\r\nB2B\r\nMortgage\r\nPolitical\r\nAutomotive \r\nDemographic\r\nHobby/Interest/Lifestyle \r\nInterest & Brand Preferences\r\nWealth"},"popularity":215},{"id":"7205290c-5848-46a6-9716-b18370665938","summary":{"name":"Diagnosis Related Groups and Classifications","subtitle":"This data package includes information regarding All Patients Refined Diagnosis Related Groups (APR DRG) weights for a..","share":{"name":"diagnosis_related_groups_and_classifications","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1682355662046,"updated_at":1695690012916,"categories":["HEALTH","SCIENCE_AND_RESEARCH"],"listingType":"STANDARD","provider_id":"f88849f8-9d77-4de2-b4c7-03d55cc3768e"},"detail":{"description":"\n**Overview**\nThis data package includes information regarding All Patients Refined Diagnosis Related Groups (APR DRG) weights for average length and capital ratios for acute care hospitals. It comprises of dataset about DRGs by procedures, hospital-aquired conditions and major diagnostic categories as well as Medicare severity, payments, average and operating ratios and percentile lengths. It also contains information about the wage index table by Core Based Statistical Area (CBSA) for the fiscal year 2017.\n\n**Description**\nThis data package contains the Diagnosis Related Groups (DRGs) patient classification system that provides a means of relating the type of patients a hospital treats to the costs incurred by the hospital. Capital Ratios for Acute Care Hospitals are used to determine whether a case qualifies for outlier payments under the hospital inpatient prospective payment system (IPPS), hospital-specific cost-to-charge ratios are applied to the total covered charges for the case. Operating and capital costs for the case are calculated separately by applying separate operating and capital cost-to-charge ratios and combining these costs to compare them with the fixed-loss outlier threshold. \n\nCase Mix Index And Wage Index Table By CMS Certification Number (CCN) Correction Notice dataset contains the correction notice of Wage Index Final Rule Table 2. Table lists each Lugar hospital that is located in a county that qualifies for an outmigration adjustment; the 3-year period for which the hospital is eligible for the outmigration adjustment; and whether or not the hospital accepted the outmigration adjustment in lieu of its urban status for each year of its 3-year eligibility. A Diagnosis-Related Group dataset contains a list of Medicare Severity (MS) DRG with a specification of the Major Diagnostic Category (MDC) and whether the MS-DRG is medical or surgical. Some MS-DRGs which contain patients from multiple MDCs do not have an MDC specified. Diagnosis-Related Group is a statistical system of classifying any inpatient stay into groups for the purposes of payment. \n\nThe Hospital-Acquired Conditions (HACs) are conditions that occur after the physician writes the inpatient admission order and that could have been reasonably prevented through the application of evidence-based guidelines and best practices. The dataset Major Diagnostic Categories contains all 25 MDCs along with the DRGs assigned to each MDC. The data is organized by MDC with a list of the surgical classes associated with that MDC listed in hierarchical order as well as the MS-DRGs that are included in each surgical class. This dataset contains the final rule/correction notice cost thresholds by Medicare Severity Diagnosis Related Group (MS-DRG) for the cost criteria for new technology add on payment applications for FY 2018. \n\nThe dataset Operating Ratios for Acute Care Hospitals contains the Fiscal Year 2017 Inpatient Prospective Payment System (IPPS) operating and capital statewide average cost-to-charge-ratios. The empty values mean that all counties in the State or Territory are classified as urban. This dataset Percentile Lengths of Hospital Stay consists of a table containing Medicare Prospective Payment System Selected Percentile Lengths of Stay for FY 2015. The dataset Statewide Average Ratios contains the Fiscal Year 2017 IPPS operating and capital statewide average cost-to-charge ratios. It also includes States with no data from hospitals paid under the IPPS where the national average IPPS total rural CCR for the rural statewide average is used for these areas.\n\n**Benefits**\n- Useful for doctors, medical health planners, policy makers, patients, medical research groups and medical students.\n- diagnosis related groups and classifications database is available on centers for medicare & medicaid services and department of health, new york state websites in raw form, whereas a jsl customer gets it in clean and normalized form ready to use in one place and create his own custom reports.\n- easy to comprehend for the customers who are unfamiliar with the medical terminologies as all abbreviated terms are replaced with full form, unlike centers for medicare & medicaid services (cms) and department of health, new york state original datasets which have many abbreviations.\n- recent datasets for 2017 is available for use.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [APR-DRG Service Intensity Weights and Average Length of Stay](https://www.johnsnowlabs.com/marketplace/apr-drg-service-intensity-weights-and-average-length-of-stay)\n\t- This dataset is the version 33 of APR-DRG Weights (All Patient Refined Diagnosis Related Group) and ALOS (Average Length of Stay) for acute care services. The APR-DRG weights effective January 1, 2017 will not be updated. For discharges beginning on or after January 1, 2018, the SIWs (Service Intensity Weights), cost thresholds and ALOS effective July 1, 2014 will be used for payment purposes with the updated APR-DRG grouper version 33, which is available in this dataset.\n- [Capital Ratios For Acute Care Hospitals](https://www.johnsnowlabs.com/marketplace/capital-ratios-for-acute-care-hospitals)\n\t- This dataset is used to determine whether a case qualifies for outlier payments under the hospital inpatient prospective payment system (IPPS), hospital-specific cost-to-charge ratios are applied to the total covered charges for the case. Operating and capital costs for the case are calculated separately by applying separate operating and capital cost-to-charge ratios and combining these costs to compare them with the fixed-loss outlier threshold.\n- [Case Mix Index And Wage Index Table By CCN Correction Notice](https://www.johnsnowlabs.com/marketplace/case-mix-index-and-wage-index-table-by-ccn-correction-notice)\n\t- This dataset contains the correction notice of Wage Index Final Rule. It lists each Lugar hospital (deemed urban under section 1886(d)(8)(B) of the Act) that is located in a county that qualifies for an outmigration adjustment; the 3-year period for which the hospital is eligible for the outmigration adjustment; and whether or not the hospital accepted the outmigration adjustment in lieu of its urban status for each year of its 3-year eligibility.\n- [Case Mix Index And Wage Index Table By CCN Final Rule](https://www.johnsnowlabs.com/marketplace/case-mix-index-and-wage-index-table-by-ccn-final-rule)\n\t- This dataset contains the non-transfer adjusted Case Mix Index (CMI) based on the Medicare Severity Diagnosis Related Groups (MS-DRGs) billed on the claim in the year the claim was incurred.\n- [Diagnosis Related Groups According to Procedure](https://www.johnsnowlabs.com/marketplace/diagnosis-related-groups-according-to-procedure)\n\t- This dataset contains a list of Medicare Severity Diagnosis Related Groups (MS-DRGs) with a specification of the Major Diagnostic Category (MDC) and whether the MS-DRG is medical or surgical. Some MS-DRGs which contain patients from multiple MDCs (e.g., 014 Allogeneic Bone Marrow Transplant) do not have an MDC specified.\n- [Diagnosis Related Groups Assigned to Hospital Acquired Conditions](https://www.johnsnowlabs.com/marketplace/diagnosis-related-groups-assigned-to-hospital-acquired-conditions)\n\t- The following dataset consists of Hospital-Acquired Conditions (HACs) which are conditions that occur after the physician writes the inpatient admission order and that could have been reasonably prevented through the application of evidence-based guidelines and best practices.\n- [Diagnosis Related Groups Assigned to Major Diagnostic Categories](https://www.johnsnowlabs.com/marketplace/diagnosis-related-groups-assigned-to-major-diagnostic-categories)\n\t- This dataset contains all 25 MDCs (Major Diagnostic Categories), along with the DRGs assigned to each MDC. The data is organized by MDC with a list of the surgical classes associated with that MDC listed in hierarchical order as well as the MS-DRGs that are included in each surgical class.\n- [Hospitals With Fewer Than 1600 Medicare Discharges And Potential](https://www.johnsnowlabs.com/marketplace/hospitals-with-fewer-than-1600-medicare-discharges-and-potential)\n\t- This dataset was taken from the United States Food and Drugs Association (US FDA).The datset is a list of Hospitals with Fewer than 1,600 Medicare Discharges Based on the December 2017 Update of the FY 2016 MedPAR File and Potentially Eligible Hospitals’ FY 2018 Low-Volume Hospital Payment Adjustment. Eligibility for the low-volume hospital payment adjustment is also dependent upon meeting the mileage criteria specified at 412.101(b)(2)(ii) of the regulations.\n- [Medicare Severity Diagnosis Related Groups MSDRG Correction Notice](https://www.johnsnowlabs.com/marketplace/medicare-severity-diagnosis-related-groups-msdrg-correction-notice)\n\t- This dataset file includes the list of Medicare Severity Diagnosis-Related Groups (MS-DRGS) Relative Weighting Factors, and Geometric and Arithmetic Mean Length of Stay Correction Notice.\n- [Medicare Severity Diagnosis Related Groups MSDRG Final Rule](https://www.johnsnowlabs.com/marketplace/medicare-severity-diagnosis-related-groups-msdrg-final-rule)\n\t- This dataset file includes the list of Medicare Severity Diagnosis-Related Groups (MSDRG) Relative Weighting Factors, and Geometric and Arithmetic Mean Length of Stay Final Rule.\n- [Medicare Uncompensated Care Payment Final Rule](https://www.johnsnowlabs.com/marketplace/medicare-uncompensated-care-payment-final-rule)\n\t- This dataset shows section 3133 of the Affordable Care Act as it amends the Medicare Disproportionate Share Hospital (DSH) adjustment provision under section 1886(d) (5) (F) of the Act, and establishes 1886(r) which provides for an additional payment for a hospital’s uncompensated care. As proposed in the FY 2014 IPPS proposed rule, the regulations that implement this provision are in proposed subpart I of 42 CFR part §412.106.\n- [New Technology Add On Payments Thresholds Final Rule](https://www.johnsnowlabs.com/marketplace/new-technology-add-on-payments-thresholds-final-rule)\n\t- This dataset contains the final rule/correction notice cost thresholds by Medicare Severity Diagnosis Related Group (MS-DRG) for the cost criteria for new technology add on payment applications for applications for FY 2018.\n- [Operating Ratios For Acute Care Hospitals](https://www.johnsnowlabs.com/marketplace/operating-ratios-for-acute-care-hospitals)\n\t- This dataset contains the Fiscal Year 2022 Inpatient Prospective Payment System (IPPS) operating and capital statewide average cost-to-charge-ratios. The empty values mean that all counties in the State or Territory are classified as urban.\n- [Percentile Lengths of Hospital Stay for MSDRG](https://www.johnsnowlabs.com/marketplace/percentile-lengths-of-hospital-stay-for-msdrg)\n\t- This dataset consists of a table containing Medicare Prospective Payment System Selected Percentile Lengths of Stay for the fiscal year 2016, 2017, 2018, 2019 & 2020.\n- [Statewide Average Ratios](https://www.johnsnowlabs.com/marketplace/statewide-average-ratios)\n\t- This dataset contains the Fiscal Year 2022 Inpatient Prospective Payment System (IPPS) operating and capital statewide average cost-to-charge-ratios. The empty values mean that all counties in the State or Territory are classified as urban. It also includes States with no data from hospitals paid under the IPPS where the national average IPPS total rural CCR for the rural statewide average are used for these areas.\n- [Wage Index Table By CBSA Correction Notice](https://www.johnsnowlabs.com/marketplace/wage-index-table-by-cbsa-correction-notice)\n\t- This dataset contains the correction notice for Wage Index. The dataset lists each Lugar hospital (deemed urban under section 1886(d)(8)(B) of the Act) that is located in a county that qualifies for an outmigration adjustment; the 3-year period for which the hospital is eligible for the outmigration adjustment; and whether or not the hospital accepted the outmigration adjustment in lieu of its urban status for each year of its 3-year eligibility.\n- [Wage Index Table By CBSA Final Rule](https://www.johnsnowlabs.com/marketplace/wage-index-table-by-cbsa-final-rule)\n\t- This dataset lists Final Rule Wage Index Table by Core-Based Statistical Areas (CBSAs). 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This dataset is ideal for organizations seeking to quantify the historical impact of weather on operations, consumer behavior, and revenue across diverse industries.\n\nIs OnPoint History: ECMWF Data Right for You?\n\n- Ideal for data scientists, analysts, and researchers who require high-resolution, hourly historical weather data for global locations.\n\n- Suited for organizations in sectors like retail, insurance, agriculture, energy, and transportation that need reliable, gap-free weather data for demand forecasting, risk modeling, trend analysis, and operational planning.\n\n- Perfect for climate scientists and academics conducting long-term climate studies, event attribution, or environmental impact assessments using a globally consistent dataset.\n\n- Valuable for businesses and developers building analytics platforms or applications that depend on accurate, spatially and temporally complete weather information.","terms_of_service":"https://business.theweathernetwork.com/privacy-policy","contact_email":"support@datarade.ai","privacy_policy_link":"https://business.theweathernetwork.com/privacy-policy","assets":["ASSET_TYPE_DATA_TABLE"]},"provider_summary":{"name":"The Weather Network For Business","description":"At The Weather Network for Business, we turn weather volatility into a predictable business advantage for enterprises globally. 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This meticulously compiled dataset, sourced from official statistical sources, including Statistics Estonia, offers a detailed insight into the population landscape of Estonia at the municipality (linnad/vallad) level. \n\nIf you require data at a less granular level, such as an Uber grid or square grid, please don't hesitate to get in touch with us at *contact@geolocet.com*. 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No lengthy sign-up processes or NDAs required on our side – just download the data you need.\n* **Data Samples for Evaluation:** To ensure you're getting exactly what you need, we provide free downloadable samples in CSV format for data evaluation.\n* **Data accuracy:** Our datasets are created using the latest demographics data available in national statistics bureaus, guaranteeing accuracy and reliability.\n* **Best-in-Class Quality:** We guarantee best-in-class quality, backed by over two decades of experience in modeling demographics data and an outstanding quality management process.\n* **Competitive Pricing:** We maintain the lowest prices on the market, ensuring that you get the most cost-efficient demographics data acquisition process.\n---\nAdditionally, for those looking to enrich their analysis or project, we offer:\n* **[Administrative Boundaries](https://geolocet.com/products/estonia-admin-level-7-municipalities):** If you require geographical boundaries (polygons) for the municipalities of Estonia, consider exploring our Administrative Boundaries product, which seamlessly integrates with this dataset.\n\n* **[Point of Interest (PoI) Data](https://geolocet.com/pages/aggregated-points-of-interest-poi-data):** If you're interested in Points of Interest data, please don't hesitate to contact us for customized datasets to enhance your geographical analysis.\n\n* **[Data Customization](https://geolocet.com/pages/data-services-by-geolocet):** We understand that your project may have specific requirements. Feel free to get in touch with us if you need any modifications or specific data formats such as Excel, JSON, GeoJSON, or Shapefile. We are here to tailor the data to your needs.\n\nIf you're looking for data for Estonia, [look here](https://geolocet.com/pages/estonia) for off-the-shelf or tailor-made datasets to suit your specific requirements.\n\nMake informed decisions, conduct precise research, and unlock the potential of Estonia demographic landscape with our comprehensive dataset. Trust in the accuracy and reliability of our official statistical data to drive your projects forward.\nContact us at *contact@geolocet.com* today to access this invaluable resource and explore Estonia census data like never before.\n\n[Geolocet LTD](https://geolocet.com)\n","terms_of_service":"https://geolocet.com/pages/terms-of-use","documentation_link":"","privacy_policy_link":"https://geolocet.com/pages/privacy-policy","geographical_coverage":"[\"Europe\"]","cost":"PAID","update_frequency":{"interval":1,"unit":"YEARLY"},"data_source":"","assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://geolocet.com/pages/terms-of-use"},"provider_summary":{"name":"Geolocet LTD","description":"Geolocet provides premium geospatial data for retail analysis, GIS, and market planning. 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See separate listings for Falkor Platform and Falkor Surface.","terms_of_service":"https://falkor.com/trust-centre/b2c/b2c-terms-of-use","documentation_link":"https://falkor.com/","privacy_policy_link":"https://falkor.com/en/trust-centre/privacy","geographical_coverage":"[\"Global\"]","assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://creativecommons.org/licenses/by/4.0/"},"provider_summary":{"name":"Falkor AS","description":"Falkor (formerly Kongsberg Digital, a KONGSBERG company) is an industrial software and AI provider for asset-intensive industries, including oil & gas, renewables & utilities, and chemicals. On Databricks Marketplace, Falkor offers its industrial intelligence platform, the Industrial Work Surface, and complementary modules such as K-Spice and LedaFlow for dynamic process and multiphase flow simulation, SiteCom, and industrial AI agents. 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This platform also offers valuable insights into job openings, salaries, locations, and job seeker profiles, benefiting various users such as HR managers and recruiters. By scraping job details from Indeed, users can extract a wealth of information about different jobs, locations, actively hiring companies, ratings, and more. Our scrapers eliminate the need for manual navigation or coding, allowing easy access to this information.\r\n\r\nWhy scrape Job postings data?\r\nScraping data offers the advantage of quickly obtaining up-to-date and extensive information. With over 250 million users worldwide, it provides a vast amount of data. Utilizing this data enables insights into job trends, tracking hiring patterns, staying informed about the job market, finding job openings that match specific criteria, and creating more targeted job postings. This benefits HR managers, recruiters, and business owners alike.\r\n\r\nWhat information does it scrape?\r\nOur datasets can extract data from job postings, including job titles, posting dates, company names, locations, salaries, company ratings, and apply URLs","terms_of_service":"https://webautomation.io/4/privacy-policy","contact_email":"support@datarade.ai","privacy_policy_link":"https://webautomation.io/4/privacy-policy","assets":["ASSET_TYPE_DATA_TABLE"]},"provider_summary":{"name":"Webautomation","description":"WebAutomation.io revolutionizes web data collection with its marketplace of pre-built extractors and ready-to-use datasets. In just minutes, users can access hundreds of extractors covering various web sources. Collect millions of data points from e-commerce sites, social media platforms, and more without coding or maintenance. The platform's user-friendly interface simplifies the process, making it accessible to users of all technical backgrounds. 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Agents search for preprints by keyword, retrieve full metadata and abstracts by DOI, and monitor the recent preprint feed covering days to a full year of activity across bioRxiv and medRxiv.\n\n**Use cases:**\n\n- Search bioRxiv or medRxiv by keyword to surface emerging research ahead of MEDLINE indexing\n- Retrieve full preprint metadata, abstracts, version history, and author information by DOI\n- Monitor the most recent preprints posted within a configurable window of 1 to 365 days\n- Track new publications in rapidly evolving research areas for competitive intelligence and R&D signal detection\n- Support manuscript preparation and advisory board materials by surveying current preprint activity\n\n**Sample Prompts:**\n\nPrompt Example 1: Early R&D Signal Detection\n\n- \"Search bioRxiv for preprints on 'GLP-1 receptor agonist neurodegeneration' and return the top 10 results. 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Learn more about Climb here: [climb.ai](https://climb.ai)\n\n![Climb logo](https://climb-static-assets.s3.us-west-2.amazonaws.com/assets/PNG/Dark/Climb+Logo+Left-Thirds.png)","terms_of_service":"https://climb.ai/terms-of-service","documentation_link":"https://api.biorxiv.org","privacy_policy_link":"https://climb.ai/privacy-policy","update_frequency":{"interval":1,"unit":"SECOND"},"assets":["ASSET_TYPE_MCP"],"license":""},"provider_summary":{"name":"bioRxiv | medRxiv","description":"This MCP Server is powered by Climb, a leading Consulting and Solutions Integrator Partner of Databricks. 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This comprehensive dataset transforms all current and future CMS data feeds from data.cms.gov into analysis-ready views, automatically updated as new files are published.\n\n**Dataset Size:** 500+ GB | **37B+ records** | **Updated daily**\n\n> **[Start Free Trial](https://trial.dataplex-consulting.com)** | **[Purchase Full Access](https://checkout.dataplex-consulting.com/b/3cIdR2e1J59Sh2w7pWbQY00)**\n\n### What's Included\n\n- **140+ data feeds** - All major CMS data sources in standardized views\n- **1,700+ report files** - Historical versions for trend analysis\n- **Medicare Part D** - Drug spending, utilization, and prescriber data\n- **Hospital Compare** - Quality metrics, outcomes, and patient experience\n- **Provider data** - Enrollment, utilization, and payment information\n- **Geographic data** - State, county, and zip code level statistics\n- **Telehealth metrics** - Virtual care utilization and trends\n- **Opioid data** - Prescribing rates and geographic patterns\n\n---\n\n## Use Cases\n\n### Healthcare Analytics\n- **Medicare spending analysis**: Track drug costs and utilization trends\n- **Hospital benchmarking**: Compare quality metrics across facilities\n- **Provider profiling**: Analyze utilization patterns by provider\n- **Geographic insights**: Map healthcare patterns by region\n\n### Health Plans & Payers\n- **Network analysis**: Evaluate provider performance metrics\n- **Cost forecasting**: Model future Medicare enrollment and spending\n- **Quality programs**: Track value-based care metrics\n- **Risk adjustment**: Support population health management\n\n### Life Sciences & Pharma\n- **Market access**: Analyze Medicare drug coverage and spending\n- **Competitive intelligence**: Track market share by therapeutic area\n- **Pricing analysis**: Monitor drug pricing trends over time\n- **HCP targeting**: Identify high-prescribing providers\n\n### Research & Policy\n- **Trend analysis**: Study long-term healthcare utilization patterns\n- **Policy evaluation**: Measure impact of CMS program changes\n- **Health equity**: Analyze disparities in access and outcomes\n- **Predictive modeling**: Train ML models on CMS metrics\n\n---\n\n## Data Quality & Updates\n\n- **Source**: CMS data.cms.gov and related CMS data sources\n- **History**: Complete historical files with version tracking\n- **Updates**: Daily automated processing as new files are published\n- **Schema alignment**: All feeds normalized to consistent data types\n- **Future-proof**: New CMS feeds automatically added to dataset\n\n---\n\n## Getting Started\n\n### Start a Free Trial\n\nGet 14 days of access to explore the CMS Data Feeds dataset:\n\n**[Start Free Trial](https://trial.dataplex-consulting.com)**\n\nTrial includes:\n- Access to all 140+ feed views with sample data\n- Complete feed metadata catalog\n- Databricks notebook with example queries\n\n### Get Full Access\n\nReady for production use? Get unlimited access to all CMS data feeds:\n\n**[Purchase Full Access](https://checkout.dataplex-consulting.com/b/3cIdR2e1J59Sh2w7pWbQY00)**\n\nFull access includes:\n- Complete access to all 140+ data feeds\n- All 37B+ records with full history\n- 1,700+ historical report files\n- Daily updates as CMS publishes new data\n- Automatic inclusion of new CMS feeds\n\n---\n\n## Example Analyses\n\n- **Medicare spending by drug over time** - Track Part D trends\n- **Hospital comparison by provider** - Benchmark inpatient facilities\n- **Telehealth utilization trends** - Monitor virtual care adoption\n- **Opioid prescribing by geography** - Map prescription patterns\n- **Medicare enrollment forecasting** - Predict future enrollment\n\n---\n\n## Documentation & Support\n\n- **Full Documentation**: [docs.dataplex-consulting.com/cms-data-feeds-dataset](https://docs.dataplex-consulting.com/data-catalog/cms-data-feeds-dataset)\n- **Data Dictionary**: Comprehensive field-level documentation\n- **Sample Notebooks**: Ready-to-run Databricks notebooks\n- **Support**: support@dataplex-consulting.com\n\n---\n\n\n*Data provided by Dataplex Consulting | [dataplex-consulting.com](https://dataplex-consulting.com)*\n","terms_of_service":"https://www.dataplex-consulting.com/terms","documentation_link":"https://docs.dataplex-consulting.com/data-catalog/cms-data-feeds-dataset","privacy_policy_link":"https://www.dataplex-consulting.com/privacy","embedded_notebook_file_infos":[{"id":"e5fa2dc9-7d28-42f1-a5cd-975034ad7553","marketplace_file_type":"EMBEDDED_NOTEBOOK","file_parent":{"parent_id":"7bfc94b8-8c6f-4706-96cd-5da0ba6766eb","file_parent_type":"LISTING"},"mime_type":"text/html","download_link":"https://marketplace-file-storage-prod.s3-fips.us-east-1.amazonaws.com/EMBEDDED_NOTEBOOK/LISTING_7bfc94b8-8c6f-4706-96cd-5da0ba6766eb/e5fa2dc9-7d28-42f1-a5cd-975034ad7553?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEGgaDnVzLWVhc3QtMS1maXBzIkYwRAIgbC8sWIoYaHR8y7Wq86nPb0pufBl4%2BM4PWpeN%2B5PW9VQCIG7BhAVBDCWTpxFDLeuKYm%2Ff1JLz4iccPMzter0YdhgWKqYECDAQABoMNDE0MzUxNzY3ODI2IgxNAJrHbXaZG20jNfMqgwQyGEb4TsoVOoNvJ%2BaA%2FZ7zeu8hGcHqCeNfblP%2FQo5g6q2aRNtpwtPRKMlhYcIE%2FUQaNT1Lqmyptu32VUQTpzjTTxRVmJrDPkheSCkV06XQ%2FpTMKjU8CC7X1b%2FqYf8CrBwZT7Cv4C5WPjEET38f2A79h0xV6bZgPy28zQWrSeIJ9w6AfZBxiE24%2FVc08zVA0ivCT6lsy7nuU0wcR%2FMg2C9nd1vaMnQ923VpD21CKfVVYcrNWL2b%2FQ%2BNAO%2BxPjPgjiN6UPQbvbvZxkSdhgrGtecIg9NgqGkMcvM5VLTDtdC1kdeZetfOC2obtA%2BT%2F0gy3tIDo6WM5hcQfw743%2FAU9oaGnRBl%2BwoxRRDCZA%2F4jnPdluICSI7NCr6C3HHOe4CSZJjRihczxPA%2FgQczAVjAPs6CrK9N9pIzt0vNJpGUTgg4AoxftTvGfciDREilSctPIcBk6GwB%2Bzt4wbtNCmEPU%2BFD46rc6CWVQmmLwbKOeHw70ipMVFlOc8NlXkEtGO2vu6H44MstSSc6daOVUYUavQSGZ6jGhEjsP2YGz8KDm9XQotB%2FtyVBqiZV01OKqDmmBJA%2FXjUpSJ8M%2FeJXhe5m%2BKcxzNObtoJRI7jci3Tpt08XZyXZ7%2FyIV8H30ya468X3cfkTXrm972uga3z7iXAdubyEHnQIua%2FuvtwOHILi6O5D1Me2MzCPvcnXBjqTAYDoMWBt%2B3AVFuqSs2dmDQNBnvEhXfMgTedKLD5PJHb3aIl15iBkLaNu36GYnVCp9ZU2lSYcTbY5QwNnNeTfJTNy9bKhwzru6Sg%2BslhN%2BTS0zkTn%2BeLMY4tHgPEtEZ2cp%2FCk6GwqswvnYVKHjwO8q%2B1Mx8lc08fVEfarE9dke1GGFqoddx%2Bq%2BZUyHbDlAVfrCmf0yw%3D%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20261110T000135Z&X-Amz-SignedHeaders=host&X-Amz-Expires=86400&X-Amz-Credential=ASIAWA6KKHEJORWBH4N3%2F20261110%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=5c1e9887a93847c79ab4dc4162c17f9d3374f3c78c161908245dc3e1f7b4cc5f","created_at":1769117346743,"updated_at":1769117346743,"display_name":"03_cms_showcase","status":"FILE_STATUS_PUBLISHED"}],"geographical_coverage":"[\"United States and Canada\"]","cost":"PAID","update_frequency":{"interval":1,"unit":"DAILY"},"collection_granularity":{"interval":1,"unit":"DAILY"},"collection_date_start":1420092000000,"collection_date_end":1769116253987,"data_source":"CMS","size":1200.0,"assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://www.dataplex-consulting.com/privacy"},"provider_summary":{"name":"Dataplex Consulting & Data Products","description":"Dataplex Consulting & Data Products delivers turnkey, analytics-ready data products that make complex public and commercial data easy to use across modern data platforms. 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The dataset spans diverse podcast genres, hosts, guests, formats, and recording styles, and supports both ready-made and custom dataset creation.\n\n**Use cases**\n- Training and evaluating automatic speech recognition (ASR) models\n- Speaker diarization and long-form dialogue understanding\n- Podcast transcription, summarization, and content indexing\n- Conversational AI and media intelligence applications\n- Benchmarking speech and audio processing pipelines\n\n**Product details**\n- Datasets include large-scale collections of podcast dialogue audio with rich acoustic, technical, and semantic metadata recorded in real-world and studio environments.\n- Sample fields include audio ID, audio URL, duration, sampling rate, channel configuration, language, speaker count, speaker segments, timestamps, podcast genre, episode metadata, background noise level, and annotated transcripts with confidence scores.","terms_of_service":"https://gurushots.com/terms","documentation_link":"","privacy_policy_link":"https://gurushots.com/privacy","geographical_coverage":"[\"Global\"]","cost":"PAID","update_frequency":{"interval":30,"unit":"DAILY"},"collection_granularity":{"interval":1,"unit":"DAILY"},"data_source":"Proprietary and curated datasets collected, licensed, and generated by DataSeeds.ai","assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"DataSeeds.ai","description":"DataSeeds.ai provides high-quality, large-scale multimodal datasets designed for modern AI and machine learning workflows. 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The models provided in this listing are\n\n- [e5_base_v2](https://huggingface.co/intfloat/e5-base-v2)\n- [e5_large_v2](https://huggingface.co/intfloat/e5-large-v2)\n- [e5_small_v2](https://huggingface.co/intfloat/e5-small-v2)\n\nE5_v2 models are licensed under the [MIT License](https://github.com/microsoft/unilm/blob/master/LICENSE). By installing this listing, you acknowledge and agree to the license.\n\nFor example notebooks of using the e5_v2 models in various use cases on Databricks, refer to [the Databricks ML example repository](https://github.com/databricks/databricks-ml-examples/tree/master/llm-models/embedding/e5-v2).\n\n**Use cases**\n\nThe e5_v2 models are embedding models that map text to low-dimensional vectors, and some example use cases are:\n- RAG implementation\n- Data visualization with clustering\n- Semantic search document search engine\n\n**Product details**\n\nThe embedding models in this listing can be deployed directly to Databricks Model Serving for immediate use, or loaded for fine-tuning or batch inference use cases. 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We are a cross-cloud platform that works across AWS, Azure, and Google.\nThis unified approach simplifies your modern data stack by eliminating the data silos that traditionally separate and complicate data engineering, analytics, BI, data science and machine learning. It’s built on open source and open standards to maximize flexibility. 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It is designed to handle question answering tasks, providing responses based on a specialized financial dataset.\n\n**Intended Use**\n\n- **Recommended applications**: This model is intended for automated financial advising, especially in the context of wealth management. You can use it in Chatbots and Copilots as a base model to answer user questions.\n- **Out-of-scope**: The model is not intended for general conversational purposes and should not be used for medical, legal, or safety-critical advice.\n\n**Model Architecture**\n\nThe model employs the `MistralForCausalLM` architecture with a `LlamaTokenizer`. It maintains the configuration of the base Mistral model but has been fine-tuned to better understand and generate responses related to wealth management.\n\n**Training Data**\n\nThe model was fine-tuned using a private Bitext dataset designed for question and answer interactions in the wealth management sector. This dataset includes instructions and responses across a variety of financial topics, ensuring that the model can handle a wide range of inquiries related to this field. The dataset covers 24 intents such as `arrange_meeting`, `calculate_portfolio_risk`, `check_balances`, `create_account`, and more specialized queries like `portfolio_performance` and `set_price_alert`. Each intent has 1000 examples, illustrating a training process aimed at understanding and generating accurate responses for financial advisory services. The dataset follows the same structured approach as our dataset published on Hugging Face as [bitext/Bitext-customer-support-llm-chatbot-training-dataset](https://huggingface.co/datasets/bitext/Bitext-customer-support-llm-chatbot-training-dataset), but with a focus on wealth management.\n\n**Training Procedure**\n\n**Hyperparameters**\n\n- **Optimizer**: AdamW with betas=(0.9, 0.999) and epsilon=1e-08\n- **Learning Rate**: 0.0002 with a cosine learning rate scheduler\n- **Epochs**: 4\n- **Batch Size**: 10\n- **Gradient Accumulation Steps**: 8\n- **Maximum Sequence Length**: 8192 tokens\n\n**Environment**\n\n- **Transformers Version**: 4.40.0.dev0\n- **Framework**: PyTorch 2.2.1+cu121\n- **Tokenizers**: Tokenizers 0.15.0\n\n**Limitations and Bias**\n\n- The model is fine-tuned on a domain-specific dataset and may not perform well outside the scope of financial advice.\n- Users should be aware of potential biases in the training data, as the model's responses may inadvertently reflect these biases. This model has been trained with a dataset that answers general wealth management questions, so potential biases may exist for specific use cases.\n\n**Ethical Considerations**\n\nThis model should be used responsibly, considering ethical implications of automated financial advice. As it is a base model for this financial field, it is crucial to ensure that the model's advice complements human expertise and adheres to relevant financial regulations.\n\n**Acknowledgments**\n\nThis model was developed by the Bitext and trained on infrastructure provided by Bitext.\n\n**License**\n\nThis model, \"Mistral-7B-Wealth-Management-v1\", is licensed under the Apache License 2.0 by Bitext Innovations International, Inc. This open-source license allows for free use, modification, and distribution of the model but requires that proper credit be given to Bitext.\n\n**Key Points of the Apache 2.0 License**\n\n- **Permissibility**: Users are allowed to use, modify, and distribute this software freely.\n- **Attribution**: You must provide proper credit to Bitext Innovations International, Inc. when using this model, in accordance with the original copyright notices and the license.\n- **Patent Grant**: The license includes a grant of patent rights from the contributors of the model.\n- **No Warranty**: The model is provided \"as is\" without warranties of any kind.\n\nYou may view the full license text at [Apache License 2.0](http://www.apache.org/licenses/LICENSE-2.0).\n\nThis licensing ensures the model can be used widely and freely while respecting the intellectual contributions of Bitext. 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The product contains **`5K+ restaurants`** updated frequently, and additional columns catered for leads and segmentation. \n\n> Additionally, we also curate restaurant menu data including dishes, prices, price history, restaurant promotions and social insights. This sample contains ~140 restaurants across Jordan. \n\nFor pricing and licensing details please [**contact us**](mailto:info@dotlas.com)\n\n**Use cases** \n\n\uD83D\uDD70 Analyze competitor opening hours on each day of the week to help create an optimal operating time frame.\n\uD83D\uDCC8 Identify trending cuisines in different regions by analyzing the frequency and distribution of various cuisine types in different cities.\n\uD83D\uDCB0 Use the distribution data to identify underserved areas with high potential for new restaurant openings based on the lack of certain cuisine types.\n\n### **Potential Sectors**\n* Food & Beverages (F&B)\n* GIS (Geographic Information Systems) and Geocoding Services\n* Marketing and Advertising Leads\n* Competitor Monitoring & Market Analysis\n* Financial tickering of F&B brand performance\n* Site Selection & Suitability Analysis\n* Real Estate Proximity Analysis\n\n**Key features** \n\n▶️ **Restaurant Identification**: Includes unique identifiers and names for each restaurant.\n\n▶️ **Geographical Data**: Detailed information about the `country`, `subregion`, `city`, `area`, and precise geographical coordinates (`latitude` and `longitude`) of each restaurant.\n\n▶️ **Contact Information**: Addresses, phone numbers, email addresses, and website URLs.\n\n▶️ **Social Media Presence**: Links to `Instagram`, `Facebook`, and `Twitter` profiles where available.\n\n▶️ **Amenities**: Information on various amenities such as acceptance of credit cards, availability of Wi-Fi, presence of outdoor seating, and more.\n\n▶️ **Additional Metadata**: Information on entertainment options, executive chef names, dress codes, dining styles, and more","terms_of_service":"https://dotlas.com/terms-of-service","documentation_link":"https://catalog.dotlas.com/restaurants/unified_restaurant_establishments/","privacy_policy_link":"https://dotlas.com/privacy-policy","geographical_coverage":"[\"Middle East\"]","cost":"FREE","update_frequency":{"interval":1,"unit":"DAILY"},"collection_granularity":{"interval":1,"unit":"DAILY"},"data_source":"Dotlas collects and coalesces information from multiple online and offline sources","assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"Dotlas","description":"Dotlas provides high-quality geographical and temporal data on restaurants, real estate and hospitality sectors around the world. 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These rules are applied to different levels such as columns and tables to provide a comprehensive assessment of the data.\n\n\nTo run Pingahla's Data Quality Accelerator Demo, simply execute the provided notebook. You will be prompted to select an Output Connection from the available options. Next, enter the credentials for all the desired connections for your data (Databricks, AWS, Azure). Upon completion, your data will be stored in the Output Connection database, where it can be viewed on a dashboard.\n\n\n**Use cases**\n- Data Integrity Verification: Ensure that the data adheres to predefined rules for accuracy, completeness, and consistency.\n- Anomaly Detection: Identify and flag potential anomalies in the data using methods like Isolation Forest.\n- Data Standardization: Apply rules to maintain consistency in data formats, such as email and phone numbers.\n- Schema Validation: Check for duplicate natural keys to maintain data integrity.\n- Cleansing: Based on the results of data quality, you’ll be able to make the cleansing to those that did not comply with it. \n\n\n**Additional Insights**\nThis data quality project is designed to be integrated with multiple platforms including Databricks, AWS, and Azure. 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Our solutions focus on data quality, data governance and data integration across industries like finance, manufacturing, retail, consumer goods and healthcare."},"popularity":1820},{"id":"344636dd-b2c5-4782-bdc4-b1491b380f17","summary":{"name":"Fall Detection Dataset - 1,047 Videos for Public Safety and Computer Vision","subtitle":"Fall detection dataset of 1,047 Full HD videos (1920×1080, 30 FPS) of staged human falls in ~40–50 indoor and ...","share":{"name":"unidata_6c737019_d339_494d_943f_772a36d808d7_samples_share","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1792401434643,"updated_at":1792401434643,"categories":["OTHER"],"listingType":"PERSONALIZED","provider_id":"a6668567-8ddc-4746-9958-8edbc3807a14"},"detail":{"description":"Overview\nThe Fall Detection Dataset is a commercial video dataset produced by Unidata. It consists of 1,047 high-resolution videos of staged human falls recorded in controlled indoor and outdoor settings, including parking areas, rooms and corridors. It is designed for fall detection, computer vision research and public safety applications, and gives detection research teams a consistent base for training, evaluating and benchmarking detection systems and detection algorithms.\nAll recordings were captured from static and moving camera perspectives at 1920×1080 resolution and 30 FPS. They span ~40–50 unique locations, and the subjects changed clothing every 10 videos to increase visual diversity.\n\nDataset at a Glance\n1. Total number of files: 1,047 videos\n2. Data type: video (MP4, MOV)\n3. Resolution and frame rate: 1920×1080 (Full HD), 30 FPS\n4. Location types: parking areas, rooms, corridors\n5. Number of locations: ~40–50\n6. Clothing change: every 10 videos\n7. Camera perspectives: static and moving\n8. Tasks: public safety, computer vision, object detection, action recognition\n\nLocation Distribution\nThe dataset has an almost even balance of outdoor and indoor scenarios. Detection systems trained on it therefore generalize across very different backgrounds, surfaces and spatial layouts:\n- Outdoors: 546 videos (52.15%). Parking areas and other open-air environments with variable backgrounds and natural conditions.\n- Indoors (room): 501 videos (47.85%). Rooms and enclosed spaces that reflect home, care facility and office conditions.\n\nLighting Distribution\nLighting is one of the main factors affecting model performance in real-world deployments. The dataset covers both lighting types:\n- Artificial lighting: 730 videos (69.72%). Indoor illumination plus outdoor scenes lit by streetlights and parking lights.\n- Natural lighting: 317 videos (30.28%). Daylight recordings with real shadows and brightness variations.\n\nTime of Day Distribution\nFor outdoor recordings the time of day is labeled separately, which helps when evaluating detection algorithms in low-light conditions:\n- Day: 317 videos (30.28%)\n- Evening/night: 229 videos (21.87%)\n- Not applicable: 501 videos (47.85%). These are indoor room recordings, where time of day is not specified.\n\nAnnotations and Metadata\nEvery video carries structured metadata with labels that support filtering, stratified training and detailed performance analysis:\n1. Action type (fall events and other actions)\n2. Camera height\n3. Camera distance\n4. Time of day\n5. Lighting source\n6. Location\n7. Number of people in the frame\n8. Recording device\n9. Resolution\n10. FPS\nThese labels let researchers build study designs that isolate specific variables. For example, they can compare how learning models perform at different camera distances or under artificial versus natural lighting.\n\nTechnical Specifications\n- Video formats: MP4, MOV\n- Resolution: 1920×1080\n- Frame rate: 30 FPS\n- Camera height: 3 m\n- Camera distance: 3–5 m\n- Recording device: iPhone 14\n- Collection method: digital cameras in controlled, simulated environments\nThe camera height of 3 m and distance of 3–5 m match typical ceiling-mounted surveillance and smart building cameras.\n\nSource and Collection Methodology\nAll data was captured with digital cameras in controlled, simulated environments to ensure consistency and safety. The dataset consists entirely of original footage recorded specifically for fall detection and computer vision research. Falls were staged by performers, so no participants were exposed to real injury during recording.\n\nUse Cases\n1. Healthcare and Assisted Living. Healthcare providers use the dataset to develop camera-based detection systems for assisted living, clinical settings and home care. The videos capture realistic fall events across varied environments, which helps train models that can identify falls during daily activity. Fall detection tools can be evaluated reliably without depending on wearable devices.\n2. Public Safety and Smart Buildings. For smart buildings and public infrastructure, the dataset enables detection systems that recognize fall events in corridors, stations, parking areas and shared facilities. Diverse scenarios let algorithms distinguish falls from normal movements such as walking, which reduces false alarms in real-time monitoring.\n3. Computer Vision and AI Research. Researchers use the dataset for detection research and algorithm benchmarking. Consistent resolution, clear metadata and multiple camera perspectives support training and evaluating machine learning models for action recognition, motion analysis and anomaly detection.\n\nCompliance and Security\nThe dataset is curated in compliance with GDPR and applicable data protection laws. All recordings were collected through lawful means with ethical data handling practices. Data is stored on AWS cloud infrastructure, and storage and management processes comply with ISO 27001 and ISO 27701 information security and privacy standards.\n\nSummary\nThe Fall Detection Dataset is a large-scale collection of 1,047 Full HD videos for advancing fall detection, action recognition and public safety technology. Its features include:\n- A balanced outdoor/indoor split (52.15% / 47.85%)\n- Coverage of day, evening and night conditions\n- Artificial and natural lighting\n- ~40–50 locations, with clothing changed every 10 videos\n- Rich metadata across 10 labeled parameters","terms_of_service":"https://unidata.pro/privacy-policy","contact_email":"support@datarade.ai","privacy_policy_link":"https://unidata.pro/privacy-policy","assets":["ASSET_TYPE_DATA_TABLE"]},"provider_summary":{"name":"Unidata","description":"Unidata (https://unidata.pro/) specializes in data collection, delivering high-quality labeled datasets for iBeta, Robotics, Biometrics, Kids Safety, Medical Imaging, Smart City, and many other domains — as well as annotation and LLM training to power your AI and ML to the next level.\r\nWe provide end-to-end AI data solutions trusted by 200+ corporate clients worldwide. Our team of 1,100+ labelers and AI experts labels over 920,000 files daily across text, audio, video, and image data — with 3-tier quality control and full legal compliance (AWS ISO 27001/27701).\r\nOur services cover the full cycle: custom data collection via the Prolific platform, computer vision and audio annotation, 3D point cloud labeling, NLP & LLM training, geospatial annotation, and AI model testing. 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This can be particularly valuable during the due diligence process of buying or investing in a property.\n**Customer Acquisition**\nReal estate professionals can use Owner Transfer data to identify potential new clients. By monitoring sales and owner transfers, they can identify individuals or businesses that have recently purchased a property and may be in need of related services.\n**Market Analysis**\nBy analyzing the data on sales and ownership transfers, businesses can gain insights into trends in the housing market. This can help them identify growth opportunities, understand the competitive landscape, and make informed strategic decisions.\n**Data Quality and Cleansing**\nOwner Transfer data can be used to validate and cleanse other data sets. For example, a business might use Owner Transfer data to confirm the accuracy of its customer database or to update its records after a property sale or transfer.\n**Fraud Remediation**\nOwner Transfer data can be used to detect and prevent fraud. For example, by monitoring for unusual patterns of sales or transfers, businesses can identify potential fraudulent activity.\n**Regulatory Reporting**\nBusinesses in the financial sector may be required to report on their property holdings, including any changes in ownership. 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teams often rely on manual reports to measure campaign success, which delays insights and limits agility.\n- **Limited Customer Engagement Visibility** : Lack of unified views across contacts, campaigns and engagement events restricts the ability to understand customer behavior.\n- **Data Consistency Challenges** : Inconsistent campaign identifiers, duplicate contacts and missing engagement data can lead to inaccurate reporting.\n- **Delayed Marketing Insights** : Without automated pipelines, campaign performance analysis and marketing attribution become time-consuming.\n\n**Dataplatr HubSpot Marketing Analytics Solution**\n\n- **Unified Email Engagement Analytics** : Consolidates Email Sends, Campaign Details and Contact Information into a centralized analytics-ready repository.\n- **Automated Campaign Performance Tracking** : Tracks email send activity with details such as Campaign ID, Contact ID, Send Time, Open Status and Click Activity.\n- **Real-Time Marketing Insights** : Enables real-time monitoring of campaign engagement metrics including open rates, click-through rates and campaign reach.\n- **Customer Engagement Intelligence** : Provides deeper visibility into how contacts interact with email campaigns and marketing content.\n- **Marketing Attribution & Reporting** : Enables marketing teams to measure campaign effectiveness and optimize strategies for higher engagement and conversion.\n\n**Medallion Architecture Implementation\nBronze Layer – Raw HubSpot Data Ingestion**\n\n- **Email Sends (HUBSPOT__EMAIL_SENDS)** : Email Send ID, Campaign ID, Contact ID, Send Timestamp, Delivery Status, Engagement Events\n- **Email Campaigns (HUBSPOT__EMAIL_CAMPAIGNS)** : Campaign ID, Campaign Name, Campaign Type, Created Date, Campaign Status, Marketing Owner\n- **Contacts (HUBSPOT__CONTACTS)** : Contact ID, Name, Email Address, Lifecycle Stage, Company, Region and Customer Attributes\n- **Engagement Metadata** : Event timestamps, email engagement status and 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periods.\n- **Contact Growth & Segmentation Insights** : Provides insights into contact acquisition, lifecycle stages and engagement patterns.\n- **Marketing Attribution Reports** : Links campaign activity to contact interactions for improved marketing ROI analysis.\n- **Executive Marketing Dashboards** : Curated datasets ready for BI tools to support marketing leadership decision-making.\n\n**Marketplace Deliverables**\n\n- **Production-Ready Pipelines** : End-to-end HubSpot data pipeline for email sends, campaigns and contacts with monitoring and logging.\n- **Config Templates** :Pre-configured JSON mappings for HubSpot Email Sends, Campaigns and Contacts data ingestion pipelines.\n- **Delta Table Schemas** : Optimized Bronze–Silver–Gold tables designed for scalable campaign performance analytics reporting.\n- **CDC Framework** : Change Data Capture pipelines enabling incremental updates from HubSpot marketing engagement data.\n- **Data Quality Framework** : Automated validation of campaign IDs, contact mappings and email engagement performance metrics.\n\n**Documentation & Support**\n\n- **Implementation Guide** : Step-by-step instructions for HubSpot API ingestion, pipeline configuration and Databricks deployment.\n- **Configuration Manual** : Field mapping documentation (e.g., Email Send ID → Engagement Event ID, Contact ID → Customer Identifier, Campaign ID → Marketing Campaign Key).\n\n**Why Choose Dataplatr HubSpot Email Engagement Accelerator**\n\n- **Marketing Analytics Expertise** : Designed with deep understanding of digital marketing analytics and campaign performance measurement.\n- **Rapid Time-to-Value** : Deploy campaign performance dashboards quickly and efficiently using pre-built pipelines and models.\n- **Enterprise-Grade Data Governance** : Includes complete data lineage tracking, monitoring and campaign-level governance.\n- **Scalable Marketing Intelligence** : Built on Databricks Lakehouse architecture for scalable high-volume marketing 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Companies using Doorda can quickly leverage continually updated, GDPR-compliant insights about UK properties, businesses, and demographics, enhancing their decision-making processes."},"popularity":1027},{"id":"cddf1739-5a1a-4554-b81c-5f9a98cb84fa","summary":{"name":"Healthcare Associated Infections","subtitle":"This data package includes the hospital, national and state-level data for the Healthcare-Associated Infection (HAI) m..","share":{"name":"healthcare_associated_infections","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1682356057737,"updated_at":1695691434591,"categories":["HEALTH","SCIENCE_AND_RESEARCH"],"listingType":"STANDARD","provider_id":"f88849f8-9d77-4de2-b4c7-03d55cc3768e"},"detail":{"description":"\n**Overview**\nThis data package includes the hospital, national and state-level data for the Healthcare-Associated Infection (HAI) measures which provide information on infections that occur while the patient is in the hospital. It also contains data regarding certain infections like Surgical Site, Clostridium difficile, Methicillin-Resistant Staphylococcus Aureus Bacteremia as well as the standardized infection ratios for all the above-mentioned infections.\n\n**Description**\nThis data package contains the Catheter-Associated Urinary Tract Infection (CAUTI) occurs when germs enter the urinary tract through the urinary catheter and cause infection. CAUTIs have been associated with increased morbidity, mortality, healthcare costs, and length of stay. A Central Line-Associated Bloodstream Infection (CLABSI) is a serious infection that occurs when germs enter the bloodstream through the central line. Healthcare providers must follow a strict protocol when inserting the line to make sure the line remains sterile and a CLABSI does not occur. Patients who get a CLABSI have a fever, and might also have red skin and soreness around the central line. Catheter-Associated bloodstream infection is serious, but often can be successfully treated with antibiotics. The catheter might need to be removed if you develop an infection. A surgical site infection is an infection that occurs after surgery in the part of the body where the surgery took place. Surgical site infections can sometimes be superficial infections involving the skin only.\n\nOther surgical site infections are more serious and can involve tissues under the skin, organs, or implanted material. The Standardized Infection Ratio (SIR) is a statistic used to track healthcare-associated infections (HAIs) over time, at a national, state, or facility level. The SIR compares the actual number of HAIs at each hospital, to the predicted number of infections. The healthcare-associated infection (HAI) measures show how often patients in a particular hospital contract certain infections during the course of their medical treatment, when compared to like hospitals. These infections can often be prevented when healthcare facilities follow guidelines for safe care. MRSA is methicillin-resistant Staphylococcus aureus, a type of staph bacteria that is resistant to many antibiotics. In a healthcare setting, such as a hospital or nursing home, MRSA can cause severe problems such as bloodstream infections, pneumonia and surgical site infections. Clostridium difficile infection (CDI) is a common and sometimes fatal health-care-associated infection; the incidence, deaths, and excess health-care costs resulting from CDIs in hospitalized patients are all at historic highs.\n\n**Benefits**\n- Useful for medical care providers, patients, consumers and hospitals. healthcare associated infections database is available on cdc.gov and centers for medicare & medicaid services, hospital compare data website in raw form, whereas a john snow labs (jsl) customer gets it in clean and normalized form ready to use in one place and create his own custom reports. easy to comprehend for the customers who are unfamiliar with the medical terminologies as all abbreviated terms are replaced with full form, unlike healthcare associated infections original datasets which are full of abbreviations.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [Catheter Associated Urinary Tract Infections](https://www.johnsnowlabs.com/marketplace/catheter-associated-urinary-tract-infections)\n\t- This dataset discusses cases of catheter-associated urinary tract infection (CAUTI) in hospitals and is reported to the National Healthcare Safety Network (NHSN). Among UTIs acquired in the hospital, approximately 75% are associated with a urinary catheter, which is a tube inserted into the bladder through the urethra to drain urine. Between 15-25% of hospitalized patients receive urinary catheters during their hospital stay.\n- [Central Line Associated Bloodstream Infections](https://www.johnsnowlabs.com/marketplace/central-line-associated-bloodstream-infections)\n\t- This dataset shows the Central line-associated bloodstream infections (CLABSIs) result in thousands of deaths each year and billions of dollars in the United States from the Centers For Disease Control and Prevention (CDC) that helps the healthcare community to end CLABSIs. The annual Health Associated Infections (HAI) Progress Reports serves as a reference for national and state prevention progress updates, and for calculating SIRs for each reporting state and facility.\n- [Changes in State Specific SIRs for CAUTI All Locations 2012-2013](https://www.johnsnowlabs.com/marketplace/changes-in-state-specific-sirs-for-cauti-all-locations-2012-2013)\n\t- The Standardized Infection Ratio (SIR) is a statistic used to track healthcare-associated infections (HAIs) over time, at a national, state, or facility level. The SIR compares the actual number of HAIs at each hospital, to the predicted number of infections. The predicted number is an estimate based on national baseline data, and it is risk adjusted. Risk adjustment takes into account that some hospitals treat sicker patients than others.\n- [Changes in State Specific SIRs for CLABSI All Locations 2012-2013](https://www.johnsnowlabs.com/marketplace/changes-in-state-specific-sirs-for-clabsi-all-locations-2012-2013)\n\t- The Standardized Infection Ratio (SIR) is a statistic used to track healthcare-associated infections (HAIs) over time, at a national, state, or facility level. The SIR compares the actual number of healthcare-associated infections at each hospital, to the predicted number of infections. The predicted number is an estimate based on national baseline data, and it is risk adjusted. Risk adjustment takes into account that some hospitals treat sicker patients than others.\n- [Changes in State Specific SIRs for Colon Surgery Infections 2012-2013](https://www.johnsnowlabs.com/marketplace/changes-in-state-specific-sirs-for-colon-surgery-infections-2012-2013)\n\t- The Standardized Infection Ratio (SIR) is a statistic used to track healthcare associated infections (HAIs) over time, at a national, state, or facility level. The SIR compares the actual number of HAIs at each hospital, to the predicted number of infections. The predicted number is an estimate based on national baseline data, and it is risk adjusted. Risk adjustment takes into account that some hospitals treat sicker patients than others.\n- [Changes in State Specific SIRs for Hysterectomy Infections 2012-2013](https://www.johnsnowlabs.com/marketplace/changes-in-state-specific-sirs-for-hysterectomy-infections-2012-2013)\n\t- The Standardized Infection Ratio (SIR) is a statistic used to track healthcare associated infections (HAIs) over time, at a national, state, or facility level. The SIR compares the actual number of HAIs at each hospital, to the predicted number of infections. The predicted number is an estimate based on national baseline data, and it is risk adjusted. Risk adjustment takes into account that some hospitals treat sicker patients than others.\n- [Healthcare Associated Infections by Hospital](https://www.johnsnowlabs.com/marketplace/healthcare-associated-infections-by-hospital)\n\t- This dataset includes the hospital data for the Healthcare-Associated Infection (HAI) measures. These measures are developed by the Centers for Disease Control and Prevention (CDC) and collected through the National Healthcare Safety Network (NHSN). They provide information on infections that occur while the patient is in the hospital.\n- [Healthcare Associated Infections by State](https://www.johnsnowlabs.com/marketplace/healthcare-associated-infections-by-state)\n\t- This dataset includes the state data for the Healthcare-Associated Infection (HAI) measures. These measures are developed by the Centers for Disease Control and Prevention (CDC) and collected through the National Healthcare Safety Network (NHSN). They provide information on infections that occur while the patient is in the hospital.\n- [Healthcare Surgical Site Infections](https://www.johnsnowlabs.com/marketplace/healthcare-surgical-site-infections)\n\t- This dataset provides information on surgical site infection is an infection that occurs after surgery in the part of the body where the surgery took place. Surgical site infections can sometimes be superficial infections involving the skin only. Other surgical site infections are more serious and can involve tissues under the skin, organs, or implanted material.\n- [Hospital Onset Clostridium Difficile Infection](https://www.johnsnowlabs.com/marketplace/hospital-onset-clostridium-difficile-infection)\n\t- This dataset is about healthcare-associated infections (HAIs); a type that is caused by the germ Clostridium difficile that was estimated to cause almost half a million infections in the United States in 2011. The report states that 29,000 died within 30 days of the initial diagnosis and mostly affected were older adults on antibiotics.\n- [Hospital Onset Methicillin Resistant Staphylococcus Aureus Bacteremia](https://www.johnsnowlabs.com/marketplace/hospital-onset-methicillin-resistant-staphylococcus-aureus-bacteremia)\n\t- MRSA is Methicillin-resistant Staphylococcus aureus (MRSA), a type of staph bacteria that is resistant to many antibiotics. In a healthcare setting, such as a hospital or nursing home, MRSA can cause severe problems such as bloodstream infections, pneumonia, and surgical site infections; values about this infection are found in this dataset.\n- [Standardized Infection Ratios for CAUTI All Locations Combined 2013](https://www.johnsnowlabs.com/marketplace/standardized-infection-ratios-for-cauti-all-locations-combined-2013)\n\t- This dataset is about the Standardized Infection Ratio (SIR) for catheter-associated urinary tract infection (CAUTI) which is a statistic used to track healthcare-associated infections (HAIs) over time, at national, state, or facility level. The SIR compares the actual number of HAIs at each hospital, to the predicted risk-adjusted number of infections based on national baseline data. Risk adjustment takes into account that some hospitals treat sicker patients than others.\n- [Standardized Infection Ratios for CAUTI Intensive Care Units 2013](https://www.johnsnowlabs.com/marketplace/standardized-infection-ratios-for-cauti-intensive-care-units-2013)\n\t- The dataset is about the Standardized Infection Ratio (SIR) for catheter-associated urinary tract infection (CAUTI) for Intensive Care Units which is used to track healthcare-associated infections (HAIs) over time, at national, state, or facility level. The SIR compares the actual number of HAIs at each hospital, to the predicted risk-adjusted number of infections based on national baseline data. Risk adjustment takes into account that some hospitals treat sicker patients than others.\n- [Standardized Infection Ratios for CAUTI Wards 2013](https://www.johnsnowlabs.com/marketplace/standardized-infection-ratios-for-cauti-wards-2013)\n\t- The dataset is about the Standardized Infection Ratio (SIR) for catheter-associated urinary tract infection (CAUTI) wards which is used to track healthcare-associated infections (HAIs) over time, at national, state, or facility level. The SIR compares the actual number of HAIs at each hospital, to the predicted risk-adjusted number of infections based on national baseline data. Risk adjustment takes into account that some hospitals treat sicker patients than others.\n- [Standardized Infection Ratios for CLABSI All Locations Combined 2013](https://www.johnsnowlabs.com/marketplace/standardized-infection-ratios-for-clabsi-all-locations-combined-2013)\n\t- This dataset is about the Standardized Infection Ratio (SIR) for central line-associated bloodstream infection (CLABSI) which is a statistic used to track healthcare-associated infections (HAIs) over time, at national, state, or facility level. The SIR compares the actual number of HAIs at each hospital, to the predicted risk-adjusted number of infections based on national baseline data. Risk adjustment takes into account that some hospitals treat sicker patients than others.\n- [Standardized Infection Ratios for CLABSI Intensive Care Units 2013](https://www.johnsnowlabs.com/marketplace/standardized-infection-ratios-for-clabsi-intensive-care-units-2013)\n\t- The dataset is about the Standardized Infection Ratio (SIR) for central line-associated bloodstream infection (CLABSI) for Intensive Care Units which is used to track healthcare-associated infections (HAIs) over time, at national, state, or facility level. The SIR compares the actual number of HAIs at each hospital, to the predicted risk-adjusted number of infections based on national baseline data. Risk adjustment takes into account that some hospitals treat sicker patients than others.\n- [Standardized Infection Ratios for CLABSI Neonatal Intensive Care 2013](https://www.johnsnowlabs.com/marketplace/standardized-infection-ratios-for-clabsi-neonatal-intensive-care-2013)\n\t- The Standardized Infection Ratio (SIR) is a statistic used to track healthcare associated infections (HAIs) over time, at a national, state, or facility level. The SIR compares the actual number of HAIs at each hospital, to the predicted number of infections. The predicted number is an estimate based on national baseline data, and it is risk adjusted. Risk adjustment takes into account that some hospitals treat sicker patients than others.\n- [Standardized Infection Ratios for CLABSI Wards 2013](https://www.johnsnowlabs.com/marketplace/standardized-infection-ratios-for-clabsi-wards-2013)\n\t- The dataset is about the Standardized Infection Ratio (SIR) for central line-associated bloodstream infection (CLABSI) wards which is used to track healthcare-associated infections (HAIs) over time, at national, state, or facility level. The SIR compares the actual number of HAIs at each hospital, to the predicted risk-adjusted number of infections based on national baseline data. Risk adjustment takes into account that some hospitals treat sicker patients than others.\n- [Standardized Infection Ratios for Colon Surgery Site Infections 2013](https://www.johnsnowlabs.com/marketplace/standardized-infection-ratios-for-colon-surgery-site-infections-2013)\n\t- The Standardized Infection Ratio (SIR) is a statistic used to track healthcare associated infections (HAIs) over time, at a national, state, or facility level. The SIR compares the actual number of HAIs at each hospital, to the predicted number of infections. The predicted number is an estimate based on national baseline data, and it is risk adjusted. Risk adjustment takes into account that some hospitals treat sicker patients than others.\n- [Standardized Infection Ratios for Hospital Onset CDI 2013](https://www.johnsnowlabs.com/marketplace/standardized-infection-ratios-for-hospital-onset-cdi-2013)\n\t- The Standardized Infection Ratio (SIR) is a statistic used to track healthcare associated infections (HAIs) over time, at a national, state, or facility level. The SIR compares the actual number of HAIs at each hospital, to the predicted number of infections. The predicted number is an estimate based on national baseline data, and it is risk adjusted. Risk adjustment takes into account that some hospitals treat sicker patients than others.\n- [Standardized Infection Ratios for Hospital Onset MRSA Bacteremia 2013](https://www.johnsnowlabs.com/marketplace/standardized-infection-ratios-for-hospital-onset-mrsa-bacteremia-2013)\n\t- The Standardized Infection Ratio (SIR) is a statistic used to track healthcare associated infections (HAIs) over time, at a national, state, or facility level. The SIR compares the actual number of HAIs at each hospital, to the predicted number of infections. The predicted number is an estimate based on national baseline data, and it is risk adjusted. Risk adjustment takes into account that some hospitals treat sicker patients than others.\n- [Standardized Infection Ratios for Hysterectomy Site Infections 2013](https://www.johnsnowlabs.com/marketplace/standardized-infection-ratios-for-hysterectomy-site-infections-2013)\n\t- The Standardized Infection Ratio (SIR) is a statistic used to track healthcare associated infections (HAIs) over time, at a national, state, or facility level. The SIR compares the actual number of HAIs at each hospital, to the predicted number of infections. The predicted number is an estimate based on national baseline data, and it is risk adjusted. Risk adjustment takes into account that some hospitals treat sicker patients than others.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated\n- Data Updates\n - Data updates support replace-on-update: outdated foreign keys are deprecated, not deleted\n\n**Our data is curated and enriched by domain experts** \n\n Each dataset is manually curated by our team of doctors, pharmacists, public health & medical billing experts:\n- Field names, descriptions, and normalized values are chosen by people who actually understand their meaning\n- Healthcare & life science experts add categories, search keywords, descriptions and more to each dataset\n- Both manual and automated data enrichment supported for clinical codes, providers, drugs, and geo-locations\n- The data is always kept up to date – even when the source requires manual effort to get updates\n- Support for data subscribers is provided directly by the domain experts who curated the data sets\n- Every data source’s license is manually verified to allow for royalty-free commercial use and redistribution.\n\n**Need Help?**\nIf you have questions about our products, contact us at [info@johnsnowlabs.com](mailto:info@johnsnowlabs.com). \n ","terms_of_service":"https://www.johnsnowlabs.com/healthcare-data-library-eula/","documentation_link":"https://www.johnsnowlabs.com/marketplace/healthcare-associated-infections-data-package","contact_email":"support@johnsnowlabs.com","privacy_policy_link":"https://www.johnsnowlabs.com/privacy-policy/","embedded_notebook_file_infos":[{"id":"ddbd23a2-0f08-481d-a492-c9150195fd3e","marketplace_file_type":"EMBEDDED_NOTEBOOK","file_parent":{"parent_id":"cddf1739-5a1a-4554-b81c-5f9a98cb84fa","file_parent_type":"LISTING"},"mime_type":"text/html","download_link":"https://marketplace-file-storage-prod.s3-fips.us-east-1.amazonaws.com/EMBEDDED_NOTEBOOK/LISTING_cddf1739-5a1a-4554-b81c-5f9a98cb84fa/ddbd23a2-0f08-481d-a492-c9150195fd3e?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEGgaDnVzLWVhc3QtMS1maXBzIkYwRAIgbC8sWIoYaHR8y7Wq86nPb0pufBl4%2BM4PWpeN%2B5PW9VQCIG7BhAVBDCWTpxFDLeuKYm%2Ff1JLz4iccPMzter0YdhgWKqYECDAQABoMNDE0MzUxNzY3ODI2IgxNAJrHbXaZG20jNfMqgwQyGEb4TsoVOoNvJ%2BaA%2FZ7zeu8hGcHqCeNfblP%2FQo5g6q2aRNtpwtPRKMlhYcIE%2FUQaNT1Lqmyptu32VUQTpzjTTxRVmJrDPkheSCkV06XQ%2FpTMKjU8CC7X1b%2FqYf8CrBwZT7Cv4C5WPjEET38f2A79h0xV6bZgPy28zQWrSeIJ9w6AfZBxiE24%2FVc08zVA0ivCT6lsy7nuU0wcR%2FMg2C9nd1vaMnQ923VpD21CKfVVYcrNWL2b%2FQ%2BNAO%2BxPjPgjiN6UPQbvbvZxkSdhgrGtecIg9NgqGkMcvM5VLTDtdC1kdeZetfOC2obtA%2BT%2F0gy3tIDo6WM5hcQfw743%2FAU9oaGnRBl%2BwoxRRDCZA%2F4jnPdluICSI7NCr6C3HHOe4CSZJjRihczxPA%2FgQczAVjAPs6CrK9N9pIzt0vNJpGUTgg4AoxftTvGfciDREilSctPIcBk6GwB%2Bzt4wbtNCmEPU%2BFD46rc6CWVQmmLwbKOeHw70ipMVFlOc8NlXkEtGO2vu6H44MstSSc6daOVUYUavQSGZ6jGhEjsP2YGz8KDm9XQotB%2FtyVBqiZV01OKqDmmBJA%2FXjUpSJ8M%2FeJXhe5m%2BKcxzNObtoJRI7jci3Tpt08XZyXZ7%2FyIV8H30ya468X3cfkTXrm972uga3z7iXAdubyEHnQIua%2FuvtwOHILi6O5D1Me2MzCPvcnXBjqTAYDoMWBt%2B3AVFuqSs2dmDQNBnvEhXfMgTedKLD5PJHb3aIl15iBkLaNu36GYnVCp9ZU2lSYcTbY5QwNnNeTfJTNy9bKhwzru6Sg%2BslhN%2BTS0zkTn%2BeLMY4tHgPEtEZ2cp%2FCk6GwqswvnYVKHjwO8q%2B1Mx8lc08fVEfarE9dke1GGFqoddx%2Bq%2BZUyHbDlAVfrCmf0yw%3D%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20261110T000135Z&X-Amz-SignedHeaders=host&X-Amz-Expires=86400&X-Amz-Credential=ASIAWA6KKHEJORWBH4N3%2F20261110%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=7fe118cbe611e4ea178cd0f07ff8d944fe7ae6dd134784c58a5e6dc514b0e6be","created_at":1682537715154,"updated_at":1682537715154,"display_name":"EDA Notebook","status":"FILE_STATUS_PUBLISHED"}]},"provider_summary":{"name":"John Snow Labs","description":"John Snow Labs, an AI and NLP for healthcare company, provides state-of-the-art software, models, and data to help healthcare and life science organizations build, deploy, and operate AI projects."},"popularity":950},{"id":"714f3421-beaf-4d02-ae31-d71791bc6c7f","summary":{"name":"Vimeo - 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The products include a range of no-code data solutions utilized by business owners and a robust infrastructure used by engineers and IT professionals.\n\nBright Data provides a cost-effective way to perform fast and stable public web data collection at scale, effortless conversion of unstructured data into structured data, and superior customer experience while being fully transparent and compliant."},"popularity":351},{"id":"a6c4afbe-3500-4a02-ad19-09bfca700112","summary":{"name":"Geolocet | Points of Interest Data | Grocery Store Locations - Netherlands","subtitle":"Complete GIS-ready dataset of 19,036 Netherlands grocery locations (brands & independents). Optimized for Databricks.","setting":{"visibility":"PUBLIC"},"created_at":1780637378570,"updated_at":1780637437712,"categories":["GEOSPATIAL","RETAIL"],"listingType":"PERSONALIZED","provider_id":"5d3ddc17-a7e8-4794-a760-13587e1de2c6"},"detail":{"description":"# **All Supermarket Locations in the Netherlands: Complete Geographic Dataset**\n---\n### **Premium GIS-Ready Retail Location Intelligence Data for Databricks Analytics**\n\nThis high-fidelity geospatial dataset provides a complete, exhaustive, and standardized overview of **19,036** verified grocery store, supermarket, and convenience locations across the Netherlands. Updated as of **May 26, 2026**, this comprehensive national dataset captures the entire Dutch grocery landscape - encompassing both major branded retail chains and independent local markets. It offers an analysis-ready foundation for full-scale retail site selection, competitive intelligence, logistics optimization, and spatial demographics mapping.\n\nFully compatible with **Apache Spark**, **Delta Lake**, and **Databricks Unity Catalog** workflows.\n\n---\n\n## **Complete National Coverage: Branded & Independent Grocers**\nUnlike top-tier only datasets, this exhaustive collection covers maximum geographic depth across all 12 Dutch provinces (including highly dense areas like Zuid-Holland, Noord-Holland, and Noord-Brabant). It includes thousands of independent local grocers alongside the top national and regional supermarket chains:\n\n- **Major National Supermarkets:** [Albert Heijn (1,261 locations)](https://geolocet.com/products/netherlands-albert-heijn-grocery), \n [Jumbo (724 locations)](https://geolocet.com/products/netherlands-jumbo-grocery), \n [PLUS (479 locations)](https://geolocet.com/products/netherlands-plus-grocery), \n [SPAR (454 locations)](https://geolocet.com/products/netherlands-spar-grocery)\n\n- **Leading Discounters & Value Chains:** [ALDI (494 locations)](https://geolocet.com/products/netherlands-aldi-grocery), \n [Lidl (449 locations)](https://geolocet.com/products/netherlands-lidl-grocery), \n [Action (423 locations)](https://geolocet.com/products/netherlands-action-grocery), \n [Dirk van den Broek (134 locations)](https://geolocet.com/products/netherlands-dirk-van-den-broek-grocery), \n [Nettorama (82 locations)](https://geolocet.com/products/Netherlands-Nettorama-Grocery)\n\n- **Strong Regional & Specialty Banners:** [Holland & Barrett (200 locations)](https://geolocet.com/products/netherlands-holland-and-barrett-grocery), \n [Vomar (108 locations)](https://geolocet.com/products/netherlands-vomar-grocery), \n [DekaMarkt (107 locations)](https://geolocet.com/products/netherlands-dekamarkt-grocery), \n [Ekoplaza (91 locations)](https://geolocet.com/products/netherlands-ekoplaza-grocery), \n [Poiesz (87 locations)](https://geolocet.com/products/netherlands-poiesz-grocery), \n [Hoogvliet (83 locations)](https://geolocet.com/products/netherlands-hoogvliet-grocery), \n [Alexanderhoeve (74 locations)](https://geolocet.com/products/netherlands-alexanderhoeve-grocery)\n\n**Plus over 13,000 independent, regional, and unbranded grocers** fully mapped and standardized across all provinces (e.g., Zuid-Holland: 3,915 sites; Noord-Holland: 3,370 sites; Noord-Brabant: 2,679 sites).\n\n---\n\n## **Technical Specifications & Schema Definitions**\nOptimized for zero-friction schema enforcement when reading via `spark.read.format(\"csv\")`. The structured schema below ensures flawless rendering in Databricks notebooks and high accuracy for LLM semantic indexing.\n\n* **`GUID`** (`STRING`): Unique global identifier for deterministic primary key mapping.\n* **`Title`** (`STRING`): Official store name, brand banner, or independent title of the location.\n* **`Latitude`** (`DOUBLE`): Precise WGS84 latitude coordinate for point-in-polygon & spatial joins.\n* **`Longitude`** (`DOUBLE`): Precise WGS84 longitude coordinate for spatial indexing (H3 / S2).\n* **`Street_No` / `Street`** (`STRING`): Normalized building/house number and street name elements.\n* **`City` / `Postal_Code`** (`STRING`): Core postal addresses and primary municipal settlement names.\n* **`Settlement`** (`STRING`): Local settlement/town classification.\n* **`Region`** (`STRING`): Macro-administrative boundary classification (e.g., Gelderland, Utrecht).\n* **`Admin_level_1` / `_2`** (`STRING`): Additional local administrative area and municipal identifiers.\n* **`Population`** (`INTEGER`): Contextual demographic population metric attributed to the location.\n* **`Wheelchair`** (`STRING`): Accessibility flags indicating wheelchair mobility support.\n* **`Popularity_Score`** (`INTEGER`): Relative foot-traffic/rating metric graded on a standardized 1-10 scale.\n* **`Opening_Hours`** (`STRING`): Standard daily operational schedules and structured operating text.\n* **`Phone` / `Website`** (`STRING`): Direct communication data, branch-specific landing URLs, and domain vectors.\n\n---\n\n## **Databricks Workflow & Spatial Analytics Use Cases**\n\n- **Randstad Catchment Area Modeling:** Run native Apache Spark SQL spatial functions or python-based `geopandas` routines to construct highly accurate drive-time and cycling-time isochrones around competitors in densely populated hubs like Amsterdam, Rotterdam, and The Hague.\n- **H3 Geospatial Indexing:** Convert the precise WGS84 `Latitude` and `Longitude` fields into Uber H3 spatial hexagons (`h3_longlattocell`) to cross-reference with granular Dutch neighborhood (buurt) demographic or income data.\n- **Supply Chain & Last-Mile Logistics Optimization:** Combine store location points with regional administrative boundary layers to optimize urban delivery routes, distribution warehouse placements, and network expansion footprints.\n\n---\n\n## **Data Evaluation & Custom Ingestion**\n\n### **Evaluate with a Free Sample Dataset**\nVerify the structure, column types, and coordinate precision before full implementation. A comprehensive schema sample is available for download in CSV/Parquet-friendly formats.\n\n*Want deeper market insights?* Learn more about the brand network in our comprehensive report: **[View the Netherlands Top Grocery Brands Report](https://geolocet.com/pages/netherlands-top-grocery-brands-report)**\n\n### **Enterprise Delivery & Tailored Formatting**\nWhile natively available as an analysis-ready CSV for DBFS ingestion, we accommodate enterprise-level data architecture needs. Alternative formats - including **GeoJSON, Esri Shapefiles, PostgreSQL dumps, Excel, or direct Delta sharing profiles** - can be generated. Reach out to our engineering team at **contact@geolocet.com** for integration support or custom European POI data requests.","terms_of_service":"https://geolocet.com/pages/terms-of-use","documentation_link":"https://geolocet.com/pages/privacy-policy","privacy_policy_link":"https://geolocet.com/pages/privacy-policy","assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://geolocet.com/pages/terms-of-use"},"provider_summary":{"name":"Geolocet LTD","description":"Geolocet provides premium geospatial data for retail analysis, GIS, and market planning. 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This wide-ranging database lets users analyze potential expenditures by both average dollars per household and total dollars spent for any geographic level—from all of \nCanada to a small trade area—regardless of where the consumer made the purchase. HouseholdSpend is produced annually with household income data from DemoStats and is based on Statistics Canada’s Survey of Household Spending. \n\nSample questions it can answer:\n\n• What product categories do households in my trade area spend the most money on and what percentage of their total spending is associated with those categories?\n\n• How much are my customers spending on average on clothing, rent and groceries?\n\n• Where can I find neighbourhoods with high childcare expenses?\n\n• Are expenditures in my trade area higher, lower or the same as the general population?","terms_of_service":"https://environicsanalytics.com/en-ca/footer/privacy","contact_email":"marketplace@datarade.ai","privacy_policy_link":"https://environicsanalytics.com/en-ca/footer/privacy","assets":["ASSET_TYPE_DATA_TABLE"]},"provider_summary":{"name":"Environics Analytics","description":"We are one of North America’s leading data, analytics and marketing services companies. 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Our industry expertise and sector depth are surpassed only by our exceptional client service."},"popularity":1191},{"id":"989d5ca5-4da3-4765-b7f0-d3b4430f1506","summary":{"name":"OSINT Web Data for Global Supply Chain Planning and Monitoring","subtitle":"HTD delivers real-time insights from across the open web to help leaders anticipate threats and maintain operations.","setting":{"visibility":"PUBLIC"},"created_at":1750866587746,"updated_at":1750866587746,"categories":["COMMERCE","ECONOMICS","ENERGY","FINANCIAL","GEOSPATIAL","HEALTH","MANUFACTURING","TRANSPORTATION_AND_LOGISTICS","TRAVEL_AND_TOURISM","RETAIL","SECURITY","SCIENCE_AND_RESEARCH"],"listingType":"PERSONALIZED","provider_id":"dc936c7b-b23a-4f98-9d0c-3422f88f8ae9"},"detail":{"description":"**Overview**\nFor executives managing global supply chains, open-source intelligence (OSINT) from Hometree Data can be an essential tool for staying ahead of disruption. 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Each record includes details about the unit's technology, capacity, and operational status, as well as fuel sources and maintenance history.\n\n\n","terms_of_service":"https://www.spglobal.com/en/privacy/privacy-policy-english","documentation_link":"https://www.marketplace.spglobal.com/en/datasets/european-power-assets-(1770666831)","privacy_policy_link":"https://www.spglobal.com/en/privacy/privacy-policy-english","geographical_coverage":"[\"Europe\"]","update_frequency":{"interval":1,"unit":"DAILY"},"collection_granularity":{"interval":1,"unit":"DAILY"},"collection_date_start":1041404324389,"assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"S&P Global Energy","description":"At S&P Energy, our complete view of global energy and commodities markets enables our customers to make decisions with conviction and create long-term, sustainable value. 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S&P Global Energy is a division of S&P Global (NYSE: SPGI)."},"popularity":1551},{"id":"4a70c046-fbde-4e4b-8580-5e4e25c4c458","summary":{"name":"Geolocet | Demographic Data Portugal","subtitle":"Demographic Data Portugal at the census tract level - age, gender, marital status, employment, education, household size","setting":{"visibility":"PUBLIC"},"created_at":1705909911768,"updated_at":1752146313217,"categories":["DEMOGRAPHICS","ADVERTISING_AND_MARKETING","GEOSPATIAL","PUBLIC_SECTOR","RETAIL"],"listingType":"PERSONALIZED","provider_id":"5d3ddc17-a7e8-4794-a760-13587e1de2c6"},"detail":{"description":"## **Get instant access to Portugal's Demographics Data with [Geolocet](https://geolocet.com)**\n\nExplore the richness of Portugal's demographic landscape with our Portugal Census Data. This meticulously compiled dataset, sourced from official statistical sources, including Database of Contemporary Portugal, offers a detailed insight into the population landscape of Portugal at the the census tract level.\n\nIf you require data at a less granular level, such as an Uber grid or square grid, please don't hesitate to get in touch with us at *contact@geolocet.com*. We understand that different projects may have varying data needs, and we are here to tailor our data to match your specific requirements.\n\n ## **Key Features:**\n---\n* **Population Data Portugal:** Gain access to the latest and most accurate population statistics for Portugal, meticulously organized by census areas.\n\n* **Demographic Insights:** Dive deep into Portugal's demographic landscape with data on 135 attributes, including: \n* Total population,\n* Gender,\n* Age bands,\n* Gender X Age bands,\n* Marital Status,\n* Total number of employed people,\n* Age bands of employed people,\n* Education level of employed people,\n* Education,\n* Gender X Education,\n* Total number of dwellings,\n* Type of Dwellings,\n* Form of occupation of conventional dwellings,\n* Total number of households,\n* Household size,\n* Commuting population,\n* Employment status of commuting population,\n* Commuting time - bands,\n* Average commuting time,\n* Means of commute.\n\n* **Geodemographic Insights:** Unlock geodemographic data Portugal-wide to inform location-based decision-making, marketing strategies, and policy formulation.\n\n* **Ready to use:** Our dataset is currently available in CSV format, providing ease of use and compatibility with a wide range of data analysis tools. However, if you prefer a different format, our team is readily available to assist you in obtaining the data in your desired format.\n---\nAdditionally, for those looking to enrich their analysis or project, we offer:\n* **[Administrative Boundaries](https://geolocet.com/products/portugal-all-available-administrative-boundaries):** If you require geographical boundaries (polygons) for Portuguese municipalities, consider exploring our Admin Boundaries product, which seamlessly integrates with this dataset.\n\n* **[Point of Interest (PoI) Data](https://geolocet.com/pages/aggregated-points-of-interest-poi-data):** If you're interested in Points of Interest data, please don't hesitate to contact us for customized datasets to enhance your geographical analysis.\n\n* **[Data Customization](https://geolocet.com/pages/data-services-by-geolocet):** We understand that your project may have specific requirements. Feel free to get in touch with us if you need any modifications or specific data formats such as Excel, JSON, GeoJSON, or Shapefile. We are here to tailor the data to your needs.\n\nMake informed decisions, conduct precise research, and unlock the potential of Portugal's demographic landscape with our comprehensive dataset. Trust in the accuracy and reliability of our official statistical data to drive your projects forward.\nContact us at *contact@geolocet.com* today to access this invaluable resource and explore Portugal's census data like never before.\n\n[Geolocet LTD](https://geolocet.com)\n","terms_of_service":"https://geolocet.com/pages/terms-of-use","documentation_link":"","privacy_policy_link":"https://geolocet.com/pages/privacy-policy","geographical_coverage":"[\"Europe\"]","cost":"PAID","data_source":"","assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://geolocet.com/pages/terms-of-use"},"provider_summary":{"name":"Geolocet LTD","description":"Geolocet provides premium geospatial data for retail analysis, GIS, and market planning. 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Social determinants of health are crucial factors that influence individuals’ health outcomes, encompassing various social, economic, and environmental element. The model has been trained using advanced machine learning techniques on a diverse range of text sources. The model can accurately recognize and classify a wide range of SDOH entities, including but not limited to factors such as socioeconomic status, education level, housing conditions, access to healthcare services, employment status, cultural and ethnic background, neighborhood characteristics, and environmental factors. The model’s accuracy and precision have been carefully validated against expert-labeled data to ensure reliable and consistent results. \n \n\n\n**Additional Model Information**\n- [Industry Use-Case Demo](https://demo.johnsnowlabs.com/healthcare/SDOH/)\n- **Domain:** Clinical Text Analysis\n- **Subdomain:** Social Determinants of Health\n- **Predictable entities:** Access_To_Care, Age, Alcohol, Childhood_Event, Community_Safety, Diet, Disability, Eating_Disorder, Education, Employment, Environmental_Condition, Exercise, Family_Member, Financial_Status, Food_Insecurity, Gender, Geographic_Entity, Healthcare_Institution, Housing, Hyperlipidemia, Hypertension, Income, Insurance_Status, Language, Legal_Issues, Marital_Status, Mental_Health, Obesity, Other_Disease, Other_SDoH_Keywords, Population_Group, Quality_Of_Life, Race_Ethnicity, Sexual_Activity, Sexual_Orientation, Smoking, Social_Exclusion, Social_Support, Spiritual_Beliefs, Substance_Duration, Substance_Frequency, Substance_Quantity, Substance_Use, Transportation, Violence_Or_Abuse\n- **Deployment Identifier:** 31. Extract Social Determinants of Health\n \n\n**How to run this model:**\n1. Contact [Sales](mailto:Databricks-Marketplace-listing@johnsnowlabs.com) at John Snow Labs for a free 15 day trial license key or purchase our most popular annual subscription license. \n2. Import this listing.\n3. See the attached notebook to deploy and use the model.\n\n\nThis model comes with optimized CPU and GPU builds. 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(Income, age, state, education, etc)\n\n**Use cases**\n\n- Use case 1\n\nA US-based rideshare company is exploring new strategies to optimize fleet management, reduce wait times, and improve driver allocation efficiency. The operations team aims to leverage real-time data to enhance key performance metrics. The company is considering several data-driven approaches, including Just-In-Time Driver Allocation, Predictive Analytics for Demand Forecasting, and Automated Driver Replenishment Systems. To make an informed decision, the team seeks to compare these strategies and their impact on ride acceptance rates, customer satisfaction, and operational ROI.\n\n\n\n\n**Product details**\n\nExample table name: rideshare_income_row_level\n\nIncluded fields:\n\n- Alias\n- Ride ID\n- Pickup Address\n- Pickup Time\n- Currency\n- Total Price\n- Drop Off Address\n- Drop Off Time\n- Ride Duration\n- Ride Distance\n- Car Type\n- Tax\n- Service Fee\n\n\n\n\n**Additional Insights**\n\n\nPlease refer to the links in the product menu for additional documentation including samples & data dictionaries.\n\n","terms_of_service":"https://www.anthology.ai/privacy","documentation_link":"https://hubs.ly/Q032K9G50","privacy_policy_link":"https://www.anthology.ai/privacy","geographical_coverage":"[\"United States and Canada\"]","cost":"PAID","update_frequency":{"interval":7,"unit":"DAILY"},"collection_date_start":1641069948946,"data_source":"Caden iOS App","assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"AnthologyAI","description":"AnthologyAI is the breakthrough Artificial Intelligence Consumer Insights & Predictions Platform with billions of realtime data points, sourced directly and ethically from consumers. 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It also contains datasets about Diagnosis Codes, National Correct Codes, Valid Modifiers and Valid Revenue of Initiative Pairs for IOCE Quarterly Release Files.\n\n**Description**\nThis data package contains the Ambulatory Payment Classifications (APCs) are the government's method of paying facilities for outpatient services for the Medicare program. Each APC is composed of services which are similar in clinical intensity, resource utilization and cost. All services which are grouped under a specific APC result in an annually updated Medicare prospective payment for that particular APC. The 'Integrated' Outpatient Code Editor (I/OCE) program processes claims for all outpatient institutional providers including hospitals that are subject to the Outpatient Prospective Payment System (OPPS). The OCE not only identifies individual errors but also indicates what actions should be taken and the reasons why these actions are necessary. In order to accommodate this functionality, the OCE is structured to return lists of edit numbers. This structure facilitates the linkage between the actions being taken, the reasons for the actions and the information on the claim that caused the action. The OCE performs all functions that require specific reference to HCPCS codes, HCPCS modifiers and ICD-9-CM diagnosis codes. Since these coding systems are complex and annually quarterly, the centralization of the direct reference to these codes and modifiers in a single program will reduce effort and reduce the chance of inconsistent processing. The Revenue codes are 4-digit numbers that are used on hospital bills to tell the insurance companies either where the patient was when they received treatment, or what type of item a patient might have received as a patient. A medical claim will not be paid if this is missing from a bill.\n\n**Benefits**\n- Useful for medical care providers, patients and medical research groups. integrated outpatient code editors database is available on centers for medicare & medicaid services website in raw form, whereas a john snow labs (jsl) customer gets it in a clean and normalized form ready to use and create his own custom reports. easy to comprehend for the customers who are unfamiliar with the medical terminologies as all abbreviated terms are replaced with full form, unlike centers for medicare & medicaid services (cms) original datasets which have many abbreviations. recent dataset for 2016 is available for use.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [Ambulatory Payment Classification for IOCE Quarterly Release Files](https://www.johnsnowlabs.com/marketplace/ambulatory-payment-classification-for-ioce-quarterly-release-files)\n\t- APCs or \"Ambulatory Payment Classifications\" are the government's method of paying facilities for outpatient services for the Medicare program.\n- [Code Pairs IOCE Quarterly Release Files 2017-01 and 2017-04](https://www.johnsnowlabs.com/marketplace/code-pairs-ioce-quarterly-release-files-2017-01-and-2017-04)\n\t- This dataset contains information about 'integrated' Outpatient Code Editor (I/OCE) program processes claims for all outpatient institutional providers including hospitals that are subject to the Outpatient Prospective Payment System (OPPS) as well as hospitals that are NOT (Non-OPPS). This dataset contains code pairs differences between January, 2017 and April, 2017 that should or should not be used together.\n- [Code Pairs for IOCE Quarterly Release Files](https://www.johnsnowlabs.com/marketplace/code-pairs-for-ioce-quarterly-release-files)\n\t- This dataset contains information about 'integrated' Outpatient Code Editor (I/OCE) program processes claims for all outpatient institutional providers including hospitals that are subject to the Outpatient Prospective Payment System (OPPS) as well as hospitals that are NOT (Non-OPPS).\n- [Diagnosis Codes Using DX10 Mapping for IOCE Quarterly Release Files](https://www.johnsnowlabs.com/marketplace/diagnosis-codes-using-dx10-mapping-for-ioce-quarterly-release-files)\n\t- This dataset contains information about 'integrated' Outpatient Code Editor (I/OCE) program processes claims for all outpatient institutional providers including hospitals that are subject to the Outpatient Prospective Payment System (OPPS) as well as hospitals that are NOT (Non-OPPS).\n- [National Correct Coding Initiative Pairs for IOCE Quarterly Release](https://www.johnsnowlabs.com/marketplace/national-correct-coding-initiative-pairs-for-ioce-quarterly-release)\n\t- This dataset contains information about 'integrated' Outpatient Code Editor (I/OCE) program processes claims for all outpatient institutional providers including hospitals that are subject to the Outpatient Prospective Payment System (OPPS) as well as hospitals that are NOT (Non-OPPS).\n- [Valid Modifiers for IOCE Quarterly Release Files](https://www.johnsnowlabs.com/marketplace/valid-modifiers-for-ioce-quarterly-release-files)\n\t- This dataset contains information about 'integrated' Outpatient Code Editor (I/OCE) program processes claims for all outpatient institutional providers including hospitals that are subject to the Outpatient Prospective Payment System (OPPS) as well as hospitals that are NOT (Non-OPPS).\n- [Valid Revenue Codes for IOCE Quarterly Release Files](https://www.johnsnowlabs.com/marketplace/valid-revenue-codes-for-ioce-quarterly-release-files)\n\t- This dataset contains information about 'integrated' Outpatient Code Editor (I/OCE) program processes claims for all outpatient institutional providers including hospitals that are subject to the Outpatient Prospective Payment System (OPPS) as well as hospitals that are NOT (Non-OPPS).\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated\n- Data Updates\n - Data updates support replace-on-update: outdated foreign keys are deprecated, not deleted\n\n**Our data is curated and enriched by domain experts** \n\n Each dataset is manually curated by our team of doctors, pharmacists, public health & medical billing experts:\n- Field names, descriptions, and normalized values are chosen by people who actually understand their meaning\n- Healthcare & life science experts add categories, search keywords, descriptions and more to each dataset\n- Both manual and automated data enrichment supported for clinical codes, providers, drugs, and geo-locations\n- The data is always kept up to date – even when the source requires manual effort to get updates\n- Support for data subscribers is provided directly by the domain experts who curated the data sets\n- Every data source’s license is manually verified to allow for royalty-free commercial use and redistribution.\n\n**Need Help?**\nIf you have questions about our products, contact us at [info@johnsnowlabs.com](mailto:info@johnsnowlabs.com). \n ","terms_of_service":"https://www.johnsnowlabs.com/healthcare-data-library-eula/","documentation_link":"https://www.johnsnowlabs.com/marketplace/integrated-outpatient-code-editors-data-package","contact_email":"support@johnsnowlabs.com","privacy_policy_link":"https://www.johnsnowlabs.com/privacy-policy/","embedded_notebook_file_infos":[{"id":"b32b9918-751b-4ad6-a4f9-aa9a5790c00f","marketplace_file_type":"EMBEDDED_NOTEBOOK","file_parent":{"parent_id":"13fd2162-5718-44a2-aff0-44f1a446e47a","file_parent_type":"LISTING"},"mime_type":"text/html","download_link":"https://marketplace-file-storage-prod.s3-fips.us-east-1.amazonaws.com/EMBEDDED_NOTEBOOK/LISTING_13fd2162-5718-44a2-aff0-44f1a446e47a/b32b9918-751b-4ad6-a4f9-aa9a5790c00f?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEGgaDnVzLWVhc3QtMS1maXBzIkYwRAIgbC8sWIoYaHR8y7Wq86nPb0pufBl4%2BM4PWpeN%2B5PW9VQCIG7BhAVBDCWTpxFDLeuKYm%2Ff1JLz4iccPMzter0YdhgWKqYECDAQABoMNDE0MzUxNzY3ODI2IgxNAJrHbXaZG20jNfMqgwQyGEb4TsoVOoNvJ%2BaA%2FZ7zeu8hGcHqCeNfblP%2FQo5g6q2aRNtpwtPRKMlhYcIE%2FUQaNT1Lqmyptu32VUQTpzjTTxRVmJrDPkheSCkV06XQ%2FpTMKjU8CC7X1b%2FqYf8CrBwZT7Cv4C5WPjEET38f2A79h0xV6bZgPy28zQWrSeIJ9w6AfZBxiE24%2FVc08zVA0ivCT6lsy7nuU0wcR%2FMg2C9nd1vaMnQ923VpD21CKfVVYcrNWL2b%2FQ%2BNAO%2BxPjPgjiN6UPQbvbvZxkSdhgrGtecIg9NgqGkMcvM5VLTDtdC1kdeZetfOC2obtA%2BT%2F0gy3tIDo6WM5hcQfw743%2FAU9oaGnRBl%2BwoxRRDCZA%2F4jnPdluICSI7NCr6C3HHOe4CSZJjRihczxPA%2FgQczAVjAPs6CrK9N9pIzt0vNJpGUTgg4AoxftTvGfciDREilSctPIcBk6GwB%2Bzt4wbtNCmEPU%2BFD46rc6CWVQmmLwbKOeHw70ipMVFlOc8NlXkEtGO2vu6H44MstSSc6daOVUYUavQSGZ6jGhEjsP2YGz8KDm9XQotB%2FtyVBqiZV01OKqDmmBJA%2FXjUpSJ8M%2FeJXhe5m%2BKcxzNObtoJRI7jci3Tpt08XZyXZ7%2FyIV8H30ya468X3cfkTXrm972uga3z7iXAdubyEHnQIua%2FuvtwOHILi6O5D1Me2MzCPvcnXBjqTAYDoMWBt%2B3AVFuqSs2dmDQNBnvEhXfMgTedKLD5PJHb3aIl15iBkLaNu36GYnVCp9ZU2lSYcTbY5QwNnNeTfJTNy9bKhwzru6Sg%2BslhN%2BTS0zkTn%2BeLMY4tHgPEtEZ2cp%2FCk6GwqswvnYVKHjwO8q%2B1Mx8lc08fVEfarE9dke1GGFqoddx%2Bq%2BZUyHbDlAVfrCmf0yw%3D%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20261110T000135Z&X-Amz-SignedHeaders=host&X-Amz-Expires=86400&X-Amz-Credential=ASIAWA6KKHEJORWBH4N3%2F20261110%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=05128212ae6fe34d376f0db2eb1a7097ee58d0ae42e216a50525a6ae2f5d12d3","created_at":1682537264572,"updated_at":1682537264572,"display_name":"EDA Notebook","status":"FILE_STATUS_PUBLISHED"}]},"provider_summary":{"name":"John Snow Labs","description":"John Snow Labs, an AI and NLP for healthcare company, provides state-of-the-art software, models, and data to help healthcare and life science organizations build, deploy, and operate AI projects."},"popularity":1667},{"id":"8366abf8-024b-4bdf-b6cc-0c4a7cd033f9","summary":{"name":"UMLS - 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There are three UMLS Knowledge Sources:\n- The Metathesaurus, which contains over one million biomedical concepts from over 100 source vocabularies\n- The Semantic Network, which defines 133 broad categories and fifty-four relationships between categories for labeling the biomedical domain\nThe SPECIALIST Lexicon & Lexical Tools, which provide lexical information and programs for language processing The Metathesaurus is a large, multi-purpose, and multi-lingual vocabulary database that contains information about biomedical and health related concepts, their various names, and the relationships among them. The Metathesaurus is built from the electronic versions of many different thesauri, classifications, code sets, and lists of controlled terms used in patient care, health services billing, public health statistics, indexing and cataloging biomedical literature, and/or basic, clinical, and health services research. The Metathesaurus contains over five million terms, or names, organized by meaning into concepts and assigned a unique identifier. The Metathesaurus is not a vocabulary. It contains many vocabularies that are standards and helps to create mappings between these vocabularies. The Semantic Network consists of:\n- Semantic types (high level categories)\n- Semantic relationships (relationships between semantic types) The Semantic Network can be used to categorize any medical vocabulary. There are 133 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types. Semantic types and semantic relationships create a network that represents the biomedical domain. Semantic types and relationships help with interpreting the meaning that has been assigned to the Metathesaurus concept.\n\n**Benefits**\n- The purpose of the umls is to improve the ability of computer programs to \"understand\" the biomedical meaning in user inquiries and to use this understanding to retrieve and integrate relevant machine-readable information for users.\n- one powerful use of the umls is linking health information, medical terms, drug names, and billing codes across different computer systems.\n- linking terms and codes between your doctor, your pharmacy, and your insurance company.\n- patient care coordination among several departments within a hospital.\n- timely access to accurate and up-to-date information improves decision making and ultimately the quality of patient care and research.\n- the umls has many other uses, including search engine retrieval, data mining, public health statistics reporting, and terminology research.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [Amphibian Concepts and Types](https://www.johnsnowlabs.com/marketplace/amphibian-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Amphibian\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Amphibian Relationships](https://www.johnsnowlabs.com/marketplace/amphibian-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Amphibian\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Animal Concepts and Types](https://www.johnsnowlabs.com/marketplace/animal-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Animal\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Animal Relationships](https://www.johnsnowlabs.com/marketplace/animal-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Animal\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Archaeon Concepts and Types](https://www.johnsnowlabs.com/marketplace/archaeon-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Archaeon\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Archaeon Relationships](https://www.johnsnowlabs.com/marketplace/archaeon-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Archaeon\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Bacterium Concepts and Types](https://www.johnsnowlabs.com/marketplace/bacterium-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Bacterium\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Bacterium Relationships](https://www.johnsnowlabs.com/marketplace/bacterium-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Bacterium\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Bird Concepts and Types](https://www.johnsnowlabs.com/marketplace/bird-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Bird\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Bird Relationships](https://www.johnsnowlabs.com/marketplace/bird-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Bird\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Eukaryote Concepts and Types](https://www.johnsnowlabs.com/marketplace/eukaryote-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Eukaryote\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Eukaryote Relationships](https://www.johnsnowlabs.com/marketplace/eukaryote-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Eukaryote\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Fish Concepts and Types](https://www.johnsnowlabs.com/marketplace/fish-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Fish\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Fish Relationships](https://www.johnsnowlabs.com/marketplace/fish-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Fish\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Fungus Concepts and Types](https://www.johnsnowlabs.com/marketplace/fungus-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Fungus\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Fungus Relationships](https://www.johnsnowlabs.com/marketplace/fungus-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Fungus\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Human Concepts and Types](https://www.johnsnowlabs.com/marketplace/human-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Human\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Human Relationships](https://www.johnsnowlabs.com/marketplace/human-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Human\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Mammal Concepts and Types](https://www.johnsnowlabs.com/marketplace/mammal-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Mammal\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Mammal Relationships](https://www.johnsnowlabs.com/marketplace/mammal-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Mammal\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Organism Concepts and Types](https://www.johnsnowlabs.com/marketplace/organism-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Organism\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Organism Relationships](https://www.johnsnowlabs.com/marketplace/organism-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Organism\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Physical Object Concepts and Types](https://www.johnsnowlabs.com/marketplace/physical-object-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Physical Object\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Physical Object Relationships](https://www.johnsnowlabs.com/marketplace/physical-object-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Physical Object\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Plant Concepts and Types](https://www.johnsnowlabs.com/marketplace/plant-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Plant\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Plant Relationships](https://www.johnsnowlabs.com/marketplace/plant-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Plant\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Reptile Concepts and Types](https://www.johnsnowlabs.com/marketplace/reptile-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Reptile\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Reptile Relationships](https://www.johnsnowlabs.com/marketplace/reptile-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Reptile\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Vertebrate Concepts and Types](https://www.johnsnowlabs.com/marketplace/vertebrate-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Vertebrate\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Vertebrate Relationships](https://www.johnsnowlabs.com/marketplace/vertebrate-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Vertebrate\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Virus Concepts and Types](https://www.johnsnowlabs.com/marketplace/virus-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Virus\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Virus Relationships](https://www.johnsnowlabs.com/marketplace/virus-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Virus\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated\n- Data Updates\n - Data updates support replace-on-update: outdated foreign keys are deprecated, not deleted\n\n**Our data is curated and enriched by domain experts** \n\n Each dataset is manually curated by our team of doctors, pharmacists, public health & medical billing experts:\n- Field names, descriptions, and normalized values are chosen by people who actually understand their meaning\n- Healthcare & life science experts add categories, search keywords, descriptions and more to each dataset\n- Both manual and automated data enrichment supported for clinical codes, providers, drugs, and geo-locations\n- The data is always kept up to date – even when the source requires manual effort to get updates\n- Support for data subscribers is provided directly by the domain experts who curated the data sets\n- Every data source’s license is manually verified to allow for royalty-free commercial use and redistribution.\n\n**Need Help?**\nIf you have questions about our products, contact us at [info@johnsnowlabs.com](mailto:info@johnsnowlabs.com). \n 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This dataset offers valuable insights into supply commitments and market demands using offtake and capacity contract data.\n\nThis dataset enables analysts to screen projects and assess their impact on supply and demand forecasts, while assisting traders in making informed purchasing or selling decisions. Research teams and strategic planners can leverage LNG Assets and Contracts to gain insight into projects and ownership structures, informing infrastructure development and long-term investment strategies.\n\nThis dataset includes:\n- Data on countries and coasts, as well as their geographic regions, for classification purposes\n- Details of liquefaction projects and liquefaction ownership that have commenced, including their geographic characteristics for mapping purposes\n- Information on regasification projects that have started, along with their geographic characteristics for mapping purposes\n- Gas and liquids production data associated with liquefaction projects, including historical and forecasted annual production\n- Estimated offtake contract buildouts based on start dates, presented in million tones per annum on a monthly basis\n- Estimated regasification capacity buildouts based on announced and estimated start dates, presented in million tones per annum on a monthly basis\n- Project breakeven costs for upstream, pipeline, and liquefaction segments, estimated for various rates of return and oil price scenarios\n- A list of all retired, existing, and future vessels along with their attributes\n\n**Use cases**\n- Asset Valuation - Quickly evaluate the competitive landscape and obtain insights on liquefaction and regasification projects, LNG vessels, and contract details\n- Risk Analysis - Enhance trading strategies, investment choices, and risk management by leveraging information on LNG assets and contracts\n\n**Product details**\nSample Fields:\nA ) LIQUEFACTION_PROJECTS\nB ) FEEDSTOCK_PROFILES\n\nSample Fields:\n\nA ) LIQUEFACTION_PROJECTS\n- LIQUEFACTION_PROJECT_ID\n- LIQUEFACTION_PROJECT\n- COUNTRY_COAST\n- SUPPLY_MARKET\n- SUPPLY_BASIN\n- MODIFIED_DATE\n\nB) FEEDSTOCK_PROFILES\n- ECONOMIC_GROUP\n- LIQUEFACTION_PROJECT\n- FEEDSTOCK_ASSET\n- SUPPLY_MARKET\n- YEAR\n- PRODUCTION_TYPE\n- PRODUCTION_UOM\n- PRODUCTION\n- MODIFIED_DATE\n\nView Descriptions:\n- LIQUEFACTION_ECONOMICS\nCOUNTRY_COASTS - A table listing countries along with their relevant regional classifications for LNG industry analysis\n- CURRENT_LIQUEFACTION - Details of liquefaction projects that have commenced, including their geographic characteristics for mapping purposes\n- CURRENT_REGASIFICATION - Information on regasification projects that have started, along with their geographic characteristics for mapping purposes\n- LIQUEFACTION_PROJECTS - List of liquefaction projects with their IDs and associated country coasts\n- LIQUEFACTION_TRAIN_OWNERSHIP - Shareholder and share information for each liquefaction train\n- LIQUEFACTION_TRAINS - Information on existing, under construction, and future liquefaction trains, including their attributes\n- OFFTAKE_CONTRACTS - List of announced and observed offtake contract relationships for liquefaction projects and company portfolios, including attributes\n- REGASIFICATION_CONTRACTS - List of announced and observed capacity contract relationships for regasification phases, including attributes\n- REGASIFICATION_PHASE_OWNERSHIP - Shareholder and share information for each regasification phase\n- REGASIFICATION_PHASES - Information on existing, under construction, and future regasification phases, including attributes\n- REGASIFICATION_PROJECTS - 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Contact Databricks.partnership@precisely.com for more information on licensing the product.\n**Use cases**\n**Location Planning**\nInform retail site selection and business investment strategies\n**Market Analysis**\nQuickly assess and compare crime levels by local regions\n**Location Data Enrichment**\nEliminates the need for complex spatial analysis\n**Risk Analysis**\nLeverage crime statistics to influence buying/selling decisions\n\n**Product details**\nThe specific crime variables measured are:\n\nOverall Crime (a composite measure based on all crime types)\n\nViolent Crime Score\n• Murder\n• Rape\n• Robbery\n• Aggravated Assault\n\nProperty Crime Score\n• Burglary\n• Larceny-Theft\n• Motor Vehicle Theft\n• Arson\n\n- Each crime variable includes both a numerical score – where higher values denote the increased likelihood of criminal activity – and a qualitative categorization that helps explain the relative crime level when compared to the Nation as a whole. \n- A State-wise score is also provided to allow users to perform within-State comparison of geographic areas. 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Open a flagged claim \n 2. Review claim details and anomaly information \n 3. Read the explanation for the flag \n 4. Mark the claim as reviewed \n 5. Configure monitoring conditions \n 6. View generated notifications \n 7. Navigate directly from a notification back to the claim \n\nThis provides a traceable workflow from claim detection to review and business action. \n \n **Product details**\nClaimsGuard is designed as a Databricks-native vehicle insurance claims application. \nThe solution uses business-ready claim data and precomputed anomaly results to provide an application experience for claims monitoring, explanation, review, alerting, and notification. \nThe current solution includes: \n- Claims monitoring dashboard \n- High-risk and flagged claim views \n- Claim details and review workflow \n- Anomaly score and risk classification \n- Plain-English claim explanations \n- Natural-language alert configuration \n- Advanced multi-condition alert configuration \n- In-app notification management \n- Reviewed / Pending Review tracking \n- Direct navigation between alerts and claims \n\n**ClaimsGuard follows a simple workflow:**\n\nDETECT → EXPLAIN → REVIEW → ALERT → NOTIFY \n\nThe solution consumes anomaly detection results generated upstream and presents the results through a Databricks App. Users can monitor high-risk and flagged claims, open individual claims, view anomaly scores and risk classifications, read plain-English explanations, mark claims as reviewed, configure alert conditions, and receive actionable in-app notifications. \nThe solution is designed for vehicle insurance claims workflows where teams need faster identification of unusual claim patterns, explainable review, configurable monitoring, and a traceable path from detection to action. \n\n**Additional Insights**\n- Databricks-native architecture built around the Databricks Lakehouse and governed business-ready data. \n- Explainable anomaly review presents business-friendly explanations instead of exposing only anomaly scores. \n- Precomputed anomaly results are consumed by the application; model training is outside the application scope. \n- Configurable alerting allows claims teams to define business monitoring conditions without writing SQL. \n- In-app notifications provide an actionable path from an alert to the underlying claim. \n- Claim review tracking supports Reviewed and Pending Review workflows. \n- Natural-language interaction simplifies the creation of supported claim alert conditions. \n- Databricks Apps provides the application interface for claims teams. \n- Databricks SQL provides access to the business-ready claim data and results. \n- ai_query() can be used upstream for generating business-friendly claim explanations; it is not the anomaly detector itself. \n- Statement Execution API via Databricks SDK provides application access to Databricks SQL. \n- The solution is focused on vehicle insurance claim anomaly detection and review, not a standalone fraud-probability prediction product. \n- The current notification channel is in-app notification. 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This data package also combines meaningful attestations from the Medicare EHR Incentive Program and certified EHR product data from the ONC Certified Health IT Product List (CHPL) to identify the unique vendors, products, and product types of each EHR used to attest to meaningful use. The National Hospital Discharge Survey (NHDS) collects information on patients discharged from hospitals in the United States. The registry is a confidential database that records a person’s consent to organ/tissue donation upon death.\n\n**Benefits**\n- The database is used by donation professionals to determine a person donation status at the time of their death.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [Childhood Cancer Registry](https://www.johnsnowlabs.com/marketplace/childhood-cancer-registry)\n\t- The number of registered cases of childhood cancer in the National Registry of Childhood Tumours (NRCT) is currently available for the five-year periods 1971-75, 1976-80, 1981-85, 1986-1990, 1991-1995, 1996-2000 and 2001-2005, and the two-year period 2006-2007. Within each period the number of cases is given for each of the major groups in the International Classification of Childhood Cancer, Third Edition, by sex and age group at diagnosis. The age groups are 0-4, 5-9 and 10-14 years.\n- [Electronic Health Records Products 2013 to 2018](https://www.johnsnowlabs.com/marketplace/electronic-health-records-products-2013-to-2018)\n\t- The Medicare Electronic Health Record Incentive Program provides financial incentives to eligible hospitals and ambulatory providers to adopt and meaningfully use certified electronic health record technology (CEHRT). Electronic Health Record (EHR) technology reported by participants is certified by the Office of the National Coordinator for Health Information Technology ONC. This dataset provides the CEHRT reported by providers when they annually attest to meaningful use.\n- [Initial Diagnosis of Disease by Age](https://www.johnsnowlabs.com/marketplace/initial-diagnosis-of-disease-by-age)\n\t- This dataset contains data from the National Hospital Discharge Survey (NHDS), which collects information on patients discharged from hospitals in the US. The data contained is by first-listed diagnosis and age of patients.\n- [Medical Marijuana Statistics by County](https://www.johnsnowlabs.com/marketplace/medical-marijuana-statistics-by-county)\n\t- This dataset contains the Medical Marijuana Registry Program Update by County as of January 31, 2014.\n- [Organs and Tissue Donor Registry](https://www.johnsnowlabs.com/marketplace/organs-and-tissue-donor-registry)\n\t- This dataset contains information regarding the number of enrollments by population in the New York State Donate Life Registry from 2008 to 2023. The database is used by donation professionals to determine a person donation status at the time of their death.\n- [Survival from Childhood Cancer Registry](https://www.johnsnowlabs.com/marketplace/survival-from-childhood-cancer-registry)\n\t- The dataset presents the percentage survival rates at five years after diagnosis for children in the National Registry of Childhood Tumours (NRCT) who were diagnosed in the five-year periods 1971-75, 1976-80, 1981-85, 1986-1990, 1991-1995, 1996-2000 and 2001-05. Data are given for most of the main groups in the International Classification of Childhood Cancer, Third Edition, and for the most important individual subtypes of childhood cancer.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated\n- Data Updates\n - Data updates support replace-on-update: outdated foreign keys are deprecated, not deleted\n\n**Our data is curated and enriched by domain experts** \n\n Each dataset is manually curated by our team of doctors, pharmacists, public health & medical billing experts:\n- Field names, descriptions, and normalized values are chosen by people who actually understand their meaning\n- Healthcare & life science experts add categories, search keywords, descriptions and more to each dataset\n- Both manual and automated data enrichment supported for clinical codes, providers, drugs, and geo-locations\n- The data is always kept up to date – even when the source requires manual effort to get updates\n- Support for data subscribers is provided directly by the domain experts who curated the data sets\n- Every data source’s license is manually verified to allow for royalty-free commercial use and redistribution.\n\n**Need Help?**\nIf you have questions about our products, contact us at [info@johnsnowlabs.com](mailto:info@johnsnowlabs.com). \n 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All data comes from a single source, ensuring **consistency and comparability** across markets.\n\nThe Sociodemographics-Bundle Basic for Bosnia and Herzegovina includes the following **variables**:\n- [population](https://www.mbi-geodata.com/en/sociodemographic-data/population/)\n- [gender](https://www.mbi-geodata.com/en/sociodemographic-data/sex-gender/)\n- [age structure](https://www.mbi-geodata.com/en/sociodemographic-data/agebands/) (15-year age bands)\n- household figures ([number of households](https://www.mbi-geodata.com/en/sociodemographic-data/households/) and [households by type](https://www.mbi-geodata.com/en/sociodemographic-data/households-by-type/))\n\nOur sociodemographic data is offered for each country on different **levels**:\n- **Postal Code Level:** This fine-grained level of detail makes the data suitable for a wide range of applications – for example, enriching customer databases, which are often organized by postal code.\n- **Administrative Level:** Covers both larger administrative areas (e.g., regions, states, provinces) as well as finer subdivisions such as districts, counties, or municipalities.\n- **Micro Level**: Subdivisions of postal code or administrative boundaries, such as census areas or street segments. 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See the attached notebook to deploy and use the model.\n\nBuild based on [Healthcare NLP **6.4.0** version](https://nlp.johnsnowlabs.com/docs/en/jsl/jsl-release-notes#section-2)","terms_of_service":"https://www.johnsnowlabs.com/healthcare-data-library-eula/","documentation_link":"https://nlp.johnsnowlabs.com/2025/05/17/pdf_obfuscate_multilingual_name_plus.html","privacy_policy_link":"https://www.johnsnowlabs.com/privacy-policy/","embedded_notebook_file_infos":[{"id":"311edfcc-85c6-42f8-b2db-b14833cd6602","marketplace_file_type":"EMBEDDED_NOTEBOOK","file_parent":{"parent_id":"e231d645-7f92-457e-a576-dfd8704c818d","file_parent_type":"LISTING"},"mime_type":"text/html","download_link":"https://marketplace-file-storage-prod.s3-fips.us-east-1.amazonaws.com/EMBEDDED_NOTEBOOK/LISTING_e231d645-7f92-457e-a576-dfd8704c818d/311edfcc-85c6-42f8-b2db-b14833cd6602?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEGgaDnVzLWVhc3QtMS1maXBzIkYwRAIgbC8sWIoYaHR8y7Wq86nPb0pufBl4%2BM4PWpeN%2B5PW9VQCIG7BhAVBDCWTpxFDLeuKYm%2Ff1JLz4iccPMzter0YdhgWKqYECDAQABoMNDE0MzUxNzY3ODI2IgxNAJrHbXaZG20jNfMqgwQyGEb4TsoVOoNvJ%2BaA%2FZ7zeu8hGcHqCeNfblP%2FQo5g6q2aRNtpwtPRKMlhYcIE%2FUQaNT1Lqmyptu32VUQTpzjTTxRVmJrDPkheSCkV06XQ%2FpTMKjU8CC7X1b%2FqYf8CrBwZT7Cv4C5WPjEET38f2A79h0xV6bZgPy28zQWrSeIJ9w6AfZBxiE24%2FVc08zVA0ivCT6lsy7nuU0wcR%2FMg2C9nd1vaMnQ923VpD21CKfVVYcrNWL2b%2FQ%2BNAO%2BxPjPgjiN6UPQbvbvZxkSdhgrGtecIg9NgqGkMcvM5VLTDtdC1kdeZetfOC2obtA%2BT%2F0gy3tIDo6WM5hcQfw743%2FAU9oaGnRBl%2BwoxRRDCZA%2F4jnPdluICSI7NCr6C3HHOe4CSZJjRihczxPA%2FgQczAVjAPs6CrK9N9pIzt0vNJpGUTgg4AoxftTvGfciDREilSctPIcBk6GwB%2Bzt4wbtNCmEPU%2BFD46rc6CWVQmmLwbKOeHw70ipMVFlOc8NlXkEtGO2vu6H44MstSSc6daOVUYUavQSGZ6jGhEjsP2YGz8KDm9XQotB%2FtyVBqiZV01OKqDmmBJA%2FXjUpSJ8M%2FeJXhe5m%2BKcxzNObtoJRI7jci3Tpt08XZyXZ7%2FyIV8H30ya468X3cfkTXrm972uga3z7iXAdubyEHnQIua%2FuvtwOHILi6O5D1Me2MzCPvcnXBjqTAYDoMWBt%2B3AVFuqSs2dmDQNBnvEhXfMgTedKLD5PJHb3aIl15iBkLaNu36GYnVCp9ZU2lSYcTbY5QwNnNeTfJTNy9bKhwzru6Sg%2BslhN%2BTS0zkTn%2BeLMY4tHgPEtEZ2cp%2FCk6GwqswvnYVKHjwO8q%2B1Mx8lc08fVEfarE9dke1GGFqoddx%2Bq%2BZUyHbDlAVfrCmf0yw%3D%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20261110T000135Z&X-Amz-SignedHeaders=host&X-Amz-Expires=86400&X-Amz-Credential=ASIAWA6KKHEJORWBH4N3%2F20261110%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=883f917caea086217326f5342641b59b69f772351bc03187b94da70ccd1b42ee","created_at":1789110326715,"updated_at":1789110326715,"display_name":"pdf_obfuscate_multilingual_name_plus","status":"FILE_STATUS_PUBLISHED"}],"assets":["ASSET_TYPE_MODEL"],"license":"","tags":[{"tag_name":"LISTING_TAG_TYPE_TASK","tag_values":["FILL_MASK","NAMED_ENTITY_RECOGNITION"]}]},"provider_summary":{"name":"John Snow Labs","description":"John Snow Labs, an AI and NLP for healthcare company, provides state-of-the-art software, models, and data to help healthcare and life science organizations build, deploy, and operate AI projects."},"popularity":75},{"id":"0b2fec6c-63a4-4409-9079-bc4a5682f263","summary":{"name":"LoanGuard","subtitle":"LoanGuard uses Databricks to verify loan documents, detect fraud indicators, calculate risk scores, and deliver loan-dec","setting":{"visibility":"PUBLIC"},"created_at":1790338527358,"updated_at":1790338554253,"categories":["FINANCIAL","ECONOMICS"],"listingType":"PERSONALIZED","provider_id":"69e292c4-7435-4dd8-8306-1f3a5214dc7b"},"detail":{"description":"**Overview**\nLoanGuard is a Databricks-based solution for document-driven loan verification and fraud-risk assessment. 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Optimized for Databricks spatial analytics.","setting":{"visibility":"PUBLIC"},"created_at":1780474037100,"updated_at":1780474061960,"categories":["GEOSPATIAL","RETAIL"],"listingType":"PERSONALIZED","provider_id":"5d3ddc17-a7e8-4794-a760-13587e1de2c6"},"detail":{"description":"%md\n# **Spain Grocery Locations Dataset: Top 25 Supermarket Brands**\n---\n### **Premium GIS-Ready Retail Location Intelligence Data for Databricks Analytics**\n\nThis high-fidelity geospatial dataset provides a consolidated, standardized overview of **18,613** verified grocery store and supermarket locations across Spain. Updated as of **May 30, 2026**, this single-source resource eliminates the need for manual web scraping or multi-vendor licensing, offering an analysis-ready foundation for retail site selection, competitive intelligence, logistics optimization, and spatial demographics mapping across all Spanish autonomous communities.\n\nFully compatible with **Apache Spark**, **Delta Lake**, and **Databricks Unity Catalog** workflows.\n\n---\n\n## **Consolidated Brand Coverage (Top 25 Spanish Chains)**\nBy consolidating multiple retail banners into a single geospatial table, this dataset provides maximum cost-efficiency and strategic utility over individual brand lists. Coverage includes:\n\n- **Major National Supermarkets & Hypermarkets:** \n [Mercadona (1,704 locations)](https://geolocet.com/products/spain-mercadona-grocery), \n [Carrefour (1,682 locations)](https://geolocet.com/products/spain-carrefour-grocery), \n [Dia (2,500 locations)](https://geolocet.com/products/spain-dia-grocery), \n [Alcampo (564 locations)](https://geolocet.com/products/spain-alcampo-grocery), \n [SuperCor (856 locations)](https://geolocet.com/products/spain-supercor-grocery)\n\n- **Leading Discounters (Hard & Soft Discount):** \n [Lidl (752 locations)](https://geolocet.com/products/spain-lidl-grocery), \n [ALDI (543 locations)](https://geolocet.com/products/spain-aldi-grocery), \n [PrimaPrix (290 locations)](https://geolocet.com/products/spain-primaprix-grocery)\n\n- **Regional & Cooperative Powerhouses:** \n [Coviran (2,006 locations)](https://geolocet.com/products/spain-coviran-grocery), \n [SPAR (1,484 locations)](https://geolocet.com/products/spain-spar-grocery), \n [Eroski (1,059 locations)](https://geolocet.com/products/spain-eroski-grocery), \n [Condis (775 locations)](https://geolocet.com/products/spain-condis-grocery), \n [Consum (601 locations)](https://geolocet.com/products/spain-consum-grocery), \n [Coaliment (573 locations)](https://geolocet.com/products/spain-coaliment-grocery), \n [Charter (529 locations)](https://geolocet.com/products/spain-charter-grocery), \n [BM (355 locations)](https://geolocet.com/products/spain-bm-grocery), \n [Unide (345 locations)](https://geolocet.com/products/spain-unide-grocery), \n [Caprabo (310 locations)](https://geolocet.com/products/spain-caprabo-grocery), \n [Ahorramas (308 locations)](https://geolocet.com/products/spain-ahorramas-grocery)\n\n- **Strong Regional Banners:** \n [Claudio (262 locations)](https://geolocet.com/products/spain-claudio-grocery), \n [Froiz (260 locations)](https://geolocet.com/products/spain-froiz-grocery), \n [MasyMas (240 locations)](https://geolocet.com/products/spain-masymas-grocery), \n [Gadis (228 locations)](https://geolocet.com/products/spain-gadis-grocery), \n [Lupa (213 locations)](https://geolocet.com/products/spain-lupa-grocery), \n [Alimerka (174 locations)](https://geolocet.com/products/spain-alimerka-grocery)\n\n---\n\n## **Technical Specifications & Schema Definitions**\nOptimized for zero-friction schema enforcement when reading via `spark.read.format(\"csv\")`. The structured schema below ensures flawless rendering in Databricks notebooks and high accuracy for LLM semantic indexing.\n\n* **`GUID`** (`STRING`): Unique global identifier for deterministic primary key mapping.\n* **`Title`** (`STRING`): Official store name and primary brand banner classification.\n* **`Latitude`** (`DOUBLE`): Precise WGS84 latitude coordinate for point-in-polygon & spatial joins.\n* **`Longitude`** (`DOUBLE`): Precise WGS84 longitude coordinate for spatial indexing (H3 / S2).\n* **`Street_No` / `Street`** (`STRING`): Normalized building/house number and street name elements.\n* **`City` / `Postal_Code`** (`STRING`): Core postal addresses and primary municipal settlement names.\n* **`Commune`** (`STRING`): Local municipal administrative boundary designation (Municipio/Comuna).\n* **`Autonomous_Community`** (`STRING`): High-level macro-administrative classification (e.g., Andalucía, Catalunya, Madrid).\n* **`Admin_level_1` / `_2`** (`STRING`): Additional local administrative area and province identifiers.\n* **`Address`** (`STRING`): Full, standardized street address of the location.\n* **`Wheelchair`** (`STRING`): Accessibility flags indicating wheelchair mobility support.\n* **`Popularity_Score`** (`INTEGER`): Relative foot-traffic/rating metric graded on a standardized 1-10 scale.\n* **`Opening_Hours`** (`STRING`): Standard daily operational schedules and structured operating text.\n* **`Phone` / `Website`** (`STRING`): Direct communication data, branch-specific landing URLs, and domain vectors.\n\n---\n\n## **Databricks Workflow & Spatial Analytics Use Cases**\n\n- **Catchment Area Modeling:** Run native Apache Spark SQL spatial functions or python-based `geopandas` routines to construct drive-time isochrones around competitors in dense retail corridors like Catalunya or Madrid.\n- **H3 Geospatial Indexing:** Convert the precise WGS84 `Latitude` and `Longitude` fields into Uber H3 spatial hexagons (`h3_longlattocell`) to cross-reference with granular Spanish demographic or statistical sector income raster files.\n- **Territory Optimization:** Combine store location points with regional administrative boundary layers to spot gaps in supply chain routing, optimize distribution center placement, or measure market penetration.\n\n---\n\n## **Data Evaluation & Custom Ingestion**\n\n### **Evaluate with a Free Sample Dataset**\nVerify the structure, column types, and coordinate precision before full implementation. A comprehensive schema sample is available for download in CSV/Parquet-friendly formats.\n\n*Want deeper market insights?* Learn more about the brand network in our comprehensive report: **[View the Spanish Top Brands Grocery Report](https://geolocet.com/pages/spain-top-brands-grocery-report)**\n\n### **Enterprise Delivery & Tailored Formatting**\nWhile natively available as an analysis-ready CSV for DBFS ingestion, we accommodate enterprise-level data architecture needs. Alternative formats - including **GeoJSON, Esri Shapefiles, PostgreSQL dumps, Excel, or direct Delta sharing profiles** - can be generated. Reach out to our engineering team at **contact@geolocet.com** for integration support or custom European POI data requests.","terms_of_service":"https://geolocet.com/pages/terms-of-use","documentation_link":"","privacy_policy_link":"https://geolocet.com/pages/privacy-policy","assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://geolocet.com/pages/terms-of-use"},"provider_summary":{"name":"Geolocet LTD","description":"Geolocet provides premium geospatial data for retail analysis, GIS, and market planning. We equip businesses with accurate, ready-to-query data to drive spatial analysis, site selection, and customer segmentation.\n\nCore Offerings:\n\nDemographics: Socioeconomic data covering population, age, and employment across diverse geographies.\n\nPoints of Interest (PoI): Datasets spanning retail, healthcare, and transport for catchment analysis.\n\nAdministrative Boundaries: GIS-ready boundaries, including municipalities and postcodes, for territory management.\n\nBrand Locations: Chain location datasets to monitor store networks and market expansion.\n\nWealth Data: Granular economic indicators like purchasing power to identify high-potential markets.\n\nWe source from trusted providers, delivering highly customizable datasets with comprehensive coverage across Europe and worldwide."},"popularity":1467},{"id":"6ee01610-7709-4ea0-be5d-bee713d3e8c8","summary":{"name":"Menu Data","subtitle":"This is a dataset that tracks menu items, ingredients, and flavors across a representative sample...\n","setting":{"visibility":"PUBLIC"},"created_at":1780664854083,"updated_at":1780868206967,"categories":["COMMERCE","TRAVEL_AND_TOURISM"],"listingType":"PERSONALIZED","provider_id":"4b102bc7-519c-44d5-985e-aac1c38399e2"},"detail":{"description":"**Overview**\nThis is a dataset that tracks menu items, ingredients, and flavors across a representative sample of the US menus across the restaurant industry. 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It is a useful tool for activities such as looking for new safety concerns that might be related to a marketed product, evaluating a manufacturer's compliance to reporting regulations and responding to outside requests for information. The reports in FAERS are evaluated by clinical reviewers, in the Center for Drug Evaluation and Research (CDER) and the Center for Biologics Evaluation and Research (CBER), to monitor the safety of products after they are approved by FDA. These reports are submitted to FDA by healthcare professionals (such as physicians, pharmacists, nurses and others) and consumers (such as patients, family members, lawyers and others). Healthcare professionals and consumers may also report to the products’ manufacturers who then send these reports to FDA. The datasets in FAERS data package contain many relevant information such as: patient demographics, drug name, dose and route of administration, diagnosis and indication for use, therapy start and end date and patient outcome.\n\n**Benefits**\n- useful for physicians, pharmacists, pharmaceutical companies, medical students and patients (general public).\n- faers database is available on fda’s website in raw form, whereas a jsl customer gets it in clean and normalized form ready to use and create his own custom reports.\n- quarter wise data merged into one file.\n- easy to comprehend for the customers who are unfamiliar with the medical terminology as all abbreviated terms are replaced with full form, unlike faers original datasets which are full of abbreviations.\n- datasets are available for the year 2014, 2015, 2016 and 2017\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [FDA Adverse Events Reporting System Demographics 2017](https://www.johnsnowlabs.com/marketplace/fda-adverse-events-reporting-system-demographics-2017)\n\t- FAERS (FDA Adverse Events Reporting System) database is designed to support the FDA’s post-marketing safety surveillance program for drug and therapeutic biologic products. \nThe Demographics dataset contains patient demographic and administrative information, a single record for each event report.\n- [FDA Adverse Events Reporting System Drug 2017](https://www.johnsnowlabs.com/marketplace/fda-adverse-events-reporting-system-drug-2017)\n\t- FAERS (FDA Adverse Events Reporting System) database is designed to support the FDA’s post-marketing safety surveillance program for drug and therapeutic biologic products. The Drug file contains drug/biologic information for as many medications as were reported for the event (1 or more per event).\n- [FDA Adverse Events Reporting System Drug Indication 2017](https://www.johnsnowlabs.com/marketplace/fda-adverse-events-reporting-system-drug-indication-2017)\n\t- FAERS (FDA Adverse Events Reporting System) database is designed to support the FDA’s post-marketing safety surveillance program for drug and therapeutic biologic products. The Indication file contains all \"Medical Dictionary for Regulatory Activities\" (MedDRA) terms coded for the indications for use (diagnoses) for the reported drugs (0 or more per drug per event).\n- [FDA Adverse Events Reporting System Drug Reaction 2017](https://www.johnsnowlabs.com/marketplace/fda-adverse-events-reporting-system-drug-reaction-2017)\n\t- FAERS (FDA Adverse Events Reporting System) database is designed to support the FDA’s post-marketing safety surveillance program for drug and therapeutic biologic products. The \"Drug Reaction\" file contains all \"Medical Dictionary for Regulatory Activities\" (MedDRA) terms coded for the adverse event (1 or more).\n- [FDA Adverse Events Reporting System Drug Therapy Dates 2017](https://www.johnsnowlabs.com/marketplace/fda-adverse-events-reporting-system-drug-therapy-dates-2017)\n\t- FAERS (FDA Adverse Events Reporting System) database is designed to support the FDA’s post-marketing safety surveillance program for drug and therapeutic biologic products. The Drug Therapy Dates file contains drug therapy start dates and end dates for the reported drugs (0 or more per drug per event).\n- [FDA Adverse Events Reporting System Patient Outcome 2017](https://www.johnsnowlabs.com/marketplace/fda-adverse-events-reporting-system-patient-outcome-2017)\n\t- FAERS (FDA Adverse Events Reporting System) database is designed to support the FDA’s post-marketing safety surveillance program for drug and therapeutic biologic products. The \"Patient Outcome\" file contains information on patient outcomes for the event (0 or more).\n- [FDA Adverse Events Reporting System Report Source 2017](https://www.johnsnowlabs.com/marketplace/fda-adverse-events-reporting-system-report-source-2017)\n\t- FAERS (FDA Adverse Events Reporting System) database is designed to support the FDA’s post-marketing safety surveillance program for drug and therapeutic biologic products. The Report Source file contains report sources for event (0 or more).\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated\n- Data Updates\n - Data updates support replace-on-update: outdated foreign keys are deprecated, not deleted\n\n**Our data is curated and enriched by domain experts** \n\n Each dataset is manually curated by our team of doctors, pharmacists, public health & medical billing experts:\n- Field names, descriptions, and normalized values are chosen by people who actually understand their meaning\n- Healthcare & life science experts add categories, search keywords, descriptions and more to each dataset\n- Both manual and automated data enrichment supported for clinical codes, providers, drugs, and geo-locations\n- The data is always kept up to date – even when the source requires manual effort to get updates\n- Support for data subscribers is provided directly by the domain experts who curated the data sets\n- Every data source’s license is manually verified to allow for royalty-free commercial use and redistribution.\n\n**Need Help?**\nIf you have questions about our products, contact us at [info@johnsnowlabs.com](mailto:info@johnsnowlabs.com). \n 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By installing this listing, you acknowledge and agree to the license.\n\nFor example notebooks of using the falcon model in various use cases on Databricks, refer to [the Databricks ML example repository](https://github.com/databricks/databricks-ml-examples/tree/master/llm-models/falcon).\n\n**Use cases**\n\n[falcon-7b](https://huggingface.co/tiiuae/falcon-7b), [falcon-40b](https://huggingface.co/tiiuae/falcon-40b):\n- Fine-tuning on specific applications.\n[falcon-7b-instruct](https://huggingface.co/tiiuae/falcon-7b-instruct), [falcon-40b-instruct](https://huggingface.co/tiiuae/falcon-40b-instruct):\n- Generating responses to text-based instructions\n\n**Product details**\n\nThe pre-trained model can be used for further fine-tuning on specific applications. The fine-tuned model in this listing can be deployed directly to Databricks Model Serving for immediate use, or loaded for fine-tuning or batch inference use cases. 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There are three UMLS Knowledge Sources:\n- The Metathesaurus, which contains over one million biomedical concepts from over 100 source vocabularies\n- The Semantic Network, which defines 133 broad categories and fifty-four relationships between categories for labeling the biomedical domain\nThe SPECIALIST Lexicon & Lexical Tools, which provide lexical information and programs for language processing The Metathesaurus is a large, multi-purpose, and multi-lingual vocabulary database that contains information about biomedical and health related concepts, their various names, and the relationships among them. The Metathesaurus is built from the electronic versions of many different thesauri, classifications, code sets, and lists of controlled terms used in patient care, health services billing, public health statistics, indexing and cataloging biomedical literature, and/or basic, clinical, and health services research. The Metathesaurus contains over five million terms, or names, organized by meaning into concepts and assigned a unique identifier. The Metathesaurus is not a vocabulary. It contains many vocabularies that are standards and helps to create mappings between these vocabularies. The Semantic Network consists of:\n- Semantic types (high level categories)\n- Semantic relationships (relationships between semantic types) The Semantic Network can be used to categorize any medical vocabulary. There are 133 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types. Semantic types and semantic relationships create a network that represents the biomedical domain. Semantic types and relationships help with interpreting the meaning that has been assigned to the Metathesaurus concept.\n\n **Benefits**\n - The purpose of the umls is to improve the ability of computer programs to \"understand\" the biomedical meaning in user inquiries and to use this understanding to retrieve and integrate relevant machine-readable information for users.\n- one powerful use of the umls is linking health information, medical terms, drug names, and billing codes across different computer systems.\n- linking terms and codes between your doctor, your pharmacy, and your insurance company.\n- patient care coordination among several departments within a hospital.\n- timely access to accurate and up-to-date information improves decision making and ultimately the quality of patient care and research.\n- the umls has many other uses, including search engine retrieval, data mining, public health statistics reporting, and terminology research.\n\n **License Information**\n The use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n **Included Datasets**\n - [Amphibian Concepts and Types](https://johnsnowlabs.com/wp-json/datasets-api/v1/amphibian-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Amphibian\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Amphibian Relationships](https://johnsnowlabs.com/wp-json/datasets-api/v1/amphibian-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Amphibian\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Animal Concepts and Types](https://johnsnowlabs.com/wp-json/datasets-api/v1/animal-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Animal\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Animal Relationships](https://johnsnowlabs.com/wp-json/datasets-api/v1/animal-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Animal\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Archaeon Concepts and Types](https://johnsnowlabs.com/wp-json/datasets-api/v1/archaeon-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Archaeon\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Archaeon Relationships](https://johnsnowlabs.com/wp-json/datasets-api/v1/archaeon-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Archaeon\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Bacterium Concepts and Types](https://johnsnowlabs.com/wp-json/datasets-api/v1/bacterium-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Bacterium\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Bacterium Relationships](https://johnsnowlabs.com/wp-json/datasets-api/v1/bacterium-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Bacterium\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Bird Concepts and Types](https://johnsnowlabs.com/wp-json/datasets-api/v1/bird-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Bird\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Bird Relationships](https://johnsnowlabs.com/wp-json/datasets-api/v1/bird-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Bird\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Eukaryote Concepts and Types](https://johnsnowlabs.com/wp-json/datasets-api/v1/eukaryote-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Eukaryote\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Eukaryote Relationships](https://johnsnowlabs.com/wp-json/datasets-api/v1/eukaryote-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Eukaryote\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Fish Concepts and Types](https://johnsnowlabs.com/wp-json/datasets-api/v1/fish-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Fish\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Fish Relationships](https://johnsnowlabs.com/wp-json/datasets-api/v1/fish-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Fish\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Fungus Concepts and Types](https://johnsnowlabs.com/wp-json/datasets-api/v1/fungus-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Fungus\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Fungus Relationships](https://johnsnowlabs.com/wp-json/datasets-api/v1/fungus-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Fungus\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Human Concepts and Types](https://johnsnowlabs.com/wp-json/datasets-api/v1/human-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Human\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Human Relationships](https://johnsnowlabs.com/wp-json/datasets-api/v1/human-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Human\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Mammal Concepts and Types](https://johnsnowlabs.com/wp-json/datasets-api/v1/mammal-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Mammal\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Mammal Relationships](https://johnsnowlabs.com/wp-json/datasets-api/v1/mammal-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Mammal\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Organism Concepts and Types](https://johnsnowlabs.com/wp-json/datasets-api/v1/organism-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Organism\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Organism Relationships](https://johnsnowlabs.com/wp-json/datasets-api/v1/organism-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Organism\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Physical Object Concepts and Types](https://johnsnowlabs.com/wp-json/datasets-api/v1/physical-object-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Physical Object\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Physical Object Relationships](https://johnsnowlabs.com/wp-json/datasets-api/v1/physical-object-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Physical Object\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Plant Concepts and Types](https://johnsnowlabs.com/wp-json/datasets-api/v1/plant-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Plant\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Plant Relationships](https://johnsnowlabs.com/wp-json/datasets-api/v1/plant-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Plant\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Reptile Concepts and Types](https://johnsnowlabs.com/wp-json/datasets-api/v1/reptile-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Reptile\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Reptile Relationships](https://johnsnowlabs.com/wp-json/datasets-api/v1/reptile-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Reptile\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Vertebrate Concepts and Types](https://johnsnowlabs.com/wp-json/datasets-api/v1/vertebrate-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Vertebrate\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Vertebrate Relationships](https://johnsnowlabs.com/wp-json/datasets-api/v1/vertebrate-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Vertebrate\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Virus Concepts and Types](https://johnsnowlabs.com/wp-json/datasets-api/v1/virus-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Virus\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Virus Relationships](https://johnsnowlabs.com/wp-json/datasets-api/v1/virus-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Virus\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n\n\n **Data Engineering Overview**\n **We deliver high-quality data**\n - Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n - Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n - Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated\n - Data Updates\n - Data updates support replace-on-update: outdated foreign keys are deprecated, not deleted\n\n **Our data is curated and enriched by domain experts** \n\n Each dataset is manually curated by our team of doctors, pharmacists, public health & medical billing experts:\n - Field names, descriptions, and normalized values are chosen by people who actually understand their meaning\n - Healthcare & life science experts add categories, search keywords, descriptions and more to each dataset\n - Both manual and automated data enrichment supported for clinical codes, providers, drugs, and geo-locations\n - The data is always kept up to date – even when the source requires manual effort to get updates\n - Support for data subscribers is provided directly by the domain experts who curated the data sets\n - Every data source’s license is manually verified to allow for royalty-free commercial use and redistribution.\n\n **Need Help?**\n If you have questions about our products, contact us at [info@johnsnowlabs.com](mailto:info@johnsnowlabs.com). \n ","terms_of_service":"https://www.johnsnowlabs.com/healthcare-data-library-eula/","documentation_link":"https://johnsnowlabs.com/wp-json/datasets-api/v1/organism-data-package","contact_email":"support@johnsnowlabs.com","privacy_policy_link":"https://www.johnsnowlabs.com/privacy-policy/"},"provider_summary":{"name":"John Snow Labs","description":"John Snow Labs, an AI and NLP for healthcare company, provides state-of-the-art software, models, and data to help healthcare and life science organizations build, deploy, and operate AI projects."},"popularity":627},{"id":"160b91da-6540-48c4-bd72-df7635a0b88d","summary":{"name":"Medical Devices Applications and Approvals","subtitle":"This data package contains all (510(k)) submissions for medical devices, reporting status of post approval studies, Pr..","share":{"name":"medical_devices_applications_and_approvals","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1682356518171,"updated_at":1695692148898,"categories":["SCIENCE_AND_RESEARCH"],"listingType":"STANDARD","provider_id":"f88849f8-9d77-4de2-b4c7-03d55cc3768e"},"detail":{"description":"\n**Overview**\nThis data package contains all (510(k)) submissions for medical devices, reporting status of post approval studies, Premarket approval (PMA) applications. it lists also all national and international standards recognized by FDA.\n\n**Description**\nThis data package contains all (510(k)) submissions for medical devices reviewed by FDA’s Center for Devices and Radiological Health (CDRH),  reporting status of post-approval studies (PAS) studies based on study timelines incorporated in study protocols and agreed upon by the CDRH and applicants, database of Premarket approval (PMA) related to the FDA process of scientific and regulatory review to evaluate the safety and effectiveness of Class III medical devices it lists also national and international standards recognized by FDA which manufacturers can declare conformity to.\n\n**Benefits**\n- Access to medical devices various fda applications.\n- access to fda premarket approval data.\n- access to updated list of standards recognized by fda.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [FDA Devices Premarket Approval Summary Review Memos](https://www.johnsnowlabs.com/marketplace/fda-devices-premarket-approval-summary-review-memos)\n\t- Premarket Approval (PMA) Summary Review Memos for 180-Day Design Changes includes 180-day supplements which are requested for a significant change in components, materials, design, specification, software, color additive, and labeling to an approved premarket application or premarket report\n- [FDA Medical Device 510k Clearances](https://www.johnsnowlabs.com/marketplace/fda-medical-device-510k-clearances)\n\t- The Food and Drug Administration (FDA) Medical Device 510k Clearances dataset lists all (510(k)) submissions for medical devices. These are reviewed by FDA’s Center for Devices and Radiological Health (CDRH), specifically, by the Office of Device Evaluation (ODE) and the Office of In Vitro Diagnostics and Radiological Health (OIR). 510(k) submissions are reviewed by ODE and OIR staff, including biomedical engineers, physicians, microbiologists, chemists, and other scientific professionals.\n- [FDA Post Approval Studies Database](https://www.johnsnowlabs.com/marketplace/fda-post-approval-studies-database)\n\t- The Post-Approval Studies (PAS) Database identifies the reporting status of PAS studies based on study timelines incorporated in study protocols and agreed upon by the CDRH and applicants. This system represents CDRH's effort to ensure that all PAS commitments are fulfilled in a timely manner.\n- [FDA Premarket Approval Database](https://www.johnsnowlabs.com/marketplace/fda-premarket-approval-database)\n\t- This dataset includes the Premarket Approval (PMA) data which is the Food and Drug Administration (FDA) process of scientific and regulatory review to assess the safety and effectiveness of Class III medical devices. These devices support human life and prevent impairment of human health. Due to the level of risk associated with Class III devices, general and special controls are insufficient to assure the safety of these devices and require a PMA application to obtain marketing clearance.\n- [FDA Recognized Consensus Standards](https://www.johnsnowlabs.com/marketplace/fda-recognized-consensus-standards)\n\t- FDA Recognized Consensus Standards database consists of those national and international standards recognized by FDA which manufacturers can declare conformity to and is part of the information the Center can use to make an appropriate decision regarding the clearance or approval of a submission.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated\n- Data Updates\n - Data updates support replace-on-update: outdated foreign keys are deprecated, not deleted\n\n**Our data is curated and enriched by domain experts** \n\n Each dataset is manually curated by our team of doctors, pharmacists, public health & medical billing experts:\n- Field names, descriptions, and normalized values are chosen by people who actually understand their meaning\n- Healthcare & life science experts add categories, search keywords, descriptions and more to each dataset\n- Both manual and automated data enrichment supported for clinical codes, providers, drugs, and geo-locations\n- The data is always kept up to date – even when the source requires manual effort to get updates\n- Support for data subscribers is provided directly by the domain experts who curated the data sets\n- Every data source’s license is manually verified to allow for royalty-free commercial use and redistribution.\n\n**Need Help?**\nIf you have questions about our products, contact us at [info@johnsnowlabs.com](mailto:info@johnsnowlabs.com). \n ","terms_of_service":"https://www.johnsnowlabs.com/healthcare-data-library-eula/","documentation_link":"https://www.johnsnowlabs.com/marketplace/medical-devices-applications-and-approvals-data-package","contact_email":"support@johnsnowlabs.com","privacy_policy_link":"https://www.johnsnowlabs.com/privacy-policy/"},"provider_summary":{"name":"John Snow Labs","description":"John Snow Labs, an AI and NLP for healthcare company, provides state-of-the-art software, models, and data to help healthcare and life science organizations build, deploy, and operate AI projects."},"popularity":529},{"id":"49328741-4798-4388-ae00-ca1fc8db2f42","summary":{"name":"Chronic Conditions | Derived Healthcare Claims Data","subtitle":"Chronic Conditions delivers detailed, long-term records for patients diagnosed with or treated for a chronic condition.","setting":{"visibility":"PUBLIC"},"created_at":1675453138983,"updated_at":1717943911241,"categories":["HEALTH"],"listingType":"PERSONALIZED","provider_id":"3f540322-735a-47ef-ad5a-8ccbe4693428"},"detail":{"description":"The Chronic Conditions Asset is a derived data product from Kythera Labs, which uses remastered healthcare claims data, data science, and statistical modeling to create individual, longitudinal records of patients diagnosed or treated for a chronic condition. These records include patients diagnosed or treated for any chronic condition defined by the Healthcare Cost and Utilization Project (HCUP) and mapped to one of 18 relevant body systems, and details like the diagnosing provider(s) and treating provider(s) for each condition. Using statistical models, Chronic Conditions identifies procedures, revenue codes, diagnostic and imaging procedures, and drugs relevant to treating the chronic conditions that each patient is experiencing, in addition to treatments explicitly listed on claims.\n\n**Use Cases**\n- The Chronic Conditions Asset reduces raw claims data missingness and errors so Life Sciences and Healthcare organizations can uncover strategic insights faster by answering questions like:\n- What chronic conditions diagnoses does a patient carry?\n- What are the affected body systems?\n- How many patients in a given Core Based Statistical Area (CBSA) have chronic conditions? How does that compare to other CBSAs?\n- Who are the top prescribers for a drug or therapy?\n- What is the population size in any market with a specific diagnosis?\n- Where are patients receiving care?\n- Which are the patients with specific comorbidities?\n- How can I see a patient's journey for a particular drug or condition?\n\n**Data Sources**\n- Kythera Labs curated, unified, and remastered claims source data from disparate vendor feeds that are de-identified, conformed, unified, enriched, and regularly updated.\n- Kythera Labs curated and maintained data directories.\n- Publicly available data directories.","terms_of_service":"https://www.kytheralabs.com/eula","documentation_link":"","privacy_policy_link":"https://www.kytheralabs.com/privacy","embedded_notebook_file_infos":[{"id":"75d185c6-c931-439c-ad98-298d132511cc","marketplace_file_type":"EMBEDDED_NOTEBOOK","file_parent":{"parent_id":"49328741-4798-4388-ae00-ca1fc8db2f42","file_parent_type":"LISTING"},"mime_type":"text/html","download_link":"https://marketplace-file-storage-prod.s3-fips.us-east-1.amazonaws.com/EMBEDDED_NOTEBOOK/LISTING_49328741-4798-4388-ae00-ca1fc8db2f42/75d185c6-c931-439c-ad98-298d132511cc?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEGgaDnVzLWVhc3QtMS1maXBzIkYwRAIgbC8sWIoYaHR8y7Wq86nPb0pufBl4%2BM4PWpeN%2B5PW9VQCIG7BhAVBDCWTpxFDLeuKYm%2Ff1JLz4iccPMzter0YdhgWKqYECDAQABoMNDE0MzUxNzY3ODI2IgxNAJrHbXaZG20jNfMqgwQyGEb4TsoVOoNvJ%2BaA%2FZ7zeu8hGcHqCeNfblP%2FQo5g6q2aRNtpwtPRKMlhYcIE%2FUQaNT1Lqmyptu32VUQTpzjTTxRVmJrDPkheSCkV06XQ%2FpTMKjU8CC7X1b%2FqYf8CrBwZT7Cv4C5WPjEET38f2A79h0xV6bZgPy28zQWrSeIJ9w6AfZBxiE24%2FVc08zVA0ivCT6lsy7nuU0wcR%2FMg2C9nd1vaMnQ923VpD21CKfVVYcrNWL2b%2FQ%2BNAO%2BxPjPgjiN6UPQbvbvZxkSdhgrGtecIg9NgqGkMcvM5VLTDtdC1kdeZetfOC2obtA%2BT%2F0gy3tIDo6WM5hcQfw743%2FAU9oaGnRBl%2BwoxRRDCZA%2F4jnPdluICSI7NCr6C3HHOe4CSZJjRihczxPA%2FgQczAVjAPs6CrK9N9pIzt0vNJpGUTgg4AoxftTvGfciDREilSctPIcBk6GwB%2Bzt4wbtNCmEPU%2BFD46rc6CWVQmmLwbKOeHw70ipMVFlOc8NlXkEtGO2vu6H44MstSSc6daOVUYUavQSGZ6jGhEjsP2YGz8KDm9XQotB%2FtyVBqiZV01OKqDmmBJA%2FXjUpSJ8M%2FeJXhe5m%2BKcxzNObtoJRI7jci3Tpt08XZyXZ7%2FyIV8H30ya468X3cfkTXrm972uga3z7iXAdubyEHnQIua%2FuvtwOHILi6O5D1Me2MzCPvcnXBjqTAYDoMWBt%2B3AVFuqSs2dmDQNBnvEhXfMgTedKLD5PJHb3aIl15iBkLaNu36GYnVCp9ZU2lSYcTbY5QwNnNeTfJTNy9bKhwzru6Sg%2BslhN%2BTS0zkTn%2BeLMY4tHgPEtEZ2cp%2FCk6GwqswvnYVKHjwO8q%2B1Mx8lc08fVEfarE9dke1GGFqoddx%2Bq%2BZUyHbDlAVfrCmf0yw%3D%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20261110T000135Z&X-Amz-SignedHeaders=host&X-Amz-Expires=86400&X-Amz-Credential=ASIAWA6KKHEJORWBH4N3%2F20261110%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=8874f7081aef6a2990e823e3e5dbf456a3120f1c6087fe02ea77f33008df56cc","created_at":1717943891872,"updated_at":1717943891872,"display_name":"Chronic Conditions - Kythera Product Overview","status":"FILE_STATUS_PUBLISHED"}],"assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"Kythera Labs Inc","description":"Kythera Labs is a healthcare data technology company that helps healthcare and life sciences organizations reduce uncertainty and make decisions with greater confidence. Through Wayfinder, our Databricks-powered platform, we transform fragmented healthcare data into higher-fidelity, analysis-ready assets that accelerate analytics, AI, and research.\n\nWayfinder integrates, remasters, and enriches claims, EHR, laboratory, prescription, and provider data to create longitudinal patient and provider views that improve accuracy, completeness, and usability. Using advanced data science and healthcare-specific methodologies, Kythera delivers trusted data products that help organizations uncover patient journeys, identify market opportunities, and generate actionable insights faster.\n\nKythera enables organizations to spend less time preparing data and more time finding answers."},"popularity":808},{"id":"95bba82f-0562-41b5-943f-5967cb3b4051","summary":{"name":"MapLab Enterprise","subtitle":"MapLab Enterprise provides a unified approach to accelerate Life Sciences & Healthcare decision-making","setting":{"visibility":"PUBLIC"},"created_at":1743430662251,"updated_at":1744836498190,"categories":["FINANCIAL","HEALTH","SCIENCE_AND_RESEARCH","OTHER"],"listingType":"PERSONALIZED","provider_id":"f77cbe35-afdb-4ddc-a9d7-dec83fe53c33"},"detail":{"description":"**MapLab Enterprise** is Komodo’s flexible analytics workspace, which empowers teams to explore, analyze, and operationalize real-world healthcare data—without friction. Built for scale, it combines direct access to Komodo’s Healthcare Map™ with a modern environment that supports everything from custom code to natural language queries and no-code dashboards.\n\nWhether you’re a data scientist building advanced models or a business stakeholder exploring outcomes trends, MapLab meets you where you are. Users can generate insights through familiar notebooks, accelerate discovery with AI-powered search and cohorting tools, and share results through dynamic, templated dashboards—no engineering support required.\n\nWith built-in governance, version control, and interoperability with tools like Databricks, MapLab streamlines the path from data to decision—helping organizations activate Komodo’s data asset with speed, confidence, and cross-functional alignment.\n\n**Key Value Highlights**:\n- **Analytics-ready from day one**: Clean, linked, and normalized data eliminates prep work so teams can start generating insights immediately\n- **Built to support complex, high-scale analytics**: Designed for custom workflows across billions of patient-level records\n- **Adaptable to any skill set**: Supports advanced coding, natural language interactions, and intuitive dashboards—enabling collaboration across business and technical users\n- **Enterprise-grade governance**: Ensures security, compliance, and traceability across teams and use cases\n\nMapLab Enterprise turns Komodo’s unmatched data asset into an execution engine—so you can go from data to delivery faster, more securely, and with greater precision.","terms_of_service":"https://www.komodohealth.com/terms-of-service/","documentation_link":"","privacy_policy_link":"https://www.komodohealth.com/privacy-notice/","geographical_coverage":"[\"United States and Canada\"]","cost":"PAID","data_source":"","assets":["ASSET_TYPE_DATA_TABLE","ASSET_TYPE_MEDIA"],"license":""},"provider_summary":{"name":"Komodo Health","description":"We’ve set the Evidentiary Standard with the industry’s most accurate, in-depth patient insights. Coupled with AI-enabled and fit-for-purpose software, we unlock a velocity advantage for our customers."},"popularity":1877},{"id":"e20294d3-2dee-490e-bd31-73332ea18fcb","summary":{"name":"Bloomberg Government Data License","subtitle":"Propel your business development strategy with the most comprehensive and accurate federal contracting data set.","setting":{"visibility":"PUBLIC"},"created_at":1687290296498,"updated_at":1698965635315,"categories":["COMMERCE","PUBLIC_SECTOR"],"listingType":"PERSONALIZED","provider_id":"636f9220-7e10-475d-94e8-b300c9daa4d8"},"detail":{"description":"**Overview**\n\nBloomberg Government Data License is one of the largest sources of quality federal government contracting opportunities and market intelligence data, designed to optimize the steps in the federal government contracting lifecycle. With over 31+ million government contracts updated daily and over 10 years’ worth of historical agency spend, Bloomberg Data license provides valuable context that helps federal contractors set current strategies and make long term business decisions. Based on historical market data, our machine learning algorithms (which are incorporated into this dataset) help predict the value of agency information, provide insights into which competitors might bid on the same project and the companies that might be best fits for partnership or acquisition.\n\n**Use cases**\n\nCustomers want our bulk data on federal contracting opportunities so they can incorporate/layer their proprietary data to perform analyses relevant to them. Without Data License, they are not able to have this comprehensive data set at their fingertips to: \n\n- Understand the market(s) they participate in, any new / adjacent market(s) they should start participating in, and the market(s) their competitors are in to inform their business strategy, investment decisions, and/or research and development direction. \n\n- Find relevant, open federal contracting opportunities available in their target market(s) that meet their specific requirements to build a pipeline of opportunities to pursue and win business. \n\n- Gain insight into where their competitors have expiring government contracts / licensing agreements that can be acquired. \n\n- Explore well-placed partner companies with that will help them win relevant federal contracting opportunities.\n- Use BGOV data to power self-service dashboards for use by business development, capture, and account team member. Enable them to get on-demand answers to frequently asked questions around agency spend, competitive landscapes, and potential partners.\n\n**Product details**\n\n16 data tables related to government contracting to fuel decision-making and resource allocation \n\n- Leading Indicators – Budget Data: Where is the government going to be making future investments? \n\n- “As Is” Indicators – Procurement / Solicitation Data: What does the government have an active requirement for a given solution / product / service? \n\n- Lagging Indicators – Historical Contracting Data: Where has the government spent money in the past and which companies won that work? \n\nFor more details, refer to the embedded notebook.\n\n**Key Customer Challenges**\n\n- Inability to track trends and forecast contracting opportunities: Customers want as much historical federal spending data as possible so that they can anticipate the final details of expected government agency budget allocations and priorities. The lack of easily accessible insights into past and current opportunities, combined with the dynamic nature of the government, makes it hard for government contractors to forecast and develop growth strategies for their business that align with government priorities. \n\n- Lack of Visibility into New and Adjacent Markets: Customers need to source opportunities in new / adjacent parts of the government where they do not have a footprint. They also want to make sure that they have not missed opportunities that they are well-placed to win – but have not been identified by their sales / business development teams. 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All Genes Related to Aging from The Human Dataset include a list of all genes related to aging in humans such as GenAge ID, symbol, aliases, name, Entrez gene id, SwissProt/UniProt, band, location start, location end, orientation, enzyme acetyl-CoA carboxylase promoter, orf, CDs, references and orthologs. Common Model Organisms in Aging as Human Homologs include a list of genes with a common ancestral DNA sequence with humans. Genes Related to Human Aging from Microarray Studies in Mammals include a database of genetic associations of gene expression signatures of aging from a meta-analysis of microarray studies in mammals. Genetic Dietary Restriction (GenDR) Expressions from Studies in Mammals dataset is an informational resource on genes related to dietary restriction (DR), the most powerful non-genetic intervention shown to extend lifespan in a wide range of model organisms. The Animal Ageing (AnAge) and Longevity Database is a database of longevity, aging, and life history in extant species employing the same engine of GenAge.\n\n**Benefits**\n- This data package can be useful for further gene research and genetic studies.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [All Genes Related to Aging from The Human Dataset](https://www.johnsnowlabs.com/marketplace/all-genes-related-to-aging-from-the-human-dataset)\n\t- This dataset of All Genes Related to Aging from The Human Dataset is essentially a list of all genes related to aging in humans. It also includes the GenAge ID, symbol, aliases, name, Entrez gene id, SwissProt/UniProt, band, location start, location end, orientation, enzyme acetyl-CoA carboxylase promoter, orf, CDs, references, and orthologs.\n- [Common Model Organisms in Aging as Human Homologs](https://www.johnsnowlabs.com/marketplace/common-model-organisms-in-aging-as-human-homologs)\n\t- This dataset of Common Model Organisms in Aging as Human Homologs is essentially a list of genes with a common ancestral DNA sequence with humans. It also includes a brief description of the species, symbol and model organism among others. For a gene to be featured, its association with aging and/or longevity must be unambiguous, and hence most genes were selected based on genetic manipulations and not mere correlations.\n- [Genes Associated With Longevity or Aging In Model Organisms](https://www.johnsnowlabs.com/marketplace/genes-associated-with-longevity-or-aging-in-model-organisms)\n\t- This dataset of genes associated with longevity or aging in model organisms is essentially a list of genes with one or a few key references, a brief description of the phenotype or effects of genetic manipulations of the gene, human homologues of the gene, and a few external links. For a gene to be featured, its association with aging and/or longevity must be unambiguous, and hence most genes were selected based on genetic manipulations and not mere correlations.\n- [Genes Related to Human Aging from Microarray Studies in Mammals](https://www.johnsnowlabs.com/marketplace/genes-related-to-human-aging-from-microarray-studies-in-mammals)\n\t- This dataset is a manually curated database of genetic associations of gene expression signatures of aging from a meta-analysis of microarray studies in mammals that reflect current knowledge of the genetics of human aging and longevity. This dataset comes from the GenAge (Genetic Aging) section of the Human Ageing Genomic Resources (HAGR) which is a repository containing information about the genetics of human aging.\n- [Genetic Dietary Restriction Expressions from Studies in Mammals](https://www.johnsnowlabs.com/marketplace/genetic-dietary-restriction-expressions-from-studies-in-mammals)\n\t- Genetic Dietary Restriction (GenDR) Expressions from Studies in Mammals dataset is an informational resource on genes related to dietary restriction (DR), the most powerful non-genetic intervention shown to extend lifespan in a wide range of model organisms. Specifically, this dataset includes a manually curated list of a conserved molecular signature of DR generated genes from a meta-analysis of DR-differentially expressed genes in mammals (\"Gene expression\").\n- [Genetic Dietary Restriction Gene Manipulations Database](https://www.johnsnowlabs.com/marketplace/genetic-dietary-restriction-gene-manipulations-database)\n\t- Genetic Dietary Restriction (GenDR) Gene Manipulations Database is an informational resource on genes related to dietary restriction (DR), the most powerful non-genetic intervention shown to extend lifespan in a wide range of model organisms. Specifically, this dataset includes a manually curated list of DR-essential genes (\"Gene manipulations\").\n- [Human Genes Commonly Altered During Aging](https://www.johnsnowlabs.com/marketplace/human-genes-commonly-altered-during-aging)\n\t- This dataset is a manually curated database of the commonly altered genes and their common names during human aging. This dataset is part of the study of genetic associations of gene expression signatures of aging from a meta-analysis of microarray studies in various mammals. This dataset comes from the GenAge (Genetic Aging) section of the Human Ageing Genomic Resources (HAGR) which is a repository containing information about the genetics of aging.\n- [Human Genetic Variants Associated With LongevityMap](https://www.johnsnowlabs.com/marketplace/human-genetic-variants-associated-with-longevitymap)\n\t- This dataset is a manually curated database of genetic association studies of longevity and reflects current knowledge of the genetics related to human longevity.\n- [The Animal Aging and Longevity Database](https://www.johnsnowlabs.com/marketplace/the-animal-aging-and-longevity-database)\n\t- The Animal Ageing (AnAge) and Longevity Database is a database of longevity, aging, and life history in extant species employing the same engine of GenAge. The most important trait in this dataset is maximum longevity (also called maximum lifespan) because it is the most widely used parameter for comparing the rate of aging between species.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated\n- Data Updates\n - Data updates support replace-on-update: outdated foreign keys are deprecated, not deleted\n\n**Our data is curated and enriched by domain experts** \n\n Each dataset is manually curated by our team of doctors, pharmacists, public health & medical billing experts:\n- Field names, descriptions, and normalized values are chosen by people who actually understand their meaning\n- Healthcare & life science experts add categories, search keywords, descriptions and more to each dataset\n- Both manual and automated data enrichment supported for clinical codes, providers, drugs, and geo-locations\n- The data is always kept up to date – even when the source requires manual effort to get updates\n- Support for data subscribers is provided directly by the domain experts who curated the data sets\n- Every data source’s license is manually verified to allow for royalty-free commercial use and redistribution.\n\n**Need Help?**\nIf you have questions about our products, contact us at [info@johnsnowlabs.com](mailto:info@johnsnowlabs.com). \n 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By combining insights on hiring intensity, job postings, technology adoption, outsourcing engagements, and leadership movements, this dataset enables HR, workforce planning, and talent intelligence teams to benchmark against competitors and identify areas of talent competition.\nThe dataset empowers decision-makers to refine recruitment strategies, anticipate skill shortages, and monitor workforce signals at both company and regional levels. From assessing IT spend linked to workforce expansion, to understanding company priorities and leadership changes, the data offers granular visibility into the dynamics shaping talent competition. This intelligence supports evidence-based workforce planning, peer benchmarking, and proactive responses to shifts in the global talent market.\n\n**Methodology & Quality**\nThe dataset is built from a blend of public filings, company reports, job postings, outsourcing announcements, press releases, and verified third-party sources. All data undergoes rigorous normalization and entity resolution to maintain consistency across companies, regions, and business units. Updates are applied on a recurring schedule to capture near real-time shifts in talent activity, hiring intensity, and competitor strategies. Standardization of fields—such as job roles, skill sets, financials, and priorities—ensures comparability across industries, while bias reduction processes minimize over-representation of certain geographies or sectors.\n\n**Key Tables Include**\n•\tCompany Overview – Core company information, including size, verticals, workforce, and R&D intensity.\n•\tCompany Financials – Annual and quarterly financial metrics to link hiring activity with business performance.\n•\tFinancials by Business Unit & Region – Revenue and investments segmented by functions and geographies.\n•\tIT Spend by Category – Insights into technology spending that drive workforce needs and talent demand.\n•\tCompany Priorities & Use Cases – Stated organizational priorities mapped to business functions and workloads.\n•\tTechnology Stack – Evidence of product usage and intensity across workloads and locations.\n•\tCompany Locations & Headcount – Distribution of talent across sites, including workforce size and workload focus.\n•\tOutsourcing Engagements – Service provider relationships, outsourced workloads, skills involved, and project intensity.\n•\tLeadership Movements – Tracking of executive shifts by business function, location, and movement type.\n•\tJob Postings Data – Standardized job roles, required skills, pay, and benefits, tied to competitor hiring activity.\n•\tMarket Signals – News and updates on workforce shifts, expansion, and talent-linked corporate signals.\n•\tCompetitor Mapping – Direct competitor listings with sectoral and vertical benchmarking.\n•\tCorporate Filings & Transcripts – Regulatory filings, shareholder data, and transcripts that highlight talent-related priorities.\n\n**Use Cases:**\n***Find companies that recently laid off employees in the last 3 months***\t\n\nTrack companies/verticals/business functions within companies that have layed off employees in the past 3 months using Draup’s workforce signals. 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INTERVAL '6' MONTH\nGROUP BY company_overview.company_name\nORDER BY total_data_science_openings DESC\nLIMIT 20;\n\n***Compare the hiring trend for Cybersecurity job roles between Palo Alto Networks & Crowdstrike in the last 12 months***\t\n\nUse Draup’s role-level hiring trend analytics to compare year-over-year or recent quarter hiring intensity across two or more peer companies.\t\n\nSELECT\n TO_CHAR(job_postings_company.date_posted, 'YYYY-MM') AS month,\n job_postings_company.company_name,\n unnest_roles.role,\n COUNT(*) AS postings\nFROM job_postings_company\nCROSS JOIN LATERAL UNNEST(job_postings_company.draup_job_role) AS unnest_roles(role)\nWHERE\n job_postings_company.company_name IN ('Palo Alto Networks', 'CrowdStrike')\n AND job_postings_company.date_posted >= (CURRENT_DATE - INTERVAL '12 months')\n AND (\n LOWER(unnest_roles.role) LIKE 'cyber%' OR\n LOWER(unnest_roles.role) LIKE '%network%'\n )\nGROUP BY\n TO_CHAR(job_postings_company.date_posted, 'YYYY-MM'),\n job_postings_company.company_name,\n unnest_roles.role\nORDER BY\n month DESC,\n postings DESC;\n\n***Find out what and where is your competitor hiring right now***\t\n\nMonitor competitor job postings by role, location, and skills to understand their hiring strategy and identify direct talent competition.\t\" \n\nWITH RecentMicrosoftPostings AS (\n SELECT\n draup_job_role,\n tech_stack,\n core_skills,\n consolidated_JDs\n FROM\n job_postings_company\n WHERE\n company_name = 'Microsoft' AND date_posted >= CURRENT_DATE - INTERVAL '1' MONTH\n)\nSELECT\n unnested_roles.role AS draup_job_role,\n unnested_locations.msa,\n unnested_locations.country,\n COUNT(*) AS total_openings\nFROM\n RecentMicrosoftPostings\nCROSS JOIN UNNEST(draup_job_role) AS t1(unnested_roles)\nCROSS JOIN UNNEST(consolidated_JDs) AS t2(unnested_locations)\nGROUP BY\n unnested_roles.role,\n unnested_locations.msa,\n unnested_locations.country\nORDER BY\n total_openings DESC;\n\n***See how does your workforce distribution by function compare with peers.***\t\n\nCompare workforce split across business functions with that of competitors to benchmark organizational structure and identify over/under-invested areas.\t\n\nWITH YourCompanyCompetitors AS (\n SELECT\n competitors_name\n FROM\n company_competitors\n WHERE\n company_name = 'Microsoft'\n),\nAllRelevantJobPostings AS (\n SELECT\n company_name,\n unnested_functions.function_name\n FROM\n job_postings_company\n CROSS JOIN UNNEST(business_function) AS t(unnested_functions)\n WHERE\n company_name = 'Microsoft'\n OR company_name IN (SELECT competitors_name FROM YourCompanyCompetitors)\n)\nSELECT\n company_name,\n function_name,\n COUNT(*) AS postings_count,\n CAST(COUNT(*) AS DOUBLE) * 100 / SUM(COUNT(*)) OVER (PARTITION BY company_name) AS percentage_share\nFROM\n AllRelevantJobPostings\nGROUP BY\n company_name,\n function_name\nORDER BY\n company_name,\n percentage_share DESC;\n\n***Track what leadership movements are happening in an industry***\t\n\nTrack senior executive shifts across competitor organizations to stay updated on leadership priorities and potential organizational changes.\t\n\nWITH UnnestedCompanyVerticals AS (\n SELECT\n company_overview.company_id,\n unnested_vertical.vertical\n FROM\n company_overview\n CROSS JOIN UNNEST(company_overview.primary_vertical) AS unnested_vertical\n)\nSELECT\n company_leadership_movement.company_name,\n company_leadership_movement.executive_name,\n company_leadership_movement.from_title,\n company_leadership_movement.to_title,\n company_leadership_movement.movement_type,\n company_leadership_movement.movement_date\nFROM\n company_leadership_movement\nJOIN\n UnnestedCompanyVerticals\n ON company_leadership_movement.company_id = UnnestedCompanyVerticals.company_id\nWHERE\n UnnestedCompanyVerticals.vertical = 'Professional Services'\n AND company_leadership_movement.movement_date >= DATE_SUB(CURRENT_DATE(), INTERVAL 1 YEAR)\nORDER BY\n company_leadership_movement.movement_date DESC;\n\n***Identify what technologies are your peers using across different workloads.***\t\n\nAnalyze competitor tech stacks segmented by workload (CRM, Cloud, Productivity, etc.) to understand adoption patterns and emerging technology choices.\t\n\nSELECT\n company_leadership_movement.company_name,\n company_leadership_movement.executive_name,\n company_leadership_movement.from_title,\n company_leadership_movement.to_title,\n company_leadership_movement.movement_type,\n company_leadership_movement.movement_date\nFROM\n company_leadership_movement\nJOIN\n company_overview\n ON company_leadership_movement.company_id = company_overview.company_id\nWHERE\n ARRAY_CONTAINS(company_overview.primary_vertical, 'Professional Services')\n AND company_leadership_movement.movement_date >= CURRENT_DATE - 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By combining unparalleled real-time global data with agentic AI, Draup enables strategic decision-making across talent and revenue functions.\n \nThrough its Talent Intelligence solution, Draup empowers HR teams to drive workforce planning, precision hiring, predictive skill architecture, and competitive benchmarking. Its Sales Intelligence solution helps go-to-market teams uncover deep account insights, identify buying triggers, and accelerate revenue growth.\n \nTrusted by over 300 global enterprises - including five of the Fortune 10 - Draup supports companies like Microsoft, PepsiCo, PayPal, and Moderna in building future-ready workforces and growth strategies."},"popularity":1362},{"id":"19f75482-e25b-4390-8afb-c71d0ce64a97","summary":{"name":"Geo Data Connector MCP Server","subtitle":"Discover and ingest geospatial data from thousands of map services into Unity Catalog, straight from your AI agent.","setting":{"visibility":"PUBLIC"},"created_at":1789379371960,"updated_at":1790197205566,"categories":["CLIMATE_AND_ENVIRONMENT","GEOSPATIAL","FINANCIAL","ENERGY","DEMOGRAPHICS","RETAIL","SCIENCE_AND_RESEARCH","TRANSPORTATION_AND_LOGISTICS","PUBLIC_SECTOR"],"listingType":"STANDARD","provider_id":"02369e84-cfa9-46ba-9d82-ee6d1dae7845"},"detail":{"description":"**Geo Data Connector (GDC)** by Smart Data Hub lets AI agents working in Databricks discover and ingest geospatial data from thousands of public and proprietary map and coverage services worldwide — straight from a conversation.\n\n**How it works with Databricks**\n\nDatabricks gives AI agents a powerful home — the AI Playground, Genie, and the Assistant for natural-language work, Unity Catalog for governed data, elastic processing power at scale, and native geospatial capabilities for spatial analytics. Geo Data Connector complements this as an **external MCP service with seamless interoperability**: an agent working in Databricks calls out to GDC to discover the right source among thousands and ingest it — scoped to just the area and resolution needed — delivering analysis-ready geometry ready to load into Unity Catalog. GDC does the heavy geospatial data engineering — source discovery, extraction, CRS standardization, and H3 indexing. GDC handles the geospatial acquisition; Databricks brings the processing power, native geospatial analytics, intelligence, and governance — so joint customers go from a plain-language question to a spatial answer in minutes.\n\n**What your agent can do**\n\nGeo Data Connector exposes 13 tools:\n\n- `search_datasets` — search geospatial datasets across sources worldwide.\n- `get_dataset_info` — explore a geospatial data source and its datasets.\n- `create_subscription` — subscribe to a dataset and ingest it (optionally scoped to an area/resolution); waits until ingestion finishes.\n- `list_subscriptions` — list your dataset subscriptions and their ingestion status.\n- `get_subscription` — get one subscription's status, details and recent runs.\n- `run_ingestion` — re-run ingestion for an existing subscription.\n- `wait_for_ingestion` — continue waiting for an in-progress ingestion run to finish.\n- `list_downloads` — list download files for a subscription's latest completed ingestion.\n- `pause_subscription` — pause a subscription (stops future ingestion; reversible).\n- `resume_subscription` — resume a paused subscription.\n- `get_analysis_guidance` — GDC's how-to tool: recipes for landing ingested data and analyzing it.\n- `whoami` — show your GDC identity, plan and current usage.\n- `start_trial` — get access to Geo Data Connector without an account.\n\n**Industry use cases**\n\n- **Forestry and land management.** An analyst asks which forest stands in a management area have changed since the last planning round. The agent uses GDC to discover a national forest-resource dataset and ingest the stand polygons scoped to the management area, and the analyst lands the result in Unity Catalog to compare against harvest plans with Databricks geospatial functions.\n- **Environmental monitoring.** A team needs to know which protected habitats lie near a planned infrastructure corridor. The agent discovers protected-area and habitat datasets published by environmental agencies, ingests them scoped to the corridor, and the team runs the proximity analysis against the route geometry in Databricks.\n- **Insurance and risk.** A risk analyst asks which insured properties fall within mapped flood hazard zones. The agent discovers the national flood-hazard dataset, ingests it scoped to the operating region, and the analyst joins it with the insured-portfolio table in Unity Catalog to quantify exposure.\n- **Real estate and urban planning.** A planner asks what land-use zoning applies around candidate development sites. The agent discovers the municipal zoning and land-use plan datasets, ingests them scoped to the city, and the planner overlays parcels and zoning in Databricks to shortlist compliant sites.\n- **Agriculture.** An agronomist asks what soil types cover a set of contracted parcels. The agent discovers a soil-survey dataset, ingests it scoped to the parcels' extent at the resolution needed, and the agronomist combines it with yield history in Databricks to plan inputs per parcel.\n- **Utilities.** A network analyst asks which grid assets sit close to watercourses or unstable ground. The agent discovers national terrain and hydrography datasets, ingests them scoped to the service area, and the analyst runs the proximity screen against the asset register in Databricks.\n\n**Getting started**\n\n1. Install the MCP server from this listing.\n2. Open the installed MCP service and click **Login** to sign in with your email. If you do not have a Geo Data Connector account yet, one is created for you automatically during sign-in.\n3. Enable the MCP service for Genie, AI Playground, or your agent.\n\nPrefer to explore without signing in? From any MCP client, your agent can call the `start_trial` tool for instant trial access with no account.\n\nFull connection instructions and tool reference:\n[Geo Data Connector MCP Server documentation](https://www.smartdatahub.io/mcp).\n\n**Third-party data responsibility**\n\nGeo Data Connector provides access to third-party data sources solely as a technical facilitator. 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By combining **LLM-powered agents, Streamlit-based control workflows and Databricks-native execution**, the framework transforms raw data into governed, business-ready insights with minimal manual effort.\n\nWith intelligent metadata enrichment, automated SQL generation and one-click deployment across Silver and Gold layers, the accelerator reduces engineering effort by up to **60%**, while improving **data quality, standardization, lineage and trust** across the Lakehouse.\n\n**Business Challenge**\n\nOrganizations building analytics on Databricks commonly face the following challenges:\n\n1. **Poor or Missing Metadata**\n\nTables and columns often lack meaningful descriptions, making datasets hard to discover, understand and govern. Manual documentation is time-consuming, inconsistent, and rarely maintained.\n\n2. **Manual Medallion Layer Engineering**\n\nCreating L1 views, Silver curated tables and Gold analytics requires repetitive SQL development, naming standardization, constraint handling and deployment orchestration—slowing down delivery and increasing errors.\n\n3. **Lack of Governance in Automation**\n\nPure automation risks incorrect assumptions, while manual validation slows progress. Teams need **AI-driven acceleration with human control**, not black-box pipelines.\n\n**Architecture Overview – Medallion + Agentic Orchestration**\n**Silver Layer 1 (L1)**\n\nStandardized relational views generated automatically from source schemas, enriched with metadata and deployed using automated Databricks jobs.\n\n**Silver Layer 2 (L2)**\n\nCurated Delta Live Tables built using agent-generated DLT SQL with primary keys, SCD logic, constraints, audit columns and data quality rules.\n\n**Gold Layer**\n\nConversational analytics layer where users describe business requirements in natural language and agents generate optimized SQL models accordingly.\n\n**Agentic Control Plane**\n\nA Streamlit-based application orchestrates configuration, review, approval, generation and deployment—bridging AI automation and enterprise governance.\n\n**Pipeline Flow**\n**Metadata Enrichment Agent (Foundation Layer)**\n\nThe pipeline begins with LLM-driven metadata enrichment, applied at both table and column levels.\n\n**How It Works** \n\n- **The agent scans selected tables and schemas.** \n- **It generates intelligent descriptions for:**\n\n1. **Tables** \n2. **Columns** \n3. **Semantic relationships** \n\n**Before persisting any metadata:**\n- **AI-generated comments are presented to users for review.**\n- **Users can approve, edit or refine descriptions.**\n- **Once approved, metadata is saved back to the catalog.**\n\n**Key Capabilities**\n\n- **Automatic schema documentation**\n- **Semantic enrichment using LLM reasoning**\n- **Human-in-the-loop (HITL) validation for governance**\n- **Improved discoverability and trust in Unity Catalog**\n- **This ensures metadata accuracy without sacrificing control, forming a reliable foundation for downstream Silver and Gold layers.**\n\n**Silver SQL Generator Agent (L1 → L2)**\n**After metadata enrichment:**\n**L1 (Silver Layer 1)**\n\n- **Standardized SQL views are generated automatically.**\n- **Naming conventions are enforced (UPPER_SNAKE_CASE, numeric handling).**\n- **Metadata and audit columns are added.**\n- **SQL scripts are stored in a configurable folder.**\n\n**A single-click Deploy SQL Runner Job:**\n\n- **Creates a Databricks Job**\n- **Executes all SQL files**\n- **Publishes views into the target catalog**\n\n**L2 (Silver Layer 2)**\n**The agent generates Delta Live Tables (DLT) SQL by identifying:**\n\n- **Primary keys**\n- **Sequence columns**\n- **Delete flags**\n- **Data quality constraints (e.g., NOT NULL rules)**\n\n**Generated DLT SQL includes:**\n\n- **Streaming source view definitions**\n- **Target table schemas with constraints**\n- **SCD Type 1 apply-changes logic**\n- **Z-Ordering**\n- **Audit columns**\n\n**The application automatically:**\n\n- **Creates a DLT pipeline**\n- **Executes all generated DLT SQL**\n- **Publishes curated Silver L2 tables**\n- **Conversational Gold Agent**\n\n **The Gold layer is driven by a conversational interface.**\n\n- **Users describe business requirements in natural language.** \n\n**The agent:**\n\n- **Clarifies intent** \n- **Generates complete Gold-layer SQL** \n- **Applies consistent logic and optimizations** \n- **SQL is production-ready and aligned with upstream Silver models.** \n- **This enables analytics teams to move from questions to datasets, without writing complex SQL.** \n\n**Key Capabilities** \n\n- **LLM-Driven Metadata Enrichment with HITL governance** \n- **Automated L1, L2 and Gold SQL Generation** \n- **DLT Pipeline Creation and Execution** \n- **One-Click Deployment via Databricks Jobs** \n- **Conversational Analytics Modeling** \n- **Enterprise Naming, Quality and Audit Standards** \n- **Unity Catalog Lineage & Governance Alignment**\n\n**Marketplace Deliverables** \n\n- **Streamlit-based Agentic Control Application** \n- **Production-ready SQL and DLT templates** \n- **Automated Databricks Jobs & DLT Pipelines** \n- **Configuration-driven metadata and layer generation** \n- **End-to-end implementation documentation** \n\n**Why Choose This Agentic ELT Accelerator** \n\n- **True End-to-End Medallion Automation** \n- **AI Acceleration with Human Control** \n- **Faster Time-to-Value** \n- **Governed, Repeatable and Scalable** \n **Built Natively for Databricks Lakehouse** \n\nNote: By clicking '**Get Access**', you agree to our **Terms of Service**. You acknowledge that **Dataplatr Corp** will recognize your organization as a customer/user for promotional and reference purposes.","terms_of_service":"https://docs.google.com/forms/d/e/1FAIpQLSdnlNmSfrFRQDa_ToOL3ydTOPcWI3m3lWor2plLbiqF1JRINg/viewform","documentation_link":"","privacy_policy_link":"https://dataplatr.com","embedded_notebook_file_infos":[{"id":"c7fc473f-b826-4ad2-a88c-e03289d1a7ca","marketplace_file_type":"EMBEDDED_NOTEBOOK","file_parent":{"parent_id":"ee13ed64-bde8-4ea5-bfd9-6e4457e0dad0","file_parent_type":"LISTING"},"mime_type":"text/html","download_link":"https://marketplace-file-storage-prod.s3-fips.us-east-1.amazonaws.com/EMBEDDED_NOTEBOOK/LISTING_ee13ed64-bde8-4ea5-bfd9-6e4457e0dad0/c7fc473f-b826-4ad2-a88c-e03289d1a7ca?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEGgaDnVzLWVhc3QtMS1maXBzIkYwRAIgbC8sWIoYaHR8y7Wq86nPb0pufBl4%2BM4PWpeN%2B5PW9VQCIG7BhAVBDCWTpxFDLeuKYm%2Ff1JLz4iccPMzter0YdhgWKqYECDAQABoMNDE0MzUxNzY3ODI2IgxNAJrHbXaZG20jNfMqgwQyGEb4TsoVOoNvJ%2BaA%2FZ7zeu8hGcHqCeNfblP%2FQo5g6q2aRNtpwtPRKMlhYcIE%2FUQaNT1Lqmyptu32VUQTpzjTTxRVmJrDPkheSCkV06XQ%2FpTMKjU8CC7X1b%2FqYf8CrBwZT7Cv4C5WPjEET38f2A79h0xV6bZgPy28zQWrSeIJ9w6AfZBxiE24%2FVc08zVA0ivCT6lsy7nuU0wcR%2FMg2C9nd1vaMnQ923VpD21CKfVVYcrNWL2b%2FQ%2BNAO%2BxPjPgjiN6UPQbvbvZxkSdhgrGtecIg9NgqGkMcvM5VLTDtdC1kdeZetfOC2obtA%2BT%2F0gy3tIDo6WM5hcQfw743%2FAU9oaGnRBl%2BwoxRRDCZA%2F4jnPdluICSI7NCr6C3HHOe4CSZJjRihczxPA%2FgQczAVjAPs6CrK9N9pIzt0vNJpGUTgg4AoxftTvGfciDREilSctPIcBk6GwB%2Bzt4wbtNCmEPU%2BFD46rc6CWVQmmLwbKOeHw70ipMVFlOc8NlXkEtGO2vu6H44MstSSc6daOVUYUavQSGZ6jGhEjsP2YGz8KDm9XQotB%2FtyVBqiZV01OKqDmmBJA%2FXjUpSJ8M%2FeJXhe5m%2BKcxzNObtoJRI7jci3Tpt08XZyXZ7%2FyIV8H30ya468X3cfkTXrm972uga3z7iXAdubyEHnQIua%2FuvtwOHILi6O5D1Me2MzCPvcnXBjqTAYDoMWBt%2B3AVFuqSs2dmDQNBnvEhXfMgTedKLD5PJHb3aIl15iBkLaNu36GYnVCp9ZU2lSYcTbY5QwNnNeTfJTNy9bKhwzru6Sg%2BslhN%2BTS0zkTn%2BeLMY4tHgPEtEZ2cp%2FCk6GwqswvnYVKHjwO8q%2B1Mx8lc08fVEfarE9dke1GGFqoddx%2Bq%2BZUyHbDlAVfrCmf0yw%3D%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20261110T000135Z&X-Amz-SignedHeaders=host&X-Amz-Expires=86400&X-Amz-Credential=ASIAWA6KKHEJORWBH4N3%2F20261110%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=bda0813148ad614d73336b11f0538617f83054fc4d680cf53ecfdc23de23ccfc","created_at":1771913773666,"updated_at":1771913773666,"display_name":"AELT_Automation_Suite","status":"FILE_STATUS_PUBLISHED"}],"assets":["ASSET_TYPE_MEDIA"],"license":""},"provider_summary":{"name":"Dataplatr Corp","description":"Dataplatr Corp specializes in Data Analytics & AI with pre-built data models for Enterprise Applications like SAP, Oracle EBS, Workday, Salesforce to empower businesses to unlock the full potential of their data. Our enterprise data engineering accelerators are designed to expedite the development of data pipelines, data transformation, and integration, saving you time and resources.\n\nOur team of experienced data engineers, scientists and analysts utilize cutting-edge data infrastructure into valuable insights and helps enterprise clients optimize their Sales, Marketing, Operations, Financials, Supply chain, Human capital and Customer experiences."},"popularity":1753},{"id":"9df06f16-e14f-430a-88f9-a842d8ae9323","summary":{"name":"Taskmaster OmniVox — Indic ASR Corpus - Full Dataset","subtitle":"1,475-hour multilingual Indic ASR corpus with 805K records across 12 languages for speech recognition research.","setting":{"visibility":"PUBLIC"},"created_at":1791026038780,"updated_at":1791026038780,"categories":["EDUCATION","MEDIA","SCIENCE_AND_RESEARCH","OTHER"],"listingType":"PERSONALIZED","provider_id":"51e1bafd-2b96-40fb-b5bd-e9d3531279e9"},"detail":{"description":"**Overview**\n\nTaskmaster OmniVox — Indic ASR Corpus-Full Dataset is a large-scale multilingual conversational speech corpus designed for automatic speech recognition (ASR), speech-to-text development, multilingual speech research, and speech recognition evaluation across 12 Indic languages.\n\nThe validated corpus contains **805,745 records across 365 Parquet files**, with a total validated duration of **1,475.444763733 hours**. The dataset provides transcription text, audio payload/reference information, duration, language, gender, and speaker metadata in a consistent seven-field schema.\n\nThe corpus is provided under the **CC BY 4.0** license and is available by request as the full-data offering associated with the free Marketplace sample.\n\n**Use cases**\n\n- Automatic speech recognition (ASR) model development and training\n- Speech-to-text system development and evaluation\n- Multilingual and cross-lingual speech research\n- Indic language speech technology development\n- Speech recognition benchmarking\n- Acoustic and linguistic model research\n- Conversational speech analysis\n- Speaker and speech metadata analysis\n- ASR preprocessing and data-pipeline development\n- Evaluation of multilingual speech recognition systems\n\n**Product details**\n\n- The dataset contains **365 Parquet files** representing conversational speech data across 12 Indic languages.\n- The 12 languages represented are Bengali, Gujarati, Hindi, Kannada, Malayalam, Marathi, Odia, Punjabi, Sanskrit, Tamil, Telugu, and Urdu.\n- The corpus contains **805,745 records** with **1,475.444763733 validated hours** of speech data.\n- The consistent dataset schema contains seven fields: `fname`, `text`, `audio_filepath`, `lang`, `duration`, `gender`, and `speaker_id`.\n- `text` contains the corresponding transcription text for each speech record.\n- `audio_filepath` is a structured field containing `bytes` and `path`.\n- `duration` contains the duration of each speech record in seconds.\n- `lang` identifies the language associated with each record.\n- `gender` provides speaker gender metadata.\n- `speaker_id` provides a speaker identifier for each record.\n\nFor more details, refer to the embedded notebook.\n\n**Additional Insights**\n\nThe full dataset is the expanded offering associated with the free **Taskmaster OmniVox — Indic ASR Corpus** Marketplace sample. The free sample provides approximately five hours of Gujarati and Tamil data for evaluation before requesting access to the complete corpus.\n\nThe validated full-corpus duration is **1,475.444763733 hours**. An earlier reference of approximately 1,684 hours is not used as the current validated corpus duration.\n\nThe dataset is provided under the **Creative Commons Attribution 4.0 International (CC BY 4.0)** license. Users should retain appropriate attribution to the underlying Indic ASR/Kathbath dataset and associated research work when using or redistributing the data.\n\nSuggested research citation:\n\n> Javed, T., Bhogale, K. S., Raman, A., Kunchukuttan, A., Kumar, P., & Khapra, M. M. (2022). *IndicSUPERB: A Speech Processing Universal Performance Benchmark for Indian Languages*.\n\nThe free sample and full dataset use the same validated seven-field schema, enabling users to prototype and evaluate workflows against the sample before working with the complete corpus.","terms_of_service":"https://docs.google.com/document/d/13yOhwgIcZaG4BE-4urbsLiGioD0ov3GNMmwe3tfunCE/edit?usp=sharing","documentation_link":"https://docs.google.com/document/d/1TBDihslhSxnDzVlbd2GvfLpqCosXVKCiAouCfTCRriw/edit?usp=sharing","privacy_policy_link":"https://taskmastervirtualally.com/privacy-statement","assets":["ASSET_TYPE_MEDIA"],"license":"https://docs.google.com/document/d/13yOhwgIcZaG4BE-4urbsLiGioD0ov3GNMmwe3tfunCE/edit?usp=sharing"},"provider_summary":{"name":"Taskmaster Virtual Ally LLC","description":"Taskmaster Virtual Ally solves the AI Data Readiness Gap with commercially cleared speech, vision, and NLP datasets alongside managed Human Infrastructure as a Service (HIaaS) Pods. 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Operating on a strict zero-data-retention model within your Databricks governance boundary, our dedicated 2-2-1 Data Pods integrate directly via Delta Sharing to curate, clean, and benchmark enterprise AI training data with 99%+ data fidelity."},"popularity":264},{"id":"865a75f1-1c7c-407d-bc18-2c15fcfcf483","summary":{"name":"D&B Finance Analytics","subtitle":"Commercial credit decisioning and portfolio risk workflows for AI","setting":{"visibility":"PUBLIC"},"created_at":1781301908411,"updated_at":1786124907392,"categories":["FINANCIAL"],"listingType":"STANDARD","provider_id":"75c8a3e7-57c7-468f-a194-da84b0b78bfb"},"detail":{"description":"**Enable AI-powered credit decisioning, portfolio monitoring, and risk management workflows using trusted D&B data and analytics.**\n\n*For inquiries, please contact : databricks@dnb.com.* \n\nD&B Finance Analytics empowers finance and credit teams to verify businesses, evaluate financial and predictive risk, accelerate credit decisions, monitor portfolio health, and take action through AI assistants, agents, and enterprise applications. By combining trusted D&B business intelligence, financial risk insights, predictive analytics, and workflow capabilities, organizations can bring credit and portfolio management processes directly into AI-driven workflows. \nAnchored by the global standard D U N S® Number and powered by the D&B Commercial Graph™, D&B Finance Analytics helps organizations reduce ambiguity, improve consistency, and make more confident commercial credit decisions at scale. It also enables teams to move from insight to action through portfolio management and workflow capabilities available within D&B Finance Analytics. \n\n**Use D&B Finance Analytics to:**\n\n**Evaluate credit recommendation:**\n\"What is the recommended credit limit for [Company Name], and what factors contributed to that recommendation?\" \n\n**Assess business risk:**\n\"Summarize the financial risk, payment behavior, and predictive risk indicators for [Company Name].\" \n\n**Monitor portfolio health:**\n\"Which customers in my portfolio show elevated risk or signs of deterioration?\" \n\n**Prioritize risk management activities:**\n\"Which customers should I review first based on recent alerts, exposure, and risk changes?\" \n\n**Analyze ownership and corporate linkage:** \n\"Show the ownership structure and related entities for [Company Name].\" \n\n**Take action on portfolio:**\n\"Create a folder named [Folder Name], move [Company Name] to the folder and assign Alert Profile [Alert Profile Name] to the folder.\" \n \n*Note: Requires a D&B Finance Analytics license.*","terms_of_service":"https://view.highspot.com/viewer/1c5acd1db08e2cc64faff50703335a1d","documentation_link":"https://view.highspot.com/viewer/403069bfda32744dedd7380b7c234c35#1","privacy_policy_link":"https://www.dnb.com/about-us/data-transparency.html","assets":["ASSET_TYPE_MCP"],"license":""},"provider_summary":{"name":"Dun & Bradstreet","description":"Dun & Bradstreet delivers the world’s most comprehensive business data and analytical insights to power today’s crucial business needs. That’s why 90% of the Fortune 500, and companies of all sizes around the world, rely on Dun & Bradstreet to help grow and protect their businesses. "},"popularity":2041},{"id":"8720116a-3dfe-4b32-b630-59504512d9ae","summary":{"name":"Geolocet | Demographic Data Germany","subtitle":"Demographic data for the Germany - age, gender, and gender crossed with age.","setting":{"visibility":"PUBLIC"},"created_at":1705910803335,"updated_at":1752138914587,"categories":["DEMOGRAPHICS","ADVERTISING_AND_MARKETING","GEOSPATIAL","RETAIL","PUBLIC_SECTOR"],"listingType":"PERSONALIZED","provider_id":"5d3ddc17-a7e8-4794-a760-13587e1de2c6"},"detail":{"description":"## **Get instant access to German Demographics Data with** [Geolocet](https://geolocet.com)\n\nExplore the richness of Germany's demographic landscape with our Germany Census Data. This meticulously compiled dataset, sourced from official statistical sources, including Federal Statistical Office, offers a detailed insight into the population landscape of Germany at the municipality (gemeinden) level. \n\nIf you require data at a less granular level, such as an Uber grid or square grid, please don't hesitate to get in touch with us at *contact@geolocet.com*. We understand that different projects may have varying data needs, and we are here to tailor our data to match your specific requirements.\n\n ## **Key Features:**\n---\n* **Population Data Germany:** Gain access to the latest and most accurate population statistics for Germany, meticulously organized by municipality (gemeinden).\n\n* **Demographic Insights:** Dive deep into Germany's demographic landscape with data on 54 attributes, including: age, gender, and gender crossed with age, all available at the municipality (gemeinden) level.\n\n* **Geodemographic Insights:** Unlock geodemographic data Germany-wide to inform location-based decision-making, marketing strategies, and policy formulation.\n\n* **Ready to use:** Our dataset is currently available in CSV format, providing ease of use and compatibility with a wide range of data analysis tools. However, if you prefer a different format, our team is readily available to assist you in obtaining the data in your desired format.\n---\nAdditionally, for those looking to enrich their analysis or project, we offer:\n* **[Administrative Boundaries](https://geolocet.com/products/germany-all-available-administrative-boundaries):** If you require geographical boundaries (polygons) for German municipalities, consider exploring our Admin Boundaries product, which seamlessly integrates with this dataset.\n\n* **[Point of Interest (PoI) Data](https://geolocet.com/pages/aggregated-points-of-interest-poi-data):** If you're interested in Points of Interest data, please don't hesitate to contact us for customized datasets to enhance your geographical analysis.\n\n* **[Data Customization](https://geolocet.com/pages/data-services-by-geolocet):** We understand that your project may have specific requirements. Feel free to get in touch with us if you need any modifications or specific data formats such as Excel, JSON, GeoJSON, or Shapefile. We are here to tailor the data to your needs.\n\nIf you're looking for data for Germany, [look here](https://geolocet.com/pages/germany) for off-the-shelf or tailor-made datasets to suit your specific requirements.\n\nMake informed decisions, conduct precise research, and unlock the potential of Germany's demographic landscape with our comprehensive dataset. Trust in the accuracy and reliability of our official statistical data to drive your projects forward.\nContact us at *contact@geolocet.com* today to access this invaluable resource and explore Germany's census data like never before.\n\n[Geolocet LTD](https://geolocet.com)\n","terms_of_service":"https://geolocet.com/pages/terms-of-use","documentation_link":"","privacy_policy_link":"https://geolocet.com/pages/privacy-policy","geographical_coverage":"[\"Europe\"]","data_source":"","assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://geolocet.com/pages/terms-of-use"},"provider_summary":{"name":"Geolocet LTD","description":"Geolocet provides premium geospatial data for retail analysis, GIS, and market planning. We equip businesses with accurate, ready-to-query data to drive spatial analysis, site selection, and customer segmentation.\n\nCore Offerings:\n\nDemographics: Socioeconomic data covering population, age, and employment across diverse geographies.\n\nPoints of Interest (PoI): Datasets spanning retail, healthcare, and transport for catchment analysis.\n\nAdministrative Boundaries: GIS-ready boundaries, including municipalities and postcodes, for territory management.\n\nBrand Locations: Chain location datasets to monitor store networks and market expansion.\n\nWealth Data: Granular economic indicators like purchasing power to identify high-potential markets.\n\nWe source from trusted providers, delivering highly customizable datasets with comprehensive coverage across Europe and worldwide."},"popularity":312},{"id":"4b56038c-34f6-4b3c-afa4-865ff120ffe6","summary":{"name":"Restaurants in Qatar (QA) - Places Data Sample","subtitle":"\uD83C\uDDF6\uD83C\uDDE6 Restaurant location & metadata table for Qatar, with 6K+ restaurant points-of-interest (PoIs) updated daily.","share":{"name":"qa_restaurant_marketplace","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1727886470658,"updated_at":1746426332864,"categories":["GEOSPATIAL","ADVERTISING_AND_MARKETING","LOOKUP_TABLES","RETAIL","TRAVEL_AND_TOURISM","COMMERCE"],"listingType":"STANDARD","provider_id":"bcde9d2e-7e2f-4266-8c17-45d2e7bdc27e"},"detail":{"description":"## **Overview**\n\n> Learn more about this dataset on [catalog.dotlas.com](https://catalog.dotlas.com/restaurants/unified_restaurant_establishments/)\n\n> This is a sample of our Qatar Restaurants data product (temporal). The product contains **`6K+ restaurants`** updated frequently, and additional columns catered for leads and segmentation. \n\n> Additionally, we also curate restaurant menu data including dishes, prices, price history, restaurant promotions and social insights. This sample contains ~170 restaurants across Qatar. \n\nFor pricing and licensing details please [**contact us**](mailto:info@dotlas.com)\n\n**Use cases** \n\n\uD83D\uDD70 Analyze competitor opening hours on each day of the week to help create an optimal operating time frame.\n\uD83D\uDCC8 Identify trending cuisines in different regions by analyzing the frequency and distribution of various cuisine types in different cities.\n\uD83D\uDCB0 Use the distribution data to identify underserved areas with high potential for new restaurant openings based on the lack of certain cuisine types.\n\n### **Potential Sectors**\n* Food & Beverages (F&B)\n* GIS (Geographic Information Systems) and Geocoding Services\n* Marketing and Advertising Leads\n* Competitor Monitoring & Market Analysis\n* Financial tickering of F&B brand performance\n* Site Selection & Suitability Analysis\n* Real Estate Proximity Analysis\n\n**Key features** \n\n▶️ **Restaurant Identification**: Includes unique identifiers and names for each restaurant.\n\n▶️ **Geographical Data**: Detailed information about the `country`, `subregion`, `city`, `area`, and precise geographical coordinates (`latitude` and `longitude`) of each restaurant.\n\n▶️ **Contact Information**: Addresses, phone numbers, email addresses, and website URLs.\n\n▶️ **Social Media Presence**: Links to `Instagram`, `Facebook`, and `Twitter` profiles where available.\n\n▶️ **Amenities**: Information on various amenities such as acceptance of credit cards, availability of Wi-Fi, presence of outdoor seating, and more.\n\n▶️ **Additional Metadata**: Information on entertainment options, executive chef names, dress codes, dining styles, and more","terms_of_service":"https://dotlas.com/terms-of-service","documentation_link":"https://catalog.dotlas.com/restaurants/unified_restaurant_establishments/","privacy_policy_link":"https://dotlas.com/privacy-policy","geographical_coverage":"[\"Middle East\"]","cost":"FREE","update_frequency":{"interval":1,"unit":"DAILY"},"collection_granularity":{"interval":1,"unit":"DAILY"},"data_source":"Dotlas collects and coalesces information from multiple online and offline sources","assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"Dotlas","description":"Dotlas provides high-quality geographical and temporal data on restaurants, real estate and hospitality sectors around the world. 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It is a useful tool for activities such as looking for new safety concerns that might be related to a marketed product, evaluating a manufacturer's compliance with reporting regulations and responding to outside requests for information. The reports in FAERS are evaluated by clinical reviewers, in the Center for Drug Evaluation and Research (CDER) and the Center for Biologics Evaluation and Research (CBER), to monitor the safety of products after they are approved by FDA. These reports are submitted to FDA by healthcare professionals (such as physicians, pharmacists, nurses and others) and consumers (such as patients, family members, lawyers and others). Healthcare professionals and consumers may also report to the products’ manufacturers who then send these reports to the FDA. The datasets in FAERS data package contain much relevant information such as patient demographics, drug name, dose and route of administration, diagnosis and indication for use, therapy starts and ends date and patient outcome.\n\n **Benefits**\n - useful for physicians, pharmacists, pharmaceutical companies, medical students and patients (general public).\n- faers database is available on fda’s website in raw form, whereas a john snow labs (jsl) customer gets it in a clean and normalized form ready to use and create his own custom reports.\n- quarter-wise data merged into one file.\n- easy to comprehend for the customers who are unfamiliar with the medical terminology as all abbreviated terms are replaced with full form, unlike faers original datasets which are full of abbreviations.\n\n **License Information**\n The use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n **Included Datasets**\n - [FDA Adverse Events Reporting System Demographics 2024](https://www.johnsnowlabs.com/wp-json/datasets-api/v1/fda-adverse-events-reporting-system-demographics-2024)\n\t- The FDA Adverse Event Reporting System (FAERS) contains information on medication errors, quality complaints and drug-related adverse events that were submitted to the FDA (U.S. Food and Drug Administration) in 2024. The Demographics dataset contains patient demographic and administrative information, a single record for each event report.\n- [FDA Adverse Events Reporting System Drug 2024](https://www.johnsnowlabs.com/wp-json/datasets-api/v1/fda-adverse-events-reporting-system-drug-2024)\n\t- The FDA Adverse Event Reporting System (FAERS) contains information on medication errors, quality complaints and drug-related adverse events that were submitted to FDA (U.S. Food and Drug Administration) in 2024. The Drug dataset contains drug/biologic information for as many medications as were reported for the event (1 or more per event).\n- [FDA Adverse Events Reporting System Drug Indication 2024](https://www.johnsnowlabs.com/wp-json/datasets-api/v1/fda-adverse-events-reporting-system-drug-indication-2024)\n\t- The FDA Adverse Event Reporting System (FAERS) contains information on medication errors, quality complaints and drug-related adverse events that were submitted to the FDA (U.S. Food and Drug Administration) in 2024. The Indication dataset contains all \"Medical Dictionary for Regulatory Activities\" (MedDRA) terms coded for the indications for use (diagnoses) for the reported drugs (0 or more per drug per event).\n- [FDA Adverse Events Reporting System Drug Reaction 2024](https://www.johnsnowlabs.com/wp-json/datasets-api/v1/fda-adverse-events-reporting-system-drug-reaction-2024)\n\t- The FDA Adverse Event Reporting System (FAERS) contains information on medication errors, quality complaints and drug-related adverse events that were submitted to FDA (U.S. Food and Drug Administration) in 2024. The drug reaction dataset contains all \"Medical Dictionary for Regulatory Activities\" (MedDRA) terms coded for the adverse event (1 or more).\n- [FDA Adverse Events Reporting System Drug Therapy Dates 2024](https://www.johnsnowlabs.com/wp-json/datasets-api/v1/fda-adverse-events-reporting-system-drug-therapy-dates-2024)\n\t- The FDA Adverse Event Reporting System (FAERS) contains information on medication errors, quality complaints and drug-related adverse events that were submitted to FDA (U.S. Food and Drug Administration) in 2024. The drug therapy dates dataset contains drug therapy start dates and end dates for the reported drugs.\n- [FDA Adverse Events Reporting System Patient Outcome 2024](https://www.johnsnowlabs.com/wp-json/datasets-api/v1/fda-adverse-events-reporting-system-patient-outcome-2024)\n\t- The FDA Adverse Event Reporting System (FAERS) contains information on medication errors, quality complaints and drug-related adverse events that were submitted to FDA (U.S. Food and Drug Administration) in 2024. The patient outcome file contains information on patient outcomes for the event (0 or more).\n- [FDA Adverse Events Reporting System Report Source 2024](https://www.johnsnowlabs.com/wp-json/datasets-api/v1/fda-adverse-events-reporting-system-report-source-2024)\n\t- The FDA Adverse Event Reporting System (FAERS) contains information on medication errors, quality complaints and drug-related adverse events that were submitted to FDA (U.S. Food and Drug Administration) in 2024. The report source dataset contains report sources for event.\n\n\n **Data Engineering Overview**\n **We deliver high-quality data**\n - Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n - Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n - Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated\n - Data Updates\n - Data updates support replace-on-update: outdated foreign keys are deprecated, not deleted\n\n **Our data is curated and enriched by domain experts** \n\n Each dataset is manually curated by our team of doctors, pharmacists, public health & medical billing experts:\n - Field names, descriptions, and normalized values are chosen by people who actually understand their meaning\n - Healthcare & life science experts add categories, search keywords, descriptions and more to each dataset\n - Both manual and automated data enrichment supported for clinical codes, providers, drugs, and geo-locations\n - The data is always kept up to date – even when the source requires manual effort to get updates\n - Support for data subscribers is provided directly by the domain experts who curated the data sets\n - Every data source’s license is manually verified to allow for royalty-free commercial use and redistribution.\n\n **Need Help?**\n If you have questions about our products, contact us at [info@johnsnowlabs.com](mailto:info@johnsnowlabs.com). \n ","terms_of_service":"https://www.johnsnowlabs.com/healthcare-data-library-eula/","documentation_link":"https://www.johnsnowlabs.com/wp-json/datasets-api/v1/fda-drug-adverse-events-reporting-system-faers-2024-data-package","contact_email":"support@johnsnowlabs.com","privacy_policy_link":"https://www.johnsnowlabs.com/privacy-policy/","assets":["ASSET_TYPE_DATA_TABLE"]},"provider_summary":{"name":"John Snow Labs","description":"John Snow Labs, an AI and NLP for healthcare company, provides state-of-the-art software, models, and data to help healthcare and life science organizations build, deploy, and operate AI projects."},"popularity":671},{"id":"8c2b3ea6-f6c4-4ff8-8750-789a82b2ce9b","summary":{"name":"Provider Data Solutions","subtitle":"HCP & HCO reference data, practice locations, and more for life science, payer, provider, and advertising organizations","setting":{"visibility":"PUBLIC"},"created_at":1757942455841,"updated_at":1759176967123,"categories":["HEALTH","ADVERTISING_AND_MARKETING","SCIENCE_AND_RESEARCH"],"listingType":"PERSONALIZED","provider_id":"7f96a44e-e17c-4214-9093-6f48f3026e29"},"detail":{"description":"**Overview**\nProvider Data Solutions delivers curated healthcare practitioner and organization reference datasets, optimized for analytics and machine learning in Databricks. 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","setting":{"visibility":"PUBLIC"},"created_at":1695231079820,"updated_at":1695234360855,"categories":["ECONOMICS","HEALTH","SCIENCE_AND_RESEARCH"],"listingType":"PERSONALIZED","provider_id":"0adfd425-2346-4579-a3cb-5d6c8ba95648"},"detail":{"description":"**Overview**\nSerif Health provides access to comprehensive price transparency data from payor machine readable file (MRF) fee schedules including specific negotiated reimbursement rates by NPI, EIN, specialty, and location for available CPT, DRG, HCPCS, and RC codes. This listing contains a sample for Aetna's rates in Texas for the top 25 billed inpatient DRGs. The data includes specific providers' negotiated rates. \n\n**Use cases**\n\nFor Providers:\n- Competitor benchmarking -- how does your reimbursement schedule compare to peers \n- Contracting and payer strategy -- negotiating higher reimbursements with commercial payers\n- Roll-ups / acquisitions / M&A -- identifying targets such as highest reimbursed practices in a region\n- Provider recruiting and partnerships -- prospecting health systems or individual practitioners to add to your practice\n- Margin expansion / improvement -- forecasting revenue impact from changing operations (reducing time per visit from ~45 min. to ~20 min., performing procedures using nurse practitioners vs. physicians, alternative coding, ...)\n\nFor Payers:\n\n- Rate and network benchmarking -- size, cost, and scope of networks across competitors and their products (HMO, PPO, ACO, self-insured)\n- Network expansion, optimization, and management -- scan market to understand relative cost of care, add providers in-network based on adequacy, create narrow high-performance networks, evaluate provider integration to become a \"pay-vider\" \n- Plan design -- right-size premiums / deductibles based on reimbursement fees, innovate on fee-for-service and create bundles / capitated arrangements\n\nFor Employers, TPAs, Brokers\n\n- Network comparison -- comparing cost of care across various networks and products \n- Direct contracting -- determining which providers to add to your network\n- Payment integrity, claims verification, claims re-pricing -- assess over/under-billing against published fee schedules\n- Network tiering and steering -- identify sites with lower cost of care and create dynamic co-pay programs to direct patients \n\nFor Pharmaceuticals, Life Sciences, Medical Devices:\n\n- Pricing analysis -- see what 3rd party commercial payers reimburse providers for injectables, medical supplies to capture their margin\n\n**Product details**\nColumns included in the sample are: \n- Payer: Aetna\n- Region: Either TX or \"Blank\" if not provided\n- Network: Open Access Managed Choice PPO\n- EIN: A 9 digit taxable identification number without hyphen \n- NPI List: Comma separated NPI values\n- NPI List Length: Count of NPIs in NPI list\n- Taxonomy: Most common NUCC Taxonomy code for the NPIs in the NPI List\n- Taxonomy Name: NUCC description associated with NUCC Taxonomy Code in \"Taxonomy\" field\n- Code: 3 character MS-DRG\n- Code Type: MS-DRG\n- Code Type Version: Year associated with published Code Type\n- Modifier List: Any billing code modifiers present on this code and rate, comma separated. If not present, will be an empty string \n- Bundled Code List: For ‘bundled’ or ‘capitation’ arrangements, comma separated list of codes included in the bundled rate, otherwise empty string.\n- Billing Class: Allowed values: ‘professional’ or ‘institutional’\n- Place Of Service List: Comma separated list of CMS place of service codes that apply to the stated rate\n- Negotiation Type: Allowed values: \"negotiated\", \"derived\", \"fee schedule\", \"percentage\", and \"per diem\"\n- Arrangement: Allowed values: ‘ffs’, ‘bundled’, ‘capitation’\n- Rate: Decimal with two digits after a period, no commas or dollar signs. e.g. 10125.85\n- Additional Information: Any commentary published by payor in MRF, e.g., why 2 different rates exist for the same provider and code\n- Is Billable: Allowed values: 'true' if CMS accepts claim from that taxonomy for given code, 'false' if CMS does not accept, 'null\" if un-determined\n- Matched On: Field explaining data extension source. Allowed values: “Type1NPI”, “Type2NPI”, “TIN”, “ImputedType2NPI”\n- Matched ID: The ID (matched or imputed) used to tag this data row\n- Entity Name: Organization or Entity Name, from TIN or NPI data sources\n- Entity Address: Organization or Entity Address, from TIN or NPI data sources\n\n\n\n\n\n","terms_of_service":"https://www.serifhealth.com/terms-of-use","documentation_link":"","privacy_policy_link":"https://www.serifhealth.com/privacy-policy","geographical_coverage":"[\"United States and Canada\"]","cost":"FREE","update_frequency":{"interval":1,"unit":"MONTHLY"},"data_source":"https://health1.aetna.com/app/public/#/one/insurerCode=AETNACVS_I&brandCode=ALICFI/machine-readable-transparency-in-coverage"},"provider_summary":{"name":"Serif Health","description":"Serif Health provides access to comprehensive price transparency data from payor machine readable file (MRF) fee schedules including specific negotiated reimbursement rates by NPI, EIN, specialty, and location for available CPT, DRG, HCPCS, and RC codes. Our data asset covers ~95% of commercially insured lives, spanning >200 private health plans and >400 network products. Customers use Serif Health to benchmark rates, determine which providers are in-network with which plans, and develop patient steering solutions to contain costs. To power these use cases, Serif has enhanced the source MRF data significantly, including building a proprietary provider directory to map individual type 1 NPIs, type 2 group NPIs, and EINs to each other and their respective entity name, address, and NUCC taxonomy as well as using claims data to QA the MRFs for any outlier or implausible rate information. 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In our initial release, we use the datasets that are collected and distributed by OpenAddresses.\n\nThe initial schema for addresses is fairly simple and is expected to expand into more complex addressing schemes.\n\nAddresses are geographic points used for locating businesses and individuals. The rules, fields, and fieldnames of an address can vary extensively between locations. Overture Maps uses a simplified schema to capture worldwide address points. This initial schema is largely based on the OpenAddresses (www.openaddresses.io) project. The address schema allows up to 5 \"admin levels\". Rather than have field names that apply across all countries, we provide an array called \"address_levels\" containing the necessary administrative levels for an address.\n\nFounded in 2022 under the Joint Development Foundation, Overture Maps Foundation (Overture Maps) is dedicated to the development of reliable, easy-to-use, and interoperable open map data that will power current and next-generation map products. We build this data through a collaborative process that combines technology, data, and support from a wide range of companies. Map data are assembled from multiple sources including members, civic organizations, and open data sources.\n\nOverture's data themes and schema are licensed under CC-BY 4.0. GERS IDs are licensed under CDLA Permissive v 2.0. Data associated with the themes through GERS can carry different licenses. However, if the data associated though GERS with an ODbL licensed theme constitutes a derivative dataset, it must be licensed under ODbL. 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It supports sales teams in spotting active deals and competitive activity, while advisors and vendor managers can use it to assess partnership intensity, monitor vendor ecosystems, and inform negotiation strategies.\n\n**Methodology & Quality**\nOutsourcing data is aggregated from a combination of public disclosures, proprietary signals, and curated sources, then normalized into a consistent schema of companies, service providers, workloads, and business functions. Draup standardizes vendor taxonomies and workload classifications, applies evidence tagging for digital technologies, and refreshes data frequently to capture new deals and contract updates. This ensures accuracy, comparability across industries, and the ability to trace relationships over time.\n\n**Key Tables Include**\no\tCompany–Service Provider Engagements: outsourcing projects with company, service provider, locations, workloads, functions, project timelines, technologies, and skills.\no\tDeal Intensity Metrics: engagement footprint, outsourced workload intensity, and geographic distribution of service providers.\no\tRelationship Insights: highlights of provider–company partnerships, outsourced size, key locations, and notable relationship details.\n\n**Use Cases:**\n\n***Discover Accounts that outsource to a India for Data Engineering work***\t\n\nUse Draup’s outsourcing deals data to identify which companies are using outsourcing services from where for specific functions. Filter by provider, client location, vendor location and function to target high-potential companys with offshore dependencies.\t\t\n\nSELECT \n company_name,\n service_provider_name,\n service_provider_location,\n outsourced_workloads,\n outsourced_business_function\nFROM deals_company_service_providers\nWHERE LOWER(service_provider_location) LIKE '%india'\n AND LOWER(outsourced_workloads) LIKE '%data engineering%';\n\n***Uncover Competitors’ Workload Share Across Verticals***\t\n\nQuickly identify how rival service providers distribute their outsourcing workloads across industries and business functions, and benchmark their customer footprint against your own.\t\t\n\nSELECT\n service_provider_name,\n service_provider_primary_vertical,\n COUNT(*) AS projects_count,\n CAST(COUNT(*) AS DOUBLE) * 100 / SUM(COUNT(*)) OVER (PARTITION BY service_provider_name) AS workload_share_percentage\nFROM\n deals_company_service_providers\nGROUP BY\n service_provider_name,\n service_provider_primary_vertical\nORDER BY\n service_provider_name,\n workload_share_percentage DESC;\n\n***Map the Service Provider Partner footprint for a Target Account***\t\n\nGet a comprehensive view of all the service provider partner ecosystem of the company. Get deeper insights with the ability to filter the outsourcing deals data by service providers, functions, workloads, tech stack leveraged or the delivery location. This can enable understanding competitive landscape or partnership opportunities. \t\t\n\nSELECT \n company_name,\n service_provider_name,\n service_provider_location,\n service_provider_primary_vertical,\n outsourced_business_function,\n outsourced_workloads,\n digital_product,\n skills\nFROM deals_company_service_providers\nWHERE company_name = 'Microsoft'\n OR service_provider_name IN ('Accenture', 'Tech Mahindra')\n OR service_provider_location IN ('India', 'USA')\n OR outsourced_business_function LIKE '%IT%'\n OR LOWER(outsourced_workloads) LIKE '%cloud%'\nORDER BY service_provider_name;\n\n***Understand the client landscape of a competing Service Provider in a function***\t\n\nUse Draup’s outsourcing deal intelligence to explore the client base of a competitor service provider for a specific function (e.g., IT, Finance, Engineering). Identify served industries, deal sizes, and delivery locations to benchmark penetration and uncover whitespace opportunities.\t\t\n\nSELECT\n deals_company_service_providers.company_name,\n deals_company_service_providers.company_location,\n company_overview.primary_vertical AS Client_primary_vertical,\n company_overview.verticals AS client_verticals,\n deals_company_service_providers.service_provider_name,\n deals_company_service_providers.service_provider_location,\n deals_company_service_providers.service_provider_vertical,\n deals_company_service_providers.service_provider_primary_vertical,\n service_providers_insights.outsourced_size,\n deals_company_service_providers.outsourced_workloads,\n deals_company_service_providers.outsourced_business_function\nFROM\n deals_company_service_providers\nINNER JOIN\n deals_company_service_providers_insights as service_providers_insights ON deals_company_service_providers.company_id = service_providers_insights.company_id\n AND deals_company_service_providers.service_provider_id = service_providers_insights.service_provider_id\nINNER JOIN\n company_overview ON deals_company_service_providers.company_id = company_overview.company_id\nWHERE\n deals_company_service_providers.service_provider_name = 'Accenture'\n AND deals_company_service_providers.outsourced_business_function = 'IT';\n\n***What workloads does a Company outsource to vendors and who are their top vendors?***\t\n\nDraup’s outsourcing deal data helps map the workloads and business functions outsourced by a company, along with its primary service providers. Analyze deal attributes by function, delivery location, or contract size to understand vendor strategy and whitespace opportunities.\t\t\n\nSELECT\n outsourced_workload,\n COUNT(*) AS project_count\nFROM\n deals_company_service_providers\nLATERAL VIEW explode(deals_company_service_providers.outsourced_workloads) t AS outsourced_workload\nWHERE\n deals_company_service_providers.company_name = 'Microsoft'\nGROUP BY\n outsourced_workload\nORDER BY\n project_count DESC;","terms_of_service":"https://draup.com/privacy-policy#:~:text=Unless%20provided%20for%20in%20this,that%20work%20on%20our%20behalf","documentation_link":"","privacy_policy_link":"https://draup.com/privacy-policy","geographical_coverage":"[\"Global\"]","cost":"FREE","update_frequency":{"interval":1,"unit":"WEEKLY"},"collection_granularity":{"interval":1,"unit":"DAILY"},"data_source":"Public Sources","assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"Draup, Inc.","description":"Draup is an enterprise agentic intelligence platform built for HR and Sales leaders navigating transformation. By combining unparalleled real-time global data with agentic AI, Draup enables strategic decision-making across talent and revenue functions.\n \nThrough its Talent Intelligence solution, Draup empowers HR teams to drive workforce planning, precision hiring, predictive skill architecture, and competitive benchmarking. Its Sales Intelligence solution helps go-to-market teams uncover deep account insights, identify buying triggers, and accelerate revenue growth.\n \nTrusted by over 300 global enterprises - including five of the Fortune 10 - Draup supports companies like Microsoft, PepsiCo, PayPal, and Moderna in building future-ready workforces and growth strategies."},"popularity":694},{"id":"77d59eea-9db5-4c35-9955-b1a619ec9937","summary":{"name":"SAMPLE People Data | 800M+ Professional Profiles | Employee Data | Data Feed | 30-Day Refresh on ...","subtitle":"B2B people data and employee data feed: roles, employers, skills. 30-day refresh on active profiles.","share":{"name":"forager_ai_48114c87_2f79_48ca_a7d8_027c574991bf_samples_share","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1794247520642,"updated_at":1794247520642,"categories":["OTHER","ADVERTISING_AND_MARKETING"],"listingType":"STANDARD","provider_id":"ba4dbcfb-6e61-49d1-92b5-b79a3480c00f"},"detail":{"description":"**People data for 800M+ people, delivered as a data feed.** Each record is a professional profile with every role, employer, school and skill. Every role carries the employer's Forager company ID, so people join to company and job post tables on one key.\n\n**Columns in the people table**\n\n- `person_id`: Forager person ID, stable across drops\n- `full_name`, `headline`: Name and profile headline\n- `location`, `country`: Location as stated, plus normalized country\n- `current_title`, `role_start_date`: Current role and when it started\n- `company_id`, `company_name`, `company_domain`: Current employer, joinable to company data\n- `company_industry`, `company_employees_range`: Employer industry and headcount band\n- `previous_title`, `previous_company`, `roles_count`: Career history summary\n- `education_school`, `education_degree`, `education_field`: Most recent education\n- `skills`, `languages`, `certifications`: Pipe-separated lists\n- `public_profile_url`: Public profile URL\n- `date_updated`: When Forager last updated the record\n\nThe full feed adds every past role with dates, and every school, as nested records.\n\n**Coverage and refresh**\n- Global\n- 30-day refresh on active profiles, written into the contract\n- The full set, or a subset by country, industry, company size or role\n\n**Access on Databricks**\n- Request access on this listing. The share includes a sample table you can query in SQL or a notebook as soon as access is granted\n- Licensed data is delivered as files, updated daily, weekly or monthly on an annual license\n- Delivery to Snowflake, Amazon S3 or Google Cloud Storage as Parquet, JSONL or CSV, ready to load into Databricks\n\n**Where it fits**\n- Marketing teams use it as B2B marketing data to build audiences by role and company.\n- The B2B marketing data segments by industry, size and seniority.\n- It is identity data for resolution: one email or phone number in, one professional profile out.\n- The identity data links personal contact points to a person's current role and employer.\n- Use it as B2B contact data: each person comes with a current role, employer and work history to match against your CRM.\n- The B2B contact data joins to company records by Forager ID, so contacts and accounts stay linked.\n- Recruiters use it as talent data to find candidates by skill, title, employer and location.\n- The talent data keeps full work history, so you can see career moves before you reach out.\n\n**Who uses it**\n- Data and product teams building people search, scoring or ML features\n- Sales-tech and recruiting platforms that want a people layer they own\n- Workforce and talent analytics teams\n\n**How to test us**\n- Start free with $5 in credits and query the same records through the Forager API\n- Send records you already know. We return what we find, field by field, so you can check fill and freshness yourself","terms_of_service":"https://www.forager.ai/privacy","contact_email":"support@datarade.ai","privacy_policy_link":"https://www.forager.ai/privacy","assets":["ASSET_TYPE_DATA_TABLE"]},"provider_summary":{"name":"Forager.ai","description":"Forager is a B2B data company. We sell people, company and job-posting data to the platforms and teams that dial, email, recruit and build agents: sales-tech and GTM platforms, recruiting and talent platforms, data providers that run enrichment waterfalls, and product and data teams that license feeds. Forager.ai, LLC is based in Florida, USA, and our team works from the United States, Ukraine, the Philippines, Poland and Mexico. Platforms and teams that build on Forager include Clay, HubSpot, Nooks, Andela and Amplemarket. The data covers 800M+ people and 65M+ companies, with 200M+ verified mobile numbers and a 30-day refresh on active profiles. Buyers use Forager two ways: a REST API on self-serve credits, and licensed data feeds delivered to their warehouse. Bring a sample and we run it, so you can measure hit rate and connect rate on your own records."},"popularity":335},{"id":"91675bbd-ebac-4b34-aaba-24d978a9b568","summary":{"name":"Patient Events Analyst","subtitle":"Turn patient events into agent-ready real-world data intelligence.","setting":{"visibility":"PUBLIC"},"created_at":1781277090910,"updated_at":1781277240726,"categories":["HEALTH","SCIENCE_AND_RESEARCH"],"listingType":"PERSONALIZED","provider_id":"3f540322-735a-47ef-ad5a-8ccbe4693428"},"detail":{"description":"**Kythera Patient Events Analyst MCP Server**\n\n**Turn patient event data into agent-ready healthcare intelligence**\n\nKythera Patient Events Analyst MCP Server helps healthcare and life sciences teams analyze real-world patient events by connecting procedures, providers, facilities, payers, diagnoses, care settings, geographies, and source claims into a unified analytics layer. Users can ask natural language questions about utilization, referral patterns, provider performance, facility activity, payer mix, diagnosis profiles, and service area trends to generate structured insights from trusted real-world data.\n\n**What Customers Can Do**\n\n* Analyze procedure volumes, utilization trends, and distinct patient event counts\n* Identify referral patterns between referring and rendering providers\n* Exclude self-referrals by default for cleaner referral analytics\n* Compare provider performance by procedure, diagnosis, payer, geography, or care setting\n* Understand facility utilization and care setting trends\n* Explore patient demographics, diagnosis profiles, and payer mix for specific events\n* Segment analyses by date range, provider, facility, payer, diagnosis, procedure, or patient region\n* Map geographic service areas and market activity\n* Trace events back to original claim sources and vendor attribution\n* Generate structured outputs for dashboards, agents, and downstream analytics workflows\n\n**Example Questions**\n\n* \"Show procedure volumes for knee replacement by facility over the last year.\"\n* \"Which providers refer the most patients for this procedure?\"\n* \"Compare rendering provider activity by payer mix.\"\n* \"What facilities have the highest event volume for this diagnosis?\"\n* \"Show care setting trends for cardiology procedures.\"\n* \"Which geographies drive the most patient events for this service line?\"\n* \"Analyze referral patterns excluding self-referrals.\"\n* \"Show diagnosis profiles and patient demographics for these events.\"\n* \"Trace these events back to source claims and vendor attribution.\"\n\n**Example Outputs**\n\n* Procedure volume summaries\n* Distinct patient event counts\n* Referral pattern analysis\n* Rendering and referring provider comparisons\n* Facility utilization summaries\n* Care setting breakdowns\n* Diagnosis and comorbidity profiles\n* Patient demographic summaries\n* Payer mix analysis\n* Geographic service area summaries\n* Source claim and vendor attribution details\n* Structured JSON, tables, and summaries for agents and applications\n\n**Designed for Agentic Real-World Data Workflows**\n\nThe Patient Events Analyst MCP Server gives agents a trusted way to analyze healthcare activity at the patient event level.\n\nIt helps teams move from fragmented claims activity to structured insight across providers, facilities, diagnoses, payers, procedures, and care settings. This makes it easier to support market analytics, referral analysis, provider targeting, facility strategy, service line planning, and real-world evidence workflows.\n\nExample workflow:\n\n**Clinical or Market Question → Patient Events → Provider and Facility Context → Referral and Utilization Signals → Segmentation → Market and Operational Strategy**","terms_of_service":"https://www.kytheralabs.com/eula","documentation_link":"","privacy_policy_link":"https://www.kytheralabs.com/privacy","assets":["ASSET_TYPE_MCP"],"license":""},"provider_summary":{"name":"Kythera Labs Inc","description":"Kythera Labs is a healthcare data technology company that helps healthcare and life sciences organizations reduce uncertainty and make decisions with greater confidence. Through Wayfinder, our Databricks-powered platform, we transform fragmented healthcare data into higher-fidelity, analysis-ready assets that accelerate analytics, AI, and research.\n\nWayfinder integrates, remasters, and enriches claims, EHR, laboratory, prescription, and provider data to create longitudinal patient and provider views that improve accuracy, completeness, and usability. Using advanced data science and healthcare-specific methodologies, Kythera delivers trusted data products that help organizations uncover patient journeys, identify market opportunities, and generate actionable insights faster.\n\nKythera enables organizations to spend less time preparing data and more time finding answers."},"popularity":1338},{"id":"c93e25f7-1ab9-4255-907d-356691379e17","summary":{"name":"SAMPLE LATAM Data Suite | 1.8M+ Sentences | Natural Language Processing (NLP) Data | TTS | ...","subtitle":"Note: The CSV format sample above is provided for preview only. Full datasets may differ in format depending on ...","share":{"name":"oxford_languages_1147cc29_aea9_4598_8672_7c77756acac2_samples_share","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1789386250366,"updated_at":1789386250366,"categories":["OTHER"],"listingType":"STANDARD","provider_id":"79c6c146-3407-48ec-8737-5b9b06406932"},"detail":{"description":"LATAM Data Suite provides high-quality datasets in Spanish, Portuguese, and American English. Ideal for NLP, AI, LLMs, translation, and education, it combines linguistic depth and regional authenticity to power scalable, multilingual language technologies.\n\nDiscover our expertly curated language datasets in the LATAM Data Suite. Compiled and annotated by language and linguistic experts, this suite offers high-quality resources tailored to your needs. This suite includes: \n\n- Monolingual and Bilingual Dictionary Data \nFeaturing headwords, definitions, word senses, part-of-speech (POS) tags, and semantic metadata. \n\n- Sentences \nCurated examples of real-world usage with contextual annotations.\n\n- Synonyms & Antonyms \nLexical relations to support semantic search, paraphrasing, and language understanding. \n\n- Audio Data \nNative speaker recordings for TTS and pronunciation modeling. \n\n- Word Lists \nFrequency-ranked and thematically grouped lists. \n\n\nLearn more about the datasets included in the data suite: \n\n1. Portuguese Monolingual Dictionary Data \n2. Portuguese Bilingual Dictionary Data \n3. Spanish Monolingual Dictionary Data \n4. Spanish Bilingual Dictionary Data \n5. Spanish Sentences Data \n6. Spanish Synonyms and Antonyms Data \n7. Spanish Audio Data \n8. Spanish Word List Data \n9. American English Monolingual Dictionary Data \n10. American English Synonyms and Antonyms Data \n11. American English Pronunciations with Audio \n \nKey Features (approximate numbers): \n\n1. Portuguese Monolingual Dictionary Data \n\nOur Portuguese monolingual covers both European and Latin American varieties, featuring clear definitions and examples, a large volume of headwords, and comprehensive coverage of the Portuguese language. \n\n- Words: 143,600 \n- Senses: 285,500 \n- Example sentences: 69,300 \n- Format: XML format \n- Delivery: Email (link-based file sharing) \n\n\n2. Portuguese Bilingual Dictionary Data \n\nThe bilingual data provides translations in both directions, from English to Portuguese and from Portuguese to English. It is annually reviewed and updated by our in-house team of language experts. Offers comprehensive coverage of the language, providing a substantial volume of translated words of excellent quality that span both European and Latin American Portuguese varieties. \n\n- Translations: 300,000 \n- Senses: 158,000 \n- Example translations: 117,800 \n- Format: XML and JSON formats \n- Delivery: Email (link-based file sharing) and REST API \n- Updated frequency: annually \n\n\n3. Spanish Monolingual Dictionary Data \n\nOur Spanish monolingual reliably offers clear definitions and examples, a large volume of headwords, and comprehensive coverage of the Spanish language. \n\n- Words: 73,000 \n- Senses: 123,000 \n- Example sentences: 104,000 \n- Format: XML and JSON formats \n- Delivery: Email (link-based file sharing) and REST API \n- Updated frequency: annually \n\n\n4. Spanish Bilingual Dictionary Data \n\nThe bilingual data provides translations in both directions, from English to Spanish and from Spanish to English. It is annually reviewed and updated by our in-house team of language experts. Offers significant coverage of the language, providing a large volume of translated words of excellent quality. \n\n- Translations: 221,300 \n- Senses: 103,500 \n- Example sentences: 74,500 \n- Example translations: 83,800 \n- Format: XML and JSON formats \n- Delivery: Email (link-based file sharing) and REST API \n- Updated frequency: annually \n\n\n5. Spanish Sentences Data \n\nSpanish sentences retrieved from corpus are ideal for NLP model training, presenting approximately 20 million words. The sentences provide a great coverage of Spanish-speaking countries and are accordingly tagged to a particular country or dialect. \n\n- Sentences volume: 1,840,000 \n- Format: XML and JSON formats \n- Delivery: Email (link-based file sharing) and REST API \n\n\n6. Spanish Synonyms and Antonyms Data \n\nThis Spanish language dataset offers a rich collection of synonyms and antonyms, accompanied by detailed definitions and part-of-speech (POS) annotations, making it a comprehensive resource for building linguistically aware AI systems and language technologies. \n\n- Synonyms: 127,700 \n- Antonyms: 9,500 \n- Format: XML format \n- Delivery: Email (link-based file sharing) \n- Updated frequency: annually \n\n\n7. Spanish Audio Data (word-level) \n\nCurated word-level audio data for the Spanish language, which covers all varieties of world Spanish, providing rich dialectal diversity in the Spanish language. \n\n- Audio files: 20,900 \n- Format: XLSX (for index), MP3 and WAV (audio files) \n\n\n8. Spanish Word List Data \n\nThis language data contains a carefully curated and comprehensive list of 450,000 Spanish words. \n\n- Wordforms: 450,000 \n- Format: CSV and TXT formats \n- Delivery: Email (link-based file sharing) \n\n9. American English Monolingual Dictionary Data \n\nOur American English Monolingual Dictionary Data is the foremost authority on American English, including detailed tagging and labelling covering parts of speech (POS), grammar, region, register, and subject, providing rich linguistic information. Additionally, all grammar and usage information is present to ensure relevance and accuracy. \n\n- Words: 140,000 \n- Senses: 222,000 \n- Example sentences: 140,000 \n- Format: XML and JSON formats \n- Delivery: Email (link-based file sharing) and REST API \n- Updated frequency: annually \n\n\n10. American English Synonyms and Antonyms Data \n\nThe American English Synonyms and Antonyms Dataset is a leading resource offering comprehensive, up-to-date coverage of word relationships in contemporary American English. It includes rich linguistic detail such as precise definitions and part-of-speech (POS) tags, making it an essential asset for developing AI systems and language technologies that require deep semantic understanding. \n\n\n- Synonyms: 600,000 \n- Antonyms: 22,000 \n- Format: XML and JSON formats \n- Delivery: Email (link-based file sharing) and REST API \n- Updated frequency: annually \n\n \n11. American English Pronunciations with Audio (word-level) \n\nThis dataset provides IPA transcriptions and mapped audio files for words in contemporary American English, with a focus on US speaker usage. It includes syllabified transcriptions, variant spellings, part-of-speech tags, and pronunciation group identifiers. Audio files are supplied separately and linked where available – ideal for TTS, ASR, and pronunciation modeling. \n\n- Transcriptions (IPA): 250,000 \n- Audio files: 180,000 \n- Format: XLSX (for transcriptions), MP3 and WAV (audio files) \n- Updated frequency: annually \n\n\nUse Cases: \n\nWe consistently work with our clients on new use cases as language technology continues to evolve. These include Natural Language Processing (NLP) applications, TTS, dictionary display tools, games, translations, word embedding, and word sense disambiguation (WSD). \n\nIf you have a specific use case in mind that isn't listed here, we’d be happy to explore it with you. Don’t hesitate to get in touch with us at Growth.OL@oup.com to start the conversation. \n \n\nPricing: \n\nOxford Languages offers flexible pricing based on use case and delivery format. Our datasets are licensed via term-based IP agreements and tiered pricing for API-delivered data. Whether you’re integrating into a product, training an LLM, or building custom NLP solutions, we tailor licensing to your specific needs. \n\nContact our team or email us at Growth.OL@oup.com to explore pricing options and discover how our language data can support your goals.\n\n\nAbout the sample: \n\nThe samples offer a brief overview of one or two language datasets (monolingual or/and bilingual dictionary data). To help you explore the structure and features of our dataset, we provide a sample in CSV format for preview purposes only. \n\nIf you need the complete original sample or more details about any dataset, please contact us (Growth.OL@oup.com) to request access or further information","terms_of_service":"https://corp.oup.com/privacy-policy","contact_email":"support@datarade.ai","privacy_policy_link":"https://corp.oup.com/privacy-policy","assets":["ASSET_TYPE_DATA_TABLE"]},"provider_summary":{"name":"Oxford Languages","description":"Oxford Languages delivers multilingual language datasets designed to power the next generation of language technologies. Built through decades of research and curated by expert lexicographers, our data fuels diverse applications – from text-to-speech (TTS) and predictive text to language models dictionary display tools, assistive tech, chatbots, games, and more. 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Billing code modifiers and place of service codes are not included. Aggregated columns include minimum negotiated rate, maximum negotiated rate, and mode negotiated rate. 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For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. 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For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [Base Provider Enrollment File](https://www.johnsnowlabs.com/marketplace/base-provider-enrollment-file)\n\t- This dataset shows a point in time snapshot of enrollment level data for providers actively enrolled in Medicare for the year 2019.\n- [Essential Community Providers Dental Services](https://www.johnsnowlabs.com/marketplace/essential-community-providers-dental-services)\n\t- This dataset shows the list of Essential Community Providers (ECPs) that provide dental services enrolled in the Centers for Medicare and Medicaid Services (CMS).\n- [Medicare Part A and B Enrollments Revalidation Phase III Target List](https://www.johnsnowlabs.com/marketplace/medicare-part-a-and-b-enrollments-revalidation-phase-iii-target-list)\n\t- This dataset consists of Medicare Phase III target list which revalidates all remaining providers including providers reassigning their benefits to some of the large organizations they are affiliated with. Large organizations are considered as groups with 200 or more reassignments.\n- [Medicare Physician National Downloadable File](https://www.johnsnowlabs.com/marketplace/medicare-physician-national-downloadable-file)\n\t- The National downloadable file is organized at the individual provider level; each line is unique at the provider/enrollment/organization/address level. Providers with multiple enrollments and/or single enrollments linking to multiple practice location addresses are listed on multiple lines.\n- [Medicare Providers and Suppliers Revalidation Due Date List 2016](https://www.johnsnowlabs.com/marketplace/medicare-providers-and-suppliers-revalidation-due-date-list-2016)\n\t- This dataset conisists of a list of due dates by which the provider/supplier's revalidation application must reach their MAC (Medicare Administrative Contractor) in order for them to remain in compliance with Medicare's provider enrollment requirements.\n- [Medicare Providers and Suppliers Revalidation Due Date List](https://www.johnsnowlabs.com/marketplace/medicare-providers-and-suppliers-revalidation-due-date-list)\n\t- This dataset conisists of a list of due dates by which the provider/supplier's revalidation application must reach their MAC (Medicare Administrative Contractor) in order for them to remain in compliance with Medicare's provider enrollment requirements.\n- [Moratoria Provider Services and Utilization at State and County Level](https://www.johnsnowlabs.com/marketplace/moratoria-provider-services-and-utilization-at-state-and-county-level)\n\t- Moratoria Provider Services and Utilization dataset provides information on the number of Medicare providers servicing a geographic region and the number of Medicare beneficiaries who use a health service area. The dataset can also be used to reveal the degree to which use of a service is related to the number of providers servicing a geographic region as well as to determine which geographic and health service areas might be considered for a moratorium on new providers.\n- [Revalidation Notice List for Providers Enrolled Before 2011-03-25](https://www.johnsnowlabs.com/marketplace/revalidation-notice-list-for-providers-enrolled-before-2011-03-25)\n\t- This dataset contains the current list of all providers and suppliers who have been mailed a revalidation notice. If a provider/supplier has been listed but has not received the request, then they have to contact their MAC (Medicare Administrative Contractors).\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated\n- Data Updates\n - Data updates support replace-on-update: outdated foreign keys 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Additional: Company Firmographic Data\n- **Officer Data:** ZoomInfo Company ID, Corp File ID, Officer Title, Contact Email Address, Contact Phone Number, Contact Address\n\nFor more details, [contact ZoomInfo sales](https://www.zoominfo.com/go-to-market/first-mover-advantage-data-cube/).\n\n**Additional Insights**\nRevOps, and sales leaders can compare existing customer/prospect data against state business registrations to identify net-new opportunities, launch initial outreach and move to create additional pipeline.\n\n**Business impact**\nHaving this data creates significant competitive advantage in building relationships, enabling businesses to more accurately, easily identify new business prospects and allows for first-in advantage for service/software providers (e.g. banking, legal, insurance, payroll, domains, web hosting, accounting, CRM, etc.).\n","terms_of_service":"https://www.zoominfo.com/about-zoominfo/terms-of-use","documentation_link":"https://www.zoominfo.com/go-to-market/first-mover-advantage-data-cube/","privacy_policy_link":"https://www.zoominfo.com/legal/privacy-policy","geographical_coverage":"[\"United States and Canada\"]","cost":"PAID","update_frequency":{"interval":30,"unit":"DAILY"},"data_source":"Government licensing records across 50 states and thousands of local jurisdictions","assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"ZoomInfo","description":"Build effective go-to-market engines rapidly and activate a unified data strategy with ZoomInfo, the most comprehensive suite of products and services to power your business with accurate and complete data. 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Multiple postal code entries supply alternate coordinates and linkage to standard Canadian census geography codes to supplement the single most representative coordinates.\n\nRefer to the Canada Postal Points File section of the product guide for additional information.\n\n**Forward Sortation Area (FSA) Boundaries/Points**\n\nForward Sortation Area (FSA) Boundaries/Points contains geographical boundary polygons and centroids for FSAs across Canada.\n\nRefer to the Forward Sortation Area (FSA) Boundaries/Points section of the product guide for additional information.\n\n**Six-Digit Postal Code Points**\n\nSix-Digit Postal Code Points is derived from an extract of the Canada Postal Points File and contains the single most representative coordinate for every Canadian postal code. 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"},"popularity":2049},{"id":"ce965d22-8098-423e-9f32-59b47325a758","summary":{"name":"Workcloud Modeling Studio","subtitle":"Zebra Workcloud Modeling studio is a low-code / no-code Machine Learning platform for forecasting demand in retail/cpg.","setting":{"visibility":"PUBLIC"},"created_at":1746545070235,"updated_at":1746545096989,"categories":["RETAIL","MANUFACTURING"],"listingType":"PERSONALIZED","provider_id":"c4e19272-fc82-49b2-9bc5-4ed34b698919"},"detail":{"description":"**Overview**\nZebra Workcloud Modeling studio is a low-code / no-code Data Science Machine Learning (DSML) platform designed for retailers and CPG companies to predict future demand with speed and accuracy. It provides a collaborative environment for data scientists and ML engineers to train, test, and deploy highly scalable machine learning forecasting pipelines to predict future demand. \n\nThe platform blends domain expertise with machine intelligence to deliver highly accurate forecasts, leveraging state-of-the-art AI and machine learning algorithms specifically fine-tuned for retail time series. \n\nOur platform helps companies stay ahead of demand volatility, leading to tangible business gains. According to McKinsey, a 10-20% improvement in forecast accuracy can reduce inventory costs by ~5% and boost revenues by 2-3%. \n\nWorkcloud Modeling Studio focuses on demand forecasting as the primary use case, yet its flexible architecture makes it extensible to other predictive analytics scenarios across Retail and CPG enterprises. \n\n**Key Features**\n1. Model Experimentation Workspace \n\nProvides an interactive sandbox for data scientists to explore various forecasting techniques (ARIMA, ETS, Machine Learning Algorithms, Neural Network Architectures like RNN, Autoencoder, etc.) and Foundational Models for forecasting. Teams can easily run model bake-offs to identify the best algorithm with automated tracking of experiments, results, and artifacts. \n\n2. Managed Infrastructure \n\nWorkcloud Modeling Studio is built on top of Databricks, which means no additional infrastructure to manage. Users simply pass their Databricks developer token into the studio, and the platform accesses their Databricks environment securely and seamlessly. There’s no new compute to provision or services to configure. Your data stays entirely within your own environment, and you only pay for the compute you use. With Modeling Studio leveraging your existing Databricks infrastructure, you get the power of a fully managed experience without the operational burden. \n\n3. Automated Pipelines \n\nEnd-to-end features to streamline all steps between experimentation and deployment. The Modeling Studio offers configurable, automated workflows for data preparation, model training, performance evaluation, and explainaibility. This allows users to go from raw dataset to high quality and intelligent forecasts within hours without any technical debt. \n\n4. Scalability and Performance \n\nBuilt on top of Apache Spark and Databricks, Workcloud Modeling Studio effortlessly scales to handle thousands of product-location combinations and years of history and any type of external data. Distributed computing ensures even fine-grained forecasts (e.g. store-SKU level) execute within tight windows. The platform’s architecture and modeling option supports analyzing data at any granularity – blending forecasts from store/item to brand/week – to provide a unified view across all channels and product hierarchies. \n\n5. MLOps \n\nTransitioning from experimentation to production is extremely simple and efficient. Workcloud Modeling Studio offers one-click deployment of ML pipelines, allowing data scientists to go from a model experiment to a production-grade deployment without rewriting code. All the MLOps functionality – including scheduling, monitoring, pipeline orchestration, artifact management, and version control – is fully managed within the platform itself. This reduces time-to-value and removes barriers to model operationalization. \n\n6. Extensibility \n\nWhile demand forecasting is the core focus, Workcloud Modeling Studio is extensible to a wide range of time-series and predictive use-cases. Users can integrate custom Python scripts or integrate external data sources (economic indicators, weather events, industry reports, etc.) to enrich the forecasts. \n\n**Target Audience**\n\nWorkcloud Modeling Studio is built for enterprise data science and analytics teams in the retail and CPG sectors. Its primary users are data scientists, ML engineers, and analytics professionals who develop and maintain forecasting models. These users will appreciate collaborative workspace, advanced modeling capabilities, and integration with existing Databricks workflows. Additionally, the output of Modeling Studio benefits demand planners, supply chain managers, and business analysts who rely on accurate forecasts to make decisions on inventory, procurement, and marketing. \n\n**Use cases**\n\n1. Demand Planning and Inventory Optimization \n\nGenerate precise forecasts for product demand at various levels (store, region, channel) to ensure optimal inventory levels. By forecasting with high accuracy, retailers can reduce excess stock and avoid stockouts, directly improving working capital and customer satisfaction. \n\n2. Promotional Forecasting \n\nPredict the uplift in demand for products during promotions or holiday campaigns. Data Scientists can model different scenarios (with promotion, without promotion) to help marketing and sales teams understand the impact of campaigns on demand and plan accordingly. \n\n3. New Product Demand Forecasting \n\nLeverage advanced machine learning techniques and algorithms to forecast demand for new product launches or new store openings where historical data is limited. \n\n4. Supply Chain and Replenishment \n\nExtend demand forecasts to upstream supply chain planning. For manufacturers and CPG companies, Workcloud Modeling Studio can forecast product demand at distributions centers or wholesalers, helping to streamline production scheduling and replenishment. This ensures better alignment between consumer demand and supply plans. \n\n5. Retail Sales and Revenue Forecasting \n\nLeverage the platform to project sales figures for financial planning and budgeting (FP&A) processes. The platform’s ability to model at aggregate levels while preserving the fine detail makes it useful for both granular operational forecasts and high-level revenue forecasts for executives. \n\n**Databricks Integration**\nWorkcloud Modeling Studio is natively integrated with your existing Databricks infrastructure. The platform does not require any additional setup or data migration on your part. Users simply create developer’s API token in their Databricks Workspace and securely passes it in Modeling Studio UI. All experimentation, model training, and production jobs are executed within the customers own Databricks environment, ensuring complete control, security, and compliance. \n\nBecause the platform runs on top of Databricks, it works seamlessly with your Lakehouse architecture, Delta Lake tables, and existing workflows. Modeling Studio does not move or copy data. This tight and secure integration enables frictionless experience, from loading data and experimentation with models to deploying forecasts and scheduling jobs. 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Lists are cached per service instance and search uses an AND-then-OR fallback with the match strategy reported in every response.\n\n**Use cases:**\n\n- Search and retrieve National Coverage Determinations including indications, limitations, and reasons for denial by condition or procedure\n- Search and retrieve Local Coverage Determinations by keyword across title and short note fields with jurisdiction context\n- Search Medicare Coverage Documents by type, such as: Decision Memos, National Coverage Analyses, Memoranda, etc. and retrieve full document text\n- Search and retrieve Technology Assessments by keyword or year for formal evidence reviews supporting coverage decisions\n- Chain search-then-lookup across all four document types to build comprehensive coverage landscapes for any service or procedure\n\n**Sample Prompts:**\n\nPrompt Example 1: Coverage Policy Research Across Document Types\n\n- \"Search NCDs for transcranial magnetic stimulation and retrieve the full policy text. Search MCDs for any related Decision Memos or National Coverage Analyses on the same service and retrieve the full document text to build a complete Medicare coverage history.\"\n\nPrompt Example 2: Technology Assessment & LCD Review\n\n- \"Search Technology Assessments for continuous glucose monitoring and retrieve the full assessment text and research question. 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This enables the detection of subtle yet important differences between customer segments, therefore allowing you to improve the relevancy & accuracy of your marketing efforts\n\nDue to the unique nature of the behavioural, lifestyle, and demographic data we have compiled at different strata, our solution is available at 3 different levels: individual, household, and postcode. This allows you to work at the level that suits your business and enables you to match an Intra code to your full customer base.\n\n**Use cases**\nUtilising the full breadth of our individual level data, we have created the socio-geodemographic segmentation Intra from over 300 attributes. \n\nEach segment has between 1% and 3% of the resident adults we have within our base, which have been numbered from 1 to 55 in order of average age of individuals in the segment, with 1 being the youngest and 55 the oldest. There is a suffix from A to E which indicates Affluence. 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This solution seamlessly handles schema transformation, data migration, and pipeline optimization, allowing businesses to move from legacy systems to modern cloud platforms with minimal disruption\n\n**Key Features**\n\n- **Pre-Built Connectors:** Native connectors to multiple RDBMS such as MySQL, SQL Server, Oracle, PostgreSQL, DB2, and Teradata.\n- **Automated Schema Transformation:** Convert legacy RDBMS schemas into the Delta Lake format, optimizing for analytical workloads.\n- **Real-Time Data Pipelines:** Support for Change Data Capture (CDC) to ensure real-time data replication and synchronization with Databricks.\n- **Data Validation & Quality Assurance:** In-built framework for ensuring schema consistency and data integrity during the migration process.\n- **Optimized ETL Framework:** Leveraging PySpark and Databricks Workflows to streamline data ingestion and transformation.\n- **Cloud-Native Integration:** Direct compatibility with Databricks Delta Lake for improved storage, data governance, and analytics.\n\n**Use cases:**\n\n- **Database Migration:** Migrate data from MySQL, SQL Server, PostgreSQL, Oracle, DB2, and Teradata to Databricks Delta Lake with automated workflows, ensuring minimal downtime and optimized schema mapping.\n- **Real-Time Data Replication:** Use CDC to synchronize data from your on-premise databases to Databricks in real-time, ensuring your analytics platforms and data lakes are always up-to-date.\n- **Schema Transformation and Optimization:** Convert complex schemas from any supported RDBMS into a unified Delta Lake model, eliminating the need for manual transformation and ensuring compatibility with Databricks’ analytics capabilities.\n- **Data Integration Across Systems:** RDBridge integrates data from multiple databases into a single, normalized model within Databricks, making it easier for data teams to perform cross-system analytics.\n- **Business Intelligence and Reporting:** Automate the transfer of historical and real-time data from on-premise RDBMS systems into Databricks, enabling seamless BI reporting and advanced data analysis.\n\n**Business Value**\n\n- **Faster Migration:** Automate the migration from MySQL, SQL Server, Oracle, and more to Databricks, cutting down migration times by 3x.\n- **Real-Time Data Synchronization:** Keep your data up-to-date with CDC and real-time data pipelines.\n- **Simplified Data Integration:** Easily manage data from various on-premise databases with RDBridge’ s unified data model, making it easier to work with in Databricks.\n- **Scalable Data Architecture:** Migrate to Delta Lake for enhanced scalability and performance, supporting high-volume data processing for advanced analytics.\n\n**Additional Insights**\n\nPlease feel free to reach out to us for a detailed discussion about our RD Bridge.\nYou can also book a convenient time for a call directly through our Calendly link: https://calendly.com/ganesanv-datapattern/30min\n","terms_of_service":"https://docs.google.com/document/d/1BNvbHG2TDQbJfJ5lJ0D9yfNh1vKQWLOd/edit?pli=1","documentation_link":"https://datapattern.ai/databrick/","privacy_policy_link":"https://docs.google.com/document/d/1BNvbHG2TDQbJfJ5lJ0D9yfNh1vKQWLOd/edit?pli=1","geographical_coverage":"[\"Global\"]","cost":"PAID","data_source":"","assets":["ASSET_TYPE_MEDIA"],"license":""},"provider_summary":{"name":"DataPattern","description":"DataPattern is a consulting and services leader specializing in Data Programs, Generative AI solutions and innovative data products. 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Every record is matched at the company level, so you can connect employee sentiment to hiring activity, skill demand, and compensation in one dataset.\n\nWhat This Employee Data Covers\n\nThis Employee Data product combines review data with enriched job postings data matched at the company level.\n\nEmployee Data Reviews (Glassdoor and Indeed):\n\n• Overall company rating (1-5 stars)\n• Sub-ratings: work-life balance, pay and benefits, job security, management, culture\n• Review title, pros text, and cons text (full content)\n• Reviewer job title, location, and employment status (current or former employee)\nEmployee Data Skills and Employment Records:\n\n• Normalized job title (from 50,000+ standardized categories)\n• Hard skills (37,000+ unique) with weighted relevance scores\n• Certifications (3,000+) and soft skills (400+)\n• Seniority level (Entry, Mid, Senior, Lead, Executive)\n• Work type (Remote, Hybrid, Onsite) and employment type\n• Salary (parsed from posting and AI-predicted where missing)\nThis Employee Data covers the United States from 2022 to present, updated weekly.\n\nWhat You Can Do With This Employee Data\n\nCompensation Benchmarking and Pay Equity\n\nHR and compensation teams use Employee Data to benchmark salaries, validate internal pay structures, and identify equity gaps across roles, seniority levels, and locations. Employee Data combines what companies post as salary with AI predictions for the 60-70% that don't show pay, giving you full compensation coverage.\n\n• Benchmark base pay by role, seniority, and location using Employee Data salary and prediction fields\n• Validate internal compensation structures against external Employee Data records\n• Analyze competitor compensation levels through Employee Data review sub-ratings for pay and benefits\n• Build compensation bands grounded in Employee Data actual posting and predicted salary ranges\nEmployer Brand and Talent Attraction\n\nRecruiting and employer brand teams use Employee Data review signals to monitor culture perception, track satisfaction trends, and benchmark employer reputation against competitors. Employee Data gives you the raw review data, not just an aggregate score, so you can understand the drivers of sentiment.\n\n• Monitor employer reputation trends using Employee Data review ratings and sub-scores over time\n• Compare culture scores, management ratings, and work-life balance signals across competitors with Employee Data\n• Identify emerging retention risks through shifts in Employee Data review sentiment by company and role\n• Benchmark your Employee Data employer brand signals against industry peers\nHR Analytics and People Intelligence\n\nPeople analytics teams use Employee Data to combine internal HR data with external market signals, connecting what employees say publicly with what skills and pay the market is offering. Employee Data at the company level gives analysts a 360-degree view of the talent environment.\n\n• Correlate Employee Data review sentiment with hiring velocity as an indicator of company health\n• Track skill demand trends by role and industry using Employee Data skills and certification fields\n• Benchmark internal HR metrics against external Employee Data market signals\n• Identify high-retention employers through Employee Data culture and management sub-ratings\nInvestment and Due Diligence\n\nInvestors and deal teams use Employee Data as a leading indicator of organizational health. Shifts in Employee Data review scores often reflect culture changes or leadership transitions before they appear in financial filings.\n\n• Monitor portfolio company employee sentiment using Employee Data review scores over time\n• Use Employee Data culture and management ratings as early signals of leadership or operational risk\n• Assess private company workforce health through Employee Data review patterns and hiring activity\n• Track compensation competitiveness of portfolio companies using Employee Data salary benchmarks\nHow We Build This Employee Data\n\nThe salary estimation model fills the pay gap in Employee Data records. About 60-70% of job postings do not include explicit salary. Trained on 50M+ observations with a MAPE under 15%, it predicts ranges using company history, geographic adjustments, role seniority, and industry norms, giving Employee Data full salary coverage across all records.\n\nThe title taxonomy model normalizes every Employee Data job title into 50,000+ standardized categories from 20M+ raw titles, enabling consistent comparison of skills and salaries across companies, industries, and geographies.\n\nThe skill taxonomy model extracts 37,000+ hard skills, 3,000+ certifications, and 400+ soft skills from Employee Data job postings using contextual AI filtering with weighted relevance scores to separate core skills from peripheral mentions.\n\nJob category models classify each Employee Data record by seniority (Entry, Mid, Senior, Lead, Executive) and work modality (Remote, Hybrid, Onsite) using LLM-based analysis of the full job description.\n\nOur annotation team validates every Employee Data model output before delivery.\n\nWhat Makes This Employee Data Different\n\n• Three-dimensional: Employee Data combines reviews, skills, and salary in one company-matched dataset\n• Raw records: Employee Data delivers individual review records, not aggregated scores, for granular analysis\n• Weighted skills: Employee Data distinguishes core skills from peripheral mentions using relevance scores\n• Full salary coverage: Employee Data AI predictions fill the 60-70% of records without explicit pay, with MAPE under 15%\n• Company-matched: every Employee Data review and skill record links to a verified company profile\n• Weekly updates: Employee Data refreshes weekly so sentiment and skill demand signals reflect current market conditions\nWho Uses This Employee Data\n\n• HR and Compensation Teams use Employee Data to benchmark salaries, validate pay equity, and track external compensation trends against internal structures\n• People Analytics Teams use Employee Data to correlate internal HR metrics with external market signals for a fuller view of the talent environment\n• Employer Brand and Recruiting Teams use Employee Data review signals to monitor reputation, track culture trends, and benchmark against competitor employers\n• Investors, PE Firms, and VC Funds use Employee Data sentiment and hiring signals as alternative data for due diligence and portfolio monitoring\n• Learning and Development Teams use Employee Data skills records to design training programs aligned with real employer demand and market skill trends\n• Compensation Consultants use Employee Data to advise clients on market rates, pay equity, and competitive compensation positioning\n• HR Tech Companies and B2B Platforms use Employee Data to power compensation benchmarking tools, employer review products, and talent intelligence applications\nDelivery and Format\n\nEmployee Data is delivered in CSV, JSON, or Parquet via AWS S3 or Google Cloud Storage. Custom filters by company, geography, date range, industry, role, seniority, review rating, and employment status. Compatible with Snowflake, Databricks, Power BI, Tableau, Salesforce, and most BI platforms.","terms_of_service":"https://decanaria.com/privacy-policy","contact_email":"support@datarade.ai","privacy_policy_link":"https://decanaria.com/privacy-policy","assets":["ASSET_TYPE_DATA_TABLE"]},"provider_summary":{"name":"Canaria Inc.","description":"Canaria is a leading provider of enriched job posting and labor market data. 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Public commercial data only, GDPR and CCPA compliant."},"popularity":1832},{"id":"a548beee-8ce1-436a-8b08-91f31a04cdc6","summary":{"name":"Cyber Security Incident Investigation using Graphistry","subtitle":"For SOC analyts, Incident Responders and Threat Hunters","setting":{"visibility":"PUBLIC"},"created_at":1695926911969,"updated_at":1696444149536,"categories":["SECURITY"],"listingType":"STANDARD","provider_id":"ea1e69ff-0127-4c94-bf39-e841fe1d19d2","git_repo":{"git_repo_url":"https://github.com/databricks-industry-solutions/incident-investigation-using-graphistry.git","git_repo_file_entrypoint":"README.md","git_provider":"GITHUB"}},"detail":{"description":"\n**Use Cases**\nIn this solution accelerator, we showcase how SOC analyts, Incident Responders and Threat Hunters can use Databricks to\n- Investigate an incident or alert to determine if it is true positive or false positive. If it is a true positive, determine the host and users impacted, so that remediation steps can be taken.\n- Investigate leads from a threat hunting exercise.\n- Hunt for threats given a piece of threat intelligence or a news release\n\n**Technical Overview**\n- Leverage any cybersecurity data in the lakehouse without transforming the data into a graph data model at ingestion time.\n- Dynamically run a query to filter the data and convert to graph data model at analysis time. This flexibility allows the analyst to tweak the graph data model at will during analysis.\n- Send the resultant data frames (nodes and edges) to graphistry for visualization\n- Perform investigation and analysis in graphistry UI without writing any code\nClick on the \"Get instant access\" button in the top right corner to clone the solution accelerator repo into your workspace. 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Technical complexity:**\nExecutives need clarity on service levels, operational efficiency, workforce alignment, and customer experience. But critical data—call handling, agent behaviour, queue dynamics, WFM schedules, QA scores—remains scattered across multiple systems. This forces leaders to make decisions without a holistic, trustworthy view of their organisation’s performance.\n**2. Poorly Connected Operational Signals:**\nHigh-value leadership KPIs such as FCR, AHT, SLA adherence, shrinkage, utilisation, and queue efficiency depend on connecting events across telephony, WFM, and CRM systems. Traditional ETL pipelines struggle to stitch these signals accurately, resulting in delayed insights and reactive management.\n\n**Architecture Overview – Medallion + Agentic Orchestration**\n- Bronze (L0): Raw ingestion of contact centre Leadership datasets:\n- Silver (L1–L2): AI agents automatically generate standardized SQL, enforce incremental load logic, and validate primary keys using Delta Live Tables (DLT).\n- Gold (L3): Conversational agent assists users in defining business tables—such as CC Leadership Summary —through natural language, validating and deploying DLT SQL automatically.\n\n**Pipeline Flow**\n- Metadata Enricher Agent (Bronze): Generates semantic descriptions for events, states, and call segments with HITL approval.\n- Silver SQL Generator Agent: Builds L1 standardized views for calls, agents, queues, and routing\n- Conversational Gold Agent: Allows CC teams to build advanced CC tables simply by describing them (“Create an CC Leadership summary”). The agent validates logic, fixes SQL errors automatically, and ensures consistency across CC metrics.\n- DLT Execution: A single Databricks Delta Live Tables pipeline constructs the complete CC lifecycle, seamlessly linking L1, L2, and L3 layers while providing full data lineage, change tracking, and auditability.\n\n**CC Leadership Insight Functional Insights (Gold Layer)**\nOnce the agentic pipeline is deployed, the accelerator delivers ready-to-use insights:\n\n**1. Executive Call Journey Intelligence**\nProvides leadership with a complete, high-level view of how customers move through the contact centre—from IVR and routing decisions to agent handling and multi-transfer flows.\nEnables rapid identification of systemic friction points, customer effort drivers, and areas impacting CX, SLAs, and cost-to-serve.\n**2. Workforce & Performance Leadership Metrics**\nElevates agent analytics into leadership-ready insights by blending productivity, adherence, and behavioural patterns.\nSurfaces trends in team efficiency, coaching opportunities, burnout indicators, and SLA contribution, helping leaders optimise staffing strategies and performance programs.\n**3. Operational Excellence Dashboarding**\nDelivers unified KPIs that matter to leaders: SLA attainment, service reliability, queue efficiency, cost efficiency, utilisation, and capacity trends.\nHelps leadership proactively manage performance, forecast operational risk, and drive strategic improvements across queues, teams, and channels.\n\n**Dashboard Widgets**\n**Empower Caps & Scorecard Review**\n- **Capacity Utilization vs. Target** – Shows the percentage of planned agent capacity actually used versus target thresholds.\n- **Scorecard Achievement Trend** – Tracks team- and agent-level performance against KPIs over time.\n- **Compliance & Adherence Scores** – Visualizes adherence to schedules, policies, and compliance standards.\n- **Performance Gaps** – Highlights areas where teams or individuals are falling short of targets.\n\n**Agent Performance**\n- **Handle Time Metrics** – Summarizes talk, hold, and wrap-up times for each agent.\n- **Occupancy & Utilization** – Illustrates workload distribution across shifts and teams.\n- **ACW Trends** – Tracks After-Call Work trends to identify inefficiencies or coaching needs.\n- **Top & Bottom Performers**– Highlights high-performing agents and those needing support, based on SLA contribution and efficiency.\n\n**Key Capabilities**\n- Autogenerates descriptions for call segments, events, and agent states.\n- AI-driven reconstruction of complete call journeys—multi-segment, multi-agent, multi-transfer.\n- DLT-ready SQL for Agent Performance, and Empower Caps & Scorecard Review Tables\n- Conversational Analytics Creation (chat-based Gold tables)\n- Agent auto-fixes SQL errors and repairs broken transformations.\n- CDC Support for near-real-time invoice lifecycle\n\n**Marketplace Deliverables**\n- **Production-Ready Notebooks**: End-to-end AELT pipeline notebooks for Bronze (Ingestion), Silver (Transformation), and Gold (Analytics) with agent-assisted automation.\n- **Config Templates**: JSON mappings for CC objects with AI-assisted metadata generation.\n- **DLT SQL Files**: Automatically generated and validated SQL for incremental tables and CC Leadership Insights views.\n- **CDC & Data Quality Frameworks**: Out-of-the-box components for real-time ingestion and data validation.\n- **Documentation & Implementation Guide**: Step-by-step Databricks setup, object mapping, and AELT deployment manual.\n\n**Why Choose Dataplatr’s CC Leadership Agentic ELT Accelerator**\n- **Agentic Automation** – Eliminates manual stitching logic & complex SQL.\n- **Rapid Time-to-Value** – Operational dashboards ready in hours, not weeks.\n- **Deep Domain Logic** – Prebuilt formulas for AHT, ASA, occupancy, and SLA.\n- **Enterprise Governance** – Unity Catalog lineage & auditability.\n- **Databricks-Native** – Powered by Delta Live Tables & Lakehouse.\n- **Scalable & Extensible** – Works with any contact centre platform.\n\n**Transform CC Leadership Analytics with Agentic ELT**\nDeliver unified Leadership Insights, real-time insights, and AI-powered operational intelligence—fully automated on Databricks.\n\nNote: By clicking '**Get Access**', you agree to our **Terms of Service**. You acknowledge that **Dataplatr Corp** will recognize your organization as a customer/user for promotional and reference purposes.\n\nFor a demo or hands-on notebook, feel free to reach out.\n\n***Contact us at**: chandra.reddy@dataplatr.com\n**For consultations or custom inquiries**: https://dataplatr.com/contact-us \n[Linkedin](https://www.linkedin.com/company/dataplatrinc)*\n\n*Please read our published article on [Medium](https://medium.com/@dataplatr) to learn more about our latest insights and innovations.\n[Medium](https://medium.com/@dataplatr)*","terms_of_service":"https://docs.google.com/forms/d/e/1FAIpQLSdnlNmSfrFRQDa_ToOL3ydTOPcWI3m3lWor2plLbiqF1JRINg/viewform","documentation_link":"","privacy_policy_link":"https://dataplatr.com","embedded_notebook_file_infos":[{"id":"fee20b98-cbf2-4ed2-a458-67fd86bfc10a","marketplace_file_type":"EMBEDDED_NOTEBOOK","file_parent":{"parent_id":"66e84999-0c62-4243-8287-bfbdff91b59a","file_parent_type":"LISTING"},"mime_type":"text/html","download_link":"https://marketplace-file-storage-prod.s3-fips.us-east-1.amazonaws.com/EMBEDDED_NOTEBOOK/LISTING_66e84999-0c62-4243-8287-bfbdff91b59a/fee20b98-cbf2-4ed2-a458-67fd86bfc10a?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEGgaDnVzLWVhc3QtMS1maXBzIkYwRAIgbC8sWIoYaHR8y7Wq86nPb0pufBl4%2BM4PWpeN%2B5PW9VQCIG7BhAVBDCWTpxFDLeuKYm%2Ff1JLz4iccPMzter0YdhgWKqYECDAQABoMNDE0MzUxNzY3ODI2IgxNAJrHbXaZG20jNfMqgwQyGEb4TsoVOoNvJ%2BaA%2FZ7zeu8hGcHqCeNfblP%2FQo5g6q2aRNtpwtPRKMlhYcIE%2FUQaNT1Lqmyptu32VUQTpzjTTxRVmJrDPkheSCkV06XQ%2FpTMKjU8CC7X1b%2FqYf8CrBwZT7Cv4C5WPjEET38f2A79h0xV6bZgPy28zQWrSeIJ9w6AfZBxiE24%2FVc08zVA0ivCT6lsy7nuU0wcR%2FMg2C9nd1vaMnQ923VpD21CKfVVYcrNWL2b%2FQ%2BNAO%2BxPjPgjiN6UPQbvbvZxkSdhgrGtecIg9NgqGkMcvM5VLTDtdC1kdeZetfOC2obtA%2BT%2F0gy3tIDo6WM5hcQfw743%2FAU9oaGnRBl%2BwoxRRDCZA%2F4jnPdluICSI7NCr6C3HHOe4CSZJjRihczxPA%2FgQczAVjAPs6CrK9N9pIzt0vNJpGUTgg4AoxftTvGfciDREilSctPIcBk6GwB%2Bzt4wbtNCmEPU%2BFD46rc6CWVQmmLwbKOeHw70ipMVFlOc8NlXkEtGO2vu6H44MstSSc6daOVUYUavQSGZ6jGhEjsP2YGz8KDm9XQotB%2FtyVBqiZV01OKqDmmBJA%2FXjUpSJ8M%2FeJXhe5m%2BKcxzNObtoJRI7jci3Tpt08XZyXZ7%2FyIV8H30ya468X3cfkTXrm972uga3z7iXAdubyEHnQIua%2FuvtwOHILi6O5D1Me2MzCPvcnXBjqTAYDoMWBt%2B3AVFuqSs2dmDQNBnvEhXfMgTedKLD5PJHb3aIl15iBkLaNu36GYnVCp9ZU2lSYcTbY5QwNnNeTfJTNy9bKhwzru6Sg%2BslhN%2BTS0zkTn%2BeLMY4tHgPEtEZ2cp%2FCk6GwqswvnYVKHjwO8q%2B1Mx8lc08fVEfarE9dke1GGFqoddx%2Bq%2BZUyHbDlAVfrCmf0yw%3D%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20261110T000135Z&X-Amz-SignedHeaders=host&X-Amz-Expires=86400&X-Amz-Credential=ASIAWA6KKHEJORWBH4N3%2F20261110%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=99d2f8a2cae298ce90e231144abde166b644eff946cb588be54fa21a9b86ccb4","created_at":1770876456637,"updated_at":1770876456637,"display_name":"AELT_Marketplace_CC_Leadership Insights","status":"FILE_STATUS_PUBLISHED"}],"assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"Dataplatr Corp","description":"Dataplatr Corp specializes in Data Analytics & AI with pre-built data models for Enterprise Applications like SAP, Oracle EBS, Workday, Salesforce to empower businesses to unlock the full potential of their data. Our enterprise data engineering accelerators are designed to expedite the development of data pipelines, data transformation, and integration, saving you time and resources.\n\nOur team of experienced data engineers, scientists and analysts utilize cutting-edge data infrastructure into valuable insights and helps enterprise clients optimize their Sales, Marketing, Operations, Financials, Supply chain, Human capital and Customer experiences."},"popularity":1350},{"id":"03085f84-4be8-4211-97a4-3c6ee764d826","summary":{"name":"Labor Force Participation Rate - 25-54 Yrs. | FRED","subtitle":"This dataset contains a historical time-series data of Labor Force Participation Rate - 25-54 Yrs. starting from 1948-01-01.","share":{"name":"fs_fred_lns11300060","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1674243925645,"updated_at":1698965635225,"categories":["ECONOMICS"],"listingType":"STANDARD","provider_id":"f00071b1-2db4-4f8f-a4bc-4c62c790f25a"},"detail":{"description":"**Overview**\n\nThis dataset contains a historical time-series data of Labor Force Participation Rate - 25-54 Yrs. (LNS11300060) starting from 1948-01-01. Starting with the 2005-02-16 release, the series reflects an increase in the universe of permit-issuing places from 19,000 to 20,000 places.\n\n**Product Details**\n\nIncludes the following tables:\n\n\n- **fs_fred.fred_lns11300060**\n - **Update frequency:** Monthly\n - **Source:** FRED\n - **Description:** The values within the data field labeled `LNS11300060` represent the amount of people ages 25-54 employed or looking for work in the US in Percent at the time of the corresponding `DATE` field. Values are seasonally adjusted.\n\n\n\n**Data Sources**\n\n\nThe data in this product comes from **FRED**.\n\nShort for Federal Reserve Economic Data, FRED is an online database consisting of hundreds of thousands of economic data time series from scores of national, international, public, and private sources. \nFRED, created and maintained by the Research Department at the Federal Reserve Bank of St. Louis, goes far beyond simply providing data: \nIt combines data with a powerful mix of tools that help the user understand, interact with, display, and disseminate the data. \nIn essence, FRED helps users tell their data stories.\n\nThis product uses data from the FRED® API but is not endorsed or certified by the Federal Reserve Bank of St. Louis.\n\n\n\n- **Source Homepage:** [Federal Reserve Economic Data | FRED | St. Louis Fed](https://fred.stlouisfed.org/)\n- **Source Data:** [Labor Force Participation Rate - 25-54 Yrs. (LNU01300060)](https://fred.stlouisfed.org/series/LNU01300060)\n- **License:** [Full FRED® Services Terms of Use](https://fred.stlouisfed.org/legal/#full-fred-terms)\n- **Frequency:** monthly\n\n\n\n\n","terms_of_service":"https://rearc-data-public-assets.s3.amazonaws.com/Rearc_Data_DSA.pdf","contact_email":"data@rearc.io","support_link":"https://www.rearc.io/data/","privacy_policy_link":"https://rearc-data-public-assets.s3.amazonaws.com/Rearc+Privacy+Policy.pdf","embedded_notebook_file_infos":[{"id":"1e6392c0-a012-44b0-8005-1d64802bc12c","marketplace_file_type":"EMBEDDED_NOTEBOOK","file_parent":{"parent_id":"03085f84-4be8-4211-97a4-3c6ee764d826","file_parent_type":"LISTING"},"mime_type":"text/html","download_link":"https://marketplace-file-storage-prod.s3-fips.us-east-1.amazonaws.com/EMBEDDED_NOTEBOOK/LISTING_03085f84-4be8-4211-97a4-3c6ee764d826/1e6392c0-a012-44b0-8005-1d64802bc12c?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEGgaDnVzLWVhc3QtMS1maXBzIkYwRAIgbC8sWIoYaHR8y7Wq86nPb0pufBl4%2BM4PWpeN%2B5PW9VQCIG7BhAVBDCWTpxFDLeuKYm%2Ff1JLz4iccPMzter0YdhgWKqYECDAQABoMNDE0MzUxNzY3ODI2IgxNAJrHbXaZG20jNfMqgwQyGEb4TsoVOoNvJ%2BaA%2FZ7zeu8hGcHqCeNfblP%2FQo5g6q2aRNtpwtPRKMlhYcIE%2FUQaNT1Lqmyptu32VUQTpzjTTxRVmJrDPkheSCkV06XQ%2FpTMKjU8CC7X1b%2FqYf8CrBwZT7Cv4C5WPjEET38f2A79h0xV6bZgPy28zQWrSeIJ9w6AfZBxiE24%2FVc08zVA0ivCT6lsy7nuU0wcR%2FMg2C9nd1vaMnQ923VpD21CKfVVYcrNWL2b%2FQ%2BNAO%2BxPjPgjiN6UPQbvbvZxkSdhgrGtecIg9NgqGkMcvM5VLTDtdC1kdeZetfOC2obtA%2BT%2F0gy3tIDo6WM5hcQfw743%2FAU9oaGnRBl%2BwoxRRDCZA%2F4jnPdluICSI7NCr6C3HHOe4CSZJjRihczxPA%2FgQczAVjAPs6CrK9N9pIzt0vNJpGUTgg4AoxftTvGfciDREilSctPIcBk6GwB%2Bzt4wbtNCmEPU%2BFD46rc6CWVQmmLwbKOeHw70ipMVFlOc8NlXkEtGO2vu6H44MstSSc6daOVUYUavQSGZ6jGhEjsP2YGz8KDm9XQotB%2FtyVBqiZV01OKqDmmBJA%2FXjUpSJ8M%2FeJXhe5m%2BKcxzNObtoJRI7jci3Tpt08XZyXZ7%2FyIV8H30ya468X3cfkTXrm972uga3z7iXAdubyEHnQIua%2FuvtwOHILi6O5D1Me2MzCPvcnXBjqTAYDoMWBt%2B3AVFuqSs2dmDQNBnvEhXfMgTedKLD5PJHb3aIl15iBkLaNu36GYnVCp9ZU2lSYcTbY5QwNnNeTfJTNy9bKhwzru6Sg%2BslhN%2BTS0zkTn%2BeLMY4tHgPEtEZ2cp%2FCk6GwqswvnYVKHjwO8q%2B1Mx8lc08fVEfarE9dke1GGFqoddx%2Bq%2BZUyHbDlAVfrCmf0yw%3D%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20261110T000135Z&X-Amz-SignedHeaders=host&X-Amz-Expires=86400&X-Amz-Credential=ASIAWA6KKHEJORWBH4N3%2F20261110%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=1da1afc7f4132ba4ddec3fd776e2df059999be61d567823ed887c2f296241612","created_at":1683045467063,"updated_at":1683045467063,"status":"FILE_STATUS_PUBLISHED"}],"assets":["ASSET_TYPE_DATA_TABLE"]},"provider_summary":{"name":"Rearc","description":"Rearc provides a vast library of data products for Healthcare, Financial Services, and ESG use cases. Whether you're looking for genomes or asset prices, there is a Rearc data product to address your team's data needs. If you're looking for something that's not yet available in our library, our data curation team can help you source that data through the Databricks Marketplace as well."},"popularity":909},{"id":"d1f73528-cb11-4b34-b4be-0e443a02ea0d","summary":{"name":"Socotra Data Ingestion Accelerator","subtitle":"Ingest your Socotra Data Lake into Unity Catalog with incremental, consistent, and customizable loads","setting":{"visibility":"PUBLIC"},"created_at":1789421275690,"updated_at":1790344028803,"categories":["FINANCIAL"],"listingType":"PERSONALIZED","provider_id":"8c2c2b17-eb7f-44a6-99d8-1e255d2b3ac9"},"detail":{"description":"**Overview**\n\nBring your **[Socotra Insurance Suite](https://www.socotra.com/)** data into Databricks, with no third-party ETL tools, connectors, or services required.\n\nThe **Socotra Data Ingestion Accelerator** is a Databricks notebook that replicates your [Socotra data](https://docs.socotra.com/features/reporting/reporting-overview) into your own Unity Catalog, using only your Socotra credentials and your existing Databricks workspace. Configure it, run it, and query current-state Socotra tables directly in Databricks on day one.\n\nBuilt to make your Socotra data easy to reach and trust: tables stay consistent with each other, runs pick up exactly where they left off, and the structure comes straight from Socotra. You own the code and how it runs, so you can schedule, extend, and maintain it like any other part of your Databricks environment.\n\n---\n\n**Product details**\n\nThe **[Socotra Data Lake](https://docs.socotra.com/features/reporting/data-model)** is a relational view of your operational data across the Socotra Insurance Suite. This accelerator loads it into Databricks using **[Socotra Delta Files](https://docs.socotra.com/features/reporting/delta-files)**, incremental CSV change exports built for replication, so each run picks up only what changed since the last one.\n\nSocotra Delta File API → Unity Catalog Volume (raw files) → Bronze tables (full history) → Silver tables (current state)\n\n**What you get**\n\n- **Current-state data:** a query-ready copy of your Socotra Data Lake in your catalog and schemas of choice\n- **Selective loading:** load only the tables you need, and add more as you go\n- **Incremental runs:** rerun any time to pull in only what's new\n- **Medallion layout:** Bronze and Silver tables, ready for your own Gold layer\n- **Multi-tenant ready:** combine tenants into shared tables, or keep each in its own schema\n- **Open code:** readable PySpark built on Auto Loader, ready to edit and schedule\n\n---\n\n**Requirements**\n\n- A **Socotra Business Account** with CSV Delta Files enabled\n- A **Databricks workspace with Unity Catalog** and cluster or serverless compute\n\n---\n\n**License**\n\nCopyright (c) 2026 Socotra\nPermission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the \"Software\"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:\nThe above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANT ABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n","terms_of_service":"https://www.socotra.com/terms-of-use/","documentation_link":"https://docs.socotra.com/","privacy_policy_link":"https://www.socotra.com/privacy-policy/","update_frequency":{"interval":90,"unit":"DAILY"},"assets":["ASSET_TYPE_NOTEBOOK"],"license":""},"provider_summary":{"name":"Socotra Inc","description":"Socotra delivers the most powerful insurance platform on the market, empowering insurers to support any number of insurance products, policies, or users. Accelerate implementations, integrations, and product updates—all with the industry's lowest total cost of ownership (TCO). Socotra's listings help customers bring their core operational data, such as quotes, policies, and transactions, into Databricks for analytics, reporting, and AI."},"popularity":628},{"id":"1596adf6-851d-4645-87ca-592fade48a33","summary":{"name":"Consumer Complaints","subtitle":"This data package contains the complaints filed by the consumer for different purposes like Environmental Complaints, ..","share":{"name":"consumer_complaints","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1682356036506,"updated_at":1695691343950,"categories":["HEALTH","SCIENCE_AND_RESEARCH"],"listingType":"STANDARD","provider_id":"f88849f8-9d77-4de2-b4c7-03d55cc3768e"},"detail":{"description":"\n**Overview**\nThis data package contains the complaints filed by the consumer for different purposes like Environmental Complaints, Building Complaints, Housing Maintenance Complaint Code, Landmark Complaint, etc.\n\n**Description**\nThis data package includes information about the actions taken by the company in response to the complaint, such as, whether the company’s response was timely, how the company responded, and whether the consumer disputed the company’s response. If the consumer opts to share it and after we take steps to remove personal information, we publish the consumer’s description of what happened. Companies also have the option to select a public response. The Department enforces compliance with these regulations and promotes worker and public safety through its review and approval of building plans, permitting and licensing functions, and inspections. In addition to Code and Zoning compliance review, emergency response and professional analysis of accidents to prevent future incidents, the Department has strengthened its education and outreach programs to provide the construction industry, property owners and tenants with a heightened understanding of safety awareness.\nhe Department of Housing Preservation and Development (HPD) records complaints that are made by the public through the 311 Citizen Services Center, Code Enforcement Borough Offices or the internet for conditions which violate the New York City Housing Maintenance Code (HMC) or the New York State Multiple Dwelling Law (MDL).\n\n**Benefits**\n- Useful to evaluate issues and public expectations with regard to the environment, buildings, landmarks, housing maintenance, sanitation, and water quality. the field descriptions along with the dataset descriptions are also useful for the user to quickly understand the values from each dataset.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [Alley Lights Out Service Request](https://www.johnsnowlabs.com/marketplace/alley-lights-out-service-request)\n\t- This dataset contains all open 311 reports of one or more lights out on a wooden pole in the alley and all completed requests since January 1, 2011.\n- [Bloomington All Open 311 Request Reports](https://www.johnsnowlabs.com/marketplace/bloomington-all-open-311-request-reports)\n\t- This dataset contains all open 311 request reports in the city of Bloomington. Open311 reports typically include requests for pothole repair, street signal light bulb replacement, graffiti removal, and sidewalk repair. Open311 cities publish these services through a web API (Application Programming Interface).\n- [Bloomington Street Signs](https://www.johnsnowlabs.com/marketplace/bloomington-street-signs)\n\t- This dataset shows the activities conducted by the Street Department that involve street signs for the City of Bloomington.\n- [Building Complaint Disposition Codes](https://www.johnsnowlabs.com/marketplace/building-complaint-disposition-codes)\n\t- This dataset contains Complaint Disposition Reports contain information on the status of complaints received and how the complaints are deposed. This report includes the community board number, dates each complaint was received and inspected, and the disposition of the complaint.\n- [CDPH Asbestos and Demolition Notification](https://www.johnsnowlabs.com/marketplace/cdph-asbestos-and-demolition-notification)\n\t- This dataset contains the information about CDPH (California Department of Public Health) Asbestos and Demolition Notification Notices of Intent (NOI) for demolition and asbestos abatement per Chapter 11 -4 Article XVIII of the Municipal Code submitted to the Department of Environment (DOE) for work started January, 1993 or submitted to the Department of Public Health (DPH) for work beginning on or after January 1, 2012.\n- [CDPH Environmental Complaints](https://www.johnsnowlabs.com/marketplace/cdph-environmental-complaints)\n\t- This dataset contains The Environmental complaints received by the Department of Environment (DOE) from January 1993 to December 31, 2011 and by the Department of Public Health (CDPH) since January 1, 2012. On January 1, 2012, the DOE was disbanded and all its inspection, permitting, and enforcement authorities were transferred to the CDPH.\nEnvironmental complaints arise from issues that pose a threat to the environment, a threat to health or an adverse impact on nature, amenity and lifestyle.\n- [CDPH Environmental Enforcement](https://www.johnsnowlabs.com/marketplace/cdph-environmental-enforcement)\n\t- This dataset contains the Municipal and State code violation notices issued by the Department of Environment (DOE) and by the Department of Public Health (CDPH) Permitting and Inspections unit since January 1, 2012. On January 1, 2012, the DOE was disbanded and all its inspection, permitting, and enforcement authorities were transferred to the CDPH.\n- [CDPH Environmental Inspections](https://www.johnsnowlabs.com/marketplace/cdph-environmental-inspections)\n\t- This dataset contains the Inspections conducted by the Department of Environment (DOE) and by the Department of Public Health (CDPH). The Department of Environment was disbanded and all its inspection, permitting, and enforcement authorities were transferred to the CDPH.\n- [CDPH Environmental Permits](https://www.johnsnowlabs.com/marketplace/cdph-environmental-permits)\n\t- This dataset contains the permits issued by the Department of Environment (DOE) from January 1993 to December 2011 and by the Chicago Department of Public Health (CDPH) since January 1, 2012. On January 1, 2012, the DOE was disbanded and all its inspection, permitting, and enforcement authorities were transferred to the CDPH.\n- [CDPH Storage Tanks](https://www.johnsnowlabs.com/marketplace/cdph-storage-tanks)\n\t- This dataset contains Aboveground Storage Tank (AST) and Underground Storage Tank (UST) information from the Chicago Department of Public Health’s (CDPH) Tank Asset Database.\n- [Chicago Building Violations](https://www.johnsnowlabs.com/marketplace/chicago-building-violations)\n\t- This dataset shows the violations issued by the Department of Buildings from 2006 to the present for the City of Chicago.\n- [Chicago City Clerk Request Log](https://www.johnsnowlabs.com/marketplace/chicago-city-clerk-request-log)\n\t- This dataset shows the FOIA (Freedom of Information Act) requests received by the Office of the Chicago City Clerk as of May 1, 2010.\n- [Chicago Food Inspections](https://www.johnsnowlabs.com/marketplace/chicago-food-inspections)\n\t- This dataset contains the restaurant inspections and other food establishments in Chicago from January 1, 2010 to the present.\n- [Chicago Life Safety Evaluations Reports](https://www.johnsnowlabs.com/marketplace/chicago-life-safety-evaluations-reports)\n\t- This dataset contains the list of the report status of all residential buildings required to submit a Life Safety Evaluation under city ordinance.\n- [Civilian Office of Police Accountability Cases](https://www.johnsnowlabs.com/marketplace/civilian-office-of-police-accountability-cases)\n\t- This dataset contains the complaints received by the Civilian Office of Police Accountability and its predecessor agency in Chicago. Complaints include excessive force, domestic violence, bias-based verbal abuse, and coercion with the threat of violence.\n- [Connecticut Fire Incidents Report](https://www.johnsnowlabs.com/marketplace/connecticut-fire-incidents-report)\n\t- This dataset contains Connecticut Fire Department (CFD) Incidents as reported to the National Fire Department Incident Reporting System (NFIRS).\n- [Connecticut Insurance Company Complaints](https://www.johnsnowlabs.com/marketplace/connecticut-insurance-company-complaints)\n\t- This dataset shows the listing of consumer complaints filed against insurance companies licensed in Connecticut. This dataset includes the company name, the line of business, nature of complaint, outcome or resolution, and recovery.\n- [Consumer Complaint Database](https://www.johnsnowlabs.com/marketplace/consumer-complaint-database)\n\t- The Consumer Complaint Database is a collection of 719,794 complaints, on a range of consumer financial products and services, sent to nearly 3,000 companies for a response. All the facts alleged in these complaints are not verified, but steps are taken to confirm a commercial relationship between the consumer and the company.\n- [Consumer Services Mediated Complaints](https://www.johnsnowlabs.com/marketplace/consumer-services-mediated-complaints)\n\t- This data set features consumer complaints against businesses that were mediated by the DCA (Department of Consumer Affairs) Consumer Services Division during the last and current calendar years. It excludes complaints that may have ongoing legal investigations.\n- [DHHL Revocable Permits](https://www.johnsnowlabs.com/marketplace/dhhl-revocable-permits)\n\t- This Dataset includes revocable permit that grants the Permittee certain rights regarding the placement of a privately owned\nimprovement in a public right-of-way or public easement.\n- [Department of Building Complaints Received](https://www.johnsnowlabs.com/marketplace/department-of-building-complaints-received)\n\t- This dataset provides the list of complaints received and associated data provided by the department of building DOB. The report lists complaints received and acted upon by the DOB (Department of Building).\n- [Department of Public Safety Weekly Population Reports](https://www.johnsnowlabs.com/marketplace/department-of-public-safety-weekly-population-reports)\n\t- This dataset contains the total number of state corrections population in the Department’s custody at the end of each month, including those in prison, in contracted county jails, in community phases of the State Intermediate Punishment (SIP) program, in Parole Violator Centers (PVCs), and on temporary transfer to other jurisdictions.\n- [Detroit Blight Violations](https://www.johnsnowlabs.com/marketplace/detroit-blight-violations)\n\t- This dataset shows Blight Violation Notices (BVN), or Blight Tickets, that have been issued to property owners who have violated City of Detroit ordinances that govern how property owners must maintain the exterior of their property.\n- [Detroit Citizen Complaints](https://www.johnsnowlabs.com/marketplace/detroit-citizen-complaints)\n\t- This dataset reflects all citizen complaints received by the Detroit Police Department (DPD) and the Board of Police Commissioners since January 1, 2019.\n- [Detroit Fire Incidents Report](https://www.johnsnowlabs.com/marketplace/detroit-fire-incidents-report)\n\t- This dataset reflects all fire incidents reported to the Detroit Fire Department.\n- [Detroit Properties Demolition](https://www.johnsnowlabs.com/marketplace/detroit-properties-demolition)\n\t- This dataset shows properties that are contracted for demolition by the Detroit Land Bank Authority (DLBA) or Detroit Building Authority (DBA). Projects listed in this dataset have a known price, contractor, and projected knock-down date.\n- [Hartford Fire Incidents Report](https://www.johnsnowlabs.com/marketplace/hartford-fire-incidents-report)\n\t- This dataset reflects all fire incidents reported to the Hartford Fire Department (HFD) from January 1, 2014, through September 26, 2018.\n- [Housing Maintenance Code Complaints](https://www.johnsnowlabs.com/marketplace/housing-maintenance-code-complaints)\n\t- This data set features Housing Maintenance Code Complaints by the Department of Housing Preservation and Development (HPD).\n- [Housing and Development Complaint Problems](https://www.johnsnowlabs.com/marketplace/housing-and-development-complaint-problems)\n\t- This dataset shows the Department of Housing Preservation and Development (HPD) complaints and information about problems associated with complaints.\n- [Landmarks Complaint Log](https://www.johnsnowlabs.com/marketplace/landmarks-complaint-log)\n\t- This dataset Landmarks Complaint Log contains the information related to designated landmark violation complaints received by the Landmarks Preservation Commission (LPC).\n- [London Borough of Barnet Complaints](https://www.johnsnowlabs.com/marketplace/london-borough-of-barnet-complaints)\n\t- This dataset contains data on all complaints received by the council in 2015, 2016 and 2017 financial year for London Borough of Barnet.\n- [Montgomery Cable Complaint](https://www.johnsnowlabs.com/marketplace/montgomery-cable-complaint)\n\t- This dataset reflects all cable complaints filed with the Montgomery County Cable Office since January 2010. Complaints are filed in Montgomery County Cable Office.\n- [NYC Council Constituent Service](https://www.johnsnowlabs.com/marketplace/nyc-council-constituent-service)\n\t- This dataset comes from CouncilStat, an application used by many Council district offices to enter and track constituent issues that can range from affordable housing to potholes and pedestrian safety in New York City.\n- [New York City Emergency Notifications](https://www.johnsnowlabs.com/marketplace/new-york-city-emergency-notifications)\n\t- This dataset contains the messages sent with information about emergency events and important New York City services.\n- [Observations And Statuses For Inspections](https://www.johnsnowlabs.com/marketplace/observations-and-statuses-for-inspections)\n\t- This dataset provides a list of observations and status for construction project inspections in the city of New York for the inspection year 2022.\n- [Oregon Consumer Complaints](https://www.johnsnowlabs.com/marketplace/oregon-consumer-complaints)\n\t- This dataset features consumer complaints registered with the Oregon Department of Justice. The database of consumer complaints is derived from consumer contacts for the past 3 years and is for information only. This database may not offer a completely accurate or comprehensive account of every incident.\n- [Rodent Baiting Requested by 311 Service](https://www.johnsnowlabs.com/marketplace/rodent-baiting-requested-by-311-service)\n\t- This dataset shows the rodent baiting request reported by the 311 Service. It includes all open requests and all completed requests since January 1, 2011.\n- [San Francisco Aircraft Noise Complaint](https://www.johnsnowlabs.com/marketplace/san-francisco-aircraft-noise-complaint)\n\t- This dataset shows the total complaints filed against aircraft noise by the San Francisco Airport (SFO). This dataset is collected and published in a monthly report which is presented at the SFO Airport Community Roundtable meetings.\n- [Street Sweeping Schedule 2019](https://www.johnsnowlabs.com/marketplace/street-sweeping-schedule-2019)\n\t- This dataset contains the Chicago street sweeping schedule by ward and ward section number for the year 2019.\n- [Streets and Sanitation Code Complaints](https://www.johnsnowlabs.com/marketplace/streets-and-sanitation-code-complaints)\n\t- This dataset contains the performance metrics and schedules for Streets & Sanitation and Sanitation Code Complaints in the city of Chicago.\n- [Streets and Sanitation Graffiti Removal](https://www.johnsnowlabs.com/marketplace/streets-and-sanitation-graffiti-removal)\n\t- This dataset contains the performance metrics and schedules for Streets & Sanitation and Graffiti Removal requests in the city of Chicago.\n- [Transportation Performance Metrics by CDOT](https://www.johnsnowlabs.com/marketplace/transportation-performance-metrics-by-cdot)\n\t- This dataset contains performance metrics tracked by the Chicago Department of Transportation (CDOT) from 2011 to 2019.\n- [Utility Customer Service Performance Indicators](https://www.johnsnowlabs.com/marketplace/utility-customer-service-performance-indicators)\n\t- This dataset includes the summary of New York State (NYS) Utility Customer Service Performance Indicators which includes Gas and Electric utilities for the years 2009 to 2019.\n- [Vacant and Abandoned Buildings Reported](https://www.johnsnowlabs.com/marketplace/vacant-and-abandoned-buildings-reported)\n\t- This dataset contains vacant and abandoned building reported on 311 calls for open and vacant buildings reported to the City of Chicago since January 1, 2010.\n- [Water Quality Complaints](https://www.johnsnowlabs.com/marketplace/water-quality-complaints)\n\t- This dataset contains running list of all water quality complaint by the Agency Department of Environmental Protection. The quality complaints about drinking water quality received from consumers should be investigated promptly by the private water supplier and the results of the investigation given quickly to the complainants.\n- [Windsor All 311 Service Requests](https://www.johnsnowlabs.com/marketplace/windsor-all-311-service-requests)\n\t- This dataset is extracted electronically from the 311 customer request management system. 311 manages the service request data for City of Windsor departments and divisions represented in this dataset. As such, 311 consulted the participating divisions and subject matter experts for this release.\n- [Windsor Winter Control Snowplow Activity](https://www.johnsnowlabs.com/marketplace/windsor-winter-control-snowplow-activity)\n\t- This dataset shows the plowing, salting and removal of accumulated snowfall during various weather events / during the applicable months when inclement weather occurs with below freezing temperatures.\n- [Work Order Management Module](https://www.johnsnowlabs.com/marketplace/work-order-management-module)\n\t- This dataset provides a List of Work Orders associated with a customer sales representative complaints.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - 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It includes datasets over facility, national and state-level data for Inpatient Psychiatric Hospital Facility Quality Reporting (IPFQR) and payment measures. It also provides Timely and Effective Care information by national and state-level data for measures of heart attack care, heart failure care, pneumonia care, surgical care and emergency department care.\n\n**Description**\nThis data package contains the Hospital Outcome of Care Surgical Measures contains a list of different hospitals and their quality of surgical outcomes to provide information to healthcare consumers. The American College of Surgeons (ACS) National Surgical Quality Improvement Program (NSQIP) is the leading nationally validated, risk-adjusted, outcomes-based program to measure and improve the quality of surgical care in the private sector. Hospital Structure Measures evaluate the infrastructure of health care settings, such as hospitals or doctor offices, and whether those health care settings are able to deliver care. These measures include staffing of facilities and the capabilities of these staff, the policy environment in which care is delivered, and the availability of resources within an institution. Inpatient Psychiatric Hospital Facility Quality Measures by Facility, State and National contains six measures. Psychiatric facilities that are eligible for this program may have their Medicare payments reduced if they do not report. \n\nThe Inpatient Psychiatric Facility Quality Reporting (IPFQR) program is a pay-for-reporting program intended to equip consumers with quality of care information to make more informed decisions about healthcare options. It is also intended to encourage hospitals and clinicians to improve the quality of inpatient care provided to beneficiaries by, first, ensuring that providers are aware of and reporting on best practices for their respective facilities and type of care. Payment and Value of Care by Hospital displays associated with a 30-day episode of care for heart attack, heart failure, and pneumonia patients. Payment by State and National level measures contain payments made for care and supplies starting the day the patient enters the hospital and for the next 30 days.\n\nThe measures are meant to reflect differences in the services and supplies provided to patients. Timely and Effective Care by Hospital, State and National level data for measures of heart attack care, heart failure care, pneumonia care, surgical care, emergency department care, preventive care, children’s asthma care, stroke care, blood clot prevention and treatment, and pregnancy and delivery care. The payments made for patients treated at a particular hospital is less than, no different than, or greater than the national average payment. For some hospitals, the number of cases is too small to reliably compare their results to the national average payment.\n\n**Benefits**\n- Useful for medical care services providers, patients, consumers and hospitals. hospital compare quality of care and measures database is available on centers for medicare & medicaid services, hospital compare website in raw form, whereas a john snow labs (jsl) customer gets it in a clean and normalized form ready to use in one place and create his own custom reports. easy to comprehend for the customers who are unfamiliar with the medical terminologies as all abbreviated terms are replaced with full form, unlike hospital compare quality of care and measures datasets which are full of abbreviations.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [Hospital Outcome of Care Surgical Measures](https://www.johnsnowlabs.com/marketplace/hospital-outcome-of-care-surgical-measures)\n\t- This dataset includes a list of different hospitals and their quality of surgical outcomes to provide information to healthcare consumers. The study comprises of Hospital Name and the data on the outcome measures for three different forms of surgeries. These \"Outcome of Care Surgical Measures\" show whether hospitals differ in what happens to patients after they have one of three types of surgeries - elderly surgery, colon surgery and lower-extremity bypass.\n- [Hospital Structural Measures](https://www.johnsnowlabs.com/marketplace/hospital-structural-measures)\n\t- This dataset includes a list of hospitals and the availability of structural measures at that hospital. A structural measure reflects the environment in which hospitals care for patients. For example, whether or not a hospital participates in a Cardiac Surgery Registry.\n- [Inpatient Psychiatric Hospital Facility Quality Measures by Facility](https://www.johnsnowlabs.com/marketplace/inpatient-psychiatric-hospital-facility-quality-measures-by-facility)\n\t- This dataset includes Psychiatric facilities that are eligible for the Inpatient Psychiatric Facility Quality Reporting (IPFQR) program are required to meet all program requirements, otherwise their Medicare payments may be reduced. It contains facility-wise data for the hospitals in United States for several inpatient psychiatric facility quality measures.\n- [Inpatient Psychiatric Hospital Facility Quality Measures by National](https://www.johnsnowlabs.com/marketplace/inpatient-psychiatric-hospital-facility-quality-measures-by-national)\n\t- This dataset includes the psychiatric facilities that are eligible for the Inpatient Psychiatric Facility Quality Reporting (IPFQR) program are required to meet all program requirements, otherwise their Medicare payments may be reduced. It contains national data for the hospitals in United States for several inpatient psychiatric facility quality measures.\n- [Inpatient Psychiatric Hospital Facility Quality Measures by State](https://www.johnsnowlabs.com/marketplace/inpatient-psychiatric-hospital-facility-quality-measures-by-state)\n\t- This dataset includes Psychiatric facilities that are eligible for the Inpatient Psychiatric Facility Quality Reporting (IPFQR) program are required to meet all program requirements, otherwise their Medicare payments may be reduced. It contains state-wise data for the hospitals in United States for several inpatient psychiatric facility quality measures.\n- [Payment and Value of Care by Hospital](https://www.johnsnowlabs.com/marketplace/payment-and-value-of-care-by-hospital)\n\t- This dataset includes provider data for the payment measures and value of care displays associated with a 30-day episode of care for heart attack, heart failure, and pneumonia patients.\n- [Payment by State](https://www.johnsnowlabs.com/marketplace/payment-by-state)\n\t- This dataset includes state-level data for the payment measures associated with a 30-day episode of care for heart attack, heart failure, and pneumonia patients.\n- [Timely and Effective Care by Hospital](https://www.johnsnowlabs.com/marketplace/timely-and-effective-care-by-hospital)\n\t- This dataset includes provider-level data for measures of heart attack care, heart failure care, pneumonia care, surgical care, emergency department care, preventive care, children’s asthma care, stroke care, blood clot prevention and treatment, and pregnancy and delivery care.\n- [Timely and Effective Care by National](https://www.johnsnowlabs.com/marketplace/timely-and-effective-care-by-national)\n\t- This dataset includes National-level data for measures of heart attack care, heart failure care, pneumonia care, surgical care, emergency department care, preventive care, children’s asthma care, stroke care, blood clot prevention and treatment, and pregnancy and delivery care.\n- [Timely and Effective Care by State](https://www.johnsnowlabs.com/marketplace/timely-and-effective-care-by-state)\n\t- This dataset includes state-level data for measures of heart attack care, heart failure care, pneumonia care, surgical care, emergency department care, preventive care, children’s asthma care, stroke care, blood clot prevention and treatment, and pregnancy and delivery care.\n- [Value of Care by National](https://www.johnsnowlabs.com/marketplace/value-of-care-by-national)\n\t- This dataset includes national-level data for the value of care displays associated with a 30-day episode of care for heart attack, heart failure, and pneumonia patients.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated\n- Data Updates\n - Data updates support replace-on-update: outdated foreign keys are deprecated, not deleted\n\n**Our data is curated and enriched by domain experts** \n\n Each dataset is manually curated by our team of doctors, pharmacists, public health & medical billing experts:\n- Field names, descriptions, and normalized values are chosen by people who actually understand their meaning\n- Healthcare & life science experts add categories, search keywords, descriptions and more to each dataset\n- Both manual and automated data enrichment supported for clinical codes, providers, drugs, and geo-locations\n- The data is always kept up to date – even when the source requires manual effort to get updates\n- Support for data subscribers is provided directly by the domain experts who curated the data sets\n- Every data source’s license is manually verified to allow for royalty-free commercial use and redistribution.\n\n**Need Help?**\nIf you have questions about our products, contact us at [info@johnsnowlabs.com](mailto:info@johnsnowlabs.com). \n 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Updated as of **April 27, 2026**, this single-source resource eliminates the need for manual web scraping or multi-vendor licensing, offering an analysis-ready foundation for retail site selection, competitive intelligence, logistics optimization, and spatial demographics mapping.\n\nFully compatible with **Apache Spark**, **Delta Lake**, and **Databricks Unity Catalog** workflows.\n\n---\n\n## **Consolidated Brand Coverage (Top 18 Canadian Chains)**\nBy consolidating multiple retail banners into a single geospatial table, this dataset provides maximum cost-efficiency and strategic utility over individual brand lists. Coverage includes:\n\n- **Major Grocers & Superstores:** [Walmart (444 locations)](https://geolocet.com/products/canada-walmart-grocery), \n [Metro (331 locations)](https://geolocet.com/products/canada-metro-grocery), \n [Sobeys (239 locations)](https://geolocet.com/products/canada-sobeys-grocery), \n [Save-On-Foods (188 locations)](https://geolocet.com/products/canada-save-on-foods-grocery), \n [Safeway (134 locations)](https://geolocet.com/products/canada-safeway-grocery), \n [Real Canadian Superstore (120 locations)](https://geolocet.com/products/canada-real-canadian-superstore-grocery)\n\n- **Discount, Value & Specialty Chains:** [No Frills (365 locations)](https://geolocet.com/products/canada-no-frills-grocery), \n [Winners (283 locations)](https://geolocet.com/products/canada-winners-grocery), \n [Giant Tiger (232 locations)](https://geolocet.com/products/canada-giant-tiger-grocery), \n [Maxi (198 locations)](https://geolocet.com/products/canada-maxi-grocery), \n [Food Basics (153 locations)](https://geolocet.com/products/canada-food-basics-grocery), \n [FreshCo (152 locations)](https://geolocet.com/products/canada-freshco-grocery), \n [Super C (120 locations)](https://geolocet.com/products/canada-super-c-grocery), \n [M&M (333 locations)](https://geolocet.com/products/canada-m-m-grocery)\n\n- **Convenience & Local Formats:** [Couche-Tard (596 locations)](https://geolocet.com/products/canada-couche-tard-grocery), \n [IGA (382 locations)](https://geolocet.com/products/canada-iga-grocery), \n [Boni-Soir (227 locations)](https://geolocet.com/products/canada-boni-soir-grocery), \n [Foodland (218 locations)](https://geolocet.com/products/canada-foodland-grocery)\n\n---\n\n## **Technical Specifications & Schema Definitions**\nOptimized for zero-friction schema enforcement when reading via `spark.read.format(\"csv\")`. The structured schema below ensures flawless rendering in Databricks notebooks and high accuracy for LLM semantic indexing.\n\n* **`GUID`** (`STRING`): Unique global identifier for deterministic primary key mapping.\n* **`Title`** (`STRING`): Official store name and primary brand banner classification.\n* **`Latitude`** (`DOUBLE`): Precise WGS84 latitude coordinate for point-in-polygon & spatial joins.\n* **`Longitude`** (`DOUBLE`): Precise WGS84 longitude coordinate for spatial indexing (H3 / S2).\n* **`Street_No` / `Street`** (`STRING`): Normalized building/house number and street name elements.\n* **`City` / `Postal_Code`** (`STRING`): Core postal addresses, primary municipal settlement names, and Canadian Forward Sortation Areas (FSAs).\n* **`Admin_level_1` / `_2`** (`STRING`): Macro-administrative boundary classification (e.g., Provinces/Territories like Ontario, Quebec, BC) and regional district identifiers.\n* **`Address`** (`STRING`): Full, standardized street address of the location.\n* **`Wheelchair`** (`STRING`): Accessibility flags indicating wheelchair mobility support.\n* **`Popularity_Score`** (`INTEGER`): Relative foot-traffic/rating metric graded on a standardized 1-10 scale.\n* **`Opening_Hours`** (`STRING`): Standard daily operational schedules and structured operating text.\n* **`Phone` / `Website`** (`STRING`): Direct communication data, branch-specific landing URLs, and domain vectors.\n\n---\n\n## **Databricks Workflow & Spatial Analytics Use Cases**\n\n- **Catchment Area Modeling:** Run native Apache Spark SQL spatial functions or python-based `geopandas` routines to construct drive-time isochrones around competitors.\n- **H3 Geospatial Indexing:** Convert the precise WGS84 `Latitude` and `Longitude` fields into Uber H3 spatial hexagons (`h3_longlattocell`) to cross-reference with Canadian census tract data.\n- **Inter-Provincial Territory Optimization:** Combine store location points with regional administrative boundary layers to spot gaps in supply chain routing, optimize inter-provincial logistics, or analyze local market penetration.\n\n---\n\n## **Data Evaluation & Custom Ingestion**\n\n### **Evaluate with a Free Sample Dataset**\nVerify the structure, column types, and coordinate precision before full implementation. A comprehensive schema sample is available for download in CSV/Parquet-friendly formats. \n\n*Want deeper market insights?* Learn more about the brand network in our comprehensive report: **[View the Canadian Top Brands Grocery Report](https://geolocet.com/pages/canada-topbrands-grocery-report)**\n\n### **Enterprise Delivery & Tailored Formatting**\nWhile natively available as an analysis-ready CSV for DBFS ingestion, we accommodate enterprise-level data architecture needs. Alternative formats—including **GeoJSON, Esri Shapefiles, PostgreSQL dumps, Excel, or direct Delta sharing profiles**—can be generated. Reach out to our engineering team at **contact@geolocet.com** for integration support or custom North American POI data requests.","terms_of_service":"https://geolocet.com/pages/terms-of-use","documentation_link":"","privacy_policy_link":"https://geolocet.com/pages/privacy-policy","assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://geolocet.com/pages/terms-of-use"},"provider_summary":{"name":"Geolocet LTD","description":"Geolocet provides premium geospatial data for retail analysis, GIS, and market planning. We equip businesses with accurate, ready-to-query data to drive spatial analysis, site selection, and customer segmentation.\n\nCore Offerings:\n\nDemographics: Socioeconomic data covering population, age, and employment across diverse geographies.\n\nPoints of Interest (PoI): Datasets spanning retail, healthcare, and transport for catchment analysis.\n\nAdministrative Boundaries: GIS-ready boundaries, including municipalities and postcodes, for territory management.\n\nBrand Locations: Chain location datasets to monitor store networks and market expansion.\n\nWealth Data: Granular economic indicators like purchasing power to identify high-potential markets.\n\nWe source from trusted providers, delivering highly customizable datasets with comprehensive coverage across Europe and worldwide."},"popularity":880},{"id":"ce0bd10f-af6e-42c6-a527-03213a5942e6","summary":{"name":"Address Fabric Australia Sample","subtitle":"Pre-Geocoded nationwide list of addresses with the most accurate geographic coordinates for each location","share":{"name":"aus_address_fabric_sample","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1732800638594,"updated_at":1783681922192,"categories":["GEOSPATIAL"],"listingType":"STANDARD","provider_id":"b2312fad-ce38-48e5-9c55-8a225d183993"},"detail":{"description":"**Overview**\nThis evaluation dataset features a subset of Victoria, AUS. This is a subset of the full product’s geographic extent. The full commercial offering features geographic coverage of Australia. This dataset is intended for trial purposes and its currency may differ from that of the commercial offering.Contact Databricks.partnership@precisely.com for more information on licensing the product.\n\nConsistent and correct location information is vital for conducting analysis with confidence while avoiding costly mistakes. Unique identifiers help you maintain your address and property databases, enrich address locations with contextual information, and perform spatial analytics. Address Fabric is the foundational data that provides the key for easily accessing this wealth of information.\n\n**Use cases**\n\n- **360-Degree Customer View**\n Persistent identifiers such as the PreciselyID and G-NAF PID enable the enrichment of location attributes such as demographics and property information. This can assist in service delivery and supply chain modelling. Derive location-based customer awareness to improve opportunities and identify target markets. NOTE: Address Fabric contains no PII, (Personally Identifiable Information) data.\n- **Contact Data Enrichment**\n Use Address Fabric to cleanse and complete your contact data. Use the PreciselyID, a unique and persistent identifier, to link to other Precisely products and potentially third party and government data. NOTE: Address Fabric contains no PII data.\n\n- **Demand Forecasting**\n Cleanse, verify and append geospatial data to existing address data. Use Address Fabric in spatial analysis to forecast demand at the address level. Assess potential markets. Develop service regions and franchise boundaries. NOTE: Address Fabric contains no PII data.\n\n- **Fraud Remediation**\n Address Fabric provides contextual details for addresses. Does the address provided exist? Is the address in a commercial or residential area? Is there a building associated with and address? Address Fabric provides persistent identifiers that can be linked to other Precisely data to provide additional information such as business attributes where applicable. NOTE: Address Fabric contains no PII data.\n\n- **Identity Resolution**\n Address Fabric can be used to verify a customer’s address. Help verify an identity where there are multiple persons of the same name. Ensure that the correct and complete address is used for optimal customer communication. NOTE: Address Fabric contains no PII data.\n- **Population Health Management**\n Address Fabric provides accurate address data to help verify and/or clean patient records. Accurate address information improves service and delivery location planning. Leverage location data at the address level to underpin variables and location analysis in health-related research. NOTE: Address Fabric contains no PII data.\n\n**Product details**\n\nAddress Fabric is a current and comprehensive list of all known physical addresses in Australia (for this evaluation data set the geography is limited to a portion of Victoria, Australia). Each record is pre-geocoded to provide the most precise latitude and longitude coordinate location with the majority being the primary building centroid with address data including physical locations that are not deliverable by the postal service. Each record contains the street address, coordinate location and address type for that addressable location. Address genealogy known as parent/child relationships for primary and secondary addresses such as flats, units, suites, and apartments are also included. Records are assigned our unique identifier that can be used for data management and exchange, or appending enriched contextual information such as demographics, psychographics, and risk factors.\n\nData Set\n\n- The Address Fabric product consists of a single data set (table) with a reference table\n\nData set content includes but is not limited to:\n\n- A unique and persistent identifier for each address, which can be used to directly join to Precisely’s enrichment data suite\n\n- Comprehensive address information\n- Address components\n- Location information (latitude & longitude)\n- G-NAF primary identifier\n\n- Location accuracy codes to convey the positional accuracy of the address point\n\n- Parent/child address relationships (flats, units, apartments, suites)\n\n- Property type information based on Mesh Block category (business vs. residential)\n\n- Building name where available\n \nAttribute fields include but are not limited to the following:\n\n- PreciselyID: a unique and persistent identifier for each address in the US postal address\n- State abbreviation\n- Postcode\n- Precision of the coordinate location\n- Latitude\n- Longitude\n- PreciselyID of parent record if applicable\n- Property type\n- G-NAF primary ID\n- Full street address\n- Date created\n- Date modified\n- Flat number\n- Flat number prefix\n- Flat number suffix\n- Level type\n- Level number\n- Building name\n- Street number (multiple associated attribute fields)\n- Lot number (multiple associated attribute fields)\n- Street name\n- Street type\n- Street suffix\n- Locality name\n\nSample Use Cases:\n\n- Manage internal address data with a single source of truth\n- Improved location information for service delivery planning\n- Improved site information for likelihood of building structure and usage at the location\n- Utilize for site selection of new assets or network expansion\n- Delineate new service or catchment areas\n- Find new customers or upsell to existing clients through look-alike 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Our portfolio, including the Precisely Data Integrity Suite, helps integrate your data, improve data quality, govern data usage, geocode and analyze location data, and enrich it with complementary datasets for confident business decisions. Over 12,000 organizations in more than 100 countries, including 95 of the Fortune 100, trust Precisely software, data, and data strategy consulting to power AI, automation, and analytics initiatives. Learn more at www.precisely.com."},"popularity":1579},{"id":"068157c0-1c4e-43b6-af4a-7a4713d46a9f","summary":{"name":"MBI Area Boundaries for Czech Republic – Sample","subtitle":"MBI's precise & seamless boundaries for well-founded decisions in the Czech Republic. Free trial data.","share":{"name":"share_boundaries_mbi_cz_r_2025","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1784900030911,"updated_at":1784900030911,"categories":["ADVERTISING_AND_MARKETING","GEOSPATIAL","MEDIA","OTHER","PUBLIC_SECTOR","RETAIL","TRANSPORTATION_AND_LOGISTICS"],"listingType":"STANDARD","provider_id":"aa09abd6-ff7f-4452-803d-b8c43b4c07cb"},"detail":{"description":"MBI'S GLOBAL AREA BOUNDARIES COVER MOST COUNTRIES WORLDWIDE AND **CAN BE LICENSED FOR EACH COUNTRY INDIVIDUALLY** AND IN BUNDLES.\n[MBI](https://www.mbi-geodata.com/en/) digital boundaries are available for virtually all countries worldwide.* Created by our experts according to consistent global standards, the polygons are so precise and harmonized that they align seamlessly across borders. The territorial vintages of the worldwide area boundaries are compatible with our globally consistent and comparable [socio-demographic](https://www.mbi-geodata.com/en/sociodemographic-data/) and [economic](https://www.mbi-geodata.com/en/purchasing-power-overview/) data.\nOur area boundaries for the Czech Republicare offered at the following levels:\n\n- **Postal Code Level** (Postcode 5-digit): This fine-grained level of detail makes the data suitable for a wide range of applications – for example, enriching customer databases, which are often organized by postal code.\n- **Administrative Level** (Obce): Covers both larger administrative areas (e.g., regions, states, provinces) as well as finer subdivisions such as districts, counties, or municipalities.\n- **Micro Level** (Statistické Obvody, Základní Sídelní Jednotky Díl): Subdivisions of postal code or administrative boundaries, such as census areas or street segments. This is the most granular level for local analysis. | on request\n\n**Use Cases:**\n\n- **Market Analysis**: Use MBI’s boundaries to visualize data on a map, learn about your customers, and uncover untapped market potential.\n- **360-Degree Customer View**: Link boundaries to customer data, sociodemographic, and economic data to gain a holistic understanding of customer needs, behaviors, and potential value.\n- **Location Planning**\n•\tUse MBI’s boundaries to visualize data on a map, learn about your customers\n•\tPlan the provision of services and products across different regions\n•\tDefine sales territories\n•\tVisualize catchment areas and plan actions to reach potential customers\n\n**Additional Insights:**\n[About Area Boundaries](https://www.mbi-geodata.com/en/area-boundaries/)\n\n**Data Structure:**\nThe area boundaries include the following variables:\nCTRYCODE: Two-digit country code by ISO 3166\nNAME: Name of administrative area / Name of the most populous administrative area / Name of the respective micro-area or the next higher level\nADMINCODE / POSTCODE / MICROCODE: Administrative code / Postal code / Micro-code\nWKT: Geography as Well Known Text (lat/long WGS84)\n\n*with only a few specific exceptions","terms_of_service":"https://service-mbi-geodata.com/s/HNp7Z2tws4WDqWd","documentation_link":"https://www.mbi-geodata.com/en/mbi-supplier-manufacturer-worldwide-geodata/europe/czech-republic-maps/","privacy_policy_link":"https://www.mbi-geodata.com/en/data-protection/","update_frequency":{"interval":1,"unit":"YEARLY"},"assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://service-mbi-geodata.com/s/wFRSHyPLMeeL7CC"},"provider_summary":{"name":"Michael Bauer International GmbH – MBI Geodata","description":"For over 15 years, Michael Bauer International (MBI) has been a leading provider of globally consistent and comparable geographic data, including more than 3 million postcode areas and other detailed datasets like sociodemographics and purchasing power. 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The datasets provide metrics to measure the overall disease burden expressed as Disability-adjusted life year (DALY), the sum of years of life lost (YLLs) and years lived with disability (YLDs), life expectancy and health-adjusted life expectancy (HALE). The datasets provide global, regional, and GBD location-specific and gender-specific data. The datasets included in this data package resulted from the Global Burden of Disease Study 2016.\n\n**Benefits**\n- This data is useful for researchers and policymakers to compare very different populations and health conditions across time. this can be used for further research\n- public health officials and program managers for potential interventions, setting priority areas for action and evaluating progress\n- policymakers for policy use and development\n- public health practitioner and clinicians' aid in designing and implementing targeted interventions and monitoring the progress of public health strategies.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [UK Deaths by Cause](https://www.johnsnowlabs.com/marketplace/uk-deaths-by-cause)\n\t- This dataset provides deaths in England in 1990 and 2013 for all ages and age‐standardized rates (per 100,000) by sex with 95% confidence interval and percentage change.\n- [UK Disability Adjusted Life Year by Risk Factor](https://www.johnsnowlabs.com/marketplace/uk-disability-adjusted-life-year-by-risk-factor)\n\t- This dataset provides Disability Adjusted Life Year (DALYs) in thousands attributable-risk factors or risk factor clusters and median percent change of age-standardized DALYs PAFs in England for both sexes, males, and females.\n- [UK Disability Adjusted Life Year](https://www.johnsnowlabs.com/marketplace/uk-disability-adjusted-life-year)\n\t- This dataset provides Disability Adjusted Life Year (DALYs) in England in 1990 and 2013 for all ages and age‐standardized rates (per 100,000) by sex with 95% confidence interval and percentage change.\n- [UK Global Burden of Disease by Cause](https://www.johnsnowlabs.com/marketplace/uk-global-burden-of-disease-by-cause)\n\t- This dataset provides results for the burden of disease and injury in the United Kingdom through the following metrics: years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life years (DALYs).\n- [UK Global Burden of Disease by Risk Factor](https://www.johnsnowlabs.com/marketplace/uk-global-burden-of-disease-by-risk-factor)\n\t- This dataset provides results for the risk factors for burden of disease and injury in the United Kingdom through the following metrics: years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life years (DALYs).\n- [UK Life Expectancy At the Age of 75](https://www.johnsnowlabs.com/marketplace/uk-life-expectancy-at-the-age-of-75)\n\t- This dataset contains indicator values for NHS (National Health Service) Outcomes Framework indicator - the average number of additional years a man or woman aged 75 can be expected to live if they continue to live in the same place and the death rates in their area remain the same for the rest of their life.\n- [UK Years Lived with Disability by Cause](https://www.johnsnowlabs.com/marketplace/uk-years-lived-with-disability-by-cause)\n\t- This dataset provides years lived with disability (YLDs) in England in 1990 and 2013 for all ages and age‐standardized rates (per 100,000) by sex with 95% confidence interval and percentage change.\n- [UK Years of Life Lost by Cause](https://www.johnsnowlabs.com/marketplace/uk-years-of-life-lost-by-cause)\n\t- This dataset provides years of life lost (YLLs) in England in 1990 and 2013 for all ages and age‐standardized rates (per 100,000) by sex with 95% confidence interval and percentage change.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated\n- Data Updates\n - Data updates support replace-on-update: outdated foreign keys are deprecated, not deleted\n\n**Our data is curated and enriched by domain experts** \n\n Each dataset is manually curated by our team of doctors, pharmacists, public health & medical billing experts:\n- Field names, descriptions, and normalized values are chosen by people who actually understand their meaning\n- Healthcare & life science experts add categories, search keywords, descriptions and more to each dataset\n- Both manual and automated data enrichment supported for clinical codes, providers, drugs, and geo-locations\n- The data is always kept up to date – even when the source requires manual effort to get updates\n- Support for data subscribers is provided directly by the domain experts who curated the data sets\n- Every data source’s license is manually verified to allow for royalty-free commercial use and redistribution.\n\n**Need Help?**\nIf you have questions about our products, contact us at [info@johnsnowlabs.com](mailto:info@johnsnowlabs.com). \n 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Terminology for SPL validation procedures and information on performing SPL validations.\n\n**Description**\nThe Structured Product Labeling (SPL) is a document markup standard approved by Health Level Seven (HL7) and adopted by FDA as a mechanism for exchanging product and facility information. SPL documents contain both the content of labeling (all text, tables and figures) for a product along with additional machine readable information (drug listing data elements). Drug listing data elements include information about the product (product and generic names, ingredients, ingredient strengths, dosage forms, routes of administration, appearance, DEA schedule) and the packaging (package quantity and type). This data package is a Structured Product Labeling (SPL) Terminology File for SPL validation procedures and contains information on performing SPL validation regarding Active Ingredient-Active Moiety Relationship/Basis of Strength, Combination Product Type Category, DEA (Drug Enforcement Administration) Controlled Substance List, Final All Over-the-Counter (OTC) Drugs Monograph, Final Over-the-Counter (OTC) Drugs Monograph, Not Final Over-the-Counter (OTC) Drugs Monograph and Vaccine Antigen Substances.\n\n**Benefits**\n- helps in gathering product and facility information\n- information about the product (product and generic names, ingredients, ingredient strengths, dosage forms, routes of administration, appearance, dea schedule) and the packaging (package quantity and type) of fda approved drugs can be obtained through this dataset.\n- the \"drug label\" includes all published material accompanying a drug, such as the actual label on a prescribed dose as well as the package insert which contains a great deal of detailed information about the drug.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [Active Ingredient Active Moiety Relationship Validation Procedure](https://www.johnsnowlabs.com/marketplace/active-ingredient-active-moiety-relationship-validation-procedure)\n\t- This dataset is a Structured Product Labeling (SPL) Terminology File for SPL validation procedures and contains information on performing SPL validation regarding Active Ingredient-Active Moiety Relationship/Basis of Strength.\n- [Controlled Substances Schedule](https://www.johnsnowlabs.com/marketplace/controlled-substances-schedule)\n\t- This dataset identifies Drug Enforcement Administration number, which category (from I to V) the controlled substance is in, whether or not it is narcotic and other names of the controlled substances.\n- [DEA Controlled Substance Validation Procedure](https://www.johnsnowlabs.com/marketplace/dea-controlled-substance-validation-procedure)\n\t- This dataset is a Structured Product Labeling (SPL) Terminology File for SPL validation procedures and contains information on performing SPL validation regarding DEA (Drug Enforcement Administration) Controlled Substance List.\n- [DEA Exempted Substance Validation Procedure](https://www.johnsnowlabs.com/marketplace/dea-exempted-substance-validation-procedure)\n\t- This dataset is a Structured Product Labeling (SPL) Terminology File for SPL validation procedures and contains information on performing SPL validation regarding DEA (Drug Enforcement Administration) Exempted Substance List.\n- [Structured Product Labeling Validation Procedures](https://www.johnsnowlabs.com/marketplace/structured-product-labeling-validation-procedures)\n\t- This dataset is a Structured Product Labeling (SPL) Terminology File for SPL validation procedures and contains information on performing SPL validation of the \"Final Over-the-Counter (OTC) Drugs Monograph\", \"Final All Over-the-Counter (OTC) Drugs Monograph\", \"Not Final Over-the-Counter (OTC) Drugs Monograph\" and \"Combination Product Type Category under the NCI Code C102833\".\n- [Vaccine Antigen Substance Validation Procedure](https://www.johnsnowlabs.com/marketplace/vaccine-antigen-substance-validation-procedure)\n\t- This dataset is a Structured Product Labeling (SPL) Terminology File for SPL validation procedures and contains information on performing SPL validation regarding Vaccine Antigen Substances.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 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Understand the potential economic impact from the local to national level resulting from damage and incidents related to weather.\n**Location Planning**\nIt is important to consider the factors associated with a location when assessing it for a specified purpose. Understanding the potential threat associated with weather trends allows for comprehensive risk analysis relative to specified location.\n**Location Data Enrichment**\nThe Precisely Historical Weather Risk dataset contains risk information that can be associated at the individual address level. 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Shared Savings Program (SSP) Accountable Care Organization (ACO) data Public Use File (PUF), which has been updated to include from each ACO the mean final prospective Centers for Medicare and Medicaid Service's (CMS) Hierarchical Condition Category (HCC) risk score for the four Medicare enrollment types (End-Stage Renal Disease (ESRD), disabled, aged/dual, aged/non-dual) for each of the benchmark year and calendar year 2013 risk scores for performance year 1. It also includes the ACO Participants from 2015 to 2018.\n\nThe Shared Savings Program ACO participants are groups of doctors and other health care providers who voluntarily work together with Medicare to give high-quality service to Medicare Fee-for-Service beneficiaries. ACOs are required to completely and accurately report quality data that are used to calculate and assess their quality performance. In addition, in order to be eligible to share in any savings generated, an ACO must meet the established quality performance standard that corresponds to its performance year. Participation in ACOs creates incentives for health care providers to work together voluntarily to coordinate care and improve quality for their patient population. Since the 2011 Shared Savings Program final rule, updates have been made to the Shared Savings Program quality measures, scoring, and quality performance standards have been made in subsequent Shared Savings Program and Physician Fee Schedule rules.\n\n**Benefits**\n- Useful to researchers involved in evaluating and deliver valuable information necessary for the improvements/adjustments of the sharing savings program and its health and financial outcomes. useful to public health specialists and specialized institutions find improvement solutions. useful to aco administrative staff and other interested parties to evaluate, compare and find better improvement approaches and the necessary strategic changes. useful to beneficiaries to understand and compare the acos and about the advantages and disadvantages of the recent implementation of the sharing savings program.\n- the fields descriptions along with the dataset description are again useful for user to quick understand the data and the dataset.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [ACO Assigned Beneficiaries by County 2016](https://www.johnsnowlabs.com/marketplace/aco-assigned-beneficiaries-by-county-2016)\n\t- This dataset shows the ACO (Accountable Care Organization) Beneficiaries by County for the year 2016. Aged beneficiaries are identified as dually-eligible if they are identified in CMS (Centers for Medicare and Medicaid Services) data systems by dual status.\n- [ACO Assigned Beneficiaries by County 2017](https://www.johnsnowlabs.com/marketplace/aco-assigned-beneficiaries-by-county-2017)\n\t- This dataset shows the ACO (Accountable Care Organization) Beneficiaries by County for the year 2017. Aged beneficiaries are identified as dually-eligible if they are identified in CMS (Centers for Medicare and Medicaid Services) data systems by dual status.\n- [ACO Assigned Beneficiaries by County 2018](https://www.johnsnowlabs.com/marketplace/aco-assigned-beneficiaries-by-county-2018)\n\t- This dataset shows the ACO (Accountable Care Organization) Beneficiaries by County for the year 2018. Aged beneficiaries are identified as dually-eligible if they are identified in CMS (Centers for Medicare and Medicaid Services) data systems by dual status.\n- [ACO Quality Measure Benchmark for Reporting Year 2016 and 2017](https://www.johnsnowlabs.com/marketplace/aco-quality-measure-benchmark-for-reporting-year-2016-and-2017)\n\t- This dataset describes methods for calculating the quality performance benchmarks for Accountable Care Organizations (ACOs) that are participating in the Medicare Shared Savings Program (Shared Savings Program) and presents the benchmarks for the 34 quality measures for the 2016 and 2017 quality reporting years.\n- [ACO Shared Savings Program Public Use File 2013](https://www.johnsnowlabs.com/marketplace/aco-shared-savings-program-public-use-file-2013)\n\t- This dataset shows the 2013 Shared Savings Program (SSP) Accountable Care Organization (ACO) data Public Use File (PUF), which has been updated to include for each ACO the mean final prospective Centers for Medicare and Medicaid Service's (CMS) Hierarchical Condition Category (HCC) risk score for the four Medicare enrollment types (End-Stage Renal Disease (ESRD), disabled, aged/dual, aged/non-dual) for each of the benchmark year and calendar year 2013 risk scores for performance year 1.\n- [ACO Shared Savings Program Public Use File 2014-2016](https://www.johnsnowlabs.com/marketplace/aco-shared-savings-program-public-use-file-2014-2016)\n\t- This dataset shows the 2014 to 2016 Shared Savings Program (SSP) Accountable Care Organization (ACO) data Public Use File (PUF), which has been updated to include for each ACO the mean final prospective Centers for Medicare and Medicaid Service's (CMS) Hierarchical Condition Category (HCC) risk score for the four Medicare enrollment types (End-Stage Renal Disease (ESRD), disabled, aged/dual, aged/non-dual) for each of the benchmark year.\n- [CMS Innovation Center Model Participants](https://www.johnsnowlabs.com/marketplace/cms-innovation-center-model-participants)\n\t- This dataset shows the Centers for Medicare and Medicaid Services (CMS) innovation center model participants.\n- [FQHC Advanced Primary Care Practice Demonstration Participants](https://www.johnsnowlabs.com/marketplace/fqhc-advanced-primary-care-practice-demonstration-participants)\n\t- This dataset shows the Federally Qualified Health Centers (FQHCs) advanced primary care practice demonstration participants by the Centers for Medicare and Medicaid Services (CMS) Innovation Center.\n- [Medicare Shared Savings Program ACO 2015-2020](https://www.johnsnowlabs.com/marketplace/medicare-shared-savings-program-aco-2015-2020)\n\t- This dataset shows the Medicare Shared Savings Program Accountable Care Organizations (ACOs) for 2015-2020. The Shared Savings Program ACO participants are groups of doctors and other health care providers who voluntarily work together with Medicare to give high-quality service to Medicare Fee-for-Service beneficiaries.\n- [Medicare Shared Savings Program ACO Beneficiary Provider Summary 2018](https://www.johnsnowlabs.com/marketplace/medicare-shared-savings-program-aco-beneficiary-provider-summary-2018)\n\t- This dataset shows the Medicare Shared Savings Program Accountable Care Organizations (ACOs) beneficiary provider summary for 2018. The Shared Savings Program ACO participants are groups of doctors and other health care providers who voluntarily work together with Medicare to give high-quality service to Medicare Fee-for-Service beneficiaries.\n- [Medicare Shared Savings Program ACO Participants](https://www.johnsnowlabs.com/marketplace/medicare-shared-savings-program-aco-participants)\n\t- This dataset shows the participants of Medicare Shared Savings Program Accountable Care Organizations (ACOs). The Shared Savings Program ACO participants are groups of doctors and other healthcare providers who voluntarily work together with Medicare to give high-quality service to Medicare Fee-for-Service beneficiaries. This dataset covers data from 2014 to 2020.\n- [Medicare Shared Savings Program ACO SNF Affiliates](https://www.johnsnowlabs.com/marketplace/medicare-shared-savings-program-aco-snf-affiliates)\n\t- This dataset is the certified list of ACO (Accountable Care Organizations) SNF (Skilled Nursing Facility) Affiliates for the year 2022.\n- [Medicare Shared Savings Program Performance Year 2013-2015 Results](https://www.johnsnowlabs.com/marketplace/medicare-shared-savings-program-performance-year-2013-2015-results)\n\t- This dataset contains the results of Medicare Shared Savings Program Performance Accountable Care Organizations (ACO) coverage for years 2013, 2014 and 2015.\n- [Quality Measures to Assess ACO Quality Performance](https://www.johnsnowlabs.com/marketplace/quality-measures-to-assess-aco-quality-performance)\n\t- This dataset shows the 34 Quality Measures for the ACO Quality Performance for 2016; prepared in January 13, 2016 by RTI International for the Pioneer ACO Model: Division of Accountable Care Organization Populations, Seamless Care Models Group and Center for Medicare and Medicaid Innovation, and the Medicare Shared Savings Program: Division of Shared Savings Program, Performance-Based Payment Policy Group, Center for Medicare and Centers for Medicare & Medicaid Services.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated\n- Data Updates\n - Data updates support replace-on-update: outdated foreign keys are deprecated, not deleted\n\n**Our data is curated and enriched by domain experts** \n\n Each dataset is manually curated by our team of doctors, pharmacists, public health & medical billing experts:\n- Field names, descriptions, and normalized values are chosen by people who actually understand their meaning\n- Healthcare & life science experts add categories, search keywords, descriptions and more to each dataset\n- Both manual and automated data enrichment supported for clinical codes, providers, drugs, and geo-locations\n- The data is always kept up to date – even when the source requires manual effort to get updates\n- Support for data subscribers is provided directly by the domain experts who curated the data sets\n- Every data source’s license is manually verified to allow for royalty-free commercial use and redistribution.\n\n**Need Help?**\nIf you have questions about our products, contact us at [info@johnsnowlabs.com](mailto:info@johnsnowlabs.com). \n 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Conditions (e.g., social, economic, and physical) in these various environments and settings (e.g., school, church, workplace, and neighborhood) have been referred to as place.\n\n**Description**\nThis data package contains information on social determinants of health such as daily smoking, HIV Incidence, population & social demographics. The social determinants of health (SDOH or SDH) are linked to the economic and social conditions and their distribution among the population that influence individual and group differences in health status. They are health-promoting factors found in one's living and working conditions such as the distribution of income, wealth, influence, and power rather than individual risk factors such as behavioral risk factors or genetics that influence the risk for a disease, or vulnerability to disease or injury. According to some viewpoints, the distributions of social determinants are shaped by public policies that reflect the influence of prevailing political ideologies of those governing a jurisdiction. The patterns of social engagement and a sense of security and well-being are also affected by where people live. Resources that enhance the quality of life can have a significant influence on population health outcomes.\n\n**Benefits**\n- Useful data package for physicians as it contains information on the conditions like social, economic and physical in these various environments and settings (e.g., school, church, workplace, and neighborhood) have been referred to as place.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [Daily Smoking Prevalence](https://www.johnsnowlabs.com/marketplace/daily-smoking-prevalence)\n\t- The Global Burden of Disease Study 2019 (GBD 2019), coordinated by the Institute for Health Metrics and Evaluation (IHME), estimated the burden of diseases, injuries, and risk factors at the global, regional, national, territorial, and, for a subset of countries, subnational level.\n- [Disability Weights](https://www.johnsnowlabs.com/marketplace/disability-weights)\n\t- This dataset contains information of GBD (Global Burden of Disease) 2019 sequelae, health states, health state lay descriptions and disability weights.\n- [HIV Incidence Prevalence and Mortality](https://www.johnsnowlabs.com/marketplace/hiv-incidence-prevalence-and-mortality)\n\t- The Global Burden of Disease Study 2015 (GBD 2015), coordinated by the Institute for Health Metrics and Evaluation (IHME), estimated the burden of diseases, injuries, and risk factors globally, for 21 regions, and for 195 countries and territories. This dataset was also published in The Lancet in July 2016 in \"Global, regional, and national incidence, prevalence, and mortality for HIV, 1980-2015: estimates from the Global Burden of Disease Study 2015.\"\n- [ICD-9 and ICD-10 Codes Mapped To Fatal Nonfatal Causes](https://www.johnsnowlabs.com/marketplace/icd-9-and-icd-10-codes-mapped-to-fatal-nonfatal-causes)\n\t- This dataset contains the List of International Classification of Diseases (ICD) codes mapped to the Global Burden of Disease cause list for causes of death and ICD-9 and ICD-10 codes used in the extraction of hospital and claims data, mapped to GBD 2015 nonfatal causes, impairments, and nature of injury categories.\n- [Opioid Overdose Mortality in the US](https://www.johnsnowlabs.com/marketplace/opioid-overdose-mortality-in-the-us)\n\t- This dataset contains age-adjusted statistical data on deaths caused by opioids overdose by state and the increase of deaths caused by opioids overdose.\n- [Population Estimates 1970-2019](https://www.johnsnowlabs.com/marketplace/population-estimates-1970-2019)\n\t- This dataset contains information of GBD (Global Burden of Disease) 2019 population estimates from 1970 to 2019.\n- [Socio-Demographic Index Values](https://www.johnsnowlabs.com/marketplace/socio-demographic-index-values)\n\t- This dataset consists of a summary measure that identifies where countries or other geographic areas sit on the spectrum of development. Expressed on a scale of 0 to 1, SDI (Socio-Demographic Index) is a composite average of the rankings of the incomes per capita, average educational attainment, and fertility rates of all areas in the GBD (Global Burden of Disease) study.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated\n- Data Updates\n - 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This meticulously compiled dataset, sourced from official statistical sources, including [Statistics Finland](https://www.stat.fi/), offers a detailed insight into the population landscape of Finland at the municipality (Kunnat) level. \n\nIf you require data at a less granular level, such as an Uber grid or square grid, please don't hesitate to get in touch with us at *contact@geolocet.com*. We understand that different projects may have varying data needs, and we are here to tailor our data to match your specific requirements.\n\n ## **Key Features:**\n---\n* **Population Data Finland:** Gain access to the latest and most accurate population statistics for Finland, meticulously organized by municipality (Kunnat).\n\n* **Demographic Insights:** Dive deep into Finland’s demographic landscape with data on 400+ attributes, including: \n* Total population,\n* Education,\n* Education split by gender,\n* Education split by age,\n* Education split by age and gender,\n* Labour force,\n* Labor force split by age,\n* Labor force split by gender and age,\n* Labor force split by gender,\n* Employment status,\n* Employment status split by age,\n* Employment status split by gender and age,\n* Employment status split by gender.\n\nAll demographic attributes are available at the municipal (Kunnat) level.\n\n* **Geodemographic Insights:** Unlock geodemographic data Finland-wide to inform location-based decision-making, marketing strategies, and policy formulation.\n* **Seamless Data Access:** Our data is available for instant download in CSV format. No lengthy sign-up processes or NDAs required on our side – just download the data you need.\n* **Data Samples for Evaluation:** To ensure you're getting exactly what you need, we provide free downloadable samples in CSV format for data evaluation.\n* **Data accuracy:** Our datasets are created using the latest demographics data available in national statistics bureaus, guaranteeing accuracy and reliability.\n* **Best-in-Class Quality:** We guarantee best-in-class quality, backed by over two decades of experience in modeling demographics data and an outstanding quality management process.\n* **Competitive Pricing:** We maintain the lowest prices on the market, ensuring that you get the most cost-efficient demographics data acquisition process.\n---\nAdditionally, for those looking to enrich their analysis or project, we offer:\n* **[Administrative Boundaries](https://geolocet.com/products/finland-all-available-administrative-boundaries):** If you require geographical boundaries (polygons) for the municipalities of Finland, consider exploring our Administrative Boundaries product, which seamlessly integrates with this dataset.\n\n* **[Point of Interest (PoI) Data](https://geolocet.com/pages/aggregated-points-of-interest-poi-data):** If you're interested in Points of Interest data, please don't hesitate to contact us for customized datasets to enhance your geographical analysis.\n\n* **[Data Customization](https://geolocet.com/pages/data-services-by-geolocet):** We understand that your project may have specific requirements. Feel free to get in touch with us if you need any modifications or specific data formats such as Excel, JSON, GeoJSON, or Shapefile. We are here to tailor the data to your needs.\n\nIf you're looking for data for Finland, [look here](https://geolocet.com/pages/finland) for off-the-shelf or tailor-made datasets to suit your specific requirements.\n\n\n\nMake informed decisions, conduct precise research, and unlock the potential of Finland's demographic landscape with our comprehensive dataset. Trust in the accuracy and reliability of our official statistical data to drive your projects forward.\nContact us at *contact@geolocet.com* today to access this invaluable resource and explore Finland census data like never before.\n\n[Geolocet LTD](https://geolocet.com)\n","terms_of_service":"https://geolocet.com/pages/terms-of-use","documentation_link":"","privacy_policy_link":"https://geolocet.com/pages/privacy-policy","geographical_coverage":"[\"Europe\"]","cost":"PAID","update_frequency":{"interval":1,"unit":"YEARLY"},"data_source":"","assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://geolocet.com/pages/terms-of-use"},"provider_summary":{"name":"Geolocet LTD","description":"Geolocet provides premium geospatial data for retail analysis, GIS, and market planning. 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We have it. Ship data like software. AIDC packages external data as governed Delta tables with Unity Catalog, complete with schema, metadata, lineage, and clear license terms. Pre‑vetted contracts minimize back‑and‑forth, so squads move from browse to pipeline in days. Quality control is operated by Kaggle Grandmasters and senior data engineers at that standard, so datasets behave in production and pass audits. Coverage spans luxury item pricing (valuation, comp, elasticity studies) to retail property analytics (site selection, underwriting, performance prediction). Instrument adoption and prove lift: reduced ETL, faster training, higher dataset usability rates. Repeatable motion across teams: import → govern → build → measure → scale. 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The data package also includes other databases related to the European Agency like rare disease designation and withdrawn applications initiated by pharmaceutical companies.\n\n**Benefits**\n- useful to users searching for data from eudragmpd.\n- useful to pharmaceutical companies and other interested parties, providing access ema datasets about the certificates issued to european and non-european entities and the designation and withdraw applications for rare diseases.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [EMA API Registration Certificates of EEA Sites](https://www.johnsnowlabs.com/marketplace/ema-api-registration-certificates-of-eea-sites)\n\t- The European Medicines Agency (EMA) API Registration Certificates of EEA Sites dataset includes API sites registration certificates delivered by EMA or national competent authorities to European Economic Area countries API manufacturers.\n- [EMA API Registration Certificates of Non-EEA Sites](https://www.johnsnowlabs.com/marketplace/ema-api-registration-certificates-of-non-eea-sites)\n\t- The European Medicines Agency (EMA) API Registration Certificates of Non-EEA Sites dataset includes API sites registration certificates delivered by EMA or national competent authorities to European Economic Area countries API manufacturers.\n- [EMA Good Distribution Practices Certificates](https://www.johnsnowlabs.com/marketplace/ema-good-distribution-practices-certificates)\n\t- The European Medicines Agency (EMA) Good Distribution Practices Certificates (GDP) dataset lists GDP Certificates delivered by EMA or national competent authorities to European Economic Area countries Wholesale distributors of pharmaceutical products.\n- [EMA Good Manufacturing Practice Certificates of EEA Countries](https://www.johnsnowlabs.com/marketplace/ema-good-manufacturing-practice-certificates-of-eea-countries)\n\t- Good manufacturing practice (GMP) describes the minimum standard that a medicine manufacturer must meet in their production processes. \nThe European Medicines Agency (EMA) coordinates inspections to verify compliance with these standards and plays a key role in harmonizing GMP activities at European Union (EU) level.\n- [EMA Good Manufacturing Practice Certificates of Non-EEA Countries](https://www.johnsnowlabs.com/marketplace/ema-good-manufacturing-practice-certificates-of-non-eea-countries)\n\t- Good manufacturing practice (GMP) describes the minimum standard that a medicine manufacturer must meet in their production processes. \nThe European Medicines Agency (EMA) coordinates inspections to verify compliance with these standards and plays a key role in harmonizing GMP activities at European Union (EU) level including inspection of Non-EEA countries sites.\n- [EMA Manufacturing and Importation Authorizations](https://www.johnsnowlabs.com/marketplace/ema-manufacturing-and-importation-authorizations)\n\t- The European Medicines Agency (EMA) Manufacturing and Importation Authorizations dataset includes manufacturing and importation authorizations delivered by EMA or national competent authorities to European Economic Area countries manufacturers and importers of pharmaceutical products.\n- [EMA Opinions And Decisions On Paediatric Investigation Plans](https://www.johnsnowlabs.com/marketplace/ema-opinions-and-decisions-on-paediatric-investigation-plans)\n\t- The European Medicines Agency (EMA) Opinions And Decisions On Paediatric Investigation Plans dataset includes information on opinions and decisions adopted by the European Medicines Agency's (EMA) Paediatric Committee (PDCO) on Paediatric Investigation Plans (PIPs) including deferrals and waivers.\n- [EMA Rare Disease Designations](https://www.johnsnowlabs.com/marketplace/ema-rare-disease-designations)\n\t- The European Medicines Agency (EMA) Rare Disease Designations dataset includes information on rare disease (orphan) designations based on applications that have been assessed by the European Medicines Agency's (EMA) Committee for Orphan Medicinal Products (COMP).\n- [EMA Wholesale Distributor Authorizations](https://www.johnsnowlabs.com/marketplace/ema-wholesale-distributor-authorizations)\n\t- The European Medicines Agency (EMA) Wholesale Distributor Authorizations dataset lists Wholesale distributors authorizations delivered by EMA or national competent authorities to European Economic Area countries Wholesale distributors of pharmaceutical products.\n- [EMA Withdrawn Applications](https://www.johnsnowlabs.com/marketplace/ema-withdrawn-applications)\n\t- The European Medicines Agency (EMA) Withdrawn Applications dataset includes information on the decision by a pharmaceutical company to withdraw an application made to the European Medicines Agency for a human medicine.\nIt includes withdrawals of new marketing authorization applications or for changes to existing marketing authorizations, which are evaluated by the Committee for Medicinal Products for Human Use (CHMP).\n- [European Public Assessment Reports](https://www.johnsnowlabs.com/marketplace/european-public-assessment-reports)\n\t- The European Medicines Agency (EMA) publishes a European Public Assessment Report (EPAR) for every medicine granted a central marketing authorisation by the European Commission following an assessment by the EMA's Committee for Medicinal Products for Human Use (CHMP). \nEPARs are full scientific assessment reports of medicines authorized at a European Union level.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated\n- Data Updates\n - Data updates support replace-on-update: outdated foreign keys are deprecated, not deleted\n\n**Our data is curated and enriched by domain experts** \n\n Each dataset is manually curated by our team of doctors, pharmacists, public health & medical billing experts:\n- Field names, descriptions, and normalized values are chosen by people who actually understand their meaning\n- Healthcare & life science experts add categories, search keywords, descriptions and more to each dataset\n- Both manual and automated data enrichment supported for clinical codes, providers, drugs, and geo-locations\n- The data is always kept up to date – even when the source requires manual effort to get updates\n- Support for data subscribers is provided directly by the domain experts who curated the data sets\n- Every data source’s license is manually verified to allow for royalty-free commercial use and redistribution.\n\n**Need Help?**\nIf you have questions about our products, contact us at [info@johnsnowlabs.com](mailto:info@johnsnowlabs.com). \n 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listing demonstrates how synthetic data can be shared on Databricks Marketplace using Delta Sharing. It includes a sample synthetic version of U.S. Census data, a pre-trained generator for producing custom variations, and a generation notebook. This approach enables data providers to offer high-utility, privacy-preserving datasets—without sharing any real personal data.\n\n## Use Cases \n- **Privacy-safe data sharing** for analytics and AI development \n- **Instant synthetic dataset generation** for rapid evaluation and onboarding \n\n## Product Details \nThis share includes the following assets:\n- Synthetic version of U.S. Census sample data \n- Generator to produce custom variations of the dataset \n- Notebook to guide generation and exploration \n\n## Additional Insights \nThis listing shows how data providers can leverage MOSTLY AI's SDK and Delta Sharing to safely distribute data with full utility, removing legal and privacy bottlenecks. \nLearn more about synthetic data at [https://mostly.ai](https://mostly.ai).\n","terms_of_service":"https://mostly.ai/terms-of-service","documentation_link":"","privacy_policy_link":"https://mostly.ai/privacy","data_source":"","assets":["ASSET_TYPE_DATA_TABLE","ASSET_TYPE_MEDIA"],"license":""},"provider_summary":{"name":"MOSTLY AI","description":"MOSTLY AI is the leader in the tabular synthetic data category, enabling safe and insightful data utilization for all. 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To learn more about the use of synthetic data visit mostly.ai.\n\nOn the Marketplace MOSTLY AI is showcasing the power of synthetic data by providing synthetic datasets from various industries for free exploration."},"popularity":1226},{"id":"349e7c77-56d7-4320-97e5-2f7390f2e616","summary":{"name":"SAMPLE Cloudlead | Company Data & B2B Contact Data, 100% Human Verified Dataset Built On Custom ...","subtitle":"With a team of 100+ human researchers, Cloudlead builds custom data lists of B2B decision makers from around the ...","share":{"name":"cloudlead_4bb3f33a_d2bb_475f_9a50_6a9a9f9e86cd_samples_share","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1761156909531,"updated_at":1765460654048,"categories":["OTHER"],"listingType":"STANDARD","provider_id":"46a53084-3b88-4e59-89fe-84ba6f25d2e2"},"detail":{"description":"Customized data research with 100% human verification includes advanced level data accuracy. 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This includes data on industries, occupations, demographics, and more. The core UK Labor Market Information (LMI) is categorized according to the UK Standard Industrial Classification (UK SIC) and the UK Standard Occupational Classification (UK SOC). Together, these tables offer a direct and detailed view into the UK labor market through officially reported government data.\n\n**Use cases**\n***Market Analysis***\nGovernment data on occupations and wages can reveal important regional differences in job opportunities and compensation. By exploring this data, you can compare employment rates, average wages, and the distribution of occupations across various geographic areas. This analysis can uncover disparities between urban and rural regions, highlight areas facing unique labor market challenges, or showcase regions that have successfully implemented economic development strategies.\n\n***Real World Data (RWD)***\nAnalyzing government occupation data over time can reveal trends in the labor market. You could examine how certain occupations have grown or declined in demand, identifying emerging industries or job sectors. For instance, you might discover that technology-related occupations have experienced significant growth, while traditional manufacturing jobs have declined.\n\n***Quantitative Analysis***\nUsing historical occupation data and projections, you can create a story about the future of the labor market. By analyzing trends, skills requirements, and industry growth, you can provide insights into the jobs and skills that will be in demand in the coming years. 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People are at the heart of everything we do, and we want to create a job market that works for everyone."},"popularity":888},{"id":"800b44cd-a86a-4b3d-9302-af647bb47490","summary":{"name":"SAMPLE Canada Business Registry PRO | 1.5M Verified Organizations + 564K Director/Officer ...","subtitle":"Canada entity master plus a people-layer: 1.49M organizations (registry IDs, names, address fields) + 564K ...","share":{"name":"quantlens_14bb0199_1dc0_466e_9f16_3e3b5753df61_samples_share","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1769010917987,"updated_at":1769010917987,"categories":["OTHER","ADVERTISING_AND_MARKETING","GEOSPATIAL"],"listingType":"STANDARD","provider_id":"1927c455-25f0-44e0-8e62-6a7e69558eb3"},"detail":{"description":"QuantLens Canada PRO v4.9 is a join-ready “entity backbone + people layer” bundle designed for KYC/compliance workflows, entity resolution (MDM), and relationship/graph analytics. 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They are headquartered in Canada."},"popularity":321},{"id":"6e6dd81a-fdae-4958-bffa-8861637a838b","summary":{"name":"SAMPLE M-ART: 5,000 Drone View Datasets | 4K Video Content | Commercially Cleared | Large ...","subtitle":"High-quality AI datasets with 5,000+ 4K video assets. 100% rights-cleared with model and property releases, ...","share":{"name":"m_art_5f697475_edbe_4918_afb6_72a0c7107b7d_samples_share","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1789465480017,"updated_at":1789559828428,"categories":["OTHER"],"listingType":"STANDARD","provider_id":"37c05363-a973-4f03-ab0f-0785c32e582a"},"detail":{"description":"M-ART delivers diverse AI training datasets with over 5,000 assets in 4K annotated images. All content is filmed on DJI drones, commercially cleared with full releases, and structured with detailed metadata to extract precise annotated images and video sequences. Key sections of the catalog include footage of cities, nature, the sea, and residential areas. \n\nM-ART also offers the ability to create customized datasets for clients, providing unique, high-quality video collections that help companies stand out from the competition and accelerate the training of AI models.","terms_of_service":"https://datarade.ai/company/legal/terms-of-service","contact_email":"support@datarade.ai","privacy_policy_link":"https://datarade.ai/company/legal/privacy-policy","assets":["ASSET_TYPE_DATA_TABLE"]},"provider_summary":{"name":"M-ART","description":"M-ART is a creative data provider based in Poland.\r\nWe specialize in producing and delivering large-scale, high-quality video and photo datasets for training AI models. M-ART combines cinematic production expertise with structured dataset solutions trusted by leading global companies.\r\n\r\nOur mission is to deliver authentic, diverse, and scalable content that empowers partners to build the next generation of AI technologies."},"popularity":1042},{"id":"c6961700-8693-4e93-980c-aad2aab8d00b","summary":{"name":"Free Sample Dataset - Biometric & Human Features Data (500 High-Res Images & Metadata)","subtitle":"DepositPhotos Biometric & Human Features Dataset","share":{"name":"biometric","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1779375293308,"updated_at":1793914744820,"categories":["ADVERTISING_AND_MARKETING","MEDIA","SCIENCE_AND_RESEARCH","OTHER"],"listingType":"STANDARD","provider_id":"4eddea04-97a3-4094-a7b0-49ce862d39cd"},"detail":{"description":"**DepositPhotos Biometric & Human Features Dataset (Sample)**\n\n\n**Overview**\n\nThis dataset is a curated sample of high-resolution **Biometric, Facial, and Human Feature** images sourced from the DepositPhotos library. This subset is designed for training, testing, and evaluating Computer Vision models for facial recognition, identity verification, emotional analysis, and healthcare AI applications.\n\nThis sample demonstrates the exact quality, diversity, and structural consistency of the enterprise data available through DepositPhotos. If you are looking to scale your machine learning models with premium, commercially licensed data at high volumes, reach out to our team at **vladyslav.shtak@vista.com** to discuss custom licensing, tailored curation, and enterprise-scale delivery.\n\n**Metadata Structure**\n\nTo help you fully evaluate the capabilities of our data pipeline, this showcase sample includes our **complete, enterprise-grade metadata** for every asset.\n\n**Fields Included in this Dataset:**\n\n- `file_name` — Formatted asset identifier used in this repository.\n- `deposit_item_id` — Unique numeric identifier for each asset.\n- `filename` — Original asset file string.\n- `description` — Manually written, high-accuracy descriptive caption provided by the original creator.\n- `title` — Alternate or shortened title provided by the creator.\n- `keywords` — Up to 50 human-reviewed, comma-separated tags per asset.\n- `type` — Asset type (e.g., `image`, `vector`).\n- `height` / `width` — Native resolution dimensions in pixels.\n- `extension` — Original file format (e.g., `jpg`, `eps`).\n- `available_for_ai_training` — Clearance flag confirming the asset is approved for AI/ML training use.\n- `royalty_model` — Licensing model applied to the asset.\n- `status` — Asset review status (e.g., `approved`).\n- `user_id` — Creator linkage for provenance tracking.\n- **Video/Audio Fields:** (Where applicable) frame rate, duration, BPM, genre, mood, and instrument data.\n\n**Why License Data from DepositPhotos?**\n\nAI and machine learning models require clean, diverse, and legally sound data at scale. DepositPhotos provides the volume and legal certainty that enterprise model development demands.\n\n- **Scale:** 250M+ royalty-free images, vectors, illustrations, videos, and audio tracks, with new assets added continuously.\n- **Commercial Safety:** All content is human-reviewed for IP infringement.\n- **Metadata Quality:** Captions and keywords are manually created and reviewed, minimizing noise and improving label reliability.\n- **Ethical Sourcing:** Creators are compensated for their work. Licensing through DepositPhotos directly supports the creative community and responsible AI development.\n- **Custom Curation:** Our data team works directly with engineering teams to filter, package, and deliver precise data slices based on your requirements.\n\n**About DepositPhotos**\n\nDepositPhotos is a global content marketplace offering premium stock photos, graphics, vectors, and videos. Trusted by corporations and technology companies worldwide, we connect millions of creators with businesses that need high-quality visual content at scale.\n\n**Contact & Licensing**\n\nReady to scale your models beyond this sample? 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Our enterprise data engineering accelerators are designed to expedite the development of data pipelines, data transformation, and integration, saving you time and resources.\n\nOur team of experienced data engineers, scientists and analysts utilize cutting-edge data infrastructure into valuable insights and helps enterprise clients optimize their Sales, Marketing, Operations, Financials, Supply chain, Human capital and Customer experiences."},"popularity":1563},{"id":"d971be99-944b-4e44-b001-42ee77509334","summary":{"name":"MBI Area Boundaries for Cyprus – Sample","subtitle":"MBI's precise & seamless boundaries for well-founded decisions in Cyprus. 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The territorial vintages of the worldwide area boundaries are compatible with our globally consistent and comparable [socio-demographic](https://www.mbi-geodata.com/en/sociodemographic-data/) and [economic](https://www.mbi-geodata.com/en/purchasing-power-overview/) data.\nOur area boundaries for Cyprus are offered at the following levels:\n\n- **Postal Code Level** (Postcode 2-digit, Postcode 4-digit on request): Suitable for a wide range of applications – for example, enriching customer databases, which are often organized by postal code.\n- **Administrative Level** (Dimoi kai Koinotites): Covers both larger administrative areas (e.g., regions, states, provinces) as well as finer subdivisions such as districts, counties, or municipalities.\n\n**Use Cases:**\n\n- **Market Analysis**: Use MBI’s boundaries to visualize data on a map, learn about your customers, and uncover untapped market potential.\n- **360-Degree Customer View**: Link boundaries to customer data, sociodemographic, and economic data to gain a holistic understanding of customer needs, behaviors, and potential value.\n- **Location Planning**\n•\tUse MBI’s boundaries to visualize data on a map, learn about your customers\n•\tPlan the provision of services and products across different regions\n•\tDefine sales territories\n•\tVisualize catchment areas and plan actions to reach potential customers\n\n**Additional Insights:**\n[About Area Boundaries](https://www.mbi-geodata.com/en/area-boundaries/)\n\n**Data Structure:**\nThe area boundaries include the following variables:\nCTRYCODE: Two-digit country code by ISO 3166\nNAME: Name of administrative area / Name of the most populous administrative area / Name of the respective micro-area or the next higher level\nADMINCODE / POSTCODE / MICROCODE: Administrative code / Postal code / Micro-code\nWKT: Geography as Well Known Text (lat/long WGS84)\n\n*with only a few specific exceptions","terms_of_service":"https://service-mbi-geodata.com/s/HNp7Z2tws4WDqWd","documentation_link":"https://www.mbi-geodata.com/en/mbi-supplier-manufacturer-worldwide-geodata/europe/cyprus-maps/","privacy_policy_link":"https://www.mbi-geodata.com/en/data-protection/","update_frequency":{"interval":1,"unit":"YEARLY"},"assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://service-mbi-geodata.com/s/L9j6HG7xrHRcyTn"},"provider_summary":{"name":"Michael Bauer International GmbH – MBI Geodata","description":"For over 15 years, Michael Bauer International (MBI) has been a leading provider of globally consistent and comparable geographic data, including more than 3 million postcode areas and other detailed datasets like sociodemographics and purchasing power. 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Founded several years ago, we operate with a skilled and agile team focused on delivering reliable, scalable, and user-centric digital products."},"popularity":538},{"id":"438f07ee-02ca-4c26-a221-7cfcf2e02f03","summary":{"name":"ＣＯ－ＯＤＥ 気象データ","subtitle":"このデータセットでは、気象庁が公開している日次の気象台観測データ(2023年1月1日～2023年1月31日)と７日間先の気象予報(2023年2月1日時点)のデータを取得・加工したサンプルデータを収録しています。","setting":{"visibility":"PUBLIC"},"created_at":1697598162944,"updated_at":1699581844701,"categories":["ADVERTISING_AND_MARKETING","COMMERCE","CLIMATE_AND_ENVIRONMENT","RETAIL"],"listingType":"PERSONALIZED","provider_id":"ccb1eead-54d5-4f89-9cdc-fca2645ff069"},"detail":{"description":"**Overview**\nこのデータセットでは、気象庁が公開している日次の気象台観測データ(2023年1月1日～2023年1月31日)と\n７日間先の気象予報(2023年2月1日時点)のデータを取得・加工したサンプルデータを収録しています。\n自社データと気象台観測地点を容易に紐づけを行えるように、マッピング用のマスタと郵便番号マスタも用意しております。\nマッピング用のマスタでは、気象台観測地点の緯度経度と全国市区町村の緯度経度情報(2017年)の2点間の距離から近い観測地点を収録しています。\nＣＯ－ＯＤＥをご契約いただくと、気象台観測地点約150地点とアメダス観測地点約1100地点の日次毎・１時間毎・10分毎のデータを取得できます。\nＣＯ－ＯＤＥ(コ・オード)の詳細については、以下のリンクから公式ページを参照ください。\nhttps://www.zdh.co.jp/products-services/external-data/co-ode/\n\n**Use cases**\n気象データを使うことで、営業活動やマーケティング活動で幅広く活用できます。\n・実店舗やオンラインショップの商品ごとの売上との相関分析\n・売上と気象を掛け合わせ、売れ筋分析による新商品開発のアイディア獲得\n・食品メーカーやアパレルメーカーによる小売業への提案で活用\n・季節ものの売上予測と販促強化\n・各店舗の在庫予測・在庫適正化\n・各店舗の適切な人員配置や適切なシフト表作成\n等。\n\n**Product details**\nSample Tables:\n-気象情報_気象台観測地点_日次毎\n-気象情報_気象台用場所マスタ\n-気象情報_天気予報_7日間\n-郵便番号_2\n\n\nSample Fields:\n-日付\n-都道府県\n-観測地点名\n-観測地点名（カナ）\n-緯度\n-経度\n-標高\n-備考欄\n-現地の平均気圧（hPa）\n-海面の平均気圧（hPa）\n-降水量（mm）\n-１時間の最大降水量（mm）\n-１０分間の最大降水量（mm）\n-平均気温（℃）\n-最高気温（℃）\n-最低気温（℃）\n-平均湿度（％）\n-最小湿度（％）\n-平均風速（m/s）\n-最大風速（m/s）\n-最大風速_風向\n-最大瞬間風速（m/s）\n-最大瞬間風速_風向\n-日照時間（h）\n-降雪（cm）\n-最深積雪（cm）\n-天気概況_昼（06:00～18:00）\n-天気概況_夜（18:00～翌日06:00）\n\n**Additional Insights**\n出典\n【気象情報_気象台観測地点_日次毎】気象庁「過去の気象データ検索」(https://www.data.jma.go.jp/obd/stats/etrn/index.php)より加工して出典\n【気象情報_気象台用場所マスタ】気象庁「過去の気象データ検索」(https://www.data.jma.go.jp/obd/stats/etrn/index.php)と国土交通省「位置情報サービス」(http://nlftp.mlit.go.jp/isj/index.html)を加工して出典\n【気象情報_天気予報_7日間】気象庁「週間天気予報」(https://www.jma.go.jp/bosai/forecast/#area_type=japan&area_code=010000)より加工して出典。\n【郵便番号_2】日本郵便株式会社(https://www.post.japanpost.jp/zipcode/download.html)を加工して出典","terms_of_service":"https://www.zdh.co.jp/products-services/external-data/co-ode/","documentation_link":"","privacy_policy_link":"https://www.zdh.co.jp/privacy/","assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"Zeal Corporation","description":"株式会社ジールは、経営を支えるデータ基盤の構築とDX人材の育成に注力し、従来のビジネスインテリジェンスサービスを超えた活動を推進しています。 そのサービスの一つにオープンデータ提供サービス「CO-ODE」があり、オープンデータとお客様独自のデータを組み合わせることでデータ活用を強力にサポートします。"},"popularity":1980},{"id":"32db0d45-89ad-4159-b15c-f031d981998a","summary":{"name":"Privacy-Safe Synthetic Data in Unity Catalog for Financial Services","subtitle":"Create privacy-preserving synthetic data for financial services using the open-source Synthetic Data SDK from MOSTLY AI","share":{"name":"ossdk_notebooks0214","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1739482540806,"updated_at":1739558729674,"categories":["FINANCIAL","PUBLIC_SECTOR","HEALTH"],"listingType":"STANDARD","provider_id":"88338f65-c6c5-44df-8b1d-25bb66cbdb00"},"detail":{"description":"## Overview \nThis Databricks Solution Accelerator enables financial institutions to generate high-quality, privacy-preserving synthetic data using the **open-source Synthetic Data SDK from MOSTLY AI**—all within **Databricks**. \n\nBy leveraging **Unity Catalog** for secure data management and governance, this accelerator helps financial services teams unlock access to **AI/ML training, analytics, fraud detection, and risk modeling—without exposing sensitive customer information**. \n\n---\n\n## \uD83D\uDCCC How to Get Started \nTo use this accelerator, **click \"Get Instant Access\"** in the top right. \n\n- The **included notebooks** will appear in the **\"Other assets\"** section of the **Delta Share**, where you can **clone them into your own workspace**. \n- The **financial data tables** will also be accessible in **Unity Catalog** via the Delta Share, allowing you to begin generating synthetic data immediately. \n\n---\n\n## \uD83D\uDD39 Use Cases \nFinancial institutions can leverage this accelerator for multiple synthetic data applications, including: \n\n✔ **AI/ML Model Training** – Use privacy-preserving synthetic data to train **fraud detection, credit risk, and predictive analytics models**. \n✔ **Self-Service Data Access** – Enable analysts and data scientists to work with **high-quality data while keeping real customer information protected**. \n✔ **Regulatory Compliance & Data Sharing** – Securely share **financial data across teams, partners, and regulatory bodies** while maintaining full compliance. \n✔ **Software Testing & Application Development** – Use synthetic data for **developing and testing banking applications** without production data risks. \n\n---\n\n## \uD83D\uDD39 Product Details \nThis accelerator provides a **streamlined, end-to-end synthetic data pipeline** for **financial services**. It consists of **two fully parameterized, widget-driven notebooks** that allow users to: \n\n\uD83D\uDCCC **Step 1**: **Train a synthetic data generator** on their financial data \n\uD83D\uDCCC **Step 2**: **Generate synthetic datasets** that retain key relationships \n\uD83D\uDCCC **All results are securely stored in Unity Catalog** \n\n---\n\n## \uD83D\uDCC2 Included Financial Data: Accounts & Transactions \nThis accelerator includes **preloaded financial data in Delta Share**, ensuring users can **immediately** train and generate synthetic data **without needing external datasets**. \n\nThe included tables are derived from the **Berka dataset**, a well-known **public banking dataset** that has been **preprocessed for seamless integration into Unity Catalog**: \n\n\uD83D\uDD39 **Accounts** – Represents **customer bank accounts**, including **creation dates** and **assigned districts**. \n\uD83D\uDD39 **Transactions** – Logs **deposits, withdrawals, and transfers** across accounts. \n\nThis structure mirrors **real-world banking data**, making it highly relevant for financial institutions needing synthetic data for: \n\n✔ **AI/ML model training** \n✔ **Data democratization** \n✔ **Regulatory compliance** \n\n\uD83D\uDCA1 **Performance Tip:** \nThis dataset includes **over 1 million transactions**. \nTo ensure **quick execution while maintaining model performance**, we recommend setting the **Max Training Time** to **10 minutes**. \n\n---\n\n## \uD83D\uDEE0️ Included Notebooks \n\n### **\uD83D\uDCCC Step 1: Train a Synthetic Data Generator** \n✔ Load financial data from **Unity Catalog** \n✔ Train a synthetic data generator using the **open-source Synthetic Data SDK** \n✔ Export the generator **to Unity Catalog Volumes** \n\n### **\uD83D\uDCCC Step 2: Generate Synthetic Data** \n✔ Use the trained generator to **produce synthetic financial datasets** \n✔ Preserve **key relationships** and store the results in **Unity Catalog** \n\n---\n\n## \uD83D\uDCA1 Key Benefits for Financial Services \n\n✅ **Privacy-Preserving Data** – Enables **AI/ML model training** without exposing real customer data, ensuring compliance with **GDPR, CCPA, and other regulations**. \n✅ **No-Code, User-Friendly Setup** – Fully **widget-driven**, allowing easy setup and execution **without coding experience**. \n✅ **Unity Catalog Integration** – Ensures **governance, auditability, and discoverability** across teams in Databricks. \n✅ **Self-Service Data Access** – Democratizes **data access** across finance teams without manual anonymization. \n✅ **AI/ML Model Training & Risk Modeling** – Ideal for **fraud detection, credit risk modeling, and predictive analytics**. \n✅ **Reusable Generators** – Once trained, a **synthetic data generator can be reused indefinitely**, reducing the need for re-training. \n✅ **Multi-Table Support** – Automatically preserves **primary and foreign key relationships** across datasets. \n\n---\n\n## \uD83D\uDD25 Scaling Beyond Open Source \n\nWhile the **open-source Synthetic Data SDK** provides powerful synthetic data generation, organizations looking to scale can unlock: \n\n✔ **Enterprise-grade observability & governance** – Gain **centralized visibility into synthetic data generation, usage, and performance**. \n✔ **Robust professional services & support** – Leverage **expert guidance** for **implementation, compliance, and best practices**. \n\n\uD83D\uDCE9 **Interested in scaling synthetic data across your organization?** \nContact **[hello@mostly.ai](mailto:hello@mostly.ai)** to discuss **enterprise solutions**. \n","terms_of_service":"https://mostly.ai/terms-of-service","documentation_link":"https://github.com/mostly-ai/mostlyai","privacy_policy_link":"https://mostly.ai/privacy","cost":"FREE","assets":["ASSET_TYPE_DATA_TABLE","ASSET_TYPE_GIT_REPO"],"license":""},"provider_summary":{"name":"MOSTLY AI","description":"MOSTLY AI is the leader in the tabular synthetic data category, enabling safe and insightful data utilization for all. Its advanced GenAI algorithms facilitate the creation of high-fidelity synthetic datasets, empowering Fortune 100 companies and beyond to work securely with data and gain valuable insights. With offices in Vienna, Austria, and New York, USA, MOSTLY AI supports global organizations to create secure, scalable, and innovative data environments while upholding privacy compliance. To learn more about the use of synthetic data visit mostly.ai.\n\nOn the Marketplace MOSTLY AI is showcasing the power of synthetic data by providing synthetic datasets from various industries for free exploration."},"popularity":2043},{"id":"62885422-6e12-4ec1-8824-2ba20efdec67","summary":{"name":"Geolocet | Demographic Data Austria","subtitle":"Austria demographics data - gender, age bands, employment status, marital status, and nationality","setting":{"visibility":"PUBLIC"},"created_at":1700158938466,"updated_at":1752144280982,"categories":["DEMOGRAPHICS","GEOSPATIAL","PUBLIC_SECTOR"],"listingType":"PERSONALIZED","provider_id":"5d3ddc17-a7e8-4794-a760-13587e1de2c6"},"detail":{"description":"Get instant access to Austrian Demographics Data with [Geolocet](https://geolocet.com)\n\nExplore the richness of Austrian demographic landscape with our Austrian Census Data. This meticulously compiled dataset, sourced from official statistical sources, offers a detailed insight into the population landscape of Austria at the municipality level. \n\nIf you require data at a less granular level, such as an Uber grid or square grid, please don't hesitate to get in touch with us at *contact@geolocet.com*. We understand that different projects may have varying data needs, and we are here to tailor our data to match your specific requirements.\n\n ## Key Features:\n---\n* **Population Data Austria:** Gain access to the latest and most accurate population statistics for Austria, meticulously organized by municipality.\n\n* **Demographic Insights:** Dive deep into Austria's demographic landscape with data on gender, age bands, employment status, marital status, and nationality, all available at the municipal level.\n\n* **Geodemographic Insights:** Unlock geodemographic data Austria-wide to inform location-based decision-making, marketing strategies, and policy formulation.\n\n* **Ready to use:** Our dataset is currently available in CSV format, providing ease of use and compatibility with a wide range of data analysis tools. However, if you prefer a different format, our team is readily available to assist you in obtaining the data in your desired format.\n---\nAdditionally, for those looking to enrich their analysis or project, we offer:\n* **[Administrative Boundaries](https://geolocet.com/pages/administrative-boundaries-data-for-european-countries):** If you require geographical boundaries (polygons) for Austrian municipalities, consider exploring our Admin Boundaries product, which seamlessly integrates with this dataset.\n\n* **[Point of Interest (PoI) Data](https://geolocet.com/pages/aggregated-points-of-interest-poi-data):** If you're interested in Points of Interest data, please don't hesitate to contact us for customized datasets to enhance your geographical analysis.\n\n* **[Data Customization](https://geolocet.com/pages/data-services-by-geolocet):** We understand that your project may have specific requirements. Feel free to get in touch with us if you need any modifications or specific data formats such as Excel, JSON, GeoJSON, or Shapefile. We are here to tailor the data to your needs.\n\nIf you're looking for data for Austria, [look here](https://geolocet.com/pages/austria) for off-the-shelf or tailor-made datasets to suit your specific requirements.\n\nMake informed decisions, conduct precise research, and unlock the potential of Austria's demographic landscape with our comprehensive dataset. Trust in the accuracy and reliability of our official statistical data to drive your projects forward.\nContact us at *contact@geolocet.com* today to access this invaluable resource and explore Austria's census data like never before.\n\n[Geolocet LTD](https://geolocet.com)\n","terms_of_service":"https://geolocet.com/pages/terms-of-use","documentation_link":"","privacy_policy_link":"https://geolocet.com/pages/privacy-policy","cost":"PAID","data_source":"Statistics Austria, OSM","assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://geolocet.com/pages/terms-of-use"},"provider_summary":{"name":"Geolocet LTD","description":"Geolocet provides premium geospatial data for retail analysis, GIS, and market planning. We equip businesses with accurate, ready-to-query data to drive spatial analysis, site selection, and customer segmentation.\n\nCore Offerings:\n\nDemographics: Socioeconomic data covering population, age, and employment across diverse geographies.\n\nPoints of Interest (PoI): Datasets spanning retail, healthcare, and transport for catchment analysis.\n\nAdministrative Boundaries: GIS-ready boundaries, including municipalities and postcodes, for territory management.\n\nBrand Locations: Chain location datasets to monitor store networks and market expansion.\n\nWealth Data: Granular economic indicators like purchasing power to identify high-potential markets.\n\nWe source from trusted providers, delivering highly customizable datasets with comprehensive coverage across Europe and worldwide."},"popularity":382},{"id":"f6f054e2-f741-4e3c-936b-657e710959ff","summary":{"name":"Sigma Software Adtech Events Sample for ML","subtitle":"This data set contains simulated Ad Campaign events to use for ML and prototyping purpose in adtech domain.","setting":{"visibility":"PUBLIC"},"created_at":1760559194632,"updated_at":1761740918133,"categories":["ADVERTISING_AND_MARKETING","MEDIA","SCIENCE_AND_RESEARCH"],"listingType":"PERSONALIZED","provider_id":"3d292fe0-4f52-4229-a7bd-693ff8166efd"},"detail":{"description":"**Overview**\nSimulated events for Ad Campaigns\n\n**Use cases**\nThe Data could be used for \n- Trafic Shaping Model\n- Click-Through Rate (CTR) Prediction Model\n- Conversion Optimization with Attribution Modeling\n\n**Product details**\nThe data table contains such columns:\nuser_id, event_name, advertiser, campaign, gender, income, page_url, region, country\n\nFor more details, refer to the embedded notebook.\n\n**Additional Insights**\nCampagn details are synthetic","terms_of_service":"https://sigma.software/terms-of-use","documentation_link":"","privacy_policy_link":"https://sigma.software/privacy-notice","embedded_notebook_file_infos":[{"id":"e0c46ccc-493a-4cd6-8d6b-11206712b429","marketplace_file_type":"EMBEDDED_NOTEBOOK","file_parent":{"parent_id":"f6f054e2-f741-4e3c-936b-657e710959ff","file_parent_type":"LISTING"},"mime_type":"text/html","download_link":"https://marketplace-file-storage-prod.s3-fips.us-east-1.amazonaws.com/EMBEDDED_NOTEBOOK/LISTING_f6f054e2-f741-4e3c-936b-657e710959ff/e0c46ccc-493a-4cd6-8d6b-11206712b429?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEGgaDnVzLWVhc3QtMS1maXBzIkYwRAIgbC8sWIoYaHR8y7Wq86nPb0pufBl4%2BM4PWpeN%2B5PW9VQCIG7BhAVBDCWTpxFDLeuKYm%2Ff1JLz4iccPMzter0YdhgWKqYECDAQABoMNDE0MzUxNzY3ODI2IgxNAJrHbXaZG20jNfMqgwQyGEb4TsoVOoNvJ%2BaA%2FZ7zeu8hGcHqCeNfblP%2FQo5g6q2aRNtpwtPRKMlhYcIE%2FUQaNT1Lqmyptu32VUQTpzjTTxRVmJrDPkheSCkV06XQ%2FpTMKjU8CC7X1b%2FqYf8CrBwZT7Cv4C5WPjEET38f2A79h0xV6bZgPy28zQWrSeIJ9w6AfZBxiE24%2FVc08zVA0ivCT6lsy7nuU0wcR%2FMg2C9nd1vaMnQ923VpD21CKfVVYcrNWL2b%2FQ%2BNAO%2BxPjPgjiN6UPQbvbvZxkSdhgrGtecIg9NgqGkMcvM5VLTDtdC1kdeZetfOC2obtA%2BT%2F0gy3tIDo6WM5hcQfw743%2FAU9oaGnRBl%2BwoxRRDCZA%2F4jnPdluICSI7NCr6C3HHOe4CSZJjRihczxPA%2FgQczAVjAPs6CrK9N9pIzt0vNJpGUTgg4AoxftTvGfciDREilSctPIcBk6GwB%2Bzt4wbtNCmEPU%2BFD46rc6CWVQmmLwbKOeHw70ipMVFlOc8NlXkEtGO2vu6H44MstSSc6daOVUYUavQSGZ6jGhEjsP2YGz8KDm9XQotB%2FtyVBqiZV01OKqDmmBJA%2FXjUpSJ8M%2FeJXhe5m%2BKcxzNObtoJRI7jci3Tpt08XZyXZ7%2FyIV8H30ya468X3cfkTXrm972uga3z7iXAdubyEHnQIua%2FuvtwOHILi6O5D1Me2MzCPvcnXBjqTAYDoMWBt%2B3AVFuqSs2dmDQNBnvEhXfMgTedKLD5PJHb3aIl15iBkLaNu36GYnVCp9ZU2lSYcTbY5QwNnNeTfJTNy9bKhwzru6Sg%2BslhN%2BTS0zkTn%2BeLMY4tHgPEtEZ2cp%2FCk6GwqswvnYVKHjwO8q%2B1Mx8lc08fVEfarE9dke1GGFqoddx%2Bq%2BZUyHbDlAVfrCmf0yw%3D%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20261110T000135Z&X-Amz-SignedHeaders=host&X-Amz-Expires=86400&X-Amz-Credential=ASIAWA6KKHEJORWBH4N3%2F20261110%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=0e98a38e03843237b4626752963ecd4737618cee8f65fceb4d61456e0bcd266b","created_at":1761740478026,"updated_at":1761740478026,"display_name":"Adtech Campaign","status":"FILE_STATUS_PUBLISHED"}],"geographical_coverage":"[\"Global\"]","cost":"FREE","collection_date_start":1759349316046,"data_source":"Simulation","size":1.0,"assets":["ASSET_TYPE_DATA_TABLE","ASSET_TYPE_GIT_REPO"],"license":"https://mit-license.org/"},"provider_summary":{"name":"sigmasoftware","description":"Sigma Software is a global technology consultancy helping organizations manage data, AI, and analytics to drive innovation. We focus on AdTech, Manufacturing, Healthcare, BFSI, and the Public Sector, delivering tailored data and AI solutions.\n \nOn Databricks marketplace, we offer domain-specific sample datasets and notebooks for training and validating AI models, enabling rapid prototyping and experimentation. With strong expertise in AI, ML, and Generative AI, we help our customers accelerate time to market.\n \nTechnical excellence and high-quality data assets are our top priorities."},"popularity":581},{"id":"d129ead5-71af-48a7-97ec-8d9c7320357c","summary":{"name":"Consumer Packaged Goods (Grocery) | Nutrition Datasets","subtitle":"This set of US and Canadian grocery/CPG products includes more than 999,000+ UPCs, representing 92% of grocery products.","setting":{"visibility":"PUBLIC"},"created_at":1736460871060,"updated_at":1736524691716,"categories":["HEALTH","SCIENCE_AND_RESEARCH","PUBLIC_SECTOR"],"listingType":"PERSONALIZED","provider_id":"b00b0400-e15d-4f0f-9804-67c86e38aa3a"},"detail":{"description":"Overview\n\nNutritionix, a Syndigo company, is the world’s largest dietitian-verified nutrition database that provides the most accurate nutritional data to power popular lifestyle apps to help consumers make the most informed nutrition decisions. Over 2,000 apps, platforms, and clinical research projects around the world use Nutritionix nutrition data across all digital channels. Datasets include over 999,000+ UPCs, 204,000+ restaurant menu items, and 38,500+ generic food items and food phrases.\nTap into the largest assortment of verified data on the market, with more than 92% coverage of all US and Canadian CPG products.\n\n\nProduct Details\n\n•\tScale: Access to over 999,000 verified grocery products in the US and Canada.\n•\tBarcode Lookup: Every product record includes the UPC information as the unique identifier. 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The models provided in this listing are\n\n- [OLMo_1B](https://huggingface.co/allenai/OLMo-1B)\n- [OLMo_7B](https://huggingface.co/allenai/OLMo-7B)\n- [OLMo_7B_Twin_2T](https://huggingface.co/allenai/OLMo-7B-Twin-2T)\n\nThe models are packaged using MLflow’s PyFunc flavor for a Hugging Face `transformers` model.\n- [OLMo_1B](https://huggingface.co/allenai/OLMo-1B), [OLMo_7B](https://huggingface.co/allenai/OLMo-7B), [OLMo_7B_Twin_2T](https://huggingface.co/allenai/OLMo-7B-Twin-2T): pretrained generative text model that can be used for further fine-tuning on specific applications.\n\nOlmo models are licensed under the [Apache 2.0 license](https://www.apache.org/licenses/LICENSE-2.0). By installing this listing, you acknowledge and agree to the license.\n\nFor example notebooks of using the olmo model in various use cases on Databricks, refer to [the Databricks ML example repository](https://github.com/databricks/databricks-ml-examples/tree/master/llm-models/olmo).\n\n**Use cases**\n\n[OLMo_1B](https://huggingface.co/allenai/OLMo-1B), [OLMo_7B](https://huggingface.co/allenai/OLMo-7B), [OLMo_7B_Twin_2T](https://huggingface.co/allenai/OLMo-7B-Twin-2T):\n- Fine-tuning on specific applications.\n\n\n**Product details**\n\nThe pre-trained model can be used for further fine-tuning on specific applications. The fine-tuned model in this listing can be deployed directly to Databricks Model Serving for immediate use, or loaded for fine-tuning or batch inference use cases. 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It offers an analysis-ready foundation for EV charging infrastructure planning, competitor benchmarking, logistics routing, and retail location intelligence.\n\nFully compatible with **Apache Spark**, **Delta Lake**, and **Databricks Unity Catalog** workflows.\n\n---\n\n## **Complete National Coverage: Branded Forecourts & Supermarket Fuel**\nUnlike single-brand extractions, this dataset aggregates all premier fuel networks and independent operators into a single, unified data model. The coverage maps maximum geographic depth across all 17 autonomous communities and autonomous cities:\n\n- **Major Fuel Networks & Multinational Banners:** \n [Repsol (3,246 locations)](https://geolocet.com/products/Spain-Repsol-Petrol-Stations), \n [Moeve / Cepsa (2,008 locations)](https://geolocet.com/products/Spain-Moeve-Cepsa-Petrol-Stations), \n [bp (757 locations)](https://geolocet.com/products/Spain-bp-Petrol-Stations), \n [Galp (545 locations)](https://geolocet.com/products/Spain-Galp-Petrol-Stations), \n [Shell (402 locations)](https://geolocet.com/products/Spain-Shell-Petrol-Stations), \n [Disa (143 locations)](https://geolocet.com/products/Spain-Disa-Petrol-Stations), \n [AVIA (118 locations)](https://geolocet.com/products/Spain-AVIA-Petrol-Stations), \n [Q8 (101 locations)](https://geolocet.com/products/Spain-Q8-Petrol-Stations)\n\n- **Automated, Express & Value Forecourts:** \n [Ballenoil (364 locations)](https://geolocet.com/products/Spain-Ballenoil-Petrol-Stations), \n [Plenergy (364 locations)](https://geolocet.com/products/Spain-Plenergy-Petrol-Stations), \n [Petroprix (197 locations)](https://geolocet.com/products/Spain-Petroprix-Petrol-Stations)\n\n- **Leading Supermarket & Commercial Fuel Banners:** \n [Carrefour (145 locations)](https://geolocet.com/products/Spain-Carrefour-Petrol-Stations), \n [AS 24 (80 locations)](https://geolocet.com/products/Spain-AS-24-Petrol-Stations), \n [BonÀrea (77 locations)](https://geolocet.com/products/Spain-BonArea-Petrol-Stations), \n [tankpool24 (95 locations)](https://geolocet.com/products/Spain-tankpool24-Petrol-Stations), \n [Alcampo (58 locations)](https://geolocet.com/products/Spain-Alcampo-Petrol-Stations)\n\n**Plus thousands of regional independent forecourts** fully mapped and standardized across all administrative regions (e.g., Andalucía: 3,053 sites; Catalunya: 2,129 sites; Comunitat Valenciana: 1,885 sites; Comunidad de Madrid: 1,182 sites).\n\n---\n\n## **Technical Specifications & Schema Definitions**\nOptimized for zero-friction schema enforcement when reading via `spark.read.format(\"csv\")`. The structured schema below ensures flawless rendering in Databricks notebooks and high accuracy for LLM semantic indexing.\n\n* **`GUID`** (`STRING`): Unique global identifier for deterministic primary key mapping and deduplication.\n* **`Title`** (`STRING`): Official station name or commercial brand banner classification (e.g., Repsol, Moeve).\n* **`Latitude`** (`DOUBLE`): Precise WGS84 latitude coordinate for point-in-polygon and spatial joins.\n* **`Longitude`** (`DOUBLE`): Precise WGS84 longitude coordinate for spatial indexing (H3 / S2).\n* **`Street_No` / `Street`** (`STRING`): Normalized building numbers and thoroughfare name elements.\n* **`City` / `Postal_Code`** (`STRING`): Core municipal settlements and primary postal routing codes.\n* **`Commune`** (`STRING`): Local municipal administrative boundary designation (Municipio / Comuna).\n* **`Autonomous_Community`** (`STRING`): High-level macro-administrative classification (e.g., Andalucía, Catalunya, Madrid).\n* **`Admin_level_1` / `_2`** (`STRING`): Additional local administrative area and province identifiers.\n* **`Address`** (`STRING`): Full, standardized street address clean-text output.\n* **`Wheelchair`** (`STRING`): Accessibility flags indicating wheelchair mobility support where available.\n\n---\n\n## **Databricks Workflow & Spatial Analytics Use Cases**\n\n- **EV Charging Whitespace Analysis:** Query the full 16,182 location points against high-voltage grid corridors or traffic volume rasters to rank optimal stations for ultra-fast EV charging infrastructure placement.\n- **H3 Geospatial Indexing:** Convert the precise WGS84 `Latitude` and `Longitude` fields into Uber H3 spatial hexagons (`h3_longlattocell`) to seamlessly cross-reference with granular Spanish small-area statistical sector (*sección censal*) income indicators.\n- **Logistics & Fleet Fleet Benchmarking:** Integrate forecourt coordinates with fleet tracking telemetry to calculate fuel stop density, build optimal corridor routing profiles, and minimize off-route deadhead mileage across the national highway network.\n\n---\n\n## **Data Evaluation & Custom Ingestion**\n\n### **Evaluate with a Free Sample Dataset**\nVerify the structure, column types, and coordinate precision before full implementation. A comprehensive schema sample is available for download in CSV/Parquet-friendly formats.\n\n*Want deeper market insights?* Learn more about the forecourt distribution and competitive trends in our comprehensive report: **[View the Spain Top Brands Petrol Stations Report](https://geolocet.com/pages/Spain-topbrands-petrol-stations-report)**\n\n### **Enterprise Delivery & Tailored Formatting**\nWhile natively available as an analysis-ready CSV for DBFS ingestion, we accommodate enterprise-level data architecture needs. Alternative formats - including **GeoJSON, Esri Shapefiles, PostgreSQL dumps, Excel, or direct Delta sharing profiles** - can be generated. Reach out to our engineering team at **contact@geolocet.com** for integration support or custom European POI data requests.","terms_of_service":"https://geolocet.com/pages/terms-of-use","documentation_link":"","privacy_policy_link":"https://geolocet.com/pages/privacy-policy","assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://geolocet.com/pages/terms-of-use"},"provider_summary":{"name":"Geolocet LTD","description":"Geolocet provides premium geospatial data for retail analysis, GIS, and market planning. 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Whether you're analysing hiring trends, exploring workforce needs, or forecasting skills demand, this dataset empowers you to make smarter, data-driven decisions.\n\nSourced from both job boards and company career sites, our data captures postings from organizations of all sizes, ensuring a comprehensive and representative picture of employer demand across the UK.\n\nQuestions answered by this dataset:\n- What skills are employers seeking for roles like computer programmers?\n- Which companies in London are hiring the most nurses?\n- What skills are most often associated with a given job title?\n- Has demand for the skill \"Python\" increased or decreased in the past six months?\n- How long does the average job posting for a web developer stay open in Liverpool?\n\n**Use cases**\n***Market Analysis***\nUncover emerging industries, identify in-demand skills, and spot trending job types. Analyze how the labour market is evolving and where the opportunities lie.\n\n***Location Planning***\nExamine where job postings are concentrated geographically. Ideal for companies planning growth and job seekers targeting regions with strong employment prospects.\n\n***Staffing***\nGain invaluable insights into how employers approach their recruitment efforts. Uncover which companies are actively hiring, the types of job postings they prioritize, and the qualifications they seek. Help employers refine their recruitment strategies and improve talent acquisition.\n\n***Empower Job Seekers***\nEquip job seekers with the knowledge they need to thrive in the competitive landscape. Highlight trending skills, showcase industries with the highest demand, and provide guidance on how to stand out in the crowd.\n\n***Industry Competitiveness***\nAnalyze the competitiveness of different industries based on job postings. 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It is optimized to answer questions and assist users with various banking transactions. It has been trained using hybrid synthetic data generated using our NLP/NLG technology and our automated Data Labeling (DAL) tools.\n \nThe goal of this model is to show that a generic verticalized model makes customization for a final use case much easier. For example, if you are \"ACME Bank\", you can create your own customized model by using this fine-tuned model and doing an additional fine-tuning using a small amount of your own data. An overview of this approach can be found at: [From General-Purpose LLMs to Verticalized Enterprise Models](https://www.bitext.com/blog/general-purpose-models-verticalized-enterprise-genai/)\n\n**Intended Use**\n- **Recommended applications**: This model is designed to be used as the first step in Bitext’s two-step approach to LLM fine-tuning for the creation of chatbots, virtual assistants and copilots for the Retail Banking domain, providing customers with fast and accurate answers about their banking needs.\n- **Out-of-scope**: It should not be used for non-banking related inquiries or for providing advice on medical, legal, or critical safety issues.\n- This model represents step one of of our two-step approach to verticalizing GenAI for enterprise use, as we describe in https://www.bitext.com/blog/general-purpose-models-verticalized-enterprise-genai/\n\n**Model Architecture**\nThe model uses the `MistralForCausalLM` architecture with a `LlamaTokenizer`. It retains the core features of the base model but is enhanced to address the specific needs of retail banking.\n\n**Training Data**\nThe model was trained using a dataset designed for retail banking interactions, now publicly available on Hugging Face as [bitext/Bitext-retail-banking-llm-chatbot-training-dataset](https://huggingface.co/datasets/bitext/Bitext-retail-banking-llm-chatbot-training-dataset). This dataset covers 26 different intents such as `check_balance`, `transfer_money`, `open_account`, and more, each with around 1000 examples.\n\nThe dataset includes:\n- 25,545 question/answer pairs\n- 4.98 million tokens\n- 1224 entity/slot types\n\nEach entry consists of:\n- Instruction: User request\n- Category: High-level semantic category\n- Intent: Specific intent of the user request\n- Response: Example response from a virtual assistant\n\nThe dataset covers a wide range of banking-related categories such as ACCOUNT, ATM, CARD, CONTACT, FEES, FIND, LOAN, PASSWORD, and TRANSFER, ensuring comprehensive training for handling diverse retail banking queries.\n\n**Training Procedure**\n\n**Hyperparameters**\n- **Optimizer**: AdamW\n- **Learning Rate**: 0.0002 with a cosine learning rate scheduler\n- **Epochs**: 3\n- **Batch Size**: 4\n- **Gradient Accumulation Steps**: 4\n- **Maximum Sequence Length**: 8192 tokens\n\n**Environment**\n- **Transformers Version**: 4.43.4\n- **Framework**: PyTorch 2.3.1+cu121\n- **Tokenizers**: Tokenizers 0.19.1\n\n**Limitations and Bias**\n- The model is specifically trained for retail banking and may not yield accurate results outside this field.\n- There may be biases in the training data, which could influence the model's responses, particularly in nuanced financial scenarios.\n\n**Ethical Considerations**\nCare should be taken to ensure that the model's automated responses do not replace professional human advice where necessary, particularly in complex financial situations.\n\n**Acknowledgments**\nThis model was developed and trained by Bitext using their proprietary technologies and resources.\n\n**License**\n\"Mistral-7B-Retail-Banking\" is licensed under the Apache License 2.0 by Bitext Innovations International, Inc. 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Compiled through extensive research and public record aggregation, it supports advanced analytics across property valuation, regional trends, and investment forecasting.\n\nFeaturing 53 structured attributes per property, this dataset enables in-depth market modeling, predictive analytics, and spatial correlation studies for urban planners, investors, and policy researchers. With anonymized identifiers and consistent formatting, it is optimized for real estate data science and economic modeling at scale.\n\n### **Provenance**\nThe dataset is built from public records and aggregated sources using ethical research methods. All data is GDPR and CCPA compliant, with personal identifiers anonymized. A sample version is distributed under the **CC BY 4.0** license, while the full dataset is available via **AIDC.app** with a full AI training license. \n\n**Acknowledgements:** \nCompiled by **Maths with Kanchana LLC** under the Creative Commons Attribution 4.0 International License.\n\n### **Use Cases**\nThis dataset is valuable for a variety of analytical and research applications, including:\n\n- **Market Forecasting:** Train predictive models to estimate California housing price trends and investment opportunities.\n- **Insurance Risk Modeling:** Incorporate property condition and neighborhood factors into risk scoring systems.\n- **Urban Planning Analytics:** Evaluate permit history and neighborhood data for zoning and infrastructure analysis.\n- **Policy and Economic Research:** Assess taxation trends and affordability for public policy studies.\n- **Business Intelligence:** Enable property-level insights for agencies, developers, and real estate investors.\n\n### **Column 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(USD).\n- **neig_primary_neigh_name** | string | Anonymized neighborhood name.\n- **agent_name** | string | Anonymized agent identifier.\n- **office_name** | string | Anonymized office 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2025 California property dataset with pricing, permits, tax, and neighborhood analytics.","size":0.0179,"assets":["ASSET_TYPE_MEDIA"],"license":"https://spdx.org/licenses/CC-BY-4.0.html"},"provider_summary":{"name":"AIDC, Inc.","description":"Great AI starts with great data. We have it. Ship data like software. AIDC packages external data as governed Delta tables with Unity Catalog, complete with schema, metadata, lineage, and clear license terms. Pre‑vetted contracts minimize back‑and‑forth, so squads move from browse to pipeline in days. Quality control is operated by Kaggle Grandmasters and senior data engineers at that standard, so datasets behave in production and pass audits. Coverage spans luxury item pricing (valuation, comp, elasticity studies) to retail property analytics (site selection, underwriting, performance prediction). Instrument adoption and prove lift: reduced ETL, faster training, higher dataset usability rates. Repeatable motion across teams: import → govern → build → measure → scale. Available now on Databricks Marketplace."},"popularity":897},{"id":"4a543dd3-13c9-49cf-b73a-c615ea670276","summary":{"name":"SAMPLE Mobile Location Data | Australia | +20M Unique Devices | +10M Daily Users | +20B Events / ...","subtitle":"Quadrant provides privacy first global location data and foot traffic data for AI, analytics, and business ...","share":{"name":"quadrant_0075fe82_1a73_4da6_a839_40d1ec242bea_samples_share","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1790087059546,"updated_at":1790087059546,"categories":["GEOSPATIAL"],"listingType":"STANDARD","provider_id":"a8064e88-f58c-4540-97d8-309869635380"},"detail":{"description":"Quadrant provides privacy first global location data and foot traffic data that help organizations understand how people interact with places across more than 200 countries and territories. Our enterprise grade datasets enable businesses to measure visitation trends, analyze mobility patterns, support AI models, improve location intelligence, and make confident data driven decisions.\n\nBuilt for analytics and AI, Quadrant's location data helps organizations uncover meaningful geographic patterns across industries including retail, commercial real estate, transportation, urban planning, financial services, market research, tourism, and consumer insights. Whether you are evaluating site performance, measuring market activity, building predictive models, or analyzing movement trends, our data provides a trusted foundation for scalable analysis.\n\nOur location data is privacy first, quality controlled, and designed to meet the needs of modern enterprises. Every dataset undergoes rigorous validation and continuous quality monitoring to ensure consistency, accuracy, and reliability while supporting responsible data practices.\n\nQuadrant offers flexible geographic coverage that adapts to your business requirements. Unlike providers limited to predefined geographic boundaries, we support reporting and analysis at the city, county, state, regional, and national levels, making it easier to analyze Points of Interest, compare markets, evaluate trade areas, and understand mobility across locations.\n\nKey benefits include:\n\nGlobal coverage across more than 200 countries and territories\nPrivacy first and quality controlled location data\nAI ready and analytics friendly datasets\nFlexible geographic reporting at the city, county, state, regional, and national levels\nHistorical data available from 2022 onwards\nTailored datasets aligned with your business requirements\nExpert technical support from our in house data specialists\n\nQuadrant's location data and foot traffic data power a wide range of applications including market intelligence, consumer behavior analysis, site selection, competitive benchmarking, catchment analysis, mobility analytics, urban planning, location based services, and AI driven business intelligence. By combining global scale with flexible delivery and a strong commitment to data quality and privacy, we help organizations transform location intelligence into better business outcomes.","terms_of_service":"https://www.quadrant.io/privacy-policy","contact_email":"support@datarade.ai","privacy_policy_link":"https://www.quadrant.io/privacy-policy","assets":["ASSET_TYPE_DATA_TABLE"]},"provider_summary":{"name":"Quadrant","description":"Quadrant, an Appen company, is a global provider of privacy first mobile location data and Point of Interest (POI) data. Our data that helps enterprises, organizations, and innovators understand how people and places interact.\r\n\r\nWith coverage across 200+ countries and territories, 1+ billion opted in unique devices, and 50+ billion daily location events, our mobile location data delivers global scale for location intelligence. Our POI data spans 200+ countries and includes 300+ million places with 50+ verified attributes, enabling organizations to build better maps, search experiences, market intelligence, and location based products. We also offer POI validation, enrichment, and enhancement services to improve existing datasets.\r\n\r\nAs part of Appen, a global leader in AI training data, Quadrant combines global scale, enterprise grade data quality, and privacy first governance to help organizations build the next generation of location enabled products and services."},"popularity":434},{"id":"cd7299d0-45ea-4a4b-97be-8f49441b8efb","summary":{"name":"Geolocet | Points of Interest Data | Petrol Station Locations - UK","subtitle":"Complete GIS-ready dataset of 10,766 UK petrol station locations (brands & independents). Optimized for Spark/Databricks","setting":{"visibility":"PUBLIC"},"created_at":1780642424715,"updated_at":1780642442645,"categories":["GEOSPATIAL","RETAIL"],"listingType":"PERSONALIZED","provider_id":"5d3ddc17-a7e8-4794-a760-13587e1de2c6"},"detail":{"description":"# **All Petrol Station Locations in the UK: Complete Geographic Dataset**\n---\n### **Premium GIS-Ready Retail Location Intelligence Data for Databricks Analytics**\n\nThis high-fidelity geospatial dataset provides a complete, exhaustive, and standardized overview of **10,766** verified fuel and petrol station locations across the United Kingdom. Updated as of **May 8, 2026**, this comprehensive national dataset captures the entire UK forecourt landscape - encompassing major multinational fuel networks, supermarket-branded fuel stations, and prominent independent operators. It offers an analysis-ready foundation for EV charging infrastructure planning, competitor benchmarking, logistics routing, and retail location intelligence.\n\nFully compatible with **Apache Spark**, **Delta Lake**, and **Databricks Unity Catalog** workflows.\n\n---\n\n## **Complete National Coverage: Branded Forecourts & Supermarket Fuel**\nUnlike single-brand extractions, this dataset aggregates all premier fuel networks and independent operators into a single, unified data model. The coverage maps maximum geographic depth across England, Scotland, Wales, and Northern Ireland:\n\n- **Major Fuel Networks & Multinational Banners:** \n [Esso (1,510 locations)](https://geolocet.com/products/uk-esso-petrol-stations), \n [bp (1,287 locations)](https://geolocet.com/products/uk-bp-petrol-stations), \n [Shell (1,185 locations)](https://geolocet.com/products/uk-shell-petrol-stations), \n [Texaco (702 locations)](https://geolocet.com/products/uk-texaco-petrol-stations), \n [Gulf (408 locations)](https://geolocet.com/products/uk-gulf-petrol-stations), \n [JET (359 locations)](https://geolocet.com/products/uk-jet-petrol-stations)\n\n- **Leading Supermarket Fuel Banners:** \n [Tesco (567 locations)](https://geolocet.com/products/uk-tesco-petrol-stations), \n [Asda (432 locations)](https://geolocet.com/products/uk-asda-petrol-stations), \n [Morrisons (428 locations)](https://geolocet.com/products/uk-morrisons-petrol-stations), \n [Sainsbury's (319 locations)](https://geolocet.com/products/uk-sainsburys-petrol-stations)\n\n**Plus thousands of regional independent forecourts** fully mapped and standardized across all home nations:\n- **England:** 8,295 forecourts\n- **Scotland:** 1,021 forecourts\n- **Northern Ireland:** 737 forecourts\n- **Wales:** 675 forecourts\n\n---\n\n## **Technical Specifications & Schema Definitions**\nOptimized for zero-friction schema enforcement when reading via `spark.read.format(\"csv\")`. The structured schema below ensures flawless rendering in Databricks notebooks and high accuracy for LLM semantic indexing.\n\n* **`GUID`** (`STRING`): Unique global identifier for deterministic primary key mapping and deduplication.\n* **`Title`** (`STRING`): Official station name or commercial brand banner classification.\n* **`Latitude`** (`DOUBLE`): Precise WGS84 latitude coordinate for point-in-polygon and spatial joins.\n* **`Longitude`** (`DOUBLE`): Precise WGS84 longitude coordinate for spatial indexing (H3 / S2).\n* **`Street_No` / `Street`** (`STRING`): Normalized building numbers and thoroughfare name elements.\n* **`City` / `Town`** (`STRING`): Core municipal settlements and primary post town designations.\n* **`Local_Authority`** (`STRING`): Local government administrative unit governing the site location.\n* **`District`** (`STRING`): Intermediate county, regional borough, or district boundary identifiers.\n* **`Postal_Code`** (`STRING`): Full outward and inward alphanumeric UK postcode string.\n* **`Address`** (`STRING`): Full, standardized street address clean-text output.\n* **`Wheelchair`** (`STRING`): Accessibility flags indicating wheelchair mobility support where available.\n\n---\n\n## **Databricks Workflow & Spatial Analytics Use Cases**\n\n- **EV Charging Whitespace Analysis:** Query the full 10,766 location points against regional grid capacity or highways traffic data to rank optimal fuel stations for high-power EV charging station conversions.\n- **H3 Geospatial Indexing:** Convert the precise WGS84 `Latitude` and `Longitude` fields into Uber H3 spatial hexagons (`h3_longlattocell`) to seamlessly cross-reference with granular UK small-area income data or ONS demographic indicators.\n- **Logistics & Fleet Fleet Benchmarking:** Integrate forecourt coordinates with fleet tracking telemetry to calculate fuel stop density, build optimal driver routing profiles, and minimize off-route deadhead mileage.\n\n---\n\n## **Data Evaluation & Custom Ingestion**\n\n### **Evaluate with a Free Sample Dataset**\nVerify the structure, column types, and coordinate precision before full implementation. A comprehensive schema sample is available for download in CSV/Parquet-friendly formats.\n\n*Want deeper market insights?* Learn more about the forecourt distribution and competitive trends in our comprehensive report: **[View the UK Top Brands Petrol Stations Report](https://geolocet.com/pages/uk-topbrands-petrol-stations-report)**\n\n### **Enterprise Delivery & Tailored Formatting**\nWhile natively available as an analysis-ready CSV for DBFS ingestion, we accommodate enterprise-level data architecture needs. Alternative formats - including **GeoJSON, Esri Shapefiles, PostgreSQL dumps, Excel, or direct Delta sharing profiles** - can be generated. Reach out to our engineering team at **contact@geolocet.com** for integration support or custom European POI data requests.","terms_of_service":"https://geolocet.com/pages/terms-of-use","documentation_link":"","privacy_policy_link":"https://geolocet.com/pages/privacy-policy","assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://geolocet.com/pages/terms-of-use"},"provider_summary":{"name":"Geolocet LTD","description":"Geolocet provides premium geospatial data for retail analysis, GIS, and market planning. We equip businesses with accurate, ready-to-query data to drive spatial analysis, site selection, and customer segmentation.\n\nCore Offerings:\n\nDemographics: Socioeconomic data covering population, age, and employment across diverse geographies.\n\nPoints of Interest (PoI): Datasets spanning retail, healthcare, and transport for catchment analysis.\n\nAdministrative Boundaries: GIS-ready boundaries, including municipalities and postcodes, for territory management.\n\nBrand Locations: Chain location datasets to monitor store networks and market expansion.\n\nWealth Data: Granular economic indicators like purchasing power to identify high-potential markets.\n\nWe source from trusted providers, delivering highly customizable datasets with comprehensive coverage across Europe and worldwide."},"popularity":141},{"id":"280ec699-b30c-4972-a14d-6e7373c386df","summary":{"name":"Medical Device Adverse Events for Total Hip Replacements","subtitle":"This accelerator contains report on Adverse Events Total Hip Replacement for year 2014, 2015 and 2016.","share":{"name":"medical_device_adverse_events_for_total_hip_replacements","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1682356517009,"updated_at":1698965635253,"categories":["SCIENCE_AND_RESEARCH"],"listingType":"STANDARD","provider_id":"f88849f8-9d77-4de2-b4c7-03d55cc3768e"},"detail":{"description":"\n**Overview**\nThis accelerator contains report on Adverse Events Total Hip Replacement for year 2014, 2015 and 2016.\n\n**Description**\nEach year, the FDA receives several hundred thousand medical device reports (MDRs) of suspected device-associated deaths, serious injuries and malfunctions. The FDA uses MDRs to monitor device performance, detect potential device-related safety issues, and contribute to benefit-risk assessments of these products. The MAUDE database houses MDRs submitted to the FDA by mandatory reporters (manufacturers, importers and device user facilities) and voluntary reporters such as health care professionals, patients and consumers.\nAlthough MDRs are a valuable source of information, this passive surveillance system has limitations, including the potential submission of incomplete, inaccurate, untimely, unverified, or biased data. In addition, the incidence or prevalence of an event cannot be determined from this reporting system alone due to potential under-reporting of events and lack of information about frequency of device use. Because of this, MDRs comprise only one of the FDA's several important postmarket surveillance data sources.\nThe reports are submitted to the FDA by both mandatory and voluntary reporters. Mandatory reports are provided by manufactures, importers, and device user facilities, such as hospitals, outpatient diagnostic or treatment facilities, nursing homes and ambulatory surgical facilities. Voluntary reports are provided by health care professionals, patients and consumers. The reports include suspected device-associated deaths, serious injuries and malfunctions. The FDA uses the reports to monitor device performance, detect potential device-related safety issues, and contribute to benefit-risk assessments of the products.\nManufacturers and importers submit reports when they become aware of information that reasonably suggests that one of their marketed devices may have caused or contributed to a death or serious injury or has malfunctioned and the malfunction of the device or a similar device that they market would be likely to cause or contribute to a death or serious injury if the malfunction were to recur. Manufacturers must send reports of such deaths, serious injuries and malfunctions to the FDA. Importers must send reports of deaths and serious injuries to the FDA and the manufacturer, and reports of malfunctions to the manufacturer. \n\nDevice user facilities include hospitals, outpatient diagnostic or treatment facilities, nursing homes and ambulatory surgical facilities. Device user facilities must submit reports when they become aware of information that reasonably suggests that a device may have caused or contributed to a death or serious injury of a patient in their facility. Death reports must be sent to the FDA and the manufacturer, if known. Serious injury reports must be sent to the manufacturer or to the FDA, if the manufacturer is not known.\n\n**Benefits**\n- Useful for physicians for hip replacement which includes: hip replacement reports,\n- medical device reports (mdr),\n- mdrs submitted by the fda,\n- death reports to fda, serious injuries reports to fda, manufacturer and user facility device experience, knee replacement.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [Adverse Events Total Hip Replacement 2014](https://www.johnsnowlabs.com/marketplace/adverse-events-total-hip-replacement-2014)\n\t- This dataset identifies adverse events associated with medical devices for total hip replacement. This dataset includes reports submitted from January 2014 through December 2014.\n- [Adverse Events Total Hip Replacement 2015](https://www.johnsnowlabs.com/marketplace/adverse-events-total-hip-replacement-2015)\n\t- This dataset identifies adverse events associated with medical devices for total hip replacement. This dataset includes reports submitted from January 2015 through December 2015.\n- [Adverse Events Total Hip Replacement 2016](https://www.johnsnowlabs.com/marketplace/adverse-events-total-hip-replacement-2016)\n\t- This dataset identifies adverse events associated with medical devices for total hip replacement. This dataset includes reports submitted from January 01, 2016 through December 31, 2016.\n- [Adverse Events Total Hip Replacement 2017](https://www.johnsnowlabs.com/marketplace/adverse-events-total-hip-replacement-2017)\n\t- This dataset identifies adverse events associated with medical devices for total hip replacement. This dataset includes reports submitted from January 01, 2017 through December 31, 2017.\n- [Adverse Events Total Hip Replacement 2018](https://www.johnsnowlabs.com/marketplace/adverse-events-total-hip-replacement-2018)\n\t- This dataset identifies adverse events associated with medical devices for total hip replacement. This dataset includes reports submitted from January 01, 2018 through December 31, 2018.\n- [Adverse Events Total Hip Replacement 2019](https://www.johnsnowlabs.com/marketplace/adverse-events-total-hip-replacement-2019)\n\t- This dataset identifies adverse events associated with medical devices for total hip replacement. This dataset includes reports submitted from January 01, 2019 through December 31, 2019.\n- [Adverse Events Total Hip Replacement 2020](https://www.johnsnowlabs.com/marketplace/adverse-events-total-hip-replacement-2020)\n\t- This dataset identifies adverse events associated with medical devices for total hip replacement. This dataset includes reports submitted from January 01, 2020 through December 31, 2020.\n- [Adverse Events Total Hip Replacement 2021](https://www.johnsnowlabs.com/marketplace/adverse-events-total-hip-replacement-2021)\n\t- This dataset identifies adverse events associated with medical devices for total hip replacement. This dataset includes reports submitted from January 01, 2021 through December 31, 2021.\n- [Adverse Events Total Hip Replacement 2022](https://www.johnsnowlabs.com/marketplace/adverse-events-total-hip-replacement-2022)\n\t- This dataset identifies adverse events associated with medical devices for total hip replacement. This dataset includes reports submitted from January 01, 2022 through April 29, 2022.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated\n- Data Updates\n - Data updates support replace-on-update: outdated foreign keys are deprecated, not deleted\n\n**Our data is curated and enriched by domain experts** \n\n Each dataset is manually curated by our team of doctors, pharmacists, public health & medical billing experts:\n- Field names, descriptions, and normalized values are chosen by people who actually understand their meaning\n- Healthcare & life science experts add categories, search keywords, descriptions and more to each dataset\n- Both manual and automated data enrichment supported for clinical codes, providers, drugs, and geo-locations\n- The data is always kept up to date – even when the source requires manual effort to get updates\n- Support for data subscribers is provided directly by the domain experts who curated the data sets\n- Every data source’s license is manually verified to allow for royalty-free commercial use and redistribution.\n\n**Need Help?**\nIf you have questions about our products, contact us at [info@johnsnowlabs.com](mailto:info@johnsnowlabs.com). \n 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Segments without an estimate are absent rather than zero.\n\nFor overall market coverage, see the [full Market coverage documentation](https://docs.tomtom.com/historical-traffic-volumes/documentation/product-information/market-coverage).\n\n\n**Documentation**\n\n[Traffic Volumes Documentation](https://docs.tomtom.com/historical-traffic-volumes/documentation/product-information/introduction) and [end-to-end use case notebooks](https://github.com/tomtom-international/tomtom-data-examples).\n\n**Pairs with TomTom Traffic Stats**\n\nTomTom Traffic Stats is a separate free listing covering the same areas. Both datasets include an H3 resolution 9 cell, so they join without map matching.\n\n**Getting more than the sample**\n\nTo ask questions about this listing, data in it or to get a wider extract, a longer period, or a commercial licence, contact our tech expert team at 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All data comes from a single source, ensuring **consistency and comparability** across markets.\n\nThe Sociodemographics-Bundle Basic for Bulgaria includes the following **variables**:\n- [population](https://www.mbi-geodata.com/en/sociodemographic-data/population/)\n- [gender](https://www.mbi-geodata.com/en/sociodemographic-data/sex-gender/)\n- [age structure](https://www.mbi-geodata.com/en/sociodemographic-data/agebands/) (15-year age bands)\n- household figures ([number of households](https://www.mbi-geodata.com/en/sociodemographic-data/households/) and [households by type](https://www.mbi-geodata.com/en/sociodemographic-data/households-by-type/))\n\nOur sociodemographic data is offered for each country on different **levels**:\n- **Postal Code Level:** This fine-grained level of detail makes the data suitable for a wide range of applications – for example, enriching customer databases, which are often organized by postal code.\n- **Administrative Level:** Covers both larger administrative areas (e.g., regions, states, provinces) as well as finer subdivisions such as districts, counties, or municipalities.\n- **Micro Level**: Subdivisions of postal code or administrative boundaries, such as census areas or street segments. 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We will be happy to help you find a product that fits your needs.\n \n**Additional Insights**: https://www.mbi-geodata.com/en/sociodemographic-data/\n\n**Data Structure**:\nAGE_F0014: Population (Nighttime) by age: 0 - 14 years, female\nAGE_F1529: Population (Nighttime) by age: 15 - 29 years, female\nAGE_F3044: Population (Nighttime) by age: 30 - 44 years, female\nAGE_F4559: Population (Nighttime) by age: 45 - 59 years, female\nAGE_F60PL: Population (Nighttime) by age: 60 years and above, female\nAGE_M0014: Population (Nighttime) by age: 0 - 14 years, male\nAGE_M1529: Population (Nighttime) by age: 15 - 29 years, male\nAGE_M3044: Population (Nighttime) by age: 30 - 44 years, male\nAGE_M4559: Population (Nighttime) by age: 45 - 59 years, male\nAGE_M60PL: Population (Nighttime) by age: 60 years and above, male\nAGE_T0014: Population (Nighttime) by age: 0 - 14 years, total\nAGE_T1529: Population (Nighttime) by age: 15 - 29 years, male\nAGE_T3044: Population (Nighttime) by age: 30 - 44 years, total\nAGE_T4559: Population (Nighttime) by age: 45 - 59 years, total\nAGE_T60PL: Population (Nighttime) by age: 60 years and above, total\nCTRYCODE: Two digit country code by ISO 3166\nFEMALE: Population (Nighttime): females\nHH_SIZE: Average household size\nHH_T: Households: total number\nMALE: Population (Nighttime): males\nNAME: Name of administrative area / Name of the most populous administrative area / Name of the respective micro-area or the next higher level\nADMINCODE / POSTCODE / MICROCODE Administrative code / Postal code / Micro-code\nP_PRM: Population (Nighttime) year average: per mill of country\nP_T: Population (Nighttime) year average: total number","terms_of_service":"https://service-mbi-geodata.com/s/HNp7Z2tws4WDqWd","documentation_link":"https://www.mbi-geodata.com/en/mbi-supplier-manufacturer-worldwide-geodata/europe/bulgaria-maps/","privacy_policy_link":"https://www.mbi-geodata.com/en/data-protection/","geographical_coverage":"[\"Europe\"]","cost":"FREE","update_frequency":{"interval":1,"unit":"YEARLY"},"collection_granularity":{"interval":1,"unit":"YEARLY"},"collection_date_start":1704095328330,"collection_date_end":1735631336100,"data_source":"MBI","assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://service-mbi-geodata.com/s/PzrDteP5bWZqdJT"},"provider_summary":{"name":"Michael Bauer International GmbH – MBI Geodata","description":"For over 15 years, Michael Bauer International (MBI) has been a leading provider of globally consistent and comparable geographic data, including more than 3 million postcode areas and other detailed datasets like sociodemographics and purchasing power. 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Each file serves a unique purpose within the course, aimed at providing hands-on experience with model deployment and inference logging.\n\n**File Descriptions**\n* inference.csv: Collected after creating a model-serving endpoint, this dataset contains raw inference data for demonstration within the MLOps course.\n* inference_table.csv: This structured inference table, gathered post-endpoint creation, replicates production-like logged predictions and metadata, solely for demonstration purposes in the MLOps course.\n* lab_inference_df.csv: Used exclusively in lab exercises, this dataset includes raw inference data collected after endpoint creation, providing hands-on experience with endpoint building, testing, and inference logging.\n\n**Purpose and Use**\nThese datasets are collected after creating a model-serving endpoint and enabling the inference table. 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It includes measures of birth, of death and other measures of health.\n\n**Description**\nThis data package identifies the elements and values in the leading causes of death and birth measures. Community Health Status Indicators (CHSI) to combat obesity, heart disease, and cancer, this dataset provides key health indicators for local communities and encourages dialogue about actions that can be taken to improve community health (e.g., obesity, heart disease, cancer). It also identifies the data elements and values in the Vulnerable Populations and Environmental Health indicator domain from the Community Health Status Indicators (CHSI) report.\n\n**Benefits**\n- Useful data package for providers to know the leading causes of death, measures of birth and death rate and prevention.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [CHSI Demographics](https://www.johnsnowlabs.com/marketplace/chsi-demographics)\n\t- This dataset identifies the data elements and values in the Demographics indicator domain by the Community Health Status Indicators (CHSI) fight diseases.\n- [CHSI Healthy People 2010 Targets](https://www.johnsnowlabs.com/marketplace/chsi-healthy-people-2010-targets)\n\t- This dataset identifies the Healthy People 2010 Targets and the 2003 United States health percentages or rates from Community Health Status Indicators (CHSI).\n- [CHSI Leading Causes of Death](https://www.johnsnowlabs.com/marketplace/chsi-leading-causes-of-death)\n\t- This dataset identifies the data elements and values in the Leading Causes of Death indicator domain from the Community Health Status Indicators (CHSI) report.\n- [CHSI Measures of Birth and Death](https://www.johnsnowlabs.com/marketplace/chsi-measures-of-birth-and-death)\n\t- This dataset identifies the data elements and values in the Measures of Birth and Death indicator domain from the Community Health Status Indicators (CHSI) report.\n- [CHSI Preventive Services Use](https://www.johnsnowlabs.com/marketplace/chsi-preventive-services-use)\n\t- This dataset identifies the data elements and values in the Preventive Services indicator domain from the Community Health Status Indicators (CHSI) report.\n- [CHSI Relative Health Importance](https://www.johnsnowlabs.com/marketplace/chsi-relative-health-importance)\n\t- This dataset identifies the data elements and values in the Relative Health Importance indicator domain from the Community Health Status Indicators (CHSI) report.\n- [CHSI Risk Factors and Access to Care](https://www.johnsnowlabs.com/marketplace/chsi-risk-factors-and-access-to-care)\n\t- This dataset identifies the data elements and values in the Risk Factors and Access to Care indicator domain from the Community Health Status Indicators (CHSI) report.\n- [CHSI Summary Measures of Health](https://www.johnsnowlabs.com/marketplace/chsi-summary-measures-of-health)\n\t- This dataset identifies the data elements and values in the Summary Measures of Health indicator domain from the Community Health Status Indicators (CHSI) report.\n- [CHSI Vulnerable Pops and Environmental Health](https://www.johnsnowlabs.com/marketplace/chsi-vulnerable-pops-and-environmental-health)\n\t- This dataset identifies the data elements and values in the Vulnerable Populations and Environmental Health indicator domain from the Community Health Status Indicators (CHSI) report.\n- [Cancer Screening Coverage](https://www.johnsnowlabs.com/marketplace/cancer-screening-coverage)\n\t- This dataset is sourced from Public Health England and consists of the percentage of people in the resident population eligible for cervical screening who were screened adequately within the previous years (2010 to 2016) for bowel, cervical and breast cancer.\n- [Venous Thromboembolism Risk Assessment](https://www.johnsnowlabs.com/marketplace/venous-thromboembolism-risk-assessment)\n\t- This dataset provides information on the number and proportion of admitted adult patients in England who have been assessed for VTE (Venous Thromboembolism) risk from 2016 to 2017.\n- [Waiting Times for Suspected and Diagnosed Cancer Patients](https://www.johnsnowlabs.com/marketplace/waiting-times-for-suspected-and-diagnosed-cancer-patients)\n\t- This dataset provides information on the waiting times of people referred by their GP (General Practitioner) with suspected cancer or breast symptoms and those subsequently diagnosed with and treated for cancer by the NHS (National Health Service) in England.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated\n- Data Updates\n - Data updates support replace-on-update: outdated foreign keys are deprecated, not deleted\n\n**Our data is curated and enriched by domain experts** \n\n Each dataset is manually curated by our team of doctors, pharmacists, public health & medical billing experts:\n- Field names, descriptions, and normalized values are chosen by people who actually understand their meaning\n- Healthcare & life science experts add categories, search keywords, descriptions and more to each dataset\n- Both manual and automated data enrichment supported for clinical codes, providers, drugs, and geo-locations\n- The data is always kept up to date – even when the source requires manual effort to get updates\n- Support for data subscribers is provided directly by the domain experts who curated the data sets\n- Every data source’s license is manually verified to allow for royalty-free commercial use and redistribution.\n\n**Need Help?**\nIf you have questions about our products, contact us at [info@johnsnowlabs.com](mailto:info@johnsnowlabs.com). \n 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Free trial data.","share":{"name":"share_socio_mbi_market_data_2024_md_postcodes_2_socio","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1763368970837,"updated_at":1779198875115,"categories":["ADVERTISING_AND_MARKETING","DEMOGRAPHICS","GEOSPATIAL"],"listingType":"STANDARD","provider_id":"aa09abd6-ff7f-4452-803d-b8c43b4c07cb"},"detail":{"description":"[MBI](https://www.mbi-geodata.com/en/)'s sociodemographic data is available for many countries worldwide. All data comes from a single source, ensuring **consistency and comparability** across markets.\n\nThe Sociodemographics-Bundle Basic for Moldova includes the following **variables**:\n- [population](https://www.mbi-geodata.com/en/sociodemographic-data/population/)\n- [gender](https://www.mbi-geodata.com/en/sociodemographic-data/sex-gender/)\n- [age structure](https://www.mbi-geodata.com/en/sociodemographic-data/agebands/) (15-year age bands)\n- household figures ([number of households](https://www.mbi-geodata.com/en/sociodemographic-data/households/) and [households by type](https://www.mbi-geodata.com/en/sociodemographic-data/households-by-type/))\n\nOur sociodemographic data is offered for each country on different **levels**:\n- **Postal Code Level:** This fine-grained level of detail makes the data suitable for a wide range of applications – for example, enriching customer databases, which are often organized by postal code.\n- **Administrative Level:** Covers both larger administrative areas (e.g., regions, states, provinces) as well as finer subdivisions such as districts, counties, or municipalities.\n- **Micro Level**: Subdivisions of postal code or administrative boundaries, such as census areas or street segments. 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We will be happy to help you find a product that fits your needs.\n \n**Additional Insights**: https://www.mbi-geodata.com/en/sociodemographic-data/\n\n**Data Structure**:\nAGE_F0014: Population (Nighttime) by age: 0 - 14 years, female\nAGE_F1529: Population (Nighttime) by age: 15 - 29 years, female\nAGE_F3044: Population (Nighttime) by age: 30 - 44 years, female\nAGE_F4559: Population (Nighttime) by age: 45 - 59 years, female\nAGE_F60PL: Population (Nighttime) by age: 60 years and above, female\nAGE_M0014: Population (Nighttime) by age: 0 - 14 years, male\nAGE_M1529: Population (Nighttime) by age: 15 - 29 years, male\nAGE_M3044: Population (Nighttime) by age: 30 - 44 years, male\nAGE_M4559: Population (Nighttime) by age: 45 - 59 years, male\nAGE_M60PL: Population (Nighttime) by age: 60 years and above, male\nAGE_T0014: Population (Nighttime) by age: 0 - 14 years, total\nAGE_T1529: Population (Nighttime) by age: 15 - 29 years, male\nAGE_T3044: Population (Nighttime) by age: 30 - 44 years, total\nAGE_T4559: Population (Nighttime) by age: 45 - 59 years, total\nAGE_T60PL: Population (Nighttime) by age: 60 years and above, total\nCTRYCODE: Two digit country code by ISO 3166\nFEMALE: Population (Nighttime): females\nHH_SIZE: Average household size\nHH_T: Households: total number\nMALE: Population (Nighttime): males\nNAME: Name of administrative area / Name of the most populous administrative area / Name of the respective micro-area or the next higher level\nADMINCODE / POSTCODE / MICROCODE Administrative code / Postal code / Micro-code\nP_PRM: Population (Nighttime) year average: per mill of country\nP_T: Population (Nighttime) year average: total number","terms_of_service":"https://service-mbi-geodata.com/s/HNp7Z2tws4WDqWd","documentation_link":"https://www.mbi-geodata.com/en/mbi-supplier-manufacturer-worldwide-geodata/europe/moldova-maps/","privacy_policy_link":"https://www.mbi-geodata.com/en/data-protection/","geographical_coverage":"[\"Europe\"]","cost":"FREE","update_frequency":{"interval":1,"unit":"YEARLY"},"collection_granularity":{"interval":1,"unit":"YEARLY"},"collection_date_start":1704098510608,"collection_date_end":1735634517292,"data_source":"MBI","assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://service-mbi-geodata.com/s/LSqeEZKJcpxJ9yr"},"provider_summary":{"name":"Michael Bauer International GmbH – MBI Geodata","description":"For over 15 years, Michael Bauer International (MBI) has been a leading provider of globally consistent and comparable geographic data, including more than 3 million postcode areas and other detailed datasets like sociodemographics and purchasing power. MBI's high-quality small-scale data spans 248 countries, supporting businesses across all industries in location planning, branch networks, and distribution strategies. Recognized as a key data source, MBI is the preferred provider for leading GIS software and technology companies worldwide as well as one of the first global distributors for HERE Technologies. MBI is part of the Michael Bauer Group, alongside Michael Bauer Research GmbH and Michael Bauer Micromarketing GmbH."},"popularity":307},{"id":"b2d6538d-f697-4ff8-b8a2-2969ea530bba","summary":{"name":"Brazilian Sign Language (Libras) Healthcare Corpus","subtitle":"Multimodal Libras dataset of ~25h video on health topics, with gloss annotations and Portuguese translations.","setting":{"visibility":"PUBLIC"},"created_at":1792585486551,"updated_at":1793112403477,"categories":["SCIENCE_AND_RESEARCH","HEALTH"],"listingType":"PERSONALIZED","provider_id":"f6e9a544-3103-4533-8706-321f7282d7d3"},"detail":{"description":"**Get this dataset on the Mozilla Data Collective.** Licensing and download happen on MDC rather than through Databricks. [See the full details and get access on MDC](https://mozilladatacollective.com/datasets/cmrftle0s01fsnv0755jw602i?utm_source=databricks&utm_medium=referral&utm_campaign=databricks_marketplace&utm_content=listing_cta).\n\nThe Brazilian Sign Language Health Dataset is a large-scale multimodal corpus developed to advance inclusive AI and accessibility research for Deaf communities in Brazil, with a focus on healthcare communication, including medical terminology, symptoms, preventive care, mental health, and clinical interactions, while maintaining an intersectional and gender-diverse approach. The dataset contains approximately 24.6 hours of Brazilian Sign Language (Libras) recordings collected across 44 participants and 16 structured recording sessions. The corpus includes more than 8,000 annotated video segments aligned with subtitles, gloss annotations, elicitation prompts, and metadata tables designed for multimodal AI research workflows and combines segmented video recordings, subtitle files, prompt metadata, technical recording metadata, and linguistic annotations, and is designed to support research and innovation in Automatic Sign Language Recognition (ASLR), Sign Language Translation (SLT), and multimodal AI systems, while also enabling downstream tasks such as subtitle alignment, gloss prediction, gesture segmentation, temporal localisation, and non-manual feature analysis.\n\n**At a glance**\n\n- **Language:** Brazilian Sign Language (Libras), Portuguese\n- **Format:** MP4, SRT, CSV, JSON\n\n**Description**\n\nThe dataset is organised into standardised folders and structured metadata tables to support multimodal research workflows and reproducible experimentation. It includes MP4 video recordings, Portuguese subtitle files aligned with video segments, segment-level annotation tables, elicitation guides, participant and recording metadata, and gloss annotations with expected gloss references. All participants in the dataset are anonymised, and no personally identifiable information is included. Researchers using the dataset are responsible for complying with applicable ethical and legal requirements related to the use of human participant data.\n\n**Dataset Format**\n\nAll participants in the dataset are anonymised, and no personally identifiable information is included. Researchers using the dataset are responsible for complying with applicable ethical and legal requirements related to the use of human participant data.\n\nThe dataset directory holds:\n\n- README.md, metadata.json, README_POR.md and metadata_POR.json\n- annotations/: segments.csv, segments_extra.csv, questions.csv, questions_extra.csv, recordings.csv and participants.csv\n- videos/: one folder per session (S01, S02 and so on)\n- subtitles/: one folder per session (S01, S02 and so on)\n\n**Dataset Contents**\n\n**Videos**\n\nThe videos/ directory contains the original interview recordings organised by session and participant.\n\nFor example, videos/S15/P41/P41_S15.MP4.\n\nSome recordings are divided into multiple parts, such as videos/S07/P29/P29_S07_part01.mp4 and videos/S07/P29/P29_S07_part02.mp4.\n\n**Subtitles**\n\nThe subtitles/ directory contains subtitle files generated from interview recordings.\n\nFor example, subtitles/S15/P41/P41_S15.srt.\n\nSubtitle timestamps may not perfectly align with sign boundaries because spoken/written language timing differs from sign articulation timing.\n\n**Annotation Files**\n\n**segments.csv**\n\nContains segment-level annotations for the main elicitation protocol.\n\nEach row corresponds to a segmented sign production or response interval.\n\nMain fields include:\n\n- **segment_id**: Unique segment identifier\n- **session_id**: Recording session identifier\n- **participant_id**: Anonymous participant identifier\n- **video_file**: Source video filename\n- **question_id**: Prompt identifier\n- **location**: How the session was recorded, such as Online\n- **start_time_raw**, **end_time_raw**: Segment start and end as mm:ss\n- **start_time**: Segment start time, in whole seconds\n- **end_time**: Segment end time, in whole seconds\n- **duration**: Segment duration, in whole seconds\n- **repetition**: Which attempt at the prompt the segment is\n- **prompt**: Prompt text\n- **section**: Semantic domain\n- **prompt_type**: Lexical or open-ended\n- **mode**: Controlled, guided, or free\n- **target_gloss**: Target elicited gloss\n- **produced_gloss**: Gloss recorded as produced; in the sample it always matches the target\n- **produced_keywords**: Keywords observed in the response\n- **non_manual_features**: Facial/body non-manual annotations\n- **signing_quality**: A level such as beginner, intermediate or complex, as for the prompts' difficulty\n- **record_date**: Recording date\n- **Comments**: Additional notes\n\n**segments_extra.csv**\n\nContains annotations for supplementary or extended elicitation prompts.\n\n**questions.csv**\n\nContains the primary elicitation question guide used during recordings.\n\n**questions_extra.csv**\n\nContains supplementary elicitation prompts and extra lexical items.\n\n**recordings.csv**\n\nContains technical metadata for all recordings.\n\nFields include:\n\n* FPS\n* Resolution\n* Codec\n* Duration\n* File size\n* Audio presence\n* Recording quality tier\n* Recording parts\n\n**participants.csv**\n\nContains anonymous participant-level metadata when available.\n\nNo personally identifiable information is included.\n\n**Recording Conditions**\n\nThe corpus includes recordings collected under heterogeneous conditions, including:\n\n* In-person recordings\n* Online recordings\n* Webcam recordings\n* Mobile recordings\n\nVideo quality varies across sessions with differences in:\n\n* Resolution\n* Frame rate\n* Compression\n* Lighting\n* Internet stability\n* Camera positioning\n\nThis variability reflects realistic communication conditions and may support robustness research in sign language recognition systems.\n\n**Annotation Methodology**\n\n**Segmentation**\n\nVideos were manually segmented into lexical productions and elicited responses.\n\nSegments containing skipped prompts, unclear productions, and/or unusable recordings were excluded during quality control.\n\n**Gloss Annotations**\n\nThe dataset primarily contains **expected gloss annotations** corresponding to elicited target concepts.\n\nThese annotations may not always represent exact produced glosses because participants occasionally:\n\n* produced explanations instead of isolated lexical signs\n* used alternative lexical variants\n* expanded responses semantically\n\nTherefore, gloss annotations should be interpreted as elicitation targets rather than fully verified production glosses.\n\n**Non-Manual Features**\n\nSome segments include annotations describing non-manual features such as:\n\n* pain expressions\n* discomfort expressions\n* emphasis\n* affective markers\n\nExample annotations:\n\n* pain face\n* disgust face\n* low energy face\n\n**Video Properties**\n\nThe dataset includes recordings with:\n\n- **Resolution**: 1920x1080, 1280x720, 640x360\n- **FPS**: 23.98, 24, 25, 29.97, 59.94\n- **Codec**: H.264\n- **Format**: MP4\n\n**Limitations**\n\nThe dataset includes some limitations that should be considered during use and analysis. Subtitle timestamps may not perfectly align with sign boundaries, and expected gloss annotations may differ from the exact signs produced by participants. Some recordings contain internet artifacts or compression noise, and recording quality may vary across sessions. In addition, certain participants produced descriptive responses rather than isolated lexical concepts, reflecting natural variation in communication styles.\n\n**Intended use**\n\nSupport research and development in Automatic Sign Language Recognition (ASLR), Sign Language Translation (SLT), multimodal AI systems, accessible healthcare technologies, human-computer interaction for Deaf communities, and linguistic and sociolinguistic research on Brazilian Sign Language (Libras). Contribute to healthcare accessibility, public health communication, and the development of educational and assistive technologies. 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It also contains data on Antimicrobial Use and Resistance Eligible Antimicrobial Agents.\n\n**Description**\nIn an effort to prospectively monitor the occurrence of antimicrobial resistance of zoonotic pathogens from human diagnostic specimens, retail meats and food animals, the National Antimicrobial Resistance Monitoring System (NARMS) was established in 1996 by the Food and Drug Administration’s Center for Veterinary Medicine in collaboration with the Center for Disease Control and Prevention (CDC), and the United States Department of Agriculture (USDA). The animal component of NARMS is housed within the Bacterial Epidemiology and Antimicrobial Resistance Research Unit (BEAR) of the Agricultural Research Service (ARS) in Athens, Georgia. The animal component of NARMS comprises the testing of isolates obtained from diagnostic animal specimens, healthy on-farm animals, and food-producing animals at slaughter. NARMS also tracks changes in the antimicrobial susceptibility of certain enteric (intestinal) bacteria found in ill people (CDC), retail meats (FDA), and food animals (USDA) in the United States. The NARMS program at CDC helps protect public health by providing information about emerging bacterial resistance, the ways in which resistance is spread, and how resistant infections differ from susceptible infections. NARMS monitors antibiotic resistance among the following four major foodborne bacteria: Salmonella, Campylobacter, Escherichia coli and Enterococcus.\n\n**Benefits**\n- monitor trends in antimicrobial resistance among foodborne bacteria from humans, retail meats, and animals\n- disseminate timely information on antimicrobial resistance to promote interventions that reduce resistance among foodborne bacteria\n- conduct research to better understand the emergence, persistence, and spread of antimicrobial resistance\n- assist the fda in making decisions related to the approval of safe and effective antimicrobial drugs for animals\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [Antimicrobial Use and Resistance Eligible Antimicrobial Agents](https://www.johnsnowlabs.com/marketplace/antimicrobial-use-and-resistance-eligible-antimicrobial-agents)\n\t- The National Healthcare Safety Network (NHSN) Antimicrobial Use and Resistance (AUR) provides a mechanism for facilities to report and analyze antimicrobial use and/or resistance as part of local or regional efforts to reduce antimicrobial resistant infections through antimicrobial stewardship efforts or interruption of transmission of resistant pathogens at their facility.\n- [NARMS Food Animal Isolate Data](https://www.johnsnowlabs.com/marketplace/narms-food-animal-isolate-data)\n\t- The National Antimicrobial Resistance Monitoring System (NARMS) Animal Isolates provides data that comprises the testing of isolates obtained from diagnostic animal specimens, healthy on-farm animals, and food-producing animals at slaughter. These tests on retail samples were performed by FDA.\n- [NARMS Human Isolate Data](https://www.johnsnowlabs.com/marketplace/narms-human-isolate-data)\n\t- The National Antimicrobial Resistance Monitoring System (NARMS) Human Isolate Data makes it easier and quicker to find out how antibiotic resistance has changed over the past 20 years for four bacteria transmitted commonly through food—Campylobacter, E. coli O157, Salmonella, and Shigella.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - 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We deliver the most complete, verified identity graph across the world’s fastest-growing economies, connecting over 1.35 billion real-world profiles across 18 frontier markets. As the identity backbone for enterprises expanding into data-scarce, high-growth regions, we provide an unmatched edge in verification, risk modeling, and regulatory-grade decisioning. Our data is exclusive, privacy-first, and licensed for compliant global use. Headquartered in the U.S. with subsidiaries across LATAM, APAC, and MENA, we partner with banks, credit bureaus, fintechs, AI platforms, and big data companies to deliver scalable intelligence with real-world impact."},"popularity":1472},{"id":"5c3558ef-315c-44dd-baef-7062ac301f22","summary":{"name":"Volve Data Village","subtitle":"All subsurface and production data from the Volve field on the Norwegian continental shelf (NCS).","share":{"name":"equinoropendata_volve","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1761051662825,"updated_at":1782989408837,"categories":["ENERGY","SCIENCE_AND_RESEARCH"],"listingType":"STANDARD","provider_id":"006d1a44-e5d8-4d20-8bd7-e9a311106007"},"detail":{"description":"**Overview**\n\nVolve is a decommissioned field in the central part of the North Sea and was discovered in 1993, the plan for development and operation (PDO) was approved in 2005. The field was shut down in 2016 and the formal removal and decommissioning will be completed by the end of 2018.\n\nThe Volve field is located five kilometers north of the Sleipner Øst field with water depths of 80 meters in the block 15/9. The produced oil was from sandstone in the Hugin Formation of the Jurassic age. \nThe reservoir is located at 2750 – 3120 m depth. The Volve Data was approved for data sharing in 2018 by the initiative of the last Operating company, Equinor and approved by the license partners ExxonMobil E&P Norway AS and Bayerngas Norge AS in the end of 2017.\n\nDuring the life of the Volve license, a limited amount of field-related data has been released to a handful of research institutes.\nThe Volve Data available will contain data covering data in regards of production data, well design, completion string design, seismic data, well logs (petrophysical and drilling), geological and stratigraphical data, static and dynamic models, surface and grid data.\n \n**Disclaimer**\n\n- The users of the information and data are expressly informed that the data provided by Equinor and the former Volve License partners is provided on an “as is” basis and may contain errors and omissions.\n- Equinor and former Volve license partners provide no warranties, expressed or implied, either relating to the content or to the relevance of the data. \n- Equinor disclaims any liability for errors and defects associated with the data to the maximum extent permitted by law. \n- Equinor and the former Volve License partners shall not be liable for any direct or indirect losses as a result of use of the data, or concerning possible infringement of any intellectual property and/or patent rights of a third party.\n- The data set shall not be resold.\n\n## Personal data and availability\n\nThe «Volve data village» was released for a two year period on June 14, 2018. The availability has been extended until June 2022. In order to comply with the agreements relevant for the release of the data, it is necessary for Equinor to maintain control of who has downloaded the data. Consequently, name, email, company and date of download will be registered when asking for access to the data, and retained for the necessary time-period. For further information, please see Equinor’s privacy policy available on equinor.com.\n\n## The Volve Data Village\n\nThe Volve Field:\n\nThe Volve field was discovered in 1993 (Discovery well 15/9-19 SR). Located in the central part of the north sea, 5 km north of Sleipner East, waterdepth 80 m.\n\nVolve was developed when oil prices were low. A non-traditional concept was chosen to extract resources easily and profitably. The plan for development and production was approved by NPD in 2005. A description of field development can be found in the PUD report.\nThe Volve development took advantage of an opportunity to use a vacant new building, Maersk Inspirer, which at the time was the world's largest jack-up rig. The rig was equipped with its own wellhead and process module for drilling and production. After the shutdown of the Volve field, this rig will be used in the Repsol-operated Yme field which is to be reopened for production.\n\n\nFSU Navion Saga was used as a storage vessel for the oil. Both installations were leased under the license and operation of the contractors Mærsk Drilling and Teekay.\n\n\nThe reservoir in the Volve field consists of Jurassic sandstones at 2750 – 3120 m TVD ss, the field was produced with water injection as pressure support.\n\n\nWhen the Volve field started well drilling in May 2007, and came into production the following year, life expectancy was from 3-5 years.\nNew wells were being drilled up until 2012-13, which contributed to the increased recovery rate and extended life of the field. However remaining resources were very limited and with the decrease in oil price over recent years, new wells were no longer profitable.\n\n\nThe Volve oil and gas field closed in 2016, having continued production 3 years longer than originally planned.\nAll possibilities to extend the life of the field were explored, which yielded very good results.\n\n\nThe field was in production for over 8 years and delivered about 9.5 million barrels of oil beyond what was expected in the development and operation plan. In total, Volve achieved a recovery rate of 54%.\n\nVolve was shut down in 2016 by decision of the partners: Statoil (now Equinor), ExxonMobil and Bayern Gas (Now Spring Energy), and with the approval of OED.\nProduction ended in October 2016 and FSU Navion Saga left the field.\nPermanent plugging of the wells and other process activities were carried out in December. 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Contact\n [Sales](mailto:Databricks-Marketplace-listing@johnsnowlabs.com) at John Snow Labs for a free 15 day trial license key or purchase our most popular annual subscription license\n2. Import this listing.\n3. See the attached notebook to deploy and use the model\n\nBuild based on [Healthcare NLP **6.4.0** version](https://nlp.johnsnowlabs.com/docs/en/jsl/jsl-release-notes#section-2)","terms_of_service":"https://www.johnsnowlabs.com/healthcare-data-library-eula/","documentation_link":"https://nlp.johnsnowlabs.com/2024/05/07/explain_clinical_doc_granular_en.html","privacy_policy_link":"https://www.johnsnowlabs.com/privacy-policy/","embedded_notebook_file_infos":[{"id":"cb2dc57d-0406-4955-8139-d1ab30dd08af","marketplace_file_type":"EMBEDDED_NOTEBOOK","file_parent":{"parent_id":"6bf333a0-af4e-4650-b4b2-1595e0df8dcb","file_parent_type":"LISTING"},"mime_type":"text/html","download_link":"https://marketplace-file-storage-prod.s3-fips.us-east-1.amazonaws.com/EMBEDDED_NOTEBOOK/LISTING_6bf333a0-af4e-4650-b4b2-1595e0df8dcb/cb2dc57d-0406-4955-8139-d1ab30dd08af?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEGgaDnVzLWVhc3QtMS1maXBzIkYwRAIgbC8sWIoYaHR8y7Wq86nPb0pufBl4%2BM4PWpeN%2B5PW9VQCIG7BhAVBDCWTpxFDLeuKYm%2Ff1JLz4iccPMzter0YdhgWKqYECDAQABoMNDE0MzUxNzY3ODI2IgxNAJrHbXaZG20jNfMqgwQyGEb4TsoVOoNvJ%2BaA%2FZ7zeu8hGcHqCeNfblP%2FQo5g6q2aRNtpwtPRKMlhYcIE%2FUQaNT1Lqmyptu32VUQTpzjTTxRVmJrDPkheSCkV06XQ%2FpTMKjU8CC7X1b%2FqYf8CrBwZT7Cv4C5WPjEET38f2A79h0xV6bZgPy28zQWrSeIJ9w6AfZBxiE24%2FVc08zVA0ivCT6lsy7nuU0wcR%2FMg2C9nd1vaMnQ923VpD21CKfVVYcrNWL2b%2FQ%2BNAO%2BxPjPgjiN6UPQbvbvZxkSdhgrGtecIg9NgqGkMcvM5VLTDtdC1kdeZetfOC2obtA%2BT%2F0gy3tIDo6WM5hcQfw743%2FAU9oaGnRBl%2BwoxRRDCZA%2F4jnPdluICSI7NCr6C3HHOe4CSZJjRihczxPA%2FgQczAVjAPs6CrK9N9pIzt0vNJpGUTgg4AoxftTvGfciDREilSctPIcBk6GwB%2Bzt4wbtNCmEPU%2BFD46rc6CWVQmmLwbKOeHw70ipMVFlOc8NlXkEtGO2vu6H44MstSSc6daOVUYUavQSGZ6jGhEjsP2YGz8KDm9XQotB%2FtyVBqiZV01OKqDmmBJA%2FXjUpSJ8M%2FeJXhe5m%2BKcxzNObtoJRI7jci3Tpt08XZyXZ7%2FyIV8H30ya468X3cfkTXrm972uga3z7iXAdubyEHnQIua%2FuvtwOHILi6O5D1Me2MzCPvcnXBjqTAYDoMWBt%2B3AVFuqSs2dmDQNBnvEhXfMgTedKLD5PJHb3aIl15iBkLaNu36GYnVCp9ZU2lSYcTbY5QwNnNeTfJTNy9bKhwzru6Sg%2BslhN%2BTS0zkTn%2BeLMY4tHgPEtEZ2cp%2FCk6GwqswvnYVKHjwO8q%2B1Mx8lc08fVEfarE9dke1GGFqoddx%2Bq%2BZUyHbDlAVfrCmf0yw%3D%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20261110T000135Z&X-Amz-SignedHeaders=host&X-Amz-Expires=86400&X-Amz-Credential=ASIAWA6KKHEJORWBH4N3%2F20261110%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=a808c82c99ed78dc78d55ca900611ee9af24d5ec861eda70d71dcdcb648e5646","created_at":1788539511787,"updated_at":1788539511787,"display_name":"en.explain_doc.clinical_granular","status":"FILE_STATUS_PUBLISHED"}],"assets":["ASSET_TYPE_MODEL"],"license":"","tags":[{"tag_name":"LISTING_TAG_TYPE_TASK","tag_values":["ASSERTION_STATUS","RELATION_EXTRACTION","NAMED_ENTITY_RECOGNITION"]}]},"provider_summary":{"name":"John Snow Labs","description":"John Snow Labs, an AI and NLP for healthcare company, provides state-of-the-art software, models, and data to help healthcare and life science organizations build, deploy, and operate AI projects."},"popularity":1544},{"id":"0a60a3d0-6e11-4b7c-b277-ce9824c1e0a8","summary":{"name":"Restaurants QA Pairs for LLM Conversational Fine-Tuning","subtitle":"A dataset for fine-tuning LLM models for the Restaurants domain, covering 30 intents and 13 categories.","share":{"name":"bitext-restaurants-training-dataset","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1727548229961,"updated_at":1727611880426,"categories":["TRAVEL_AND_TOURISM","OTHER"],"listingType":"STANDARD","provider_id":"07860fe8-4a74-42d7-ba10-cef96b03b517"},"detail":{"description":"**Overview**\n\nThis hybrid synthetic dataset is designed to be used to fine-tune Large Language Models such as GPT, Mistral and OpenELM, and has been generated using our NLP/NLG technology and our automated Data Labeling (DAL) tools. The goal is to demonstrate how Verticalization/Domain Adaptation for the [restaurants] sector can be easily achieved using our two-step approach to LLM Fine-Tuning. An overview of this approach can be found at: [From General-Purpose LLMs to Verticalized Enterprise Models](https://www.bitext.com/blog/general-purpose-models-verticalized-enterprise-genai/)\n\nThe dataset has the following specifications:\n\n- Use Case: Intent Detection\n- Vertical: Restaurants\n- 30 intents assigned to 13 categories\n- 30000 question/answer pairs, with approximately 1000 per intent\n- 54 entity/slot types\n- 10 different types of language generation tags\n\nThe categories and intents are derived from Bitext's extensive experience across various industry-specific datasets, ensuring the relevance and applicability across diverse contexts.\n\n**Dataset Token Count**\n\nThe dataset contains a total of 3.67 million tokens across 'instruction' and 'response' columns. This extensive corpus is crucial for training sophisticated LLMs that can perform a variety of functions including conversational AI, question answering, and virtual assistant tasks in the restaurants domain.\n\n**Fields of the Dataset**\n\nEach entry in the dataset comprises the following fields:\n\n- tags\n- instruction: a user request from the Restaurants domain\n- category: the high-level semantic category for the intent\n- intent: the specific intent corresponding to the user instruction\n- response: an example of an expected response from the virtual assistant\n\n**Categories and Intents**\n\nThe dataset covers a wide range of restaurant-related categories and intents, which are:\n\n- **CATERING**: cancel_catering, change_catering, information_catering, order_catering\n- **COMPANY**: information_company, locations\n- **COMPLAINT**: file_complaint\n- **CONTACT**: customer_service, human_agent\n- **EVENTS**: events\n- **FEEDBACK**: leave_review\n- **FRANCHISE**: apply_franchise, find_franchise, franchising\n- **LEGAL**: privacy_policy\n- **MENU**: check_menu, check_offers, information_allergens\n- **ONLINE_ORDER**: cancel_order, change_order, delivery_time, order_food_online, order_issue, track_order\n- **PAYMENT**: payment_methods, report_payment_issue\n- **RESERVATIONS**: cancel_reservation, change_reservation, make_reservation\n- **RESTAURANT**: find_restaurant\n\n**Entities**\n\nThe entities covered by the dataset include:\n\n- **{{WEBSITE_URL}}**, common with all intents.\n- **{{RESTAURANT}}**, featured in intents like find_franchise, find_restaurant.\n- **{{FRANCHISE_SECTION}}**, associated with intents like apply_franchise, find_franchise.\n- **{{CHANGE_ORDER}}**, relevant to intents such as change_catering.\n- **{{LOCATION}}**, featured in intents like cancel_reservation.\n\nThis comprehensive list of entities ensures that the dataset is well-equipped to train models that are highly adept at understanding and processing a wide range of restaurant-related queries and tasks.\n\n**Language Generation Tags**\n\nThe dataset includes tags indicative of various language variations and styles adapted for the Restaurants domain, enhancing the robustness and versatility of models trained on this data. These tags categorize the utterances into different registers such as colloquial, formal, or containing specific restaurants jargon, ensuring that the trained models can understand and generate a range of conversational styles appropriate for different customer interactions in the restaurants sector.\n\n**Language Generation Tags**\n\nThe dataset includes tags that reflect various language variations and styles, crucial for creating adaptable and responsive conversational AI models within the restaurants sector. These tags help in understanding and generating appropriate responses based on the linguistic context and user interaction style.\n\n**Tags for Lexical variation**\n\n- **M - Morphological variation**: Adjusts for inflectional and derivational forms.\n - Example: \"is my account active\", \"is my account activated\"\n- **L - Semantic variations**: Handles synonyms, use of hyphens, and compounding.\n - Example: “what's my balance date\", “what's my billing date”\n\n**Tags for Syntactic structure variation**\n\n- **B - Basic syntactic structure**: Simple, direct commands or statements.\n - Example: \"activate my card\", \"I need to check my balance\"\n- **I - Interrogative structure**: Structuring sentences in the form of questions.\n - Example: “can you show my balance?”, “how do I transfer money?”\n- **C - Coordinated syntactic structure**: Complex sentences coordinating multiple ideas or tasks.\n - Example: “I want to transfer money and check my balance, what should I do?”\n- **N - Negation**: Expressing denial or contradiction.\n - Example: \"I do not wish to proceed with this transaction, how can I stop it?\"\n\n**Tags for language register variations**\n\n- **P - Politeness variation**: Polite forms often used in customer service.\n - Example: “could you please help me check my account balance?”\n- **Q - Colloquial variation**: Informal language that might be used in casual customer interactions.\n - Example: \"can u tell me my balance?\"\n- **W - Offensive language**: Handling potentially offensive language which might occasionally appear in frustrated customer interactions.\n - Example: “I’m upset with these charges, this is ridiculous!”\n\n**Tags for stylistic variations**\n\n- **K - Keyword mode**: Responses focused on keywords.\n - Example: \"balance check\", \"account status\"\n- **E - Use of abbreviations**: Common abbreviations.\n - Example: “acct for account”, “trans for transaction”\n- **Z - Errors and Typos**: Includes common misspellings or typographical errors found in customer inputs.\n - Example: “how can I chek my balance”\n\n**Other tags not in use in this Dataset**\n\n- **D - Indirect speech**: Expressing commands or requests indirectly.\n - Example: “I was wondering if you could show me my last transaction.”\n- **G - Regional variations**: Adjustments for regional language differences.\n - Example: American vs British English: \"checking account\" vs \"current account\"\n- **R - Respect structures - Language-dependent variations**: Formality levels appropriate in different languages.\n - Example: Using “vous” in French for formal addressing instead of “tu.”\n- **Y - Code switching**: Switching between languages or dialects within the same conversation.\n - Example: “Can you help me with my cuenta, please?”\n\nThese tags not only aid in training models for a wide range of customer interactions but also ensure that the models are culturally and linguistically sensitive, enhancing the customer experience in the restaurant environments.\n\n**License**\n\nThe `Bitext-restaurants-llm-chatbot-training-dataset` is released under the **Community Data License Agreement (CDLA) Sharing 1.0**. This license facilitates broad sharing and collaboration while ensuring that the freedom to use, share, modify, and utilize the data remains intact for all users.\n\n**Key Aspects of CDLA-Sharing 1.0**\n\n- **Attribution and ShareAlike**: Users must attribute the dataset and continue to share derivatives under the same license.\n- **Non-Exclusivity**: The license is non-exclusive, allowing multiple users to utilize the data simultaneously.\n- **Irrevocability**: Except in cases of material non-compliance, rights under this license are irrevocable.\n- **No Warranty**: The dataset is provided without warranties regarding its accuracy, completeness, or fitness for a particular purpose.\n- **Limitation of Liability**: Both users and data providers limit their liability for damages arising from the use of the dataset.\n\n**Usage Under CDLA-Sharing 1.0**\n\nBy using the `Bitext-restaurants-llm-chatbot-training-dataset`, you agree to adhere to the terms set forth in the CDLA-Sharing 1.0. 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Pharma, biotech, and functional-genomics teams can use these predictions to prioritize regulatory variants, evaluate promoter candidates, and investigate genomic regions before committing to laboratory experiments.\n\nThe models run on Genomic Intelligence’s hosted GPU infrastructure. Your team gets biological prediction tools without downloading model weights or managing inference infrastructure. Explore the platform and integrations: https://genomicintelligence.ai\n\nFor teams working in Databricks, these tools provide a sequence-to-function layer alongside existing genomic and experimental data. Build workflows that select genes or coordinates from Delta tables, retrieve reference DNA, run predictions, and write results back for comparison with your research evidence.\n\n**Use cases**\n1. Prioritize promoters for gene-therapy research. Compare candidate regulatory sequences in target and off-target biological contexts. Use predicted expression to rank starting sequences and score proposed edits within an agent-driven optimization workflow. Bring a focused candidate panel into reporter assays and broader specificity testing. See our glioblastoma promoter-design demonstration: https://genomicintelligence.ai/blog/designing-glioblastoma-promoters-with-claude-code\n\n2. Investigate regulatory variants in drug-response genes. Build workflows that construct matched reference and alternative DNA sequences, compare predicted expression, and combine the differences with pharmacogenomic evidence. Prioritize variants for functional testing, including candidates that may be difficult to study through population associations alone. Our Biomni collaboration screened 5,317 variants across 41 pharmacogenes, nominating 22 regulatory candidates for validation: https://genomicintelligence.ai/blog/pharmacogenomics-variant-prioritization\n\n3. Explore mechanisms behind genetic associations. Investigate whether a variant near a gene’s transcription start site could alter its predicted expression in a relevant biological context. Combine sequence-derived predictions with association results and experimental annotations to develop hypotheses for follow-up studies. Our GLP1R analysis https://genomicintelligence.ai/blog/glp1r-ozempic-response-part-2 illustrates this approach, from a published association to a broader screen of nearby variants.\n\n4. Characterize poorly annotated genomic regions. Predict candidate genes and transcript structures, examine splice signals, and estimate expression through the combined gene-finding and expression workflow. Use the results to guide annotation review and downstream functional analysis. Our genome-annotation walkthrough https://genomicintelligence.ai/blog/agentic-genome-annotation-walkthrough shows how GI predictions can be combined with external sequence-analysis tools to reconstruct and identify a candidate protein.\n\nThese workflows combine Genomic Intelligence predictions with your agent’s orchestration, sequence preparation, and downstream analysis.\n\n**Product details**\nOne hosted MCP server provides six DNA-analysis capabilities:\n\n1. Promoter detection: Identify candidate promoter regions with prediction scores.\n2. Splice-site prediction: Detect donor and acceptor signals with scored sequence spans.\n3. Enhancer prediction: Estimate developmental and housekeeping enhancer activity using Drosophila-trained models.\n4. Chromatin prediction: Score 919 chromatin features, including accessibility, binding, and histone-associated signals.\n5. Expression prediction: Estimate expression as log(TPM+1) from a transcription-start-site-centered DNA window and a biological-context description.\n6. Gene annotation: Predict gene and transcript structure, including exons, introns, and coding regions.\n\nModel and organism coverage varies by task. See the task documentation: https://docs.genomicintelligence.ai/tasks\n\nThe MCP server includes reference-sequence retrieval, model discovery, prediction tools, and a combined gene-finding and expression workflow. Sequence handles let agents pass references between tools without carrying large DNA strings through the language model’s context. Predictions return structured results for downstream analysis. MCP documentation: https://docs.genomicintelligence.ai/mcp\n\nSequence requests support up to 500 kb, subject to task requirements. This is the submission limit; individual models use task-specific scoring windows. Input requirements and limits: https://docs.genomicintelligence.ai/reference/limits\n\nInference runs on Genomic Intelligence infrastructure outside your Databricks workspace. A REST API is also available for programmatic workflows: https://docs.genomicintelligence.ai/rest-api\n\n\n\n**Additional Insights**\nStart with a focused research question: scan a gene region for promoters, compare candidate sequences in a defined expression context, or identify genes within a genomic interval. Explore our research demonstrations https://genomicintelligence.ai/blog\n\n*Try it with a free demo key*\n\nUse this shared demo key to evaluate Genomic Intelligence:\n\ngi_gGnP7pIZ0wHBUU4c8erLTOV9sMLUM2VK\n\nClick Install, then paste the key into the Bearer Token field. Paste only the key, without the Bearer prefix.\n\nDemo access is rate-limited, with a shared quota across evaluators. For a dedicated API key, higher throughput, concurrency requirements, or help developing a research workflow, contact contact@genomicintelligence.ai.\n\nFor research and development use. Predictions are computational hypotheses requiring experimental validation. Not for clinical or diagnostic decisions.\n","terms_of_service":"https://genomicintelligence.ai/terms","documentation_link":"https://docs.genomicintelligence.ai","privacy_policy_link":"https://genomicintelligence.ai/privacy","assets":["ASSET_TYPE_MCP"],"license":""},"provider_summary":{"name":"Genomic Intelligence","description":"Genomic Intelligence builds transformer DNA language models and serves them from a hosted GPU platform, so teams can analyze DNA sequence without provisioning GPUs, downloading model weights, or managing Python dependencies. 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It must be mentioned that OECD available data cover predominantly two major areas: population health status and healthcare services (resources and utilization).\n\n**Description**\nAccording to OECD, its mission is to advise “policy makers and informing public and private stakeholders and citizens on how to address demands for more and better quality health care”. As part of its mission, OECD develops “reliable statistics of health system performance and help countries benchmark their policies against high-performing health systems.” The health data for member and partner countries, and for the countries accession negotiations with OECD are continuously collected and analyzed by the OECD Secretariat, the decision based on these data being taken by OECD Council which recommends the necessary healthcare policies to be implemented by the countries in order to improve access, efficiency and quality of health care. \n\n\n\nAs mentioned in the summary the major areas that OECD available data are covering are about population heath status and health services/systems. An important component of OECD statistics are the well-known healthcare quality indicators (HCQI).\n\n\n\nPopulation health status is depicted through data about:\n\n• Population dynamics: mortality, infant mortality, maternal mortality, life expectancy and years of potential life lost \n\n• Population structure (static)\n\n• Morbidity: communicable and non-communicable disease, perceived health status, absence from work due to illness\n\n• Behavioral health risk factors\n\n\n\nThe main healthcare services/system indicators topics covered by the OECD available indicators are as follows:\n\n• Preventive health services coverage\n\n• Primary health services effectiveness \n\n• Ambulatory health services utilization and quality indicators\n\n• Hospital health services utilization and quality indicators\n\n• Long term care services recipients\n\n• Patients safety and survival\n\n• Pharmaceuticals utilization \n\n• Health services/systems financing, workforce and infrastructure\n• Hospital health services utilization and quality indicators\n• Long term care services recipients\n• Patients safety and survival\n• Pharmaceuticals utilization \n• Health services/systems financing, workforce and infrastructure\"\n\n**Benefits**\n- \n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [OECD Absence From Work Due To Illness Statistics](https://www.johnsnowlabs.com/marketplace/oecd-absence-from-work-due-to-illness-statistics)\n\t- This dataset contains statistics for the absences from work and compensated absences from work due to illness for countries members of OECD (The Organization for Economic Co-operation and Development) and countries in accession negotiations with OECD. The perceived health status data cover the period 1970-2018.\n- [OECD Behavioral Health Risk Factors Exposed Population](https://www.johnsnowlabs.com/marketplace/oecd-behavioral-health-risk-factors-exposed-population)\n\t- This dataset contains statistics regarding the population exposed to tobacco, foods or overweight/obesity for country members and partners of OECD (The Organization for Economic Co-operation and Development) and for countries in accession negotiations with OECD. The exposure levels to health risk or protection factors statistics cover the period 1960-2018.\n- [OECD Behavioral Health Risk Factors Exposure Levels](https://www.johnsnowlabs.com/marketplace/oecd-behavioral-health-risk-factors-exposure-levels)\n\t- This dataset contains statistics regarding the levels of exposure (consumption) to tobacco, alcohol, foods, macronutrients and calories for country members and partners of OECD (The Organization for Economic Co-operation and Development) and for countries in accession negotiations with OECD. The exposure levels to health risk or protection factors statistics cover the period 1960-2018.\n- [OECD Cancer Statistics](https://www.johnsnowlabs.com/marketplace/oecd-cancer-statistics)\n\t- This dataset contains cancer statistics for countries members of OECD (The Organization for Economic Co-operation and Development), for OECD key partners and countries in accession negotiations with OECD. The estimated values for the two types of indicators, cancer frequency and cancer incidence, cover the years 1998, 2000, 2002, 2008 and 2012.\n- [OECD Communicable Diseases Statistics](https://www.johnsnowlabs.com/marketplace/oecd-communicable-diseases-statistics)\n\t- This dataset contains communicable diseases statistics for countries members of OECD (The Organization for Economic Co-operation and Development) and countries in accession negotiations with OECD. The values of the indicator cover periods from 1980 to 2016.\n- [OECD Countries Demographic Indicators](https://www.johnsnowlabs.com/marketplace/oecd-countries-demographic-indicators)\n\t- This dataset contains the demographic indicators included by OECD in the group of health indicators. The indicators values are for the total population, population structure by gender, the population of 65, 85 and over, fertility, live births, civilian employment population and their values cover periods of time between the years 1960 and 2016.\n- [OECD Doctors And Nurses Migration Indicators By Country Of Origin](https://www.johnsnowlabs.com/marketplace/oecd-doctors-and-nurses-migration-indicators-by-country-of-origin)\n\t- This dataset contains internationally comparable indicators regarding the migration of health care workforce for country members or partners of OECD (The Organization for Economic Co-operation and Development) and for countries in accession negotiations with OECD. The indicators values cover the period 2000-2016.\n- [OECD Health Financial Indicators](https://www.johnsnowlabs.com/marketplace/oecd-health-financial-indicators)\n\t- This dataset contains internationally comparable financial indicators regarding the financing of healthcare and the expenditure on healthcare services by financing scheme, healthcare functions and type of providers. The indicators values cover the period 2000-2018.\n- [OECD Hospital And Ambulatory Health Care Services Utilization](https://www.johnsnowlabs.com/marketplace/oecd-hospital-and-ambulatory-health-care-services-utilization)\n\t- This dataset contains indicators of the health care services utilization by the type of delivery setting (ambulatory, hospital or all) in country members and partners of OECD (The Organization for Economic Co-operation and Development) and for countries in accession negotiations with OECD. The indicators values cover the period 1960-2016.\n- [OECD Hospital Average Length Of Stay And Discharges By Disease](https://www.johnsnowlabs.com/marketplace/oecd-hospital-average-length-of-stay-and-discharges-by-disease)\n\t- This dataset contains indicators hospital health care services utilization by diseases in country members OECD (The Organization for Economic Co-operation and Development) and for countries in accession negotiations with OECD. The indicators values cover the period 1970-2018.\n- [OECD Infant And Maternal Mortality Statistics](https://www.johnsnowlabs.com/marketplace/oecd-infant-and-maternal-mortality-statistics)\n\t- This dataset contains infant, neonatal, perinatal and maternal mortality estimates for countries members of OECD (The Organization for Economic Co-operation and Development), for OECD key partners and countries in accession negotiations with OECD. The estimated values of the four birth and related indicators, cover periods from 1960 to 2018.\n- [OECD Life Expectancy And Years Of Potential Life Lost Statistics](https://www.johnsnowlabs.com/marketplace/oecd-life-expectancy-and-years-of-potential-life-lost-statistics)\n\t- This dataset contains life expectancy and Potential Years of Life Lost (YPLL) statistics for country members of OECD (The Organization for Economic Co-operation and Development), for OECD key partners and countries in accession negotiations with OECD. The estimated values of the two indicators, cover periods from 2000 to 2016.\n- [OECD Long Term Care Resources And Recipients Statistics](https://www.johnsnowlabs.com/marketplace/oecd-long-term-care-resources-and-recipients-statistics)\n\t- This dataset contains internationally comparable indicators regarding the long term care workforce, beds and recipients in country members of OECD (The Organization for Economic Co-operation and Development) and in countries in accession negotiations with OECD. The indicators values cover the period 2005-2018.\n- [OECD Mortality Statistics By Cause Of Death](https://www.johnsnowlabs.com/marketplace/oecd-mortality-statistics-by-cause-of-death)\n\t- This dataset contains mortality statistics for countries members of OECD (The Organization for Economic Co-operation and Development), for OECD key partners and countries in accession negotiations with OECD. The estimated values of the three mortality indicators: number of deaths, crude rate and age-adjusted rate, cover periods from 1960 to 2015.\n- [OECD Patients Mortality And Survival Indicators](https://www.johnsnowlabs.com/marketplace/oecd-patients-mortality-and-survival-indicators)\n\t- This dataset contains internationally comparable indicators regarding patients mortality and survival for country members and partners of OECD (The Organization for Economic Co-operation and Development) and for countries in accession negotiations with OECD. The indicators values cover the period 2000-2015.\n- [OECD Perceived Health Status Statistics](https://www.johnsnowlabs.com/marketplace/oecd-perceived-health-status-statistics)\n\t- This dataset contains perceived health status statistics for countries members of OECD (The Organization for Economic Co-operation and Development) and countries in accession negotiations with OECD. The perceived health status data cover periods from 1980 to 2015.\n- [OECD Pharmaceutical Consumption And Sales By ATC Codes](https://www.johnsnowlabs.com/marketplace/oecd-pharmaceutical-consumption-and-sales-by-atc-codes)\n\t- This dataset contains internationally comparable indicators regarding the pharmaceutical consumption and sales, including OTC (over-the-counter) drugs for country members of OECD (The Organization for Economic Co-operation and Development) and for countries in accession negotiations with OECD. The indicators values cover the period 1980-2016.\n- [OECD Preventive Health Services Coverage Indicators](https://www.johnsnowlabs.com/marketplace/oecd-preventive-health-services-coverage-indicators)\n\t- This dataset contains indicators for the preventive health services coverage by the type of preventive method and target disease in country members and partners of OECD (The Organization for Economic Co-operation and Development) and for countries in accession negotiations with OECD. The indicators values cover the period 1980-2016.\n- [OECD Primary Health Care Services Effectiveness Indicators](https://www.johnsnowlabs.com/marketplace/oecd-primary-health-care-services-effectiveness-indicators)\n\t- This dataset contains internationally comparable indicators of the effectiveness of primary care for country members of OECD (The Organization for Economic Co-operation and Development) and for countries in accession negotiations with OECD. The indicators values cover the period 2000-2015.\n- [OECD Responsiveness And Patient Experiences Health Care Indicators](https://www.johnsnowlabs.com/marketplace/oecd-responsiveness-and-patient-experiences-health-care-indicators)\n\t- This dataset contains indicators of responsiveness and patient experiences quality indicators for the health care services available and delivered in country members of OECD (The Organization for Economic Co-operation and Development). The indicators values cover the period 2000-2018.\n- [OECD Social And Private Health Insurance Coverage](https://www.johnsnowlabs.com/marketplace/oecd-social-and-private-health-insurance-coverage)\n\t- This dataset contains internationally comparable indicators regarding health insurance coverage (social and private) for country members or partners of OECD (The Organization for Economic Co-operation and Development) and for countries in accession negotiations with OECD. The indicators values cover the period 1960-2016.\n- [OECD Surgery Procedures By Day Inpatient And Outpatient Cases](https://www.johnsnowlabs.com/marketplace/oecd-surgery-procedures-by-day-inpatient-and-outpatient-cases)\n\t- This dataset contains indicators for surgical procedures broken-down by day cases, inpatient and outpatient cases, in country members OECD (The Organization for Economic Co-operation and Development) and for countries in accession negotiations with OECD. The indicators values cover the period 1990-2016.\n- [OECD Surgical Patients Safety Indicators](https://www.johnsnowlabs.com/marketplace/oecd-surgical-patients-safety-indicators)\n\t- This dataset contains internationally comparable indicators of the surgical patients safety for country members of OECD (The Organization for Economic Co-operation and Development). The indicators values cover the period 2009-2017.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated\n- Data Updates\n - Data updates support replace-on-update: outdated foreign keys are deprecated, not deleted\n\n**Our data is curated and enriched by domain experts** \n\n Each dataset is manually curated by our team of doctors, pharmacists, public health & medical billing experts:\n- Field names, descriptions, and normalized values are chosen by people who actually understand their meaning\n- Healthcare & life science experts add categories, search keywords, descriptions and more to each dataset\n- Both manual and automated data enrichment supported for clinical codes, providers, drugs, and geo-locations\n- The data is always kept up to date – even when the source requires manual effort to get updates\n- Support for data subscribers is provided directly by the domain experts who curated the data sets\n- Every data source’s license is manually verified to allow for royalty-free commercial use and redistribution.\n\n**Need Help?**\nIf you have questions about our products, contact us at [info@johnsnowlabs.com](mailto:info@johnsnowlabs.com). \n 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The models provided in this listing are\n\n- [CodeLlama_13b_Instruct_hf](https://huggingface.co/codellama/CodeLlama-13b-Instruct-hf)\n- [CodeLlama_13b_Python_hf](https://huggingface.co/codellama/CodeLlama-13b-Python-hf)\n- [CodeLlama_13b_hf](https://huggingface.co/codellama/CodeLlama-13b-hf)\n- [CodeLlama_34b_Instruct_hf](https://huggingface.co/codellama/CodeLlama-34b-Instruct-hf)\n- [CodeLlama_34b_Python_hf](https://huggingface.co/codellama/CodeLlama-34b-Python-hf)\n- [CodeLlama_34b_hf](https://huggingface.co/codellama/CodeLlama-34b-hf)\n- [CodeLlama_70b_Instruct_hf](https://huggingface.co/codellama/CodeLlama-70b-Instruct-hf)\n- [CodeLlama_70b_Python_hf](https://huggingface.co/codellama/CodeLlama-70b-Python-hf)\n- [CodeLlama_70b_hf](https://huggingface.co/codellama/CodeLlama-70b-hf)\n- [CodeLlama_7b_Instruct_hf](https://huggingface.co/codellama/CodeLlama-7b-Instruct-hf)\n- [CodeLlama_7b_Python_hf](https://huggingface.co/codellama/CodeLlama-7b-Python-hf)\n- [CodeLlama_7b_hf](https://huggingface.co/codellama/CodeLlama-7b-hf)\n\nThe models are packaged using MLflow’s transformers flavor.\n- [CodeLlama_13b_Instruct_hf](https://huggingface.co/codellama/CodeLlama-13b-Instruct-hf), [CodeLlama_34b_Instruct_hf](https://huggingface.co/codellama/CodeLlama-34b-Instruct-hf), [CodeLlama_70b_Instruct_hf](https://huggingface.co/codellama/CodeLlama-70b-Instruct-hf), [CodeLlama_7b_Instruct_hf](https://huggingface.co/codellama/CodeLlama-7b-Instruct-hf): fine-tuned for short-form instruction following.\n- [CodeLlama_13b_Python_hf](https://huggingface.co/codellama/CodeLlama-13b-Python-hf), [CodeLlama_34b_Python_hf](https://huggingface.co/codellama/CodeLlama-34b-Python-hf), [CodeLlama_70b_Python_hf](https://huggingface.co/codellama/CodeLlama-70b-Python-hf), [CodeLlama_7b_Python_hf](https://huggingface.co/codellama/CodeLlama-7b-Python-hf): fine-tuned for short-form instruction following.\n- [CodeLlama_13b_hf](https://huggingface.co/codellama/CodeLlama-13b-hf), [CodeLlama_34b_hf](https://huggingface.co/codellama/CodeLlama-34b-hf), [CodeLlama_70b_hf](https://huggingface.co/codellama/CodeLlama-70b-hf), [CodeLlama_7b_hf](https://huggingface.co/codellama/CodeLlama-7b-hf): fine-tuned for short-form instruction following.\n\nCodellama models are licensed under the [LLAMA 2 Community License, Copyright © Meta Platforms, Inc. All Rights Reserved](https://ai.meta.com/resources/models-and-libraries/llama-downloads/). By installing this listing, you acknowledge and agree to the license.\n\nFor example notebooks of using the codellama model in various use cases on Databricks, refer to [the Databricks ML example repository](https://github.com/databricks/databricks-ml-examples/tree/master/llm-models/code_generation/codellama).\n\n**Use cases**\n\n[CodeLlama_13b_Instruct_hf](https://huggingface.co/codellama/CodeLlama-13b-Instruct-hf), [CodeLlama_34b_Instruct_hf](https://huggingface.co/codellama/CodeLlama-34b-Instruct-hf), [CodeLlama_70b_Instruct_hf](https://huggingface.co/codellama/CodeLlama-70b-Instruct-hf), [CodeLlama_7b_Instruct_hf](https://huggingface.co/codellama/CodeLlama-7b-Instruct-hf):\n- It is for instruction following and safer deployment.\n\n[CodeLlama_13b_Python_hf](https://huggingface.co/codellama/CodeLlama-13b-Python-hf), [CodeLlama_34b_Python_hf](https://huggingface.co/codellama/CodeLlama-34b-Python-hf), [CodeLlama_70b_Python_hf](https://huggingface.co/codellama/CodeLlama-70b-Python-hf), [CodeLlama_7b_Python_hf](https://huggingface.co/codellama/CodeLlama-7b-Python-hf):\n- It is a base models specifically for Python code synthesis and understanding.\n\n[CodeLlama_13b_hf](https://huggingface.co/codellama/CodeLlama-13b-hf), [CodeLlama_34b_hf](https://huggingface.co/codellama/CodeLlama-34b-hf), [CodeLlama_70b_hf](https://huggingface.co/codellama/CodeLlama-70b-hf), [CodeLlama_7b_hf](https://huggingface.co/codellama/CodeLlama-7b-hf):\n- It is a base models designed for general code synthesis and understanding.\n\n\n**Product details**\n\nThe pre-trained model can be used for further fine-tuning on specific applications. The fine-tuned model in this listing can be deployed directly to Databricks Model Serving for immediate use, or loaded for fine-tuning or batch inference use cases. For more details, install the listing and view the provided model cards for each model.","terms_of_service":"https://cms.databricks.com/sites/default/files/2023-01/marketplace-consumer-terms_0.pdf","documentation_link":"","privacy_policy_link":"https://www.databricks.com/legal/privacynotice","embedded_notebook_file_infos":[{"id":"a88989d2-382e-4af4-b322-f7ecfd5aa7ff","marketplace_file_type":"EMBEDDED_NOTEBOOK","file_parent":{"parent_id":"4b0b9034-0f9b-4d25-ac7c-52ca956af838","file_parent_type":"LISTING"},"mime_type":"text/html","download_link":"https://marketplace-file-storage-prod.s3-fips.us-east-1.amazonaws.com/EMBEDDED_NOTEBOOK/LISTING_4b0b9034-0f9b-4d25-ac7c-52ca956af838/a88989d2-382e-4af4-b322-f7ecfd5aa7ff?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEGgaDnVzLWVhc3QtMS1maXBzIkYwRAIgbC8sWIoYaHR8y7Wq86nPb0pufBl4%2BM4PWpeN%2B5PW9VQCIG7BhAVBDCWTpxFDLeuKYm%2Ff1JLz4iccPMzter0YdhgWKqYECDAQABoMNDE0MzUxNzY3ODI2IgxNAJrHbXaZG20jNfMqgwQyGEb4TsoVOoNvJ%2BaA%2FZ7zeu8hGcHqCeNfblP%2FQo5g6q2aRNtpwtPRKMlhYcIE%2FUQaNT1Lqmyptu32VUQTpzjTTxRVmJrDPkheSCkV06XQ%2FpTMKjU8CC7X1b%2FqYf8CrBwZT7Cv4C5WPjEET38f2A79h0xV6bZgPy28zQWrSeIJ9w6AfZBxiE24%2FVc08zVA0ivCT6lsy7nuU0wcR%2FMg2C9nd1vaMnQ923VpD21CKfVVYcrNWL2b%2FQ%2BNAO%2BxPjPgjiN6UPQbvbvZxkSdhgrGtecIg9NgqGkMcvM5VLTDtdC1kdeZetfOC2obtA%2BT%2F0gy3tIDo6WM5hcQfw743%2FAU9oaGnRBl%2BwoxRRDCZA%2F4jnPdluICSI7NCr6C3HHOe4CSZJjRihczxPA%2FgQczAVjAPs6CrK9N9pIzt0vNJpGUTgg4AoxftTvGfciDREilSctPIcBk6GwB%2Bzt4wbtNCmEPU%2BFD46rc6CWVQmmLwbKOeHw70ipMVFlOc8NlXkEtGO2vu6H44MstSSc6daOVUYUavQSGZ6jGhEjsP2YGz8KDm9XQotB%2FtyVBqiZV01OKqDmmBJA%2FXjUpSJ8M%2FeJXhe5m%2BKcxzNObtoJRI7jci3Tpt08XZyXZ7%2FyIV8H30ya468X3cfkTXrm972uga3z7iXAdubyEHnQIua%2FuvtwOHILi6O5D1Me2MzCPvcnXBjqTAYDoMWBt%2B3AVFuqSs2dmDQNBnvEhXfMgTedKLD5PJHb3aIl15iBkLaNu36GYnVCp9ZU2lSYcTbY5QwNnNeTfJTNy9bKhwzru6Sg%2BslhN%2BTS0zkTn%2BeLMY4tHgPEtEZ2cp%2FCk6GwqswvnYVKHjwO8q%2B1Mx8lc08fVEfarE9dke1GGFqoddx%2Bq%2BZUyHbDlAVfrCmf0yw%3D%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20261110T000135Z&X-Amz-SignedHeaders=host&X-Amz-Expires=86400&X-Amz-Credential=ASIAWA6KKHEJORWBH4N3%2F20261110%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=2cfa8536c415e4bdfa6b6882eedb26f27b3db0a66eebbc67d0d14ea9739b6939","created_at":1701816768261,"updated_at":1701816768261,"display_name":"codellama_marketplace_listing_example","status":"FILE_STATUS_PUBLISHED"}],"assets":["ASSET_TYPE_MODEL"],"license":"","tags":[{"tag_name":"LISTING_TAG_TYPE_TASK","tag_values":["CODE_GENERATION","TEXT_GENERATION"]}]},"provider_summary":{"name":"Databricks","description":"As the world’s first lakehouse platform in the cloud, Databricks combines the best elements of data lakes and data warehouses to deliver the reliability, strong governance, and performance of data warehouses with the openness, flexibility, and machine learning support of data lakes. 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It is a useful tool for activities such as looking for new safety concerns that might be related to a marketed product, evaluating a manufacturer's compliance to reporting regulations and responding to outside requests for information. The reports in FAERS are evaluated by clinical reviewers, in the Center for Drug Evaluation and Research (CDER) and the Center for Biologics Evaluation and Research (CBER), to monitor the safety of products after they are approved by FDA. These reports are submitted to FDA by healthcare professionals (such as physicians, pharmacists, nurses and others) and consumers (such as patients, family members, lawyers and others). Healthcare professionals and consumers may also report to the products’ manufacturers who then send these reports to FDA. The datasets in FAERS data package contain many relevant information such as: patient demographics, drug name, dose and route of administration, diagnosis and indication for use, therapy start and end date and patient outcome.\n\n**Benefits**\n- useful for physicians, pharmacists, pharmaceutical companies, medical students and patients (general public).\n- faers database is available on fda’s website in raw form, whereas a jsl customer gets it in clean and normalized form ready to use and create his own custom reports.\n- quarter wise data merged into one file.\n- easy to comprehend for the customers who are unfamiliar with the medical terminology as all abbreviated terms are replaced with full form, unlike faers original datasets which are full of abbreviations.\n- datasets are available for the year 2014, 2015, 2016, 2017 and 2018\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [FDA Adverse Events Reporting System Demographics 2018](https://www.johnsnowlabs.com/marketplace/fda-adverse-events-reporting-system-demographics-2018)\n\t- The FDA Adverse Event Reporting System (FAERS) contains information on medication errors, quality complaints and drug-related adverse events that were submitted to FDA (U.S. Food and Drug Administration) in 2018. The Demographics dataset contains patient demographic and administrative information, a single record for each event report.\n- [FDA Adverse Events Reporting System Drug 2018](https://www.johnsnowlabs.com/marketplace/fda-adverse-events-reporting-system-drug-2018)\n\t- The FDA Adverse Event Reporting System (FAERS) contains information on medication errors, quality complaints and drug-related adverse events that were submitted to FDA (U.S. Food and Drug Administration) in 2018. The Drug dataset contains drug/biologic information for as many medications as were reported for the event (1 or more per event).\n- [FDA Adverse Events Reporting System Drug Indication 2018](https://www.johnsnowlabs.com/marketplace/fda-adverse-events-reporting-system-drug-indication-2018)\n\t- The FDA Adverse Event Reporting System (FAERS) contains information on medication errors, quality complaints and drug-related adverse events that were submitted to FDA (U.S. Food and Drug Administration) in 2018. The Indication dataset contains all \"Medical Dictionary for Regulatory Activities\" (MedDRA) terms coded for the indications for use (diagnoses) for the reported drugs (0 or more per drug per event).\n- [FDA Adverse Events Reporting System Drug Reaction 2018](https://www.johnsnowlabs.com/marketplace/fda-adverse-events-reporting-system-drug-reaction-2018)\n\t- The FDA Adverse Event Reporting System (FAERS) contains information on medication errors, quality complaints and drug-related adverse events that were submitted to FDA (U.S. Food and Drug Administration) in 2018. The drug reaction dataset contains all \"Medical Dictionary for Regulatory Activities\" (MedDRA) terms coded for the adverse event (1 or more).\n- [FDA Adverse Events Reporting System Drug Therapy Dates 2018](https://www.johnsnowlabs.com/marketplace/fda-adverse-events-reporting-system-drug-therapy-dates-2018)\n\t- The FDA Adverse Event Reporting System (FAERS) contains information on medication errors, quality complaints and drug-related adverse events that were submitted to FDA (U.S. Food and Drug Administration) in 2018. The drug therapy dates dataset contains drug therapy start dates and end dates for the reported drugs.\n- [FDA Adverse Events Reporting System Patient Outcome 2018](https://www.johnsnowlabs.com/marketplace/fda-adverse-events-reporting-system-patient-outcome-2018)\n\t- The FDA Adverse Event Reporting System (FAERS) contains information on medication errors, quality complaints and drug-related adverse events that were submitted to FDA (U.S. Food and Drug Administration) in 2018. The patient outcome file contains information on patient outcomes for the event (0 or more).\n- [FDA Adverse Events Reporting System Report Source 2018](https://www.johnsnowlabs.com/marketplace/fda-adverse-events-reporting-system-report-source-2018)\n\t- The FDA Adverse Event Reporting System (FAERS) contains information on medication errors, quality complaints and drug-related adverse events that were submitted to FDA (U.S. Food and Drug Administration) in 2018. The report source dataset contains report sources for event.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated\n- Data Updates\n - Data updates support replace-on-update: outdated foreign keys are deprecated, not deleted\n\n**Our data is curated and enriched by domain experts** \n\n Each dataset is manually curated by our team of doctors, pharmacists, public health & medical billing experts:\n- Field names, descriptions, and normalized values are chosen by people who actually understand their meaning\n- Healthcare & life science experts add categories, search keywords, descriptions and more to each dataset\n- Both manual and automated data enrichment supported for clinical codes, providers, drugs, and geo-locations\n- The data is always kept up to date – even when the source requires manual effort to get updates\n- Support for data subscribers is provided directly by the domain experts who curated the data sets\n- Every data source’s license is manually verified to allow for royalty-free commercial use and redistribution.\n\n**Need Help?**\nIf you have questions about our products, contact us at [info@johnsnowlabs.com](mailto:info@johnsnowlabs.com). \n ","terms_of_service":"https://www.johnsnowlabs.com/healthcare-data-library-eula/","documentation_link":"https://www.johnsnowlabs.com/marketplace/fda-drug-adverse-events-reporting-system-faers-2018-data-package","contact_email":"support@johnsnowlabs.com","privacy_policy_link":"https://www.johnsnowlabs.com/privacy-policy/","assets":["ASSET_TYPE_DATA_TABLE"]},"provider_summary":{"name":"John Snow Labs","description":"John Snow Labs, an AI and NLP for healthcare company, provides state-of-the-art software, models, and data to help healthcare and life science organizations build, deploy, and operate AI projects."},"popularity":437},{"id":"32d19941-62fc-466d-8379-93f63bc19ab1","summary":{"name":"Collibra MCP Server","subtitle":"The Collibra MCP server allows you to find, access, and engage with governed metadata and business context information ","setting":{"visibility":"PUBLIC"},"created_at":1761909516349,"updated_at":1782731369311,"categories":["OTHER"],"listingType":"PERSONALIZED","provider_id":"6b4c3f00-c69d-47ed-a3a2-541171c84c7d"},"detail":{"description":"Overview\nThe Collibra Model Context Protocol (MCP) server enables AI/ML developers, business users and LLMs to access and engage with governed metadata and business context information in the Collibra Platform. This offering additionally simplifies the integration of Collibra with agents that are built on LLMs and connected to other agents in complex workflows.\nThe MCP server provides a standardized access layer that addresses common challenges including inconsistent or low-quality context retrieval for LLMs, fragmented integration workflows, and lack of standardized communication among information sources.\n\n# Read Tools\n- **discover_business_glossary** | Searches for glossary definitions via Collibra AI Copilot. Note that this tool leverages Collibra AI and therefore consumes Collibra Units (CUs).\n - Note: this requires Collibra AI Copilot Public Preview to be enabled\n- **discover_data_assets** | Searches for data assets via Collibra AI Copilot. Note that this tool leverages Collibra AI and therefore consumes Collibra Units (CUs).\n - Note: this requires Collibra AI Copilot Public Preview to be enabled\n- **get_asset_details** | Retrieves a comprehensive, pre-formatted summary of a specific data asset by its ID\n- **get_business_term_data** | Trace a business term back to its connected physical data assets\n- **get_column_semantics** | Retrieve data attributes, measures, and business assets connected to a column\n- **get_lineage_downstream** | Get downstream technical lineage (consumers) for a data entity\n- **get_lineage_entity** | Get metadata about a specific entity in the technical lineage graph\n- **get_lineage_transformation** | Get details and logic of a specific data transformation\n- **get_lineage_upstream** | Get upstream technical lineage (sources) for a data entity\n- **get_measure_data** | Trace a measure back to its underlying physical columns and tables\n- **get_table_semantics** | Retrieve the semantic layer for a table: columns, data attributes, and connected measures\n- **list_asset_types** | Lists available asset types in the system. \n- **list_data_contract** | Retrieves the details of existing data contracts. Use this for general queries to view data contracts or get contract details, and always return both their IDs and names in the response.\n - Note: requires Data Contracts Private Preview to be enabled\n- **prepare_add_business_term** | Validate business term data, resolve domains, check for duplicates, and hydrate attribute schemas. Always call this before add_business_term.\n- **prepare_create_asset** | Resolve asset type, domain, hydrate full attribute schema, and check duplicates. Always call this before create_asset.\n- **pull_data_contract_manifest** | Downloads the manifest file for the currently active version of a specific data contract. \n- **search_asset_keyword** | Searches for data assets, business terms, communities, or other resources. This tool allows filtering by name, type, status, community or domain.\n- **search_data_class** | Retrieves a list of available data classes with filtering capabilities. \n- **search_data_classification_match** | Finds associations between data classes and assets.. Supports filtering by asset IDs, classification IDs, statuses, and - asset types.\n- **search_lineage_entities** | Search for entities in the technical lineage graph\n- **search_lineage_transformations** | Search for transformations in the technical lineage graph\n\n# Write Tools\n- **add_business_term** | Create a business term asset with definition and optional attributes in Collibra.\n- **add_data_classification_match** | Assigns a data classification to a specific data asset. This tool allows for associating a data class with an asset. \n- **create_asset** | Create a new data asset with optional attributes in Collibra.\n- **push_data_contract_manifest** | Upload a new version of a data contract manifest to Collibra. The manifestID and version are automatically parsed from the manifest content if it adheres to the Open Data Contract Standard.\n- **remove_data_classification_match** | Removes a data classification from a specific data asset. This tool allows for the removal of a data class from an asset. \n- **edit_asset** - Edit an existing asset via* *a list of typed operations:\n - *update_attribute*, *add_attribute*, remove_attribute - change, append, or clear an attribute value (e.g. Definition, Note)\n - *update_property* - rename the asset (name), change its displayName, or change its statusId (status name or UUID accepted)\n - *add_relation*, *remove_relation* - link or unlink the asset to another asset by relation role (e.g. is synonym of)\n - *add_tag* - append a free-text tag without replacing existing tags\n - *set_responsibility* - assign a user or group to a resource role (e.g. Steward, Owner) by username, email, or UUID\n\n# Documentation on integrating with Collibra\nFor integrating Collibra with Databricks, please see the Collibra Product Documentation [here](https://productresources.collibra.com/docs/collibra/latest/Content/ModelContextProtocol/ta_databricks-mcp.htm)\n\n","terms_of_service":"https://www.collibra.com/legal/documents/collibra-privacy-policy","documentation_link":"","privacy_policy_link":"https://www.collibra.com/legal/documents/collibra-privacy-policy","cost":"FREE","assets":["ASSET_TYPE_MCP"],"license":""},"provider_summary":{"name":"Collibra","description":"Collibra is the Data Intelligence company. 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The products include a range of no-code data solutions utilized by business owners and a robust infrastructure used by engineers and IT professionals.\n\nBright Data provides a cost-effective way to perform fast and stable public web data collection at scale, effortless conversion of unstructured data into structured data, and superior customer experience while being fully transparent and compliant."},"popularity":1808},{"id":"c517c7bb-a6b2-41f2-bc44-76b77fe9a30d","summary":{"name":"New Movers","subtitle":"Each year, 36 million people move. Movers are ideal for customer acquisition, retention, and CRM matching. They will nee","setting":{"visibility":"PUBLIC"},"created_at":1711567932335,"updated_at":1712588430430,"categories":["ADVERTISING_AND_MARKETING"],"listingType":"PERSONALIZED","provider_id":"40a8da22-d632-4e9c-b557-7ebc800c14a2"},"detail":{"description":"**Overview**\nEach year, 36 million people move. Movers are ideal for customer acquisition, retention, and CRM matching. They will need an array of products and \nservices both in the months leading up to their move and the months following. Speedeon provides best in class multii sourced movers for your marketing campaigns. \n\n\n**Use cases**\nThis data is used for direct marketing\n\n**Product details**\nFNAME\nLNAME\nADDRESS\nCITY\nSTATE\nZIP\nZIP4\nPREV_ADDRESS (Where avaialble)\nPREV_CITY (Where avaialble)\nPREV_STATE (Where avaialble)\nPREV_ZIP (Where avaialble)\nPREV_ZIP4 (Where avaialble)\nSPEEDEONID\nMOVE_DATE\nEMAIL (Where avaialble)\n\n\n\n**Additional Insights**\n\nContact us for pricing","terms_of_service":"https://speedeondata.com/privacy-policy/","documentation_link":"","privacy_policy_link":"https://speedeondata.com/privacy-policy/","geographical_coverage":"[\"United States and Canada\"]","cost":"PAID","update_frequency":{"interval":7,"unit":"DAILY"},"collection_granularity":{"interval":1,"unit":"DAILY"},"collection_date_start":1708979310414,"collection_date_end":1711481313313,"data_source":"Our mover data is updated daily and undergoes additional data enhancements and verifications such as CASS and NCOA. 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By linking company and provider profiles, the dataset surfaces the context of vendor engagements and their alignment with strategic priorities.\nThis data enables sourcing, strategy, and market intelligence teams to benchmark outsourcing practices across industries, identify major service provider relationships, and evaluate workload distribution by geography and function. It supports sales teams in spotting active deals and competitive activity, while advisors and vendor managers can use it to assess partnership intensity, monitor vendor ecosystems, and inform negotiation strategies.\n\n**Methodology & Quality**\nOutsourcing data is aggregated from a combination of public disclosures, proprietary signals, and curated sources, then normalized into a consistent schema of companies, service providers, workloads, and business functions. Draup standardizes vendor taxonomies and workload classifications, applies evidence tagging for digital technologies, and refreshes data frequently to capture new deals and contract updates. This ensures accuracy, comparability across industries, and the ability to trace relationships over time.\n\n**Key Tables Include**\no\tCompany–Service Provider Engagements: outsourcing projects with company, service provider, locations, workloads, functions, project timelines, technologies, and skills.\no\tDeal Intensity Metrics: engagement footprint, outsourced workload intensity, and geographic distribution of service providers.\no\tRelationship Insights: highlights of provider–company partnerships, outsourced size, key locations, and notable relationship details.\n\n**Use Cases:**\n\n***Discover Accounts that outsource to a India for Data Engineering work***\n\nUse Draup’s outsourcing deals data to identify which companies are using outsourcing services from where for specific functions. Filter by provider, client location, vendor location and function to target high-potential companys with offshore dependencies.\n\nSELECT\ncompany_name,\nservice_provider_name,\nservice_provider_location,\noutsourced_workloads,\noutsourced_business_function\nFROM deals_company_service_providers\nWHERE LOWER(service_provider_location) LIKE '%india'\nAND LOWER(outsourced_workloads) LIKE '%data engineering%';\n\n***Uncover Competitors’ Workload Share Across Verticals***\n\nQuickly identify how rival service providers distribute their outsourcing workloads across industries and business functions, and benchmark their customer footprint against your own.\n\nSELECT\nservice_provider_name,\nservice_provider_primary_vertical,\nCOUNT() AS projects_count,\nCAST(COUNT() AS DOUBLE) * 100 / SUM(COUNT(*)) OVER (PARTITION BY service_provider_name) AS workload_share_percentage\nFROM\ndeals_company_service_providers\nGROUP BY\nservice_provider_name,\nservice_provider_primary_vertical\nORDER BY\nservice_provider_name,\nworkload_share_percentage DESC;\n\n***Map the Service Provider Partner footprint for a Target Account***\n\nGet a comprehensive view of all the service provider partner ecosystem of the company. Get deeper insights with the ability to filter the outsourcing deals data by service providers, functions, workloads, tech stack leveraged or the delivery location. This can enable understanding competitive landscape or partnership opportunities.\n\nSELECT\ncompany_name,\nservice_provider_name,\nservice_provider_location,\nservice_provider_primary_vertical,\noutsourced_business_function,\noutsourced_workloads,\ndigital_product,\nskills\nFROM deals_company_service_providers\nWHERE company_name = 'Microsoft'\nOR service_provider_name IN ('Accenture', 'Tech Mahindra')\nOR service_provider_location IN ('India', 'USA')\nOR outsourced_business_function LIKE '%IT%'\nOR LOWER(outsourced_workloads) LIKE '%cloud%'\nORDER BY service_provider_name;\n\n***Understand the client landscape of a competing Service Provider in a function***\n\nUse Draup’s outsourcing deal intelligence to explore the client base of a competitor service provider for a specific function (e.g., IT, Finance, Engineering). Identify served industries, deal sizes, and delivery locations to benchmark penetration and uncover whitespace opportunities.\n\nSELECT\ndeals_company_service_providers.company_name,\ndeals_company_service_providers.company_location,\ncompany_overview.primary_vertical AS Client_primary_vertical,\ncompany_overview.verticals AS client_verticals,\ndeals_company_service_providers.service_provider_name,\ndeals_company_service_providers.service_provider_location,\ndeals_company_service_providers.service_provider_vertical,\ndeals_company_service_providers.service_provider_primary_vertical,\nservice_providers_insights.outsourced_size,\ndeals_company_service_providers.outsourced_workloads,\ndeals_company_service_providers.outsourced_business_function\nFROM\ndeals_company_service_providers\nINNER JOIN\ndeals_company_service_providers_insights as service_providers_insights ON deals_company_service_providers.company_id = service_providers_insights.company_id\nAND deals_company_service_providers.service_provider_id = service_providers_insights.service_provider_id\nINNER JOIN\ncompany_overview ON deals_company_service_providers.company_id = company_overview.company_id\nWHERE\ndeals_company_service_providers.service_provider_name = 'Accenture'\nAND deals_company_service_providers.outsourced_business_function = 'IT';\n\n***What workloads does a Company outsource to vendors and who are their top vendors?***\n\nDraup’s outsourcing deal data helps map the workloads and business functions outsourced by a company, along with its primary service providers. 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Current year estimates and five-year projections were produced by a team of demographers, statisticians and geographers with extensive industry experience, using data-driven and reliable methodologies.\n\nThis sample data set covers the San Francisco, CA area. This represents a subset of the full product’s geographic extent. The full commercial offering features geographic coverage of the entire United States. Contact Databricks.partnership@precisely.com for more information on licensing the product.\n**Use cases**\n**Customer Acquisition**\nImprove customer acquisition rates and reduce costs by delivering demographic and socioeconomic information in a geographic context, for a deeper understanding of current and potential markets.\n**Market Analysis**\nUnderstand the makeup of markets using GroundView's information such as population by age and race, household income, and consumer spending potential.\n**Personalize Customer Experiences**\nInform communication strategies, building strong and profitable customer relationships\n\n**Product details**\nGroundView: Complete provides the following data components:\n• Demographics – population and household characteristics bundled in themed datasets, plus additional datasets such as Consumer Spend Potential, Retail Sales Potential, and Household Wealth and Financial Assets.\n• GeoEnrichment – current year estimates are transformed into relative values and provided in a set of themed datasets which correspond to the current year demographics data sets. Included with these components is the PreciselyID lookup.\nGroundView datasets deliver demographic and socioeconomic information in a geographic context, for a deeper understanding of current and potential markets. Data includes information such as population by age and race, household income, and consumer spending potential. The GeoEnrichment percentage and mean/median data components are based on the current year GroundView demographic estimates. Each current year GroundView estimates dataset is transformed into relative values at the Block Group level, resulting in the set of GeoEnrichment files. An accompanying lookup table with the PreciselyID PBKEY and Block Group code enables the linkage to the derived GeoEnrichment data and thus delivers demographic information on a PreciselyID for operational efficiency at the address level.\nPrecisely GroundView data is used to understand, estimate, project, compare and differentiate the demographic, economic and geographic characteristics of markets – including by customized trade areas. The annually updated GroundView suite of demographic datasets provide users with a sound basis for making assessments about current and target market sizes and characteristics, and for enhancing their\ncustomer databases with added contextual information.\n\n**Additional Insights**\nhttps://help.precisely.com/r/t/1014166678/2023-12-31/GroundView-Complete/pub/Latest/en-US/GroundView-Complete-US-Demographic-Estimates-and-Projections-Data-Suite-Product-Guide/Getting-started\n\nhttps://www.precisely.com/data-guide/products/groundview-demographics","terms_of_service":"https://www.precisely.com/legal/licensing/trial-evaluation-agreement-terms-u-s-may-2021","documentation_link":"https://help.precisely.com/r/t/1014166678/2023-12-31/GroundView-Complete/pub/Latest/en-US/GroundView-Complete-US-Demographic-Estimates-and-Projections-Data-Suite-Product-Guide/Getting-started","privacy_policy_link":"https://www.precisely.com/legal/privacy-policy","embedded_notebook_file_infos":[{"id":"f8c9ab7f-7151-452a-99ac-c84d97ae8315","marketplace_file_type":"EMBEDDED_NOTEBOOK","file_parent":{"parent_id":"09b5c8c6-f31b-4000-9c17-9983b4152661","file_parent_type":"LISTING"},"mime_type":"text/html","download_link":"https://marketplace-file-storage-prod.s3-fips.us-east-1.amazonaws.com/EMBEDDED_NOTEBOOK/LISTING_09b5c8c6-f31b-4000-9c17-9983b4152661/f8c9ab7f-7151-452a-99ac-c84d97ae8315?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEGgaDnVzLWVhc3QtMS1maXBzIkYwRAIgbC8sWIoYaHR8y7Wq86nPb0pufBl4%2BM4PWpeN%2B5PW9VQCIG7BhAVBDCWTpxFDLeuKYm%2Ff1JLz4iccPMzter0YdhgWKqYECDAQABoMNDE0MzUxNzY3ODI2IgxNAJrHbXaZG20jNfMqgwQyGEb4TsoVOoNvJ%2BaA%2FZ7zeu8hGcHqCeNfblP%2FQo5g6q2aRNtpwtPRKMlhYcIE%2FUQaNT1Lqmyptu32VUQTpzjTTxRVmJrDPkheSCkV06XQ%2FpTMKjU8CC7X1b%2FqYf8CrBwZT7Cv4C5WPjEET38f2A79h0xV6bZgPy28zQWrSeIJ9w6AfZBxiE24%2FVc08zVA0ivCT6lsy7nuU0wcR%2FMg2C9nd1vaMnQ923VpD21CKfVVYcrNWL2b%2FQ%2BNAO%2BxPjPgjiN6UPQbvbvZxkSdhgrGtecIg9NgqGkMcvM5VLTDtdC1kdeZetfOC2obtA%2BT%2F0gy3tIDo6WM5hcQfw743%2FAU9oaGnRBl%2BwoxRRDCZA%2F4jnPdluICSI7NCr6C3HHOe4CSZJjRihczxPA%2FgQczAVjAPs6CrK9N9pIzt0vNJpGUTgg4AoxftTvGfciDREilSctPIcBk6GwB%2Bzt4wbtNCmEPU%2BFD46rc6CWVQmmLwbKOeHw70ipMVFlOc8NlXkEtGO2vu6H44MstSSc6daOVUYUavQSGZ6jGhEjsP2YGz8KDm9XQotB%2FtyVBqiZV01OKqDmmBJA%2FXjUpSJ8M%2FeJXhe5m%2BKcxzNObtoJRI7jci3Tpt08XZyXZ7%2FyIV8H30ya468X3cfkTXrm972uga3z7iXAdubyEHnQIua%2FuvtwOHILi6O5D1Me2MzCPvcnXBjqTAYDoMWBt%2B3AVFuqSs2dmDQNBnvEhXfMgTedKLD5PJHb3aIl15iBkLaNu36GYnVCp9ZU2lSYcTbY5QwNnNeTfJTNy9bKhwzru6Sg%2BslhN%2BTS0zkTn%2BeLMY4tHgPEtEZ2cp%2FCk6GwqswvnYVKHjwO8q%2B1Mx8lc08fVEfarE9dke1GGFqoddx%2Bq%2BZUyHbDlAVfrCmf0yw%3D%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20261110T000135Z&X-Amz-SignedHeaders=host&X-Amz-Expires=86400&X-Amz-Credential=ASIAWA6KKHEJORWBH4N3%2F20261110%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=762187fd73970e32f62457ff1e7eb8d22b7167e9ac7b72318aab92a8906233a0","created_at":1768524056094,"updated_at":1768524056094,"display_name":"GroundView USA Notebook","status":"FILE_STATUS_PUBLISHED"}],"geographical_coverage":"[\"United States and Canada\"]","cost":"FREE","update_frequency":{"interval":1,"unit":"QUARTERLY"},"data_source":"","assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://www.precisely.com/legal/licensing/trial-evaluation-agreement-terms-u-s-may-2021"},"provider_summary":{"name":"Precisely","description":"As a global leader in data integrity, Precisely ensures that your data is accurate, consistent, and contextual. 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The dataset covers diverse meeting formats, speakers, accents, and acoustic conditions, and supports both ready-made and custom dataset creation.\n\n**Use cases**\n- Training and evaluating speech recognition (ASR) models\n- Speaker diarization and identification\n- Meeting transcription, summarization, and action item extraction\n- Conversational AI, voice assistants, and productivity tools\n- Benchmarking audio and speech processing pipelines\n\n**Product details**\n- Datasets include large-scale collections of office meeting audio with rich acoustic, technical, and semantic metadata recorded in real-world environments.\n\n- Sample fields include audio ID, audio URL, duration, sampling rate, number of channels, language, speaker count, speaker segments, timestamps, background noise level, meeting type, and annotated transcripts with confidence scores.","terms_of_service":"https://gurushots.com/terms","documentation_link":"","privacy_policy_link":"https://gurushots.com/privacy","geographical_coverage":"[\"Global\"]","cost":"PAID","update_frequency":{"interval":30,"unit":"DAILY"},"collection_granularity":{"interval":1,"unit":"DAILY"},"data_source":"Proprietary and curated datasets collected, licensed, and generated by DataSeeds.ai","assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"DataSeeds.ai","description":"DataSeeds.ai provides high-quality, large-scale multimodal datasets designed for modern AI and machine learning workflows. 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We manage the database and update content 24/7.\n\n**Key benefits of LSEG Quantitative Analytics Database:**\n\n- Eliminate the effort of addressing different time frequencies, currencies and units, or adjusting data for corporate actions.\n\n- Access point-in-time values and data for delisted companies.\n\n- Enjoy a seamless user experience featuring programming languages such as Python and C# and a database that integrates with statistical packages, including R, SAS, MATLAB and S-PLUS.\n\n- Easily map and analyse all securities associated with a single entity and build factors using data from different sources.\n\n- Shorten time to market and reduce your workload by syncing proprietary data with our time series data sets spanning a range of identifiers – including SEDOL and RIC.\n\n- Gain access to an unrivalled library of multi-asset securities pricing, company data and historical constituent lists.\n\n- Back-test over multiple economic cycles and variable market conditions with trusted timestamps and point-in-time data that helps you avoid look-ahead and survivorship biases.\n\n- Leverage our partnership with Databricks to begin your quant or machine learning initiatives without the capital lock-in associated with deployed quantitative data solutions.\n\n- Access quantitative analytics data as a service in the cloud and leverage Databricks’ data sharing capabilities to combine data sets in your Databricks account and run queries on the full content set.\n\n- Utilise select, preloaded third-party data integrated into Quantitative Analytics, and seamlessly access information that is concorded and historically mapped.","terms_of_service":"https://catalogartifact.azureedge.net/publicartifacts/londonstockexchangegroupplc1694088398525.analytics_api-31b1f21b-23b7-461b-94cc-e5d2e08d218c/Artifacts/Documents/TermsOfUse.txt","documentation_link":"https://www.lseg.com/content/dam/data-analytics/en_us/documents/fact-sheets/lseg-quant-cloud-fact-sheet.pdf","privacy_policy_link":"https://www.lseg.com/en/policies/privacy-statement","geographical_coverage":"[\"Global\"]","update_frequency":{"interval":15,"unit":"MINUTE"},"assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"LSEG","description":"The London Stock Exchange Group (LSEG) is one of the world’s leading providers of financial markets infrastructure and delivers financial data, analytics, news and index products to more than 40,000 customers in 190 countries. \n \nWe help organisations fund innovation, manage risk and create jobs by partnering with customers at every point in the trade lifecycle: from informing their pre-trade decisions and executing trades to raising capital, clearing and optimisation. \n \nBacked by more than three centuries of experience, innovative technologies and a team of 23,000 people in 70 countries, we are driving financial stability, empowering economies and enabling you to grow sustainably."},"popularity":1767},{"id":"9639e141-a8bd-4768-aedb-9915bd3f4de5","summary":{"name":"Healthcare: Biosurveillance","subtitle":"This web dataset tracks signals of infectious disease activity and emerging biological threats with data back to 2021.","setting":{"visibility":"PUBLIC"},"created_at":1749487530738,"updated_at":1761846792477,"categories":["HEALTH","GEOSPATIAL","PUBLIC_SECTOR","SCIENCE_AND_RESEARCH","SECURITY","CLIMATE_AND_ENVIRONMENT","COMMERCE","DEMOGRAPHICS","ECONOMICS","EDUCATION","ENERGY","FINANCIAL","MANUFACTURING","MEDIA","TRANSPORTATION_AND_LOGISTICS","TRAVEL_AND_TOURISM"],"listingType":"PERSONALIZED","provider_id":"dc936c7b-b23a-4f98-9d0c-3422f88f8ae9"},"detail":{"description":"**Overview**\nThe Healthcare: Biosurveillance dataset tracks early signals of infectious disease activity, emerging biological threats, and unusual health events reported across global online sources. It is a multilingual dataset, capturing content in many languages to ensure broad geographic and cultural coverage. With historical data going back to 2021, it supports retrospective analysis as well as real-time monitoring. By surfacing early warnings—such as mentions of outbreaks, new pathogens, and cross-species infections—it helps protect human life by enabling faster public health response, better preparedness, and more informed decision-making.\n**Use cases**\n\n1. **Public Health Institutions:** To detect outbreaks early, issue advisories, and prepare containment strategies.\n2. **National Governments & Security Agencies:** Biological threat detection—natural or manmade—and geopolitical risk monitoring.\n3. **Global Health NGOs & Humanitarian Organizations:** To triage response priorities, mobilize medical aid, and protect vulnerable populations.\n4. **Hospitals, Clinics & Health Systems:** For operational planning, staffing, and managing patient surges during regional outbreaks.\n5. **Pharmaceutical & Biotech Companies:** To track pathogen evolution, anticipate demand for vaccines and treatments, and plan R&D investments. \n6. **Food & Agriculture Corporations:** Especially those operating in cross-border environments, where zoonotic spillover and avian or livestock diseases can disrupt trade, supply chains, or public trust.\n7. **Travel, Aviation & Tourism Industries:** For assessing regional health risks, route planning, and crisis communication with travelers.\n8. **Manufacturing & Supply Chain Firms:** Outbreaks can shut down production hubs, delay logistics, or affect supplier operations—early knowledge is critical for business continuity.\n9. **Insurance & Reinsurance Providers:** To recalibrate risk models and policy exposure in light of emerging health threats.\n10. **Corporate Risk & Business Continuity Teams (All Sectors):** Biosurveillance can help in scenario planning, workforce safety, office closures, and investment strategies.\n11. **Investors & Financial Analysts:** To assess sector-specific exposure to biological risks and make more informed decisions in volatile markets.\n12. **Media & Communications Professionals:** For accurate and timely reporting on health events, preventing misinformation, and shaping public understanding.\n\n\n**Product details**\n\ndomain -Amplifying domains. In cases without amplification, the contents of this field are the same as the contents of \"url_domain\".\nstory language - Two-letter ISO 639-1 code indicating the document's original language.\ntext - Complete scraped text from the website.\ntitle - Original text found by crawling the story URL. \ntitle_translated - English language translation of the document's title field. \nTime Stamp - ts_date Alternative date string format (\"YYYYMMDD\") for the date_added field.\nTime Stamp - ts_hour - Alternative format indicating the hour of the day (0-23) when the document was added.\nip - IP address derived from the domain name.\nurl -Full-length URL of the story.\nurl_domain - Domain culled from the URL, representing the \"source domain\" of the document.\n\nFor more details, refer to the embedded notebook.\n\n**Additional Insights**\nThis dataset contains fields that support NLP, network/graph analysis, and AI/ML workflows.","terms_of_service":"https://www.hometreedata.com/","documentation_link":"","privacy_policy_link":"https://www.hometreedata.com/","geographical_coverage":"[\"Global\"]","cost":"PAID","update_frequency":{"interval":1,"unit":"DAILY"},"collection_granularity":{"interval":1,"unit":"DAILY"},"collection_date_start":1622576672762,"collection_date_end":1761933186372,"data_source":"Curated open web data / OSINT","size":0.4,"assets":["ASSET_TYPE_DATA_TABLE","ASSET_TYPE_GIT_REPO"],"license":""},"provider_summary":{"name":"Hometree Data, Inc.","description":"At HomeTreeData, our mission is to empower organizations with real-time, verified intelligence from across the open web—cutting through information chaos and delivering clarity when it matters most. We fuse speed with trust, enabling businesses and governments to detect emerging risks, monitor reputational threats, and make confident, reality-based decisions.\n\nWith our AI-enhanced platform, we support urgent awareness, trend analysis, and informed decision-making powering scalable AI modeling, and mission-critical operations. Our high-signal intelligence integrates with leading BI tools to enable our to customers spot emerging risks, monitor reputational threats, and act decisively with confidence.\n\nIn an age of information overload and uncertainty, we ensure that what you know is both timely—and true."},"popularity":291},{"id":"c51c135f-4b2b-4aa8-9994-0ee08bda7781","summary":{"name":"MBI Area Boundaries for Romania – Sample","subtitle":"MBI's precise & seamless boundaries for well-founded decisions in Romania. Free trial data.","share":{"name":"share_boundaries_mbi_ro_r_2025","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1785151771635,"updated_at":1785151771635,"categories":["ADVERTISING_AND_MARKETING","CLIMATE_AND_ENVIRONMENT","ENERGY","GEOSPATIAL","MEDIA","OTHER","PUBLIC_SECTOR","RETAIL","TRANSPORTATION_AND_LOGISTICS","TRAVEL_AND_TOURISM"],"listingType":"STANDARD","provider_id":"aa09abd6-ff7f-4452-803d-b8c43b4c07cb"},"detail":{"description":"MBI'S GLOBAL AREA BOUNDARIES COVER MOST COUNTRIES WORLDWIDE AND **CAN BE LICENSED FOR EACH COUNTRY INDIVIDUALLY** AND IN BUNDLES.\n[MBI](https://www.mbi-geodata.com/en/) digital boundaries are available for virtually all countries worldwide.* Created by our experts according to consistent global standards, the polygons are so precise and harmonized that they align seamlessly across borders. The territorial vintages of the worldwide area boundaries are compatible with our globally consistent and comparable [socio-demographic](https://www.mbi-geodata.com/en/sociodemographic-data/) and [economic](https://www.mbi-geodata.com/en/purchasing-power-overview/) data.\nOur area boundaries for Romania are offered at the following levels:\n\n- **Postal Code Level** (Postcode 2-digit, 6-digit on request): This fine-grained level of detail makes the data suitable for a wide range of applications – for example, enriching customer databases, which are often organized by postal code.\n- **Administrative Level** (Comune): Covers both larger administrative areas (e.g., regions, states, provinces) as well as finer subdivisions such as districts, counties, or municipalities.\n\n**Use Cases:**\n\n- **Market Analysis**: Use MBI’s boundaries to visualize data on a map, learn about your customers, and uncover untapped market potential.\n- **360-Degree Customer View**: Link boundaries to customer data, sociodemographic, and economic data to gain a holistic understanding of customer needs, behaviors, and potential value.\n- **Location Planning**\n•\tUse MBI’s boundaries to visualize data on a map, learn about your customers\n•\tPlan the provision of services and products across different regions\n•\tDefine sales territories\n•\tVisualize catchment areas and plan actions to reach potential customers\n\n**Additional Insights:**\n[About Area Boundaries](https://www.mbi-geodata.com/en/area-boundaries/)\n\n**Data Structure:**\nThe area boundaries include the following variables:\nCTRYCODE: Two-digit country code by ISO 3166\nNAME: Name of administrative area / Name of the most populous administrative area / Name of the respective micro-area or the next higher level\nADMINCODE / POSTCODE / MICROCODE: Administrative code / Postal code / Micro-code\nWKT: Geography as Well Known Text (lat/long WGS84)\n\n*with only a few specific exceptions","terms_of_service":"https://service-mbi-geodata.com/s/HNp7Z2tws4WDqWd","documentation_link":"https://www.mbi-geodata.com/en/mbi-supplier-manufacturer-worldwide-geodata/europe/romania-maps/","privacy_policy_link":"https://www.mbi-geodata.com/en/data-protection/","update_frequency":{"interval":1,"unit":"YEARLY"},"assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://service-mbi-geodata.com/s/j7HSym96cFW3DtH"},"provider_summary":{"name":"Michael Bauer International GmbH – MBI Geodata","description":"For over 15 years, Michael Bauer International (MBI) has been a leading provider of globally consistent and comparable geographic data, including more than 3 million postcode areas and other detailed datasets like sociodemographics and purchasing power. MBI's high-quality small-scale data spans 248 countries, supporting businesses across all industries in location planning, branch networks, and distribution strategies. Recognized as a key data source, MBI is the preferred provider for leading GIS software and technology companies worldwide as well as one of the first global distributors for HERE Technologies. MBI is part of the Michael Bauer Group, alongside Michael Bauer Research GmbH and Michael Bauer Micromarketing GmbH."},"popularity":647},{"id":"76243c99-1bd1-4f47-924e-3889e1c337e2","summary":{"name":"Salon sample","subtitle":"This is a sample of 50 salons and other personal care providers from Enigma’s data on small businesses.","share":{"name":"salon-samples","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1728670921301,"updated_at":1728670921301,"categories":["ADVERTISING_AND_MARKETING","FINANCIAL","COMMERCE"],"listingType":"STANDARD","provider_id":"d40f7797-9097-408c-aebc-6d8dccd3c0f3"},"detail":{"description":"**Overview**\nThis is a sample of 50 salons and other personal care providers from Enigma’s data on the identity and financial health of small businesses. You can see data on the corresponding locations for each of these restaurants in the location level sample.\n\nBy engineering better data from hundreds of public and third-party sources, Enigma tells the complete story of every business, so that companies of every size can access the financial services they need to grow and thrive.\n\n**Use cases**\n\n\uD83C\uDFAF **Sales and marketing**\nEnigma’s Sales & Marketing data platform powers better targeting, segmentation, and engagement. Prospect for the long tail of businesses, no matter how small or unique.\n\n⛳ **Risk and underwriting**\nGain early visibility into business performance. Accurate intelligence about the identity and financial health of businesses helps you dynamically manage risk.\n\n\uD83D\uDCCB **Onboarding and KYB**\nStreamline KYB with more instant approvals. Cut operational overhead, onboard more customers, and reduce a lengthy KYB process with Enigma.\n\n\uD83D\uDD75️ **Customer and transaction screening**\nEnigma offers industry leading performance in sanctions and watchlist screening, helping institutions achieve compliance with greater confidence, speed, and accuracy than ever before.\n\n**Data dictionary**\n- **business_id** - Enigma’s unique identifier for this business.\nbusiness_name - The name of the business. A single name in this simplified sample, but can include all known names or aliases of the business.\n- **operating_status** - Enigma’s assessment of whether the business is active in the real world as indicated by transactions with customers. Confidence can be included.\n- **last_12m_monthly_card_revenue** - The business' average monthly card revenue in the 12 months leading up to the date this sample was published.\n- **annual_card_revenue_growth_rate** - The business' change in revenue in the past 12 months from the 12 months prior, expressed as a percentage.\n- **last_12m_daily_customers** - The average number of customers that transacted with this business each day in the past 12 months.\n- **last_12m_monthly_card_transactions** - The average monthly transaction counts (swipes) a business receives from credit and debit card transactions.\n- **has_online_payments** - Whether a business ever has an online payment service.\t\n**technologies_used** - The payment technologies used by the business.\n- **primary_phone_number** - The phone number(s) associated with the business. Separately, Enigma also has phone numbers for specific contacts at the business.\n- **primary_website** - The primary website associated with the business. A single website in this simplified sample, but can include all known websites of the business.\n- **primary_address_street1** - The street name (and number) of the mailing address associated with the main business.\n- **primary_address_street2** - The suite name, floor name, or other granular information of the mailing address associated with the main business.\n- **primary_address_city** - The city of the mailing address associated with the main business.\n- **primary_address_state** - The US state of the mailing address associated with the main business.\n- **primary_address_postal_code** - The US zip code of the mailing address associated with the main business.\n- **primary_industry_NAICS_code** - The North American Industry Classification System (NAICS) code of the business industry. \n- **industry_NAICS_description** - The NAICS category name. Enigma also offers its own brief descriptions of the industry the business operates in.\n\nSee our [documentation](https://developers.enigma.com/docs/welcome) for more information on Enigma’s data.","terms_of_service":"https://www.enigma.com/company/terms-of-service","documentation_link":"https://developers.enigma.com/docs/welcome","privacy_policy_link":"https://www.enigma.com/company/privacy-policy","geographical_coverage":"[\"United States and Canada\"]","cost":"FREE","update_frequency":{"interval":1,"unit":"MONTHLY"},"collection_granularity":{"interval":1,"unit":"MONTHLY"},"data_source":"Hundreds of public and third-party sources","assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"Enigma","description":"Enigma provides comprehensive intelligence about the identity and financial health of small and medium businesses across the United States. By engineering better data from hundreds of public and third-party sources, Enigma tells the complete story of every business, so that companies of every size can access the financial services they need to grow and thrive."},"popularity":1034},{"id":"c2073c67-6204-4d49-b63f-e25c86656211","summary":{"name":"Pediatric Appointment Call Dataset: 500 Voices Simulating Real-World Care Coordination","subtitle":"500 voices, 10 pediatric scheduling calls each—great for building child-focused healthcare AI & voice-based support tool","setting":{"visibility":"PUBLIC"},"created_at":1756314509486,"updated_at":1756314509486,"categories":["HEALTH","SCIENCE_AND_RESEARCH"],"listingType":"PERSONALIZED","provider_id":"3d28060f-1726-48a7-b699-f4a84328832e"},"detail":{"description":"**Overview**\nThis dataset includes 5,000 high-quality, authentically synthesized pediatric appointment call recordings voiced by 500 real, consented English-speaking participants. Each speaker voices 10 unique appointment-related call scripts designed around pediatric care scenarios. These recordings simulate realistic parent/caregiver interactions with clinics and hospitals to schedule, cancel, or follow up on appointments for children.\n\nAll voice files are paired with a single metadata CSV that includes speaker demographic details and call-specific pediatric information such as the child’s age, reason for visit, provider type, and urgency level. This makes the dataset suitable for training pediatric-specific voice applications in healthcare settings.\n\n**Product details**\n\n- Datasets represented include pediatric_appointment_calls.csv and audio_files/ (10 recordings per speaker)\n\n- All metadata is stored in a single CSV file combining speaker and call attributes\n\nSample Fields Include:\n\nSpeaker Metadata\n\n- speaker_id, gender, age_range, region, native_language, voice_conditions\n\nCall Metadata (Pediatric Context)\n\n- text: Spoken call script\n- patient_gender (child’s gender)\n- patient_age (child’s age)\n- appointment_type (e.g., new visit, follow-up, specialist referral)\n- reason_for_visit (e.g., vaccination, cough & fever, behavioral check)\n- provider_type (e.g., pediatrician, ENT, urgent care)\n- priority_level (e.g., urgent, routine)\n- insurance_status (e.g., Medicaid, private, uninsured)\n- action_needed (e.g., schedule now, cancel, leave voicemail)\n\n**Additional Insights**\nThis dataset is ethically sourced and fully consented - intenional recordings from diverse speakers, and synthisization consent, ensures compliance with responsible and growth AI practices. It is invaluable for responsible AI teams, corporate AI model training, and content moderation system refinement.\n\nFor more details, please feel free to reach out to us at sales.databricks@destined.ai","terms_of_service":"https://destined.ai/terms/","documentation_link":"","privacy_policy_link":"https://destined.ai/privacy-policy-2024/","geographical_coverage":"[\"United States and Canada\"]","cost":"PAID","data_source":"destined.ai","size":10.0,"assets":["ASSET_TYPE_DATA_TABLE","ASSET_TYPE_MEDIA"],"license":""},"provider_summary":{"name":"Destined","description":"Destined helps companies mitigate unwanted bias in AI by providing fully consented and culturally relevant data. From thousands of authentic voices to unique faces across many skin tones, we've helped Fortune 500 companies deploy AI responsibly.\n\nOur growing community of data contributors globally represents everyone from people of color to rural white southerners. Our Culture Pulse™ technology taps into this diverse network, ensuring your AI systems mitigate unwanted bias and are also enriched with a wide spectrum of perspectives and experiences. \n\nWith Destined, you’re not just getting data; you’re gaining insight into the heart of humanity.\n"},"popularity":177},{"id":"2b05ea0b-d954-4681-8646-478eac290eb5","summary":{"name":"Surgical Outcomes","subtitle":"This data package shows the Incidence & Surgical Procedures for Diabetes and PAD HRR Level, Readmission of Diabetic Pa..","share":{"name":"surgical_outcomes","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1682356069912,"updated_at":1698965635253,"categories":["HEALTH","SCIENCE_AND_RESEARCH"],"listingType":"STANDARD","provider_id":"f88849f8-9d77-4de2-b4c7-03d55cc3768e"},"detail":{"description":"\n**Overview**\nThis data package shows the Incidence & Surgical Procedures for Diabetes and PAD HRR Level, Readmission of Diabetic Patients Post Bariatric Surgery HRR Level and Revascularization HRR Level for the year 2014.\n\n**Description**\nThis report from the Dartmouth Atlas series on variation in the care of surgical conditions carefully details the scope of the problem of diabetes and peripheral arterial disease and, as in previous Atlas analyses, emphasizes geographic practice variation in both preventive services and in surgical treatment rates. This Dartmouth Atlas of Health Care series reports on unwarranted regional variation in the care of several conditions for which surgery is one important treatment option.\n\n**Benefits**\n- Useful to compare the surgical interventions for diabetes complications. useful to analyze the impact of factors like age, gender and race/ethnicity on the utilization of surgical healthcare services for diabetes complications. useful for preventive and curative healthcare resources management.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [Incidence and Surgical Procedures for Diabetes and PAD HRR Level 2014](https://www.johnsnowlabs.com/marketplace/incidence-and-surgical-procedures-for-diabetes-and-pad-hrr-level-2014)\n\t- This report from the Dartmouth Atlas series on variation in the care of surgical conditions carefully details the scope of the problem of diabetes and peripheral arterial disease and, as in previous Atlas analyses, emphasizes geographic practice variation in both preventive services and in surgical treatment rates.\n- [Readmission of Diabetic Patients Post Bariatric Surgery HRR Level 2014](https://www.johnsnowlabs.com/marketplace/readmission-of-diabetic-patients-post-bariatric-surgery-hrr-level-2014)\n\t- This report from The Dartmouth Atlas series in support by The Michigan Bariatric Surgery Collaborative (MBSC) carefully details the scope of the ever-increasing problem of obesity and, as in previous Atlas analyses, emphasizes geographic practice variation in surgical treatment rates. However, the report also takes a more longitudinal view.\n- [Readmission of Diabetic Patients Post Revascularization HRR Level 2014](https://www.johnsnowlabs.com/marketplace/readmission-of-diabetic-patients-post-revascularization-hrr-level-2014)\n\t- This report from the Dartmouth Atlas series on variation in the care of surgical conditions carefully details the scope of the problem of diabetes and peripheral arterial disease and, as in previous Atlas analyses, emphasizes geographic practice variation in both preventive services and in surgical treatment rates.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - 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Your agent (LLM, Agent Bricks supervisor, or custom orchestrator) parses user intent and issues structured tool calls to Kythera, which returns precise, confidence-scored code sets.\n\nFor example, when a user asks \"Find patients with IPF treated with nintedanib, using ICD-10-CM for conditions and NDC for drugs,\" the supervisor agent calls:\n\n- `kythera_map_condition(\"idiopathic pulmonary fibrosis\", codeset=\"ICD10CM\")` → ICD-10-CM code J84.112 with confidence 100\n- `kythera_map_drug(\"nintedanib\", target_code=\"ndc\")` → NDC codes for all nintedanib formulations with confidence 95\n\nThe agent assembles those results and uses them directly in downstream queries against claims, EHR, or pharmacy data.\n\n**Use cases**\n\n- **Real-world data retrieval:** Map clinical intent to query-ready code sets for claims, EHR, pharmacy, lab, and registry databases — without manual coding.\n- **Cohort building:** Translate plain-language inclusion and exclusion criteria into structured vocabulary logic for patient identification pipelines.\n- **Clinical trial feasibility:** Convert protocol eligibility criteria into code sets across ICD-10, SNOMED, RxNorm, NDC, and LOINC for site and population assessment.\n- **Real-world evidence (RWE) studies:** Build multi-vocabulary concept sets for phenotype definitions without manual curation.\n- **Drug and formulary intelligence:** Resolve medication names to NDC or RxNorm codes, or retrieve all drugs indicated for a given condition by ICD-10, SNOMED, or MedDRA code.\n- **Clinical AI pipelines:** Provide any LLM-based agent with structured medical coding for extracting and classifying clinical data from unstructured text or user input.\n- **Provider network analytics:** Standardize specialty descriptions across provider databases using Kythera's provider taxonomy.\n\n**Product details**\n\n15 MCP tools organized in two categories.\n\n**Semantic Mapping Tools** — call these first for any free-text clinical term:\n\n- **kythera_map_condition** — condition/diagnosis text → ICD-10CM, SNOMED, MeSH, HPO, HemOnc, KCD7, and 20+ others\n- **kythera_map_procedure** — procedure text → CPT4, ICD-10PCS, SNOMED, LOINC, HCPCS, OPCS4, and 25+ others\n- **kythera_map_measurement** — lab/vital name → LOINC, SNOMED, OMOP, CDISC, NCIt, HPO, and 35+ others\n- **kythera_map_measurement_unit** — unit description → UCUM codes\n- **kythera_map_drug** — drug name or RxCUI → NDC or RxNorm; supports term→NDC, term→RxNorm, RxNorm→NDC\n- **kythera_map_provider_specialty** — provider specialty text → Kythera standardized provider taxonomy\n- **kythera_map_drugs_for_condition** — condition code(s) → indicated drug names (accepts MeSH ID)\n- **kythera_get_drugs_in_class** — lists all drugs belonging to a pharmacologic class by type (EPC, MOA, ATC, VA, and more); supports multi-type aggregation for comprehensive results\n- **kythera_get_drug_classes** — reverse lookup: given a drug name or RxCUI, returns all classes it belongs to (EPC, MOA, ATC, indications, and more), grouped by class type\n- **kythera_search_drug_classes** — finds drug class IDs and names by keyword; use before kythera_get_drugs_in_class when the exact class name is unknown\n\n**Vocabulary Lookup Tools** — for exploration, verification, and code-first workflows; also the fallback when a mapping tool does not support your target vocabulary:\n\n- **kythera_search_concepts** — semantic search across all OMOP standardized vocabularies\n- **kythera_get_concept** — full details for a known OMOP concept_id\n- **kythera_get_concept_by_code** — look up a concept by vocabulary-specific code\n- **kythera_get_concept_hierarchy** — traverse ancestors and descendants in the OMOP hierarchy\n- **kythera_list_vocabularies** — discover all available code systems in OMOP\n\nVocabulary coverage (100+): ICD-10-CM, ICD-10-PCS, ICD-9-CM, SNOMED CT, RxNorm, NDC, LOINC, CPT4, HCPCS, UCUM, ATC, MeSH, HPO, HemOnc, OncoTree, OMOP, OMOP Extension, CDISC, NCIt, ClinVar, NAACCR, OPCS4, ICD-10-CN, ICD-10-GM, KCD7, CIM10, CCAM, and more.\n\n\n**Getting access**\n\nKythera's Clinical Semantic Bridge uses OAuth 2.0 client credentials. To connect:\n\n1. Contact Kythera Labs to request credentials and describe your intended use case.\n2. Kythera provisions a Service Principal and shares a client ID and client secret with you.\n3. At Marketplace install time, enter the client ID and client secret when prompted.\n4. The installed server is available as a Unity Catalog HTTP connection — add it to any Agent Bricks agent or MCP-compatible framework. Token refresh is handled automatically.","terms_of_service":"https://www.kytheralabs.com/eula","documentation_link":"","privacy_policy_link":"https://www.kytheralabs.com/privacy","assets":["ASSET_TYPE_MCP"],"license":""},"provider_summary":{"name":"Kythera Labs Inc","description":"Kythera Labs is a healthcare data technology company that helps healthcare and life sciences organizations reduce uncertainty and make decisions with greater confidence. 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Using advanced data science and healthcare-specific methodologies, Kythera delivers trusted data products that help organizations uncover patient journeys, identify market opportunities, and generate actionable insights faster.\n\nKythera enables organizations to spend less time preparing data and more time finding answers."},"popularity":1828},{"id":"d3fe26e2-0217-478d-a000-8ecb70b60277","summary":{"name":"Smart Grid Monitoring ","subtitle":"Real-time smart-meter monitoring POC using Databricks Lakehouse to detect anomalies, predict outages, and enable alerts.","setting":{"visibility":"PUBLIC"},"created_at":1790334076285,"updated_at":1790336348263,"categories":["ENERGY"],"listingType":"PERSONALIZED","provider_id":"69e292c4-7435-4dd8-8306-1f3a5214dc7b"},"detail":{"description":"**Overview**\nSmart Grid Monitoring & Outage Prediction is a real-time monitoring POC designed to ingest smart-meter telemetry, detect abnormal operating conditions, calculate outage risk, and provide operational visibility through a Databricks dashboard and alerts. The POC uses a historical Household Electric Power Consumption dataset as the source, generates meter events into a landing location, and processes them through a Databricks Lakehouse flow using Auto Loader, Bronze, Silver, and Gold layers. The solution demonstrates how utilities can move from delayed batch-based issue identification toward near real-time anomaly monitoring and risk-based operational awareness.\n\n**Use cases**\n• Smart Meter Telemetry Ingestion – Generate and ingest meter readings as individual JSON events and process incoming data using Auto Loader. \n• Data Cleansing and Standardization – Transform raw meter events into structured Silver data with event timestamps, appropriate data types, and required filtering. \n• Anomaly Detection – Identify voltage, power, and current abnormalities using defined operational thresholds and flag abnormal meter conditions. \n• Outage Risk Scoring – Combine detected anomalies into an outage risk score and classify the resulting risk level for operational monitoring. \n• Monitoring, Dashboard and Alerts – Provide a consolidated Databricks dashboard and SQL alerting capability to surface anomalies and high-risk conditions. \n\n**Product details**\nThe Smart Grid Monitoring POC follows the lifecycle: Ingest → Transform → Detect → Score → Monitor → Alert. The solution uses the Household Electric Power Consumption dataset as the source, a Python event generator to simulate incoming meter telemetry, a Databricks landing volume for incoming JSON events, Auto Loader for incremental ingestion, Bronze for raw events, Silver for cleaned telemetry, and Gold tables for anomaly and outage-risk analytics. Databricks SQL dashboards provide visualization, while SQL alerts provide notification for configured abnormal conditions. \n\n**Additional Insights**\n• The event generator simulates continuous smart-meter telemetry from historical data for the POC. \n• Auto Loader enables incremental ingestion of newly arriving JSON meter events into the Bronze layer. \n• Silver processing creates a structured telemetry layer suitable for downstream anomaly analysis. \n• Voltage is treated as anomalous when it is below 220 V, above 250 V, or NULL. \n• Power is treated as anomalous when Global_active_power exceeds 5 kW, and current is anomalous when Global_intensity exceeds 20 A. \n• Outage risk scoring assigns 40 points for a voltage anomaly, 30 for a power anomaly, and 30 for a current anomaly. \n• The dashboard provides operational visibility into anomaly conditions, event trends, and outage-risk indicators. \n• The POC demonstrates a reusable Databricks Lakehouse pattern that can be extended to additional telemetry sources and production monitoring scenarios. Add any additional insights on the listing here. This can include relevant links pertaining to research related to the listing dataset, etc.","terms_of_service":"https://docs.google.com/document/d/1BNvbHG2TDQbJfJ5lJ0D9yfNh1vKQWLOd/edit?pli=1","documentation_link":"","privacy_policy_link":"https://docs.google.com/document/d/1BNvbHG2TDQbJfJ5lJ0D9yfNh1vKQWLOd/edit?pli=1","embedded_notebook_file_infos":[{"id":"ef131646-8285-49a9-a915-de46f8af8193","marketplace_file_type":"EMBEDDED_NOTEBOOK","file_parent":{"parent_id":"d3fe26e2-0217-478d-a000-8ecb70b60277","file_parent_type":"LISTING"},"mime_type":"text/html","download_link":"https://marketplace-file-storage-prod.s3-fips.us-east-1.amazonaws.com/EMBEDDED_NOTEBOOK/LISTING_d3fe26e2-0217-478d-a000-8ecb70b60277/ef131646-8285-49a9-a915-de46f8af8193?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEGgaDnVzLWVhc3QtMS1maXBzIkYwRAIgbC8sWIoYaHR8y7Wq86nPb0pufBl4%2BM4PWpeN%2B5PW9VQCIG7BhAVBDCWTpxFDLeuKYm%2Ff1JLz4iccPMzter0YdhgWKqYECDAQABoMNDE0MzUxNzY3ODI2IgxNAJrHbXaZG20jNfMqgwQyGEb4TsoVOoNvJ%2BaA%2FZ7zeu8hGcHqCeNfblP%2FQo5g6q2aRNtpwtPRKMlhYcIE%2FUQaNT1Lqmyptu32VUQTpzjTTxRVmJrDPkheSCkV06XQ%2FpTMKjU8CC7X1b%2FqYf8CrBwZT7Cv4C5WPjEET38f2A79h0xV6bZgPy28zQWrSeIJ9w6AfZBxiE24%2FVc08zVA0ivCT6lsy7nuU0wcR%2FMg2C9nd1vaMnQ923VpD21CKfVVYcrNWL2b%2FQ%2BNAO%2BxPjPgjiN6UPQbvbvZxkSdhgrGtecIg9NgqGkMcvM5VLTDtdC1kdeZetfOC2obtA%2BT%2F0gy3tIDo6WM5hcQfw743%2FAU9oaGnRBl%2BwoxRRDCZA%2F4jnPdluICSI7NCr6C3HHOe4CSZJjRihczxPA%2FgQczAVjAPs6CrK9N9pIzt0vNJpGUTgg4AoxftTvGfciDREilSctPIcBk6GwB%2Bzt4wbtNCmEPU%2BFD46rc6CWVQmmLwbKOeHw70ipMVFlOc8NlXkEtGO2vu6H44MstSSc6daOVUYUavQSGZ6jGhEjsP2YGz8KDm9XQotB%2FtyVBqiZV01OKqDmmBJA%2FXjUpSJ8M%2FeJXhe5m%2BKcxzNObtoJRI7jci3Tpt08XZyXZ7%2FyIV8H30ya468X3cfkTXrm972uga3z7iXAdubyEHnQIua%2FuvtwOHILi6O5D1Me2MzCPvcnXBjqTAYDoMWBt%2B3AVFuqSs2dmDQNBnvEhXfMgTedKLD5PJHb3aIl15iBkLaNu36GYnVCp9ZU2lSYcTbY5QwNnNeTfJTNy9bKhwzru6Sg%2BslhN%2BTS0zkTn%2BeLMY4tHgPEtEZ2cp%2FCk6GwqswvnYVKHjwO8q%2B1Mx8lc08fVEfarE9dke1GGFqoddx%2Bq%2BZUyHbDlAVfrCmf0yw%3D%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20261110T000135Z&X-Amz-SignedHeaders=host&X-Amz-Expires=86400&X-Amz-Credential=ASIAWA6KKHEJORWBH4N3%2F20261110%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=2e7f6c02f346a9dc1d078adaa2eb8f8a36597159f2e4db9ce72db27709719e9b","created_at":1790336139132,"updated_at":1790336139132,"display_name":"Smart Grid Monitoring","status":"FILE_STATUS_PUBLISHED"}],"geographical_coverage":"[\"Global\"]","assets":["ASSET_TYPE_NOTEBOOK"],"license":""},"provider_summary":{"name":"DataPattern","description":"DataPattern is a consulting and services leader specializing in Data Programs, Generative AI solutions and innovative data products. We collaborate with businesses and startups to unlock innovation, streamline operations, and deliver actionable insights through advanced technologies like IoT, AI/ML and Analytics. In addition to consulting, we design and deliver cutting-edge GenAI and data products, empowering organizations to harness the full potential of their data. Our expertise in data-driven strategies and application development ensures tailored solutions that address unique challenges, driving measurable outcomes. With a focus on excellence and client success, DataPattern enables organizations to thrive in the rapidly evolving digital landscape."},"popularity":1520},{"id":"23cf9bf8-fc8a-4110-af3b-072e59abed7d","summary":{"name":"Telco Customer Dataset","subtitle":"This dataset provides data to support a model that predicts customer churn.","share":{"name":"dbacademy_dataset_telco","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1731645003961,"updated_at":1731691828915,"categories":["EDUCATION"],"listingType":"STANDARD","provider_id":"ea1e69ff-0127-4c94-bf39-e841fe1d19d2"},"detail":{"description":"# Telco Customer Dataset License Information\n\n## Attribution\n\nThis course uses the IBM customer Churn Dataset, which is licensed under Apache 2.0, Copyright IBM.\n\n## License Information\n\nYou can find more details about the \"Churn Dataset\" in the [GitHub repository](https://github.com/IBM/telco-customer-churn-on-icp4d).\n\nThe \"Churn Dataset\" is distributed under the [Apache 2.0 License](https://github.com/IBM/telco-customer-churn-on-icp4d/blob/master/LICENSE). Please review the license terms before using the dataset.\n\nFor any questions or inquiries regarding the dataset or its usage, please refer to the GitHub repository for contact information.\n\n---\nNote: This README is provided as part of the dataset attribution for educational purposes. Please ensure compliance with the dataset's licensing terms and conditions.\n","terms_of_service":"https://cms.databricks.com/sites/default/files/2023-01/marketplace-consumer-terms_0.pdf","documentation_link":"https://github.com/IBM/telco-customer-churn-on-icp4d","privacy_policy_link":"https://www.databricks.com/legal/privacynotice","data_source":"https://github.com/IBM/telco-customer-churn-on-icp4d","assets":["ASSET_TYPE_MEDIA"],"license":"https://github.com/IBM/telco-customer-churn-on-icp4d/blob/master/LICENSE"},"provider_summary":{"name":"Databricks","description":"As the world’s first lakehouse platform in the cloud, Databricks combines the best elements of data lakes and data warehouses to deliver the reliability, strong governance, and performance of data warehouses with the openness, flexibility, and machine learning support of data lakes. We are a cross-cloud platform that works across AWS, Azure, and Google.\nThis unified approach simplifies your modern data stack by eliminating the data silos that traditionally separate and complicate data engineering, analytics, BI, data science and machine learning. It’s built on open source and open standards to maximize flexibility. And, its common approach to data management, security and governance helps companies operate more efficiently and innovate faster."},"popularity":2010},{"id":"ebf8ebca-c7d0-42a3-a10a-ea468fe7a1d8","summary":{"name":"Draup Buyers Data","subtitle":"Executive profiles with skills, org roles, budget authority, contact data, and career paths","setting":{"visibility":"PUBLIC"},"created_at":1756522419308,"updated_at":1757052621451,"categories":["COMMERCE","ECONOMICS","EDUCATION","ADVERTISING_AND_MARKETING","OTHER","SCIENCE_AND_RESEARCH"],"listingType":"PERSONALIZED","provider_id":"9900a255-6aa9-496f-9b6a-421a3568af3b"},"detail":{"description":"**Overview**\nDraup’s Buyers dataset delivers a multi-dimensional view of key executives and decision-makers across global organizations. It empowers sales, marketing, strategy, and partnership teams with detailed professional profiles, enabling precise identification of stakeholders, influencers, and budget owners within target accounts. Each profile includes role, seniority, organizational context, and functional responsibilities, providing the context needed to engage the right people at the right time.\nBeyond contact details, the dataset goes deeper into professional and personal attributes such as education, certifications, projects, awards, investments, and patents. By linking verified skills, languages, and career trajectories with budget authority and organizational influence, Draup Buyers Data reveals the decision-making networks and potential career movements that shape enterprise buying processes. This intelligence supports personalized outreach, accelerates account penetration, and enables stronger, data-driven engagement strategies.\n\n**Methodology & Quality**\nDraup aggregates and standardizes professional data from diverse proprietary and public sources, ensuring consistency across companies, functions, and roles. 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The national forecast, which is updated quarterly, is publicly available via Databricks Marketplace, with projections by service line and demographic segments. Premium licenses offer more granular geographies, with forecasts by state, core-based statistical area and zip code. \n\n**Use cases**\n* Develop strategic plans to drive return on invested capital; \n* Create financial projections based on evolving market demand for healthcare technologies, care modalities, pharmaceuticals and medical devices; \n* Inform provider needs analysis with a detailed understanding of demand at the service-line level. \n\n**Product details**\nTrilliant Health's demand forecast consists of the following tables, each corresponding to the described service line. \n\n- demand_forecast_behavioral_health_us\n- demand_forecast_emergency_us\n- demand_forecast_medical_us\n- demand_forecast_oncology_us\n- demand_forecast_pcp_us\n- demand_forecast_physical_therapy_us\n- demand_forecast_radiology_us\n- demand_forecast_surgical_us\n- demand_forecast_urgent_care_us\n \nEach table has the following fields:\n\n- Segment Parameters\n - geoid\n - name\n - inpatient\n - age_group_start\n - gender\n - year\n - severity - _Emergency Department forecast only_\n - icd_chapter - _Medical, Urgent Care and Primary Care forecasts only_\n - icd_section - _Medical, Urgent Care and Primary Care forecasts only_\n - body_region - _Physical Therapy forecast only_\n - body_sub_region - _Physical Therapy forecast only_\n - modality - _Radiology forecast only_\n - service_line - _Surgical forecast only_\n - procedure_sub_type - _Surgical forecast only_\n- Segment Forecast\n - population\n - count_mean\n - count_percentile_10_0\n - count_percentile_25_0\n - count_percentile_50_0\n - count_percentile_75_0\n - count_percentile_90_0\n - ir_mean\n - ir_observed\n - ir_percentile_10_0\n - ir_percentile_25_0\n - ir_percentile_50_0\n - ir_percentile_75_0\n - ir_percentile_90_0\n\n**Additional Insights**\nTo learn more about Trilliant Health's Demand Forecast offering, please explore the starter notebook provided, or [the explainer on our Tech Blog](https://medium.com/trilliant-health/how-to-build-a-demand-forecast-fd7c7f1ce780).","terms_of_service":"https://www.trillianthealth.com/terms-of-service-provider-directory","documentation_link":"","privacy_policy_link":"https://www.trillianthealth.com/privacy-policy","embedded_notebook_file_infos":[{"id":"ee8c6dfa-f21d-4a90-9ef8-bec5dfa0509c","marketplace_file_type":"EMBEDDED_NOTEBOOK","file_parent":{"parent_id":"187d7973-1883-4c43-b6c6-056df56d8f97","file_parent_type":"LISTING"},"mime_type":"text/html","download_link":"https://marketplace-file-storage-prod.s3-fips.us-east-1.amazonaws.com/EMBEDDED_NOTEBOOK/LISTING_187d7973-1883-4c43-b6c6-056df56d8f97/ee8c6dfa-f21d-4a90-9ef8-bec5dfa0509c?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEGgaDnVzLWVhc3QtMS1maXBzIkYwRAIgbC8sWIoYaHR8y7Wq86nPb0pufBl4%2BM4PWpeN%2B5PW9VQCIG7BhAVBDCWTpxFDLeuKYm%2Ff1JLz4iccPMzter0YdhgWKqYECDAQABoMNDE0MzUxNzY3ODI2IgxNAJrHbXaZG20jNfMqgwQyGEb4TsoVOoNvJ%2BaA%2FZ7zeu8hGcHqCeNfblP%2FQo5g6q2aRNtpwtPRKMlhYcIE%2FUQaNT1Lqmyptu32VUQTpzjTTxRVmJrDPkheSCkV06XQ%2FpTMKjU8CC7X1b%2FqYf8CrBwZT7Cv4C5WPjEET38f2A79h0xV6bZgPy28zQWrSeIJ9w6AfZBxiE24%2FVc08zVA0ivCT6lsy7nuU0wcR%2FMg2C9nd1vaMnQ923VpD21CKfVVYcrNWL2b%2FQ%2BNAO%2BxPjPgjiN6UPQbvbvZxkSdhgrGtecIg9NgqGkMcvM5VLTDtdC1kdeZetfOC2obtA%2BT%2F0gy3tIDo6WM5hcQfw743%2FAU9oaGnRBl%2BwoxRRDCZA%2F4jnPdluICSI7NCr6C3HHOe4CSZJjRihczxPA%2FgQczAVjAPs6CrK9N9pIzt0vNJpGUTgg4AoxftTvGfciDREilSctPIcBk6GwB%2Bzt4wbtNCmEPU%2BFD46rc6CWVQmmLwbKOeHw70ipMVFlOc8NlXkEtGO2vu6H44MstSSc6daOVUYUavQSGZ6jGhEjsP2YGz8KDm9XQotB%2FtyVBqiZV01OKqDmmBJA%2FXjUpSJ8M%2FeJXhe5m%2BKcxzNObtoJRI7jci3Tpt08XZyXZ7%2FyIV8H30ya468X3cfkTXrm972uga3z7iXAdubyEHnQIua%2FuvtwOHILi6O5D1Me2MzCPvcnXBjqTAYDoMWBt%2B3AVFuqSs2dmDQNBnvEhXfMgTedKLD5PJHb3aIl15iBkLaNu36GYnVCp9ZU2lSYcTbY5QwNnNeTfJTNy9bKhwzru6Sg%2BslhN%2BTS0zkTn%2BeLMY4tHgPEtEZ2cp%2FCk6GwqswvnYVKHjwO8q%2B1Mx8lc08fVEfarE9dke1GGFqoddx%2Bq%2BZUyHbDlAVfrCmf0yw%3D%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20261110T000135Z&X-Amz-SignedHeaders=host&X-Amz-Expires=86400&X-Amz-Credential=ASIAWA6KKHEJORWBH4N3%2F20261110%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=91535d3c7adfe8be91249f2e2898745084b262b601cf14594c851d80792751e0","created_at":1712863257091,"updated_at":1712863257091,"display_name":"Demand Forecast Starter Notebook","status":"FILE_STATUS_PUBLISHED"}],"geographical_coverage":"[\"United States and Canada\"]","cost":"FREE","data_source":"Trilliant Health's national all-payer proprietary and public datasets","assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"Trilliant Health","description":"Trilliant Health combines healthcare industry expertise, market research and predictive analytics to form Evidence-Based Strategy for Healthcare™. 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It is a useful tool for activities such as looking for new safety concerns that might be related to a marketed product, evaluating a manufacturer's compliance with reporting regulations and responding to outside requests for information. The reports in FAERS are evaluated by clinical reviewers, in the Center for Drug Evaluation and Research (CDER) and the Center for Biologics Evaluation and Research (CBER), to monitor the safety of products after they are approved by FDA. These reports are submitted to FDA by healthcare professionals (such as physicians, pharmacists, nurses and others) and consumers (such as patients, family members, lawyers and others). Healthcare professionals and consumers may also report to the products’ manufacturers who then send these reports to the FDA. The datasets in FAERS data package contain much relevant information such as patient demographics, drug name, dose and route of administration, diagnosis and indication for use, therapy starts and ends date and patient outcome.\n\n**Benefits**\n- useful for physicians, pharmacists, pharmaceutical companies, medical students and patients (general public).\n- faers database is available on fda’s website in raw form, whereas a john snow labs (jsl) customer gets it in a clean and normalized form ready to use and create his own custom reports.\n- quarter-wise data merged into one file.\n- easy to comprehend for the customers who are unfamiliar with the medical terminology as all abbreviated terms are replaced with full form, unlike faers original datasets which are full of abbreviations.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [FDA Adverse Events Reporting System Demographics 2022](https://www.johnsnowlabs.com/marketplace/fda-adverse-events-reporting-system-demographics-2022)\n\t- The FDA Adverse Event Reporting System (FAERS) contains information on medication errors, quality complaints and drug-related adverse events that were submitted to the FDA (U.S. Food and Drug Administration) in 2022. The Demographics dataset contains patient demographic and administrative information, a single record for each event report.\n- [FDA Adverse Events Reporting System Drug 2022](https://www.johnsnowlabs.com/marketplace/fda-adverse-events-reporting-system-drug-2022)\n\t- The FDA Adverse Event Reporting System (FAERS) contains information on medication errors, quality complaints and drug-related adverse events that were submitted to FDA (U.S. Food and Drug Administration) in 2022. The Drug dataset contains drug/biologic information for as many medications as were reported for the event (1 or more per event).\n- [FDA Adverse Events Reporting System Drug Indication 2022](https://www.johnsnowlabs.com/marketplace/fda-adverse-events-reporting-system-drug-indication-2022)\n\t- The FDA Adverse Event Reporting System (FAERS) contains information on medication errors, quality complaints and drug-related adverse events that were submitted to the FDA (U.S. Food and Drug Administration) in 2022. The Indication dataset contains all \"Medical Dictionary for Regulatory Activities\" (MedDRA) terms coded for the indications for use (diagnoses) for the reported drugs (0 or more per drug per event).\n- [FDA Adverse Events Reporting System Drug Reaction 2022](https://www.johnsnowlabs.com/marketplace/fda-adverse-events-reporting-system-drug-reaction-2022)\n\t- The FDA Adverse Event Reporting System (FAERS) contains information on medication errors, quality complaints and drug-related adverse events that were submitted to FDA (U.S. Food and Drug Administration) in 2022. The drug reaction dataset contains all \"Medical Dictionary for Regulatory Activities\" (MedDRA) terms coded for the adverse event (1 or more).\n- [FDA Adverse Events Reporting System Drug Therapy Dates 2022](https://www.johnsnowlabs.com/marketplace/fda-adverse-events-reporting-system-drug-therapy-dates-2022)\n\t- The FDA Adverse Event Reporting System (FAERS) contains information on medication errors, quality complaints and drug-related adverse events that were submitted to FDA (U.S. Food and Drug Administration) in 2022. The drug therapy dates dataset contains drug therapy start dates and end dates for the reported drugs.\n- [FDA Adverse Events Reporting System Patient Outcome 2022](https://www.johnsnowlabs.com/marketplace/fda-adverse-events-reporting-system-patient-outcome-2022)\n\t- The FDA Adverse Event Reporting System (FAERS) contains information on medication errors, quality complaints and drug-related adverse events that were submitted to FDA (U.S. Food and Drug Administration) in 2022. The patient outcome file contains information on patient outcomes for the event (0 or more).\n- [FDA Adverse Events Reporting System Report Source 2022](https://www.johnsnowlabs.com/marketplace/fda-adverse-events-reporting-system-report-source-2022)\n\t- The FDA Adverse Event Reporting System (FAERS) contains information on medication errors, quality complaints and drug-related adverse events that were submitted to FDA (U.S. Food and Drug Administration) in 2022. 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Optimized for Spark/Databricks.","setting":{"visibility":"PUBLIC"},"created_at":1780637821819,"updated_at":1780637849894,"categories":["GAMING","RETAIL"],"listingType":"PERSONALIZED","provider_id":"5d3ddc17-a7e8-4794-a760-13587e1de2c6"},"detail":{"description":"# **All Supermarket Locations in Austria: Complete Geographic Dataset**\n---\n### **Premium GIS-Ready Retail Location Intelligence Data for Databricks Analytics**\n\nThis high-fidelity geospatial dataset provides a complete, exhaustive, and standardized overview of **9,934** verified grocery store and supermarket locations across Austria. Updated as of **May 27, 2026**, this comprehensive national dataset captures the entire Austrian grocery landscape - encompassing both major branded retail chains and independent local markets. It offers an analysis-ready foundation for full-scale retail site selection, competitive intelligence, logistics optimization, and spatial demographics mapping.\n\nFully compatible with **Apache Spark**, **Delta Lake**, and **Databricks Unity Catalog** workflows.\n\n---\n\n## **Complete National Coverage: Branded & Independent Grocers**\nUnlike top-tier only datasets, this exhaustive collection covers maximum geographic depth across all 9 Austrian federal states (including highly dense areas like Niederösterreich, Wien, and Steiermark). It includes thousands of independent local grocers alongside the top national and regional supermarket chains:\n\n- **Major National Supermarkets & Hypermarkets:** \n [BILLA (1,279 locations)](https://geolocet.com/products/austria-billa-grocery), \n [SPAR (1,235 locations)](https://geolocet.com/products/austria-spar-grocery), \n [EUROSPAR (238 locations)](https://geolocet.com/products/austria-eurospar-grocery), \n [INTERSPAR (75 locations)](https://geolocet.com/products/austria-interspar-grocery)\n\n- **Leading Discounters & Value Brands:** \n [HOFER (524 locations)](https://geolocet.com/products/austria-hofer-grocery), \n [PENNY (300 locations)](https://geolocet.com/products/austria-penny-grocery), \n [Lidl (250 locations)](https://geolocet.com/products/austria-lidl-grocery), \n [NORMA (23 locations)](https://geolocet.com/products/austria-norma-grocery)\n\n- **Regional, Proximity & Specialty Banners:** \n [BIPA (543 locations)](https://geolocet.com/products/austria-bipa-grocery), \n [ADEG (249 locations)](https://geolocet.com/products/austria-adeg-grocery), \n [MPREIS (249 locations)](https://geolocet.com/products/austria-mpreis-grocery), \n [Nah&Frisch (207 locations)](https://geolocet.com/products/austria-nah-and-frisch-grocery), \n [UNIMARKT (92 locations)](https://geolocet.com/products/austria-unimarkt-grocery), \n [Denns (27 locations)](https://geolocet.com/products/austria-denns-grocery)\n\n**Plus thousands of independent, regional, and unbranded grocers** fully mapped and standardized across all federal states (e.g., Niederösterreich: 2,004 sites; Wien: 1,759 sites; Oberösterreich: 1,545 sites).\n\n---\n\n## **Technical Specifications & Schema Definitions**\nOptimized for zero-friction schema enforcement when reading via `spark.read.format(\"csv\")`. The structured schema below ensures flawless rendering in Databricks notebooks and high accuracy for LLM semantic indexing.\n\n* **`GUID`** (`STRING`): Unique global identifier for deterministic primary key mapping.\n* **`Title`** (`STRING`): Official store name, brand banner, or independent title of the location.\n* **`Latitude`** (`DOUBLE`): Precise WGS84 latitude coordinate for point-in-polygon & spatial joins.\n* **`Longitude`** (`DOUBLE`): Precise WGS84 longitude coordinate for spatial indexing (H3 / S2).\n* **`Street_No` / `Street`** (`STRING`): Normalized building/house number and street name elements.\n* **`City` / `Postal_Code`** (`STRING`): Core postal addresses and primary municipal settlement names.\n* **`Gemeinde`** (`STRING`): Austrian municipal administrative boundary designation (Gemeinde).\n* **`Region`** (`STRING`): Macro-administrative boundary classification or federal state (e.g., Wien, Salzburg, Tirol).\n* **`Admin_level_1` / `_2`** (`STRING`): Additional local administrative area and district identifiers.\n* **`Population`** (`INTEGER`): Contextual demographic population metric attributed to the location.\n* **`Wheelchair`** (`STRING`): Accessibility flags indicating wheelchair mobility support.\n* **`Popularity_Score`** (`INTEGER`): Relative foot-traffic/rating metric graded on a standardized 1-10 scale.\n* **`Opening_Hours`** (`STRING`): Standard daily operational schedules and structured operating text.\n* **`Phone` / `Website`** (`STRING`): Direct communication data, branch-specific landing URLs, and domain vectors.\n\n---\n\n## **Databricks Workflow & Spatial Analytics Use Cases**\n\n- **Whitespace & Underserved Market Analysis:** Query the full 9,934 locations against population density tables to identify true retail deserts and expansion opportunities untouched by major national networks.\n- **H3 Geospatial Indexing:** Convert the precise WGS84 `Latitude` and `Longitude` fields into Uber H3 spatial hexagons (`h3_longlattocell`) to cross-reference with granular Austrian demographic or regional income datasets.\n- **Supply Chain & Last-Mile Logistics Optimization:** Combine store location points with regional administrative boundary layers to optimize delivery routes, distribution warehouse placements, and network expansion footprints across the Alpine terrain.\n\n---\n\n## **Data Evaluation & Custom Ingestion**\n\n### **Evaluate with a Free Sample Dataset**\nVerify the structure, column types, and coordinate precision before full implementation. A comprehensive schema sample is available for download in CSV/Parquet-friendly formats.\n\n*Want deeper market insights?* Learn more about the brand network in our comprehensive report: **[View the Austria Top Brands Grocery Report](https://geolocet.com/pages/austria-topbrands-grocery-report)**\n\n### **Enterprise Delivery & Tailored Formatting**\nWhile natively available as an analysis-ready CSV for DBFS ingestion, we accommodate enterprise-level data architecture needs. Alternative formats - including **GeoJSON, Esri Shapefiles, PostgreSQL dumps, Excel, or direct Delta sharing profiles** - can be generated. Reach out to our engineering team at **contact@geolocet.com** for integration support or custom European POI data requests.","terms_of_service":"https://geolocet.com/pages/terms-of-use","documentation_link":"","privacy_policy_link":"https://geolocet.com/pages/privacy-policy","assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://geolocet.com/pages/terms-of-use"},"provider_summary":{"name":"Geolocet LTD","description":"Geolocet provides premium geospatial data for retail analysis, GIS, and market planning. 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Free trial data.","share":{"name":"share_socio_mbi_market_data_2024_fi_postcodes_5_socio","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1763380651258,"updated_at":1779198982524,"categories":["ADVERTISING_AND_MARKETING","DEMOGRAPHICS","GEOSPATIAL"],"listingType":"STANDARD","provider_id":"aa09abd6-ff7f-4452-803d-b8c43b4c07cb"},"detail":{"description":"[MBI](https://www.mbi-geodata.com/en/)'s sociodemographic data is available for many countries worldwide. All data comes from a single source, ensuring **consistency and comparability** across markets.\n\nThe Sociodemographics-Bundle Basic for Finland includes the following **variables**:\n- [population](https://www.mbi-geodata.com/en/sociodemographic-data/population/)\n- [gender](https://www.mbi-geodata.com/en/sociodemographic-data/sex-gender/)\n- [age structure](https://www.mbi-geodata.com/en/sociodemographic-data/agebands/) (15-year age bands)\n- household figures ([number of households](https://www.mbi-geodata.com/en/sociodemographic-data/households/) and [households by type](https://www.mbi-geodata.com/en/sociodemographic-data/households-by-type/))\n\nOur sociodemographic data is offered for each country on different **levels**:\n- **Postal Code Level:** This fine-grained level of detail makes the data suitable for a wide range of applications – for example, enriching customer databases, which are often organized by postal code.\n- **Administrative Level:** Covers both larger administrative areas (e.g., regions, states, provinces) as well as finer subdivisions such as districts, counties, or municipalities.\n- **Micro Level**: Subdivisions of postal code or administrative boundaries, such as census areas or street segments. This is the most granular level for local analysis.\n\n**Please note:** The dataset available for free download is a **sample dataset** covering a limited number of geographic areas. If you wish to access the full dataset (covering all areas) or if you require additional variables or geographic levels, please contact us. We will be happy to help you find a product that fits your needs.\n \n**Additional Insights**: https://www.mbi-geodata.com/en/sociodemographic-data/\n\n**Data Structure**:\nAGE_F0014: Population (Nighttime) by age: 0 - 14 years, female\nAGE_F1529: Population (Nighttime) by age: 15 - 29 years, female\nAGE_F3044: Population (Nighttime) by age: 30 - 44 years, female\nAGE_F4559: Population (Nighttime) by age: 45 - 59 years, female\nAGE_F60PL: Population (Nighttime) by age: 60 years and above, female\nAGE_M0014: Population (Nighttime) by age: 0 - 14 years, male\nAGE_M1529: Population (Nighttime) by age: 15 - 29 years, male\nAGE_M3044: Population (Nighttime) by age: 30 - 44 years, male\nAGE_M4559: Population (Nighttime) by age: 45 - 59 years, male\nAGE_M60PL: Population (Nighttime) by age: 60 years and above, male\nAGE_T0014: Population (Nighttime) by age: 0 - 14 years, total\nAGE_T1529: Population (Nighttime) by age: 15 - 29 years, male\nAGE_T3044: Population (Nighttime) by age: 30 - 44 years, total\nAGE_T4559: Population (Nighttime) by age: 45 - 59 years, total\nAGE_T60PL: Population (Nighttime) by age: 60 years and above, total\nCTRYCODE: Two digit country code by ISO 3166\nFEMALE: Population (Nighttime): females\nHH_SIZE: Average household size\nHH_T: Households: total number\nMALE: Population (Nighttime): males\nNAME: Name of administrative area / Name of the most populous administrative area / Name of the respective micro-area or the next higher level\nADMINCODE / POSTCODE / MICROCODE Administrative code / Postal code / Micro-code\nP_PRM: Population (Nighttime) year average: per mill of country\nP_T: Population (Nighttime) year average: total number","terms_of_service":"https://service-mbi-geodata.com/s/HNp7Z2tws4WDqWd","documentation_link":"https://www.mbi-geodata.com/en/mbi-supplier-manufacturer-worldwide-geodata/europe/finland-maps/","privacy_policy_link":"https://www.mbi-geodata.com/en/data-protection/","geographical_coverage":"[\"Europe\"]","cost":"FREE","update_frequency":{"interval":1,"unit":"YEARLY"},"collection_granularity":{"interval":1,"unit":"YEARLY"},"collection_date_start":1704110211151,"collection_date_end":1735646217260,"data_source":"MBI","assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://service-mbi-geodata.com/s/fWwRyn8HmQLiFem"},"provider_summary":{"name":"Michael Bauer International GmbH – MBI Geodata","description":"For over 15 years, Michael Bauer International (MBI) has been a leading provider of globally consistent and comparable geographic data, including more than 3 million postcode areas and other detailed datasets like sociodemographics and purchasing power. 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MBI is part of the Michael Bauer Group, alongside Michael Bauer Research GmbH and Michael Bauer Micromarketing GmbH."},"popularity":230},{"id":"5f183624-2cc9-450d-98cd-a524bb036b57","summary":{"name":"Airbnb Sample Data","subtitle":"This dataset contains publicly available data from Airbnb, obtained from the Inside Airbnb project.","share":{"name":"dbacademy_dataset_airbnb","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1729714127463,"updated_at":1729714127463,"categories":["EDUCATION"],"listingType":"STANDARD","provider_id":"ea1e69ff-0127-4c94-bf39-e841fe1d19d2"},"detail":{"description":"**Overview**\nThis folder contains publicly available data sourced from [http://insideairbnb.com/get-the-data.html](Inside Airbnb).\n\n**Disclaimers**\n\n- This data is not associated with or endorsed by Airbnb or any of Airbnb's competitors.\n- The data utilizes public information compiled by InsideAirbnb from the Airbnb web-site including the availability calendar for 365 days in the future, and the reviews for each listing. Data was verified, cleansed, analyzed and aggregated by InsideAirbnb. Databricks has not confirmed the accuracy of the data in the data set. The dataset is provided by Databricks in an unmodified form, as well as in a form subjected to additional cleansing.\n- All copyright and registered trademarks remain the property of their owners.","terms_of_service":"https://cms.databricks.com/sites/default/files/2023-01/marketplace-consumer-terms_0.pdf","documentation_link":"https://insideairbnb.com/","privacy_policy_link":"https://www.databricks.com/legal/privacynotice","data_source":"","assets":["ASSET_TYPE_MEDIA"],"license":"https://creativecommons.org/publicdomain/zero/1.0/"},"provider_summary":{"name":"Databricks","description":"As the world’s first lakehouse platform in the cloud, Databricks combines the best elements of data lakes and data warehouses to deliver the reliability, strong governance, and performance of data warehouses with the openness, flexibility, and machine learning support of data lakes. 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And, its common approach to data management, security and governance helps companies operate more efficiently and innovate faster."},"popularity":2116},{"id":"84d07cd9-d3cc-4cc0-9275-e0d18fd852d7","summary":{"name":"Global Fixed Income Reference Data | Bond Data | ESG Data | 850K issues","subtitle":"Gain access to the most detailed descriptive data parameters of worldwide fixed income descriptive parameters, such ...","share":{"name":"cbonds_f4cdd028_3697_4d17_be26_8f59d105b5ac_samples_share","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1731060676914,"updated_at":1734699586156,"categories":["FINANCIAL"],"listingType":"PERSONALIZED","provider_id":"af6daabc-425a-40d0-81e4-5865d3ef8e8e"},"detail":{"description":"It is important for financial companies and investors to get all the reference data for each bond aggregated in one set.\r\nSubscribers to “Reference bond data” are getting immediate access to the following information:\r\n - more than 100 fields of static reference data\r\n - types of bonds: sovereign and corporate, senior and subordinated, convertible, sinkable, fixed and floating rate and other types of bonds.\r\n - ISIN, SEDOL and other identifiers\r\n - bond descriptions: country, currency, nominal, issue price, minimum settlement amount, outstanding amount\r\n\r\nGlobal Fixed Income Data.\r\nReference data on more than 850K bonds worldwide.\r\nHistorical data from 2000 onwards.\r\nPay only for the parameters you need.\r\nFlexible in customizing our product to the customer's needs.\r\nFree test access as long as you need for integration.\r\nReliable sources: issues' documents, disclosure website, global depositories data and other open sources.\r\nThe cost depends on the amount of required parameters and re-distribution right.","terms_of_service":"https://cbonds.com/user-agreement","contact_email":"marketplace@datarade.ai","privacy_policy_link":"https://cbonds.com/user-agreement","assets":["ASSET_TYPE_DATA_TABLE"]},"provider_summary":{"name":"Cbonds","description":"Cbonds is a data vendor for financial market professionals and investors. We provide our clients with comprehensive information on global financial markets, with the main focus being on the bond market. Developing data business since 2000, the company has gained a reputation as a respected and competent source of timely and accurate data.\r\nAmong our clients there are regional banks, asset managers, brokerage companies, family offices and software platforms. We offer our clients subscription services to our platforms https://cbonds.com/ and https://cbn-data.com/, data feeds and API solutions."},"popularity":735},{"id":"542a6df3-bb60-48fc-9bb1-244715b4b714","summary":{"name":"Ambee Global Wildfire Data – Historical | Present | Risk Forecast | Climatology","subtitle":" Present wildfire intelligence, 30 years of archives, and 4-week wildfire risk forecasts for any global location.","share":{"name":"client_schema","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1757663888665,"updated_at":1759301493925,"categories":["CLIMATE_AND_ENVIRONMENT"],"listingType":"STANDARD","provider_id":"e8f52c61-ad23-4333-82f9-6f83cd446474"},"detail":{"description":"**Overview**\n\nAmbee’s wildfire dataset ingests and homogenizes multiple inputs, including satellite detections, reported fire events, modeled wildfire risk forecasts, fire radiative power (FRP), Fire Weather Index (FWI), fuel load, soil moisture, terrain, and weather conditions that are combined to create a globally harmonized dataset. The Wildfire dataset provides wildfire intelligence for present detections, 4-week wildfire risk forecasts, historical archives extending back three decades, and climatological baseline risk.\n\n**Use cases**\n\n- Insurance risk modeling and catastrophe exposure\n- Utility and asset protection\n- Disaster-aware logistics planning\n- Emergency management and response\n- Deep histories of fire activity and research\n\n**Sample fields**\n\n- frp: Fire radiative power (MW)\n- fwi: Fire Weather Index\n- fireType: Reported or detected fire\n- fireCategory: Wildfire, prescribed, or normal\n- areaBurnt: Total burned area (hectares)\n- predicted_risk: Modeled wildfire risk level (numeric)\n- predicted_risk_category: Low, Moderate, High\n\n**Tables included**\n\n- Wildfire_present_data\n- Wildfire_forecast_risk_4week\n- Wildfire_historical_data\n- Wildfire_climatology\n\n**Benefits to Databricks Marketplace users**\n\n- Instantly available: Delivered directly for immediate integration into any Databricks workflow.\n- Global coverage: Current wildfire detections and modeled risk forecasts for any geography.\n- Risk forecasting: Forward-looking wildfire risk projections up to 4 weeks.\n- Historical climatology: 30+ years of wildfire records, enabling model training and validation.\n- Consistent schema: Unified structures across present, forecast, and historical data.\n- Analytics-ready: Optimized for Databricks-native tools, including Spark and MLflow.","terms_of_service":"https://www.getambee.com/terms-and-conditions","documentation_link":"https://docs.ambeedata.com/apis/fire","privacy_policy_link":"https://www.getambee.com/privacy-policy","geographical_coverage":"[\"Global\"]","cost":"FREE","update_frequency":{"interval":1,"unit":"HOURLY"},"collection_granularity":{"interval":1,"unit":"HOURLY"},"data_source":"","assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://www.getambee.com/servicesagreement"},"provider_summary":{"name":"Ambee","description":"Ambee is a global climate tech company delivering high-resolution, real-time, historical, and forecast environmental intelligence. 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The service includes full order book data, with microsecond-level market event records and nanosecond timestamp precision, enabling detailed analysis of market activity, liquidity, order flow, and trade surveillance across global trading venues. The service contains more than 45 petabytes of data covering both exchange-traded and over-the-counter (OTC) instruments from more than 400 exchanges and third-party contributors worldwide.\n\n**30 years of high-quality historical data** \n\nCelebrating 30 years in 2026, Tick History provides normalized and structured historical market data dating back to 1996, delivering a consistent foundation for analytics, research, backtesting, regulatory, and trading use cases.\n\n**High-precision point-in-time data**\n\nAccess point-in-time historical records with microsecond-level market event granularity and nanosecond timestamp precision, enabling accurate reconstruction of market activity for quantitative research, algorithmic trading, trade surveillance, transaction cost analysis, market quality analysis, and regulatory reporting.\n\n**Aligned with the LSEG Data Model**\n\nTick History uses the LSEG data model and normalized datasets, enabling historical data to be seamlessly combined with reference data, news, sentiment, and other content across the LSEG ecosystem. 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Clients can leverage Databricks' best-in-class services and tooling to enrich, analyse, and derive insights from historical market data at scale, accelerating the development of trading models, investment strategies, and analytics applications.\n\n**Rich developer resources**\n\nTick History offers a comprehensive library of documentation, examples, and resources to support implementation, query development, and integration with Tick History services.\n\n**Powering quantitative and regulatory workflows**\n\nTick History supports a wide range of front-office, risk, compliance, and research workflows, including trade surveillance, transaction cost analysis (TCA), best execution analysis, quantitative research, alpha generation, strategy backtesting, liquidity modelling, market microstructure analysis, and regulatory reporting. 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With consistent, real-world market signals and privacy-first design, our Global Dataset supports expansion planning, benchmarking, risk analysis, and growth worldwide.\n\n**Datasets Leveraged:**\n\n- **Point-of-Interest (POI):** Single, unified dataset covering more than 370 million global business locations across 250+ countries and territories\n- **Foot Traffic & Mobility:** Privacy-safe, aggregated insights into real-world movement and visitation patterns\n- **Brands:** Standardized brand and chain data covering 15,000+ global brands \n- **Trade Areas:** Behavior-based maps that show where visitors actually come from, based on real movement data\n- **Polygons:** Detailed location boundaries that help to better understand space, layout, and on-site activity\n\n**Data Built for Scale & Consistency:**\nDataplor provides a globally standardized, continuously updated foundation for organizations that need to understand how businesses perform in the real world—at scale and with precision. \n\n- **Global Business Profiles:** Verified business records with official names, brand affiliations, ownership details, industry classifications, and unique location IDs.\n- **Business Classification & Context:** Detailed business categorization by industry, product type, brand, operational status, and parent company relationships.\n- **Location Precision:** Accurate address data, geographic coordinates, H3 indexing, and polygon boundaries for precise spatial analysis.\n- **Operational Intelligence:** Business hours, verified openings and closures, and historical status tracking to monitor location changes over time.\n- **Foot Traffic & Visitation Metrics:** Weekly aggregated visit counts, dwell-time metrics, popularity indices, sentiment indicators, and proprietary performance signals derived from privacy-compliant mobility data. \n- **Data Quality & Validation:** Continuous quality checks powered by machine learning, human review, and perpetual accuracy monitoring.\n- **Reliable Data Delivery:** Weekly delivery via S3, SFTP, Snowflake, Box, Google Drive (CSV or JSON formats) \n\n**Use Cases:**\nWe help organizations understand where businesses are, how they operate, and when people interact with them across every market. \n\n**- Market & Competitive Analysis:** Benchmark markets, categories, and competitors using rich location and visitation trends across regions.\n**- Spatial Analytics & GIS Integration:** Enhance maps, spatial models, and GIS workflows with precise geocoded POIs and polygon boundaries.\n**- Predictive Modeling & Analytics:** Feed high-quality global data into predictive models, risk frameworks, and econometric analyses with confidence in completeness and consistency.\n**- Operational Intelligence:** Power internal dashboards and analytics systems with near-real-time insights into location activity, business openings/closures, and visitation trends.\n**- Strategic Growth & Expansion Planning:** Identify growth opportunities, regions with untapped demand, and emerging market signals using global footprint and movement data.\n**- Risk & Compliance Modeling:** Support risk frameworks and compliance models with reliable, global location inputs that reflect real-world change rather than outdated snapshots.\n\nRequest a data sample from our team: contact@dataplor.com","terms_of_service":"https://www.dataplor.com/sample-primary-services-agreement/","documentation_link":"","privacy_policy_link":"https://www.dataplor.com/privacy-policy/","geographical_coverage":"[\"Global\"]","assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"Dataplor","description":"Dataplor is a leading provider of global location intelligence, telling the story of places and how the world interacts with them. Spanning more than 370 million places and 15,000 global brands across 250+ countries and territories, the company delivers highly accurate, consistently structured data on business activity, including openings and closures, foot traffic, brand attributes, polygons, and trade area intelligence. This data is accessible as a comprehensive global dataset for seamless system integration or through an intuitive platform, turning global complexity into clear, actionable insight. To learn more or see Dataplor in action, visit dataplor.com."},"popularity":17},{"id":"7ea40add-377f-4fda-8127-8da2a17de713","summary":{"name":"Helios Comprehend Voice Tone Analysis","subtitle":"Voice-based tonal analytics of 1000's of Earnings calls with deep history for traders and research.","setting":{"visibility":"PUBLIC"},"created_at":1686926539542,"updated_at":1698965635273,"categories":["FINANCIAL"],"listingType":"PERSONALIZED","provider_id":"58748858-4900-4cdc-8f2a-7fd0fb8a7851"},"detail":{"description":"**Overview**\nHelios Comprehend is the first widely available data product that can systematically assess affect in the voice of an executive during earnings conference calls to produce novel and meaningful sources of quantitative information.\n\nDuring earnings conference calls, executives exhibit specific voice-based affect signatures that represent their understanding of company fundamentals. Thus, the affect in their voice may or may not coincide with the specific words they are saying, particularly if the company is under stress.\n\nThis voice analytics intelligence can provide an unparalleled informational advantage for investors who integrate signal from Helios’s voice data into their investment strategy.\n\nThis dataset contains a static copy of the Helios Comprehend data available for easy evaluation of the dataset.\n\n**Use cases**\n- **Sentment Analysis** - Incorporate voice-based tonal analysis into your sentiment modeling for a deeper, more accurate picture of executive sentiment.\n- **Quantitative Analysis** - Use Helios' Voice Tone Analysis data to create novel signal to power alpha models.\n- **Risk Analysis** - Incorporate Helios Voice Tone Analysis data into risk models for a more complete look at company risk.\n\n**Product details**\n\n- Datasets represented includes COMPREHEND_ELITE\n- Sample fields include Ticker, Event Name, Event Date, Event Start Time, Event ID, Executive Participants, Executive Titles, Executive IDs, 200+ scores","terms_of_service":"https://www.helioslife.enterprises/privacy-policy","documentation_link":"","privacy_policy_link":"https://www.helioslife.enterprises/privacy-policy","assets":["ASSET_TYPE_DATA_TABLE"]},"provider_summary":{"name":"Helios Life Enterprises","description":"Helios offers investors a new way to analyze meaning using the true metric of human nature: the voice. Research shows that the tone of voice accounts for 38% of human communication. Helios leverages advanced voice-processing and AI technology to extract and analyze information leaked from the voice that is not picked up by traditional text-based analysis."},"popularity":324},{"id":"ab09effc-3a5b-4731-a593-06dcfcacb9a9","summary":{"name":"Draup Sales Intelligence Consolidated Datasets - Sample","subtitle":"Targeted Account Planning","share":{"name":"account_intelligence","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1751488183831,"updated_at":1788941507483,"categories":["ADVERTISING_AND_MARKETING","COMMERCE","ECONOMICS","EDUCATION","OTHER","SCIENCE_AND_RESEARCH"],"listingType":"STANDARD","provider_id":"9900a255-6aa9-496f-9b6a-421a3568af3b"},"detail":{"description":"Draup for Sales Intelligence empowers B2B sales, marketing, and account management teams with actionable, data-driven insights to target, engage, and win high-value customers. Leveraging advanced AI and human curation, Draup delivers a unified view of global enterprises, technology landscapes, buying centers, and decision-makers—enabling smarter go-to-market strategies and faster deal cycles.\n\n**Key Features**\nComprehensive Account Intelligence:\n Access detailed profiles of 35,000+ global enterprises, including org charts, technology stacks, business priorities, and digital transformation initiatives.\n\n**Decision-Maker Mapping:**\n Identify and map 6M+ verified decision-makers and influencers, complete with role context, reporting lines, and contact intelligence.\n\n**Opportunity Identification:**\n Discover high-propensity accounts and in-market buyers using real-time signals, intent data, and predictive analytics.\n\n**Technology & Solution Fit:**\n Analyze enterprise technology adoption, spending patterns, and solution gaps to tailor your value proposition and maximize win rates.\n\n**Custom Segmentation & Prioritization:**\n Leverage advanced filters to segment accounts by industry, geography, digital maturity, and business need for precise targeting.\n\n**Deal Intelligence & Playbooks:**\n Access competitive intelligence, use-case playbooks, and account-specific insights to craft winning pitches and accelerate pipeline velocity.\n\n**Use Cases**\nPrioritize high-potential accounts and verticals\nMap and engage key decision-makers with personalized outreach\nUncover cross-sell and upsell opportunities within existing clients\nIdentify and act on real-time buying signals and market shifts\nShorten sales cycles and improve conversion rates\n\n**Subscription Details**\nDelivered natively in Databricks for seamless integration with your analytics and CRM workflows\nRegularly updated with the latest company, contact, and market data\nFlexible subscription plans and scalable data access\nFree trial available for marketplace users\nWho Should Subscribe\nSales and Business Development Teams\nAccount Managers and Customer Success Leaders\nMarketing and Demand Generation Teams\nSales Operations and Revenue Enablement Professionals\nDrive smarter prospecting, deeper account engagement, and faster revenue growth with Draup for Sales Intelligence—now available as a subscription on the Databricks Marketplace.","terms_of_service":"https://draup.com/privacy-policy#:~:text=Unless%20provided%20for%20in%20this,that%20work%20on%20our%20behalf","documentation_link":"","privacy_policy_link":"https://draup.com/privacy-policy","geographical_coverage":"[\"Global\"]","cost":"FREE","update_frequency":{"interval":1,"unit":"WEEKLY"},"collection_granularity":{"interval":1,"unit":"DAILY"},"data_source":"Public Sources","assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"Draup, Inc.","description":"Draup is an enterprise agentic intelligence platform built for HR and Sales leaders navigating transformation. By combining unparalleled real-time global data with agentic AI, Draup enables strategic decision-making across talent and revenue functions.\n \nThrough its Talent Intelligence solution, Draup empowers HR teams to drive workforce planning, precision hiring, predictive skill architecture, and competitive benchmarking. Its Sales Intelligence solution helps go-to-market teams uncover deep account insights, identify buying triggers, and accelerate revenue growth.\n \nTrusted by over 300 global enterprises - including five of the Fortune 10 - Draup supports companies like Microsoft, PepsiCo, PayPal, and Moderna in building future-ready workforces and growth strategies."},"popularity":234},{"id":"39879cf9-14f4-4c23-9b94-8c515aa9cb9b","summary":{"name":"Replit","subtitle":"Replit is an AI-powered platform where anyone can build and ship production-ready apps using natural language.","setting":{"visibility":"PUBLIC"},"created_at":1781202023682,"updated_at":1781202023682,"categories":["ADVERTISING_AND_MARKETING","COMMERCE","ECONOMICS","EDUCATION","FINANCIAL","LOOKUP_TABLES","MANUFACTURING","MEDIA","RETAIL","TRANSPORTATION_AND_LOGISTICS","OTHER","GAMING","TRAVEL_AND_TOURISM","SECURITY","SCIENCE_AND_RESEARCH"],"listingType":"PERSONALIZED","provider_id":"4aad5ccd-fd15-421d-a0b2-b6ee56134a8f"},"detail":{"description":"**Overview**\n\nReplit is an AI-powered software development platform where anyone can build and ship production-ready applications by describing them in natural language, no coding required. Trusted by 40M+ users and enterprise teams at Zillow, Accenture, Talkdesk, Rokt, and Hg Capital, Replit empowers non-technical users across product, finance, operations, and business teams to build full-stack enterprise applications, front end to back end, in a single session. With SOC 2 Type II certification, SSO/SAML, SCIM, and enterprise-grade security built in, teams can ship internal tools, prototypes, and customer-facing apps in hours rather than months, without managing infrastructure or waiting on engineering queues. With the Replit x Databricks integration, teams can build applications in Replit and deploy them securely into their Databricks environment, inheriting existing security and governance controls.\n\n**Use cases**:\n- Empowering non-technical users to build full-stack enterprise applications using only natural language\n- End-to-end development from front end to back end in a single session with no environment setup\n- Rapid prototyping from idea to working prototype, ready to hand off as working source code\n- Shipping customer-facing applications faster with production-grade infrastructure included\n- Building internal AI companions and assistants tailored to your team's workflows and knowledge base\n\n**Product details**\nApps created on Replit span internal tools, business apps, customer portals, data dashboards, workflow automations, prototypes, and AI assistants, built and deployed end-to-end from a single browser-based environment with enterprise-grade authentication and security controls.\n\n**Additional Insights:**\nProduct overview: https://replit.com/enterprise\nDatabricks Integration: https://replit.com/partners/databricks\nDocs: https://docs.replit.com","terms_of_service":"https://replit.com/terms-of-service","documentation_link":"https://docs.replit.com/","privacy_policy_link":"https://replit.com/privacy-policy","assets":["ASSET_TYPE_GIT_REPO"],"license":""},"provider_summary":{"name":"Replit","description":"Replit is an AI-powered software development platform where anyone can build and ship production-ready applications by describing them in natural language, no coding required. Trusted by 40M+ users and enterprise teams at Zillow, Accenture, Talkdesk, Rokt, and Hg Capital, Replit empowers non-technical users across product, finance, operations, and business teams to build full-stack enterprise applications, front end to back end, in a single session. With SOC 2 Type II certification, SSO/SAML, SCIM, and enterprise-grade security built in, teams can ship internal tools, prototypes, and customer-facing apps in hours rather than months, without managing infrastructure or waiting on engineering queues. With the Replit x Databricks integration, teams can build applications in Replit and deploy them securely into their Databricks environment, inheriting existing security and governance controls."},"popularity":1927},{"id":"b0933fb9-7836-49d5-96bd-9964d569c78f","summary":{"name":"Alation MCP Server","subtitle":"Alation MCP Server links Alation’s Agentic Platform with AI tools like Databricks, enabling trusted, governed data use.","setting":{"visibility":"PUBLIC"},"created_at":1763563867373,"updated_at":1763579364805,"categories":["ADVERTISING_AND_MARKETING","CLIMATE_AND_ENVIRONMENT","COMMERCE","DEMOGRAPHICS","ECONOMICS","EDUCATION","ENERGY","FINANCIAL","GAMING","GEOSPATIAL","HEALTH","LOOKUP_TABLES","MANUFACTURING","MEDIA","OTHER","PUBLIC_SECTOR","RETAIL","SCIENCE_AND_RESEARCH","SECURITY","SPORTS","TRANSPORTATION_AND_LOGISTICS","TRAVEL_AND_TOURISM"],"listingType":"STANDARD","provider_id":"327843b1-48d8-4ea6-91fb-fe286e1a05f8"},"detail":{"description":"**Overview**\nYour AI’s Trusted Knowledge Layer. \n\nThe Alation MCP (Model Context Protocol) Server connects the Alation Agentic Platform seamlessly with leading AI environments such as Databricks and Anthropic’s MCP. This integration empowers enterprise AI agents to reason over structured, governed, and contextualized data with full confidence.\n\nBy embedding Alation’s Agentic Knowledge Layer directly into Databricks’ Agent Bricks, the integration ensures every agent interaction is explainable, auditable, and grounded in trusted metadata, lineage, policies, and business definitions—not approximations. Enterprises can connect in minutes, govern continuously, and operationalize AI safely at scale. With Alation’s context layer, agents gain understanding, not just access—reducing hallucinations, ensuring compliance, and accelerating adoption of enterprise AI initiatives.\n\n**Use cases**\n\n- Knowledge Assistants that Cite the Truth\nBuild intelligent agents that access Alation’s governed metadata: business definitions, certified metrics, and lineage before generating responses. Every answer is traceable to its data source and includes quality signals, ensuring users receive explainable and trustworthy insights within Databricks.\n- Policy-Aware Automation and Context-Rich Copilots\nEnable autonomous agents and copilots that check ownership, governance policies, and certification status from Alation before acting. Agents automatically select approved datasets, respect governance policies, and avoid deprecated sources - ensuring compliant, high-quality automation and analytics workflows within Databricks.\n\n\n**Additional Insights**\nAgentic Knowledge Layer Advantage: The Alation + Databricks integration bridges the critical “context gap” in enterprise AI. Instead of relying on schema-level information, agents gain access to curated business knowledge, governance policies, and usage patterns already managed by your data stewards.\n\nComplementary Strengths: Alation extends and complements Unity Catalog’s governance capabilities, serving as a single source of truth for metadata and policies across your entire data estate—while Databricks handles the data, compute, and orchestration layers.\n\nQuick Start:\nIn Databricks: Visit Marketplace → MCP Servers to connect Alation via the MCP tab in Agents.\n\nIn Alation: Ensure glossaries, lineage, and policies are up to date for instant context inheritance by agents.","terms_of_service":"https://www.alation.com/legal/terms-and-conditions/","documentation_link":"","privacy_policy_link":"https://www.alation.com/legal/privacy-policy/","geographical_coverage":"[\"Global\"]","cost":"FREE","data_source":"Alation","assets":["ASSET_TYPE_MCP"],"license":""},"provider_summary":{"name":"Alation","description":"Your AI's Trusted Knowledge Layer. \n\nThe Alation MCP (Model Context Protocol) Server seamlessly connects the Alation Agentic Platform to leading AI environments - such as Databricks and Anthropic’s MCP, so enterprise agents can reason over structured, governed data with confidence.\n\nPowered by the Alation Agentic Knowledge Layer, the integration delivers trusted metadata, lineage, policies, and business definitions directly into AI workflows. Every response is explainable and auditable, grounded in authoritative enterprise context - not approximations.\n\nBuilt-in evaluation and governance capabilities ensure continuous quality assurance and traceability, while native MCP support lets organizations connect in minutes, test or swap models without disruption, and operationalize AI with precision, transparency, and speed.\n\nAlation is trusted by over 650 organizations across Financial Services, Healthcare, Retail, Manufacturing, Energy, and more. "},"popularity":2040},{"id":"98b9b982-fb31-49f1-aea3-2a5520b5b4ba","summary":{"name":"Draup Global Job Postings Data (Talent)","subtitle":"Comprehensive and near real-time view of hiring demand across companies worldwide","setting":{"visibility":"PUBLIC"},"created_at":1756523490498,"updated_at":1757051936283,"categories":["ADVERTISING_AND_MARKETING","COMMERCE","ECONOMICS","EDUCATION","OTHER","SCIENCE_AND_RESEARCH"],"listingType":"PERSONALIZED","provider_id":"9900a255-6aa9-496f-9b6a-421a3568af3b"},"detail":{"description":"**Overview**\nDraup’s Global Job Postings dataset provides a comprehensive and near real-time view of hiring demand across companies worldwide. It captures detailed information on job roles, skill requirements, educational backgrounds, certifications, pay, and benefits, while also mapping postings to business functions and workloads. This enables HR teams, workforce planners, and talent intelligence leaders to analyze emerging workforce needs, identify skill gaps, and anticipate hiring patterns across geographies and industries.\nFor talent-focused decision-makers, the dataset reveals trends in role evolution, technology adoption within jobs, and shifts in functional demand—helping organizations align workforce planning with business priorities. By offering visibility into hiring activity at both company and market levels, it equips talent acquisition leaders to design smarter sourcing strategies, benchmark against competitors, and anticipate future demand for critical skills.\n\n**Methodology & Quality**\nThe dataset is aggregated from global job boards, company career portals, and verified postings across industries. Data is continuously refreshed to ensure relevance, with postings standardized through a structured pipeline that normalizes job titles, roles, skills, and workloads. Automated translation and lemmatization processes improve comparability across geographies, while deduplication and validation routines minimize noise and bias. Updates are applied on a frequent basis to maintain timeliness, and robust standardization ensures consistency for longitudinal analysis of workforce demand.\n\n**Key Tables Include**\n•\tCompany & Job Identifiers – Standardized company and posting IDs for consistent linkage across datasets.\n•\tJob Role & Titles – Normalized job roles, standardized titles, and translated raw job titles.\n•\tJob Descriptions – Consolidated and translated job descriptions for consistent role analysis.\n•\tSkills & Tech Stack – Extracted core skills, soft skills, and associated technology stacks for each posting.\n•\tBusiness Functions & Workloads – Mapped to functional areas and workload categories to align postings with organizational structures.\n•\tEducation & Certifications – Requirements including degrees, certifications, and qualifications linked to roles.\n•\tCompensation & Benefits – Pay ranges, benefits, and perks as advertised in postings.\n•\tExperience Requirements – Minimum experience levels and role-specific experience indices.\n•\tLanguage Requirements – Language skills and multilingual expectations for specific roles.\n•\tPosting Metadata – Posting dates, update timestamps, and system load dates for time-based analysis.\n\n**Use Cases:**\n***Discover what roles, locations, and skills are your competitors hiring for.***\n\nTrack competitor hiring activity by monitoring the job roles, skills, and geographies they are targeting. This visibility helps HR teams understand competitor workforce strategies, anticipate skill shortages, and sharpen their own talent acquisition efforts.\t\n\nSELECT\n company_name,\n role AS job_role,\n location.country,\n location.region,\n location.location,\n skill AS core_skill,\n date_posted\nFROM job_postings_company \nLATERAL VIEW explode(jd.draup_job_role) role_tbl AS role\nLATERAL VIEW explode(jd.consolidated_JDs) loc_tbl AS location\nLATERAL VIEW explode(jd.core_skills) core_tbl AS skill\nWHERE jd.date_posted >= date_add(current_date, -180);\n\n***Has the demand for a specific job role increased or decreased in a location?***\t\n\nAnalyze hiring demand trends for any job role in a given geography. These insights help talent teams anticipate attrition risks—if demand is surging, existing employees are more likely to be poached—allowing HR to design retention and workforce planning strategies accordingly.\t\n\nSELECT\nrole_tbl.role AS job_role,\nloc_tbl.country,\nloc_tbl.region,\nDATE_TRUNC('month', jd.date_posted) AS posting_month,\nCOUNT(*) AS total_job_postings\nFROM job_postings_company jd\nLATERAL VIEW explode(jd.draup_job_role) role_tbl AS role\nLATERAL VIEW explode(jd.consolidated_JDs) loc_tbl AS loc\nWHERE role_tbl.role = 'Data Engineer'\nAND loc_tbl.country = 'India'\nAND jd.date_posted >= date_add(current_date, -365)\nGROUP BY role_tbl.role, loc_tbl.country, loc_tbl.region, DATE_TRUNC('month', jd.date_posted)\nORDER BY posting_month;\n\n***Find out what benefits are most commonly offered by product companies in India***\t\n\nBenchmark compensation packages beyond salary by analyzing benefits offered by companies in a target geography. These insights guide HR in refining employer value propositions and designing competitive offers to attract and retain top talent.\t\n\nSELECT\n benefit,\n COUNT(DISTINCT job_descriptions.company_name) AS companies_offering,\n COUNT(*) AS total_job_postings\nFROM job_postings_company\nJOIN company_overview\n ON job_postings_company.company_id = company_overview.company_id\nLATERAL VIEW explode(jd.benefits.categories) benefit_tbl AS benefit\nLATERAL VIEW explode(jd.consolidated_JDs) consoli_jds AS job_descriptions\nWHERE job_descriptions.country = 'India'\n AND LOWER(company_overview.company_type) = 'product' \nGROUP BY benefit\nORDER BY companies_offering DESC, total_job_postings DESC;\n\n***Find what new skills are emerging within a particular job role in a location***\n\nIdentify rising or emerging skills associated with a role in a region, based on the latest job postings. This helps organizations adjust training programs, update role definitions, and proactively build the skills needed to stay competitive in fast-changing markets.\t\n\nWITH recent_skills AS (\n SELECT DISTINCT\n role AS job_role,\n job_descriptions.country,\n skill\n FROM job_postings_company\n LATERAL VIEW explode(jd.draup_job_role) role_tbl AS role\n LATERAL VIEW explode(jd.consolidated_JDs) consolid_jds AS job_description\n LATERAL VIEW explode(jd.core_skills) skill_tbl AS skill\n WHERE role = 'Data Engineer'\n AND job_descriptions.country = 'India'\n AND job_postings_company.date_posted >= date_add(current_date, -180) \n),\npast_skills AS (\n SELECT DISTINCT\n role AS job_role,\n job_descriptions.country,\n skill\n FROM job_postings_company\n LATERAL VIEW explode(jd.draup_job_role) role_tbl AS role\n LATERAL VIEW explode(jd.consolidated_JDs) consoli_jds AS job_descriptions\n LATERAL VIEW explode(jd.core_skills) skill_tbl AS skill\n WHERE role = 'Data Engineer'\n AND job_descriptions.country = 'India'\n AND job_postings_company.date_posted BETWEEN date_add(current_date, -365) AND date_add(current_date, -181) \n)\nSELECT\n recent_skills.job_role,\n recent_skills.country,\n recent_skills.skill AS emerging_skill\nFROM recent_skills\nLEFT JOIN past_skills p\n ON recent_skills.job_role = past_skills.job_role\n AND recent_skills.country = past_skills.country\n AND recent_skills.skill = past_skills.skill\nWHERE past_skills.skill IS NULL;","terms_of_service":"https://draup.com/privacy-policy#:~:text=Unless%20provided%20for%20in%20this,that%20work%20on%20our%20behalf","documentation_link":"","privacy_policy_link":"https://draup.com/privacy-policy","geographical_coverage":"[\"Global\"]","cost":"PAID","update_frequency":{"interval":1,"unit":"WEEKLY"},"collection_granularity":{"interval":1,"unit":"DAILY"},"data_source":"Public Sources","assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"Draup, Inc.","description":"Draup is an enterprise agentic intelligence platform built for HR and Sales leaders navigating transformation. By combining unparalleled real-time global data with agentic AI, Draup enables strategic decision-making across talent and revenue functions.\n \nThrough its Talent Intelligence solution, Draup empowers HR teams to drive workforce planning, precision hiring, predictive skill architecture, and competitive benchmarking. Its Sales Intelligence solution helps go-to-market teams uncover deep account insights, identify buying triggers, and accelerate revenue growth.\n \nTrusted by over 300 global enterprises - including five of the Fortune 10 - Draup supports companies like Microsoft, PepsiCo, PayPal, and Moderna in building future-ready workforces and growth strategies."},"popularity":1002},{"id":"3d741aca-431f-4018-8e28-7c27f202e27c","summary":{"name":"Reflex: Sandbox","subtitle":"The secure execution sandbox for Reflex Build — runs your AI-generated apps in isolation, inside your workspace.","setting":{"visibility":"PUBLIC"},"created_at":1780966311124,"updated_at":1781713194235,"categories":["OTHER"],"listingType":"STANDARD","provider_id":"e4b293a5-79ea-4dad-ae89-ed99d28cbf10"},"detail":{"description":"**The execution engine behind Reflex**\n\nReflex: Sandbox is the companion app that **runs and live-previews the code Reflex generates**. When you prompt Reflex, it hands the generated Reflex app to this sandbox to execute in isolation — keeping user-generated code separated from the builder itself, and keeping all execution inside your Databricks workspace.\n\nYou don't interact with the Sandbox directly; the Reflex app reaches it over the Databricks Apps OAuth proxy, authenticated by a shared secret you provision. It persists each project's working files to a Unity Catalog volume you choose, so generated apps survive restarts.\n\n**Install this first**\n\nReflex is two apps, and the Sandbox must be installed **before** the main Reflex app:\n\n1. **Create the shared control secret** (one-time, with the [Databricks CLI](https://docs.databricks.com/dev-tools/cli/)). Reflex drives this Sandbox over an internal control channel that both apps authenticate with a secret they each read, so create it **before installing either app**:\n\n databricks secrets create-scope reflex-control-auth\n\n databricks secrets put-secret reflex-control-auth sandbox-control-auth --string-value \"$(openssl rand -hex 32)\"\n\n2. **Install this Reflex: Sandbox app**, selecting a Unity Catalog volume for its `scm-volume` resource (any volume you have write access to) and the `reflex-control-auth` scope (key `sandbox-control-auth`) for its `control-auth` resource.\n3. **Run the `01_create_resources` notebook** as a workspace admin — it provisions the shared Lakebase instance and secret scopes and prints what to select when installing the main app. Import it into your workspace (**Workspace → Import → URL**):\n\n https://raw.githubusercontent.com/reflex-dev/flexgen-databricks-marketplace-init/main/01_create_resources.py\n\n4. **Install the Reflex app** and finish setup as 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Taxonomy code set is a collection of unique alp..","share":{"name":"nucc_health_provider_taxonomy_code_set_and_certificates","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1682356088042,"updated_at":1698965635315,"categories":["HEALTH","SCIENCE_AND_RESEARCH"],"listingType":"STANDARD","provider_id":"f88849f8-9d77-4de2-b4c7-03d55cc3768e"},"detail":{"description":"\n**Overview**\nThis data package contain information of Taxonomy code & certificates. Taxonomy code set is a collection of unique alphanumeric codes, ten characters in length. The code set is structured into three distinct \"Levels\" including Provider Grouping, Classification, and Area of Specialization. Its allow a single provider (individual, group, or institution) to identify their specialty category.\n\n**Description**\nThe National Uniform Claim Committee (NUCC) is presently maintaining the code set. It is used in transactions specified in HIPAA and the National Provider Identifier (NPI) application for enumeration. Effective 2001, the NUCC took over the administration of the code set. Ongoing duties, including processing taxonomy code requests and maintenance of the code set, fall under the NUCC Code Subcommittee. Taxonomy code set is an external, nonmedical data code set designed for use in an electronic environment, specifically within the ASC X12N Health Care transactions. This includes the transactions mandated under HIPAA. \nThe ABMS (American Board of Medical Specialties) has identified what candidates must accomplish in order to be eligible for certification. Generally, this involves:\n• finishing four years of premedical education in a college or university;\n• earning a medical degree (MD, DO or other credential approved by the ABMS Member Board) from a qualified medical school;\n• completing three to five years of full-time experience in a residency training program accredited by the Accreditation Council for Graduate Medical Education (ACGME); and\n• Obtaining an unrestricted medical license to practice medicine in the United States or Canada.\n\nThe ABMS (American Board of Medical Specialties) has set the professional and educational standards for medical specialty certification and practice. Candidates for certification must has to pass an exam created and administered by their specialty’s board. These exams are developed by Board Certified specialists and others who are subject experts in the specific area of medicine.\n\nCandidates for certification must then pass an exam created and administered by their specialty’s board. Candidates for certification must then pass an exam created and administered by their specialty’s board. These exams are developed by Board Certified specialists and others who are subject experts in the specific area of medicine. Exams are developed against learning requirements which are extensively tested and refined over time. This is done to assure that the exam is a thorough, relevant, and fair assessment of a specialist’s medical knowledge, clinical judgment, and diagnostic skills. Candidates who have passed the exam and completed all other requirements are considered certified as a specialist and a diplomate of their specialty board.\n\n**Benefits**\n- Useful data package for health care provider and billing companies as it contain information of taxonomy code & certificates. its help a single provider (individual, group, or institution) to identify their specialty category.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [Medicare Provider Supplier To Healthcare Provider Taxonomy](https://www.johnsnowlabs.com/marketplace/medicare-provider-supplier-to-healthcare-provider-taxonomy)\n\t- This crosswalk links the types of providers and suppliers who are eligible to apply for enrollment in the Medicare program with the appropriate Healthcare Provider Taxonomy Codes. This crosswalk includes the Medicare Specialty Codes for those provider/supplier types who have Medicare Specialty Codes.\n- [NUCC Health Provider Taxonomy Code Set](https://www.johnsnowlabs.com/marketplace/nucc-health-provider-taxonomy-code-set)\n\t- This dataset shows the Health Care Provider Taxonomy code set. These code set are a collection of unique alphanumeric codes, ten characters in length. The code set is structured into three distinct \"Levels\" including Provider Grouping, Classification, and Area of Specialization.\n- [Physician Secondary Specialty](https://www.johnsnowlabs.com/marketplace/physician-secondary-specialty)\n\t- This dataset provides lists of any secondary specialties associated with enrollment identification in the provider enrollment files.\n- [Specialty and Subspecialty Certificates](https://www.johnsnowlabs.com/marketplace/specialty-and-subspecialty-certificates)\n\t- The dataset provides chart lists the approved Specialty and Subspecialty Certificates in which ABMS (American Board of Medical Specialties) Member Boards can offer certification.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - 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It also gives detailed information on retail prescription drugs filled at pharmacies in the United States.\n\n**Description**\nThis data package contains the datasets that estimates on the number of prescription items dispensed in England for the treatment of alcohol and smoking dependence, and also UK Prescription Cost Analysis, which provides a national level annual summary of prescriptions dispensed during 2016, providing the detail for each individual item prescribed in 2016 in UK. The number of prescription items dispensed in 2016 for the treatment of alcohol dependence was 188 thousand which was 4 percent lower than 2015. The total Net Ingredient Cost (NIC) was £4.87 million. This is 24 percent higher than in 2015 and more than double the level ten years ago. The number of prescription items dispensed for the treatment of smoking cessation in England in 2015-2016 was 1.2 million, compared to 2.2 million ten years ago. In 2015-2016, 644 thousand items of NRT (Nicotine Replacement Therapies) and 489 thousand items of Varenicline were dispensed. Bupropion is the least common item, with only 21 thousand dispensed in 2015-2016. The data package also contains two datasets that provide one year data on retail prescription drugs filled at each state and regional zone, categorized by age, gender, prescription drug and by per capita annually as well as by total prescription drugs and total sales for each state and regional zone in US for the year 2014 and 2016.\n\n**Benefits**\n- Provides statistics on prescription numbers and net ingredient cost in uk as well as in us.\n- annual stats for easy analysis\n- useful for the healthcare researchers.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [FDA Pharmacy Prescriptions Per Capita Income and Total Sales](https://www.johnsnowlabs.com/marketplace/fda-pharmacy-prescriptions-per-capita-income-and-total-sales)\n\t- This dataset is a two-years data on retail prescription drugs filled at each state and regional zone; it is for pharmacies per capita categorized by age, gender, prescription drug and by per capita annually as well as by total prescription drugs and total sales for each state and regional zone. This data is for 2014 and 2016 for pharmacies, stores and merchandises at 814 defined regional zones.\n- [Number of Prescription Items for the Treatment of Alcohol Dependence](https://www.johnsnowlabs.com/marketplace/number-of-prescription-items-for-the-treatment-of-alcohol-dependence)\n\t- This dataset provides statistics on alcohol related hospital admissions as well as the number of prescriptions for the treatment of alcohol dependence in 2018 in England.\n- [Prescription Items For the Treatment of Smoking Dependence](https://www.johnsnowlabs.com/marketplace/prescription-items-for-the-treatment-of-smoking-dependence)\n\t- This dataset presents information on the number of prescription items written for pharmacotherapies for the treatment of smoking dependence, prescribed in primary care and dispensed in the community, by Commissioning Region, Region and Clinical Commissioning Group.\n- [UK Prescription Cost Analysis](https://www.johnsnowlabs.com/marketplace/uk-prescription-cost-analysis)\n\t- Prescription Cost Analysis (PCA) provides details of the number of items and the Net Ingredient Cost (NIC) of all prescriptions dispensed in the community in England. The drugs that are dispensed in the community are listed by British National Formulary (BNF) therapeutic class chapters 1 to 15, using the classification system prior to BNF edition 70. Items such as dressings and appliances, have been classified into six pseudo BNF chapters 18 to 23.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated\n- Data Updates\n - Data updates support replace-on-update: outdated foreign keys are deprecated, not deleted\n\n**Our data is 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American RTO/ISO regions.\n\nThis high-quality data set for the ISO New England (ISO-NE) region helps asset owners, project managers, and energy traders make confident, data-driven decisions at the site-specific and regional level to effectively manage investments, reduce future risks, and gain a competitive edge in energy markets.\n\n- Look ahead with 7-day power forecasts at 1-hour intervals at the plant and regional level for real-time and day-ahead markets.\n\n- Look back at site-level estimated actual power data in 15-minute intervals for the last two days.\n\nXweather combines cutting-edge weather and environmental modeling, robust data science, and powerful supercomputing to produce accurate energy forecasts and actual power estimates for wind and solar projects.\n\nThis sample data set contains 1 day of forecast data and 1 day of estimated actual power data for sites in the ERCOT Solar region in our database.\n\nAll data provided is interval ending.\n\nPlease contact us using the Contact Vaisala link in the listing if you would like to purchase a regularly updated data feed for the ISO-NE region.\n\n\nTables included:\n\nLATEST 7-DAY FORECAST\nLAST 2 DAYS ESTIMATED ACTUAL POWER\nREGIONAL SITES METADATA\n\n\nFields included:\n\nLATEST 7-DAY FORECAST\n- NAME — renewable energy project name\n- TIME — date and time for the forecast\n- FORECAST — forecasted estimated power in MW\n- INTERVAL — the time between forecasts\n- TIME ZONE — local time zone for time values\n- FORECAST CREATION TIME — date and time the forecast was created\n- MODEL INITIALIZATION TIME — date and time when the forecast model was initiated\n\nLAST 2 DAYS ESTIMATED ACTUAL POWER\n- NAME — renewable energy project name\n- TIME — date and time for the observation\n- ESTIMATED ACTUAL POWER — estimated actual power in MW\n- INTERVAL — the time between observations\n- TIME ZONE — local time zone for time values\n- FILE CREATION TIME — date and time the file was created\n\nREGIONAL SITES METADATA\n- NAME — renewable energy project name\n- ALTERNATE_NAME — alternative name\n- EIA_PLANT_CODE — U.S. Energy Information Administration project ID\n- MAX_CAPACITY — project capacity in MW\n- MODEL — turbine model used in wind projects\n- ONLINE_DATE — operational start date\n- LATITUDE — location of the project\n- LONGITUDE — location of the project\n- STATE — location of the project\n- COUNTY — location of the project\n\n\n**DATA FORMAT AND GRANULARITY**\n\nData is provided for projects and sites within the ISO-NE region in our database.\n\nThe data set includes three CSV files.\n\n1) The first file contains site-specific metadata about each project in the region such as name, location, and capacity.\n\n2) The second file provides a 7-day energy forecast (168 hours) in 1-hour intervals for each site in the region and a forecast for the ISO-NE region as a whole.\n\n3) The third file contains the estimated actual power output from each site in the region and for the region as a whole over the last 2 days in 15-minute intervals.\n\n\n**SOURCES**\n\nThe total actual power for the region as a whole comes from ISO-NE. The estimated actual power for each site in the region and the 7-day energy forecasts are produced using Vaisala's proprietary blend of numerical weather prediction trained models, statistical data modeling, and sophisticated machine-learning algorithms. Wind energy estimates and forecasts combine the actual operating characteristics of each project with the most advanced weather forecasts available.\n\n**Business Needs**\n**Risk Analysis**\nAsset owners, power marketers, and energy traders use power estimates for regional transmission grids to take a position on balancing reserves and congestion mitigation.\n**Asset Valuation**\nAsset owners and project managers use power estimates for regional sites to take a position on managing each project’s operations and the budgetary impact on their farms.\n**Demand Forecasting**\nGrid operators and balancing authorities use these data sets to run forecasts per site in their area to understand how demand is being met by the energy they produce and supply.\nPricing Analysis\nPower marketers, energy traders, and nodal traders use these data sets to see the power estimates from farms within the same transmission line to get insights into congestion and determine negative locational marginal prices.\n**Market Analysis**\nAsset owners, power marketers, and energy traders use these data sets to capitalize on opportunities, mitigate risk, and make better investment decisions in real-time and day-ahead energy trading markets.\n**Machine Learning**\nData scientists and market analysts combine these data sets with weather data and other sources to train machine learning models for forecasting renewable energy production.","terms_of_service":"https://www.xweather.com/terms","documentation_link":"https://www.xweather.com/renewable-energy-forecaster?utm_medium=other&utm_source=snowflake&utm_campaign=WEA-GLO-EN-EBU_RE-Default-Other-Snowflake","privacy_policy_link":"https://www.xweather.com/privacy","embedded_notebook_file_infos":[{"id":"ebc44227-25b0-430c-9d3c-81fbd68bc9bc","marketplace_file_type":"EMBEDDED_NOTEBOOK","file_parent":{"parent_id":"da913e99-af44-4a76-8e32-962fd944789e","file_parent_type":"LISTING"},"mime_type":"text/html","download_link":"https://marketplace-file-storage-prod.s3-fips.us-east-1.amazonaws.com/EMBEDDED_NOTEBOOK/LISTING_da913e99-af44-4a76-8e32-962fd944789e/ebc44227-25b0-430c-9d3c-81fbd68bc9bc?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEGgaDnVzLWVhc3QtMS1maXBzIkYwRAIgbC8sWIoYaHR8y7Wq86nPb0pufBl4%2BM4PWpeN%2B5PW9VQCIG7BhAVBDCWTpxFDLeuKYm%2Ff1JLz4iccPMzter0YdhgWKqYECDAQABoMNDE0MzUxNzY3ODI2IgxNAJrHbXaZG20jNfMqgwQyGEb4TsoVOoNvJ%2BaA%2FZ7zeu8hGcHqCeNfblP%2FQo5g6q2aRNtpwtPRKMlhYcIE%2FUQaNT1Lqmyptu32VUQTpzjTTxRVmJrDPkheSCkV06XQ%2FpTMKjU8CC7X1b%2FqYf8CrBwZT7Cv4C5WPjEET38f2A79h0xV6bZgPy28zQWrSeIJ9w6AfZBxiE24%2FVc08zVA0ivCT6lsy7nuU0wcR%2FMg2C9nd1vaMnQ923VpD21CKfVVYcrNWL2b%2FQ%2BNAO%2BxPjPgjiN6UPQbvbvZxkSdhgrGtecIg9NgqGkMcvM5VLTDtdC1kdeZetfOC2obtA%2BT%2F0gy3tIDo6WM5hcQfw743%2FAU9oaGnRBl%2BwoxRRDCZA%2F4jnPdluICSI7NCr6C3HHOe4CSZJjRihczxPA%2FgQczAVjAPs6CrK9N9pIzt0vNJpGUTgg4AoxftTvGfciDREilSctPIcBk6GwB%2Bzt4wbtNCmEPU%2BFD46rc6CWVQmmLwbKOeHw70ipMVFlOc8NlXkEtGO2vu6H44MstSSc6daOVUYUavQSGZ6jGhEjsP2YGz8KDm9XQotB%2FtyVBqiZV01OKqDmmBJA%2FXjUpSJ8M%2FeJXhe5m%2BKcxzNObtoJRI7jci3Tpt08XZyXZ7%2FyIV8H30ya468X3cfkTXrm972uga3z7iXAdubyEHnQIua%2FuvtwOHILi6O5D1Me2MzCPvcnXBjqTAYDoMWBt%2B3AVFuqSs2dmDQNBnvEhXfMgTedKLD5PJHb3aIl15iBkLaNu36GYnVCp9ZU2lSYcTbY5QwNnNeTfJTNy9bKhwzru6Sg%2BslhN%2BTS0zkTn%2BeLMY4tHgPEtEZ2cp%2FCk6GwqswvnYVKHjwO8q%2B1Mx8lc08fVEfarE9dke1GGFqoddx%2Bq%2BZUyHbDlAVfrCmf0yw%3D%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20261110T000135Z&X-Amz-SignedHeaders=host&X-Amz-Expires=86400&X-Amz-Credential=ASIAWA6KKHEJORWBH4N3%2F20261110%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=142863cf286a57f191e104296e96533c87a2d373ece7e1a0d46b104e2eab29b1","created_at":1685095190848,"updated_at":1685095190848,"display_name":"ISO-NE Regional","status":"FILE_STATUS_PUBLISHED"}],"assets":["ASSET_TYPE_DATA_TABLE"]},"provider_summary":{"name":"Vaisala Oyj","description":"Vaisala is a global leader in weather and environmental measurements with over 85 years of experience in providing observations for a better world. Vaisala Xweather delivers sensor-enhanced environmental data and solutions to customers in weather-sensitive industries. We provide accurate forecasts, uninterrupted real-time observations, and comprehensive historical datasets. Let's solve the world's weather and environmental challenges. 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We define a FoWS as a consumer who spends with teams, leagues, media companies, and consumer brands that are directly tied to professional women's sports, such as the WNBA, NWSL, and more. This data also reveals psychographic behavioral characteristics to better understand who they are and what kind of lifestyle activities they participate in. It also uncovers the top categories they spend with and the preferred brands they buy from.\n\n**Use cases**\nThis data set helps us answer questions like:\n- How quickly is the women’s sports fan community growing?\n- Which brands command the most consumer spending with these fans?\n- What else are fans of women’s sport interested in?\n- When did people make their first women’s sport-related purchase?\n- Are members of the women’s sports community digitally savvy relative to the general population?\n- How much do fans of women’s sports spend on other sports? Entertainment? Exercise? Streaming?\n\n**Product details**\nSample Tables:\n- ACQUISITION_RETENTION_ANNUAL_RATES_SPORTS_FANS\n- ACQUISITION_RETENTION_MONTHLY_RATES_SPORTS_FANS\n- AVG_ANNUAL_SPENDING_LEAGUE_LEVEL\n- AVG_ANNUAL_SPENDING_TEAMS_LEVEL\n- FOWS_ANNUAL_CHARACTERISTIC_INDEXING\n- FOWS_TOP_CATEGORIES_RANKED_ANNUALLY\n- FOWS_TOP_PREFERRED_BRANDS\n- FOWS_TOP_SPORTS_CONSUMPTION_BRANDS\n\nSample Fields:\n- Characteristic/Audience: Who are we analyzing?\n- Team\n- League\n- Average Spend Per Consumer: How much is an average consumer spending towards a specific team in total in a given year?\n- Average Spend Per Transaction: How much is a consumer spending towards a specific team per transaction in a given year?\n- Average Transactions Per Consumer: How many transactions is an average consumer making with a specific team in a given year?\n\nFor the full data dictionary across all tables, refer to the embedded notebook.","terms_of_service":"https://www.sportsilab.com/termsofservice","documentation_link":"","privacy_policy_link":"https://www.sportsilab.com/privacy-policy","embedded_notebook_file_infos":[{"id":"5ff1c9d2-6086-48a6-905b-22366fd09623","marketplace_file_type":"EMBEDDED_NOTEBOOK","file_parent":{"parent_id":"b3d3ad77-f591-478a-b0e0-23814fd3b27a","file_parent_type":"LISTING"},"mime_type":"text/html","download_link":"https://marketplace-file-storage-prod.s3-fips.us-east-1.amazonaws.com/EMBEDDED_NOTEBOOK/LISTING_b3d3ad77-f591-478a-b0e0-23814fd3b27a/5ff1c9d2-6086-48a6-905b-22366fd09623?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEGgaDnVzLWVhc3QtMS1maXBzIkYwRAIgbC8sWIoYaHR8y7Wq86nPb0pufBl4%2BM4PWpeN%2B5PW9VQCIG7BhAVBDCWTpxFDLeuKYm%2Ff1JLz4iccPMzter0YdhgWKqYECDAQABoMNDE0MzUxNzY3ODI2IgxNAJrHbXaZG20jNfMqgwQyGEb4TsoVOoNvJ%2BaA%2FZ7zeu8hGcHqCeNfblP%2FQo5g6q2aRNtpwtPRKMlhYcIE%2FUQaNT1Lqmyptu32VUQTpzjTTxRVmJrDPkheSCkV06XQ%2FpTMKjU8CC7X1b%2FqYf8CrBwZT7Cv4C5WPjEET38f2A79h0xV6bZgPy28zQWrSeIJ9w6AfZBxiE24%2FVc08zVA0ivCT6lsy7nuU0wcR%2FMg2C9nd1vaMnQ923VpD21CKfVVYcrNWL2b%2FQ%2BNAO%2BxPjPgjiN6UPQbvbvZxkSdhgrGtecIg9NgqGkMcvM5VLTDtdC1kdeZetfOC2obtA%2BT%2F0gy3tIDo6WM5hcQfw743%2FAU9oaGnRBl%2BwoxRRDCZA%2F4jnPdluICSI7NCr6C3HHOe4CSZJjRihczxPA%2FgQczAVjAPs6CrK9N9pIzt0vNJpGUTgg4AoxftTvGfciDREilSctPIcBk6GwB%2Bzt4wbtNCmEPU%2BFD46rc6CWVQmmLwbKOeHw70ipMVFlOc8NlXkEtGO2vu6H44MstSSc6daOVUYUavQSGZ6jGhEjsP2YGz8KDm9XQotB%2FtyVBqiZV01OKqDmmBJA%2FXjUpSJ8M%2FeJXhe5m%2BKcxzNObtoJRI7jci3Tpt08XZyXZ7%2FyIV8H30ya468X3cfkTXrm972uga3z7iXAdubyEHnQIua%2FuvtwOHILi6O5D1Me2MzCPvcnXBjqTAYDoMWBt%2B3AVFuqSs2dmDQNBnvEhXfMgTedKLD5PJHb3aIl15iBkLaNu36GYnVCp9ZU2lSYcTbY5QwNnNeTfJTNy9bKhwzru6Sg%2BslhN%2BTS0zkTn%2BeLMY4tHgPEtEZ2cp%2FCk6GwqswvnYVKHjwO8q%2B1Mx8lc08fVEfarE9dke1GGFqoddx%2Bq%2BZUyHbDlAVfrCmf0yw%3D%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20261110T000135Z&X-Amz-SignedHeaders=host&X-Amz-Expires=86400&X-Amz-Credential=ASIAWA6KKHEJORWBH4N3%2F20261110%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=42495f6148f2df1e8e05678646732867f56a0a666d623516fb3d3d747aa79a84","created_at":1713567173708,"updated_at":1713567173708,"display_name":"Sports Innovation Lab - Fans of Women's Sports (FoWS) Listing","status":"FILE_STATUS_PUBLISHED"}],"geographical_coverage":"[\"United States and Canada\"]","cost":"PAID","update_frequency":{"interval":1,"unit":"MONTHLY"},"assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"Sports Innovation Lab","description":"Sports Innovation Lab is an Insight, Data and Analytics company that enables brands and sports properties to build more effective sponsorships, improve targeted advertising, and enhance fan experiences. 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Learn how global brands including the NFL, Nike, NHL, Ally Financial, WNBA, and NASCAR use Sports Innovation Lab's data, audiences, and strategic advisory to drive revenue by visiting sportsilab.com or following on LinkedIn, Instagram, and Twitter."},"popularity":209},{"id":"377a5739-3a2b-4183-ad9c-d7db2fa49701","summary":{"name":"Geolocet | Latest Demographic Data Finland - Languages and Origin - 40+ attributes","subtitle":"Demographic data for the municipalities (Kunnat) of Finland and their population - main language, country of birth","setting":{"visibility":"PUBLIC"},"created_at":1705994189559,"updated_at":1752142313479,"categories":["DEMOGRAPHICS","ADVERTISING_AND_MARKETING","GEOSPATIAL","RETAIL","PUBLIC_SECTOR"],"listingType":"PERSONALIZED","provider_id":"5d3ddc17-a7e8-4794-a760-13587e1de2c6"},"detail":{"description":"## **Get instant access to the latest Demographics Data for Finland with [Geolocet](https://geolocet.com)**\n\nExplore the richness of Finland’s demographic landscape with our Finland Census Data. This meticulously compiled dataset, sourced from official statistical sources, including [Statistics Finland](https://www.stat.fi/), offers a detailed insight into the population landscape of Finland at the municipality (Kunnat) level. \n\nIf you require data at a less granular level, such as an Uber grid or square grid, please don't hesitate to get in touch with us at *contact@geolocet.com*. 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No lengthy sign-up processes or NDAs required on our side – just download the data you need.\n* **Data Samples for Evaluation:** To ensure you're getting exactly what you need, we provide free downloadable samples in CSV format for data evaluation.\n* **Data accuracy:** Our datasets are created using the latest demographics data available in national statistics bureaus, guaranteeing accuracy and reliability.\n* **Best-in-Class Quality:** We guarantee best-in-class quality, backed by over two decades of experience in modeling demographics data and an outstanding quality management process.\n* **Competitive Pricing:** We maintain the lowest prices on the market, ensuring that you get the most cost-efficient demographics data acquisition process.\n---\nAdditionally, for those looking to enrich their analysis or project, we offer:\n* **[Administrative Boundaries](https://geolocet.com/products/finland-all-available-administrative-boundaries):** If you require geographical boundaries (polygons) for the municipalities of Finland, consider exploring our Administrative Boundaries product, which seamlessly integrates with this dataset.\n\n* **[Point of Interest (PoI) Data](https://geolocet.com/pages/aggregated-points-of-interest-poi-data):** If you're interested in Points of Interest data, please don't hesitate to contact us for customized datasets to enhance your geographical analysis.\n\n* **[Data Customization](https://geolocet.com/pages/data-services-by-geolocet):** We understand that your project may have specific requirements. Feel free to get in touch with us if you need any modifications or specific data formats such as Excel, JSON, GeoJSON, or Shapefile. We are here to tailor the data to your needs.\n\nIf you're looking for data for Finland, [look here](https://geolocet.com/pages/finland) for off-the-shelf or tailor-made datasets to suit your specific requirements.\n\n\n\nMake informed decisions, conduct precise research, and unlock the potential of Finland's demographic landscape with our comprehensive dataset. Trust in the accuracy and reliability of our official statistical data to drive your projects forward.\nContact us at *contact@geolocet.com* today to access this invaluable resource and explore Finland census data like never before.\n\n[Geolocet LTD](https://geolocet.com)\n","terms_of_service":"https://geolocet.com/pages/terms-of-use","documentation_link":"","privacy_policy_link":"https://geolocet.com/pages/privacy-policy","geographical_coverage":"[\"Europe\"]","cost":"PAID","update_frequency":{"interval":1,"unit":"YEARLY"},"data_source":"","assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://geolocet.com/pages/terms-of-use"},"provider_summary":{"name":"Geolocet LTD","description":"Geolocet provides premium geospatial data for retail analysis, GIS, and market planning. 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Information on reported Research Payments which provide the total value of a payment or other transfer of value made for research purposes to a particular recipient for a particular date.\n\n**Benefits**\n- Useful to policymakers, media and patients. useful for organizations which protect patients interest or medical practice ethics. the fields descriptions along with the dataset description are again useful for the user to quickly understand the data and the dataset.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [CMS Deleted and Removed Records from Previously Published Years](https://www.johnsnowlabs.com/marketplace/cms-deleted-and-removed-records-from-previously-published-years)\n\t- Dataset contains the Record ID, Payment Type, and Program Year of records that were previously published and have been deleted or removed from the Open Payments system for that program year. \nEach record also has a Change Type indicator, which shows if the record was deleted or removed.\n- [CMS General Payment Details for Covered Recipients 2013](https://www.johnsnowlabs.com/marketplace/cms-general-payment-details-for-covered-recipients-2013)\n\t- This is the dataset for reported General Payments for the 2013 program year. General Payments are defined as payments or other transfers of value made to a covered recipient (physician or teaching hospital) not in connection with a research agreement or research protocol. \nThis dataset has been refreshed to include the most recent attested-to data for program year 2013.\n- [CMS General Payment Details for Covered Recipients 2014](https://www.johnsnowlabs.com/marketplace/cms-general-payment-details-for-covered-recipients-2014)\n\t- This is the dataset for reported General Payments for the 2014 program year. General Payments are defined as payments or other transfers of value made to a covered recipient (physician or teaching hospital) not in connection with a research agreement or research protocol. \nThis dataset has been refreshed to include the most recent attested-to data for program year 2014.\n- [CMS General Payment Details for Covered Recipients 2015](https://www.johnsnowlabs.com/marketplace/cms-general-payment-details-for-covered-recipients-2015)\n\t- This is the data set for reported General Payments for the 2015 program year. General Payments are defined as payments or other transfers of value made to a covered recipient (physician or teaching hospital) not in connection with a research agreement or research protocol. \nThis data set includes the most recent attested-to data for program year 2015.\n- [CMS General Payment Details for Covered Recipients 2016](https://www.johnsnowlabs.com/marketplace/cms-general-payment-details-for-covered-recipients-2016)\n\t- This is the dataset for reported General Payments for the 2016 program year. General Payments are defined as payments or other transfers of value made to a covered recipient (physician or teaching hospital) not in connection with a research agreement or research protocol. \nThis dataset includes the most recent attested-to data for program year 2016.\n- [CMS General Payment Details for Covered Recipients 2017](https://www.johnsnowlabs.com/marketplace/cms-general-payment-details-for-covered-recipients-2017)\n\t- This is the dataset for reported General Payments for the 2017 program year. General Payments are defined as payments or other transfers of value made to a covered recipient (physician or teaching hospital) not in connection with a research agreement or research protocol.\nThis dataset includes the most recent attested-to data for program year 2017.\n- [CMS General Payment Details for Covered Recipients 2018](https://www.johnsnowlabs.com/marketplace/cms-general-payment-details-for-covered-recipients-2018)\n\t- This is the dataset for reported General Payments for the 2018 program year. General Payments are defined as payments or other transfers of value made to a covered recipient (physician or teaching hospital) not in connection with a research agreement or research protocol.\nThis dataset includes the most recent attested-to data for program year 2018.\n- [CMS Physician Ownership or Investment Interests Details 2013](https://www.johnsnowlabs.com/marketplace/cms-physician-ownership-or-investment-interests-details-2013)\n\t- Dataset for reported Ownership and Investment Interest Information for the 2013 program year. It is the Information about physicians who hold an ownership or investment interest in an applicable manufacturer or applicable Group Purchasing Organization (GPO) or who have an immediate family member holding such interest. \nThis dataset has been refreshed to include the most recent attested-to data for program year 2013.\n- [CMS Physician Ownership or Investment Interests Details 2014](https://www.johnsnowlabs.com/marketplace/cms-physician-ownership-or-investment-interests-details-2014)\n\t- Dataset for reported Ownership and Investment Interest Information for the 2014 program year. Ownership and Investment Interest Information is defined as information about the value of ownership or investment interest physician themselves or by an immediate family member held in an applicable manufacturer or applicable group purchasing organization. \nThis dataset has been refreshed to include the most recent attested-to data for program year 2014.\n- [CMS Physician Ownership or Investment Interests Details 2015](https://www.johnsnowlabs.com/marketplace/cms-physician-ownership-or-investment-interests-details-2015)\n\t- Data set for reported Ownership and Investment Interest Information for the 2015 program year. Ownership and Investment Interest Information is defined as information about the value of ownership or investment interest physician themselves or by an immediate family member held in an applicable manufacturer or applicable group purchasing organization. \nThis data set includes the most recent attested-to data for program year 2015.\n- [CMS Physician Ownership or Investment Interests Details 2016](https://www.johnsnowlabs.com/marketplace/cms-physician-ownership-or-investment-interests-details-2016)\n\t- Data set for reported Ownership and Investment Interest Information for the 2016 program year. Ownership and Investment Interest Information is defined as information about the value of ownership or investment interest physician themselves or by an immediate family member held in an applicable manufacturer or applicable group purchasing organization. \nThis data set includes the most recent attested-to data for program year 2016.\n- [CMS Physician Ownership or Investment Interests Details 2017](https://www.johnsnowlabs.com/marketplace/cms-physician-ownership-or-investment-interests-details-2017)\n\t- Data set for reported Ownership and Investment Interest Information for the 2017 program year. Ownership and Investment Interest Information is defined as information about the value of ownership or investment interest physician themselves or by an immediate family member held in an applicable manufacturer or applicable group purchasing organization. \nThis data set includes the most recent attested-to data for program year 2017.\n- [CMS Physician Ownership or Investment Interests Details 2018](https://www.johnsnowlabs.com/marketplace/cms-physician-ownership-or-investment-interests-details-2018)\n\t- Data set for reported Ownership and Investment Interest Information for the 2018 program year. Ownership and Investment Interest Information is defined as information about the value of ownership or investment interest physician themselves or by an immediate family member held in an applicable manufacturer or applicable group purchasing organization. \nThis data set includes the most recent attested-to data for program year 2018.\n- [CMS Physician Ownership or Investment Interests Details 2020](https://www.johnsnowlabs.com/marketplace/cms-physician-ownership-or-investment-interests-details-2020)\n\t- Data set for reported Ownership and Investment Interest Information for the 2020 program year. Ownership and Investment Interest Information is defined as information about the value of ownership or investment interest physician themselves or by an immediate family member held in an applicable manufacturer or applicable group purchasing organization. \nThis data set includes the most recent attested-to data for program year 2020.\n- [CMS Physicians and Principal Investigators Details](https://www.johnsnowlabs.com/marketplace/cms-physicians-and-principal-investigators-details)\n\t- Dataset contains all of the identifying information for physicians who were indicated as recipients of payments, other transfers of value, or ownership and investment interest in records published by CMS through Open Payments as well as physician principal investigators who were associated with payments or other transfers of value in records published in Open Payments. This file contains only physicians and principal investigators having at least one payment record published.\n- [CMS Research Payment Details for Covered Recipients 2013](https://www.johnsnowlabs.com/marketplace/cms-research-payment-details-for-covered-recipients-2013)\n\t- Dataset for reported Research Payments for the 2013 program year. Research payment records provide the total value of a payment or other transfer of value made for research purposes to a particular recipient for a particular date. \nThis dataset has been refreshed to include the most recent attested-to data for program year 2013.\n- [CMS Research Payment Details for Covered Recipients 2014](https://www.johnsnowlabs.com/marketplace/cms-research-payment-details-for-covered-recipients-2014)\n\t- Dataset for reported Research Payments for the 2014 program year. Research payment records provide the total value of a payment or other transfer of value made for research purposes to a particular recipient for a particular date. \nThis dataset has been refreshed to include the most recent attested-to data for program year 2014.\n- [CMS Research Payment Details for Covered Recipients 2015](https://www.johnsnowlabs.com/marketplace/cms-research-payment-details-for-covered-recipients-2015)\n\t- Dataset for reported Research Payments for the 2015 program year. Research payment records provide the total value of a payment or other transfer of value made for research purposes to a particular recipient for a particular date. \nThis dataset includes the most recent attested-to data for program year 2015.\n- [CMS Research Payment Details for Covered Recipients 2016](https://www.johnsnowlabs.com/marketplace/cms-research-payment-details-for-covered-recipients-2016)\n\t- Dataset for reported Research Payments for the 2016 program year. Research payment records provide the total value of a payment or other transfer of value made for research purposes to a particular recipient for a particular date. \nThis dataset has been refreshed to include the most recent attested-to data for program year 2016.\n- [CMS Research Payment Details for Covered Recipients 2017](https://www.johnsnowlabs.com/marketplace/cms-research-payment-details-for-covered-recipients-2017)\n\t- Dataset for reported Research Payments for the 2017 program year. Research payment records provide the total value of a payment or other transfer of value made for research purposes to a particular recipient for a particular date. \nThis dataset has been refreshed to include the most recent attested-to data for program year 2017.\n- [CMS Research Payment Details for Covered Recipients 2018](https://www.johnsnowlabs.com/marketplace/cms-research-payment-details-for-covered-recipients-2018)\n\t- This dataset shows reported research payments for the 2018 program year. Research payment records provide the total value of a payment or other transfer of value made for research purposes to a particular recipient for a particular date.\nThis dataset has been refreshed to include the most recent attested-to data for program year 2018.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated\n- Data Updates\n - Data updates support replace-on-update: outdated foreign keys are deprecated, not deleted\n\n**Our data is curated and enriched by domain experts** \n\n Each dataset is manually curated by our team of doctors, pharmacists, public health & medical billing experts:\n- Field names, descriptions, and normalized values are chosen by people who actually understand their meaning\n- Healthcare & life science experts add categories, search keywords, descriptions and more to each dataset\n- Both manual and automated data enrichment supported for clinical codes, providers, drugs, and geo-locations\n- The data is always kept up to date – even when the source requires manual effort to get updates\n- Support for data subscribers is provided directly by the domain experts who curated the data sets\n- Every data source’s license is manually verified to allow for royalty-free commercial use and redistribution.\n\n**Need Help?**\nIf you have questions about our products, contact us at [info@johnsnowlabs.com](mailto:info@johnsnowlabs.com). \n ","terms_of_service":"https://www.johnsnowlabs.com/healthcare-data-library-eula/","documentation_link":"https://www.johnsnowlabs.com/marketplace/cms-healthcare-industry-providers-financial-relationships-data-package","contact_email":"support@johnsnowlabs.com","privacy_policy_link":"https://www.johnsnowlabs.com/privacy-policy/","assets":["ASSET_TYPE_DATA_TABLE"]},"provider_summary":{"name":"John Snow Labs","description":"John Snow Labs, an AI and NLP for healthcare company, provides state-of-the-art software, models, and data to help healthcare and life science organizations build, deploy, and operate AI projects."},"popularity":1238},{"id":"c9436b3a-e7e8-4d91-bdab-c0b7fddc073c","summary":{"name":"B2B Commercial Data (Contact)","subtitle":"ZoomInfo Data Cubes are powerful data solutions that provide seamless access to the world’s best B2B data, at scale.","share":{"name":"marketplace_databricks_sample_share-share","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1695009807634,"updated_at":1757364660577,"categories":["ADVERTISING_AND_MARKETING","RETAIL","OTHER","COMMERCE","DEMOGRAPHICS","ECONOMICS","FINANCIAL","MANUFACTURING","MEDIA","TRANSPORTATION_AND_LOGISTICS","EDUCATION"],"listingType":"STANDARD","provider_id":"7b188ec5-4695-43ac-a955-f6486e1ab53c"},"detail":{"description":"**Overview**\nThe power of accurate and comprehensive commercial B2B data cannot be overstated, as it is the foundation for success in today’s fast-paced business landscape. To build a strong and competitive organization, it is crucial to embrace best-in-class third party B2B data as a core part of your business strategy. ZoomInfo Data Cubes are powerful data solutions that provide seamless access to the world’s best B2B data, at scale. Whether you need to build your master data, supplement your multi-vendor strategy, unite siloed data, or run advanced analysis and modeling, ZoomInfo Data Cubes have you covered. With unlimited access to ZoomInfo data delivered directly to your existing systems of work, you can build a reliable go-to-market data foundation to analyze, predict, and engage with the right accounts to drive revenue. By actualizing your total addressable market, pinpointing your ideal customer profile, increasing segmentation accuracy, predicting look-alikes, sharing go-to-market insights and more, ZoomInfo Data Cubes provide holistic account intelligence for your go-to-market motions.\n\nUse the ZoomInfo Contact Data Cube to pinpoint your ideal customer profile with complete account intelligence. ZoomInfo Contact Data has:\n- Over 300 million contact profiles\n- More than 6 million C-level contact\n- Over 176 million email addresses \n- More than 71 million direct dial phone numbers \n\nThe ZoomInfo Data Cube will fill the gaps in your accounts and map your ideal customer profile with criteria-based access to the industry’s most comprehensive contact data attributes.\n\n__\n**Use cases**\nHere are a few benefits of accessing ZoomInfo contact data via the Contact Data Cube:\n- Fill the whitespace in your TAM with key information about your contacts \n- Keep data decay at bay with recurring data deliveries of contact profiles \n- Ensure complete and accurate contact profiles for improved analysis, reporting, and modeling.\n- Skip the hassle of credit limitations with contact data delivery at scale\n- Reduce time spent on manual data prep and validation\nImprove data governance and regulatory compliance\n\n__\n**Product details**\nContact Data Cube: Fill account gaps and define ideal customer profiles (ICPs) with criteria-based access to the industry’s most comprehensive contact data attributes. \n\nSample Data Cube: zi_sample_contacts_us\nDetails: Sample 100 Contacts in the US\n\nFor more details, refer to the embedded notebook.\n\n__\n**Full Suite of Data Cubes**\n**Company Data Cube:** *Firmographic Data purpose-built for Sales & Marketing*\nUnderstand and expand your Total Addressable Market (TAM) with access to 200+ ZoomInfo company data attributes at the HQ and location-level, refreshed and delivered quarterly. ZoomInfo’s firmographic data supports industry-leading match & fill rates built on logical business relations - making it the preferred foundational dataset for Sales and Marketing motions.\n\n**Company Data Cube Add-on:** *Technographic Data*\nGain in-depth insight into the complementary and competitive technologies companies are using to tailor your campaigns with precision. \n\n**Company Data Cube Add-on:** *Advanced Attributes* \nLeveraging complex data modeling and ZoomInfo’s proprietary rules engine, Advanced Attributes delivers a comparative analysis of department size and strength, technology spend, and a blended measure of department-level sophistication. \n\n\n**Hierarchy:** *Target the right companies with accurate corporate hierarchy linkages*\nUnderstand the relationship between accounts with unlimited access to ZoomInfo’s corporate hierarchy data. Refreshed and delivered quarterly, this cube contains 55 fields including ultimate parent, domestic parent, immediate parent, subsidiary, headquarter, individual location and franchise data.\n\n**Intent Data Cube:** *Turn research into revenue with insights into prospects actively seeking solutions like your*\nPrioritize prospects actively seeking solutions like yours with direct access to ZoomInfo Intent Data, refreshed and delivered weekly. Subscribe to your choice of 4,000+ Standard Intent Topics to use as fuel for your modeling, scoring, analysis, and other advanced data initiatives.\n\n**Contact Data Cube:** *Pinpoint your ideal customer profile with complete account intelligence*\nFill the gaps in your accounts and map your ideal customer profile with criteria-based access to the industry’s most comprehensive contact data attributes, refreshed and delivered monthly.\n\n**Contact Data Cube Add-on:** *Job History*\nTrack your champions, discover patterns across career moves, recruit for culture-fit candidates, refine outreach based on responsibilities, and more with complete career histories for customers and prospects. \n\n\n**Additional Resources**\n[Capital One Case Study](https://www.zoominfo.com/b2b/customer-success/Capital-One)\nSee how Capital One uses ZoomInfo Data Cube to improve data quality and enable sales reps at scale. Read case study.\n\n[eBook: 5 Ways Go-To-Market Teams use Data as a Service](https://www.zoominfo.com/resources/5-ways-go-to-market-teams-use-data-as-a-service)","terms_of_service":"https://www.zoominfo.com/about-zoominfo/terms-of-use","documentation_link":"https://www.zoominfo.com/data","privacy_policy_link":"https://www.zoominfo.com/about-zoominfo/privacy-policy","embedded_notebook_file_infos":[{"id":"edd2635a-c73f-4601-a3a1-4fe6e89db23b","marketplace_file_type":"EMBEDDED_NOTEBOOK","file_parent":{"parent_id":"c9436b3a-e7e8-4d91-bdab-c0b7fddc073c","file_parent_type":"LISTING"},"mime_type":"text/html","download_link":"https://marketplace-file-storage-prod.s3-fips.us-east-1.amazonaws.com/EMBEDDED_NOTEBOOK/LISTING_c9436b3a-e7e8-4d91-bdab-c0b7fddc073c/edd2635a-c73f-4601-a3a1-4fe6e89db23b?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEGgaDnVzLWVhc3QtMS1maXBzIkYwRAIgbC8sWIoYaHR8y7Wq86nPb0pufBl4%2BM4PWpeN%2B5PW9VQCIG7BhAVBDCWTpxFDLeuKYm%2Ff1JLz4iccPMzter0YdhgWKqYECDAQABoMNDE0MzUxNzY3ODI2IgxNAJrHbXaZG20jNfMqgwQyGEb4TsoVOoNvJ%2BaA%2FZ7zeu8hGcHqCeNfblP%2FQo5g6q2aRNtpwtPRKMlhYcIE%2FUQaNT1Lqmyptu32VUQTpzjTTxRVmJrDPkheSCkV06XQ%2FpTMKjU8CC7X1b%2FqYf8CrBwZT7Cv4C5WPjEET38f2A79h0xV6bZgPy28zQWrSeIJ9w6AfZBxiE24%2FVc08zVA0ivCT6lsy7nuU0wcR%2FMg2C9nd1vaMnQ923VpD21CKfVVYcrNWL2b%2FQ%2BNAO%2BxPjPgjiN6UPQbvbvZxkSdhgrGtecIg9NgqGkMcvM5VLTDtdC1kdeZetfOC2obtA%2BT%2F0gy3tIDo6WM5hcQfw743%2FAU9oaGnRBl%2BwoxRRDCZA%2F4jnPdluICSI7NCr6C3HHOe4CSZJjRihczxPA%2FgQczAVjAPs6CrK9N9pIzt0vNJpGUTgg4AoxftTvGfciDREilSctPIcBk6GwB%2Bzt4wbtNCmEPU%2BFD46rc6CWVQmmLwbKOeHw70ipMVFlOc8NlXkEtGO2vu6H44MstSSc6daOVUYUavQSGZ6jGhEjsP2YGz8KDm9XQotB%2FtyVBqiZV01OKqDmmBJA%2FXjUpSJ8M%2FeJXhe5m%2BKcxzNObtoJRI7jci3Tpt08XZyXZ7%2FyIV8H30ya468X3cfkTXrm972uga3z7iXAdubyEHnQIua%2FuvtwOHILi6O5D1Me2MzCPvcnXBjqTAYDoMWBt%2B3AVFuqSs2dmDQNBnvEhXfMgTedKLD5PJHb3aIl15iBkLaNu36GYnVCp9ZU2lSYcTbY5QwNnNeTfJTNy9bKhwzru6Sg%2BslhN%2BTS0zkTn%2BeLMY4tHgPEtEZ2cp%2FCk6GwqswvnYVKHjwO8q%2B1Mx8lc08fVEfarE9dke1GGFqoddx%2Bq%2BZUyHbDlAVfrCmf0yw%3D%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20261110T000135Z&X-Amz-SignedHeaders=host&X-Amz-Expires=86400&X-Amz-Credential=ASIAWA6KKHEJORWBH4N3%2F20261110%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=ae4bb70d37c2556eaa2400467fb6cf59bb2dce6ec4c60906228e9aa59b55df6e","created_at":1700592333944,"updated_at":1700592333944,"display_name":"Sample 100 Contacts in United States","status":"FILE_STATUS_PUBLISHED"}],"cost":"PAID","data_source":"","assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"ZoomInfo","description":"Build effective go-to-market engines rapidly and activate a unified data strategy with ZoomInfo, the most comprehensive suite of products and services to power your business with accurate and complete data. 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This meticulously compiled dataset, sourced from official statistical sources, including Departament d'Estadística del Govern d'Andorra, offers a detailed insight into the population landscape of Andorra at the municipality (Parish) level. \n\n ## **Key Features:**\n---\n* **Population Data Andorra:** Gain access to the latest and most accurate population statistics for Andorra, meticulously organized by municipality (Parish).\n\n* **Demographic Insights:** Dive deep into Andorra’s demographic landscape with data on 38 attributes, including: \n* gender,\n* age bands, \n* nationality\n\nAll demographic attributes are available at the municipal (Parish) level.\n\n* **Geodemographic Insights:** Unlock geodemographic data Andorra-wide to inform location-based decision-making, marketing strategies, and policy formulation.\n* **Seamless Data Access:** Our data is available for instant download in CSV format. No lengthy sign-up processes or NDAs required on our side – just download the data you need.\n---\nAdditionally, for those looking to enrich their analysis or project, we offer:\n* **[Administrative Boundaries](https://geolocet.com/products/andorra-all-available-administrative-boundaries):** If you require geographical boundaries (polygons) for the municipalities of Andorra, consider exploring our Administrative Boundaries product, which seamlessly integrates with this dataset.\n\n* **[Point of Interest (PoI) Data](https://geolocet.com/pages/aggregated-points-of-interest-poi-data):** If you're interested in Points of Interest data, please don't hesitate to contact us for customized datasets to enhance your geographical analysis.\n\n* **[Data Customization](https://geolocet.com/pages/data-services-by-geolocet):** We understand that your project may have specific requirements. Feel free to get in touch with us if you need any modifications or specific data formats such as Excel, JSON, GeoJSON, or Shapefile. We are here to tailor the data to your needs.\n\n[Geolocet LTD](https://geolocet.com)\n","terms_of_service":"https://geolocet.com/pages/terms-of-use","documentation_link":"","privacy_policy_link":"https://geolocet.com/pages/privacy-policy","geographical_coverage":"[\"Europe\"]","cost":"FREE","update_frequency":{"interval":1,"unit":"YEARLY"},"assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://geolocet.com/pages/terms-of-use"},"provider_summary":{"name":"Geolocet LTD","description":"Geolocet provides premium geospatial data for retail analysis, GIS, and market planning. 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It is calculated using the rest of the vulnerability information proivded in this dataset into one combined score for easy prioritisation.\n\n- **EPSS (Exploitation Predictive Score):** This predictive score estimates the likelihood of a CVE being exploited in the next 30 days. It is generated using a gradient-boosted machine learning model.\n\n- **CTI Count:** This attribute counts how often a CVE has been found in a collection of monitored cyber threat intelligence reports and articles feeds.\n\n- **Social Media Audience:** It provides an estimate of the number of people who have seen a CVE being discussed on social media platforms, particularly Twitter and Reddit.\n\n- **Vendors:** The dataset includes a list of vendors affected by each CVE.\n\n- **Software CPEs:** This list enumerates the Common Platform Enumerations (CPEs) affected by the CVE. 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The agent verifies intent, produces optimized DLT SQL, and deploys models seamlessly.\n\n**Pipeline Flow**\n- Metadata Enricher Agent (Bronze):Ingests raw Workday HR extracts—including Employees, Job Profiles, Positions, Organizations, Compensation, Time Tracking, and Talent-while automatically documenting schemas, field definitions, HR-specific attributes, and relationships. AI-generated metadata descriptions are enriched with optional HITL review for accuracy and compliance.\n- Silver SQL Generator Agent:Automatically generates standardized L1 relational views for core HR entities and produces L2 incremental DLT SQL pipelines with SCD logic, effective-dating, organizational hierarchy resolution, employee–position linking, and data quality checks for dates, IDs, compensation fields, and employment statuses.\n- Conversational Gold Agent: Empowers HR and People Analytics teams to create curated business-level tables simply by describing them—e.g., “Build a monthly Headcount Snapshot,” “Create an Attrition Events table,” or “Generate a Diversity & Demographics summary.” The agent validates logic, corrects SQL automatically, and ensures consistency across HR KPIs and definitions.\n- DLT Execution: A unified Databricks Delta Live Tables pipeline orchestrates the full HR analytics lifecycle, seamlessly connecting L1, L2, and L3 layers. 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You acknowledge that **Dataplatr Corp** will recognize your organization as a customer/user for promotional and reference purposes\n\nFor a demo or hands-on notebook, feel free to reach out.\n\n***Contact us at**: chandra.reddy@dataplatr.com\n**For consultations or custom inquiries**: https://dataplatr.com/contact-us \n[Linkedin](https://www.linkedin.com/company/dataplatrinc)*\n\n*Please read our published article on [Medium](https://medium.com/@dataplatr) to learn more about our latest insights and innovations.\n[Medium](https://medium.com/@dataplatr)*","terms_of_service":"https://docs.google.com/forms/d/e/1FAIpQLSdnlNmSfrFRQDa_ToOL3ydTOPcWI3m3lWor2plLbiqF1JRINg/viewform","documentation_link":"","privacy_policy_link":"https://dataplatr.com","embedded_notebook_file_infos":[{"id":"d18c0292-fda7-41fb-9e54-27967a1b7475","marketplace_file_type":"EMBEDDED_NOTEBOOK","file_parent":{"parent_id":"77b96f4f-6717-46a6-9fa3-a5169b5b4a63","file_parent_type":"LISTING"},"mime_type":"text/html","download_link":"https://marketplace-file-storage-prod.s3-fips.us-east-1.amazonaws.com/EMBEDDED_NOTEBOOK/LISTING_77b96f4f-6717-46a6-9fa3-a5169b5b4a63/d18c0292-fda7-41fb-9e54-27967a1b7475?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEGgaDnVzLWVhc3QtMS1maXBzIkYwRAIgbC8sWIoYaHR8y7Wq86nPb0pufBl4%2BM4PWpeN%2B5PW9VQCIG7BhAVBDCWTpxFDLeuKYm%2Ff1JLz4iccPMzter0YdhgWKqYECDAQABoMNDE0MzUxNzY3ODI2IgxNAJrHbXaZG20jNfMqgwQyGEb4TsoVOoNvJ%2BaA%2FZ7zeu8hGcHqCeNfblP%2FQo5g6q2aRNtpwtPRKMlhYcIE%2FUQaNT1Lqmyptu32VUQTpzjTTxRVmJrDPkheSCkV06XQ%2FpTMKjU8CC7X1b%2FqYf8CrBwZT7Cv4C5WPjEET38f2A79h0xV6bZgPy28zQWrSeIJ9w6AfZBxiE24%2FVc08zVA0ivCT6lsy7nuU0wcR%2FMg2C9nd1vaMnQ923VpD21CKfVVYcrNWL2b%2FQ%2BNAO%2BxPjPgjiN6UPQbvbvZxkSdhgrGtecIg9NgqGkMcvM5VLTDtdC1kdeZetfOC2obtA%2BT%2F0gy3tIDo6WM5hcQfw743%2FAU9oaGnRBl%2BwoxRRDCZA%2F4jnPdluICSI7NCr6C3HHOe4CSZJjRihczxPA%2FgQczAVjAPs6CrK9N9pIzt0vNJpGUTgg4AoxftTvGfciDREilSctPIcBk6GwB%2Bzt4wbtNCmEPU%2BFD46rc6CWVQmmLwbKOeHw70ipMVFlOc8NlXkEtGO2vu6H44MstSSc6daOVUYUavQSGZ6jGhEjsP2YGz8KDm9XQotB%2FtyVBqiZV01OKqDmmBJA%2FXjUpSJ8M%2FeJXhe5m%2BKcxzNObtoJRI7jci3Tpt08XZyXZ7%2FyIV8H30ya468X3cfkTXrm972uga3z7iXAdubyEHnQIua%2FuvtwOHILi6O5D1Me2MzCPvcnXBjqTAYDoMWBt%2B3AVFuqSs2dmDQNBnvEhXfMgTedKLD5PJHb3aIl15iBkLaNu36GYnVCp9ZU2lSYcTbY5QwNnNeTfJTNy9bKhwzru6Sg%2BslhN%2BTS0zkTn%2BeLMY4tHgPEtEZ2cp%2FCk6GwqswvnYVKHjwO8q%2B1Mx8lc08fVEfarE9dke1GGFqoddx%2Bq%2BZUyHbDlAVfrCmf0yw%3D%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20261110T000135Z&X-Amz-SignedHeaders=host&X-Amz-Expires=86400&X-Amz-Credential=ASIAWA6KKHEJORWBH4N3%2F20261110%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=cdc40dd3d435432cae1ed3cbf2c9928b032cb244dd10a934b771eca6cee3123d","created_at":1774336380213,"updated_at":1774336380213,"display_name":"AELT_Marketplace_HR_databricks","status":"FILE_STATUS_PUBLISHED"}],"assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"Dataplatr Corp","description":"Dataplatr Corp specializes in Data Analytics & AI with pre-built data models for Enterprise Applications like SAP, Oracle EBS, Workday, Salesforce to empower businesses to unlock the full potential of their data. 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Typically, we see this ratio the highest during the holiday period (Nov/Dec), due to an influx of new customers.\n\nIf the ratio is above 1:1, it can help inform a brand that they should focus on retention and cultivation of the new customers and if the ratio is below 1:1 then the brand should focus on trying to acquire new customers.\n\n**What we'll build**\nThis notebook includes an end-to-end solution with Databricks and Amperity. We will analyze results pre- and post- Identity Resolution to determine how Amperity Stitch changes the makeup of their customer base - and in turn - provides more accurate customer insights. \n\nThe accelerator will follow these steps:\n\n- Ingest and prepare a sample set of customer data for analysis.\n- Run the Leaky Bucket Analysis based on AmperityID\n- For comparison, run the Leaky Bucket analysis against CustomerID\n\nTo access the Solution Accelerator Notebook:\n1. Select \"Get Instant Access\" on the top right of the page\n2. Click \"Open\"\n3. 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The goal is to demonstrate how Verticalization/Domain Adaptation for the [Wealth Management] sector can be easily achieved using our two-step approach to LLM Fine-Tuning. An overview of this approach can be found at: [From General-Purpose LLMs to Verticalized Enterprise Models](https://www.bitext.com/blog/general-purpose-models-verticalized-enterprise-genai/)\n\nThe dataset has the following specifications:\n\n- Use Case: Intent Detection\n- Vertical: Wealth Management\n- 24 intents assigned to 5 categories\n- 23806 question/answer pairs, with approximately 1000 per intent\n- 2152 entity/slot types\n- 10 different types of language generation tags\n\nThe categories and intents are derived from Bitext's extensive experience across various industry-specific datasets, ensuring the relevance and applicability across diverse contexts.\n\n**Dataset Token Count**\n\nThe dataset contains a total of 4.74 million tokens across 'instruction' and 'response' columns. This extensive corpus is crucial for training sophisticated LLMs that can perform a variety of functions including conversational AI, question answering, and virtual assistant tasks in the wealth management domain.\n\n**Fields of the Dataset**\n\nEach entry in the dataset comprises the following fields:\n\n- flags: tags\n- instruction: a user request from the Wealth Management domain\n- category: the high-level semantic category for the intent\n- intent: the specific intent corresponding to the user instruction\n- response: an example of an expected response from the virtual assistant\n\n**Categories and Intents**\n\nThe dataset covers a wide range of wealth management-related categories and intents, which are:\n\n- **CONTACT**: contact_manager, customer_service, human_agent\n- **ACCOUNT**: create_account, delete_account, recover_password, requirements_to_create_account\n- **BECOME_CLIENT**: arrange_meeting, become_client, calculate_portfolio_risk, check_fees, check_services, get_manager, minimum_amount_to_invest, run_simulator\n- **MANAGEMENT**: implement_own_plan, transfer_money_to_account, withdraw_money_from_account\n- **PORTFOLIO**: check_balances, check_portfolio, portfolio_performance, portfolio_value, search_for_stocks, set_price_alert\n\n**Entities**\n\nThe entities covered by the dataset include:\n\n- **{{Customer Support Phone Number}}**, **{{Company Website URL}}**, common with all intents.\n- **{{Email Address}}**, **{{Name}}**, common with most intents.\n- **{{Pricing}}**, featured in intents like check_fees.\n- **{{Portfolio}}**, featured in intents like check_portfolio, portfolio_performance, portfolio_value.\n- **{{Investment Platform}}**, relevant to intents such as calculate_portfolio_risk, check_balances.\n- **{{Investments}}**, associated with intents like check_balances, portfolio_performance, set_price_alert.\n- **{{Account Settings}}**, important for intents including delete_account, withdraw_money_from_account.\n- **{{Alerts}}**, typically present in intents such as set_price_alert.\n- **{{Withdrawal}}**, featured in intents like withdraw_money_from_account.\n- **{{Transfer}}**, relevant to intents such as transfer_money_to_account, withdraw_money_from_account.\n- **{{Investment Dashboard}}**, associated with intents like calculate_portfolio_risk, check_portfolio.\n- **{{Investment Summary}}**, important for intents including check_balances, portfolio_performance.\n\nThis comprehensive list of entities ensures that the dataset is well-equipped to train models that are highly adept at understanding and processing a wide range of wealth management-related queries and tasks.\n\n**Language Generation Tags**\n\nThe dataset includes tags indicative of various language variations and styles adapted for Wealth Management, enhancing the robustness and versatility of models trained on this data. These tags categorize the utterances into different registers such as colloquial, formal, or containing specific wealth management jargon, ensuring that the trained models can understand and generate a range of conversational styles appropriate for different customer interactions in the wealth management sector.\n\n**Language Generation Tags**\n\nThe dataset includes tags that reflect various language variations and styles, crucial for creating adaptable and responsive conversational AI models within the wealth management sector. These tags help in understanding and generating appropriate responses based on the linguistic context and user interaction style.\n\n**Tags for Lexical variation**\n\n- **M - Morphological variation**: Adjusts for inflectional and derivational forms.\n - Example: \"is my account active\", \"is my account activated\"\n- **L - Semantic variations**: Handles synonyms, use of hyphens, and compounding.\n - Example: “what's my balance date\", “what's my billing date”\n\n**Tags for Syntactic structure variation**\n\n- **B - Basic syntactic structure**: Simple, direct commands or statements.\n - Example: \"activate my card\", \"I need to check my balance\"\n- **I - Interrogative structure**: Structuring sentences in the form of questions.\n - Example: “can you show my balance?”, “how do I transfer money?”\n- **C - Coordinated syntactic structure**: Complex sentences coordinating multiple ideas or tasks.\n - Example: “I want to transfer money and check my balance, what should I do?”\n- **N - Negation**: Expressing denial or contradiction.\n - Example: \"I do not wish to proceed with this transaction, how can I stop it?\"\n\n**Tags for language register variations**\n\n- **P - Politeness variation**: Polite forms often used in customer service.\n - Example: “could you please help me check my account balance?”\n- **Q - Colloquial variation**: Informal language that might be used in casual customer interactions.\n - Example: \"can u tell me my balance?\"\n- **W - Offensive language**: Handling potentially offensive language which might occasionally appear in frustrated customer interactions.\n - Example: “I’m upset with these charges, this is ridiculous!”\n\n**Tags for stylistic variations**\n\n- **K - Keyword mode**: Responses focused on keywords.\n - Example: \"balance check\", \"account status\"\n- **E - Use of abbreviations**: Common abbreviations.\n - Example: “acct for account”, “trans for transaction”\n- **Z - Errors and Typos**: Includes common misspellings or typographical errors found in customer inputs.\n - Example: “how can I chek my balance”\n\n**Other tags not in use in this Dataset**\n\n- **D - Indirect speech**: Expressing commands or requests indirectly.\n - Example: “I was wondering if you could show me my last transaction.”\n- **G - Regional variations**: Adjustments for regional language differences.\n - Example: American vs British English: \"checking account\" vs \"current account\"\n- **R - Respect structures - Language-dependent variations**: Formality levels appropriate in different languages.\n - Example: Using “vous” in French for formal addressing instead of “tu.”\n- **Y - Code switching**: Switching between languages or dialects within the same conversation.\n - Example: “Can you help me with my cuenta, please?”\n\nThese tags not only aid in training models for a wide range of customer interactions but also ensure that the models are culturally and linguistically sensitive, enhancing the customer experience in wealth management environments.\n\n**License**\n\nThe `Bitext-wealth-management-llm-chatbot-training-dataset` is released under the **Community Data License Agreement (CDLA) Sharing 1.0**. This license facilitates broad sharing and collaboration while ensuring that the freedom to use, share, modify, and utilize the data remains intact for all users.\n\n**Key Aspects of CDLA-Sharing 1.0**\n\n- **Attribution and ShareAlike**: Users must attribute the dataset and continue to share derivatives under the same license.\n- **Non-Exclusivity**: The license is non-exclusive, allowing multiple users to utilize the data simultaneously.\n- **Irrevocability**: Except in cases of material non-compliance, rights under this license are irrevocable.\n- **No Warranty**: The dataset is provided without warranties regarding its accuracy, completeness, or fitness for a particular purpose.\n- **Limitation of Liability**: Both users and data providers limit their liability for damages arising from the use of the dataset.\n\n**Usage Under CDLA-Sharing 1.0**\n\nBy using the `Bitext-wealth-management-llm-chatbot-training-dataset`, you agree to adhere to the terms set forth in the CDLA-Sharing 1.0. 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The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [Anophelines Database in Africa](https://www.johnsnowlabs.com/marketplace/anophelines-database-in-africa)\n\t- This dataset contains 13,465 unique survey locations where anophelines were sampled in the Afrotropical Region south of the Sahara from 1898 to 2016. 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financial health at various organizational levels.\n- **P&L and Expense Analysis** : Detailed breakdown by Account Type, Department and Location for budget vs. actuals tracking.\n- **Cash Flow Summaries** : Tracks opening balance, movements and closing balances by GL Account and Subsidiary.\n- **Account Rollforwards** : Detailed movement for key accounts such as Accounts Receivable, Accounts Payable and Cash.\n- **Compliance Dashboards** : Consolidated financial reports with detailed Posting Periods, Journal Entry IDs and complete audit trails.\n\n\n**Marketplace Deliverables**\n- **Production-Ready Notebooks** : End-to-end NetSuite GL pipeline with error handling, CDC tracking and detailed logging.\n- **Config Templates** : Pre-configured JSON mappings for Journal Entries, Chart of Accounts, Subsidiaries and Departments.\n- **Delta Table Schemas** : Optimized Bronze–Silver–Gold tables for Trial Balance, P&L and Account Rollforwards.\n- **CDC Framework** : Efficient Change Data Capture for near real-time updates to balances and journal entries.\n- **Data Quality Framework** : Automated validation of Debit vs Credit balances, Posting Period completeness and currency conversions.\n\n\n**Documentation & Support**\n- **Implementation Guide** : Step-by-step instructions for NetSuite Saved Searches, ingestion configuration and Databricks deployment.\n- **Configuration Manual** : Field mappings (e.g., Journal Entry Number → Transaction ID, Subsidiary → Ledger Name, Account Code → GL Account Code) with usage guidelines and audit best practices.\n\n\n\n**Why Choose Dataplatr NetSuite GL Accelerator**\n- **Finance Domain Expertise** : Designed by accounting specialists experienced with GL reconciliation, multi-subsidiary consolidation and audit preparation.\n- **Rapid Time-to-Value** : Deploy Trial Balance and P&L analytics quickly using pre-built pipelines and dashboards.\n- **Enterprise-Grade Security & Compliance** : Includes audit trails, data lineage tracking and 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Built on NetSuite and Databricks best practices with finance-first design, this solution delivers immediate visibility into GL transactions while enabling advanced analytics and AI-driven optimization.\n\n\n[Transform financial decision-making with Databricks](https://medium.com/@dataplatr/empowering-financial-decision-making-using-databricks-and-power-bi-insights-5beb6ee7a08c) using CDC-driven pipelines and Lakehouse architecture to deliver actionable insights across GL, AP, AR and Fixed Assets with automated, finance-ready data models.\n\n[Leverage the Databricks Lakehouse Platform to transform People Analytics](https://medium.com/@dataplatr/maximizing-people-analytics-with-the-power-of-databricks-lakehouse-platform-and-omni-analytics-523e4a9cf5e5) with CDC-driven pipelines and real-time insights across headcount, hiring and HR metrics using a scalable, finance-grade data architecture.\n\nNote: By clicking '**Get Access**', you agree to our **Terms of Service**. 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For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [California Death Records by ZIP Code by Age](https://www.johnsnowlabs.com/marketplace/california-death-records-by-zip-code-by-age)\n\t- This dataset contains the count of deaths of residents of the state of California from 2013 to 2016, according to age group. Data is from the California Health and Human Services Open Data Portal.\n- [California ER Asthma Attack Rate by County](https://www.johnsnowlabs.com/marketplace/california-er-asthma-attack-rate-by-county)\n\t- This dataset collects the count of Emergency Department visits with a diagnosis of asthma in the state of California by age group. Data is from the California Health and Human Services Open Data Portal.\n- [Congenital Disease Screening in Newborn in California](https://www.johnsnowlabs.com/marketplace/congenital-disease-screening-in-newborn-in-california)\n\t- This dataset presents the count of newborns with congenital diseases by region and by State of California. Data is from the California Health and Human Services Open Data Portal.\n- [Emergency Admission Diagnosis](https://www.johnsnowlabs.com/marketplace/emergency-admission-diagnosis)\n\t- This dataset from the state of California shows the counts of visits or encounters and admissions in emergency departments from 2010 to 2014 for all hospitals in the state. The data is organized by principal diagnosis, county, facility and year.\n- [Patients with Infections in California by Sex and Year](https://www.johnsnowlabs.com/marketplace/patients-with-infections-in-california-by-sex-and-year)\n\t- This dataset contains data about the infectious diseases reported in California counties by gender and year. The data cover the number of infections, the infections rate and also the confidence intervals calculated for each rate, that are helping for a better understanding the risk for a specific type of infection by location (county), age and gender of the individuals.\n- [Premature Birth Rate by California County](https://www.johnsnowlabs.com/marketplace/premature-birth-rate-by-california-county)\n\t- This dataset contains the count and ratio of all the preterm and very preterm births in the State of California for the period of three years categorized according to the World Health Organization definitions for preterm and very preterm birth.\n- [Surgical Procedure Infections in California](https://www.johnsnowlabs.com/marketplace/surgical-procedure-infections-in-california)\n\t- This dataset contains the Surgical Site Infections (SSI) collected data from the California Department of Public Health (CDPH) Healthcare Associated Infections (HAI) for surgical procedures performed during 2021. 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The territorial vintages of the worldwide area boundaries are compatible with our globally consistent and comparable [socio-demographic](https://www.mbi-geodata.com/en/sociodemographic-data/) and [economic](https://www.mbi-geodata.com/en/purchasing-power-overview/) data.\nOur area boundaries for Turkey are offered at the following levels:\n\n- **Postal Code Level** (Postcode 5-digit): This fine-grained level of detail makes the data suitable for a wide range of applications – for example, enriching customer databases, which are often organized by postal code.\n- **Administrative Level** (İlçeler): Covers both larger administrative areas (e.g., regions, states, provinces) as well as finer subdivisions such as districts, counties, or municipalities.\n- **Micro Level** (Mahalleler): Subdivisions of postal code or administrative boundaries, such as census areas or street segments. 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Bias is reduced through multi-source validation and standardized normalizations across industries and geographies.\n\n**Key Tables Include**\n•\tJob Roles & Pathways – Role-level insights including current skillsets, career transitions leading to and from each role.\n•\tSkill Adjacencies – Mapping of skills to related or adjacent skills, associated job roles, and estimated time to proficiency.\n•\tCareer Path Transitions – Data on forward and backward career moves, including differentiators and required duration.\n•\tSkill Gap Analysis – Transition-based mapping of skills required to move between roles, categorized by type.\n•\tCourse Library – Curated database of training courses with metadata (duration, difficulty, provider, outcomes, and ratings).\n•\tCertification Library – Repository of certifications with issuing authority, skill coverage, cost, and difficulty levels.\n\n**Use Cases:**\n***Map trending skills required for the Digital Marketing role in the Retail Vertical***\t\n\nDraup’s skill demand analytics help identify emerging and high-frequency skills required for a job role within/across industries. Filter by geography, years of experience, or budget to shape role definitions, learning paths, or workforce readiness programs.\t\n\nSELECT\n core_skills.skill,\n COUNT(*) AS skill_count\nFROM job_role_core_and_soft_skills AS core_skills\nJOIN vertical_split_country_job_role AS job_roles\n ON core_skills.job_role = job_roles.job_role\nWHERE core_skills.job_role = 'Digital Marketing'\n AND LOWER(job_roles.industry) LIKE '%retail%'\nGROUP BY core_skills.skill\nORDER BY skill_count DESC;\n\n***Analyze emerging vs. declining skills within Product Management in the Healthcare sector.***\n\nDraup’s longitudinal skill tracking highlights which skills are growing or waning in across roles. Use these insights to future-proof teams, update skill taxonomies, or target relevant upskilling content.\t\n\nSELECT\n core_skills.skill,\n COUNT(*) AS skill_count\nFROM job_role_core_and_soft_skills AS core_skills\nJOIN master_job_role AS master_roles\n ON core_skills.job_role = master_roles.job_role\nJOIN vertical_split_country_job_role AS job_roles\n ON core_skills.job_role = job_roles.job_role\nWHERE LOWER(core_skills.job_role) LIKE '%product%'\n AND job_roles.industry = 'Hospital & Healthcare'\nGROUP BY core_skills.skill\nORDER BY skill_count DESC;\"\nIdentify high-demand skills for Clinical Research & Development family in Southern Asia\tUsing Draup’s real-time demand signals, identify the most sought-after skills segmented by job family, country, or role type to align L&D programs, hiring criteria, or market-entry strategies.\t\"SELECT\n core_skills.skill,\n COUNT(*) AS skill_demand_count\nFROM job_role_core_and_soft_skills AS core_skills\nJOIN master_job_role AS master_roles\n ON core_skills.job_role = master_roles.job_role\nJOIN talent_metrics_country_job_role AS talent_roles\n ON core_skills.job_role = talent_roles.job_role\nWHERE master_roles.job_family = 'Clinical Research & Development'\n AND talent_roles.country IN (\n 'Afghanistan', 'Bangladesh', 'Bhutan', 'India',\n 'Maldives', 'Nepal', 'Pakistan', 'Sri Lanka'\n )\nGROUP BY core_skills.skill\nORDER BY skill_demand_count DESC;\n\n***Analyze emerging vs. declining skills within Product Management in the Healthcare sector.***\t\n\nDraup’s longitudinal skill tracking highlights which skills are growing or waning in across roles. Use these insights to future-proof teams, update skill taxonomies, or target relevant upskilling content.\t\n\nSELECT\n core_skills.skill,\n COUNT(*) AS skill_count\nFROM job_role_core_and_soft_skills AS core_skills\nJOIN master_job_role AS master_roles\n ON core_skills.job_role = master_roles.job_role\nJOIN vertical_split_country_job_role AS job_roles\n ON core_skills.job_role = job_roles.job_role\nWHERE LOWER(core_skills.job_role) LIKE '%product%'\n AND job_roles.industry = 'Hospital & Healthcare'\nGROUP BY core_skills.skill\nORDER BY skill_count DESC;\n\n***Top soft skills in need for Data Scientist  talent in the USA***\t\n\nExplore the most frequently requested soft skills for a job role. Draup’s job description intelligence allows filtering by country, role, and industry to support well-rounded talent assessment, hiring frameworks, or curriculum design.\t\n\nSELECT\n core_skills.skill,\n COUNT(*) AS skill_count\nFROM job_role_core_and_soft_skills AS core_skills\nJOIN master_job_role AS master_roles\n ON core_skills.job_role = master_roles.job_role\nJOIN talent_metrics_country_job_role AS talent_roles\n ON core_skills.job_role = talent_roles.job_role\nWHERE master_roles.job_role = 'Data Scientist'\n AND core_skills.is_soft_skill = TRUE\n AND talent_roles.country IN ('United States', 'United States of America')\nGROUP BY core_skills.skill\nORDER BY skill_count DESC;\n\n***What are the top career paths ahead of Database Administrators and top courses to bridge the skills gap to complete a career transition***\n\nDraup’s career path intelligence reveals the most common forward transitions for a job role; e.g. Data Engineers, Cloud DBAs, or Platform Architects for Database Administrators. Combine this with Draup’s skill gap insights to recommend targeted certifications and courses that accelerate reskilling and enable successful career progression.\t\n\nWITH forward_paths AS (\n SELECT \n start_role as start_role,\n end_role AS target_role\n FROM reskill_career_path_transition\n WHERE LOWER(start_role) = 'database administrator'\n),\nskill_gaps AS (\n SELECT \n f.start_role,\n f.target_role,\n s.skills AS skill_name,\n s.skill_category\n FROM forward_paths f\n JOIN reskill_transition_skill_gap_analysis s\n ON LOWER(s.start_role) = LOWER(f.start_role)\n AND LOWER(s.end_role) = LOWER(f.target_role)\n),\ncourses AS (\n SELECT \n t.skill_name,\n COLLECT_LIST(\n course_title || ' (' || course_provider || ', ' || level_of_difficulty || ', ' || course_duration || ')'\n ) AS recommended_courses\n FROM reskill_course_library\n LATERAL VIEW explode(skills) t AS skill_name\n GROUP BY t.skill_name\n),\ncertifications AS (\n SELECT \n t.skill_name,\n COLLECT_LIST(\n course_title || ' (' || issuing_authority || ')'\n ) AS recommended_certifications\n FROM reskill_certfication_library\n LATERAL VIEW explode(skills) t AS skill_name\n GROUP BY t.skill_name\n)\nSELECT \n sg.start_role,\n sg.target_role,\n sg.skill_name,\n sg.skill_category,\n c.recommended_courses,\n cert.recommended_certifications\nFROM skill_gaps sg\nLEFT JOIN courses c\n ON LOWER(sg.skill_name) = LOWER(c.skill_name)\nLEFT JOIN certifications cert\n ON LOWER(sg.skill_name) = LOWER(cert.skill_name)\nORDER BY sg.target_role, sg.skill_category, sg.skill_name","terms_of_service":"https://draup.com/privacy-policy#:~:text=Unless%20provided%20for%20in%20this,that%20work%20on%20our%20behalf","documentation_link":"","privacy_policy_link":"https://draup.com/privacy-policy","geographical_coverage":"[\"Global\"]","cost":"FREE","update_frequency":{"interval":1,"unit":"WEEKLY"},"collection_granularity":{"interval":1,"unit":"DAILY"},"data_source":"Public Sources","assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"Draup, Inc.","description":"Draup is an enterprise agentic intelligence platform built for HR and Sales leaders navigating transformation. By combining unparalleled real-time global data with agentic AI, Draup enables strategic decision-making across talent and revenue functions.\n \nThrough its Talent Intelligence solution, Draup empowers HR teams to drive workforce planning, precision hiring, predictive skill architecture, and competitive benchmarking. Its Sales Intelligence solution helps go-to-market teams uncover deep account insights, identify buying triggers, and accelerate revenue growth.\n \nTrusted by over 300 global enterprises - including five of the Fortune 10 - Draup supports companies like Microsoft, PepsiCo, PayPal, and Moderna in building future-ready workforces and growth strategies."},"popularity":890},{"id":"32cff7e1-8a55-485b-8cf4-adf300ac18f6","summary":{"name":"Alesco New and Expecting Parents: Reach a High-Value Audience","subtitle":"The Alesco New and Expecting Parents database is your direct line to over 44,000 U.S. addresses of individuals who have ","share":{"name":"alesco_databricks_prenataldb_share","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1756314686468,"updated_at":1756314686468,"categories":["ADVERTISING_AND_MARKETING","DEMOGRAPHICS","MEDIA"],"listingType":"STANDARD","provider_id":"99b197c3-5e13-4d19-91ac-0da396d59a89"},"detail":{"description":"Are you a business that caters to families? The Alesco New and Expecting Parents database is your direct line to over 44,000 U.S. addresses of individuals who have recently welcomed a new baby or are preparing to. Our data allows you to connect with this highly engaged and valuable consumer group at a pivotal moment in their lives.\n\nThis meticulously maintained database is routinely updated to ensure you have a fresh and accurate list of leads. It includes new parents, single mothers, and adoptive parents, giving you broad coverage of this essential market segment.\n\n**Why Choose Our New Parent Data?**\n•\tPerfect Timing: Our data allows you to reach new and expecting parents when they are actively purchasing a wide range of products—from baby furniture and diapers to larger items like new vehicles and homes.\n•\tFresh & Accurate Leads: We continuously update our records to provide you with the most current information, eliminating bad data and ensuring your campaigns are as effective as possible.\n•\tTargeted Audience: These new parents are a prime consumer group with specific needs, making them an ideal audience for products and services related to:\no\tBaby food, diapers, and wipes\no\tStrollers, car seats, and safety products\no\tNew furniture, home decor, and cleaning supplies\no\tClothing, toys, and other essentials\n**Powerful Use Cases:**\n•\tCustomer Acquisition: Identify and acquire new customers who are ready to buy a variety of products and services.\n•\tDirect Marketing: Power your direct mail, email, and catalog campaigns with a targeted list of new and expecting parents.\n•\tAudience Targeting: Create highly specific audience segments for your advertising and promotions to maximize ROI.\n","terms_of_service":"https://alescodata.com/alesco-acceptable-use-policy/","documentation_link":"","privacy_policy_link":"https://alescodata.com/privacy-policy/","data_source":"","assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"Alesco Data","description":"Alesco Data is a leading provider of comprehensive and accurate data, data analytics, and state-of-the-art data technology, empowering businesses to unlock the true potential of their marketing strategies. With our deep expertise in data and data analytics, we help organizations drive growth, enhance customer experiences, and maximize their return on investment."},"popularity":842},{"id":"67b0552b-eb46-4997-8fa9-c609207c0849","summary":{"name":"Falkor Operate","subtitle":"Streamline daily operations and improve asset performance with Falkor's digital twin","setting":{"visibility":"PUBLIC"},"created_at":1790338103244,"updated_at":1790338103244,"categories":["ENERGY","MANUFACTURING"],"listingType":"PERSONALIZED","provider_id":"039595fa-0183-453c-8dc3-a01d601b90f5"},"detail":{"description":"**Overview**\nFalkor Operate is a configurable digital work surface for daily operations at heavy-asset industrial sites. It brings together operational data, registers, tasks, shift logs, handovers, targets, alarms, reports and meetings into role-based views, so operators, shift leads, supervisors and managers always know what is happening, what needs attention, and what to do next.\n\nLarge industrial assets run in complex environments where production, safety, maintenance, integrity, emissions, permits, alarms, work orders and shift activities all need to be coordinated across many roles and systems. Too often, critical information is still scattered across spreadsheets, paper logs, standalone tools and informal shift handovers, creating blind spots, manual rework, and slow response to emerging risk. Falkor Operate replaces that fragmentation with one connected operational surface.\n\nBeyond visualization, Falkor Operate turns operational signals into structured follow-up automatically: deviations become tasks, register items trigger workflows, shift events become handover content, alarms and target exceedances become visible risk indicators, and reports draw from the same live operational record the frontline uses.\n\n**Key benefits**\n- Improve daily operational control with one surface showing plant status, risks, tasks, alarms, targets, maintenance activity, permits and critical events in context.\n- Reduce production losses and inefficiency by centralizing work execution, risk follow-up and operational records, cutting duplicate entry and missed actions.\n- Strengthen safety, compliance and audit readiness through structured logging, workflow approvals, task history and handover records.\n- Improve shift continuity across 24/7 operations with continuous capture of comments, tasks, register updates and observations, structured into reliable handovers.\n- Enable leaner, more scalable operating models with role-based views, mobile field execution, remote access, and enterprise-level KPI aggregation.\n- Build a foundation for analytics and AI by capturing operational activity as structured, contextualized, searchable data linked to roles, shifts, equipment and events.\n\n**Key features**\n*Identify & Scope:* a situational awareness cockpit unifying status, risks, alarms, targets and tasks; configurable operational registers for risk visibility; alarm and target monitoring with real-time trends; role-based context by area, shift and authority; a connected data foundation spanning maintenance, integrity, permits, planning, engineering, HSSE and historian systems; and AI-assisted, model-based decision support.\n\n*Execute & Report:* task management and operator rounds with scheduling, assignment and close-out; mitigation task follow-up linked to equipment, documents and 3D location; structured shift logs and handovers; configurable workflows and approvals with escalation logic; reports and meetings generated in Excel, CSV or PDF; and two-way API integration to read from and write back to connected systems, including Power BI.\n\n**Use cases**\n- Integrated daily operations work surface across many source systems\n- Shift log, shift handover and crew handover\n- Operator rounds and field task execution on desktop or handheld devices\n- Operational risk and register management (leaks, valves, permits, overrides, deferrals)\n- Alarm, target and abnormal situation follow-up\n- Maintenance and operations coordination\n- Operational meetings and enterprise performance monitoring\n\n**Who is it for**\nControl room operators, field operators, shift leads and production supervisors, operations managers and asset leadership, reliability/maintenance/integrity teams, and remote specialists, contractors and administrators who configure the platform.\n\n**Requirements**\nFalkor Operate runs on the Falkor Platform and depends on integration with real-time historians, control and monitoring systems, maintenance, permit, planning, HSSE, integrity, document/engineering and management-of-change systems. Successful deployment requires defined roles, workflow ownership, register governance and reporting needs, plus trusted data, consistent equipment references and clear asset hierarchy. Access is available via browser and mobile for control room, field and remote use.","terms_of_service":"https://falkor.com/trust-centre/b2c/b2c-terms-of-use","documentation_link":"https://falkor.com/","privacy_policy_link":"https://falkor.com/en/trust-centre/privacy","geographical_coverage":"[\"Global\"]","assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://creativecommons.org/licenses/by/4.0/"},"provider_summary":{"name":"Falkor AS","description":"Falkor (formerly Kongsberg Digital, a KONGSBERG company) is an industrial software and AI provider for asset-intensive industries, including oil & gas, renewables & utilities, and chemicals. On Databricks Marketplace, Falkor offers its industrial intelligence platform, the Industrial Work Surface, and complementary modules such as K-Spice and LedaFlow for dynamic process and multiphase flow simulation, SiteCom, and industrial AI agents. Together they contextualise real-time, transactional and master data into digital twins, cockpits and end-to-end workflows for operations, maintenance, asset management and risk management. Through open APIs, customers can connect Falkor solutions to their Databricks lakehouse to unify OT/IT data and scale analytics and AI."},"popularity":769},{"id":"644bbf18-b80a-4418-a1b6-b25119f0fc69","summary":{"name":"IQVIA Data-as-a-Service (DaaS+)","subtitle":"IQVIA's Data-as-a-Service (DaaS+) is a centralized data management solution for clients subscribing to IQVIA data.","setting":{"visibility":"PUBLIC"},"created_at":1788966394743,"updated_at":1788966607974,"categories":["HEALTH"],"listingType":"PERSONALIZED","provider_id":"7fab49b9-819a-4cd8-a7e2-79af51df6f72"},"detail":{"description":"**PLEASE NOTE**: Data availability is determined by your organization’s or customer’s data subscriptions with IQVIA. The licensing of these data assets are subject to local privacy laws, authorizations, and restrictions which may vary by country. \n\n**Overview**\n**IQVIA Data-as-a-Service (DaaS+)** is a centralized data management solution that transforms individual IQVIA country data subscriptions into a globally standardized and harmonized data foundation. This enables organizations to drive consistent enterprise-wide analytics, accelerate data-driven decision-making, and support AI use cases. \n\nAs part of DaaS+, your IQVIA data subscriptions are standardized and integrated through IQVIA’s **Global Data Model (GDM)**. The GDM is the industry’s most comprehensive healthcare data standard, providing a robust and scalable framework for health data integration and exchange. It consolidates hundreds of country-specific data structures into a single global model, enabling a streamlined, end-to-end automated, **one pipeline** approach to data ingestion and management. \n\nBy establishing a common language and integration framework across data sources, the GDM improves data consistency, interoperability, and governance. Combined with a rich business data dictionary, it creates a trusted foundation that enhances data quality, simplifies cross-market analytics, and strengthens organizational readiness for AI-driven applications. \n\nThe GDM also provides an extensible framework for data enrichment and continuously evolves to meet changing industry requirements and emerging business needs. \n\nA second key pillar of DaaS+ is **content harmonization**. DaaS+ delivers IQVIA country data as a single globally harmonized data source, powered by IQVIA’s **MIDAS Product Master**, which provides standardized product definitions and mappings across more than 95 countries. \n\n**Together, the Global Data Model and content harmonization provide a scalable data foundation driving AI-readiness, that helps organizations unlock greater value from their IQVIA data investments and accelerate enterprise-wide analytics initiatives.** \n\n**Benefits customers see are:**\n\nSingle source of truth for IQVIA data providing consistent reporting \n\nContent harmonization using international data attributes supports Product MDM and heavily reduces manual data stewardship (cost reduction) \n\nKey accelerator for building a global Data Foundation (Cost reduction & quick scaling) \n\nBuild globally scalable commercial data products (Cost reduction & quick scaling) \n\nCentralized, standardized and harmonized data including rich data dictionaries is a must for high-quality, reliable AI-based analytics (Cost reduction, Time to insights) \n\n**Product details**\nIQVIA data products available via Data-as-a-Service include but are not limited to: \n\n-National Sales Data: Continuous country level market surveys that measure sales of registered medicines either into or out of retail pharmacies, hospitals, or other dispensing channels. \n\n-National Prescription Data: Continuous country level market survey that measures sales of registered medicines against a prescription from retail pharmacies to consumers, based on a representative pharmacy sample. Unlike sales audits, includes information on number of prescriptions and prescribing activity by doctor speciality. \n\n-Sub-National Sales Data: Continuous Information regarding sales of registered drugs either into or out of retail pharmacies or hospitals, and reported in detailed geographic territories, or bricks, enabling more granular understanding of volume of sales per territory. \n\n-Sub-National Prescription Data: Continuous market survey that measures prescriptions of registered medicines dispensed from retail pharmacies, and reported in detailed geographic territories, or bricks, and in some markets with the ability to link the data back to the origin of the prescription and doctor specialty. \n\n \n\n-OneKey: OneKey is a reference data asset that provides a complete and integrated view of the evolving environment by linking prescribers and their many locations to their affiliates with group practices, hospitals, clinics and other healthcare provider organizations, with insights into 680K organizations and 9M+ professionals. Please see the usage examples below for a sample of fields provided. \n\n-Xponent: A suite of sub-national reporting providing granular prescription performance aligned to help manage customer operations, sales targeting, and representative incentive compensation \n\n-DDD: For over 40 years, DDD has been providing pharmaceutical manufacturers with insight into which non-retail outlets are purchasing their and their competitors’ products and how sales change over time. DDD provides zip-code and account-level sales of prescription products sold into retail, non-retail and mail channels. \n\n-APLD: APLD is IQVIA’s Anonymized Patient Level Data – the lowest level of granularity of transactional patient data containing longitudinal prescription (LRx) and medical claims (Dx) data. \n\n-LAAD: LAAD is an all-inclusive master patient centric dataset, which integrates Longitudinal Prescriptions (LRx), Lifecycle (FIA) and Professional Medical (Dx) transaction claims with the option to also include Institutional Medical (Hx) and Remittance claims. LAAD not only integrates the datasets, LAAD claims are cleansed and business logic is applied. Primary and Secondary payer information and copays are integrated into LAAD as well. \n\n-Channel Dynamics: Continuous information service providing key metrics and benchmark insights into competitive sales force and marketing channel activity of the pharma industry, based on a proprietary online daily diary survey of over 30,000 HCPs across 30+ countries, capturing their exposure to life-science communication channels, the resulting data are processed and projected to show both promotional contact volume and investment by channel at company and brand level. \n\n-LRx: Longitudinal patient-level prescription data at national and subnational level sourced from computerized dispensed prescription records at the anonymized patient level collected from retail pharmacies.\n\n**Terms relating to IQVIA data and services are addressed separately in contracts between IQVIA and its clients.**","terms_of_service":"https://www.iqvia.com/about-us/privacy","documentation_link":"","privacy_policy_link":"https://www.iqvia.com/about-us/privacy","assets":["ASSET_TYPE_DATA_TABLE","ASSET_TYPE_MEDIA"],"license":""},"provider_summary":{"name":"IQVIA","description":"IQVIA (NYSE:IQV) is a leading global provider of advanced analytics, technology solutions and clinical research services to the life sciences industry dedicated to delivering actionable insights. IQVIA has the largest global healthcare data networks in the world, with over 1 million data sources providing access to 800M+ non-identified patient records and 95B+ healthcare records processed annually in over 100 countries. Learn more at www.iqvia.com."},"popularity":1986},{"id":"d726072c-5185-4569-bb57-3ca38851ab57","summary":{"name":"Health Plan Price Transparency MRF","subtitle":"Sample health plan price transparency MRF data parsed with Databricks accelerator for analytics and insights","share":{"name":"exchange_health_care_mrf","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1764972532623,"updated_at":1765295462544,"categories":["HEALTH"],"listingType":"STANDARD","provider_id":"ea1e69ff-0127-4c94-bf39-e841fe1d19d2"},"detail":{"description":"**Databricks Healthcare & Life Sciences** team is providing a sample of health plan price transparency Machine Readable Files (MRFs) for the purpose of organizations being able to POC and/or MVP data products regarding insights from this data. \n\nThe file(s) are stored in Databricks volumes in json or[ json.gz](http://json.gz) format, are often hundreds of GB in size, and follow the schemas set forth by [CMS](https://github.com/CMSgov/price-transparency-guide/tree/master). Because of the size of each individual json object, we cannot use traditional json parsers and instead use an [open source Databricks solution accelerator](https://github.com/databricks-industry-solutions/hls-payer-mrf-sparkstreaming) to parse data into relational tables (see Notebooks attached). \n\nThe relational data is derived from running the accelerator and parsing out the data to consume it for analytics and insights. The sample notebook shows how to use this accelerator and run queries against this data. The tables shared are the outputs of running the notebook on the MRF data. \n\n\nFor a deeper understanding please check out the following:\n\n- [Blog](https://www.databricks.com/blog/2023/02/02/creating-health-plan-price-transparency-coverage-lakehouse.html) on how the json data is parsed into relational tables in the accelerator \n- [Workshop](https://vimeo.com/810277096) on how to better leverage the MRF data and how customers are using this information in their organization 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CDER dataset contains a list of biological products regulated by the Center for Drug Evaluation and Research (CDER).\n\n**Benefits**\n- available in clean well curated dataset making it easier for medical professionals to search for generic equivalents to brand-name drugs, drug patents and drug exclusivity.\n- patients and doctors can see approved uses for drugs and patent expiration dates for name-brand drugs. for example, a search for the prescription antidepressant drug prozac shows that the drug is available in four dosage forms of varying strengths, all of which are capsules taken orally.\n- at par with electronic versions of the orange book, as its updated monthly.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes.\n\n**Included Datasets**\n- [Approved Drug Products with Therapeutic Equivalence Exclusivity](https://www.johnsnowlabs.com/marketplace/approved-drug-products-with-therapeutic-equivalence-exclusivity/)\n\t- This dataset is also called Orange Book. The dataset contains application type, exclusivity code, exclusivity date etc for approved drug products with therapeutic equivalence.\n- [Approved Drug Products with Therapeutic Equivalence Patent](https://www.johnsnowlabs.com/marketplace/approved-drug-products-with-therapeutic-equivalence-patent/)\n\t- This dataset is also called Orange Book. The dataset contains drug ingredient, dosage form, route, product number, therapeutic equivalence code, whether or not it is reference listed drug etc.\n- [Approved Drug Products with Therapeutic Equivalence Products](https://www.johnsnowlabs.com/marketplace/approved-drug-products-with-therapeutic-equivalence-products/)\n\t- This dataset is also called Orange Book. The dataset contains drug ingredient, dosage form, route, trade name, strength, product number, therapeutic equivalence code etc. for approved drug products with therapeutic equivalence.\n- [Purple Book Database of Licensed Biological Products](https://www.johnsnowlabs.com/marketplace/purple-book-database-of-licensed-biological-products/)\n\t- This dataset is a download from the FDA Purple Book, the searchable database of biological products licensed by the U.S. FDA under the Public Health Service Act. 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Derived through extensive multi-party sourcing, this structured, high-quality data originates from:\n- 1st-party proprietary research and direct surveys\n- Regulatory filings from global jurisdictions\n- Direct submissions from fund managers and investors\n- Intelligence sourced through PERE events and networks\n- Continuous input from the industry’s most recognised editorial team\n\nEach table is professionally validated through:\n- Multi-stage programmatic and human review\n- Cross-referencing across historical datasets\n- Manual validation by real estate specialists\n- Ongoing updates and historical enrichment\n- Normalised schema alignment across institutions, funds, performance, and commitments\n\nThe result is **an actionable dataset built for accuracy, completeness, and reliability** to enhance smarter investing decisions.\n\n\n**What’s included?**\nIn this sample dataset you will receive 10 rows of data across the below categories. \n\n*The industry’s leading editorial analysis and archive*\nDecades of PERE reporting — interviews, features, analyses, rankings, trend narratives, market shifts — transformed into a structured, queryable knowledge layer that contextualises fundraising, deal activity, and strategy evolution.\n\n*Investor (LP) Profiles*\nDeep intelligence on pension funds, insurance companies, sovereign wealth funds, family offices, endowments, wealth managers, and governments actively allocating to private real estate funds:\n- Mandates, sector focus and asset-class allocations\n- Ticket sizes, pacing, re-ups and geographic preferences\n- AUM history and commitment behaviour\n- LP strategy shifts surfaced early via editorial coverage\n\n*Investor commitments*\nComprehensive LP → fund commitments:\n- Amounts, dates, pacing patterns\n- Co-investment participation\n- Fundraising cycle behaviour\n- Forward-looking allocation signals\n\n*Investor mandates*\nForward-looking, actionable data and insights including:\n- Allocation targets and ranges\n- ESG 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Please review the license terms before using the dataset.\n\nFor any questions or inquiries regarding the dataset or its usage, please refer to the above pages for contact information.\n\n---\nNote: This README is provided as part of the dataset attribution for educational purposes. 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All data comes from a single source, ensuring **consistency and comparability** across markets.\n\nThe Sociodemographics-Bundle Basic for Austria includes the following **variables**:\n- [population](https://www.mbi-geodata.com/en/sociodemographic-data/population/)\n- [gender](https://www.mbi-geodata.com/en/sociodemographic-data/sex-gender/)\n- [age structure](https://www.mbi-geodata.com/en/sociodemographic-data/agebands/) (15-year age bands)\n- household figures ([number of households](https://www.mbi-geodata.com/en/sociodemographic-data/households/) and [households by type](https://www.mbi-geodata.com/en/sociodemographic-data/households-by-type/))\n\nOur sociodemographic data is offered for each country on different **levels**:\n- **Postal Code Level:** This fine-grained level of detail makes the data suitable for a wide range of applications – for example, enriching customer databases, which are often organized by postal code.\n- **Administrative Level:** Covers both larger administrative areas (e.g., regions, states, provinces) as well as finer subdivisions such as districts, counties, or municipalities.\n- **Micro Level**: Subdivisions of postal code or administrative boundaries, such as census areas or street segments. 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The platform unifies market-leading data quality and observability with catalog, lineage, and reference data to help organizations streamline their data management tech stack. The embedded ONE AI Agent acts as a digital data steward, continuously monitoring and improving data reliability, maximizing time-to-value, and team efficiency. 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By securely integrating with your own first-party data, the platform builds highly accurate lookalike audiences and B2C intent segments modeled on your most valuable fans, including season ticket holders, merchandise buyers, and high-value collectors.\n\nThe platform provides real-time B2C intent signals from millions of daily fan interactions, giving sports and collectibles organizations the ability to capture demand exactly as it emerges. Whether optimizing ticket pricing, targeting merchandise campaigns, enhancing in-stadium fan experiences, or identifying collectors searching for specific players or teams, the system provides the intelligence needed to engage fans at the right moment. 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Our proprietary ontology links and normalizes a variety of content sources to ensure consistency, transparency, and data integrity across your business."},"popularity":513},{"id":"e6e93183-b315-4eb8-8a7c-947327974648","summary":{"name":"SAMPLE Premium USA B2C Database | 290M+ Personal Emails |105M+ Households","subtitle":"Premium USA B2C database with nationwide coverage, featuring 290M+ verified personal emails and regularly updated ...","share":{"name":"lakeb2b_4c8ba298_bf4f_4ac3_abca_43b9df4ecc19_samples_share","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1769781568856,"updated_at":1792009682782,"categories":["OTHER","ADVERTISING_AND_MARKETING"],"listingType":"STANDARD","provider_id":"258841ce-a620-465c-9af8-a230e2bac7e2"},"detail":{"description":"\uD835\uDC15\uD835\uDC1E\uD835\uDC2B\uD835\uDC22\uD835\uDC1F\uD835\uDC22\uD835\uDC1E\uD835\uDC1D \uD835\uDC02\uD835\uDC28\uD835\uDC27\uD835\uDC2C\uD835\uDC2E\uD835\uDC26\uD835\uDC1E\uD835\uDC2B \uD835\uDC04\uD835\uDC26\uD835\uDC1A\uD835\uDC22\uD835\uDC25 & \uD835\uDC03\uD835\uDC1E\uD835\uDC26\uD835\uDC28\uD835\uDC20\uD835\uDC2B\uD835\uDC1A\uD835\uDC29\uD835\uDC21\uD835\uDC22\uD835\uDC1C \uD835\uDC03\uD835\uDC1A\uD835\uDC2D\uD835\uDC1A – \uD835\uDFD0\uD835\uDFD7\uD835\uDFCE\uD835\uDC0C+ \uD835\uDC11\uD835\uDC1E\uD835\uDC1C\uD835\uDC28\uD835\uDC2B\uD835\uDC1D\uD835\uDC2C\n\nLake B2B offers one of the most comprehensive consumer data assets in the market, comprising \uD835\uDC28\uD835\uDC2F\uD835\uDC1E\uD835\uDC2B \uD835\uDFD0\uD835\uDFD7\uD835\uDFCE \uD835\uDC26\uD835\uDC22\uD835\uDC25\uD835\uDC25\uD835\uDC22\uD835\uDC28\uD835\uDC27 \uD835\uDC2F\uD835\uDC1E\uD835\uDC2B\uD835\uDC22\uD835\uDC1F\uD835\uDC22\uD835\uDC1E\uD835\uDC1D \uD835\uDC29\uD835\uDC1E\uD835\uDC2B\uD835\uDC2C\uD835\uDC28\uD835\uDC27\uD835\uDC1A\uD835\uDC25 \uD835\uDC1C\uD835\uDC28\uD835\uDC27\uD835\uDC2C\uD835\uDC2E\uD835\uDC26\uD835\uDC1E\uD835\uDC2B \uD835\uDC1E\uD835\uDC26\uD835\uDC1A\uD835\uDC22\uD835\uDC25 \uD835\uDC2B\uD835\uDC1E\uD835\uDC1C\uD835\uDC28\uD835\uDC2B\uD835\uDC1D\uD835\uDC2C, enriched with detailed demographic, geographic, lifestyle, and behavioral intelligence.\n\nThis robust dataset is designed to support \uD835\uDC21\uD835\uDC22\uD835\uDC20\uD835\uDC21-\uD835\uDC29\uD835\uDC2B\uD835\uDC1E\uD835\uDC1C\uD835\uDC22\uD835\uDC2C\uD835\uDC22\uD835\uDC28\uD835\uDC27 \uD835\uDC2D\uD835\uDC1A\uD835\uDC2B\uD835\uDC20\uD835\uDC1E\uD835\uDC2D\uD835\uDC22\uD835\uDC27\uD835\uDC20, \uD835\uDC1A\uD835\uDC1D\uD835\uDC2F\uD835\uDC1A\uD835\uDC27\uD835\uDC1C\uD835\uDC1E\uD835\uDC1D \uD835\uDC1A\uD835\uDC2E\uD835\uDC1D\uD835\uDC22\uD835\uDC1E\uD835\uDC27\uD835\uDC1C\uD835\uDC1E \uD835\uDC2C\uD835\uDC1E\uD835\uDC20\uD835\uDC26\uD835\uDC1E\uD835\uDC27\uD835\uDC2D\uD835\uDC1A\uD835\uDC2D\uD835\uDC22\uD835\uDC28\uD835\uDC27, \uD835\uDC1A\uD835\uDC27\uD835\uDC1D \uD835\uDC1D\uD835\uDC1A\uD835\uDC2D\uD835\uDC1A-\uD835\uDC1D\uD835\uDC2B\uD835\uDC22\uD835\uDC2F\uD835\uDC1E\uD835\uDC27 \uD835\uDC26\uD835\uDC1A\uD835\uDC2B\uD835\uDC24\uD835\uDC1E\uD835\uDC2D\uD835\uDC22\uD835\uDC27\uD835\uDC20, \uD835\uDC1A\uD835\uDC27\uD835\uDC1A\uD835\uDC25\uD835\uDC32\uD835\uDC2D\uD835\uDC22\uD835\uDC1C\uD835\uDC2C, \uD835\uDC1A\uD835\uDC27\uD835\uDC1D \uD835\uDC2B\uD835\uDC1E\uD835\uDC2C\uD835\uDC1E\uD835\uDC1A\uD835\uDC2B\uD835\uDC1C\uD835\uDC21 \uD835\uDC22\uD835\uDC27\uD835\uDC22\uD835\uDC2D\uD835\uDC22\uD835\uDC1A\uD835\uDC2D\uD835\uDC22\uD835\uDC2F\uD835\uDC1E\uD835\uDC2C across a wide range of industries.\n\nEvery record is validated for \uD835\uDC1A\uD835\uDC1C\uD835\uDC1C\uD835\uDC2E\uD835\uDC2B\uD835\uDC1A\uD835\uDC1C\uD835\uDC32, \uD835\uDC1F\uD835\uDC2B\uD835\uDC1E\uD835\uDC2C\uD835\uDC21\uD835\uDC27\uD835\uDC1E\uD835\uDC2C\uD835\uDC2C, \uD835\uDC1A\uD835\uDC27\uD835\uDC1D \uD835\uDC1C\uD835\uDC28\uD835\uDC26\uD835\uDC29\uD835\uDC25\uD835\uDC22\uD835\uDC1A\uD835\uDC27\uD835\uDC1C\uD835\uDC1E, ensuring strong deliverability, reliability, and suitability for compliant outreach, audience modeling, enrichment, and insight generation.\n\n\uD835\uDC02\uD835\uDC28\uD835\uDC2B\uD835\uDC1E \uD835\uDC02\uD835\uDC28\uD835\uDC27\uD835\uDC2D\uD835\uDC1A\uD835\uDC1C\uD835\uDC2D & \uD835\uDC08\uD835\uDC1D\uD835\uDC1E\uD835\uDC27\uD835\uDC2D\uD835\uDC22\uD835\uDC2D\uD835\uDC32 \uD835\uDC00\uD835\uDC2D\uD835\uDC2D\uD835\uDC2B\uD835\uDC22\uD835\uDC1B\uD835\uDC2E\uD835\uDC2D\uD835\uDC1E\uD835\uDC2C\n• Verified Personal Email Address\n• Contact Name\n• First Name\n• Last Name\n• Gender\n• Age\n• Marital Status\n• Presence of Children\n\nThese attributes support \uD835\uDC29\uD835\uDC1E\uD835\uDC2B\uD835\uDC2C\uD835\uDC28\uD835\uDC27\uD835\uDC1A\uD835\uDC25\uD835\uDC22\uD835\uDC33\uD835\uDC1E\uD835\uDC1D \uD835\uDC1C\uD835\uDC28\uD835\uDC26\uD835\uDC26\uD835\uDC2E\uD835\uDC27\uD835\uDC22\uD835\uDC1C\uD835\uDC1A\uD835\uDC2D\uD835\uDC22\uD835\uDC28\uD835\uDC27, \uD835\uDC1D\uD835\uDC1E\uD835\uDC26\uD835\uDC28\uD835\uDC20\uD835\uDC2B\uD835\uDC1A\uD835\uDC29\uD835\uDC21\uD835\uDC22\uD835\uDC1C \uD835\uDC1A\uD835\uDC27\uD835\uDC1A\uD835\uDC25\uD835\uDC32\uD835\uDC2C\uD835\uDC22\uD835\uDC2C, \uD835\uDC1A\uD835\uDC27\uD835\uDC1D \uD835\uDC22\uD835\uDC27\uD835\uDC1D\uD835\uDC22\uD835\uDC2F\uD835\uDC22\uD835\uDC1D\uD835\uDC2E\uD835\uDC1A\uD835\uDC25- \uD835\uDC1A\uD835\uDC27\uD835\uDC1D \uD835\uDC21\uD835\uDC28\uD835\uDC2E\uD835\uDC2C\uD835\uDC1E\uD835\uDC21\uD835\uDC28\uD835\uDC25\uD835\uDC1D-\uD835\uDC25\uD835\uDC1E\uD835\uDC2F\uD835\uDC1E\uD835\uDC25 \uD835\uDC29\uD835\uDC2B\uD835\uDC28\uD835\uDC1F\uD835\uDC22\uD835\uDC25\uD835\uDC22\uD835\uDC27\uD835\uDC20.\n\n\uD835\uDC06\uD835\uDC1E\uD835\uDC28\uD835\uDC20\uD835\uDC2B\uD835\uDC1A\uD835\uDC29\uD835\uDC21\uD835\uDC22\uD835\uDC1C & \uD835\uDC0B\uD835\uDC28\uD835\uDC1C\uD835\uDC1A\uD835\uDC2D\uD835\uDC22\uD835\uDC28\uD835\uDC27 \uD835\uDC03\uD835\uDC1A\uD835\uDC2D\uD835\uDC1A\n• Address\n• City\n• State\n• ZIP Code\n• Country\n• Length of Residence\n\nEnables \uD835\uDC2B\uD835\uDC1E\uD835\uDC20\uD835\uDC22\uD835\uDC28\uD835\uDC27\uD835\uDC1A\uD835\uDC25 \uD835\uDC2D\uD835\uDC1A\uD835\uDC2B\uD835\uDC20\uD835\uDC1E\uD835\uDC2D\uD835\uDC22\uD835\uDC27\uD835\uDC20, \uD835\uDC25\uD835\uDC28\uD835\uDC1C\uD835\uDC1A\uD835\uDC25\uD835\uDC22\uD835\uDC33\uD835\uDC1E\uD835\uDC1D \uD835\uDC1C\uD835\uDC1A\uD835\uDC26\uD835\uDC29\uD835\uDC1A\uD835\uDC22\uD835\uDC20\uD835\uDC27 \uD835\uDC1E\uD835\uDC31\uD835\uDC1E\uD835\uDC1C\uD835\uDC2E\uD835\uDC2D\uD835\uDC22\uD835\uDC28\uD835\uDC27, \uD835\uDC2D\uD835\uDC1E\uD835\uDC2B\uD835\uDC2B\uD835\uDC22\uD835\uDC2D\uD835\uDC28\uD835\uDC2B\uD835\uDC32 \uD835\uDC29\uD835\uDC25\uD835\uDC1A\uD835\uDC27\uD835\uDC27\uD835\uDC22\uD835\uDC27\uD835\uDC20, \uD835\uDC1A\uD835\uDC27\uD835\uDC1D \uD835\uDC26\uD835\uDC1A\uD835\uDC2B\uD835\uDC24\uD835\uDC1E\uD835\uDC2D \uD835\uDC1E\uD835\uDC31\uD835\uDC29\uD835\uDC1A\uD835\uDC27\uD835\uDC2C\uD835\uDC22\uD835\uDC28\uD835\uDC27 \uD835\uDC1A\uD835\uDC27\uD835\uDC1A\uD835\uDC25\uD835\uDC32\uD835\uDC2C\uD835\uDC22\uD835\uDC2C.\n\n\uD835\uDC07\uD835\uDC28\uD835\uDC2E\uD835\uDC2C\uD835\uDC1E\uD835\uDC21\uD835\uDC28\uD835\uDC25\uD835\uDC1D & \uD835\uDC12\uD835\uDC28\uD835\uDC1C\uD835\uDC22\uD835\uDC28\uD835\uDC1E\uD835\uDC1C\uD835\uDC28\uD835\uDC27\uD835\uDC28\uD835\uDC26\uD835\uDC22\uD835\uDC1C \uD835\uDC08\uD835\uDC27\uD835\uDC1D\uD835\uDC22\uD835\uDC1C\uD835\uDC1A\uD835\uDC2D\uD835\uDC28\uD835\uDC2B\uD835\uDC2C\n• Household Income Range\n• Homeowner / Renter Status\n• Dwelling Type\n\nIdeal for \uD835\uDC1F\uD835\uDC22\uD835\uDC27\uD835\uDC1A\uD835\uDC27\uD835\uDC1C\uD835\uDC22\uD835\uDC1A\uD835\uDC25 \uD835\uDC2C\uD835\uDC1E\uD835\uDC20\uD835\uDC26\uD835\uDC1E\uD835\uDC27\uD835\uDC2D\uD835\uDC1A\uD835\uDC2D\uD835\uDC22\uD835\uDC28\uD835\uDC27, \uD835\uDC2B\uD835\uDC1E\uD835\uDC1A\uD835\uDC25 \uD835\uDC1E\uD835\uDC2C\uD835\uDC2D\uD835\uDC1A\uD835\uDC2D\uD835\uDC1E \uD835\uDC22\uD835\uDC27\uD835\uDC2C\uD835\uDC22\uD835\uDC20\uD835\uDC21\uD835\uDC2D\uD835\uDC2C, \uD835\uDC22\uD835\uDC27\uD835\uDC2C\uD835\uDC2E\uD835\uDC2B\uD835\uDC1A\uD835\uDC27\uD835\uDC1C\uD835\uDC1E \uD835\uDC26\uD835\uDC28\uD835\uDC1D\uD835\uDC1E\uD835\uDC25\uD835\uDC22\uD835\uDC27\uD835\uDC20, \uD835\uDC25\uD835\uDC2E\uD835\uDC31\uD835\uDC2E\uD835\uDC2B\uD835\uDC32 \uD835\uDC2F\uD835\uDC2C. \uD835\uDC2F\uD835\uDC1A\uD835\uDC25\uD835\uDC2E\uD835\uDC1E-\uD835\uDC1B\uD835\uDC1A\uD835\uDC2C\uD835\uDC1E\uD835\uDC1D \uD835\uDC2D\uD835\uDC1A\uD835\uDC2B\uD835\uDC20\uD835\uDC1E\uD835\uDC2D\uD835\uDC22\uD835\uDC27\uD835\uDC20, and consumer affordability analysis.\n\n\uD835\uDC0B\uD835\uDC22\uD835\uDC1F\uD835\uDC1E\uD835\uDC2C\uD835\uDC2D\uD835\uDC32\uD835\uDC25\uD835\uDC1E, \uD835\uDC08\uD835\uDC27\uD835\uDC2D\uD835\uDC1E\uD835\uDC2B\uD835\uDC1E\uD835\uDC2C\uD835\uDC2D & \uD835\uDC01\uD835\uDC1E\uD835\uDC21\uD835\uDC1A\uD835\uDC2F\uD835\uDC22\uD835\uDC28\uD835\uDC2B\uD835\uDC1A\uD835\uDC25 \uD835\uDC00\uD835\uDC2D\uD835\uDC2D\uD835\uDC2B\uD835\uDC22\uD835\uDC1B\uD835\uDC2E\uD835\uDC2D\uD835\uDC1E\uD835\uDC2C\n• Interest Categories\n• Buyer Type\n• Traveler Type\n• Wine Enthusiast Indicator\n• Wine Price Preference\n• Charity Donor Indicator\n• Charity Affiliation\n\nHelps brands align messaging with \uD835\uDC1C\uD835\uDC28\uD835\uDC27\uD835\uDC2C\uD835\uDC2E\uD835\uDC26\uD835\uDC1E\uD835\uDC2B \uD835\uDC29\uD835\uDC2B\uD835\uDC1E\uD835\uDC1F\uD835\uDC1E\uD835\uDC2B\uD835\uDC1E\uD835\uDC27\uD835\uDC1C\uD835\uDC1E\uD835\uDC2C, \uD835\uDC29\uD835\uDC2E\uD835\uDC2B\uD835\uDC1C\uD835\uDC21\uD835\uDC1A\uD835\uDC2C\uD835\uDC22\uD835\uDC27\uD835\uDC20 \uD835\uDC22\uD835\uDC27\uD835\uDC2D\uD835\uDC1E\uD835\uDC27\uD835\uDC2D, \uD835\uDC25\uD835\uDC22\uD835\uDC1F\uD835\uDC1E\uD835\uDC2C\uD835\uDC2D\uD835\uDC32\uD835\uDC25\uD835\uDC1E \uD835\uDC1B\uD835\uDC1E\uD835\uDC21\uD835\uDC1A\uD835\uDC2F\uD835\uDC22\uD835\uDC28\uD835\uDC2B, \uD835\uDC1A\uD835\uDC27\uD835\uDC1D \uD835\uDC1B\uD835\uDC2B\uD835\uDC1A\uD835\uDC27\uD835\uDC1D \uD835\uDC1A\uD835\uDC1F\uD835\uDC1F\uD835\uDC22\uD835\uDC27\uD835\uDC22\uD835\uDC2D\uD835\uDC32.\n\n\uD835\uDC05\uD835\uDC1A\uD835\uDC26\uD835\uDC22\uD835\uDC25\uD835\uDC32 & \uD835\uDC0F\uD835\uDC1E\uD835\uDC2D \uD835\uDC0E\uD835\uDC30\uD835\uDC27\uD835\uDC1E\uD835\uDC2B\uD835\uDC2C\uD835\uDC21\uD835\uDC22\uD835\uDC29 \uD835\uDC03\uD835\uDC1A\uD835\uDC2D\uD835\uDC1A\n• Pet Ownership Indicator\n• Pet Type\n\nHighly valuable for \uD835\uDC29\uD835\uDC1E\uD835\uDC2D \uD835\uDC1B\uD835\uDC2B\uD835\uDC1A\uD835\uDC27\uD835\uDC1D\uD835\uDC2C, \uD835\uDC2F\uD835\uDC1E\uD835\uDC2D\uD835\uDC1E\uD835\uDC2B\uD835\uDC22\uD835\uDC27\uD835\uDC1A\uD835\uDC2B\uD835\uDC22\uD835\uDC1A\uD835\uDC27\uD835\uDC2C, \uD835\uDC22\uD835\uDC27\uD835\uDC2C\uD835\uDC2E\uD835\uDC2B\uD835\uDC1E\uD835\uDC2B\uD835\uDC2C, \uD835\uDC2C\uD835\uDC2E\uD835\uDC1B\uD835\uDC2C\uD835\uDC1C\uD835\uDC2B\uD835\uDC22\uD835\uDC29\uD835\uDC2D\uD835\uDC22\uD835\uDC28\uD835\uDC27 \uD835\uDC2C\uD835\uDC1E\uD835\uDC2B\uD835\uDC2F\uD835\uDC22\uD835\uDC1C\uD835\uDC1E\uD835\uDC2C, \uD835\uDC1A\uD835\uDC27\uD835\uDC1D \uD835\uDC25\uD835\uDC22\uD835\uDC1F\uD835\uDC1E\uD835\uDC2C\uD835\uDC2D\uD835\uDC32\uD835\uDC25\uD835\uDC1E \uD835\uDC26\uD835\uDC1A\uD835\uDC2B\uD835\uDC24\uD835\uDC1E\uD835\uDC2D\uD835\uDC1E\uD835\uDC2B\uD835\uDC2C.\n\n\uD835\uDC00\uD835\uDC2E\uD835\uDC2D\uD835\uDC28\uD835\uDC26\uD835\uDC28\uD835\uDC2D\uD835\uDC22\uD835\uDC2F\uD835\uDC1E \uD835\uDC0E\uD835\uDC30\uD835\uDC27\uD835\uDC1E\uD835\uDC2B\uD835\uDC2C\uD835\uDC21\uD835\uDC22\uD835\uDC29 \uD835\uDC03\uD835\uDC1E\uD835\uDC2D\uD835\uDC1A\uD835\uDC22\uD835\uDC25\uD835\uDC2C\n• Auto Owner Indicator\n• Vehicle Make\n• Vehicle Model\n\nSupports \uD835\uDC1A\uD835\uDC2E\uD835\uDC2D\uD835\uDC28\uD835\uDC26\uD835\uDC28\uD835\uDC2D\uD835\uDC22\uD835\uDC2F\uD835\uDC1E \uD835\uDC26\uD835\uDC1A\uD835\uDC2B\uD835\uDC24\uD835\uDC1E\uD835\uDC2D\uD835\uDC22\uD835\uDC27\uD835\uDC20, \uD835\uDC22\uD835\uDC27\uD835\uDC2C\uD835\uDC2E\uD835\uDC2B\uD835\uDC1A\uD835\uDC27\uD835\uDC1C\uD835\uDC1E \uD835\uDC2E\uD835\uDC27\uD835\uDC1D\uD835\uDC1E\uD835\uDC2B\uD835\uDC30\uD835\uDC2B\uD835\uDC22\uD835\uDC2D\uD835\uDC22\uD835\uDC27\uD835\uDC20, \uD835\uDC1A\uD835\uDC1F\uD835\uDC2D\uD835\uDC1E\uD835\uDC2B\uD835\uDC26\uD835\uDC1A\uD835\uDC2B\uD835\uDC24\uD835\uDC1E\uD835\uDC2D \uD835\uDC2C\uD835\uDC1A\uD835\uDC25\uD835\uDC1E\uD835\uDC2C, \uD835\uDC28\uD835\uDC30\uD835\uDC27\uD835\uDC1E\uD835\uDC2B\uD835\uDC2C\uD835\uDC21\uD835\uDC22\uD835\uDC29 \uD835\uDC25\uD835\uDC22\uD835\uDC1F\uD835\uDC1E\uD835\uDC1C\uD835\uDC32\uD835\uDC1C\uD835\uDC25\uD835\uDC1E \uD835\uDC1A\uD835\uDC27\uD835\uDC1A\uD835\uDC25\uD835\uDC32\uD835\uDC2C\uD835\uDC22\uD835\uDC2C, and upgrade targeting.\n\n\uD835\uDC0F\uD835\uDC2B\uD835\uDC1E-\uD835\uDC01\uD835\uDC2E\uD835\uDC22\uD835\uDC25\uD835\uDC2D \uD835\uDC00\uD835\uDC2E\uD835\uDC1D\uD835\uDC22\uD835\uDC1E\uD835\uDC27\uD835\uDC1C\uD835\uDC1E \uD835\uDC12\uD835\uDC1E\uD835\uDC20\uD835\uDC26\uD835\uDC1E\uD835\uDC27\uD835\uDC2D\uD835\uDC2C (\uD835\uDC12\uD835\uDC1A\uD835\uDC26\uD835\uDC29\uD835\uDC25\uD835\uDC1E \uD835\uDC02\uD835\uDC28\uD835\uDC2E\uD835\uDC27\uD835\uDC2D\uD835\uDC2C)\n\nLake B2B’s consumer database includes \uD835\uDC26\uD835\uDC22\uD835\uDC25\uD835\uDC25\uD835\uDC22\uD835\uDC28\uD835\uDC27\uD835\uDC2C \uD835\uDC28\uD835\uDC1F \uD835\uDC22\uD835\uDC27\uD835\uDC1D\uD835\uDC22\uD835\uDC2F\uD835\uDC22\uD835\uDC1D\uD835\uDC2E\uD835\uDC1A\uD835\uDC25\uD835\uDC2C \uD835\uDC1A\uD835\uDC1C\uD835\uDC2B\uD835\uDC28\uD835\uDC2C\uD835\uDC2C \uD835\uDC21\uD835\uDC22\uD835\uDC20\uD835\uDC21-\uD835\uDC1D\uD835\uDC1E\uD835\uDC26\uD835\uDC1A\uD835\uDC27\uD835\uDC1D \uD835\uDC1A\uD835\uDC2E\uD835\uDC1D\uD835\uDC22\uD835\uDC1E\uD835\uDC27\uD835\uDC1C\uD835\uDC1E \uD835\uDC2C\uD835\uDC1E\uD835\uDC20\uD835\uDC26\uD835\uDC1E\uD835\uDC27\uD835\uDC2D\uD835\uDC2C, such as:\n\n• \uD835\uDC12\uD835\uDC28\uD835\uDC1C\uD835\uDC22\uD835\uDC1A\uD835\uDC25 \uD835\uDC0C\uD835\uDC1E\uD835\uDC1D\uD835\uDC22\uD835\uDC1A \uD835\uDC14\uD835\uDC2C\uD835\uDC1E\uD835\uDC2B\uD835\uDC2C: 41,062,857\n• \uD835\uDC05\uD835\uDC1A\uD835\uDC26\uD835\uDC22\uD835\uDC25\uD835\uDC22\uD835\uDC1E\uD835\uDC2C \uD835\uDC30\uD835\uDC22\uD835\uDC2D\uD835\uDC21 \uD835\uDC02\uD835\uDC21\uD835\uDC22\uD835\uDC25\uD835\uDC1D\uD835\uDC2B\uD835\uDC1E\uD835\uDC27: 30,134,158\n• \uD835\uDC00\uD835\uDC29\uD835\uDC29\uD835\uDC1A\uD835\uDC2B\uD835\uDC1E\uD835\uDC25 \uD835\uDC01\uD835\uDC2E\uD835\uDC32\uD835\uDC1E\uD835\uDC2B\uD835\uDC2C: 23,031,455\n• \uD835\uDC07\uD835\uDC1E\uD835\uDC1A\uD835\uDC25\uD835\uDC2D\uD835\uDC21 & \uD835\uDC05\uD835\uDC22\uD835\uDC2D\uD835\uDC27\uD835\uDC1E\uD835\uDC2C\uD835\uDC2C \uD835\uDC04\uD835\uDC27\uD835\uDC2D\uD835\uDC21\uD835\uDC2E\uD835\uDC2C\uD835\uDC22\uD835\uDC1A\uD835\uDC2C\uD835\uDC2D\uD835\uDC2C: 21,872,541\n• \uD835\uDC16\uD835\uDC1E\uD835\uDC1A\uD835\uDC2B\uD835\uDC1A\uD835\uDC1B\uD835\uDC25\uD835\uDC1E \uD835\uDC13\uD835\uDC1E\uD835\uDC1C\uD835\uDC21\uD835\uDC27\uD835\uDC28\uD835\uDC25\uD835\uDC28\uD835\uDC20\uD835\uDC32 \uD835\uDC14\uD835\uDC2C\uD835\uDC1E\uD835\uDC2B\uD835\uDC2C: 20,527,813\n• \uD835\uDC0C\uD835\uDC2E\uD835\uDC2C\uD835\uDC22\uD835\uDC1C \uD835\uDC0B\uD835\uDC28\uD835\uDC2F\uD835\uDC1E\uD835\uDC2B\uD835\uDC2C: 20,135,168\n• \uD835\uDC16\uD835\uDC22\uD835\uDC27\uD835\uDC1E \uD835\uDC04\uD835\uDC27\uD835\uDC2D\uD835\uDC21\uD835\uDC2E\uD835\uDC2C\uD835\uDC22\uD835\uDC1A\uD835\uDC2C\uD835\uDC2D\uD835\uDC2C: 11,260,827\n\nCustom audiences can be built using 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It connects unrivaled editorial insight with more than 17 million datapoints for a unified, trusted view across global markets.\n\nBuilt to power specialist private equity investing workflows, the premium dataset includes investor and manager profiles, fundraising and fund performance data, LP commitments and mandates – enriched with the industry’s most trusted journalist insight and asset class defining rankings.\n\n**Leverage PEI Private Equity Editorial Insight & Data to**: \n- Benchmark fund terms, strategies, and performance (IRR/TVPI/DPI/RVPI) \n- Build fundraising strategies backed by verified investor allocation, mandate and commitment data \n- Strengthen strategic planning with award-winning editorial context combined with extensive market, fundraising and deal data\n\nBuilt for decision-makers, **PEI filters out the noise to surface only the most relevant, verified intelligence your teams actually need**. Derived through extensive multi-party sourcing, this structured, high-quality data originates from:\n- 1st-party proprietary research and direct surveys\n- Regulatory filings from global jurisdictions\n- Direct submissions from fund managers and investors\n- Intelligence sourced through PEI's global events and networks\n- Continuous input from the industry’s most recognised editorial team\n\nEach table is professionally validated through:\n- Multi-stage programmatic and human review\n- Cross-referencing across historical datasets\n- Manual validation by infrastructure specialists\n- Ongoing updates and historical enrichment\n- Normalised schema alignment across institutions, funds, performance, and commitments\n\nThe result is **an actionable dataset built for accuracy, completeness, and reliability** to enhance smarter investing decisions.\n\n\n**What’s included?**\nIn this sample dataset you will receive 10 rows of data across the below categories. \n\n*The industry’s leading 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Built on the medallion architecture (Bronze–Silver–Gold) using Delta Lake and configuration-driven execution, the accelerator delivers audit-ready, reporting-optimized outputs while ensuring scalability for future finance use cases.\n\nManaging and analyzing General Ledger data is central to ensuring finance integrity, audit readiness, and strategic decision-making. Yet, most enterprises struggle with fragmented data pipelines, delayed reconciliations, and manual reporting processes. Dataplatr’s Data Analytics Consulting helps streamline your GL operations, enabling finance teams to move faster, reduce risk, and drive efficiency.\n\nThe **Dataplatr Financial Analytics (GL) Accelerator** is a production-grade solution purpose-built to modernize General Ledger (GL) reporting pipelines on the Databricks Lakehouse. 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With minimal setup and configuration, financial teams can accelerate audit-ready reporting, ledger visibility, and compliance.\nDesigned with a **medallion ( Bronze–Silver–Gold ) architecture,** the accelerator simplifies complex ELT logic using Delta Lake and leverages configuration-driven execution, making it adaptable and scalable for varied financial use cases.\n\n**Business Outcomes**\n\n- **General Ledger-Ready Financial Views**\nGenerate structured outputs including Opening Balances, Net Movement, and Closing Balances—aligned with enterprise reporting standards.\n- **Audit-Compliant Reporting**\nBuilt-in tracking fields (e.g., SourceSystem, DwCreateDate) and lineage logs ensure full traceability for audit and reconciliation processes.\n- **Rapid Time-to-Insight**\nPredefined financial transformations reduce manual effort and enable finance teams to focus on strategic analysis instead of pipeline maintenance.\n- **Marketplace Deployable**\nEasily consumable via the Databricks Marketplace with production-ready notebooks, config templates, and supporting documentation.\n\n**Key Functional Capabilities**\n\n- **Plug-and-Play Finance Pipeline**\nReady-to-use Databricks notebook for ingesting\n and transforming GL data from Databricks\n- **Configuration-Driven Execution**\nCentralized JSON config governs source tables, keys, CDC flags, and layer-specific logic.\n- **CDC-Aware Ingestion**\nCaptures only changes since the last load using Change Data Capture (CDC) logic for efficiency\n- **Dynamic SQL Generation**\nAuto-generates SQL MERGE and INSERT statements based on real-time schema introspection\n- **Layered Output (Bronze → Gold)**\nStructured flow from raw ingestion (Bronze) to business-ready outputs (Gold)\n- **Audit & Error Logging**\nLogs all load history and errors with run-specific tracking for compliance and recovery\n- **Secure Credential Handling**\nCredentials for databricks are securely isolated and disposed of after execution\n- **Reporting-Ready Outputs**\nDelivers clean, aggregated GL Balances for direct use in Power BI, Tableau, or Looker dashboards\n- **GL Balances: Dashboard**\nThe accelerator’s power is demonstrated through GL Balances, a foundational need for any finance team. \nThe accelerator:\n\n1. Extracts journals and ledgers data from Databricks\n2. Normalizes and deduplicates records across time\n3. Produces monthly/fiscal snapshots of balances\n4. Enables segment-based aggregation (Account, Department, Entity)\n\n\nThese outputs directly feed financial dashboards, audits, or reconciliation processes—supporting strategic insights without the need for manual SQL development or ETL tooling.\n\n**Marketplace Deliverables**\n- **Databricks Notebook:** Full ingestion-to-transformation pipeline\n- **Config Templates**: Editable JSON for quick table onboarding\n- **Delta Table Schemas**: Predefined Bronze & Silver structures\n- **Logging & CDC Tracking**: Prebuilt Delta tables for load status and errors\n- **Documentation Bundle:** Setup guide, pipeline overview, functional flow\n- **Report-Ready Views**: Outputs modeled for seamless integration with finance reporting tools\n\n\n**Why Choose Dataplatr Financial GL Accelerator**\n- **Finance-Aware by Design** Built for ledger logic, not generic ETL\n- **Minimal Code, Maximum Reuse** Configuration-first setup supports new tables without rewriting pipelines\n- **Enterprise Ready** Aligned to Databricks best practices (Unity Catalog, Delta Lake, Workflows)\n- **Scalable Across Domains** Adaptable beyond GL to AP, AR, and Fixed Assets\n- **Optimized for Reporting** Delivers curated, audit-ready financial outputs for BI and compliance reports\n\n\nThe Dataplatr Financial GL Accelerator offers a ready-to-deploy, reporting-ready solution for automating financial data pipelines on Databricks. With its configuration-driven design and GL-focused outputs, it accelerates time-to-insight while ensuring auditability and reporting precision. Ideal for enterprises modernizing their ERP reporting workflows. While General Ledger is at the core of financial reporting, it rarely operates in isolation. That’s why Dataplatr extends its expertise beyond GL to provide a comprehensive, connected view of your finance function. Our consulting services are designed to integrate seamlessly across key modules, Accounts Payable such as Accounts Receivable, Fixed Assets, and Procurement & Spend, ensuring end-to-end visibility, control, and automation.\n\nExplore our Medium articles for deeper insights and real-world implementations of our Databricks-based analytics solutions.\n[Transform financial decision-making with Databricks](https://medium.com/@dataplatr/empowering-financial-decision-making-using-databricks-and-power-bi-insights-5beb6ee7a08c) using CDC-driven pipelines and Lakehouse architecture to deliver actionable insights across GL, AP, AR, and Fixed Assets with automated, finance-ready data models.\n\n[Leverage the Databricks Lakehouse Platform to transform People Analytics](https://medium.com/@dataplatr/maximizing-people-analytics-with-the-power-of-databricks-lakehouse-platform-and-omni-analytics-523e4a9cf5e5) with CDC-driven pipelines and real-time insights across headcount, hiring, and HR metrics using a scalable, finance-grade data architecture.\n\nNote: By clicking '**Get Access**', you agree to our **Terms of Service**. You acknowledge that **Dataplatr Corp** will recognize your organization as a customer/user for promotional and reference purposes.\n\n\nContact us at: chandra.reddy@dataplatr.com\nFor consultations or custom inquiries: https://dataplatr.com/contact-us \n","terms_of_service":"https://docs.google.com/forms/d/e/1FAIpQLSdnlNmSfrFRQDa_ToOL3ydTOPcWI3m3lWor2plLbiqF1JRINg/viewform","documentation_link":"https://medium.com/@dataplatr/empowering-financial-decision-making-using-databricks-and-power-bi-insights-5beb6ee7a08c","privacy_policy_link":"https://dataplatr.com","embedded_notebook_file_infos":[{"id":"4924df5e-cdbe-4d40-912d-cf5343849a1c","marketplace_file_type":"EMBEDDED_NOTEBOOK","file_parent":{"parent_id":"208b8ed9-df0f-46a6-a08a-6b327a44ec13","file_parent_type":"LISTING"},"mime_type":"text/html","download_link":"https://marketplace-file-storage-prod.s3-fips.us-east-1.amazonaws.com/EMBEDDED_NOTEBOOK/LISTING_208b8ed9-df0f-46a6-a08a-6b327a44ec13/4924df5e-cdbe-4d40-912d-cf5343849a1c?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEGgaDnVzLWVhc3QtMS1maXBzIkYwRAIgbC8sWIoYaHR8y7Wq86nPb0pufBl4%2BM4PWpeN%2B5PW9VQCIG7BhAVBDCWTpxFDLeuKYm%2Ff1JLz4iccPMzter0YdhgWKqYECDAQABoMNDE0MzUxNzY3ODI2IgxNAJrHbXaZG20jNfMqgwQyGEb4TsoVOoNvJ%2BaA%2FZ7zeu8hGcHqCeNfblP%2FQo5g6q2aRNtpwtPRKMlhYcIE%2FUQaNT1Lqmyptu32VUQTpzjTTxRVmJrDPkheSCkV06XQ%2FpTMKjU8CC7X1b%2FqYf8CrBwZT7Cv4C5WPjEET38f2A79h0xV6bZgPy28zQWrSeIJ9w6AfZBxiE24%2FVc08zVA0ivCT6lsy7nuU0wcR%2FMg2C9nd1vaMnQ923VpD21CKfVVYcrNWL2b%2FQ%2BNAO%2BxPjPgjiN6UPQbvbvZxkSdhgrGtecIg9NgqGkMcvM5VLTDtdC1kdeZetfOC2obtA%2BT%2F0gy3tIDo6WM5hcQfw743%2FAU9oaGnRBl%2BwoxRRDCZA%2F4jnPdluICSI7NCr6C3HHOe4CSZJjRihczxPA%2FgQczAVjAPs6CrK9N9pIzt0vNJpGUTgg4AoxftTvGfciDREilSctPIcBk6GwB%2Bzt4wbtNCmEPU%2BFD46rc6CWVQmmLwbKOeHw70ipMVFlOc8NlXkEtGO2vu6H44MstSSc6daOVUYUavQSGZ6jGhEjsP2YGz8KDm9XQotB%2FtyVBqiZV01OKqDmmBJA%2FXjUpSJ8M%2FeJXhe5m%2BKcxzNObtoJRI7jci3Tpt08XZyXZ7%2FyIV8H30ya468X3cfkTXrm972uga3z7iXAdubyEHnQIua%2FuvtwOHILi6O5D1Me2MzCPvcnXBjqTAYDoMWBt%2B3AVFuqSs2dmDQNBnvEhXfMgTedKLD5PJHb3aIl15iBkLaNu36GYnVCp9ZU2lSYcTbY5QwNnNeTfJTNy9bKhwzru6Sg%2BslhN%2BTS0zkTn%2BeLMY4tHgPEtEZ2cp%2FCk6GwqswvnYVKHjwO8q%2B1Mx8lc08fVEfarE9dke1GGFqoddx%2Bq%2BZUyHbDlAVfrCmf0yw%3D%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20261110T000135Z&X-Amz-SignedHeaders=host&X-Amz-Expires=86400&X-Amz-Credential=ASIAWA6KKHEJORWBH4N3%2F20261110%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=9b1403b6de68cdafaf67eb583b6d9ae24756567da063258c5a6bf05d2c3c758c","created_at":1774425443337,"updated_at":1774425443337,"display_name":"GL_Accounting_GLBalances_Accelerator_v3","status":"FILE_STATUS_PUBLISHED"}],"geographical_coverage":"[\"Global\"]","cost":"FREE","data_source":"Oracle E-Business Suite ","assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://dataplatr.com/"},"provider_summary":{"name":"Dataplatr Corp","description":"Dataplatr Corp specializes in Data Analytics & AI with pre-built data models for Enterprise Applications like SAP, Oracle EBS, Workday, Salesforce to empower businesses to unlock the full potential of their data. 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Data is stable, compliant, and not reliant on third-party scraping policies.\nSignal: We capture high-intent negative feedback (legal disputes, warranty issues, safety hazards) that serves as a leading indicator for revenue misses and stock price drawdowns.\nScope: Ideal for Investors Data analysis across Consumer Tech, Retail, Services, Automotive sectors and other.\n\nThis dataset helps asset managers and equity researchers \"look under the hood\" of a public company to assess real operational stress vs. management claims.","terms_of_service":"https://www.pissedconsumer.ai/privacy-policy","contact_email":"support@datarade.ai","privacy_policy_link":"https://www.pissedconsumer.ai/privacy-policy","assets":["ASSET_TYPE_DATA_TABLE"]},"provider_summary":{"name":"PissedConsumer","description":"PissedConsumer is a US-based consumer feedback and resolution platform operating since 2006. 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For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [Big Cities Mother And Child Health](https://www.johnsnowlabs.com/marketplace/big-cities-mother-and-child-health)\n\t- This dataset contains estimates of morbidity levels related to mother and child shared by the Big Cities Health Coalition members represented by the largest metropolitan health departments in the United States. The estimated values of morbidity levels cover the 2010-2015 period and are described by gender and race/ethnicity.\n- [Contraceptive Use and Contraception Methods](https://www.johnsnowlabs.com/marketplace/contraceptive-use-and-contraception-methods)\n\t- The Contraceptive Use and Contraception Methods dataset includes data about contraceptive use and different contraception methods among women aged between 15 and 44 in the United States by race between 1986 and 2015.\n- [Infant and Neonatal Mortality Rates](https://www.johnsnowlabs.com/marketplace/infant-and-neonatal-mortality-rates)\n\t- The Infant and Neonatal Mortality Rates dataset includes infant, neonatal, postneonatal, fetal, late fetal and perinatal mortality rates by detailed race and Hispanic origin of mother in the United States between 1983 and 2014.\n- [PRAMS Pregnancy Data for Year 2011](https://www.johnsnowlabs.com/marketplace/prams-pregnancy-data-for-year-2011)\n\t- This dataset includes information over the Pregnancy Risk Assessment Monitoring System (PRAMS). It is a surveillance system collecting state-specific, population-based data on maternal attitudes and experiences before, during, and shortly after pregnancy.\n- [US Breastfeeding Statistics](https://www.johnsnowlabs.com/marketplace/us-breastfeeding-statistics)\n\t- The US Breastfeeding Statistics dataset includes data about Breastfeeding among mothers aged between 15 and 44 in the United States by detailed race and Hispanic origin between 1986 and 2013.\n- [US Children Health Conditions](https://www.johnsnowlabs.com/marketplace/us-children-health-conditions)\n\t- The US Children Health Conditions dataset includes data about health conditions among children under age 18 in the United States by age, gender, race, poverty level, and health insurance status between 1997 and 2017.\n- [US Low Birth Weight Live Births](https://www.johnsnowlabs.com/marketplace/us-low-birth-weight-live-births)\n\t- The US Low Birth Weight Live Births dataset includes data about low birth weight and very low birth weight in the United States by detailed race and Hispanic origin of newborns between 1970 and 2015.\n- [US Natality Trends by Race of Mother](https://www.johnsnowlabs.com/marketplace/us-natality-trends-by-race-of-mother)\n\t- This dataset contains information on the number of live births, birth rates and fertility rates by race of mother from 1960-2016 published by National Center for Health Statistics (NCHS).\n- [US Nonmarital Childbearing](https://www.johnsnowlabs.com/marketplace/us-nonmarital-childbearing)\n\t- The US Nonmarital Childbearing dataset includes data about nonmarital childbearing by detailed race and Hispanic origin of mother and maternal age in the United States between 1970 and 2017.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 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Modern healthcare organizations require sophisticated analytical frameworks that can identify at-risk patients, predict readmission likelihood, and generate actionable interventions to break the cycle of avoidable returns.\n\n**Objective:** This notebook demonstrates how advanced predictive models and clinical decision support tools can identify patients at high risk for readmission and enable targeted interventions throughout the care continuum. 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It captures the software products, platforms, and tools in use, along with developer affiliations, product categories, and intensity of adoption. By tracking evidence of technology usage over time, the dataset reveals both current deployments and emerging technology adoption trends.\nThis dataset supports technology strategists, IT decision-makers, sales, and market intelligence teams in benchmarking enterprise technology portfolios, identifying whitespace opportunities, and targeting accounts with solution-aligned needs. Sales and business development professionals can pinpoint opportunities for engagement by understanding where specific technologies are deployed, in which workloads, and across which locations—enabling tailored solution selling and competitive positioning.\n\n**Methodology & Quality**\nTech stack insights are aggregated from proprietary and public signals, standardized into Draup’s controlled taxonomy of products, categories, and workloads. Vendor-product hierarchies are normalized, usage intensity scores are assigned to reduce noise, and adoption timelines are tracked to capture both legacy and new technology footprints. 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For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [Bloomington Police Department Employee Data](https://www.johnsnowlabs.com/marketplace/bloomington-police-department-employee-data)\n\t- This dataset of raw data identifies race, gender, and age of all Bloomington Police Department employees, as well as lists the education of all sworn personnel.\n- [Brooklyn Public Library Catalog](https://www.johnsnowlabs.com/marketplace/brooklyn-public-library-catalog)\n\t- Brooklyn Public Library is a selective Federal Depository for government publications. The dataset includes Library Catalog, with Call Numbers, Author, Title, Edition, Publication Information among others.\n- [CPS Elementary School Progress Report Card 2013-2014](https://www.johnsnowlabs.com/marketplace/cps-elementary-school-progress-report-card-2013-2014)\n\t- This dataset shows the 2013 School Progress Report Card data elements for each CPS (Chicago Public Schools) elementary school.\n- [City Population Annual Time Series](https://www.johnsnowlabs.com/marketplace/city-population-annual-time-series)\n\t- This dataset contains city population yearly time series for female and male, and for both genders, collected by the United Nations Statistics Division and published by UNData.\n- [Classification of the Functions of Government](https://www.johnsnowlabs.com/marketplace/classification-of-the-functions-of-government)\n\t- This dataset contains the codes, description and explanatory notes to describe the classification of different functions of the Government by the United Nations (UN) Statistics Division. The salient functions involve general public services, defense, public order and safety, economic affairs, environmental protection, housing and community amenities, health, recreation, culture and religion, education and social protection.\n- [Deaths in US Cities 2015](https://www.johnsnowlabs.com/marketplace/deaths-in-us-cities-2015)\n\t- This dataset identifies the total number of death certificates processed and the number of those for which pneumonia or influenza was listed as the underlying or contributing cause of death by age group in 122 Cities of United States. This dataset includes the report submitted for the year 2015.\n- [Demographic Statistics By Zip Code](https://www.johnsnowlabs.com/marketplace/demographic-statistics-by-zip-code)\n\t- The Demographic Reports are produced by the Economic, Demographic and Statistical Research unit within the Countywide Service Integration and Planning Management (CSIPM) Division of the Fairfax County Department of Neighborhood and Community Services. Information produced by the Economic, Demographic and Statistical Research unit is used by every county department, board, authority and the Fairfax County Public Schools.\n- [FDNY Line Of Duty Deaths](https://www.johnsnowlabs.com/marketplace/fdny-line-of-duty-deaths)\n\t- This dataset shows the rank, name, unit and date of New York Fire Department (FDNY) line of duty deaths.\n- [Headcount Enrollment by Student Level and Student Load by Institutions](https://www.johnsnowlabs.com/marketplace/headcount-enrollment-by-student-level-and-student-load-by-institutions)\n\t- This dataset contains the headcount enrollment by student level and student load by institutions of the State University of New York.\n- [Impaired Driving Death Rate by Age and Race](https://www.johnsnowlabs.com/marketplace/impaired-driving-death-rate-by-age-and-race)\n\t- This dataset contains information on Death Rate according to age and race which is caused by impaired driving in year 2012 and 2014.\n- [Marital Status](https://www.johnsnowlabs.com/marketplace/marital-status)\n\t- This dataset defines the set of codes that can be used to indicate the marital status of a person. It is required to enter in the patient demographic information.\n- [Most Popular Baby First Names 2011 to 2023](https://www.johnsnowlabs.com/marketplace/most-popular-baby-first-names-2011-to-2023)\n\t- This dataset is from the different website that has released its poular baby names from 2011 to 2023, showing which names proved to be the most popular during these years. Data were collected through civil birth registration. Each record represents the ranking of a baby name in the order of frequency.\n- [NYS English and Mathematics Exam](https://www.johnsnowlabs.com/marketplace/nys-english-and-mathematics-exam)\n\t- This dataset contains the information of New York City Results on the New York State English Language Arts (ELA) & Mathematics Exams (Grades 3 - 8) 2013 - 2015 Notes Starting in 2013, the NY State Education Department (NYSED) changed the exams to be Common Core aligned.\n- [NYS Math Test Results By Grade 2006-2011](https://www.johnsnowlabs.com/marketplace/nys-math-test-results-by-grade-2006-2011)\n\t- This dataset contains the information of New York City Results on the New York State Mathematics Tests, Grades 3 - 8.\n- [Name Prefixes](https://www.johnsnowlabs.com/marketplace/name-prefixes)\n\t- This dataset contains prefixes used in people's names. It is used to enter in patient demographics information.\n- [Name Suffixes](https://www.johnsnowlabs.com/marketplace/name-suffixes)\n\t- This dataset contains suffixes used in people's names. It is used in patient demographics information.\n- [Occurrence Of Firearm Discharge](https://www.johnsnowlabs.com/marketplace/occurrence-of-firearm-discharge)\n\t- One of the most traumatic and life changing incidents that can occur in the course of a police officer’s career is the line-of-duty discharge of his or her firearm. In an effort to guarantee that these incidents only occur when necessary, and thus prevent avoidable Member of Service (MOS) and civilian trauma, the Department began collecting in-depth data of police related firearms discharges in 1971.\n- [Popular 5000 Last Names in the US](https://www.johnsnowlabs.com/marketplace/popular-5000-last-names-in-the-us)\n\t- This dataset represents the 5000 popular last names in the United States. The data is split by race to show the percentages against each option.\n- [Popular American Indian and Alaskan Native Last Names in the US](https://www.johnsnowlabs.com/marketplace/popular-american-indian-and-alaskan-native-last-names-in-the-us)\n\t- This dataset represents the popular last names in the United States for American Indian and Alaskan Native.\n- [Popular Asian and Pacific Islander Last Names in the US](https://www.johnsnowlabs.com/marketplace/popular-asian-and-pacific-islander-last-names-in-the-us)\n\t- This dataset represents the popular last names in the United States for Asian and Pacific Islanders.\n- [Popular Baby Names for Each Religion](https://www.johnsnowlabs.com/marketplace/popular-baby-names-for-each-religion)\n\t- This dataset represents the popular baby names for each religion with genders.\n- [Popular Black Last Names in the US](https://www.johnsnowlabs.com/marketplace/popular-black-last-names-in-the-us)\n\t- This dataset represents the popular last names in the United States for Black.\n- [Popular Hispanic Last Names in the US](https://www.johnsnowlabs.com/marketplace/popular-hispanic-last-names-in-the-us)\n\t- This dataset represents the popular last names in the United States for Hispanic.\n- [Popular Last Names for People of Two Or More Races in the US](https://www.johnsnowlabs.com/marketplace/popular-last-names-for-people-of-two-or-more-races-in-the-us)\n\t- This dataset represents the popular last names in the United States for people of two or more races.\n- [Popular Names for Each Indian Caste](https://www.johnsnowlabs.com/marketplace/popular-names-for-each-indian-caste)\n\t- This dataset represents the popular names for each indian caste (Brahmin, Kshatriya, Vaisya and Sudra).\n- [Popular White Last Names in the US](https://www.johnsnowlabs.com/marketplace/popular-white-last-names-in-the-us)\n\t- This dataset represents the popular last names in the United States for White.\n- [Population Figures By Country](https://www.johnsnowlabs.com/marketplace/population-figures-by-country)\n\t- The dataset represents the world population, it shows the population figures of all countries and known territories. Figures are derived from census data and official numbers and estimates from the respective countries.\n- [Ratio of Homeless Population to General Population](https://www.johnsnowlabs.com/marketplace/ratio-of-homeless-population-to-general-population)\n\t- Homelessness is a social crisis in the United States of America. According to McKinney–Vento Homeless Assistance Act, homeless people are those who lack a fixed, regular and adequate nighttime residence. \"Ratio of Homeless Population to General Population in major US Cities in 2012.\n- [Regents Exam Results](https://www.johnsnowlabs.com/marketplace/regents-exam-results)\n\t- This dataset contains the information of Regents results by School for all subjects from cohort years 2001 to 2011 and classes of 2005 to 2015 by the New York State Education Department.\n- [School Attendance 4PM Report](https://www.johnsnowlabs.com/marketplace/school-attendance-4pm-report)\n\t- This dataset is a statistical report that provides daily school-wide attendance each day for all schools at 4:00pm classes.\n- [School Level Graduation Outcomes 2010-2011](https://www.johnsnowlabs.com/marketplace/school-level-graduation-outcomes-2010-2011)\n\t- This dataset is the statistical record that provides School level graduation outcomes by years 2010 to 2011.\n- [School Progress Reports 2006-2007](https://www.johnsnowlabs.com/marketplace/school-progress-reports-2006-2007)\n\t- This dataset is the statistical record that provides School Progress Reports for all schools in the United States for the school years 2006 to 2007.\n- [Top 23 Artists to Watch in 2023](https://www.johnsnowlabs.com/marketplace/top-23-artists-to-watch-in-2023)\n\t- This dataset contains the essential new and rising artists poised to make a major impact in 2023 year.\n- [Top 25 American Film Schools 2022](https://www.johnsnowlabs.com/marketplace/top-25-american-film-schools-2022)\n\t- This dataset contains 2022’s Top 25 American Film Schools, Ranked. These 25 programs offer students the best chance at navigating Hollywood’s changing terrain.\n- [Top New Films Arriving in 2023](https://www.johnsnowlabs.com/marketplace/top-new-films-arriving-in-2023)\n\t- This dataset shows top most anticipated movies of 2023. Warner Bros. commit to streaming all of their major 2021 releases simultaneously on HBO Max, with mixed results, while other studios incorporated some combination of theatrical and digital strategies.\n- [Top Sculpture Artists to Watch in 2023](https://www.johnsnowlabs.com/marketplace/top-sculpture-artists-to-watch-in-2023)\n\t- This dataset contains the promising artists to watch in 2023 who have seen a significant rise in interest from collectors over the last year.\n- [Top World Airports](https://www.johnsnowlabs.com/marketplace/top-world-airports)\n\t- This dataset shows the the world's top airports announced by the Centre of Aviation CAPA (Corrective and Preventive Actions).\n- [US Citizenship Type](https://www.johnsnowlabs.com/marketplace/us-citizenship-type)\n\t- This dataset contains US Citizenship Types. It is used to enter in patient demographics information.\n- [US Race and Ethnicity Codes](https://www.johnsnowlabs.com/marketplace/us-race-and-ethnicity-codes)\n\t- This dataset contains Race/Ethinicty codes. It is used to enter in patient demographics information.\n- [World Best Hospitals 2023](https://www.johnsnowlabs.com/marketplace/world-best-hospitals-2023)\n\t- This dataset shows the the world's best hospital in 2023 issued by the Newsweek and Statista.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated\n- Data Updates\n - Data updates support replace-on-update: outdated foreign keys are deprecated, not deleted\n\n**Our data is curated and enriched by domain experts** \n\n Each dataset is manually curated by our team of doctors, pharmacists, public health & medical billing experts:\n- Field names, descriptions, and normalized values are chosen by people who actually understand their meaning\n- Healthcare & life science experts add categories, search keywords, descriptions and more to each dataset\n- Both manual and automated data enrichment supported for clinical codes, providers, drugs, and geo-locations\n- The data is always kept up to date – even when the source requires manual effort to get updates\n- Support for data subscribers is provided directly by the domain experts who curated the data sets\n- Every data source’s license is manually verified to allow for royalty-free commercial use and redistribution.\n\n**Need Help?**\nIf you have questions about our products, contact us at [info@johnsnowlabs.com](mailto:info@johnsnowlabs.com). \n 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Mention the desired region and product, topic, or category of the dataset in the request details. \n\nFor example: \"KSA Smart Watches\" ","terms_of_service":"https://www.vdd.ai/terms","documentation_link":"","privacy_policy_link":"https://www.vdd.ai/privacy","geographical_coverage":"[\"Global\"]","update_frequency":{"interval":1,"unit":"WEEKLY"},"assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"VDD AI","description":"VDD AI employs a unique approach, enhancing enterprise profitability and efficiency through its portfolio of enterprise SaaS products (AI Pricing Co Pilot, AI Stock Co Pilot, Social Graph Analysis, etc) and tailored AI and Data Infrastructure services."},"popularity":216},{"id":"567eaffb-1eda-40dc-9e05-2216aca3ac06","summary":{"name":"Monday.com MCP Server","subtitle":"MCP server for monday.com integration.","setting":{"visibility":"PUBLIC"},"created_at":1776819546943,"updated_at":1778808181506,"categories":["OPEN_SOURCE"],"listingType":"STANDARD","provider_id":"f0b2edc1-e839-40c6-8d0e-782015c17c14"},"detail":{"description":"**This MCP server is not part of the Databricks Services, and its use is subject to the source's license terms. The MCP server and the information below are reproduced here for your convenience from publicly available listings. Any issues should be submitted to the MCP provider.**\n\n**Overview**\nThe monday.com MCP Server connects AI agents directly to the monday.com Work OS, allowing them to manage projects, automate workflows, and interact with the Apps framework through natural language. It supports multiple modes to balance between standard platform automation and advanced app development:\n* **Default (Standard)** — Enables core operations for project management, including board creation, item updates, and column management.\n* **Apps (`--mode apps`)** — Dedicated to developers; allows AI to scaffold apps, manage versions, deploy code, and handle environment variables.\n* **Dynamic (`--enable-dynamic-api-tools`)** — Provides full access to the monday.com GraphQL schema, allowing agents to generate custom queries for any endpoint beyond predefined tools.\n\nConnect locally via `npx -y @mondaydotcomorg/monday-api-mcp -t `. It supports both Read-Only modes for safety and enterprise-grade OAuth through its hosted service.\n\n**Tools**\nKey capabilities across modes:\n* **Item & Board Operations** — Create, move, and update items; modify column values; and search across boards by name.\n* **Collaboration** — Add updates/comments to items and manage user mentions.\n* **App Lifecycle Management** — Create apps from manifests, promote versions to live, and check deployment status.\n* **Schema Exploration** — Dynamically fetch GraphQL types and build complex reports across multiple data points.\n* **Account Management** — Search for users and teams to assign tasks or notify stakeholders.\n\n**Use Cases**\n* **AI Project Assistant:** Update task statuses, move items between groups as they progress, and summarize board activity for daily briefings.\n* **Automated App Scaffolding:** Use natural language to \"Create a new app with a board view and dashboard widget\" and deploy it to the monday.com infrastructure.\n* **Advanced Reporting:** Generate complex cross-board insights by allowing the agent to write custom GraphQL queries on the fly.\n* **Workflow Automation:** Automatically create monday.com items based on external triggers like customer support tickets or sign-ups.\n\n**Learn More:** [github.com/mondaycom/mcp](https://github.com/mondaycom/mcp)","terms_of_service":"https://cms.databricks.com/sites/default/files/2023-01/marketplace-consumer-terms_0.pdf","documentation_link":"https://github.com/mondaycom/mcp","privacy_policy_link":"https://www.databricks.com/legal/privacynotice","assets":["ASSET_TYPE_MCP"],"license":"https://github.com/mondaycom/mcp/blob/master/LICENSE"},"provider_summary":{"name":"Monday.com"},"popularity":1918},{"id":"fc40e796-a19b-4c53-a579-4febe1954998","summary":{"name":"PACE Tax Liens","subtitle":"Nationwide lien data for qualified environmentally beneficial home property projects","setting":{"visibility":"PUBLIC"},"created_at":1789677732471,"updated_at":1789677732471,"categories":["FINANCIAL"],"listingType":"PERSONALIZED","provider_id":"26040e80-2e39-4e01-8fea-0667064d9fce"},"detail":{"description":"PACE (Property Assessed Clean Energy) programs provide a unique type of financing that allows homeowners to finance environmentally beneficial improvements to residences through property taxes. These include energy efficient appliances/upgrades, water-saving projects, and solar panels. \nCotality delivers Property Assessed Clean Energy (PACE) lien data for California, Florida, and Missouri to help financial institutions and marketing teams with risk analysis, asset valuation, targeted marketing, market analysis, and more. With property tax jurisdiction details, lien amounts, tax year, and other records, the dataset provides a clearer view of PACE-financed property improvements such as energy efficiency upgrades, water-saving projects, and solar installations.\n\n**Risk Analysis**\nCrucial in assessing potential risks associated with property investments. Understanding if a property has a tax lien allows financial institutions and other stakeholders to make informed decisions and manage risks accordingly.\n**Asset Valuation**\nValidate the status of a property. Knowing if a property has a tax lien can help with asset valuation and due diligence processes.\n**Data Quality and Cleansing**\nEnrich and clean existing data sets. The precise and accurate information provided can ensure the accuracy and completeness of the company's data repository.\n**Customer Acquisition**\nFor financial institutions offering loan consolidation services, understanding if a potential customer has a tax lien on their property can inform targeted acquisition strategies.\n**Market Analysis**\nProvide insights into the wider property market, specifically concerning properties under PACE programs. Analyzing this data can reveal market trends, aiding in strategy development and decision-making.\n**ESG Investment Analysis**\nAs PACE programs fund environmentally beneficial improvements, data on PACE Tax Liens can inform ESG investors about properties with a commitment to environmental sustainability, aiding in investment decisions.","terms_of_service":"https://www.cotality.com/","documentation_link":"https://www.cotality.com/products/pace-tax-liens?utm_campaign=412443580-EDSG-ALL-CloudMarketplace-Databricks-2026&utm_source=databricks&utm_medium=marketplace&utm_term=pacetaxliens","privacy_policy_link":"https://www.cotality.com/","assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"Cotality","description":"We illuminate data, insights, and workflows across the property ecosystem, analyzing the largest objective dataset in the U.S. for real-time insights in minutes, not months. Together, we’ll identify hidden risks and powerful opportunities for transforming your business and solving society’s greatest challenges."},"popularity":1146},{"id":"113c3685-cb6a-40d7-8289-c1fd68f49d80","summary":{"name":"Restaurants in Oman (OM) - Places Data Sample","subtitle":"\uD83C\uDDF4\uD83C\uDDF2 Restaurant location & metadata table for Oman, with 5K+ restaurant points-of-interest (PoIs) updated daily.","share":{"name":"om_restaurant_marketplace","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1727886563339,"updated_at":1746426406403,"categories":["ADVERTISING_AND_MARKETING","GEOSPATIAL","LOOKUP_TABLES","RETAIL"],"listingType":"STANDARD","provider_id":"bcde9d2e-7e2f-4266-8c17-45d2e7bdc27e"},"detail":{"description":"## **Overview**\n\n> Learn more about this dataset on [catalog.dotlas.com](https://catalog.dotlas.com/restaurants/unified_restaurant_establishments/)\n\n> This is a sample of our Oman Restaurants data product (temporal). The product contains **`5K+ restaurants`** updated frequently, and additional columns catered for leads and segmentation. \n\n> Additionally, we also curate restaurant menu data including dishes, prices, price history, restaurant promotions and social insights. This sample contains ~140 restaurants across Oman. \n\nFor pricing and licensing details please [**contact us**](mailto:info@dotlas.com)\n\n**Use cases** \n\n\uD83D\uDD70 Analyze competitor opening hours on each day of the week to help create an optimal operating time frame.\n\uD83D\uDCC8 Identify trending cuisines in different regions by analyzing the frequency and distribution of various cuisine types in different cities.\n\uD83D\uDCB0 Use the distribution data to identify underserved areas with high potential for new restaurant openings based on the lack of certain cuisine types.\n\n### **Potential Sectors**\n* Food & Beverages (F&B)\n* GIS (Geographic Information Systems) and Geocoding Services\n* Marketing and Advertising Leads\n* Competitor Monitoring & Market Analysis\n* Financial tickering of F&B brand performance\n* Site Selection & Suitability Analysis\n* Real Estate Proximity Analysis\n\n**Key features** \n\n▶️ **Restaurant Identification**: Includes unique identifiers and names for each restaurant.\n\n▶️ **Geographical Data**: Detailed information about the `country`, `subregion`, `city`, `area`, and precise geographical coordinates (`latitude` and `longitude`) of each restaurant.\n\n▶️ **Contact Information**: Addresses, phone numbers, email addresses, and website URLs.\n\n▶️ **Social Media Presence**: Links to `Instagram`, `Facebook`, and `Twitter` profiles where available.\n\n▶️ **Amenities**: Information on various amenities such as acceptance of credit cards, availability of Wi-Fi, presence of outdoor seating, and more.\n\n▶️ **Additional Metadata**: Information on entertainment options, executive chef names, dress codes, dining styles, and more","terms_of_service":"https://dotlas.com/terms-of-service","documentation_link":"https://catalog.dotlas.com/restaurants/unified_restaurant_establishments/","privacy_policy_link":"https://dotlas.com/privacy-policy","geographical_coverage":"[\"Middle East\"]","cost":"FREE","update_frequency":{"interval":1,"unit":"DAILY"},"collection_granularity":{"interval":1,"unit":"DAILY"},"data_source":"Dotlas collects and coalesces information from multiple online and offline sources","assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"Dotlas","description":"Dotlas provides high-quality geographical and temporal data on restaurants, real estate and hospitality sectors around the world. We collect data from various public and online sources and coalesce this information in-house and enrich it with hyperlocal context, in order to create datasets that can reliably drive decision-making and generative AI applications."},"popularity":542},{"id":"cf00a1d2-b520-47fb-a721-3b3e9ac69272","summary":{"name":"MBI Area Boundaries for North Macedonia – Sample","subtitle":"MBI's precise & seamless boundaries for well-founded decisions in North Macedonia. Free trial data.","share":{"name":"share_boundaries_mbi_mk_r_2025","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1785151263645,"updated_at":1785151263645,"categories":["ADVERTISING_AND_MARKETING","CLIMATE_AND_ENVIRONMENT","ENERGY","GEOSPATIAL","MEDIA","OTHER","PUBLIC_SECTOR","RETAIL","TRANSPORTATION_AND_LOGISTICS","TRAVEL_AND_TOURISM"],"listingType":"STANDARD","provider_id":"aa09abd6-ff7f-4452-803d-b8c43b4c07cb"},"detail":{"description":"MBI'S GLOBAL AREA BOUNDARIES COVER MOST COUNTRIES WORLDWIDE AND **CAN BE LICENSED FOR EACH COUNTRY INDIVIDUALLY** AND IN BUNDLES.\n[MBI](https://www.mbi-geodata.com/en/) digital boundaries are available for virtually all countries worldwide.* Created by our experts according to consistent global standards, the polygons are so precise and harmonized that they align seamlessly across borders. The territorial vintages of the worldwide area boundaries are compatible with our globally consistent and comparable [socio-demographic](https://www.mbi-geodata.com/en/sociodemographic-data/) and [economic](https://www.mbi-geodata.com/en/purchasing-power-overview/) data.\nOur area boundaries for North Macedonia are offered at the following levels:\n\n- **Postal Code Level** (Postcode 2-digit, 5-digit on request): This fine-grained level of detail makes the data suitable for a wide range of applications – for example, enriching customer databases, which are often organized by postal code.\n- **Administrative Level** (Opštini): Covers both larger administrative areas (e.g., regions, states, provinces) as well as finer subdivisions such as districts, counties, or municipalities.\n\n**Use Cases:**\n\n- **Market Analysis**: Use MBI’s boundaries to visualize data on a map, learn about your customers, and uncover untapped market potential.\n- **360-Degree Customer View**: Link boundaries to customer data, sociodemographic, and economic data to gain a holistic understanding of customer needs, behaviors, and potential value.\n- **Location Planning**\n•\tUse MBI’s boundaries to visualize data on a map, learn about your customers\n•\tPlan the provision of services and products across different regions\n•\tDefine sales territories\n•\tVisualize catchment areas and plan actions to reach potential customers\n\n**Additional Insights:**\n[About Area Boundaries](https://www.mbi-geodata.com/en/area-boundaries/)\n\n**Data Structure:**\nThe area boundaries include the following variables:\nCTRYCODE: Two-digit country code by ISO 3166\nNAME: Name of administrative area / Name of the most populous administrative area / Name of the respective micro-area or the next higher level\nADMINCODE / POSTCODE / MICROCODE: Administrative code / Postal code / Micro-code\nWKT: Geography as Well Known Text (lat/long WGS84)\n\n*with only a few specific exceptions","terms_of_service":"https://service-mbi-geodata.com/s/HNp7Z2tws4WDqWd","documentation_link":"https://www.mbi-geodata.com/en/mbi-supplier-manufacturer-worldwide-geodata/europe/north-macedonia-maps/","privacy_policy_link":"https://www.mbi-geodata.com/en/data-protection/","update_frequency":{"interval":1,"unit":"YEARLY"},"assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://service-mbi-geodata.com/s/E4fAPgiww5qSzNG"},"provider_summary":{"name":"Michael Bauer International GmbH – MBI Geodata","description":"For over 15 years, Michael Bauer International (MBI) has been a leading provider of globally consistent and comparable geographic data, including more than 3 million postcode areas and other detailed datasets like sociodemographics and purchasing power. MBI's high-quality small-scale data spans 248 countries, supporting businesses across all industries in location planning, branch networks, and distribution strategies. Recognized as a key data source, MBI is the preferred provider for leading GIS software and technology companies worldwide as well as one of the first global distributors for HERE Technologies. MBI is part of the Michael Bauer Group, alongside Michael Bauer Research GmbH and Michael Bauer Micromarketing GmbH."},"popularity":135},{"id":"11a7584e-1a94-4998-aed4-add351636c57","summary":{"name":"Physician Dictation Data : Infectious Disease","subtitle":"Comprehensive physician dictation and transcript data: high-quality, redacted medical records of different work types.","share":{"name":"physiciandictationspeech(&transcript)infectiousdisease","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1723102371366,"updated_at":1723106073078,"categories":["HEALTH","SCIENCE_AND_RESEARCH"],"listingType":"STANDARD","provider_id":"dc00cb61-5b9a-403e-8b4f-71e78dd44d6c"},"detail":{"description":"**Overview**\n\nShaip's Physician Dictation Speech (& Transcript) Data is a comprehensive resource designed for the healthcare sector, offering transcribed medical records from various work types such as History & Physical, Operative Reports, Discharge Summaries, Consultation Notes, Admit Notes, ED Notes, Clinic Notes, and Radiology Reports. \n\nThis dataset provides a broad spectrum of medical insights. Geographically, the dataset encompasses dictation audio from physicians across almost all US states. \n\nThis rich dataset is invaluable for developing and enhancing medical NLP applications, automated transcription services, and clinical decision support systems.\n\n**Use cases**\n\n**Clinical Natural Language Processing (NLP)** \n\n- Model development: Training NLP models to understand and extract medical information from spoken language.\n- Entity recognition: Identifying and classifying medical entities like diseases, medications, and treatments within dictation.\n- Relation extraction: Identifying relationships between medical entities (e.g., drug-interaction, symptom-disease).\n\n**Clinical Decision Support**\n\n- Predictive modeling: Developing models to predict patient outcomes, disease progression, or treatment effectiveness.\n- Risk assessment: Identifying patients at risk for certain conditions or complications.\n- Treatment optimization: Recommending optimal treatment plans based on patient data and clinical guidelines.\n\n**Healthcare Operations**\n\n- Documentation improvement: Analyzing dictation for consistency, completeness, and adherence to clinical guidelines.\n- Coding and billing accuracy: Improving the accuracy of medical coding and billing through NLP analysis.\n- Quality improvement: Identifying areas for improvement in patient care based on dictation analysis.\n\n**Research and Development**\n\n- Drug discovery: Analyzing medical language to identify potential drug targets or repurposing opportunities.\n- Clinical trial recruitment: Identifying eligible patients for clinical trials based on dictation data.\n- Population health management: Understanding disease prevalence and patient populations.\n\n**Product details**\n\n**Dictation Audio Devices:** Audio captured from various devices-\n- Telephone Dictation (54.3%)\n- Digital Recorder (24.9%)\n- SpeechMic (5.4%)\n- Smartphone (2.7%)\n- Unknown (12.7%).\n\n**Transcribed Medical Records:** Available from various work types, including \n\n- Consultation\n- Progress Report\n- History and Physical\n- Discharge Summary\n- Operative Report\n- Procedure Report\n- Radiology Report\n- Office Visit\n- Emergency Room Report\n- Transfer Summary\n- Letter\n- Clinic Note etc.\n\n**Geographical Coverage:** Dictation audio from physicians across almost all US states.\n\n**Physician Age Groups:** Dictation audio from physicians aged \n- 30-50 years (32%)\n- 50-70 years (54%)\n- 70+ years (13%).\n\n**Additional Insights**\nAll audio and transcripts are redacted for Personally Identifiable Information (PII) in accordance with Safe Harbor Guidelines and HIPAA compliance, ensuring privacy and security.","terms_of_service":"https://www.shaip.com/terms-of-service/","documentation_link":"","privacy_policy_link":"https://www.shaip.com/privacy-policy/","geographical_coverage":"[\"United States and Canada\"]","cost":"FREE","assets":["ASSET_TYPE_MEDIA"],"license":""},"provider_summary":{"name":"Shaip","description":"Shaip is a leading AI data platform and services provider specializing in the sourcing, creation, licensing, and transformation of high-quality, ethical, and domain-specific data for AI and machine learning applications. Shaip supports Healthcare AI, Computer Vision, Generative AI, and Conversational AI use cases for leading AI labs and enterprise customers worldwide. Learn more at shaip.com"},"popularity":889},{"id":"de6c488e-f4c0-4ce8-8e9c-6b31b78fc601","summary":{"name":"Global Beauty & Personal Care Product Data","subtitle":"Fresh beauty product data with pricing and ratings from Sephora, Ulta, Amazon, Notino & 18+ other retailers","share":{"name":"sephora","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1745995342286,"updated_at":1746621725824,"categories":["COMMERCE"],"listingType":"STANDARD","provider_id":"4213e2cd-deae-4bc8-b94b-fb9f745f8c02"},"detail":{"description":"**Overview**\nGroupBWT’s Beauty Product Dataset offers structured, up-to-date product-level data from 18 global beauty retailers.\nThis dataset enables analytics teams to track price dynamics, stock availability, product performance, and assortment strategies across regions and platforms.\n\n**Use cases**\n- Competitive pricing analysis\n- Assortment comparison across retailers\n- Stock availability tracking\n- Trend monitoring by brand/category\n- Input for ML models (e.g. demand forecasting, price optimization)\n\n**Retailer Coverage**\nThe dataset currently includes structured product data from 18 leading global and regional beauty retailers.\nWe continuously monitor and maintain these sources to ensure data freshness and consistency. If your use case requires coverage beyond this list, we can easily expand it to include additional retailers upon request.\n\n● Sephora (all locales)\n● Ulta\n● Douglas\n● Notino\n● Boots\n● Amazon\n● Rossmann\n● DM\n● Lookfantastic\n● Cult Beauty\n● ASOS\n● Beauty Bay\n● Selfridges\n● Space NK\n● Zalando Beauty\n● Farfetch\n● Tmall\n● YesStyle\n\n**Additional Insights**\nData is collected directly from public product listings across 18 global and regional retailers, including Sephora, Ulta, Amazon, Notino, and niche platforms like YesStyle and Space NK. The dataset is refreshed regularly to ensure accuracy and timeliness. A sample of 2,000 records from Sephora is available for preview.\n\nWe can provide data from any country-specific locale or retailer website upon request.\nProducts are not matched across sources in this version, but cross-retailer product mapping can be offered as an additional service.","terms_of_service":"https://groupbwt.com/terms-of-use/","documentation_link":"https://drive.google.com/file/d/1AoISJ8tm__RnDEtOBK3gtc_EetBgFv--/view","privacy_policy_link":"https://groupbwt.com/privacy-policy/","geographical_coverage":"[\"Global\"]","cost":"FREE","update_frequency":{"interval":1,"unit":"DAILY"},"collection_granularity":{"interval":1,"unit":"DAILY"},"collection_date_start":1740815089730,"collection_date_end":1746081898813,"data_source":"Sephora, Ulta, Douglas, Notino, Boots, Amazon, Lookfantastic, Rossmann, DM, Cult Beauty, ASOS, Beauty Bay, Selfridges, Space NK, Zalando Beauty, Farfetch, Tmall, YesStyle","size":1.0,"assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"GroupBWT","description":"GroupBWT is a data provider specializing in large-scale web data aggregation for enterprise analytics, forecasting, and machine learning.\nWe serve industries such as retail, e-commerce, beauty & personal care, micromobility, logistics, and public procurement.\nOur typical data assets include product and pricing intelligence, store and location data, availability signals, and public tenders — all collected from complex online sources and delivered in clean, analysis-ready formats."},"popularity":1574},{"id":"068829f8-df48-491e-9501-1f7aafbfd11b","summary":{"name":"Draup Location Characteristics & Demographics - Sample","subtitle":"Compare country & metro demographics, workforce stats, and location traits for strategy & planning","share":{"name":"location_demograhpics_data","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1755722794764,"updated_at":1788933446783,"categories":["ADVERTISING_AND_MARKETING","COMMERCE","ECONOMICS","EDUCATION","OTHER","SCIENCE_AND_RESEARCH"],"listingType":"STANDARD","provider_id":"9900a255-6aa9-496f-9b6a-421a3568af3b"},"detail":{"description":"**Overview**\nDraup’s Location Characteristics and Demographics dataset provides a structured view of demographic and ecosystem attributes at both country and metropolitan statistical area (MSA) levels. Covering population dynamics, workforce-related indicators, and other location-specific characteristics, the dataset enables HR, workforce strategy, and market intelligence teams to better understand how geography shapes talent availability and operational potential.\nThis dataset supports multiple decision-making use cases such as workforce planning, geographic expansion, and market entry assessment. By comparing demographic trends across countries and cities, organizations can identify high-potential regions for talent sourcing, evaluate diversity and workforce composition, and align business strategies with the socio-economic profile of a target location.\n\n**Methodology & Quality**\nThe dataset is built using a combination of government records, public demographic databases, and validated secondary sources. All records are normalized for consistency across geographies, ensuring comparability between countries and MSAs. Updates are applied regularly to reflect the most recent demographic shifts, while missing values and outliers are treated through structured standardization techniques. Bias reduction measures are applied by harmonizing demographic definitions across regions, minimizing distortions that arise from variations in data reporting standards.\n\n**Key Tables Include**\n•\tCountry-Level Demographics – Population and workforce-related demographic attributes aggregated at the country level.\n•\tMetropolitan Area (MSA) Demographics – Granular demographic indicators captured at the city or metropolitan level for localized workforce analysis.\n\n**Use Cases:**\n***What is the average age of working population in San Fransisco***\t\n\nLocation Characteristics data helps the users profile each location based on its demographic attributes\t\n\nWITH data AS (\n SELECT msa_name,\n demographics_taxonomy_id,\n CAST(json_extract(attr_value, '$.yearly_data') AS MAP(VARCHAR, JSON)) AS yearly\n FROM ecosystem_location_msa_demographics\n WHERE demographics_taxonomy_id = 14\n),\nyears AS (\n SELECT data.msa_name,\n data.demographics_taxonomy_id,\n year,\n json_extract_scalar(d.yearly[k], '$.median_age') AS median_age,\n ROW_NUMBER() OVER (PARTITION BY data.msa_name ORDER BY CAST(k AS INTEGER) DESC) AS rn\n FROM data\n CROSS JOIN UNNEST(MAP_KEYS(d.yearly)) AS t(year)\n)\nSELECT msa_name,\n demographics_taxonomy_id,\n year AS latest_year,\n median_age AS latest_median_age\nFROM years\nWHERE rn = 1 and msa_name like '%San Francisco%' \n\n***Identify locations that offer the best working/ expansion conditions based on factors like crime rate, commute time, meal costs, pollution, etc***\t\n\nData attributes like crime index, travel time, cost per meal, population size, pollution index, etc provide a deep understanding of an ecosystem. Build composite indices by combining various attributes to prioritize expansion or new office locations aligned with workforce and business needs.\t\n\nWITH extracted AS (\n SELECT\n msa,\n demographics_taxonomy_id,\n md.year,\n md.month,\n CASE\n WHEN demographics_taxonomy_id = 26 THEN CAST(md.value['crime_value'] AS DOUBLE)\n WHEN demographics_taxonomy_id = 30 THEN CAST(md.value['meals_2_people_price'] AS DOUBLE)\n WHEN demographics_taxonomy_id = 23 THEN CAST(md.value['pollution'] AS DOUBLE)\n END AS metric_value\n FROM ecosystem_location_msa_demographics t\n CROSS JOIN UNNEST(\n CAST(json_extract(attr_value, '$.monthly_data') AS MAP>)\n ) AS m(year, arr)\n CROSS JOIN UNNEST(arr) AS md(value)\n)\n, aggregated AS (\n SELECT\n msa,\n demographics_taxonomy_id,\n AVG(metric_value) AS avg_value\n FROM extracted\n WHERE metric_value IS NOT NULL\n GROUP BY msa, demographics_taxonomy_id\n)\n, pivoted AS (\n SELECT\n msa,\n MAX(CASE WHEN demographics_taxonomy_id = 26 THEN avg_value END) AS avg_crime,\n MAX(CASE WHEN demographics_taxonomy_id = 30 THEN avg_value END) AS avg_meal_cost,\n MAX(CASE WHEN demographics_taxonomy_id = 23 THEN avg_value END) AS avg_pollution\n FROM aggregated\n GROUP BY msa\n)\nSELECT\n msa,\n avg_crime,\n avg_meal_cost,\n avg_pollution,\n ( (avg_crime * 0.4) + (avg_meal_cost * 0.3) + (avg_pollution * 0.3) ) AS composite_index\nFROM pivoted\nORDER BY composite_index ASC\n\n***See what is the gender and ethnic diversity landscape of your country like, and if you fit.***\n\nEvaluate demographic diversity at national or metro levels to benchmark inclusivity, inform DEI initiatives, and identify regions with diverse talent pools.\t\n\nWITH yearly_data AS (\n SELECT\n msa_name,\n demographics_taxonomy_id,\n JSON_EXTRACT(attr_value, '$.yearly_data') AS yearly_data\n FROM ecosystem_location_msa_demographics\n WHERE demographics_taxonomy_id = 4\n),\nlatest_year AS (\n SELECT\n msa_name,\n demographics_taxonomy_id,\n JSON_EXTRACT_SCALAR(\n JSON_KEYS(yearly_data),\n CONCAT('$[', JSON_ARRAY_LENGTH(JSON_KEYS(yearly_data)) - 1, ']')\n ) AS last_year\n FROM yearly_data\n)\nSELECT\n y.msa_name,\n y.demographics_taxonomy_id,\n l.last_year,\n JSON_EXTRACT_SCALAR(\n JSON_EXTRACT(y.yearly_data, CONCAT('$.', l.last_year)),\n '$.ethnicity'\n ) AS ethnicity_distribution\nFROM yearly_data y\nJOIN latest_year l\n ON y.msa_name = l.msa_name\n AND y.demographics_taxonomy_id = l.demographics_taxonomy_id;\n\n***Analyze how unemployment and inflation trends in a location signal potential attrition risks.***\t\n\nCorrelate macroeconomic indicators with workforce stability to anticipate attrition and adjust retention or recruitment strategies proactively.\t\n\nWITH demographics_data AS (\n SELECT\n msa_name,\n demographics_taxonomy_id,\n jsonb_each_text(attr_value::jsonb -> 'monthly_data') AS monthy\n FROM ecosystem_country_demographics\n WHERE demographics_taxonomy_id IN (10, 20)\n),\ndetailed_demographics_data AS (\n SELECT\n msa_name,\n demographics_taxonomy_id,\n (monthy.key)::text AS period, \n (monthy.value)::numeric AS value \n FROM demographics_data\n)\nSELECT\n inflation.msa_name,\n inflation.period,\n inflation.value AS inflation_rate,\n unemployed.value AS unemployment_rate\nFROM detailed_demographics_data as inflation\nJOIN detailed_demographics_data as unemployed\n ON inflation.msa_name = unemployed.msa_name\n AND inflation.period = unemployed.period\n AND inflation.demographics_taxonomy_id = 10\n AND unemployed.demographics_taxonomy_id = 20\nORDER BY inflation.msa_name, inflation.period;\n\n***Which countries or regions are most attractive for market entry or expansion based on GDP growth trends?***\t\n\nLeverage economic growth and demographic shifts to identify high-potential markets and guide global workforce and investment planning.\t\n\nWITH parsed_data AS (\n SELECT\n country_name,\n demographics_taxonomy_id,\n jsonb_each_text(attr_value::jsonb -> 'yearly_data') AS kv\n FROM ecosystem_country_demographics\n WHERE demographics_taxonomy_id = 7 \n),\nunpacked AS (\n SELECT\n country_name,\n (kv.key)::text AS year,\n (kv.value)::numeric AS gdp_growth\n FROM parsed_data\n)\nSELECT\n country_name,\n year,\n gdp_growth\nFROM (\n SELECT\n country_name,\n year,\n gdp_growth,\n ROW_NUMBER() OVER (PARTITION BY country_name ORDER BY year DESC) AS rn\n FROM unpacked\n) t\nWHERE rn = 1\nORDER BY gdp_growth DESC\nLIMIT 10;\n\n***How does compensation vary by geography for similar roles?***\t\n\nBenchmark compensation patterns across geographies using demographic and economic data to refine pay strategies and remain competitive in local labor markets.\t\n\nWITH unpacked AS (\n SELECT\n msa_name,\n demographics_taxonomy_id,\n (jsonb_each_text(attr_value::jsonb -> 'yearly_data')).key::int AS year,\n (jsonb_each_text(attr_value::jsonb -> 'yearly_data')).value::numeric AS metric_value\n FROM workforce_data\n WHERE demographics_taxonomy_id IN (8,25)\n),\nlatest AS (\n SELECT\n msa_name,\n demographics_taxonomy_id,\n year,\n metric_value,\n ROW_NUMBER() OVER (PARTITION BY msa_name, demographics_taxonomy_id ORDER BY year DESC) AS rn\n FROM unpacked\n)\nSELECT\n msa_name,\n MAX(CASE WHEN demographics_taxonomy_id = 8 THEN metric_value END) AS gdp_per_capita,\n MAX(CASE WHEN demographics_taxonomy_id = 25 THEN metric_value END) AS cost_of_living,\n (MAX(CASE WHEN demographics_taxonomy_id = 8 THEN metric_value END) /\n NULLIF(MAX(CASE WHEN demographics_taxonomy_id = 25 THEN metric_value END), 0)) AS compensation_proxy\nFROM latest\nWHERE rn = 1\nGROUP BY msa_name\nORDER BY compensation_proxy DESC;","terms_of_service":"https://draup.com/privacy-policy#:~:text=Unless%20provided%20for%20in%20this,that%20work%20on%20our%20behalf","documentation_link":"","privacy_policy_link":"https://draup.com/privacy-policy","geographical_coverage":"[\"Global\"]","cost":"FREE","update_frequency":{"interval":1,"unit":"WEEKLY"},"collection_granularity":{"interval":1,"unit":"DAILY"},"data_source":"Public Sources","assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"Draup, Inc.","description":"Draup is an enterprise agentic intelligence platform built for HR and Sales leaders navigating transformation. By combining unparalleled real-time global data with agentic AI, Draup enables strategic decision-making across talent and revenue functions.\n \nThrough its Talent Intelligence solution, Draup empowers HR teams to drive workforce planning, precision hiring, predictive skill architecture, and competitive benchmarking. Its Sales Intelligence solution helps go-to-market teams uncover deep account insights, identify buying triggers, and accelerate revenue growth.\n \nTrusted by over 300 global enterprises - including five of the Fortune 10 - Draup supports companies like Microsoft, PepsiCo, PayPal, and Moderna in building future-ready workforces and growth strategies."},"popularity":1140},{"id":"c6ed5de0-3822-49e2-bfc0-271d2d6e8092","summary":{"name":"SAMPLE Oxford Dictionaries API (25+ languages, JSON format, 9 endpoints) | Oxford Languages","subtitle":"Note: Our API delivers dictionary data exclusively in JSON format. The CSV sample above is for preview purposes ...","share":{"name":"oxford_languages_346b49e7_eafb_4969_ae56_494af9dd0b4c_samples_share","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1786349170852,"updated_at":1786349170852,"categories":["OTHER"],"listingType":"STANDARD","provider_id":"79c6c146-3407-48ec-8737-5b9b06406932"},"detail":{"description":"The Oxford Dictionaries API delivers direct, efficient access to Oxford's authoritative dictionary content.\n\nOur API provides instant access to language data for over 25 languages, featuring monolingual and bilingual dictionaries, thesauri, sentence examples, and audio data in JSON format. Effortlessly integrate accurate definitions, translations, synonyms, and pronunciations into your project or app.\n\nThrough 9 endpoints, you can use the API in different ways:\n•\tEntries endpoint \n•\tWords endpoint \n•\tLemmas endpoint \n•\tInflections endpoint \n•\tSearch endpoint \n•\tThesaurus endpoint \n•\tSentences endpoint \n•\tTranslations endpoint \n•\tUtility endpoint \n\nOur lexical datasets include features such as:\n•\tDefinitions\n•\tDomains\n•\tEtymologies\n•\tExamples\n•\tInflections\n•\tAudio and transcription data\n•\tRegional differences \n•\tRegisters\n•\tSynonyms\n•\tAntonyms\n•\tTranslations\n•\tAlternative spellings\n\nWhy choose our API?\n\n1.\tOn-demand data: Get only the features you need in our datasets from our API for your projects or apps.\n\n2.\tScalable and seamless integration: There’s no need to reengineer integration for each language; our unified schema and call format reduce the learning curve and let you quickly build or scale your project or app across languages.\n\n3.\tComprehensive multilingual coverage: The API includes datasets for over 25 languages. Unlike most dictionary APIs that support only a single language or data type, we deliver a comprehensive pipeline across multiple languages and support your expansion.\n\n4.\tAccess updated data: As language evolves, so does our data. Accessing our API gives you immediate updates to lexical data as it changes.\n\n\nUse Cases: \n\nWe consistently work with our clients on new use cases as language technology continues to evolve. These include Natural Language Processing (NLP) applications, TTS, dictionary display tools, games, translation machine, AI and machine learning training and fine-tuning, word embedding, and word sense disambiguation (WSD). \n\nIf you have a specific use case in mind that isn't listed here, we’d be happy to explore it with you. Don’t hesitate to get in touch with us at Growth.OL@oup.com to start the conversation or visit our Oxford Dictionaries API website for more information: https://developer.oxforddictionaries.com/\n\n \nPricing: \n \n1. API Lite\n\nAPI Lite gives full access to the Oxford Dictionaries API at £50 per month (billed annually at £600), allowing up to 5,000 calls per month. Each additional call costs £0.05. This suits those who want basic monthly access with access to all languages and endpoints.\n\nPlan includes:\n- 5,000 calls per month\n- Access to all languages and endpoints\n- Overage available at £0.05 per call\n\n2. Growing\n\nGain expanded access to the Oxford Dictionaries API from £200 per month (billed annually at £2,400). Each call costs £0.004, offering a 60% discount.\n\nPlan includes:\n- 50,000 calls per month per language and per endpoint\n- Access to all languages and endpoints\n- Bespoke language packages available\n- Dedicated customer success support\n- Overage accessible at £0.01 per call\n\n3. Enterprise\n\nAccess the highest-volume package for heavy API use, starting at £415 per month (billed annually at £5,000). Each call is valued at £0.0008, reflecting a 92% discount.\n\nPlan includes:\n- 500,000 calls per month per language and per endpoint\n- Access to all languages and endpoints\n- Bespoke language packages available\n- Dedicated customer success support\n- Marketing & collaboration opportunities\n- Overage accessible at £0.005 per call\n\nIdeal for businesses of all sizes and developers building dictionary applications and features in eBook and audiobook platforms, language learning, other educational apps, and more.\n\nReady to get started? Choose a plan and sign up for API now: https://account.oxforddictionaries.com/pricing\n\nYou can contact our team or email us at Growth.OL@oup.com.\n\n\nAbout the sample: \n\nTo help you explore the structure and features of our dataset on this platform, we provide a sample in CSV and/or JSON formats for one of the presented datasets, for preview purposes only, as shown on this page. This sample offers a quick and accessible overview of the data's contents and organization. \n\nIf you would like to try the data directly from the API, you can explore with our free trial plan: http://tiny.cc/ODAPI","terms_of_service":"https://corp.oup.com/privacy-policy","contact_email":"support@datarade.ai","privacy_policy_link":"https://corp.oup.com/privacy-policy","assets":["ASSET_TYPE_DATA_TABLE"]},"provider_summary":{"name":"Oxford Languages","description":"Oxford Languages delivers multilingual language datasets designed to power the next generation of language technologies. Built through decades of research and curated by expert lexicographers, our data fuels diverse applications – from text-to-speech (TTS) and predictive text to language models dictionary display tools, assistive tech, chatbots, games, and more. With over 60 languages and a wide range of features, our structured datasets ensure linguistic accuracy, cultural nuance, and domain relevance – ideal for AI, NLP, and ML development."},"popularity":1434},{"id":"5f3154ec-d498-4117-b9ee-81200e5e6e47","summary":{"name":"ＣＯ－ＯＤＥ 有価証券報告書データ","subtitle":"このデータセットでは、金融庁が運営するEDINETにて公開している有価証券報告書・四半期報告書\n（株式会社ジールの親会社である株式会社アバントグループの１社分）の財務データを加工したサンプルデータを収録しております。","setting":{"visibility":"PUBLIC"},"created_at":1697598455090,"updated_at":1699581901535,"categories":["FINANCIAL"],"listingType":"PERSONALIZED","provider_id":"ccb1eead-54d5-4f89-9cdc-fca2645ff069"},"detail":{"description":"**Overview**\nこのデータセットでは、金融庁が運営するEDINETにて公開している有価証券報告書・四半期報告書\n（株式会社ジールの親会社である株式会社アバントグループの１社分）の財務データを加工したサンプルデータを収録しております。\nＣＯ－ＯＤＥをご契約いただくと、上場企業約3700社分と非上場約600社分の有価証券報告書・四半期報告書の財務データを取得できます。\nＣＯ－ＯＤＥ(コ・オード)の詳細については、以下のリンクから公式ページを参照ください。\nhttps://www.zdh.co.jp/products-services/external-data/co-ode/\n\n**Use cases**\n経営分析において、有価証券報告書のデータから同業他社の財務状況を把握することができ、\n自社の財務状況を比較することで相対的に判断することができます。\n・ベンチマークしている会社のPL＆BSとの比較分析\n・円安や燃料費高騰によるコスト増による業績影響度合いを把握\n・経営ダッシュボードで他社比較分析\n・他社の利益や販管費と自社の比較\n\n**Product details**\nSample Tables:\n-有価証券報告書_上場企業一覧\n-有価証券報告書_並び順\n-有価証券報告書_基本情報\n-四半期報告書_並び順\n-四半期報告書_基本情報\n\nSample Fields:\n-EDINETコード\n-決算日\n-提出日\n-項目名\n-コンテキストID\n-相対年度※１\n-連結・個別※２\n-ユニットID※３\n-単位\n-値\n\n※１：当期/当期末を収録\n※２：連結/個別/その他を収録\n※３：JPY：日本円\n　　　pure：割合（％）、整数、小数及び人数に利用\n　　　shares：株式数\n　　　JPYPerShares：１株当たりの金額（日本円）\n\n**Additional Insights**\n出典\nEDINET閲覧（提出）サイト（https://disclosure.edinet-fsa.go.jp/）より加工して出典","terms_of_service":"https://www.zdh.co.jp/products-services/external-data/co-ode/","documentation_link":"","privacy_policy_link":"https://www.zdh.co.jp/privacy/","assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"Zeal Corporation","description":"株式会社ジールは、経営を支えるデータ基盤の構築とDX人材の育成に注力し、従来のビジネスインテリジェンスサービスを超えた活動を推進しています。 そのサービスの一つにオープンデータ提供サービス「CO-ODE」があり、オープンデータとお客様独自のデータを組み合わせることでデータ活用を強力にサポートします。"},"popularity":1354},{"id":"1a364b26-0b8d-4905-8ed1-fbc3a80b13d2","summary":{"name":"Hospital Compare General Information","subtitle":"This data package contains information about Abeyance, Dispersal and GP Practice Codes for Prescribing Cost Centres an..","share":{"name":"hospital_compare_general_information","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1682356018334,"updated_at":1695691203786,"categories":["HEALTH","SCIENCE_AND_RESEARCH"],"listingType":"STANDARD","provider_id":"f88849f8-9d77-4de2-b4c7-03d55cc3768e"},"detail":{"description":"\n**Overview**\nThis data package contains information about Abeyance, Dispersal and GP Practice Codes for Prescribing Cost Centres and Relevant Health Trusts. It comprises of datasets on Hospital Compare by General Practitioners and Long Term Care Hospitals by General Information and Provider data. It also includes datasets on Nurse Prescribers, Pharmacy Headquarters and Private Controlled Drug Prescribers in England as well as Medical Center Location of US Hospital.\n\n**Description**\nThis data package contains the Abeyance and Dispersal General Practitioners (GP) Codes that are a part of this data package. The Organization Data Service (ODS) is provided by the Health and Social Care Information Centre (HSCIC). It is responsible for the publication of all organization and practitioner codes and national policy and standards with regard to the majority of organization codes and encompasses the functionality and services previously provided by the National Administrative Codes Service (NACS). These code standards form part of the NHS data standards. For dispensaries in England, each dispensary can only be linked to one Headquarter (HQ). Data for General Medical Practices, General Medical Practitioners (GPs), Prescribing Cost Centres and Dispensaries are supplied by the National Health Service (NHS) Prescription Services and are used to facilitate their remit to remunerate dispensing contractors and provide the NHS with financial, prescribing and drug information.\n\nThe General Practitioners (GPs) Practice Codes by relevant Health Trusts in England and Wales includes Practitioners and Parent Organization Code, Join and Left Parent Date information for several health trusts in England and Wales. The dataset of Codes for GP branch surgeries in England includes information over data for GPs and GP Surgeries that has traditionally been supplied by the NHS Prescription Service of the NHS Business Services Authority. Hospital General Information and Performance Measures include a list of all hospitals that have been registered with Medicare. Hospital Compare provides information on how well hospitals provide recommended care to their patients. This information can help consumers make informed decisions about where to go for health care. Hospital Compare allows consumers to select multiple hospitals and directly compare performance measure information as it includes measures on Mortality, Safety of Care, Readmissions, Patients' Experience, Timeliness of Care, Effectiveness of Care and Efficient Use of Medical Imaging.\n\nThis data package also includes a list of long-term care hospitals. Long Term Care Hospitals (LTCHs) provide care to patients with medically complex problems. These complex diagnoses include Traumatic Brain Injury, conditions requiring prolonged mechanical ventilation, paralysis, very significant wound care, and other conditions resulting in organ failure resulting in the patient requiring a hospital-level of care for an extended period. US Hospital Medical Center Location provides a list of hospitals which includes medical centers in the USA with detailed geolocation identifiers. It includes only hospital facilities and does not include nursing homes. Data for all the states was acquired from respective states departments and then geocoded and converted into a spatial database.\n\n**Benefits**\n- Useful for general medical practitioners, medical care providers, hospitals, patients and consumers. hospital compare general information database is available on data.gov.uk, (national health service, england nhs digital), centers for medicare & medicaid services (cms) and homeland infrastructure foundation-level data websites in raw form, whereas a jsl customer gets it in clean and normalized form ready to use in one place and create his own custom reports. easy to comprehend for the customers who are unfamiliar with the medical terminologies as all abbreviated terms are replaced with full form, unlike data.gov.uk, (national health service, england nhs digital), centers for medicare & medicaid services (cms) and homeland infrastructure foundation-level original datasets which are full of abbreviations. recent dataset for 2016 is available for use.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [Abeyance and Dispersal GP Codes](https://www.johnsnowlabs.com/marketplace/abeyance-and-dispersal-gp-codes)\n\t- This dataset comprises of Abeyance and Dispersal General Practitioners (GP) Codes. It includes name and address information, identifying codes, Joined and Left Parent dates and contact telephone number for abeyance and dispersal GP Codes.\n- [Clinics in England](https://www.johnsnowlabs.com/marketplace/clinics-in-england)\n\t- This dataset contains information about clinics in England. National Health Service (NHS) Choices considers clinics as locations that provide predominantly outpatient services. It includes information about organization and postcodes, telephone number and email address for several clinics organizations in England.\n- [Dispensaries in England](https://www.johnsnowlabs.com/marketplace/dispensaries-in-england)\n\t- This dataset comprises of codes for dispensaries in England. Each dispensary can only be linked to one HQ (Headquarter). It includes information about the organization and zip codes, open and close date information and telephone number for several dispensaries in England.\n- [GP Practice Codes by Relevant Health Trusts in England and Wales](https://www.johnsnowlabs.com/marketplace/gp-practice-codes-by-relevant-health-trusts-in-england-and-wales)\n\t- This dataset comprises of General Practitioners (GPs) Practice Codes by relevant Health Trusts in England and Wales. It includes Practitioners and Parent Organization Code, Join and Left Parent Date information for several health trusts in England and Wales.\n- [GP Practices Currently Followed and Prescribing Cost Centers](https://www.johnsnowlabs.com/marketplace/gp-practices-currently-followed-and-prescribing-cost-centers)\n\t- This dataset comprises of General Practitioner (GP) Practices and the prescribed Cost Centres in England and Wales. It includes the name and address information and identifying codes for GP Practices in England and Wales.\n- [General Medical Practitioners Currently Working in England and Wales](https://www.johnsnowlabs.com/marketplace/general-medical-practitioners-currently-working-in-england-and-wales)\n\t- This dataset comprises of General Medical Practitioners (GPs) currently working in England and Wales. It includes name and address information and identifying codes for General Practitioners working in England and Wales. These identifying codes are known as General National Codes (GNC).\n- [General Practitioner Branch Surgeries in England](https://www.johnsnowlabs.com/marketplace/general-practitioner-branch-surgeries-in-england)\n\t- This dataset comprises of Codes for General Practitioner (GP) branch surgeries in England. It includes information about Government Office Region and Parent Organization Code, Joined and Left Parent Organization Date information and Telephone Number for several branch surgeries in England.\n- [General Practitioner Branches in England](https://www.johnsnowlabs.com/marketplace/general-practitioner-branches-in-england)\n\t- This dataset contains a list of active General Practioner (GP) branches. The data is aligned with the Organization Data Service (ODS) GP data. It includes information about organization and postcodes, telephone number and email address for several GP branches in England.\n- [General Practitioners Codes by GP Practice](https://www.johnsnowlabs.com/marketplace/general-practitioners-codes-by-gp-practice)\n\t- This dataset comprises of General Medical Practitioner (GP) codes to the relevant GP Practice or Practices at which they work. It includes Practitioners and Parent Organization Code, Joined and Left Parent Organization Date information.\n- [Hospital Acquired Conditions Reduction Program National](https://www.johnsnowlabs.com/marketplace/hospital-acquired-conditions-reduction-program-national)\n\t- This dataset identifies hospitals that are subject to a payment reduction based on rank with respect to Hospital Acquired Conditions (HAC) quality measures. The Affordable Care Act established the HAC Reduction Program to incentivize hospitals to reduce HACs. Hospitals that rank in the worst-performing 25 percent of all subsection (d) hospitals. These hospitals may have their payments reduced by 99 percent of what would have been paid for such discharges.\n- [Hospital General Information and Performance Measures Comparison](https://www.johnsnowlabs.com/marketplace/hospital-general-information-and-performance-measures-comparison)\n\t- This dataset includes a list of all hospitals that have been registered with Medicare. The list includes addresses, phone numbers and hospital type. This dataset also comprises of the measures such as Mortality, Safety of Care, Readmissions, Patients' Experience, Timeliness of Care, Effectiveness of Care and Efficient Use of Medical Imaging.\n- [Hospitals in England](https://www.johnsnowlabs.com/marketplace/hospitals-in-england)\n\t- This dataset contains information about hospitals in England. National Health Service (NHS) Choices considers hospitals as locations that provide predominantly inpatient services. It includes information about organization and post codes, telephone number and email address for several hospital organizations in England.\n- [Long Term Care Hospital General Information](https://www.johnsnowlabs.com/marketplace/long-term-care-hospital-general-information)\n\t- This dataset includes a list of long-term care hospitals with information such as address, phone number, ownership data and more. This dataset also shows the total number of beds each long-term care hospital owns.\n- [Long Term Care Hospital National Data](https://www.johnsnowlabs.com/marketplace/long-term-care-hospital-national-data)\n\t- This dataset includes the data on the quality of patient care measures. This dataset also shows the corresponding scores against each of the measures for quality of patient care nation-wide.\n- [Long Term Care Hospital Provider Data](https://www.johnsnowlabs.com/marketplace/long-term-care-hospital-provider-data)\n\t- This dataset includes a list of long-term care hospitals with information such as address, phone number, data on the quality of patient care measures and more. This dataset also shows the corresponding scores against each of the measures for quality of patient care.\n- [MHAC Hospital Performance By PPC FY2016](https://www.johnsnowlabs.com/marketplace/mhac-hospital-performance-by-ppc-fy2016)\n\t- This dataset comprises Maryland Hospital Acquired Conditions (MHAC) program which was implemented in state fiscal year (FY) 2011 to link hospital payment with hospital performance using 3M’s Potentially Preventable Complication (PPC) classification system. PPCs are post-admission complications that may result from hospital care and treatment.\n- [MHAC PPC Improvement by Hospital FY 2016](https://www.johnsnowlabs.com/marketplace/mhac-ppc-improvement-by-hospital-fy-2016)\n\t- This dataset comprises Maryland Hospital Acquired Conditions (MHAC) program FY 2016, which was implemented in state fiscal year (FY) 2011 to link hospital payment with hospital performance using 3M’s Potentially Preventable Complication (PPC) classification system. PPCs are post-admission complications that may result from hospital care and treatment.\n- [Nurse Prescribers in England](https://www.johnsnowlabs.com/marketplace/nurse-prescribers-in-england)\n\t- This dataset comprises of codes for Nurse Prescribers in England. 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It includes information about organization and post codes, open and close date information and telephone number for several pharmacy HQ organizations in England.\n- [Private Controlled Drug Prescribers in England](https://www.johnsnowlabs.com/marketplace/private-controlled-drug-prescribers-in-england)\n\t- This dataset comprises of current Private Controlled Drug Prescribers (PCDP) in England. It includes name and address information and identifying codes for Drug Prescribers working in England.\n- [US Hospital Medical Center Location](https://www.johnsnowlabs.com/marketplace/us-hospital-medical-center-location)\n\t- This dataset provides a list of hospitals that include medical centers in the USA with detailed geolocation identifiers, such as latitude and longitude of each hospital. The information about the location of hospitals was obtained by geocoding of addresses and then was corrected using satellite images. It includes only hospital facilities and does not include nursing homes. The dataset represents the location of hospitals for 50 states and Washington D.C., Puerto Rico and US territories.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated\n- Data Updates\n - Data updates support replace-on-update: outdated foreign keys are deprecated, not deleted\n\n**Our data is curated and enriched by domain experts** \n\n Each dataset is manually curated by our team of doctors, pharmacists, public health & medical billing experts:\n- Field names, descriptions, and normalized values are chosen by people who actually understand their meaning\n- Healthcare & life science experts add categories, search keywords, descriptions and more to each dataset\n- Both manual and automated data enrichment supported for clinical codes, providers, drugs, and geo-locations\n- The data is always kept up to date – even when the source requires manual effort to get updates\n- Support for data subscribers is provided directly by the domain experts who curated the data sets\n- Every data source’s license is manually verified to allow for royalty-free commercial use and redistribution.\n\n**Need Help?**\nIf you have questions about our products, contact us at [info@johnsnowlabs.com](mailto:info@johnsnowlabs.com). \n 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This meticulously compiled dataset, sourced from official statistical sources, including Instituto Nacional de Estadística, offers a detailed insight into the population landscape of Spain at the municipality (communes) level. \n\nIf you require data at a less granular level, such as an Uber grid or square grid, please don't hesitate to get in touch with us at *contact@geolocet.com*. We understand that different projects may have varying data needs, and we are here to tailor our data to match your specific requirements.\n\n ## **Key Features:**\n---\n* **Population Data Spain:** Gain access to the latest and most accurate population statistics for Spain, meticulously organized by municipality (communes).\n\n* **Demographic Insights:** Dive deep into Spain demographic landscape with data on 104 attributes, including: \n* Total Population,\n* Gender,\n* Age bands,\n* Gender X Age,\n* Marital Status,\n* Gender X Marital Status,\n* Number of households,\n* Household composition,\n* Household size,\n* Property type - owned/rented,\n* Dwelling type,\nall available at the municipal (communes) level.\n\n* **Geodemographic Insights:** Unlock geodemographic data Spain-wide to inform location-based decision-making, marketing strategies, and policy formulation.\n\n* **Ready to use:** Our dataset is currently available in CSV format, providing ease of use and compatibility with a wide range of data analysis tools. However, if you prefer a different format, our team is readily available to assist you in obtaining the data in your desired format.\n---\nAdditionally, for those looking to enrich their analysis or project, we offer:\n* **[Administrative Boundaries](https://geolocet.com/products/spain-all-available-administrative-boundaries):** If you require geographical boundaries (polygons) for the municipalities of Spain, consider exploring our Admin Boundaries product, which seamlessly integrates with this dataset.\n\n* **[Point of Interest (PoI) Data](https://geolocet.com/pages/aggregated-points-of-interest-poi-data):** If you're interested in Points of Interest data, please don't hesitate to contact us for customized datasets to enhance your geographical analysis.\n\n* **[Data Customization](https://geolocet.com/pages/data-services-by-geolocet):** We understand that your project may have specific requirements. Feel free to get in touch with us if you need any modifications or specific data formats such as Excel, JSON, GeoJSON, or Shapefile. We are here to tailor the data to your needs.\n\n\nIf you're looking for data for Spain, [look here](https://geolocet.com/pages/spain) for off-the-shelf or tailor-made datasets to suit your specific requirements.\n\nMake informed decisions, conduct precise research, and unlock the potential of Spain's demographic landscape with our comprehensive dataset. Trust in the accuracy and reliability of our official statistical data to drive your projects forward.\nContact us at *contact@geolocet.com* today to access this invaluable resource and explore Spain census data like never before.\n\n[Geolocet LTD](https://geolocet.com)\n","terms_of_service":"https://geolocet.com/pages/terms-of-use","documentation_link":"","privacy_policy_link":"https://geolocet.com/pages/privacy-policy","cost":"PAID","data_source":"","assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://geolocet.com/pages/terms-of-use"},"provider_summary":{"name":"Geolocet LTD","description":"Geolocet provides premium geospatial data for retail analysis, GIS, and market planning. 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Connect with Acxiom on LinkedIn and discover more at Acxiom.com."},"popularity":2073},{"id":"d4af8d8f-db7b-4fb7-abc3-51fb2c4623da","summary":{"name":"Agentic AI Informatica-to-Databricks Migration Accelerator","subtitle":"AI-powered accelerator for migrating Informatica ETL workloads to Databricks.","setting":{"visibility":"PUBLIC"},"created_at":1785938725815,"updated_at":1785938853281,"categories":["ADVERTISING_AND_MARKETING"],"listingType":"PERSONALIZED","provider_id":"3e1fd420-8722-4ebc-abaa-79f86ceffda0"},"detail":{"description":"Contact us at: chandra.reddy@dataplatr.com\nFor consultations or custom inquiries: https://dataplatr.com/contact-us\n\n**Overview**\nThe Dataplatr Agentic AI Informatica-to-Databricks Migration Accelerator is an AI-driven, Databricks-native methodology that converts Informatica PowerCenter / IICS mappings, sessions and workflows into production-ready\nDatabricks pipelines. At its core is an AI agent that orchestrates the entire migration, backed by a translation engine that pairs rules-based parsing with a dedicated agentic AI translation skill, which plans, generates and self-checks code for logic the rules cannot confidently resolve on their own. This agentic approach replaces slow, manual,mapping-by-mapping rewrites with a structured, auditable process, delivering a fast, low-risk path from legacy Informatica ETL to a governed Databricks\nLakehouse, with proof at every stage that the new pipeline matches the old one.\n\nEnterprises running Informatica PowerCenter or IICS estates face rising licensing costs, a shrinking pool of Informatica-skilled engineers, and ETL logic that has accumulated years of business rules across thousands of mappings, sessions and workflows. Rewriting this estate by hand is slow and resource-intensive, and needs disciplined validation to keep downstream\nreporting and compliance processes accurate through the transition.\n\nDataplatr's accelerator addresses this with a five-stage, agentic methodology: **Pre-Migration Assessment**, **AI-Powered Code Translation**, **Testing**, **Reconciliation**, and **Scheduling**, all coordinated end to end by AI agents. Each stage produces auditable evidence, including an inventory, translated code, execution logs, a reconciliation report, and\na working Lakeflow Job, so migration leads can track and sign off progress wave by wave, rather than discovering gaps at cutover. The outcome is a Databricks-native estate built on Delta Lake and Unity Catalog, with the existing Informatica flow logic migrated as-is and validated at every step, not just approximately reproduced.\n\n**Key Features**\n* **Automated Repository Assessment** – Parses the Informatica repository export to inventory every mapping, session and workflow, capturing transformation counts and dependency relationships, and classifies each object by translation complexity.\n* **Hybrid Rules-Based + Agentic AI Translation Engine** – A library of rule-based parsers converts well-understood Informatica constructs directly to PySpark/Delta Lake; a dedicated translation agent skill handles complex or ambiguous expression logic, reasoning over mapping context to generate idiomatic Spark code.\n* **100% Databricks-Native Output** – Generated pipelines are built on PySpark, Delta Lake and Unity Catalog, carrying the existing Informatica mapping and workflow structure over to Databricks as-is.\n* **Automated Functional Testing** – Every converted pipeline is executed end-to-end against representative data and checked for clean, error-free completion before being marked migration-complete.\n* **Row, Column & Aggregate Reconciliation** – Comparison between legacy Informatica outputs and new Databricks outputs across every migrated mapping, requiring an exact match on row counts, column values and business aggregates.\n* **Workflow-Equivalent Scheduling** – Informatica workflow structure (session order, decision/event-wait/command tasks, worklets) is translated into equivalent Databricks Lakeflow Jobs task graphs, preserving dependencies and failure handling.\n* **Agentic Migration Orchestration** – An AI agent sequences each mapping through assessment, translation, testing, reconciliation and scheduling end-to-end, invoking the right skill at each stage.\n* **Audit & Migration Logging** – Every stage (assessment, translation, test, reconciliation, scheduling) is logged with run IDs and status, giving migration leads a live, auditable view of progress.\n\n**Benefits**\n* **Faster Time-to-Migration** – Hybrid rules-based and agentic AI translation converts high-volume mapping inventories far faster than manual rewrites.\n* **Proven Equivalence** – Built-in testing and reconciliation gates prove the new pipeline matches legacy output before go-live.\n* **Minimal Re-Engineering of Operations** – Workflow-equivalent scheduling preserves existing dependency and failure-handling behavior, so operational runbooks need minimal change.\n* **Reduced Cost of Ownership** – Retiring Informatica licensing and specialist staffing needs in favor of a Databricks-native estate.\n\n## Informatica-to-Databricks Migration Accelerator User Manual\n\n**Informatica PowerCenter/IICS to Databricks ETL Migration (Assessment → Translation → Testing → Reconciliation → Scheduling)**\n\n**1. Purpose**\nThis accelerator automates the migration of Informatica PowerCenter/IICS\nmappings, sessions and workflows into Databricks using PySpark and Delta\nLake, following a five-stage methodology with built-in testing,\nreconciliation and workflow-equivalent scheduling.\n\n**2. Architecture Overview**\n\n**Migration Flow:** Informatica Repository Export → Assessment → Code\nTranslation → Testing → Reconciliation → Scheduling (Lakeflow Jobs)\n\n**Key Components:**\n* Repository parser for mappings, sessions, workflows and parameter files.\n* Translation engine: rule-based parser library + an agent skill for complex logic.\n* Delta Lake target tables for translated pipelines, mirroring the existing Informatica data flow structure.\n* Automated test harness for generated pipeline execution.\n* Reconciliation framework covering row, column and aggregate comparison across every migrated object.\n* Workflow-equivalence mapper for Databricks Lakeflow Jobs.\n* An AI agent that sequences each mapping through the five stages, invoking the assessment, translation, testing, reconciliation and scheduling skills in turn.\n* Migration log tables tracking status per mapping/workflow across all stages.\n\n**3. Migration Execution Steps**\n\n**Step 1: Assessment**\n* Load Informatica repository export and parse mappings, sessions and workflows.\n* Classify each object by transformation type and translation complexity.\n* Capture mapping, session and workflow counts, the full transformation inventory, and the object dependency graph across the estate.\n* Produce inventory and wave-based migration plan.\n\n**Step 2: Code Translation**\n* For each mapping in the current wave, the AI agent invokes the translation engine.\n* Rule-based parsers handle standard transformations (Expression, Filter, Joiner, Lookup, Aggregator, Router, Sequence Generator, Update Strategy).\n* The translation agent skill reasons over complex/ambiguous expressions and generates idiomatic PySpark logic for cases the rule-based parsers cannot resolve.\n* Generated code is written to the target notebook/module.\n\n**Step 3: Testing**\n* Execute the translated pipeline end-to-end against representative data.\n* Confirm the run completes without schema, runtime or dependency errors.\n* Any failure routes the mapping back to the translation agent skill for remediation, then re-testing.\n* Log results (pass/fail, error detail) to the migration log table.\n\n**Step 4: Reconciliation**\n* Run row-count comparison between legacy and new outputs across all migrated mappings.\n* Run column-level and aggregate-level comparison for key business metrics across all migrated mappings.\n* Any mismatch is flagged for remediation and re-translation; reconciliation must reach an exact match before sign-off.\n\n**Step 5: Scheduling**\n* Map the Informatica workflow's session order, decision/event-wait/command tasks to an equivalent Databricks Lakeflow Jobs task graph.\n* Preserve dependency order, trigger conditions and retry/failure-handling behavior.\n* Configure native Databricks Jobs alerting to match legacy notification patterns.\n* Publish the job and record it against the migration log as \"Scheduled\".\n\n **4. Operational Notes**\n\n* **Cluster Requirements:** Use a cluster sized for the translated PySpark workloads; scale per wave volume.\n* **Credential Handling:** Do not hardcode source system credentials; store in Databricks Secrets and reference from configuration.\n* **Wave Sequencing:** Migrate in waves grouped by shared source systems or downstream consumers to simplify reconciliation and cutover coordination.\n\n**Migrate Off Informatica with Agentic AI**\n\nDataplatr's Informatica-to-Databricks Migration Accelerator is built natively on Databricks to streamline your move off Informatica. To learn more or request access, please get in touch with our team.\n\n**Email:** chandra.reddy@dataplatr.com\n**Contact us:** https://dataplatr.com/contact-us\n\n*Note: By clicking 'Get Access', you agree to our Terms of Service. 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Buyers use Forager two ways: a REST API on self-serve credits, and licensed data feeds delivered to their warehouse. Bring a sample and we run it, so you can measure hit rate and connect rate on your own records."},"popularity":189},{"id":"bc3c9f86-6797-484e-82e5-016f09de8b7b","summary":{"name":"ＣＯ－ＯＤＥ 人口データ","subtitle":"このデータセットでは、総務省が公開している国勢調査と住民基本台帳に基づく人口推移、\n国立社会保障・人口問題研究所が公開している2045年までの将来人口推移のデータを加工したサンプルデータを収録しています。","setting":{"visibility":"PUBLIC"},"created_at":1697598254754,"updated_at":1697598623712,"categories":["ADVERTISING_AND_MARKETING","COMMERCE","DEMOGRAPHICS","RETAIL"],"listingType":"PERSONALIZED","provider_id":"ccb1eead-54d5-4f89-9cdc-fca2645ff069"},"detail":{"description":"**Overview**\nこのデータセットでは、総務省が公開している国勢調査と住民基本台帳に基づく人口推移、\n国立社会保障・人口問題研究所が公開している2045年までの将来人口推移のデータを加工したサンプルデータを収録しています。\n（地点は全て東京都目黒区のみに絞ったデータとなります）\nＣＯ－ＯＤＥをご契約いただくと、国勢調査は５年に１度の市区町村別・大字別・町丁目別の粒度の人口・世帯情報を取得でき、住民基本台帳に基づく人口は、年次で公開される年齢階級別の人口推移等の値を取得することができます。\nＣＯ－ＯＤＥ(コ・オード)の詳細については、以下のリンクから公式ページを参照ください。\nhttps://www.zdh.co.jp/products-services/external-data/co-ode/\n\n**Use cases**\n人口データを使うことで、エリアごとの営業活動やマーケティング活動で幅広く活用できます。\n・実店舗やオンラインショップを展開する時の商圏分析\n・自社会員が住んでいる地域の商圏分析\n・指定商圏の人口把握による小売店舗、商業施設、ホテルなどの出退店戦略\n・優良顧客と近しい年齢、性別、世帯に近い地域の分析による潜在顧客層の発掘\n・指定商圏の人口把握による、保険やローンの新規加盟店への営業\n・指定商圏の人口把握による住宅設備の売れ行き予測\n・鉄道業・運輸業では、電車やバスのダイヤ改正や適正な駅員数の調整\n等。\n\n**Product details**\nSample Tables:\n-人口・世帯_2020年国勢調査\n-2020年国勢調査_市区町村_1\n-人口・世帯_住民基本台帳\n-人口_住民基本台帳_年齢階級別\n-将来人口推移\n\nSample Fields:\n-年\n-都道府県コード\n-市区町村コード\n-都道府県\n-政令市・郡\n-市区町村\n-総人口\n-総人口＿男\n-総人口＿女\n-世帯数\n-０～４歳人口\n-５～９歳人口\n-１０～１４歳人口\n-１５～１９歳人口\n-２０～２４歳人口\n-２５～２９歳人口\n-３０～３４歳人口\n-３５～３９歳人口\n-４０～４４歳人口\n-４５～４９歳人口\n-５０～５４歳人口\n-５５～５９歳人口\n-６０～６４歳人口\n-６５～６９歳人口\n-７０～７４歳人口\n-７５～７９歳人口\n-８０～８４歳人口\n-８５～８９歳人口\n-９０～９４歳人口\n-９５～９９歳人口\n-１００歳以上人口\n-年齢不詳\n-平均年齢\n-１５歳未満人口\n-１５歳～６４歳人口\n-６５歳以上人口\n-７５歳以上人口\n-８５歳以上人口\n-外国人人口\n-０～４歳人口＿男\n-５～９歳人口＿男\n-１０～１４歳人口＿男\n-１５～１９歳人口＿男\n-２０～２４歳人口＿男\n-２５～２９歳人口＿男\n-３０～３４歳人口＿男\n-３５～３９歳人口＿男\n-４０～４４歳人口＿男\n-４５～４９歳人口＿男\n-５０～５４歳人口＿男\n-５５～５９歳人口＿男\n-６０～６４歳人口＿男\n-６５～６９歳人口＿男\n-７０～７４歳人口＿男\n-７５～７９歳人口＿男\n-８０～８４歳人口＿男\n-８５～８９歳人口＿男\n-９０～９４歳人口＿男\n-９５～９９歳人口＿男\n-１００歳以上人口＿男\n-年齢不詳＿男\n-平均年齢＿男\n-１５歳未満人口＿男\n-１５歳～６４歳人口＿男\n-６５歳以上人口＿男\n-７５歳以上人口＿男\n-８５歳以上人口＿男\n-外国人人口＿男\n-０～４歳人口＿女\n-５～９歳人口＿女\n-１０～１４歳人口＿女\n-１５～１９歳人口＿女\n-２０～２４歳人口＿女\n-２５～２９歳人口＿女\n-３０～３４歳人口＿女\n-３５～３９歳人口＿女\n-４０～４４歳人口＿女\n-４５～４９歳人口＿女\n-５０～５４歳人口＿女\n-５５～５９歳人口＿女\n-６０～６４歳人口＿女\n-６５～６９歳人口＿女\n-７０～７４歳人口＿女\n-７５～７９歳人口＿女\n-８０～８４歳人口＿女\n-８５～８９歳人口＿女\n-９０～９４歳人口＿女\n-９５～９９歳人口＿女\n-１００歳以上人口＿女\n-年齢不詳＿女\n-平均年齢＿女\n-１５歳未満人口＿女\n-１５歳～６４歳人口＿女\n-６５歳以上人口＿女\n-７５歳以上人口＿女\n-８５歳以上人口＿女\n-外国人人口＿女\n\n**Additional Insights**\n出典\n【国勢調査】政府統計の総合窓口(e-Stat)（https://www.e-stat.go.jp/）の「国勢調査」（総務省）を加工して出典\n【住民基本台帳】政府統計の総合窓口(e-Stat)（https://www.e-stat.go.jp/）の「住民基本台帳に基づく人口、人口動態及び世帯数調査」（総務省）を加工して出典\n【将来人口推移】「日本の将来推計人口(平成29年推計)」(国立社会保障・人口問題研究所)(https://www.ipss.go.jp/syoushika/tohkei/Mainmenu.asp）を加工して出典","terms_of_service":"https://www.zdh.co.jp/products-services/external-data/co-ode/","documentation_link":"","privacy_policy_link":"https://www.zdh.co.jp/privacy/","assets":["ASSET_TYPE_DATA_TABLE"],"license":"","tags":[{"tag_name":"LISTING_TAG_TYPE_TASK"}]},"provider_summary":{"name":"Zeal Corporation","description":"株式会社ジールは、経営を支えるデータ基盤の構築とDX人材の育成に注力し、従来のビジネスインテリジェンスサービスを超えた活動を推進しています。 そのサービスの一つにオープンデータ提供サービス「CO-ODE」があり、オープンデータとお客様独自のデータを組み合わせることでデータ活用を強力にサポートします。"},"popularity":1584},{"id":"c810a876-e037-4741-9a1b-0d0a62a8f276","summary":{"name":"Geolocet | Latest Demographic Data | Ukraine at Municipality Level","subtitle":"Demographic data for the municipalities (район) of Ukraine, offering a snapshot of their population.","setting":{"visibility":"PUBLIC"},"created_at":1706106444110,"updated_at":1752136649551,"categories":["DEMOGRAPHICS","ADVERTISING_AND_MARKETING","GEOSPATIAL","PUBLIC_SECTOR","RETAIL"],"listingType":"PERSONALIZED","provider_id":"5d3ddc17-a7e8-4794-a760-13587e1de2c6"},"detail":{"description":"## **Get instant access to the latest Demographics Data for Ukraine with [Geolocet](https://geolocet.com)**\n\nExplore the richness of Ukraine’s demographic landscape with our Ukraine Census Data. This meticulously compiled dataset, sourced from official statistical sources, including Держстат України, offers a detailed insight into the population landscape of Ukraine at the municipality (район) level. \n\nIf you require data at a less granular level, such as an Uber grid or square grid, please don't hesitate to get in touch with us at *contact@geolocet.com*. We understand that different projects may have varying data needs, and we are here to tailor our data to match your specific requirements.\n\n ## **Key Features:**\n---\n* **Population Data Ukraine:** Gain access to the latest and most accurate population statistics for Ukraine, meticulously organized by municipality (район).\n\n* **Demographic Insights:** Dive deep into Ukraine’s demographic landscape with data on 24 attributes, including: \n* Total population,\n* Gender,\n* Age bands,\n* Age bands split per gender,\n* Rural/Urban population,\n* Rural/Urban population split per age,\n* Rural/Urban population split per gender.\n\nAll demographic attributes are available at the municipal (район) level.\n\n* **Geodemographic Insights:** Unlock geodemographic data Ukraine-wide to inform location-based decision-making, marketing strategies, and policy formulation.\n* **Seamless Data Access:** Our data is available for instant download in CSV format. No lengthy sign-up processes or NDAs required on our side – just download the data you need.\n* **Data Samples for Evaluation:** To ensure you're getting exactly what you need, we provide free downloadable samples in CSV format for data evaluation.\n* **Data accuracy:** Our datasets are created using the latest demographics data available in national statistics bureaus, guaranteeing accuracy and reliability.\n* **Best-in-Class Quality:** We guarantee best-in-class quality, backed by over two decades of experience in modeling demographics data and an outstanding quality management process.\n* **Competitive Pricing:** We maintain the lowest prices on the market, ensuring that you get the most cost-efficient demographics data acquisition process.\n---\nAdditionally, for those looking to enrich their analysis or project, we offer:\n\n* **[Point of Interest (PoI) Data](https://geolocet.com/pages/aggregated-points-of-interest-poi-data):** If you're interested in Points of Interest data, please don't hesitate to contact us for customized datasets to enhance your geographical analysis.\n\n* **[Data Customization](https://geolocet.com/pages/data-services-by-geolocet):** We understand that your project may have specific requirements. Feel free to get in touch with us if you need any modifications or specific data formats such as Excel, JSON, GeoJSON, or Shapefile. We are here to tailor the data to your needs.\n\nMake informed decisions, conduct precise research, and unlock the potential of the Ukraine demographic landscape with our comprehensive dataset. Trust in the accuracy and reliability of our official statistical data to drive your projects forward.\nContact us at *contact@geolocet.com* today to access this invaluable resource and explore Ukraine census data like never before.\n\n[Geolocet LTD](https://geolocet.com)\n","terms_of_service":"https://geolocet.com/pages/terms-of-use","documentation_link":"","privacy_policy_link":"https://geolocet.com/pages/privacy-policy","geographical_coverage":"[\"Europe\"]","cost":"PAID","update_frequency":{"interval":1,"unit":"YEARLY"},"data_source":"","assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://geolocet.com/pages/terms-of-use"},"provider_summary":{"name":"Geolocet LTD","description":"Geolocet provides premium geospatial data for retail analysis, GIS, and market planning. We equip businesses with accurate, ready-to-query data to drive spatial analysis, site selection, and customer segmentation.\n\nCore Offerings:\n\nDemographics: Socioeconomic data covering population, age, and employment across diverse geographies.\n\nPoints of Interest (PoI): Datasets spanning retail, healthcare, and transport for catchment analysis.\n\nAdministrative Boundaries: GIS-ready boundaries, including municipalities and postcodes, for territory management.\n\nBrand Locations: Chain location datasets to monitor store networks and market expansion.\n\nWealth Data: Granular economic indicators like purchasing power to identify high-potential markets.\n\nWe source from trusted providers, delivering highly customizable datasets with comprehensive coverage across Europe and worldwide."},"popularity":157},{"id":"252db075-34cf-4e54-b326-25c072707eb8","summary":{"name":"USDA Yield and Retention","subtitle":"This data package contains information on USDA statistics on milk and dairy products production and supply, their util..","share":{"name":"usda_yield_and_retention","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1682356579301,"updated_at":1698965635225,"categories":["SCIENCE_AND_RESEARCH"],"listingType":"STANDARD","provider_id":"f88849f8-9d77-4de2-b4c7-03d55cc3768e"},"detail":{"description":"\n**Overview**\nThis data package contains information on USDA statistics on milk and dairy products production and supply, their utilization and commercial disappearance. It also provides information on wheat, meat and poultry cooking yield and adoption of genetically engineered crops in the United States.\n\n**Description**\nThis data package is a collection of datasets obtained by USDA (United States Department of Agriculture). The data is updated annually. The datasets include following statistics; - Number of milk cows, production of milk per cow and total milk production by state and region in the United States. - Dairy product per capita consumption; fluid milk sales; milk supply by State and region; milk production and supply and utilization of milk in all dairy products. - Commercial disappearance data of milk products: milk-equivalent milk-fat, milk-equivalent skim-solids, milk fat and skim solids. - Meat and Poultry cooking yields and retention factors are important because they serve as a major resource for U.S. and international food composition databases. - Planted and harvested acreage of five varieties of wheat, including hard red winter, hard red spring, soft red winter, white, and durum and rye. The estimation of the production, yields and farm prices have also been included in this dataset. - The adoption of herbicide-tolerant and insect-resistant corn, cotton, and soybeans. The data is obtained by USDA's (United States Department of Agriculture) National Agricultural Statistics Service (NASS)\n\n**Benefits**\n- useful for analytical purposes.\n- usda data available in normalized form\n- provides data on commodities exchange rates which are important for agricultural projections.\n- provides estimates of productivity growth in the u.s. farm sector.\n- comprehensive list of farmers markets helps to locate a market in specific location.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [Adoption of Genetically Engineered Crops](https://www.johnsnowlabs.com/marketplace/adoption-of-genetically-engineered-crops)\n\t- This dataset summarizes the adoption of herbicide-tolerant and insect-resistant corn, cotton, and soybeans. The data is obtained by USDA's (United States Department of Agriculture) National Agricultural Statistics Service (NASS) in the June Agricultural Survey for 2000 through 2019.\n- [Commercial Disappearance of Milk and Dairy Products](https://www.johnsnowlabs.com/marketplace/commercial-disappearance-of-milk-and-dairy-products)\n\t- This dataset contains commercial disappearance data of milk products: milk-equivalent milk-fat, milk-equivalent skim-solids, milk fat and skim solids. It also contains data on seven dairy product categories: butter, nonfat dry milk, American cheese, other-than-American cheese, dry whey, whey protein concentrate and lactose.\n- [Meat and Poultry Cooking Yields](https://www.johnsnowlabs.com/marketplace/meat-and-poultry-cooking-yields)\n\t- This dataset shows United States Department of Agriculture (USDA) cooking yields and retention factors are important because they serve as a major resource for U.S. and international food composition databases.\n- [Milk Cows and Milk Production in the US](https://www.johnsnowlabs.com/marketplace/milk-cows-and-milk-production-in-the-us)\n\t- This dataset provides information on the number of milk cows, production of milk per cow and total milk production by state and region in the United States from the year 1970 to 2021.\n- [Milk Supply and Dairy Products Utilization](https://www.johnsnowlabs.com/marketplace/milk-supply-and-dairy-products-utilization)\n\t- This dataset includes data on dairy product per capita consumption; fluid milk sales; milk supply by State and region; milk production and supply and utilization of milk in all dairy products.\n- [Wheat Production Yield and Farm Price](https://www.johnsnowlabs.com/marketplace/wheat-production-yield-and-farm-price)\n\t- This dataset contains information on planted and harvested acreage of five varieties of wheat, including hard red winter, hard red spring, soft red winter, white, and durum and rye. The estimation of the production, yields and farm prices have also been included in this dataset.\n- [World Production of Fruits and Vegetables for US Importation](https://www.johnsnowlabs.com/marketplace/world-production-of-fruits-and-vegetables-for-us-importation)\n\t- This dataset is a list of commodities including 23 fruits and 19 vegetables for which ERS (Economic Research Service) conducts market analysis. These commodities are included in the Census of Agriculture, published by USDA's National Agricultural Statistics Service (NASS). The list includes only those fruits and vegetables sold and consumed fresh. It does not include dry edible beans and does not include fruits and vegetables that have been dried, canned, or otherwise processed.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated\n- Data Updates\n - Data updates support replace-on-update: outdated foreign keys are deprecated, not deleted\n\n**Our data is curated and enriched by domain experts** \n\n Each dataset is manually curated by our team of doctors, pharmacists, public health & medical billing experts:\n- Field names, descriptions, and normalized values are chosen by people who actually understand their meaning\n- Healthcare & life science experts add categories, search keywords, descriptions and more to each dataset\n- Both manual and automated data enrichment supported for clinical codes, providers, drugs, and geo-locations\n- The data is always kept up to date – even when the source requires manual effort to get updates\n- Support for data subscribers is provided directly by the domain experts who curated the data sets\n- Every data source’s license is manually verified to allow for royalty-free commercial use and redistribution.\n\n**Need Help?**\nIf you have questions about our products, contact us at [info@johnsnowlabs.com](mailto:info@johnsnowlabs.com). \n 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The dataset contains 3,809,704 annotated images collected across 93 countries, making it one of the most geographically comprehensive resources available for plate detection and OCR tasks.\nData was collected by parsing videos from real-world road environments and traffic cameras, ensuring diverse coverage of traffic conditions, road types, lighting scenarios, and urban areas across multiple continents.\n\nDataset Composition\n- Total images: 3,809,704\n- Countries covered: 93\n- Image format: PNG\n- Annotation format: CSV\n- Tasks supported: Detection, Classification, OCR\n- Labeling fields: model, details, plate text, tag, country\nThe OCR-annotated subset covers 1,200,000+ images with full character recognition markup, enabling training of recognition algorithms for automatic number plate identification across different regions and environments.\n\nGeographic Coverage\nThe dataset spans all major continents, with the heaviest concentration in Europe:\n- Europe — 2,434,844 images\n- Asia — 128,627 images\n- North America — 84,629 images\n- Oceania — 5,265 images\n- South America — 4,687 images\n- Africa — 2,596 images\n\nTop 10 countries by image count:\n1. Russia — 1,299,630\n2. France — 222,912\n3. Poland — 220,474\n4. Germany — 200,062\n5. Ukraine — 189,947\n6. United Kingdom — 135,718\n7. Spain — 125,919\n8. Hungary — 107,272\n9. USA — 105,136\n10. Netherlands — 101,582\nAdditional well-represented countries include Italy, Belgium, China, Serbia, Belarus, Lithuania, Turkey, Kazakhstan, Austria, and Romania, among others. The dataset also includes plates from Vietnam, Latvia, Czech Republic, Croatia, Norway, Canada, Indonesia, Greece, Georgia, Finland, Estonia, Thailand, Uzbekistan, Israel, UAE, Sweden, Moldova, Azerbaijan, Kyrgyzstan, Armenia, Singapore, Argentina, Brazil, Morocco, Mexico, Japan, South Korea, Saudi Arabia, Egypt, Mongolia, and more.\n\nVehicle Tags & Plate Categories\nEach image is tagged by vehicle type and plate category, enabling fine-grained filtering for specialized training scenarios. Top tags by frequency:\n- Truck — 266,654\n- Transferred / re-issued plate — 226,645\n- Tractor unit — 148,792\n- Trailer — 106,685\n- Bus — 83,659\n- Cabriolet — 73,272\n- Oldtimer — 71,065\n- New letter combination — 56,709\n- Motorcycle — 52,111\n -Taxicab — 43,570\n -Motorhome — 35,760\n- Non-standard plate — 26,572\n- Plate for brand or model of vehicle — 26,268\n- Abandoned vehicle — 22,309\n- Dump truck — 15,975\n- Vinyl wrapping — 15,728\n- Electric vehicle — 15,150\n- Police — 14,185\n- Damaged — 11,235\n- Tuning — 9,556\nThis breadth of vehicle and plate types ensures that models trained on this dataset can handle real-world traffic flows with high accuracy — including edge cases such as damaged plates, non-standard formats, and specialty vehicles.\n\nTechnical Specifications\nImages are provided in PNG format with CSV annotation files. Each annotation record contains: plate text (OCR), country of origin, vehicle model and tag classification. \nCollection methodology: frames extracted from road camera footage and video streams captured across diverse road conditions — highways, urban streets, parking lots, and intersections — under varying lighting and weather conditions.\n\nUse Cases\n- Intelligent Transportation Systems. Training LPR and ALPR models for real-time vehicle identification in traffic management and monitoring systems. The dataset's coverage of 93 countries supports multi-region deployment without additional data collection.\n- Parking & Access Control. Building automated parking management solutions that recognize number plates across different vehicle types, including trucks, buses, motorcycles, and electric vehicles.\n- Law Enforcement & Security. Powering ANPR camera systems for vehicle registration tracking, enforcement workflows, and security monitoring in smart city infrastructure.\nAutonomous Driving Research. Supplying training data for autonomous vehicles and ADAS systems that require reliable plate detection across diverse road environments and traffic scenarios.\n- OCR Model Development. The 1,200,000+ OCR-annotated images with character-level labels and segmentation masks make the dataset directly applicable to training and benchmarking plate recognition algorithms for multi-language and multi-script number plates.\n\nSummary\nThe Car License Plate Detection Dataset is a production-grade resource for teams building LPR, ALPR, and ANPR systems at scale. With 3,809,704 images spanning 93 countries, 20+ vehicle and plate tag categories, it covers the full spectrum of real-world plate detection challenges. 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It also comprises of data about CCS for Mortality Reporting Program. It contains information regarding single level CCS for ICD-9 diagnosis and procedure codes.\n\n**Description**\nThis data package contains the Agency for Healthcare Research and Quality (AHRQ) developed the Clinical Classifications Software (CCS), a tool for clustering patient diagnoses and procedures into a manageable number of clinically meaningful categories. CCS offers researchers the ability to group conditions and procedures without having to sort through thousands of codes and makes it easier to quickly understand patterns of diagnoses and procedures so that health plans, policymakers, and researchers can analyze costs, utilization, and outcomes associated with particular illnesses and procedures. CCS for ICD-10-CM and Procedure Classification System (PCS) is a diagnosis and procedure categorization scheme that can be employed in many types of projects analyzing data on diagnoses and procedures. CCS is based on the International Classification of Diseases, 10th Revision (ICD-10), a uniform and standardized coding system. CCS consists of two related classification systems, single-level and multi-level, which are designed to meet different needs. Single-level CCS is most useful for ranking of diagnoses and procedures and for direct integration into risk adjustment and other software. Multi-level CCS is ideal for evaluating larger aggregations of conditions and procedures or exploring these groupings in greater detail.\n\n**Benefits**\n- Useful for medical health planners, policymakers and medical research groups and medical students. clinical classifications software (ccs) for icd-10-cm database is available on hcup website in raw form, whereas a john snow labs(jsl) customer gets it in a clean and normalized form ready to use and create his own custom reports. easy to comprehend for the customers who are unfamiliar with the medical terminologies as all abbreviated terms are replaced with full form, unlike healthcare cost and utilization project (hcup) original datasets which are full of abbreviations. recent dataset for 2018 is available for use.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [Clinical Classification Software Refined for ICD-10 CM](https://www.johnsnowlabs.com/marketplace/clinical-classification-software-refined-for-icd-10-cm)\n\t- The Clinical Classifications Software Refined (CCSR) is one in a family of databases and software tools developed as part of the Healthcare Cost and Utilization Project (HCUP), a Federal-State-Industry partnership sponsored by the Agency for Healthcare Research and Quality (AHRQ). HCUP databases, tools, and software inform decision making at the national, state and community levels.\n- [Clinical Classification Software for ICD-10 CM](https://www.johnsnowlabs.com/marketplace/clinical-classification-software-for-icd-10-cm)\n\t- Clinical Classifications Software for ICD-10-CM Codes provides a way to classify diagnoses into a limited number of categories by aggregating individual ICD-10-CM codes into broad diagnosis groups to facilitate statistical analysis and reporting.\n- [Clinical Classification Software for ICD-10 PCS](https://www.johnsnowlabs.com/marketplace/clinical-classification-software-for-icd-10-pcs)\n\t- Clinical Classifications Software for ICD-10-PCS Codes provides a way to classify procedures into a limited number of categories by aggregating individual codes into broad diagnosis groups to facilitate statistical analysis and reporting.\n- [Clinical Classification Software for Mortality Reporting Program](https://www.johnsnowlabs.com/marketplace/clinical-classification-software-for-mortality-reporting-program)\n\t- The Clinical Classifications Software (CCS) for Mortality Reporting is a diagnosis categorization scheme that can be employed in many types of projects analyzing data on diagnoses. This tool is based on the International Classification of Diseases, 10th Revision (ICD-10), a uniform and standardized coding system, which has been used in the U.S. for mortality reporting since 1999.\n- [Multi-Level Clinical Classification Software ICD-10 CM and PCS Codes](https://www.johnsnowlabs.com/marketplace/multi-level-clinical-classification-software-icd-10-cm-and-pcs-codes)\n\t- Clinical Classifications Software for ICD-10-Clinical Modification (CM) and Procedural Classification System (PCS) Codes provides a way to classify diagnoses into a limited number of categories by aggregating multi-level ICD-10-CM and PCS codes into broad diagnosis groups to facilitate statistical analysis and reporting.\n- [Single Level Clinical Classification Software ICD-9 Diagnosis Codes](https://www.johnsnowlabs.com/marketplace/single-level-clinical-classification-software-icd-9-diagnosis-codes)\n\t- Clinical Classifications Software for ICD-9 Diagnosis Codes provides a way to classify diagnosis and procedures into a limited number of categories by aggregating individual ICD-9-CM codes into broad diagnosis and procedure groups to facilitate statistical analysis and reporting.\n- [Single Level Clinical Classification Software ICD-9 Procedure Codes](https://www.johnsnowlabs.com/marketplace/single-level-clinical-classification-software-icd-9-procedure-codes)\n\t- Clinical Classifications Software for ICD-9 Procedure Codes provides a way to classify diagnosis and procedures into a limited number of categories by aggregating individual ICD-9-CM codes into broad diagnosis and procedure groups to facilitate statistical analysis and reporting.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 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Exclusively for Quebec businesses: REQ roles (directors, administrators, shareholders) enriched with phone numbers verified via Canada411. Each entry includes official titles, a Canada411 proof link, and the NEQ identifier – for impeccable traceability and full compliance.\nWhat You Get: A Powerful Tool to Boost Your Business\nA premium lead database focused on decision-makers, ideal for prospecting in commercial insurance, life insurance, investments, and more in Quebec. Names come directly from the REQ (Quebec Business Registry), enhanced with fresh phone coordinates from Canada411.\n\nSave Tons of Time: Forget endless searches – jump straight to the calls that matter, without wasting time on dead-end leads.\nMaximize Your Chances of Success: Reach the people who say \"YES\" (or influence the decision) for renewals, policy adjustments, and new opportunities: life insurance (individual, group, term, permanent – to protect families from the unexpected), investments (RRSP for tax-deferred retirement, TFSA for tax-free withdrawals, mutual funds, stocks, bonds to maximize returns), liability (GL for covering mishaps), property and real estate (for buildings and inventory), fleet auto (for company vehicles), cyber-security (against hacks that cost a fortune), group benefits (to retain employees with group plans), and much more.\nProof and Transparency: Every lead is backed by a Canada411 link + NEQ, for ethical and effective prospecting, without the risk of compliance issues.\n\nPerfect for Ambitious Insurance Brokers\n\nFast Prospecting Without Junk: Say goodbye to \"junk leads\" – target real decision-makers for campaigns that convert, whether in life insurance to protect families and businesses (with policies that cover major surprises), or investments to optimize portfolios and maximize returns (RRSP for building without immediate taxes, TFSA for total flexibility).\nRenewals and Account Development: Resubmit quotes, adjust coverages, increase limits, modify deductibles, add endorsements (cyber for data breaches, fleet auto for business vehicles, property/real estate for assets, GL for lawsuits, equipment breakdown for failing machinery, business interruption for revenue losses, E&O/professional for pro errors, life insurance with investment options, diversified investments like RRSP and TFSA) – with contacts who have the power to act.\nHyper-Targeted Local Campaigns: Filter by city, region, or province for personalized and impactful approaches, tailored to specific needs in life insurance (estate planning to pass on assets without heavy taxes, mortgage protection to secure the home), and investments (tax strategies to minimize burdens, retirement with RRSP and TFSA for a comfortable cushion).\nIrresistible Call Scripts: Use REQ roles as your angle (owner for patrimonial budgets and RRSP/TFSA, finance for risks and investments) – turn every conversation into an opportunity, emphasizing secure financial growth.\n\nWho Are These VIP Contacts? (REQ Roles Decoded)\nThese roles signal massive decision-making or influence power – prioritize your calls for maximum ROI, especially for products like life insurance and investments:\n\nPresident: The ultimate boss; often the key to final decisions on overall protection and investment strategies (RRSP, TFSA for retirement).\nVice-President: Powerful influencer; handles delegations and speeds up approvals for life policies and portfolios (mutual funds, bonds).\nAdministrator: Governance master; strategic impact on business choices, including long-term financial planning (permanent life insurance).\nShareholder: Engaged owner; controls budgets and prioritizes life protections and profitable investments (stocks for growth).\nPartner: Key partner; decision-maker based on structure, ideal for SMEs seeking life and investment solutions (TFSA for quick liquidity).\nTreasurer: Finance expert; highly receptive to costs, risks, life insurance savings, and investment opportunities (RRSP for tax deductions).\nSecretary: Compliance guardian; excellent relay for navigating internal regulatory aspects of financial products.\nOther: Atypical roles; explore for hidden opportunities in life insurance, investments (mutual funds, etc.).\n\nAdapt This List to Your Strategy!\nFilter, segment, and personalize easily for your specific campaigns, integrating life insurance for personal/business security (term for covering loans), investments for asset growth (RRSP and TFSA to beat inflation), and more. Whether targeting growing SMEs or large enterprises, this list adapts to your needs – for maximum marketing results. Ready to multiply your closings in life insurance, investments, and beyond? Contact QuantLens today and propel your prospecting!\nAvailable exclusively on QuantLens Marketplace – For insurance brokers who play to win.\nFull disclaimer \nLegal disclaimer: This lead list is provided “as is” and is compiled from publicly available sources (e.g., Québec REQ role information and Canada411 lookups). Data may be incomplete, inaccurate, or change over time; QuantLens does not guarantee deliverability, reachability, identity, or conversion results. Any links included are provided as reference to the enrichment source used and do not constitute verification or certification of the phone number. The buyer is solely responsible for validating data before use and for complying with all applicable laws and regulations (including privacy and consent requirements, anti-spam rules such as CASL, and telemarketing rules such as DNCL requirements where applicable). QuantLens is not affiliated with, endorsed by, or sponsored by the Québec Registre des entreprises or Canada411. Nothing in this product constitutes legal advice.","terms_of_service":"https://www.quantlens.net/privacy","contact_email":"marketplace@datarade.ai","privacy_policy_link":"https://www.quantlens.net/privacy","assets":["ASSET_TYPE_DATA_TABLE"]},"provider_summary":{"name":"QuantLens","description":"QuantLens is a data provider offering curated, ML-ready alternative datasets built from public institutional sources (e.g., SEC, FDA, USPTO, clinical trials, federal contracts, and crypto market data) for quantitative and AI research. 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The territorial vintages of the worldwide area boundaries are compatible with our globally consistent and comparable [socio-demographic](https://www.mbi-geodata.com/en/sociodemographic-data/) and [economic](https://www.mbi-geodata.com/en/purchasing-power-overview/) data.\nOur area boundaries for Italy are offered at the following levels:\n\n- **Postal Code Level** (Postcode 5-digit): This fine-grained level of detail makes the data suitable for a wide range of applications – for example, enriching customer databases, which are often organized by postal code.\n- **Administrative Level** (Comuni): Covers both larger administrative areas (e.g., regions, states, provinces) as well as finer subdivisions such as districts, counties, or municipalities.\n- **Micro Level** (Sezioni di Censimento): Subdivisions of postal code or administrative boundaries, such as census areas or street segments. 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No Corporate Self-Disclosures, No Value-Based Judgements.\n \nWorld-Class Data Engineering connects and standardizes ESG-relevant fact-based data sources into a Spatial-Level reference data solution known as a Knowledge Graph. Fit for Purpose for Financial Sector Workflows.\n \nAsset Level Reporting mapped to Corporate Owners.\n \nThe SRS Spatial Factors unlocks the ability to evaluate a location based on:\n \n- Climate/Natural Disaster Risks\n- Estimated Annual Financial loss from Climate/Natural Disaster Risks\n- Environmental Hazards\n- Socio-Economic factors - Vulnerability and Resilience\n- Social Cost of Carbon.\n- Scope I and Scope II Climate/Carbon Accounting\n- EPA facilities\n- Super Fund Sites\n- Greenhouse Gas Emissions\n\n**Transaction Due Diligence**\nSRS provides a comprehensive view of asset locations and critical factors. SRS' Turn-key reporting\nsolution saves time and money from the cumbersome efforts of building asset-level profiles.\n\n**Location Planning**\nThe SRS Spatial Risk Factors unlocks the ability to measure and quantify the risks to an asset location or\njurisdiction based upon the following core data dimensions.\n \nThousands of factors can mapped to a specific address location.\n \n- Climate/Natural Disaster Events (18)\n- Estimated Annual Losses from Natural Disaster Events\n- Environmental Hazards (Toxic Release, Air, Water, Proximity to EPA Super Fund Sites)\n- Socio-Economic factors (Income, Education, Poverty, Race)\n- Carbon Emissions -Scope I and Scope II (Corporate, Government, Non-Profit)\n\n**Fundamental Analysis**\nSpatial Portfolio Analytics: 30,000 Corporate and Municipal Issuers, 1.1 million securities, 600,000 Asset\nLocations.\n \nSRS provides a turn-key solution illuminating hundreds of key factors for both Corporate Level and\nFacility Level Spatial Factors for use in Portfolio Analytics, Fundamental Research, and Credit/Risk\nAnalysis.","terms_of_service":"https://spatialrisksystems.com/terms-of-service/","documentation_link":"https://spatialrisksystems.com/wp-content/uploads/2023/09/SRS-Data-Dictionaries.zip","privacy_policy_link":"https://spatialrisksystems.com/privacy/","geographical_coverage":"[\"Global\"]","cost":"PAID","assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"Spatial Risk Systems","description":"SRS, the market leader for spatial-level data governance and reference data, is an innovative data and analytics company that has engineered the first global 'spatial knowledge graph' to measure and quantify key spatial-level risk factors for Finance, Insurance, and Supply Chains."},"popularity":1008},{"id":"2470cf84-e000-47a3-8242-5fda0445ee3a","summary":{"name":"SAMPLE Global Late Payment Index Dataset | +200 Countries | Payment Behavior Metrics | Credit ...","subtitle":"This sample dataset presents payment behavior evaluations for 15 companies using a 0-4 scale indexing system that ...","share":{"name":"coface_business_informati_93bed2db_c716_4f00_b734_ec0c2e3b085e_samples_share","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1779285908770,"updated_at":1779285908770,"categories":["OTHER"],"listingType":"STANDARD","provider_id":"ab33ff81-b391-4b26-bb1d-f4b9bbbffbda"},"detail":{"description":"Late Payment Index (LPI) helps organisations evaluate prospects and current business partners and their potential risk of future non-payment. Helping businesses to make strategic decisions ahead of time.​\n\nThe Late Payment Index is a comprehensive metric developed by Coface to evaluate payment behaviour of companies globally. \nIt reflects the age, frequency, severity of claims, and other vital factors to assess payment difficulties accurately.\n\n\nDataset Structure and Components:\nSample Size: 15 company records with unique identifiers\nAssessment Date: All entries dated \nProduct Type: All categorized as \"LPI\" (Late Payment Index)\nEvaluation Format: Numeric rating system (0-4) with corresponding descriptive explanations\n\nRating Classification System:\nThe dataset employs a standardized 0-4 scale to categorize payment behavior:\n\n0: Not available - Assessment data unavailable or insufficient\n1: In progress - Assessment currently being conducted\n2: Considerable negative experience - Significant payment issues detected\n3: Some negative experience - Minor or occasional payment issues detected\n4: No negative experience - Clean payment history with no detected issues\n\nApplication Context:\nThis sample illustrates how payment behavior can be systematically tracked and categorized to support credit decision-making and business relationship management. 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[Learn more about SalesIntel.](https://www.salesintel.io)\n\n**Use cases**\nWe work with partners across industries to integrate this data into their datasets or augment their product offerings. 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SiteCom’s intuitive visualisations and plug-and-play applications drive operational resilience, efficiency, and sustainability.\n\nFor over two decades, SiteCom has been at the forefront, driving the adoption of new technologies and innovative working practices to transform the drilling and well operations domain.\n\n**Key benefits**\n- *Streamline well operations planning:* Integrate onshore planning, offshore drilling, well operations, and marine data to ensure smoother and more efficient operations.\n- *Keep your data safe, accessible, and reliable:* Maintain the safety, integration, and usability of your drilling and well operations data within a vendor-neutral and secure ecosystem.\n- *Advance offshore well procedures and rig automation:* Integrate apps with models and simulators to optimise the planning and engineering of a complete well design while staying informed about construction progress.\n- *Implement real-time analysis support:* Monitor well operations in real-time, optimise cost analysis, and capture lessons learned through post-well campaigns to continuously improve performance.\n- *Optimise OpEx and improve CapEx:* Achieve superior well execution and placement – reducing drilling time, optimising SME time offshore and onshore, and increasing production volumes.\n- *Drive ESG excellence:* Cut environmental impact by optimising Plug & Abandonment and minimising well construction. Lower your carbon footprint and reduce fuel combustion, the source of 85% of upstream CO2 emissions.\n\n**Additional insights**\n\n**Real-time insights, real-world impact**\nSiteCom ensures the reliable, low-latency delivery of real-time data, reducing non-productive time (NPT), driving stronger ESG outcomes, and optimising the entire energy production lifecycle from planning to execution. By combining live data with planning and contextual insights, SiteCom offers a deeper understanding of critical metrics like KPIs, hole cleaning, operation classification, and much more.\n\n**We are WITSML v2.1 ready – are you?​**\nFor over 30 years, WITSML (Well-site Information Transfer Standard Markup Language) has streamlined drilling and well operations, cutting costs and risks. The latest WITSML v2.1 delivers improved data quality, reliability, and performance. SiteCom supports both WITSML v1.4.1 and v2.1 for seamless data exchange between legacy and modern systems, ensuring smooth third-party integration and OSDU compatibility. Native ETP v1.2 (Energy Transfer Protocol) support reduces latency, optimises resource consumption, and strengthens security.\n\n**SiteCom’s strategic partnerships**\nIn collaboration with trusted partners like Microsoft for cloud solutions, Exebenus for machine learning capabilities, and Keystone for integrating planned and actual activities to enable digitally optimised processes, SiteCom goes beyond standard oilfield data to deliver flexibility, interoperability, and a robust infrastructure for ongoing innovation.","terms_of_service":"https://falkor.com/trust-centre/b2c/b2c-terms-of-use","documentation_link":"https://falkor.com/products/sitecom","privacy_policy_link":"https://falkor.com/en/trust-centre/privacy","geographical_coverage":"[\"Global\"]","assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://creativecommons.org/licenses/by/4.0/"},"provider_summary":{"name":"Falkor AS","description":"Falkor (formerly Kongsberg Digital, a KONGSBERG company) is an industrial software and AI provider for asset-intensive industries, including oil & gas, renewables & utilities, and chemicals. 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Luminate Partner.","setting":{"visibility":"PUBLIC"},"created_at":1701207794283,"updated_at":1712865058219,"categories":["RETAIL","MANUFACTURING","COMMERCE"],"listingType":"PERSONALIZED","provider_id":"f0b27b03-a28b-4654-80c1-3348b4c266fc"},"detail":{"description":"**Overview**\nThe world of data is constantly evolving. To excel in this competitive market, you need analytics that can keep up with the evolving retail landscape and deliver powerful insights.\n\nEngine is your partner in data science and omni analytics, offering ML and AI powered solutions that harness the power of your data to drive meaningful results and avoid data fragmentation, disconnected processes and teams, and extended vendor lock-in.\n\nIn addition to automatically connecting to and ingesting data from major Retailers, eCommerce sites, and many 3rd party datasets, Engine is a Walmart Luminate Certified API Partner with proven expertise in integrating Retailer POS, Supply Chain, Category, and Marketing data to deliver the power of Walmart data for your brands.\n\n**Data is just the beginning - partner with Engine to accelerate growth:**\n- **Intelligent Data Platform:** Transform disparate data sources into limitless possibilities. Engine creates seamless connectivity across clouds and platforms to bring consistent, reliable insights to your teams. Built on open-source technologies like Databricks Delta Lake, we handle the complexity so you can focus on driving meaningful results.\n- **Interactive Content Hub:** Engine consolidates dashboarding and interactive visualization tools into a single, easy-to-use interface, enhancing efficiency and highlighting essential metrics. Content accelerators ensure a swift setup, while BI specialists provide expert guidance to maximize data utility.\n- **Industry vetted Data Science Solutions:** A set of advanced ML/AI-driven products that solve business problems specific to Retail and CPG. 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Iterate and test strategies that cut through the noise and amplify the signal.\n - **Planogram Automation:** Leverage historical planograms and analytics for smarter mod planning, seamlessly connected with assortment and clustering for comprehensive insights and streamlined relays.\n - **Marketing Measurement:** Elevate your next campaign with consistent, unbiased marketing measurement with Lift Bridge: our expert-driven, consultative solution that adapts with you.\n\nOur mission is to accelerate your data journey, simplify your data integration roadmap, and save your time to focus on the insights and actions that grow your category, improve your distribution, drive your impressions, and expand your consumption.\n\n**Unparalleled tools. \nFanatical customer service. \nUnleash Your Data's Potential | www.engine.net**","terms_of_service":"https://www.engine.net/legals/terms-of-service","documentation_link":"","privacy_policy_link":"https://www.engine.net/legals/privacy-policy","cost":"PAID","assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"Engine","description":"Engine is a leading omni-analytics and data science company combining unparalleled access to your CPG and Retail data with machine learning, artificial intelligence, and data visualization. Drive insights at scale with our intelligent data platform and suite of SaaS solutions designed to accelerate your data journey. Unparalleled tools. Fanatic customer service. Engine | Quality. Reliability. Speed."},"popularity":1617},{"id":"ef20a721-ffe9-423f-8023-b40c4cd602d2","summary":{"name":"Moody's Orbis","subtitle":"Global public and private entities, with extensive ownership, risk, and specialized data to drive strategic analysis.","setting":{"visibility":"PUBLIC"},"created_at":1779823421287,"updated_at":1784831261081,"categories":["FINANCIAL","OTHER"],"listingType":"PERSONALIZED","provider_id":"757a382d-5f20-411a-bca0-e3a363112082"},"detail":{"description":"Access one of the world’s leading resources for comprehensive entity insights with Moody’s Orbis. Orbis delivers an expansive database of over 630 million public and private entities worldwide, extending far beyond foundational data into deep corporate ownership structures, risk analytics, and highly specialized datasets. This includes M&A deals, patents, ESG risk ratings, and advanced credit risk metrics. Moody’s ingests, enriches, and standardizes data from over 150 providers and makes it available in Orbis to deliver a holistic view of the global business landscape. \n\nLeveraging this data via Databricks means Orbis content can be easily and rapidly ingested into enterprise applications to support advanced analytic workflows. With rigorously validated data, extensive contextual metadata, and traceability, Moody’s Orbis allows organizations to drive strategic decision-making, navigate complex risks, and uncover opportunities with unmatched confidence. \n\n**Use Orbis For:**\n\n- Data Management & Enrichment\n- Compliance and Due Diligence\n- Global Supply Chain Risk Analysis\n- Competitive Intelligence & Market Sizing\n- Strategic Risk Assessment\n- Global M&A Analysis\n- Credit Analysis\n\n\n**Orbis Includes:**\n\n- Company identifiers, legal status, location, and industry classifications\n- Detailed financials\n- Extensive corporate hierarchies and beneficial ownership data\n- Profiles of directors, shareholders, and key individuals\n- Moody's Probability of Default (PD) metrics and other financial strength scores\n- Specialized datasets including:\n\n
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\n - Patents and intellectual property\n - ESG and Cyber Risk ratings\n - Sanctions and Politically Exposed Persons (PEPs)\n - Shell Company Indicators\n ","terms_of_service":"https://www.moodys.com/web/en/us/legal/terms-of-use.html","documentation_link":"","privacy_policy_link":"https://www.moodys.com/web/en/us/legal/privacy-policy.html","update_frequency":{"interval":1,"unit":"WEEKLY"},"assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"Moody's","description":"Moody’s provides data, analytics, and risk management tools that global markets depend on to help business and financial leaders make confident decisions. Moody’s has highly skilled analysts, rich data, robust tools supported by groundbreaking technologies, and a view of the future informed by more than 115 years of expertise. "},"popularity":2061},{"id":"3c1e9e76-b879-49e3-ae40-9e054c69a5fe","summary":{"name":"Anti-Spoofing Real Videos Dataset - 87,340 Files for Facial Recognition and Anti-spoofing","subtitle":"The dataset contains 87,340 videos and selfies of faces from 170 countries, featuring male and female participants ...","share":{"name":"unidata_dd057f37_9eb0_4856_9a36_beb2a0e2c64d_samples_share","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1775774626914,"updated_at":1775774626914,"categories":["OTHER"],"listingType":"PERSONALIZED","provider_id":"a6668567-8ddc-4746-9958-8edbc3807a14"},"detail":{"description":"Overview\nThe Anti-Spoofing Real Videos Dataset is a large-scale biometric collection produced by Unidata, containing 87,340 files — videos and selfies of genuine, living faces recorded by participants from 170 countries. The dataset serves as a foundational resource for developing and benchmarking face antispoofing systems, facial recognition algorithms, and liveness detection pipelines.\nUnlike attack-focused datasets, this collection captures exclusively real faces and natural behavior, providing the clean \"genuine\" class that every antispoofing system requires. Researchers can pair it with attack data to train binary classifiers, or use it standalone to evaluate how reliably a spoofing detection model accepts legitimate users — a critical metric for any production biometric authentication deployment.\nThe dataset supports development toward iBeta Level 2 certification, the leading benchmark for robust presentation attack detection (PAD) in biometric security.\n\nContent & Structure\n\n- 87,340 total files: video clips and selfie images\n- Participants from 170 countries across all major world regions\n- Recorded on a wide range of popular consumer devices: iPhone, Samsung, Xiaomi, and others\n- Videos feature individuals performing natural head movements — turning left, right, up, and down — simulating real-world liveness cues\n- Both video and static image formats included, supporting multi-modal recognition system development\n\nSubject Demographics\nThe dataset reflects broad demographic representation, enabling models to generalize across diverse real-world populations rather than overfit to a narrow group.\nGender split:\n- Male: 52%\n- Female: 48%\n\nAge groups:\n- 18–25\n- 26–35\n- 36–45\n- 46–60\n- 60+\n\nGeographic & ethnic coverage spans 170 countries, including Caucasian, Asian, African, Latin American, Middle Eastern, and South Asian participants — directly supporting domain generalization in anti-spoofing algorithms and reducing demographic bias in biometric systems.\n\nMetadata & Annotations\nEvery file in the dataset is accompanied by structured metadata, giving researchers the context needed for rigorous model training and analysis:\n1. Gender\n2. Age\n3. Ethnicity\n4. Video resolution\n5. Duration\n6. Frames per second (FPS)\n7. Recording device model\n8. Country of origin\nThis annotation depth allows teams to segment training data by demographic subset, evaluate detection algorithms for fairness across age or ethnicity groups, and build anti-spoofing solutions with measurably stronger domain generalization.\n\nTechnical Specifications\n- File formats: MP4 (video), jpg(selfies)\n- Recording devices: iPhone (multiple generations), Samsung Galaxy series, Xiaomi Mi series, and additional Android devices\n- Resolutions: HD to Full HD (1080p), varying by device\n- Head movement types captured: frontal, left, right, upward tilt, downward tilt\n- Collection method: crowdsourced, reflecting natural real-world variability in lighting, backgrounds, and angles\n\nCrowdsourced capture ensures the dataset represents the unpredictable conditions authentication systems encounter in production — varied lighting, informal backgrounds, and inconsistent camera positioning — rather than the overly clean conditions of lab recordings.\n\nUse Cases\n- Liveness Detection Training. The core application. Anti-spoofing techniques and detection algorithms require high-quality genuine faces as the positive class. This dataset provides 87,340 clean, diverse real-face samples to anchor that training, reducing false-acceptance rates against photo attacks, 3D masks, printed photos, and replay attacks.\n- Facial Recognition & Identity Verification. Developers building facial recognition and identity verification pipelines use demographically diverse real-face data to improve accuracy across population groups. The dataset's coverage of 170 countries directly addresses one of the most common failure modes in recognition technology: poor performance on underrepresented ethnicities.\n- iBeta Level 2 Certification. Organizations pursuing iBeta Level 2 — the most rigorous standard for biometric presentation attack detection — need a validated real-face baseline for system evaluation. This dataset provides that foundation, supporting compliance with ISO/IEC 30107 requirements for liveness detection in security systems.\n\nCompliance & Data Protection\nThe dataset was collected under consent-based conditions through crowdsourcing platforms. All biometric data complies with GDPR and applicable international data protection regulations. Data storage is hosted on AWS infrastructure certified to ISO 27001 and ISO 27701 standards, meeting enterprise requirements for handling sensitive biometric information.\n\nSummary\nThe Anti-Spoofing Real Videos Dataset is a production-ready collection of 87,340 genuine face videos and selfies from 170 countries, built for teams developing face antispoofing, liveness detection, and facial recognition systems. Its demographic breadth — 52% male / 48% female, spanning all major age groups and ethnicities — combined with detailed per-file metadata, natural facial movements, and real-world capture conditions, makes it an essential component of any serious biometric security research or certification workflow. 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Our team of 1,100+ labelers and AI experts labels over 920,000 files daily across text, audio, video, and image data — with 3-tier quality control and full legal compliance (AWS ISO 27001/27701).\r\nOur services cover the full cycle: custom data collection via the Prolific platform, computer vision and audio annotation, 3D point cloud labeling, NLP & LLM training, geospatial annotation, and AI model testing. Data is legally sourced, precisely matched to your requirements, and delivered fast — with a dedicated PM and SLA guarantee.\r\nExplore 70+ ready-made datasets and start building smarter AI today — free estimate within 24h."},"popularity":1372},{"id":"d1ff7ae5-04d9-4649-a9c5-069666c31d54","summary":{"name":"Geolocet | Macedonia Demographics Data","subtitle":"Demographic dataset for the municipalities (opshtini) of Macedonia, offering 1300+ attributes, including age, gender,...","setting":{"visibility":"PUBLIC"},"created_at":1705917664060,"updated_at":1752137697206,"categories":["DEMOGRAPHICS","ADVERTISING_AND_MARKETING","GEOSPATIAL","RETAIL","PUBLIC_SECTOR"],"listingType":"PERSONALIZED","provider_id":"5d3ddc17-a7e8-4794-a760-13587e1de2c6"},"detail":{"description":"## **Get instant access to the latest Demographics Data for Macedonia with [Geolocet](https://geolocet.com)**\n\nExplore the richness of Macedonia's demographic landscape with our Macedonia Census Data. This meticulously compiled dataset, sourced from official statistical sources, including Republic of North Macedonia State Statistical Office, offers a detailed insight into the population landscape of Macedonia at the municipality (opshtini) level. \n\nIf you require data at a less granular level, such as an Uber grid or square grid, please don't hesitate to get in touch with us at *contact@geolocet.com*. We understand that different projects may have varying data needs, and we are here to tailor our data to match your specific requirements.\n\n ## **Key Features:**\n---\n* **Population Data Macedonia:** Gain access to the latest and most accurate population statistics for Macedonia, meticulously organized by municipality (opshtini).\n\n* **Demographic Insights**: Dive deep into Macedonia demographic landscape with data on 1300+ attributes, including: \n\n- Total Population,\n- Gender,\n- 5-years age bands,\n- Gender divided by 5-years age bands,\n- Rural/Urban population,\n- Rural/Urban population divided by gender,\n- Rural/Urban population divided by age,\n- Rural/Urban population divided by gender and age,\n- Marital status,\n- Marital status divided by age,\n- Marital status divided by gender,\n- Marital status divided by gender and age,\n- Education attainment,\n- Education attainment divided by gender,\n- Field of education,\n- Field of education divided by gender,\n- Literacy,\n- Literacy divided by age,\n- Literacy divided by gender,\n- Literacy divided by age and gender,\n- Ethnic affiliation,\n- Ethnic affiliation divided by gender,\n- Native language,\n- Native language divided by gender,\n- Religion,\n- Religion divided by gender,\n- Employment status,\n- Employment status divided by age,\n- Employment status divided by gender,\n- Education of unemployed people,\n- Education of unemployed people divided by gender,\n- Field of employment,\n- Field of employment divided by gender,\n- Population with disabilities,\n- Population with disabilities divided by age,\n- Population with disabilities divided by gender,\n- Population with disabilities divided by gender and age,\n- Population with disabilities by type of disability,\n- Population with disabilities by type of disability divided by age,\n- Means of commute,\n- Means of commute divided by gender,\n- Families,\n- Family size,\n- Households,\n- Households size and structure,\n- Household heating systems per rural/urban area,\n- Household heating systems,\n- Households water supply systems,\n- Household water supply systems per rural/urban area,\n- Dwellings,\n- Useful floor area,\n- Living conditions in the dwellings,\n- Occupancy type,\n- Number of rooms,\n- Useful floor area divided by number of rooms,\n- Number of people per number of rooms.\n\nAll demographic attributes are available at the municipal (opshtini) level.\n\n* **Geodemographic Insights**: Unlock geodemographic data Macedonia-wide to inform location-based decision-making, marketing strategies, and policy formulation.\n**Seamless Data Access**: Our data is available for instant download in CSV format. No lengthy sign-up processes or NDA agreements required on our side – just download the data you need.\n* **Data Samples for Evaluation**: To ensure you're getting exactly what you need, we provide free downloadable samples in CSV format for data evaluation.\n* **Data accuracy**: Our datasets are created using the latest demographics data available in national statistics bureaus, guaranteeing accuracy and reliability.\n* **Best-in-Class Quality**: We guarantee best-in-class quality, backed by over two decades of experience in modeling demographics data and an outstanding quality management process.\n* **Competitive Pricing:** We maintain the lowest prices on the market, ensuring that you get the most cost-effective demographics data acquisition process.\n---\nAdditionally, for those looking to enrich their analysis or project, we offer:\n* **[Administrative Boundaries](https://geolocet.com/products/macedonia-all-available-administrative-boundaries):** If you require geographical boundaries (polygons) for the municipalities of Macedonia, consider exploring our Administrative Boundaries product, which seamlessly integrates with this dataset.\n\n* **[Point of Interest (PoI) Data](https://geolocet.com/pages/aggregated-points-of-interest-poi-data):** If you're interested in Points of Interest data, please don't hesitate to contact us for customized datasets to enhance your geographical analysis.\n\n* **[Data Customization](https://geolocet.com/pages/data-services-by-geolocet):** We understand that your project may have specific requirements. Feel free to get in touch with us if you need any modifications or specific data formats such as Excel, JSON, GeoJSON, or Shapefile. We are here to tailor the data to your needs.\n\nIf you're looking for data for Macedonia, [look here](https://geolocet.com/pages/macedonia) for off-the-shelf or tailor-made datasets to suit your specific requirements.\n\n\nMake informed decisions, conduct precise research, and unlock the potential of the Macedonia demographic landscape with our comprehensive dataset. 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The territorial vintages of the worldwide area boundaries are compatible with our globally consistent and comparable [socio-demographic](https://www.mbi-geodata.com/en/sociodemographic-data/) and [economic](https://www.mbi-geodata.com/en/purchasing-power-overview/) data.\nOur area boundaries for Finland are offered at the following levels:\n\n- **Postal Code Level** (Postcode 5-digit): This fine-grained level of detail makes the data suitable for a wide range of applications – for example, enriching customer databases, which are often organized by postal code.\n- **Administrative Level** (Kunnat): Covers both larger administrative areas (e.g., regions, states, provinces) as well as finer subdivisions such as districts, counties, or municipalities.\n\n**Use Cases:**\n\n- **Market Analysis**: Use MBI’s boundaries to visualize data on a map, learn about your customers, and uncover untapped market potential.\n- **360-Degree Customer View**: Link boundaries to customer data, sociodemographic, and economic data to gain a holistic understanding of customer needs, behaviors, and potential value.\n- **Location Planning**\n•\tUse MBI’s boundaries to visualize data on a map, learn about your customers\n•\tPlan the provision of services and products across different regions\n•\tDefine sales territories\n•\tVisualize catchment areas and plan actions to reach potential customers\n\n**Additional Insights:**\n[About Area Boundaries](https://www.mbi-geodata.com/en/area-boundaries/)\n\n**Data Structure:**\nThe area boundaries include the following variables:\nCTRYCODE: Two-digit country code by ISO 3166\nNAME: Name of administrative area / Name of the most populous administrative area / Name of the respective micro-area or the next higher level\nADMINCODE / POSTCODE / MICROCODE: Administrative code / Postal code / Micro-code\nWKT: Geography as Well Known Text (lat/long WGS84)\n\n*with only a few specific exceptions","terms_of_service":"https://www.mbi-geodata.com/en/data-protection/","documentation_link":"https://www.mbi-geodata.com/en/mbi-supplier-manufacturer-worldwide-geodata/europe/finland-maps/","privacy_policy_link":"https://www.mbi-geodata.com/en/data-protection/","geographical_coverage":"[\"Europe\"]","update_frequency":{"interval":1,"unit":"YEARLY"},"assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://service-mbi-geodata.com/s/NDtz54fXKg8fCnw"},"provider_summary":{"name":"Michael Bauer International GmbH – MBI Geodata","description":"For over 15 years, Michael Bauer International (MBI) has been a leading provider of globally consistent and comparable geographic data, including more than 3 million postcode areas and other detailed datasets like sociodemographics and purchasing power. 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It enables the ethical and compliant use of critical medical data, fostering advanced research and knowledge dissemination while rigorously maintaining data privacy and security.\n \n\n\n**Additional Model Information**\n- [Automated De-Identification, Consistent Obfuscation, and Regulatory Grade Validation of 2 Billion Patient Notes](https://www.johnsnowlabs.com/peer-reviewed-papers/)\n- [Industry Use-Case Demo](https://demo.johnsnowlabs.com/healthcare/DEID_PHI_TEXT_MULTI/)\n- [Full model info on John Snow Labs Models Hub](https://nlp.johnsnowlabs.com/2024/10/03/clinical_deidentification_docwise_wip_en.html)\n- **Domain:** Clinical Data Privacy\n- **Subdomain:** PHI Masking and Obfuscation for De-identification\n- **Predictable entities:** LOCATION, CONTACT, PROFESSION, NAME, DATE, ID, AGE, MEDICALRECORD, ORGANIZATION, HEALTHPLAN, DOCTOR, USERNAME, LOCATION-OTHER, URL, DEVICE, CITY, ZIP, STATE, PATIENT, COUNTRY, STREET, PHONE, HOSPITAL, EMAIL, IDNUM, BIOID, FAX, LOCATION_OTHER, DLN, SSN, ACCOUNT, PLATE, VIN, LICENSE, IP\n\n **How to run this model:**\n1. 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Updated as of **May 8, 2026**, this comprehensive national dataset captures the entire French forecourt landscape - encompassing major multinational fuel networks, national champions, and prominent independent operators. It offers an analysis-ready foundation for EV charging infrastructure planning, competitor benchmarking, logistics routing, and retail location intelligence.\n\nFully compatible with **Apache Spark**, **Delta Lake**, and **Databricks Unity Catalog** workflows.\n\n---\n\n## **Complete National Coverage: Branded Forecourts & Regional Operators**\nUnlike single-brand extractions, this dataset aggregates all premier fuel networks and independent operators into a single, unified data model. The coverage maps maximum geographic depth across all French regions:\n\n- **Major Fuel Networks & National Champions:** \n [TOTAL (3,081 locations)](https://geolocet.com/products/france-total-petrol-stations), \n [Intermarché (1,897 locations)](https://geolocet.com/products/france-intermarche-petrol-stations), \n [Carrefour (1,219 locations)](https://geolocet.com/products/france-carrefour-petrol-stations), \n [Esso (887 locations)](https://geolocet.com/products/france-esso-petrol-stations), \n [E.Leclerc (781 locations)](https://geolocet.com/products/france-eleclerc-petrol-stations)\n\n- **Regional & Dynamic Growth Banners:** \n [AVIA (826 locations)](https://geolocet.com/products/france-avia-petrol-stations), \n [Elan (335 locations)](https://geolocet.com/products/france-elan-petrol-stations), \n [Auchan (312 locations)](https://geolocet.com/products/france-auchan-petrol-stations), \n [AS 24 (273 locations)](https://geolocet.com/products/france-as-24-petrol-stations), \n [Eni (233 locations)](https://geolocet.com/products/france-eni-petrol-stations), \n [bp (238 locations)](https://geolocet.com/products/france-bp-petrol-stations), \n [tankpool24 (96 locations)](https://geolocet.com/products/france-tankpool24-petrol-stations), \n [Shell (88 locations)](https://geolocet.com/products/france-shell-petrol-stations)\n\n**Plus thousands of regional independent forecourts** fully mapped and standardized across all administrative regions (e.g., Auvergne-Rhône-Alpes: 2,043 sites; Nouvelle-Aquitaine: 1,669 sites; Occitanie: 1,624 sites; Île-de-France: 1,340 sites).\n\n---\n\n## **Technical Specifications & Schema Definitions**\nOptimized for zero-friction schema enforcement when reading via `spark.read.format(\"csv\")`. The structured schema below ensures flawless rendering in Databricks notebooks and high accuracy for LLM semantic indexing.\n\n* **`GUID`** (`STRING`): Unique global identifier for deterministic primary key mapping and deduplication.\n* **`Title`** (`STRING`): Official station name or commercial brand banner classification (e.g., TOTAL, Carrefour, Esso).\n* **`Latitude`** (`DOUBLE`): Precise WGS84 latitude coordinate for point-in-polygon and spatial joins.\n* **`Longitude`** (`DOUBLE`): Precise WGS84 longitude coordinate for spatial indexing (H3 / S2).\n* **`Street_No` / `Street`** (`STRING`): Normalized building numbers and thoroughfare name elements.\n* **`City` / `Postal_Code`** (`STRING`): Core municipal settlements and primary postal routing codes.\n* **`Commune`** (`STRING`): Local municipal administrative boundary designation (Commune).\n* **`Region`** (`STRING`): Macro-administrative boundary classification (e.g., Auvergne-Rhône-Alpes, Bretagne).\n* **`Admin_level_1` / `_2`** (`STRING`): Additional local administrative area and department identifiers.\n* **`Address`** (`STRING`): Full, standardized street address clean-text output.\n* **`Wheelchair`** (`STRING`): Accessibility flags indicating wheelchair mobility support where available.\n\n---\n\n## **Databricks Workflow & Spatial Analytics Use Cases**\n\n- **EV Charging Whitespace Analysis:** Query the full 14,424 location points against high-voltage grid corridors or traffic volume rasters to rank optimal stations for ultra-fast EV charging infrastructure placement.\n- **H3 Geospatial Indexing:** Convert the precise WGS84 `Latitude` and `Longitude` fields into Uber H3 spatial hexagons (`h3_longlattocell`) to seamlessly cross-reference with granular French IRIS (Îlots Regroupés pour l'Information Statistique) income indicators or demographic datasets.\n- **Logistics & Fleet Benchmarking:** Integrate forecourt coordinates with fleet tracking telemetry to calculate fuel stop density, build optimal corridor routing profiles, and minimize off-route deadhead mileage across the national highway network.\n\n---\n\n## **Data Evaluation & Custom Ingestion**\n\n### **Evaluate with a Free Sample Dataset**\nVerify the structure, column types, and coordinate precision before full implementation. A comprehensive schema sample is available for download in CSV/Parquet-friendly formats.\n\n*Want deeper market insights?* Learn more about the French retail landscape in our comprehensive reports:\n* **[View the France Top Brands Petrol Stations Report](https://geolocet.com/pages/france-topbrands-petrol-stations-report)**\n* **[View the France Top Brands Grocery Report](https://geolocet.com/pages/france-top-brands-grocery-report)**\n\n### **Enterprise Delivery & Tailored Formatting**\nWhile natively available as an analysis-ready CSV for DBFS ingestion, we accommodate enterprise-level data architecture needs. Alternative formats - including **GeoJSON, Esri Shapefiles, PostgreSQL dumps, Excel, or direct Delta sharing profiles** - can be generated. Reach out to our engineering team at **contact@geolocet.com** for integration support or custom European POI data requests.","terms_of_service":"https://geolocet.com/pages/terms-of-use","documentation_link":"","privacy_policy_link":"https://geolocet.com/pages/privacy-policy","assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://geolocet.com/pages/terms-of-use"},"provider_summary":{"name":"Geolocet LTD","description":"Geolocet provides premium geospatial data for retail analysis, GIS, and market planning. 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The model is specifically designed for organizations that require accurate document intelligence while maintaining security, compliance, and operational control\n\n**Performance**\n\n- 860 on OCRBench (state-of-the-art for models under 3B parameters)\n- 94.10 overall on OmniDocBench with 0.042 text edit distance, 94.73 formula, 91.81 table\n- 85.21 on Wild-OmniDocBench (degraded scans with folds and lighting changes)\n- 91.03 on DocML multilingual document parsing across 14 non-English non-Chinese languages\n- 92.29 cards, 92.53 receipts, 92.87 video subtitles on information extraction\n- 0.9574 Table TEDS, 0.9706 Formula CDM (English)\n- 0.039 BBox CER on FUNSD - #1 of 15 models in the JSL Vision Benchmark Series\n- 4.7x lower CER than Tesseract 5.5, 6.1x lower than EasyOCR on the same FUNSD benchmark\n- 100% parse rate - valid bounding-box output produced for every page\n\nBuilt for organizations that require security, control, and high-quality structured outputs, the Vision OCR LLM enables enterprises to unlock value from document repositories while reducing operational costs and accelerating automation initiatives.\n\n**Additional Model Information**\n\n- [BBox OCR Benchmark: The most critical metric for visual PHI removal](https://medium.com/john-snow-labs/bbox-ocr-benchmark-1-out-of-15-including-every-frontier-api-fcc3dcf1b7de)\n\n\n**How to run this model**:\n\n1. Contact [Sales](mailto:Databricks-Marketplace-listing@johnsnowlabs.com) at John Snow Labs for a free 15 day trial license key or purchase our most popular annual subscription license. \n2. Import this listing.\n3. 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The share includes a sample table of example lookups you can query in SQL before you talk to us\n- Production access is the Forager REST API: POST /person_role_search/ with paged results and a total match count, JSON, one page of results per call\n- Bulk runs: send a file and get it back with results appended, as files\n- Pass any person ID to the mobile or email lookups; a lookup that returns nothing costs 0 credits\n\n**Where it fits**\n- It works as B2B leads data at the account level, with each company keyed by its domain.\n- Score and route the B2B leads data in your CRM by industry, size and location.\n- Each company resolves to one Forager organization ID across names and domains, so it serves as company identity data.\n- Use the identity data to merge duplicate accounts in your CRM.\n- Pair it with Forager people records, joined by organization ID, to turn these accounts into B2B contact data.\n- The B2B contact data side, people with roles at each company, comes from the same IDs.\n- Headcount and team size per company give an employee data view of every account.\n- Track the employee data over time to see which companies grow.\n\n**Who uses it**\n- Sales-tech platforms building lists for their users\n- Agencies and sales teams finding buyers at target accounts\n- Recruiting platforms sourcing by role and skills\n\n**How to test us**\n- Start free with $5 in credits. 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Buyers use Forager two ways: a REST API on self-serve credits, and licensed data feeds delivered to their warehouse. 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The earliest reported period is 2016-11-07. The dataset is updated daily to capture all future ABS-EE filings as they are published in Edgar.\n\nThe Schedule AL is a statement of disclosure of assets and liabilities and is part of Reg AB II Asset-level Requirements Compliance that went into effect on November 23, 2016.\n\nForm ABS-EE is a form that issuers of asset-backed securities use to file exhibits EX-102 (Asset Data File) and EX-103 (Asset Related Document). More information can be found at https://www.sec.gov/oit/announcement/regabii-asset-level-requirements-compliance and https://www.sec.gov/divisions/corpfin/guidance/form-abs-ee-interps.\n\nAll Conduits since 2018 are represented in this dataset along with information about their loans and properties. This dataset contains the original XML submission, no processing has been done whatsoever to the data submitted.\n\n**Use cases**\nTenant name inconsistencies across datasets can hinder CRE analysis. 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The Hospital Medicare Cost Report data is the parent of the other data and contains the unique primary key field Report_Record_Number. The field Report_Record_Number is a foreign key in the other values. Includes the alpha data, numeric data, and the report data.\n\n**Benefits**\n- Useful to public health specialists and specialized institutions to evaluate the medicare costs associated with hospital healthcare services. useful to hospital administrative staff and other interested parties to evaluate and compare the hospital's financial aspects from the medicare reimbursement perspective. the fields descriptions along with the dataset description are again useful for the user to quickly understand the data and the dataset.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [Hospital Medicare Cost Report Alpha Data](https://www.johnsnowlabs.com/marketplace/hospital-medicare-cost-report-alpha-data)\n\t- The Healthcare Provider Cost Report Information System (HCRIS) contains annual reports submitted by Medicare-certified institutional providers to Medicare Administrative Contractors (MAC). This dataset contains all alpha data reported on a cost report such as hospital name, address, dates, and questions that require a ‘yes’ response.\n- [Hospital Medicare Cost Report Data](https://www.johnsnowlabs.com/marketplace/hospital-medicare-cost-report-data)\n\t- The Healthcare Cost Report Information System (HCRIS) contains annual reports submitted by Medicare-certified institutional providers to Medicare Administrative Contractors (MAC). It provides information to CMS that assists with the annual settlement summary between CMS and the institutional provider. The cost report information includes facility level: utilization statistics, costs, charges, medicare payments, and financial information.\n- [Hospital Medicare Cost Report Numeric Data](https://www.johnsnowlabs.com/marketplace/hospital-medicare-cost-report-numeric-data)\n\t- The Healthcare Provider Cost Report Information System (HCRIS) contains annual reports submitted by Medicare-certified institutional providers to Medicare Administrative Contractors (MAC). This dataset contains all numeric data reported on a cost report such as costs, charges, ratios, number of beds, etc.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated\n- Data Updates\n - Data updates support replace-on-update: outdated foreign keys are deprecated, not deleted\n\n**Our data is curated and enriched by domain experts** \n\n Each dataset is manually curated by our team of doctors, pharmacists, public health & medical billing experts:\n- Field names, descriptions, and normalized values are chosen by people who actually understand their meaning\n- Healthcare & life science experts add categories, search keywords, descriptions and more to each dataset\n- Both manual and automated data enrichment supported for clinical codes, providers, drugs, and geo-locations\n- The data is always kept up to date – even when the source requires manual effort to get updates\n- Support for data subscribers is provided directly by the domain experts who curated the data sets\n- Every data source’s license is manually verified to allow for royalty-free commercial use and redistribution.\n\n**Need Help?**\nIf you have questions about our products, contact us at [info@johnsnowlabs.com](mailto:info@johnsnowlabs.com). \n ","terms_of_service":"https://www.johnsnowlabs.com/healthcare-data-library-eula/","documentation_link":"https://www.johnsnowlabs.com/marketplace/hospital-medicare-cost-report-data-package","contact_email":"support@johnsnowlabs.com","privacy_policy_link":"https://www.johnsnowlabs.com/privacy-policy/","embedded_notebook_file_infos":[{"id":"fe16492c-0d43-406e-969a-5ca4a394d758","marketplace_file_type":"EMBEDDED_NOTEBOOK","file_parent":{"parent_id":"bef66b5d-c283-479e-880d-6eb21886994c","file_parent_type":"LISTING"},"mime_type":"text/html","download_link":"https://marketplace-file-storage-prod.s3-fips.us-east-1.amazonaws.com/EMBEDDED_NOTEBOOK/LISTING_bef66b5d-c283-479e-880d-6eb21886994c/fe16492c-0d43-406e-969a-5ca4a394d758?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEGgaDnVzLWVhc3QtMS1maXBzIkYwRAIgbC8sWIoYaHR8y7Wq86nPb0pufBl4%2BM4PWpeN%2B5PW9VQCIG7BhAVBDCWTpxFDLeuKYm%2Ff1JLz4iccPMzter0YdhgWKqYECDAQABoMNDE0MzUxNzY3ODI2IgxNAJrHbXaZG20jNfMqgwQyGEb4TsoVOoNvJ%2BaA%2FZ7zeu8hGcHqCeNfblP%2FQo5g6q2aRNtpwtPRKMlhYcIE%2FUQaNT1Lqmyptu32VUQTpzjTTxRVmJrDPkheSCkV06XQ%2FpTMKjU8CC7X1b%2FqYf8CrBwZT7Cv4C5WPjEET38f2A79h0xV6bZgPy28zQWrSeIJ9w6AfZBxiE24%2FVc08zVA0ivCT6lsy7nuU0wcR%2FMg2C9nd1vaMnQ923VpD21CKfVVYcrNWL2b%2FQ%2BNAO%2BxPjPgjiN6UPQbvbvZxkSdhgrGtecIg9NgqGkMcvM5VLTDtdC1kdeZetfOC2obtA%2BT%2F0gy3tIDo6WM5hcQfw743%2FAU9oaGnRBl%2BwoxRRDCZA%2F4jnPdluICSI7NCr6C3HHOe4CSZJjRihczxPA%2FgQczAVjAPs6CrK9N9pIzt0vNJpGUTgg4AoxftTvGfciDREilSctPIcBk6GwB%2Bzt4wbtNCmEPU%2BFD46rc6CWVQmmLwbKOeHw70ipMVFlOc8NlXkEtGO2vu6H44MstSSc6daOVUYUavQSGZ6jGhEjsP2YGz8KDm9XQotB%2FtyVBqiZV01OKqDmmBJA%2FXjUpSJ8M%2FeJXhe5m%2BKcxzNObtoJRI7jci3Tpt08XZyXZ7%2FyIV8H30ya468X3cfkTXrm972uga3z7iXAdubyEHnQIua%2FuvtwOHILi6O5D1Me2MzCPvcnXBjqTAYDoMWBt%2B3AVFuqSs2dmDQNBnvEhXfMgTedKLD5PJHb3aIl15iBkLaNu36GYnVCp9ZU2lSYcTbY5QwNnNeTfJTNy9bKhwzru6Sg%2BslhN%2BTS0zkTn%2BeLMY4tHgPEtEZ2cp%2FCk6GwqswvnYVKHjwO8q%2B1Mx8lc08fVEfarE9dke1GGFqoddx%2Bq%2BZUyHbDlAVfrCmf0yw%3D%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20261110T000135Z&X-Amz-SignedHeaders=host&X-Amz-Expires=86400&X-Amz-Credential=ASIAWA6KKHEJORWBH4N3%2F20261110%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=b6c11e04022e428fef4c3611f078de0e1eb015ba03f5f3542685e61c2797ccd9","created_at":1682537664377,"updated_at":1682537664377,"display_name":"EDA Notebook","status":"FILE_STATUS_PUBLISHED"}],"assets":["ASSET_TYPE_DATA_TABLE"]},"provider_summary":{"name":"John Snow Labs","description":"John Snow Labs, an AI and NLP for healthcare company, provides state-of-the-art software, models, and data to help healthcare and life science organizations build, deploy, and operate AI projects."},"popularity":1176},{"id":"6887ce2c-70cc-4221-91b7-1f5de7588c61","summary":{"name":"Improve Relevance through Weather Targeting - 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This analytical process can be reviewed like a template for those interested in weather-based triggering for their own business needs.\n\nData used within this analysis is provided publicly through US Department of Agriculture Data Products which contains historical weekly retail sales data by product category.\n\n**Dataset details**\n- Daily Historical Weather in a Weekly Rollup (AccuWeather)\n- Weekly Retail Sales ([USDA](https://www.ers.usda.gov/webdocs/DataFiles/100189/StateAndCategory.xlsx?v=7373.2))\n\nFor more details, refer to the embedded notebook.","terms_of_service":"https://www.accuweather.com/en/privacy","documentation_link":"https://docs.google.com/spreadsheets/d/1sxeWreQiyI3C0s4BjxwBVmYy4VbPNkl3I77dtkP9wms/edit?usp=sharing","privacy_policy_link":"https://www.accuweather.com/en/privacy","embedded_notebook_file_infos":[{"id":"2b48a253-b0c0-45c6-bc5a-86f603d5527d","marketplace_file_type":"EMBEDDED_NOTEBOOK","file_parent":{"parent_id":"6887ce2c-70cc-4221-91b7-1f5de7588c61","file_parent_type":"LISTING"},"mime_type":"text/html","download_link":"https://marketplace-file-storage-prod.s3-fips.us-east-1.amazonaws.com/EMBEDDED_NOTEBOOK/LISTING_6887ce2c-70cc-4221-91b7-1f5de7588c61/2b48a253-b0c0-45c6-bc5a-86f603d5527d?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEGgaDnVzLWVhc3QtMS1maXBzIkYwRAIgbC8sWIoYaHR8y7Wq86nPb0pufBl4%2BM4PWpeN%2B5PW9VQCIG7BhAVBDCWTpxFDLeuKYm%2Ff1JLz4iccPMzter0YdhgWKqYECDAQABoMNDE0MzUxNzY3ODI2IgxNAJrHbXaZG20jNfMqgwQyGEb4TsoVOoNvJ%2BaA%2FZ7zeu8hGcHqCeNfblP%2FQo5g6q2aRNtpwtPRKMlhYcIE%2FUQaNT1Lqmyptu32VUQTpzjTTxRVmJrDPkheSCkV06XQ%2FpTMKjU8CC7X1b%2FqYf8CrBwZT7Cv4C5WPjEET38f2A79h0xV6bZgPy28zQWrSeIJ9w6AfZBxiE24%2FVc08zVA0ivCT6lsy7nuU0wcR%2FMg2C9nd1vaMnQ923VpD21CKfVVYcrNWL2b%2FQ%2BNAO%2BxPjPgjiN6UPQbvbvZxkSdhgrGtecIg9NgqGkMcvM5VLTDtdC1kdeZetfOC2obtA%2BT%2F0gy3tIDo6WM5hcQfw743%2FAU9oaGnRBl%2BwoxRRDCZA%2F4jnPdluICSI7NCr6C3HHOe4CSZJjRihczxPA%2FgQczAVjAPs6CrK9N9pIzt0vNJpGUTgg4AoxftTvGfciDREilSctPIcBk6GwB%2Bzt4wbtNCmEPU%2BFD46rc6CWVQmmLwbKOeHw70ipMVFlOc8NlXkEtGO2vu6H44MstSSc6daOVUYUavQSGZ6jGhEjsP2YGz8KDm9XQotB%2FtyVBqiZV01OKqDmmBJA%2FXjUpSJ8M%2FeJXhe5m%2BKcxzNObtoJRI7jci3Tpt08XZyXZ7%2FyIV8H30ya468X3cfkTXrm972uga3z7iXAdubyEHnQIua%2FuvtwOHILi6O5D1Me2MzCPvcnXBjqTAYDoMWBt%2B3AVFuqSs2dmDQNBnvEhXfMgTedKLD5PJHb3aIl15iBkLaNu36GYnVCp9ZU2lSYcTbY5QwNnNeTfJTNy9bKhwzru6Sg%2BslhN%2BTS0zkTn%2BeLMY4tHgPEtEZ2cp%2FCk6GwqswvnYVKHjwO8q%2B1Mx8lc08fVEfarE9dke1GGFqoddx%2Bq%2BZUyHbDlAVfrCmf0yw%3D%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20261110T000135Z&X-Amz-SignedHeaders=host&X-Amz-Expires=86400&X-Amz-Credential=ASIAWA6KKHEJORWBH4N3%2F20261110%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=19179b1241609132c745299fbf2bcb5b461badc6e18b0faf2f9b8164469d1c02","created_at":1687537344704,"updated_at":1687537344704,"display_name":"Beverage Sales Campaign","status":"FILE_STATUS_PUBLISHED"}],"geographical_coverage":"[\"United States and Canada\"]","cost":"FREE","collection_granularity":{"interval":1,"unit":"WEEKLY"},"collection_date_start":1570393882825,"collection_date_end":1671399097714,"data_source":"AccuWeather and US Department of Agriculture Data Products","assets":["ASSET_TYPE_DATA_TABLE","ASSET_TYPE_GIT_REPO"],"license":""},"provider_summary":{"name":"AccuWeather","description":"AccuWeather provides global weather data powered by Superior Accuracy™ with 170+ forecast models and over 60 years of meteorological expertise. The AccuWeather Data Suite offers global forecast and historical coverage including 30+ years of historical data and 90-day forecasts tailored for operational decision making. With over 240 parameters, AccuWeather enables smarter decision making with trusted data."},"popularity":373},{"id":"b4a9752e-033a-4454-a9eb-366b7d626cfd","summary":{"name":"Draup Technographics Data - Sample","subtitle":"Company-level tech stack with products, usage intensity, adoption timelines, and workloads","share":{"name":"techstack_data","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1755720216440,"updated_at":1788933550886,"categories":["ADVERTISING_AND_MARKETING","COMMERCE","ECONOMICS","EDUCATION","OTHER","SCIENCE_AND_RESEARCH"],"listingType":"STANDARD","provider_id":"9900a255-6aa9-496f-9b6a-421a3568af3b"},"detail":{"description":"**Overview**\nDraup’s Tech Stack Data provides a detailed and dynamic view of the technologies adopted by companies across industries and geographies. It captures the software products, platforms, and tools in use, along with developer affiliations, product categories, and intensity of adoption. By tracking evidence of technology usage over time, the dataset reveals both current deployments and emerging technology adoption trends.\nThis dataset supports technology strategists, IT decision-makers, sales, and market intelligence teams in benchmarking enterprise technology portfolios, identifying whitespace opportunities, and targeting accounts with solution-aligned needs. Sales and business development professionals can pinpoint opportunities for engagement by understanding where specific technologies are deployed, in which workloads, and across which locations—enabling tailored solution selling and competitive positioning.\n\n**Methodology & Quality**\nTech stack insights are aggregated from proprietary and public signals, standardized into Draup’s controlled taxonomy of products, categories, and workloads. Vendor-product hierarchies are normalized, usage intensity scores are assigned to reduce noise, and adoption timelines are tracked to capture both legacy and new technology footprints. Data is refreshed frequently to ensure recency and accuracy, with evidence tagging that provides transparency into the sources and confidence levels of adoption signals.\n\n**Key Tables Include**\n•\tCompany–Technology Mapping: company identifiers, company names, and adopted products.\n•\tProduct Details: product name, developer company, product sub-category, and product category.\n•\tUsage Intensity & Evidence: confidence levels of adoption, new product flags, and first/recent evidence dates.\n•\tWorkloads & Usage Context: workloads where the product is applied and product usage locations.\n•\tProduct Metadata: product picture URLs, data creation, and update timestamps.\n\n**Use Cases:**\n***Discover Accounts that use a Tech Stack (Ex: Tableau or PowerBI) in EduTech market***\t\n\nExplore Draup’s tech stack data to find companies using specific tools like Tableau or Power BI. Understand adoption levels, deployment regions, and use cases to tailor tech-driven outreach.\t\n\nSELECT\n company_overview.company_name,\n tech_stack_company.techstack_product_name,\n tech_stack_company.product_usage_locations,\n tech_stack_company.usage_intensity,\n company_overview.primary_vertical,\n company_overview.sub_vertical\nFROM\n company_overview\nJOIN\n tech_stack_company ON company_overview.company_id = tech_stack_company.company_id\nWHERE\n LOWER(company_overview.sub_vertical) LIKE '%education%'\n AND LOWER(tech_stack_company.techstack_product_name) IN ('tableau', 'powerbi');\n\n***Comprehensive map of the Tech Stack of a Target Account***\t\n\nGet a detailed view of the technology products and platforms adopted by a target company.\t\n\nSELECT \n company_name,\n techstack_product_name,\n product_sub_category,\n product_category,\n usage_intensity,\n product_workloads,\n product_usage_locations\nFROM tech_stack_company\nWHERE company_name = 'Microsoft';\n\n***Discover Retail companies that use a competitors' product in the USA***\t\n\nLeverage Draup’s tech stack intelligence to identify companies(of various verticals) in any location using a competing technology or platform. Filter by usage region, deployment function, and company size to sharpen competitive displacement or co-sell strategies.\t\n\nSELECT\n company_techstack.company_name,\n company_techstack.primary_vertical,\n company_techstack.techstack_product_name,\n company_techstack.product_category,\n company_techstack.developer_company\nFROM (\n SELECT\n company_overview.company_name,\n company_overview.company_id,\n company_overview.primary_vertical,\n tech_stack_company.techstack_product_name,\n tech_stack_company.product_category,\n tech_stack_company.developer_company\n FROM\n company_overview\n JOIN\n tech_stack_company ON company_overview.company_id = tech_stack_company.company_id\n WHERE\n company_overview.primary_vertical ILIKE '%retail%'\n AND tech_stack_company.product_usage_locations ILIKE '%usa%'\n) as company_techstack\nJOIN (\n SELECT\n company_overview.primary_vertical AS developer_company_vertical,\n tech_stack_company.company_id,\n tech_stack_company.techstack_product_name,\n tech_stack_company.developer_company\n FROM\n company_overview\n JOIN\n tech_stack_company ON company_overview.company_name = tech_stack_company.developer_company\n WHERE \n company_overview.primary_vertical ILIKE '%retail%'\n) as developer_company_details\nON company_techstack.company_id = developer_company_details.company_id\nWHERE \n company_techstack.company_name <> developer_company_details.developer_company\"\nWhat are the popular Cybersecurity tools used in Automotive Vertical\tExplore the most widely adopted technologies (can be filtered based on functions) within any industry using Draup’s tech stack intelligence.\t\"WITH automotive_company AS (\n SELECT\n company_overview.company_name,\n primary_vertical,\n techstack_product_name,\n product_workloads\n FROM\n company_overview\n INNER JOIN\n tech_stack_company ON company_overview.company_id = tech_stack_company.company_id\n WHERE\n primary_vertical = 'Automotive'\n)\nSELECT\n techstack_product_name,\n COUNT(company_name) AS total_no_of_companies_used,\n product_workloads\nFROM\n automotive_company\nWHERE\n LOWER(product_workloads) LIKE '%cybersecurity%'\nGROUP BY\n techstack_product_name, product_workloads\nORDER BY\n total_no_of_companies_used DESC;\n\n***Analyze HR Technology Stacks of the Top 5 Companies in Building Materials***\t\n\nIdentify the leading technologies used by the largest companies within a chosen industry, highlighting adoption patterns, vendor preferences, and deployment intensity. This analysis provides a scalable framework to explore technology stacks across different workloads, industries, company sizes, and geographies, enabling flexible insights into enterprise digital adoption strategies.\t\n\nWITH top_companies AS (\n SELECT\n company_overview.company_id,\n company_overview.company_name,\n company_overview.employee_count\n FROM\n company_overview\n WHERE\n (company_overview.primary_vertical = 'Chemicals & Materials'\n OR company_overview.primary_vertical = 'Construction & Heavy Machinery'\n OR company_overview.primary_vertical = 'Industrial'\n OR company_overview.primary_vertical = 'Engineering, Procurement and Construction')\n ORDER BY\n company_overview.employee_count DESC\n LIMIT 5\n)\nSELECT\n top_companies.company_name,\n tech_stack_company.techstack_product_name AS hr_platform,\n tech_stack_company.developer_company,\n tech_stack_company.product_category,\n tech_stack_company.product_sub_category,\n tech_stack_company.usage_intensity\nFROM\n top_companies\nJOIN\n tech_stack_company ON top_companies.company_id = tech_stack_company.company_id\nWHERE\n tech_stack_company.product_category IN ('HCM', 'Recruiting', 'Payroll', 'Performance Management', 'Workforce Management Software', 'Contact Center Workforce Software')\nORDER BY\n top_companies.company_name, tech_stack_company.product_category;","terms_of_service":"https://draup.com/privacy-policy#:~:text=Unless%20provided%20for%20in%20this,that%20work%20on%20our%20behalf","documentation_link":"","privacy_policy_link":"https://draup.com/privacy-policy","geographical_coverage":"[\"Global\"]","cost":"FREE","update_frequency":{"interval":1,"unit":"WEEKLY"},"collection_granularity":{"interval":1,"unit":"DAILY"},"data_source":"Public Sources","assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"Draup, Inc.","description":"Draup is an enterprise agentic intelligence platform built for HR and Sales leaders navigating transformation. By combining unparalleled real-time global data with agentic AI, Draup enables strategic decision-making across talent and revenue functions.\n \nThrough its Talent Intelligence solution, Draup empowers HR teams to drive workforce planning, precision hiring, predictive skill architecture, and competitive benchmarking. Its Sales Intelligence solution helps go-to-market teams uncover deep account insights, identify buying triggers, and accelerate revenue growth.\n \nTrusted by over 300 global enterprises - including five of the Fortune 10 - Draup supports companies like Microsoft, PepsiCo, PayPal, and Moderna in building future-ready workforces and growth strategies."},"popularity":667},{"id":"8da79664-cbcc-4f07-854a-75f45ed48983","summary":{"name":"Domestic & International Online Supermarket Data Pipelines ","subtitle":"Real-time supermarket data exposes granular consumer behavior and buying trends from any public online retail website.","share":{"name":"supermarket_sample","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1717415627792,"updated_at":1734532202961,"categories":["COMMERCE","FINANCIAL","RETAIL"],"listingType":"STANDARD","provider_id":"44c5899b-14cd-4e50-94f2-1f710c0e20dd"},"detail":{"description":"**Overview**\n\nAn increasingly inflationary environment has turned the food and CPG retail industry into a cat-and-mouse game between retailers and consumers. As consumers defect to competitors at record-breaking rates to chase value, retailers try to win back their business by aggressively lowering prices. In an already low-margin industry, this quickly becomes a dangerous race to the bottom—especially when so many pricing and marketing decisions are based on instinct rather than strategic insights. \n\nHow can supermarket retailers win this game and earn back much-needed profit? One effective solution is real-time competitive pricing powered by high-quality data.\n\nNimble’s supermarket data pipelines deliver comprehensive, granular, and real-time insights into consumer behavior, competitor activities, and purchasing trends by leveraging dynamic structured datasets collected from public online retail sources. This enables both e-commerce and brick-and-mortar retailers to monitor inventory, pricing, and promotional strategies with unparalleled accuracy, allowing for smarter data-driven decisions that fuel growth. With our pipelines, retail inventory planners can avoid out-of-stock scenarios, pricing strategist can optimize their tactics to boost revenue, and investors can predict trends to maximize returns. \n\nNimble’s RAG-ready web data is the fastest, most accurate way to reveal consumer sentiment and track seller performance in the e-commerce space. It also natively integrates with the Databricks platform for a seamless experience. \n\n**Use Cases**\n\nDiscover the ways real-time, AI-ready grocery data can transform your business operations:\n\n1. **Dynamic Pricing Optimization**: Maximize revenue, retain loyal customers, and maintain your competitive edge using insights derived from real-time, continuously updated dynamic datasets that capture market conditions, competitor pricing, and consumer demand. \n2. **Share of Voice Analytics**: Understand how your brand visibility compares to competitors, quickly respond to customer feedback, and optimize your marketing efforts by monitoring and analyzing your share of voice across various key digital channels and platforms. \n3. **Digital Shelf Insights**: Identify opportunities to improve product positioning and ensure your products are displayed correctly and compliantly online with real-time critical insights into product placement, availability, and visibility on digital shelves. \n4. **Personalized Marketing Campaigns**: Utilize consumer purchasing patterns and preferences to create personalized, targeted marketing campaigns that boost customer engagement, foster company loyalty, and maximize revenue through tailored offers and promotions. \n5. **Competitive Analysis and Benchmarking**: Benchmark performance, identify market trends, and develop strategic responses to competitor initiatives by leveraging continuous monitoring of competitor pricing, promotions, and product launches. \n\n**Included Data Sources**\n\nOur Domestic and International Online Retail Supermarket Data Pipelines can provide data from the following sources. You can pick and choose between different sources, data delivery frequencies, and the types of data you want to focus on. \n\n* 7-Eleven \n* Walgreens \n* Safeway \n* Winn-Dixie \n* Albertsons \n* Sprouts \n* Meijer \n* Morton Williams \n* Walmart \n* Costco \n* Metro \n* Food Basics \n* Rexall \n* Co-op \n* Sainsbury's \n* ASDA \n* Waitrose \n* Iceland \n* Tesco Whoosh \n* Instacart \n* Deliveroo \n* DoorDash \n* JustEat\n\n**Key Features**\n\n* **Real-Time Data**: Get unlimited access to real-time, continuously updated datasets on supermarket inventory, pricing, and sales trends so you can stay ahead of the market. \n* **Comprehensive Coverage**: Get dynamically updated structured datasets from public online retail sources for detailed data spanning product pricing, availability, customer reviews, and sales metrics for an all-encompassing view of the market. \n* **Actionable Intelligence**: Get structured data that delivers clear, actionable insights tailored to supermarket operations so you can make strategic decisions on inventory management, pricing strategies, and promotional campaigns. \n* **Localized Insights**: Data is segmented by region, store location, and demographic information to provide relevant insights for each supermarket’s operating area that can enhance regional marketing and stocking strategies. \n* **Customizable Data Pipelines**: Our pipelines can be tailored to focus on specific product categories, brands, or competitive metrics based on your unique business needs. \n* **Scalable Solution**: Our pipelines are capable of processing supermarket data at any scale, from local store chains to global networks—ensuring reliable, robust performance no matter how large your data requirements grow.\n\n**Why Choose Nimble’s Dynamic Datasets Over Traditional Static Datasets**\n\nTraditional static datasets provide snapshots of information frozen in time from a predetermined set of data points and data sources. 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Free trial data.","share":{"name":"share_socio_mbi_market_data_2024_es_postcodes_5_socio","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1763382499144,"updated_at":1779199106511,"categories":["ADVERTISING_AND_MARKETING","DEMOGRAPHICS","GEOSPATIAL"],"listingType":"STANDARD","provider_id":"aa09abd6-ff7f-4452-803d-b8c43b4c07cb"},"detail":{"description":"[MBI](https://www.mbi-geodata.com/en/)'s sociodemographic data is available for many countries worldwide. All data comes from a single source, ensuring **consistency and comparability** across markets.\n\nThe Sociodemographics-Bundle Basic for Spain includes the following **variables**:\n- [population](https://www.mbi-geodata.com/en/sociodemographic-data/population/)\n- [gender](https://www.mbi-geodata.com/en/sociodemographic-data/sex-gender/)\n- [age structure](https://www.mbi-geodata.com/en/sociodemographic-data/agebands/) (15-year age bands)\n- household figures ([number of households](https://www.mbi-geodata.com/en/sociodemographic-data/households/) and [households by type](https://www.mbi-geodata.com/en/sociodemographic-data/households-by-type/))\n\nOur sociodemographic data is offered for each country on different **levels**:\n- **Postal Code Level:** This fine-grained level of detail makes the data suitable for a wide range of applications – for example, enriching customer databases, which are often organized by postal code.\n- **Administrative Level:** Covers both larger administrative areas (e.g., regions, states, provinces) as well as finer subdivisions such as districts, counties, or municipalities.\n- **Micro Level**: Subdivisions of postal code or administrative boundaries, such as census areas or street segments. 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All data comes from a single source, ensuring **consistency and comparability** across markets.\n\nThe Sociodemographics-Bundle Basic for the Netherlands includes the following **variables**:\n- [population](https://www.mbi-geodata.com/en/sociodemographic-data/population/)\n- [gender](https://www.mbi-geodata.com/en/sociodemographic-data/sex-gender/)\n- [age structure](https://www.mbi-geodata.com/en/sociodemographic-data/agebands/) (15-year age bands)\n- household figures ([number of households](https://www.mbi-geodata.com/en/sociodemographic-data/households/) and [households by type](https://www.mbi-geodata.com/en/sociodemographic-data/households-by-type/))\n\nOur sociodemographic data is offered for each country on different **levels**:\n- **Postal Code Level:** This fine-grained level of detail makes the data suitable for a wide range of applications – for example, enriching customer databases, which are often organized by postal code.\n- **Administrative Level:** Covers both larger administrative areas (e.g., regions, states, provinces) as well as finer subdivisions such as districts, counties, or municipalities.\n- **Micro Level**: Subdivisions of postal code or administrative boundaries, such as census areas or street segments. 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This meticulously compiled dataset containing modeled data, based on official statistical sources, including INSEE, offers a detailed insight into the population landscape of France at the municipality (commune) level. \n\nIf you require data at a less granular level, such as an Uber grid or square grid, please don't hesitate to get in touch with us at *contact@geolocet.com*. We understand that different projects may have varying data needs, and we are here to tailor our data to match your specific requirements.\n\n ## **Key Features:**\n---\n* **Population Data France:** Gain access to the latest and most accurate population statistics for France, meticulously organized by municipality (commune).\n\n* **Demographic Insights:** Dive deep into France's demographic landscape with data on 54 attributes, including: \n* Total number of dwellings,\n* Main residence,\n* Secondary residence and occasional logs,\n* Vacant housing,\n* Houses,\n* Apartments,\n* Main residence size distribution,\n* Main residence occupancy distribution.\n\nAll demographic attributes are available at the municipal (commune) level.\n\n* **Geodemographic Insights:** Unlock geodemographic data France-wide to inform location-based decision-making, marketing strategies, and policy formulation.\n* **Seamless Data Access:** Our data is available for instant download in CSV format. No lengthy sign-up processes or NDAs required on our side - just download the data you need.\n* **Data Samples for Evaluation:** To ensure you're getting exactly what you need, we provide free downloadable samples in CSV format for data evaluation.\n* **Data accuracy:** Our datasets are created using the latest demographics data available in national statistics bureaus, guaranteeing accuracy and reliability.\n* **Best-in-Class Quality:** We guarantee best-in-class quality, backed by over two decades of experience in modeling demographics data and an outstanding quality management process.\n* **Competitive Pricing:** We maintain the lowest prices on the market, ensuring that you get the most cost-efficient demographics data acquisition process.\n---\nAdditionally, for those looking to enrich their analysis or project, we offer:\n* **[Administrative Boundaries](https://geolocet.com/products/france-admin-level-8-municipalities):** If you require geographical boundaries (polygons) for the municipalities of France, consider exploring our Administrative Boundaries product, which seamlessly integrates with this dataset.\n\n* **[Point of Interest (PoI) Data](https://geolocet.com/pages/aggregated-points-of-interest-poi-data):** If you're interested in Points of Interest data, please don't hesitate to contact us for customized datasets to enhance your geographical analysis.\n\n* **[Data Customization](https://geolocet.com/pages/data-services-by-geolocet):** We understand that your project may have specific requirements. Feel free to get in touch with us if you need any modifications or specific data formats such as Excel, JSON, GeoJSON, or Shapefile. We are here to tailor the data to your needs.\n\nIf you're looking for data for France, [look here](https://geolocet.com/pages/france) for off-the-shelf or tailor-made datasets to suit your specific requirements.\n\n\n\nMake informed decisions, conduct precise research, and unlock the potential of the French demographic landscape with our comprehensive dataset. Trust in the accuracy and reliability of our official statistical data to drive your projects forward.\nContact us at *contact@geolocet.com* today to access this invaluable resource and explore France census data like never before.\n\n[Geolocet LTD](https://geolocet.com)\n","terms_of_service":"https://geolocet.com/pages/terms-of-use","documentation_link":"","privacy_policy_link":"https://geolocet.com/pages/privacy-policy","cost":"PAID","data_source":"","assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://geolocet.com/pages/terms-of-use"},"provider_summary":{"name":"Geolocet LTD","description":"Geolocet provides premium geospatial data for retail analysis, GIS, and market planning. 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The product includes comprehensive information for every building–such as area, interior and exterior attributes, and unit composition. Building Detail supports use cases such as prospecting and property valuation by enabling more accurate assessment of individual building characteristics.\n\n**Asset Valuation**\nBuilding Detail can be used by real estate investors, property managers, and insurance companies to validate the details and the condition of a building, including its size, features, and any modifications. This information can help in making informed decisions regarding investment, insurance underwriting, or maintenance planning.\n**Location Data Enrichment**\nBy providing comprehensive data about each building on a parcel, Building Detail can enrich location data for real estate platforms, GIS applications, or any other system that requires detailed property information. This can improve the accuracy and value of these systems for their users.\n**Risk Analysis**\nInsurance companies, real estate investors, and financial institutions can use Building Detail to perform a detailed risk analysis. This could involve assessing the risk of damage to the building based on its structure and features, or evaluating the financial risk associated with investing in or lending against the property.\n**Customer Onboarding**\nFor businesses providing services related to property management, construction, or renovation, Building Detail can enhance the customer onboarding process by providing a deep understanding of the customer's property. This can help to tailor services to the specific needs of the customer and their property.\n**Market Analysis**\nReal estate professionals, researchers, and policy makers can use Building Detail to conduct detailed market analyses. This could involve studying trends in building features and modifications, understanding the distribution of different types of properties, or analyzing the relationship between building characteristics and property values.\n**Regulatory Reporting**\nBuilding Detail can assist businesses in meeting regulatory reporting requirements, particularly those related to building safety, insurance, or property taxes. 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This dataset offers a reliable, comprehensive foundation for anyone looking to understand employment dynamics in Singapore.\n\nWhether you’re a job seeker, employer, policymaker, or market analyst, our data equips you with the insights you need to make informed, strategic decisions, helping you stay ahead in a constantly evolving job market.\n\nSourced from both job boards and company career sites, our data ensures comprehensive coverage across geographies and employer sizes, from businesses of every size.\n\nQuestions answered by this dataset include:\n- What skills are employers seeking for roles like computer programmers?\n- What skills are most often associated with a given job title?\n- Has demand for the skill \"Python\" increased or decreased in the past six months?\n- How long do job postings for web developers typically remain open?\n\n**Use cases**\n***Market Analysis***\nUncover emerging industries, identify in-demand skills, and spot trending job types. 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It also contains data on population figures and geolocation of cities and countries.\n\n**Description**\nThis data package is useful when data on various codes and common abbreviations used to represent the states and territories of the United States, their spatial relations, population figures and major cities of the world and their geolocation is required.\nIn addition to the above data, this data package also contains detailed information on the following:\n\n- Airport codes, i.e., International Air Transport Association (IATA) airport code, a three-letter code which is used in passenger reservation, ticketing and baggage-handling systems and the International Civil Aviation Organization.\n- A list of countries by continent. Continent codes and country codes are also included in this dataset\n- All IETF (Internet Engineering Task Force) language tags of the official resource indicated by Library of Congress Unicode. 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The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [Airport Codes](https://www.johnsnowlabs.com/marketplace/airport-codes)\n\t- The Airport codes includes the International Air Transport Association (IATA) airport code, a three-letter code which is used in passenger reservation, ticketing and baggage-handling systems and the International Civil Aviation Organization (ICAO) airport code which is a four letter code used by Air traffic control (ATC) systems and for airports that do not have an IATA airport code\n- [All Countries Latitude Longitude](https://www.johnsnowlabs.com/marketplace/all-countries-latitude-longitude)\n\t- This dataset provides country code, postal code, latitude, longitude, as well as names of state, county/province, community etc. for all countries where the data is available.\n- [Classification of Countries by Income Economies](https://www.johnsnowlabs.com/marketplace/classification-of-countries-by-income-economies)\n\t- This dataset lists the World Bank member countries and all other economies and classifies them according to low, middle and high income economies with populations of more than 30,000.\n- [Countries Geographical Territory Containment](https://www.johnsnowlabs.com/marketplace/countries-geographical-territory-containment)\n\t- This dataset contains a list of different geographical or continental regions and the relative list of codes of the territories/countries they contain.\n- [IMO IMDG Classification Codes](https://www.johnsnowlabs.com/marketplace/imo-imdg-classification-codes)\n\t- The International Maritime Dangerous Goods (IMDG) Code of the International Maritime Organization (IMO) was developed as a uniform international code for the transport of dangerous goods by sea covering such matters as packing, container traffic and stowage, with particular reference to the segregation of incompatible substances.\n- [ISO 3166 Country Codes ITU Dialing Codes ISO 4217 Currency Codes](https://www.johnsnowlabs.com/marketplace/iso-3166-country-codes-itu-dialing-codes-iso-4217-currency-codes)\n\t- This dataset contains a list of comprehensive data about (International Organization for Standardization)ISO-3166 Country Codes in English, (International Telecommunications Union) ITU Dialing Codes, ISO Currency Codes and the other related data with geographic information for different countries around the world. The list consists of 251 countries and islands.\n- [ISO 6346 Container Codes](https://www.johnsnowlabs.com/marketplace/iso-6346-container-codes)\n\t- This dataset consists of coded list of ISO 6346 shipping containers, used in international trade and electronic shipping messages.\n- [Major Cities of The World](https://www.johnsnowlabs.com/marketplace/major-cities-of-the-world)\n\t- This dataset lists cities which consists of above 15,000 inhabitants. Each city is associated with its country and sub-country to reduce the number of ambiguities. Subcountry can be the name of a state (eg in the United Kingdom or the United States of America) or the major administrative section (eg \"region\" in \"France\").\n- [SMDG Master Terminal Facilities List](https://www.johnsnowlabs.com/marketplace/smdg-master-terminal-facilities-list)\n\t- This Codes List has been created and will be maintained by the Secretariat of SMDG (User Group for Shipping Lines and Container Terminals) with the purpose of harmonizing the codes between EDI (Electronic Data Interchange) partners. The lists can be downloaded by all interested parties and used for the benefit of standardization worldwide. The members of SMDG and the SMDG secretariat cannot be held responsible for the correctness and completeness of the lists.\n- [Spatial Relations Between Countries and Geographical Standards](https://www.johnsnowlabs.com/marketplace/spatial-relations-between-countries-and-geographical-standards)\n\t- This dataset contains a list of two-letter codes in English for different countries around the world and their geographical information. The list consists of ISO 3166-1-alpha-2 code elements for 250 countries, neighboring countries, geographical location and Universal Transverse Mercator (UTM) Grid information.\n- [UN-CEFACT Package Codes](https://www.johnsnowlabs.com/marketplace/un-cefact-package-codes)\n\t- This dataset is a coded representation of package type names used in international trade, revision 8, annex v and annex vi (UN/ECE CEFACT Trade Facilitation Recommendation No.21).\n- [US State New Jersey Municipalities With Geoname IDs](https://www.johnsnowlabs.com/marketplace/us-state-new-jersey-municipalities-with-geoname-ids)\n\t- This dataset includes data about 565 municipalities of New Jersey, US state, including the GeoName ID for each municipality. The data describes New Jersey's municipalities with regard to the population size, county, type, the form of government and the aspects related to its establishment, change of type and name.\n- [US States and Territories](https://www.johnsnowlabs.com/marketplace/us-states-and-territories)\n\t- This dataset contains various codes and common abbreviations used to represent the states and territories of the United States.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - 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This means that one drug can have more than one code, for example acetylsalicylic acid (aspirin) has A01AD05 (WHO) as a drug for local oral treatment, B01AC06 (WHO) as a platelet inhibitor, and N02BA01 (WHO) as an analgesic and antipyretic); as well as one code can represent more than one active ingredient, for example C09BB04 (WHO) is the combination of perindopril with amlodipine, two active ingredients that have their own codes (C09AA04 (WHO) and C08CA01 (WHO) respectively) when prescribed alone.\nThe ATC classification system is a strict hierarchy, meaning that each code necessarily has one and only one parent code, except for the 14 codes at the topmost level which have no parents. The codes are semantic identifiers, meaning they depict in themselves the complete lineage of parenthood.\n\n**Benefits**\n- Useful data package as the anatomical therapeutic chemical (atc) classification system is used for the classification of active ingredients of drugs according to the organ or system on which they act and their therapeutic, pharmacological and chemical properties.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [Anatomical Therapeutic Chemical Alterations 2005 to 2022](https://www.johnsnowlabs.com/marketplace/anatomical-therapeutic-chemical-alterations-2005-to-2022)\n\t- This dataset contains the ATC (Anatomical Therapeutic Chemical) Alterations from 2005 to 2022.\n- [Anatomical Therapeutic Chemical Codes 2018 to 2023](https://www.johnsnowlabs.com/marketplace/anatomical-therapeutic-chemical-codes-2018-to-2023)\n\t- This dataset contains the Anatomical Therapeutic Chemical (ATC) classification system. 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Each country within Europe is also available as a standalone dataset.\n\n\n### **Access & Usage:**\n\n- Instant download upon purchase for all datasets.\n- Free sample available for instant data evaluation.\n\nElevate your geographical and demographic analysis with the most thorough and accessible dataset available on the Eurozone municipalities - Geolocet’s flagship product.\n\n## **Coverage Details:**\n- **[Austria](https://geolocet.com/products/Austria-geodemographics-dataset-with-boundaries)**: 9,105K people\n- **[Belgium](https://geolocet.com/products/Belgium-geodemographics-dataset-with-boundaries)**: 11,686K people\n- **[Estonia](https://geolocet.com/products/Estonia-geodemographics-dataset-with-boundaries)**: 1,322K people\n- **[Finland](https://geolocet.com/products/Finland-geodemographics-dataset-with-boundaries)**: 5,548K people\n- **[France](https://geolocet.com/products/France-geodemographics-dataset-with-boundaries)**: 67,733K people\n- **[Germany](https://geolocet.com/products/Germany-geodemographics-dataset-with-boundaries)**: 83,237K people\n- **[Greece](https://geolocet.com/products/Greece-geodemographics-dataset-with-boundaries)**: 10,370K people\n- **[Ireland](https://geolocet.com/products/Ireland-geodemographics-dataset-with-boundaries)**: 5,057K people\n- **[Italy](https://geolocet.com/products/Italy-geodemographics-dataset-with-boundaries)**: 58,871K people\n- **[Latvia](https://geolocet.com/products/Latvia-geodemographics-dataset-with-boundaries)**: 1,876K people\n- **[Lithuania](https://geolocet.com/products/Lithuania-geodemographics-dataset-with-boundaries)**: 2,857K people\n- **[Luxembourg](https://geolocet.com/products/Luxembourg-geodemographics-dataset-with-boundaries)**: 661K people\n- **[Malta](https://geolocet.com/products/Malta-geodemographics-dataset-with-boundaries)**: 536K people\n- **[Netherlands](https://geolocet.com/products/Netherlands-geodemographics-dataset-with-boundaries)**: 17,811K people\n- **[Portugal](https://geolocet.com/products/Portugal-geodemographics-dataset-with-boundaries)**: 10,467K people\n- **[Slovakia](https://geolocet.com/products/Slovakia-geodemographics-dataset-with-boundaries)**: 5,795K people\n- **[Slovenia](https://geolocet.com/products/Slovenia-geodemographics-dataset-with-boundaries)**: 2,117K people\n- **[Spain](https://geolocet.com/products/Spain-geodemographics-dataset-with-boundaries)**: 45,136K people\n\n## **Customize Your Data Experience**\n\nNeed the data in another format? Your requirements drive our solutions. Reach out to us at **contact@geolocet.com** for alternative formats or if you have specific data needs that go beyond the downloadable list of Germany locations available on our platform.\n\n## **Explore further with our extensive retail datasets**\n\nIf you are exploring other brands and require additional data, we encourage you to **contact@geolocet.com**. Our comprehensive datasets and dedicated team stand ready to assist you.\n\n","terms_of_service":"https://geolocet.com/pages/terms-of-use","documentation_link":"","privacy_policy_link":"https://geolocet.com/pages/privacy-policy","geographical_coverage":"[\"Europe\"]","cost":"PAID","data_source":"","assets":["ASSET_TYPE_MEDIA"],"license":""},"provider_summary":{"name":"Geolocet LTD","description":"Geolocet provides premium geospatial data for retail analysis, GIS, and market planning. 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The data is classified by Health Service Area and county.\n\n**Description**\nThis data package shows the Inpatient De-identified data from 2009 to 2014 of the state of New York Office of Quality and Patient Safety, collected from hospitals and treatment centers of New York. Patients age group, gender, race and ethnicity are listed and contains information on the admission, diagnosis, procedure and discharge, as well as payment and costs. The dataset also indicates abortion records. This data is from the Statewide Planning and Research Cooperative System (SPARCS), a reporting system that protects confidentiality and privacy of patient’s data under Section 28.16 of the Public Health Law (PHL). Inpatient data must be submitted to SPARCS by all Article 28 facilities.\n\n**Benefits**\n- Useful for providers of new york for inpatient discharges from the year 2009 to 2014\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [State of New York Inpatient Discharges 2009](https://www.johnsnowlabs.com/marketplace/state-of-new-york-inpatient-discharges-2009)\n\t- This contains the Inpatient De-identified data of 2009 of the state of New York Office of Quality and Patient Safety, collected from hospitals and treatment centers of New York. This dataset displays information on hospital discharges at patient level data with basic record details without showing protected health information (PHI) and was made not identifiable.\n- [State of New York Inpatient Discharges 2010](https://www.johnsnowlabs.com/marketplace/state-of-new-york-inpatient-discharges-2010)\n\t- This contains the Inpatient De-identified data of 2010 of the state of New York Office of Quality and Patient Safety, collected from hospitals and treatment centers of New York. This dataset displays information on hospital discharges at patient level data with basic record details without showing protected health information (PHI) and was made not identifiable.\n- [State of New York Inpatient Discharges 2011](https://www.johnsnowlabs.com/marketplace/state-of-new-york-inpatient-discharges-2011)\n\t- This contains the Inpatient De-identified data of 2011 of the state of New York Office of Quality and Patient Safety, collected from hospitals and treatment centers of New York. This dataset displays information on hospital discharges at patient level data with basic record details without showing protected health information (PHI) and was made not identifiable.\n- [State of New York Inpatient Discharges 2012](https://www.johnsnowlabs.com/marketplace/state-of-new-york-inpatient-discharges-2012)\n\t- This contains the Inpatient De-identified data of 2012 of the state of New York Office of Quality and Patient Safety, collected from hospitals and treatment centers of New York. This dataset displays information on hospital discharges at patient level data with basic record details without showing protected health information (PHI) and was made not identifiable.\n- [State of New York Inpatient Discharges 2013](https://www.johnsnowlabs.com/marketplace/state-of-new-york-inpatient-discharges-2013)\n\t- This contains the Inpatient De-identified data of 2013 of the state of New York Office of Quality and Patient Safety, collected from hospitals and treatment centers of New York. This dataset displays information on hospital discharges at patient level data with basic record details without showing protected health information (PHI) and was made not identifiable.\n- [State of New York Inpatient Discharges 2014](https://www.johnsnowlabs.com/marketplace/state-of-new-york-inpatient-discharges-2014)\n\t- This contains the Inpatient De-identified data of 2014 of the state of New York Office of Quality and Patient Safety, collected from hospitals and treatment centers of New York. 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By installing this listing, you acknowledge and agree to the license.\n\nFor example notebooks of using the t5 model in various use cases on Databricks, refer to [the Databricks ML example repository](https://github.com/databricks/databricks-ml-examples/tree/master/llm-models/t5).\n\n**Use cases**\n\n- Natural language processing tasks such as summarization, translation, text classification, and question answering\n- Fine-tuning on specific applications\n\n**Product details**\n\nThe pre-trained model can be used for further fine-tuning on specific applications. The fine-tuned model in this listing can be deployed directly to Databricks Model Serving for immediate use, or loaded for fine-tuning or batch inference use cases. 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Users can leverage this dataset to make informed decisions based on real-time weather updates or forecasted trends, ensuring adaptability and strategic planning across various sectors reliant on weather-sensitive information.\nThis dataset includes:\n\n- Comprehensive historical and forecast data\n\n- Real-time weather updates\n\n- Key weather metrics\n\n\nSource: Custom Weather\n\n**Use cases**\n- Quantitative Analysis - Leverage historical and forecast weather data to identify patterns, assess market drivers, and enhance quantitative models across energy and commodity markets. Analyze the impact of temperature, precipitation, and other weather indicators on supply, demand, pricing, and market fundamentals.\n- Supply Chain - Utilize weather data to monitor conditions that may affect production, transportation, logistics, storage, and operational activities across global commodity supply chains. 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This dataset offers a 24-month historical look-back that is aggregated daily and refreshed every 24 hours, enabling organizations to uncover actionable correlations between mobile app usage, consumer behavior, and market trends.\n\nEach record includes app behavior enriched with Deep Sync’s proprietary demographic, interest, and ownership data, allowing analysts to connect app-level activity with demographic composition including gender, age, household information, income bands, employment, home ownership, and more.\n\nThis dataset is ideal for quantitative research, attribution, competitive analysis, and investment decision-making where persistent, reliable, and timely behavioral context is critical.\n\n\n**Datasets:**\n\n***App Download***\n- RECORD_TYPE\n- MOBILE_APP_ID\n- MOBILE_APP_TITLE \n- PRIMARY_APP_GENRE\n- COHORT\n- INTERVAL \n- DATE\n- DOWNLOADED_ACTIVE\n- DOWNLOADED_INACTIVE\n\n***App Removal***\n- RECORD_TYPE\n- MOBILE_APP_ID\n- MOBILE_APP_TITLE \n- PRIMARY_APP_GENRE\n- COHORT\n- INTERVAL \n- DATE\n- UNINSTALLED_ACTIVE\n- UNINSTALLED_INACTIVE\n\n***App Engagement***\n- RECORD_TYPE\n- MOBILE_APP_ID\n- MOBILE_APP_TITLE \n- PRIMARY_APP_GENRE\n- COHORT\n- INTERVAL \n- DATE\n- NUM_DEVICES\n- FOCUS_FREQUENCY_PCT\n- TOTAL_DWELL_MINUTES\n\n***Mobility Data***\n- RECORD_TYPE\n- ACTON_TYPE\n- ATTRIBUTE_SOURCE\n- REPORTING_DEVICE_UNIVERSE\n- COHORT\n- INTERVAL \n- DATE\n\n***Attribute Data***\n- TOP_AGE_RANK\n- GENDER_DISTRIBUTION\n- MARITAL_DISTRIBUTION\n- HH_INCOME_DISTRIBUTION\n- EDUCATION_DISTRIBUTION\n- TOP_OCCUPATION_RANK\n- HOME_OWNER_DISTRIBUTION\n- DWELLING_TYPE_DISTRIBUTION\n- RESIDENCE_DURATION_AVERAGE\n- HOUSEHOLD_SIZE_AVERAGE\n- NET_WORTH_DISTRIBUTION\n- HOME_VALUE_MEDIAN\n- RECENT_HOMEBUYERS_PERCENT\n\n\n**Why Deep Sync?**\nDeep Sync is the industry leader in deterministic identity. Leveraging our 35-year foundation of compiling direct mail grade datasets, Deep Sync develops data-first technologies that power marketing, measurement, AI, and business intelligence for agencies and brands with our fully deterministic identity graph, privacy-first audiences, comprehensive data solutions, and integrations with leading platforms and cloud providers.\nAt the core of Deep Sync’s identity graph is our proprietary identity spine, a highly accurate, deterministic offline identity asset of more than 260 million direct mail-grade records that recognizes consumers and households across the United States. Our identity spine enables us to connect a variety of data assets into actionable audiences through the use of offline personal information with observed linkages to digital identifiers.\n\n**Additional Considerations - What Comes Next?**\nWe welcome you to contact our team at sales@deepsync.com for additional assistance.\n\n","terms_of_service":"https://deepsync.com/terms-conditions/","documentation_link":"","privacy_policy_link":"https://deepsync.com/privacy-policy/","geographical_coverage":"[\"United States and Canada\"]","cost":"PAID","update_frequency":{"interval":24,"unit":"HOURLY"},"data_source":"","assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"Deep Sync","description":"Decades of Industry Leadership.\nPrivacy-Safe Focus.\nPowerful Hygiene and Identity Solutions.\nBest-in-Class Data.\n\nDeep Sync drives marketing success for agencies and brands alike with privacy-first datasets and best-in-class data solutions that enable the identification, targeting, and engagement of desired audiences across devices and channels.\n\nFrom precision audience creation and targeting to a variety of top-tier data services that empower marketers to improve data hygiene and quality, consolidate identities, enhance files with valuable attributes, and predict marketing outcomes, Deep Sync’s comprehensive suite of solutions powers billions of data connections for thousands of customers annually."},"popularity":1134},{"id":"caa55ea8-b491-4271-880c-e601af7a38a2","summary":{"name":"BOD and TSS Numeric Violations in the US","subtitle":"List of NPDES Permit Violations for Biological Oxygen Demand (BOD) and Total Suspended Solids (TSS) in the US.","share":{"name":"bod-tss-share","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1749059483166,"updated_at":1749059557703,"categories":["HEALTH","CLIMATE_AND_ENVIRONMENT"],"listingType":"STANDARD","provider_id":"6dffdf32-fcc3-4442-9365-0600a62a578a"},"detail":{"description":"\n**Datasets**: Biological Oxygen Demand (BOD) Violations and Total Suspended Solids (TSS) NPDES Permit Violations in the US - Sample data.\n\n## Overview\n\nThis README provides detailed information about two datasets: `public_bod_violations_sample` and `public_suspended_violations_sample`. These datasets contain information about violations related to Biochemical Oxygen Demand (BOD) and suspended solids, respectively. The datasets include various columns, with a focus on geolocation, industry name, type, violation description, parameter description, and exceedances.\n\n## Datasets\n\n### 1. `public_bod_violations_sample`\n\nThis dataset contains information about violations related to Biochemical Oxygen Demand (BOD). Below are the key columns of interest:\n\n- **Geolocation:**\n - `LOCATION_ADDRESS`: The address of the facility.\n - `city`: The city where the facility is located.\n - `county_code`: The county code of the facility's location.\n - `state_code`: The state code of the facility's location.\n - `zip`: The ZIP code of the facility's location.\n - `latitude`: The latitude coordinate of the facility.\n - `longitude`: The longitude coordinate of the facility.\n\n- **Industry Information:**\n - `facility_name`: The name of the facility.\n - `naics_code`: The North American Industry Classification System (NAICS) code for the facility.\n - `naics_desc`: The description of the NAICS code.\n\n- **Violation Information:**\n - `VIOLATION_DESC`: A description of the violation.\n - `PARAMETER_DESC`: A description of the parameter related to the violation.\n - `EXCEEDENCE_PCT`: The percentage by which the parameter exceeded the limit.\n\n### 2. `public_suspended_violations_sample`\n\nThis dataset contains information about violations related to suspended solids. Below are the key columns of interest:\n\n- **Geolocation:**\n - `LOCATION_ADDRESS`: The address of the facility.\n - `city`: The city where the facility is located.\n - `county_code`: The county code of the facility's location.\n - `state_code`: The state code of the facility's location.\n - `zip`: The ZIP code of the facility's location.\n - `latitude`: The latitude coordinate of the facility.\n - `longitude`: The longitude coordinate of the facility.\n\n- **Industry Information:**\n - `facility_name`: The name of the facility.\n - `naics_code`: The North American Industry Classification System (NAICS) code for the facility.\n - `naics_desc`: The description of the NAICS code.\n\n- **Violation Information:**\n - `VIOLATION_DESC`: A description of the violation.\n - `PARAMETER_DESC`: A description of the parameter related to the violation.\n - `EXCEEDENCE_PCT`: The percentage by which the parameter exceeded the limit.\n\n## Column Descriptions\n\n### Common Columns\n\n- `NPDES_ID`: The National Pollutant Discharge Elimination System (NPDES) ID of the facility.\n- `VERSION_NMBR`: The version number of the record.\n- `ACTIVITY_ID`: The activity ID associated with the violation.\n- `NPDES_VIOLATION_ID`: The NPDES violation ID.\n- `PERM_FEATURE_NMBR`: The permit feature number.\n- `PERMIT_ACTIVITY_ID`: The permit activity ID.\n- `LIMIT_SET_DESIGNATOR`: The limit set designator.\n- `MONITORING_LOCATION_CODE`: The monitoring location code.\n- `DMR_FORM_VALUE_ID`: The Discharge Monitoring Report (DMR) form value ID.\n- `DMR_VALUE_NMBR`: The DMR value number.\n- `DMR_VALUE_ID`: The DMR value ID.\n- `DMR_PARAMETER_ID`: The DMR parameter ID.\n- `NODI_CODE`: The No Data Indicator (NODI) code.\n- `ADJUSTED_DMR_VALUE_NMBR`: The adjusted DMR value number.\n- `LIMIT_VALUE_STANDARD_UNITS`: The limit value in standard units.\n- `VIOLATION_TYPE_CODE`: The violation type code.\n- `VIOLATION_TYPE_DESC`: The violation type description.\n- `VIOLATION_CODE`: The violation code.\n- `PARAMETER_CODE`: The parameter code.\n- `STANDARD_UNIT_CODE`: The standard unit code.\n- `STANDARD_UNIT_DESC`: The standard unit description.\n- `MONITORING_PERIOD_END_DATE`: The end date of the monitoring period.\n- `NMBR_OF_REPORT`: The number of reports.\n- `VALUE_QUALIFIER_CODE`: The value qualifier code.\n- `UNIT_CODE`: The unit code.\n- `VALUE_RECEIVED_DATE`: The date the value was received.\n- `DAYS_LATE`: The number of days late.\n- `ADJUSTED_DMR_STANDARD_UNITS`: The adjusted DMR value in standard units.\n- `LIMIT_ID`: The limit ID.\n- `DMR_VALUE_STANDARD_UNITS`: The DMR value in standard units.\n- `VALUE_TYPE_CODE`: The value type code.\n- `RNC_DETECTION_CODE`: The RNC detection code.\n- `RNC_DETECTION_DESC`: The RNC detection description.\n- `RNC_DETECTION_DATE`: The RNC detection date.\n- `RNC_RESOLUTION_CODE`: The RNC resolution code.\n- `RNC_RESOLUTION_DESC`: The RNC resolution description.\n- `RNC_RESOLUTION_DATE`: The RNC resolution date.\n- `STATISTICAL_BASE_CODE`: The statistical base code.\n- `STATISTICAL_BASE_MONTHLY_AVG`: The statistical base monthly average.\n- `STATISTICAL_BASE_SHORT_DESC`: The statistical base short description.\n- `registry_id`: The registry ID.\n- `facility_type_code`: The facility type code.\n- `IMPAIRED_WATERS`: Indicates if the waters are impaired.\n\n## Usage\n\nThese datasets can be used for various analyses, including:\n- Identifying facilities with frequent violations.\n- Analyzing the geographic distribution of violations.\n- Understanding the types of industries and their compliance with environmental regulations.\n- Examining the exceedance percentages to identify severe violations.\n\n## Access\n\nThese datasets are publicly accessible and can be queried using Databricks SQL.\n\n## Contact\n\nFor any questions or further information, please contact 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management issues, while providing mission-critical insights and 24/7 services for operators across all industries. \n\nLearn more at www.ketos.co"},"popularity":148},{"id":"a65d2cdf-44be-413f-9602-6bfd5622f507","summary":{"name":"MBI Sociodemographic Data for Portugal – Sample","subtitle":"MBI's fresh sociodemographic data for well-founded decisions in Portugal. Free trial data.","share":{"name":"share_socio_mbi_market_data_2024_pt_postcodes_4_socio","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1763382314995,"updated_at":1779199103263,"categories":["ADVERTISING_AND_MARKETING","DEMOGRAPHICS","GEOSPATIAL"],"listingType":"STANDARD","provider_id":"aa09abd6-ff7f-4452-803d-b8c43b4c07cb"},"detail":{"description":"[MBI](https://www.mbi-geodata.com/en/)'s sociodemographic data is available for many countries worldwide. All data comes from a single source, ensuring **consistency and comparability** across markets.\n\nThe Sociodemographics-Bundle Basic for Portugal includes the following **variables**:\n- [population](https://www.mbi-geodata.com/en/sociodemographic-data/population/)\n- [gender](https://www.mbi-geodata.com/en/sociodemographic-data/sex-gender/)\n- [age structure](https://www.mbi-geodata.com/en/sociodemographic-data/agebands/) (15-year age bands)\n- household figures ([number of households](https://www.mbi-geodata.com/en/sociodemographic-data/households/) and [households by type](https://www.mbi-geodata.com/en/sociodemographic-data/households-by-type/))\n\nOur sociodemographic data is offered for each country on different **levels**:\n- **Postal Code Level:** This fine-grained level of detail makes the data suitable for a wide range of applications – for example, enriching customer databases, which are often organized by postal code.\n- **Administrative Level:** Covers both larger administrative areas (e.g., regions, states, provinces) as well as finer subdivisions such as districts, counties, or municipalities.\n- **Micro Level**: Subdivisions of postal code or administrative boundaries, such as census areas or street segments. This is the most granular level for local analysis.\n\n**Please note:** The dataset available for free download is a **sample dataset** covering a limited number of geographic areas. If you wish to access the full dataset (covering all areas) or if you require additional variables or geographic levels, please contact us. We will be happy to help you find a product that fits your needs.\n \n**Additional Insights**: https://www.mbi-geodata.com/en/sociodemographic-data/\n\n**Data Structure**:\nAGE_F0014: Population (Nighttime) by age: 0 - 14 years, female\nAGE_F1529: Population (Nighttime) by age: 15 - 29 years, female\nAGE_F3044: Population (Nighttime) by age: 30 - 44 years, female\nAGE_F4559: Population (Nighttime) by age: 45 - 59 years, female\nAGE_F60PL: Population (Nighttime) by age: 60 years and above, female\nAGE_M0014: Population (Nighttime) by age: 0 - 14 years, male\nAGE_M1529: Population (Nighttime) by age: 15 - 29 years, male\nAGE_M3044: Population (Nighttime) by age: 30 - 44 years, male\nAGE_M4559: Population (Nighttime) by age: 45 - 59 years, male\nAGE_M60PL: Population (Nighttime) by age: 60 years and above, male\nAGE_T0014: Population (Nighttime) by age: 0 - 14 years, total\nAGE_T1529: Population (Nighttime) by age: 15 - 29 years, male\nAGE_T3044: Population (Nighttime) by age: 30 - 44 years, total\nAGE_T4559: Population (Nighttime) by age: 45 - 59 years, total\nAGE_T60PL: Population (Nighttime) by age: 60 years and above, total\nCTRYCODE: Two digit country code by ISO 3166\nFEMALE: Population (Nighttime): females\nHH_SIZE: Average household size\nHH_T: Households: total number\nMALE: Population (Nighttime): males\nNAME: Name of administrative area / Name of the most populous administrative area / Name of the respective micro-area or the next higher level\nADMINCODE / POSTCODE / MICROCODE Administrative code / Postal code / Micro-code\nP_PRM: Population (Nighttime) year average: per mill of country\nP_T: Population (Nighttime) year average: total number","terms_of_service":"https://service-mbi-geodata.com/s/HNp7Z2tws4WDqWd","documentation_link":"https://www.mbi-geodata.com/en/mbi-supplier-manufacturer-worldwide-geodata/europe/portugal-maps/","privacy_policy_link":"https://www.mbi-geodata.com/en/data-protection/","geographical_coverage":"[\"Europe\"]","cost":"FREE","update_frequency":{"interval":1,"unit":"YEARLY"},"collection_granularity":{"interval":1,"unit":"YEARLY"},"collection_date_start":1704111876262,"collection_date_end":1735647881037,"data_source":"MBI","assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://service-mbi-geodata.com/s/BbZdn2DMzPXwM2t"},"provider_summary":{"name":"Michael Bauer International GmbH – MBI Geodata","description":"For over 15 years, Michael Bauer International (MBI) has been a leading provider of globally consistent and comparable geographic data, including more than 3 million postcode areas and other detailed datasets like sociodemographics and purchasing power. MBI's high-quality small-scale data spans 248 countries, supporting businesses across all industries in location planning, branch networks, and distribution strategies. Recognized as a key data source, MBI is the preferred provider for leading GIS software and technology companies worldwide as well as one of the first global distributors for HERE Technologies. MBI is part of the Michael Bauer Group, alongside Michael Bauer Research GmbH and Michael Bauer Micromarketing GmbH."},"popularity":245},{"id":"f71cfca4-e0ea-4f4c-b34c-902a20f77ffd","summary":{"name":"Facebook Datasets","subtitle":"Facebook Posts, Comments and Reels Datasets","share":{"name":"facebook","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1719230136169,"updated_at":1776059469949,"categories":["MEDIA"],"listingType":"STANDARD","provider_id":"63866f42-27f8-4a1f-b243-37e7be863700"},"detail":{"description":"Note: This is a FREE SAMPLE dataset that includes 1,000 rows. For expanded datasets, additional records, or continuous data refreshes, please contact us.\n\nFREE SAMPLE DATASET - 1,000 Records\n\nThis is a complimentary sample of our comprehensive Facebook Posts, Comments and Reels Datasets. Please note: this sample includes 1,000 records and represents a static historical snapshot.\n\nNeed MORE records or FRESH, continuously updated data?\nDownload this free sample and our team will contact you within 24 hours to discuss full dataset options and exclusive Databricks Marketplace pricing.\n\nWe provide 2 datasets:\n\n**1. Facebook - Posts:**\nThe Facebook Posts dataset provides comprehensive information about posts on the platform, including URLs, post identifiers, user profile URLs, raw usernames, post content, posting dates, associated hashtags, engagement metrics (comments, shares, likes), and post types.\n**Use Cases:**\n-Facebook - Posts with Hashtags: This subset focuses on posts containing hashtags, enabling analysis of their impact on content visibility and user engagement. It helps explore hashtag usage patterns, popular topics, and trends across different demographics, aiding in effective hashtag strategies and content optimization.\n-Facebook - Posts with More Than 20 Comments: This subset is valuable for analyzing user engagement and content virality. By focusing on posts with significant interaction (more than 20 comments), researchers can uncover popular topics, effective content strategies, and community dynamics, informing marketing strategies and content creation.\n**Benefits:**\n-Informed Content Strategy: Understand user engagement and content trends to optimize content strategies.\n-Targeted Marketing: Identify popular topics and effective content strategies for highly engaging posts.\n-Insightful Analytics: Gain insights into community dynamics, enabling data-driven decision-making for Facebook marketing efforts.\n\n**2. 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For polygonal ZIP centroids, a modified geographic center point is used.\n - ZIP Code Alternate Names provides all names associated with a particular ZIP Code in a separate table for faster geocoding and better hit rates in the main ZIP Code boundary file\n- **Attribute fields include but are not limited to:**\n - 5-digit ZIP Code\n - Primary postal town name\n - ZIP Code type\n - State abbreviation\n - State FIPS Code\n - County FIPS Code\n - County name\n - 3-digit ZIP Prefix\n - Latitude (ZIP Centroid only)\n - Longitude (ZIP Centroid only)\n - Total count of residential deliveries on route\n - Total count of multifamily deliveries on route\n - Total count of single-family deliveries on route\n - Total count of residential PO Box deliveries on route\n - Total count of business deliveries on route\n - Enclosing ZIP Code of centroid. 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The healthcare market is complex — our data makes it clearer. We cut through the noise to deliver the insights you need to make smarter, faster, more strategic decisions. Because when you succeed, healthcare gets better for everyone."},"popularity":1383},{"id":"21811f14-3869-41bb-a07a-c0f95f7d90fe","summary":{"name":"Energy Efficiency Portfolio Standard Program","subtitle":"This data package has the Energy Efficiency Portfolio Standard Program (EEPS). The EEPS Program encourages cost-effect..","share":{"name":"energy_efficiency_portfolio_standard_program","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1682355735470,"updated_at":1698965635225,"categories":["HEALTH","SCIENCE_AND_RESEARCH"],"listingType":"STANDARD","provider_id":"f88849f8-9d77-4de2-b4c7-03d55cc3768e"},"detail":{"description":"\n**Overview**\nThis data package has the Energy Efficiency Portfolio Standard Program (EEPS). The EEPS Program encourages cost-effective electric and natural gas energy efficiency across New York State. New York State’s utilities and the New York State Energy Research and Development Authority (NYSERDA) administer energy efficiency programs to achieve energy efficiency savings. This data package includes the list of energy efficiency programs and the estimated energy savings reported for each program.\n\n**Description**\nThis data package contains In May 2007 the New York Public Service Commission (PSC) issued an order instituting a proceeding to develop an Energy Efficiency Portfolio Standard (EEPS). The order set goal of reducing electricity usage in New York by 15% from projected electricity usage in 2015. After examining comments and input from staff and stakeholders, the PSC issued a further order in June 2008 establishing detailed program targets, ratepayer collections to fund energy efficiency programs, and various other protocols for the EEPS. The June 2008 order also established collections from natural gas customers to fund gas efficiency programs although at the time targets for natural gas efficiency was still under development. In May 2009 the PSC issued another order establishing gas efficiency targets. In October 2011 the PSC issued a further order reauthorizing EEPS programs through December 31, 2015, and suspending a portion of the program that provides utilities with financial incentives for achieving efficiency targets. A subsequent March 2012 order reinstated the utility incentives for 2012 - 2015 under a revised structure.\n\n**Benefits**\n- Useful to estimate energy savings and to evaluate the consumption in government building for new york state. the fields descriptions along with the dataset description are again useful for the user to quickly understand the data and the dataset.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [Chicago Covered Buildings](https://www.johnsnowlabs.com/marketplace/chicago-covered-buildings)\n\t- This dataset shows the full list of buildings required to comply with the Chicago Energy Benchmarking Ordinance. The list includes all commercial, institutional, and residential buildings larger than 50,000 square feet.\n- [Chicago Energy Benchmarking](https://www.johnsnowlabs.com/marketplace/chicago-energy-benchmarking)\n\t- This dataset contains the Chicago Energy Benchmarking for the FY 2014 to 2020 by the Environment and Sustainable Development.\n- [Estimated Energy Savings Data](https://www.johnsnowlabs.com/marketplace/estimated-energy-savings-data)\n\t- This dataset contains the Energy Efficiency Portfolio Standard (EEPS) Program encourages cost-effective electric and natural gas energy efficiency across New York State.\n- [NYS Government Building Energy Use Intensity Data](https://www.johnsnowlabs.com/marketplace/nys-government-building-energy-use-intensity-data)\n\t- This dataset shows the data of energy intensity for NYS (New York State) Government building for Beginning State Fiscal Year 2010 to 2018.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated\n- Data Updates\n - Data updates support replace-on-update: outdated foreign keys are deprecated, not deleted\n\n**Our data is curated and enriched by domain experts** \n\n Each dataset is manually curated by our team of doctors, pharmacists, public health & medical billing experts:\n- Field names, descriptions, and normalized values are chosen by people who actually understand their meaning\n- Healthcare & life science experts add categories, search keywords, descriptions and more to each dataset\n- Both manual and automated data enrichment supported for clinical codes, providers, drugs, and geo-locations\n- The data is always kept up to date – even when the source requires manual effort to get updates\n- Support for data subscribers is provided directly by the domain experts who curated the data sets\n- Every data source’s license is manually verified to allow for royalty-free commercial use and redistribution.\n\n**Need Help?**\nIf you have questions about our products, contact us at [info@johnsnowlabs.com](mailto:info@johnsnowlabs.com). \n 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database of dietary supplements, International Food Consumption Database and Nutrition Assistance Program Participation and Cost Database.\n\n**Description**\nAdverse Food Events 2004 to 2018 dataset is a quarterly updated file that contains data extracted from the US Food and Drug Administration (FDA) Center for Food Safety and Applied Nutrition (CFSAN) Adverse Event Reporting System (CAERS) for the years 2004 to 2018. The Dietary Supplement Ingredient Database (DSID) provides estimated levels of ingredients in dietary supplement products sold in the United States. The DSID data are reported by ingredient type and ingredient level for each product category. International Food Consumption Database analyzes expenditures on major consumption categories including food and different food subcategories across 114 countries. The dataset is created from USDA (United States Department of Agriculture)-Economic Research Service calculations using 2005 International Comparison Program (ICP) data. Supplemental Nutrition Assistance Program (SNAP) is the new name for the federal Food Stamp Program. This dataset contains participation and cost data for SNAP. The data is furthered divided by annual, state, and monthly levels categorized by persons participating, households participating, benefits provided, average monthly benefits per person and average monthly benefits per household.\n\n**Benefits**\n- helps in collecting, harmonizing and disseminating data available at national and sub-national level all over the world.\n- provides statistical information on aspects of all major food and nutrition service (fns) food and nutrition assistance program.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [Adverse Food Events 2004 to 2020](https://www.johnsnowlabs.com/marketplace/adverse-food-events-2004-to-2020)\n\t- This dataset is a quarterly updated file that contains data extracted from the US Food and Drug Administration (FDA) Center for Food Safety and Applied Nutrition (CFSAN) Adverse Event Reporting System (CAERS) for the years 2004 to 2020.\n- [Dietary Supplement Ingredient Database](https://www.johnsnowlabs.com/marketplace/dietary-supplement-ingredient-database)\n\t- The Dietary Supplement Ingredient Database (DSID) provides estimated levels of ingredients in dietary supplement products sold in the United States. The DSID data are reported by ingredient type and ingredient level for each product category.\n- [International Food Consumption Database](https://www.johnsnowlabs.com/marketplace/international-food-consumption-database)\n\t- This dataset analyzes expenditures on major consumption categories including food and different food subcategories across 114 countries. The dataset is created from USDA (United States Department of Agriculture)-Economic Research Service calculations using 2005 International Comparison Program (ICP) data.\n- [Nutrition Assistance Program Participation and Cost Database](https://www.johnsnowlabs.com/marketplace/nutrition-assistance-program-participation-and-cost-database)\n\t- Supplemental Nutrition Assistance Program (SNAP) is the new name for the federal Food Stamp Program. This dataset contains participation and cost data for SNAP. The data is furthered divided by annual, state, and monthly levels categorized by persons participating, households participating, benefits provided, average monthly benefits per person and average monthly benefits per household.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated\n- Data Updates\n - 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Search for talent, review professional profiles, and support recruiting workflows using your existing SeekOut account and permissions.\n\n**Use cases**\n\n- Find candidates by skills, experience, role, and location.\n- Review candidate profiles and evaluate fit against hiring requirements.\n- Analyze talent markets to inform sourcing strategies.\n- Retrieve contact information, organize candidates in SeekOut workspaces, and export to configured recruiting systems where authorized.\n\n**Product details**\n\nThis listing provides access to SeekOut’s remotely hosted MCP server, rather than a downloadable dataset. Information is retrieved on demand from SeekOut and authorized recruiting integrations.\n\nEach user signs in to SeekOut through OAuth. Available tools and data depend on their subscription, permissions, and configured integrations.\n\n**Requirements**\n\nAn active SeekOut Recruit account with MCP access enabled. Specialized data sources and ATS workflows require the relevant entitlements and integrations. Contact retrieval and export actions may consume SeekOut credits.","terms_of_service":"https://www.seekout.com/terms/","documentation_link":"https://docs.seekout.com/","privacy_policy_link":"https://www.seekout.com/privacy/","assets":["ASSET_TYPE_MCP"],"license":""},"provider_summary":{"name":"SeekOut","description":"SeekOut is an AI-powered talent intelligence platform that helps organizations find, understand, and engage qualified talent. SeekOut serves recruiting, talent acquisition, and workforce planning teams with governed access to professional profiles, talent market insights, and recruiting workflows. 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It also provides data on U.S. price indices and quantities of farm output and input and real commodity trade weighted exchange rates.\n\n**Description**\nThis data package contains datasets that provide estimates of productivity growth in farm sector including indices of farm output, input, and total factor productivity for the United States as well as real trade-weighted exchange rate indexes for many commodities and aggregations important to U.S. agriculture. It also contains real trade-weighted exchange rate indexes for many commodities and aggregations important to U.S. agriculture.\n\n**Benefits**\n- This data package, updated annually, helps to improve the agricultural productivity which has been the world's primary means of assuring that the needs of a growing population don't outstrip the ability to supply food. over the past 50 years, productivity growth in agriculture has allowed food to become more abundant and cheaper even as world population more than doubled.\n- one of the most informative measures of agricultural productivity is total factor productivity (tfp). tfp takes into account all of the land, labor, capital, and material resources employed in farm production and compares them with the total amount of crop and livestock output. if total output is growing faster than total inputs, then the total productivity of the factors of production (i.e., total factor productivity, or tfp) is increasing.\n- he data also provides a practicable way of measuring changes in agricultural tfp over time given available data on agricultural outputs, inputs, and their prices.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [Real Commodity Trade Weighted Exchange Rates](https://www.johnsnowlabs.com/marketplace/real-commodity-trade-weighted-exchange-rates)\n\t- This dataset contains real trade-weighted exchange rate indices for many commodities and aggregations important to U.S. agriculture. The data covers information from 1970 to 2023.\n- [US Farmers Market Location](https://www.johnsnowlabs.com/marketplace/us-farmers-market-location)\n\t- This dataset lists markets that feature two or more farm vendors selling agricultural products directly to customers at a common, recurrent physical location. It includes address, city, county, state and latitude/longitude information of the farm market of the USA. It also contains information about directions, operating times, product offerings, accepted forms of payment and contact information for each market as well as URLs of their websites and communities in the popular social networks.\n- [United States Price Indices and Quantities of Farm Output and Input](https://www.johnsnowlabs.com/marketplace/united-states-price-indices-and-quantities-of-farm-output-and-input)\n\t- This dataset provides estimates of productivity growth in the U.S. farm sector for 1948-2019, including Price indices and implicit quantities of farm outputs and inputs.\n- [United States Total Farm Output Input and Total Factor Productivity](https://www.johnsnowlabs.com/marketplace/united-states-total-farm-output-input-and-total-factor-productivity)\n\t- This dataset provides estimates of productivity growth in the U.S. farm sector for 1948-2019, including indices of farm output, input, and total factor productivity for the United States. 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Transportation data includes highways, footways, cycleways, railways, ferry routes and public transportation.\n\n1. Segments: Segments are paths which can be traveled by people or things. Segment geometry represents the center-line of a path which may be traveled. Segment properties describe both the physical attributes (e.g. road surface and width) and non-physical attributes (e.g. access restriction rules) of that path. Two Segments are physically connected if a common Connector is referenced from the connectors property of both Segments. Where this occurs, both Segment geometries must contain the common Connector's coordinates. A physical connection between Segments indicates that it may be possible to travel from one segment to the next at the connected location, provided the Segment properties do not indicate a restriction, such as a turn restriction, which would prevent the transition in a particular case. Conversely, two segments are not physically connected—even if their geometries intersect—if they do not share a Connector in common.\n2. Connectors: Connectors create physical connections between Segments. Connectors help define the topology of the transportation network by defining points of physical connection between two or more Segments.\n\nFounded in 2022 under the Joint Development Foundation, Overture Maps Foundation (Overture Maps) is dedicated to the development of reliable, easy-to-use, and interoperable open map data that will power current and next-generation map products. We build this data through a collaborative process that combines technology, data, and support from a wide range of companies. Map data are assembled from multiple sources including members, civic organizations, and open data sources.\n\nOverture's data themes and schema are licensed under CC-BY 4.0. GERS IDs are licensed under CDLA Permissive v 2.0. 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It captures detailed information on job roles, skill requirements, educational backgrounds, certifications, pay, and benefits, while also mapping postings to business functions and workloads. This enables HR teams, workforce planners, and talent intelligence leaders to analyze emerging workforce needs, identify skill gaps, and anticipate hiring patterns across geographies and industries.\nFor talent-focused decision-makers, the dataset reveals trends in role evolution, technology adoption within jobs, and shifts in functional demand—helping organizations align workforce planning with business priorities. By offering visibility into hiring activity at both company and market levels, it equips talent acquisition leaders to design smarter sourcing strategies, benchmark against competitors, and anticipate future demand for critical skills.\n\n**Methodology & Quality**\nThe dataset is aggregated from global job boards, company career portals, and verified postings across industries. Data is continuously refreshed to ensure relevance, with postings standardized through a structured pipeline that normalizes job titles, roles, skills, and workloads. Automated translation and lemmatization processes improve comparability across geographies, while deduplication and validation routines minimize noise and bias. Updates are applied on a frequent basis to maintain timeliness, and robust standardization ensures consistency for longitudinal analysis of workforce demand.\n\n**Key Tables Include**\n•\tCompany & Job Identifiers – Standardized company and posting IDs for consistent linkage across datasets.\n•\tJob Role & Titles – Normalized job roles, standardized titles, and translated raw job titles.\n•\tJob Descriptions – Consolidated and translated job descriptions for consistent role analysis.\n•\tSkills & Tech Stack – Extracted core skills, soft skills, and associated technology stacks for each posting.\n•\tBusiness Functions & Workloads – Mapped to functional areas and workload categories to align postings with organizational structures.\n•\tEducation & Certifications – Requirements including degrees, certifications, and qualifications linked to roles.\n•\tCompensation & Benefits – Pay ranges, benefits, and perks as advertised in postings.\n•\tExperience Requirements – Minimum experience levels and role-specific experience indices.\n•\tLanguage Requirements – Language skills and multilingual expectations for specific roles.\n•\tPosting Metadata – Posting dates, update timestamps, and system load dates for time-based analysis.\n\n**Use Cases:**\n***Discover what roles, locations, and skills are your competitors hiring for.***\n\nTrack competitor hiring activity by monitoring the job roles, skills, and geographies they are targeting. This visibility helps HR teams understand competitor workforce strategies, anticipate skill shortages, and sharpen their own talent acquisition efforts.\t\n\nSELECT\n company_name,\n role AS job_role,\n location.country,\n location.region,\n location.location,\n skill AS core_skill,\n date_posted\nFROM job_postings_company \nLATERAL VIEW explode(jd.draup_job_role) role_tbl AS role\nLATERAL VIEW explode(jd.consolidated_JDs) loc_tbl AS location\nLATERAL VIEW explode(jd.core_skills) core_tbl AS skill\nWHERE jd.date_posted >= date_add(current_date, -180);\n\n***Has the demand for a specific job role increased or decreased in a location?***\t\n\nAnalyze hiring demand trends for any job role in a given geography. These insights help talent teams anticipate attrition risks—if demand is surging, existing employees are more likely to be poached—allowing HR to design retention and workforce planning strategies accordingly.\t\n\nSELECT\nrole_tbl.role AS job_role,\nloc_tbl.country,\nloc_tbl.region,\nDATE_TRUNC('month', jd.date_posted) AS posting_month,\nCOUNT(*) AS total_job_postings\nFROM job_postings_company jd\nLATERAL VIEW explode(jd.draup_job_role) role_tbl AS role\nLATERAL VIEW explode(jd.consolidated_JDs) loc_tbl AS loc\nWHERE role_tbl.role = 'Data Engineer'\nAND loc_tbl.country = 'India'\nAND jd.date_posted >= date_add(current_date, -365)\nGROUP BY role_tbl.role, loc_tbl.country, loc_tbl.region, DATE_TRUNC('month', jd.date_posted)\nORDER BY posting_month;\n\n***Find out what benefits are most commonly offered by product companies in India***\t\n\nBenchmark compensation packages beyond salary by analyzing benefits offered by companies in a target geography. 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Its Sales Intelligence solution helps go-to-market teams uncover deep account insights, identify buying triggers, and accelerate revenue growth.\n \nTrusted by over 300 global enterprises - including five of the Fortune 10 - Draup supports companies like Microsoft, PepsiCo, PayPal, and Moderna in building future-ready workforces and growth strategies."},"popularity":10},{"id":"80aaf535-eba1-4ee6-b47b-79131ad61b77","summary":{"name":"Digital Pathology Solution Accelerator","subtitle":"Improve the efficiency of diagnostic teams with deep learning","setting":{"visibility":"PUBLIC"},"created_at":1695926286735,"updated_at":1696444204619,"categories":["HEALTH"],"listingType":"STANDARD","provider_id":"ea1e69ff-0127-4c94-bf39-e841fe1d19d2","git_repo":{"git_repo_url":"https://github.com/databricks-industry-solutions/digital-pathology.git","git_repo_file_entrypoint":"README.md","git_provider":"GITHUB"}},"detail":{"description":"https://www.databricks.com/solutions/accelerators/digital-pathology\n\nThe tumor proliferation speed or tumor growth is an important biomarker for predicting patient outcomes. Proper assessment of this biomarker is crucial for informing the decisions for the treatment plan for the patient. In a clinical setting, the most common method is to count mitotic figures under a microscope by a pathologist. The manual counting and subjectivity of the process pose a reproducibility challenge. This has been the main motivation for many efforts to automate this process and use advanced ML techniques.\n\nOne of the main challenges however for automating this task, is the fact that whole slide images are rather large. WSI images can vary anywhere between 0.5 to 3.5GB in size, and that can slow down the image preprocessing step which is necessary for any downstream ML application.\n\nIn this solution accelerator, we walk you through a step-by-step process to use databricks capabilities to perform image segmentation and pre-processing on WSI and train a binary classifier that produces a metastasis probability map over a whole slide image (WSI).\n\nClick on the \"Get instant access\" button in the top right corner to clone the solution accelerator repo into your workspace. 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It offers an analysis-ready foundation for site selection, competitive benchmarking, delivery zone optimization, and hyper-local consumer insights.\n\nFully compatible with **Apache Spark**, **Delta Lake**, and **Databricks Unity Catalog** workflows.\n\n---\n\n## **Complete National Coverage: Branded Chains & Independent Eateries**\nThis exhaustive registry provides maximum geographic depth across England, Scotland, Wales, and Northern Ireland. It aggregates major global brands and regional operators into a unified data model.\n\n### **Leading QSR & Dining Chains**\n- **[Greggs](https://geolocet.com/products/uk-greggs-fastfood):** 2,761 locations\n- **[Subway](https://geolocet.com/products/uk-subway-fastfood):** 2,050 locations\n- **[McDonald's](https://geolocet.com/products/uk-mcdonalds-fastfood):** 1,493 locations\n- **[KFC](https://geolocet.com/products/uk-kfc-fastfood):** 980 locations\n- **[Burger King](https://geolocet.com/products/uk-burger-king-fastfood):** 567 locations\n- **[Nando's](https://geolocet.com/products/uk-nandos-fastfood):** 479 locations\n- **[Pret A Manger](https://geolocet.com/products/uk-pret-a-manger-fastfood):** 479 locations\n- **[Pizza Hut](https://geolocet.com/products/uk-pizza-hut-fastfood):** 402 locations\n- **[Papa Johns](https://geolocet.com/products/uk-papa-johns-fastfood):** 399 locations\n- **[PizzaExpress](https://geolocet.com/products/uk-pizzaexpress-fastfood):** 359 locations\n- **[Pepe's](https://geolocet.com/products/uk-pepes-fastfood):** 244 locations\n- **[Five Guys](https://geolocet.com/products/uk-five-guys-fastfood):** 176 locations\n\n*Plus over 90,000 additional independent, regional, and specialty dining locations mapped across the UK (England: 87,850; Scotland: 7,955; Wales: 4,694; Northern Ireland: 2,994).*\n\n---\n\n## **Technical Specifications & Schema Definitions**\nOptimized for zero-friction schema enforcement when reading via `spark.read.format(\"csv\")`. The structured schema below ensures flawless rendering in Databricks notebooks and high accuracy for LLM semantic indexing.\n\n* **`GUID`** (`STRING`): Unique global identifier for deterministic primary key mapping and deduplication.\n* **`Title`** (`STRING`): Official restaurant name or commercial brand banner classification.\n* **`Latitude`** (`DOUBLE`): Precise WGS84 latitude coordinate for point-in-polygon and spatial joins.\n* **`Longitude`** (`DOUBLE`): Precise WGS84 longitude coordinate for spatial indexing (H3 / S2).\n* **`Street_No` / `Street`** (`STRING`): Normalized building numbers and thoroughfare name elements.\n* **`Town`** (`STRING`): Core municipal settlements and primary post town designations.\n* **`Admin_level_1` / `_2`** (`STRING`): Regional and district boundary identifiers.\n* **`Local_authority`** (`STRING`): Local government administrative unit governing the site.\n* **`Postal_Code`** (`STRING`): Full outward and inward alphanumeric UK postcode string.\n* **`Address`** (`STRING`): Full, standardized street address clean-text output.\n* **`Wheelchair`** (`STRING`): Accessibility flags indicating wheelchair mobility support.\n* **`Phone` / `Website`** (`STRING`): Direct communication data, site-specific landing URLs, and domain vectors.\n* **`Opening_Hours`** (`STRING`): Standard daily operational schedules and structured operating text.\n\n---\n\n## **Databricks Workflow & Spatial Analytics Use Cases**\n\n- **Whitespace & Catchment Analysis:** Query the full 104,260 location points against population density tables to identify true \"dining deserts\" and untapped territory for new restaurant expansion.\n- **H3 Geospatial Indexing:** Convert the precise WGS84 `Latitude` and `Longitude` fields into Uber H3 spatial hexagons (`h3_longlattocell`) to seamlessly cross-reference with granular UK income data, ONS demographic indicators, or transit accessibility scores.\n- **Supply Chain & Delivery Logistics:** Integrate site coordinates with delivery platform data to optimize \"last-mile\" hub placements, assess competition density, and build sophisticated demand-forecasting models for multi-unit operators.\n\n---\n\n## **Data Evaluation & Custom Ingestion**\n\n### **Evaluate with a Free Sample Dataset**\nVerify the structure, column types, and coordinate precision before full implementation. A comprehensive schema sample is available for download in CSV/Parquet-friendly formats.\n\n*Want deeper market insights?* Learn more about sector distribution and competitive trends in our comprehensive report: **[View the UK Top Brands Fast Food and Restaurants Report](https://geolocet.com/pages/uk-topbrands-fastfood-and-restaurants-report)**\n\n### **Enterprise Delivery & Tailored Formatting**\nWhile natively available as an analysis-ready CSV for DBFS ingestion, we accommodate enterprise-level data architecture needs. Alternative formats - including **GeoJSON, Esri Shapefiles, PostgreSQL dumps, Excel, or direct Delta sharing profiles** - can be generated. Reach out to our engineering team at **contact@geolocet.com** for integration support or custom European POI data requests.","terms_of_service":"https://geolocet.com/pages/terms-of-use","documentation_link":"","privacy_policy_link":"https://geolocet.com/pages/privacy-policy","assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://geolocet.com/pages/terms-of-use"},"provider_summary":{"name":"Geolocet LTD","description":"Geolocet provides premium geospatial data for retail analysis, GIS, and market planning. 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All data comes from a single source, ensuring **consistency and comparability** across markets.\n\nThe Sociodemographics-Bundle Basic for Hungary includes the following **variables**:\n- [population](https://www.mbi-geodata.com/en/sociodemographic-data/population/)\n- [gender](https://www.mbi-geodata.com/en/sociodemographic-data/sex-gender/)\n- [age structure](https://www.mbi-geodata.com/en/sociodemographic-data/agebands/) (15-year age bands)\n- household figures ([number of households](https://www.mbi-geodata.com/en/sociodemographic-data/households/) and [households by type](https://www.mbi-geodata.com/en/sociodemographic-data/households-by-type/))\n\nOur sociodemographic data is offered for each country on different **levels**:\n- **Postal Code Level:** This fine-grained level of detail makes the data suitable for a wide range of applications – for example, enriching customer databases, which are often organized by postal code.\n- **Administrative Level:** Covers both larger administrative areas (e.g., regions, states, provinces) as well as finer subdivisions such as districts, counties, or municipalities.\n- **Micro Level**: Subdivisions of postal code or administrative boundaries, such as census areas or street segments. 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With millions of verified and ranked events aggregated from hundreds of data sources, PredictHQ stands as a single, global source of truth for events - from sports to festivals, school holidays, severe weather, and beyond. Benefit from customised event data, tailored to the categories, timeframes, and locations most critical to your business. \n\n**Use cases**\nPredictHQ's event data provides significant value across a range of applications, facilitating better decision-making and strategy:\n\n- **Demand Forecasting:** Enhance your forecasting models with event data for more accurate demand prediction and increased profitability.\n\n- **Event Visibility:** Rapidly and easily identify relevant impactful events for better strategic planning and risk mitigation.\n\n- **Inventory Management:** Leverage event data to understand demand patterns, aiding in maintaining lean supply chains, avoiding stockouts, and maximizing sales.\n\n- **Workforce Optimization:** Tailor your workforce strategies to event-driven demand to reduce labour costs and increase profitability.\n\n- **Dynamic Pricing:** Anticipate demand fluctuations using event data, allowing your models or team to adjust prices in advance to maximize revenue.\n\n- **Targeted Marketing:** Identify optimal locations for Out-of-Home (OOH) advertisements and pinpoint opportunities to accurately reach your target market.\n\n- **Delivery Optimization:** Employ event data to circumnavigate event-driven traffic jams, ensuring swifter, more dependable delivery times for food, grocery, liquor, and snack delivery companies.\n\n- **Site Selection:** Utilize event data to find the most advantageous locations for business expansion or targeted marketing efforts.\n\n- **Visualizations & Insights:** Swiftly visualize the impact of events through BI tools, gaining actionable insights for better decision-making.\n\n\nFor further use cases and to explore how event data can transform your business operations, refer to PredictHQ's website or reach out to us directly.\n\n\n**Product details**\nWe offer 19 event categories, 450 event labels, and event ranking (PHQ Rank™, Local Rank™, Aviation Rank™) at both aggregate and event levels. Our dataset includes fields like Title, Category, Labels, Description, Start and End dates and times, Predicted end time, Country, Lat / Lon (or Polygon), Venue Name and Address, Rank, PHQ Attendance, Event status, Place Hierarchy, and Created / updated timestamps. \n\nFeel free to check out our sample listings here on Databricks marketplace to get familiar with the data we offer, but bear in mind those are just sample listings. Our pipeline runs 24/7, updating your tailored datasets to reflect changes in millions of global events in real-time and we could curate the categories, time duration and location of data to match your business use-case.\n\n**Additional Insights**\nOur datasets are event-level and don't contain Personally Identifiable Information (PII), ensuring compliance with data governance rules across regions. 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PredictHQ brings all events into one place, combines it with world-first tools and intelligence to allow organizations to better predict and respond to changing customer demand created by events in an easy, reliable, and scalable way. We meet customers exactly where they are, ensuring they can access our data the way that suits them best. 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This timestamp provides precise information on when the news item became public in GST (UTC+4) timezone.\n- TICKER: The stock symbol for the company as per the trading system of the exchange it's listed on.\n- SENTIMENT_DAILY: The sentiment score derived from the analysis of the news headline for the ticker on the specific published date and time, ranging from -1 (negative) to +1 (positive).\n- SENTIMENT_7MA: The 7-day moving average of the news headline sentiment score for the ticker, smoothing out daily sentiment fluctuations to show a clearer trend.\n- SENTIMENT_30MA: The 30-day moving average of the news headline sentiment score, providing a medium-term perspective on how sentiment from news headlines is trending for the company.\n- SENTIMENT_90MA: The 90-day moving average of the news headline sentiment score, offering a long-term view of the company's sentiment trend based on news headlines.\n- ATTENTION_BUZZ_DAILY: A measure of the media focus or interest in the ticker, quantified by the count of news headlines in which the ticker was mentioned.\n- ATTENTION_BUZZ_7RSUM: The total media focus or interest in the ticker over the past 7 days, quantified by the count of news headlines in which the ticker was mentioned on a 7-day rolling sum basis.\n- ATTENTION_BUZZ_30RSUM: The total media focus or interest in the ticker over the past 30 days, quantified by the count of news headlines in which the ticker was mentioned on a 30-day rolling sum basis.\n- ATTENTION_BUZZ_90RSUM: The total media focus or interest in the ticker over the past 90 days, quantified by the count of news headlines in which the ticker was mentioned on a 90-day rolling sum basis.\n- ATTENTION_BUZZ_WEIGHTED_SENTIMENT: The news headline sentiment score for the day, weighted by the volume of headlines the ticker received, combining sentiment with the level of attention.\n\n**Business Needs**\n- ***Sentiment Analysis***: Capture alpha-driving insights and uncover early risk signals with NLPify's cutting-edge Language AI and text quantification techniques. Our news sentiment dataset quantifies market breadth and asset sentiment, allows for further evidence to explain and interpret asset returns, and generates information edge which is not necessarily captured in \"the numbers\" or \"the charts\". \n- ***Quantitative Analysis***: Predict equity movements and detect anomalies in real time by deploying NLPify's tickerized sentiment analysis. With our quantified textual sentiment scores and news attention buzz, you are able to construct alpha-generating quantitative trading programs and enhance existing legacy systematic strategies.\n- ***Machine Learning***: Using NLPify's news sentiment scores and related metrics, you can train your own ML algorithm or use existing ML models to predict stock price and produce NLP-driven trading signals.\n- ***Market Analysis***: Given that our news sentiment scores are mapped to individual stocks, sector and market analyses are facilitated by our dataset. To that extent, correlation analysis can be conducted among stocks from a sentiment vs. sentiment & sentiment vs. price perspectives. Further more, the dataset enables the discovery and evaluation of individual outliers within a portfolio, sector, or market. Also, it can play a key role in validating asset allocation strategies and portfolio rebalancing based on periodic sentiment shifts.\n- ***Risk Analysis***: Risk management and capital preservation are enhanced substantially by using our news attention buzz-weighted sentiment scores. Spot risky assets based on sentiment-driven surveillance, detect unusual sentiment change in stocks, and minimize portfolio drawdowns by embedding sentiment analysis into your trading strategies. High negative sentiment accompanied by larger than usual news attention is an effective early risk indication of a critical company event that could precede a sudden price move.\n\nFor more details, refer to the embedded notebook.","terms_of_service":"https://www.nlpify.ai/terms-of-use","documentation_link":"","privacy_policy_link":"https://www.nlpify.ai/privacy-policy","geographical_coverage":"[\"Middle East\"]","cost":"PAID","update_frequency":{"interval":1,"unit":"DAILY"},"collection_granularity":{"interval":1,"unit":"DAILY"},"collection_date_start":1601921457710,"collection_date_end":1713463991444,"data_source":"NLPify Data Technologies","size":0.0011,"assets":["ASSET_TYPE_DATA_TABLE","ASSET_TYPE_MEDIA"],"license":""},"provider_summary":{"name":"NLPify Data Technologies","description":"NLPify is the Middle East's premier alternative data provider for finance, and an AI text analytics platform that extracts sentiment from financial textual and voice data covering equites in MENAT (GCC, Egypt, Morocco, and Turkey). 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These can be consumed directly via Databricks, clean rooms, or client environments.\n\n**What Sets Us Apart:**\n\n- Trained on expansive, privacy-compliant offline PII datasets \n- AI scores each individual (0–1) for insurance-specific intent \n- Combines transactional, behavioral, and life-stage indicators \n- Built for scalable segmentation, enrichment, and omnichannel activation \n- Supports custom audience creation by line, geography, or carrier model \n\n**Key Attributes**\n\n- Insurance product behavior and modeled propensity by line \n- Carrier switching signals and crossline coverage gaps \n- Homeownership, vehicle ownership, household income, family stage \n- Health coverage signals including Medicare and supplemental plan interest \n- Life event data (new parents, retirees, recent movers) triggering insurance needs \n- Engagement with financial, loyalty, and online comparison platforms \n\n**Segment Categories**\n\n- Each category includes multiple AI-scored segments – below are illustrative examples and business applications. New categories are released regularly.\n- Auto Insurance Intenders – In-market for auto coverage; includes new car buyers, carrier switchers, and multi-policy prospects \n- Use case: Target for quote comparison tools, mobile-first sign-up, or usage-based policies. \n- Homeowners & Property Coverage Seekers – Homeowners, buyers, and renovators exploring homeowners or renters’ coverage \n- Use case: Promote bundled savings with auto/home or competitive rate switches. \n- Life Insurance Buyers – Individuals at transitional life stages (e.g., parenthood, retirement, home purchase) seeking protection plans \n- Use case: Align offers to term, whole, or universal products with education content. \n- Health & Medical Policy Shoppers – Consumers actively comparing health, dental, vision, and supplemental policies \n- Use case: Engage during open enrollment, Medicare enrollment, or life-stage triggers. \n- Multi-Line Policy Holders & Cross-Sell Targets – Current policyholders with high potential to add additional coverage lines \n- Use case: Power cross-sell campaigns using policy gap intelligence. \n- Brand Switchers & Rate Shoppers – Price-sensitive or dissatisfied consumers showing strong likelihood of switching providers \n- Use case: Trigger competitive displacement campaigns with aggressive offer messaging.\n\n \n\n**Key Features**\n\n- AI-modeled insurance intent scoring by product line \n- Enriched with over 100 data sources (property, auto, household, credit proxies) \n- Captures brand loyalty, bundling likelihood, and switching behavior \n- Turnkey and customizable segments available \n- Deployable via HEMs, identity spine, match keys, or platform connectors \n\n**Use Cases**\n\n- Launch intent-based acquisition campaigns by insurance type \n- Increase cross-sell of home + auto or life + health coverage combinations \n- Improve quote-to-bind conversion by targeting high-scoring segments \n- Activate churn-prevention flows using early switching indicators \n- Personalize onboarding or renewal journeys based on policyholder profile \n- Support lead scoring and routing for digital insurance platforms \n\n**Next Steps**\n\n- Request listing access \n- Data Axle team will contact you to finalize audience, data cut, and delivery method / frequency \n- Need a Custom Audience? 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Learn more at www.precisely.com."},"popularity":653},{"id":"22b69626-8ab5-4a7f-a829-773b036c4086","summary":{"name":"Geolocet | Points of Interest Data | Grocery Store Locations - Germany","subtitle":"Supercharge Your Germany Grocery Retail Strategies: 45,000+ supermarkets, convenience stores, frozen food, and more","setting":{"visibility":"PUBLIC"},"created_at":1712659610857,"updated_at":1780387546873,"categories":["RETAIL","COMMERCE"],"listingType":"PERSONALIZED","provider_id":"5d3ddc17-a7e8-4794-a760-13587e1de2c6"},"detail":{"description":"# **All Supermarket Locations in Germany: Complete Geographic Dataset**\n---\n### **Premium GIS-Ready Retail Location Intelligence Data for Databricks Analytics**\n\nThis high-fidelity geospatial dataset provides a complete, exhaustive, and standardized overview of **78,951** verified grocery store and supermarket locations across Germany. Updated as of **May 27, 2026**, this complete national dataset encompasses both major branded chains and independent local grocers. It offers an analysis-ready foundation for full-scale retail site selection, competitive intelligence, logistics optimization, and spatial demographics mapping.\n\nFully compatible with **Apache Spark**, **Delta Lake**, and **Databricks Unity Catalog** workflows.\n\n---\n\n## **Complete National Coverage: Branded & Independent Grocers**\nUnlike top-tier only datasets, this complete collection captures the entire German grocery landscape, offering unmatched geographic depth. It includes thousands of independent markets alongside the top 27 national supermarket chains:\n\n- **Discounter & Hypermarket Leaders:** \n [ALDI (4,329 locations)](https://geolocet.com/products/germany-aldi-grocery), \n [Lidl (3,322 locations)](https://geolocet.com/products/germany-lidl-grocery), \n [Netto (4,765 locations)](https://geolocet.com/products/germany-netto-grocery), \n [PENNY (2,163 locations)](https://geolocet.com/products/germany-penny-grocery), \n [Kaufland (821 locations)](https://geolocet.com/products/germany-kaufland-grocery), \n [Norma (1,371 locations)](https://geolocet.com/products/germany-norma-grocery)\n\n- **Full-Range Supermarkets:** \n [EDEKA (6,469 locations)](https://geolocet.com/products/germany-edeka-grocery), \n [REWE (5,014 locations)](https://geolocet.com/products/germany-rewe-grocery), \n [Combi (205 locations)](https://geolocet.com/products/germany-combi-grocery), \n [HIT (104 locations)](https://geolocet.com/products/germany-hit-grocery), \n [tegut... (299 locations)](https://geolocet.com/products/germany-tegut-grocery), \n [famila (179 locations)](https://geolocet.com/products/germany-famila-grocery), \n [FRISTO (206 locations)](https://geolocet.com/products/germany-fristo-grocery)\n\n- **Organic & Specialty Chains:** \n [Alnatura (131 locations)](https://geolocet.com/products/germany-alnatura-grocery), \n [Denns BioMarkt (317 locations)](https://geolocet.com/products/germany-denns-biomarkt-grocery), \n [Mix Markt (199 locations)](https://geolocet.com/products/germany-mix-markt-grocery)\n\n- **Wholesale & Local Formats:** \n [METRO (95 locations)](https://geolocet.com/products/germany-metro-grocery), \n [GLOBUS (85 locations)](https://geolocet.com/products/germany-globus-grocery), \n [Nahkauf (547 locations)](https://geolocet.com/products/germany-nahkauf-grocery), \n [Tante-M (74 locations)](https://geolocet.com/products/germany-tante-m-grocery), \n [K + K Klaas (199 locations)](https://geolocet.com/products/germany-kk-klaas-grocery), \n [Konsum (124 locations)](https://geolocet.com/products/germany-konsum-grocery), \n [HOL'AB! (159 locations)](https://geolocet.com/products/germany-holab-grocery), \n [NP-Markt (195 locations)](https://geolocet.com/products/germany-np-markt-grocery), \n [SPAR (289 locations)](https://geolocet.com/products/germany-spar-grocery), \n [EuroShop (287 locations)](https://geolocet.com/products/germany-euroshop-grocery), \n [MÄC-GEIZ (190 locations)](https://geolocet.com/products/germany-mac-geiz-grocery)\n\n**Plus over 46,000 independent and regional grocers** fully mapped and standardized across all 16 federal states (e.g., Bayern: 14,623 sites; Nordrhein-Westfalen: 13,424 sites; Baden-Württemberg: 11,756 sites).\n\n---\n\n## **Technical Specifications & Schema Definitions**\nOptimized for zero-friction schema enforcement when reading via `spark.read.format(\"csv\")`. The structured schema below ensures flawless rendering in Databricks notebooks and high accuracy for LLM semantic indexing.\n\n* **`GUID`** (`STRING`): Unique global identifier for deterministic primary key mapping.\n* **`Title`** (`STRING`): Official store name, brand, or independent title of the location.\n* **`Latitude`** (`DOUBLE`): Precise WGS84 latitude coordinate for point-in-polygon & spatial joins.\n* **`Longitude`** (`DOUBLE`): Precise WGS84 longitude coordinate for spatial indexing (H3 / S2).\n* **`Street_No` / `Street`** (`STRING`): Normalized building/house number and street name elements.\n* **`City` / `Postal_Code`** (`STRING`): Core postal addresses and primary municipal settlement names.\n* **`Federal_State`** (`STRING`): High-level regional classification (e.g., Bayern, Nordrhein-Westfalen).\n* **`Admin_level_1` / `_2`** (`STRING`): Local administrative area boundaries and regional district identifiers.\n* **`Gemainde`** (`STRING`): German local municipality classification.\n* **`Population`** (`INTEGER`): Contextual demographic population metric attributed to the location.\n* **`Wheelchair`** (`STRING`): Accessibility flags indicating wheelchair mobility support.\n* **`Popularity_Score`** (`INTEGER`): Relative foot-traffic/rating metric graded on a standardized 1-10 scale.\n* **`Opening_Hours`** (`STRING`): Standard daily operational schedules and structured operating text.\n* **`Phone` / `Website`** (`STRING`): Direct communication data, branch-specific landing URLs, and domain vectors.\n\n---\n\n## **Databricks Workflow & Spatial Analytics Use Cases**\n\n- **Whitespace & Underserved Market Analysis:** Query the full 78,951 locations against population density tables to identify true retail deserts and expansion opportunities untouched by major chains.\n- **H3 Geospatial Indexing:** Convert the precise WGS84 `Latitude` and `Longitude` fields into Uber H3 spatial hexagons (`h3_longlattocell`) to cross-reference with granular socio-economic raster files.\n- **Supply Chain & Territory Optimization:** Combine store location points with regional administrative boundary layers (available via Geolocet) to optimize last-mile delivery routes and distribution warehouse placements.\n\n---\n\n## **Data Evaluation & Custom Ingestion**\n\n### **Evaluate with a Free Sample Dataset**\nVerify the structure, column types, and coordinate precision before full implementation. A comprehensive schema sample is available for download in CSV/Parquet-friendly formats.\n\n### **Enterprise Delivery & Tailored Formatting**\nWhile natively available as an analysis-ready CSV for DBFS ingestion, we accommodate enterprise-level data architecture needs. Alternative formats—including **GeoJSON, Esri Shapefiles, PostgreSQL dumps, or direct Delta sharing profiles**—can be generated. Reach out to our engineering team at **contact@geolocet.com** for integration support or custom European POI data requests.","terms_of_service":"https://geolocet.com/pages/terms-of-use","documentation_link":"","privacy_policy_link":"https://geolocet.com/pages/privacy-policy","cost":"PAID","update_frequency":{"interval":1,"unit":"QUARTERLY"},"size":0.001,"assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://geolocet.com/pages/terms-of-use"},"provider_summary":{"name":"Geolocet LTD","description":"Geolocet provides premium geospatial data for retail analysis, GIS, and market planning. 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Companies can customize their own data sets to align with the supply chain processes that they manage on the platform. Example data sets:\nFinancial:\n•\tInvoice cycle time\n•\tSupplier invoice compliance\n•\tPayments forecast\nOrder fulfillment\n•\tOrder cycle times\n•\tOn time and in full\nTransportation:\n•\tTransit time \n•\tCarrier performance \n•\tDwell at Port of Load and Port of Discharge\n","terms_of_service":"https://www.infor.com/about/legal","documentation_link":"","privacy_policy_link":"https://www.infor.com/about/privacy","geographical_coverage":"[\"Europe\",\"United States and Canada\",\"Asia-Pacific\",\"Middle East\"]","cost":"PAID","update_frequency":{"interval":1,"unit":"DAILY"},"data_source":"","assets":["ASSET_TYPE_GIT_REPO"],"license":""},"provider_summary":{"name":"Infor Nexus","description":"Infor Nexus is a leading end-to-end global supply chain business network platform, helping companies achieve unmatched visibility and collaboration for over 25 years. 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Offers comprehensive coverage of the language, providing a substantial volume of translated words of excellent quality that span European, African and Canadian French varieties. \n\n- Translations: 380,000 \n- Senses: 199,000 \n- Example translations: 146,000 \n- Format: XML and JSON formats \n- Delivery: Email (link-based file sharing) and REST API \n- Updated frequency: annually \n\n\nUse Cases: \n\nWe consistently work with our clients on new use cases as language technology continues to evolve. These include Natural Language Processing (NLP) applications, TTS, dictionary display tools, games, translations, word embedding, and word sense disambiguation (WSD). \n\nIf you have a specific use case in mind that isn't listed here, we’d be happy to explore it with you. Don’t hesitate to get in touch with us at Growth.OL@oup.com to start the conversation. \n\n \nPricing: \n\nOxford Languages offers flexible pricing based on use case and delivery format. Our datasets are licensed via term-based IP agreements and tiered pricing for API-delivered data. Whether you’re integrating into a product, training an LLM, or building custom NLP solutions, we tailor licensing to your specific needs. \n\nContact our team or email us at Growth.OL@oup.com to explore pricing options and discover how our language data can support your goals.\n\n\nAbout the sample: \n\nThe samples offer a brief overview of one or two language datasets (monolingual or/and bilingual dictionary data). To help you explore the structure and features of our dataset, we provide a sample in CSV format for preview purposes only. \n\nIf you need the complete original sample or more details about any dataset, please contact us (Growth.OL@oup.com) to request access or further information.","terms_of_service":"https://corp.oup.com/privacy-policy","contact_email":"support@datarade.ai","privacy_policy_link":"https://corp.oup.com/privacy-policy","assets":["ASSET_TYPE_DATA_TABLE"]},"provider_summary":{"name":"Oxford Languages","description":"Oxford Languages delivers multilingual language datasets designed to power the next generation of language technologies. Built through decades of research and curated by expert lexicographers, our data fuels diverse applications – from text-to-speech (TTS) and predictive text to language models dictionary display tools, assistive tech, chatbots, games, and more. 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Useful for SEO, generative engine optimization (GEO) and answer engine optimization (AEO) work.\n\n**Columns**\n- domain: the company's website\n- aiVisibility: High, Medium or Low, relative to all companies we score (High = roughly the top quarter)\n- readByAIEnginesLast30Days: yes if AI engine crawlers read the company's Engagemii page in the last 30 days\n- businessRelationshipsOnRecord: yes if the company has business relationships on record in the Engagemii network\n\n**Want more?**\nOur free 500-company sample shows all 113 columns of company data: exact scores, AI naming, Network Authority, AI crawler reads by engine, industry, B2B or B2C, location, tech stack, SEO signals and more. The full dataset of 82M+ companies is self-serve at engagemii.com/aeo/data: 10 cents a record, 4 cents from 5,000. 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The estimated actual power for each site in the region and the 10-day energy forecasts are produced using Vaisala's proprietary blend of numerical weather prediction trained models, statistical data modeling, and sophisticated machine-learning algorithms. Wind energy estimates and forecasts combine the actual operating characteristics of each project with the most advanced weather forecasts available.","terms_of_service":"https://www.xweather.com/terms","documentation_link":"https://www.xweather.com/renewable-energy-forecaster?utm_medium=other&utm_source=snowflake&utm_campaign=WEA-GLO-EN-EBU_RE-Default-Other-Snowflake","privacy_policy_link":"https://www.xweather.com/privacy","embedded_notebook_file_infos":[{"id":"61be1929-7a8b-4470-8847-01981f9d11de","marketplace_file_type":"EMBEDDED_NOTEBOOK","file_parent":{"parent_id":"9f99c097-91e5-493c-a59e-7b5ef97b53dc","file_parent_type":"LISTING"},"mime_type":"text/html","download_link":"https://marketplace-file-storage-prod.s3-fips.us-east-1.amazonaws.com/EMBEDDED_NOTEBOOK/LISTING_9f99c097-91e5-493c-a59e-7b5ef97b53dc/61be1929-7a8b-4470-8847-01981f9d11de?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEGgaDnVzLWVhc3QtMS1maXBzIkYwRAIgbC8sWIoYaHR8y7Wq86nPb0pufBl4%2BM4PWpeN%2B5PW9VQCIG7BhAVBDCWTpxFDLeuKYm%2Ff1JLz4iccPMzter0YdhgWKqYECDAQABoMNDE0MzUxNzY3ODI2IgxNAJrHbXaZG20jNfMqgwQyGEb4TsoVOoNvJ%2BaA%2FZ7zeu8hGcHqCeNfblP%2FQo5g6q2aRNtpwtPRKMlhYcIE%2FUQaNT1Lqmyptu32VUQTpzjTTxRVmJrDPkheSCkV06XQ%2FpTMKjU8CC7X1b%2FqYf8CrBwZT7Cv4C5WPjEET38f2A79h0xV6bZgPy28zQWrSeIJ9w6AfZBxiE24%2FVc08zVA0ivCT6lsy7nuU0wcR%2FMg2C9nd1vaMnQ923VpD21CKfVVYcrNWL2b%2FQ%2BNAO%2BxPjPgjiN6UPQbvbvZxkSdhgrGtecIg9NgqGkMcvM5VLTDtdC1kdeZetfOC2obtA%2BT%2F0gy3tIDo6WM5hcQfw743%2FAU9oaGnRBl%2BwoxRRDCZA%2F4jnPdluICSI7NCr6C3HHOe4CSZJjRihczxPA%2FgQczAVjAPs6CrK9N9pIzt0vNJpGUTgg4AoxftTvGfciDREilSctPIcBk6GwB%2Bzt4wbtNCmEPU%2BFD46rc6CWVQmmLwbKOeHw70ipMVFlOc8NlXkEtGO2vu6H44MstSSc6daOVUYUavQSGZ6jGhEjsP2YGz8KDm9XQotB%2FtyVBqiZV01OKqDmmBJA%2FXjUpSJ8M%2FeJXhe5m%2BKcxzNObtoJRI7jci3Tpt08XZyXZ7%2FyIV8H30ya468X3cfkTXrm972uga3z7iXAdubyEHnQIua%2FuvtwOHILi6O5D1Me2MzCPvcnXBjqTAYDoMWBt%2B3AVFuqSs2dmDQNBnvEhXfMgTedKLD5PJHb3aIl15iBkLaNu36GYnVCp9ZU2lSYcTbY5QwNnNeTfJTNy9bKhwzru6Sg%2BslhN%2BTS0zkTn%2BeLMY4tHgPEtEZ2cp%2FCk6GwqswvnYVKHjwO8q%2B1Mx8lc08fVEfarE9dke1GGFqoddx%2Bq%2BZUyHbDlAVfrCmf0yw%3D%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20261110T000135Z&X-Amz-SignedHeaders=host&X-Amz-Expires=86400&X-Amz-Credential=ASIAWA6KKHEJORWBH4N3%2F20261110%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=eaba5131fa024c0bd3b8114494d0e7c300aaafd701a2ff12e8cbeeb1ba3866f1","created_at":1685096153484,"updated_at":1685096153484,"display_name":"NYISO Regional","status":"FILE_STATUS_PUBLISHED"}],"assets":["ASSET_TYPE_DATA_TABLE"]},"provider_summary":{"name":"Vaisala Oyj","description":"Vaisala is a global leader in weather and environmental measurements with over 85 years of experience in providing observations for a better world. Vaisala Xweather delivers sensor-enhanced environmental data and solutions to customers in weather-sensitive industries. We provide accurate forecasts, uninterrupted real-time observations, and comprehensive historical datasets. Let's solve the world's weather and environmental challenges. Together."},"popularity":700},{"id":"63399dba-5039-4fef-b82d-43cb38c3546f","summary":{"name":"automation-test-listing-2","subtitle":"automation-test-description-2","share":{"name":"api_test_share","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1789121337895,"updated_at":1791898229103,"categories":["COMMERCE","ENERGY"],"listingType":"STANDARD","provider_id":"6b4c3f00-c69d-47ed-a3a2-541171c84c7d"},"detail":{"description":"**Overview**\nAdd a general description of your listing and its benefits here.\n\n**Use cases**\nAdd use cases for the listing here.\n- Use case 1\n- Use case 2\n\n**Product details**\nAdd a maximum of 10 sample datasets and field names that represent your dataset.\n\n- Datasets represented include table 1, table 2, and table 3.\n- Sample fields include field 1, field 2, field 3, field 4 and field 5.\n\nFor more details, refer to the embedded notebook.\n\n**Additional Insights**\nAdd any additional insights on the listing here. This can include relevant links pertaining to research related to the listing dataset, etc.","terms_of_service":"https://www.collibra.com/legal/documents/master-collibra-agreement","documentation_link":"","privacy_policy_link":"https://www.collibra.com/legal/documents/collibra-privacy-policy","assets":["ASSET_TYPE_DATA_TABLE","ASSET_TYPE_MEDIA","ASSET_TYPE_MODEL","ASSET_TYPE_NOTEBOOK"],"license":"","tags":[{"tag_name":"LISTING_TAG_TYPE_TASK","tag_values":["ASSERTION_STATUS"]}]},"provider_summary":{"name":"Collibra","description":"Collibra is the Data Intelligence company. We accelerate trusted business outcomes by connecting the right data, insights, and algorithms to all Data Citizens. Our cloud-based platform connects IT and the business to build a data-driven culture for the digital enterprise. Global organizations choose Collibra to unlock the value of their data and turn it into a strategic, competitive asset.\n"},"popularity":1547},{"id":"aff7f95f-8d3c-457f-b438-aab43cea3abc","summary":{"name":"MBI Area Boundaries for Belarus – Sample","subtitle":"MBI's precise & seamless boundaries for well-founded decisions in Belarus. Free trial data.","share":{"name":"share_boundaries_mbi_by_r_2025","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1764667060901,"updated_at":1784885107818,"categories":["ADVERTISING_AND_MARKETING","GEOSPATIAL","OTHER","MEDIA","PUBLIC_SECTOR","RETAIL","TRANSPORTATION_AND_LOGISTICS"],"listingType":"STANDARD","provider_id":"aa09abd6-ff7f-4452-803d-b8c43b4c07cb"},"detail":{"description":"MBI'S GLOBAL AREA BOUNDARIES COVER MOST COUNTRIES WORLDWIDE AND **CAN BE LICENSED FOR EACH COUNTRY INDIVIDUALLY** AND IN BUNDLES.\n[MBI](https://www.mbi-geodata.com/en/) digital boundaries are available for virtually all countries worldwide.* Created by our experts according to consistent global standards, the polygons are so precise and harmonized that they align seamlessly across borders. The territorial vintages of the worldwide area boundaries are compatible with our globally consistent and comparable [socio-demographic](https://www.mbi-geodata.com/en/sociodemographic-data/) and [economic](https://www.mbi-geodata.com/en/purchasing-power-overview/) data.\nOur area boundaries for Belarus are offered at the following levels:\n\n- **Postal Code Level** (Postcode 5-digit): This fine-grained level of detail makes the data suitable for a wide range of applications – for example, enriching customer databases, which are often organized by postal code.\n- **Administrative Level** (Gminy/Miasta): Covers both larger administrative areas (e.g., regions, states, provinces) as well as finer subdivisions such as districts, counties, or municipalities.\n- **Micro Level** (Census Enumeration Areas): Subdivisions of postal code or administrative boundaries, such as census areas or street segments. This is the most granular level for local analysis. | on request\n\n**Use Cases:**\n\n- **Market Analysis**: Use MBI’s boundaries to visualize data on a map, learn about your customers, and uncover untapped market potential.\n- **360-Degree Customer View**: Link boundaries to customer data, sociodemographic, and economic data to gain a holistic understanding of customer needs, behaviors, and potential value.\n- **Location Planning**\n•\tUse MBI’s boundaries to visualize data on a map, learn about your customers\n•\tPlan the provision of services and products across different regions\n•\tDefine sales territories\n•\tVisualize catchment areas and plan actions to reach potential customers\n\n**Additional Insights:**\n[About Area Boundaries](https://www.mbi-geodata.com/en/area-boundaries/)\n\n**Data Structure:**\nThe area boundaries include the following variables:\nCTRYCODE: Two-digit country code by ISO 3166\nNAME: Name of administrative area / Name of the most populous administrative area / Name of the respective micro-area or the next higher level\nADMINCODE / POSTCODE / MICROCODE: Administrative code / Postal code / Micro-code\nWKT: Geography as Well Known Text (lat/long WGS84)\n\n*with only a few specific exceptions","terms_of_service":"https://service-mbi-geodata.com/s/HNp7Z2tws4WDqWd","documentation_link":"https://www.mbi-geodata.com/en/mbi-supplier-manufacturer-worldwide-geodata/europe/belarus-maps/","privacy_policy_link":"https://www.mbi-geodata.com/en/data-protection/","geographical_coverage":"[\"Global\"]","cost":"FREE","update_frequency":{"interval":1,"unit":"YEARLY"},"collection_granularity":{"interval":1,"unit":"YEARLY"},"collection_date_start":1704100550030,"collection_date_end":1735636558098,"data_source":"MBI","assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://service-mbi-geodata.com/s/btQQHqK2KA3KtBT"},"provider_summary":{"name":"Michael Bauer International GmbH – MBI Geodata","description":"For over 15 years, Michael Bauer International (MBI) has been a leading provider of globally consistent and comparable geographic data, including more than 3 million postcode areas and other detailed datasets like sociodemographics and purchasing power. MBI's high-quality small-scale data spans 248 countries, supporting businesses across all industries in location planning, branch networks, and distribution strategies. Recognized as a key data source, MBI is the preferred provider for leading GIS software and technology companies worldwide as well as one of the first global distributors for HERE Technologies. MBI is part of the Michael Bauer Group, alongside Michael Bauer Research GmbH and Michael Bauer Micromarketing GmbH."},"popularity":218},{"id":"1978166d-767c-41d5-8446-99bded74f36a","summary":{"name":"FDA Drug Adverse Events Reporting System FAERS 2025","subtitle":"The Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) data package contains information on med..","share":{"name":"fda_drug_adverse_events_reporting_system_faers_2025","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1775231444032,"updated_at":1775231896001,"categories":["SCIENCE_AND_RESEARCH"],"listingType":"STANDARD","provider_id":"f88849f8-9d77-4de2-b4c7-03d55cc3768e"},"detail":{"description":"\n **Overview**\n The Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) data package contains information on medication errors, quality complaints and drug-related adverse events that were submitted to FDA in 2025.\n\n **Description**\n FAERS data package is designed to support the FDA's post-marketing safety surveillance program for drug and therapeutic biologic products. It is a useful tool for activities such as looking for new safety concerns that might be related to a marketed product, evaluating a manufacturer's compliance with reporting regulations and responding to outside requests for information. The reports in FAERS are evaluated by clinical reviewers, in the Center for Drug Evaluation and Research (CDER) and the Center for Biologics Evaluation and Research (CBER), to monitor the safety of products after they are approved by FDA. These reports are submitted to FDA by healthcare professionals (such as physicians, pharmacists, nurses and others) and consumers (such as patients, family members, lawyers and others). Healthcare professionals and consumers may also report to the products’ manufacturers who then send these reports to the FDA. The datasets in FAERS data package contain much relevant information such as patient demographics, drug name, dose and route of administration, diagnosis and indication for use, therapy starts and ends date and patient outcome.\n\n **Benefits**\n - useful for physicians, pharmacists, pharmaceutical companies, medical students and patients (general public).\n- faers database is available on fda’s website in raw form, whereas a john snow labs (jsl) customer gets it in a clean and normalized form ready to use and create his own custom reports.\n- quarter-wise data merged into one file.\n- easy to comprehend for the customers who are unfamiliar with the medical terminology as all abbreviated terms are replaced with full form, unlike faers original datasets which are full of abbreviations.\n\n **License Information**\n The use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n **Included Datasets**\n - [FDA Adverse Events Reporting System Demographics 2025](https://www.johnsnowlabs.com/wp-json/datasets-api/v1/fda-adverse-events-reporting-system-demographics-2025)\n\t- The FDA Adverse Event Reporting System (FAERS) contains information on medication errors, quality complaints and drug-related adverse events that were submitted to the FDA (U.S. Food and Drug Administration) in 2025. The Demographics dataset contains patient demographic and administrative information, a single record for each event report.\n- [FDA Adverse Events Reporting System Drug 2025](https://www.johnsnowlabs.com/wp-json/datasets-api/v1/fda-adverse-events-reporting-system-drug-2025)\n\t- The FDA Adverse Event Reporting System (FAERS) contains information on medication errors, quality complaints and drug-related adverse events that were submitted to FDA (U.S. Food and Drug Administration) in 2025. The Drug dataset contains drug/biologic information for as many medications as were reported for the event (1 or more per event).\n- [FDA Adverse Events Reporting System Drug Indication 2025](https://www.johnsnowlabs.com/wp-json/datasets-api/v1/fda-adverse-events-reporting-system-drug-indication-2025)\n\t- The FDA Adverse Event Reporting System (FAERS) contains information on medication errors, quality complaints and drug-related adverse events that were submitted to the FDA (U.S. Food and Drug Administration) in 2025. The Indication dataset contains all \"Medical Dictionary for Regulatory Activities\" (MedDRA) terms coded for the indications for use (diagnoses) for the reported drugs (0 or more per drug per event).\n- [FDA Adverse Events Reporting System Drug Reaction 2025](https://www.johnsnowlabs.com/wp-json/datasets-api/v1/fda-adverse-events-reporting-system-drug-reaction-2025)\n\t- The FDA Adverse Event Reporting System (FAERS) contains information on medication errors, quality complaints and drug-related adverse events that were submitted to FDA (U.S. Food and Drug Administration) in 2025. The drug reaction dataset contains all \"Medical Dictionary for Regulatory Activities\" (MedDRA) terms coded for the adverse event (1 or more).\n- [FDA Adverse Events Reporting System Drug Therapy Dates 2025](https://www.johnsnowlabs.com/wp-json/datasets-api/v1/fda-adverse-events-reporting-system-drug-therapy-dates-2025)\n\t- The FDA Adverse Event Reporting System (FAERS) contains information on medication errors, quality complaints and drug-related adverse events that were submitted to FDA (U.S. Food and Drug Administration) in 2025. The drug therapy dates dataset contains drug therapy start dates and end dates for the reported drugs.\n- [FDA Adverse Events Reporting System Report Source 2025](https://www.johnsnowlabs.com/wp-json/datasets-api/v1/fda-adverse-events-reporting-system-report-source-2025)\n\t- The FDA Adverse Event Reporting System (FAERS) contains information on medication errors, quality complaints and drug-related adverse events that were submitted to FDA (U.S. Food and Drug Administration) in 2025. The report source dataset contains report sources for event.\n\n\n **Data Engineering Overview**\n **We deliver high-quality data**\n - Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n - Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n - Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated\n - Data Updates\n - Data updates support replace-on-update: outdated foreign keys are deprecated, not deleted\n\n **Our data is curated and enriched by domain experts** \n\n Each dataset is manually curated by our team of doctors, pharmacists, public health & medical billing experts:\n - Field names, descriptions, and normalized values are chosen by people who actually understand their meaning\n - Healthcare & life science experts add categories, search keywords, descriptions and more to each dataset\n - Both manual and automated data enrichment supported for clinical codes, providers, drugs, and geo-locations\n - The data is always kept up to date – even when the source requires manual effort to get updates\n - Support for data subscribers is provided directly by the domain experts who curated the data sets\n - Every data source’s license is manually verified to allow for royalty-free commercial use and redistribution.\n\n **Need Help?**\n If you have questions about our products, contact us at [info@johnsnowlabs.com](mailto:info@johnsnowlabs.com). \n ","terms_of_service":"https://www.johnsnowlabs.com/healthcare-data-library-eula/","documentation_link":"https://www.johnsnowlabs.com/wp-json/datasets-api/v1/fda-drug-adverse-events-reporting-system-faers-2025-data-package","contact_email":"support@johnsnowlabs.com","privacy_policy_link":"https://www.johnsnowlabs.com/privacy-policy/","assets":["ASSET_TYPE_DATA_TABLE"]},"provider_summary":{"name":"John Snow Labs","description":"John Snow Labs, an AI and NLP for healthcare company, provides state-of-the-art software, models, and data to help healthcare and life science organizations build, deploy, and operate AI projects."},"popularity":1275},{"id":"81a464b7-ae69-4db4-b44a-7484f31ba176","summary":{"name":"Medical Domain Mexican Spanish Speech Corpus (MEDMEX)","subtitle":"10.5 hours of read-speech in the medical domain, read by licensed nurses in Mexico.","setting":{"visibility":"PUBLIC"},"created_at":1792585483044,"updated_at":1793112399352,"categories":["SCIENCE_AND_RESEARCH","HEALTH"],"listingType":"PERSONALIZED","provider_id":"f6e9a544-3103-4533-8706-321f7282d7d3"},"detail":{"description":"**Get this dataset on the Mozilla Data Collective.** Licensing and download happen on MDC rather than through Databricks. [See the full details and get access on MDC](https://mozilladatacollective.com/datasets/cmrdfjave008uo307ett7d4km?utm_source=databricks&utm_medium=referral&utm_campaign=databricks_marketplace&utm_content=listing_cta).\n\n10.5 hours (~5.25 hours each from two speakers, one male one female) of read-speech in the medical domain (drug/medical equipment descriptions, including name, amount, and mode of application) by licensed nurses.\n\n**At a glance**\n\n- **Language:** Spanish (Mexico)\n- **Task:** Automatic speech recognition (ASR)\n- **Format:** MP3, TSV\n\n**Motivation**\n\nSpeech technologies, particularly apart from those for English, often struggle with domain-specific content. One particularly valuable domain that speech technology systems may struggle with is medicine.\n\nThe present dataset was created with two main motivations:\n\n1. Offer a specialized medical-domain speech corpus of Mexican Spanish for the evaluation of ASR systems, and\n2. To provide specialized speech data for training a domain-specific TTS system.\n\n**Speakers**\n\nThe two speakers represented in the data are both trained and practicing nurses from the state of Puebla, Mexico. They are monolingual Spanish speakers.\n\n**Dataset creation process**\n\n**Text**: The source text is from Mexico's National Platform for Open Data, specifically: [datos.gob.mx/dataset/medicamentos_productos_farmaceuticos_adquiridos_hgmgg](https://www.datos.gob.mx/dataset/medicamentos_productos_farmaceuticos_adquiridos_hgmgg), \"Medicines requested by the General Hospital Dr. Manuel Gea Gonzalez, desegregated by description, unit, requested amount, and destination\". Our text corpus includes the descriptions and the requested amount for each drug. We publish audio for 2,623 rows, containing of 60k tokens.\n\n**Audio**: Two nursing professionals were recruited to read descriptions of medication, including drug and medical equipment names, form of administration or material, and quantities/dosages. Abbreviations are read out in full (e.g. \"miligramos\" instead of \"mg\"). Speakers were instructed to practice each sentence until they could read it comfortably and naturally, as if they were reading the descriptions to a doctor or patient.\n\nIn total, each speaker contributed approximately 5 hours of recorded speech. Recordings were made in a web application and recorded in webmedia format (.webm), and later converted to .mp3, using usb microphones.\n\n**Text preprocessing**\n\nMost descriptions come in the form drug name, quantity, and mode of application. We attempted to separate each of these fields when present by a pipe character | to assist readers in pacing. The pipe characters are omitted in the final dataset.\n\n* *Cánulas Orofaríngeas De plástico transparente o translucido Tipo: guedel/berman. Tamaño: 5 Longitud: 100 mm. Pieza*\n* *Espaciadores de volumen De plástico rígido resistente que se adapte a los diferentes medicamentos broncodilatadores en aerosol Puede tener o no ensamblada una mascarilla o una boquilla. Vida útil: tres meses. Adulto. Pieza.*\n* *Irbesartán-hidroclorotiazida Tableta Cada Tableta contiene: Irbesartán 300 mg Hidroclorotiazida 12.5 mg Envase con 28 Tabletas.*\n\n**Structure and format**\n\nThe dataset directory holds a metadata.tsv file, and an mp3/ directory with one MP3 file per recording (mono, 48 kHz, 64 kbps), named {key}.mp3.\n\nThe metadata.tsv file has the following columns:\n\n- **key**: The ID of the audio file. To get the audio path from the key, prepend mp3/ and append .mp3 to the key.\n- **sentence**: The sentence text, as written in the source. 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There are three UMLS Knowledge Sources: \n- The Metathesaurus, which contains over one million biomedical concepts from over 100 source vocabularies \n- The Semantic Network, which defines 133 broad categories and fifty-four relationships between categories for labeling the biomedical domain \n- The SPECIALIST Lexicon & Lexical Tools, which provide lexical information and programs for language processing The Metathesaurus is a large, multi-purpose, and multi-lingual vocabulary database that contains information about biomedical and health related concepts, their various names, and the relationships among them. The Metathesaurus is built from the electronic versions of many different thesauri, classifications, code sets, and lists of controlled terms used in patient care, health services billing, public health statistics, indexing and cataloging biomedical literature, and/or basic, clinical, and health services research. The Metathesaurus contains over five million terms, or names, organized by meaning into concepts and assigned a unique identifier. The Metathesaurus is not a vocabulary. It contains many vocabularies that are standards and helps to create mappings between these vocabularies. The Semantic Network consists of: \n- Semantic types (high level categories) \n- Semantic relationships (relationships between semantic types) The Semantic Network can be used to categorize any medical vocabulary. There are 133 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types. Semantic types and semantic relationships create a network that represents the biomedical domain. Semantic types and relationships help with interpreting the meaning that has been assigned to the Metathesaurus concept.\n\n**Benefits**\n- he purpose of the umls is to improve the ability of computer programs to \"understand\" the biomedical meaning in user inquiries and to use this understanding to retrieve and integrate relevant machine-readable information for users.\n- one powerful use of the umls is linking health information, medical terms, drug names, and billing codes across different computer systems.\n- linking terms and codes between your doctor, your pharmacy, and your insurance company.\n- patient care coordination among several departments within a hospital.\n- timely access to accurate and up-to-date information improves decision making and ultimately the quality of patient care and research.\n- the umls has many other uses, including search engine retrieval, data mining, public health statistics reporting, and terminology research.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [Behavior Concepts and Types](https://www.johnsnowlabs.com/marketplace/behavior-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Behavior\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Behavior Relationships](https://www.johnsnowlabs.com/marketplace/behavior-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Behavior\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Daily or Recreational Activity Concepts and Types](https://www.johnsnowlabs.com/marketplace/daily-or-recreational-activity-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Daily or Recreational Activity\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Daily or Recreational Activity Relationships](https://www.johnsnowlabs.com/marketplace/daily-or-recreational-activity-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Daily or Recreational Activity\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Diagnostic Procedure Concepts and Types](https://www.johnsnowlabs.com/marketplace/diagnostic-procedure-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Diagnostic Procedure\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Diagnostic Procedure Relationships](https://www.johnsnowlabs.com/marketplace/diagnostic-procedure-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Diagnostic Procedure\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Educational Activity Concepts and Types](https://www.johnsnowlabs.com/marketplace/educational-activity-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Educational Activity\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Educational Activity Relationships](https://www.johnsnowlabs.com/marketplace/educational-activity-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Educational Activity\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Governmental or Regulatory Activity Concepts and Types](https://www.johnsnowlabs.com/marketplace/governmental-or-regulatory-activity-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Governmental or Regulatory Activity\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Governmental or Regulatory Activity Relationships](https://www.johnsnowlabs.com/marketplace/governmental-or-regulatory-activity-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Governmental or Regulatory Activity\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Health Care Activity Concepts and Types](https://www.johnsnowlabs.com/marketplace/health-care-activity-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Health Care Activity\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Health Care Activity Relationships](https://www.johnsnowlabs.com/marketplace/health-care-activity-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Health Care Activity\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Individual Behavior Concepts and Types](https://www.johnsnowlabs.com/marketplace/individual-behavior-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Individual Behavior\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Individual Behavior Relationships](https://www.johnsnowlabs.com/marketplace/individual-behavior-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Individual Behavior\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Laboratory Procedure Concepts and Types](https://www.johnsnowlabs.com/marketplace/laboratory-procedure-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Laboratory Procedure\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Laboratory Procedure Relationships](https://www.johnsnowlabs.com/marketplace/laboratory-procedure-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Laboratory Procedure\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Machine Activity Concepts and Types](https://www.johnsnowlabs.com/marketplace/machine-activity-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Machine Activity\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Machine Activity Relationships](https://www.johnsnowlabs.com/marketplace/machine-activity-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Machine Activity\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Molecular Biology Research Technique Concepts and Types](https://www.johnsnowlabs.com/marketplace/molecular-biology-research-technique-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Molecular Biology Research Technique\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Molecular Biology Research Technique Relationships](https://www.johnsnowlabs.com/marketplace/molecular-biology-research-technique-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Molecular Biology Research Technique\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Occupational Activity Concepts and Types](https://www.johnsnowlabs.com/marketplace/occupational-activity-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Occupational Activity\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Occupational Activity Relationships](https://www.johnsnowlabs.com/marketplace/occupational-activity-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Occupational Activity\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Research Activity Concepts and Types](https://www.johnsnowlabs.com/marketplace/research-activity-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Research Activity\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Research Activity Relationships](https://www.johnsnowlabs.com/marketplace/research-activity-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Research Activity\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Social Behavior Concepts and Types](https://www.johnsnowlabs.com/marketplace/social-behavior-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Social Behavior\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Social Behavior Relationships](https://www.johnsnowlabs.com/marketplace/social-behavior-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Social Behavior\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Therapeutic or Preventive Procedure Concepts and Types](https://www.johnsnowlabs.com/marketplace/therapeutic-or-preventive-procedure-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Therapeutic or Preventive Procedure\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Therapeutic or Preventive Procedure Relationships](https://www.johnsnowlabs.com/marketplace/therapeutic-or-preventive-procedure-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Therapeutic or Preventive Procedure\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated\n- Data Updates\n - Data updates support replace-on-update: outdated foreign keys are deprecated, not deleted\n\n**Our data is curated and enriched by domain experts** \n\n Each dataset is manually curated by our team of doctors, pharmacists, public health & medical billing experts:\n- Field names, descriptions, and normalized values are chosen by people who actually understand their meaning\n- Healthcare & life science experts add categories, search keywords, descriptions and more to each dataset\n- Both manual and automated data enrichment supported for clinical codes, providers, drugs, and geo-locations\n- The data is always kept up to date – even when the source requires manual effort to get updates\n- Support for data subscribers is provided directly by the domain experts who curated the data sets\n- Every data source’s license is manually verified to allow for royalty-free commercial use and redistribution.\n\n**Need Help?**\nIf you have questions about our products, contact us at [info@johnsnowlabs.com](mailto:info@johnsnowlabs.com). \n ","terms_of_service":"https://www.johnsnowlabs.com/healthcare-data-library-eula/","documentation_link":"https://www.johnsnowlabs.com/marketplace/activities-data-package","contact_email":"support@johnsnowlabs.com","privacy_policy_link":"https://www.johnsnowlabs.com/privacy-policy/","embedded_notebook_file_infos":[{"id":"339d02b4-b0ca-4c3d-af48-68674d8e18f6","marketplace_file_type":"EMBEDDED_NOTEBOOK","file_parent":{"parent_id":"0135c478-2d77-408c-ba79-26bf0960005c","file_parent_type":"LISTING"},"mime_type":"text/html","download_link":"https://marketplace-file-storage-prod.s3-fips.us-east-1.amazonaws.com/EMBEDDED_NOTEBOOK/LISTING_0135c478-2d77-408c-ba79-26bf0960005c/339d02b4-b0ca-4c3d-af48-68674d8e18f6?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEGgaDnVzLWVhc3QtMS1maXBzIkYwRAIgbC8sWIoYaHR8y7Wq86nPb0pufBl4%2BM4PWpeN%2B5PW9VQCIG7BhAVBDCWTpxFDLeuKYm%2Ff1JLz4iccPMzter0YdhgWKqYECDAQABoMNDE0MzUxNzY3ODI2IgxNAJrHbXaZG20jNfMqgwQyGEb4TsoVOoNvJ%2BaA%2FZ7zeu8hGcHqCeNfblP%2FQo5g6q2aRNtpwtPRKMlhYcIE%2FUQaNT1Lqmyptu32VUQTpzjTTxRVmJrDPkheSCkV06XQ%2FpTMKjU8CC7X1b%2FqYf8CrBwZT7Cv4C5WPjEET38f2A79h0xV6bZgPy28zQWrSeIJ9w6AfZBxiE24%2FVc08zVA0ivCT6lsy7nuU0wcR%2FMg2C9nd1vaMnQ923VpD21CKfVVYcrNWL2b%2FQ%2BNAO%2BxPjPgjiN6UPQbvbvZxkSdhgrGtecIg9NgqGkMcvM5VLTDtdC1kdeZetfOC2obtA%2BT%2F0gy3tIDo6WM5hcQfw743%2FAU9oaGnRBl%2BwoxRRDCZA%2F4jnPdluICSI7NCr6C3HHOe4CSZJjRihczxPA%2FgQczAVjAPs6CrK9N9pIzt0vNJpGUTgg4AoxftTvGfciDREilSctPIcBk6GwB%2Bzt4wbtNCmEPU%2BFD46rc6CWVQmmLwbKOeHw70ipMVFlOc8NlXkEtGO2vu6H44MstSSc6daOVUYUavQSGZ6jGhEjsP2YGz8KDm9XQotB%2FtyVBqiZV01OKqDmmBJA%2FXjUpSJ8M%2FeJXhe5m%2BKcxzNObtoJRI7jci3Tpt08XZyXZ7%2FyIV8H30ya468X3cfkTXrm972uga3z7iXAdubyEHnQIua%2FuvtwOHILi6O5D1Me2MzCPvcnXBjqTAYDoMWBt%2B3AVFuqSs2dmDQNBnvEhXfMgTedKLD5PJHb3aIl15iBkLaNu36GYnVCp9ZU2lSYcTbY5QwNnNeTfJTNy9bKhwzru6Sg%2BslhN%2BTS0zkTn%2BeLMY4tHgPEtEZ2cp%2FCk6GwqswvnYVKHjwO8q%2B1Mx8lc08fVEfarE9dke1GGFqoddx%2Bq%2BZUyHbDlAVfrCmf0yw%3D%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20261110T000135Z&X-Amz-SignedHeaders=host&X-Amz-Expires=86400&X-Amz-Credential=ASIAWA6KKHEJORWBH4N3%2F20261110%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=f5b9762e9c3c7148bbd2854da748e8fc94fe8b444f64b13d5ca997ddd5d20553","created_at":1682537213670,"updated_at":1682537213670,"display_name":"EDA Notebook","status":"FILE_STATUS_PUBLISHED"}]},"provider_summary":{"name":"John Snow Labs","description":"John Snow Labs, an AI and NLP for healthcare company, provides state-of-the-art software, models, and data to help healthcare and life science organizations build, deploy, and operate AI projects."},"popularity":571},{"id":"7f782bc2-e4bf-4985-b735-9d8006adc722","summary":{"name":"AnthologyAI - 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Providing investors with unique inputs into factors like interest rates, policy changes, and the global business cycle, this dataset combines accuracy, granularity and speed of acquisition. \n\n**Use Case**\n\n\nA newly launched neobank, a subsidiary of a US regional lender, is seeking to develop an integrated marketing and customer segmentation strategy that aligns with current investment and spending trends. The bank is interested in leveraging opportunities arising from changes in consumer behavior, especially shifts in spending patterns across different sectors, as identified by the latest data insights. 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This data package also contains incidence and mortality rates of diseases included in the Global Burden of Diseases, Injuries, and Risk Factors 2010 study.\n\n**Benefits**\n- This data is useful for public health officials, clinicians, and researchers seeking to improve health and reduce geographic disparities. this can be used for further research, public health officials and program managers for potential interventions, setting priority areas for action and evaluating progress, policymakers for policy use and development\n- public health practitioner and clinicians' aid in designing and implementing targeted interventions and monitoring the progress of public health strategies.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [Adult Mortality Estimates by Country](https://www.johnsnowlabs.com/marketplace/adult-mortality-estimates-by-country)\n\t- This dataset provides global estimates of adult mortality risk, 45q15 (probability of death between the ages of 15 years and 60 years), between 1970 and 2010.\n- [Africa Child Mortality 1998 to 2017](https://www.johnsnowlabs.com/marketplace/africa-child-mortality-1998-to-2017)\n\t- This dataset provides estimates of the probability of child death from 1998 to 2017 for each country in Africa at the first administrative divisions.\n- [Brazil HIV and Tuberculosis Mortality Rates by Municipality 2001-2015](https://www.johnsnowlabs.com/marketplace/brazil-hiv-and-tuberculosis-mortality-rates-by-municipality-2001-2015)\n\t- This dataset provides estimates for age-standardized mortality rates by cause and sex at the state level and the municipality level for each state of Brazil for 2001-2015. Data comes from the Institute for Health Metrics and Evaluation (IHME) and the Global Health Data Exchange (GHDE).\n- [Child Health Indicators Italy](https://www.johnsnowlabs.com/marketplace/child-health-indicators-italy)\n\t- This dataset provides child health indicators in Italy. It includes different indicators for child (Polio immunization coverage among 1-year-olds, Diphtheria tetanus toxoid and pertussis immunization coverage etc).\n- [Child Health Indicators Japan](https://www.johnsnowlabs.com/marketplace/child-health-indicators-japan)\n\t- This dataset provides child health indicators in Japan. It includes different indicators for child (Low-birth-weight newborns, Overweight prevalence among children under 5 years of age, Prevalence of wasted children under 5 years of age etc).\n- [Child Mortality Estimates by Country 1970 to 2010](https://www.johnsnowlabs.com/marketplace/child-mortality-estimates-by-country-1970-to-2010)\n\t- This dataset provides estimates of neonatal, postneonatal, childhood, and under-5 mortality for 187 countries between 1970 and 2010. This dataset is from a compiled a database of 16,174 measurements of mortality in children younger than 5 years for 187 countries from 1970 to 2009.\n- [Child Mortality Estimates by Country 1990 to 2011](https://www.johnsnowlabs.com/marketplace/child-mortality-estimates-by-country-1990-to-2011)\n\t- This dataset provides estimates of neonatal, postneonatal, childhood, and under-5 mortality between 1990 and 2011.\n- [Global Breast and Cervical Cancer Mortality Estimates 1980 to 2010](https://www.johnsnowlabs.com/marketplace/global-breast-and-cervical-cancer-mortality-estimates-1980-to-2010)\n\t- This dataset provides estimates of breast and cervical cancer incidence and mortality for women aged 15-79 between 1980 and 2010.\n- [Global Child Mortality 2000 to 2016](https://www.johnsnowlabs.com/marketplace/global-child-mortality-2000-to-2016)\n\t- This dataset includes estimates for stillbirth rate, neonatal, post-neonatal, child, and under-5 mortality rates, number of stillbirths, and total number of under-5 deaths in 2016 and annualised rate of change in under-5 mortality for 2000–16, for global, Socio Demographic Index (SDI) groups, Global Burden of Disease (GBD) regions and super-regions, countries and territories, both sexes combined from 1990 to 2016.\n- [Global Malaria Mortality 1980 to 2010](https://www.johnsnowlabs.com/marketplace/global-malaria-mortality-1980-to-2010)\n\t- This dataset provides estimates of malaria mortality and cumulative probability of death from malaria for 105 countries with local malaria transmission.\n- [Global Mortality Rate 1970 to 2016](https://www.johnsnowlabs.com/marketplace/global-mortality-rate-1970-to-2016)\n\t- This dataset includes estimates for age-standardized adult mortality rate and 95% uncertainty interval estimates by location, male, female and both sexes combined in 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005, 2010, 2016. This age-specific mortality dataset is used to enable health systems to target interventions for the adult populations.\n- [Global Stillbirth Rates 1970 to 2016](https://www.johnsnowlabs.com/marketplace/global-stillbirth-rates-1970-to-2016)\n\t- This dataset includes estimates for stillbirth rates (per 1,000 live births) and 95% uncertainty interval estimates by location from 1970 to 2016. This age-specific mortality dataset is used to enable health systems to target interventions to reduce stillbirths.\n- [Global Under 5 Mortality 1970 to 2016](https://www.johnsnowlabs.com/marketplace/global-under-5-mortality-1970-to-2016)\n\t- This dataset includes estimates for under-5 mortality rates and 95% uncertainty interval estimates by location and gender, male, female and both sexes combined in 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005, 2010, 2016. This age-specific mortality dataset is used to enable health systems to target interventions to the under 5 years of age population.\n- [Italy Mortality and Global Health Estimates](https://www.johnsnowlabs.com/marketplace/italy-mortality-and-global-health-estimates)\n\t- This dataset provides mortality and global health estimates (probability of dying between birth and age 1 per 1000 live births) residing in Italy from 1990 to 2019.\n- [Japan Health Systems Indicators](https://www.johnsnowlabs.com/marketplace/japan-health-systems-indicators)\n\t- This dataset shows health systems indicators for Japan.\n- [Japan Mortality and Global Health Estimates](https://www.johnsnowlabs.com/marketplace/japan-mortality-and-global-health-estimates)\n\t- This dataset provides mortality and global health estimates (probability of dying between birth and age 1 per 1000 live births) residing in Japan from 1950 to 2019.\n- [Maternal Mortality Estimates by Country 1980 to 2008](https://www.johnsnowlabs.com/marketplace/maternal-mortality-estimates-by-country-1980-to-2008)\n\t- This dataset provides global, regional, and national level estimates of the maternal mortality ratio (MMR - the number of maternal deaths per 100,000 live births) as well as the number of maternal deaths from 1980 to 2008.\n- [Maternal Mortality Estimates by Country 1990 to 2011](https://www.johnsnowlabs.com/marketplace/maternal-mortality-estimates-by-country-1990-to-2011)\n\t- This dataset provides global and country estimates of where maternal mortality ratio (MMR - the number of maternal deaths per 100,000 live births), the number of maternal deaths, and the annualized rate of decline in MMR were estimated from 1990 to 2011.\n- [Surgery Mortality Rates in Scotland 2007-2012](https://www.johnsnowlabs.com/marketplace/surgery-mortality-rates-in-scotland-2007-2012)\n\t- This dataset includes information about Information Services Division (ISD) of National Services Scotland provided information on the mortality rates of surgeries in Scotland from 2007 to 2012 in response to a freedom of information request.\n- [Sustainable Development Goals Indicators for Italy](https://www.johnsnowlabs.com/marketplace/sustainable-development-goals-indicators-for-italy)\n\t- This dataset provides sustainable development goals indicators for Italy. It includes different indicators (Adolescent birth rate (per 1000 women aged 15-19 years), Crude suicide rates (per 100 000 population) etc).\n- [Sustainable Development Goals Indicators for Japan](https://www.johnsnowlabs.com/marketplace/sustainable-development-goals-indicators-for-japan)\n\t- This dataset provides sustainable development goals indicators for Japan. It includes different indicators (Estimated direct deaths from major conflicts (per 100 000 population), Ambient and household air pollution attributable death rate etc).\n- [Tuberculosis Indicators Japan](https://www.johnsnowlabs.com/marketplace/tuberculosis-indicators-japan)\n\t- This dataset provides indicators for tuberculosis in Japan. Incidence of tuberculosis is the estimated number of new and relapse tuberculosis cases arising in a given year, expressed as the rate per 100,000 population.\n- [World Health Statistics Indicators Italy](https://www.johnsnowlabs.com/marketplace/world-health-statistics-indicators-italy)\n\t- This dataset provides world health statistics indicators for Italy. It includes different indicators for heath (Population living in urban areas, Literacy rate among adults aged, Civil registration coverage of births etc).\n- [World Health Statistics Indicators Japan](https://www.johnsnowlabs.com/marketplace/world-health-statistics-indicators-japan)\n\t- This dataset provides world health statistics indicators for Japan. It includes different indicators for heath (Civil registration coverage of causes of deaths, Dentistry personnel density, Hospital beds, Hepatitis B surface antigen (HBsAg) prevalence among children under 5 years etc).\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated\n- Data Updates\n - 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analysis.\n\n**Technical Specifications:**\n\n- **File Format:** Geopackage (.gpkg)\n- **Spatial Reference:** WGS 84\n- **Resolution:** High-precision vector data\n- **Attributes:** country name, municipality name, alternative name, short name, local name, polygon\n- **Update Date:** 9th April 2024\n- **Source:** OSM (OpenStreetMap) Contributors\n\n**Purchase Now** and embark on a journey of discovery, analysis, and innovation with the most detailed and comprehensive European municipality boundaries shapefile available on the market.\n\n**Countries Included**\n\n- [Albania](https://geolocet.com/products/albania-admin-level-7-municipalities)\n- [Andorra](https://geolocet.com/products/andorra-admin-level-7-communes)\n- [Austria](https://geolocet.com/products/austria-admin-level-8-citymunicipality)\n- [Belarus](https://geolocet.com/products/belarus-admin-level-6-regions)\n- [Belgium](https://geolocet.com/products/belgium-admin-level-8-municipalities)\n- [Bosnia and 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[Guernsey](https://geolocet.com/products/guernsey-all-available-administrative-boundaries)\n- [Hungary](https://geolocet.com/products/hungary-admin-level-7-lau-1)\n- [Iceland](https://geolocet.com/products/iceland-admin-level-6-municipalities)\n- [Ireland](https://geolocet.com/products/ireland-and-northern-ireland-admin-level-8-borough-and-town-council-in-ireland-dublin-postal-districts)\n- [Isle of Man](https://geolocet.com/products/isle-of-man-all-available-administrative-boundaries)\n- [Italy](https://geolocet.com/products/italy-admin-level-8-municipalities)\n- [Jersey](https://geolocet.com/products/jersey-all-available-administrative-boundaries)\n- [Kosovo](https://geolocet.com/products/kosovo-admin-level-6-municipalities)\n- [Latvia](https://geolocet.com/products/latvia-admin-level-6-municipalities-cities)\n- [Liechtenstein](https://geolocet.com/products/liechtenstein-admin-level-8-municipalities)\n- [Lithuania](https://geolocet.com/products/lithuania-admin-level-5-municipalities)\n- [Luxembourg](https://geolocet.com/products/luxembourg-admin-level-8-municipalities)\n- [Macedonia](https://geolocet.com/products/macedonia-admin-level-7-municipalities)\n- [Malta](https://geolocet.com/products/malta-all-available-administrative-boundaries)\n- [Moldova](https://geolocet.com/products/moldova-admin-level-4-districts)\n- [Monaco](https://geolocet.com/products/monaco-admin-level-10-administrative-districts)\n- [Montenegro](https://geolocet.com/products/montenegro-admin-level-6-municipalities)\n- [Netherlands](https://geolocet.com/products/netherlands-admin-level-8-municipalities)\n- [Norway](https://geolocet.com/products/norway-admin-level-7-muncipiality)\n- [Poland](https://geolocet.com/products/poland-admin-level-7-municipalities)\n- [Portugal](https://geolocet.com/products/portugal-admin-level-7-municipalities)\n- 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[Ukraine](https://geolocet.com/products/ukraine-admin-level-6-crimea-municipalities-municipalities-in-oblasts-cities-of-regional-significance)\n\n---\nAdditionally, for those looking to enrich their analysis or project, we offer:\n* **[Demographics data](https://geolocet.com/collections/demographics-data):** If you require demographics data for countries in Europe at the municipality level, consider exploring our Demographics Data product, which seamlessly integrates with this dataset.\n\n* **[Point of Interest (PoI) Data](https://geolocet.com/collections/points-of-interest-data):** If you're interested in Points of Interest data, please don't hesitate to contact us for customized datasets to enhance your geographical analysis.\n\n* **[Data Customization](https://geolocet.com/pages/data-services-by-geolocet):** We understand that your project may have specific requirements. Feel free to get in touch with us if you need any modifications or specific data formats such as GeoJSON or Shapefile. We are here to tailor the data to your needs.\n\nContact us at *contact@geolocet.com* today.\n\n[Geolocet LTD](https://geolocet.com)\n\n","terms_of_service":"https://geolocet.com/pages/terms-of-use","documentation_link":"","privacy_policy_link":"https://geolocet.com/pages/privacy-policy","geographical_coverage":"[\"Europe\"]","cost":"PAID","update_frequency":{"interval":1,"unit":"DAILY"},"assets":["ASSET_TYPE_MEDIA","ASSET_TYPE_DATA_TABLE"],"license":"https://geolocet.com/pages/terms-of-use"},"provider_summary":{"name":"Geolocet LTD","description":"Geolocet provides premium geospatial data for retail analysis, GIS, and market planning. 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Sourced directly from monthly SEC EDGAR Form ABS-EE filings with full CIK and filing-level provenance. 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Monthly filings from November 2016 to present.\n\n**Pricing**\n\n$2,500/month. Contact cairn@cmdrvl.com for enterprise licensing or to request a trial.\n\n**Related Listings**\n\nPairs with **CMBS Property Collateral** for loan-to-property analysis, **CMBS Servicer Commentary** for workout narratives, and **CMBS Deal Counterparties** for sponsor exposure. **CMBS Tenant Names** is available free.","terms_of_service":"https://cmdrvl.com/terms/","documentation_link":"https://dealcharts.org/capitalmarkets/abs/cmbs/","privacy_policy_link":"https://cmdrvl.com/privacy/","cost":"PAID","update_frequency":{"interval":1,"unit":"DAILY"},"collection_date_start":1480512030642,"data_source":"SEC.gov EDGAR ABS-EE Filings","assets":["ASSET_TYPE_DATA_TABLE"]},"provider_summary":{"name":"CMD+RVL","description":"We believe that every person should have access to the resources necessary to improve their mastery of data analytics. 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The `02_ioc_matching` notebook addresses this use case.\n\n* **Continuous IOC matching**: The approach in the `02_ioc_matching` notebook can be easily adapted to perform incremental or continuous IOC matching using [Delta Live Tables (DLT)](https://docs.databricks.com/data-engineering/delta-live-tables/index.html). An example is given in the `03_dlt_ioc_matching` notebook.\n\n* **Ad hoc historical IOC search**: Historical IOC search at interactive speeds can be done using summary tables constructed using DLT. An example is given in the `04_dlt_summary_table` notebook. The `06_verify_dlt` notebook provides a series of steps to verify the DLT capabilities.\n\n* **Multi-cloud/region federated query**: Log ingestion and IOC matching can happen in each cloud or region without incurring egress costs. Hunting and triage of IOC hits can use federated queries from a single workspace to get results back from the workspaces in each cloud or region. The `07_multicloud` notebook demonstrates the use of multi-cloud and multi-region federated queries. \n\n* **Fully-automated continuous IOC matching with continuous IOC updates**: The streaming IOC matching approach in the `03_dlt_ioc_matching` notebook and the summary table approach in the `04_dlt_summary_table` notebook can be combined and extended to fully automate the IOC matching process even when the curated set of IOCs are constantly updated. In particular, when a new IOC is added, not only should newly ingested log data be matched against the new IOC, but the historical data needs to be matched against the new IOC. The `08_handling_ioc_updates` notebook demonstrates these concepts.\n\n\nClick on the \"Get instant access\" button in the top right corner to clone the solution accelerator repo into your workspace. Once the repo is cloned into your workspace, please execute the **RUNME** notebook in the repo in order to create the cluster and job you can use to run the 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It brings your curated data models, semantic definitions, and validated workbooks directly into the conversation, so the AI assistant answers with business context instead of generic guesses.\n\n* **Please Note**: this MCP listing is region-specific. It connects to Sigma organizations hosted on **Google Cloud Platform (GCP)**, in the **United States Central (Iowa)** region. If your Sigma organization is hosted in a different cloud or region, use the matching listing from the ***MCP Listings*** section below — each Sigma cloud/region combination is published as its own Databricks Marketplace listing because the connection endpoint is region-specific.\n\n## ***Key capabilities***\n\n* **Search** — Discover the right workbook, data model, or connection in your Sigma organization with a plain-language question (for example, \"What data do we have on customer retention?\"). Sigma's semantic search surfaces the most relevant assets, including tables, elements, and documents.\n* **Analyze** — Ask questions about your data in natural language and get results back in the same conversation, with no SQL required. Follow-up questions (drilling into an anomaly, slicing by a new dimension, cross-referencing a second model) build on the existing context instead of starting over.\n* **Explore** — Inspect column names, data types, definitions, and descriptions for any connected data source to confirm it fits the analysis before running a query.\n* **Build** — Turn an analysis into a Sigma workbook without leaving the conversation, adding pages, tables, charts, and controls from the data you've already explored. Preview any page or element as an inline screenshot to check the result before you share it.\n\n## ***Use cases***\n\n* **Ask questions, get governed answers** — Business users get real answers from the warehouse in the same conversation where they're already working, with permissions enforced automatically.\n* **Investigate anomalies before anyone has to ask** — Schedule an agent to monitor key metrics in Sigma and automatically drill into revenue or usage drops, surfacing a plain-English summary before the next standup.\n* **Reduce tool switching** — Discovery, exploration, and analysis all happen inside the AI assistant, without opening a separate BI tab.\n\n## ***Governed by design***\n\nThe Sigma MCP Server does not introduce a new security surface. Every request inherits the requesting user's existing Sigma permissions:\n\n* **Account-level access** — Search and Analyze respect the user's assigned Sigma account type and license.\n* **Connection-level access** — The AI assistant can only reach data connections the user already has **Can use** access to.\n* **Column-level and row-level security** — Any CLS/RLS policies configured in Sigma apply identically through the AI assistant. It cannot describe a column, or return a row, the user cannot already see in Sigma.\n* **Workspace permissions** — The AI assistant cannot find or query a workbook that has not been shared with the requesting user.\n\n## ***Requirements***\n* View permissions in Sigma for whatever you want to search, explore, or query (**View connections**, **View data models**, and/or **View workbooks**, plus document- and connection-level access).\n* An AI provider enabled for your Sigma organization.\n* If your organization uses an IP allowlist, the AI assistant's outbound IP ranges added to the allowlist with **Scope** set to **API**.\n\n## ***MCP Listings for Sigma on Databricks Marketplace*** \n* **Currently Supported**\n * **AWS**: [US West (`us-west-2)`](https://marketplace.databricks.com/details/b4dc5668-3131-47ec-9eb6-6a567182b022/Sigma-Computing_Sigma-MCP-AWS-US-West)\n * **GCP**: [US Central (`us-central-1`)](https://marketplace.databricks.com/details/d7f62b00-fb22-417d-89b3-4a8db9b6ab18/Sigma-Computing_Sigma-MCP-GCP-Central)\n * **Azure**: [US East (`eastus2`)](https://marketplace.databricks.com/details/9a874516-73d4-4e5a-ac35-a87696216981/Sigma-Computing_Sigma-MCP-Azure-US-East)\n\n* **Future Support**\n * **AWS**: [US East (`us-east-1`)](https://marketplace.databricks.com/provider/cfccf3c5-0092-49bd-9df2-4e62774aee96/Sigma-Computing), [Canada (`ca-central-1`)](https://marketplace.databricks.com/provider/cfccf3c5-0092-49bd-9df2-4e62774aee96/Sigma-Computing), [Europe (`eu-central-1`)](https://marketplace.databricks.com/provider/cfccf3c5-0092-49bd-9df2-4e62774aee96/Sigma-Computing), [Asia Pacific (`ap-southeast-2`)](https://marketplace.databricks.com/provider/cfccf3c5-0092-49bd-9df2-4e62774aee96/Sigma-Computing), [United Kingdom (`eu-west-2`)](https://marketplace.databricks.com/provider/cfccf3c5-0092-49bd-9df2-4e62774aee96/Sigma-Computing)\n * **Azure**: [US East (`eastus2`)](https://marketplace.databricks.com/provider/cfccf3c5-0092-49bd-9df2-4e62774aee96/Sigma-Computing), [Europe (`westeurope`)](https://marketplace.databricks.com/provider/cfccf3c5-0092-49bd-9df2-4e62774aee96/Sigma-Computing), [Canada (`canadacentral`)](https://marketplace.databricks.com/provider/cfccf3c5-0092-49bd-9df2-4e62774aee96/Sigma-Computing), [United Kingdom (`uksouth`)](https://marketplace.databricks.com/provider/cfccf3c5-0092-49bd-9df2-4e62774aee96/Sigma-Computing), [Australia (`australiaeast`)](https://marketplace.databricks.com/provider/cfccf3c5-0092-49bd-9df2-4e62774aee96/Sigma-Computing)\n * **GCP**: [Kingdom of Saudi Arabia (`me-central2`)](https://marketplace.databricks.com/provider/cfccf3c5-0092-49bd-9df2-4e62774aee96/Sigma-Computing)\n\n## ***Resources***\n* [MCP Documentation](https://help.sigmacomputing.com/docs/use-sigma-mcp-server)\n* [Supported regions](https://help.sigmacomputing.com/docs/region-warehouse-and-feature-support#supported-cloud-platforms-and-regions)","terms_of_service":"https://www.sigmacomputing.com/legal/terms-of-service","documentation_link":"https://help.sigmacomputing.com/docs/use-sigma-mcp-server","privacy_policy_link":"https://www.sigmacomputing.com/legal/privacy-policy","assets":["ASSET_TYPE_MCP"],"license":""},"provider_summary":{"name":"Sigma Computing","description":"Sigma is the AI, Apps and Analytics platform connected to the cloud data warehouse.\n\nUsing Sigma, business and technical teams can build intelligent, production-ready AI apps that accelerate and automate operational workflows. Sigma provides a spreadsheet interface, SQL and Python editors, visual builders, and native AI to help teams turn live data into interactive applications, analysis, reports, and embedded experiences."},"popularity":1803},{"id":"8b440026-623a-45c1-9c10-2e771d820778","summary":{"name":"Platts Market Data - Energy Transition","subtitle":"Platts Energy Transition industry benchmarks and price assessments.","setting":{"visibility":"PUBLIC"},"created_at":1691165351551,"updated_at":1786434389962,"categories":["ENERGY","FINANCIAL"],"listingType":"PERSONALIZED","provider_id":"eff4b18d-fe1b-4884-9591-eab9d16d2e2a"},"detail":{"description":"**Overview**\n\nPlatts energy transition price assessments provide a basis for trade across global environmental markets as well as providing understanding of the reasons behind market shifts. In these emerging trading markets, understanding the correlation between regions and commodities to compete effectively has never been more important.\n\nPlatts Market Data – Energy Transition package provides access to the full breadth and depth of our energy transition market data, including contract price assessments, plus:\n\n-A robust, transparent, and proven methodology allowing you to access the best possible reflection of market value, on the day, in a variety of regions\n\n-Historical data to enable you to look at past market trends to inform your future plans and back testing needs\n\nDataset Overview:\n\n-Platts’ full suite of Voluntary Carbon Market Price Assessments. These are the most in-depth in the market, covering a wide range of projects. They are designed to bring much needed transparency to a market looking to scale and fulfill its potential as an important tool in the mission to achieve 2050 climate targets.\n\n-Platts’ CARBEX carbon credit indices, produced in partnership with Viridios AI. CARBEX indices reflect the current market value for specific project types that carry defined sets of Sustainable Development Goals co-benefits, as defined by the United Nations General Assembly.\n\n-Platts’ full suite of daily hydrogen price assessments, which bring transparency across regions and production pathways including North America, Europe, Asia and Australia. These demonstrate the production cost of hydrogen for Steam Methane Reforming (SMR) production methods, including some regions Carbon Capture and Storage (CCS), along with prices for Proton Exchange Membrane (PEM) Electrolysis, Alkaline Electrolysis, Auto Thermal Reforming (ATR), Coal Gasification and Lignite Gasification production pathways, depending on the production hubs.\n\n-Platts’ growing suite of Ammonia assessments. These include cargo prices on a CFR basis into Asia, Northwest Europe, and the US Gulf Coast, and cargo prices on an FOB basis loaded in the Middle East and the Black Sea.\n\n-Daily assessments of Methane Performance Certificates (MPCs). These are certificates traded on the spot market that represent avoided methane emissions from the production of a specific volume of natural gas\n\n**Use cases**\n\n- Asset Valuation - Evaluate ROI for Energy projects and assets\n- Quantitative Analysis - Leverage SPGE data to design and implement complex and insightful models to support energy and commodities trading\n- ESG Investment Analysis - Combine SPGE data with project data-sets into models for visibility, optimizations, and collaboration\n- Risk Analysis - Utilize SPGE's independent, verifiable Risk data to support all risk management functions including validation of internal valuations and better align risk tolerance with business strategy\n- Fundamental Analysis - Connect SPGE pricing data with financial and operations data, and sensing technology to optimize decision-making on spending, productivity and cycle time\n- Pricing Analysis - Enable consistent benchmarking and forward-looking pricing analysis for emerging energy transition commodities and related instruments by integrating Platts energy transition pricing signals into internal analytics to support strategy, investment planning, and risk management decisions.\n\n**Product details**\n\nSample Tables:\nA ) PRICEDATA\nB ) REF_DATA_MARKETDATA\n\nSample Fields:\nA ) PRICEDATA\n- SYMBOL\n- MDC\n- DESCRIPTION\n- BATE\n- ASSESSDATE\n- UOM\n- CURRENCY\n\nB ) REF_DATA_MARKETDATA\n- COMMODITY\n- DELIVERY_REGION\n- DESCRIPTION\n\nView Descriptions:\n\n- PRICEDATA - Get the latest and historical assessments by Symbol\n- CORRECTIONS - Get all corrections to the assessments by Symbol\n- REF_DATA_MARKETDATA - Get additional reference data for Symbols\n- MARKETDATA_DICTIONARY - Contains descriptions and examples of each of the columns in the above views for a better understanding of the dataset","terms_of_service":"https://www.spglobal.com/en/privacy/privacy-policy-english","documentation_link":"https://www.marketplace.spglobal.com/en/datasets/platts-market-data-energy-transition-(264)","privacy_policy_link":"https://www.spglobal.com/en/privacy/privacy-policy-english","geographical_coverage":"[\"Global\"]","cost":"PAID","update_frequency":{"interval":1,"unit":"SECOND"},"collection_granularity":{"interval":1,"unit":"SECOND"},"collection_date_start":1546362529288,"assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"S&P Global Energy","description":"At S&P Energy, our complete view of global energy and commodities markets enables our customers to make decisions with conviction and create long-term, sustainable value. For more than 100 years, we've been a trusted connector that brings together thought leaders, market participants, governments, and regulators to co-create solutions that lead to progress. Vital to navigating Energy Transition, S&P Global Energy's coverage includes oil and gas, power, chemicals, metals, agriculture, and shipping. S&P Global Energy is a division of S&P Global (NYSE: SPGI)."},"popularity":728},{"id":"dac99d10-281b-42cc-9ffb-8656e28961f0","summary":{"name":"Hospital Treatment Events Costs","subtitle":"This data package contains the Information including the U.S. national trends in the number of inpatient stays, Health..","share":{"name":"hospital_treatment_events_costs","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1682355743363,"updated_at":1698965635315,"categories":["HEALTH","SCIENCE_AND_RESEARCH"],"listingType":"STANDARD","provider_id":"f88849f8-9d77-4de2-b4c7-03d55cc3768e"},"detail":{"description":"\n**Overview**\nThis data package contains the Information including the U.S. national trends in the number of inpatient stays, Healthcare Resource Group (HRG) unit costs for acute hospital procedures, Medicare Inpatient Prospective Payment System (IPPS) for discharges costs, Ambulatory Payment Classification (APC) Groups, Short-Stay Hospitals discharges information for Aged Beneficiaries, All Beneficiaries, Information on Office visit per Medicare beneficiaries and hospitalization counts and rates.\n\n**Description**\nThis data package contains the Information including the U.S. national trends in the number of inpatient stays, the average cost per stay (actual and inflation-adjusted), the average length of stay, and in-hospital mortality rate stratified by age, sex, expected payer, community-level income, and hospitalization type. Healthcare Resource Group unit costs for acute hospital procedures, broken down by hospital site, specialty on diagnosis at state and national level. Utilization trends for Emergency Department Services Level, Emergency Medical Services Authority (EMSA) Trauma Center Designation Level and Emergency Department services available for all licensed, comparable hospitals and Kaiser Foundation Hospitals in California. Medicare Inpatient Prospective Payment System (IPPS) payments for discharges paid under Medicare based on a rate per discharge using the Medicare Severity Diagnosis Related Group (MS-DRG).\n\nInformation on the discharges, total days of care, total charges, and program payments for Medicare beneficiaries discharged from Short-stay hospitals for aged beneficiaries, all beneficiaries, disabled beneficiaries. Information on office visit per Medicare beneficiaries at Hospital Referral Regions (HRR), Hospital Service Areas (HSA), State level. Information on estimated hospital-specific charges for select Ambulatory Payment Classification (APC) Groups paid under the Medicare Outpatient Prospective Payment System (OPPS) is also made available. Information on hospitalization counts and rates, statewide and by county, for ambulatory care sensitive conditions plus a number of composite measures which help to assess the quality of health services in the community are included in this data package.\n\n**Benefits**\n- Useful to public health specialists and specialized institutions to evaluate the utilization and the medicare costs associated with hospital inpatient, emergency and ambulatory healthcare services. useful to hospital administrative staff and other interested parties to evaluate and compare the hospital's healthcare services financial aspects from the medicare reimbursement perspective and of the healthcare services type. the field's descriptions along with the dataset description are again useful for the user to quickly understand the data and the dataset.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [HCUP National Trend Inpatient Stay 1994-2013](https://www.johnsnowlabs.com/marketplace/hcup-national-trend-inpatient-stay-1994-2013)\n\t- The dataset has United States national trends in the number of inpatient stays, average cost per stay (actual and inflation-adjusted), average length of stay, and in-hospital mortality rate from 1994-2013. The trends are stratified by age, sex, expected payer, community-level income, and hospitalization type.\n- [HCUP National Trend Inpatient Stay 1994-2019](https://www.johnsnowlabs.com/marketplace/hcup-national-trend-inpatient-stay-1994-2019)\n\t- The dataset has United States national trends in the number of inpatient stays, average cost per stay (actual and inflation-adjusted), average length of stay, and in-hospital mortality rate from 1994-2019. The trends are stratified by age, sex, expected payer, community-level income, and hospitalization type.\n- [HCUP State Level Trend Inpatient Stay 2003-2015](https://www.johnsnowlabs.com/marketplace/hcup-state-level-trend-inpatient-stay-2003-2015)\n\t- The dataset has information on the number of inpatient stays for every available calendar quarter at the level of State, hospitalization type, and expected payer. This results in 36 rows of data per available State.\n- [HCUP State Level Trend Inpatient Stay 2003-2021](https://www.johnsnowlabs.com/marketplace/hcup-state-level-trend-inpatient-stay-2003-2021)\n\t- The dataset has information on the number of inpatient stays for every available calendar quarter at the level of State, hospitalization type, and expected payer. This results in 36 rows of data per available State.\n- [Health Resource Group Costs by Provider HRG Code and Location](https://www.johnsnowlabs.com/marketplace/health-resource-group-costs-by-provider-hrg-code-and-location)\n\t- The dataset contains information of Healthcare Resource Group unit costs for acute hospital procedures, broken down by hospital site. This a merged dataset from years 2013 to 2020 for cost different hospital treatment criteria. Thirty-six (36) separate files were merged for this dataset.\n- [Health Resource Group Costs by Specialty HRG Code and Location](https://www.johnsnowlabs.com/marketplace/health-resource-group-costs-by-specialty-hrg-code-and-location)\n\t- The dataset contains information of Healthcare Resource Group unit costs for acute hospital procedures, broken down by Specialty on diagnosis. This a merged dataset from the year 2013 to 2020 for cost different hospital treatment criteria. Thirty-six (36) separate files were merged for this dataset.\n- [Hospital Emergency Medical Services Utilization Trends 2009-2013](https://www.johnsnowlabs.com/marketplace/hospital-emergency-medical-services-utilization-trends-2009-2013)\n\t- The dataset contains Emergency Department Services Level, Emergency Medical Services Authority (EMSA) Trauma Center Designation Level and Emergency Department services availability for all licensed, comparable hospitals and Kaiser Foundation Hospitals in California, 2009 through 2013.\n- [Inpatient Utilization and Payment Data 2013](https://www.johnsnowlabs.com/marketplace/inpatient-utilization-and-payment-data-2013)\n\t- The dataset includes hospital-specific charges for U.S. hospitals that receive Medicare Inpatient Prospective Payment System (IPPS) payments for 2013 for discharges paid under Medicare based on a rate per discharge using the Medicare Severity Diagnosis Related Group (MS-DRG).\n- [Inpatient Utilization and Payment Data 2014](https://www.johnsnowlabs.com/marketplace/inpatient-utilization-and-payment-data-2014)\n\t- The dataset includes hospital-specific charges for U.S. hospitals that receive Medicare Inpatient Prospective Payment System (IPPS) payments for 2014 for discharges paid under Medicare based on a rate per discharge using the Medicare Severity Diagnosis Related Group (MS-DRG).\n- [Inpatient Utilization and Payment Data 2015](https://www.johnsnowlabs.com/marketplace/inpatient-utilization-and-payment-data-2015)\n\t- The dataset provided here include hospital-specific charges for the U.S. hospitals that receive Medicare Inpatient Prospective Payment System (IPPS) payments for discharges, paid under Medicare based on a rate per discharge using the Medicare Severity Diagnosis Related Group (MS-DRG) for Fiscal Year (FY) 2015. These MS-DRGs represent more than 7 million discharges or 75 percent of total Medicare IPPS discharges.\n- [Inpatient Utilization and Payment Data 2016](https://www.johnsnowlabs.com/marketplace/inpatient-utilization-and-payment-data-2016)\n\t- The dataset provided here include hospital-specific charges for the U.S. hospitals that receive Medicare Inpatient Prospective Payment System (IPPS) payments for discharges, paid under Medicare based on a rate per discharge using the Medicare Severity Diagnosis Related Group (MS-DRG) for Fiscal Year (FY) 2016. These MS-DRGs represent more than 7 million discharges or 75 percent of total Medicare IPPS discharges.\n- [Inpatient Utilization and Payment Data 2017](https://www.johnsnowlabs.com/marketplace/inpatient-utilization-and-payment-data-2017)\n\t- The dataset provided here include hospital-specific charges for the U.S. hospitals that receive Medicare Inpatient Prospective Payment System (IPPS) payments for discharges, paid under Medicare based on a rate per discharge using the Medicare Severity Diagnosis Related Group (MS-DRG) for Fiscal Year (FY) 2017. These MS-DRGs represent more than 7 million discharges or 75 percent of total Medicare IPPS discharges.\n- [Inpatient Utilization and Payment Data 2018](https://www.johnsnowlabs.com/marketplace/inpatient-utilization-and-payment-data-2018)\n\t- The dataset provided here include hospital-specific charges for the U.S. hospitals that receive Medicare Inpatient Prospective Payment System (IPPS) payments for discharges, paid under Medicare based on a rate per discharge using the Medicare Severity Diagnosis Related Group (MS-DRG) for Fiscal Year (FY) 2018. These MS-DRGs represent more than 7 million discharges or 75 percent of total Medicare IPPS discharges.\n- [Inpatient Utilization and Payment Data 2019](https://www.johnsnowlabs.com/marketplace/inpatient-utilization-and-payment-data-2019)\n\t- The dataset provided here include hospital-specific charges for the U.S. hospitals that receive Medicare Inpatient Prospective Payment System (IPPS) payments for discharges, paid under Medicare based on a rate per discharge using the Medicare Severity Diagnosis Related Group (MS-DRG) for Fiscal Year (FY) 2019. These MS-DRGs represent more than 7 million discharges or 75 percent of total Medicare IPPS discharges.\n- [Inpatient Utilization and Payment Data 2020](https://www.johnsnowlabs.com/marketplace/inpatient-utilization-and-payment-data-2020)\n\t- The dataset provided here include hospital-specific charges for the U.S. hospitals that receive Medicare Inpatient Prospective Payment System (IPPS) payments for discharges, paid under Medicare based on a rate per discharge using the Medicare Severity Diagnosis Related Group (MS-DRG) for Fiscal Year (FY) 2020. These MS-DRGs represent more than 7 million discharges or 75 percent of total Medicare IPPS discharges.\n- [Medicare Beneficiary Office Visit by HRR HSA and State Levels](https://www.johnsnowlabs.com/marketplace/medicare-beneficiary-office-visit-by-hrr-hsa-and-state-levels)\n\t- The dataset gives information on Office visit per Medicare beneficiaries at Hospital Service Area (HSA), Hospital Referral Region (HRR) and State level for the year 2012. To calculate annual office visits to primary care clinicians, to general internists and family physicians and nurse practitioners are included. To calculate the rate (a proportion), the event (the numerator) was a beneficiary with at least one primary care clinician visit. These rates were calculated for the year 2012.\n- [Medicare Beneficiary Short Stay Hospital Charges and Discharges](https://www.johnsnowlabs.com/marketplace/medicare-beneficiary-short-stay-hospital-charges-and-discharges)\n\t- The dataset gives information on the Discharges, Total Days of Care, Total Charges, and Program Payments for Medicare Beneficiaries Discharged from Short-Stay Hospitals, by Type of Entitlement for the Calendar Years 1972-2011. Here the type of entitlement is for Aged Beneficiaries.\n- [Outpatient Utilization and Payment Data 2013](https://www.johnsnowlabs.com/marketplace/outpatient-utilization-and-payment-data-2013)\n\t- The dataset includes estimated hospital-specific charges for select Ambulatory Payment Classification (APC) Groups paid under the Medicare Outpatient Prospective Payment System (OPPS) for Calendar Year 2013.\n- [Outpatient Utilization and Payment Data 2014](https://www.johnsnowlabs.com/marketplace/outpatient-utilization-and-payment-data-2014)\n\t- The dataset includes estimated hospital-specific charges for select Ambulatory Payment Classification (APC) Groups paid under the Medicare Outpatient Prospective Payment System (OPPS) for Calendar Year 2014.\n- [Outpatient Utilization and Payment Data 2015](https://www.johnsnowlabs.com/marketplace/outpatient-utilization-and-payment-data-2015)\n\t- The dataset includes estimated hospital-specific charges for select Ambulatory Payment Classification (APC) Groups paid under the Medicare Outpatient Prospective Payment System (OPPS) for Calendar Year 2015.\n- [Outpatient Utilization and Payment Data 2016](https://www.johnsnowlabs.com/marketplace/outpatient-utilization-and-payment-data-2016)\n\t- The dataset includes estimated hospital-specific charges for select Ambulatory Payment Classification (APC) Groups paid under the Medicare Outpatient Prospective Payment System (OPPS) for Calendar Year 2016.\n- [Outpatient Utilization and Payment Data 2017](https://www.johnsnowlabs.com/marketplace/outpatient-utilization-and-payment-data-2017)\n\t- The dataset includes estimated hospital-specific charges for select Ambulatory Payment Classification (APC) Groups paid under the Medicare Outpatient Prospective Payment System (OPPS) for Calendar Year 2017.\n- [Outpatient Utilization and Payment Data 2018](https://www.johnsnowlabs.com/marketplace/outpatient-utilization-and-payment-data-2018)\n\t- The dataset includes estimated hospital-specific charges for select Ambulatory Payment Classification (APC) Groups paid under the Medicare Outpatient Prospective Payment System (OPPS) for Calendar Year 2018.\n- [Outpatient Utilization and Payment Data 2019](https://www.johnsnowlabs.com/marketplace/outpatient-utilization-and-payment-data-2019)\n\t- The dataset includes estimated hospital-specific charges for select Ambulatory Payment Classification (APC) Groups paid under the Medicare Outpatient Prospective Payment System (OPPS) for Calendar Year 2019.\n- [Outpatient Utilization and Payment Data 2020](https://www.johnsnowlabs.com/marketplace/outpatient-utilization-and-payment-data-2020)\n\t- The dataset includes estimated hospital-specific charges for select Ambulatory Payment Classification (APC) Groups paid under the Medicare Outpatient Prospective Payment System (OPPS) for Calendar Year 2020.\n- [Rates of Preventable Hospitalizations by County](https://www.johnsnowlabs.com/marketplace/rates-of-preventable-hospitalizations-by-county)\n\t- The dataset contains hospitalization counts and rates, statewide and by county, for 12 ambulatory care sensitive conditions plus 4 composite measures.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated\n- Data Updates\n - Data updates support replace-on-update: outdated foreign keys are deprecated, not deleted\n\n**Our data is curated and enriched by domain experts** \n\n Each dataset is manually curated by our team of doctors, pharmacists, public health & medical billing experts:\n- Field names, descriptions, and normalized values are chosen by people who actually understand their meaning\n- Healthcare & life science experts add categories, search keywords, descriptions and more to each dataset\n- Both manual and automated data enrichment supported for clinical codes, providers, drugs, and geo-locations\n- The data is always kept up to date – even when the source requires manual effort to get updates\n- Support for data subscribers is provided directly by the domain experts who curated the data sets\n- Every data source’s license is manually verified to allow for royalty-free commercial use and redistribution.\n\n**Need Help?**\nIf you have questions about our products, contact us at [info@johnsnowlabs.com](mailto:info@johnsnowlabs.com). \n ","terms_of_service":"https://www.johnsnowlabs.com/healthcare-data-library-eula/","documentation_link":"https://www.johnsnowlabs.com/marketplace/hospital-treatment-events-costs-data-package","contact_email":"support@johnsnowlabs.com","privacy_policy_link":"https://www.johnsnowlabs.com/privacy-policy/","embedded_notebook_file_infos":[{"id":"4d660365-17ed-459d-82fe-b922af7091be","marketplace_file_type":"EMBEDDED_NOTEBOOK","file_parent":{"parent_id":"dac99d10-281b-42cc-9ffb-8656e28961f0","file_parent_type":"LISTING"},"mime_type":"text/html","download_link":"https://marketplace-file-storage-prod.s3-fips.us-east-1.amazonaws.com/EMBEDDED_NOTEBOOK/LISTING_dac99d10-281b-42cc-9ffb-8656e28961f0/4d660365-17ed-459d-82fe-b922af7091be?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEGgaDnVzLWVhc3QtMS1maXBzIkYwRAIgbC8sWIoYaHR8y7Wq86nPb0pufBl4%2BM4PWpeN%2B5PW9VQCIG7BhAVBDCWTpxFDLeuKYm%2Ff1JLz4iccPMzter0YdhgWKqYECDAQABoMNDE0MzUxNzY3ODI2IgxNAJrHbXaZG20jNfMqgwQyGEb4TsoVOoNvJ%2BaA%2FZ7zeu8hGcHqCeNfblP%2FQo5g6q2aRNtpwtPRKMlhYcIE%2FUQaNT1Lqmyptu32VUQTpzjTTxRVmJrDPkheSCkV06XQ%2FpTMKjU8CC7X1b%2FqYf8CrBwZT7Cv4C5WPjEET38f2A79h0xV6bZgPy28zQWrSeIJ9w6AfZBxiE24%2FVc08zVA0ivCT6lsy7nuU0wcR%2FMg2C9nd1vaMnQ923VpD21CKfVVYcrNWL2b%2FQ%2BNAO%2BxPjPgjiN6UPQbvbvZxkSdhgrGtecIg9NgqGkMcvM5VLTDtdC1kdeZetfOC2obtA%2BT%2F0gy3tIDo6WM5hcQfw743%2FAU9oaGnRBl%2BwoxRRDCZA%2F4jnPdluICSI7NCr6C3HHOe4CSZJjRihczxPA%2FgQczAVjAPs6CrK9N9pIzt0vNJpGUTgg4AoxftTvGfciDREilSctPIcBk6GwB%2Bzt4wbtNCmEPU%2BFD46rc6CWVQmmLwbKOeHw70ipMVFlOc8NlXkEtGO2vu6H44MstSSc6daOVUYUavQSGZ6jGhEjsP2YGz8KDm9XQotB%2FtyVBqiZV01OKqDmmBJA%2FXjUpSJ8M%2FeJXhe5m%2BKcxzNObtoJRI7jci3Tpt08XZyXZ7%2FyIV8H30ya468X3cfkTXrm972uga3z7iXAdubyEHnQIua%2FuvtwOHILi6O5D1Me2MzCPvcnXBjqTAYDoMWBt%2B3AVFuqSs2dmDQNBnvEhXfMgTedKLD5PJHb3aIl15iBkLaNu36GYnVCp9ZU2lSYcTbY5QwNnNeTfJTNy9bKhwzru6Sg%2BslhN%2BTS0zkTn%2BeLMY4tHgPEtEZ2cp%2FCk6GwqswvnYVKHjwO8q%2B1Mx8lc08fVEfarE9dke1GGFqoddx%2Bq%2BZUyHbDlAVfrCmf0yw%3D%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20261110T000135Z&X-Amz-SignedHeaders=host&X-Amz-Expires=86400&X-Amz-Credential=ASIAWA6KKHEJORWBH4N3%2F20261110%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=c0dc9b8e6c2e1fe5808ec427cd8202e47b8eaf4bb199c542a5c4260305991d24","created_at":1682537752479,"updated_at":1682537752479,"display_name":"EDA Notebook","status":"FILE_STATUS_PUBLISHED"}],"assets":["ASSET_TYPE_DATA_TABLE"]},"provider_summary":{"name":"John Snow Labs","description":"John Snow Labs, an AI and NLP for healthcare company, provides state-of-the-art software, models, and data to help healthcare and life science organizations build, deploy, and operate AI projects."},"popularity":98},{"id":"1d5a1fe2-e250-4490-b26a-5220362db5e0","summary":{"name":"Job Market Data - 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The dataset is updated daily and tracks when new jobs are added and when they are removed.\n\nTables included:\n- Job records\n- Job descriptions\n- Point-in-time company reference\n- Company analytics\n- Ticker analytics\n- Additional reference data and identifiers\n\n**Use cases**:\n\n**Quantitative Analysis**\nMany of the world’s largest and most successful systematic hedge funds use LinkUp’s proprietary data to generate excess returns.\n\nJob listings and, conversely, removals have been demonstrated to be a high-frequency indicator of overall company performance and forward-looking employer plans for their workforce needs.\n\nWhen correlating LinkUp job listing data to portfolio performance, Refinitiv analysts found that portfolios with stocks in the top quintile of job signals outperformed the benchmark compared to other portfolios with stocks in the bottom quintile.\n\n**Fundamental Analysis**\nBecause job listings indicate a company’s intent to hire, incorporating real-time job listing data into predictive models may increase visibility into a company’s future performance.\n\nLinkUp’s data can offer insight into fundamental drivers such as same-store sales and new product development or new strategic initiatives or business lines. On the other side, LinkUp’s data has been used to get ahead of bankruptcies and analyst downgrades.\n\nExamples:\n$HNNMY: When comparing active job listing counts with same-store sales for H&M across time, you can observe a strong relationship between changes in active job listings and trends in same-store sales.\n$MGM: Back in early 2020, when others were decreasing hiring, MGM bet big on iGaming, and LinkUp observed a massive spike in job listings for those types of occupations, before BetMGM was announced in late 2020.\n\n**Macro Analysis**\nLinkUp’s data is highly correlated with, and predictive of, JOLTS and NFP numbers. We publish our JOLTS and NFP predictions a month before the BLS releases its numbers. Our data has also been used in CPI forecasts and also economic forecasts.\n\nOur data can help understand trends across geography, industries, and occupations. By filtering our data by the geographic labels included in job listings, we can track how labor demand has changed over time at the state level or compare local labor markets in real-time.\n\nLinkUp’s unique job listing data can provide a glimpse into labor demand at both the macro and micro levels. Organizing job listings according to the North American Industry Classification System (NAICS) categories, for example, allows for increased visibility into which industries are driving job growth or how labor demand is distributed across the economy.\n\n**Economic Impact Analysis**\nOur data capture how the labor landscapes of cities and states are changing, generating data-driven insights into which cities may be showing promising growth in a particular industry or occupation, or which states may be showing signs of a slump.\n\n**Market Analysis**\nLinkUp delivers leading talent management and corporate intelligence solutions. Our data helps corporations and consultants with:\n\n- Competitive intelligence\n- Supply chain sourcing and monitoring\n- Workforce planning and development\n- Talent intelligence\n- People Analytics\n- Real estate (site selection, population mapping)\n\n\n**Staffing**\nUpdated daily, our custom feeds can be leveraged for both strategic and tactical uses:\n- Assessing and predicting labor demand, growth, and decline\n- Identify real-time opportunities by tracking changes at the location, company, and\nindustry level\n- Create powerful competitive intelligence analyses\n- Provide high-quality job content to users with no redirects or sign-ups\n- Increase your company’s SEO & SEM efforts\n\nBecause our job listings come directly from company websites, our job feeds provide optimized user experiences, better page ranking, and click-through rates, and help to mitigate bias in job listings commonly found in feeds hosted by third-parties.\n\n\n**Product details**:\n- Historical start date: 2007\n- Total Historical Job Count: 230,000,000\n- Monthly Active Job Count (12 month range low to high): 7,000,000 - 10,000,000\n- Countries: 195\n- Companies: 60,000\n- Tickers: 6,000\n- Sectors and Industries: All\n- Unique Job Attributes: 14\n- Unique Company Attributes: 17\n\n","terms_of_service":"https://www.linkup.com/terms-of-use#:~:text=General%20Rules%20for%20Use%20of,your%20access%20to%20the%20Services.","documentation_link":"https://data.support.linkup.com/kb/article/23-linkup-raw-overview-user-guide/","privacy_policy_link":"https://www.linkup.com/privacy-policy#:~:text=LinkUp%20gathers%20and%20retains%20personal,the%20email%20address%20you%20provide.","geographical_coverage":"[\"Global\"]","update_frequency":{"interval":1,"unit":"DAILY"},"collection_date_start":1186002879138,"data_source":"direct sourcing from employer websites","assets":["ASSET_TYPE_DATA_TABLE"],"license":"","tags":[{"tag_name":"LISTING_TAG_TYPE_TASK"}]},"provider_summary":{"name":"LinkUp","description":"LinkUp combines more than 20 years of experience in human capital management and employment data with proprietary technology that indexes millions of job listings daily directly from employer websites around the world. From this unique jobs dataset, LinkUp provides our clients with valuable, actionable insights into the global labor market at a macro, industry, geographic, and individual company level."},"popularity":1613},{"id":"7abf0b66-7e79-4f3a-9aa4-e7067598bb09","summary":{"name":"Geolocet | Points of Interest Data | Grocery Store Locations - Slovakia","subtitle":"Complete GIS-ready dataset of 8,380 Slovakia grocery locations (brands & independents). Optimized for Spark/Databricks.","setting":{"visibility":"PUBLIC"},"created_at":1780638999219,"updated_at":1780639014255,"categories":["GEOSPATIAL","RETAIL"],"listingType":"PERSONALIZED","provider_id":"5d3ddc17-a7e8-4794-a760-13587e1de2c6"},"detail":{"description":"# **All Supermarket Locations in Slovakia: Complete Geographic Dataset**\n---\n### **Premium GIS-Ready Retail Location Intelligence Data for Databricks Analytics**\n\nThis high-fidelity geospatial dataset provides a complete, exhaustive, and standardized overview of **8,380** verified grocery store, supermarket, and convenience locations across Slovakia. Updated as of **May 30, 2026**, this comprehensive national dataset captures the entire Slovak grocery landscape - encompassing both major branded retail chains and independent local markets. It offers an analysis-ready foundation for full-scale retail site selection, competitive intelligence, logistics optimization, and spatial demographics mapping.\n\nFully compatible with **Apache Spark**, **Delta Lake**, and **Databricks Unity Catalog** workflows.\n\n---\n\n## **Complete National Coverage: Branded & Independent Grocers**\nUnlike top-tier only datasets, this exhaustive collection covers maximum geographic depth across all 8 Slovak regions (Kraje) (including highly dense areas like Bratislavský kraj, Žilinský kraj, and Prešovský kraj). It includes thousands of independent local grocers alongside the top national and regional supermarket chains:\n\n- **Major National Supermarkets & Hypermarkets:** \n [Billa (190 locations)](https://geolocet.com/products/slovakia-billa-grocery), \n [Tesco (186 locations)](https://geolocet.com/products/slovakia-tesco-grocery), \n [Kaufland (79 locations)](https://geolocet.com/products/slovakia-kaufland-grocery)\n\n- **Leading Discounters:** \n [Lidl (186 locations)](https://geolocet.com/products/slovakia-lidl-grocery)\n\n- **Cooperative, Regional & Proximity Formats:** \n [Coop (1,903 locations)](https://geolocet.com/products/slovakia-coop-grocery), \n [Milk Agro (186 locations)](https://geolocet.com/products/slovakia-milk-agro-grocery), \n [Terno (111 locations)](https://geolocet.com/products/slovakia-terno-grocery), \n [Koruna (97 locations)](https://geolocet.com/products/slovakia-koruna-grocery)\n\n**Plus over 5,400 independent, regional, and unbranded grocers** fully mapped and standardized across all macro-regions (e.g., Žilinský kraj: 1,272 sites; Prešovský kraj: 1,172 sites; Banskobystrický kraj: 1,124 sites).\n\n---\n\n## **Technical Specifications & Schema Definitions**\nOptimized for zero-friction schema enforcement when reading via `spark.read.format(\"csv\")`. The structured schema below ensures flawless rendering in Databricks notebooks and high accuracy for LLM semantic indexing.\n\n* **`GUID`** (`STRING`): Unique global identifier for deterministic primary key mapping.\n* **`Title`** (`STRING`): Official store name, brand banner, or independent title of the location.\n* **`Latitude`** (`DOUBLE`): Precise WGS84 latitude coordinate for point-in-polygon & spatial joins.\n* **`Longitude`** (`DOUBLE`): Precise WGS84 longitude coordinate for spatial indexing (H3 / S2).\n* **`Street_No` / `Street`** (`STRING`): Normalized building/house number and street name elements.\n* **`City` / `Postal_Code`** (`STRING`): Core postal addresses and primary municipal settlement names.\n* **`Area`** (`STRING`): Local neighborhood or borough associated with the retail point.\n* **`Municipality`** (`STRING`): Slovak municipal administrative boundary designation (Obec / Mesto).\n* **`Region`** (`STRING`): Macro-administrative boundary classification (Kraj, e.g., Bratislavský kraj, Košický kraj).\n* **`Admin_level_1` / `_2`** (`STRING`): Additional local administrative area and district (Okres) identifiers.\n* **`Population`** (`INTEGER`): Contextual demographic population metric attributed to the location.\n* **`Address`** (`STRING`): Full, standardized street address of the location.\n* **`Wheelchair`** (`STRING`): Accessibility flags indicating wheelchair mobility support.\n* **`Popularity_Score`** (`INTEGER`): Relative foot-traffic/rating metric graded on a standardized 1-10 scale.\n* **`Opening_Hours`** (`STRING`): Standard daily operational schedules and structured operating text.\n* **`Phone` / `Website`** (`STRING`): Direct communication data, branch-specific landing URLs, and domain vectors.\n\n---\n\n## **Databricks Workflow & Spatial Analytics Use Cases**\n\n- **Whitespace & Underserved Market Analysis:** Query the full 8,380 locations against population density tables to identify true retail deserts and expansion opportunities untouched by major national networks.\n- **H3 Geospatial Indexing:** Convert the precise WGS84 `Latitude` and `Longitude` fields into Uber H3 spatial hexagons (`h3_longlattocell`) to cross-reference with granular Slovak municipal demographic datasets or local purchasing power data.\n- **Supply Chain & Last-Mile Logistics Optimization:** Combine store location points with regional administrative boundary layers to optimize delivery routes, distribution warehouse placements, and network expansion footprints across Slovakia's varying terrain.\n\n---\n\n## **Data Evaluation & Custom Ingestion**\n\n### **Evaluate with a Free Sample Dataset**\nVerify the structure, column types, and coordinate precision before full implementation. A comprehensive schema sample is available for download in CSV/Parquet-friendly formats.\n\n### **Enterprise Delivery & Tailored Formatting**\nWhile natively available as an analysis-ready CSV for DBFS ingestion, we accommodate enterprise-level data architecture needs. Alternative formats - including **GeoJSON, Esri Shapefiles, PostgreSQL dumps, Excel, or direct Delta sharing profiles** - can be generated. Reach out to our engineering team at **contact@geolocet.com** for integration support or custom European POI data requests.","terms_of_service":"https://geolocet.com/pages/terms-of-use","documentation_link":"","privacy_policy_link":"https://geolocet.com/pages/privacy-policy","assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://geolocet.com/pages/terms-of-use"},"provider_summary":{"name":"Geolocet LTD","description":"Geolocet provides premium geospatial data for retail analysis, GIS, and market planning. We equip businesses with accurate, ready-to-query data to drive spatial analysis, site selection, and customer segmentation.\n\nCore Offerings:\n\nDemographics: Socioeconomic data covering population, age, and employment across diverse geographies.\n\nPoints of Interest (PoI): Datasets spanning retail, healthcare, and transport for catchment analysis.\n\nAdministrative Boundaries: GIS-ready boundaries, including municipalities and postcodes, for territory management.\n\nBrand Locations: Chain location datasets to monitor store networks and market expansion.\n\nWealth Data: Granular economic indicators like purchasing power to identify high-potential markets.\n\nWe source from trusted providers, delivering highly customizable datasets with comprehensive coverage across Europe and worldwide."},"popularity":327},{"id":"f18a4c8b-2400-4c95-a23a-f0857fd6045f","summary":{"name":"SAMPLE Yahoo Data | Yahoo Finance Business Dataset | 500k+ Records","subtitle":"Yahoo Finance business dataset provides information on top traded companies.\n","share":{"name":"yahoo","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1704098742497,"updated_at":1776250360422,"categories":["FINANCIAL"],"listingType":"STANDARD","provider_id":"63866f42-27f8-4a1f-b243-37e7be863700"},"detail":{"description":"Note: This is a FREE SAMPLE dataset that includes 1,000 rows. For expanded datasets, additional records, or continuous data refreshes, please contact the provider.\n\nFREE SAMPLE DATASET - 1,000 Records\n\nThis is a complimentary sample of our comprehensive Yahoo Finance Business Dataset. Please note: this sample includes 1,000 records and represents a static historical snapshot.\n\nNeed MORE records or FRESH, continuously updated data?\nDownload this free sample and our team will contact you within 24 hours to discuss full dataset options and exclusive Databricks Marketplace pricing.\n\n\nThe Yahoo Finance Business Dataset is a comprehensive resource that offers insights into the financial landscape of top-traded companies. This dataset encompasses crucial financial information for each company, including stock ticker details, risk scores, and general company attributes such as location and industry classification.\n\n\nRelevant Use Cases:\n\n1. Customer Sentiment Analysis: Gain valuable insights into user experiences and sentiments related to local businesses, enabling a deeper understanding of customer perceptions.\n\n2.User Interaction Insights: Explore user interactions with businesses and analyze how users engage with and respond to various companies, providing valuable data for strategic decision-making.\n\n3.Review Contextualization: Contextualize business performance by leveraging detailed user feedback, allowing for a nuanced understanding of factors influencing company reputation.\n\n\n\nDataset Insights:\n\n- Financial Metrics:\nExplore key financial indicators such as closing price, previous close, open, bid, ask, day range, week range, and volume for a comprehensive view of each company's market performance.\n\n- Earnings Information:\nAccess earnings dates and currency details, providing insights into financial events and facilitating analysis in different currencies.\n\n- Market Exchange Details:\nExamine the exchange information for each company to understand where their stocks are traded and the associated market dynamics.\n\n- Company Profiles:\nUtilize company summaries and entity types to quickly understand each business, aiding in industry and business classification analysis.\n\n\n\nExample Use Case:\n\nTelecom vs. Retail Companies: This dataset includes a subset focusing on two specific industries: Telecom and Retail. By filtering based on these industries, users can compare the business and financial performance of companies within or across industries. 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So can you!"},"popularity":1232},{"id":"ec35ec75-7b97-4fb2-80f8-32b345e8ca29","summary":{"name":"Silent Compute: A Cryptographic VM","subtitle":"A powerful VM to enable financial institutions to compute and collaborate without sharing data, using cryptography","share":{"name":"share_1","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1736515942926,"updated_at":1742656615064,"categories":["FINANCIAL","SCIENCE_AND_RESEARCH","SECURITY"],"listingType":"STANDARD","provider_id":"4b630912-780f-452a-b996-5cce2e373e03"},"detail":{"description":"## **Overview**\n**Silent Compute** is a powerful suite of APIs designed to empower financial institutions to process and analyze sensitive financial data without the need to move or share that data. Built with advanced cryptographic techniques, **Silent Compute** ensures compliance and security while unlocking valuable insights from financial data. This tool is ideal for fintech companies, banks, and financial institutions working with highly sensitive information, such as transaction data, credit scores, and cross-border payment data.\n\n---\n\n## **How It Works (Key Functionalities Explained)**\n**Silent Compute** leverages state-of-the-art privacy-preserving computational protocols, allowing financial institutions to securely perform various operations on their sensitive data. The tool does not require data to be moved or shared between parties, ensuring that sensitive information stays protected throughout the process. Here's a detailed look at how the suite operates:\n\n1. **Sensitive Data Handling**:\n - **Transaction Data**: Institutions can securely validate transaction details, ensuring compliance with regulatory standards and avoiding fraud or errors, all while keeping transaction data private.\n - **Credit Data**: Financial institutions can perform credit and risk scoring checks without exposing individual credit scores or other sensitive data. This allows for accurate assessments while maintaining full privacy.\n - **Cross-Border Data**: The tool facilitates secure cross-border data computations, enabling institutions to handle international payments, validate compliance, and ensure data privacy in the face of complex global regulations.\n\n2. **Unlocking Financial Data for Key Use Cases**:\n - **Open Banking**: **Silent Compute** enables secure and private data sharing between institutions for Open Banking applications. Financial data can be analyzed and processed without breaching privacy, supporting innovation in financial services.\n - **Compliance Checks**: Financial institutions can perform essential compliance checks (e.g., anti-money laundering, KYC) securely without transferring sensitive data, ensuring regulatory compliance without compromising data privacy.\n - **Credit/Risk Scoring**: Silent Compute allows financial institutions to conduct robust credit and risk scoring without accessing or sharing sensitive individual data. This ensures accurate assessments while safeguarding customer privacy.\n\n3. **Cross-Border Transfers**:\n - **Silent Compute** makes it possible to securely process cross-border transactions, ensuring compliance with international regulations without requiring data to leave the institution’s local environment. The suite supports institutions in securely validating transactions across borders, ensuring both security and compliance.\n\n---\n\n## **Example Use Case: Private Sanction Check (PSC)**\n**Silent Compute** offers a powerful solution for **Private Sanction Check (PSC)**, ensuring that transactions are compliant with local and international regulations, without exposing sensitive data. Here's how **Silent Compute** can be applied:\n\n- **Scenario**: A financial institution needs to verify that a cross-border payment transaction complies with sanctions regulations, but without revealing any transaction details to a third party.\n \n- **Steps**:\n 1. **OB (Originating Bank)** submits the transaction details to **Silent Compute**, ensuring no sensitive data is shared.\n 2. **BB (Beneficiary Bank)** securely inputs its encrypted sanctions list.\n 3. **Silent Compute** processes these inputs using **Multiparty Computation (MPC)** techniques, performing the necessary compliance checks against the sanction list.\n 4. **OB** receives a secure, encrypted result, indicating whether the transaction complies with the relevant sanctions regulations (whether the transaction can proceed or be blocked due to non-compliance).\n\nFor detailed information on how to implement Private Sanction Check (PSC) with Silent Compute, please refer to the **private_sanction_list_check.ipynb** and **private_sanction_list_check_distributed.ipynb** notebook for step-by-step instructions and examples.\n\n---\n## **Example Use Case: Capital Flow Management (CFM)**\n\n**Silent Compute** enables privacy-preserving **Capital Flow Management (CFM)**, ensuring that transactions comply with macroprudential capital controls, while keeping customer and transaction details fully confidential.\n\n**Scenario:** A central bank wants to enforce capital flow limits (e.g., maximum allowed outflows), but neither the bank nor the transacting party wants to reveal sensitive data—like the total capital already moved or the transaction amount.\n\n**Steps:**\n\n1. **OB (Originating Bank)** proposes a new cross-border transaction by submitting encrypted details (e.g., transaction amount, customer identity) via Silent Compute.\n2. **CB (Central Bank)** inputs its private state: a list of prior capital flows for that customer and the jurisdictional capital limit.\n3. **Silent Compute** processes these inputs using secure multiparty computation (MPC), ensuring that:\n - The total of past and proposed capital flows is computed,\n - A secure comparison is made against the limit,\n - No private data is leaked during the process.\n4. **OB** receives a secure verdict indicating whether the transaction is **within** the allowed limit or **exceeds** it—without ever learning the CB’s prior capital flow data, and vice versa.\n\nFor implementation details, please refer to the **cfm.ipynb** notebook for a full walkthrough of the protocol and implementation.\n\n---\n\n**Silent Compute** ensures that all data remains secure throughout this process, enabling compliance checks to be completed without exposing any private data. This use case is ideal for institutions managing cross-border payments or transactions subject to stringent regulatory frameworks.\n\n---\n\n# **Why Choose Silent Compute?**\n\nSilent Compute offers a comprehensive solution tailored for the financial sector, combining cutting-edge cryptography, regulatory compliance, and scalability to meet the demands of modern financial institutions:\n\n- **Privacy-Preserving Computation:** \n Sensitive financial data, including transaction or customer details and personal identifiers, remains securely within your control, ensuring customer and institutional privacy.\n\n- **Regulatory Assurance:** \n Meet local and international compliance requirements such as GDPR, CCPA, DPDPA, and PSD2 while safeguarding sensitive data and maintaining trust.\n\n- **Optimized for Financial Use Cases:** \n Specifically designed for fintech applications, Silent Compute supports Open Banking, Private Sanction Checks (PSC), credit scoring, cross-border collaborations, and more, empowering institutions to innovate responsibly.\n\n- **State-of-the-Art Security:** \n Leveraging advanced cryptographic techniques like Multiparty Computation (MPC), Silent Compute ensures secure, transparent, and tamper-proof data processing without the need to centralize or expose raw data outside the host environment.\n\n- **Scalable and High-Performance:** \n Built to handle high-volume data processing with minimal latency, Silent Compute efficiently scales to meet the needs of both small financial firms and large multinational institutions.\n\nChoose Silent Compute to drive innovation, enhance security, and ensure compliance in an increasingly complex financial landscape.\n\n---\n\nSilent Compute is your ultimate solution for secure, compliant, and privacy-preserving financial data processing. 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The synthetic generation approach eliminates personally identifiable information (PII) and removes the need for consent management or anonymization workflows, enabling safe experimentation and rapid deployment.\n\n## **Use Cases**\n\nThis dataset enables high-value analytics and ML workflows on Databricks:\n\n* **Multimodal Credit Risk Modeling:** Combine tabular risk features with NLP sentiment signals to improve default prediction accuracy.\n* **NLP-Driven Distress Detection:** Train models to detect early financial stress using customer support language patterns.\n* **Thin-File Underwriting Research:** Prototype alternative credit scoring approaches for Gen Z and underbanked consumers.\n* **Model Validation & Benchmarking:** Stress-test imbalance-aware ML pipelines against a realistic 5.4% default rate.\n* **Feature Engineering at Scale:** Develop and test cross-modal feature pipelines using Spark, MLflow, and Delta Lake.\n\n## **Column Dictionary**\n\n* **Transaction_ID** (string): Unique synthetic identifier for each BNPL transaction\n* **User_ID** (string): Unique synthetic customer identifier\n* **Transaction_Date** (string): Date of the BNPL transaction\n* **Customer_Age** (integer): Age of the customer at transaction time\n* **Customer_State** (string): Customer’s U.S. state of residence\n* **Job_Title** (string): Synthetic occupation descriptor\n* **Annual_Income** (integer): Geo-adjusted annual income estimate\n* **FICO_Proxy_Score** (integer): Synthetic proxy for credit score\n* **Merchant_Name** (string): Merchant associated with the BNPL purchase\n* **Category** (string): Purchase category\n* **Loan_Amount** (integer): Principal BNPL loan amount\n* **Term_Length_Months** (integer): Repayment term length\n* **Interest_Rate** (decimal): Applied interest rate\n* **Bureau_Inquiries_Last_12m** (integer): Credit inquiries in last 12 months\n* **Default_Flag** (integer): Binary indicator of default\n* **Customer_Support_Log** (string): Synthetic text interactions (emails, chat, call notes)\n\n## **Keywords**\n BNPL, credit risk, synthetic data, fintech, NLP, multimodal AI, default prediction, thin-file borrowers","terms_of_service":"https://www.aidc.app/sample-terms-of-service","documentation_link":"https://app.aidatacoop.com/datasets/c1d46c7d-a843-4c2b-9c6a-134750df9235#c95f8cdd-f444-4ff7-9d8a-bf4798d119a5","privacy_policy_link":"https://www.aidc.app/faq","embedded_notebook_file_infos":[{"id":"172b10ca-edd4-4a80-8993-829e2dc71c3c","marketplace_file_type":"EMBEDDED_NOTEBOOK","file_parent":{"parent_id":"1ba7f7ec-d99f-4e41-9d12-1004ff600b4a","file_parent_type":"LISTING"},"mime_type":"text/html","download_link":"https://marketplace-file-storage-prod.s3-fips.us-east-1.amazonaws.com/EMBEDDED_NOTEBOOK/LISTING_1ba7f7ec-d99f-4e41-9d12-1004ff600b4a/172b10ca-edd4-4a80-8993-829e2dc71c3c?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEGgaDnVzLWVhc3QtMS1maXBzIkYwRAIgbC8sWIoYaHR8y7Wq86nPb0pufBl4%2BM4PWpeN%2B5PW9VQCIG7BhAVBDCWTpxFDLeuKYm%2Ff1JLz4iccPMzter0YdhgWKqYECDAQABoMNDE0MzUxNzY3ODI2IgxNAJrHbXaZG20jNfMqgwQyGEb4TsoVOoNvJ%2BaA%2FZ7zeu8hGcHqCeNfblP%2FQo5g6q2aRNtpwtPRKMlhYcIE%2FUQaNT1Lqmyptu32VUQTpzjTTxRVmJrDPkheSCkV06XQ%2FpTMKjU8CC7X1b%2FqYf8CrBwZT7Cv4C5WPjEET38f2A79h0xV6bZgPy28zQWrSeIJ9w6AfZBxiE24%2FVc08zVA0ivCT6lsy7nuU0wcR%2FMg2C9nd1vaMnQ923VpD21CKfVVYcrNWL2b%2FQ%2BNAO%2BxPjPgjiN6UPQbvbvZxkSdhgrGtecIg9NgqGkMcvM5VLTDtdC1kdeZetfOC2obtA%2BT%2F0gy3tIDo6WM5hcQfw743%2FAU9oaGnRBl%2BwoxRRDCZA%2F4jnPdluICSI7NCr6C3HHOe4CSZJjRihczxPA%2FgQczAVjAPs6CrK9N9pIzt0vNJpGUTgg4AoxftTvGfciDREilSctPIcBk6GwB%2Bzt4wbtNCmEPU%2BFD46rc6CWVQmmLwbKOeHw70ipMVFlOc8NlXkEtGO2vu6H44MstSSc6daOVUYUavQSGZ6jGhEjsP2YGz8KDm9XQotB%2FtyVBqiZV01OKqDmmBJA%2FXjUpSJ8M%2FeJXhe5m%2BKcxzNObtoJRI7jci3Tpt08XZyXZ7%2FyIV8H30ya468X3cfkTXrm972uga3z7iXAdubyEHnQIua%2FuvtwOHILi6O5D1Me2MzCPvcnXBjqTAYDoMWBt%2B3AVFuqSs2dmDQNBnvEhXfMgTedKLD5PJHb3aIl15iBkLaNu36GYnVCp9ZU2lSYcTbY5QwNnNeTfJTNy9bKhwzru6Sg%2BslhN%2BTS0zkTn%2BeLMY4tHgPEtEZ2cp%2FCk6GwqswvnYVKHjwO8q%2B1Mx8lc08fVEfarE9dke1GGFqoddx%2Bq%2BZUyHbDlAVfrCmf0yw%3D%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20261110T000135Z&X-Amz-SignedHeaders=host&X-Amz-Expires=86400&X-Amz-Credential=ASIAWA6KKHEJORWBH4N3%2F20261110%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=7a99175df07a92364409e6fe312bdda7371ed896860a9b9b82d961247c4d4c5c","created_at":1768602229418,"updated_at":1768602229418,"display_name":"2025_bnpl_risk_nlp_sample_notebook_20260116","status":"FILE_STATUS_PUBLISHED"}],"geographical_coverage":"[\"Global\"]","cost":"FREE","update_frequency":{"interval":1,"unit":"MONTHLY"},"collection_granularity":{"interval":1,"unit":"DAILY"},"collection_date_start":1765498917524,"collection_date_end":1765585322250,"data_source":"This dataset is **100% synthetic**, generated based on real-world data patterns. 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The territorial vintages of the worldwide area boundaries are compatible with our globally consistent and comparable [socio-demographic](https://www.mbi-geodata.com/en/sociodemographic-data/) and [economic](https://www.mbi-geodata.com/en/purchasing-power-overview/) data.\nOur area boundaries for Switzerland are offered at the following levels:\n\n- **Postal Code Level** (Postcode 4-digit): This fine-grained level of detail makes the data suitable for a wide range of applications – for example, enriching customer databases, which are often organized by postal code.\n- **Administrative Level** (Gemeinden): Covers both larger administrative areas (e.g., regions, states, provinces) as well as finer subdivisions such as districts, counties, or municipalities.\n- **Micro Level** (PC 6-digit): Subdivisions of postal code or administrative boundaries, such as census areas or street segments. This is the most granular level for local analysis. | on request\n\n**Use Cases:**\n\n- **Market Analysis**: Use MBI’s boundaries to visualize data on a map, learn about your customers, and uncover untapped market potential.\n- **360-Degree Customer View**: Link boundaries to customer data, sociodemographic, and economic data to gain a holistic understanding of customer needs, behaviors, and potential value.\n- **Location Planning**\n•\tUse MBI’s boundaries to visualize data on a map, learn about your customers\n•\tPlan the provision of services and products across different regions\n•\tDefine sales territories\n•\tVisualize catchment areas and plan actions to reach potential customers\n\n**Additional Insights:**\n[About Area Boundaries](https://www.mbi-geodata.com/en/area-boundaries/)\n\n**Data Structure:**\nThe area boundaries include the following variables:\nCTRYCODE: Two-digit country code by ISO 3166\nNAME: Name of administrative area / Name of the most populous administrative area / Name of the respective micro-area or the next higher level\nADMINCODE / POSTCODE / MICROCODE: Administrative code / Postal code / Micro-code\nWKT: Geography as Well Known Text (lat/long WGS84)\n\n*with only a few specific exceptions","terms_of_service":"https://service-mbi-geodata.com/s/HNp7Z2tws4WDqWd","documentation_link":"https://www.mbi-geodata.com/en/mbi-supplier-manufacturer-worldwide-geodata/europe/switzerland-maps/","privacy_policy_link":"https://www.mbi-geodata.com/en/data-protection/","update_frequency":{"interval":1,"unit":"YEARLY"},"assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://service-mbi-geodata.com/s/A77Md5MoPEmTMDJ"},"provider_summary":{"name":"Michael Bauer International GmbH – MBI Geodata","description":"For over 15 years, Michael Bauer International (MBI) has been a leading provider of globally consistent and comparable geographic data, including more than 3 million postcode areas and other detailed datasets like sociodemographics and purchasing power. 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Data includes the extent of adoption of herbicide-tolerant (HT), insect-resistant (BT), and both traits \"stacked\" genetically engineered (GE) crops. \nThe Center for Nutrition Policy and Promotion (CNPP) Food Prices Database presents the cost of these consumed foods for 2003-04. It shows the actual cost of an apple consumed, the cost of a glass of juice drunk, the cost of lasagna eaten, etc. It also covers data on the average cost of a market basket of nutritious food items relative to income for female-headed households with children under the age of 18, for California, its regions, counties, and cities/towns. The affordability ratio uses data from the U.S. Department of Agriculture and the U.S. Census Bureau. \n\nThe Healthy Eating Index (HEI) is a measure of diet quality that assesses conformance to the Dietary Guidelines for Americans. USDA’s primary use of the HEI is to monitor the diet quality of the U.S. population and the low-income subpopulation. A dataset in this package consists of 20 variables and 6795 observations from 6795 respondents who were surveyed during the National Health and Nutrition Examination Survey 2009-2010 in order to assess the health and nutritional status of adults and children in the United States. The UK Controlled Drugs List dataset includes the most commonly encountered drugs currently controlled under both the Misuse of Drugs Act 1971 (MDA) and the Misuse of Drugs Regulations 2001 (MDR).\n\n**Benefits**\n- Useful for healthcare researchers. useful for agricultural predictions and analysis. easy access controlled drug list in a normalized form.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [Adoption of Herbicide Tolerant and Insect Resistant Crops](https://www.johnsnowlabs.com/marketplace/adoption-of-herbicide-tolerant-and-insect-resistant-crops)\n\t- This dataset covers genetically engineered (GE) varieties of corn, cotton, and soybeans for the U.S. and States. Data includes the extent of adoption of herbicide-tolerant (HT), insect-resistant (Bt), and both traits (\"stacked\") genetically engineered (GE) crops.\n- [CNPP Food Prices Database 2003-2004](https://www.johnsnowlabs.com/marketplace/cnpp-food-prices-database-2003-2004)\n\t- This dataset shows the Center for Nutrition Policy and Promotion (CNPP) Food Prices Database presents the cost of these consumed foods for 2003-2004. 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USDA’s (United States Department of Agriculture) primary use of the HEI is to monitor the diet quality of the U.S. population and the low-income subpopulation. 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It is a useful tool for activities such as looking for new safety concerns that might be related to a marketed product, evaluating a manufacturer's compliance to reporting regulations and responding to outside requests for information. The reports in FAERS are evaluated by clinical reviewers, in the Center for Drug Evaluation and Research (CDER) and the Center for Biologics Evaluation and Research (CBER), to monitor the safety of products after they are approved by FDA. These reports are submitted to FDA by healthcare professionals (such as physicians, pharmacists, nurses and others) and consumers (such as patients, family members, lawyers and others). Healthcare professionals and consumers may also report to the products’ manufacturers who then send these reports to FDA. The datasets in FAERS data package contain many relevant information such as: patient demographics, drug name, dose and route of administration, diagnosis and indication for use, therapy start and end date and patient outcome.\n\n**Benefits**\n- useful for physicians, pharmacists, pharmaceutical companies, medical students and patients (general public).\n- faers database is available on fda’s website in raw form, whereas a jsl customer gets it in clean and normalized form ready to use and create his own custom reports.\n- quarter wise data merged into one file.\n- easy to comprehend for the customers who are unfamiliar with the medical terminology as all abbreviated terms are replaced with full form, unlike faers original datasets which are full of abbreviations.\n- datasets are available for the year 2014, 2015, 2016 and 2017\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [FDA Adverse Events Reporting System Demographics 2016](https://www.johnsnowlabs.com/marketplace/fda-adverse-events-reporting-system-demographics-2016)\n\t- FAERS database is designed to support the FDA’s post-marketing safety surveillance program for drug and therapeutic biologic products. \nThe Demographics data file contains patient demographic and administrative information, a single record for each event report.\n- [FDA Adverse Events Reporting System Drug 2016](https://www.johnsnowlabs.com/marketplace/fda-adverse-events-reporting-system-drug-2016)\n\t- FAERS database is designed to support the FDA’s post-marketing safety surveillance program for drug and therapeutic biologic products. \nThe Drug file contains drug/biologic information for as many medications as were reported for the event (1 or more per event).\n- [FDA Adverse Events Reporting System Drug Indication 2016](https://www.johnsnowlabs.com/marketplace/fda-adverse-events-reporting-system-drug-indication-2016)\n\t- FAERS database is designed to support the FDA’s post-marketing safety surveillance program for drug and therapeutic biologic products. \nThe Indication file contains all \"Medical Dictionary for Regulatory Activities\" (MedDRA) terms coded for the indications for use (diagnoses) for the reported drugs (0 or more per drug per event).\n- [FDA Adverse Events Reporting System Drug Reaction 2016](https://www.johnsnowlabs.com/marketplace/fda-adverse-events-reporting-system-drug-reaction-2016)\n\t- FAERS database is designed to support the FDA’s post-marketing safety surveillance program for drug and therapeutic biologic products. \nThe Reaction file contains all \"Medical Dictionary for Regulatory Activities\" (MedDRA) terms coded for the adverse event (1 or more).\n- [FDA Adverse Events Reporting System Drug Therapy Dates 2016](https://www.johnsnowlabs.com/marketplace/fda-adverse-events-reporting-system-drug-therapy-dates-2016)\n\t- FAERS database is designed to support the FDA’s post-marketing safety surveillance program for drug and therapeutic biologic products. The Drug Therapy Dates file contains drug therapy start dates and end dates for the reported drugs (0 or more per drug per event).\n- [FDA Adverse Events Reporting System Patient Outcome 2016](https://www.johnsnowlabs.com/marketplace/fda-adverse-events-reporting-system-patient-outcome-2016)\n\t- FAERS database is designed to support the FDA’s post-marketing safety surveillance program for drug and therapeutic biologic products. \nThe Outcome file contains patient outcomes for the event (0 or more).\n- [FDA Adverse Events Reporting System Report Source 2016](https://www.johnsnowlabs.com/marketplace/fda-adverse-events-reporting-system-report-source-2016)\n\t- FAERS database is designed to support the FDA’s post-marketing safety surveillance program for drug and therapeutic biologic products. \nThe Report Source file contains report sources for event (0 or more).\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated\n- Data Updates\n - Data updates support replace-on-update: outdated foreign keys are deprecated, not deleted\n\n**Our data is curated and enriched by domain experts** \n\n Each dataset is manually curated by our team of doctors, pharmacists, public health & medical billing experts:\n- Field names, descriptions, and normalized values are chosen by people who actually understand their meaning\n- Healthcare & life science experts add categories, search keywords, descriptions and more to each dataset\n- Both manual and automated data enrichment supported for clinical codes, providers, drugs, and geo-locations\n- The data is always kept up to date – even when the source requires manual effort to get updates\n- Support for data subscribers is provided directly by the domain experts who curated the data sets\n- Every data source’s license is manually verified to allow for royalty-free commercial use and redistribution.\n\n**Need Help?**\nIf you have questions about our products, contact us at [info@johnsnowlabs.com](mailto:info@johnsnowlabs.com). \n ","terms_of_service":"https://www.johnsnowlabs.com/healthcare-data-library-eula/","documentation_link":"https://www.johnsnowlabs.com/marketplace/fda-drug-adverse-events-reporting-system-faers-2016-data-package","contact_email":"support@johnsnowlabs.com","privacy_policy_link":"https://www.johnsnowlabs.com/privacy-policy/","assets":["ASSET_TYPE_DATA_TABLE"]},"provider_summary":{"name":"John Snow Labs","description":"John Snow Labs, an AI and NLP for healthcare company, provides state-of-the-art software, models, and data to help healthcare and life science organizations build, deploy, and operate AI projects."},"popularity":557},{"id":"0f983883-6d85-4620-a7f9-1741c2734201","summary":{"name":"SAMPLE Enriched Skill Taxonomy Data for HR Analytics & Workforce Planning | Skill Taxonomy Data ...","subtitle":"Skill Taxonomy Data covering 37,000+ hard skills, 3,000+ certifications, and 400+ soft skills mapped to 70,000+ ...","share":{"name":"canaria_inc_4c655ce3_5c98_47bd_9a3b_35b9cd719a68_samples_share","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1768711634652,"updated_at":1783433304240,"categories":["OTHER"],"listingType":"STANDARD","provider_id":"24eb2934-4a3f-4aec-b02c-4465c271a025"},"detail":{"description":"Most skill data is just keyword lists. Every mention of Python, SQL, or project management gets extracted and dumped into a flat list, regardless of whether the skill is core to the role or buried in a footnote. Our Skill Taxonomy Data does something different: it assigns weighted relevance scores to every extracted skill so you know what employers actually require, not just what they mention.\n\nThis Skill Taxonomy Data is built from 600M+ US job postings and covers 37,000+ hard skills, 3,000+ certifications, and 400+ soft skills mapped to 70,000+ normalized job titles. Whether you are building a workforce intelligence platform, conducting skills gap analysis, or training models on structured labor market data, Skill Taxonomy Data gives you contextual skill intelligence at scale.\n\nWhat This Skill Taxonomy Data Covers\n\nThis Skill Taxonomy Data is sourced from 600M+ US job postings processed through AI and NLP pipelines. Every record includes:\n\n• Normalized job title (70,000+ unique titles standardized from 20M+ raw)\n• Hard skills: up to 20 per title from 37,000+ unique skills with weighted relevance scores\n• Soft skills: up to 10 per title from 400+ unique categories\n• Certifications: 3,000+ unique credentials mapped per title\n• Qualification levels across 8 standardized categories\n• Skill hierarchy and definitions for each extracted skill\n• Seniority level and work modality classification per posting\nThis Skill Taxonomy Data covers the United States from 2022 to present, updated monthly.\n\nWhat You Can Do With This Skill Taxonomy Data\n\nHR Analytics and Talent Management\n\nHR and talent teams use Skill Taxonomy Data to understand what employers actually require, not just what they list, separating core skills from peripheral mentions through weighted relevance scores.\n\n• Identify which hard skills and certifications drive salary premiums using Skill Taxonomy Data relevance weights\n• Benchmark job descriptions against market standards using Skill Taxonomy Data skill distributions\n• Analyze competitor hiring skill requirements across roles and geographies with Skill Taxonomy Data\n• Track shifts in skill demand over time using Skill Taxonomy Data historical snapshots\nSkills Gap Analysis and Workforce Planning\n\nWorkforce planning teams and economic development organizations use Skill Taxonomy Data to map employer demand to labor supply, identify skills gaps, and build evidence for grant applications and regional economic strategy.\n\n• Identify in-demand skills and emerging occupations across regions using Skill Taxonomy Data records\n• Map skill gaps between employer demand and labor supply with Skill Taxonomy Data\n• Build evidence for workforce grant applications backed by Skill Taxonomy Data employer demand signals\n• Inform sector partnership strategies by mapping skill requirements to key industries using Skill Taxonomy Data\nLearning and Development\n\nL&D teams use Skill Taxonomy Data to design training programs aligned with real employer demand rather than internal assumptions, validating which skills and credentials carry the most market weight.\n\n• Design training curricula aligned with Skill Taxonomy Data employer skill demand signals\n• Prioritize certification programs by identifying the highest-weighted Skill Taxonomy Data credentials\n• Validate internal L&D investments against external Skill Taxonomy Data skill frequency benchmarks\n• Identify skills with the fastest growth trajectories using Skill Taxonomy Data trend data\nCareer Pathing and Recruiting Intelligence\n\nRecruiting teams use Skill Taxonomy Data to map career progressions, build role-to-skill frameworks, and identify skills that differentiate senior from junior candidates across 70,000+ normalized titles.\n\n• Map skill adjacencies and career progression paths using Skill Taxonomy Data normalized titles\n• Distinguish core skills from peripheral skills in candidate evaluation using Skill Taxonomy Data weights\n• Build role-to-skill frameworks for structured interviewing using Skill Taxonomy Data field definitions\n• Power ATS and talent intelligence tools with structured Skill Taxonomy Data skill and certification fields\nData Science and AI Applications\n\nData science and ML teams use Skill Taxonomy Data as structured training input for NLP models, job matching systems, and labor market research.\n\n• Train NLP models on skill relationships and job-to-candidate matching using Skill Taxonomy Data\n• Fine-tune classification models using Skill Taxonomy Data relevance weights as a supervised signal\n• Conduct longitudinal labor market research with Skill Taxonomy Data historical snapshots\nHow We Build This Skill Taxonomy Data\n\nThe skill taxonomy model is the core extraction engine for this Skill Taxonomy Data. It uses Aho-Corasick extraction with NLP-based relevance filtering to remove false positives, then assigns weighted relevance scores based on frequency, uniqueness, and role context. This makes Skill Taxonomy Data a contextual skill map, not a flat keyword dump. The model produces 37,000+ distinct hard skills, 3,000+ certifications, and 400+ soft skills across all records.\n\nThe title taxonomy model normalizes every Skill Taxonomy Data job title into 50,000+ standardized categories from 20M+ raw titles, stripping location noise, qualifiers, and formatting variations for consistent cross-company analysis.\n\nJob category models classify each Skill Taxonomy Data record by seniority (Entry, Mid, Senior, Lead, Executive) and work modality (Remote, Hybrid, Onsite) using LLM-based analysis of the full job description.\n\nOur annotation team validates every Skill Taxonomy Data model output before delivery.\n\nWhat Makes This Skill Taxonomy Data Different\n\n• Weighted relevance scores: Skill Taxonomy Data distinguishes core skills from peripheral mentions, giving you a true signal of employer demand\n• Contextual extraction: Skill Taxonomy Data uses NLP filtering, not plain keyword matching, to remove false positives and irrelevant terms\n• Scale: Skill Taxonomy Data maps 37,000+ hard skills, 3,000+ certifications, and 400+ soft skills across 70,000+ normalized titles\n• Skill hierarchy: Skill Taxonomy Data includes definitions, groupings, and categories for every extracted skill\n• Matchable: Skill Taxonomy Data joins directly with job postings, company profiles, and salary records for multi-dimensional analysis\n• Monthly updates: Skill Taxonomy Data refreshes monthly so skill demand signals reflect current employer activity\nWho Uses This Skill Taxonomy Data\n\n• HR and Talent Acquisition Teams use Skill Taxonomy Data to benchmark job requirements, analyze competitor hiring, and build sharper candidate evaluation frameworks\n• People Analytics Teams use Skill Taxonomy Data to track skill demand trends and build workforce planning and compensation models\n• Learning and Development Teams use Skill Taxonomy Data to design training programs and validate certification priorities against real employer demand\n• Workforce Development Organizations use Skill Taxonomy Data to map in-demand skills, support grant applications, and inform regional economic strategy\n• Recruiting and Staffing Agencies use Skill Taxonomy Data to understand role requirements and build structured candidate sourcing pipelines\n• Data Science and ML Teams use Skill Taxonomy Data as structured training input for NLP models, job matching systems, and skill classification applications\n• HR Tech Companies and B2B Platforms use Skill Taxonomy Data to power skills intelligence APIs, candidate matching tools, and workforce analytics products\nDelivery and Format\n\nSkill Taxonomy Data is delivered in CSV, JSON, or Parquet via AWS S3 or Google Cloud Storage. Custom filters by geography, industry, seniority, job title, skill category, and certification type. Compatible with Snowflake, Databricks, Power BI, Tableau, Salesforce, and most BI platforms.","terms_of_service":"https://decanaria.com/privacy-policy","contact_email":"support@datarade.ai","privacy_policy_link":"https://decanaria.com/privacy-policy","assets":["ASSET_TYPE_DATA_TABLE"]},"provider_summary":{"name":"Canaria Inc.","description":"Canaria is a leading provider of enriched job posting and labor market data. We collect 1B+ job postings from Indeed, LinkedIn and 200,000+ employer career sites and deduplicate them to 400M+ unique jobs, with deepest coverage in the US plus postings from 243 other countries and territories since 2022.\r\n\r\nData arrives analysis-ready: SOC occupation and NAICS industry codes, normalized titles across 99,799 canonical roles, a 43,000+ entry skills and credentials taxonomy, salary estimates backed by 44,000+ benchmark cells with 95% confidence intervals, worker type (W2 vs 1099), stock tickers for listed employers, and a confidence score on every classified field.\r\n\r\nHedge funds, HR tech platforms, workforce planners and researchers choose Canaria when they want enterprise-grade enrichment without an enterprise contract. Delivery by API or as CSV and Parquet files to S3, GCS, Snowflake or SFTP. Public commercial data only, GDPR and CCPA compliant."},"popularity":1283},{"id":"0d1ddb65-1579-423c-87b6-eab5b7f784f5","summary":{"name":"Geolocet | Demographic Data Italy","subtitle":"The dataset contains demographic data Italy at a small area level - Age, Gender, Marital status, Income, and more","setting":{"visibility":"PUBLIC"},"created_at":1700208809216,"updated_at":1752138751645,"categories":["DEMOGRAPHICS","GEOSPATIAL","PUBLIC_SECTOR"],"listingType":"PERSONALIZED","provider_id":"5d3ddc17-a7e8-4794-a760-13587e1de2c6"},"detail":{"description":"## Get access to Italian Demographics Data with [Geolocet](https://geolocet.com)\n\nExplore the richness of Italy's demographic landscape with our Italian Census Data. This meticulously compiled dataset, sourced from official statistical sources, including IstatData, offers a detailed insight into the population landscape of Italy at a small area level. \n\nIf you require data at a less granular level, such as an Uber grid or square grid, please don't hesitate to get in touch with us at *contact@geolocet.com*. We understand that different projects may have varying data needs, and we are here to tailor our data to match your specific requirements.\n\n ## Key Features:\n---\n* **Population Data Italy:** Gain access to the latest and most accurate population statistics for Italy, meticulously organized by small areas.\n\n* **Demographic Insights:** Dive deep into Italy's demographic landscape with data on 54 attributes, including: **gender, age bands, gender crossed with age, marital status, income, and more**, all available at the small area level.\n\n* **Geodemographic Insights:** Unlock geodemographic data Italy-wide to inform location-based decision-making, marketing strategies, and policy formulation.\n\n* **Ready to use:** Our dataset is currently available in CSV format, providing ease of use and compatibility with a wide range of data analysis tools. However, if you prefer a different format, our team is readily available to assist you in obtaining the data in your desired format.\n---\nAdditionally, for those looking to enrich their analysis or project, we offer:\n* **[Administrative Boundaries](https://geolocet.com/pages/administrative-boundaries-data-for-european-countries):** If you require geographical boundaries (polygons) for Italian municipalities, consider exploring our Admin Boundaries product, which seamlessly integrates with this dataset.\n\n* **[Point of Interest (PoI) Data](https://geolocet.com/pages/aggregated-points-of-interest-poi-data):** If you're interested in Points of Interest data, please don't hesitate to contact us for customized datasets to enhance your geographical analysis.\n\n* **[Data Customization](https://geolocet.com/pages/data-services-by-geolocet):** We understand that your project may have specific requirements. Feel free to get in touch with us if you need any modifications or specific data formats such as Excel, JSON, GeoJSON, or Shapefile. We are here to tailor the data to your needs.\n\nMake informed decisions, conduct precise research, and unlock the potential of Italy's demographic landscape with our comprehensive dataset. Trust in the accuracy and reliability of our official statistical data to drive your projects forward.\n\nIf you're looking for data for Italy, [look here](https://geolocet.com/pages/italy) for off-the-shelf or tailor-made datasets to suit your specific requirements.\n\nContact us at *contact@geolocet.com* today to access this invaluable resource and explore Italy's census data like never before.\n\n[Geolocet LTD](https://geolocet.com)\n","terms_of_service":"https://geolocet.com/pages/terms-of-use","documentation_link":"","privacy_policy_link":"https://geolocet.com/pages/privacy-policy","cost":"PAID","update_frequency":{"interval":1,"unit":"YEARLY"},"data_source":"","assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://geolocet.com/pages/terms-of-use"},"provider_summary":{"name":"Geolocet LTD","description":"Geolocet provides premium geospatial data for retail analysis, GIS, and market planning. 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The unit of data column is Dollars and the values are not seasonally adjusted.\n\n\n\n**Data Sources**\n\n\nThe data in this product comes from **FRED**.\n\nShort for Federal Reserve Economic Data, FRED is an online database consisting of hundreds of thousands of economic data time series from scores of national, international, public, and private sources. \nFRED, created and maintained by the Research Department at the Federal Reserve Bank of St. Louis, goes far beyond simply providing data: \nIt combines data with a powerful mix of tools that help the user understand, interact with, display, and disseminate the data. \nIn essence, FRED helps users tell their data stories.\n\nThis product uses data from the FRED® API but is not endorsed or certified by the Federal Reserve Bank of St. Louis.\n\n\n\n- **Source Homepage:** [FRED](https://fred.stlouisfed.org/)\n- **Source Data:** [Median Sales Price of Houses Sold for the United States | FRED](https://fred.stlouisfed.org/series/MSPUS)\n- **License:** [Full FRED® Services Terms of Use](https://fred.stlouisfed.org/legal/#full-fred-terms)\n- **Frequency:** Quarterly\n\n\n\n\n","terms_of_service":"https://rearc-data-public-assets.s3.amazonaws.com/Rearc_Data_DSA.pdf","contact_email":"data@rearc.io","support_link":"https://www.rearc.io/data/","privacy_policy_link":"https://rearc-data-public-assets.s3.amazonaws.com/Rearc+Privacy+Policy.pdf","embedded_notebook_file_infos":[{"id":"88e65528-fc35-47a6-a154-9002d441fad0","marketplace_file_type":"EMBEDDED_NOTEBOOK","file_parent":{"parent_id":"7c067496-ec22-4368-9702-5864edad3e16","file_parent_type":"LISTING"},"mime_type":"text/html","download_link":"https://marketplace-file-storage-prod.s3-fips.us-east-1.amazonaws.com/EMBEDDED_NOTEBOOK/LISTING_7c067496-ec22-4368-9702-5864edad3e16/88e65528-fc35-47a6-a154-9002d441fad0?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEGgaDnVzLWVhc3QtMS1maXBzIkYwRAIgbC8sWIoYaHR8y7Wq86nPb0pufBl4%2BM4PWpeN%2B5PW9VQCIG7BhAVBDCWTpxFDLeuKYm%2Ff1JLz4iccPMzter0YdhgWKqYECDAQABoMNDE0MzUxNzY3ODI2IgxNAJrHbXaZG20jNfMqgwQyGEb4TsoVOoNvJ%2BaA%2FZ7zeu8hGcHqCeNfblP%2FQo5g6q2aRNtpwtPRKMlhYcIE%2FUQaNT1Lqmyptu32VUQTpzjTTxRVmJrDPkheSCkV06XQ%2FpTMKjU8CC7X1b%2FqYf8CrBwZT7Cv4C5WPjEET38f2A79h0xV6bZgPy28zQWrSeIJ9w6AfZBxiE24%2FVc08zVA0ivCT6lsy7nuU0wcR%2FMg2C9nd1vaMnQ923VpD21CKfVVYcrNWL2b%2FQ%2BNAO%2BxPjPgjiN6UPQbvbvZxkSdhgrGtecIg9NgqGkMcvM5VLTDtdC1kdeZetfOC2obtA%2BT%2F0gy3tIDo6WM5hcQfw743%2FAU9oaGnRBl%2BwoxRRDCZA%2F4jnPdluICSI7NCr6C3HHOe4CSZJjRihczxPA%2FgQczAVjAPs6CrK9N9pIzt0vNJpGUTgg4AoxftTvGfciDREilSctPIcBk6GwB%2Bzt4wbtNCmEPU%2BFD46rc6CWVQmmLwbKOeHw70ipMVFlOc8NlXkEtGO2vu6H44MstSSc6daOVUYUavQSGZ6jGhEjsP2YGz8KDm9XQotB%2FtyVBqiZV01OKqDmmBJA%2FXjUpSJ8M%2FeJXhe5m%2BKcxzNObtoJRI7jci3Tpt08XZyXZ7%2FyIV8H30ya468X3cfkTXrm972uga3z7iXAdubyEHnQIua%2FuvtwOHILi6O5D1Me2MzCPvcnXBjqTAYDoMWBt%2B3AVFuqSs2dmDQNBnvEhXfMgTedKLD5PJHb3aIl15iBkLaNu36GYnVCp9ZU2lSYcTbY5QwNnNeTfJTNy9bKhwzru6Sg%2BslhN%2BTS0zkTn%2BeLMY4tHgPEtEZ2cp%2FCk6GwqswvnYVKHjwO8q%2B1Mx8lc08fVEfarE9dke1GGFqoddx%2Bq%2BZUyHbDlAVfrCmf0yw%3D%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20261110T000135Z&X-Amz-SignedHeaders=host&X-Amz-Expires=86400&X-Amz-Credential=ASIAWA6KKHEJORWBH4N3%2F20261110%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=56d76113f2dd166016ead78dee5ec494a3d7b1e9890a230541fc5b26d3b2fa09","created_at":1682452146619,"updated_at":1682452146619,"status":"FILE_STATUS_PUBLISHED"}],"assets":["ASSET_TYPE_DATA_TABLE"]},"provider_summary":{"name":"Rearc","description":"Rearc provides a vast library of data products for Healthcare, Financial Services, and ESG use cases. 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Krutrim, a part of the Ola group, is working on creating the AI computing stack of the future. 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No engineering resources required.\n\n\n> **[Start Free Trial](https://trial.dataplex-consulting.com)** | **[Purchase Full Access](https://checkout.dataplex-consulting.com/b/14A6oAcXFfOw13y9y4bQY01)**\n\nOur automated platform monitors the CDC's entire open data catalog daily, detects new and updated datasets, and delivers production-ready tables directly to your Unity Catalog. Data is processed within 12 hours of publication and organized into **56 topic-specific schemas** covering infectious diseases, chronic conditions, vaccinations, environmental health, vital statistics, and more.\n\n**Pricing:** $565/month with 14-day free trial\n\n---\n\n## What's Included\n\n- **1,300+ datasets** across 56 organized topic schemas\n- **2 billion+ records** spanning public health surveillance, epidemiology, and population health\n- **Daily automated updates** — new data appears within 12 hours of CDC publication\n- **56 topic schemas** organized by subject area (e.g., vaccination data, NCHS statistics, NNDSS surveillance, chronic disease indicators)\n- **Full historical coverage** from 2015 to present\n- **Standardized column naming** and data types across all datasets\n- **Lineage tracking** — every row traces back to its source dataset and batch\n\n### Top Schemas by Coverage\n\n- **NNDSS Data** — 295 tables — Nationally Notifiable Disease Surveillance\n- **NCHS Statistics** — 235 tables — National Center for Health Statistics\n- **Vaccination Data** — 88 tables — Immunization coverage and trends\n- **Public Health Surveillance** — 74 tables — Disease monitoring and outbreak data\n- **CDC Cities (500 Cities/PLACES)** — 64 tables — Local health estimates\n- **CDC Data Catalog** — 61 tables — Cross-cutting health indicators\n- **Other Data** — 60 tables — Specialized public health topics\n\n---\n\n## Why Buy vs. Build?\n\nBuilding and maintaining CDC data pipelines in-house means your engineering team is responsible for:\n\n- **Monitoring 1,300+ CDC endpoints** for new data, schema changes, and retracted datasets\n- **Handling rate limits, API failures, and retry logic** across government data sources\n- **Running daily ETL jobs** on provisioned compute to extract, transform, and load data\n- **Debugging pipeline failures** — including weekends and holidays when CDC publishes updates\n- **Managing schema evolution** as the CDC adds, renames, or removes columns\n- **Building and maintaining quality checks** to catch data anomalies before they reach analysts\n\nWith this listing, all of that is handled for you. Your team can focus on **analysis, not infrastructure**.\n\n**Estimated build cost to replicate:** 2–3 senior data engineers × 6+ months, plus ongoing maintenance.\n**Cost with this listing:** $565/month — less than a single day of engineering time.\n\n---\n\n## Use Cases\n\n### Population Health Analytics\nTrack disease prevalence, mortality trends, and health disparities across geographies. Combine CDC surveillance data with your clinical or claims datasets to identify at-risk populations and measure intervention outcomes.\n\n### Life Sciences & Pharma\nAccess real-world evidence from CDC surveillance systems including FAERS-adjacent drug safety signals, vaccination coverage by demographics, and chronic disease burden data. Support regulatory submissions, pharmacovigilance, and market research with continuously updated public health data.\n\n### Health Plans & Payers\nBenchmark population health metrics across service areas using CDC PLACES data (500+ health indicators at county and census tract level). Identify high-cost chronic disease concentrations and design targeted care management programs.\n\n### Government & Policy Research\nAnalyze NNDSS notifiable disease trends, NCHS vital statistics, and environmental health indicators for evidence-based policy development. Access standardized datasets spanning a decade of public health reporting.\n\n---\n\n## Featured Datasets\n\n- **ABCs (Active Bacterial Core surveillance)** — Invasive bacterial disease incidence across surveillance sites\n- **NNDSS Weekly Tables** — Nationally notifiable disease case counts by jurisdiction\n- **BRFSS (Behavioral Risk Factor Surveillance)** — Health-related risk behaviors and chronic conditions survey\n- **500 Cities / PLACES** — Local health estimates for counties and census tracts\n- **NCHS Vital Statistics** — Birth, death, and mortality data from national vital records\n- **COVID-19 Case Surveillance** — Detailed case-level COVID-19 public use data\n- **Vaccination Coverage (NIS)** — National Immunization Survey data by demographics\n- **CDC Wonder Mortality** — Detailed mortality statistics by cause, demographics, geography\n- **Chronic Disease Indicators** — 200+ indicators across 35+ chronic disease topics\n- **Environmental Health Tracking** — Environmental hazards and health outcome indicators\n\n---\n\n## Data Quality & Updates\n\n- **Update frequency:** Daily automated pipeline\n- **Processing time:** Within 12 hours of CDC publication\n- **Historical coverage:** January 2015 – present\n- **Quality checks:** Row count validation, schema drift detection, null rate monitoring\n- **Lineage:** Every record includes BATCH_ID linking to source dataset metadata\n- **Schema management:** Automatic detection and handling of CDC schema changes\n- **Monitoring:** 24/7 pipeline health monitoring with automated alerting\n\n---\n\n## Getting Started\n\n### Free Trial\nRequest access to start a **14-day free trial** with full access to all 1,300+ datasets. Explore the data, run queries, and validate the product fits your use case — no commitment required.\n\n### Full Access\nAfter trial, continue with a monthly subscription at **$565/month**. Includes:\n- All current and future CDC datasets\n- Daily automated updates\n- Full historical backfill\n- Schema and lineage metadata\n- Email support from the Dataplex data engineering team\n\n---\n\n## Example Queries\n\n- **Count datasets by topic schema:** Query information_schema.tables filtered to cdc_dwv_% schemas grouped by table_schema\n- **Explore vaccination data:** Select from cdc_dwv_vacc_data schema tables\n- **Search by topic:** Query cdc_dwv.datasets and filter description by keyword (e.g., mortality, diabetes, vaccination)\n- **Latest batch data:** Join any data table to cdc_dwv.datasets_batches on batch_id and filter to is_latest_batch = TRUE\n\nSee the included sample notebook for full working query examples.\n\n\n---\n\n## Documentation & Support\n\n- **Data Catalog:** [docs.dataplex-consulting.com/data-catalog/cdc-open-data-product](https://docs.dataplex-consulting.com/data-catalog/cdc-open-data-product)\n- **Support:** support@dataplex-consulting.com\n- **Provider:** [Dataplex Consulting & Data Products](https://www.dataplex-consulting.com)\n\n> **[Start Free Trial](https://trial.dataplex-consulting.com)** | **[Purchase Full Access](https://checkout.dataplex-consulting.com/b/14A6oAcXFfOw13y9y4bQY01)**\n","terms_of_service":"https://www.dataplex-consulting.com/terms","documentation_link":"https://docs.dataplex-consulting.com/data-catalog/cdc-open-data-product","privacy_policy_link":"https://www.dataplex-consulting.com/privacy","embedded_notebook_file_infos":[{"id":"e72ce6ef-b295-4289-acd2-de2a9ba188ae","marketplace_file_type":"EMBEDDED_NOTEBOOK","file_parent":{"parent_id":"9ad35afb-9b10-4ca7-a26b-b812fbc9a3dc","file_parent_type":"LISTING"},"mime_type":"text/html","download_link":"https://marketplace-file-storage-prod.s3-fips.us-east-1.amazonaws.com/EMBEDDED_NOTEBOOK/LISTING_9ad35afb-9b10-4ca7-a26b-b812fbc9a3dc/e72ce6ef-b295-4289-acd2-de2a9ba188ae?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEGgaDnVzLWVhc3QtMS1maXBzIkYwRAIgbC8sWIoYaHR8y7Wq86nPb0pufBl4%2BM4PWpeN%2B5PW9VQCIG7BhAVBDCWTpxFDLeuKYm%2Ff1JLz4iccPMzter0YdhgWKqYECDAQABoMNDE0MzUxNzY3ODI2IgxNAJrHbXaZG20jNfMqgwQyGEb4TsoVOoNvJ%2BaA%2FZ7zeu8hGcHqCeNfblP%2FQo5g6q2aRNtpwtPRKMlhYcIE%2FUQaNT1Lqmyptu32VUQTpzjTTxRVmJrDPkheSCkV06XQ%2FpTMKjU8CC7X1b%2FqYf8CrBwZT7Cv4C5WPjEET38f2A79h0xV6bZgPy28zQWrSeIJ9w6AfZBxiE24%2FVc08zVA0ivCT6lsy7nuU0wcR%2FMg2C9nd1vaMnQ923VpD21CKfVVYcrNWL2b%2FQ%2BNAO%2BxPjPgjiN6UPQbvbvZxkSdhgrGtecIg9NgqGkMcvM5VLTDtdC1kdeZetfOC2obtA%2BT%2F0gy3tIDo6WM5hcQfw743%2FAU9oaGnRBl%2BwoxRRDCZA%2F4jnPdluICSI7NCr6C3HHOe4CSZJjRihczxPA%2FgQczAVjAPs6CrK9N9pIzt0vNJpGUTgg4AoxftTvGfciDREilSctPIcBk6GwB%2Bzt4wbtNCmEPU%2BFD46rc6CWVQmmLwbKOeHw70ipMVFlOc8NlXkEtGO2vu6H44MstSSc6daOVUYUavQSGZ6jGhEjsP2YGz8KDm9XQotB%2FtyVBqiZV01OKqDmmBJA%2FXjUpSJ8M%2FeJXhe5m%2BKcxzNObtoJRI7jci3Tpt08XZyXZ7%2FyIV8H30ya468X3cfkTXrm972uga3z7iXAdubyEHnQIua%2FuvtwOHILi6O5D1Me2MzCPvcnXBjqTAYDoMWBt%2B3AVFuqSs2dmDQNBnvEhXfMgTedKLD5PJHb3aIl15iBkLaNu36GYnVCp9ZU2lSYcTbY5QwNnNeTfJTNy9bKhwzru6Sg%2BslhN%2BTS0zkTn%2BeLMY4tHgPEtEZ2cp%2FCk6GwqswvnYVKHjwO8q%2B1Mx8lc08fVEfarE9dke1GGFqoddx%2Bq%2BZUyHbDlAVfrCmf0yw%3D%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20261110T000135Z&X-Amz-SignedHeaders=host&X-Amz-Expires=86400&X-Amz-Credential=ASIAWA6KKHEJORWBH4N3%2F20261110%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=12b95821b2c22c279b823f0d24d32ef32908fbe572355eabc4cd9e23193c31e3","created_at":1772667957898,"updated_at":1772667957898,"display_name":"04_cdc_showcase","status":"FILE_STATUS_PUBLISHED"}],"geographical_coverage":"[\"United States and Canada\"]","cost":"PAID","update_frequency":{"interval":1,"unit":"DAILY"},"collection_granularity":{"interval":1,"unit":"DAILY"},"collection_date_start":1420155921501,"data_source":"CDC (Centers for Disease Control and Prevention)","size":400.0,"assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://www.dataplex-consulting.com/terms"},"provider_summary":{"name":"Dataplex Consulting & Data Products","description":"Dataplex Consulting & Data Products delivers turnkey, analytics-ready data products that make complex public and commercial data easy to use across modern data platforms. 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Every interaction reads from the full patient record in Databricks, and every outcome writes back as queryable data.\n\n**Use cases**\n- Optimize scheduling and reduce no-shows through proactive, personalized reminders and outreach\n- Support patients between visits with 24/7 conversational health support aligned to custom provider care protocols\n- Automate adherence reminders across medications and care plans\n- Deliver digital health applications on the unified record\n\n**Additional Insights**\nEngagement data is recorded in the lakehouse as a queryable asset, analyzable alongside clinical and claims data.\n\nThe workflows listed are representative; Concurrence builds custom applications for any workflow in this outcome area on an existing Databricks foundation.","terms_of_service":"https://www.concurrence.com/terms-of-service","documentation_link":"https://docs.concurrence.com","privacy_policy_link":"https://www.concurrence.com/privacy","assets":["ASSET_TYPE_DATA_TABLE","ASSET_TYPE_MODEL","ASSET_TYPE_MEDIA","ASSET_TYPE_NOTEBOOK"],"license":"","tags":[{"tag_name":"LISTING_TAG_TYPE_TASK","tag_values":["ASSERTION_STATUS","EMBEDDINGS","ENTITY_RESOLUTION","NAMED_ENTITY_RECOGNITION","QUESTION_ANSWERING","RELATION_EXTRACTION","SUMMARIZATION","TEXT_CLASSIFICATION","TEXT_GENERATION","TRANSLATION","ZERO_SHOT_CLASSIFICATION","CODE_GENERATION","AUTOMATIC_SPEECH_RECOGNITION","TEXT_TO_SPEECH","DOCUMENT_QUESTION_ANSWERING"]}]},"provider_summary":{"name":"Concurrence","description":"Concurrence is the platform for healthcare organizations to deploy custom AI for clinical and operational workflows on a unified data foundation."},"popularity":60},{"id":"9045fdc1-cab9-4a52-9d51-27cb1a4162f0","summary":{"name":"Corporate Linkage Data","subtitle":"Corporate hierarchy: immediate parent, domestic ultimate and global ultimate. 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The full product is delivered under a separate agreement.\n\n**Use cases**\n\n- Rolling up spend, exposure or revenue from subsidiaries to a global ultimate.\n- Counterparty and concentration risk, where the real exposure sits at group level.\n- Deduplicating a customer list that contains several entities of the same group.\n- Territory and account assignment that follows the corporate family rather than the legal entity.\n\n**Product details**\n\n- Dataset represented: entity_relationship_sample, 300 records, 16 columns.\n- Sample fields include CompanyName, Country, SubsidiaryIndicator, HierarchyCode, FamilyMembers, ImmediateParent_RecordID, ImmediateParent_Name, DomesticUltimate_Name and GlobalUltimate_Name.\n- This product joins our other listings on RecordID.\n\nFor more details, refer to the embedded notebook.\n\n**Additional Insights**\n\nThis product contains no ownership percentages and is not a beneficial-ownership or UBO product. 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Easily identify underserved markets with low competition.\n\n**Revenue Cycle Management**\nCompare billed and paid claim amounts to actual contracted rates to ensure that your revenue is accurate. 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It also includes information for Inpatient and Daycase Activity Stays and Episodes by Health Board Residence and Treatment as well as NHS Staffed Beds and Percentage Occupancy and Procedures Performed for Children In An Acute Setting For NHS Scotland.\n\n**Description**\nThis data package contains the Acute Hospital Outpatient Activity By Health Board Residence and Treatment includes information split by location for health board of residence/treatment, hospital and council area, specialty, age and sex, and Scottish Index of Multiple Deprivation (SIMD) for new and Did Not Attend (DNA) appointments; and split by specialty and health board of treatment for return and total appointments and the number of attendances for outpatient activity for acute hospital care services. 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The term \"unintentional injury\" is preferred to \"accidents\" as the latter implies events are inevitable and unavoidable whereas a high proportion of these incidents are now regarded as being preventable. Inpatient and Daycase Activity Stays By Health Board Residence/Treatment includes information split by location for health board of residence/treatment, hospital and council area, specialty, age and sex, and Scottish Index of Multiple Deprivation (SIMD) for the length of stays in the specific hospital. Measures include emergency admissions, elective admissions, transfers, inpatients, day cases, and total inpatients and day cases activity stays for acute hospital care services.\n\nNHS Staffed Beds and Percentage Occupancy includes information split by location for health board of available staffed beds, hospital and council area. Due to the variability in the number of available beds in a ward or specialty, the total number of available beds is calculated by taking the average of the number of beds available in each day of the quarter and percentage occupancy for acute specialties for acute hospital care services. Procedures Performed For Children and Adults in an Acute Setting for NHS Scotland provides information about procedures performed in acute hospitals by treatment across Scotland. It includes statistics which are derived from data collected on discharges from non-obstetric and non-psychiatric hospitals (SMR01) in Scotland.\n\n**Benefits**\n- Useful for acute hospital care services, patients, staff working in health board of residence and treatment. acute hospital activity and nhs beds information database are available on information services division (isd), nhs national services scotland website in raw form, whereas a jsl customer gets it in clean and normalized form ready to use in one place and create his own custom reports. easy to comprehend for the customers who are unfamiliar with the medical terminologies as all abbreviated terms are replaced with full form, unlike acute hospital activity and nhs beds information original datasets which are full of abbreviations.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [Acute Hospital Outpatient Activity By Health Board Residence](https://www.johnsnowlabs.com/marketplace/acute-hospital-outpatient-activity-by-health-board-residence)\n\t- This dataset includes information split by location for health board of residence, hospital and council area, specialty, age and sex, and Scottish Index of Multiple Deprivation (SIMD) for new and Did Not Attend (DNA) appointments; and split by specialty and health board of treatment for return and total appointments and the number of attendances for outpatient activity for acute hospital care services.\n- [Acute Hospital Outpatient Activity By Health Board Treatment](https://www.johnsnowlabs.com/marketplace/acute-hospital-outpatient-activity-by-health-board-treatment)\n\t- This dataset includes information split by location for health board of treatment, hospital and council area, specialty, age and sex, and Scottish Index of Multiple Deprivation (SIMD) for new and Did Not Attend (DNA) appointments; and split by specialty and health board of treatment for return and total appointments and the number of attendances for outpatient activity for acute hospital care services.\n- [Acute Hospital Outpatient Activity Cross Boundary Flow](https://www.johnsnowlabs.com/marketplace/acute-hospital-outpatient-activity-cross-boundary-flow)\n\t- This dataset includes a list of locations for health board of treatment/residence hospital and council area and the number of attendances for outpatient activity for acute hospital care services. Cross boundary flow shows the relationship between where patients live and where they are treated.\n- [Attendances at Accident and Emergency Services in Scottish Hospitals](https://www.johnsnowlabs.com/marketplace/attendances-at-accident-and-emergency-services-in-scottish-hospitals)\n\t- This dataset provides information about statistics on attendances at Accident and Emergency (A&E) services across Scotland. The information includes trends in the number of attendances and performance against the 4-hour standard.\n- [Emergency Hospital Admissions Due to Unintentional Injuries](https://www.johnsnowlabs.com/marketplace/emergency-hospital-admissions-due-to-unintentional-injuries)\n\t- This dataset includes information about the Information Services Division reports on emergency hospital admissions and deaths as a result of unintentional injury. Unintentional injuries can occur in any age group, but children and the elderly are generally more vulnerable.\n- [Inpatient and Daycase Activity Cross Boundary Flow](https://www.johnsnowlabs.com/marketplace/inpatient-and-daycase-activity-cross-boundary-flow)\n\t- This dataset includes a list of locations for health board of treatment/residence, hospital and council area and the number of stays and episodes for inpatient and day case activity for acute hospital care services. Cross boundary flow shows the relationship between where patients live and where they are treated.\n- [Inpatient and Daycase Activity Episodes By Health Board Residence](https://www.johnsnowlabs.com/marketplace/inpatient-and-daycase-activity-episodes-by-health-board-residence)\n\t- This dataset includes information split by location for health board of residence, hospital and council area, specialty, age and sex, and Scottish Index of Multiple Deprivation (SIMD) for episodes and length of stays in the specific hospital. Measures include emergency admissions, elective admissions, transfers, inpatients, day cases, and total inpatients and day cases activity for acute hospital care services.\n- [Inpatient and Daycase Activity Episodes By Health Board Treatment](https://www.johnsnowlabs.com/marketplace/inpatient-and-daycase-activity-episodes-by-health-board-treatment)\n\t- This dataset includes information split by location for health board of treatment, hospital and council area, specialty, age and sex, and Scottish Index of Multiple Deprivation (SIMD) for episodes and length of stays in the specific hospital. Measures include emergency admissions, elective admissions, transfers, inpatients, day cases, and total inpatients and day cases activity for acute hospital care services.\n- [Inpatient and Daycase Activity Stays By Health Board Residence](https://www.johnsnowlabs.com/marketplace/inpatient-and-daycase-activity-stays-by-health-board-residence)\n\t- This dataset includes information split by location for health board of residence, hospital and council area, specialty, age and sex, and Scottish Index of Multiple Deprivation (SIMD) for the length of stays in the specific hospital. Measures include emergency admissions, elective admissions, transfers, inpatients, day cases, and total inpatients and day cases activity stays for acute hospital care services.\n- [Inpatient and Daycase Activity Stays By Health Board Treatment](https://www.johnsnowlabs.com/marketplace/inpatient-and-daycase-activity-stays-by-health-board-treatment)\n\t- This dataset includes information split by location for health board of treatment, hospital and council area, specialty, age and sex, and Scottish Index of Multiple Deprivation (SIMD) for the length of stays in the specific hospital. Measures include emergency admissions, elective admissions, transfers, inpatients, day cases, and total inpatients and day cases activity stays for acute hospital care services.\n- [NHS Staffed Beds and Percentage Occupancy](https://www.johnsnowlabs.com/marketplace/nhs-staffed-beds-and-percentage-occupancy)\n\t- This dataset includes information split by location for health board of available staffed beds, hospital and council area. Due to the variability in the number of available beds in a ward or specialty, the total number of available beds is calculated by taking the average of the number of beds available in each day of the quarter and percentage occupancy for acute specialties for acute hospital care services.\n- [Outpatient Consultant Led Activity Annual Trends](https://www.johnsnowlabs.com/marketplace/outpatient-consultant-led-activity-annual-trends)\n\t- This dataset includes a list of locations for New and Return attendances and Did Not Attend (DNA) rates by specialty for Scotland and National Health Service (NHS) Boards of treatment for financial years 2012-13 to 2021-22.\n- [Procedures Performed For Children In An Acute Setting For NHS Scotland](https://www.johnsnowlabs.com/marketplace/procedures-performed-for-children-in-an-acute-setting-for-nhs-scotland)\n\t- This dataset provides information about procedures performed for children in an acute setting across Scotland. It includes admission type, operations group and episodes for operation group information for acute care hospital services.\n- [Procedures Performed in Acute Hospitals by Treatment Setting](https://www.johnsnowlabs.com/marketplace/procedures-performed-in-acute-hospitals-by-treatment-setting)\n\t- This dataset provides information about NHS procedures performed in acute hospitals by treatment across Scotland. It includes procedure, procedure type and admission type information for acute care hospital services.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated\n- Data Updates\n - Data updates support replace-on-update: outdated foreign keys are deprecated, not deleted\n\n**Our data is curated and enriched by domain experts** \n\n Each dataset is manually curated by our team of doctors, pharmacists, public health & medical billing experts:\n- Field names, descriptions, and normalized values are chosen by people who actually understand their meaning\n- Healthcare & life science experts add categories, search keywords, descriptions and more to each dataset\n- Both manual and automated data enrichment supported for clinical codes, providers, drugs, and geo-locations\n- The data is always kept up to date – even when the source requires manual effort to get updates\n- Support for data subscribers is provided directly by the domain experts who curated the data sets\n- Every data source’s license is manually verified to allow for royalty-free commercial use and redistribution.\n\n**Need Help?**\nIf you have questions about our products, contact us at [info@johnsnowlabs.com](mailto:info@johnsnowlabs.com). \n 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Contact [Sales](mailto:Databricks-Marketplace-listing@johnsnowlabs.com) at John Snow Labs for a free 15 day trial license key or purchase our most popular annual subscription license.\n2. Import this listing.\n3. 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Access verified business emails, direct ...","share":{"name":"lakeb2b_8c6a080a_8c4e_40a0_a885_cf311c7c239c_samples_share","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1768307782207,"updated_at":1793204118379,"categories":["OTHER","FINANCIAL"],"listingType":"STANDARD","provider_id":"258841ce-a620-465c-9af8-a230e2bac7e2"},"detail":{"description":"\uD835\uDC06\uD835\uDC25\uD835\uDC28\uD835\uDC1B\uD835\uDC1A\uD835\uDC25 \uD835\uDC01\uD835\uDFD0\uD835\uDC01 \uD835\uDC0C\uD835\uDC1A\uD835\uDC2B\uD835\uDC24\uD835\uDC1E\uD835\uDC2D\uD835\uDC22\uD835\uDC27\uD835\uDC20 \uD835\uDC03\uD835\uDC1A\uD835\uDC2D\uD835\uDC1A | \uD835\uDC06\uD835\uDC03\uD835\uDC0F\uD835\uDC11-\uD835\uDC02\uD835\uDC28\uD835\uDC26\uD835\uDC29\uD835\uDC25\uD835\uDC22\uD835\uDC1A\uD835\uDC27\uD835\uDC2D \uD835\uDC02\uD835\uDC28\uD835\uDC2B\uD835\uDC29\uD835\uDC28\uD835\uDC2B\uD835\uDC1A\uD835\uDC2D\uD835\uDC1E \uD835\uDC02\uD835\uDC28\uD835\uDC27\uD835\uDC2D\uD835\uDC1A\uD835\uDC1C\uD835\uDC2D \uD835\uDC08\uD835\uDC27\uD835\uDC2D\uD835\uDC1E\uD835\uDC25\uD835\uDC25\uD835\uDC22\uD835\uDC20\uD835\uDC1E\uD835\uDC27\uD835\uDC1C\uD835\uDC1E\n\n\uD835\uDC0F\uD835\uDC2B\uD835\uDC28\uD835\uDC1D\uD835\uDC2E\uD835\uDC1C\uD835\uDC2D \uD835\uDC0E\uD835\uDC2F\uD835\uDC1E\uD835\uDC2B\uD835\uDC2F\uD835\uDC22\uD835\uDC1E\uD835\uDC30\nAccelerate your global go-to-market strategy with the world’s most accurate, fully compliant database of B2B decision-makers. This premium dataset delivers comprehensive firmographic profiles, verified direct-dial phone numbers, and up-to-date business email addresses across \uD835\uDFD0\uD835\uDFCE\uD835\uDFCE+ \uD835\uDC1C\uD835\uDC28\uD835\uDC2E\uD835\uDC27\uD835\uDC2D\uD835\uDC2B\uD835\uDC22\uD835\uDC1E\uD835\uDC2C and territories. Built specifically for enterprise revenue teams, marketing agencies, and data-driven platforms, our global B2B marketing data bridges the gap between massive scale and high-precision accuracy.\n\nEvery record in this dataset undergoes rigorous automated and human verification loops to ensure a \uD835\uDFD7\uD835\uDFD3%+ \uD835\uDC1A\uD835\uDC1C\uD835\uDC1C\uD835\uDC2E\uD835\uDC2B\uD835\uDC1A\uD835\uDC1C\uD835\uDC32 \uD835\uDC2B\uD835\uDC1A\uD835\uDC2D\uD835\uDC1E on core contact fields. By removing structural data decay and stale contact coordinates, we help our partners minimize bounce rates, protect domain sender reputations, and maximize cold outreach conversion metrics.\n\n\uD835\uDC02\uD835\uDC28\uD835\uDC2B\uD835\uDC1E \uD835\uDC03\uD835\uDC1A\uD835\uDC2D\uD835\uDC1A \uD835\uDC05\uD835\uDC1E\uD835\uDC1A\uD835\uDC2D\uD835\uDC2E\uD835\uDC2B\uD835\uDC1E\uD835\uDC2C & \uD835\uDC13\uD835\uDC1E\uD835\uDC1C\uD835\uDC21\uD835\uDC27\uD835\uDC22\uD835\uDC1C\uD835\uDC1A\uD835\uDC25 \uD835\uDC00\uD835\uDC2D\uD835\uDC2D\uD835\uDC2B\uD835\uDC22\uD835\uDC1B\uD835\uDC2E\uD835\uDC2D\uD835\uDC1E\uD835\uDC2C\n\n\uD835\uDC06\uD835\uDC25\uD835\uDC28\uD835\uDC1B\uD835\uDC1A\uD835\uDC25 \uD835\uDC06\uD835\uDC1E\uD835\uDC28\uD835\uDC20\uD835\uDC2B\uD835\uDC1A\uD835\uDC29\uD835\uDC21\uD835\uDC22\uD835\uDC1C \uD835\uDC12\uD835\uDC1C\uD835\uDC1A\uD835\uDC25\uD835\uDC1E: Comprehensive coverage spanning North America, Europe (EMEA), Asia-Pacific (APAC), and Latin America (LATAM).\n\n\uD835\uDC0C\uD835\uDC2E\uD835\uDC25\uD835\uDC2D\uD835\uDC22-\uD835\uDC0B\uD835\uDC1A\uD835\uDC32\uD835\uDC1E\uD835\uDC2B\uD835\uDC1E\uD835\uDC1D \uD835\uDC15\uD835\uDC1E\uD835\uDC2B\uD835\uDC22\uD835\uDC1F\uD835\uDC22\uD835\uDC1C\uD835\uDC1A\uD835\uDC2D\uD835\uDC22\uD835\uDC28\uD835\uDC27: Continuous AI-driven validation paired with manual data cleansing to maintain strict 95%+ validity thresholds.\n\n\uD835\uDC01\uD835\uDC22-\uD835\uDC0C\uD835\uDC28\uD835\uDC27\uD835\uDC2D\uD835\uDC21\uD835\uDC25\uD835\uDC32 \uD835\uDC03\uD835\uDC1A\uD835\uDC2D\uD835\uDC1A \uD835\uDC11\uD835\uDC1E\uD835\uDC1F\uD835\uDC2B\uD835\uDC1E\uD835\uDC2C\uD835\uDC21\uD835\uDC1E\uD835\uDC2C: The entire database undergoes a complete synchronization loop every 60 days to purge legacy records, track job movements, and update fresh corporate entries.\n\n\uD835\uDC01\uD835\uDC2E\uD835\uDC25\uD835\uDC25\uD835\uDC1E\uD835\uDC2D\uD835\uDC29\uD835\uDC2B\uD835\uDC28\uD835\uDC28\uD835\uDC1F \uD835\uDC11\uD835\uDC1E\uD835\uDC20\uD835\uDC2E\uD835\uDC25\uD835\uDC1A\uD835\uDC2D\uD835\uDC28\uD835\uDC2B\uD835\uDC32 \uD835\uDC02\uD835\uDC28\uD835\uDC26\uD835\uDC29\uD835\uDC25\uD835\uDC22\uD835\uDC1A\uD835\uDC27\uD835\uDC1C\uD835\uDC1E: Fully aligned with GDPR, CCPA, and global privacy mandates. Every record features verifiable opt-in data provenance for secure enterprise usage.\n\n\uD835\uDC02\uD835\uDC28\uD835\uDC26\uD835\uDC29\uD835\uDC2B\uD835\uDC1E\uD835\uDC21\uD835\uDC1E\uD835\uDC27\uD835\uDC2C\uD835\uDC22\uD835\uDC2F\uD835\uDC1E \uD835\uDC03\uD835\uDC1A\uD835\uDC2D\uD835\uDC1A \uD835\uDC12\uD835\uDC1C\uD835\uDC21\uD835\uDC1E\uD835\uDC26\uD835\uDC1A (\uD835\uDC16\uD835\uDC21\uD835\uDC1A\uD835\uDC2D \uD835\uDC08\uD835\uDC2C \uD835\uDC08\uD835\uDC27\uD835\uDC1C\uD835\uDC25\uD835\uDC2E\uD835\uDC1D\uD835\uDC1E\uD835\uDC1D)\nTo support seamless CRM integration and data lake alignment, this product is delivered with an exhaustive schema mapping. Core entities and fields include:\n\n\uD835\uDFCF. \uD835\uDC08\uD835\uDC27\uD835\uDC1D\uD835\uDC22\uD835\uDC2F\uD835\uDC22\uD835\uDC1D\uD835\uDC2E\uD835\uDC1A\uD835\uDC25 \uD835\uDC0F\uD835\uDC2B\uD835\uDC28\uD835\uDC1F\uD835\uDC1E\uD835\uDC2C\uD835\uDC2C\uD835\uDC22\uD835\uDC28\uD835\uDC27\uD835\uDC1A\uD835\uDC25 \uD835\uDC0C\uD835\uDC1E\uD835\uDC2D\uD835\uDC1A\uD835\uDC1D\uD835\uDC1A\uD835\uDC2D\uD835\uDC1A\n\n\uD835\uDC05\uD835\uDC2E\uD835\uDC25\uD835\uDC25 \uD835\uDC0D\uD835\uDC1A\uD835\uDC26\uD835\uDC1E & \uD835\uDC08\uD835\uDC1D\uD835\uDC1E\uD835\uDC27\uD835\uDC2D\uD835\uDC22\uD835\uDC1F\uD835\uDC22\uD835\uDC1E\uD835\uDC2B: First Name, Last Name, Professional Title, Seniority Level (e.g., C-Suite, VP, Director, Manager).\n\n\uD835\uDC03\uD835\uDC22\uD835\uDC2B\uD835\uDC1E\uD835\uDC1C\uD835\uDC2D \uD835\uDC02\uD835\uDC28\uD835\uDC28\uD835\uDC2B\uD835\uDC1D\uD835\uDC22\uD835\uDC27\uD835\uDC1A\uD835\uDC2D\uD835\uDC1E\uD835\uDC2C: Corporate Email Address (100% verified), Direct Dial Phone Number, Mobile Number (where available), Corporate HQ Phone Extension.\n\n\uD835\uDC12\uD835\uDC28\uD835\uDC1C\uD835\uDC22\uD835\uDC1A\uD835\uDC25 \uD835\uDC02\uD835\uDC28\uD835\uDC28\uD835\uDC2B\uD835\uDC1D\uD835\uDC22\uD835\uDC27\uD835\uDC1A\uD835\uDC2D\uD835\uDC1E\uD835\uDC2C: Professional Profile URL, Department/Functional Role mapping.\n\n\uD835\uDFD0. \uD835\uDC02\uD835\uDC28\uD835\uDC2B\uD835\uDC29\uD835\uDC28\uD835\uDC2B\uD835\uDC1A\uD835\uDC2D\uD835\uDC1E \uD835\uDC05\uD835\uDC22\uD835\uDC2B\uD835\uDC26\uD835\uDC28\uD835\uDC20\uD835\uDC2B\uD835\uDC1A\uD835\uDC29\uD835\uDC21\uD835\uDC22\uD835\uDC1C \uD835\uDC0C\uD835\uDC1E\uD835\uDC2D\uD835\uDC1A\uD835\uDC1D\uD835\uDC1A\uD835\uDC2D\uD835\uDC1A\n\n\uD835\uDC02\uD835\uDC28\uD835\uDC26\uD835\uDC29\uD835\uDC1A\uD835\uDC27\uD835\uDC32 \uD835\uDC08\uD835\uDC1D\uD835\uDC1E\uD835\uDC27\uD835\uDC2D\uD835\uDC22\uD835\uDC2D\uD835\uDC32: Legal Entity Name, Doing-Business-As (DBA) Name, Website Domain, Corporate LinkedIn Page URL.\n\n\uD835\uDC05\uD835\uDC22\uD835\uDC2B\uD835\uDC26\uD835\uDC28\uD835\uDC20\uD835\uDC2B\uD835\uDC1A\uD835\uDC29\uD835\uDC21\uD835\uDC22\uD835\uDC1C \uD835\uDC12\uD835\uDC1C\uD835\uDC1A\uD835\uDC25\uD835\uDC1E: Employee Headcount Range, Annual Revenue Bracket, Year Founded, Operational Status.\n\n\uD835\uDC0B\uD835\uDC28\uD835\uDC1C\uD835\uDC1A\uD835\uDC2D\uD835\uDC22\uD835\uDC28\uD835\uDC27 & \uD835\uDC02\uD835\uDC25\uD835\uDC1A\uD835\uDC2C\uD835\uDC2C\uD835\uDC22\uD835\uDC1F\uD835\uDC22\uD835\uDC1C\uD835\uDC1A\uD835\uDC2D\uD835\uDC22\uD835\uDC28\uD835\uDC27: Global HQ Address, Country, City, Postal Code, Primary Industry Sector, NAICS Codes, SIC Codes.\n\n\uD835\uDC0F\uD835\uDC2B\uD835\uDC22\uD835\uDC26\uD835\uDC1A\uD835\uDC2B\uD835\uDC32 \uD835\uDC13\uD835\uDC1A\uD835\uDC2B\uD835\uDC20\uD835\uDC1E\uD835\uDC2D \uD835\uDC00\uD835\uDC2E\uD835\uDC1D\uD835\uDC22\uD835\uDC1E\uD835\uDC27\uD835\uDC1C\uD835\uDC1E\uD835\uDC2C & \uD835\uDC14\uD835\uDC2C\uD835\uDC1E \uD835\uDC02\uD835\uDC1A\uD835\uDC2C\uD835\uDC1E\uD835\uDC2C\n\nThis multi-dimensional B2B dataset is engineered to feed a variety of corporate data architectures and operational workflows:\n\n\uD835\uDC00\uD835\uDC1C\uD835\uDC1C\uD835\uDC28\uD835\uDC2E\uD835\uDC27\uD835\uDC2D-\uD835\uDC01\uD835\uDC1A\uD835\uDC2C\uD835\uDC1E\uD835\uDC1D \uD835\uDC0C\uD835\uDC1A\uD835\uDC2B\uD835\uDC24\uD835\uDC1E\uD835\uDC2D\uD835\uDC22\uD835\uDC27\uD835\uDC20 (\uD835\uDC00\uD835\uDC01\uD835\uDC0C): Build hyper-targeted audience segments based on precise seniority levels, specific NAICS industry codes, and geographic regions to optimize paid media matching.\n\n\uD835\uDC0E\uD835\uDC2E\uD835\uDC2D\uD835\uDC1B\uD835\uDC28\uD835\uDC2E\uD835\uDC27\uD835\uDC1D \uD835\uDC12\uD835\uDC1A\uD835\uDC25\uD835\uDC1E\uD835\uDC2C & \uD835\uDC0F\uD835\uDC2B\uD835\uDC28\uD835\uDC2C\uD835\uDC29\uD835\uDC1E\uD835\uDC1C\uD835\uDC2D\uD835\uDC22\uD835\uDC27\uD835\uDC20: Equipping sales development representatives (SDRs) with valid corporate emails and direct dials to bypass standard company switchboards.\n\n\uD835\uDC03\uD835\uDC1A\uD835\uDC2D\uD835\uDC1A \uD835\uDC04\uD835\uDC27\uD835\uDC2B\uD835\uDC22\uD835\uDC1C\uD835\uDC21\uD835\uDC26\uD835\uDC1E\uD835\uDC27\uD835\uDC2D & \uD835\uDC02\uD835\uDC11\uD835\uDC0C \uD835\uDC02\uD835\uDC25\uD835\uDC1E\uD835\uDC1A\uD835\uDC27\uD835\uDC2C\uD835\uDC22\uD835\uDC27\uD835\uDC20: Instantly append missing firmographic fields, corporate domains, and updated direct lines to your existing legacy Hubspot, Salesforce, or Marketo database.\n\n\uD835\uDC0B\uD835\uDC1E\uD835\uDC1A\uD835\uDC1D \uD835\uDC06\uD835\uDC1E\uD835\uDC27\uD835\uDC1E\uD835\uDC2B\uD835\uDC1A\uD835\uDC2D\uD835\uDC22\uD835\uDC28\uD835\uDC27 & \uD835\uDC0C\uD835\uDC1A\uD835\uDC2B\uD835\uDC24\uD835\uDC1E\uD835\uDC2D \uD835\uDC00\uD835\uDC27\uD835\uDC1A\uD835\uDC25\uD835\uDC32\uD835\uDC2C\uD835\uDC22\uD835\uDC2C: Identify total addressable markets (TAM) across new geographic territories and map 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Rather than exposing dozens of narrow, API-style endpoints, it provides a\ncompact set of high-level tools, several of which delegate work to Hightouch's marketing agents. Those\nagents already have the customer's schema, permissions, brand context, and 300+ destination\nintegrations, so the calling client (for example, Genie) can hand off a plain-language request and get\nback a governed result. 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This dataset is a valuable resource for understanding user engagement, topic trends, and community interactions within various subreddits.\n**Key Features:**\nPost details: URL, username, title, content, upvotes, downvotes, comments, date\nSubreddit information: name, description\nSupports sentiment and trend analysis\n**Popular Use Cases:**\n1.User Behavior Analysis:\n Analyze how different types of posts perform in various subreddits.\n2.Sentiment Analysis:\n Examine sentiment across diverse topics and communities.\n3.Content Strategy:\n Develop content strategies based on post performance and engagement metrics.\n\nNote: these are samples.\n","terms_of_service":"https://brightdata.com/license","documentation_link":"https://brightdata.com/products/datasets/reddit","privacy_policy_link":"https://brightdata.com/privacy","data_source":"","assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://brightdata.com/license"},"provider_summary":{"name":"Bright Data","description":"Bright Data is the world’s #1 web data platform.\nFortune 500 companies, academic institutions, and small businesses rely on Bright Data’s scalable platform for web scraping infrastructure to retrieve crucial public web data in the most efficient, reliable, and flexible way, so they can research, monitor, and analyze data to make better decisions.\nThe platform is used worldwide by 20,000+ customers in nearly every industry. 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The full USA product is available as Paid access. Contact Databricks.partnership@precisely.com for full product licensing information.\n\nZIP Plus 4s includes over 65 million records, detailing the 5-digit ZIP Code and 4-digit extensions representing ZIP Code Sector and ZIP Code Segment in the United States. Over 40 postal attribute fields are also included to provide the detail needed to assist in utilizing ZIP Plus 4 data for internal processing and analysis. Additionally, Census block group codes, which include state, county, and tract codes, are provided for each ZIP Plus 4 record. A ZIP Code can represent one side of a street, a floor in a building, or a specific Post Office Box. The ZIP Code Sector (1234) represents a collection of addresses below the ZIP Code level. The ZIP Code Segment (1234) is more specific and always refers to a smaller set of addresses or a single address. In the case of PO Boxes, the ZIP segment is almost always the last two digits of the PO Box number. The four-digit ZIP Code extension is used by the United States Postal Service (USPS) to facilitate efficient mail handling and delivery.\n\n**Use cases**\n- **360-Degree Customer View** - What’s in a ZIP Code? Append additional ZIP Code, address and census data to your ZIP Code lists.\n- **Personalize Customer Experiences** - ZIP+4 provide an alternative to utilizing and displaying individual customer records but retaining a relatively accurate location. This allows you to aggregate demographic information and target and segment audiences based on address records but at an anonymous data point level. NOTE: this product contains no direct personally identifiable information.\n- **Real World Data (RWD)** - Verify or derive ZIP Code and ZIP Plus 4 locations, identify the ZIP Code type and append valuable Census location data.\n- **Identity Resolution** - Address verification. (NOTE: This product does not contain personally identifiable information but does allow for the verification of ZIP Code/Address data through cross reference.)\n- **Location Data Enrichment** - Enrich your addresses with FIPS codes, CBSA, Congressional Districts and more\n- **Supply Chain** - Convert ZIP Codes to locations (latitude longitude) to aid in supply and delivery planning.\n\n**Product details**\n- The ZIP Plus 4s product consists of two data sets\n- The base product table containing over 40 attribute fields.\n- An ‘unmatched’ file containing approximately 6 million ZIP+4 records that have coordinates representing either an interpolated ZIP+4, a ZIP+2, or a 5-digit ZIP centroid. The positional accuracy results for these records do not have the geographic elements required to meet Precisely’s quality criteria. However, these records are provided as they can still be used for more broad-based location matching and analysis. The unmatched ZIP+4 file has the same record layout as the base file but does not contain census tract, block group, or block values.\n- **Over 40 attribute fields include but are not limited to the following:**\n - 5-digit ZIP Code\n - ZIP Plus 4 code extension\n - 5-digit ZIP Code and Plus 4 code extension\n - Point match type: 1 = ZIP+4 centroid, 2 = ZIP+2 centroid, 3 = 5-digit ZIP Code\n - Record type code: F- Firm ,G – General Delivery ,H – High-rise ,P – PO Box (and more)\n - 4-digit USPS carrier route ID\n - Street name\n - Street suffix abbreviation\n - Primary low number in address range\n - Primary high number in address range\n - Address primary odd/even code: O – Odd ,E – Even ,B – Both\n - Building or firm name.\n - ZIP Sector low number\n - ZIP Sector high number\n - ZIP Segment low number\n - ZIP Segment high number\n - LACS status code converted.\n - Government building indicator.\n - State FIPS Code\n - State abbreviation\n - County FIPS Code\n - County Name\n - Minor Civil Division code\n - CBSA Code\n - Congressional District number\n - Census block group code (state, county, tract, and block group codes)\n - Census block code (state, county, tract, block group and block codes)\n - Latitude\n - Longitude\n\nFor more details, refer to the embedded notebook.\n\n**Additional Insights**\nhttps://www.precisely.com/data-guide/products/usps-zip-codes-plus-four-carrier-routes","terms_of_service":"https://www.precisely.com/legal/licensing/trial-evaluation-agreement-terms-u-s","documentation_link":"https://help.precisely.com/r/ZIP-Plus-4-Database-Premium-Plus-Centroids/Latest/en-US/ZIP-Plus-4-Database-Premium-Plus-Centroids-Product-Guide","privacy_policy_link":"https://www.precisely.com/legal/privacy-policy","embedded_notebook_file_infos":[{"id":"976e0b68-e075-4fd7-9887-c2f7bbb01da7","marketplace_file_type":"EMBEDDED_NOTEBOOK","file_parent":{"parent_id":"02004508-aaa0-4fbd-a4b4-3d3f1b70811b","file_parent_type":"LISTING"},"mime_type":"text/html","download_link":"https://marketplace-file-storage-prod.s3-fips.us-east-1.amazonaws.com/EMBEDDED_NOTEBOOK/LISTING_02004508-aaa0-4fbd-a4b4-3d3f1b70811b/976e0b68-e075-4fd7-9887-c2f7bbb01da7?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEGgaDnVzLWVhc3QtMS1maXBzIkYwRAIgbC8sWIoYaHR8y7Wq86nPb0pufBl4%2BM4PWpeN%2B5PW9VQCIG7BhAVBDCWTpxFDLeuKYm%2Ff1JLz4iccPMzter0YdhgWKqYECDAQABoMNDE0MzUxNzY3ODI2IgxNAJrHbXaZG20jNfMqgwQyGEb4TsoVOoNvJ%2BaA%2FZ7zeu8hGcHqCeNfblP%2FQo5g6q2aRNtpwtPRKMlhYcIE%2FUQaNT1Lqmyptu32VUQTpzjTTxRVmJrDPkheSCkV06XQ%2FpTMKjU8CC7X1b%2FqYf8CrBwZT7Cv4C5WPjEET38f2A79h0xV6bZgPy28zQWrSeIJ9w6AfZBxiE24%2FVc08zVA0ivCT6lsy7nuU0wcR%2FMg2C9nd1vaMnQ923VpD21CKfVVYcrNWL2b%2FQ%2BNAO%2BxPjPgjiN6UPQbvbvZxkSdhgrGtecIg9NgqGkMcvM5VLTDtdC1kdeZetfOC2obtA%2BT%2F0gy3tIDo6WM5hcQfw743%2FAU9oaGnRBl%2BwoxRRDCZA%2F4jnPdluICSI7NCr6C3HHOe4CSZJjRihczxPA%2FgQczAVjAPs6CrK9N9pIzt0vNJpGUTgg4AoxftTvGfciDREilSctPIcBk6GwB%2Bzt4wbtNCmEPU%2BFD46rc6CWVQmmLwbKOeHw70ipMVFlOc8NlXkEtGO2vu6H44MstSSc6daOVUYUavQSGZ6jGhEjsP2YGz8KDm9XQotB%2FtyVBqiZV01OKqDmmBJA%2FXjUpSJ8M%2FeJXhe5m%2BKcxzNObtoJRI7jci3Tpt08XZyXZ7%2FyIV8H30ya468X3cfkTXrm972uga3z7iXAdubyEHnQIua%2FuvtwOHILi6O5D1Me2MzCPvcnXBjqTAYDoMWBt%2B3AVFuqSs2dmDQNBnvEhXfMgTedKLD5PJHb3aIl15iBkLaNu36GYnVCp9ZU2lSYcTbY5QwNnNeTfJTNy9bKhwzru6Sg%2BslhN%2BTS0zkTn%2BeLMY4tHgPEtEZ2cp%2FCk6GwqswvnYVKHjwO8q%2B1Mx8lc08fVEfarE9dke1GGFqoddx%2Bq%2BZUyHbDlAVfrCmf0yw%3D%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20261110T000135Z&X-Amz-SignedHeaders=host&X-Amz-Expires=86400&X-Amz-Credential=ASIAWA6KKHEJORWBH4N3%2F20261110%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=8e1ef3d62529d1134f7deec89b04e37d9e588f4d6bc1417d22290171152c4dee","created_at":1793603159007,"updated_at":1793603159007,"display_name":"ZIP Plus 4s USA Notebook","status":"FILE_STATUS_PUBLISHED"}],"geographical_coverage":"[\"United States and Canada\"]","cost":"FREE","update_frequency":{"interval":1,"unit":"QUARTERLY"},"data_source":"USPS & spatial datasets","assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://www.precisely.com/legal/licensing/trial-evaluation-agreement-terms-u-s"},"provider_summary":{"name":"Precisely","description":"As a global leader in data integrity, Precisely ensures that your data is accurate, consistent, and contextual. Our portfolio, including the Precisely Data Integrity Suite, helps integrate your data, improve data quality, govern data usage, geocode and analyze location data, and enrich it with complementary datasets for confident business decisions. Over 12,000 organizations in more than 100 countries, including 95 of the Fortune 100, trust Precisely software, data, and data strategy consulting to power AI, automation, and analytics initiatives. Learn more at www.precisely.com."},"popularity":1626},{"id":"ba9e6214-3ccc-4628-97f7-3a7fa318e1f4","summary":{"name":"Oracle EBS Finance Analytics with Agentic AI","subtitle":"Agentic AI-powered Databricks accelerator for Oracle EBS Finance Analytics, automating workflows and insights.","setting":{"visibility":"PUBLIC"},"created_at":1793955908394,"updated_at":1793961693809,"categories":["ADVERTISING_AND_MARKETING"],"listingType":"PERSONALIZED","provider_id":"3e1fd420-8722-4ebc-abaa-79f86ceffda0"},"detail":{"description":"Contact us at: chandra.reddy@dataplatr.com\nFor consultations or custom inquiries: https://dataplatr.com/contact-us\n\n**Overview**\n**Close faster, trust every number, and stop rebuilding the same finance reports every period.** Dataplatr's Agentic Harness is an Agentic AI accelerator on Databricks that transforms Oracle EBS Finance operations. It enables finance teams to ask questions in natural language and receive accurate, source-traceable insights across General Ledger, Payables, Receivables, Purchasing, and Treasury.\n\nIt automates recurring financial reports, reconciliations, and close activities through governed workflows, while identifying data issues and improving reporting accuracy. This helps finance teams reduce manual effort, accelerate financial close, and make faster, more informed decisions.\n\n**Business Outcomes**\n* **Faster Financial Close** – Recurring close checks, reconciliations and exception reports run automatically on schedule instead of being rebuilt by hand every period.\n* **Audit-Ready, Trusted Numbers** – Every figure in an answer is traced to the Oracle EBS journals, invoices and bank lines behind it, with results explained in plain language.\n* **Fewer Late Surprises** – Data freshness, ledger balance and unusual movements are checked before reporting, so issues surface early instead of at review.\n* **Less Leakage and Better Compliance** – Duplicate invoices, late purchase orders and policy exceptions are flagged daily or weekly, with the owner notified automatically.\n* **Self-Service Finance Analytics** – Controllers and analysts get answers in minutes without waiting on a BI queue or writing SQL.\n* **Finance Knowledge That Stays** – Definitions and rules learned in conversation are approved by their owners and reused in every future answer, so know-how survives team changes.\n* **Faster Root-Cause Analysis** – Follow-up questions drill from a variance or spike to the vendor, customer or journal behind it.\n\n **Who It's For**\n\n* **CFOs and Finance Leaders** – a faster, more predictable close and numbers they can defend.\n* **Controllers and Accounting** – trial balance views, journal reviews and reconciliations on request and on schedule.\n* **AP and Procurement** – duplicate invoice checks, PO compliance against policy, supplier spend analysis.\n* **AR and Collections** – past-due receivables and DSO trends by customer.\n* **FP&A** – budget vs actual variance commentary by cost center.\n* **Treasury** – cash position and unreconciled bank lines.\n* **Finance IT and Data Teams** – one governed, Databricks-native Agentic Harness instead of a backlog of one-off reports.\n\n**Outcomes by Finance Process**\n1. **Finance Process:** Record to Report \n**Oracle EBS Modules:** GL, Subledger Accounting\n**Business Outcome:** Faster and cleaner period close.\n**Example Workflow:** Period-end journal review and trial balance on request.\n\n2. **Finance Process:** Procure to Pay\n**Oracle EBS Modules:** AP, PO\n**Business Outcome:** Fewer duplicate payments and better policy compliance.\n**Example Workflow:** Duplicate invoice checks and PO compliance against policy.\n\n3. **Finance Process:** Order to Cash\n**Oracle EBS Modules:** AR, OM\n**Business Outcome:** Faster collections and lower DSO.\n**Example Workflow:** Past-due receivables digest.\n\n4. **Finance Process:** FP&A\n**Oracle EBS Modules:** GL budgets and actuals\n**Business Outcome:** Financial variances explained, not just reported.\n**Example Workflow:** Budget vs. actual commentary.\n\n5. **Finance Process:** Tax\n**Oracle EBS Modules:** E-Business Tax, AP, AR\n**Business Outcome:** Tax exceptions identified before filing.\n**Example Workflow:** Tax code exception checks.\n\n6. **Finance Process:** Treasury\n**Oracle EBS Modules:** Cash Management, GL\n**Business Outcome:** Daily cash visibility and timely reconciliation.\n**Example Workflow:** Identification of unreconciled bank lines.\n\nStart with the close (Record to Report), then extend to the other processes.\n\n**Key Features**\n\n* **Finance Workflows Built in Chat** – Describe a recurring finance task in plain language; the Agentic Harness drafts the steps, test-runs them on recent EBS data, and saves them as a Lakeflow Job that runs on demand or on schedule, writes results to Delta tables and notifies the owner.\n* **Natural Language Questions on Oracle EBS** – Ask about ledgers, periods, entities, suppliers and customers with no SQL or schema knowledge, from a Databricks App, Teams or Slack.\n* **Lineage to Source EBS Rows** – Every figure is traced through Unity Catalog to the EBS rows it came from.\n* **Policy and Document Aware** – Attach policies, invoices or contracts; rules in a policy can be turned straight into a query and a workflow.\n* **Business Context and Anomaly Flags** – The harness remembers the usual numbers and flags moves outside the normal range.\n* **Basic Checks** – Scheduled checks for data freshness since the last EBS load, ledger balance, row counts against EBS, metric drift and job runs, with alerts when a check fails.\n* **Self-Improving, Human-Approved Memory** – A nightly job captures what was learned in chat as silver context; the metric owner approves it through a pull request before it becomes trusted golden context.\n* **Databricks-Native and Governed** – Runs in your workspace on Unity Catalog, Mosaic AI Model Serving, Databricks SQL and Lakeflow Jobs, under your existing permissions.\n\n**Oracle EBS Finance Analytics Agentic Harness User Manual**\n\n**Agentic Harness for Oracle EBS Finance Analytics on Databricks\n(Ask → Answer → Automate → Capture → Approve → Remember)**\n\n**1. Purpose**\nGive finance teams on Oracle EBS an always-on AI finance analyst on Databricks that answers in plain language with lineage to source, turns recurring close and compliance work into scheduled workflows, catches data issues before reporting, and learns from every conversation under human approval.\n\n**2. Architecture Overview**\n**Data Flow:** Oracle EBS → Lakeflow pipelines → Unity Catalog (gold Delta tables, finance metric views) → Agentic Harness → Workflows and answers\n\n**Key Components:**\n* Oracle EBS as the source: GL, AP, AR, PO, Cash Management and subledgers.\n* Lakeflow pipelines land EBS extracts on a schedule and curate them into gold fact and dimension tables.\n* Unity Catalog holds the gold Delta tables, finance metric views, Volumes for policies and invoices, and lineage down to EBS rows, with permissions and row filters by ledger.\n* A foundation model on Mosaic AI Model Serving, governed by AI Gateway, understands the question, recalls only the context it needs, and writes SQL on approved tables and metric views, run on a Databricks SQL warehouse.\n* Workflows run as Lakeflow Jobs; notifications go to Teams, Slack or email; basic checks run as scheduled jobs with SQL alerts and data quality monitoring.\n* A Databricks App, plus Teams and Slack, as the user interface; MLflow Tracing and system-table audit logs for observability.\n* Golden context (versioned `.md` files in a Git folder, seeded from metric views, dbt docs, LookML and existing SQL) and silver context (AI-generated nightly from conversation logs).\n\n**3. How It Works**\n**Step 1: Ask**\n- A finance user asks a question in plain language, optionally attaching a document such as a policy PDF.\n\n**Step 2: Answer**\n- The harness resolves ledger, period and entity, writes SQL on approved tables, and returns a table, chart or narrative with lineage to the source EBS rows and checks alongside (for example, debits = credits).\n- Example: a controller asks for the adjusted trial balance for a ledger and period, then switches to post-closing, by entity, comparative or consolidated views in conversation.\n\n**Step 3: Automate with Workflows**\n\n- Save any answer, or describe a recurring task, as a workflow; refine the steps in chat, test-run it, and schedule it as a Lakeflow Job.\n- Example: attach the AP PO policy and ask for monthly on-time vs late POs by ledger and org. The harness reads the policy, confirms an extra supplier exclusion, writes the query, and saves a 5-step workflow that writes results to a Delta table and sends the AP Manager a summary on Teams every Monday.\n\n**Step 4: Capture**\n\n- A corrected rule (for example, that Accrual Reversal journals count as adjustments) applies immediately, and a nightly job records it as silver context.\n\n**Step 5: Approve**\n\n- The metric owner reviews each silver note as a pull request; past questions are replayed so an approved change cannot quietly alter an old number.\n\n**Step 6: Remember**\n* Approved notes become golden context, trusted in every future answer and workflow run; rejected notes are never treated as fact.\n\n**4. Operational Notes**\n* **Starting Scope:** Start with the close (Record to Report), then extend to Procure to Pay, Order to Cash, FP&A, Tax and Treasury.\n* **Golden Context Seeding:** Seeded at setup from existing metric views, dbt docs, LookML and SQL, so the harness starts from the finance definitions you already trust.\n* **System Health:** When a basic check fails, the owner is alerted on Teams or email with the failing check and run ID; anyone can also ask in chat, \"is everything running fine?\".\n* **Access Control:** The harness queries only through Unity Catalog, so existing permissions and row filters by ledger apply to every answer and workflow.\n\n**Contact Dataplatr**\n\n**Close Faster and Trust Every Number on Oracle EBS**\n\nDataplatr's Agentic Harness for Oracle EBS Finance Analytics is built natively on Databricks and tailored to your ledgers, metrics and policies. To learn more or request a demo on your own data, please get in touch with our team.\n\n**Email:** chandra.reddy@dataplatr.com\n**Contact us:** https://dataplatr.com/contact-us\n\n*Note: By clicking 'Get Access', you agree to our Terms of Service. You acknowledge that Dataplatr Corp will recognize your organization as a customer/user for promotional and reference purposes.*","terms_of_service":"https://docs.google.com/forms/d/17ojn95HGwLbM-b3S7PHT-wf_nFlqY9GwbsWcDDJIjro/viewform","documentation_link":"","privacy_policy_link":"https://dataplatr.com/privacy-policy","embedded_notebook_file_infos":[{"id":"dafaddc6-c65e-4335-b973-7bdc06f69ac6","marketplace_file_type":"EMBEDDED_NOTEBOOK","file_parent":{"parent_id":"ba9e6214-3ccc-4628-97f7-3a7fa318e1f4","file_parent_type":"LISTING"},"mime_type":"text/html","download_link":"https://marketplace-file-storage-prod.s3-fips.us-east-1.amazonaws.com/EMBEDDED_NOTEBOOK/LISTING_ba9e6214-3ccc-4628-97f7-3a7fa318e1f4/dafaddc6-c65e-4335-b973-7bdc06f69ac6?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEGgaDnVzLWVhc3QtMS1maXBzIkYwRAIgbC8sWIoYaHR8y7Wq86nPb0pufBl4%2BM4PWpeN%2B5PW9VQCIG7BhAVBDCWTpxFDLeuKYm%2Ff1JLz4iccPMzter0YdhgWKqYECDAQABoMNDE0MzUxNzY3ODI2IgxNAJrHbXaZG20jNfMqgwQyGEb4TsoVOoNvJ%2BaA%2FZ7zeu8hGcHqCeNfblP%2FQo5g6q2aRNtpwtPRKMlhYcIE%2FUQaNT1Lqmyptu32VUQTpzjTTxRVmJrDPkheSCkV06XQ%2FpTMKjU8CC7X1b%2FqYf8CrBwZT7Cv4C5WPjEET38f2A79h0xV6bZgPy28zQWrSeIJ9w6AfZBxiE24%2FVc08zVA0ivCT6lsy7nuU0wcR%2FMg2C9nd1vaMnQ923VpD21CKfVVYcrNWL2b%2FQ%2BNAO%2BxPjPgjiN6UPQbvbvZxkSdhgrGtecIg9NgqGkMcvM5VLTDtdC1kdeZetfOC2obtA%2BT%2F0gy3tIDo6WM5hcQfw743%2FAU9oaGnRBl%2BwoxRRDCZA%2F4jnPdluICSI7NCr6C3HHOe4CSZJjRihczxPA%2FgQczAVjAPs6CrK9N9pIzt0vNJpGUTgg4AoxftTvGfciDREilSctPIcBk6GwB%2Bzt4wbtNCmEPU%2BFD46rc6CWVQmmLwbKOeHw70ipMVFlOc8NlXkEtGO2vu6H44MstSSc6daOVUYUavQSGZ6jGhEjsP2YGz8KDm9XQotB%2FtyVBqiZV01OKqDmmBJA%2FXjUpSJ8M%2FeJXhe5m%2BKcxzNObtoJRI7jci3Tpt08XZyXZ7%2FyIV8H30ya468X3cfkTXrm972uga3z7iXAdubyEHnQIua%2FuvtwOHILi6O5D1Me2MzCPvcnXBjqTAYDoMWBt%2B3AVFuqSs2dmDQNBnvEhXfMgTedKLD5PJHb3aIl15iBkLaNu36GYnVCp9ZU2lSYcTbY5QwNnNeTfJTNy9bKhwzru6Sg%2BslhN%2BTS0zkTn%2BeLMY4tHgPEtEZ2cp%2FCk6GwqswvnYVKHjwO8q%2B1Mx8lc08fVEfarE9dke1GGFqoddx%2Bq%2BZUyHbDlAVfrCmf0yw%3D%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20261110T000135Z&X-Amz-SignedHeaders=host&X-Amz-Expires=86400&X-Amz-Credential=ASIAWA6KKHEJORWBH4N3%2F20261110%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=c35ace78db4575a56c8cbd03996dc2d4b79201d57c7f5083478bbb06b9f57b1f","created_at":1793955939974,"updated_at":1793955939974,"display_name":"Oracle_EBS_Finance_Analytics_Agentic_AI","status":"FILE_STATUS_PUBLISHED"}],"assets":["ASSET_TYPE_NOTEBOOK"],"license":""},"provider_summary":{"name":"Dataplatr Corp","description":"Dataplatr Corp specializes in Data Analytics & AI with pre-built data models for Enterprise Applications like SAP, Oracle EBS, Workday, Salesforce to empower businesses to unlock the full potential of their data. Our enterprise data engineering accelerators are designed to expedite the development of data pipelines, data transformation, and integration, saving you time and resources.\n\nOur team of experienced data engineers, scientists and analysts utilize cutting-edge data infrastructure into valuable insights and helps enterprise clients optimize their Sales, Marketing, Operations, Financials, Supply chain, Human capital and Customer experiences."},"popularity":660},{"id":"e2d8a154-dd72-43f9-9f38-4cceb5c9e261","summary":{"name":"Geolocet | Points of Interest Data | Petrol Station Locations - Norway","subtitle":"Complete GIS-ready dataset of 2,700+ Norway petrol station locations (brands & independents). Optimized for Databricks.","setting":{"visibility":"PUBLIC"},"created_at":1780644250169,"updated_at":1780644250169,"categories":["GEOSPATIAL","RETAIL"],"listingType":"PERSONALIZED","provider_id":"5d3ddc17-a7e8-4794-a760-13587e1de2c6"},"detail":{"description":"# **All Petrol Station Locations in Norway: Complete Geographic Dataset**\n---\n### **Premium GIS-Ready Retail Location Intelligence Data for Databricks Analytics**\n\nThis high-fidelity geospatial dataset provides a complete, exhaustive, and standardized overview of over **2,700** verified fuel and petrol station locations across Norway. Updated as of **July 2025**, this comprehensive national dataset captures the entire Norwegian forecourt landscape - encompassing premier multinational fuel networks, regional automated value stations, and prominent independent operators. It offers an analysis-ready foundation for EV charging infrastructure planning, competitor benchmarking, logistics routing, and retail location intelligence.\n\nFully compatible with **Apache Spark**, **Delta Lake**, and **Databricks Unity Catalog** workflows.\n\n---\n\n## **Complete National Coverage: Branded Forecourts & Regional Banners**\nUnlike single-brand extractions, this dataset aggregates all premier fuel networks and independent operators into a single, unified data model. The coverage maps maximum geographic depth across the country:\n\n- **Major Fuel Networks & Multinational Banners:** \n [Circle K (489 locations)](https://geolocet.com/products/norway-circle-k-petrol-stations), \n [Esso (293 locations)](https://geolocet.com/products/norway-esso-petrol-stations), \n [St1 (276 locations)](https://geolocet.com/products/norway-st1-petrol-stations), \n [Shell (194 locations)](https://geolocet.com/products/norway-shell-petrol-stations)\n\n- **Automated, Express & Value Forecourts:** \n [UNO-X (351 locations)](https://geolocet.com/products/norway-uno-x-petrol-stations)\n\n- **Regional & Independent Banners:** \n [Bunker Oil (66 locations)](https://geolocet.com/products/norway-bunker-oil-petrol-stations)\n\n**Plus thousands of regional independent forecourts** fully mapped and standardized across all provinces to provide complete national coverage.\n\n---\n\n## **Technical Specifications & Schema Definitions**\nOptimized for zero-friction schema enforcement when reading via `spark.read.format(\"csv\")`. The structured schema below ensures flawless rendering in Databricks notebooks and high accuracy for LLM semantic indexing.\n\n* **`GUID`** (`STRING`): Unique global identifier for deterministic primary key mapping and deduplication.\n* **`Title`** (`STRING`): Official station name or commercial brand banner classification (e.g., Circle K, Shell, Esso).\n* **`Latitude`** (`DOUBLE`): Precise WGS84 latitude coordinate for point-in-polygon and spatial joins.\n* **`Longitude`** (`DOUBLE`): Precise WGS84 longitude coordinate for spatial indexing (H3 / S2).\n* **`Street_No` / `Street`** (`STRING`): Normalized building numbers and thoroughfare name elements.\n* **`City` / `Postal_Code`** (`STRING`): Core municipal settlements and primary postal routing codes.\n* **`Admin_level_1` / `_2`** (`STRING`): High-level administrative district/regional classifications.\n* **`Address`** (`STRING`): Full, standardized street address clean-text output.\n* **`Wheelchair`** (`STRING`): Accessibility flags indicating wheelchair mobility support where available.\n* **`Opening_Hours`** (`STRING`): Standard daily operational schedules and structured operating text (Optional enrichment field).\n* **`Popularity_Score` / `Ratings`** (`INTEGER`): Relative foot-traffic/rating metric graded on a standardized scale (Optional enrichment field).\n\n---\n\n## **Databricks Workflow & Spatial Analytics Use Cases**\n\n- **EV Charging Whitespace Analysis:** Query the full 2,700+ location points against high-voltage grid corridors or traffic volume rasters to rank optimal stations for ultra-fast EV charging infrastructure placement.\n- **H3 Geospatial Indexing:** Convert the precise WGS84 `Latitude` and `Longitude` fields into Uber H3 spatial hexagons (`h3_longlattocell`) to seamlessly cross-reference with granular local demographic indicators.\n- **Logistics & Fleet Benchmarking:** Integrate forecourt coordinates with fleet tracking telemetry to calculate fuel stop density, build optimal corridor routing profiles, and minimize off-route deadhead mileage across Norway's unique geography.\n\n---\n\n## **Data Evaluation & Custom Ingestion**\n\n### **Evaluate with a Free Sample Dataset**\nVerify the structure, column types, and coordinate precision before full implementation. A comprehensive schema sample is available for download in CSV/Parquet-friendly formats.\n\n*Want deeper market insights?* Explore how demographic mapping supports location intelligence workflows on our dedicated country library: **[Explore Data and Demographics for Norway](https://geolocet.com/pages/norway)**\n\n### **Enterprise Delivery & Tailored Formatting**\nWhile natively available as an analysis-ready CSV for DBFS ingestion, we accommodate enterprise-level data architecture needs. Alternative formats - including **GeoJSON, Esri Shapefiles, PostgreSQL dumps, Excel, or direct Delta sharing profiles** - can be generated. 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Includes events in the Sports, Concerts, Expos, Conferences, Festivals, Performing Arts and Community event categories.\n\n**Dataset:** Attended Events Sample Dataset\n**Location:** Seattle (Washington)\n**Visibility Window:** 1-Year Historical, 30-Days Future, taken on Feb 1st 2023\n**Categories:** Sports, concerts, expos, conferences, festivals, performing arts, community\n**Features:** Event-Level and Features API Datasets\n**Update Frequency:** Bi-Annually","terms_of_service":"https://www.predicthq.com/legal/terms","documentation_link":"https://docs.predicthq.com/resources/events#event-fields","privacy_policy_link":"https://www.predicthq.com/legal/privacy","assets":["ASSET_TYPE_DATA_TABLE"]},"provider_summary":{"name":"PredictHQ","description":"PredictHQ is the world’s first and only company that provides the missing context for the biggest external factor that impacts businesses demand – events. 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The main demographic indicators provided for the population groups, stratified not only by location but also for the majority by demographical and socioeconomic characteristics, are population number, females and males, usual residents and private dwellings. The primary use of the data at the Health Region level is for health surveillance and population health research. Federal and provincial departments of health and human resources, social service agencies, and other types of government agencies use the information to monitor, plan, implement and evaluate programs to improve the health of Canadians and the efficiency of health services. Researchers from various fields use the information to conduct research to improve health. Non-profit health organizations and the media use the health region data to raise awareness about health, an issue of concern to all Canadians.\n\nThe Census population counts for a particular geographic area representing the number of Canadians whose usual place of residence is in that area, regardless of where they happened to be on Census Day. Also included are any Canadians who were staying in that area on Census Day and who had no usual place of residence elsewhere in Canada, as well as those considered to be 'non-permanent residents'. National Household Survey (NHS) provides demographic data for various levels of geography, including provinces and territories, census metropolitan areas/census agglomerations, census divisions, census subdivisions, census tracts, federal electoral districts and health regions. In order to provide a comprehensive overview of an area, this product presents data from both the NHS and the Census. NHS data topics include immigration and ethnocultural diversity; aboriginal peoples; education and labor; mobility and migration; language of work; income and housing. 2011 Census data topics include population and dwelling counts; age and sex; families, households and marital status; structural type of dwelling and collectives; and language. The data are collected for private dwellings occupied by usual residents. A private dwelling is a dwelling in which a person or a group of persons permanently reside. Information for the National Household Survey does not include information for collective dwellings. Collective dwellings are dwellings used for commercial, institutional or communal purposes, such as a hotel, a hospital or a work camp.\n\n**Benefits**\n- Useful for canada public health stakeholders, for public health specialist or specialized public and other interested parties. for health surveillance and population health research. for monitoring, planning, implementation and evaluation of health-related programs. media agencies may use the health regions data to raise awareness about health, an issue of concern to all canadians. giving the addition of longitude and latitude in some of the datasets the data can be useful to transpose the values into geographical representations. the fields descriptions along with the dataset description are useful for the user to quickly understand the data and the dataset.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [Canadian Population and Dwelling by FSA 2011](https://www.johnsnowlabs.com/marketplace/canadian-population-and-dwelling-by-fsa-2011)\n\t- This Canadian Census dataset covers data on population, total private dwellings and private dwellings occupied by usual residents by forward sortation area (FSA). It is enriched with the percentage of the population or dwellings versus the total amount as well as the geographical area, province, and latitude and longitude. The whole Canada's population is marked as 100, referring to 100% for the percentages.\n- [Detailed Canadian Population Statistics by CMAs and CAs 2011](https://www.johnsnowlabs.com/marketplace/detailed-canadian-population-statistics-by-cmas-and-cas-2011)\n\t- This dataset covers the population statistics of Canada by Census Metropolitan Areas (CMAs) and Census Agglomerations (CAs). It is categorized also by citizen/immigration status, ethnic origin, religion, mobility, education, language, work, housing, income etc. There is detailed characteristics categorization within these stated categories that are in 5 layers.\n- [Detailed Canadian Population Statistics by FED 2011](https://www.johnsnowlabs.com/marketplace/detailed-canadian-population-statistics-by-fed-2011)\n\t- This dataset covers the population statistics of Canada from 2011 by Federal Electoral District of 2013 Representation Order. It is categorized also by citizen/immigration status, ethnic origin, religion, mobility, education, language, work, housing, income etc. There is detailed characteristics categorization within these stated categories that are in 5 layers.\n- [Detailed Canadian Population Statistics by Health Region 2011](https://www.johnsnowlabs.com/marketplace/detailed-canadian-population-statistics-by-health-region-2011)\n\t- This dataset covers the population statistics of Canada by health region. It is categorized also by citizen/immigration status, ethnic origin, religion, mobility, education, language, work, housing, income etc. There is detailed characteristics categorization within these stated categories that are in 5 layers.\n- [Detailed Canadian Population Statistics by Province 2011](https://www.johnsnowlabs.com/marketplace/detailed-canadian-population-statistics-by-province-2011)\n\t- This dataset covers the population statistics of Canada by provinces and territories. It is categorized also by citizen/immigration status, ethnic origin, religion, mobility, education, language, work, housing, income etc. There is detailed characteristics categorization within these stated categories that are in 5 layers.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated\n- Data Updates\n - 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Which means, after our contract is finished you keep all data received to date.\r\n· We do not have onerous redistribution rules.\r\n· We tailor our service to meet your needs.\r\n\r\nEDI’s extensive reference data includes Worldwide Equity and Fixed Income Corporate Actions, Dividends, Static Reference Data, Closing Prices, Shares Outstanding, Economic Indicators and Global Economic Data, delivered via data feeds and a web platform."},"popularity":1748},{"id":"1f2580ff-836a-4d71-94cb-a2b0de10280c","summary":{"name":"COVIDcast (COVID-19) Epidemiological Data | CMU","subtitle":"A wide-ranging collection of epidemiological signals about COVID-19 from CMU Delphi Research Group's COVIDCast system.","share":{"name":"hls_covidcast_cmu","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1675361595440,"updated_at":1698965635225,"categories":["HEALTH"],"listingType":"STANDARD","provider_id":"f00071b1-2db4-4f8f-a4bc-4c62c790f25a"},"detail":{"description":"**Overview**\n\nThis resource contains an archived collection of datasets from Carnegie Mellon University Delphi Research Group's COVID-19 Surveillance Streams Data (COVIDcast).\n\nDelphi's COVIDcast datasets are based on a variety of data sources including a CMU-run Facebook health survey, a Google-run health survey, lab test results provided by Quidel Inc, search data released by Google Health Trends, and outpatient doctor visits provided by a national health system.\n\n**Product Details**\n\nIncludes the following tables:\n\n\n- **hls_covid19_usa.covid19_usa_smoothed_wwearing_mask_7d_daily_cmu**\n - **Update frequency:** daily\n - **Source:** Delphi Research Group - COVID-19 Trends and Impact Survey\n - **Description:** Estimated percentage of people who wore a mask for most or all of the time while in public in the past 7 days; those not in public in the past 7 days are not counted.\n\n- **hls_covid19_usa.covid19_usa_smoothed_wcovid_vaccinated_appointment_or_accept_daily_cmu**\n - **Update frequency:** daily\n - **Source:** Delphi Research Group - COVID-19 Trends and Impact Survey\n - **Description:** Estimated percentage of respondents who either have already received a COVID vaccine or have an appointment to get a COVID vaccine or would definitely or probably choose to get vaccinated, if a vaccine were offered to them today.\n\n- **hls_covid19_usa.covid19_usa_sum_anosmia_ageusia_smoothed_search_daily_cmu**\n - **Update frequency:** daily\n - **Source:** Delphi Research Group - Google Symptoms\n - **Description:** The sum of Google search volume for anosmia and ageusia related searches, in an arbitrary units that are normalized for overall search users, smoothed by 7-day average.\n\n- **hls_covid19_usa.covid19_usa_smoothed_wcli_daily_cmu**\n - **Update frequency:** daily\n - **Source:** Delphi Research Group - COVID-19 Trends and Impact Survey\n - **Description:** Estimated percentage of people with COVID-like illness\n\n- **hls_covid19_usa.covid19_usa_smoothed_whh_cmnty_cli_daily_cmu**\n - **Update frequency:** daily\n - **Source:** Delphi Research Group - COVID-19 Trends and Impact Survey\n - **Description:** Estimated percentage of people reporting illness in their local community, including their household\n\n- **hls_covid19_usa.covid19_usa_smoothed_adj_cli_daily_cmu**\n - **Update frequency:** daily\n - **Source:** Delphi Research Group - Doctor Visits\n - **Description:** Estimated percentage of outpatient doctor visits primarily about COVID-related symptoms\n\n- **hls_covid19_usa.covid19_usa_covid_ag_smoothed_pct_positive_daily_cmu**\n - **Update frequency:** daily\n - **Source:** Delphi Research Group - Quidel COVID-19 Tests\n - **Description:** Percentage of antigen tests that were positive for COVID-19 (all ages), smoothed by pooling together the last 7 days of tests\n\n- **hls_covid19_usa.covid19_usa_confirmed_7dav_incidence_prop_daily_cmu**\n - **Update frequency:** daily\n - **Source:** Delphi Research Group - JHU Cases and Deaths\n - **Description:** Number of new confirmed COVID-19 cases per 100,000 population, daily\n\n- **hls_covid19_usa.covid19_usa_confirmed_admissions_covid_1d_prop_7dav_daily_cmu**\n - **Update frequency:** daily\n - **Source:** Delphi Research Group - Department of Health & Human Services\n - **Description:** Sum of adult and pediatric confirmed COVID-19 hospital admissions occurring each day, per 100,000 population\n\n- **hls_covid19_usa.covid19_usa_deaths_7dav_incidence_prop_daily_cmu**\n - **Update frequency:** daily\n - **Source:** Delphi Research Group - JHU Cases and Deaths\n - **Description:** Number of new confirmed deaths due to COVID-19 per 100,000 population, daily\n\n\n\n**Data Sources**\n\nThe data in this product comes from the following data sources:\n\n\n**Delphi Research Group - COVID-19 Trends and Impact Survey**\n\nPer the Delphi Research Group, this data source is based on the COVID-19 Trends and Impact Survey (CTIS) run by the Delphi group at Carnegie Mellon. Facebook directed a random sample of its users to these surveys, which were voluntary. Users age 18 or older were eligible to complete the surveys, and their survey responses are held by CMU and are sharable with other health researchers under a data use agreement.\n\nData from Delphi COVIDcast. Obtained via the Delphi Epidata API. https://cmu-delphi.github.io/delphi-epidata/api/covidcast.html\n\n\n\n- **Source Homepage:** [Delphi Research Group (CMU)](https://delphi.cmu.edu/)\n- **Source Data:** [Delphi Research Group - COVID-19 Trends and Impact Survey](https://cmu-delphi.github.io/delphi-epidata/api/covidcast-signals/fb-survey.html)\n- **License:** [CC BY](https://creativecommons.org/licenses/by/4.0/)\n- **Frequency:** static\n\n\n\n**Delphi Research Group - Google Symptoms**\n\nPer the Delphi Research Group, this data source is based on the COVID-19 Search Trends symptoms dataset. Using this search data, they estimate the volume of searches mapped to symptom sets related to COVID-19.\n\nMore information can be found on the Delphi Group [Google Symptoms](https://cmu-delphi.github.io/delphi-epidata/api/covidcast-signals/google-symptoms.html) page.\n\nData from Delphi COVIDcast. Obtained via the Delphi Epidata API. https://cmu-delphi.github.io/delphi-epidata/api/covidcast.html\n\n\n\n- **Source Homepage:** [Delphi Research Group (CMU)](https://delphi.cmu.edu/)\n- **Source Data:** [Delphi Research Group - Google Symptoms](https://cmu-delphi.github.io/delphi-epidata/api/covidcast-signals/google-symptoms.html)\n- **License:** [Google Terms of Service](https://policies.google.com/terms)\n- **Frequency:** static\n\n\n\n**Delphi Research Group - Doctor Visits**\n\nPer the Delphi Research Group, this data source is based on information about outpatient visits, provided to them by health system partners.\n\nMore information can be found on the Delphi Group [Doctor Visits](https://cmu-delphi.github.io/delphi-epidata/api/covidcast-signals/doctor-visits.html) page.\n\nData from Delphi COVIDcast. Obtained via the Delphi Epidata API. https://cmu-delphi.github.io/delphi-epidata/api/covidcast.html\n\n\n\n- **Source Homepage:** [Delphi Research Group (CMU)](https://delphi.cmu.edu/)\n- **Source Data:** [Delphi Research Group - Doctor Visits](https://cmu-delphi.github.io/delphi-epidata/api/covidcast-signals/doctor-visits.html)\n- **License:** [CC BY](https://creativecommons.org/licenses/by/4.0/)\n- **Frequency:** daily\n\n\n\n**Delphi Research Group - Quidel COVID-19 Tests**\n\nPer the Delphi Research Group, this data source is based on COVID-19 Antigen tests, provided to them by Quidel, Inc.\n\nMore information can be found on the Delphi Group [Quidel](https://cmu-delphi.github.io/delphi-epidata/api/covidcast-signals/quidel.html) page.\n\nData from Delphi COVIDcast. 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This table uses the COVID-19 Hospitalization: States - daily resolution, state aggregates dataset, which contains dozens of columns that break down hospital resource usage in different ways.\n\nMore information can be found on the Delphi Group [Department of Health & Human Services](https://cmu-delphi.github.io/delphi-epidata/api/covidcast-signals/hhs.html) page.\n\nData from Delphi COVIDcast. 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The dataset combines a property CLIP® identifier, standardized Cotality data, 40+ appended public record elements, nearly 500 listing attributes, and optional property images. \nData is sourced directly from more than 130 multiple listing organizations through the Partner InfoNet Program (PIN) and includes over a decade of historical coverage. This approach delivers a consistent, normalized view of listing activity across regional MLS boards—making it easier to analyze market behavior at scale. \n\nMLS photos are delivered through a secure, Cotality-encrypted path, enabling licensed clients to access property images via web services. The dataset also supports valuation, risk analysis, and location enrichment by providing detailed, property-level listing and historical information.\n\n**Market Analysis**\nComprehensive data that can be used to analyze the real estate market. Businesses can gain insights into housing trends, pricing patterns, and property features, enabling them to make informed decisions about property investments or development plans.\n**Asset Valuation**\nThe detailed information provided in MLS Listings helps validate the value and features of a property asset. This can be useful for lenders, investors, and property management companies seeking to confirm property details or estimate a property's market value.\n**Risk Analysis**\nBy providing a comprehensive view of a property’s history, including previous listing prices and days on market, MLS Listings can assist businesses in assessing risks associated with property investments.\n**Location Data Enrichment**\nMLS Listings enrich location data with specific details about a property and its surrounding area. This information can enhance decision-making in areas such as site selection, property investment, or targeted marketing campaigns.\n**Pricing Analysis** \nWith detailed property pricing information, businesses can perform in-depth pricing analyses to understand market trends, inform pricing strategies, or estimate property values.\n**Customer Acquisition**\nReal estate agents, brokers, and other professionals can use MLS Listings to identify potential properties that align with their clients' needs and preferences, aiding in customer acquisition efforts.","terms_of_service":"https://www.cotality.com/","documentation_link":"https://www.cotality.com/products/mls-listings?utm_campaign=412443580-EDSG-ALL-CloudMarketplace-Databricks-2026&utm_source=databricks&utm_medium=marketplace&utm_term=mlslistings","privacy_policy_link":"https://www.cotality.com/","assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"Cotality","description":"We illuminate data, insights, and workflows across the property ecosystem, analyzing the largest objective dataset in the U.S. for real-time insights in minutes, not months. 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This leads to inefficiencies in working capital management, missed discount opportunities, and increased compliance risk.\n\nThe **Dataplatr AP Accelerator** is a ready-to-use solution that streamlines the ingestion, transformation and analysis of **Accounts Payable** data from ERP systems such as **Oracle EBS**, **Oracle Fusion**, **SAP** and **NetSuite**. Built on the proven medallion architecture (Bronze–Silver–Gold) using Delta Lake and configuration-driven execution, the accelerator provides audit-ready, compliance-focused outputs while ensuring scalability for enterprise AP use cases.\nBy leveraging advanced analytics and automation, the Dataplatr AP Accelerator empowers enterprises to gain real-time insights into payment cycles, identify bottlenecks and proactively mitigate risks associated with delayed or missed payments. Its intuitive dashboards and reporting capabilities enable finance teams to optimize working capital, strengthen vendor relationships and improve overall AP efficiency, while reducing manual effort and ensuring data consistency across diverse ERP environments \n\n**Business Challenge**\n**The AP Analytics Challenge** -Most enterprises struggle with - \n\n- **Disconnected Payment Systems** Multiple payment platforms creating data silos across EBS AP runs , Oracle Payments/IBY and bank portals; multi-org (operating units) fragmentation; inconsistent supplier/site IDs and bank accounts leading to duplicate/late payments and difficult enterprise cash positioning.\n- **Manual Reconciliation Processes**: Time-intensive two/three-way matching across PO , receipts, and invoices; frequent price/quantity/tax variances and AP_HOLDS_ALL resolutions; prepayment applications and unmatched receipts; unaccounted transactions delaying period close and accruals; partial payments and write-offs requiring manual review.\n- **Limited Cash Flow Visibility**:Poor forecasting of near-term obligations due to scattered payment schedules, complex terms/discounts, installment invoices, currency effects, payment runs in-flight (PPR status in IBY), and invoices stuck in approval/hold not reflected in cash projections.\n- **Vendor Relationship Gaps:** Insufficient insights into supplier performance (on-time payments, discount capture, dispute cycle time), master-data quality issues, and inconsistent remit-to usage; limited view of chargebacks/credit memos and aging leading to strained negotiations and missed early-pay opportunities.\n\n**The Dataplatr AP Solution**\nOur AP Accelerator transforms these challenges into competitive advantages by providing:\n\n- **Unified Payment Analytics** Consolidates invoice, supplier, and payment data across Oracle EBS AP, Oracle Payments, and bank systems into a single analytical model. This delivers an enterprise-wide view of liabilities, payment runs, and settlement trends across multiple operating units and ledgers.\n- **Automated Invoice Processing** Tracks invoices from receipt to final payment, highlighting delays in matching, holds, or approvals. Exception dashboards provide drill-through into root causes (pricing mismatches, missing receipts, tax variances) to reduce cycle time and automate manual reconciliations.\n- **Predictive Cash Flow Management** Integrates payment schedules, terms, discounts, and approval status with historical payment trends. Machine learning models project future cash outflows, discount capture opportunities, and liquidity requirements—giving finance leaders actionable visibility into working capital.\n- **Vendor Performance Intelligence** Measures supplier reliability with metrics on invoice disputes, on-time payments, credit memo utilization, and discount capture rates. Enables supplier scorecards to improve negotiations, foster stronger partnerships, and ensure procurement objectives align with AP execution.\n- **Audit-Ready Compliance** Provides a complete lineage of invoices, approvals, payments, and accounting entries. Automates regulatory reporting (e.g., 1099, VAT, WHT, TDS), enforces segregation-of-duties monitoring, and ensures compliance through standardized dashboards and exportable audit packs.\n\n**Medallion Architecture Implementation**\n**Bronze Layer (Raw AP Data Ingestion)**\n- **Invoice Data**: Header and line-item details ingested from Oracle EBS (AP_INVOICES_ALL, AP_INVOICE_LINES_ALL) and other AP systems.\n- **Vendor Master**: Supplier records, banking details, and payment terms sourced from AP_SUPPLIERS and AP_SUPPLIER_SITES_ALL.\n- **Purchase Orders**: PO headers and lines (PO_HEADERS_ALL, PO_LINES_ALL) captured for three-way matching and spend visibility.\n- **Payment Records**: Disbursements, payment methods, and settlement status from AP_PAYMENT_SCHEDULES_ALL and AP_CHECKS_ALL.\n- **Receipt Data**: Goods and services receipts from RCV_TRANSACTIONS for matching with invoices and POs.\n\n**Silver Layer (Cleansed & Standardized AP Data)**\n- **Unified Invoice Repository**: Deduplicated invoices across multiple systems with standardized tax, date, and GL coding formats.\n- **Vendor Master Data**: Cleansed and enriched supplier master with duplicate elimination, data quality scoring, and banking validation.\n- **Standardized Chart of Accounts**: Harmonized expense and liability categorization across operating units and ledgers.\n- **Payment Harmonization**: Normalized payment transactions with cross-ledger, cross-currency standardization for consolidated reporting.\n- **Exception Management**: Categorized exceptions (e.g., missing PO, unmatched receipt, tax mismatch) with automated workflows and resolution tracking.\n\n**Gold Layer (Business-Ready AP Analytics)**\n- **Cash Flow Projections**: Forward-looking payment obligations and cash requirements\n- **Vendor Performance Scorecards**: Multi-dimensional assessment of vendors based on on-time payments, disputes, credit memo usage, and discount capture.\n- **Compliance Dashboards**: Audit-ready reports with invoice lifecycle traceability, 1099/TDS/VAT compliance monitoring, and segregation-of-duties checks.\n- **Executive KPI Views**: High-level AP metrics such as Days Payable Outstanding (DPO), discount utilization, exception aging, and AP cycle time for CFO reporting.\n\n**Vendor Intelligence & Risk Management**\n- **Vendor Payment Risk Monitoring**: Identifies vendors with chronic overdue invoices and maps impact on supply chain continuity.\n- **Early Payment Discount Optimization**: Highlights opportunities to capture discounts by accelerating payments.\n- **Contract Compliance Analytics**: Tracks adherence to vendor terms, late-payment penalties and contractual obligations.\n\n \n**Marketplace Deliverables**\n- **Production-Ready Notebooks**: Complete AP data pipeline with error handling and logging\n- **Config Templates**: Pre-configured JSON files for Oracle EBS, Fusion, SAP and NetSuite\n- **Delta Table Schemas**: Optimized table structures for Bronze, Silver and Gold layers\n- **CDC Framework**: Change data capture implementation for real-time data processing\n- **Data Quality Framework**: Automated validation rules and exception handling processes\n\n**Documentation & Support**\nImplementation Guide: Step-by-step setup instructions with best practices\nConfiguration Manual: Detailed explanation of all configurable parameters\n \n\n**Why Choose Dataplatr AP Accelerator**\n\n- **AP Domain Expertise** Built by financial process experts with deep understanding of accounts payable operations, vendor management, and cash flow optimization—not generic data engineers.\n- **Minimal Code, Maximum Reuse** Configuration-first setup supports new tables without rewriting pipelines\n- **Enterprise-Grade Security & Compliance** Built-in audit trails, data lineage tracking and compliance frameworks ensure regulatory requirements are met from day one.\n- **Cost-Effective Solution** Leverage existing Databricks investments while eliminating expensive third-party AP analytics tools and reducing operational costs.\n- **Proven Results** Based on real-world implementations with measurable ROI and documented success across multiple industries and organizational sizes. \n- **Optimized for Reporting** Delivers curated, audit-ready financial outputs for BI and compliance reports\n \n\nTransform your accounts payable operations from reactive payment processing to predictive cash flow management with Dataplatr's AP Accelerator. Built on Databricks best practices with finance-first design, this solution delivers immediate efficiency gains while providing the foundation for advanced AI-driven AP optimization. \n\nExplore our Medium articles for deeper insights and real-world implementations of our Databricks-based analytics solutions.\n[Transform financial decision-making with Databricks](https://medium.com/@dataplatr/empowering-financial-decision-making-using-databricks-and-power-bi-insights-5beb6ee7a08c) using CDC-driven pipelines and Lakehouse architecture to deliver actionable insights across GL, AP, AR, and Fixed Assets with automated, finance-ready data models.\n\nNote: By clicking '**Get Access**', you agree to our **Terms of Service**. You acknowledge that **Dataplatr Corp** will recognize your organization as a customer/user for promotional and reference purposes.\n\n\nContact us at: chandra.reddy@dataplatr.com\nFor consultations or custom inquiries: https://dataplatr.com/contact-us ","terms_of_service":"https://docs.google.com/forms/d/e/1FAIpQLSdnlNmSfrFRQDa_ToOL3ydTOPcWI3m3lWor2plLbiqF1JRINg/viewform","documentation_link":"https://medium.com/@dataplatr/empowering-financial-decision-making-using-databricks-and-power-bi-insights-5beb6ee7a08c","privacy_policy_link":"https://dataplatr.com","embedded_notebook_file_infos":[{"id":"0cc67632-2551-4110-a692-8a1872d1e45b","marketplace_file_type":"EMBEDDED_NOTEBOOK","file_parent":{"parent_id":"5dcd1de9-74b6-44cf-834c-dbc9a3f60d43","file_parent_type":"LISTING"},"mime_type":"text/html","download_link":"https://marketplace-file-storage-prod.s3-fips.us-east-1.amazonaws.com/EMBEDDED_NOTEBOOK/LISTING_5dcd1de9-74b6-44cf-834c-dbc9a3f60d43/0cc67632-2551-4110-a692-8a1872d1e45b?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEGgaDnVzLWVhc3QtMS1maXBzIkYwRAIgbC8sWIoYaHR8y7Wq86nPb0pufBl4%2BM4PWpeN%2B5PW9VQCIG7BhAVBDCWTpxFDLeuKYm%2Ff1JLz4iccPMzter0YdhgWKqYECDAQABoMNDE0MzUxNzY3ODI2IgxNAJrHbXaZG20jNfMqgwQyGEb4TsoVOoNvJ%2BaA%2FZ7zeu8hGcHqCeNfblP%2FQo5g6q2aRNtpwtPRKMlhYcIE%2FUQaNT1Lqmyptu32VUQTpzjTTxRVmJrDPkheSCkV06XQ%2FpTMKjU8CC7X1b%2FqYf8CrBwZT7Cv4C5WPjEET38f2A79h0xV6bZgPy28zQWrSeIJ9w6AfZBxiE24%2FVc08zVA0ivCT6lsy7nuU0wcR%2FMg2C9nd1vaMnQ923VpD21CKfVVYcrNWL2b%2FQ%2BNAO%2BxPjPgjiN6UPQbvbvZxkSdhgrGtecIg9NgqGkMcvM5VLTDtdC1kdeZetfOC2obtA%2BT%2F0gy3tIDo6WM5hcQfw743%2FAU9oaGnRBl%2BwoxRRDCZA%2F4jnPdluICSI7NCr6C3HHOe4CSZJjRihczxPA%2FgQczAVjAPs6CrK9N9pIzt0vNJpGUTgg4AoxftTvGfciDREilSctPIcBk6GwB%2Bzt4wbtNCmEPU%2BFD46rc6CWVQmmLwbKOeHw70ipMVFlOc8NlXkEtGO2vu6H44MstSSc6daOVUYUavQSGZ6jGhEjsP2YGz8KDm9XQotB%2FtyVBqiZV01OKqDmmBJA%2FXjUpSJ8M%2FeJXhe5m%2BKcxzNObtoJRI7jci3Tpt08XZyXZ7%2FyIV8H30ya468X3cfkTXrm972uga3z7iXAdubyEHnQIua%2FuvtwOHILi6O5D1Me2MzCPvcnXBjqTAYDoMWBt%2B3AVFuqSs2dmDQNBnvEhXfMgTedKLD5PJHb3aIl15iBkLaNu36GYnVCp9ZU2lSYcTbY5QwNnNeTfJTNy9bKhwzru6Sg%2BslhN%2BTS0zkTn%2BeLMY4tHgPEtEZ2cp%2FCk6GwqswvnYVKHjwO8q%2B1Mx8lc08fVEfarE9dke1GGFqoddx%2Bq%2BZUyHbDlAVfrCmf0yw%3D%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20261110T000135Z&X-Amz-SignedHeaders=host&X-Amz-Expires=86400&X-Amz-Credential=ASIAWA6KKHEJORWBH4N3%2F20261110%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=82d3de5bb535a41e464fcca4e3ad7b5ef4fe9bea5364bedf3f4cdde70a66c38c","created_at":1770817190469,"updated_at":1770817190469,"display_name":"Finance_AP_Accelerator_Oracle_v1","status":"FILE_STATUS_PUBLISHED"}],"assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://dataplatr.com/"},"provider_summary":{"name":"Dataplatr Corp","description":"Dataplatr Corp specializes in Data Analytics & AI with pre-built data models for Enterprise Applications like SAP, Oracle EBS, Workday, Salesforce to empower businesses to unlock the full potential of their data. 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But in this increasingly integrated and globalized trading environment, understanding the correlation between regions and sectors in order to compete effectively has never been more important.\n\nThis is why we have developed the new Platts Market Data – Refined package which provides access to the full breadth and depth of our refined market data, including benchmarks and contract price assessments, plus:\n\n– A more connected view of the influences on the price of refined products through access to the industry’s only integrated data sets across commodities, regions and verticals\n\n– Access to spot to rack PADDs prices.\n\n– Diverse new data sets to stimulate fresh thinking and inform new strategies\n\n– Historical data to enable you to look at past market trends to inform your future plans and back testing needs.\n\n– Enterprise-wide access to our refined products market data, making it available to both those who find it essential and those who find it useful. Combining this with the power of Platts standardized benchmark data will drive consistency and reduce basis risk throughout your organization — from strategy, trading and planning, to treasury and asset optimization.\n\n– A robust and transparent methodology that brings consistency to our refined products price assessments through principles and parameters that guide our market specialists at every stage of the process.\n\nMarkets Covered:\n– Bunker Fuel\n– Fuel Oil\n– Gasoil, gasoline and heating oil\n– Distillates\n– Liquid Petroleum Gas (LPG) and Natural Gas Liquids (NGLs)\n– Lubricants and Methyl Tert-Butyl Ether (MTBE)\n– Feedstocks\n– Naphtha\n\nDataset Overview:\n\nPlatts Market Data – Refined Key Market Data and Benchmarks\n\nNorth America\n-North American refined products\n–US Fuel Oil\n–US LPG\n\nEMEA\n-MOPS gasoil\n-MOPAG refined products\n-MOPFUJ Fujairah refined products\n\nAPAC\n-MOPS (FOB Singapore) refined products\n-MOPJ naphtha\n\nLATAM\n-Brazilian refined products\n\n**Use cases**\n\n- Asset Valuation - Evaluate ROI for Energy projects and assets\n- Quantitative Analysis - Leverage SPGE data to design and implement complex and insightful models to support energy and commodities trading\n- ESG Investment Analysis - Combine SPGE data with project data-sets into models for visibility, optimizations, and collaboration\n- Risk Analysis - Utilize SPGE's independent, verifiable Risk data to support all risk management functions including validation of internal valuations and better align risk tolerance with business strategy\n- Fundamental Analysis - Connect SPGE pricing data with financial and operations data, and sensing technology to optimize decision-making on spending, productivity and cycle time\n- Pricing Analysis - Enhance refined products pricing benchmarking, crack spread/margin analysis, and volatility trend monitoring by integrating Platts refined pricing signals into internal analytics to support procurement, trading, and risk management decisions.\n\n**Product details**\n\nSample Tables:\nA ) PRICEDATA\nB ) REF_DATA_MARKETDATA\n\nSample Fields:\nA ) PRICEDATA\n- SYMBOL\n- MDC\n- DESCRIPTION\n- BATE\n- ASSESSDATE\n- UOM\n- CURRENCY\n\nB ) REF_DATA_MARKETDATA\n- COMMODITY\n- DELIVERY_REGION\n- DESCRIPTION\n\nView Descriptions:\n\n- PRICEDATA - Get the latest and historical assessments by Symbol\n- CORRECTIONS - Get all corrections to the assessments by Symbol\n- REF_DATA_MARKETDATA - Get additional reference data for Symbols\n- MARKETDATA_DICTIONARY - Contains descriptions and examples of each of the columns in the above views for a better understanding of the dataset","terms_of_service":"https://www.spglobal.com/en/privacy/privacy-policy-english","documentation_link":"https://www.marketplace.spglobal.com/en/datasets/platts-market-data-oil-(28)","privacy_policy_link":"https://www.spglobal.com/en/privacy/privacy-policy-english","geographical_coverage":"[\"Global\"]","cost":"PAID","update_frequency":{"interval":1,"unit":"SECOND"},"collection_granularity":{"interval":1,"unit":"SECOND"},"assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"S&P Global Energy","description":"At S&P Energy, our complete view of global energy and commodities markets enables our customers to make decisions with conviction and create long-term, sustainable value. 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S&P Global Energy is a division of S&P Global (NYSE: SPGI)."},"popularity":1269},{"id":"1651a9f3-3987-49b9-9331-38266aa15e78","summary":{"name":"Price Transparency Data Sample | United MA Pediatrics - CPT 99213","subtitle":"In-Network Negotiated Rates for Pediatrics providers in MA\n","share":{"name":"share_united_ma_pediatrics","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1764882074446,"updated_at":1764882074446,"categories":["HEALTH","ECONOMICS"],"listingType":"STANDARD","provider_id":"9fb6a4f6-92cc-4ba3-85d6-9b2075b783db"},"detail":{"description":"This sample contains negotiated reimbursement rates sourced from Transparency in Coverage machine-readable files (MRFs).\n\nUnlike other datasets that only cover select plans and networks, PayerPrice processes every available file across all network types (PPO, HMO, EPO, ACO, self-insured) each month.\n\nRates are normalized and mapped across all code types, with EIN, TIN, NPI, and CCN relationships mapped. Medicare baselining and claims-driven filtering intelligently remove \"ghost\" rates to deliver you the most accurate reimbursement data.\n\nThis sample includes 100 rows of **United's rates for CPT 99213 in MA covering Pediatrics providers**. The full dataset includes all commercial payers nationwide, including all covered services and provider types.\n\nFor full access to all codes, payers, and states nationwide, please contact us for a quote.\n\n**Use Cases:**\n\n**Reimbursement Benchmarking**\nCompare negotiated reimbursement rates across payers, specialties, and regions.\n\n**Risk Analysis**\nCompare different payer networks to evaluate how much your prospective claims volume would cost across networks.\n\n**Life Sciences Commercialization**\nIdentify highly-reimbursed prospects to prioritize your sales targeting and market access strategy.\n\n**Market Analysis**\nEvaluate provider density and healthcare network adequacy across markets. Easily identify underserved markets with low competition.\n\n**Revenue Cycle Management**\nCompare billed and paid claim amounts to actual contracted rates to ensure that your revenue is accurate. Automatically refresh payer fee schedules monthly, sourced directly from what payers are publishing.\n","terms_of_service":"https://payerprice.com/terms-of-use","documentation_link":"https://payerprice.com/solutions/api","privacy_policy_link":"https://payerprice.com/privacy-policy","geographical_coverage":"[\"United States and Canada\"]","cost":"FREE","update_frequency":{"interval":1,"unit":"MONTHLY"},"collection_granularity":{"interval":1,"unit":"MONTHLY"},"collection_date_start":1754078374494,"collection_date_end":1759348778310,"data_source":"Machine Readable Files published by commercial insurers under the Transparency in Coverage Act.","assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://payerprice.com/terms-of-use"},"provider_summary":{"name":"PayerPrice","description":"PayerPrice shows you exactly what commercial health insurers pay providers, using every available Transparency in Coverage file across all reported plans – not just a subset of popular PPOs or HMOs.\n\nWe process data from 10,000+ files and organize it across CPT, HCPCS, DRG, and RC codes so it’s ready for real contract analysis. Health systems, RCM vendors, and managed care teams use PayerPrice to benchmark competitor contracts, negotiate stronger rates, reprice claims, and identify underpayments."},"popularity":528},{"id":"6c736415-dec5-41b8-8f72-3a26f3f2385d","summary":{"name":"Geolocet | Latest Socio-demographic Data Sweden - 480+ attributes","subtitle":"Demographic data for the municipalities (kommun) of Sweden and their population - age, gender, nationality, ...","setting":{"visibility":"PUBLIC"},"created_at":1705994857841,"updated_at":1752136441420,"categories":["DEMOGRAPHICS","ADVERTISING_AND_MARKETING","RETAIL","PUBLIC_SECTOR","GEOSPATIAL"],"listingType":"PERSONALIZED","provider_id":"5d3ddc17-a7e8-4794-a760-13587e1de2c6"},"detail":{"description":"## **Get instant access to the latest Demographics Data for Sweden with [Geolocet](https://geolocet.com)**\n\nExplore the richness of Sweden’s demographic landscape with our Sweden Census Data. This meticulously compiled dataset, sourced from official statistical sources, including Statistics Sweden, offers a detailed insight into the population landscape of Sweden at the municipality (kommun) level. \n\nIf you require data at a less granular level, such as an Uber grid or square grid, please don't hesitate to get in touch with us at *contact@geolocet.com*. We understand that different projects may have varying data needs, and we are here to tailor our data to match your specific requirements.\n\n ## **Key Features:**\n---\n* **Population Data Sweden:** Gain access to the latest and most accurate population statistics for Sweden, meticulously organized by municipality (kommun).\n\n* **Demographic Insights:** Dive deep into Sweden’s demographic landscape with data on 480+ attributes, including: \n* Gender,\n* Age,\n* Nationality,\n* Nationality split by gender,\n* Age - 5-years age bands,\n* Gender split by 5-years age bands,\n* Nationality split by 5-years age bands,\n* Nationality split by gender and 5-years age bands,\n* Employment status,\n* Employment status split by age bands,\n* Education,\n* Education split by gender,\n* Education split by age bands,\n* Education split by gender and age bands.\n\nAll demographic attributes are available at the municipal (kommun) level.\n\n* **Geodemographic Insights:** Unlock geodemographic data Sweden-wide to inform location-based decision-making, marketing strategies, and policy formulation.\n* **Seamless Data Access:** Our data is available for instant download in CSV format. No lengthy sign-up processes or NDAs required on our side – just download the data you need.\n* **Data Samples for Evaluation:** To ensure you're getting exactly what you need, we provide free downloadable samples in CSV format for data evaluation.\n* **Data accuracy:** Our datasets are created using the latest demographics data available in national statistics bureaus, guaranteeing accuracy and reliability.\n* **Best-in-Class Quality:** We guarantee best-in-class quality, backed by over two decades of experience in modeling demographics data and an outstanding quality management process.\n* **Competitive Pricing:** We maintain the lowest prices on the market, ensuring that you get the most cost-efficient demographics data acquisition process.\n---\nAdditionally, for those looking to enrich their analysis or project, we offer:\n* **[Administrative Boundaries](https://geolocet.com/products/sweden-all-available-administrative-boundaries):** If you require geographical boundaries (polygons) for the municipalities of Sweden, consider exploring our Administrative Boundaries product, which seamlessly integrates with this dataset.\n\n* **[Point of Interest (PoI) Data](https://geolocet.com/pages/aggregated-points-of-interest-poi-data):** If you're interested in Points of Interest data, please don't hesitate to contact us for customized datasets to enhance your geographical analysis.\n\n* **[Data Customization](https://geolocet.com/pages/data-services-by-geolocet):** We understand that your project may have specific requirements. Feel free to get in touch with us if you need any modifications or specific data formats such as Excel, JSON, GeoJSON, or Shapefile. We are here to tailor the data to your needs.\n\nIf you're looking for data for Sweden, [look here](https://geolocet.com/pages/sweden) for off-the-shelf or tailor-made datasets to suit your specific requirements.\n\nMake informed decisions, conduct precise research, and unlock the potential of Sweden's demographic landscape with our comprehensive dataset. Trust in the accuracy and reliability of our official statistical data to drive your projects forward.\nContact us at *contact@geolocet.com* today to access this invaluable resource and explore Sweden census data like never before.\n\n[Geolocet LTD](https://geolocet.com)\n","terms_of_service":"https://geolocet.com/pages/terms-of-use","documentation_link":"","privacy_policy_link":"https://geolocet.com/pages/privacy-policy","geographical_coverage":"[\"Europe\"]","cost":"PAID","update_frequency":{"interval":1,"unit":"YEARLY"},"data_source":"","assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://geolocet.com/pages/terms-of-use"},"provider_summary":{"name":"Geolocet LTD","description":"Geolocet provides premium geospatial data for retail analysis, GIS, and market planning. We equip businesses with accurate, ready-to-query data to drive spatial analysis, site selection, and customer segmentation.\n\nCore Offerings:\n\nDemographics: Socioeconomic data covering population, age, and employment across diverse geographies.\n\nPoints of Interest (PoI): Datasets spanning retail, healthcare, and transport for catchment analysis.\n\nAdministrative Boundaries: GIS-ready boundaries, including municipalities and postcodes, for territory management.\n\nBrand Locations: Chain location datasets to monitor store networks and market expansion.\n\nWealth Data: Granular economic indicators like purchasing power to identify high-potential markets.\n\nWe source from trusted providers, delivering highly customizable datasets with comprehensive coverage across Europe and worldwide."},"popularity":363},{"id":"f2c8f388-b23a-4a9d-9a10-14838f96e991","summary":{"name":"Commodities at Sea","subtitle":"Commodities at Sea offers near real-time visibility into vessel-by-vessel cargo intelligence.","setting":{"visibility":"PUBLIC"},"created_at":1761758714739,"updated_at":1789549078557,"categories":["ENERGY","FINANCIAL"],"listingType":"PERSONALIZED","provider_id":"eff4b18d-fe1b-4884-9591-eab9d16d2e2a"},"detail":{"description":"**Overview**\n\nCommodities at Sea (CAS): Commodity Movements (Liquid & Dry Bulk) datasets, provides transparency into market movements with near real-time visibility into supply and demand through the waterborne flows of major traded liquid and dry bulk commodities and through sophisticated fleet metrics.\nYou can utilize this data to elevate your market analysis to make winning decisions into the physical, financial and freight markets that drive the global economy. The service is used to help traders and analysts, investors and portfolio managers, charterers, and fleet operators.\n\nThis dataset includes:\n- Intra-day updates with users able to access fresh data twice daily, 7 days a week, 365 days per year\n- Historical loadings data dating back to 2016, with a complete accounting of daily loading and discharges around the world beginning January 2017\n- Global waterborne commodity flows at the terminal, port, country, region and even company level of detail\n- Cross-commodity / cross-sector visibility on all major commodities moved on oil and products tankers, LPG carriers and dry bulk carriers. Commodities covered include Crude Oil and Refined Products, NGLs, Coal, Industrial Raw Materials & Minerals, Agricultural Products (Grains, Sugar, etc) and Fertilizers\n\n**Use cases**\n- Fundamental Analysis - Commodities at Sea (CAS) datasets offer near-real time visibility into commodity trade flows (imports/exports), waterborne and onshore inventories and fleet dynamics that collectively can help users better understand shifts in supply and demand and support the construction of short term market outlooks / forecasts for trade and the price of commodities and freight.\n\n- Market Analysis - Commodities at Sea (CAS) datasets offer near-real time visibility into commodity trade flows (imports/exports), waterborne and onshore inventories and fleet dynamics that is built around ship-specific cargo details and that collectively can help users with a wide variety of market analysis including identifying shifts in supply and demand and building short term market outlooks / forecasts for trade and the price of commodities and freight.\n- Supply Chain - Commodities at Sea (CAS) datasets offer near-real time visibility into cargoes on tankers globally and into the supply chains of commodities such as oil, fuel and NGL/LPG,Coal, Iron Ore, Fertilizer, Agri Bulk and Minor Bulk in ways that can help users to identify disruptions and assess impacts, monitor supply chain chokepoints and spot changes in trade relationships, commodity trade flows (imports/exports), waterborne and onshore inventories and fleet dynamics.\n\n**Product details**\nSample Fields:\nA ) COMMODITY_MOVEMENTS_LIQUIDBULK\nB ) OIL_ON_WATER\n\nSample Fields:\n\nA ) COMMODITY_MOVEMENTS_LIQUIDBULK\n\n- VESSEL_NAME \n- VESSEL_SIZECLASS \n- VESSEL_DWT \n- LOAD_DATE_UTC \n- LOAD_PORT_DEPARTURE_DATE \n- DISCHARGE_COUNTRY_TERRITORY_FIRST_ARRIVAL_DATE \n- DISCHARGE_DATE_UTC \n- DISCHARGE_PORT_ARRIVAL_DATE \n- LOAD_TERMINAL \n- LOAD_PORT \n- LOAD_COUNTRY_TERRITORY \n- DISCHARGE_TERMINAL \n- DISCHARGE_PORT \n- DISCHARGE_COUNTRY_TERRITORY \n- COMMODITY_CATEGORY \n- COMMODITY_TYPE \n- COMMODITY_GRADE \n- COMMODITY_QUANTITY \n\nB) OIL_ON_WATER\n\n- LOAD_COUNTRY_TERRITORY \n- LOAD_REGION \n- DISCHARGE_COUNTRY_TERRITORY \n- DISCHARGE_REGION \n- COMMODITY_QUANTITY_UNITS \n- GLOBAL_REGION\n\nC) VESSEL_POSITION_AGGREGATES_DRYBULK\n\n- SOURCE_ID\n- PARAMETER_DATE_PING\n- PARAMETER_LOCATION_REGION\n- PARAMETER_LOCATION_SUBREGION\n- PARAMETER_LOCATION_BASIN\n- PARAMETER_VESSEL_SIZECLASS\n- PARAMETER_VESSEL_SIZECLASS_SUBCATEGORY\n- VESSEL_COUNT_TOTAL\n- VESSEL_COUNT_LADEN_TOTAL\n- VESSEL_COUNT_BALLAST_TOTAL\n- VESSEL_CAPACITY_DWT_TOTAL_LADEN\n- VESSEL_CAPACITY_DWT_TOTAL_BALLAST\n- VESSEL_CAPACITY_DWT_TOTAL_LADEN_MINUS_BALLAST\n- VESSEL_COUNT_LADEN_ANCHORED\n- VESSEL_COUNT_BALLAST_ANCHORED\n- VESSEL_COUNT_LADEN_MOORED\n- VESSEL_COUNT_BALLAST_MOORED\n- VESSEL_COUNT_LADEN_UNDER_WAY_ENGINE\n- VESSEL_COUNT_BALLAST_UNDER_WAY_ENGINE\n- VESSEL_COUNT_LADEN_OTHER_STATUS\n- VESSEL_COUNT_BALLAST_OTHER_STATUS\n- VESSEL_SPEED_AVERAGE_LADEN\n- VESSEL_SPEED_AVERAGE_BALLAST\n\nD) COMMODITY_MOVEMENTS_DRYBULK\n\n- SOURCE_ID\n- VESSEL_IMO\n- VESSEL_NAME\n- VESSEL_SIZECLASS\n- VESSEL_DWT\n- LOAD_DATE_UTC\n- LOAD_PORT_DEPARTURE_DATE\n- DISCHARGE_DATE_UTC\n- DISCHARGE_PORT_ARRIVAL_DATE\n- LOAD_TERMINAL\n- LOAD_PORT\n- LOAD_COUNTRY_TERRITORY\n- DISCHARGE_TERMINAL\n- DISCHARGE_PORT\n- DISCHARGE_COUNTRY_TERRITORY\n- COMMODITY_CATEGORY\n- COMMODITY_TYPE\n- COMMODITY_GRADE\n- COMMODITY_QUANTITY_TONNES\n- COMPANY_SHIPPER\n- COMPANY_RECEIVER\n- COMPANY_CHARTERER\n- LOAD_ZONE\n- LOAD_REGION\n- DISCHARGE_ZONE\n- DISCHARGE_REGION\n- JOURNEY_STATUS\n- JOURNEY_TYPE\n- IN_TRANSIT_AIS_DESTINATION\n- IN_TRANSIT_AIS_STATUS\n- IN_TRANSIT_DESTINATION_SOURCE\n- LOAD_PORT_ANCHORAGE_ARRIVAL_DATE\n- LOAD_PORT_ANCHORAGE_DEPARTURE_DATE\n- LOAD_PORT_ARRIVAL_DATE\n- LOAD_PORT_DEPARTURE_DATE_LOCAL_OFFSET\n- LOAD_PORT_WAIT_TIME\n- LOAD_TERMINAL_ARRIVAL_DATE\n- LOAD_TERMINAL_DEPARTURE_DATE\n- DISCHARGE_PORT_ANCHORAGE_ARRIVAL_DATE\n- DISCHARGE_PORT_ANCHORAGE_DEPARTURE_DATE\n- DISCHARGE_PORT_ARRIVAL_DATE_LOCAL_OFFSET\n- DISCHARGE_PORT_DEPARTURE_DATE\n- DISCHARGE_PORT_WAIT_TIME\n- DISCHARGE_TERMINAL_ARRIVAL_DATE\n- DISCHARGE_TERMINAL_DEPARTURE_DATE\n- PRIOR_DISCHARGE_COUNTRY_TERRITORY\n- PRIOR_DISCHARGE_PORT\n- PRIOR_DISCHARGE_PORT_DEPARTURE_DATE\n- PRIOR_DISCHARGE_REGION\n- PRIOR_DISCHARGE_TERMINAL\n- PRIOR_DISCHARGE_TERMINAL_DEPARTURE_DATE\n- VESSEL_AGE_DURING_VOYAGE\n- VESSEL_DATE_OF_BUILD\n- VESSEL_FLAG_NAME\n- VESSEL_GROUP_BENEFICIAL_OWNER\n- VESSEL_OPERATOR\n- VESSEL_SCRUBBER_FITTED\n- VESSEL_SCRUBBER_RETROFITTED\n- VESSEL_SCRUBBER_RETROFITTED_YEAR\n- VESSEL_SCRUBBER_TYPE\n- VESSEL_SHIP_MANAGER\n- VESSEL_SIZECLASS_SUBCATEGORY\n- VESSEL_TYPE\n- COMMODITY_GROUP\n- VOYAGE_ID\n- MODEL_RUN_TIMESTAMP\n- JOURNEY_ROUTE\n\nView Descriptions:\n\n- CAS_DICTIONARY - Contains descriptions and examples of each of the columns in the above views for a better understanding of the dataset\n- COMMODITY_MOVEMENTS_LIQUIDBULK - Describes the cargoes, volumes, timing, origins/destinations and related commercial details associated with individual laden voyages of specific oil, products and LPG tankers\n- OIL_ON_WATER - Measures waterborne inventories of crude oil, refined products and NGLs based on cargo volumes at sea over time\n- TEMPORARY_FLOATING_STORAGE_CLEAN - Measures the waterborne inventories of refined products based on the cargo volumes categorized as short or long term temporary storage at sea over time\n- TEMPORARY_FLOATING_STORAGE_DIRTY - Measure the waterborne inventories of crude oil and fuel oil based onthe cargo volumes categorized as short or long term temporary storage at sea over time\n- VESSEL_POSITION_AGGREGATES_LIQUIDBULK - Measures the count and tonnage of the global fleet of oil, products and LPG tankers that are laden or ballast by region - with additional details on speed, cargoes, etc.\n- COMMODITY_MOVEMENTS_DRYBULK - Describes the cargoes, volumes,, volumes, timing, origins/destinations and related commercial details associated with individual laden voyages of specific dry bulk carriers\t \n- VESSEL_POSITION_AGGREGATES_DRYBULK - Measures the count and tonnage of the global fleet of dry bulk carriers that are laden or ballast by region - with additional details on speed, cargoes, etc.\n\n","terms_of_service":"https://www.spglobal.com/en/privacy/privacy-policy-english","documentation_link":"https://www.marketplace.spglobal.com/en/datasets/commodities-at-sea-commodity-movements-dry-bulk-(241)","privacy_policy_link":"https://www.spglobal.com/en/privacy/privacy-policy-english","geographical_coverage":"[\"Global\"]","cost":"PAID","update_frequency":{"interval":1,"unit":"DAILY"},"collection_granularity":{"interval":1,"unit":"SECOND"},"collection_date_start":1451668910361,"assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"S&P Global Energy","description":"At S&P Energy, our complete view of global energy and commodities markets enables our customers to make decisions with conviction and create long-term, sustainable value. 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Our proprietary data processing and filtering methods identify and remove corrupted, duplicated, and low quality records, while additional quality controls evaluate data completeness, latency, consistency, and other integrity factors.\n\nWe also conduct provider level overlap analysis to assess the uniqueness and value of individual data sources. Our data science team evaluates providers based on the contribution of unique data values rather than volume alone, helping maintain a high quality and reliable data product.\n\nTo support privacy and responsible data use, our mobile location data is provided as aggregated insights rather than individual device level movement trails. Location signals are abstracted into standardized geographic areas, and temporal information is provided in aggregated time intervals. Small cohorts are suppressed to help prevent individual re identification.\n\nThe dataset can include aggregated geographic identifiers and mobility attributes designed for analysis while avoiding direct identification of individual devices or physical addresses. Historical data is available through 2022, subject to product availability and applicable data retention policies.\n\nOur in house data science team also provides technical documentation, data processing guidance, location data methodologies, and analytical queries to help data buyers get started quickly. Our goal is to provide reliable, privacy conscious data that is fit for purpose.","terms_of_service":"https://www.quadrant.io/privacy-policy","contact_email":"support@datarade.ai","privacy_policy_link":"https://www.quadrant.io/privacy-policy","assets":["ASSET_TYPE_DATA_TABLE"]},"provider_summary":{"name":"Quadrant","description":"Quadrant, an Appen company, is a global provider of privacy first mobile location data and Point of Interest (POI) data. Our data that helps enterprises, organizations, and innovators understand how people and places interact.\r\n\r\nWith coverage across 200+ countries and territories, 1+ billion opted in unique devices, and 50+ billion daily location events, our mobile location data delivers global scale for location intelligence. Our POI data spans 200+ countries and includes 300+ million places with 50+ verified attributes, enabling organizations to build better maps, search experiences, market intelligence, and location based products. We also offer POI validation, enrichment, and enhancement services to improve existing datasets.\r\n\r\nAs part of Appen, a global leader in AI training data, Quadrant combines global scale, enterprise grade data quality, and privacy first governance to help organizations build the next generation of location enabled products and services."},"popularity":55},{"id":"3df24506-3506-41a1-bd8d-cf29f2b28575","summary":{"name":"SAMPLE B2B Contact Data | B2B Database | Decision Makers | 220M+ Contacts | (Verified E-mail, ...","subtitle":"Boost your business globally with our B2B Contact Data! Connect with over 200M+ verified business contacts anywhere ...","share":{"name":"exellius_systems_cad3f6bc_d02a_4fbd_87b6_242e5512406c_samples_share","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1706552893291,"updated_at":1770893964119,"categories":["OTHER","GEOSPATIAL"],"listingType":"STANDARD","provider_id":"3c844684-0836-4f38-bbd9-35304ff66675"},"detail":{"description":"Global B2B Contact Data Solution\n\nAccess a scalable database of 200M+ verified B2B contacts designed to support targeted outreach, revenue growth, and global market expansion. 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This meticulously compiled dataset, containing modeled data, based on official statistical sources, including Agency of Statistics Bosnia and Herzegovina, offers a detailed insight into the population landscape of Bosnia and Herzegovina at the municipality (grad i opshtina) level. \n\nIf you require data at a less granular level, such as an Uber grid or square grid, please don't hesitate to get in touch with us at *contact@geolocet.com*. We understand that different projects may have varying data needs, and we are here to tailor our data to match your specific requirements.\n\n ## **Key Features:**\n---\n* **Population Data Bosnia and Herzegovina:** Gain access to the latest and most accurate population statistics for Bosnia and Herzegovina, meticulously organized by municipality (grad i opshtina).\n\n* **Demographic Insights:** Dive deep into Bosnia and Herzegovina's demographic landscape with data on 43 attributes, including: \n* Total population,\n* Gender,\n* Age bands,\n* Ethnicity,\n* Ethnicity split by gender,\n* Household size.\n\nAll demographic attributes are available at the municipal (grad i opshtina) level.\n\n* **Geodemographic Insights:** Unlock geodemographic data Bosnia and Herzegovina-wide to inform location-based decision-making, marketing strategies, and policy formulation.\n* **Seamless Data Access:** Our data is available for instant download in CSV format. No lengthy sign-up processes or NDAs required on our side - just download the data you need.\n* **Data Samples for Evaluation:** To ensure you're getting exactly what you need, we provide free downloadable samples in CSV format for data evaluation.\n* **Data accuracy:** Our datasets are created using the latest demographics data available in national statistics bureaus, guaranteeing accuracy and reliability.\n* **Best-in-Class Quality:** We guarantee best-in-class quality, backed by over two decades of experience in modeling demographics data and an outstanding quality management process.\n* **Competitive Pricing:** We maintain the lowest prices on the market, ensuring that you get the most cost-efficient demographics data acquisition process.\n---\nAdditionally, for those looking to enrich their analysis or project, we offer:\n* **[Administrative Boundaries](https://geolocet.com/products/bosnia-herzegovina-admin-level-8-cities-and-municipalities):** If you require geographical boundaries (polygons) for the municipalities of Bosnia and Herzegovina, consider exploring our Administrative Boundaries product, which seamlessly integrates with this dataset.\n\n* **[Point of Interest (PoI) Data](https://geolocet.com/pages/aggregated-points-of-interest-poi-data):** If you're interested in Points of Interest data, please don't hesitate to contact us for customized datasets to enhance your geographical analysis.\n\n* **[Data Customization](https://geolocet.com/pages/data-services-by-geolocet):** We understand that your project may have specific requirements. Feel free to get in touch with us if you need any modifications or specific data formats such as Excel, JSON, GeoJSON, or Shapefile. We are here to tailor the data to your needs.\n\nMake informed decisions, conduct precise research, and unlock the potential of the demographic landscape of Bosnia and Herzegovina with our comprehensive dataset. Trust in the accuracy and reliability of our official statistical data to drive your projects forward.\nContact us at *contact@geolocet.com* today to access this invaluable resource and explore Bosnia and Herzegovina census data like never before.\n\n[Geolocet LTD](https://geolocet.com)\n\n","terms_of_service":"https://geolocet.com/pages/terms-of-use","documentation_link":"","privacy_policy_link":"https://geolocet.com/pages/privacy-policy","geographical_coverage":"[\"Europe\"]","cost":"PAID","assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://geolocet.com/pages/terms-of-use"},"provider_summary":{"name":"Geolocet LTD","description":"Geolocet provides premium geospatial data for retail analysis, GIS, and market planning. 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The goal is to demonstrate how Verticalization/Domain Adaptation for the [insurance] sector can be easily achieved using our two-step approach to LLM Fine-Tuning. An overview of this approach can be found at: [From General-Purpose LLMs to Verticalized Enterprise Models](https://www.bitext.com/blog/general-purpose-models-verticalized-enterprise-genai/)\n\nThe dataset has the following specifications:\n\n- Use Case: Intent Detection\n- Vertical: Insurance\n- 39 intents assigned to 17 categories\n- 39000 question/answer pairs, with approximately 1000 per intent\n- 100 entity/slot types\n- 10 different types of language generation tags\n\nThe categories and intents are derived from Bitext's extensive experience across various industry-specific datasets, ensuring the relevance and applicability across diverse contexts.\n\n**Dataset Token Count**\n\nThe dataset contains a total of 5.13 million tokens across 'instruction' and 'response' columns. 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These tags categorize the utterances into different registers such as colloquial, formal, or containing specific insurance jargon, ensuring that the trained models can understand and generate a range of conversational styles appropriate for different customer interactions in the insurance sector.\n\n**Language Generation Tags**\n\nThe dataset includes tags that reflect various language variations and styles, crucial for creating adaptable and responsive conversational AI models within the insurance sector. These tags help in understanding and generating appropriate responses based on the linguistic context and user interaction style.\n\n**Tags for Lexical variation**\n\n- **M - Morphological variation**: Adjusts for inflectional and derivational forms.\n - Example: \"is my account active\", \"is my account activated\"\n- **L - Semantic variations**: Handles synonyms, use of hyphens, and compounding.\n - Example: “what's my balance date\", “what's my billing date”\n\n**Tags for Syntactic structure variation**\n\n- **B - Basic syntactic structure**: Simple, direct commands or statements.\n - Example: \"activate my card\", \"I need to check my balance\"\n- **I - Interrogative structure**: Structuring sentences in the form of questions.\n - Example: “can you show my balance?”, “how do I transfer money?”\n- **C - Coordinated syntactic structure**: Complex sentences coordinating multiple ideas or tasks.\n - Example: “I want to transfer money and check my balance, what should I do?”\n- **N - Negation**: Expressing denial or contradiction.\n - Example: \"I do not wish to proceed with this transaction, how can I stop it?\"\n\n**Tags for language register variations**\n\n- **P - Politeness variation**: Polite forms often used in customer service.\n - Example: “could you please help me check my account balance?”\n- **Q - Colloquial variation**: Informal language that might be used in casual customer interactions.\n - Example: \"can u tell me my balance?\"\n- **W - Offensive language**: Handling potentially offensive language which might occasionally appear in frustrated customer interactions.\n - Example: “I’m upset with these charges, this is ridiculous!”\n\n**Tags for stylistic variations**\n\n- **K - Keyword mode**: Responses focused on keywords.\n - Example: \"balance check\", \"account status\"\n- **E - Use of abbreviations**: Common abbreviations.\n - Example: “acct for account”, “trans for transaction”\n- **Z - Errors and Typos**: Includes common misspellings or typographical errors found in customer inputs.\n - Example: “how can I chek my balance”\n\n**Other tags not in use in this Dataset**\n\n- **D - Indirect speech**: Expressing commands or requests indirectly.\n - Example: “I was wondering if you could show me my last transaction.”\n- **G - Regional variations**: Adjustments for regional language differences.\n - Example: American vs British English: \"checking account\" vs \"current account\"\n- **R - Respect structures - Language-dependent variations**: Formality levels appropriate in different languages.\n - Example: Using “vous” in French for formal addressing instead of “tu.”\n- **Y - Code switching**: Switching between languages or dialects within the same conversation.\n - Example: “Can you help me with my cuenta, please?”\n\nThese tags not only aid in training models for a wide range of customer interactions but also ensure that the models are culturally and linguistically sensitive, enhancing the customer experience in insurance environments.\n\n**License**\n\nThe `Bitext-insurance-llm-chatbot-training-dataset` is released under the **Community Data License Agreement (CDLA) Sharing 1.0**. This license facilitates broad sharing and collaboration while ensuring that the freedom to use, share, modify, and utilize the data remains intact for all users.\n\n**Key Aspects of CDLA-Sharing 1.0**\n\n- **Attribution and ShareAlike**: Users must attribute the dataset and continue to share derivatives under the same license.\n- **Non-Exclusivity**: The license is non-exclusive, allowing multiple users to utilize the data simultaneously.\n- **Irrevocability**: Except in cases of material non-compliance, rights under this license are irrevocable.\n- **No Warranty**: The dataset is provided without warranties regarding its accuracy, completeness, or fitness for a particular purpose.\n- **Limitation of Liability**: Both users and data providers limit their liability for damages arising from the use of the dataset.\n\n**Usage Under CDLA-Sharing 1.0**\n\nBy using the `Bitext-insurance-llm-chatbot-training-dataset`, you agree to adhere to the terms set forth in the CDLA-Sharing 1.0. 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Information on Inpatient Prospective Payment System (IPPS) payments, Inpatient Rehabilitation Facilities (IRFs)\n\n**Description**\nInformation on Home Health Agency and Hospice providers Utilization and Payment data, Inpatient Prospective Payment System (IPPS) payments for the most frequently billed discharges, paid under Medicare based on a rate per discharge using the Medicare Severity Diagnosis Related Group (MS-DRG), list of inpatient rehabilitation facilities with data on the number of times people with Medicare who had certain medical conditions were treated in the last year. General information on the Inpatient Rehabilitation Facilities (IRFs) and information about the Inpatient Rehabilitation Facilities (IRFs) Prospective Payment System (PPS) Rate Settings and data regarding different measure codes and their corresponding scores for majority of the IRFs. Data on the spending and utilization data in the Home Health Agency PUF are aggregated to the identification number for the agency, the Home Health Resource Group (HHRG) and by state. Inforamation on the annual most frequent hospice diagnoses, the number of patients, and average length of stay. The Medicare Part D Prescriber Public Use File provides information on prescription drug events (PDEs) incurred by Medicare beneficiaries with a Part D prescription drug plan by National Provider Identifier (NPI) and drug name and contains information on drug utilization (claim counts and day supply) and total drug costs. The Skilled Nursing Facility Public Use File contains information on utilization, payment (allowed amount, Medicare payment and standard payment), submitted charges, and beneficiary demographic and chronic condition indicators organized by Centres for Medicare and Medicaid Services Certification Number (6-digit provider identification number), Resource Utilization Group (RUG), and state of service.\n\n**Benefits**\n- Useful to researchers and analysts who seek to compare, understand and find useful information about the geographical variations of medicare costs and financing, utilization and beneficiaries by type of healthcare provider.\n- provides to public health specialists and specialized institutions a complete view, for different categories of healthcare providers, on the supply and costs of healtcare services, covered by medicare. the fields descriptions along with the dataset description are again useful for user to quick understand the data and the dataset.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [HCRIS Hospice Cost Report 1999-2014](https://www.johnsnowlabs.com/marketplace/hcris-hospice-cost-report-1999-2014)\n\t- This dataset Healthcare Cost Report Information System (HCRIS) contains cost and statistical data for free-standing Hospice providers. The dataset includes only the most precise version of each cost report from 1999 to 2014.\n- [HCRIS Hospice Cost Report 1999-2022](https://www.johnsnowlabs.com/marketplace/hcris-hospice-cost-report-1999-2022)\n\t- This dataset Healthcare Cost Report Information System (HCRIS) contains cost and statistical data for free-standing Hospice providers. The dataset includes only the most precise version of each cost report from 1999 to 2022.\n- [Home Health Agency Utilization and Payment Data 2013](https://www.johnsnowlabs.com/marketplace/home-health-agency-utilization-and-payment-data-2013)\n\t- The Home Health Agency Utilization and Payment data 2013 presents information on services provided to Medicare beneficiaries by home health agencies. The Home Health Agency PUF includes data for providers that had a valid identification number and submitted a Medicare Part A institutional claim during the 2013 calendar year. To protect the privacy of Medicare beneficiaries, any aggregated records which are derived from 10 or fewer beneficiaries are excluded.\n- [Home Health Agency Utilization and Payment Data 2014](https://www.johnsnowlabs.com/marketplace/home-health-agency-utilization-and-payment-data-2014)\n\t- The Home Health Agency Utilization and Payment data 2014 presents information on services provided to Medicare beneficiaries by home health agencies. The Home Health Agency PUF includes data for providers that had a valid identification number and submitted a Medicare Part A institutional claim during the 2014 calendar year. To protect the privacy of Medicare beneficiaries, any aggregated records which are derived from 10 or fewer beneficiaries are excluded.\n- [Home Health Agency Utilization and Payment Data 2015](https://www.johnsnowlabs.com/marketplace/home-health-agency-utilization-and-payment-data-2015)\n\t- The Home Health Agency Utilization and Payment Data 2015 presents information on services provided to Medicare beneficiaries by home health agencies. The dataset includes data for providers that had a valid identification number and submitted a Medicare Part A institutional claim during the 2015 calendar year. To protect the privacy of Medicare beneficiaries, any aggregated records which are derived from 10 or fewer beneficiaries are excluded.\n- [Home Health Agency Utilization and Payment Data 2016](https://www.johnsnowlabs.com/marketplace/home-health-agency-utilization-and-payment-data-2016)\n\t- The Home Health Agency Utilization and Payment Data 2016 presents information on services provided to Medicare beneficiaries by home health agencies. The dataset includes data for providers that had a valid identification number and submitted a Medicare Part A institutional claim during the 2015 calendar year. To protect the privacy of Medicare beneficiaries, any aggregated records which are derived from 10 or fewer beneficiaries are excluded.\n- [Hospice Providers Utilization and Payment Data 2014](https://www.johnsnowlabs.com/marketplace/hospice-providers-utilization-and-payment-data-2014)\n\t- The Hospice providers utilization and payment data 2014 provides information on services provided to Medicare beneficiaries by hospice providers. The Hospice PUF contains information on utilization, payment (Medicare payment and standard payment), submitted charges, primary diagnoses, sites of service, and hospice beneficiary demographics organized by CMS Certification Number (6-digit provider identification number), and state.\n- [Inpatient Prospective Payment System Top 100 DRGS 2013](https://www.johnsnowlabs.com/marketplace/inpatient-prospective-payment-system-top-100-drgs-2013)\n\t- The dataset provided here include hospital-specific charges for the more than 3,000 U.S. hospitals that receive Medicare Inpatient Prospective Payment System (IPPS) payments for the top 100 most frequently billed discharges, paid under Medicare based on a rate per discharge using the Medicare Severity Diagnosis Related Group (MS-DRG) for Fiscal Year (FY) 2013. These DRGs represent more than 7 million discharges or 60 percent of total Medicare IPPS discharges.\n- [Inpatient Rehabilitation Facility Conditions](https://www.johnsnowlabs.com/marketplace/inpatient-rehabilitation-facility-conditions)\n\t- This dataset includes the list of inpatient rehabilitation facilities with data on the number of times people with Medicare who had certain medical conditions were treated in the last year.\n- [Inpatient Rehabilitation Facility General Information](https://www.johnsnowlabs.com/marketplace/inpatient-rehabilitation-facility-general-information)\n\t- This dataset shows characteristics of the Inpatient Rehabilitation Facilities (IRFs) that are shown on Inpatient Rehabilitation Facility Compare. It includes a list of IRFs with information such as address, phone number, ownership data and more. This dataset also shows the information about Centers for Medicare & Medicaid Services (CMS) Regions where each of the IRFs are located.\n- [Inpatient Rehabilitation Facility PPS Rate Settings 2017](https://www.johnsnowlabs.com/marketplace/inpatient-rehabilitation-facility-pps-rate-settings-2017)\n\t- This dataset contains the information about the Inpatient Rehabilitation Facilities (IRFs) Prospective Payment System (PPS) Rate Settings for fiscal year 2017. This file contains data for each of the 1,133 inpatient rehabilitation facilities used to estimate the policy updates in the final inpatient rehabilitation prospective payment system regulation.\n- [Inpatient Rehabilitation Facility Provider Data](https://www.johnsnowlabs.com/marketplace/inpatient-rehabilitation-facility-provider-data)\n\t- This dataset shows characteristics of the Inpatient Rehabilitation Facilities (IRFs) that are shown on Inpatient Rehabilitation Facility Compare. It includes a list of IRFs with information such as address, phone number, state, city and county data with the information about Centers for Medicare & Medicaid Services (CMS) Regions where each of the IRFs are located. This dataset also contains data regarding different measure codes and their corresponding scores for majority of the IRFs.\n- [Medicare HHRG Aggregate Table 2013](https://www.johnsnowlabs.com/marketplace/medicare-hhrg-aggregate-table-2013)\n\t- The dataset “HHRG Aggregate Table” contains information on utilization, Medicare payment, and submitted charges organized by HHRG.\n- [Medicare HHRG Aggregate Table By State 2013](https://www.johnsnowlabs.com/marketplace/medicare-hhrg-aggregate-table-by-state-2013)\n\t- The “HHRG by State Aggregate Table” contains information on utilization, Medicare payment, and submitted charges organized by HHRG by state.\n- [Medicare Hospice Cost Report 1999-2009](https://www.johnsnowlabs.com/marketplace/medicare-hospice-cost-report-1999-2009)\n\t- This dataset shows the top 20 hospice diagnoses, the number of patients, and average length of stay for calendar years 1999 to 2009.\n- [Medicare Part D Prescriber Utilization and Payment Data 2013](https://www.johnsnowlabs.com/marketplace/medicare-part-d-prescriber-utilization-and-payment-data-2013)\n\t- The Medicare Part D Prescriber Public Use File provides information on prescription drug events (PDEs) incurred by Medicare beneficiaries with a Part D prescription drug plan for the year 2013. The Part D Prescriber PUF is organized by National Provider Identifier (NPI) and drug name and contains information on drug utilization (claim counts and day supply) and total drug costs.\n- [Medicare Part D Prescriber Utilization and Payment Data 2014](https://www.johnsnowlabs.com/marketplace/medicare-part-d-prescriber-utilization-and-payment-data-2014)\n\t- The Medicare Part D Prescriber Public Use File provides information on prescription drug events (PDEs) incurred by Medicare beneficiaries with a Part D prescription drug plan for the year 2014. The Part D Prescriber PUF is organized by National Provider Identifier (NPI) and drug name and contains information on drug utilization (claim counts and day supply) and total drug costs.\n- [Medicare Part D Prescriber Utilization and Payment Data 2015](https://www.johnsnowlabs.com/marketplace/medicare-part-d-prescriber-utilization-and-payment-data-2015)\n\t- The Medicare Part D Prescriber Public Use File provides information on prescription drug events (PDEs) incurred by Medicare beneficiaries with a Part D prescription drug plan for the year 2015. The Part D Prescriber PUF is organized by National Provider Identifier (NPI) and drug name and contains information on drug utilization (claim counts and day supply) and total drug costs.\n- [Medicare Part D Prescriber Utilization and Payment Data 2016](https://www.johnsnowlabs.com/marketplace/medicare-part-d-prescriber-utilization-and-payment-data-2016)\n\t- The Medicare Part D Prescriber Public Use File provides information on prescription drug events (PDEs) incurred by Medicare beneficiaries with a Part D prescription drug plan for the year 2016. The Part D Prescriber PUF is organized by National Provider Identifier (NPI) and drug name and contains information on drug utilization (claim counts and day supply) and total drug costs.\n- [Medicare Part D Prescriber Utilization and Payment Data 2017](https://www.johnsnowlabs.com/marketplace/medicare-part-d-prescriber-utilization-and-payment-data-2017)\n\t- The Medicare Part D Prescriber Public Use File provides information on prescription drug events (PDEs) incurred by Medicare beneficiaries with a Part D prescription drug plan for the year 2017. The Part D Prescriber PUF is organized by National Provider Identifier (NPI) and drug name and contains information on drug utilization (claim counts and day supply) and total drug costs.\n- [POA Exempt Add Codes](https://www.johnsnowlabs.com/marketplace/poa-exempt-add-codes)\n\t- This dataset shows the list of codes added to the Present on Admission (POA) exempt list from 2016 to 2022 by the Centers for Medicare and Medicaid Services (CMS). Present On Admission is defined as present at the time the order for inpatient admission occurs — conditions that develop during an outpatient encounter, including emergency department, observation, or outpatient surgery, are considered POA.\n- [POA Exempt Codes](https://www.johnsnowlabs.com/marketplace/poa-exempt-codes)\n\t- This dataset shows the list of ICD-10-CM code that is exempt from the Present on Admission (POA) reporting from 2016 to 2022 by the Centers for Medicare and Medicaid Services (CMS). Present On Admission is defined as present at the time the order for inpatient admission occurs — conditions that develop during an outpatient encounter, including emergency department, observation, or outpatient surgery, are considered POA.\n- [POA Exempt Delete Codes](https://www.johnsnowlabs.com/marketplace/poa-exempt-delete-codes)\n\t- This dataset shows the list of ICD-10-CM code that is deleted from the Present on Admission (POA) reporting from 2016 to 2022 by the Centers for Medicare and Medicaid Services (CMS). Present On Admission is defined as present at the time the order for inpatient admission occurs — conditions that develop during an outpatient encounter, including emergency department, observation, or outpatient surgery, are considered POA.\n- [POA Exempt Revise Codes](https://www.johnsnowlabs.com/marketplace/poa-exempt-revise-codes)\n\t- This dataset shows the list of ICD-10-CM code that is revised from the Present on Admission (POA) reporting from 2017 to 2020 by the Centers for Medicare and Medicaid Services (CMS). Present On Admission is defined as present at the time the order for inpatient admission occurs — conditions that develop during an outpatient encounter, including emergency department, observation, or outpatient surgery, are considered POA.\n- [Skilled Nursing Facility Utilization and Payment Data 2013](https://www.johnsnowlabs.com/marketplace/skilled-nursing-facility-utilization-and-payment-data-2013)\n\t- This dataset provides information on services provided to Medicare beneficiaries residing in skilled nursing facilities. The Skilled Nursing Facility Public Use File contains information on utilization, payment, submitted charges, and beneficiary demographic and chronic condition indicators organized by Centres for Medicare and Medicaid Services Certification Number (6-digit provider identification number), Resource Utilization Group (RUG), and state of service.\n- [Skilled Nursing Facility Utilization and Payment Data 2014](https://www.johnsnowlabs.com/marketplace/skilled-nursing-facility-utilization-and-payment-data-2014)\n\t- The Skilled Nursing Facility Utilization and Payment data 2014 provides information on services provided to Medicare beneficiaries residing in skilled nursing facilities. The Skilled Nursing Facility Public Use File contains information on utilization, payment, submitted charges, and beneficiary demographic and chronic condition indicators organized by Centers for Medicare and Medicaid Services (CMS) Certification Number, Resource Utilization Group (RUG), and state of service.\n- [Skilled Nursing Facility Utilization and Payment Data 2015](https://www.johnsnowlabs.com/marketplace/skilled-nursing-facility-utilization-and-payment-data-2015)\n\t- This dataset provides information on services provided to Medicare beneficiaries residing in skilled nursing facilities. The Skilled Nursing Facility Public Use File contains information on utilization, payment (allowed amount, Medicare payment and standard payment), submitted charges, and beneficiary demographic and chronic condition indicators organized by Centers for Medicare and Medicaid Services (CMS) Certification Number, Resource Utilization Group (RUG), and state of service.\n- [Skilled Nursing Facility Utilization and Payment Data 2016](https://www.johnsnowlabs.com/marketplace/skilled-nursing-facility-utilization-and-payment-data-2016)\n\t- This dataset provides information on services provided to Medicare beneficiaries residing in skilled nursing facilities. The Skilled Nursing Facility Public Use File contains information on utilization, payment (allowed amount, Medicare payment and standard payment), submitted charges, and beneficiary demographic and chronic condition indicators organized by Centers for Medicare and Medicaid Services (CMS) Certification Number, Resource Utilization Group (RUG), and state of service.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated\n- Data Updates\n - Data updates support replace-on-update: outdated foreign keys are deprecated, not deleted\n\n**Our data is curated and enriched by domain experts** \n\n Each dataset is manually curated by our team of doctors, pharmacists, public health & medical billing experts:\n- Field names, descriptions, and normalized values are chosen by people who actually understand their meaning\n- Healthcare & life science experts add categories, search keywords, descriptions and more to each dataset\n- Both manual and automated data enrichment supported for clinical codes, providers, drugs, and geo-locations\n- The data is always kept up to date – even when the source requires manual effort to get updates\n- Support for data subscribers is provided directly by the domain experts who curated the data sets\n- Every data source’s license is manually verified to allow for royalty-free commercial use and redistribution.\n\n**Need Help?**\nIf you have questions about our products, contact us at [info@johnsnowlabs.com](mailto:info@johnsnowlabs.com). \n ","terms_of_service":"https://www.johnsnowlabs.com/healthcare-data-library-eula/","documentation_link":"https://www.johnsnowlabs.com/marketplace/medicare-provider-utilization-and-payment-data-data-package","contact_email":"support@johnsnowlabs.com","privacy_policy_link":"https://www.johnsnowlabs.com/privacy-policy/","embedded_notebook_file_infos":[{"id":"73c69ba5-d15c-425a-88fc-e03aecf71193","marketplace_file_type":"EMBEDDED_NOTEBOOK","file_parent":{"parent_id":"ee629f0c-b105-4c89-bd25-5c4116cbff27","file_parent_type":"LISTING"},"mime_type":"text/html","download_link":"https://marketplace-file-storage-prod.s3-fips.us-east-1.amazonaws.com/EMBEDDED_NOTEBOOK/LISTING_ee629f0c-b105-4c89-bd25-5c4116cbff27/73c69ba5-d15c-425a-88fc-e03aecf71193?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEGgaDnVzLWVhc3QtMS1maXBzIkYwRAIgbC8sWIoYaHR8y7Wq86nPb0pufBl4%2BM4PWpeN%2B5PW9VQCIG7BhAVBDCWTpxFDLeuKYm%2Ff1JLz4iccPMzter0YdhgWKqYECDAQABoMNDE0MzUxNzY3ODI2IgxNAJrHbXaZG20jNfMqgwQyGEb4TsoVOoNvJ%2BaA%2FZ7zeu8hGcHqCeNfblP%2FQo5g6q2aRNtpwtPRKMlhYcIE%2FUQaNT1Lqmyptu32VUQTpzjTTxRVmJrDPkheSCkV06XQ%2FpTMKjU8CC7X1b%2FqYf8CrBwZT7Cv4C5WPjEET38f2A79h0xV6bZgPy28zQWrSeIJ9w6AfZBxiE24%2FVc08zVA0ivCT6lsy7nuU0wcR%2FMg2C9nd1vaMnQ923VpD21CKfVVYcrNWL2b%2FQ%2BNAO%2BxPjPgjiN6UPQbvbvZxkSdhgrGtecIg9NgqGkMcvM5VLTDtdC1kdeZetfOC2obtA%2BT%2F0gy3tIDo6WM5hcQfw743%2FAU9oaGnRBl%2BwoxRRDCZA%2F4jnPdluICSI7NCr6C3HHOe4CSZJjRihczxPA%2FgQczAVjAPs6CrK9N9pIzt0vNJpGUTgg4AoxftTvGfciDREilSctPIcBk6GwB%2Bzt4wbtNCmEPU%2BFD46rc6CWVQmmLwbKOeHw70ipMVFlOc8NlXkEtGO2vu6H44MstSSc6daOVUYUavQSGZ6jGhEjsP2YGz8KDm9XQotB%2FtyVBqiZV01OKqDmmBJA%2FXjUpSJ8M%2FeJXhe5m%2BKcxzNObtoJRI7jci3Tpt08XZyXZ7%2FyIV8H30ya468X3cfkTXrm972uga3z7iXAdubyEHnQIua%2FuvtwOHILi6O5D1Me2MzCPvcnXBjqTAYDoMWBt%2B3AVFuqSs2dmDQNBnvEhXfMgTedKLD5PJHb3aIl15iBkLaNu36GYnVCp9ZU2lSYcTbY5QwNnNeTfJTNy9bKhwzru6Sg%2BslhN%2BTS0zkTn%2BeLMY4tHgPEtEZ2cp%2FCk6GwqswvnYVKHjwO8q%2B1Mx8lc08fVEfarE9dke1GGFqoddx%2Bq%2BZUyHbDlAVfrCmf0yw%3D%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20261110T000135Z&X-Amz-SignedHeaders=host&X-Amz-Expires=86400&X-Amz-Credential=ASIAWA6KKHEJORWBH4N3%2F20261110%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=242edd17f4e546fb3d4e1013f7399e0510b11265187f4c9c1e271caa011fa305","created_at":1682537787169,"updated_at":1682537787169,"display_name":"EDA Notebook","status":"FILE_STATUS_PUBLISHED"}],"assets":["ASSET_TYPE_DATA_TABLE"]},"provider_summary":{"name":"John Snow Labs","description":"John Snow Labs, an AI and NLP for healthcare company, provides state-of-the-art software, models, and data to help healthcare and life science organizations build, deploy, and operate AI projects."},"popularity":589},{"id":"05db2fbc-c2a2-407d-be9d-6e02e5a15ae5","summary":{"name":"Serviceability Extended Attributes USA Sample","subtitle":"Linkage to Addresses with info mail delivery indicators, land use information, & building designations.","share":{"name":"usa_serviceability_extended_attributes_sample","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1725023126602,"updated_at":1764990746463,"categories":["GEOSPATIAL","TRANSPORTATION_AND_LOGISTICS","COMMERCE"],"listingType":"STANDARD","provider_id":"b2312fad-ce38-48e5-9c55-8a225d183993"},"detail":{"description":"**Overview**\nServiceability Extended Attributes (SEA) is a separately licensed add-on dataset to the Precisely United States Address Fabric. This product allows users to identify and better understand serviceable addresses, as defined by Precisely, or develop their own definitions by leveraging supplemental mailing and tax assessor information.\n\nThis is a free sample of Precisely's Serviceability Extended Attributes (SEA) USA product covering the San Francisco, CA area. The full USA product is available as Paid access. Contact Databricks.partnership@precisely.com for full product licensing information.\n\n\n**Use cases**\n**Customer Acquisition**\nIdentify serviceable addresses and delineate areas for new service or catchment\n**Economic Impact Analysis**\nUtilize for site selection of new assets or network expansion\n**Location Data Enrichment**\nAppend enriched data to the address through a unique identifier, such as points of interest, property assessment and risk factors, demographics, and more\n\n**Product details**\nServiceability Extended Attributes also defines parent building addresses as multi-unit dwellings, multi-tenant units, or mixed-designation.\nData Sets:\n- Serviceability Extended Attributes is comprised of one table\n\nData Set Content Includes (but is not limited to):\n\n- USPS residential/business indicator of an address\n- Serviceable Flag for an address\n- Standardized Land Use values from each county\n- Genealogy designating the address record as either a parent or a child address\n- Genealogy Count of child addresses for a parent address\n- Building Designation showing whether building contains only residential addresses, only business addresses, or a mixture of both.\n\nFor more details, refer to the embedded notebook.\n\n**Additional Insights**\nhttps://help.precisely.com/r/n/Serviceability-Extended-Attributes/pub/Latest/en-US/Serviceability-Extended-Attributes-Product-Guide/Introduction\n","terms_of_service":"https://www.precisely.com/legal/licensing/trial-evaluation-agreement-terms-u-s","documentation_link":"https://help.precisely.com/r/n/Serviceability-Extended-Attributes/pub/Latest/en-US/Serviceability-Extended-Attributes-Product-Guide/Introduction","privacy_policy_link":"https://www.precisely.com/legal/privacy-policy","embedded_notebook_file_infos":[{"id":"52a64907-0055-4d2b-a19e-68dce6aa15f9","marketplace_file_type":"EMBEDDED_NOTEBOOK","file_parent":{"parent_id":"05db2fbc-c2a2-407d-be9d-6e02e5a15ae5","file_parent_type":"LISTING"},"mime_type":"text/html","download_link":"https://marketplace-file-storage-prod.s3-fips.us-east-1.amazonaws.com/EMBEDDED_NOTEBOOK/LISTING_05db2fbc-c2a2-407d-be9d-6e02e5a15ae5/52a64907-0055-4d2b-a19e-68dce6aa15f9?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEGgaDnVzLWVhc3QtMS1maXBzIkYwRAIgbC8sWIoYaHR8y7Wq86nPb0pufBl4%2BM4PWpeN%2B5PW9VQCIG7BhAVBDCWTpxFDLeuKYm%2Ff1JLz4iccPMzter0YdhgWKqYECDAQABoMNDE0MzUxNzY3ODI2IgxNAJrHbXaZG20jNfMqgwQyGEb4TsoVOoNvJ%2BaA%2FZ7zeu8hGcHqCeNfblP%2FQo5g6q2aRNtpwtPRKMlhYcIE%2FUQaNT1Lqmyptu32VUQTpzjTTxRVmJrDPkheSCkV06XQ%2FpTMKjU8CC7X1b%2FqYf8CrBwZT7Cv4C5WPjEET38f2A79h0xV6bZgPy28zQWrSeIJ9w6AfZBxiE24%2FVc08zVA0ivCT6lsy7nuU0wcR%2FMg2C9nd1vaMnQ923VpD21CKfVVYcrNWL2b%2FQ%2BNAO%2BxPjPgjiN6UPQbvbvZxkSdhgrGtecIg9NgqGkMcvM5VLTDtdC1kdeZetfOC2obtA%2BT%2F0gy3tIDo6WM5hcQfw743%2FAU9oaGnRBl%2BwoxRRDCZA%2F4jnPdluICSI7NCr6C3HHOe4CSZJjRihczxPA%2FgQczAVjAPs6CrK9N9pIzt0vNJpGUTgg4AoxftTvGfciDREilSctPIcBk6GwB%2Bzt4wbtNCmEPU%2BFD46rc6CWVQmmLwbKOeHw70ipMVFlOc8NlXkEtGO2vu6H44MstSSc6daOVUYUavQSGZ6jGhEjsP2YGz8KDm9XQotB%2FtyVBqiZV01OKqDmmBJA%2FXjUpSJ8M%2FeJXhe5m%2BKcxzNObtoJRI7jci3Tpt08XZyXZ7%2FyIV8H30ya468X3cfkTXrm972uga3z7iXAdubyEHnQIua%2FuvtwOHILi6O5D1Me2MzCPvcnXBjqTAYDoMWBt%2B3AVFuqSs2dmDQNBnvEhXfMgTedKLD5PJHb3aIl15iBkLaNu36GYnVCp9ZU2lSYcTbY5QwNnNeTfJTNy9bKhwzru6Sg%2BslhN%2BTS0zkTn%2BeLMY4tHgPEtEZ2cp%2FCk6GwqswvnYVKHjwO8q%2B1Mx8lc08fVEfarE9dke1GGFqoddx%2Bq%2BZUyHbDlAVfrCmf0yw%3D%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20261110T000135Z&X-Amz-SignedHeaders=host&X-Amz-Expires=86400&X-Amz-Credential=ASIAWA6KKHEJORWBH4N3%2F20261110%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=d61bfc47cf05c25817b68e7eaffb69ae8ceca4fefc3bd5493eb0930adda40676","created_at":1764990729658,"updated_at":1764990729658,"display_name":"Serviceability Extended Attributes Notebook","status":"FILE_STATUS_PUBLISHED"}],"geographical_coverage":"[\"United States and Canada\"]","cost":"FREE","update_frequency":{"interval":1,"unit":"MONTHLY"},"data_source":"","assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://www.precisely.com/legal/licensing/trial-evaluation-agreement-terms-u-s"},"provider_summary":{"name":"Precisely","description":"As a global leader in data integrity, Precisely ensures that your data is accurate, consistent, and contextual. 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Diseases include cervical cance..","share":{"name":"us_clinical_trials","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1682356508836,"updated_at":1698965635225,"categories":["SCIENCE_AND_RESEARCH"],"listingType":"STANDARD","provider_id":"f88849f8-9d77-4de2-b4c7-03d55cc3768e"},"detail":{"description":"\n**Overview**\nThis data package contains datasets on clinical trials conducted in the United States. Diseases include cervical cancer, diabetes, acute respiratory infection as well as stress. This data package also includes clinical trials registry and results database.\n\n**Description**\nThis data package contains the clinical trial on different diseases and conditions. The Clinical Trials Registry and Results Database compiles information on publicly and privately supported clinical trial studies on a wide range of diseases and conditions. The dataset on Cervical Cancer Screening provides demographic information, habits, and historic medical records of the 858 patients from Hospital Universitario de Caracas in Caracas, Venezuela. Diabetes prevalence study seeks to understand the prevalence of obesity, diabetes, and other cardiovascular risk factors in central Virginia for African Americans. Dobutamine stress cardiography seeks to determine if a drug called “Dobutamine” can be used effectively in a test to measure a patient’s risk of having a heart attack or cardiac event. WHO ARI Young Infant Multicenter Study was conducted to develop a clinical prediction model to assist health workers in developing countries in the presentation of an acutely ill young infant.\n\n**Benefits**\n- This data can be useful for patients, their family members, healthcare professionals, researchers, and the public as it provides an easy access to both privately and publicly funded clinical trials information.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [Cervical Cancer Screening](https://www.johnsnowlabs.com/marketplace/cervical-cancer-screening)\n\t- This dataset is composed of responses from 858 patients and 36 variables focusing on the prediction of indicators or diagnosis of cervical cancer. The dataset provides demographic information, habits, and historic medical records of the 858 patients from Hospital Universitario de Caracas in Caracas, Venezuela. A number of the patients did not answer some of the questions due to privacy concerns.\n- [Clinical Trials Registry and Results Database](https://www.johnsnowlabs.com/marketplace/clinical-trials-registry-and-results-database)\n\t- The Clinical Trials Registry and Results Database compiles information on publicly and privately supported clinical trial studies on a wide range of diseases and conditions. Its main goal is to provide an easy access to both privately and publicly funded clinical trials information for patients, their family members, healthcare professionals, researchers, and the public.\n- [Diabetes Prevalence in African Americans](https://www.johnsnowlabs.com/marketplace/diabetes-prevalence-in-african-americans)\n\t- The dataset consists of 19 variables on 403 subjects from 1046 subjects who were interviewed in a study to understand the prevalence of obesity, diabetes, and other cardiovascular risk factors in central Virginia for African Americans.\n- [Dobutamine Stress Echocardiography Data](https://www.johnsnowlabs.com/marketplace/dobutamine-stress-echocardiography-data)\n\t- The dataset contains the complete data for the final study population for the Dobutamine Stress Echocardiography test which includes 558 subjects and 31 variables.\n- [WHO ARI Young Infant Multicenter Study](https://www.johnsnowlabs.com/marketplace/who-ari-young-infant-multicenter-study)\n\t- This dataset contains 4552 observations and 97 variables. This dataset is from The World Health Organization Multicentre Acute Respiratory Infection (WHO ARI) Study of Clinical Signs and Etiological Agents of Pneumonia, Sepsis, and Meningitis in Young Infants.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated\n- Data Updates\n - Data updates support replace-on-update: outdated foreign keys are deprecated, not deleted\n\n**Our data is curated and enriched by domain experts** \n\n Each dataset is manually curated by our team of doctors, pharmacists, public health & medical billing experts:\n- Field names, descriptions, and normalized values are chosen by people who actually understand their meaning\n- Healthcare & life science experts add categories, search keywords, descriptions and more to each dataset\n- Both manual and automated data enrichment supported for clinical codes, providers, drugs, and geo-locations\n- The data is always kept up to date – even when the source requires manual effort to get updates\n- Support for data subscribers is provided directly by the domain experts who curated the data sets\n- Every data source’s license is manually verified to allow for royalty-free commercial use and redistribution.\n\n**Need Help?**\nIf you have questions about our products, contact us at [info@johnsnowlabs.com](mailto:info@johnsnowlabs.com). \n 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S&P Global Energy is a division of S&P Global (NYSE: SPGI)."},"popularity":1382},{"id":"b8491d78-33f8-40e3-a0a2-89b5f71c8335","summary":{"name":"Geolocet | Latest Demographic Data Estonia at Municipality Level","subtitle":"Demographic data for 79 municipalities (linnad/vallad) of Estonia, offering a snapshot of their population.","setting":{"visibility":"PUBLIC"},"created_at":1706106888190,"updated_at":1752144050356,"categories":["DEMOGRAPHICS","ADVERTISING_AND_MARKETING","GEOSPATIAL","RETAIL","PUBLIC_SECTOR"],"listingType":"PERSONALIZED","provider_id":"5d3ddc17-a7e8-4794-a760-13587e1de2c6"},"detail":{"description":"## **Get instant access to the latest Census Data for Estonia with [Geolocet](https://geolocet.com)**\n\nExplore the richness of Estonia’s demographic landscape with our Estonia Census Data. This meticulously compiled dataset, sourced from official statistical sources, including Statistics Estonia, offers a detailed insight into the population landscape of Estonia at the municipality (linnad/vallad) level. \n\nIf you require data at a less granular level, such as an Uber grid or square grid, please don't hesitate to get in touch with us at *contact@geolocet.com*. 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No lengthy sign-up processes or NDAs required on our side – just download the data you need.\n* **Data Samples for Evaluation:** To ensure you're getting exactly what you need, we provide free downloadable samples in CSV format for data evaluation.\n* **Data accuracy:** Our datasets are created using the latest demographics data available in national statistics bureaus, guaranteeing accuracy and reliability.\n* **Best-in-Class Quality:** We guarantee best-in-class quality, backed by over two decades of experience in modeling demographics data and an outstanding quality management process.\n* **Competitive Pricing:** We maintain the lowest prices on the market, ensuring that you get the most cost-efficient demographics data acquisition process.\n---\nAdditionally, for those looking to enrich their analysis or project, we offer:\n* **[Administrative Boundaries](https://geolocet.com/products/estonia-admin-level-7-municipalities):** If you require geographical boundaries (polygons) for the municipalities of Estonia, consider exploring our Administrative Boundaries product, which seamlessly integrates with this dataset.\n\n* **[Point of Interest (PoI) Data](https://geolocet.com/pages/aggregated-points-of-interest-poi-data):** If you're interested in Points of Interest data, please don't hesitate to contact us for customized datasets to enhance your geographical analysis.\n\n* **[Data Customization](https://geolocet.com/pages/data-services-by-geolocet):** We understand that your project may have specific requirements. Feel free to get in touch with us if you need any modifications or specific data formats such as Excel, JSON, GeoJSON, or Shapefile. We are here to tailor the data to your needs.\n\nIf you're looking for data for Estonia, [look here](https://geolocet.com/pages/estonia) for off-the-shelf or tailor-made datasets to suit your specific requirements.\n\nMake informed decisions, conduct precise research, and unlock the potential of Estonia demographic landscape with our comprehensive dataset. Trust in the accuracy and reliability of our official statistical data to drive your projects forward.\nContact us at *contact@geolocet.com* today to access this invaluable resource and explore Estonia demographic data like never before.\n\n[Geolocet LTD](https://geolocet.com)\n","terms_of_service":"https://geolocet.com/pages/terms-of-use","documentation_link":"","privacy_policy_link":"https://geolocet.com/pages/privacy-policy","geographical_coverage":"[\"Europe\"]","cost":"PAID","update_frequency":{"interval":1,"unit":"YEARLY"},"data_source":"","assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://geolocet.com/pages/terms-of-use"},"provider_summary":{"name":"Geolocet LTD","description":"Geolocet provides premium geospatial data for retail analysis, GIS, and market planning. 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The territorial vintages of the worldwide area boundaries are compatible with our globally consistent and comparable [socio-demographic](https://www.mbi-geodata.com/en/sociodemographic-data/) and [economic](https://www.mbi-geodata.com/en/purchasing-power-overview/) data.\nOur area boundaries for Sweden are offered at the following levels:\n\n- **Postal Code Level** (Postcode 5-digit): This fine-grained level of detail makes the data suitable for a wide range of applications – for example, enriching customer databases, which are often organized by postal code.\n- **Administrative Level** (Kommuner): Covers both larger administrative areas (e.g., regions, states, provinces) as well as finer subdivisions such as districts, counties, or municipalities.\n\n**Use Cases:**\n\n- **Market Analysis**: Use MBI’s boundaries to visualize data on a map, learn about your customers, and uncover untapped market potential.\n- **360-Degree Customer View**: Link boundaries to customer data, sociodemographic, and economic data to gain a holistic understanding of customer needs, behaviors, and potential value.\n- **Location Planning**\n•\tUse MBI’s boundaries to visualize data on a map, learn about your customers\n•\tPlan the provision of services and products across different regions\n•\tDefine sales territories\n•\tVisualize catchment areas and plan actions to reach potential customers\n\n**Additional Insights:**\n[About Area Boundaries](https://www.mbi-geodata.com/en/area-boundaries/)\n\n**Data Structure:**\nThe area boundaries include the following variables:\nCTRYCODE: Two-digit country code by ISO 3166\nNAME: Name of administrative area / Name of the most populous administrative area / Name of the respective micro-area or the next higher level\nADMINCODE / POSTCODE / MICROCODE: Administrative code / Postal code / Micro-code\nWKT: Geography as Well Known Text (lat/long WGS84)\n\n*with only a few specific exceptions","terms_of_service":"https://service-mbi-geodata.com/s/HNp7Z2tws4WDqWd","documentation_link":"https://www.mbi-geodata.com/en/mbi-supplier-manufacturer-worldwide-geodata/europe/sweden-maps/","privacy_policy_link":"https://www.mbi-geodata.com/en/data-protection/","update_frequency":{"interval":1,"unit":"YEARLY"},"assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://service-mbi-geodata.com/s/ZnJYiEFqnii5gRd"},"provider_summary":{"name":"Michael Bauer International GmbH – MBI Geodata","description":"For over 15 years, Michael Bauer International (MBI) has been a leading provider of globally consistent and comparable geographic data, including more than 3 million postcode areas and other detailed datasets like sociodemographics and purchasing power. 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Countries included in the dataset have employment by NACE Revision 2 industry per Lightcast Administrative Area, which is based on NUTS 3 regions in Europe, counties in the US, etc.\n\nFor more details please visit [Global LMI overview](https://kb.lightcast.io/en/articles/7199115-global-lmi-overview).\n\nCountries included:\n- Australia\n- Austria\n- Belgium\n- Brazil\n- Bulgaria\n- Canada\n- Costa Rica\n- Croatia\n- Cyprus\n- Czechia\n- Denmark\n- Estonia\n- Finland\n- France\n- Germany\n- Greece\n- Hungary\n- India\n- Ireland\n- Italy\n- Latvia\n- Lithuania\n- Luxembourg\n- Malta\n- Mexico\n- Netherlands\n- New Zealand\n- Norway\n- Philippines\n- Poland\n- Portugal\n- Romania\n- Singapore\n- Slovakia\n- Slovenia\n- South Africa\n- Spain\n- Sweden\n- Switzerland\n- United Kingdom\n- United States\n\n\n**Use cases**\n***Real World Data (RWD)***\nAnalyzing government occupation data over time can reveal trends in the labor market. 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Our database includes over one billion job postings and career profiles, enriched by machine learning, expert modelers, and human-reviewed taxonomies to deliver unmatched accuracy and depth. We combine that with curated input from dozens of other statistical sources, like government agencies, to provide the most complete view possible of the fast-changing labor market.\n\nWe empower businesses, educators, and communities to make informed decisions about hiring, workforce development, curriculum design, and regional planning. Our ultimate goal is connecting individuals with the right skills and right jobs in the right places. People are at the heart of everything we do, and we want to create a job market that works for everyone."},"popularity":104},{"id":"524566d2-38ba-49a0-9aa8-22fba10770e3","summary":{"name":"Geolocet | Latest Demographic Data Poland at Municipality Level","subtitle":"Latest Poland Demographic Data at Municipality Level - age, gender, rural/urban distributions, and more","setting":{"visibility":"PUBLIC"},"created_at":1705924249432,"updated_at":1752137447796,"categories":["DEMOGRAPHICS","ADVERTISING_AND_MARKETING","GEOSPATIAL","PUBLIC_SECTOR","RETAIL"],"listingType":"PERSONALIZED","provider_id":"5d3ddc17-a7e8-4794-a760-13587e1de2c6"},"detail":{"description":"## **Get instant access to the latest Demographics Data for Poland with [Geolocet](https://geolocet.com)**\n\nExplore the richness of Poland’s demographic landscape with our Poland Census Data. This meticulously compiled dataset, sourced from official statistical sources, including Statistics Poland, offers a detailed insight into the population landscape of Poland at the municipality (gmina) level. \n\nIf you require data at a less granular level, such as an Uber grid or square grid, please don't hesitate to get in touch with us at *contact@geolocet.com*. We understand that different projects may have varying data needs, and we are here to tailor our data to match your specific requirements.\n\n ## **Key Features:**\n---\n* **Population Data Poland:** Gain access to the latest and most accurate population statistics for Poland, meticulously organized by municipality (gmina).\n\n* **Demographic Insights:** Dive deep into Poland’s demographic landscape with data on 180 attributes, including: \n* Total Population,\n* Gender,\n* Rural/Urban population,\n* Age,\n* Age split by Gender,\n* Gender split by Rural/Urban,\n* Age split by Rural/Urban,\n* Rural/Urban split by gender and age.\n\n\nAll demographic attributes are available at the municipal (gmina) level.\n\n* **Geodemographic Insights:** Unlock geodemographic data Poland-wide to inform location-based decision-making, marketing strategies, and policy formulation.\n* **Seamless Data Access:** Our data is available for instant download in CSV format. No lengthy sign-up processes or NDAs required on our side – just download the data you need.\n* **Data Samples for Evaluation:** To ensure you're getting exactly what you need, we provide free downloadable samples in CSV format for data evaluation.\n* **Data accuracy:** Our datasets are created using the latest demographics data available in national statistics bureaus, guaranteeing accuracy and reliability.\n* **Best-in-Class Quality:** We guarantee best-in-class quality, backed by over two decades of experience in modeling demographics data and an outstanding quality management process.\n* **Competitive Pricing:** We maintain the lowest prices on the market, ensuring that you get the most cost-efficient demographics data acquisition process.\n---\nAdditionally, for those looking to enrich their analysis or project, we offer:\n* **[Administrative Boundaries](https://geolocet.com/products/poland-all-available-administrative-boundaries):** If you require geographical boundaries (polygons) for the municipalities of Poland, consider exploring our Administrative Boundaries product, which seamlessly integrates with this dataset.\n\n* **[Point of Interest (PoI) Data](https://geolocet.com/pages/aggregated-points-of-interest-poi-data):** If you're interested in Points of Interest data, please don't hesitate to contact us for customized datasets to enhance your geographical analysis.\n\n* **[Data Customization](https://geolocet.com/pages/data-services-by-geolocet):** We understand that your project may have specific requirements. Feel free to get in touch with us if you need any modifications or specific data formats such as Excel, JSON, GeoJSON, or Shapefile. We are here to tailor the data to your needs.\n\nIf you're looking for data for Poland, [look here](https://geolocet.com/pages/poland) for off-the-shelf or tailor-made datasets to suit your specific requirements.\n\nMake informed decisions, conduct precise research, and unlock the potential of Poland's demographic landscape with our comprehensive dataset. Trust in the accuracy and reliability of our official statistical data to drive your projects forward.\nContact us at *contact@geolocet.com* today to access this invaluable resource and explore Poland census data like never before.\n\n[Geolocet LTD](https://geolocet.com)\n","terms_of_service":"https://geolocet.com/pages/terms-of-use","documentation_link":"","privacy_policy_link":"https://geolocet.com/pages/privacy-policy","geographical_coverage":"[\"Europe\"]","cost":"PAID","data_source":"","assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://geolocet.com/pages/terms-of-use"},"provider_summary":{"name":"Geolocet LTD","description":"Geolocet provides premium geospatial data for retail analysis, GIS, and market planning. 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It is optimized to answer questions and assist users with various Events-related and Ticketing-related procedures. It has been trained using hybrid synthetic data generated using our NLP/NLG technology and our automated Data Labeling (DAL) tools.\n \nThe goal of this model is to show that a generic verticalized model makes customization for a final use case much easier. An overview of this approach can be found at: [From General-Purpose LLMs to Verticalized Enterprise Models](https://www.bitext.com/blog/general-purpose-models-verticalized-enterprise-genai/)\n\n**Intended Use**\n\n- **Recommended applications**: This model is designed to be used as the first step in Bitext’s two-step approach to LLM fine-tuning for the creation of chatbots, virtual assistants and copilots for the Events and Ticketing domains, providing customers with fast and accurate answers about their needs.\n- **Out-of-scope**: This model is not suited for non-events or ticketing related questions and should not be used for providing health, legal, or critical safety advice.\n\n**Model Architecture**\n\nThis model utilizes the `MistralForCausalLM` architecture with a `LlamaTokenizer`, ensuring it retains the foundational capabilities of the base model while being specifically enhanced for events-related and ticketing-related interactions.\n\n**Training Data**\n\nThe model was fine-tuned on the [Bitext Events and Ticketing Dataset](https://huggingface.co/datasets/bitext/Bitext-events-ticketing-llm-chatbot-training-dataset) comprising various events-related and ticketing-related intents, including: book_hotel, store_luggage, book_parking_space, get_refund, search_hotel, host_event, check_in, and more. Totaling 25 intents, and each intent is represented by approximately 1000 examples.\n\nThis comprehensive training helps the model address a broad spectrum of events-related and ticketing-related questions effectively. The dataset follows the same structured approach as our dataset published on Hugging Face as [bitext/Bitext-customer-support-llm-chatbot-training-dataset](https://huggingface.co/datasets/bitext/Bitext-customer-support-llm-chatbot-training-dataset), but with a focus on events and ticketing.\n\n**Training Procedure**\n\n**Hyperparameters**\n\n- **Optimizer**: AdamW\n- **Learning Rate**: 0.0002 with a cosine learning rate scheduler\n- **Epochs**: 4\n- **Batch Size**: 4\n- **Gradient Accumulation Steps**: 4\n- **Maximum Sequence Length**: 8192 tokens\n\n**Environment**\n\n- **Transformers Version**: 4.43.4\n- **Framework**: PyTorch 2.3.1+cu121\n- **Tokenizers**: Tokenizers 0.19.1\n\n**Limitations and Bias**\n\n- The model is trained for events and ticketing-specific contexts but may underperform in unrelated areas.\n- Potential biases in the training data could affect the neutrality of the responses; users are encouraged to evaluate responses critically.\n\n**Ethical Considerations**\n\nIt is important to use this technology thoughtfully, ensuring it does not substitute for human judgment where necessary, especially in sensitive situations.\n\n**Acknowledgments**\n\nThis model was developed and trained by Bitext using proprietary data and technology.\n\n**License**\n\nThis model, \"Mistral-7B-Events-Ticketing\", is licensed under the Apache License 2.0 by Bitext Innovations International, Inc. This open-source license allows for free use, modification, and distribution of the model but requires that proper credit be given to Bitext.\n\n**Key Points of the Apache 2.0 License**\n\n- **Permissibility**: Users are allowed to use, modify, and distribute this software freely.\n- **Attribution**: You must provide proper credit to Bitext Innovations International, Inc. when using this model, in accordance with the original copyright notices and the license.\n- **Patent Grant**: The license includes a grant of patent rights from the contributors of the model.\n- **No Warranty**: The model is provided \"as is\" without warranties of any kind.\n\nYou may view the full license text at [Apache License 2.0](http://www.apache.org/licenses/LICENSE-2.0).\n\nThis licensing ensures the model can be used widely and freely while respecting the intellectual contributions of Bitext. 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The Physician and Other Supplier PUF is covering a broad spectrum of variables related to Medicare healthcare services providers, utilization, payment and submitted charges.\n\n**Description**\nThis data package covers data for utilization and payment information with total submitted charges, Medicare allowed amount and Medicare payment amount, Medicare standardized amount aggregated by National Provider Identifier (NPI) and Healthcare Common Procedure Coding System (HCPCS) code.\nUtilization and payments on HCPCS codes on national level with Average submitted charges, average Medicare allowed amount, average Medicare payment amount, Medicare standardized payment amount aggregated on Healthcare Common Procedure Coding System (HCPCS) code.\nUtilization and payments on HCPCS codes on state level with average submitted charges, average Medicare allowed amount, average Medicare payment amount, Medicare standardized payment amount aggregated on Healthcare Common Procedure Coding System (HCPCS) code for each state.\nUtilization and payment data of physicians and other suppliers organized by NPI, HCPCS code and place of service. The source of data included are the CMS administrative claims data about the beneficiaries enrolled in the Medicare fee-for-service program.\n\n**Benefits**\n- Useful to researchers and analysts who seek to compare, understand and find useful information about the geographical variations of medicare costs and financing, utilization and beneficiaries of the various types of healthcare services. provides to public health specialists and specialized institutions a complete view on the supply and costs of healthcare services provided by different medical specialties and covered by medicare. the fields descriptions along with the dataset description are useful for the user to quickly understand the data and the dataset.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [Medicare NPI Physician And Other Supplier Payment Aggregate Report](https://www.johnsnowlabs.com/marketplace/medicare-npi-physician-and-other-supplier-payment-aggregate-report)\n\t- The dataset contains information on utilization and payments (Total submitted charges, Medicare allowed amount, Medicare Payment amount, Medicare standardized payment amount) organized by National Provider Identity. Sub-totals for medical type services and drug type services are included as well as overall utilization, payment and charges.\n- [Physician and Other Supplier Payment National HCPCS Aggregate Report](https://www.johnsnowlabs.com/marketplace/physician-and-other-supplier-payment-national-hcpcs-aggregate-report)\n\t- The dataset contains information on average utilization and payments (submitted charges, Medicare allowed amount, Medicare Payment amount, Medicare standardized payment amount) on Healthcare Common Procedure Coding System (HCPCS) codes on the national level. This Public Use File is based on information from Centers for Medicare and Medicaid Services administrative claims data for Medicare beneficiaries enrolled in the fee-for-service program available from the Chronic Condition Data Warehouse.\n- [Physician and Other Supplier Payment State HCPCS Aggregate Report](https://www.johnsnowlabs.com/marketplace/physician-and-other-supplier-payment-state-hcpcs-aggregate-report)\n\t- The dataset contains information on average utilization and payments (submitted charges, Medicare allowed amount, Medicare Payment amount, Medicare standardized payment amount) on Healthcare Common Procedure Coding System (HCPCS) codes on the state level. This Public Use File is based on information from Centers for Medicare and Medicaid Services administrative claims data for Medicare beneficiaries enrolled in the fee-for-service program available from the CMS Chronic Condition Data Warehouse.\n- [Provider of Services-CLIA](https://www.johnsnowlabs.com/marketplace/provider-of-services-clia)\n\t- The dataset contains the data on characteristics of hospitals and other types of healthcare facilities, including the name and address of the facility and the type of Medicare services the facility provides, among other information. The dataset includes provider number, name, and address and characterizes the participating institutional providers.\n- [Utilization and Payment Data Medical Equipment and Supplies 2013](https://www.johnsnowlabs.com/marketplace/utilization-and-payment-data-medical-equipment-and-supplies-2013)\n\t- The Centers for Medicare & Medicaid Services (CMS) publishes the Provider Utilization and Payment Data: Referring Durable Medical Equipment, Prosthetics, Orthotics and Supplies (DMEPOS) Public Use File referred here as “Referring Provider DMEPOS PUF” in efforts to make the healthcare system more transparent, affordable, and accountable.\n- [Utilization and Payment Data Physician and Other Supplier 2012](https://www.johnsnowlabs.com/marketplace/utilization-and-payment-data-physician-and-other-supplier-2012)\n\t- The dataset contains information on ‘Provider Utilization and Payment Data Physician and Other Supplier Public Use File (PUF)’ prepared by the Centers for Medicare & Medicaid Services (CMS) and organized by National Provider Identifier (NPI), Healthcare Common Procedure Coding System code, and place of service. This PUF is based on information from CMS’s National Claims History (NCH) Standard Analytic Files (SAFs).\n- [Utilization and Payment Data Physician and Other Supplier 2013](https://www.johnsnowlabs.com/marketplace/utilization-and-payment-data-physician-and-other-supplier-2013)\n\t- The dataset contains information on ‘Provider Utilization and Payment Data Physician and Other Supplier Public Use File (PUF)’ prepared by the Centers for Medicare & Medicaid Services (CMS) and organized by National Provider Identifier (NPI), Healthcare Common Procedure Coding System code, and place of service. This PUF is based on information from CMS’s National Claims History (NCH) Standard Analytic Files (SAFs).\n- [Utilization and Payment Data Physician and Other Supplier 2014](https://www.johnsnowlabs.com/marketplace/utilization-and-payment-data-physician-and-other-supplier-2014)\n\t- The dataset contains information on ‘Provider Utilization and Payment Data Physician and Other Supplier Public Use File (PUF)’ prepared by the Centers for Medicare & Medicaid Services (CMS) and organized by National Provider Identifier (NPI), Healthcare Common Procedure Coding System code, and place of service. This PUF is based on information from CMS’s National Claims History (NCH) Standard Analytic Files (SAFs).\n- [Utilization and Payment Data Physician and Other Supplier 2015](https://www.johnsnowlabs.com/marketplace/utilization-and-payment-data-physician-and-other-supplier-2015)\n\t- The dataset contains information on ‘Provider Utilization and Payment Data Physician and Other Supplier Public Use File (PUF)’ prepared by the Centers for Medicare & Medicaid Services (CMS) and organized by National Provider Identifier (NPI), Healthcare Common Procedure Coding System code, and place of service. This PUF is based on information from CMS’s National Claims History (NCH) Standard Analytic Files (SAFs).\n- [Utilization and Payment Data Physician and Other Supplier 2016](https://www.johnsnowlabs.com/marketplace/utilization-and-payment-data-physician-and-other-supplier-2016)\n\t- The dataset contains information on ‘Provider Utilization and Payment Data Physician and Other Supplier Public Use File (PUF)’ prepared by the Centers for Medicare & Medicaid Services (CMS) and organized by National Provider Identifier (NPI), Healthcare Common Procedure Coding System code, and place of service. This PUF is based on information from CMS’s National Claims History (NCH) Standard Analytic Files (SAFs).\n- [Utilization and Payment Data Physician and Other Supplier 2017](https://www.johnsnowlabs.com/marketplace/utilization-and-payment-data-physician-and-other-supplier-2017)\n\t- The dataset contains information on ‘Provider Utilization and Payment Data Physician and Other Supplier Public Use File (PUF)’ prepared by the Centers for Medicare & Medicaid Services (CMS) and organized by National Provider Identifier (NPI), Healthcare Common Procedure Coding System code, and place of service. This PUF is based on information from CMS’s National Claims History (NCH) Standard Analytic Files (SAFs).\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated\n- Data Updates\n - Data updates support replace-on-update: outdated foreign keys are deprecated, not deleted\n\n**Our data is curated and enriched by domain experts** \n\n Each dataset is manually curated by our team of doctors, pharmacists, public health & medical billing experts:\n- Field names, descriptions, and normalized values are chosen by people who actually understand their meaning\n- Healthcare & life science experts add categories, search keywords, descriptions and more to each dataset\n- Both manual and automated data enrichment supported for clinical codes, providers, drugs, and geo-locations\n- The data is always kept up to date – even when the source requires manual effort to get updates\n- Support for data subscribers is provided directly by the domain experts who curated the data sets\n- Every data source’s license is manually verified to allow for royalty-free commercial use and redistribution.\n\n**Need Help?**\nIf you have questions about our products, contact us at [info@johnsnowlabs.com](mailto:info@johnsnowlabs.com). \n 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The Division of Policy and Shortage Designation (DPSD) in the HRSA, Bureau of Health Workforce (BHW) develops shortage designation criteria and uses them to decide whether or not a geographic area or population group is a Health Professional Shortage Area (HPSA), Medically Underserved Area (MUA), or Medically Underserved Population (MUP).\n\n**Benefits**\n- Useful for healthcare practitioner\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [Business Wire Healthcare Press Release Distribution List](https://www.johnsnowlabs.com/marketplace/business-wire-healthcare-press-release-distribution-list)\n\t- This dataset contains Business Wire’s national distribution list that the company uses to disseminate full-text press releases from thousands of companies and organizations worldwide to news media, financial markets, disclosure systems, investors, information websites, databases, bloggers, social networks and other audiences across the United States of America.\n- [Health Professional Shortage Area Dental Care](https://www.johnsnowlabs.com/marketplace/health-professional-shortage-area-dental-care)\n\t- This dataset shows Health Professional Shortage Area (HPSA) in geographic areas with shortages of dental health providers. The Health Resource and Service Administration (HRSA) Data Warehouse (HDW) extracts HPSA data from the Shortage Designation Management System (SDMS), which is the transactional source system of record for all HPSA disciplines. This dataset is made available for a subscription according to the data refresh cycle indicated on the Data Marketplace page.\n- [Health Professional Shortage Area Mental Health](https://www.johnsnowlabs.com/marketplace/health-professional-shortage-area-mental-health)\n\t- This dataset shows Health Professional Shortage Area (HPSA) in geographic areas with shortages of mental health providers. The Health Resource and Service Administration (HRSA) Data Warehouse (HDW) extracts HPSA data from the Shortage Designation Management System (SDMS), which is the transactional source system of record for all HPSA disciplines. This dataset is made available for a subscription according to the data refresh cycle indicated on the Data Marketplace page.\n- [Health Professional Shortage Area Primary Care](https://www.johnsnowlabs.com/marketplace/health-professional-shortage-area-primary-care)\n\t- This dataset shows Health Professional Shortage Area (HPSA) in geographic areas with shortages of primary care health providers. The Health Resource and Service Administration (HRSA) Data Warehouse (HDW) extracts HPSA data from the Shortage Designation Management System (SDMS), which is the transactional source system of record for all HPSA disciplines. This dataset is made available for a subscription according to the data refresh cycle indicated on the Data Marketplace page.\n- [Physician Evaluation and Management Medicare Service Events](https://www.johnsnowlabs.com/marketplace/physician-evaluation-and-management-medicare-service-events)\n\t- This dataset shows the health indicator which contains information about the rate of physician evaluation and management service events among Medicare fee-for-service beneficiaries.\n- [Physician Patient Transcripts With 4C Analysis](https://www.johnsnowlabs.com/marketplace/physician-patient-transcripts-with-4c-analysis)\n\t- This dataset shows This dataset consists of 405 transcriptions of audio recorded physician-patient interactions conducted at Veterans Health Administration (VHA) medical center primary care clinics. 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The NPDB, according to the Health Care Quality Improvement Act of 1986 collects and discloses de-identified professional competence and conduct of professionals reports about practitioners, providers, suppliers and entities figuring malpractice payments, adverse reactions and other actions or decisions.\n- [US Healthcare Practitioner Malpractice Payments II](https://www.johnsnowlabs.com/marketplace/us-healthcare-practitioner-malpractice-payments-ii)\n\t- This dataset contains the second part of the National Practitioner Data Bank (NPDB) Public Use Data File, which contains reports from 1991 to March 2016 of sequence number 1357833 to 1544073. 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Trust Verified Sources.**\nWhen researching knowledge on a topic, sources like YouTube, news sites, and search engines flood you with unreliable noise—third-party commentators, indirect quotes, misinformation.\n\n**What makes CEOInterviews different:**\n\n- Our rigorous AI + Human verification process ensures you receive only authentic, directly-sourced statements from influential figures\n- When you search for Elon Musk, you get his exact words—not someone else's interpretation\n- Accuracy matters. Trust verified sources. 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An overview of this approach can be found at: [From General-Purpose LLMs to Verticalized Enterprise Models](https://www.bitext.com/blog/general-purpose-models-verticalized-enterprise-genai/)\n\n**Intended Use**\n\n- **Recommended applications**: This model is designed to be used as the first step in Bitext’s two-step approach to LLM fine-tuning for the creation of chatbots, virtual assistants and copilots for the Customer Support domain, providing customers with fast and accurate answers about their banking needs.\n- **Out-of-scope**: The model is not intended for general conversational purposes and should not be used for medical, legal, or safety-critical advice.\n\n**Model Architecture**\n\nThe model employs the `MistralForCausalLM` architecture with a `LlamaTokenizer`. It maintains the configuration of the base Mistral model but has been fine-tuned to better understand and generate responses related to customer service.\n\n**Training Data**\n\nThe model was fine-tuned using the [bitext/Bitext-customer-support-llm-chatbot-training-dataset](https://huggingface.co/datasets/bitext/Bitext-customer-support-llm-chatbot-training-dataset), which is designed for question and answer interactions in the customer service sector. This dataset includes instructions and responses across a variety of customer service topics, ensuring that the model can handle a wide range of inquiries related to this field. The dataset covers 27 intents assigned to 10 categories such as `cancel_order`, `place_order`, `change_order`, and `check_invoice`. Each intent has around 1000 examples, illustrating a training process aimed at understanding and generating accurate responses for customer service interactions.\n\n**Training Procedure**\n\n**Hyperparameters**\n\n- **Optimizer**: AdamW with betas=(0.9, 0.999) and epsilon=1e-08\n- **Learning Rate**: 0.0002 with a cosine learning rate scheduler\n- **Epochs**: 1\n- **Batch Size**: 8\n- **Gradient Accumulation Steps**: 4\n- **Maximum Sequence Length**: 1024 tokens\n\n**Environment**\n\n- **Transformers Version**: 4.40.0.dev0\n- **Framework**: PyTorch 2.2.1+cu121\n- **Tokenizers**: Tokenizers 0.15.0\n\n**Dataset Description**\n\n**Overview**\n\nThe dataset used for fine-tuning can train Large Language Models for both Fine Tuning and Domain Adaptation. It includes:\n\n- **Use Case**: Intent Detection\n- **Vertical**: Customer Service\n- **27 intents** assigned to 10 categories\n- **26,872 question/answer pairs**, around 1000 per intent\n- **30 entity/slot types**\n- **12 different types of language generation tags**\n\n**Categories and Intents**\n\nThe dataset covers the following categories and intents:\n\n- **ACCOUNT**: create_account, delete_account, edit_account, switch_account\n- **CANCELLATION_FEE**: check_cancellation_fee\n- **DELIVERY**: delivery_options\n- **FEEDBACK**: complaint, review\n- **INVOICE**: check_invoice, get_invoice\n- **NEWSLETTER**: newsletter_subscription\n- **ORDER**: cancel_order, change_order, place_order\n- **PAYMENT**: check_payment_methods, payment_issue\n- **REFUND**: check_refund_policy, track_refund\n- **SHIPPING_ADDRESS**: change_shipping_address, set_up_shipping_address\n\n**Entities**\n\nThe dataset includes various entities such as:\n\n- {{Order Number}}, {{Invoice Number}}, {{Online Order Interaction}}, {{Online Payment Interaction}}, {{Online Navigation Step}}, {{Online Customer Support Channel}}, {{Profile}}, {{Profile Type}}, {{Settings}}, {{Online Company Portal Info}}, {{Date}}, {{Date Range}}, {{Shipping Cut-off Time}}, {{Delivery City}}, {{Delivery Country}}, {{Salutation}}, {{Client First Name}}, {{Client Last Name}}, {{Customer Support Phone Number}}, {{Customer Support Email}}, {{Live Chat Support}}, {{Website URL}}, {{Upgrade Account}}, {{Account Type}}, {{Account Category}}, {{Account Change}}, {{Program}}, {{Refund Amount}}, {{Money Amount}}, {{Store Location}}\n\n**Language Generation Tags**\n\nThe dataset contains tags for various linguistic phenomena:\n\n- **Lexical Variation**: Morphological (M), Semantic (L)\n- **Syntactic Structure Variation**: Basic (B), Interrogative (I), Coordinated (C), Negation (N)\n- **Language Register Variations**: Politeness (P), Colloquial (Q), Offensive (W)\n- **Stylistic Variations**: Keyword (K), Abbreviations (E), Errors and Typos (Z)\n- **Other Tags**: Indirect Speech (D), Regional Variations (G), Respect Structures (R), Code Switching (Y)\n\n**Limitations and Bias**\n\n- The model is fine-tuned on a domain-specific dataset and may not perform well outside the scope of customer service.\n- Users should be aware of potential biases in the training data, as the model's responses may inadvertently reflect these biases. The dataset aims to cover general customer service inquiries, but biases may exist for specific use cases.\n\n**Ethical Considerations**\n\nThis model should be used responsibly, considering ethical implications of automated customer service. It is important to ensure that the model's advice complements human expertise and adheres to relevant customer service guidelines.\n\n**Acknowledgments**\n\nThis model was developed by Bitext and trained on infrastructure provided by Bitext.\n\n**License**\n\nThis model, \"Mistral-7B-Customer-Support-v1,\" is licensed under the Apache License 2.0 by Bitext Innovations International, Inc. This open-source license allows for free use, modification, and distribution of the model but requires that proper credit be given to Bitext.\n\n**Key Points of the Apache 2.0 License**\n\n- **Permissibility**: Users are allowed to use, modify, and distribute this software freely.\n- **Attribution**: You must provide proper credit to Bitext Innovations International, Inc. when using this model, in accordance with the original copyright notices and the license.\n- **Patent Grant**: The license includes a grant of patent rights from the contributors of the model.\n- **No Warranty**: The model is provided \"as is\" without warranties of any kind.\n\nYou may view the full license text at [Apache License 2.0](http://www.apache.org/licenses/LICENSE-2.0).\n\nThis licensing ensures the model can be used widely and freely while respecting the intellectual contributions of Bitext. 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The datasets include information such as peak load, generation, electric purchases, sales, revenues, customer counts and demand-side management programs, green pricing and net metering programs, and distributed generation capacity.\n\n**Description**\nThis data package contains the Information on monthly electric consumption and cost data by borough and development, utility vendor and meter information. Information on utility or customer-owned distributed and dispersed generation such as the number, capacity, and types of generators. Data include the number of generators, number of generators less than one megawatt, total aggregate capacity, aggregate capacity used only for backup, and capacity by technology type. Information on Electric Retail Revenue Sales Customer Counts By State And Class Of Service contains information on retail revenue, sales, and customer counts. Information on the Control Area Operators or entity (usually electric utility) is responsible for the application of a common, automatic generation and transmission control scheme within a specific electrically bordered area.\n\nControl Area Operators balance demand with available capacity, maintain system frequency and voltage within strict parameters and control power flows into, and out of, the region. Information on aggregate data of the number of customers by State and sector for Green Pricing (for 2007 forward) the revenue and sales from Green Pricing programs and (for 2010 and forward) installed capacity by generation type: photovoltaic; wind; CHP/cogen; and other. Green Pricing programs allow customers to purchase power generated from renewable resources and to pay for renewable energy development. Information on aggregate data of the number of customers by State and sector for Net Metering programs, the energy displaced by Net Metering programs, and (for 2010 and forward) installed capacity by generation type: photovoltaic; wind; CHP/cogen; and other. Net Metering programs allow customers to sell excess power they generate back to the electrical grid to offset consumption.\n\n**Benefits**\n- Useful to evaluate the utilization and costs/revenues related to electricity provision. useful also to evaluate and improve electrical infrastructure and electricity provision. contains data that can serve to evaluate electricity production-related aspects of as well.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [Dispersed Electric Generation Data By Capacity Number Type Generator](https://www.johnsnowlabs.com/marketplace/dispersed-electric-generation-data-by-capacity-number-type-generator)\n\t- The dataset contains information on utility or customer-owned dispersed generation (NOT grid-connected) such as the number, capacity and types of generators.\n- [Distributed Electric Generation Data By Capacity Number Type Generator](https://www.johnsnowlabs.com/marketplace/distributed-electric-generation-data-by-capacity-number-type-generator)\n\t- The dataset includes information on distributed electric generation (grid-connected) by the number of generators, number of generators less than one megawatt, total aggregate capacity, aggregate capacity used only for backup, and capacity by technology type.\n- [Electric Consumption And Cost 2010 to 2020](https://www.johnsnowlabs.com/marketplace/electric-consumption-and-cost-2010-to-2020)\n\t- This Dataset contains the monthly consumption and cost data by borough and development from 2010 to 2020. The dataset also includes utility vendor and meter information.\n- [Electric Investor Owned Utilities Companies and Rates 2011-2018](https://www.johnsnowlabs.com/marketplace/electric-investor-owned-utilities-companies-and-rates-2011-2018)\n\t- The Energy Information Administration data files include information such as peak load, generation, electric purchases, sales, revenues, customer counts and demand-side management programs, green pricing and net metering programs, and distributed generation capacity.\n- [Electric Investor Owned Utilities Companies and Rates 2011-2020](https://www.johnsnowlabs.com/marketplace/electric-investor-owned-utilities-companies-and-rates-2011-2020)\n\t- The Energy Information Administration data files include information such as peak load, generation, electric purchases, sales, revenues, customer counts and demand-side management programs, green pricing and net metering programs, and distributed generation capacity.\n- [Electric Non Investor Owned Utilities Companies and Rates 2011-2018](https://www.johnsnowlabs.com/marketplace/electric-non-investor-owned-utilities-companies-and-rates-2011-2018)\n\t- The Energy Information Administration data files include information such as peak load, generation, electric purchases, sales, revenues, customer counts and demand-side management programs, green pricing and net metering programs, and distributed generation capacity.\n- [Electric Non Investor Owned Utilities Companies and Rates 2011-2020](https://www.johnsnowlabs.com/marketplace/electric-non-investor-owned-utilities-companies-and-rates-2011-2020)\n\t- The Energy Information Administration data files include information such as peak load, generation, electric purchases, sales, revenues, customer counts and demand-side management programs, green pricing and net metering programs, and distributed generation capacity.\n- [Electric Power Annual Utilization Sales And 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Sector](https://www.johnsnowlabs.com/marketplace/net-metering-energy-displaced-capacity-by-generation-type-state-sector)\n\t- The Energy Information Administration dataset files include information such as peak load, generation, electric purchases, sales, revenues, customer counts and demand-side management programs, green pricing and net metering programs, and distributed generation capacity.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read 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The dataset enables alerts and legacy IDs integration to support seamless operational, analytical, and trading workflows.\n\nDataset Overview:\n\n- Coverage spans 38,000+ meter points across US, Canada, and Mexico\n\n- Daily, real-time, and historical data with granularity from intraday to annual\n\n- 20+ years of history, starting from 2005, supporting robust time series analysis\n\n- Point-in-time (PIT) updates and customizable alerts for rapid response\n\n- Reference metadata with 35+ attributes, including pipeline-level throughput and modeled flows\n\n- Legacy Bentek and PointLogic IDs for integration with established workflows\n\n**Use cases**\n\n- Demand Forecasting - All three tiles contribute inputs for forecasting demand, including flows, market fundamentals, infrastructure constraints, weather impacts, storage, and project data\n\n- Supply Chain - Gaslytics provides visibility into production, transportation, storage, pipeline operations, and infrastructure developments that support supply chain planning and optimization\n\n- Asset Valuation - Infrastructure usage, production outlooks, market balances, facility performance, and project development data help evaluate energy assets and investment opportunities\n\n- Quantitative Analysis - All Gaslytics datasets provide historical and near real-time data that can be used for modeling, forecasting, scenario analysis, benchmarking, and market intelligence\n\n- Regulatory Reporting - Pipeline, storage, market balance, gas quality, and FERC-related datasets support compliance, market monitoring, and reporting requirements\n\n**Product details**\n\nSample Tables:\nA) PIPELINE_FLOWS\nB) REFERENCE_DATA_ PIPELINE_FLOWS\n\nSample Fields:\nA) PIPELINE_FLOWS\n\n- FLOWDATE\n- LASTMODIFIEDDATE\n- DATEFREQUENCY\n- DATEFREQUENCYDESC\n- NOMINATIONCYCLE\n- LEGACYPOINTLOGICPOINTID\n- LEGACYPOINTLOGICLCIID\n- LEGACYBENTEKPOINTID\n- COMPONENTPOINTID\n- COMPONENTCREATEDATE\n- PIPELINEOPERATORID\n- PIPELINEOPERATORNAME\n- PIPELINEID\n- PIPELINENAME\n- POINTNAME\n- METERTYPEPRIMARY\n- METERTYPEIDPRIMARY\n- METERTYPEIDSECONDARY\n- METERTYPESECONDARY\n- LOCATIONTYPECODE\n- LOCATIONDESCRIPTION\n- LOCATIONTYPEID\n- FLOWDIRECTION\n- FLOWDIRECTIONCODE\n- FLOWDIRECTIONID\n- ZONE\n- COMPANYID\n- CONNECTINGPARTY\n- LOCPROP\n- POINTISACTIVE\n- DOMAIN\n- DOMAINID\n- REGIONID\n- REGION\n- SUBREGIONID\n- SUBREGION\n- STATEID\n- STATE\n- COUNTYID\n- COUNTY\n- PRODUCINGAREA\n- PRODUCINGAREAID\n- LATITUDE\n- LONGITUDE\n- DESIGNCAPACITY\n- UOM\n- OPERATIONALCAPACITY\n- SCHEDULEDVOLUME\n- UTILIZATION\n- BESTAVAILABLE\n\nB) REFERENCE_DATA_ PIPELINE_FLOWS\n\n- LASTMODIFIEDDATE\n- LEGACYPOINTLOGICPOINTID\n- LEGACYPOINTLOGICLCIID\n- LEGACYBENTEKPOINTID\n- COMPONENTPOINTID\n- PIPELINEOPERATORID\n- PIPELINEOPERATORNAME\n- PIPELINEID\n- PIPELINENAME\n- POINTNAME\n- METERTYPEPRIMARY\n- METERTYPEIDPRIMARY\n- METERTYPEIDSECONDARY\n- METERTYPESECONDARY\n- LOCATIONTYPECODE\n- LOCATIONDESCRIPTION\n- LOCATIONTYPEID\n- FLOWDIRECTION\n- FLOWDIRECTIONCODE\n- FLOWDIRECTIONID\n- ZONE\n- COMPANYID\n- CONNECTINGPARTY\n- LOCPROP\n- POINTISACTIVE\n- DOMAIN\n- DOMAINID\n- REGIONID\n- REGION\n- SUBREGIONID\n- SUBREGION\n- STATEID\n- STATE\n- COUNTYID\n- COUNTY\n- PRODUCINGAREA\n- PRODUCINGAREAID\n- LATITUDE\n- LONGITUDE\n\nView Descriptions:\n\n- PIPELINE_FLOWS - Daily natural gas pipeline flows in the US lower 48, Canada and Mexico that is accompanied with an array of metadata attributes.\n- REFERENCE_DATA_PIPELINE_FLOWS - A single source for pipeline metadata that captures 35 different attributes. Mimics pipeline flows dataset with the removal of volume metric time series data.\n","terms_of_service":"https://www.spglobal.com/en/privacy/privacy-policy-english","documentation_link":"https://www.marketplace.spglobal.com/en/datasets/gaslytics-pipeline-flows-(1755275929)","privacy_policy_link":"https://www.spglobal.com/en/privacy/privacy-policy-english","geographical_coverage":"[\"Latin America and Caribbean\",\"United States and Canada\"]","update_frequency":{"interval":1,"unit":"SECOND"},"collection_granularity":{"interval":1,"unit":"SECOND"},"collection_date_start":1104565372120,"assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"S&P Global Energy","description":"At S&P Energy, our complete view of global energy and commodities markets enables our customers to make decisions with conviction and create long-term, sustainable value. 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S&P Global Energy is a division of S&P Global (NYSE: SPGI)."},"popularity":1185},{"id":"aded23de-814f-44d3-a150-1e13c16c063d","summary":{"name":"SAMPLE Spire-ch-aviation Mapping - Mapping of Spire Global ADS-B data with ch-aviation Fleet ...","subtitle":"Allows Spire ADS-B customers to map each flight to ch-aviation’s Aircraft Data (Aircraft History, Aircraft, ...","share":{"name":"ch_aviation_d7f7c91d_efae_40ef_a5fe_1482414f2075_samples_share","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1793781980000,"updated_at":1793781980000,"categories":["OTHER","GEOSPATIAL","TRAVEL_AND_TOURISM"],"listingType":"STANDARD","provider_id":"bd00c255-49fc-490a-9143-7a3fccbc5f00"},"detail":{"description":"The Spire-ch-aviation Mapping data files allow Spire Global flight IDs to be mapped to ch-aviation Aircraft History Data (aircraft_id and the date_from_full (the combined primary key of ch-aviation’s aircraft histories data)), while also providing regional partnership, cargo customer, wet-lease customer, flight type information for each flight. This allows Spire Global ADS-B customers to map each flight to ch-aviation’s Aircraft Data (Aircraft History, Aircraft, Operators) without having to build any of the logic on your side. We do all the mapping on your behalf.\n\nThe data set is updated daily.\n\nThe sample data shows flights on 2025-03-30 involving Swiss, Alaska Airlines, Horizon Air, Jet Aviation Business Jets, and RVR Aviation as operators or wet lease customers.\n\nContact us to get access to ch-aviation's AWS S3 sample data bucket as well allowing you to build proof of concepts with all of our sample data. \n\nThe direct bucket URL for this data set is: https://eu-central-1.console.aws.amazon.com/s3/buckets/dataservices-standardised-samples?region=eu-central-1&bucketType=general&prefix=spire_ch_flight_mapping/&showversions=false\n\nFull Technical Data Dictionary: https://about.ch-aviation.com/spire-ch-aviation-mapping/","terms_of_service":"https://www.ch-aviation.com/privacy","contact_email":"support@datarade.ai","privacy_policy_link":"https://www.ch-aviation.com/privacy","assets":["ASSET_TYPE_DATA_TABLE"]},"provider_summary":{"name":"ch-aviation","description":"For more than 20 years, we have been supplying decision-makers around the globe with relevant and up-to-date aviation intelligence and insightful news. Today, ch-aviation proudly serves more than 800 customers, and many of the industry’s most reputable companies use its data to make informed decisions.\r\n\r\nUsing its extensive network of sources and data feeds from across the globe, ch-aviation accurately tracks and monitors aviation-specific data sets, presenting them in an easy-to-use format."},"popularity":224},{"id":"2de54147-756b-4f92-87d8-723c0e127254","summary":{"name":"Geolocet | Latest Demographic Data UK - Average Personal Income","subtitle":"The dataset contains average personal income data, split in 15 bands, for the LSOA/SDZ census level.","setting":{"visibility":"PUBLIC"},"created_at":1708503468516,"updated_at":1752136599909,"categories":["DEMOGRAPHICS","ADVERTISING_AND_MARKETING","GEOSPATIAL","RETAIL","PUBLIC_SECTOR"],"listingType":"PERSONALIZED","provider_id":"5d3ddc17-a7e8-4794-a760-13587e1de2c6"},"detail":{"description":"## **Get instant access to the latest Average Personal Income Data for the UK with [Geolocet](https://geolocet.com)**\n\nIf you require data at a less granular level, such as census output areas or square grid, please don't hesitate to get in touch with us at *contact@geolocet.com*. We understand that different projects may have varying data needs, and we are here to tailor our data to match your specific requirements.\n\n ## **Key Features:**\n---\n* **Average personal income data UK:** Gain access to the latest and most accurate population income statistics for the UK, meticulously organized by LSOAs and SDZs.\n\n* **Geodemographic Insights:** Unlock geodemographic data UK-wide to inform location-based decision-making, marketing strategies, and policy formulation.\n* **Seamless Data Access:** Our data is available for instant download in CSV format. No lengthy sign-up processes or NDAs required on our side – just download the data you need.\n* **Data Samples for Evaluation:** To ensure you're getting exactly what you need, we provide free downloadable samples in CSV format for data evaluation.\n* **Data accuracy:** Our datasets are created using the latest income data available in national statistics bureaus, guaranteeing accuracy and reliability.\n* **Best-in-Class Quality:** We guarantee best-in-class quality, backed by over two decades of experience in modeling demographics data and an outstanding quality management process.\n* **Competitive Pricing:** We maintain the lowest prices on the market, ensuring that you get the most cost-efficient demographics data acquisition process.\n---\nAdditionally, for those looking to enrich their analysis or project, we offer:\n* **[Latest Demographics Data UK](https://marketplace.databricks.com/details/ef2f41e3-eeb9-47ee-8bf2-c2e66185a143/Geolocet-LTD_Geolocet-Latest-Base-Demographic-Data-UK-):** If you require demographics data for the UK, consider exploring our Base Demographics Data product, which seamlessly integrate with this dataset.\n\n* **[Administrative and Census Boundaries](https://geolocet.com/search?q=united+kingdom&options%5Bprefix%5D=last):** If you require geographical boundaries (polygons) for the administrative or census divisions of the UK, consider exploring our Administrative and Census Boundaries products, which seamlessly integrate with this dataset.\n\n* **[Point of Interest (PoI) Data](https://geolocet.com/pages/aggregated-points-of-interest-poi-data):** If you're interested in Points of Interest data, please don't hesitate to contact us for customized datasets to enhance your geographical analysis.\n\n* **[Data Customization](https://geolocet.com/pages/data-services-by-geolocet):** We understand that your project may have specific requirements. Feel free to get in touch with us if you need any modifications or specific data formats such as Excel, JSON, GeoJSON, or Shapefile. We are here to tailor the data to your needs.\n\nIf you're looking for data for the UK, [look here](https://geolocet.com/pages/united-kingdom) for off-the-shelf or tailor-made datasets to suit your specific requirements.\n\nMake informed decisions, conduct precise research, and unlock the potential of the UK demographic landscape with our comprehensive dataset. Trust in the accuracy and reliability of our official statistical data to drive your projects forward.\nContact us at *contact@geolocet.com* today to access this invaluable resource and explore UK demographics data like never before.\n\n[Geolocet LTD](https://geolocet.com)\n","terms_of_service":"https://geolocet.com/pages/terms-of-use","documentation_link":"","privacy_policy_link":"https://geolocet.com/pages/privacy-policy","cost":"PAID","update_frequency":{"interval":1,"unit":"YEARLY"},"data_source":"","assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://geolocet.com/pages/terms-of-use"},"provider_summary":{"name":"Geolocet LTD","description":"Geolocet provides premium geospatial data for retail analysis, GIS, and market planning. We equip businesses with accurate, ready-to-query data to drive spatial analysis, site selection, and customer segmentation.\n\nCore Offerings:\n\nDemographics: Socioeconomic data covering population, age, and employment across diverse geographies.\n\nPoints of Interest (PoI): Datasets spanning retail, healthcare, and transport for catchment analysis.\n\nAdministrative Boundaries: GIS-ready boundaries, including municipalities and postcodes, for territory management.\n\nBrand Locations: Chain location datasets to monitor store networks and market expansion.\n\nWealth Data: Granular economic indicators like purchasing power to identify high-potential markets.\n\nWe source from trusted providers, delivering highly customizable datasets with comprehensive coverage across Europe and worldwide."},"popularity":1095},{"id":"a6fe684c-fc59-4204-a80a-def481c9934a","summary":{"name":"SAMPLE Think Data Group Auto Data | US VIN Data | 250MM+ Automotive Records | Updated Monthly","subtitle":"US Automotive VIN Data provides nationwide coverage with 250MM+ VIN-linked records across 125MM+ households, ...","share":{"name":"think_data_group_040e8180_f9c8_4189_9a46_29ecfade3dc2_samples_share","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1789466709457,"updated_at":1789466709457,"categories":["OTHER","ADVERTISING_AND_MARKETING"],"listingType":"STANDARD","provider_id":"0a8fb76a-1a6d-4331-ab98-a9c17198419f"},"detail":{"description":"US Automotive VIN Data delivers nationwide coverage with 250MM+ VIN-linked records across 125MM+ households, providing marketers, agencies, and analytics teams with deterministic vehicle ownership intelligence at scale. Designed for precision targeting and enrichment, the dataset helps organizations move beyond modeled assumptions and engage verified vehicle owners with confidence.\n\nVerified, High-Quality Automotive Intelligence:\n\nBuilt from multiple factual and permissioned sources, records are continuously maintained through rigorous data hygiene processes to improve match quality, freshness, and reliability. Suppression logic helps remove outdated, inactive, exported, and invalid vehicle records where applicable, creating a cleaner and more actionable dataset.\n\nBroad Attribute Coverage:\n\nThe dataset supports granular segmentation across a wide range of automotive signals, including:\n\n- Vehicle make, model, and model year\n- Luxury, mass market, EV, truck, SUV, and specialty segments\n- Household ownership insights\n- Vehicle age and ownership lifecycle indicators\n- Likely in-market timing signals\n- Brand affinity and competitive conquesting opportunities\n\nKey Use Cases:\n\nIdeal for brands and agencies seeking to power:\n\n- Programmatic, CTV, and omnichannel audience activation\n- OEM and dealership conquest campaigns\n- Insurance and financial services targeting\n- Aftermarket parts, service, and maintenance campaigns\n- CRM enrichment and identity resolution\n- Clean room collaboration and audience analytics\n\nScalable National Reach:\n\nWith comprehensive U.S. coverage and household-level depth, this automotive dataset offers the scale, precision, and flexibility needed to drive stronger targeting, smarter media investment, and measurable performance outcomes.","terms_of_service":"https://www.thinkdatagroup.com/privacy-policy","contact_email":"support@datarade.ai","privacy_policy_link":"https://www.thinkdatagroup.com/privacy-policy","assets":["ASSET_TYPE_DATA_TABLE"]},"provider_summary":{"name":"Think Data Group","description":"Think Data Group is an advisory firm composed of leading veterans of the data and information-services industry. Our experts each have many decades’ experience helping buyers and sellers of data achieve and exceed their data goals. We represent more than 100 data companies, many with very unique data and extensive scale. \r\n\r\nWe offer clients state-of-the-art consulting and implementation support across the full data lifecycle — a skill set that few companies develop in-house and few competitors can match.\r\n\r\nWhether you’re a startup or a billion-dollar enterprise, we can help you maximize the value of your data and find the data you are looking for to drive innovation and value.\r\n\r\nAt TDG, we offer a range of consumer and B2B data at scale:\r\n- Over 100 Audiences to reach your markets\r\n- Meets all regulatory/compliance standards\r\n- Custom audiences at your request\r\n- Multi-use data licensing \r\n- Private and first-party data delivery"},"popularity":682},{"id":"602e83d0-436b-492b-a99b-a0d34b07d16c","summary":{"name":"Anonymized Vehicle Standstill Events Data","subtitle":"Standstill events data provides insights into where and for how long vehicles remain stationary.","setting":{"visibility":"PUBLIC"},"created_at":1741267811749,"updated_at":1760440813342,"categories":["GEOSPATIAL","TRANSPORTATION_AND_LOGISTICS"],"listingType":"PERSONALIZED","provider_id":"534843a5-204d-4f28-9f5f-a6331173bba6"},"detail":{"description":"**Description**\nStandstill events data provides insights into where and for how long vehicles remain stationary. This data helps analyze stop locations and durations, offering valuable context for traffic and mobility trends.\n\n**Data Source**\nThe data is sourced by millions of vehicles globally from our vetted partners including:\n\n- Automotive OEMs; vehicles with embedded connectivity from the car manufacturer\n- Fleets with after-sales in-vehicle hardware telematics devices\n\n**Use Cases**\nStandstill events data becomes even more powerful when combined with Probe Data, allowing for a deeper understanding of trip patterns, traffic volumes, and stop durations. Key applications include:\n\n- Site-selection: Identify optimal locations for retail store expansions and EV charging station deployments based on vehicle stop frequency and dwell times.\n- Route Planning: Companies with large vehicle fleets (e.g. transportation companies) use this data to optimise routes, reduce fuel consumption, and improve overall operational efficiency.\n- Transportation analytics: Traffic consultants, road operators, municipalities and SaaS analytics platforms use our data for understanding road safety, road usage, people movement and performing targeted before & after analytics to assess infrastructure and signage changes.\n\n**​Standstill Events Data Schema**\n**Main attributes**\nThe following list provides the main data attributes that are available via the Mobito Standstill Events Data Product:\n\n- start_time\n- end_time\n- h3\n- latitude\n- longitude\n- duration\n- fuel_type\n- vehicle_type\n\n**Standstill Events Data Specs**\n**Historicity**\nHistorical data availability as of 2022. Availability differs per country.\n\n**Geographical granularity**\nData can be delivered on the following geographical levels:\n\n- Custom polygon area\n- Specific road segment\n\n**​Vehicle Types**\nThe following vehicle types area available in the datasets (availability varies per country):\n\n- Passenger car\n- Light commercial vehicle (LCV). This is a van type of vehicle. Examples: (Renault Master, Renault Trafic, Mercedes-Benz Sprinter, Peugeot Boxer, Volkswagen Crafter, Fiat Ducato, Opel Crossland, Citroen Berlingo etc.)\n- Heavy truck (more than 3.5 tones)\n- Bus\n- Other (unclassified)\n\n**Data Anonymisation**\nA data anonymisation is applied on the data. Key features include:\n\n- **No Exact Locations:** Rather than providing precise latitude and longitude coordinates, Mobito utilizes the [H3 hexagonal grid system](https://h3geo.org/) to generalize locations.\n- **[H3-Resolution-11 Precision:](https://h3geo.org/docs/core-library/restable/#average-area-in-m2)** Each location is mapped to a hexagonal area ensuring privacy while preserving analytical value.\n\nFor more details, visit the Mobito [Standstill Data](https://support.mobito.io/en/articles/10641990-standstill-events-data-product) page.","terms_of_service":"https://support.mobito.io/en/articles/10627684-data-license-terms","documentation_link":"https://support.mobito.io/en/articles/10641990-standstill-events-data-product","privacy_policy_link":"https://www.mobito.io/privacy-policy","geographical_coverage":"[\"Global\"]","cost":"PAID","data_source":"Car manufacturers and fleet service providers","assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://support.mobito.io/en/articles/10627684-data-license-terms"},"provider_summary":{"name":"Mobito","description":"Mobito is a mobility data solutions provider that enables organizations to monetize and utilize mobility data. We offer access to anonymized vehicle data from millions of vehicles globally, sourced from vetted car manufacturers and fleet service providers. \n\nOur data products include timestamped vehicle location data (probe data), anonymized harsh driving behavior events (such as harsh braking, acceleration, cornering, crashes), standstill (stop location) events, and origin-destination insights. \n\nThese data sets are used in various use cases, including traffic management, transportation analytics, site selection (e.g. EV charging station locations), and road safety."},"popularity":387},{"id":"c60cd877-a2a9-4054-87c4-0d2cbd68e554","summary":{"name":"Insider One","subtitle":"Access Insider One customer engagement data directly in Databricks for analytics, personalization, and AI use cases.","setting":{"visibility":"PUBLIC"},"created_at":1786635499622,"updated_at":1786638965388,"categories":["ADVERTISING_AND_MARKETING"],"listingType":"PERSONALIZED","provider_id":"e694e319-8dab-4c8d-8b45-dcdb9091805c"},"detail":{"description":"**Overview**\n\nA zero-copy data integration that brings Insider One engagement and customer data directly into Databricks Lakehouse. Use your Insider One data alongside your existing business data for analytics, customer insights, and AI use cases, without building or maintaining custom ETL pipelines.\n\n**Use cases**\n\n- **360-Degree Customer View:** Combine Insider One customer and engagement data with transactional, behavioral, and other business data in Databricks to build a more complete view of your customers and their interactions.\n\n- **Audience Segmentation:** Combine customer attributes and engagement data from Insider One with your existing Databricks datasets to create more granular audience segments, uncover deeper customer insights, and identify high-value audiences.\n\n- **Campaign & Engagement Analytics:** Analyze engagement across channels and campaigns alongside your business data to understand customer behavior, measure campaign performance, and identify opportunities for optimization.\n\n- **AI & Advanced Analytics:** Combine Insider One data with your data in Databricks to build AI and machine learning models, generate predictive insights, identify customer behaviors and patterns, and support data-driven decision making.\n\n- **Data Enrichment:** Enrich existing customer and business datasets with engagement events, campaign interactions, and personalization data from Insider One.\n\n\n**Product details**\n\nThe data product provides access to customer, campaign, engagement, and interaction data generated across Insider One's channels and personalization capabilities.\n\nAvailable data spans a wide range of customer engagement touchpoints, including SMS, WhatsApp, Email, App Push, Web Push, Onsite Personalization, Mobile App Personalization, Product Recommendations, and more, along with the associated customer interactions and engagement events.\n\nThis provides a comprehensive view of how customers interact with different campaigns, channels, and personalized experiences, making it possible to combine Insider One data with other business datasets in Databricks for deeper analysis and advanced use cases.\n\nData is shared directly with the Databricks environment, eliminating the need for manual file transfers or custom data pipelines.\n\nFor more information on available data, supported tables, and how to enable data flows from Databricks to Insider One, please refer to the [Insider One documentation](https://academy.insiderone.com/).\n","terms_of_service":"https://insiderone.com/legal/","documentation_link":"https://academy.insiderone.com/","privacy_policy_link":"https://insiderone.com/privacy-policy/","update_frequency":{"interval":1,"unit":"HOURLY"},"assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"Insider One","description":"Insider One is an AI-native customer experience platform that unifies customer data and activates it across every channel — web, app, email, SMS, WhatsApp, and ads. 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Free trial data.","share":{"name":"share_boundaries_mbi_at_r_2025","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1764336645963,"updated_at":1784884872962,"categories":["ADVERTISING_AND_MARKETING","GEOSPATIAL","MEDIA","OTHER","PUBLIC_SECTOR","RETAIL","TRANSPORTATION_AND_LOGISTICS"],"listingType":"STANDARD","provider_id":"aa09abd6-ff7f-4452-803d-b8c43b4c07cb"},"detail":{"description":"MBI'S GLOBAL AREA BOUNDARIES COVER MOST COUNTRIES WORLDWIDE AND **CAN BE LICENSED FOR EACH COUNTRY INDIVIDUALLY** AND IN BUNDLES.\n[MBI](https://www.mbi-geodata.com/en/) digital boundaries are available for virtually all countries worldwide.* Created by our experts according to consistent global standards, the polygons are so precise and harmonized that they align seamlessly across borders. The territorial vintages of the worldwide area boundaries are compatible with our globally consistent and comparable [socio-demographic](https://www.mbi-geodata.com/en/sociodemographic-data/) and [economic](https://www.mbi-geodata.com/en/purchasing-power-overview/) data.\nOur area boundaries for Austria are offered at the following levels:\n\n- **Postal Code Level** (Postcode 5-digit): This fine-grained level of detail makes the data suitable for a wide range of applications – for example, enriching customer databases, which are often organized by postal code.\n- **Administrative Level** (Gminy/Miasta): Covers both larger administrative areas (e.g., regions, states, provinces) as well as finer subdivisions such as districts, counties, or municipalities.\n- **Micro Level** (Census Enumeration Areas): Subdivisions of postal code or administrative boundaries, such as census areas or street segments. This is the most granular level for local analysis. | on request\n\n**Use Cases:**\n\n- **Market Analysis**: Use MBI’s boundaries to visualize data on a map, learn about your customers, and uncover untapped market potential.\n- **360-Degree Customer View**: Link boundaries to customer data, sociodemographic, and economic data to gain a holistic understanding of customer needs, behaviors, and potential value.\n- **Location Planning**\n•\tUse MBI’s boundaries to visualize data on a map, learn about your customers\n•\tPlan the provision of services and products across different regions\n•\tDefine sales territories\n•\tVisualize catchment areas and plan actions to reach potential customers\n\n**Additional Insights:**\n[About Area Boundaries](https://www.mbi-geodata.com/en/area-boundaries/)\n\n**Data Structure:**\nThe area boundaries include the following variables:\nCTRYCODE: Two-digit country code by ISO 3166\nNAME: Name of administrative area / Name of the most populous administrative area / Name of the respective micro-area or the next higher level\nADMINCODE / POSTCODE / MICROCODE: Administrative code / Postal code / Micro-code\nWKT: Geography as Well Known Text (lat/long WGS84)\n\n*with only a few specific exceptions","terms_of_service":"https://service-mbi-geodata.com/s/HNp7Z2tws4WDqWd","documentation_link":"https://www.mbi-geodata.com/en/mbi-supplier-manufacturer-worldwide-geodata/europe/austria-maps/","privacy_policy_link":"https://www.mbi-geodata.com/en/data-protection/","geographical_coverage":"[\"Global\"]","cost":"FREE","update_frequency":{"interval":1,"unit":"YEARLY"},"collection_granularity":{"interval":1,"unit":"YEARLY"},"collection_date_start":1704115800699,"collection_date_end":1735047007548,"data_source":"MBI","assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://service-mbi-geodata.com/s/QewSJWzWX324DTg"},"provider_summary":{"name":"Michael Bauer International GmbH – MBI Geodata","description":"For over 15 years, Michael Bauer International (MBI) has been a leading provider of globally consistent and comparable geographic data, including more than 3 million postcode areas and other detailed datasets like sociodemographics and purchasing power. 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MBI is part of the Michael Bauer Group, alongside Michael Bauer Research GmbH and Michael Bauer Micromarketing GmbH."},"popularity":160},{"id":"5a67c519-3521-4510-9b88-ec9ae2f3d425","summary":{"name":"1M+ Car Images | AI Training Data | Object Detection Data | Annotated imagery data | Global Coverage","subtitle":"A global dataset of 1M+ car images with full EXIF and object/scene metadata for AI training","setting":{"visibility":"PUBLIC"},"created_at":1767012225656,"updated_at":1779975821084,"categories":["SCIENCE_AND_RESEARCH","MEDIA","OTHER"],"listingType":"PERSONALIZED","provider_id":"dd3c5efe-9a75-4b37-b710-1d72fa504ee8"},"detail":{"description":"**Overview**\nA large-scale image dataset containing 1M+ car images captured globally, designed for computer vision research and AI experimentation. The dataset covers a wide variety of vehicle types, environments, and viewpoints, and supports both ready-made and custom dataset creation.\n\n**Use cases**\n- Training and evaluating computer vision models for car detection and classification\n- Vehicle attribute recognition (make, model, body type, color)\n- Autonomous driving and ADAS perception systems\n- Traffic analysis, smart mobility, and transportation analytics\n- Benchmarking vehicle-focused vision and image processing pipelines\n\n**Product details**\n- Datasets include large-scale collections of car and vehicle images with rich visual, technical, and semantic metadata captured from real-world environments.\n- Sample fields include image ID, image URL, camera make and model, exposure time, aperture (F-number), ISO, focal length, GPS coordinates, capture date, orientation, and labeled vehicle attributes (e.g., car, sedan, SUV, truck, color, angle, environment), along with label confidence and ranking scores.","terms_of_service":"https://gurushots.com/terms","documentation_link":"","privacy_policy_link":"https://gurushots.com/privacy","embedded_notebook_file_infos":[{"id":"e6e1e4a0-7027-42b3-8bac-ef0eb1da2e3d","marketplace_file_type":"EMBEDDED_NOTEBOOK","file_parent":{"parent_id":"5a67c519-3521-4510-9b88-ec9ae2f3d425","file_parent_type":"LISTING"},"mime_type":"text/html","download_link":"https://marketplace-file-storage-prod.s3-fips.us-east-1.amazonaws.com/EMBEDDED_NOTEBOOK/LISTING_5a67c519-3521-4510-9b88-ec9ae2f3d425/e6e1e4a0-7027-42b3-8bac-ef0eb1da2e3d?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEGgaDnVzLWVhc3QtMS1maXBzIkYwRAIgbC8sWIoYaHR8y7Wq86nPb0pufBl4%2BM4PWpeN%2B5PW9VQCIG7BhAVBDCWTpxFDLeuKYm%2Ff1JLz4iccPMzter0YdhgWKqYECDAQABoMNDE0MzUxNzY3ODI2IgxNAJrHbXaZG20jNfMqgwQyGEb4TsoVOoNvJ%2BaA%2FZ7zeu8hGcHqCeNfblP%2FQo5g6q2aRNtpwtPRKMlhYcIE%2FUQaNT1Lqmyptu32VUQTpzjTTxRVmJrDPkheSCkV06XQ%2FpTMKjU8CC7X1b%2FqYf8CrBwZT7Cv4C5WPjEET38f2A79h0xV6bZgPy28zQWrSeIJ9w6AfZBxiE24%2FVc08zVA0ivCT6lsy7nuU0wcR%2FMg2C9nd1vaMnQ923VpD21CKfVVYcrNWL2b%2FQ%2BNAO%2BxPjPgjiN6UPQbvbvZxkSdhgrGtecIg9NgqGkMcvM5VLTDtdC1kdeZetfOC2obtA%2BT%2F0gy3tIDo6WM5hcQfw743%2FAU9oaGnRBl%2BwoxRRDCZA%2F4jnPdluICSI7NCr6C3HHOe4CSZJjRihczxPA%2FgQczAVjAPs6CrK9N9pIzt0vNJpGUTgg4AoxftTvGfciDREilSctPIcBk6GwB%2Bzt4wbtNCmEPU%2BFD46rc6CWVQmmLwbKOeHw70ipMVFlOc8NlXkEtGO2vu6H44MstSSc6daOVUYUavQSGZ6jGhEjsP2YGz8KDm9XQotB%2FtyVBqiZV01OKqDmmBJA%2FXjUpSJ8M%2FeJXhe5m%2BKcxzNObtoJRI7jci3Tpt08XZyXZ7%2FyIV8H30ya468X3cfkTXrm972uga3z7iXAdubyEHnQIua%2FuvtwOHILi6O5D1Me2MzCPvcnXBjqTAYDoMWBt%2B3AVFuqSs2dmDQNBnvEhXfMgTedKLD5PJHb3aIl15iBkLaNu36GYnVCp9ZU2lSYcTbY5QwNnNeTfJTNy9bKhwzru6Sg%2BslhN%2BTS0zkTn%2BeLMY4tHgPEtEZ2cp%2FCk6GwqswvnYVKHjwO8q%2B1Mx8lc08fVEfarE9dke1GGFqoddx%2Bq%2BZUyHbDlAVfrCmf0yw%3D%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20261110T000135Z&X-Amz-SignedHeaders=host&X-Amz-Expires=86400&X-Amz-Credential=ASIAWA6KKHEJORWBH4N3%2F20261110%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=e4cc65d399a7f1ad738e63648b545c72692fd3f11e7016f93732f6f75b18dbb2","created_at":1767012230846,"updated_at":1767012230846,"display_name":"Car_Image_Dataset","status":"FILE_STATUS_PUBLISHED"}],"geographical_coverage":"[\"Global\"]","cost":"PAID","update_frequency":{"interval":30,"unit":"DAILY"},"collection_granularity":{"interval":1,"unit":"DAILY"},"data_source":"Proprietary and curated datasets collected, licensed, and generated by DataSeeds.ai","assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"DataSeeds.ai","description":"DataSeeds.ai provides high-quality, large-scale multimodal datasets designed for modern AI and machine learning workflows. Our offerings include curated image, video, and audio data with rich metadata, optimized for computer vision, generative AI, media analysis, and multimodal model training.\n\nDatasets are delivered through Databricks Marketplace using Delta Sharing, enabling secure, scalable, and zero-copy access for analytics and ML pipelines. DataSeeds.ai serves teams building production-grade AI systems that require reliable, well-structured multimodal data.\n\nIn addition to our existing dataset catalog, DataSeeds.ai also creates custom datasets on demand, tailored to specific customer requirements, data specifications, and use cases."},"popularity":401},{"id":"2b74b5a9-11a4-4067-91db-be9e14b19470","summary":{"name":"Concepts","subtitle":"This data package contains the entire concept structure (sematic type, concepts and replationship) of UMLS Metathesaur..","share":{"name":"concepts","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1682356167639,"updated_at":1695691751733,"categories":["HEALTH","SCIENCE_AND_RESEARCH"],"listingType":"STANDARD","provider_id":"f88849f8-9d77-4de2-b4c7-03d55cc3768e"},"detail":{"description":"\n**Overview**\nThis data package contains the entire concept structure (sematic type, concepts and replationship) of UMLS Metathesaurus for the biomedical domain \"Concepts\".\n\n**Description**\nThe UMLS, or Unified Medical Language System, is a set of files and software that brings together many health and biomedical vocabularies and standards to enable interoperability between computer systems. There are three UMLS Knowledge Sources: \n- The Metathesaurus, which contains over one million biomedical concepts from over 100 source vocabularies \n- The Semantic Network, which defines 133 broad categories and fifty-four relationships between categories for labeling the biomedical domain \n- The SPECIALIST Lexicon & Lexical Tools, which provide lexical information and programs for language processing The Metathesaurus is a large, multi-purpose, and multi-lingual vocabulary database that contains information about biomedical and health related concepts, their various names, and the relationships among them. The Metathesaurus is built from the electronic versions of many different thesauri, classifications, code sets, and lists of controlled terms used in patient care, health services billing, public health statistics, indexing and cataloging biomedical literature, and/or basic, clinical, and health services research. The Metathesaurus contains over five million terms, or names, organized by meaning into concepts and assigned a unique identifier. The Metathesaurus is not a vocabulary. It contains many vocabularies that are standards and helps to create mappings between these vocabularies. The Semantic Network consists of: \n- Semantic types (high level categories) \n- Semantic relationships (relationships between semantic types) The Semantic Network can be used to categorize any medical vocabulary. There are 133 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types. Semantic types and semantic relationships create a network that represents the biomedical domain. Semantic types and relationships help with interpreting the meaning that has been assigned to the Metathesaurus concept.\n\n**Benefits**\n- The purpose of the umls is to improve the ability of computer programs to \"understand\" the biomedical meaning in user inquiries and to use this understanding to retrieve and integrate relevant machine-readable information for users.\n- one powerful use of the umls is linking health information, medical terms, drug names, and billing codes across different computer systems.\n- linking terms and codes between your doctor, your pharmacy, and your insurance company.\n- patient care coordination among several departments within a hospital.\n- timely access to accurate and up-to-date information improves decision making and ultimately the quality of patient care and research.\n- the umls has many other uses, including search engine retrieval, data mining, public health statistics reporting, and terminology research.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [Activity Concepts and Types](https://www.johnsnowlabs.com/marketplace/activity-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Activity\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Activity Relationships](https://www.johnsnowlabs.com/marketplace/activity-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Activity\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Age Group Concepts and Types](https://www.johnsnowlabs.com/marketplace/age-group-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Age Group\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Age Group Relationships](https://www.johnsnowlabs.com/marketplace/age-group-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Age Group\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Amino Acid Sequence Concepts and Types](https://www.johnsnowlabs.com/marketplace/amino-acid-sequence-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Amino Acid Sequence\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Amino Acid Sequence Relationships](https://www.johnsnowlabs.com/marketplace/amino-acid-sequence-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Amino Acid Sequence\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Biomedical Occupation or Discipline Concepts and Types](https://www.johnsnowlabs.com/marketplace/biomedical-occupation-or-discipline-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Biomedical Occupation or Discipline\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Biomedical Occupation or Discipline Relationships](https://www.johnsnowlabs.com/marketplace/biomedical-occupation-or-discipline-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Biomedical Occupation or Discipline\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Body Location or Region Concepts and Types](https://www.johnsnowlabs.com/marketplace/body-location-or-region-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Body Location or Region\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Body Location or Region Relationships](https://www.johnsnowlabs.com/marketplace/body-location-or-region-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Body Location or Region\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Body Space or Junction Concepts and Types](https://www.johnsnowlabs.com/marketplace/body-space-or-junction-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Body Space or Junction\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Body Space or Junction Relationships](https://www.johnsnowlabs.com/marketplace/body-space-or-junction-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Body Space or Junction\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Body System Concepts and Types](https://www.johnsnowlabs.com/marketplace/body-system-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Body System\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Body System Relationships](https://www.johnsnowlabs.com/marketplace/body-system-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Body System\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Carbohydrate Sequence Concepts and Types](https://www.johnsnowlabs.com/marketplace/carbohydrate-sequence-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Carbohydrate Sequence\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Carbohydrate Sequence Relationships](https://www.johnsnowlabs.com/marketplace/carbohydrate-sequence-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Carbohydrate Sequence\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Classification Concepts and Types](https://www.johnsnowlabs.com/marketplace/classification-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Classification\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Classification Relationships](https://www.johnsnowlabs.com/marketplace/classification-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Classification\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Clinical Attribute Concepts and Types](https://www.johnsnowlabs.com/marketplace/clinical-attribute-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Clinical Attribute\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Clinical Attribute Relationships](https://www.johnsnowlabs.com/marketplace/clinical-attribute-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Clinical Attribute\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Conceptual Entity Concepts and Types](https://www.johnsnowlabs.com/marketplace/conceptual-entity-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Conceptual Entity\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Conceptual Entity Relationships](https://www.johnsnowlabs.com/marketplace/conceptual-entity-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Conceptual Entity\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Family Group Concepts and Types](https://www.johnsnowlabs.com/marketplace/family-group-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Family Group\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Family Group Relationships](https://www.johnsnowlabs.com/marketplace/family-group-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Family Group\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Functional Concept Concepts and Types](https://www.johnsnowlabs.com/marketplace/functional-concept-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Functional Concept\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Functional Concept Relationships](https://www.johnsnowlabs.com/marketplace/functional-concept-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Functional Concept\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Geographic Area Concepts and Types](https://www.johnsnowlabs.com/marketplace/geographic-area-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Geographic Area\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Geographic Area Relationships](https://www.johnsnowlabs.com/marketplace/geographic-area-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Geographic Area\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Group Attribute Concepts and Types](https://www.johnsnowlabs.com/marketplace/group-attribute-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Group Attribute\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Group Attribute Relationships](https://www.johnsnowlabs.com/marketplace/group-attribute-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Group Attribute\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Group Concepts and Types](https://www.johnsnowlabs.com/marketplace/group-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Group\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Group Relationships](https://www.johnsnowlabs.com/marketplace/group-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Group\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Health Care Related Organization Concepts and Types](https://www.johnsnowlabs.com/marketplace/health-care-related-organization-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Health Care Related Organization\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Health Care Related Organization Relationships](https://www.johnsnowlabs.com/marketplace/health-care-related-organization-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Health Care Related Organization\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Idea or Concept Concepts and Types](https://www.johnsnowlabs.com/marketplace/idea-or-concept-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Idea or Concept\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Idea or Concept Relationships](https://www.johnsnowlabs.com/marketplace/idea-or-concept-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Idea or Concept\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Intellectual Product Concepts and Types](https://www.johnsnowlabs.com/marketplace/intellectual-product-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Intellectual Product\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Intellectual Product Relationships](https://www.johnsnowlabs.com/marketplace/intellectual-product-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Intellectual Product\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Language Concepts and Types](https://www.johnsnowlabs.com/marketplace/language-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Language\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Language Relationships](https://www.johnsnowlabs.com/marketplace/language-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Language\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Molecular Sequence Concepts and Types](https://www.johnsnowlabs.com/marketplace/molecular-sequence-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Molecular Sequence\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Molecular Sequence Relationships](https://www.johnsnowlabs.com/marketplace/molecular-sequence-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Molecular Sequence\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Nucleotide Sequence Concepts and Types](https://www.johnsnowlabs.com/marketplace/nucleotide-sequence-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Nucleotide Sequence\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Nucleotide Sequence Relationships](https://www.johnsnowlabs.com/marketplace/nucleotide-sequence-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Nucleotide Sequence\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Occupation or Discipline Concepts and Types](https://www.johnsnowlabs.com/marketplace/occupation-or-discipline-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Occupation or Discipline\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Occupation or Discipline Relationships](https://www.johnsnowlabs.com/marketplace/occupation-or-discipline-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Occupation or Discipline\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Organism Attribute Concepts and Types](https://www.johnsnowlabs.com/marketplace/organism-attribute-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Organism Attribute\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Organism Attribute Relationships](https://www.johnsnowlabs.com/marketplace/organism-attribute-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Organism Attribute\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Organization Concepts and Types](https://www.johnsnowlabs.com/marketplace/organization-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Organization\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Organization Relationships](https://www.johnsnowlabs.com/marketplace/organization-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Organization\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Patient or Disabled Group Concepts and Types](https://www.johnsnowlabs.com/marketplace/patient-or-disabled-group-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Patient or Disabled Group\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Patient or Disabled Group Relationships](https://www.johnsnowlabs.com/marketplace/patient-or-disabled-group-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Patient or Disabled Group\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Population Group Concepts and Types](https://www.johnsnowlabs.com/marketplace/population-group-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Population Group\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Population Group Relationships](https://www.johnsnowlabs.com/marketplace/population-group-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Population Group\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Professional Society Concepts and Types](https://www.johnsnowlabs.com/marketplace/professional-society-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Professional Society\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Professional Society Relationships](https://www.johnsnowlabs.com/marketplace/professional-society-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Professional Society\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Professional or Occupational Group Concepts and Types](https://www.johnsnowlabs.com/marketplace/professional-or-occupational-group-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Professional or Occupational Group\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Professional or Occupational Group Relationships](https://www.johnsnowlabs.com/marketplace/professional-or-occupational-group-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Professional or Occupational Group\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Qualitative Concept Concepts and Types](https://www.johnsnowlabs.com/marketplace/qualitative-concept-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Qualitative Concept\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Qualitative Concept Relationships](https://www.johnsnowlabs.com/marketplace/qualitative-concept-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Qualitative Concept\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Quantitative Concept Concepts and Types](https://www.johnsnowlabs.com/marketplace/quantitative-concept-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Quantitative Concept\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Quantitative Concept Relationships](https://www.johnsnowlabs.com/marketplace/quantitative-concept-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Quantitative Concept\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Regulation or Law Concepts and Types](https://www.johnsnowlabs.com/marketplace/regulation-or-law-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Regulation or Law\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Regulation or Law Relationships](https://www.johnsnowlabs.com/marketplace/regulation-or-law-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Regulation or Law\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Self-help or Relief Organization Concepts and Types](https://www.johnsnowlabs.com/marketplace/self-help-or-relief-organization-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Self-help or Relief Organization\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Self-help or Relief Organization Relationships](https://www.johnsnowlabs.com/marketplace/self-help-or-relief-organization-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Self-help or Relief Organization\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Spatial Concept Concepts and Types](https://www.johnsnowlabs.com/marketplace/spatial-concept-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Spatial Concept\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Spatial Concept Relationships](https://www.johnsnowlabs.com/marketplace/spatial-concept-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Spatial Concept\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n- [Temporal Concept Concepts and Types](https://www.johnsnowlabs.com/marketplace/temporal-concept-concepts-and-types)\n\t- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type \"Temporal Concept\". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.\n- [Temporal Concept Relationships](https://www.johnsnowlabs.com/marketplace/temporal-concept-relationships)\n\t- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type \"Temporal Concept\". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated\n- Data Updates\n - Data updates support replace-on-update: outdated foreign keys are deprecated, not deleted\n\n**Our data is curated and enriched by domain experts** \n\n Each dataset is manually curated by our team of doctors, pharmacists, public health & medical billing experts:\n- Field names, descriptions, and normalized values are chosen by people who actually understand their meaning\n- Healthcare & life science experts add categories, search keywords, descriptions and more to each dataset\n- Both manual and automated data enrichment supported for clinical codes, providers, drugs, and geo-locations\n- The data is always kept up to date – even when the source requires manual effort to get updates\n- Support for data subscribers is provided directly by the domain experts who curated the data sets\n- Every data source’s license is manually verified to allow for royalty-free commercial use and redistribution.\n\n**Need Help?**\nIf you have questions about our products, contact us at [info@johnsnowlabs.com](mailto:info@johnsnowlabs.com). \n 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for Devices and Radiological Health (CDRH), complete list of recalls that were issued from 2015 to 2017, and descriptions of radiation-emitting products that have been recalled under an approved corrective action.\n\n**Description**\nThis data package contains the lists of medical device names and associated information developed by the Center for Devices and Radiological Health (CDRH) in support of its mission, FDA medical devices recalls from 2015 to 2017 and  electronic products for which manufacturers are required to perform corrective actions when a radiation safety problem exists that was caused by the design, manufacturing, or assembly of the product.\n\n**Benefits**\n- Access to fda medical device recalls of product that could cause serious health problems or death.\n- access to medical device classification, names and their associated product codes.\n- access to descriptions of radiation-emitting products that have been recalled from fda under an approved corrective action plan to remove defective and noncompliant products from the market.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [Medical Devices Classification](https://www.johnsnowlabs.com/marketplace/medical-devices-classification)\n\t- The Medical Devices classification Database dataset contains medical device names and associated information developed by the Center for Devices and Radiological Health (CDRH) in support of its mission.\n- [Medical Devices Recalls](https://www.johnsnowlabs.com/marketplace/medical-devices-recalls)\n\t- The Food and Drug Administration Medical Devices Recalls dataset contains a complete list of recalls that were issued since 2015.\n- [Radiation-Emitting Electronic Products Corrective Actions](https://www.johnsnowlabs.com/marketplace/radiation-emitting-electronic-products-corrective-actions)\n\t- Radiation-Emitting Electronic Products Corrective Actions provides descriptions of radiation-emitting products that have been recalled under an approved corrective action plan to remove defective and noncompliant products from the market.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 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That foundation is what makes autonomous action trustworthy at enterprise scale, and it's what powers every agent in the platform.\n\nDatabricks Ventures is an investor in Anomalo, and Databricks field teams actively recommend Anomalo to enterprise customers, because native data quality features don't replace what a purpose-built intelligence layer does."},"popularity":1826},{"id":"b5d5802b-8312-4c49-80c6-26dff80b4d37","summary":{"name":"MBI Area Boundaries for Germany – Sample","subtitle":"MBI's precise & seamless boundaries for well-founded decisions in Germany. 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Get unlimited access to all 9.67M provider golden records:\n\n[Purchase Full Access](https://checkout.dataplex-consulting.com/b/bJe9AM9LtcCk27C9y4bQY07)\n\nFull access includes:\n\n- All 9.67M providers across all 11 tables\n- Weekly refreshes as CMS publishes NPPES updates\n- Full FHIR R4 resource set and duplicate-detection tables\n- Email support from the Dataplex data engineering team\n\n## Documentation & Support\n\n- **Full Documentation:** [docs.dataplex-consulting.com/data-catalog/nppes-validated-dataset](https://docs.dataplex-consulting.com/data-catalog/nppes-validated-dataset)\n- **Support:** support@dataplex-consulting.com\n\nData provided by Dataplex Consulting | [dataplex-consulting.com](https://dataplex-consulting.com)","terms_of_service":"https://www.dataplex-consulting.com/terms","documentation_link":"https://docs.dataplex-consulting.com/data-catalog/nppes-validated-dataset","privacy_policy_link":"https://www.dataplex-consulting.com/privacy","embedded_notebook_file_infos":[{"id":"bcd95ab4-c4e5-4e1e-82f5-8a03fe90f10e","marketplace_file_type":"EMBEDDED_NOTEBOOK","file_parent":{"parent_id":"efacc7fd-7919-4315-af45-a3246192de85","file_parent_type":"LISTING"},"mime_type":"text/html","download_link":"https://marketplace-file-storage-prod.s3-fips.us-east-1.amazonaws.com/EMBEDDED_NOTEBOOK/LISTING_efacc7fd-7919-4315-af45-a3246192de85/bcd95ab4-c4e5-4e1e-82f5-8a03fe90f10e?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEGgaDnVzLWVhc3QtMS1maXBzIkYwRAIgbC8sWIoYaHR8y7Wq86nPb0pufBl4%2BM4PWpeN%2B5PW9VQCIG7BhAVBDCWTpxFDLeuKYm%2Ff1JLz4iccPMzter0YdhgWKqYECDAQABoMNDE0MzUxNzY3ODI2IgxNAJrHbXaZG20jNfMqgwQyGEb4TsoVOoNvJ%2BaA%2FZ7zeu8hGcHqCeNfblP%2FQo5g6q2aRNtpwtPRKMlhYcIE%2FUQaNT1Lqmyptu32VUQTpzjTTxRVmJrDPkheSCkV06XQ%2FpTMKjU8CC7X1b%2FqYf8CrBwZT7Cv4C5WPjEET38f2A79h0xV6bZgPy28zQWrSeIJ9w6AfZBxiE24%2FVc08zVA0ivCT6lsy7nuU0wcR%2FMg2C9nd1vaMnQ923VpD21CKfVVYcrNWL2b%2FQ%2BNAO%2BxPjPgjiN6UPQbvbvZxkSdhgrGtecIg9NgqGkMcvM5VLTDtdC1kdeZetfOC2obtA%2BT%2F0gy3tIDo6WM5hcQfw743%2FAU9oaGnRBl%2BwoxRRDCZA%2F4jnPdluICSI7NCr6C3HHOe4CSZJjRihczxPA%2FgQczAVjAPs6CrK9N9pIzt0vNJpGUTgg4AoxftTvGfciDREilSctPIcBk6GwB%2Bzt4wbtNCmEPU%2BFD46rc6CWVQmmLwbKOeHw70ipMVFlOc8NlXkEtGO2vu6H44MstSSc6daOVUYUavQSGZ6jGhEjsP2YGz8KDm9XQotB%2FtyVBqiZV01OKqDmmBJA%2FXjUpSJ8M%2FeJXhe5m%2BKcxzNObtoJRI7jci3Tpt08XZyXZ7%2FyIV8H30ya468X3cfkTXrm972uga3z7iXAdubyEHnQIua%2FuvtwOHILi6O5D1Me2MzCPvcnXBjqTAYDoMWBt%2B3AVFuqSs2dmDQNBnvEhXfMgTedKLD5PJHb3aIl15iBkLaNu36GYnVCp9ZU2lSYcTbY5QwNnNeTfJTNy9bKhwzru6Sg%2BslhN%2BTS0zkTn%2BeLMY4tHgPEtEZ2cp%2FCk6GwqswvnYVKHjwO8q%2B1Mx8lc08fVEfarE9dke1GGFqoddx%2Bq%2BZUyHbDlAVfrCmf0yw%3D%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20261110T000135Z&X-Amz-SignedHeaders=host&X-Amz-Expires=86400&X-Amz-Credential=ASIAWA6KKHEJORWBH4N3%2F20261110%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=2b8d6d01df7c4200c811a58c4fa28c5d8fe7e661712d57e3d3e4f13f914f3c92","created_at":1784560069491,"updated_at":1784560069491,"display_name":"07_nppes_validated_showcase","status":"FILE_STATUS_PUBLISHED"}],"geographical_coverage":"[\"United States and Canada\"]","update_frequency":{"interval":7,"unit":"DAILY"},"assets":["ASSET_TYPE_DATA_TABLE","ASSET_TYPE_NOTEBOOK"],"license":""},"provider_summary":{"name":"Dataplex Consulting & Data Products","description":"Dataplex Consulting & Data Products delivers turnkey, analytics-ready data products that make complex public and commercial data easy to use across modern data platforms. 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Choose from three historical EOD price equity packages:\n\n- Recent History Intraday: January 2000 to current\n- 1990s Intraday: from January 1990 to December 1999\n- Extended History Intraday: from January 1968 to December 1989\n\nSupplement the pricing data with the add-on packages including:\n\n- Splits: A time series of all stock splits in a security's history, including the rate of the split, associated dates (exdate, paydate, etc.) and the split adjustment factor \n- Dividends: All dividends paid in a security's history according to date and type\n- Capitalization: The pre-calculated market cap for a company, including limited partner shares, the total enterprise value, and the non-traded shares outstanding","terms_of_service":"https://www.spglobal.com/en/terms-of-use","documentation_link":"https://www.marketplace.spglobal.com/en/datasets/market-data-(17)","privacy_policy_link":"https://www.spglobal.com/en/privacy/privacy-policy-english","geographical_coverage":"[\"Global\"]","assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"S&P Global Market Intelligence","description":"At S&P Global Market Intelligence, we understand the importance of accurate, deep and insightful information. Our team of experts delivers unrivaled insights and leading data and technology solutions, partnering with customers to expand their perspective, operate with confidence, and make decisions with conviction."},"popularity":1990},{"id":"1b768024-1648-430f-90d6-80a5d48a3662","summary":{"name":"SAMPLE Asian Continent (51 Countries) Detailed Customs Data | Trade Activities Insights | Trade ...","subtitle":"This data product describes the detailed direct customs data and trade data of all 51 Asian countries, which is a ...","share":{"name":"market_inside_data_155a85ff_e6a4_48e9_bce2_65606f39bb51_samples_share","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1753791669925,"updated_at":1780306221782,"categories":["OTHER"],"listingType":"STANDARD","provider_id":"4c5ab4b7-2cfc-4119-bc07-157e0f05b6e6"},"detail":{"description":"This vast repository houses crucial information on Asian customs data and trade transactions, capturing the intricate details of both export and import activities of all Asian countries. \r\n\r\nThe Export Database contains meticulous records of outbound shipments, offering valuable insights into the products, exporters, and destinations involved in each transaction. On the other hand, the Import Database provides a comprehensive view of inbound shipments, shedding light on the importers, origins, and details of the products acquired. Together, these two databases present a holistic perspective on global trade dynamics, encompassing critical metadata such as dates, product descriptions, quantities, values, and transportation specifics. \r\n\r\nWhether you are an analyst, researcher, or business professional, this comprehensive database will undoubtedly prove to be an invaluable resource for gaining a deep understanding of international trade patterns and market dynamics. Explore the wealth of information within and unlock new opportunities in the world of trade and commerce.\r\n\r\nThe Export Database contains information related to export transactions. Each entry in the database represents a specific export event. The metadata fields in this database hold crucial details about the exported products and the transaction itself. \r\n• The \"DATE\" field indicates the date of the export. \r\n• \"EXPORTER NAME\" refers to the name of the entity or company responsible for exporting the goods. \r\n• \"DESTINATION COUNTRY\" indicates the country to which the products are being shipped. \r\n• The \"HS CODE\" represents the Harmonized System code, a standardized numerical system used to classify traded products. \r\n• The \"PRODUCT DESCRIPTION\" field provides a brief description of the exported item. \r\n• The \"BRAND\" field specifies the brand associated with the product.\r\n• \"QUANTITY\" indicates the total quantity of the product being exported.\r\n• \"UNIT OF QUANTITY\" represents the measurement unit used for quantity. \r\n• \"SUBITEM QUANTITY\" refers to the quantity of a subitem within the main exported product. \r\n• The \"PACKAGES\" field indicates the number of packages used for shipment.\r\n• \"GROSS WEIGHT\" represents the total weight of the exported products.\r\n• \"SUBITEM FOB VALUE\" and \"TOTAL FOB VALUE\" denote the Free on Board (FOB) value of the subitem and the total FOB value of the export, respectively. \r\n• \"TOTAL CIF VALUE\" indicates the total cost, insurance, and freight value.\r\n• \"ITEM NUMBER\" is a unique identifier for each product item. \r\n• \"TRANSPORT TYPE\" specifies the mode of transportation used for the export. \r\n• \"INCOTERMS\" refers to the standardized international trade terms defining the responsibilities of buyers and sellers during transportation. \r\n• \"CUSTOMS\" indicates the customs information related to the export.\r\n• \"VARIETY\" and \"ATTRIBUTES\" hold additional details about the product. \r\n• The \"OPERATION TYPE\" field indicates the type of export operation, such as direct export or re-export. \r\n• \"MONTH\" and \"YEAR\" represent the month and year when the export occurred.\r\n\r\nThe Import Database contains information related to import transactions. Each entry in the database represents a specific import event. The metadata fields in this database hold crucial details about the imported products and the transaction itself. \r\n• The \"DATE\" field indicates the date of the import. \r\n• \"IMPORTER NAME\" refers to the name of the entity or company responsible for importing the goods. \r\n• \"SALES COUNTRY\" indicates the country from which the products are being purchased. \r\n• \"ORIGIN COUNTRY\" denotes the country where the imported products originate. \r\n• The \"HS CODE\" represents the Harmonized System code, a standardized numerical system used to classify traded products. \r\n• The \"PRODUCT DESCRIPTION\" field provides a brief description of the imported item. \r\n• \"QUANTITY\" indicates the total quantity of the product being imported.\r\n• \"UNIT OF QUANTITY\" represents the measurement unit used for quantity.\r\n• \"SUBITEM QUANTITY\" refers to the quantity of a subitem within the main imported product. \r\n• The \"PACKAGES\" field indicates the number of packages used for shipment.\r\n• \"GROSS WEIGHT\" represents the total weight of the imported products.\r\n• \"TOTAL CIF VALUE\" indicates the total cost, insurance, and freight value.\r\n• \"TOTAL FREIGHT VALUE\" and \"TOTAL INSURANCE VALUE\" represent the respective values for freight and insurance. \r\n• \"ITEM FOB VALUE,\" \"SUBITEM FOB VALUE,\" and \"ITEM CIF VALUE\" denote the Free on Board (FOB) value of the item, subitem, and the cost, insurance, and freight value of the item, respectively. \r\n• \"ORIGIN PORT\" specifies the port from which the products were shipped.\r\n• \"TRANSPORT TYPE\" specifies the mode of transportation used for the import. \r\n• \"INCOTERMS\" refers to the standardized international trade terms defining the responsibilities of buyers and sellers during transportation. \r\n• \"ITEM NUMBER\" is a unique identifier for each product item. \r\n• \"CUSTOMS\" indicates the customs information related to the import.\r\n• \"OPERATION TYPE\" field indicates the type of import operation, such as direct import or import for re-export. \r\n• \"MONTH\" and \"YEAR\" represent the month and year when the import occurred.\r\n\r\nUse Cases:\r\nMarket Research, Company Analysis, Company Valuation, Business Development, Competitive Intelligence, Competitor analysis, Competition Analysis, Company Based Marketing, Data Enhancement, Data Enrichment, Demand Forecasting, Direct Marketing, Due Diligence, Fraud Prevention, Investment Banking & Investment Management, Lead Generation, Lead Prioritization, Location Based Targeting, Location Based Marketing, Market Intelligence, Market Analytics, Operational Intelligence, Product Research, Risk Analysis, Risk Assessment, Risk Mitigation, Sales Enablement, Ship Tracking, Stock Picking, Stock Valuation, Supply Market Analysis, Supply Market Intelligence, Commodity Analysis, Cost & Unit Price Analysis, Identify Global Buyers & Suppliers\r\n\r\nLabels: Import Export Data, Trade Data, Customs Data, Transport & Logistics Data, Bill of Lading Data","terms_of_service":"https://www.marketinsidedata.com","contact_email":"support@datarade.ai","privacy_policy_link":"https://www.marketinsidedata.com","assets":["ASSET_TYPE_DATA_TABLE"]},"provider_summary":{"name":"Market Inside Data","description":"Market Inside is a global leader in import-export information and analytics for the major industries and markets. \r\n\r\nBy using our dashboard, you can access:\r\n•\tBill of lading data by HS Code or product description.\r\n•\tProduct specifications like brand, model, type, etc.\r\n•\tCompany information like name, size, location and so on.\r\n•\tCompanies’ business information such as market share, industry, etc.\r\nHow import-export data which you get from us benefits to your business?\r\n•\tStudy trade relationships for business decisions.\r\n•\tDetermine market share of companies to choose the best prospects.\r\n•\tVisualize latest market trends to discover new business opportunities.\r\n•\tFind active suppliers in any area of the world.\r\n•\tIdentify suitable buyers globally to supply your product.\r\n•\tDirectly contact companies and their key decision makers to expand business relationships.\r\n•\tTrack bilateral trade between countries to discover new spots to import and export."},"popularity":1272},{"id":"646b491e-6731-4793-a1f5-17e746450c87","summary":{"name":"Global Labor Market Data & Talent Intelligence","subtitle":"Workforce supply, demand, pay, skills, diversity, migration & hiring trends by role and region","setting":{"visibility":"PUBLIC"},"created_at":1788589402633,"updated_at":1788589402633,"categories":["ADVERTISING_AND_MARKETING","COMMERCE","ECONOMICS","OTHER","EDUCATION","SCIENCE_AND_RESEARCH"],"listingType":"PERSONALIZED","provider_id":"9900a255-6aa9-496f-9b6a-421a3568af3b"},"detail":{"description":"**Overview**\nDraup’s Global Labor Market Data & Talent Intelligence provides a multi-dimensional view of workforce dynamics across countries, regions, industries, and job roles. By combining supply and demand metrics with compensation, hiring difficulty, and migration trends, the dataset helps HR leaders, workforce planners, and people analytics teams anticipate talent shortages, design competitive hiring strategies, and optimize global workforce distribution.\nBeyond headcounts and demand signals, the dataset enriches job roles with context on core and soft skills, educational pipelines, certifications, diversity representation, and career pathways. It also highlights top employers, competitor hiring intensity, and common job titles, allowing organizations to benchmark against peers and align workforce investments with business priorities. This holistic perspective supports decisions on location strategy, diversity initiatives, reskilling needs, and long-term workforce planning in a competitive global market.\n\n**Methodology & Quality**\nThe dataset is built by consolidating data from millions of structured and unstructured sources, including public job postings, professional profiles, labor market reports, and education and certification databases. Advanced normalization ensures consistency across countries, job roles, and industries, while Draup’s proprietary job taxonomy links occupations to responsibilities, adjacent roles, and standardized ONET classifications. Bias is minimized by weighting across multiple sources, and updates are refreshed frequently to capture the latest demand and supply signals. Each dataset is standardized for comparability across geographies and industries, providing HR and talent teams with a trusted, current, and scalable foundation for workforce planning.\n\n**Key Tables Include**\n•\tTalent Metrics by Country & Job Role – Supply, demand, pay benchmarks, and hiring difficulty at a global scale.\n•\tTalent Metrics by Location (MSA) & Job Role – Localized workforce size, demand, and compensation at metro or regional levels.\n•\tJob Role Master – Standardized taxonomy with descriptions, responsibilities, ONET mappings, and adjacent job families.\n•\tIndustry Split by Country or MSA – Workforce distribution across industries for specific job roles.\n•\tTop Employers by Role & Location – Company-level hiring intensity and workforce size indicators.\n•\tJob Role Skills (Core & Soft) – Required technical and non-technical skills with workforce distribution intensity.\n•\tTech Stack by Role & Location – Technologies most commonly associated with each role.\n•\tEducation & Certifications by Role & Location – Top universities, majors, and certifications tied to role pipelines.\n•\tUniversity Talent Supply – Graduate counts by job role, major, and institution.\n•\tJob Role Path Predictor – Forward and backward career transitions for job roles.\n•\tCompetitors by Role & Location – Hiring activity and job posting intensity among peer employers.\n•\tSeniority Distribution – Workforce experience levels across job roles.\n•\tDiversity Metrics – Gender and ethnicity breakdowns by job role and geography.\n•\tTalent Movements – Inbound and outbound migration and mobility flows by geography and role.\n•\tCommon Job Titles – Normalized job title variations with posting intensity.\n\n**Use Cases:**\n***Discover emerging talent hubs in Eastern Europe that have high supply of Data Science talent but lower median salaries***\n\nDraup’s talent location intelligence enables discovery of high-supply regions for talent with favorable compensation benchmarks. Filter by geography, job family, skill depth, and salary quartiles to identify cost-effective emerging hubs for strategic hiring or delivery center expansion.\t\n\nWITH filtered AS (\n SELECT\n country,\n job_role,\n talent_size,\n median_base_pay,\n date_updated\n FROM talent_metics_country_job_role\n WHERE job_role = 'Data Scientist'\n AND country IN ('Poland', 'Romania', 'Bulgaria', 'Hungary', 'Czechia')\n AND date_updated >= (CURRENT_DATE - INTERVAL '365 day')\n),\nstats AS (\n SELECT\n PERCENTILE_CONT(0.75) WITHIN GROUP (ORDER BY talent_size) AS talent_size_75,\n PERCENTILE_CONT(0.40) WITHIN GROUP (ORDER BY median_base_pay) AS pay_40\n FROM filtered\n)\nSELECT\n filtered.country,\n filtered.job_role,\n filtered.talent_size,\n filtered.median_base_pay,\n CASE\n WHEN filtered.talent_size >= stats.talent_size_75\n AND filtered.median_base_pay <= stats.pay_40\n THEN 1 ELSE 0\n END AS is_target\nFROM filtered\nCROSS JOIN stats\nWHERE filtered.talent_size IS NOT NULL\n AND filtered.median_base_pay IS NOT NULL\nORDER BY is_target DESC, filtered.talent_size DESC, filtered.median_base_pay ASC;\n\n***Compare the talent availability, demand & compensation benchmarks for Cybersecurity Engineers across the top 10 locations in India***\t\n\nDraup’s integrated talent analytics allows multi-location benchmarking of supply, demand, compensation, Hiring Difficulty, and peer comparison for different job roles. Evaluate hiring hotspots, cost-to-hire, and market competitiveness across cities to guide location strategy, workforce allocation, and budget planning.\t\n\nWITH ranked_salaries AS (\n SELECT\n job_role,\n msa,\n median_base_pay,\n talent_size,\n talent_demand,\n ROW_NUMBER() OVER (\n PARTITION BY msa\n ORDER BY median_base_pay\n ) AS row_number_asc,\n ROW_NUMBER() OVER (\n PARTITION BY msa\n ORDER BY median_base_pay DESC\n ) AS row_number_desc\n FROM talent_metics_location_msa_job_role\n WHERE job_role = 'Cybersecurity Engineer'\n AND msa LIKE '%India%'\n)\nSELECT\n ranked_salaries.msa,\n SUM(ranked_salaries.talent_size) AS total_talent,\n SUM(ranked_salaries.talent_demand) AS total_demand,\n AVG(ranked_salaries.median_base_pay) AS exact_median_base_pay\nFROM ranked_salaries\nWHERE ranked_salaries.row_number_asc = ranked_salaries.row_number_desc\n OR ranked_salaries.row_number_asc + 1 = ranked_salaries.row_number_desc\nGROUP BY ranked_salaries.msa\nORDER BY total_talent DESC\nLIMIT 10;\n\n***Discover gender inclusive locations to hire for Product Manager talent in Germany***\t\n\nDraup’s diversity analytics aid in reaching inclusive hiring goals and regional outreach strategies by filtering based on location, function, job family, or seniority to highlight regions based on requirement.\t\n\nSELECT \n country,\n job_role,\n Male,\n Female,\n (Female * 100.0 / NULLIF(Male + Female, 0)) AS female_percentage\nFROM genderdiversity_country_job_role\nWHERE job_role = 'Product Manager'\n AND country = 'Germany'\nORDER BY female_percentage DESC;\n\n***Identify hotspots in the US for Underwriting talent with high availability and low hiring difficulty***\t\n\nDraup’s talent supply and Hiring Difficulty Index (HDI) framework helps uncover areas with favorable conditions for hiring a target job role. Combine filters for talent pool size, skill relevance, and hiring friction to guide cost-effective location decisions.\t\n\nWITH ranked AS (\n SELECT\n job_role,\n msa,\n COUNT(*) AS total_talent,\n AVG(hiring_difficulty_index) AS avg_hdi,\n ROW_NUMBER() OVER (ORDER BY COUNT(*) DESC) AS talent_rank,\n ROW_NUMBER() OVER (ORDER BY AVG(hiring_difficulty_index) ASC) AS hdi_rank\n FROM talent_metics_location_msa_job_role\n WHERE LOWER(msa) LIKE '%united states'\n AND LOWER(job_role) = 'underwriter'\n GROUP BY msa, job_role\n)\nSELECT \n msa,\n total_talent,\n avg_hdi,\n (talent_rank + hdi_rank) AS combined_rank\nFROM ranked\nORDER BY combined_rank ASC\nLIMIT 10;\n\n***What are the top 10 locations that has seen the highest demand for Embedded C skill in the last quarter***\t\n\nUse Draup’s real-time demand signals to identify global or regional hotspots where hiring for a particular job role has surged recently. Analyze demand intensity by location, job role, or industry to align GTM, hiring, or skilling efforts with emerging trends.\t\n\nSELECT\n unnest_result.jd_msa AS location_msa,\n unnest_result.jd_country AS country,\n COUNT(*) AS demand_count\nFROM job_postings_company\nCROSS JOIN LATERAL UNNEST(consolidated_jds) AS unnest_result (\n jd_location, jd_synon_location, jd_msa, jd_region, jd_state, jd_country,\n jd_latitude, jd_longitude, jd_source, jd_jd_url, jd_data_lake_id, jd_loaded_at, jd_date_updated\n)\nWHERE POSITION('Embedded C' IN core_skills) > 0\n AND date_posted >= (DATE_TRUNC('quarter', CURRENT_DATE) - INTERVAL '3 months')\n AND date_posted < DATE_TRUNC('quarter', CURRENT_DATE)\n AND unnest_result.jd_msa IS NOT NULL\nGROUP BY unnest_result.jd_msa, unnest_result.jd_country\nORDER BY demand_count DESC\nLIMIT 10;\n\n***Map key feeder locations contributing to the Product Manager talent pool in Bay Area.***\t\n\nDraup’s talent mobility and migration insights enable mapping of feeder cities and states contributing a specific talent to a target location. Evaluate volume, role types, and career transitions to support sourcing, internal mobility, or relocation programs.\t\n\nSELECT\n msa AS feeder_location,\n SUM(talent_size) AS total_talent\nFROM talent_metics_location_msa_job_role\nWHERE job_role = 'Product Manager'\n AND msa = 'San Francisco Bay Area, United States'\nGROUP BY msa\nORDER BY total_talent DESC\nLIMIT 5;","terms_of_service":"https://draup.com/privacy-policy#:~:text=Unless%20provided%20for%20in%20this,that%20work%20on%20our%20behalf","documentation_link":"","privacy_policy_link":"https://draup.com/privacy-policy","geographical_coverage":"[\"Global\"]","update_frequency":{"interval":1,"unit":"MONTHLY"},"collection_granularity":{"interval":1,"unit":"DAILY"},"assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"Draup, Inc.","description":"Draup is an enterprise agentic intelligence platform built for HR and Sales leaders navigating transformation. By combining unparalleled real-time global data with agentic AI, Draup enables strategic decision-making across talent and revenue functions.\n \nThrough its Talent Intelligence solution, Draup empowers HR teams to drive workforce planning, precision hiring, predictive skill architecture, and competitive benchmarking. Its Sales Intelligence solution helps go-to-market teams uncover deep account insights, identify buying triggers, and accelerate revenue growth.\n \nTrusted by over 300 global enterprises - including five of the Fortune 10 - Draup supports companies like Microsoft, PepsiCo, PayPal, and Moderna in building future-ready workforces and growth strategies."},"popularity":776},{"id":"c278799e-833b-4a65-b7e8-c4b74b6f2fc5","summary":{"name":"Logical Observation Identifiers Names and Codes LOINC","subtitle":"This data package shows the information of Logical Observation Identifiers Names Codes Map to Source Organization, Co..","share":{"name":"logical_observation_identifiers_names_and_codes_loinc","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1682356475225,"updated_at":1698965635315,"categories":["HEALTH","SCIENCE_AND_RESEARCH"],"listingType":"STANDARD","provider_id":"f88849f8-9d77-4de2-b4c7-03d55cc3768e"},"detail":{"description":"\n**Overview**\nThis data package shows the information of Logical Observation Identifiers Names Codes Map to Source Organization, Codes Source Organization and Names and Codes.\n\n**Description**\nThis data package contains the data on LOINC organization. LOINC is a common language (set of identifiers, names, and codes) for clinical and laboratory observations. LOINC provides the lingua franca for interoperable data exchange.\n\n**Benefits**\n- Logical observation identifier names and codes (loinc) database is useful to identify a public use set of codes and names for electronic reporting of clinical laboratory test results.\n- loinc codes are universal identifiers for laboratory test results and other clinical observations.\n- loinc has been developed to facilitate particularly the transmission and storing of clinical laboratory results (blood haemoglobin or serum potassium etc.) to support clinical care, outcomes management, and clinical research.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [Logical Observation Identifiers Names Codes Map to Source Organization](https://www.johnsnowlabs.com/marketplace/logical-observation-identifiers-names-codes-map-to-source-organization)\n\t- Logical Observation Identifiers Names and Codes (LOINC) is a database and universal standard for identifying medical laboratory observations. This dataset shows the codes which maps to source organization. Mapping each entity-specific code to its corresponding universal code can represent a significant investment of both human and financial capital.\n- [Logical Observation Identifiers Names and Codes Source Organization](https://www.johnsnowlabs.com/marketplace/logical-observation-identifiers-names-and-codes-source-organization)\n\t- Logical Observation Identifiers Names and Codes (LOINC) is a database and universal standard for identifying medical laboratory observations. This dataset shows the LOINC source organization names.\n- [Logical Observation Identifiers Names and Codes](https://www.johnsnowlabs.com/marketplace/logical-observation-identifiers-names-and-codes)\n\t- Logical Observation Identifiers Names and Codes is a common language (set of identifiers, names, and codes) for clinical and laboratory observations. LOINC provides the lingua franca for interoperable data exchange, it contains concepts for everything from a serum alpha 1 antitrypsin level to a zygomatic arch x-ray report. For each concept, LOINC contains many other rich details, such as synonyms, units of measure, and carefully crafted descriptions.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated\n- Data Updates\n - Data updates support replace-on-update: outdated foreign keys are deprecated, not deleted\n\n**Our data is curated and enriched by domain experts** \n\n Each dataset is manually curated by our team of doctors, pharmacists, public health & medical billing experts:\n- Field names, descriptions, and normalized values are chosen by people who actually understand their meaning\n- Healthcare & life science experts add categories, search keywords, descriptions and more to each dataset\n- Both manual and automated data enrichment supported for clinical codes, providers, drugs, and geo-locations\n- The data is always kept up to date – even when the source requires manual effort to get updates\n- Support for data subscribers is provided directly by the domain experts who curated the data sets\n- Every data source’s license is manually verified to allow for royalty-free commercial use and redistribution.\n\n**Need Help?**\nIf you have questions about our products, contact us at [info@johnsnowlabs.com](mailto:info@johnsnowlabs.com). \n 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This server is fully compliant with the Model Context Protocol standard.\n\nTools & Capabilities\nThis server exposes the following tools to your AI agents:\n\nverify_address: Validates US addresses against USPS standards and returns standardized formats.\n\nproperty_lookup: Retrieves deep insights on a specific property, including tax, deed, and building specs.\n\nskip_trace: (Requires permission) Fetches verified homeowner contact information (phone/email).\n\nproperty_search: executes complex queries to find properties matching specific criteria (e.g., \"Find 3bd/2ba homes in Phoenix with >$500k equity\").\n\ngeocode: Converts addresses to precise latitude/longitude coordinates.\n\nTechnical Specifications\n\nProtocol Version: MCP 1.0\n\nAuthentication: API Key (Bearer Token)\n\nTransport: SSE (Server-Sent Events) over HTTP\n\nHosting: Fully managed by BatchData (no infrastructure required on your end)\n\nUse Cases\n\nAI Real Estate Analyst: \"Find me all distressed properties in zip code 85001 and list the owners' phone numbers.\"\n\nIntelligent CRM: An agent automatically cleans and enriches incoming leads in your CRM with tax assessor data.\n\nChatbot Support: A customer service bot that can instantly verify a user's address and pull up their home's square footage during a chat.\n\nSources\nData is sourced dynamically from BatchData's live API, aggregating records from County Assessors, Recorders, and USPS.","terms_of_service":"https://batchdata.io/terms-of-service","documentation_link":"https://developer.batchdata.com/","privacy_policy_link":"https://batchdata.io/privacy-policy","geographical_coverage":"[\"United States and Canada\"]","cost":"PAID","update_frequency":{"interval":1,"unit":"DAILY"},"collection_granularity":{"interval":1,"unit":"DAILY"},"data_source":"","assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"BatchData","description":"BatchData is a real estate technology company providing a comprehensive data platform and APIs for property and owner information, helping businesses with lead generation, skip tracing, data enrichment, and workflow automation in the real estate, proptech, and home services industries."},"popularity":856},{"id":"1e20e1ca-47e1-4ec8-92e1-c84849762dac","summary":{"name":"Geolocet | Latest Demographic Data Isle of Man - 36 attributes","subtitle":"Demographic data for the 24 municipalities (parishes, cities, villages) of Isle of Man - age, gender, income, and more","setting":{"visibility":"PUBLIC"},"created_at":1708500627546,"updated_at":1708500655656,"categories":["DEMOGRAPHICS","ADVERTISING_AND_MARKETING","GEOSPATIAL","RETAIL","MEDIA","PUBLIC_SECTOR"],"listingType":"PERSONALIZED","provider_id":"5d3ddc17-a7e8-4794-a760-13587e1de2c6"},"detail":{"description":"## **Get instant access to the latest Demographics Data for Isle of Man with [Geolocet](https://geolocet.com)**\n\nExplore the richness of Isle of Man’s demographic landscape with our Isle of Man Demographics Data. This meticulously compiled dataset, sourced from official statistical sources, offers a detailed insight into the population landscape of Isle of Man at the municipality (parish) level. \n\nIf you require data at a less granular level, such as an Uber grid or square grid, please don't hesitate to get in touch with us at *contact@geolocet.com*. We understand that different projects may have varying data needs, and we are here to tailor our data to match your specific requirements.\n\n ## **Key Features:**\n---\n* **Population Data Isle of Man:** Gain access to the latest and most accurate population statistics for Isle of Man, meticulously organized by municipality (parish).\n\n* **Demographic Insights:** Dive deep into Isle of Man’s demographic landscape with data on 36 attributes, including: \n* Total population,\n* Gender,\n* Age bands,\n* Education,\n* Household size,\n* Income bands.\n\nAll demographic attributes are available at the municipal (parish) level.\n\n* **Geodemographic Insights:** Unlock geodemographic data Isle of Man-wide to inform location-based decision-making, marketing strategies, and policy formulation.\n* **Seamless Data Access:** Our data is available for instant download in CSV format. No lengthy sign-up processes or NDAs required on our side – just download the data you need.\n* **Data Samples for Evaluation:** To ensure you're getting exactly what you need, we provide free downloadable samples in CSV format for data evaluation.\n* **Data accuracy:** Our datasets are created using the latest demographics data available in national statistics bureaus, guaranteeing accuracy and reliability.\n* **Best-in-Class Quality:** We guarantee best-in-class quality, backed by over two decades of experience in modeling demographics data and an outstanding quality management process.\n* **Competitive Pricing:** We maintain the lowest prices on the market, ensuring that you get the most cost-efficient demographics data acquisition process.\n---\nAdditionally, for those looking to enrich their analysis or project, we offer:\n* **[Administrative Boundaries](https://geolocet.com/pages/administrative-boundaries-data-for-european-countries):** If you require geographical boundaries (polygons) for the municipalities of Isle of Man, consider exploring our Administrative Boundaries product, which seamlessly integrates with this dataset.\n\n* **[Point of Interest (PoI) Data](https://geolocet.com/pages/aggregated-points-of-interest-poi-data):** If you're interested in Points of Interest data, please don't hesitate to contact us for customized datasets to enhance your geographical analysis.\n\n* **[Data Customization](https://geolocet.com/pages/data-services-by-geolocet):** We understand that your project may have specific requirements. Feel free to get in touch with us if you need any modifications or specific data formats such as Excel, JSON, GeoJSON, or Shapefile. We are here to tailor the data to your needs.\n\nMake informed decisions, conduct precise research, and unlock the potential of the Isle of Man demographic landscape with our comprehensive dataset. Trust in the accuracy and reliability of our official statistical data to drive your projects forward.\nContact us at *contact@geolocet.com* today to access this invaluable resource and explore Isle of Man census data like never before.\n\n[Geolocet LTD](https://geolocet.com)\n","terms_of_service":"https://geolocet.com/pages/terms-of-use","documentation_link":"","privacy_policy_link":"https://geolocet.com/pages/privacy-policy","cost":"PAID","assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://geolocet.com/pages/terms-of-use"},"provider_summary":{"name":"Geolocet LTD","description":"Geolocet provides premium geospatial data for retail analysis, GIS, and market planning. We equip businesses with accurate, ready-to-query data to drive spatial analysis, site selection, and customer segmentation.\n\nCore Offerings:\n\nDemographics: Socioeconomic data covering population, age, and employment across diverse geographies.\n\nPoints of Interest (PoI): Datasets spanning retail, healthcare, and transport for catchment analysis.\n\nAdministrative Boundaries: GIS-ready boundaries, including municipalities and postcodes, for territory management.\n\nBrand Locations: Chain location datasets to monitor store networks and market expansion.\n\nWealth Data: Granular economic indicators like purchasing power to identify high-potential markets.\n\nWe source from trusted providers, delivering highly customizable datasets with comprehensive coverage across Europe and worldwide."},"popularity":507},{"id":"ea5db358-155c-457b-86d5-aff34b1adab8","summary":{"name":"SAMPLE Africa B2B Data | Algeria, Ghana, Nigeria, Egypt | Decision Makers, Owner, Founder | 53M+ ...","subtitle":"Africa B2B database with 53M+ verified contacts, including founders, owners, and senior decision-makers. Features ...","share":{"name":"exellius_systems_042bdfea_1e8f_4a0f_b93e_93eeb3164bb1_samples_share","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1773237577673,"updated_at":1780306199824,"categories":["OTHER"],"listingType":"STANDARD","provider_id":"3c844684-0836-4f38-bbd9-35304ff66675"},"detail":{"description":"Africa B2B Contact Database – 53M+ Verified Decision-Makers | Direct Access to Founders, Owners & Business Leaders Across Africa\n\nExellius Systems provides a high-coverage Africa B2B contact database built for organizations seeking structured access to decision-makers across the continent’s rapidly expanding private sector.\n\nOur dataset includes 53+ million business contacts, with strong penetration among founders, owners, directors, and senior executives across SMEs, mid-market companies, and large enterprises throughout Africa.\n\nThis solution is designed for outbound sales teams, distributors, recruitment firms, consultants, and organizations expanding into African markets.\n\n---------------------------------------------------------------------------------------------\n\n- Strategic Market Positioning\n\nAfrican economies are heavily relationship-driven and founder-led. Access to direct decision-makers is critical for market entry and business development.\n\nOur Africa database is structured to provide:\n\n* Strong owner and founder-level coverage\n* SME-heavy company representation\n* Direct phone availability (where applicable)\n* Access beyond capital cities and tier-1 markets\n\nWe focus on practical, sales-ready contact intelligence for high-growth African markets.\n\n---------------------------------------------------------------------------------------------\n\n- Geographic Coverage\n\nCoverage spans across major and emerging African economies, including:\n\n* Nigeria\n* South Africa\n* Kenya\n* Egypt\n* Ghana\n* Morocco\n* Ethiopia\n* Tanzania\n* Uganda\n* Algeria\n* Côte d’Ivoire\n* Senegal\n* Zambia\n* Rwanda\n* Additional Sub-Saharan and North African markets\n\nCountry-level and regional segmentation available where applicable.\n\n---------------------------------------------------------------------------------------------\n\n- Database Volume & Company Representation\n\n* 53M+ professional B2B contacts\n* Strong coverage of SMEs and privately held companies\n* Founder and owner-level penetration\n* Representation across local enterprises and multinational operations\n\nThe dataset includes companies often underrepresented in global-only data platforms.\n\n---------------------------------------------------------------------------------------------\n\n- Contact Data Fields\n\nRecords may include:\n\n* First & Last Name\n* Job Title\n* Seniority Level\n* Department / Function\n* Company Name\n* Company Website\n* Industry Classification\n* Employee Size Band\n* Revenue Size Band\n* Country / Region / City\n* Verified Business Email\n* Direct Phone Number (where available)\n* Company LinkedIn (if applicable)\n\nCustom segmentation available based on campaign requirements.\n\n---------------------------------------------------------------------------------------------\n\n- Advanced Targeting Filters\n\nThe database supports granular filtering by:\n\n* Job function (Sales, IT, HR, Finance, Operations, Procurement, etc.)\n* Seniority (Founder, Owner, CXO, Director, Manager)\n* Industry vertical\n* Employee size bands\n* Revenue segmentation\n* Country and regional targeting\n* SME-focused campaigns\n* Enterprise-only targeting\n\nThis enables accurate ICP alignment for outbound sales and expansion initiatives.\n\n---------------------------------------------------------------------------------------------\n\n- Primary Use Cases\n\nOur Africa B2B database supports:\n\n* Direct outbound sales campaigns\n* Market entry into African countries\n* Distributor and partner sourcing\n* Channel development\n* Recruitment and executive search\n* Investor and founder outreach\n* Cross-border trade expansion\n* Supplier identification\n\nThe dataset is optimized for structured manual outreach and sales engagement workflows.\n\n---------------------------------------------------------------------------------------------\n\n- Data Accuracy & Validation\n\nExellius Systems applies structured aggregation methodologies and validation workflows to maintain operational usability.\n\n* Regular refresh cycles\n* Email verification processes\n* Deduplication and cleansing protocols\n* Replacement support for invalid records (subject to agreement)\n\nOur objective is to provide campaign-ready contact intelligence suitable for real-world sales execution.\n\n---------------------------------------------------------------------------------------------\n\n- Compliance & Responsible Use\n\nWe prioritize responsible B2B data practices.\n\n* Sourced from publicly available and business-relevant channels\n* Structured for professional B2B communication\n* Designed to align with GDPR, POPIA (South Africa), and applicable data regulations\n* Opt-out and suppression support available upon request\n\nWe encourage compliant and responsible outreach practices.\n\n---------------------------------------------------------------------------------------------\n\n- Delivery & Commercial Model\n\n* Delivered in CSV or Excel format\n* Custom-built lists available\n* One-time purchase or structured supply options\n* Sample dataset available upon request\n* Flexible pricing based on geography, volume, and seniority\n\nFast turnaround on filtered datasets.\n\n---------------------------------------------------------------------------------------------\n\n- Engage Africa with Structured Market Access\n\nAfrica represents one of the fastest-evolving business regions globally, driven by entrepreneurial growth, private sector expansion, and increasing regional trade integration.\n\nExellius Systems equips your team with verified access to founders and decision-makers across African markets, enabling confident expansion, partnership development, and revenue generation.\n\nContact us to request a sample dataset 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The model outperforms other state-of-the-art language models such as GPT-3.5 and Mixtral-8x7b on multiple benchmarks, and excels at a broad set of natural language tasks such as: text summarization, question-answering, extraction, and coding. \n- [dbrx_base](https://huggingface.co/databricks/dbrx-base): pretrained generative text model that can be used for further fine-tuning on specific applications.\n\n**License**\n\nDBRX models are licensed under the [Databricks Open Model License](https://www.databricks.com/legal/open-model-license) License. By installing this listing, you acknowledge and agree to the license and the [Databricks Open Model Acceptable Use Policy](https://www.databricks.com/legal/acceptable-use-policy-open-model).\n\n\n**Product details**\n\nDBRX Instruct can be deployed directly to Databricks Model Serving for immediate use, or loaded for fine-tuning or batch inference use cases. DBRX Base can be used for further fine-tuning on specific applications. 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We are a cross-cloud platform that works across AWS, Azure, and Google.\nThis unified approach simplifies your modern data stack by eliminating the data silos that traditionally separate and complicate data engineering, analytics, BI, data science and machine learning. It’s built on open source and open standards to maximize flexibility. 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Lightcast's Profiles database contains workforce information for over a hundred million distinct individuals, providing insight into how people build skills, move between roles and employers, and progress throughout their careers.\n\nLightcast applies machine learning to deduplicate and enrich profile information. Job titles and company names are standardized, skills are extracted and classified, and education information is standardized to make workforce experiences easier to analyze consistently at scale.\n\nThe sample is centered around a primary Profiles table, with supplementary Jobs, Education, and Skills tables that can be joined using the profile identifier. Together, these tables connect profile-level attributes with employment history, educational experience, and skills, allowing users to explore how Global Profiles data is structured and queried.\n\nUnlike Global Profiles, this offering excludes identity and contact fields such as names, email addresses, phone numbers, and social profile URLs. It is designed for workforce analytics, labor-market research, skills intelligence, and career pathway analysis rather than individual contact discovery or outreach.\n\nThis sample is intended to demonstrate the structure, relationships, geographic coverage, and analytical potential of Lightcast Profiles data. It is not statistically representative of the full Profiles dataset and should not be used to estimate workforce population distributions.\n\nContact Lightcast to learn more about the full Profiles offering and available data fields. For further information, please visit [Lightcast Profiles Methodology.](https://kb.lightcast.io/en/articles/6957504-profiles-methodology)\n\n**Use Cases**\n\n*Market Analysis*\nGlobal Profiles data provides insight into workforce supply across occupations, skills, employers, industries, and international labor markets. By analyzing professional experience, education, skills, and geography, organizations can explore talent concentrations and compare workforce characteristics across countries and regions. These insights support international talent market assessments, global location strategy, and workforce expansion planning.\n\n*Global Workforce & Career Mobility*\nExplore professional career pathways across countries, industries, occupations, and employers. By analyzing pseudonymized employment histories and educational backgrounds, organizations can examine how workers progress through their careers, compare occupational transitions across labor markets, and investigate international career experiences where supported by the data. These insights support workforce mobility research, international talent strategy, and career pathway analysis.\n\n*Global Skills & Talent Intelligence*\nCompare the skills, qualifications, and professional backgrounds associated with occupations across international labor markets. By connecting skills with employment histories, education, and geographic attributes, organizations can explore differences in workforce capabilities, identify transferable skills, and understand how professional qualifications vary across countries. These insights support global talent planning, workforce development, and international skills research.\n\n*Real World Data (RWD)*\nGlobal Profiles provide real-world information about professional experience, education, skills, and employment histories across international labor markets. By connecting profile records with supplementary Jobs, Education, and Skills tables, organizations can explore career histories and examine relationships between education, employment, and occupational experiences across countries. This data supports comparative workforce research and analysis of professional development across different labor markets.\n\n*Quantitative Analysis*\nAnalyze workforce characteristics across occupations, skills, employers, education, geography, and time to investigate patterns in global labor supply. Organizations can compare talent pools across countries, examine occupational and career transitions, and explore differences in workforce composition between markets. These analyses support international workforce planning, economic research, and strategic talent decisions.","terms_of_service":"https://legal.lightcast.io/lightcast-legal/general-terms-of-service","documentation_link":"https://kb.lightcast.io/en/articles/6957504-profiles-methodology","privacy_policy_link":"https://clickwrap.us.spotdraft.com/259502/lightcast-legal/privacy-policy-86c41a2d","geographical_coverage":"[\"Global\"]","update_frequency":{"interval":1,"unit":"MONTHLY"},"collection_granularity":{"interval":1,"unit":"DAILY"},"collection_date_start":1514838592988,"collection_date_end":1793997006228,"assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"Lightcast","description":"Lightcast provides the most comprehensive labor market data available, with insight into job postings, skills, compensation, demographics, and more—backed by 3x deeper historical data than any other source. 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It is the same forecast delivered to over 1 billion users, the forecast that influenced what consumers bought, when they bought it, and where demand surged.\n\nWith 90+ daily forecast parameters across nearly 90,000 global locations going back to 2020, including day/night part and hourly breakdowns out to 120 hours and daily forecasts out to 15 days, teams can train predictive models on the forecast that actually drove behavior, and validate prescriptive decisioning systems against real forecast confidence and lead-time behavior before going to production.\n\n**What’s included:**\n\n- 90+ daily historical forecast weather parameters\n- 15 days of forecasts snapshotted each day\n- Filtered to your specific locations of interest\n- Records dating back to 2020, updated every day\n\n**How it works:**\n\n1. Download a free sample dataset to test.\n2. Request access to the most comprehensive weather database available.\n3. Collaborate with AccuWeather data experts to build a custom dataset aligned to your goals.\n4. Receive secure access via a Databricks Delta Share\n\n**Why AccuWeather**\n\n- **The forecast consumers actually saw.** AccuWeather's forecast reach over 1 billion users. Consumer purchase timing, store visits, and replenishment demand respond to AccuWeather forecasts. Training on the AccuWeather historical forecast record means modeling the same signal that shaped behavior, rather than observed weather or a public model snapshot.\n- **Proprietary forecast, not a public model snapshot.** AccuWeather's Historical Forecast is the proprietary AccuWeather forecast, refined by AccuWeather meteorologists and drawn from over 170 forecast inputs. It is not a snapshot of publicly available model output and cannot be reproduced by re-running a public model.\n- **Preserved as issued.** Each snapshot is the forecast as it existed at issue time, including lead time and parameters available at decision time. Training and production share the same information environment, which matters for quantitative trading, automated operations, supply planning, and other decision systems that require defensibility.\n- **Superior Accuracy™.** AccuWeather's forecast accuracy is verified as superior to other sources in independent studies. More than half of the Fortune 500 use AccuWeather, with a 97% annual renewal rate.\n\n**Business needs**\n\n- **Demand Forecasting.** Train demand, yield, and outcome models on the forecasts consumers and operators actually saw.\n- **Inventory Management.** Route inventory, plan promotions, and stage advertising ahead of weather-driven demand surges.\n- **Risk Analysis.** Deploy weather-driven trading, hedging, and risk strategies on the same forecast record that will drive the model in production.\n- **Automated decisioning.** Power recommendations on confidence thresholds calibrated against real historical forecast behavior.\n- **Alerting and operational triggers.**Validate rules against years of historical forecasts before production to reduce false positives.","terms_of_service":"https://www.accuweather.com/en/privacy","documentation_link":"https://docs.google.com/spreadsheets/d/1sxeWreQiyI3C0s4BjxwBVmYy4VbPNkl3I77dtkP9wms/edit?gid=0#gid=0","privacy_policy_link":"https://www.accuweather.com/en/privacy","geographical_coverage":"[\"Global\"]","update_frequency":{"interval":1,"unit":"HOURLY"},"collection_granularity":{"interval":1,"unit":"HOURLY"},"assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"AccuWeather","description":"AccuWeather provides global weather data powered by Superior Accuracy™ with 170+ forecast models and over 60 years of meteorological expertise. 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This updated mapping file is derived from the September 2017 SNOMED CT United States (US) Edition and 2018 version of ICD-10-CM. There are a total of 119,270 SNOMED CT concepts mapped in this updated release.\n\n**Benefits**\n- this is a “readable” version of the map that can be understood by a non-technical individual.\n- all current pre-coordinated snomed ct concepts within three hierarchies (clinical findings, events, and situations with explicit context) are mapped to icd-10-cm.\n- real-time use by the healthcare provider\n- retrospective coding by coding professionals\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [SNOMED CT Snapshot Extended Map Reference Set to ICD-10-CM Map](https://www.johnsnowlabs.com/marketplace/snomed-ct-snapshot-extended-map-reference-set-to-icd-10-cm-map)\n\t- This dataset is one of the three separate files that the ICD-10-CM MAP contains. Extended Map contains the core publication of MAP data. There are one or more map records for each source concept mapped, including the ICD-10-CM target codes. This dataset is an update of the SNOMED CT to ICD-10-CM Cross Map. The purpose of this update is to synchronize with the latest release of the US Edition of SNOMED CT by removing obsolete content.\n- [SNOMED CT Snapshot Module Dependency Reference Set to ICD-10-CM Map](https://www.johnsnowlabs.com/marketplace/snomed-ct-snapshot-module-dependency-reference-set-to-icd-10-cm-map)\n\t- This dataset is one of the three separate files that the ICD-10-CM MAP contains. Module Dependency reference set designates the versions of other SNOMED CT modules upon which this publication of the MAP depends. This dataset is an update of the SNOMED CT to ICD-10-CM Cross Map. The purpose of this update is to synchronize with the latest release of the US Edition of SNOMED CT by removing obsolete content.\n- [SNOMED CT Snapshot Refset Descriptor Reference Set to ICD-10-CM Map](https://www.johnsnowlabs.com/marketplace/snomed-ct-snapshot-refset-descriptor-reference-set-to-icd-10-cm-map)\n\t- This dataset is one of the three separate files that the ICD-10-CM MAP contains. Refset Descriptor contains metadata from the SNOMED CT metadata hierarchy which describes the attributes of the publication refsets and their information content. This dataset is an update of the SNOMED CT to ICD-10-CM Cross Map. The purpose of this update is to synchronize with the latest release of the US Edition of SNOMED CT by removing obsolete content.\n- [SNOMED CT to ICD-10-CM Human Readable Cross Map](https://www.johnsnowlabs.com/marketplace/snomed-ct-to-icd-10-cm-human-readable-cross-map)\n\t- This dataset contains the human readable form of the SNOMED CT to ICD-10-CM cross map. The Map can be used in real-time by the healthcare providers and by coding professionals for retrospective coding purposes.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated\n- Data Updates\n - Data updates support replace-on-update: outdated foreign keys are deprecated, not deleted\n\n**Our data is curated and enriched by domain experts** \n\n Each dataset is manually curated by our team of doctors, pharmacists, public health & medical billing experts:\n- Field names, descriptions, and normalized values are chosen by people who actually understand their meaning\n- Healthcare & life science experts add categories, search keywords, descriptions and more to each dataset\n- Both manual and automated data enrichment supported for clinical codes, providers, drugs, and geo-locations\n- The data is always kept up to date – even when the source requires manual effort to get updates\n- Support for data subscribers is provided directly by the domain experts who curated the data sets\n- Every data source’s license is manually verified to allow for royalty-free commercial use and redistribution.\n\n**Need Help?**\nIf you have questions about our products, contact us at [info@johnsnowlabs.com](mailto:info@johnsnowlabs.com). \n ","terms_of_service":"https://www.johnsnowlabs.com/healthcare-data-library-eula/","documentation_link":"https://www.johnsnowlabs.com/marketplace/snomed-clinical-terminology-to-icd-10-cm-map-data-package","contact_email":"support@johnsnowlabs.com","privacy_policy_link":"https://www.johnsnowlabs.com/privacy-policy/","assets":["ASSET_TYPE_DATA_TABLE"]},"provider_summary":{"name":"John Snow Labs","description":"John Snow Labs, an AI and NLP for healthcare company, provides state-of-the-art software, models, and data to help healthcare and life science organizations build, deploy, and operate AI projects."},"popularity":1156},{"id":"e37d9f85-33b8-40ad-9b05-00c7d2492dda","summary":{"name":"Data+AI Summit Session Summary","subtitle":"This dataset contains session details from a major global conference focused on data, analytics, and AI.","share":{"name":"dbacademy_dataset_dais","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1731471114704,"updated_at":1731471114704,"categories":["EDUCATION"],"listingType":"STANDARD","provider_id":"ea1e69ff-0127-4c94-bf39-e841fe1d19d2"},"detail":{"description":"The **Data + AI Summit dataset** contains session details from a major global conference focused on data, analytics, and artificial intelligence. The event featured a variety of sessions and keynotes from industry leaders. Topics covered include advancements in **data lakehouses**, **machine learning lifecycle management**, and **open-source technologies** like **MLflow**. Specific tracks explored industry applications across sectors such as healthcare, the public sector, and financial services.","terms_of_service":"https://cms.databricks.com/sites/default/files/2023-01/marketplace-consumer-terms_0.pdf","documentation_link":"","privacy_policy_link":"https://www.databricks.com/legal/privacynotice","data_source":"https://www.databricks.com/","assets":["ASSET_TYPE_DATA_TABLE","ASSET_TYPE_MEDIA"],"license":""},"provider_summary":{"name":"Databricks","description":"As the world’s first lakehouse platform in the cloud, Databricks combines the best elements of data lakes and data warehouses to deliver the reliability, strong governance, and performance of data warehouses with the openness, flexibility, and machine learning support of data lakes. We are a cross-cloud platform that works across AWS, Azure, and Google.\nThis unified approach simplifies your modern data stack by eliminating the data silos that traditionally separate and complicate data engineering, analytics, BI, data science and machine learning. It’s built on open source and open standards to maximize flexibility. And, its common approach to data management, security and governance helps companies operate more efficiently and innovate faster."},"popularity":1572},{"id":"0a181f15-5a72-4898-8fe1-095ab6a42cee","summary":{"name":"Pedestal Insights & Predictions","subtitle":"A governed AI analyst in your Databricks workspace: accurate answers, proactive recommendations and predictions.","setting":{"visibility":"PUBLIC"},"created_at":1793029705794,"updated_at":1793229290096,"categories":["TRANSPORTATION_AND_LOGISTICS","MANUFACTURING","RETAIL"],"listingType":"STANDARD","provider_id":"c9117bfa-38f4-44aa-b339-4f976bf85a08"},"detail":{"description":"**Overview**\n Pedestal is a governed AI analyst for retail, supply chain, and manufacturing that runs entirely inside your Databricks workspace. Ask a question in plain English and get an answer, a chart, or a report. Build a predictive model the same way. Pedestal also surfaces proactive recommendations as your data changes. No data scientist required, and nothing leaves your workspace.\n\nAdmins decide which Unity Catalog data to onboard, then capture the business knowledge behind it: table and column descriptions inherited from Unity Catalog, a shared glossary, the quirks of business logic that trip up every new analyst. Answers are grounded in that knowledge. Every query runs under the user’s own Unity Catalog permissions, and all AI inference goes through Databricks-hosted foundation models via AI Gateway.\n\n**Use cases**\n- Self-serve analytics: business users ask questions of governed data and get explainable answers, with the SQL-backed view that produced them.\n- Proactive recommendations: Pedestal flags changes worth acting on in your data, with the reasoning and the view behind each one.\n- Shared definitions: capture metrics, terms, and caveats once, so every answer across the company means the same thing.\n- Predictions without a data science team: forecast demand, delivery times, or equipment failure from your own tables, with models trained, evaluated, and registered in your workspace.\n- Validated reporting: promote trusted views into reusable reports that stay current as the data changes.\nProduct details\n\nInstalls as a single Databricks App. State lives in a Lakebase database you own, files in a Unity Catalog volume you choose, and queries run through your SQL warehouse.\n- Admin onboarding: pick catalogs and schemas, inherit Unity Catalog comments and tags, and review and approve knowledge before answers depend on it.\n- Knowledge management: a glossary, curated knowledge items, validated views, and feedback loops that improve answers over time.\n- Questions, reports, and recommendations: natural-language questions, generated views, charts, saved reports, and proactive recommendations.\n- Predictions: build, evaluate, and run predictive models on your data; models are tracked with MLflow in your workspace.\n- Identity: users act as themselves. 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Magazine for excellence and innovation, HatchWorks combines cutting-edge AI with deep technical expertise to help businesses transform ideas into high-quality, scalable, and market-ready digital products."},"popularity":1938},{"id":"3841e9a6-4463-4cde-925c-247491524e3f","summary":{"name":"US Population Forecast by Zipcode 2010-2035","subtitle":"Granular demographic insights and forecasts for every Zip Code until 2035.","share":{"name":"share-population-forecast","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1743546149060,"updated_at":1743546149060,"categories":["DEMOGRAPHICS"],"listingType":"STANDARD","provider_id":"880a243e-7a4c-4949-b389-e1abc4110411"},"detail":{"description":"Aterio’s Population Dataset provides comprehensive insights into current population figures, historical trends, and future projections. It integrates data from the U.S. Census Bureau, CDC (fertility and mortality rates), National Cancer Institute, migration patterns, and social media audiences to accurately estimate population distribution. This dataset is a valuable tool for researchers, analysts, and decision-makers, enabling precise demographic analysis and informed planning.\n\n\n### Dataset Components:\n\nComprehensive Demographic Data – Sourced from the latest U.S. Census Bureau, CDC, and NCI, enriched with META (Facebook Audience Insights) and updated monthly to track evolving trends.\nEnhanced Accuracy – Incorporates proprietary data and advanced modeling techniques, leveraging machine learning to account for key population growth factors.\nSeamless Data Integration – Metadata mapping connects attribute names to table IDs, ZIP codes to cities/counties, and Core-Based Statistical Areas (CBSAs) for streamlined analysis.\n\n### Primary Fields Included:\n- Zip Codes\n- Aterio Unique ID\n- County\n- City\n- CBSAs\n- State\n- Latitude/Longitude\n\n### Business Needs:\n- **Market Analysis:** Identify attractive investment opportunities by leveraging predictive modeling to pinpoint locations and neighborhoods with high population growth potential.\n- **Real Estate:** Real estate developers and investors can make more informed decisions about property investments by assessing population growth and migration trends in specific zip codes, enabling them to identify areas with potential for higher property values and rental income.\n- **Location Data Enrichment:** A dataset focusing on population forecasts and migration patterns by zip code addresses a fundamental business need for accurate and localized data, empowering businesses and organizations to make strategic choices, allocate resources effectively, and stay ahead of changing market dynamics.\n- **Healthcare Analytics:** Healthcare providers and analysts can utilize our detailed demographic data to enhance service line forecasting and resource allocation. By understanding population dynamics and future trends at a hyper-local level, healthcare organizations can optimize their operations, improve patient outcomes, and strategically plan for future demands.\n- **Rental Property Management:** Rental property owners and managers can leverage our demographic insights to better understand population trends and housing demands. By analyzing data on household units availability, geographical mobility, and income status at the zip code level, they can optimize rental pricing, identify high-demand areas, and improve occupancy rates.\n- **Demographic and Population Research:** Research institutions focusing on demographics and population studies can utilize our comprehensive dataset to conduct in-depth analysis and projections. With access to historical, current, and forecasted population data, researchers can study trends, identify patterns, and publish findings that contribute to the broader understanding of population dynamics and their implications.\n\n### Why Choose Aterio?\n\n- **Hyper-Local Precision:** Dive deep into population trends at the zip code level.\n- **Monthly Data Refresh:** Stay consistently ahead with monthly dataset updates, ensuring you're equipped with the freshest information to guide your decisions.\n- **Projection:** Forecast values until 2035\"","terms_of_service":"https://www.aterio.io/terms-of-service","documentation_link":"","privacy_policy_link":"https://www.aterio.io/privacy","geographical_coverage":"[\"United States and Canada\"]","cost":"FREE","update_frequency":{"interval":1,"unit":"MONTHLY"},"collection_date_start":1262388098132,"collection_date_end":2080164108892,"data_source":"","assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"aterio.io","description":"Advance Data and Insights in the US Real Estate Market available to investors, developers, and city officials for better investment decisions and returns."},"popularity":1562},{"id":"dd19b610-cb81-4584-8e6b-9f68681796e7","summary":{"name":"Sample of AccuWeather: Lifestyle & Health Indices","subtitle":"Access 50+ lifestyle & health indices such as AccuLumen Brightness Index™, skin care, and allergen forecasts.","share":{"name":"daily_indices_demo_share","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1771521084145,"updated_at":1771974313756,"categories":["CLIMATE_AND_ENVIRONMENT","ADVERTISING_AND_MARKETING","HEALTH","TRANSPORTATION_AND_LOGISTICS","TRAVEL_AND_TOURISM","RETAIL","SCIENCE_AND_RESEARCH","SPORTS","ENERGY","COMMERCE"],"listingType":"STANDARD","provider_id":"f0a29bd4-946b-477e-a43a-1c3f2a6e6506"},"detail":{"description":"**Overview**\nAccuWeather’s Lifestyle & Health Indices Dataset is powered by the world’s largest, most advanced collection of real-time and historical weather observations from ground stations, remote-sensing technologies, and AI-driven forecast models. These indices translate complex weather data into clear, actionable signals that help businesses understand how environmental conditions influence human health, daily routines, and lifestyle decisions.\n\nThis comprehensive dataset is curated and continually validated by AccuWeather’s expert team of meteorologists and data scientists to ensure each index reflects real-world conditions. By combining raw meteorological parameters with behavioral modeling, AccuWeather delivers the most trusted health and lifestyle signals available on the market.\n\nAccuWeather’s Lifestyle & Health Indices Dataset enables organizations to anticipate demand, mitigate risk, and engage consumers at precisely the moments when weather most influences decisions.\n\n**What’s Included**\n\n- 50+ proprietary health, lifestyle & activity Indices including allergy, cold & flu, outdoor activities, thirst, golf, and many more\n- Available for 15-day daily forecasts\n- Index values derived from 200+ underlying weather parameters\n- Location-specific outputs, filtered to your markets of interest\n\n**How it works**\n1. Download a free sample dataset to test\n2. Request access the most comprehensive weather database available.\n2. Collaborate with AccuWeather data experts to build a custom dataset aligned to your goals.\n3. Receive secure access via a Databricks Delta Share\n\n**Why AccuWeather**\nAccuWeather’s Lifestyle & Health Indices deliver the clarity, depth, and scientific rigor businesses need to understand how weather shapes human behavior. No other provider combines global scale, proprietary modeling, expert validation, and real-time delivery to produce health and lifestyle signals this precise and actionable.\n\nBy purchasing AccuWeather’s indices dataset, organizations gain a trusted foundation for decisions that protect well-being, enhance experiences, and drive measurable business outcomes.\n\n**Business Needs**\nDemand Forecasting\n- Increase forecast accuracy and reduce lost sales by linking purchasing behavior to lifestyle suitability signals. Retailers and eCommerce platforms combine lifestyle indices with sales and behavioral data to understand how weather conditions influence demand for apparel, sporting goods, seasonal products, and everyday essentials. This enables more precise assortment planning, inventory allocation, and regional merchandising strategies.\n\nAudience Segmentation\n\n- Strengthen audience insights and decision-making by incorporating trusted lifestyle and health context into data models. Media, advertising, and analytics companies use AccuWeather’s lifestyle and health indices as high-value contextual signals within audience segmentation, attribution, and measurement frameworks. These indices provide scalable, privacy-safe insights into consumer mindset and readiness, extending their value far beyond on-platform activation.\n\nOutdoor Recreation & Sports Analytics\n\n- Optimize operations, scheduling, and customer experiences by understanding when conditions support or limit participation. Outdoor recreation, sports, and leisure organizations leverage lifestyle indices such as Outdoor Activities and Golf to evaluate real-time and forecasted suitability across locations. These insights inform staffing, pricing, event planning, and operational decisions, helping organizations maximize participation while minimizing weather-related disruptions.\n\nHealthcare & Life Sciences Planning\n\n- Enable proactive healthcare planning and resource optimization by anticipating weather-driven health risks. Healthcare providers, pharmaceutical companies, and life sciences organizations use AccuWeather’s Allergy and Cold & Flu indices to forecast symptom surges, align medication distribution, and time patient outreach based on real-world environmental conditions. These insights support earlier interventions, improved preparedness, and more effective population health management.\n","terms_of_service":"https://www.accuweather.com/en/legal","documentation_link":"https://docs.google.com/spreadsheets/d/1sxeWreQiyI3C0s4BjxwBVmYy4VbPNkl3I77dtkP9wms/edit?gid=0#gid=0","privacy_policy_link":"https://www.accuweather.com/en/privacy","geographical_coverage":"[\"Global\"]","cost":"FREE","update_frequency":{"interval":1,"unit":"HOURLY"},"collection_granularity":{"interval":1,"unit":"DAILY"},"data_source":"","assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"AccuWeather","description":"AccuWeather provides global weather data powered by Superior Accuracy™ with 170+ forecast models and over 60 years of meteorological expertise. The AccuWeather Data Suite offers global forecast and historical coverage including 30+ years of historical data and 90-day forecasts tailored for operational decision making. With over 240 parameters, AccuWeather enables smarter decision making with trusted data."},"popularity":1426},{"id":"f9b4742e-832e-4851-91a2-4f49975a61e8","summary":{"name":"MBI Sociodemographic Data for Serbia – Sample","subtitle":"MBI's fresh sociodemographic data for well-founded decisions in Serbia. Free trial data.","share":{"name":"share_socio_mbi_market_data_2024_rs_postcodes_5_socio","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1763369570225,"updated_at":1779198931735,"categories":["ADVERTISING_AND_MARKETING","DEMOGRAPHICS","GEOSPATIAL"],"listingType":"STANDARD","provider_id":"aa09abd6-ff7f-4452-803d-b8c43b4c07cb"},"detail":{"description":"[MBI](https://www.mbi-geodata.com/en/)'s sociodemographic data is available for many countries worldwide. All data comes from a single source, ensuring **consistency and comparability** across markets.\n\nThe Sociodemographics-Bundle Basic for Serbia includes the following **variables**:\n- [population](https://www.mbi-geodata.com/en/sociodemographic-data/population/)\n- [gender](https://www.mbi-geodata.com/en/sociodemographic-data/sex-gender/)\n- [age structure](https://www.mbi-geodata.com/en/sociodemographic-data/agebands/) (15-year age bands)\n- household figures ([number of households](https://www.mbi-geodata.com/en/sociodemographic-data/households/) and [households by type](https://www.mbi-geodata.com/en/sociodemographic-data/households-by-type/))\n\nOur sociodemographic data is offered for each country on different **levels**:\n- **Postal Code Level:** This fine-grained level of detail makes the data suitable for a wide range of applications – for example, enriching customer databases, which are often organized by postal code.\n- **Administrative Level:** Covers both larger administrative areas (e.g., regions, states, provinces) as well as finer subdivisions such as districts, counties, or municipalities.\n- **Micro Level**: Subdivisions of postal code or administrative boundaries, such as census areas or street segments. This is the most granular level for local analysis.\n\n**Please note:** The dataset available for free download is a **sample dataset** covering a limited number of geographic areas. If you wish to access the full dataset (covering all areas) or if you require additional variables or geographic levels, please contact us. We will be happy to help you find a product that fits your needs.\n \n**Additional Insights**: https://www.mbi-geodata.com/en/sociodemographic-data/\n\n**Data Structure**:\nAGE_F0014: Population (Nighttime) by age: 0 - 14 years, female\nAGE_F1529: Population (Nighttime) by age: 15 - 29 years, female\nAGE_F3044: Population (Nighttime) by age: 30 - 44 years, female\nAGE_F4559: Population (Nighttime) by age: 45 - 59 years, female\nAGE_F60PL: Population (Nighttime) by age: 60 years and above, female\nAGE_M0014: Population (Nighttime) by age: 0 - 14 years, male\nAGE_M1529: Population (Nighttime) by age: 15 - 29 years, male\nAGE_M3044: Population (Nighttime) by age: 30 - 44 years, male\nAGE_M4559: Population (Nighttime) by age: 45 - 59 years, male\nAGE_M60PL: Population (Nighttime) by age: 60 years and above, male\nAGE_T0014: Population (Nighttime) by age: 0 - 14 years, total\nAGE_T1529: Population (Nighttime) by age: 15 - 29 years, male\nAGE_T3044: Population (Nighttime) by age: 30 - 44 years, total\nAGE_T4559: Population (Nighttime) by age: 45 - 59 years, total\nAGE_T60PL: Population (Nighttime) by age: 60 years and above, total\nCTRYCODE: Two digit country code by ISO 3166\nFEMALE: Population (Nighttime): females\nHH_SIZE: Average household size\nHH_T: Households: total number\nMALE: Population (Nighttime): males\nNAME: Name of administrative area / Name of the most populous administrative area / Name of the respective micro-area or the next higher level\nADMINCODE / POSTCODE / MICROCODE Administrative code / Postal code / Micro-code\nP_PRM: Population (Nighttime) year average: per mill of country\nP_T: Population (Nighttime) year average: total number","terms_of_service":"https://service-mbi-geodata.com/s/HNp7Z2tws4WDqWd","documentation_link":"https://www.mbi-geodata.com/en/mbi-supplier-manufacturer-worldwide-geodata/europe/serbia-maps/","privacy_policy_link":"https://www.mbi-geodata.com/en/data-protection/","geographical_coverage":"[\"Europe\"]","cost":"FREE","update_frequency":{"interval":1,"unit":"YEARLY"},"collection_granularity":{"interval":1,"unit":"YEARLY"},"collection_date_start":1698828707725,"collection_date_end":1735635120303,"data_source":"MBI","assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://service-mbi-geodata.com/s/t4oKzZ9F2XaAGQw"},"provider_summary":{"name":"Michael Bauer International GmbH – MBI Geodata","description":"For over 15 years, Michael Bauer International (MBI) has been a leading provider of globally consistent and comparable geographic data, including more than 3 million postcode areas and other detailed datasets like sociodemographics and purchasing power. MBI's high-quality small-scale data spans 248 countries, supporting businesses across all industries in location planning, branch networks, and distribution strategies. Recognized as a key data source, MBI is the preferred provider for leading GIS software and technology companies worldwide as well as one of the first global distributors for HERE Technologies. MBI is part of the Michael Bauer Group, alongside Michael Bauer Research GmbH and Michael Bauer Micromarketing GmbH."},"popularity":20},{"id":"7d61c445-fd08-4891-b2bc-0c632520dd5c","summary":{"name":"Moody's Growth & Strategy MCP","subtitle":"GenAI-ready company, market, and relationship insights to accelerate GTM strategy, targeting, and revenue growth.","setting":{"visibility":"PUBLIC"},"created_at":1787677394104,"updated_at":1787766004043,"categories":["FINANCIAL"],"listingType":"PERSONALIZED","provider_id":"757a382d-5f20-411a-bca0-e3a363112082"},"detail":{"description":"Moody’s Growth & Strategy MCP supports AI-driven sales, marketing, and revenue operations by providing GenAI-ready data, analytics, and insights to inform go-to-market execution. It brings together firmographic, ownership, relationship, and market datasets into a unified intelligence layer to help teams identify opportunities, prioritize accounts, and support more effective customer engagement. \n \nThe solution combines comprehensive Moody’s entity intelligence, including global company profiles, hierarchies, and ownership linkages, with augmented signals such as news, market activity, and risk indicators. This integrated view helps organizations gain additional visibility into total addressable markets, identify whitespace opportunities, and align sales and marketing strategies around high-value prospects. \n \nBy bringing together structured company data and contextual insights into a single interface, Moody’s Growth & Strategy MCP helps streamline data-to-insight workflows and supports improvements in targeting precision, pipeline quality, and revenue outcomes.\n\n**Data Includes:**\n\n- Global company firmographics and identifiers \n- Corporate hierarchies and ownership structures \n- Industry classifications and segmentation \n- Financial indicators and company scale metrics \n- News, market signals, and company events \n- Risk indicators and early warning signals \n- Economic data and research \n\n**Use Moody's Growth & Strategy MCP for:**\n\n- GTM Planning – define and size target markets with comprehensive company coverage \n- Account Prioritization – identify and prioritize high-value prospects and customers and support more sophisticated opportunity scoring by combining company-level data with market-level indicators. \n- Territory Design – support geographic and account planning by highlighting industries, regions or countries with stronger growth prospects \n- Pipeline Acceleration – surface in-market signals and help support timely engagement based on macroeconomic trends, investment activity, hiring trends or sector-level forecasts \n- Cross-Sell & Upsell – help identify whitespace within existing customer portfolios and support AI-generated account briefs that incorporate industry outlooks, economic scenarios, and strategic talking points \n- CRM Enrichment – enrich account records with company data and insights \n- Marketing Segmentation – develop data-driven audience segments using company, market, and relationship insights ","terms_of_service":"https://www.moodys.com/web/en/us/legal/terms-of-use.html","documentation_link":"","privacy_policy_link":"https://www.moodys.com/web/en/us/legal/privacy-policy.html","assets":["ASSET_TYPE_MCP"],"license":""},"provider_summary":{"name":"Moody's","description":"Moody’s provides data, analytics, and risk management tools that global markets depend on to help business and financial leaders make confident decisions. Moody’s has highly skilled analysts, rich data, robust tools supported by groundbreaking technologies, and a view of the future informed by more than 115 years of expertise. "},"popularity":1453},{"id":"413b6c69-485d-48c3-81b2-0ac16214a3ef","summary":{"name":"Meta Llama 3.1 Models","subtitle":"The Llama 3.1 - Large language models (LLMs) with 8, 70, and 405 billion parameters.\n\nKeywords: Llama 3, Llama3.1, LLM","share":{"name":"llama_v3_1","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1727219118114,"updated_at":1727315101062,"categories":["OTHER"],"listingType":"STANDARD","provider_id":"ea1e69ff-0127-4c94-bf39-e841fe1d19d2"},"detail":{"description":"**Overview**\n\nThe Llama_v3_1 models are large language models (LLMs) developed by Meta AI. The models provided in this listing are\n\n- [meta_llama_v3_1_405b](https://huggingface.co/meta-llama/Meta-Llama-3.1-405B)\n- [meta_llama_v3_1_70b](https://huggingface.co/meta-llama/Meta-Llama-3.1-70B)\n- [meta_llama_v3_1_8b](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B)\n- [meta_llama_v3_1_405b_instruct_fp8](https://huggingface.co/meta-llama/Meta-Llama-3.1-405B-Instruct-FP8)\n- [meta_llama_v3_1_70b_instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-70B-Instruct)\n- [meta_llama_v3_1_8b_instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct)\n\nThe models are packaged using MLflow’s transformers flavor.\n- [meta_llama_v3_1_405b_instruct_fp8](https://huggingface.co/meta-llama/Meta-Llama-3.1-405B-Instruct-FP8), [meta_llama_v3_1_70b_instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-70B-Instruct), [meta_llama_v3_1_8b_instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct): fine-tuned for dialogue-based use cases.\n- [meta_llama_v3_1_405b](https://huggingface.co/meta-llama/Meta-Llama-3.1-405B), [meta_llama_v3_1_70b](https://huggingface.co/meta-llama/Meta-Llama-3.1-70B), [meta_llama_v3_1_8b](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B): pretrained generative text model that can be used for further fine-tuning on specific applications.\n\nLlama_v3_1 models are licensed under the [Meta Llama 3.1 Community License, Copyright © Meta Platforms, Inc. All Rights Reserved](https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/LICENSE). By installing this listing, you acknowledge and agree to the license.\n\n**Use cases**\n\n[meta_llama_v3_1_405b_instruct_fp8](https://huggingface.co/meta-llama/Meta-Llama-3.1-405B-Instruct-FP8), [meta_llama_v3_1_70b_instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-70B-Instruct), [meta_llama_v3_1_8b_instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct):\n- Generating responses to text-based instructions\n- Chatbots and conversational AI systems\n\n[meta_llama_v3_1_405b](https://huggingface.co/meta-llama/Meta-Llama-3.1-405B), [meta_llama_v3_1_70b](https://huggingface.co/meta-llama/Meta-Llama-3.1-70B), [meta_llama_v3_1_8b](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B):\n- Fine-tuning on specific applications.\n\n\n**Product details**\n\nThe pre-trained model can be used for further fine-tuning on specific applications. The fine-tuned model in this listing can be deployed directly to Databricks Model Serving for immediate use, or loaded for fine-tuning or batch inference use cases. For more details, install the listing and view the provided model cards for each model.","terms_of_service":"https://cms.databricks.com/sites/default/files/2023-01/marketplace-consumer-terms_0.pdf","documentation_link":"","privacy_policy_link":"https://www.databricks.com/legal/privacynotice","assets":["ASSET_TYPE_MODEL"],"license":"https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/LICENSE","tags":[{"tag_name":"LISTING_TAG_TYPE_TASK","tag_values":["TEXT_GENERATION"]}]},"provider_summary":{"name":"Databricks","description":"As the world’s first lakehouse platform in the cloud, Databricks combines the best elements of data lakes and data warehouses to deliver the reliability, strong governance, and performance of data warehouses with the openness, flexibility, and machine learning support of data lakes. We are a cross-cloud platform that works across AWS, Azure, and Google.\nThis unified approach simplifies your modern data stack by eliminating the data silos that traditionally separate and complicate data engineering, analytics, BI, data science and machine learning. It’s built on open source and open standards to maximize flexibility. And, its common approach to data management, security and governance helps companies operate more efficiently and innovate faster."},"popularity":2108},{"id":"5cd71b60-1abc-44b0-acc4-55a7af86d5c7","summary":{"name":"Conversational Speech in Manipuri in the Finance Domain","subtitle":"25 hours of Manipuri Language Conversational Speech in the Finance Domain","setting":{"visibility":"PUBLIC"},"created_at":1792585489013,"updated_at":1793112401294,"categories":["SCIENCE_AND_RESEARCH","FINANCIAL"],"listingType":"PERSONALIZED","provider_id":"f6e9a544-3103-4533-8706-321f7282d7d3"},"detail":{"description":"**Get this dataset on the Mozilla Data Collective.** Licensing and download happen on MDC rather than through Databricks. [See the full details and get access on MDC](https://mozilladatacollective.com/datasets/cmrmkmodd01h0nu07rkbq6yu9?utm_source=databricks&utm_medium=referral&utm_campaign=databricks_marketplace&utm_content=listing_cta).\n\nThis dataset provides 25 hours of natural, multi-speaker conversational dialogue speech in Manipuri, specifically recorded within the high-stakes domain of Finance for training domain-specific automated speech systems.\n\nThis dataset captures real-world domain interactions in Manipuri (a Tibeto-Burman language). The collection focuses on spontaneous conversational turn-taking, technical jargon handling, and colloquial linguistic shifts within economic settings.\n\nMulti-speaker dialogue setup containing contributions from distinct native speakers, domain professionals (microfinance agents), and local consumers.\n\n**At a glance**\n\n- **Language:** Manipuri (Meitei)\n- **Task:** Automatic speech recognition (ASR)\n- **Format:** WAV, JSON\n\n**Dataset creation**\n\nThe dataset is strictly comprised of natural, spontaneous speech.\n\nSpeakers were explicitly instructed not to follow a script. They were told to pick a topic they felt comfortable with and think: \"How do I talk about this in real life?\"\n\nTo help them sustain natural dialogue for the required 9 to 10 minutes per session, speakers were given a tip to map out 4 to 5 simple bullet points or practice chatting with their partner once before hitting record. They were told to keep it personal, share their own stories, what they have seen around them, and what they think or feel.\n\nThe recordings feature natural pairs of individuals who already have an established real-life relationship. Users were told to record the conversations with someone they know, like a friend, a cousin, or a neighbour.\n\nTo ensure acoustic and conversational diversity, a user could only record with the same partner for a maximum of 3 sessions, and they had to select an entirely different topic each time.\n\nFor every recording, speaker metadata was logged for the conversational partner, including age, gender, phone number and the PIN code of where they are originally from. The files carry only the partner's gender and PIN code, and the gender of the person who made the recording; age and phone number are not included.\n\nSetup and Recording Instructions Given to Speakers:\n\n* Speakers were instructed to talk in a quiet place, so their voices remained clear. While minor background noise was acceptable, it could not interfere with clarity, and both individuals had to be equally audible using the phone’s standard built-in microphone.\n* Speakers were given an IMPORTANT safety instruction: they were strictly forbidden from mentioning their real names, their partner's names, where they work, or where they live. All identifying personal details were intentionally kept out of the conversations.\n* Every submission was meant to last 9 to 10 minutes, though some recordings are shorter. Participants were required to complete a minimum of 4 and a maximum of 8 conversation recordings per assigned domain.\n\n**Directory structure**\n\nThe root directory contains pairs of WAV and JSON files that share a prefix: each recording's .wav file has a matching .merged.json file.\n\n**File structure**\n\nThe JSON file has three parts: “audio”, “metadata” and “transcriptions”:\n\n* **audio**: The filename and file format information\n* **metadata**: The metadata of the speaker and the topic\n* **transcriptions**: The time aligned transcriptions, in segments of at most 20 seconds, each marked SPEAKER1 or SPEAKER2\n\nThe transcriptions also mark pauses, speech heard over background noise, and sounds that are not words, with tags such as PAUSE, noise and [inaudible]. The English words speakers use now and then are kept in Latin script.\n\nAn example file is in the [full datasheet on MDC](https://mozilladatacollective.com/datasets/cmrmkmodd01h0nu07rkbq6yu9?utm_source=databricks&utm_medium=referral&utm_campaign=databricks_marketplace&utm_content=listing_description).\n\n**Translated Examples**\n\n1. ꯍꯣꯍꯣ ꯑꯗꯨꯕꯨꯗꯤꯅꯦ ꯏꯅꯦ bank account ꯂꯩꯔꯕꯨꯗꯤ ꯌꯥꯝ ꯐꯔꯦ ꯑꯗꯨꯗꯅꯦ ꯍꯦꯛꯇ ꯊꯝꯖꯟꯒ꯭ꯔꯣ fix diposit ꯑꯣꯏꯅꯅꯦ ꯏꯅꯦꯅꯥꯅꯦ ꯂꯥꯎꯊꯣꯛꯅꯤꯡꯕ ꯃꯥꯇꯝ ꯗ ꯂꯥꯎꯊꯣꯛ ꯄꯒꯤ ꯃꯑꯣꯡ ꯗ ꯊꯝꯒꯦꯁꯨ ꯌꯥꯏ ꯅꯠꯇꯔꯒꯅꯦ ꯆꯍꯤ ꯃꯉꯥ ꯑꯃ ꯇꯔꯥ ꯑꯃ ꯑꯗꯨꯝ ꯂꯥꯎꯊꯣꯛꯇꯕꯤꯗ ꯊꯝꯃꯒ ꯍꯌꯦꯡꯅꯦ ꯄꯩꯁꯥꯗꯣ ꯁꯦꯟꯗꯣꯏꯒ ꯀꯩꯒ mature ꯑꯣꯏꯔꯛꯑꯀꯟꯗꯅꯦ ꯆꯥꯎꯅ ꯐꯪꯒꯦꯁꯨ ꯌꯥꯏ ꯏꯅꯦ ꯃꯥꯈꯟ ꯃꯈꯥꯟ ꯂꯩꯕꯅꯤ ꯑꯗꯨꯒꯤꯗꯤꯅꯦ ꯃꯣꯏꯒꯤ bank ꯀꯤ ꯃꯤꯁꯤꯡꯗ ꯍꯪꯉꯥꯒꯅꯦ ꯑꯀꯨꯞꯄ ꯃꯔꯣꯜ ꯈꯪꯉꯥꯅꯤ꯫ (*If you have a bank account, it's very good. You don't have to worry much. Fixed deposits are such that when you want to withdraw money, you can withdraw it in the way you want. Or, if you withdraw it after five years or something, you can withdraw it. If the money matures, you can withdraw it. There's so many ways to save money and it'll be better to ask the bank staff for further details.*)\n2. ꯑꯣ ꯑꯗꯨꯕꯨꯗꯤꯅꯦ ꯏꯅꯦ ꯑ insurance ꯌꯥꯎꯕꯁꯦ ꯌꯝꯕꯨꯗꯤ ꯀꯥꯟꯅꯩ ꯍꯥꯏꯗꯤꯅꯦ ꯑꯍꯜ ꯑꯣꯏꯒꯦꯔꯥ ꯅꯍꯥ ꯑꯣꯏꯒꯦꯔꯥ ꯃꯤ ꯑꯁꯦ ꯁꯤꯕ ꯅꯥꯕ ꯖꯦ ꯇꯝꯕ ꯉꯝꯕ ꯅꯠꯇꯦ ꯑꯗꯣ ꯅꯥꯔꯛꯑꯦ ꯌꯦꯛꯂꯛꯑꯦ ꯏꯅꯦꯅꯅꯦ ꯀꯔꯤꯒꯨꯝꯕ ꯍꯥꯏꯕꯨꯗꯤ ꯍꯥꯏꯍꯩꯗꯦꯀꯣ ꯏꯅꯦ accident ꯀ ꯇꯧꯗꯅꯒꯖꯦ ꯌꯥꯝꯅ ꯆꯥꯎꯅꯒ ꯅꯔꯦꯗꯅꯀꯣ ꯏꯅꯦꯒꯤ ꯑꯔꯨꯕ ꯑꯗꯣ ꯏꯅꯦꯒꯤꯅꯦ ꯏꯆꯥ ꯏꯁꯨ ꯂꯩꯍꯧꯔꯤꯕꯁꯤꯡꯁꯤꯅꯅꯦ ꯄꯩꯁꯥ ꯐꯪꯗꯣꯏꯅꯤ ꯏꯅꯦ꯫ (*Oh, that's right. Aunt, having Insurance is very beneficial. Whether one is old or young, human beings cannot predict death or illness. If one falls sick or becomes ill .Aunt, I'm not saying it will happen but if a major accident occurs, [PAUSE] Aunt, for such difficult times, the children and grandchildren who are left behind will receive the money.*)\n3. ꯏꯃꯥ ꯏꯕꯦꯝ ꯏꯕꯦꯝꯃ ꯏꯅꯦꯖꯦ ꯉꯔꯥꯡꯖꯦ ꯃꯔꯨꯞ ꯑꯃ ꯐꯥꯎꯋꯦꯕ ꯑꯥ ꯑꯗ ꯄꯩꯁꯥꯖꯤꯗꯤꯅꯦ ꯌꯨꯝꯗ ꯀꯨꯏꯅ ꯊꯝꯖꯟꯕꯖꯦ ꯀꯤꯔꯕ ꯑꫭ ꯍꯧꯖꯤꯛꯀꯥꯟꯗꯤ ꯄꯩꯁꯥꯖꯦ ꯃꯔꯨꯞ ꯐꯥꯎꯋꯦꯖꯦ ꯄꯥꯎ ꯍꯦꯛ ꯇꯥꯒꯤꯕꯗ ꯌꯥꯝꯅ ꯍꯥꯠꯇꯣꯛꯅꯩꯗꯅ ꯫ (*Mother, my dear, Aunt got her Marup yesterday. ah But keeping money at home for a long time is scary. ah Nowadays, as soon as people hear news about money being kept for Marup, they come to rob or kill.*)","terms_of_service":"https://mozilladatacollective.com/terms/consumers?utm_source=databricks&utm_medium=referral&utm_campaign=databricks_marketplace&utm_content=listing_terms","documentation_link":"https://mozilladatacollective.com/datasets/cmrmkmodd01h0nu07rkbq6yu9?utm_source=databricks&utm_medium=referral&utm_campaign=databricks_marketplace&utm_content=listing_documentation","privacy_policy_link":"https://mozilladatacollective.com/privacy?utm_source=databricks&utm_medium=referral&utm_campaign=databricks_marketplace&utm_content=listing_privacy","embedded_notebook_file_infos":[{"id":"b5d7bb9b-4d85-4586-87a4-a004ea32cef1","marketplace_file_type":"EMBEDDED_NOTEBOOK","file_parent":{"parent_id":"5cd71b60-1abc-44b0-acc4-55a7af86d5c7","file_parent_type":"LISTING"},"mime_type":"text/html","download_link":"https://marketplace-file-storage-prod.s3-fips.us-east-1.amazonaws.com/EMBEDDED_NOTEBOOK/LISTING_5cd71b60-1abc-44b0-acc4-55a7af86d5c7/b5d7bb9b-4d85-4586-87a4-a004ea32cef1?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEGgaDnVzLWVhc3QtMS1maXBzIkYwRAIgbC8sWIoYaHR8y7Wq86nPb0pufBl4%2BM4PWpeN%2B5PW9VQCIG7BhAVBDCWTpxFDLeuKYm%2Ff1JLz4iccPMzter0YdhgWKqYECDAQABoMNDE0MzUxNzY3ODI2IgxNAJrHbXaZG20jNfMqgwQyGEb4TsoVOoNvJ%2BaA%2FZ7zeu8hGcHqCeNfblP%2FQo5g6q2aRNtpwtPRKMlhYcIE%2FUQaNT1Lqmyptu32VUQTpzjTTxRVmJrDPkheSCkV06XQ%2FpTMKjU8CC7X1b%2FqYf8CrBwZT7Cv4C5WPjEET38f2A79h0xV6bZgPy28zQWrSeIJ9w6AfZBxiE24%2FVc08zVA0ivCT6lsy7nuU0wcR%2FMg2C9nd1vaMnQ923VpD21CKfVVYcrNWL2b%2FQ%2BNAO%2BxPjPgjiN6UPQbvbvZxkSdhgrGtecIg9NgqGkMcvM5VLTDtdC1kdeZetfOC2obtA%2BT%2F0gy3tIDo6WM5hcQfw743%2FAU9oaGnRBl%2BwoxRRDCZA%2F4jnPdluICSI7NCr6C3HHOe4CSZJjRihczxPA%2FgQczAVjAPs6CrK9N9pIzt0vNJpGUTgg4AoxftTvGfciDREilSctPIcBk6GwB%2Bzt4wbtNCmEPU%2BFD46rc6CWVQmmLwbKOeHw70ipMVFlOc8NlXkEtGO2vu6H44MstSSc6daOVUYUavQSGZ6jGhEjsP2YGz8KDm9XQotB%2FtyVBqiZV01OKqDmmBJA%2FXjUpSJ8M%2FeJXhe5m%2BKcxzNObtoJRI7jci3Tpt08XZyXZ7%2FyIV8H30ya468X3cfkTXrm972uga3z7iXAdubyEHnQIua%2FuvtwOHILi6O5D1Me2MzCPvcnXBjqTAYDoMWBt%2B3AVFuqSs2dmDQNBnvEhXfMgTedKLD5PJHb3aIl15iBkLaNu36GYnVCp9ZU2lSYcTbY5QwNnNeTfJTNy9bKhwzru6Sg%2BslhN%2BTS0zkTn%2BeLMY4tHgPEtEZ2cp%2FCk6GwqswvnYVKHjwO8q%2B1Mx8lc08fVEfarE9dke1GGFqoddx%2Bq%2BZUyHbDlAVfrCmf0yw%3D%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20261110T000135Z&X-Amz-SignedHeaders=host&X-Amz-Expires=86400&X-Amz-Credential=ASIAWA6KKHEJORWBH4N3%2F20261110%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=3c80c80542b7890f44234dfe3fe89afe05644cc03ece1c3705df4e4d86ad5a98","created_at":1793112312565,"updated_at":1793112312565,"display_name":"Manipuri finance conversations - first look","status":"FILE_STATUS_PUBLISHED"}],"assets":["ASSET_TYPE_DATA_TABLE","ASSET_TYPE_MEDIA"],"license":"https://community.mozilladatacollective.com/mdc-data-licence-agreement-1-0?utm_source=databricks&utm_medium=referral&utm_campaign=databricks_marketplace&utm_content=listing_licence"},"provider_summary":{"name":"Mozilla Data Collective","description":"Mozilla Data Collective is building a Multilingual, Multicultural, Multimodal tech future by giving everyone the Data Platform for Human Agency and Fair Value Exchange."},"popularity":394},{"id":"d38b09ba-00ad-4a21-af38-38b304747aea","summary":{"name":"SAMPLE American English Language Datasets | 150+ Years of Research | Dictionary Data | Audio ...","subtitle":"Note: The CSV format sample above is provided for preview only. Full datasets may differ in format depending on ...","share":{"name":"oxford_languages_f4bdfac8_9a41_41b2_be2c_425589a69233_samples_share","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1789386260793,"updated_at":1789386260793,"categories":["OTHER"],"listingType":"STANDARD","provider_id":"79c6c146-3407-48ec-8737-5b9b06406932"},"detail":{"description":"Derived from over 150 years of lexical research, these comprehensive textual and audio data, focused on American English, provide linguistically annotated data. Ideal for NLP applications, LLM training and/or fine-tuning, as well as educational and game apps. \n\nOne of our flagship datasets, the American English data is expertly curated and linguistically annotated by professionals, with annual updates to ensure accuracy and relevance. The below datasets in American English are available for license:\n\n1.\tAmerican English Monolingual Dictionary Data \n2.\tAmerican English Synonyms and Antonyms Data \n3.\tAmerican English Pronunciations with Audio\n\nKey Features (approximate numbers):\n\n1.\tAmerican English Monolingual Dictionary Data\n\nOur American English Monolingual Dictionary Data is the foremost authority on American English, including detailed tagging and labelling covering parts of speech (POS), grammar, region, register, and subject, providing rich linguistic information. Additionally, all grammar and usage information is present to ensure relevance and accuracy.\n\n- Headwords: 140,000 \n- Senses: 222,000 \n- Sentence examples: 140,000 \n- Format: XML and JSON format \n- Delivery: Email (link-based file sharing) and REST API \n- Updated frequency: annually \n \n \n2. American English Synonyms and Antonyms Data\n\nThe American English Synonyms and Antonyms Dataset is a leading resource offering comprehensive, up-to-date coverage of word relationships in contemporary American English. It includes rich linguistic details such as precise definitions and part-of-speech (POS) tags, making it an essential asset for developing AI systems and language technologies that require deep semantic understanding.\n \n- Synonyms: 600,000 \n- Antonyms: 22,000\n- Format: XML and JSON format \n- Delivery: Email (link-based file sharing) and REST API\n- Updated frequency: annually \n\n3. American English Pronunciations with Audio (word-level)\n\nThis dataset provides IPA transcriptions and clean audio data in contemporary American English. It includes syllabified transcriptions, variant spellings, POS tags, and pronunciation group identifiers. The audio files are supplied separately and linked where available for seamless integration - perfect for teams building TTS systems, ASR models, and pronunciation engines.\n \n- Transcriptions (IPA): 250,000 \n- Audio files: 180,000 \n- Format: XLSX (for transcriptions), MP3 and WAV (audio files) \n- Updated frequency: annually\n \n\nUse Cases: \n\nWe consistently work with our clients on new use cases as language technology continues to evolve. These include NLP applications, TTS, dictionary display tools, games, translation machine, AI training and fine-tuning, word embedding, and word sense disambiguation (WSD). \n\nIf you have a specific use case in mind that isn't listed here, we’d be happy to explore it with you. Don’t hesitate to get in touch with us at Growth.OL@oup.com to start the conversation.\n \nPricing: \n \nOxford Languages offers flexible pricing based on use case and delivery format. Our datasets are licensed via term-based IP agreements and tiered pricing for API-delivered data. Whether you’re integrating into a product, training an LLM, or building custom NLP solutions, we tailor licensing to your specific needs. \n\nContact our team or email us at Growth.OL@oup.com to explore pricing options and discover how our language data can support your goals. \nPlease note that some datasets may have rights restrictions. Contact us for more information. \n \nAbout the sample: \n\nTo help you explore the structure and features of our dataset on this platform, we provide a sample in CSV and/or JSON formats for one of the presented datasets, for preview purposes only, as shown on this page. This sample offers a quick and accessible overview of the data's contents and organization. \n\nOur full datasets are available in various formats, depending on the language and type of data you require. These may include XML, JSON, TXT, XLSX, CSV, WAV, MP3, and other file types. 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When used alongside the **Meta Pixel**, the Meta Conversions API improves measurement, reporting, and optimization by providing a more complete view of the customer journey.\n\n**Use cases**\n* **Optimizing ad performance:** Fuel the delivery engine using Meta AI with high-quality signals to decrease cost per action.\n* **Improving measurement:** Accurately track actions in multiple ways, including deep-funnel milestones like subscription renewals or qualified leads.\n* **Increasing match quality:** Share hashed first-party data to improve the event match quality (EMQ) and enhance audience retargeting.\n* **Tracking offline conversions:** Connect in-store purchases and phone-call conversions directly to your Meta ad campaigns.\n\n**Implementation details**\nThe example notebook and sample web-event dataset provide a step-by-step walkthrough of how to run Meta Conversions API pipelines on Databricks. 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The territorial vintages of the worldwide area boundaries are compatible with our globally consistent and comparable [socio-demographic](https://www.mbi-geodata.com/en/sociodemographic-data/) and [economic](https://www.mbi-geodata.com/en/purchasing-power-overview/) data.\nOur area boundaries for Kosovo are offered at the following levels:\n\n- **Postal Code Level** (Postcode 2-digit, 5-digit on request): This fine-grained level of detail makes the data suitable for a wide range of applications – for example, enriching customer databases, which are often organized by postal code.\n- **Administrative Level** (Komunatë): Covers both larger administrative areas (e.g., regions, states, provinces) as well as finer subdivisions such as districts, counties, or municipalities.\n\n**Use Cases:**\n\n- **Market Analysis**: Use MBI’s boundaries to visualize data on a map, learn about your customers, and uncover untapped market potential.\n- **360-Degree Customer View**: Link boundaries to customer data, sociodemographic, and economic data to gain a holistic understanding of customer needs, behaviors, and potential value.\n- **Location Planning**\n•\tUse MBI’s boundaries to visualize data on a map, learn about your customers\n•\tPlan the provision of services and products across different regions\n•\tDefine sales territories\n•\tVisualize catchment areas and plan actions to reach potential customers\n\n**Additional Insights:**\n[About Area Boundaries](https://www.mbi-geodata.com/en/area-boundaries/)\n\n**Data Structure:**\nThe area boundaries include the following variables:\nCTRYCODE: Two-digit country code by ISO 3166\nNAME: Name of administrative area / Name of the most populous administrative area / Name of the respective micro-area or the next higher level\nADMINCODE / POSTCODE / MICROCODE: Administrative code / Postal code / Micro-code\nWKT: Geography as Well Known Text (lat/long WGS84)\n\n*with only a few specific exceptions","terms_of_service":"https://service-mbi-geodata.com/s/HNp7Z2tws4WDqWd","documentation_link":"https://www.mbi-geodata.com/en/mbi-supplier-manufacturer-worldwide-geodata/europe/kosovo-maps/","privacy_policy_link":"https://www.mbi-geodata.com/en/data-protection/","update_frequency":{"interval":1,"unit":"YEARLY"},"assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://service-mbi-geodata.com/s/aMtfey9EStJPDai"},"provider_summary":{"name":"Michael Bauer International GmbH – MBI Geodata","description":"For over 15 years, Michael Bauer International (MBI) has been a leading provider of globally consistent and comparable geographic data, including more than 3 million postcode areas and other detailed datasets like sociodemographics and purchasing power. 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Featuring a comprehensive inventory of products available through Google Shopping, this dataset empowers businesses and researchers to uncover valuable insights, optimize advertising strategies, and understand consumer preferences in the ever-evolving landscape of e-commerce.\n\n**Use Case:**\n\n- Market Analysis: Gain deep insights into market trends, product performance, and consumer behavior within the Google Shopping ecosystem.\n- Advertising Strategy Optimization: Refine your advertising strategies based on comprehensive data on product details, ratings, reviews, and pricing information.\n- Consumer Behavior Understanding: Dive into customer reviews and ratings to uncover insights into product satisfaction levels, quality, and overall consumer sentiment.\n- Competitor Evaluation: Assess product performance, analyze seller credibility, and stay ahead of the competition in the realm of online retail.\n\n**Key Features:**\n\n- Comprehensive Data Attributes: Access a wealth of information covering product details, ratings, reviews, pricing data, seller information, and more.\n- Tailored Subsets: Explore curated subsets such as \"Products with more than 100 reviews\" or \"Products with the Highest Rating\" to focus your analysis on specific areas of interest.\n- Seamless Integration: Effortlessly integrate the dataset into your analytics tools or platforms for streamlined analysis and decision-making.\n- Versatile Applications: Whether you're maximizing your online presence, investigating e-commerce trends, or refining marketing strategies, the Google Shopping Products Dataset offers a valuable resource for uncovering opportunities and driving success in the competitive landscape of online retail.\n\nNote: This is a sample.","terms_of_service":"https://brightdata.com/license","documentation_link":"https://drive.google.com/file/d/1tljNSB3SQykv16oqPSqhp9EedXAFE4_o/view?usp=drive_link","privacy_policy_link":"https://brightdata.com/privacy","data_source":"","assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://brightdata.com/license"},"provider_summary":{"name":"Bright Data","description":"Bright Data is the world’s #1 web data platform.\nFortune 500 companies, academic institutions, and small businesses rely on Bright Data’s scalable platform for web scraping infrastructure to retrieve crucial public web data in the most efficient, reliable, and flexible way, so they can research, monitor, and analyze data to make better decisions.\nThe platform is used worldwide by 20,000+ customers in nearly every industry. 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With access to millions of POIs, you can deepen your analysis with detailed classification that reflects business diversity.\n\n**Use cases**\n\n- **Economic Impact Analysis**\nUnderstand and assess the types and attributes of businesses (revenue, staffing, ownership) at a location. Monthly updates to the data allow analysis of changes to individual businesses or coverage of businesses over time.\n\n- **Accelerating Advertising Revenue**\nUnderstand which businesses populate a given location to create targeted advertising campaigns to optimize returns.\n- **Fundamental Analysis**\nUnderstand which types of business populate a given location. Deep dive into the attributes of the businesses.\n- **Personalize Customer Experiences**\nUnderstand the type of businesses at given locations allowing for enhanced customer communication. 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The sources of data are the Centers for Disease Control and Prevention (CDC) and the Big Cities Health Coalition (BCHC), which is described as forum for the leaders of America’s largest metropolitan health departments.\n\n**Description**\nThe CDC mission, as agency of the U.S. Department of Health & Human Services, is to protect America from health, safety and security threats, both foreign and in the U.S, regardless of where the disease starts (at home or abroad), is chronic or acute, curable or preventable, caused by a human error or deliberate attack. On the other side BCHC is a project of the National Association of County and City Health Officials (NACCHO) and it purpose is to facilitate the exchange of strategies and to address issues to promote and protect the health and safety of all people they serve.The main indicators related to vaccination coverage at country level are listed below:• Immunization among children 19-35 months old- DTaP (Diphtheria, Tetanus, and Pertussis)- IPV (Polio)- MMR (Measles, Mumps, and Rubella (MMR)- Hib (Herophilus Influenzae Type B)- Hepatitis A-Hepatitis B- Varicella- PCV (Pneumococcal Conjugate Vaccine)• Immunization among kindergarten students- DTaP (Diphtheria, Tetanus, and Pertussis)• Immunization among children 13-17 years old- DTaP (Diphtheria, Tetanus, and Pertussis)- Meningococcal Conjugate Vaccine- HPV (Human Papilloma Virus)• Global population immunization, time series (Diphtheria, Tetanus, and Pertussis)At city level the data is available for two types of immunizations: influenza and pneumococcal among most vulnerable age groups teenagers and elders.\n\n**Benefits**\n- Useful for epidemiologists, public health specialists and specialized institutions that plan and assess health services.\n- useful for researchers seeking to improve the methods, regulations, health policies and strategy.\n- the raw version can be found on authors website, but jsl is offering a clean, standardized and easier to use version, alone or in combination with other datasets found in jsl dataset library. \n- the fields descriptions along with the dataset description are again useful for user to quick understand the data and the dataset.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [Big Cities Immunization Coverage Rates](https://www.johnsnowlabs.com/marketplace/big-cities-immunization-coverage-rates)\n\t- This dataset contains estimates of influenza and pneumococcal immunization coverage rates shared by the Big Cities Health Coalition members represented by the largest metropolitan health departments in United States. Influenza immunization estimates are for two moajor vulnerable categories, adults of 65 years and older and children and teenagers under 18 years old.\n- [CDC Immunization Coverage 13 to 17 Year Olds 2011 to 2014](https://www.johnsnowlabs.com/marketplace/cdc-immunization-coverage-13-to-17-year-olds-2011-to-2014)\n\t- Vaccinations are one of the best ways to put an end to the serious effects of certain diseases. Vaccination helps to check the occurrence of epidemics as well as control the mortality and morbidity rates. Vaccination in the absence of prevalence of any disease is necessary that it will avoid the reoccurrence of the disease and save future generations from the agony of the deadly disease. The dataset carries vaccination statistics of 13 to 17 year olds.\n- [CDC Immunization Coverage 19 to 35 Months 2011 to 2014](https://www.johnsnowlabs.com/marketplace/cdc-immunization-coverage-19-to-35-months-2011-to-2014)\n\t- Vaccinations are one of the best ways to put an end to the serious effects of certain diseases. Vaccination helps to check the occurrence of epidemics as well as control the mortality and morbidity rates. Vaccination in the absence of prevalence of any disease is necessary that it will avoid the reoccurrence of the disease and save future generations from the agony of the deadly disease. The dataset carries vaccination statistics of 19 to 35 Months Old Children.\n- [CDC Immunization Coverage Kindergarten Student 2011 to 2014](https://www.johnsnowlabs.com/marketplace/cdc-immunization-coverage-kindergarten-student-2011-to-2014)\n\t- Vaccinations are one of the best ways to put an end to the serious effects of certain diseases. Vaccination helps to check the occurrence of epidemics as well as control the mortality and morbidity rates. Vaccination in the absence of prevalence of any disease is necessary that it will avoid the reoccurrence of the disease and save future generations from the agony of the deadly disease. The dataset carries vaccination statistics of kindergarten students.\n- [Child Immunization Annual Uptake Rates by NHS Board in Scotland](https://www.johnsnowlabs.com/marketplace/child-immunization-annual-uptake-rates-by-nhs-board-in-scotland)\n\t- This dataset provides information about child immunization uptake rates for the 6-1n-1 vaccine by 12 and 24 months of rates by Scottish Index of Multiple Deprivation (SIMD) 2016 quintile by National Health Service (NHS) board in Scotland. It also includes Pneumococcal conjugate vaccine (PCV), Rotavirus and Meningococcal serogroup B (MenB) by 12 months of age, Measles, Mumps, and Rubella (MMR1), Hib/MenC, PCV Booster (PCVB) and MenB by 24 months of age for evaluation period in the Year 2018.\n- [DTP3 Immunization Coverage 1986 to 2006](https://www.johnsnowlabs.com/marketplace/dtp3-immunization-coverage-1986-to-2006)\n\t- This dataset provides estimates with confidence intervals of the coverage of three-dose diphtheria, tetanus, and pertussis (DTP3) vaccination.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field 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chandra.reddy@dataplatr.com\nFor consultations or custom inquiries: https://dataplatr.com/contact-us\n\n**Dataplatr NetSuite AR Transaction Details Accelerator**\n\n**Purpose-Built for NetSuite Customer Invoice & Payment Data Pipelines**\n**Transforming Customer Invoice Aging Analytics with Databricks Lakehouse Architecture**\n\nEnterprises running NetSuite often face challenges modernizing Accounts Receivable operations due to fragmented **Vendor Bills** and **Vendor Payments** across subsidiaries, manual reconciliation of Vendor Bills with Vendor Credits and partial payments and limited visibility into Outstanding Balances and collection trends.\n\nThese gaps lead to inefficient cash-flow forecasting, delayed collections, higher Days Sales Outstanding and missed opportunities to offer early-payment discounts or improve customer credit management.\nThe Dataplatr NetSuite AR Accelerator is a ready-to-use solution that streamlines the ingestion, transformation and analysis of **Vendor 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opportunities and customer payment performance increases DSO and cash-flow risks.\n- **Compliance Complexity**: Difficulty maintaining As-of Date AR Aging snapshots, capturing Posting Dates and generating consolidated audit-ready reports across subsidiaries.\n\n**Dataplatr NetSuite AR Solution**\n\n- **Unified Receivable Analytics**: Consolidates Vendor Bills, Vendor Payments, Vendor Credits and Outstanding Balances across subsidiaries and currencies.\n- **Automated Invoice Lifecycle Tracking**: Tracks every Vendor Bill Number from Bill Date through Posting Date to final payment, including unapplied credits and Pending Approval status.\n- **Predictive Cash-Flow & DSO Management**: Combines balance amounts, Vendor Bill Aging and historical payment behavior to forecast collections and improve liquidity.\n- **Customer Payment Performance Intelligence**: Provides scorecards for Top 10 Customers by Outstanding Balance, on-time vs late payments and early-payment patterns to support better 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loads\n- **CDC Framework**: Near real-time updates of Vendor Payments and Vendor Credits to reflect up-to-date balances, transactional integrity checks and seamless downstream synchronization\n- **Data-Quality Framework**: Automated validation for Outstanding Balances, Posting Dates, Customer IDs, GL Accounts, data-type consistency and duplicate record detection\n\n**Documentation & Support**\n\n- **Implementation Guide**: Step-by-step setup for NetSuite Saved Searches, ingestion configuration, and Databricks deployment\n- **Configuration Manual**: Detailed field mappings (e.g., Vendor Bill Number → Vendor Bill Number; Subsidiary → Subsidiary Name), usage guidelines, and KPI definitions\n\nWhy Choose Dataplatr NetSuite AR Accelerator\n\n- **AR Domain Expertise**: Built by finance process specialists with deep knowledge of Vendor Bill lifecycles, collections processes, and cash-flow forecasting\n- **Rapid Time-to-Value**: Deploy Vendor Bill Aging and DSO analytics rapidly with pre-built pipelines and curated dashboards\n- **Enterprise-Grade Security & Compliance**: Embedded audit trails, data lineage, and subsidiary-level governance\n- **Cost-Effective Solution**: Fully leverages Databricks Lakehouse investment eliminates the need for third-party AR analytics tools\n- **Proven Results**: Demonstrated improvements in DSO reduction, cash-flow predictability, and collection prioritization\n- **Optimized for Reporting**: Produces curated, audit-ready outputs for BI tools, compliance reporting, and executive dashboards\n\nTransform your accounts receivable operations from manual transaction tracking to intelligent AR insights with Dataplatr's NetSuite AR Transaction Details Accelerator. Built on NetSuite and Databricks best practices with finance-first design, this solution delivers immediate visibility into AR transactions while enabling advanced analytics and AI-driven optimization.\n\n[Transform financial decision-making with Databricks](https://medium.com/@dataplatr/empowering-financial-decision-making-using-databricks-and-power-bi-insights-5beb6ee7a08c) using CDC-driven pipelines and Lakehouse architecture to deliver actionable insights across GL, AP, AR and Fixed Assets with automated, finance-ready data models.\n\n[Leverage the Databricks Lakehouse Platform to transform People Analytics](https://medium.com/@dataplatr/maximizing-people-analytics-with-the-power-of-databricks-lakehouse-platform-and-omni-analytics-523e4a9cf5e5) with CDC-driven pipelines and real-time insights across headcount, hiring and HR metrics using a scalable, finance-grade data architecture.\n\nNote: By clicking '**Get Access**', you agree to our **Terms of Service**. 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Our enterprise data engineering accelerators are designed to expedite the development of data pipelines, data transformation, and integration, saving you time and resources.\n\nOur team of experienced data engineers, scientists and analysts utilize cutting-edge data infrastructure into valuable insights and helps enterprise clients optimize their Sales, Marketing, Operations, Financials, Supply chain, Human capital and Customer experiences."},"popularity":1396},{"id":"24a36fb6-fd43-401c-9d81-1ac46725b936","summary":{"name":"Real-time Job Change Data","subtitle":"Live Data continuously monitors the employment status and job change events for 125M+ individuals across 10M+ companies.","setting":{"visibility":"PUBLIC"},"created_at":1703024569613,"updated_at":1790792707198,"categories":["OTHER","ECONOMICS","DEMOGRAPHICS"],"listingType":"PERSONALIZED","provider_id":"3fc7d7d0-a665-4eb8-aaa0-5054b781d94a"},"detail":{"description":"**Real-time Job Change Data**\nLive Data's patented dataset and analytics continuously monitor the open web for the real-time employment status and job change events of 85M+ individuals across 4M+ companies.\n\n**Better data, better decisions.**\nLive Data enhances the data function for multiple industries, including:\n- **Investment Decisions:** Human capital changes are a leading indicator of company performance. Live Data informs investment decisions for leading VCs, and investors in public, private, and secondary markets.\n- **Application Developers:** Your services perform poorly when key data is incorrect. Live Data powers top GTM and Talent platforms with the most up-to-date employment data and job change events.\n- **Data Vendors:** People change jobs, and contact data decays at a rate of 1-2% monthly. The top contact vendors rely on Live Data to ensure freshness and accuracy across their core offerings.\n\n**Sample Datasets**\nWe are happy to provide a sample dataset or data feed focused on the people and companies you care most about.\n\nPlease explore our [data dictionary](https://docs.google.com/spreadsheets/d/1zcPrqjv9HtDs0l43teLgWPLlBOgFS26Np-5mB6zYKSQ/edit#gid=0) to get a better idea of the types of data we have available.\n\n**Insights**\nFor the latest insights about how to use real-time job change data, please visit the [Live Data Insights page](https://www.livedatatechnologies.com) or [reach out](https://www.livedatatechnologies.com/contact-us) to our team to learn more.","terms_of_service":"https://www.livedatatechnologies.com/legals","documentation_link":"https://docs.livedatatech.com","privacy_policy_link":"https://www.livedatatechnologies.com/legals","geographical_coverage":"[\"Global\"]","cost":"PAID","update_frequency":{"interval":1,"unit":"DAILY"},"collection_granularity":{"interval":1,"unit":"DAILY"},"collection_date_start":79233182,"collection_date_end":1790792692084,"data_source":"Publicly available information on the open web","size":32.0,"assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"Live Data Technologies","description":"Live Data provides continuously updated, real-time job change and employment data on 125M+ professionals across 10M+ companies. 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Build targeted business contact lists or match the data against existing records to fill missing contact and company details.\n\n**What you can do with the data**\n- Build sales prospect lists by country, industry, company and job title.\n- Find business email addresses and direct or mobile phone numbers, where available.\n- Enrich existing CRM records with contact details, job titles and company information.\n- Research target companies and identify relevant business decision-makers.\nAvailable contact and company fields\n\nContact fields can include first name, last name, job title, business email, direct or mobile phone number, professional LinkedIn URL, city, state and country.\n\nCompany fields can include company name, website, company LinkedIn URL, industry, employee-size range and revenue range.\n\nField availability varies by record, country and selected dataset. Not every contact has both an email address and a phone number. The available fields, record counts and coverage for your requirements are confirmed before purchase.\n\n**Choose the data that fits your requirements**\n\nRequest coverage for your target countries, industries, companies and job roles. Tell us which fields you need and how you intend to use the data so we can confirm the suitable dataset and licensing terms.\n\n**Access, delivery and updates**\n\nThis is a paid dataset available by request. Pricing, permitted use, delivery scope and refresh arrangements are agreed with Exellius before access is approved.\n\nDelivery through Databricks sharing is configured for the agreed dataset. Table names, column definitions and access instructions are provided during onboarding. 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This data package contains Comparative Toxicogenomics Database (CTD) datasets, providing details regarding relationships between genes, chemicals and diseases and the significance of these inferences.\n\n**Benefits**\n- helps curating scientific data describing relationships between chemicals, genes/proteins, diseases, taxa, phenotypes, go annotations, pathways, and interaction modules\n- comparative toxicogenomics database is normalized according to jsl standards and split up into 14 datasets category wise.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [Chemical Disease Associations](https://www.johnsnowlabs.com/marketplace/chemical-disease-associations)\n\t- This dataset contains the relationships between chemicals and diseases. These relationships were inferred due to the fact that the chemical and the disease in some way share independent relationships with a same gene or group of genes; the inference was made through curation of research publications, the building of diagrams and statistical analysis.\n- [Chemical Gene Interaction Types](https://www.johnsnowlabs.com/marketplace/chemical-gene-interaction-types)\n\t- This dataset contains the terms of the vocabulary used in the Comparative Toxicogenomics Database (CTD) to describe the type of relationship between the chemical and the gene, these terms define different effects of the chemical over the gene or the products of a gene at a molecular level. The vocabulary used in the CTD is organized in a hierarchical manner, for which many of the terms have a parent term and at the same time could have a descendant term.\n- [Chemical Gene Interactions](https://www.johnsnowlabs.com/marketplace/chemical-gene-interactions)\n\t- This dataset is a list of research publications, curated in the Comparative Toxicogenomics Database (CTD), that show evidence of interaction between a chemical and a gene. The data collect the type of interaction, the degree of such and what element of the gene is affected. The studies listed were performed in human beings and in other species.\n- [Chemical Gene Ontology Enriched Associations](https://www.johnsnowlabs.com/marketplace/chemical-gene-ontology-enriched-associations)\n\t- This dataset contains the results of Gene Ontology (GO) enrichment analyses performed for groups of genes that are in some way affected by a chemical. This analysis was done using the tool GO-TermFinder resulting in GO terms shared between the genes, creating information used to inferences in the biological processes, molecular functions or cellular components that might be involved in the effect of the chemical over the genes and/or the mechanism of disease.\n- [Chemical Pathway Enriched Associations](https://www.johnsnowlabs.com/marketplace/chemical-pathway-enriched-associations)\n\t- This dataset contains the results of pathway enrichment analyses performed for gene groups that are in some way affected by a chemical. The enrichment analysis system permits to find common pathways between genes. Each analysis is based in the list of genes related to the specified chemical, resulting in the output of pathways shared between the genes, creating information used in the biological processes that might be involved in the effect of the chemical over the genes or disease mechanism.\n- [Chemical Pathway Vocabulary](https://www.johnsnowlabs.com/marketplace/chemical-pathway-vocabulary)\n\t- This dataset contains the terms of the vocabulary used in the Comparative Toxicogenomics Database (CTD) to describe the biological or pathological process or reaction (pathway) involved in the interaction between a chemical and disease or gene. These pathways are identified through their codes in the KEGG and REACTOME databases. This dataset is part of the CTD and can be better understood and used along with the other datasets that belong to the CTD.\n- [Chemical Vocabulary](https://www.johnsnowlabs.com/marketplace/chemical-vocabulary)\n\t- This dataset contains the terms of the vocabulary, organized in a hierarchical manner, used in the Comparative Toxicogenomics Database (CTD) to describe the chemicals inferred to have an interaction over a gene or disease. The dataset contains different types of standardized identifications for the chemical to provide a cross-platform compatibility making able to identify the chemical in major scientific databases.\n- [Disease Pathway Associations](https://www.johnsnowlabs.com/marketplace/disease-pathway-associations)\n\t- This dataset contains the relationships between biological pathways and diseases. These relationships were inferred due to the fact that the pathway and the disease in some way share independent relationships with a same gene or group of genes; the inference was made through curation of research publications, the building of diagrams and statistical analysis.\n- [Exposure Event Associations](https://www.johnsnowlabs.com/marketplace/exposure-event-associations)\n\t- This dataset is from the exposure science module added in 2016 to the Comparative Toxicogenomics Database (CTD). The Exposure Events Database collects the effects of chemicals in the environment onto human biology and the measurable events resulting from the exposure to the chemical. The data displayed is a list of research studies with details on the exposure and the outcome of such.\n- [Gene Disease Associations](https://www.johnsnowlabs.com/marketplace/gene-disease-associations)\n\t- This dataset contains the relationships between genes and diseases. These relationships were inferred due to the fact that the gene and the disease in some way share independent relationships with the same chemical; the inference was made through curation of research publications, the building of diagrams and statistical analysis.\n- [Gene Expression Vocabulary](https://www.johnsnowlabs.com/marketplace/gene-expression-vocabulary)\n\t- This dataset contains the terms of the vocabulary used in the Comparative Toxicogenomics Database (CTD) to describe the activity of genes inferred to have an interaction with a chemical or disease. The dataset contains different types of standardized identifications for the gene to provide a cross-platform compatibility making able to identify the gene and its characteristics in major scientific databases.\n- [Gene Ontology Disease Gene Inference Networks](https://www.johnsnowlabs.com/marketplace/gene-ontology-disease-gene-inference-networks)\n\t- This dataset from the Comparative Toxicogenomics Database (CTD) contains the relationships between gene ontology terms and diseases. These relationships were inferred due to the fact that the gene ontology term and the disease in some way share independent relationships with a same gene or group of genes; the inference was made through curation of research publications, the building of diagrams and statistical analysis.\n- [Gene Pathways](https://www.johnsnowlabs.com/marketplace/gene-pathways)\n\t- This dataset contains the biological pathways on which the analyzed genes are part of. These pathways are useful on inferring relationships between chemicals, genes and diseases when the gene and the disease or chemical in some way share independent relationships with the same pathway.\n- [Health Diseases Vocabulary](https://www.johnsnowlabs.com/marketplace/health-diseases-vocabulary)\n\t- This dataset contains the terms of the vocabulary, organized in a hierarchical manner, used in the Comparative Toxicogenomics Database (CTD) to describe the diseases inferred to have a relationship with a gene or chemical. The dataset contains different types of standardized identifications for the disease to provide a cross-platform compatibility making able to identify the chemical in major scientific databases.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated\n- Data Updates\n - Data updates support replace-on-update: outdated foreign keys are deprecated, not deleted\n\n**Our data is curated and enriched by domain experts** \n\n Each dataset is manually curated by our team of doctors, pharmacists, public health & medical billing experts:\n- Field names, descriptions, and normalized values are chosen by people who actually understand their meaning\n- Healthcare & life science experts add categories, search keywords, descriptions and more to each dataset\n- Both manual and automated data enrichment supported for clinical codes, providers, drugs, and geo-locations\n- The data is always kept up to date – even when the source requires manual effort to get updates\n- Support for data subscribers is provided directly by the domain experts who curated the data sets\n- Every data source’s license is manually verified to allow for royalty-free commercial use and redistribution.\n\n**Need Help?**\nIf you have questions about our products, contact us at [info@johnsnowlabs.com](mailto:info@johnsnowlabs.com). \n 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Readily fused with other data sets provided by FiscalNote AI Solutions to create additional factors and dimensions for both augmented analytical insights and quantitative modeling.\n\n**Quantitative Dimensions & Fields**\n\n- Company identifiers\n- Entity sector\n- Entity mention counts\n- Document level sentiment\n- Entity level Sentiment\n- Financial impact scores\n- URL audit trail\n- Time identifiers to support time series\n\n**About FiscalNote**\nFiscalNote is the premier information services company focused on global policy and market intelligence. By combining AI technology, expert analysis, and legislative, regulatory, and geopolitical data, FiscalNote is reinventing the way that organizations minimize risk and capitalize on opportunity. FiscalNote empowers more than 5,000 clients worldwide to monitor, manage, and act on the issues that matter most to them. 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Unlike streaming charts or sales rankings, the data reflects musical works performed in public settings and reported to GEMA, providing a distinctive perspective on Germany's regional music landscape.\nThe dataset covers all German municipalities that meet the inclusion criteria of more than 20,000 inhabitants and at least 100 registered music events in the city profile.\n\n**Use cases**\n- Identify the Top 100 musical works in individual municipalities.\n- Discover geographic differences in musical preferences.\n- Analyze regional patterns in public music performance.\n- Compare cultural activity using the number of registered music events.\n- Identify songs and artists with broad geographic Top-100 presence.\n- Support tourism, cultural policy, city benchmarking and location-intelligence initiatives.\n- Enrich demographic, geographic or market analyses with cultural indicators.\n\n**Product details**\nData represented in this listing include:\n- Official municipality code\n- Municipality name\n- Federal state\n- Population\n- Number of registered music events\n- Top-10 ranking position\n- Musical work ID\n- Song title\n- Artist information, where available\n- Data status\nEach record represents one ranked musical work within one municipality. Each municipality therefore contains ten records representing its local Top 100.\nMunicipality-level attributes such as population and number of events are repeated across the ten corresponding work records to provide a flat, analysis-ready dataset.\nFor additional examples and exploratory analyses, refer to the embedded notebook.\n\n**Additional insights**\nThe dataset supports both municipality-level comparisons and analyses across Germany. 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These data are combined with municipality-level event and administrative information to provide geographic and demographic context. Municipality-level administrative and population data are provided by Statistisches Bundesamt Deutschland via [https://www.statistikportal.de](https://www.statistikportal.de). 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It also provides datasets over Hospice General Information, Provider data and CASPER or ASPEN Information about hospice agencies.\n\n**Description**\nThis data package contains the Home Health Care Agencies List and Ratings contains the list of all Home Health Agencies that have been registered with Medicare. To receive the annual payment update (APU) for any given year, home health agencies that do not qualify for an exemption from participating in the Home Health Care Consumer Assessment of Healthcare Providers and Systems (HHCAHPS) Survey for the specified APU must contract with an approved HHCAHPS Survey vendor and administer the survey on an ongoing (monthly) basis and submit HHCAHPS Survey data to the HHCAHPS data center on a quarterly basis. Home Health Care Patient Survey State Data is designed to measure the experiences of people receiving home health care from Medicare-certified home health agencies. This survey is designed to produce comparable data on the patient’s perspective that allows objective and meaningful comparisons between home health agencies on domains that are important to consumers, Public reporting of survey results will create incentives for agencies to improve their quality of care and Public reporting will enhance public accountability in health care by increasing the transparency of the quality of care provided in return for public investment. Hospice Agencies General Information Hospice Agencies General Information works with many of the Medicare and Medi-Cal Certified Hospice Agencies in the San Francisco area, and is surveyed by agencies of the state and federal governments, and by the Joint Commission on Accreditation of Healthcare Organizations. Hospice offers specialized care and support to individuals, their families, and caregivers during an advanced illness. The focus of hospice is on comfort and quality of life, rather than cure. Hospice CASPER Or ASPEN Contacts Information contains a list of Regional Office (RO) and State coordinators. It includes the Continuous Activity Scheduling Planning Execution and Replanning (CASPER)/Automated Scheduling and Planning Environment (ASPEN) information about hospice agencies. The information on Hospice Compare should be used with other information one gathers about hospices in their area. In addition to reviewing the information on Hospice Compare, individuals are also encouraged to talk to their doctor, social worker, or other health care providers when choosing a hospice. The quality of care that hospices provide to patients and families can vary. Hospice Compare reports information on hospices across the nation and allows patients, family members, and health care providers to get a snapshot of the quality of care each hospice provides. Hospice compare enables different individuals to find hospices that serve in their area and compare them based on the quality of care they provide.\n\n**Benefits**\n- Useful for home health care and hospice agencies and providers, patients, consumers and hospitals.\n- home health and hospice compare data database is available on centers for medicare & medicaid services (cms) websites in raw form, whereas a jsl customer gets it in clean and normalized form ready to use in one place and create his own custom reports.\n- easy to comprehend for the customers who are unfamiliar with the medical terminologies as all abbreviated terms are replaced with full form, unlike home health and hospice compare data original datasets which are full of abbreviations.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [Home Health Care Agencies List and Ratings](https://www.johnsnowlabs.com/marketplace/home-health-care-agencies-list-and-ratings)\n\t- This dataset contains the list of all Home Health Agencies that have been registered with Medicare. The list includes addresses, phone numbers, and quality measure ratings for each agency.\n- [Home Health Care Patient Survey 2019](https://www.johnsnowlabs.com/marketplace/home-health-care-patient-survey-2019)\n\t- This dataset contains The Consumer Assessment of Healthcare Providers and Systems (CAHPS) Home Health Care Survey results. This survey is designed to measure the experiences of people receiving home health care from Medicare-certified home health agencies. The Home Health Care Consumer Assessment of Healthcare Providers and Systems (HHCAHPS) is conducted for home health agencies by approved HHCAHPS Survey vendors.\n- [Home Health Care Patient Survey 2020](https://www.johnsnowlabs.com/marketplace/home-health-care-patient-survey-2020)\n\t- This dataset contains The Consumer Assessment of Healthcare Providers and Systems (CAHPS) Home Health Care Survey results. This survey is designed to measure the experiences of people receiving home health care from Medicare-certified home health agencies. The Home Health Care Consumer Assessment of Healthcare Providers and Systems (HHCAHPS) is conducted for home health agencies by approved HHCAHPS Survey vendors.\n- [Home Health Care Patient Survey 2021](https://www.johnsnowlabs.com/marketplace/home-health-care-patient-survey-2021)\n\t- This dataset contains The Consumer Assessment of Healthcare Providers and Systems (CAHPS) Home Health Care Survey results. This survey is designed to measure the experiences of people receiving home health care from Medicare-certified home health agencies. The Home Health Care Consumer Assessment of Healthcare Providers and Systems (HHCAHPS) is conducted for home health agencies by approved HHCAHPS Survey vendors.\n- [Home Health Care Patient Survey State Data](https://www.johnsnowlabs.com/marketplace/home-health-care-patient-survey-state-data)\n\t- This dataset contains State data of several home health agency quality measures for Home Health Agencies. This survey is designed to measure the experiences of people receiving home health care from Medicare-certified home health agencies. The Home Health Care Consumer Assessment of Healthcare Providers and Systems (HHCAHPS) is conducted for home health agencies by approved HHCAHPS Survey vendors.\n- [Home Health Care Patient Survey](https://www.johnsnowlabs.com/marketplace/home-health-care-patient-survey)\n\t- This dataset contains The Consumer Assessment of Healthcare Providers and Systems (CAHPS) Home Health Care Survey results. This survey is designed to measure the experiences of people receiving home health care from Medicare-certified home health agencies. The Home Health Care Consumer Assessment of Healthcare Providers and Systems (HHCAHPS) is conducted for home health agencies by approved HHCAHPS Survey vendors.\n- [Home Health Care State Averages For HHA Quality Measures](https://www.johnsnowlabs.com/marketplace/home-health-care-state-averages-for-hha-quality-measures)\n\t- This dataset contains State averages of several home health agency quality measures for Home Health Agencies. This survey is designed to measure the experiences of people receiving home health care from Medicare-certified home health agencies. The Home Health Care Consumer Assessment of Healthcare Providers and Systems (HHCAHPS) is conducted for home health agencies by approved HHCAHPS Survey vendors.\n- [Hospice Agencies General Information](https://www.johnsnowlabs.com/marketplace/hospice-agencies-general-information)\n\t- The dataset shows characteristics of the hospice agencies that are shown on Hospice Compare, including information about hospice agencies such as address, phone number, ownership data and more.\n- [Hospice CASPER Or ASPEN Contacts Information](https://www.johnsnowlabs.com/marketplace/hospice-casper-or-aspen-contacts-information)\n\t- This dataset shows a list of Regional Office (RO) and State coordinators. It includes the Continuous Activity Scheduling Planning Execution and Replanning (CASPER)/Automated Scheduling and Planning Environment (ASPEN) information about hospice agencies such as the Centers for Medicare & Medicaid Services (CMS) Region, phone number, and email ID.\n- [Hospice Compare Provider CAHPS Survey Data](https://www.johnsnowlabs.com/marketplace/hospice-compare-provider-cahps-survey-data)\n\t- This dataset contains a list of hospice agencies with data on their scores on the Consumer Assessment of Healthcare Providers and Systems (CAHPS) Hospice Survey measures. It includes information about hospice agencies such as an address, phone number, CMS region data and different Centers for Medicare & Medicaid Services (CMS) Regions they belong to. This dataset also contains data regarding the corresponding scores against each of the measures for quality of patient care.\n- [Hospice Provider Data](https://www.johnsnowlabs.com/marketplace/hospice-provider-data)\n\t- This dataset includes a list of hospice agencies with data on the quality of patient care measures shown on Hospice Compare. It includes information about hospice agencies such as address, phone number, ownership data and different Centers for Medicare & Medicaid Services (CMS) Regions they belong to. This dataset also contains data regarding the corresponding scores against each of the measures for quality of patient care.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated\n- Data Updates\n - Data updates support replace-on-update: outdated foreign keys are deprecated, not deleted\n\n**Our data is curated and enriched by domain experts** \n\n Each dataset is manually curated by our team of doctors, pharmacists, public health & medical billing experts:\n- Field names, descriptions, and normalized values are chosen by people who actually understand their meaning\n- Healthcare & life science experts add categories, search keywords, descriptions and more to each dataset\n- Both manual and automated data enrichment supported for clinical codes, providers, drugs, and geo-locations\n- The data is always kept up to date – even when the source requires manual effort to get updates\n- Support for data subscribers is provided directly by the domain experts who curated the data sets\n- Every data source’s license is manually verified to allow for royalty-free commercial use and redistribution.\n\n**Need Help?**\nIf you have questions about our products, contact us at [info@johnsnowlabs.com](mailto:info@johnsnowlabs.com). \n 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This meticulously compiled dataset, sourced from official statistical sources, including Statistics Lithuania, offers a detailed insight into the population landscape of Lithuania at the municipality (savivaldybė) level. \n\nIf you require data at a less granular level, such as an Uber grid or square grid, please don't hesitate to get in touch with us at *contact@geolocet.com*. We understand that different projects may have varying data needs, and we are here to tailor our data to match your specific requirements.\n\n ## **Key Features:**\n---\n* **Population Data Lithuania:** Gain access to the latest and most accurate population statistics for Lithuania, meticulously organized by municipality (savivaldybė).\n\n* **Demographic Insights:** Dive deep into Lithuania’s demographic landscape with data on 85 attributes, including: \n* Total Population,\n* Gender,\n* Rural/Urban population,\n* Age,\n* Nationality,\n* Marital status,\n* Marital status split by gender,\n* Education,\n* Religion,\n* Employment status,\n* Property ownership,\n* Property ownership split by Rural/Urban.\n\nAll demographic attributes are available at the municipal (savivaldybė) level.\n\n* **Geodemographic Insights:** Unlock geodemographic data Lithuania-wide to inform location-based decision-making, marketing strategies, and policy formulation.\n* **Seamless Data Access:** Our data is available for instant download in CSV format. No lengthy sign-up processes or NDAs required on our side – just download the data you need.\n* **Data Samples for Evaluation:** To ensure you're getting exactly what you need, we provide free downloadable samples in CSV format for data evaluation.\n* **Data accuracy:** Our datasets are created using the latest demographics data available in national statistics bureaus, guaranteeing accuracy and reliability.\n* **Best-in-Class Quality:** We guarantee best-in-class quality, backed by over two decades of experience in modeling demographics data and an outstanding quality management process.\n* **Competitive Pricing:** We maintain the lowest prices on the market, ensuring that you get the most cost-efficient demographics data acquisition process.\n---\nAdditionally, for those looking to enrich their analysis or project, we offer:\n* **[Administrative Boundaries](https://geolocet.com/products/lithuania-all-available-administrative-boundaries):** If you require geographical boundaries (polygons) for the municipalities of Lithuania, consider exploring our Administrative Boundaries product, which seamlessly integrates with this dataset.\n\n* **[Point of Interest (PoI) Data](https://geolocet.com/pages/aggregated-points-of-interest-poi-data):** If you're interested in Points of Interest data, please don't hesitate to contact us for customized datasets to enhance your geographical analysis.\n\n* **[Data Customization](https://geolocet.com/pages/data-services-by-geolocet):** We understand that your project may have specific requirements. Feel free to get in touch with us if you need any modifications or specific data formats such as Excel, JSON, GeoJSON, or Shapefile. We are here to tailor the data to your needs. \n\nIf you're looking for data for Lithuania, [look here](https://geolocet.com/pages/lithuania) for off-the-shelf or tailor-made datasets to suit your specific requirements.\n\n\nMake informed decisions, conduct precise research, and unlock the potential of Lithuania's demographic landscape with our comprehensive dataset. Trust in the accuracy and reliability of our official statistical data to drive your projects forward.\nContact us at *contact@geolocet.com* today to access this invaluable resource and explore Lithuania census data like never before.\n\n[Geolocet LTD](https://geolocet.com)\n","terms_of_service":"https://geolocet.com/pages/terms-of-use","documentation_link":"","privacy_policy_link":"https://geolocet.com/pages/privacy-policy","geographical_coverage":"[\"Europe\"]","cost":"PAID","data_source":"","assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://geolocet.com/pages/terms-of-use"},"provider_summary":{"name":"Geolocet LTD","description":"Geolocet provides premium geospatial data for retail analysis, GIS, and market planning. 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reducing manual workload for finance teams\n- **Customer Payment Intelligence**:Deep, actionable analytics reveal payment behaviors, preferences, and recurring patterns, allowing organizations to proactively tailor engagement and anticipate liquidity needs.\n- **Proactive Credit Risk Management**: Credit monitoring with predictive early warning indicators helps identify risk shifts and high-risk accounts, supporting early intervention and revenue protection.\n- **Unified Dashboards and Reporting** Customizable, interactive dashboards provide instant visibility into AR health, cash conversion cycles, and risk profiles, streamlining analysis and decision-making across teams.\n- **Faster Implementation & Enhanced Agility** Ready-to-use, configuration-driven architecture ensures rapid deployment, reduced operational costs, and swift responsiveness to changing business needs.\n\n**Medallion Architecture Implementation**\n**Bronze Layer (Raw AR Data Ingestion)**\n- **Customer Invoices**: All 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performance obligation definitions\n- **Credit Risk Harmonization**: Integrated, consistent view of internal and external credit information\n\n**Gold Layer (Business-Ready AR Analytics)**\n- **AR Aging Reports**: Standard and configurable aging analysis with interactive drill-down capabilities\n- **Collections Dashboards**:Priority-based collections queues and detailed agent performance tracking metrics\n- **Customer Analytics**: Complete payment behavior analysis and customer relationship insights\n- **Credit Risk Scorecards**: Customer-specific creditworthiness ratings and portfolio risk exposure analysis\n\n**Marketplace Deliverables**\n**Core AR Accelerator Package**- Databricks Implementation Assets\n- **Production-Ready Notebooks**: Complete AR data pipeline with robust, complete error handling and monitoring\n- **Config Templates**: Pre-configured JSON templates for Oracle EBS, Fusion, SAP and NetSuite AR modules\n- **Delta Table Schemas**: Optimized, scalable table structures for AR-specific Bronze, Silver and Gold layers\n- **CDC Framework**: Real-time, reliable change data capture for immediate AR data processing\n\n**Documentation & Training**\n- **Implementation Guide**: Complete setup instructions with proven AR best practices and recommendations\n- **Configuration Manual**:Detailed parameter explanation with customization options and usage guidelines\n- **User Training Materials**: Role-based, interactive training for collections teams and AR management staff\n\n**Why Choose Dataplatr AR Accelerator**\n- **AR Domain Expertise**: Built by accounts receivable and credit management experts with deep understanding of collections processes, customer behavior and revenue recognition requirements not generic data scientists.\n- **Immediate Impact**: Deploy production-ready AR analytics in weeks with pre-built models, process templates and compliance frameworks that deliver value from day one.\n- **Scalable Architecture**: Databricks-native design scales with organizational growth while providing foundation for advanced AI and automation capabilities.\n\nTransform your accounts receivable operations from reactive collections management to predictive customer intelligence with Dataplatr's proven AR Accelerator. Built on Databricksbest practices with compliance-first design, this solution delivers immediate cash flow improvements while providing the foundation for advanced AI-driven customer relationship optimization.\n\n\n[Transform financial decision-making with Databricks](https://medium.com/@dataplatr/empowering-financial-decision-making-using-databricks-and-power-bi-insights-5beb6ee7a08c) using CDC-driven pipelines and Lakehouse architecture to deliver actionable insights across GL, AP, AR and Fixed Assets with automated, finance-ready data models.\n\n[Leverage the Databricks Lakehouse Platform to transform People Analytics](https://medium.com/@dataplatr/maximizing-people-analytics-with-the-power-of-databricks-lakehouse-platform-and-omni-analytics-523e4a9cf5e5) with CDC-driven pipelines and real-time insights across headcount, hiring and HR metrics using a scalable, finance-grade data architecture.\n\nNote: By clicking '**Get Access**', you agree to our **Terms of Service**. You acknowledge that **Dataplatr Corp** will recognize your organization as a customer/user for promotional and reference purposes.\n\n\nContact us at: chandra.reddy@dataplatr.com\nFor consultations or custom inquiries: https://dataplatr.com/contact-us \n\n\n","terms_of_service":"https://docs.google.com/forms/d/e/1FAIpQLSdnlNmSfrFRQDa_ToOL3ydTOPcWI3m3lWor2plLbiqF1JRINg/viewform","documentation_link":"https://dataplatr.com","privacy_policy_link":"https://dataplatr.com","embedded_notebook_file_infos":[{"id":"a814c461-5904-45ac-945d-953e3e178de7","marketplace_file_type":"EMBEDDED_NOTEBOOK","file_parent":{"parent_id":"7a3abed7-da19-4e9b-8d7f-392b8af34d81","file_parent_type":"LISTING"},"mime_type":"text/html","download_link":"https://marketplace-file-storage-prod.s3-fips.us-east-1.amazonaws.com/EMBEDDED_NOTEBOOK/LISTING_7a3abed7-da19-4e9b-8d7f-392b8af34d81/a814c461-5904-45ac-945d-953e3e178de7?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEGgaDnVzLWVhc3QtMS1maXBzIkYwRAIgbC8sWIoYaHR8y7Wq86nPb0pufBl4%2BM4PWpeN%2B5PW9VQCIG7BhAVBDCWTpxFDLeuKYm%2Ff1JLz4iccPMzter0YdhgWKqYECDAQABoMNDE0MzUxNzY3ODI2IgxNAJrHbXaZG20jNfMqgwQyGEb4TsoVOoNvJ%2BaA%2FZ7zeu8hGcHqCeNfblP%2FQo5g6q2aRNtpwtPRKMlhYcIE%2FUQaNT1Lqmyptu32VUQTpzjTTxRVmJrDPkheSCkV06XQ%2FpTMKjU8CC7X1b%2FqYf8CrBwZT7Cv4C5WPjEET38f2A79h0xV6bZgPy28zQWrSeIJ9w6AfZBxiE24%2FVc08zVA0ivCT6lsy7nuU0wcR%2FMg2C9nd1vaMnQ923VpD21CKfVVYcrNWL2b%2FQ%2BNAO%2BxPjPgjiN6UPQbvbvZxkSdhgrGtecIg9NgqGkMcvM5VLTDtdC1kdeZetfOC2obtA%2BT%2F0gy3tIDo6WM5hcQfw743%2FAU9oaGnRBl%2BwoxRRDCZA%2F4jnPdluICSI7NCr6C3HHOe4CSZJjRihczxPA%2FgQczAVjAPs6CrK9N9pIzt0vNJpGUTgg4AoxftTvGfciDREilSctPIcBk6GwB%2Bzt4wbtNCmEPU%2BFD46rc6CWVQmmLwbKOeHw70ipMVFlOc8NlXkEtGO2vu6H44MstSSc6daOVUYUavQSGZ6jGhEjsP2YGz8KDm9XQotB%2FtyVBqiZV01OKqDmmBJA%2FXjUpSJ8M%2FeJXhe5m%2BKcxzNObtoJRI7jci3Tpt08XZyXZ7%2FyIV8H30ya468X3cfkTXrm972uga3z7iXAdubyEHnQIua%2FuvtwOHILi6O5D1Me2MzCPvcnXBjqTAYDoMWBt%2B3AVFuqSs2dmDQNBnvEhXfMgTedKLD5PJHb3aIl15iBkLaNu36GYnVCp9ZU2lSYcTbY5QwNnNeTfJTNy9bKhwzru6Sg%2BslhN%2BTS0zkTn%2BeLMY4tHgPEtEZ2cp%2FCk6GwqswvnYVKHjwO8q%2B1Mx8lc08fVEfarE9dke1GGFqoddx%2Bq%2BZUyHbDlAVfrCmf0yw%3D%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20261110T000135Z&X-Amz-SignedHeaders=host&X-Amz-Expires=86400&X-Amz-Credential=ASIAWA6KKHEJORWBH4N3%2F20261110%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=baab30f87baf38291bfb52ca4d84eeef98cfc1f2d58bab843d08eb2ab44c54e0","created_at":1770816759625,"updated_at":1770816759625,"display_name":"Finance_AR_Accelerator_Oracle_v1","status":"FILE_STATUS_PUBLISHED"}],"assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"Dataplatr Corp","description":"Dataplatr Corp specializes in Data Analytics & AI with pre-built data models for Enterprise Applications like SAP, Oracle EBS, Workday, Salesforce to empower businesses to unlock the full potential of their data. Our enterprise data engineering accelerators are designed to expedite the development of data pipelines, data transformation, and integration, saving you time and resources.\n\nOur team of experienced data engineers, scientists and analysts utilize cutting-edge data infrastructure into valuable insights and helps enterprise clients optimize their Sales, Marketing, Operations, Financials, Supply chain, Human capital and Customer experiences."},"popularity":1790},{"id":"dde09c77-4dd5-436d-bd20-fb8c748b640a","summary":{"name":"Detecting Sudden Patient Satisfaction Anomalies","subtitle":"By catching dissatisfaction trends early through automations, health orgs can transform patient outcomes","share":{"name":"patient_sat_anomaly","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1751182726370,"updated_at":1752079365512,"categories":["HEALTH"],"listingType":"STANDARD","provider_id":"6c6166cf-26dc-4aec-a16d-6d1cc8dde719"},"detail":{"description":"**Overview**\n\nHealthcare organizations rely on patient satisfaction metrics to assess care quality and maintain regulatory compliance. The Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) survey is the first national, standardized, publicly reported survey of patients' perspectives of hospital care. Over 4,400 hospitals participate in HCAHPS and nearly two million patients complete the survey each year.\n\nSince 2012, HCAHPS patient satisfaction scores have been pivotal in hospital reimbursement through the Hospital Value-Based Purchasing Program. Hospitals with higher HCAHPS scores earn higher reimbursements, while those with exceptionally low scores received financial penalties of up to 2% of their Medicare payments.\n\nPatient satisfaction has evolved from a quality metric to a critical business imperative directly tied to organizational success. Traditional approaches to satisfaction management are reactive, addressing issues only after poor experiences have already occurred and patients have potentially been lost. Predictive analytics transforms this paradigm by identifying at-risk encounters before dissatisfaction materializes, enabling healthcare organizations to intervene proactively and prevent negative outcomes. \n\n**Objective**: This demonstration analysis develops predictive models to identify patients at risk for poor satisfaction scores before their care episode concludes, enabling real-time interventions to improve experiences. By leveraging machine learning techniques on historical satisfaction data, clinical indicators, and operational metrics, we create actionable intelligence that transforms reactive satisfaction management into proactive experience optimization.\n\n**Key Focus Areas**\n- Development of risk stratification models using satisfaction survey responses and clinical data\n- Predictive modeling to identify at-risk encounters\n- Integration of operational variables including wait times, staffing levels, and service utilization\n\n**Use cases:**\n- Automatically identify unusual patterns in patient satisfaction scores\n- Track which departments or time periods show concerning trends\n- Identify early warning signals predicting broader satisfaction issues\n- Quantify the value of early anomaly detection\n\n**Product details**\n\n- 1,000+ patient responses across 4 major service lines (Emergency Department, Surgery, Inpatient, ICU)\n- 2+ years of longitudinal data enabling robust trend analysis\n\n**Key Data Elements**\n- Weekly aggregated metrics with department-level segmentation\n- Core HCAHPS measures including communication, cleanliness, pain management, discharge coordination\n\n**Key Performance Indicators Tracked:**\n- Overall Hospital Rating (0-10 scale)\n- Likelihood to Recommend (0-10 scale)\n- Communication Quality Scores\n- Environmental Cleanliness Ratings\n- Pain Management Effectiveness\n- Discharge Information Quality\n \n*This product uses synthetic data and simplified models for illustration purposes. Results shown are not indicative of actual performance. Health Catalyst's production platform includes comprehensive data security, HIPAA compliance, clinical validation, and continuous monitoring that cannot be fully represented in this demonstration environment.*\n\n","terms_of_service":"https://www.healthcatalyst.com/evaluation-agreement","documentation_link":"","privacy_policy_link":"https://www.healthcatalyst.com/privacy-policy","embedded_notebook_file_infos":[{"id":"05d9159b-877b-4133-90a0-9b603d944a08","marketplace_file_type":"EMBEDDED_NOTEBOOK","file_parent":{"parent_id":"dde09c77-4dd5-436d-bd20-fb8c748b640a","file_parent_type":"LISTING"},"mime_type":"text/html","download_link":"https://marketplace-file-storage-prod.s3-fips.us-east-1.amazonaws.com/EMBEDDED_NOTEBOOK/LISTING_dde09c77-4dd5-436d-bd20-fb8c748b640a/05d9159b-877b-4133-90a0-9b603d944a08?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEGgaDnVzLWVhc3QtMS1maXBzIkYwRAIgbC8sWIoYaHR8y7Wq86nPb0pufBl4%2BM4PWpeN%2B5PW9VQCIG7BhAVBDCWTpxFDLeuKYm%2Ff1JLz4iccPMzter0YdhgWKqYECDAQABoMNDE0MzUxNzY3ODI2IgxNAJrHbXaZG20jNfMqgwQyGEb4TsoVOoNvJ%2BaA%2FZ7zeu8hGcHqCeNfblP%2FQo5g6q2aRNtpwtPRKMlhYcIE%2FUQaNT1Lqmyptu32VUQTpzjTTxRVmJrDPkheSCkV06XQ%2FpTMKjU8CC7X1b%2FqYf8CrBwZT7Cv4C5WPjEET38f2A79h0xV6bZgPy28zQWrSeIJ9w6AfZBxiE24%2FVc08zVA0ivCT6lsy7nuU0wcR%2FMg2C9nd1vaMnQ923VpD21CKfVVYcrNWL2b%2FQ%2BNAO%2BxPjPgjiN6UPQbvbvZxkSdhgrGtecIg9NgqGkMcvM5VLTDtdC1kdeZetfOC2obtA%2BT%2F0gy3tIDo6WM5hcQfw743%2FAU9oaGnRBl%2BwoxRRDCZA%2F4jnPdluICSI7NCr6C3HHOe4CSZJjRihczxPA%2FgQczAVjAPs6CrK9N9pIzt0vNJpGUTgg4AoxftTvGfciDREilSctPIcBk6GwB%2Bzt4wbtNCmEPU%2BFD46rc6CWVQmmLwbKOeHw70ipMVFlOc8NlXkEtGO2vu6H44MstSSc6daOVUYUavQSGZ6jGhEjsP2YGz8KDm9XQotB%2FtyVBqiZV01OKqDmmBJA%2FXjUpSJ8M%2FeJXhe5m%2BKcxzNObtoJRI7jci3Tpt08XZyXZ7%2FyIV8H30ya468X3cfkTXrm972uga3z7iXAdubyEHnQIua%2FuvtwOHILi6O5D1Me2MzCPvcnXBjqTAYDoMWBt%2B3AVFuqSs2dmDQNBnvEhXfMgTedKLD5PJHb3aIl15iBkLaNu36GYnVCp9ZU2lSYcTbY5QwNnNeTfJTNy9bKhwzru6Sg%2BslhN%2BTS0zkTn%2BeLMY4tHgPEtEZ2cp%2FCk6GwqswvnYVKHjwO8q%2B1Mx8lc08fVEfarE9dke1GGFqoddx%2Bq%2BZUyHbDlAVfrCmf0yw%3D%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20261110T000135Z&X-Amz-SignedHeaders=host&X-Amz-Expires=86400&X-Amz-Credential=ASIAWA6KKHEJORWBH4N3%2F20261110%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=70bd612c83cff681cb850729393a7ac6a591c07ed57d9cc3ddec43ac6e0ccbe9","created_at":1751315079338,"updated_at":1751315079338,"display_name":"Health Catalyst: Patient Satisfaction Anomaly Detection","status":"FILE_STATUS_PUBLISHED"}],"data_source":"","assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://www.healthcatalyst.com/evaluation-agreement"},"provider_summary":{"name":"Health Catalyst","description":"Health Catalyst is a leading provider of data and analytics technology and services to healthcare organizations, committed to being the catalyst for massive, measurable, data-informed healthcare improvement."},"popularity":1681},{"id":"b7b7cbb2-834f-4945-a469-28e847b6d923","summary":{"name":"DeepFake Videos Dataset - 5,000 Files for Facial Recognition and Computer Vision","subtitle":"DeepFake Videos Dataset contains 5,000 real videos with AI-generated faces for deepfake detection and facial ...","share":{"name":"unidata_3b7d8bf4_7c81_476a_8621_03abd7a3842d_samples_share","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1776282505417,"updated_at":1776282505417,"categories":["OTHER"],"listingType":"PERSONALIZED","provider_id":"a6668567-8ddc-4746-9958-8edbc3807a14"},"detail":{"description":"Overview\nDeepFake Videos Dataset is a commercial synthetic video dataset produced by Unidata, containing 5,000 video files featuring real people with AI-generated faces overlaid. Designed specifically for deepfake detection, facial recognition, and computer vision research, it provides realistic and diverse training material for building and benchmarking deepfake detectors and detection algorithms. Each video was created by generating fake faces and overlaying them onto authentic source videos of real individuals turning their heads in different directions.\n\nDataset Composition\n- 5,000 video files, each corresponding to a unique person\n- 5,000 unique subjects with individual metadata records\n- AI face generation sources: aisaver.io, faceswapvideo.ai, magichour.ai\n- Data type: real videos with AI-generated faces overlaid\n- Labeling: metadata per subject — age, gender, ethnicity\n\nSubject Demographics\nBoth male and female participants are represented in the dataset:\n- Gender breakdown: Male and Female subjects\n- Ethnicity breakdown: Asian (30%), African (70%)\n- Age range: Min = 18, Max = 80, Mean = 45\nEach video is labeled with structured metadata covering age, gender, and ethnicity, enabling targeted filtering and stratified model training across demographic groups.\n\nTechnical Specifications\n- Formats: MP4, MOV\n- Resolutions: 1920×1080p, 1280×720p, 720×480p, 640×480p, 480×360p, 1920×920p\n- Video duration: Mean = 9 sec, Median = 9 sec, Min = 2 sec, Max = 34 sec\n- Frames per second: Mean = 26.6 FPS\n- Recording devices: iPhone 13 (30%), Google Pixel (70%)\nVideos cover diverse individuals, backgrounds, and scenarios, making the dataset suitable for a wide range of detection methods and recognition systems.\n\nData Collection Methodology\nThe dataset was built by generating fake faces using generative AI models and overlaying them onto real video clips. This hybrid approach — combining real-world source videos with AI-generated faces — produces synthetic data that closely mimics actual deepfake content encountered in the wild, ensuring better results during model training and evaluation.\n\n\nUse Cases\n- Cybersecurity & Digital Forensics. Provides critical training data for developing deepfake detectors and detection algorithms. The dataset consists of both real videos and fake videos generated using advanced deepfake technology, allowing analysts to train detection systems capable of identifying synthetic media and protecting against identity fraud and misinformation.\n- AI & Machine Learning Research. Widely used in deep learning and machine learning projects. Models trained on this dataset achieve better accuracy in spotting AI-generated videos and distinguishing between real and fake content. The dataset consists of thousands of video clips with manually labelled metadata, supporting robust model development.\n- Media & Journalism. News organizations use such datasets to enhance video detection tools that verify YouTube videos, interviews, and shared clips. By training recognition systems on datasets containing both source videos and generated faces, journalists can validate footage and identify manipulated content.\n\nCompliance & Security\nThe DeepFake Videos Dataset complies with GDPR and applicable data protection regulations. All data is stored on AWS cloud infrastructure certified to ISO 27001 and ISO 27701 standards, ensuring internationally recognized information security and privacy management.\n\nSummary\nThe DeepFake Videos Dataset is a large-scale synthetic media collection purpose-built for deepfake detection research, facial recognition, and computer vision tasks. With 5,000 video clips covering 5,000 unique individuals, demographic metadata across age, gender, and ethnicity, multiple resolutions up to 1080p, and an average of 26.6 FPS — it delivers the diversity and scale needed to train detection systems that achieve better results against real-world deepfake content. Whether used for building detection algorithms, training deep learning models, or validating recognition systems, this dataset provides a comprehensive and legally compliant resource for teams working on synthetic media detection.","terms_of_service":"https://unidata.pro/privacy-policy","contact_email":"marketplace@datarade.ai","privacy_policy_link":"https://unidata.pro/privacy-policy","assets":["ASSET_TYPE_DATA_TABLE"]},"provider_summary":{"name":"Unidata","description":"Unidata (https://unidata.pro/) specializes in data collection, delivering high-quality labeled datasets for iBeta, Robotics, Biometrics, Kids Safety, Medical Imaging, Smart City, and many other domains — as well as annotation and LLM training to power your AI and ML to the next level.\r\nWe provide end-to-end AI data solutions trusted by 200+ corporate clients worldwide. Our team of 1,100+ labelers and AI experts labels over 920,000 files daily across text, audio, video, and image data — with 3-tier quality control and full legal compliance (AWS ISO 27001/27701).\r\nOur services cover the full cycle: custom data collection via the Prolific platform, computer vision and audio annotation, 3D point cloud labeling, NLP & LLM training, geospatial annotation, and AI model testing. Data is legally sourced, precisely matched to your requirements, and delivered fast — with a dedicated PM and SLA guarantee.\r\nExplore 70+ ready-made datasets and start building smarter AI today — free estimate within 24h."},"popularity":1690},{"id":"43222997-f285-4b71-b91e-e29017b49432","summary":{"name":"World Refinery Database","subtitle":"This dataset provides an in-depth, historical and forward-looking view of the entire downstream value chain.","setting":{"visibility":"PUBLIC"},"created_at":1702035409330,"updated_at":1763116104457,"categories":["ENERGY","FINANCIAL"],"listingType":"PERSONALIZED","provider_id":"eff4b18d-fe1b-4884-9591-eab9d16d2e2a"},"detail":{"description":"**Overview**\n\nS&P Global Energy World Refinery Database provides an in-depth, historical and forward-looking view of the entire downstream value chain, from crude inputs to detailed product outputs.\n\nYou can utilize this dataset to analyze specific refineries, or get a regional, national or global perspective with up-to-date and historic information for every refinery in the world. Quickly retrieve, download and integrate data into your own models, dashboards, business intelligence and visualization tools to develop confident trading, investment, and risk management strategies.\n\nThis dataset includes:\n\n- Capacity data by unit and configuration\n\n- Detailed ownership status and history\n\n- Refinery-by-refinery crude slate, yields and outages data\n\n- A complete view of the entire downstream value chain — from crude inputs to - detailed product outputs at the refinery level\n\n**Use cases**\n- Fundamental Analysis - Analyze specific refineries, or get a regional, national or global perspective with up-to-date and historic information and forward-looking ownership and capacity data for every refinery in the world. Gain a comprehensive view of the full process flow with automated access to outage, capacity, runs and yields data for crude and refined products\n- Market Analysis - Quickly assess the competition and get a single view by company, region or facility\n- Risk Analysis - Rapidly retrieve, download and integrate data into your own models, dashboards, business intelligence and visualization tools to develop confident trading, investment, and risk management strategies.\n\n**Product details**\n\nSample Tables:\nA ) CAPACITY\nB ) MARGINS\nC ) OUTAGES\n\nSample Fields:\nA ) CAPACITY\nYEAR\nQUARTER\nCAPACITY_ID\nMBCD\nMBSD\nMMTCD\n\nB ) MARGINS\nDATE\nMARGIN\nMODIFIED_DATE\nMARGIN_TYPE_ID\nMARGIN_TYPE_NAME\n\nC ) OUTAGES\nYEAR\nQUARTER\nDATE\nPLANNING_STATUS\nOUTAGE_ID\n\nView Descriptions:\n\n- CAPACITY - Get capacity changes\n- MARGINS - Get margin data\n- OUTAGES - Get outages by OutageId, Year, Quarter, Date, RefineryId, ProcessUnitId, OwnerId, and PlanningStatus\n- OWNERSHIP - Get ownership history by Year, Quarter, RefineryId, and OwnerId\n- RUNS - Get the annual crude runs for each refinery.\n- YIELDS - Get refinery yields. Every refinery is evaluated for annual product yields\n- REF_DATA_CAPACITY_STATUSES - Get the list of Capacity statuses and their corresponding Ids\n- REF_DATA_CITIES - Get a list of all cities and their corresponding Ids\n- REF_DATA_CONFIGURATIONS - Get refinery configurations and their corresponding Ids\n- REF_DATA_COUNTRIES - Get a collection of all countries and their corresponding Ids\n- REF_DATA_MARGIN_TYPES - Get all margin types and their corresponding Ids\n- REF_DATA_OPERATORS - Get all refinery operators\n- REF_DATA_OUTAGE_UNITS - Get all outage process units\n- REF_DATA_OWNERS - Get all refinery owners. Users can query by owner for all the data including capacity, crude run, product yield and outage. Owner groups are also provided to allow search by various categories such as OPEC, major integrated oil companies, independents, state-owned, etc\n- REF_DATA_PADDS - Get all PADDs\n- REF_DATA_PROCESS_UNITS - Get all refinery process units. Process Units with Id below 200 are applicable to Capacity, while starting at 200 and below 300 – applicable to Runs and Ids at 300 and above – to Yields\n- REF_DATA_PROCESS_UNIT_CATEGORIES - Get a single refinery process unit category\n- REF_DATA_PROCESS_UNIT_GROUPS - Get all refinery process unit groups. Process units are combined into Process Unit Groups\n- REF_DATA_REFINERIES - Get all refineries and corresponding information such as latitude/longitude, Country name, City Name and Owner, or refinery configuration and owner\n- REF_DATA_REGIONS - Get all regions\n- REF_DATA_STATES - Get all states/provinces\n- WORLDREFINERYDATA_DICTIONARY - Contains descriptions and examples of each of the columns in the above views for a better understanding of the dataset","terms_of_service":"https://www.spglobal.com/en/privacy/privacy-policy-english","documentation_link":"https://www.marketplace.spglobal.com/en/datasets/platts-world-refinery-database-(29)","privacy_policy_link":"https://www.spglobal.com/en/privacy/privacy-policy-english","geographical_coverage":"[\"Global\"]","cost":"PAID","update_frequency":{"interval":1,"unit":"DAILY"},"collection_date_start":473427184574,"data_source":"","assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"S&P Global Energy","description":"At S&P Energy, our complete view of global energy and commodities markets enables our customers to make decisions with conviction and create long-term, sustainable value. For more than 100 years, we've been a trusted connector that brings together thought leaders, market participants, governments, and regulators to co-create solutions that lead to progress. Vital to navigating Energy Transition, S&P Global Energy's coverage includes oil and gas, power, chemicals, metals, agriculture, and shipping. S&P Global Energy is a division of S&P Global (NYSE: SPGI)."},"popularity":1710},{"id":"aa7c7506-f11a-45a8-8b3d-7b1798c6ef8a","summary":{"name":"Accelerating Interoperability With Databricks Lakehouse","subtitle":"From FHIR ingestion to patient outcomes analysis","setting":{"visibility":"PUBLIC"},"created_at":1695926338069,"updated_at":1696444231844,"categories":["HEALTH"],"listingType":"STANDARD","provider_id":"ea1e69ff-0127-4c94-bf39-e841fe1d19d2","git_repo":{"git_repo_url":"https://github.com/databricks-industry-solutions/interop.git","git_repo_file_entrypoint":"README.md","git_provider":"GITHUB"}},"detail":{"description":"https://www.databricks.com/solutions/accelerators/fhir\nIn this solution accelerator, we demonstrate how we can leverage the lakehouse approach, for an in-depth analysis of patient outcomes, using EHR data. Consider a scenario that we have a collection of FHIR bundles and want to explore the effect of different factors on Covid outcomes. However, FHIR standard is primarily designed for the exchange of information and not optimized for analytics. To solve this problem, we need to flatten the the bundles (stored as nested json files) and extract resources such as patients, encounters, conditions etc. so that we can create a dataset which is ready for exploratory data analysis. We can decompose this process in 3 main steps:\n\n* **Data ingestion**\n- Simplify ingestion, from all kind of sources. As example, we'll use Databricks Labs dbignite library to ingest FHIR bundle as tables ready to be queried in SQL in one line.\n- Query and explore the data ingested\n- Optionally we can secure data access\n\n* **Exploratory Analysis/Data Curation**\n- Create cohorts\n- Create a patient level data structure (a patient dashboard) from the bundles\n- Investigate rate of hospital admissions among covid patients and explore correlations among different factors such as SDOH, disease history and hospital admission\n\n* **Data Science / Advanced Analytics**\n- Create patient features\n- Create a training dataset to build a model predicting and analysing our cohort\n- Use SHAP for explaining the effect of different features on the outcome under study\n\nClick on the \"Get instant access\" button in the top right corner to clone the solution accelerator repo into your workspace. Once the repo is cloned into your workspace, please execute the **RUNME** notebook in the repo in order to create the cluster and job you can use to run the 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the world’s first lakehouse platform in the cloud, Databricks combines the best elements of data lakes and data warehouses to deliver the reliability, strong governance, and performance of data warehouses with the openness, flexibility, and machine learning support of data lakes. We are a cross-cloud platform that works across AWS, Azure, and Google.\nThis unified approach simplifies your modern data stack by eliminating the data silos that traditionally separate and complicate data engineering, analytics, BI, data science and machine learning. It’s built on open source and open standards to maximize flexibility. And, its common approach to data management, security and governance helps companies operate more efficiently and innovate faster."},"popularity":2014},{"id":"1f5fad8c-464c-4cbc-b649-ff199cada062","summary":{"name":"CDC STATE System Tobacco Legislation","subtitle":"This data package is about tobacco use prevention and control policies, E-Cigarette legislation about youth access, in..","share":{"name":"cdc_state_system_tobacco_legislation","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1682356046956,"updated_at":1698965635225,"categories":["HEALTH","SCIENCE_AND_RESEARCH"],"listingType":"STANDARD","provider_id":"f88849f8-9d77-4de2-b4c7-03d55cc3768e"},"detail":{"description":"\n**Overview**\nThis data package is about tobacco use prevention and control policies, E-Cigarette legislation about youth access, information related to the Healthy People 2020 Tobacco Use objectives, its operational definitions, baselines, and targets.\n\n**Description**\nHealthy People 2020 provides a framework for action to reduce tobacco use to the point that it is no longer a public health problem for the Nation. One of the datasets in this data package includes information related to the Healthy People 2020 Tobacco Use objectives, operational definitions, baselines, and targets. Baseline years may vary by objective. Targets represented correspond to the year 2020. Another dataset includes information related to state legislation on smoke-free indoor air in areas such as: Bars, Commercial Day Care Centers, Government Multi-Unit Housing, Government Worksites, Home-Based Day Care Centers, Hotels and Motels, Personal Vehicles, Private Multi-Unit Housing, Private Worksites, Restaurants, Bingo Halls, Casinos, Enclosed Arenas, Grocery Stores, Hospitals, Hospital Campuses, Malls, Mental Health Outpatient and Residential Facilities, Prisons, Public Transportation, Racetrack Casinos, Substance Abuse Outpatient and Residential Facilities.\n\n**Benefits**\n- information about e-cigarette usage among youth\n- detailed data on healthy people 2020 tobacco use, the purpose of which is to reduce illness, disability, and death related to tobacco use and secondhand smoke exposure.\n- geo-locations are available for all the facilities that fall in cdc smoke-free indoor policy.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [CDC STATE System Smokefree Indoor Air Summary](https://www.johnsnowlabs.com/marketplace/cdc-state-system-smokefree-indoor-air-summary)\n\t- This dataset includes Centers for Disease Control and Prevention (CDC) information related to state legislation on smokefree indoor air in various areas per state. The State System houses current and historical state-level legislative dataset on tobacco use prevention and control policies data are reported on a quarterly basis.\n- [CDC State System E-Cigarette Legislation About Youth Access](https://www.johnsnowlabs.com/marketplace/cdc-state-system-e-cigarette-legislation-about-youth-access)\n\t- This dataset includes information over State Tobacco Activities Tracking and Evaluation (STATE) System houses current and historical state-level legislative data on E-Cigarette tobacco use prevention and control policies and about youth access to it.\n- [Global Tobacco Surveillance System Health Professions Student Survey](https://www.johnsnowlabs.com/marketplace/global-tobacco-surveillance-system-health-professions-student-survey)\n\t- This dataset includes information on demographics, the prevalence of cigarette smoking and other tobacco use (smoked and smokeless), knowledge and attitudes about tobacco use, exposure to second-hand smoke, desire for smoking cessation and training received regarding patient counseling on smoking cessation techniques.\n- [Global Tobacco Surveillance System Youth Tobacco Survey](https://www.johnsnowlabs.com/marketplace/global-tobacco-surveillance-system-youth-tobacco-survey)\n\t- The Global Youth Tobacco Survey (GYTS) is a school-based survey that collects data on students aged 13–15 years using a standardized methodology for constructing the sample frame, selecting schools and classes, and processing data. The GYTS surveillance system is intended to enhance the capacity of countries to design, implement, and evaluate tobacco control and prevention programs.\n- [Healthy People 2020 Tobacco Use Objectives](https://www.johnsnowlabs.com/marketplace/healthy-people-2020-tobacco-use-objectives)\n\t- Healthy People 2020 dataset provides a framework for action to reduce tobacco use to the point that it is no longer a public health problem for the Nation. This dataset includes information related to the Healthy People 2020 Tobacco Use objectives, operational definitions, baselines, and targets. Baseline years may vary by objective. Targets represented correspond to the year 2020.\n- [YRBS State Tobacco Variables by Sex and Grade for 2013](https://www.johnsnowlabs.com/marketplace/yrbs-state-tobacco-variables-by-sex-and-grade-for-2013)\n\t- The Youth Risk Behavior Surveillance System (YRBSS) monitors six types of health-risk behaviors that contribute to the leading causes of death and disability among youth and adults. This file contains state-level results for 13 tobacco-use variables by sex and grade for 2013.\n- [Youth Risk Behavior Surveillance System YRBSS Data on Smoking](https://www.johnsnowlabs.com/marketplace/youth-risk-behavior-surveillance-system-yrbss-data-on-smoking)\n\t- The YRBSS is conducted biennially and collects data on a variety of youth risk behaviors including tobacco use. The Youth Risk Behavior Surveillance System YRBSS Data on Smoking dataset uses a two-stage cluster sample design to produce representative samples of students in high schools (grades 9-12).\n- [Youth Tobacco Survey Data YTS](https://www.johnsnowlabs.com/marketplace/youth-tobacco-survey-data-yts)\n\t- The YTS (Youth Tobacco Survey) was developed to provide states with comprehensive data on both middle school and high school students regarding tobacco use, exposure to environmental tobacco smoke, smoking cessation, school curriculum, minors' ability to purchase or otherwise obtain tobacco products, knowledge and attitudes about tobacco, and familiarity with pro-tobacco and anti-tobacco media messages.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - 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The app enables teams to define business-specific quality checks, track results through centralized dashboards, and use AI-powered suggestions to identify risks before they impact reporting, operations, downstream AI systems, or business decisions.\n\n**Use cases**\n- Continuously monitor data quality across Databricks tables.\n- Define and operationalize quality checks for completeness, accuracy, consistency, freshness, and custom business rules.\n- Generate AI-powered data quality check recommendations based on table profiling.\n- Track quality results, trends, and anomalies from a centralized dashboard.\n- Help business and technical users identify data issues before they affect reporting, analytics, or AI initiatives.\n\n**Product details**\nThe Data Quality App supports configurable quality checks and dashboarding directly within Databricks.\n\n- Datasets represented may include customer data, transaction data, finance data, supply chain data, marketing analytics data, and other business-critical tables.\n- Sample fields may include customer_id, transaction_id, order_date, revenue_amount, product_id, region, status, created_timestamp, updated_timestamp, and source_system.\n\n**Additional Insights**\nReliable data is foundational to trusted reporting, analytics, and AI outcomes. 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The dataset uses product clustering and matching techniques to identify equivalent products across retailers, enabling cross-store price comparison, price trend analysis, and competitive benchmarking. Organizations can use this data to monitor pricing dynamics, identify savings opportunities, and analyze market trends in the Italian grocery sector.\n\n**Use cases**\n\n- Compare prices for equivalent products across multiple Italian grocery retailers.\n- Track week-over-week and historical price changes for products and categories.\n- Analyze retailer competitiveness and pricing strategies.\n- Identify discounts, promotions, and price-drop opportunities.\n- Support market research, consumer insights, and retail analytics initiatives.\n\n**Product details**\nThe listing contains curated datasets supporting grocery price intelligence and product matching.\n\ncluster_retailer_counts\nDescription: Pre-computed count of retailers that have actual prices for each cluster in the latest scrape week.\n\nUse case: Find which products are available at the most retailers this week.\n\nSELECT s.PRODUCT_GROUP_NAME, rc.RETAILER_COUNT\nFROM price_comparison_app.dbo.cluster_retailer_counts rc\nJOIN price_comparison_app.dbo.cluster_search_index s\n ON rc.CLUSTER_ID = s.CLUSTER_ID\nWHERE rc.RETAILER_COUNT >= 5\nORDER BY rc.RETAILER_COUNT DESC\n\n\ncluster_search_index\nDescription:19K-row deduplicated product index. One row per cluster. LIKE queries run here for fast search.\nUse case: Find all Coca-Cola product clusters available in Italy.\n\nSELECT CLUSTER_ID, PRODUCT_GROUP_NAME, EAN, CLUSTER_SIZE, SOURCE_COUNT\nFROM price_comparison_app.dbo.cluster_search_index\nWHERE LOWER(PRODUCT_GROUP_NAME) LIKE '%coca cola%'\nORDER BY CLUSTER_SIZE DESC\nLIMIT 20\n\n\n\nmart_product_latest\nDescription: Core table — one row per product listing for the latest scrape week only, enriched with retailer, seller, price, discount, stock status, and cluster info.\n\nUse case: Compare prices for Nutella across all Italian retailers right now.\n\n\nSELECT SOURCE_NAME, SELLER_NAME, SHELF_PRICE, BASE_PRICE, DISCOUNT_PCT, STOCK_STATUS, OFFER\nFROM price_comparison_app.dbo.mart_product_latest\nWHERE CLUSTER_ID = 1114 -- Barilla Pesto\n AND COUNTRY_ID = 4\n AND SHELF_PRICE IS NOT NULL\nORDER BY SHELF_PRICE ASC\n\n\n\nmart_price_alerts\n\nDescription: Pre-computed deals table — flags products as HOT_DEAL (≥50% off), GOOD_DEAL (≥30% off), or PRICE_DROP (≤-20% WoW change) for the latest week.\nUse case: Find the top 10 hot deals on Everli this week.\n\n\nSELECT PRODUCT_GROUP_NAME, SHELF_PRICE, BASE_PRICE, DISCOUNT_PCT, ALERT_TYPE\nFROM price_comparison_app.dbo.mart_price_alerts\nWHERE COUNTRY_ID = 4\n AND SOURCE_NAME = 'Everli'\n AND ALERT_TYPE = 'HOT_DEAL'\nORDER BY DISCOUNT_PCT DESC\nLIMIT 10\n\n\n\ncluster_price_history\nDescription: All weekly price observations for every clustered product in Italy — used to track price trends over time across retailers.\n\nUse case: Track how Barilla Pesto prices changed week by week across retailers.\n\n\nSELECT\n SCRAP_WEEK,\n SOURCE_NAME,\n ROUND(MIN(SHELF_PRICE), 2) AS min_price,\n ROUND(AVG(SHELF_PRICE), 2) AS avg_price\nFROM price_comparison_app.dbo.cluster_price_history\nWHERE CLUSTER_ID = 1114 -- Barilla Pesto\nGROUP BY SCRAP_WEEK, SOURCE_NAME\nORDER BY SCRAP_WEEK, SOURCE_NAME\n\n\n\nmart_market_overview\nDescription: View that aggregates product count, avg price, avg discount %, and stock stats per retailer per week — auto-refreshes from mart_product_latest.\n\nUse case: See which retailer has the most products and best average discount this week.\n\nSELECT SOURCE_NAME, PRODUCT_COUNT, AVG_SHELF_PRICE, AVG_DISCOUNT_PCT, PCT_ON_DISCOUNT\nFROM price_comparison_app.dbo.mart_market_overview\nWHERE COUNTRY_ID = 4\n AND SCRAP_WEEK = (SELECT MAX(SCRAP_WEEK) FROM price_comparison_app.dbo.mart_market_overview)\nORDER BY PCT_ON_DISCOUNT DESC\n\n\n unique_product_clusters_italy\nDescription: Full PSGC → CLUSTER_ID mapping (369K rows). 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All data comes from a single source, ensuring **consistency and comparability** across markets.\n\nThe Sociodemographics-Bundle Basic for Belgium includes the following **variables**:\n- [population](https://www.mbi-geodata.com/en/sociodemographic-data/population/)\n- [gender](https://www.mbi-geodata.com/en/sociodemographic-data/sex-gender/)\n- [age structure](https://www.mbi-geodata.com/en/sociodemographic-data/agebands/) (15-year age bands)\n- household figures ([number of households](https://www.mbi-geodata.com/en/sociodemographic-data/households/) and [households by type](https://www.mbi-geodata.com/en/sociodemographic-data/households-by-type/))\n\nOur sociodemographic data is offered for each country on different **levels**:\n- **Postal Code Level:** This fine-grained level of detail makes the data suitable for a wide range of applications – for example, enriching customer databases, which are often organized by postal code.\n- **Administrative Level:** Covers both larger administrative areas (e.g., regions, states, provinces) as well as finer subdivisions such as districts, counties, or municipalities.\n- **Micro Level**: Subdivisions of postal code or administrative boundaries, such as census areas or street segments. This is the most granular level for local analysis.\n\n**Please note:** The dataset available for free download is a **sample dataset** covering a limited number of geographic areas. If you wish to access the full dataset (covering all areas) or if you require additional variables or geographic levels, please contact us. We will be happy to help you find a product that fits your needs.\n \n**Additional Insights**: https://www.mbi-geodata.com/en/sociodemographic-data/\n\n**Data Structure**:\nAGE_F0014: Population (Nighttime) by age: 0 - 14 years, female\nAGE_F1529: Population (Nighttime) by age: 15 - 29 years, female\nAGE_F3044: Population (Nighttime) by age: 30 - 44 years, female\nAGE_F4559: Population (Nighttime) by age: 45 - 59 years, female\nAGE_F60PL: Population (Nighttime) by age: 60 years and above, female\nAGE_M0014: Population (Nighttime) by age: 0 - 14 years, male\nAGE_M1529: Population (Nighttime) by age: 15 - 29 years, male\nAGE_M3044: Population (Nighttime) by age: 30 - 44 years, male\nAGE_M4559: Population (Nighttime) by age: 45 - 59 years, male\nAGE_M60PL: Population (Nighttime) by age: 60 years and above, male\nAGE_T0014: Population (Nighttime) by age: 0 - 14 years, total\nAGE_T1529: Population (Nighttime) by age: 15 - 29 years, male\nAGE_T3044: Population (Nighttime) by age: 30 - 44 years, total\nAGE_T4559: Population (Nighttime) by age: 45 - 59 years, total\nAGE_T60PL: Population (Nighttime) by age: 60 years and above, total\nCTRYCODE: Two digit country code by ISO 3166\nFEMALE: Population (Nighttime): females\nHH_SIZE: Average household size\nHH_T: Households: total number\nMALE: Population (Nighttime): males\nNAME: Name of administrative area / Name of the most populous administrative area / Name of the respective micro-area or the next higher level\nADMINCODE / POSTCODE / MICROCODE Administrative code / Postal code / Micro-code\nP_PRM: Population (Nighttime) year average: per mill of country\nP_T: Population (Nighttime) year average: total number","terms_of_service":"https://service-mbi-geodata.com/s/HNp7Z2tws4WDqWd","documentation_link":"https://www.mbi-geodata.com/en/mbi-supplier-manufacturer-worldwide-geodata/europe/belgium-maps/","privacy_policy_link":"https://www.mbi-geodata.com/en/data-protection/","geographical_coverage":"[\"Europe\"]","cost":"FREE","update_frequency":{"interval":1,"unit":"YEARLY"},"collection_granularity":{"interval":1,"unit":"YEARLY"},"collection_date_start":1704116017836,"collection_date_end":1735652026823,"data_source":"MBI","assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://service-mbi-geodata.com/s/QsTrWQmGyGWrSiF"},"provider_summary":{"name":"Michael Bauer International GmbH – MBI Geodata","description":"For over 15 years, Michael Bauer International (MBI) has been a leading provider of globally consistent and comparable geographic data, including more than 3 million postcode areas and other detailed datasets like sociodemographics and purchasing power. 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Once identified, the PHI is effectively masked or obfuscated, rendering the text safe for broader use while maintaining its informational integrity.\n\n\n**Key Features:**\n- The model is tuned to identify wide range of PHI elements in medical texts, ensuring comprehensive de-identification.\n- The process aligns with GDPR and other healthcare privacy regulations, aiding in legal compliance and data protection.\n- Ideal for research, analytics, and training purposes, this model enables the safe utilization of medical texts without compromising patient privacy.\n\n**Covered entities**: MEDICALRECORD, ORGANIZATION, PROFESSION, DOCTOR, USERNAME, URL, CITY, DATE, SEX, PATIENT, SSN, COUNTRY, ZIP, STREET, PHONE, HOSPITAL, EMAIL, IDNUM, AGE, E-MAIL, ID, ACCOUNT, PLATE, IPADDR\n\nThis model is a useful asset in the healthcare and research sectors, where the protection of patient privacy is paramount. It allows for the ethical and legal use of valuable medical data, promoting research and analysis while upholding the highest standards of data privacy and security.\n \n\n\n**Additional Model Information**\n- [Industry Use-Case Demo](https://demo.johnsnowlabs.com/healthcare/DEID_PHI_TEXT_MULTI/)\n- [Full model info on John Snow Labs Models Hub](https://nlp.johnsnowlabs.com/2023/06/17/clinical_deidentification_it.html)\n- **Domain:** Clinical Data Privacy\n- **Subdomain:** PHI Masking and De-identification\n \n\n **How to run this model:**\n1. Contact [Sales](mailto:Databricks-Marketplace-listing@johnsnowlabs.com) at John Snow Labs for a free 15 day trial license key or purchase our most popular annual subscription license. \n2. Import this listing.\n3. 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This meticulously compiled dataset, sourced from official statistical sources, offers a detailed insight into the population landscape of the UK at the Lower layer Super Output (LSOA) Areas of England and Wales and Super Data Zones (SDZ) for Scotland and Northern Ireland level.\n\nIf you require data at a less granular level, such as census output areas, an Uber grid or square grid, please don't hesitate to get in touch with us at *contact@geolocet.com*. We understand that different projects may have varying data needs, and we are here to tailor our data to match your specific requirements.\n\n ## **Key Features:**\n---\n* **Population Data UK:** Gain access to the latest and most accurate population statistics for the UK, meticulously organized by LSOAs and SDZs.\n\n* **Demographic Insights:** Dive deep into the UK’s demographic landscape with data on: \n* gender,\n* 5-year age bands, \n* education, \n* household size.\n\n* **Geodemographic Insights:** Unlock geodemographic data UK-wide to inform location-based decision-making, marketing strategies, and policy formulation.\n* **Seamless Data Access:** Our data is available for instant download in CSV format. No lengthy sign-up processes or NDAs required on our side – just download the data you need.\n* **Data Samples for Evaluation:** To ensure you're getting exactly what you need, we provide free downloadable samples in CSV format for data evaluation.\n* **Data accuracy:** Our datasets are created using the latest demographics data available in national statistics bureaus, guaranteeing accuracy and reliability.\n* **Best-in-Class Quality:** We guarantee best-in-class quality, backed by over two decades of experience in modeling demographics data and an outstanding quality management process.\n* **Competitive Pricing:** We maintain the lowest prices on the market, ensuring that you get the most cost-efficient demographics data acquisition process.\n---\nAdditionally, for those looking to enrich their analysis or project, we offer:\n* **[Administrative and Census Boundaries](https://geolocet.com/search?q=united+kingdom&options%5Bprefix%5D=last):** If you require geographical boundaries (polygons) for the administrative or census divisions of the UK, consider exploring our Administrative and Census Boundaries products, which seamlessly integrate with this dataset.\n\n* **[Point of Interest (PoI) Data](https://geolocet.com/pages/aggregated-points-of-interest-poi-data):** If you're interested in Points of Interest data, please don't hesitate to contact us for customized datasets to enhance your geographical analysis.\n\n* **[Data Customization](https://geolocet.com/pages/data-services-by-geolocet):** We understand that your project may have specific requirements. 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Covering population dynamics, workforce-related indicators, and other location-specific characteristics, the dataset enables HR, workforce strategy, and market intelligence teams to better understand how geography shapes talent availability and operational potential.\nThis dataset supports multiple decision-making use cases such as workforce planning, geographic expansion, and market entry assessment. By comparing demographic trends across countries and cities, organizations can identify high-potential regions for talent sourcing, evaluate diversity and workforce composition, and align business strategies with the socio-economic profile of a target location.\n\n**Methodology & Quality**\nThe dataset is built using a combination of government records, public demographic databases, and validated secondary sources. All records are normalized for consistency across geographies, ensuring comparability between countries and MSAs. Updates are applied regularly to reflect the most recent demographic shifts, while missing values and outliers are treated through structured standardization techniques. Bias reduction measures are applied by harmonizing demographic definitions across regions, minimizing distortions that arise from variations in data reporting standards.\n\n**Key Tables Include**\n•\tCountry-Level Demographics – Population and workforce-related demographic attributes aggregated at the country level.\n•\tMetropolitan Area (MSA) Demographics – Granular demographic indicators captured at the city or metropolitan level for localized workforce analysis.\n\n**Use Cases:**\n***What is the average age of working population in San Fransisco***\t\n\nLocation Characteristics data helps the users profile each location based on its demographic attributes\t\n\nWITH data AS (\n SELECT msa_name,\n demographics_taxonomy_id,\n CAST(json_extract(attr_value, '$.yearly_data') AS MAP(VARCHAR, JSON)) AS yearly\n FROM ecosystem_location_msa_demographics\n WHERE demographics_taxonomy_id = 14\n),\nyears AS (\n SELECT data.msa_name,\n data.demographics_taxonomy_id,\n year,\n json_extract_scalar(d.yearly[k], '$.median_age') AS median_age,\n ROW_NUMBER() OVER (PARTITION BY data.msa_name ORDER BY CAST(k AS INTEGER) DESC) AS rn\n FROM data\n CROSS JOIN UNNEST(MAP_KEYS(d.yearly)) AS t(year)\n)\nSELECT msa_name,\n demographics_taxonomy_id,\n year AS latest_year,\n median_age AS latest_median_age\nFROM years\nWHERE rn = 1 and msa_name like '%San Francisco%' \n\n***Identify locations that offer the best working/ expansion conditions based on factors like crime rate, commute time, meal costs, pollution, etc***\t\n\nData attributes like crime index, travel time, cost per meal, population size, pollution index, etc provide a deep understanding of an ecosystem. Build composite indices by combining various attributes to prioritize expansion or new office locations aligned with workforce and business needs.\t\n\nWITH extracted AS (\n SELECT\n msa,\n demographics_taxonomy_id,\n md.year,\n md.month,\n CASE\n WHEN demographics_taxonomy_id = 26 THEN CAST(md.value['crime_value'] AS DOUBLE)\n WHEN demographics_taxonomy_id = 30 THEN CAST(md.value['meals_2_people_price'] AS DOUBLE)\n WHEN demographics_taxonomy_id = 23 THEN CAST(md.value['pollution'] AS DOUBLE)\n END AS metric_value\n FROM ecosystem_location_msa_demographics t\n CROSS JOIN UNNEST(\n CAST(json_extract(attr_value, '$.monthly_data') AS MAP>)\n ) AS m(year, arr)\n CROSS JOIN UNNEST(arr) AS md(value)\n)\n, aggregated AS (\n SELECT\n msa,\n demographics_taxonomy_id,\n AVG(metric_value) AS avg_value\n FROM extracted\n WHERE metric_value IS NOT NULL\n GROUP BY msa, demographics_taxonomy_id\n)\n, pivoted AS (\n SELECT\n msa,\n MAX(CASE WHEN demographics_taxonomy_id = 26 THEN avg_value END) AS avg_crime,\n MAX(CASE WHEN demographics_taxonomy_id = 30 THEN avg_value END) AS avg_meal_cost,\n MAX(CASE WHEN demographics_taxonomy_id = 23 THEN avg_value END) AS avg_pollution\n FROM aggregated\n GROUP BY msa\n)\nSELECT\n msa,\n avg_crime,\n avg_meal_cost,\n avg_pollution,\n ( (avg_crime * 0.4) + (avg_meal_cost * 0.3) + (avg_pollution * 0.3) ) AS composite_index\nFROM pivoted\nORDER BY composite_index ASC\n\n***See what is the gender and ethnic diversity landscape of your country like, and if you fit.***\n\nEvaluate demographic diversity at national or metro levels to benchmark inclusivity, inform DEI initiatives, and identify regions with diverse talent pools.\t\n\nWITH yearly_data AS (\n SELECT\n msa_name,\n demographics_taxonomy_id,\n JSON_EXTRACT(attr_value, '$.yearly_data') AS yearly_data\n FROM ecosystem_location_msa_demographics\n WHERE demographics_taxonomy_id = 4\n),\nlatest_year AS (\n SELECT\n msa_name,\n demographics_taxonomy_id,\n JSON_EXTRACT_SCALAR(\n JSON_KEYS(yearly_data),\n CONCAT('$[', JSON_ARRAY_LENGTH(JSON_KEYS(yearly_data)) - 1, ']')\n ) AS last_year\n FROM yearly_data\n)\nSELECT\n y.msa_name,\n y.demographics_taxonomy_id,\n l.last_year,\n JSON_EXTRACT_SCALAR(\n JSON_EXTRACT(y.yearly_data, CONCAT('$.', l.last_year)),\n '$.ethnicity'\n ) AS ethnicity_distribution\nFROM yearly_data y\nJOIN latest_year l\n ON y.msa_name = l.msa_name\n AND y.demographics_taxonomy_id = l.demographics_taxonomy_id;\n\n***Analyze how unemployment and inflation trends in a location signal potential attrition risks.***\t\n\nCorrelate macroeconomic indicators with workforce stability to anticipate attrition and adjust retention or recruitment strategies proactively.\t\n\nWITH demographics_data AS (\n SELECT\n msa_name,\n demographics_taxonomy_id,\n jsonb_each_text(attr_value::jsonb -> 'monthly_data') AS monthy\n FROM ecosystem_country_demographics\n WHERE demographics_taxonomy_id IN (10, 20)\n),\ndetailed_demographics_data AS (\n SELECT\n msa_name,\n demographics_taxonomy_id,\n (monthy.key)::text AS period, \n (monthy.value)::numeric AS value \n FROM demographics_data\n)\nSELECT\n inflation.msa_name,\n inflation.period,\n inflation.value AS inflation_rate,\n unemployed.value AS unemployment_rate\nFROM detailed_demographics_data as inflation\nJOIN detailed_demographics_data as unemployed\n ON inflation.msa_name = unemployed.msa_name\n AND inflation.period = unemployed.period\n AND inflation.demographics_taxonomy_id = 10\n AND unemployed.demographics_taxonomy_id = 20\nORDER BY inflation.msa_name, inflation.period;\n\n***Which countries or regions are most attractive for market entry or expansion based on GDP growth trends?***\t\n\nLeverage economic growth and demographic shifts to identify high-potential markets and guide global workforce and investment planning.\t\n\nWITH parsed_data AS (\n SELECT\n country_name,\n demographics_taxonomy_id,\n jsonb_each_text(attr_value::jsonb -> 'yearly_data') AS kv\n FROM ecosystem_country_demographics\n WHERE demographics_taxonomy_id = 7 \n),\nunpacked AS (\n SELECT\n country_name,\n (kv.key)::text AS year,\n (kv.value)::numeric AS gdp_growth\n FROM parsed_data\n)\nSELECT\n country_name,\n year,\n gdp_growth\nFROM (\n SELECT\n country_name,\n year,\n gdp_growth,\n ROW_NUMBER() OVER (PARTITION BY country_name ORDER BY year DESC) AS rn\n FROM unpacked\n) t\nWHERE rn = 1\nORDER BY gdp_growth DESC\nLIMIT 10;\n\n***How does compensation vary by geography for similar roles?***\t\n\nBenchmark compensation patterns across geographies using demographic and economic data to refine pay strategies and remain competitive in local labor markets.\t\n\nWITH unpacked AS (\n SELECT\n msa_name,\n demographics_taxonomy_id,\n (jsonb_each_text(attr_value::jsonb -> 'yearly_data')).key::int AS year,\n (jsonb_each_text(attr_value::jsonb -> 'yearly_data')).value::numeric AS metric_value\n FROM workforce_data\n WHERE demographics_taxonomy_id IN (8,25)\n),\nlatest AS (\n SELECT\n msa_name,\n demographics_taxonomy_id,\n year,\n metric_value,\n ROW_NUMBER() OVER (PARTITION BY msa_name, demographics_taxonomy_id ORDER BY year DESC) AS rn\n FROM unpacked\n)\nSELECT\n msa_name,\n MAX(CASE WHEN demographics_taxonomy_id = 8 THEN metric_value END) AS gdp_per_capita,\n MAX(CASE WHEN demographics_taxonomy_id = 25 THEN metric_value END) AS cost_of_living,\n (MAX(CASE WHEN demographics_taxonomy_id = 8 THEN metric_value END) /\n NULLIF(MAX(CASE WHEN demographics_taxonomy_id = 25 THEN metric_value END), 0)) AS compensation_proxy\nFROM latest\nWHERE rn = 1\nGROUP BY msa_name\nORDER BY compensation_proxy DESC;","terms_of_service":"https://draup.com/privacy-policy#:~:text=Unless%20provided%20for%20in%20this,that%20work%20on%20our%20behalf","documentation_link":"","privacy_policy_link":"https://draup.com/privacy-policy","geographical_coverage":"[\"Global\"]","cost":"PAID","update_frequency":{"interval":1,"unit":"WEEKLY"},"collection_granularity":{"interval":1,"unit":"DAILY"},"data_source":"Public Sources","assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"Draup, Inc.","description":"Draup is an enterprise agentic intelligence platform built for HR and Sales leaders navigating transformation. By combining unparalleled real-time global data with agentic AI, Draup enables strategic decision-making across talent and revenue functions.\n \nThrough its Talent Intelligence solution, Draup empowers HR teams to drive workforce planning, precision hiring, predictive skill architecture, and competitive benchmarking. Its Sales Intelligence solution helps go-to-market teams uncover deep account insights, identify buying triggers, and accelerate revenue growth.\n \nTrusted by over 300 global enterprises - including five of the Fortune 10 - Draup supports companies like Microsoft, PepsiCo, PayPal, and Moderna in building future-ready workforces and growth strategies."},"popularity":425},{"id":"5549691a-cbdb-4759-a7d7-9b62a73c1ec3","summary":{"name":"Customer Enrichment Core","subtitle":"An in-depth view of over 90m individuals to help you understand your customers better","setting":{"visibility":"PUBLIC"},"created_at":1747917694332,"updated_at":1782919388500,"categories":["ADVERTISING_AND_MARKETING","COMMERCE","DEMOGRAPHICS","ENERGY","FINANCIAL","HEALTH","MEDIA","RETAIL","TRAVEL_AND_TOURISM"],"listingType":"PERSONALIZED","provider_id":"b86d3de4-ce4e-408c-a0f3-6f8735bb3d4d"},"detail":{"description":"**Overview**\nAn in-depth view of over 50m UK adults: key indicators of who they are, their life stage, who they live with, what they earn, how they earn it, the financial products they hold, what they're interested in, which causes and issues are important to them, and much more.\n\nThis product, with over 100 actual and modelled variables, is designed to help you gain better insight into your customers and prospects. The Customer Enrichment dataset provides users with a set of predictive and descriptive attributes which support more informed, targeted and relevant marketing to consumers.\n\n\n**What is it?**\nCustomer Enrichment Core is an individual level dataset, containing self-declared, freely given socio-demographic data on over 90m individuals. The data is obtained from a range of sources, including: Satisfaction & Lifestyle surveys, Website Registrations, Newsletter & Service subscriptions, Offers & Competition websites and public Social Media feeds.\n\n**Use cases**\n-\tUsing key information, appended from Customer Enrichment, to create personalised messaging for direct mail & digital marketing campaigns\n-\tUsing Profiling & Predictive messaging to identify important cohorts within the customer base, and those that can be “Forgotten”\n-\tSee how the current customer base compares to the UK base, so you can identify which potential audiences you are missing and also those that your business excels in.\n-\tSegment your customers into distinct groups so that you can offer them the right products through the most appropriate channels\n\n\n**Additional Insights**\nCustomer Enrichment Core can be used alongside SmartLink to ensure full coverage. By connecting disparate data sources & understanding various permutations of each individual, you can make the best decisions possible, safe in the knowledge they apply to your full customer base.\n\nCustomer Enrichment Core, Property & Geo can all be used modularly, allowing you to understand the full picture of your customer base, considering not only their individual variance but also where they live & those around them. ","terms_of_service":"https://www.sagacitysolutions.co.uk/privacy-policy/","documentation_link":"https://www.sagacitysolutions.co.uk/services/solutions/data-quality-and-enhancement/customer-enrichment","privacy_policy_link":"https://www.sagacitysolutions.co.uk/privacy-policy/","cost":"PAID","assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"Sagacity","description":"Sagacity are the data intelligence people. We know more about individuals and businesses than any other company. Our advanced analytics and data allow you to identify and generate value quicker. Think of Sagacity as your data co-pilots for sales, marketing, ops, billing, credit and debt, with clients typically seeing 5x returns within the first few months."},"popularity":1237},{"id":"72325b19-444e-42b4-9a69-339383b2acc5","summary":{"name":"Intelligent Event Data Sample - Non-Attended Events, Seattle","subtitle":"Sample \"Non-Attended Event\" data for Seattle between Feb 2022 and March 2023.","share":{"name":"predicthq_non_attended_events_seattle_sample","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1685506990593,"updated_at":1698965635273,"categories":["ADVERTISING_AND_MARKETING","CLIMATE_AND_ENVIRONMENT","EDUCATION","COMMERCE","OTHER","RETAIL","SCIENCE_AND_RESEARCH","TRANSPORTATION_AND_LOGISTICS","TRAVEL_AND_TOURISM"],"listingType":"STANDARD","provider_id":"75082c74-8371-43f8-b6c2-8dfbf076ac3c"},"detail":{"description":"Free sample dataset containing PredictHQ's \"Non-Attended Event\" data for a 1-year historical period and a 30-day future period for the Seattle, Washington area. Includes events in the Public Holidays, School holidays, Observances, politics, Daylight Savings and Academic (University holiday and exam period) event categories.\n\n**Dataset:** Non-Attended Events Sample Dataset\n**Location:** Seattle (Washington)\n**Visibility Window:** 1-Year Historical, 30-Days Future, taken on Feb 1st 2023\n**Categories:** Public Holidays, School holidays, Observances, politics, Daylight Savings, Academic\n**Features:** Event-Level and Features API Datasets\n**Update Frequency:** Bi-Annually ","terms_of_service":"https://www.predicthq.com/legal/terms","documentation_link":"https://docs.predicthq.com/resources/events#event-fields","privacy_policy_link":"https://www.predicthq.com/legal/privacy","assets":["ASSET_TYPE_DATA_TABLE"]},"provider_summary":{"name":"PredictHQ","description":"PredictHQ is the world’s first and only company that provides the missing context for the biggest external factor that impacts businesses demand – events. PredictHQ’s intelligent data of verified global events enables businesses to forecast shifts in demand from events to be able to adjust their inventory, make changes to labor, dynamically price and operate more efficiently. Think conferences, sports games, college graduations, floods, and more. PredictHQ brings all events into one place, combines it with world-first tools and intelligence to allow organizations to better predict and respond to changing customer demand created by events in an easy, reliable, and scalable way. We meet customers exactly where they are, ensuring they can access our data the way that suits them best. "},"popularity":347},{"id":"00c457fd-5378-48b9-b7ef-212e46620054","summary":{"name":"Sigma | AI, Apps & Analytics Software","subtitle":"Sigma is the AI apps and analytics platform connected to the cloud data warehouse.","setting":{"visibility":"PUBLIC"},"created_at":1781055296391,"updated_at":1781300768885,"categories":["OTHER"],"listingType":"PERSONALIZED","provider_id":"cfccf3c5-0092-49bd-9df2-4e62774aee96"},"detail":{"description":"**Sigma Computing — AI Applications & Analytics for Databricks**\n\nSigma is the AI Applications platform and cloud analytics layer built directly on Databricks. Business teams, analysts and data engineers use Sigma to build AI Applications, automate agentic workflows, and explore data at scale — all without extracting a single row from Databricks. Your Delta tables stay where they are. Unity Catalog governance, row-level security and column-level masking are inherited automatically — no duplication, no configuration drift.\n\n---\n**Product Functionality**\n\n* **AI Applications and Sigma Agents** - Build human-in-the-loop AI Applications that read from and write back to Databricks in real time. Sigma Agents orchestrate AI tools like Genie, routing structured questions to Databricks SQL and complex multi-step reasoning — all from a single interface.\n* **Spreadsheet-native analytics at Lakehouse scale** - Give every analyst a familiar spreadsheet UI that translates drag-and-drop interactions into machine-optimized SQL. Pivot, filter and join billions of rows live from Delta tables with no data extract required. Business users work independently without waiting on engineering.\n* **Writeback and agentic action** - Sigma AI Applications write rich, real-time outputs directly back to Databricks. Automate data entry, approvals, enrichments and operational updates — closing the loop between insight and action inside the warehouse boundary.\n* **Pixel-perfect reporting and embedded analytics** - Publish production-grade dashboards and pixel-perfect reports, or embed Sigma directly into your product. Every embedded experience inherits the end user's Unity Catalog permissions automatically.\n* **Python and SQL Editors** - Data teams write Python and SQL directly in Sigma workbooks alongside business-friendly visualizations. Analytics engineers can build and publish reusable data models without leaving the platform.\n\n---\n\n**Databricks-native architecture**\n\n- Sigma is designed to stay entirely inside the Databricks platform.\n- Warehouse-native execution: All AI processing — from SQL generation to AI function calls — runs on Databricks compute. Nothing leaves the Databricks platform.\n- Zero-copy query model: Sigma queries your live Delta tables directly. No BI silo, no proprietary cache, no ETL.\n- Inherited Unity Catalog governance: Row/column-level policies, dynamic data masking and access controls are enforced at query time based on user identity.\n- End-to-end lineage: Every query and AI action is traceable across your Lakehouse pipeline.\n- Private connectivity: Sigma supports AWS PrivateLink and Azure Private Link — keeping traffic entirely on your cloud provider's private backbone.\n\n---\n\n**Common use cases**\n\n- Self-service analytics: Give business teams direct, governed access to Delta tables without BI bottlenecks.\n- AI-powered operational apps: Build AI Applications that surface insights and trigger actions back into Databricks in real time.\n- Embedded analytics: Deliver customer-facing dashboards with per-user Unity Catalog security baked in.\n- Financial reporting: Model and publish pixel-perfect reports from Databricks data with no extract required.\n- Data exploration at scale: Let analysts pivot and join billions of rows live, without spinning up separate compute.\n- Agentic data workflows: Automate multi-step data tasks using Sigma Agents orchestrating Genie and Mosaic AI.\n\n---\n\n**Security and compliance**\n\n- SOC 2 Type II certified\n- ISO/IEC 27001 certified\n- GDPR and CCPA aligned\n- Per-user OAuth or service account authentication\n- Role-aware access control mapped to Databricks warehouse roles\n- Admin event audit logs (logins, permission changes, connection changes)\n- Full compliance artifact library at security.sigmacomputing.com\n\n---\n\n**Getting started**\n\n1. Sign up for a Sigma trial at sigmacomputing.com or request access through this listing.\n2. In Databricks, navigate to Partner Connect and select Sigma Computing — or add a new connection directly in Sigma using your Databricks SQL warehouse hostname and HTTP path.\n3. Configure catalog, schema and table access for your users.\n4. Start building workbooks, AI Applications or embedded analytics against your live Delta tables.\n","terms_of_service":"https://community.sigmacomputing.com/tos","documentation_link":"https://help.sigmacomputing.com/","privacy_policy_link":"https://security.sigmacomputing.com/","assets":["ASSET_TYPE_GIT_REPO"],"license":""},"provider_summary":{"name":"Sigma Computing","description":"Sigma is the AI, Apps and Analytics platform connected to the cloud data warehouse.\n\nUsing Sigma, business and technical teams can build intelligent, production-ready AI apps that accelerate and automate operational workflows. Sigma provides a spreadsheet interface, SQL and Python editors, visual builders, and native AI to help teams turn live data into interactive applications, analysis, reports, and embedded experiences."},"popularity":2092},{"id":"3820f857-faf9-406f-8381-a5ef495488b3","summary":{"name":"Faros AI","subtitle":"Faros AI is an observability platform for engineering workflows that helps improve engineering productivity and dev exp","setting":{"visibility":"PUBLIC"},"created_at":1724992741803,"updated_at":1730907403068,"categories":["OTHER"],"listingType":"PERSONALIZED","provider_id":"fbd8ae83-0862-414a-b5bf-0f5ad5178132"},"detail":{"description":"**Overview**\nFaros AI is an observability platform for engineering workflows, with solutions for Engineering Productivity, DORA Metrics, Developer Experience, AI Copilot Impact, Software Quality, Initiative Tracking, Investment Strategy, Software Capitalization, Team Health & Onboarding, and more.\nWith no prerequisites to refactor or standardize data in advance, Faros AI’s out-of-the-box intelligence modules provide rapid visibility and insight into productivity, delivery, outcomes, budgets, and talent. Users can also build custom metrics, dashboards, and reports to support recurring operational cadences and impromptu business analysis. \nThe platform’s Lighthouse AI features leverage statistical analysis, machine learning, and GenAI to deliver critical insights, identify friction and root causes, and suggest team-tailored recommendations. \n\n**Key Features**\n- **Compatible with the way you work:** No need to refactor your data or processes to gain insights into productivity, quality, developer satisfaction, and the impact of new AI tooling.\n- **Holistic visibility:** Faros AI provides a complete view into engineering by connecting and normalizing data from various sources, including vendor tools, custom-built tools, pipelines, HR systems, surveys, and spreadsheets.\n- **Customizable, extensible, and AI-powered:** Faros AI offers the flexibility of an internal data warehouse plus embedded benchmarks, best practices, conversational chat, AI insights, and rich dashboards.\n- **Enterprise-ready:** Faros meets strict security, compliance, and privacy requirements and supports various deployment models to suit your needs.\n\n**Use cases**\n- **Conduct business-oriented conversations on technical topics:** Centralize engineering data on Databricks alongside financial, vendor, customer, product, and support data. Draw correlations between engineering KPIs, roadmap delivery, and business metrics.\n- **Eliminate spreadsheets:** Answer questions about costs, resources, progress, and outcomes all in one place. \n- **Get notifications on hotspots, bottlenecks, and risks:** Utilize reminders, alerts, and configurable thresholds to keep engineering healthy and to ensure SLAs and SLOs are met. \n\n**Faros data details**\nFaros datasets include data about:\n- Applications and Services\n- Builds and Artifacts\n- Calendars\n- Code Quality and Tests \n- Commits and Pull Requests \n- Deployments\n- Incidents in Production\n- Org Structure and Teams\n- Pull Requests\n- Code Repositories\n- Boards, Sprints, Epics and Tasks\n- Vulnerabilities\n- Gen AI Tools Usage\n- Custom Metrics and many more\n\n**Learn more**\nwww.faros.ai","terms_of_service":"https://www.faros.ai/terms","documentation_link":"https://docs.faros.ai","privacy_policy_link":"https://www.faros.ai/privacy","geographical_coverage":"[\"Global\"]","cost":"PAID","assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://www.faros.ai/pricing"},"provider_summary":{"name":"Faros AI","description":"Faros AI is an operational data platform that brings all engineering data in one place to give engineering leaders a single-pane view of their entire software development lifecycle.\n\nFaros AI integrates all your engineering data sources to give you holistic visibility into the entire software development lifecycle. It takes the guesswork out of planning so that you can make decisions, allocate resources and improve productivity based on actual data. Adding a new data source is as simple as flipping a switch, even if it is a homegrown system."},"popularity":1375},{"id":"ea296770-0ee0-4b74-a202-a2c0873add7c","summary":{"name":"Northern Lights","subtitle":"Relevant well data and reports from 31/5-7 CCS verification well in exploitation license EL001.","share":{"name":"equinoropendata_northernlights","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1761115032187,"updated_at":1768804663601,"categories":["ENERGY"],"listingType":"STANDARD","provider_id":"006d1a44-e5d8-4d20-8bd7-e9a311106007"},"detail":{"description":"The Northern Lights partnership releases all relevant well data and reports from 31/5-7 CCS verification well in exploitation license EL001.\n\n**Overview**\n\nThe Norwegian Full-Scale CCS project is the first industry scale project for capturing CO2 from industrial sources and storage on the Norwegian continental shelf.\n\nIn January 2019, Exploitation license EL001 for CO2 storage, located south of the Troll field was awarded to Equinor and the confirmation well 31/5-7 (Eos) was drilled between December 2019 and March 2020 with Shell and Total as partners. The aim was to confirm that EL001 is suitable for CO2 storage. The CO2 storage complex is defined by the Lower Jurassic Dunlin Group at depths between approximately 2510 to 2830m below sea level at the Eos well location. The Eos well targeted the Dunlin Gp. as the primary storage, where the sandstone-bearing Cook and Johansen Formations both serve as storage units for the injected CO2. They are capped by the Drake Formation consisting of impermeable claystones, preventing the CO2 from migrating out of the Dunlin Group vertically. The Northern Lights partnership releases all relevant well data and reports from 31/5-7 CCS verification well in exploitation license EL001. The dataset contains log data from all sections, biostratigraphy, formation pressure, fluid data and well test data among many others.","terms_of_service":"https://equinoropendata.blob.core.windows.net/disclaimers/Equinor%20open%20data%20-%20Terms%20and%20conditions.pdf","documentation_link":"","privacy_policy_link":"https://www.equinor.com/about-us/privacy-policy","geographical_coverage":"[\"Europe\"]","cost":"FREE","data_source":"Dataset from NO 31/5-7 (Eos) well","size":80.0,"assets":["ASSET_TYPE_MEDIA"],"license":"https://equinoropendata.blob.core.windows.net/disclaimers/Northern%20Lights%20data%20sharing.pdf"},"provider_summary":{"name":"Equinor ASA","description":"In Equinor, we are determined to deliver the energy the world needs today, safely and reliably. At the same time, we aim to be a leading company in the energy transition and to achieve net zero emissions in 2050. \n\nTo foster research, study, development and innovation, we are making data available to suppliers and academia."},"popularity":2093},{"id":"fa0defcf-f668-4689-aa8b-db8ca2e989cd","summary":{"name":"Geolocet | Points of Interest Data | Top German Grocery Brand Stores","subtitle":"This dataset provides a detailed overview of over 32,000 stores of top grocery brands across Germany","setting":{"visibility":"PUBLIC"},"created_at":1714377038449,"updated_at":1780387415186,"categories":["RETAIL","GEOSPATIAL"],"listingType":"PERSONALIZED","provider_id":"5d3ddc17-a7e8-4794-a760-13587e1de2c6"},"detail":{"description":"%md\n# **Germany Grocery Locations Dataset: Top 27 Supermarket Brands**\n---\n### **Premium GIS-Ready Retail Location Intelligence Data for Databricks Analytics**\n\nThis high-fidelity geospatial dataset provides a consolidated, standardized overview of **32,138** verified grocery store and supermarket locations across Germany. Updated as of **May 27, 2026**, this single-source resource eliminates the need for manual web scraping or multi-vendor licensing, offering an analysis-ready foundation for retail site selection, competitive intelligence, logistics optimization, and spatial demographics mapping.\n\nFully compatible with **Apache Spark**, **Delta Lake**, and **Databricks Unity Catalog** workflows.\n\n---\n\n## **Consolidated Brand Coverage (Top 27 German Chains)**\nBy consolidating multiple retail banners into a single geospatial table, this dataset provides maximum cost-efficiency and strategic utility over individual brand lists. Coverage includes:\n\n- **Discounter & Hypermarket Leaders:** \n [ALDI (4,329 locations)](https://geolocet.com/products/germany-aldi-grocery), \n [Lidl (3,322 locations)](https://geolocet.com/products/germany-lidl-grocery), \n [Netto (4,765 locations)](https://geolocet.com/products/germany-netto-grocery), \n [PENNY (2,163 locations)](https://geolocet.com/products/germany-penny-grocery), \n [Kaufland (821 locations)](https://geolocet.com/products/germany-kaufland-grocery), \n [Norma (1,371 locations)](https://geolocet.com/products/germany-norma-grocery)\n\n- **Full-Range Supermarkets:** \n [EDEKA (6,469 locations)](https://geolocet.com/products/germany-edeka-grocery), \n [REWE (5,014 locations)](https://geolocet.com/products/germany-rewe-grocery), \n [Combi (205 locations)](https://geolocet.com/products/germany-combi-grocery), \n [HIT (104 locations)](https://geolocet.com/products/germany-hit-grocery), \n [tegut... (299 locations)](https://geolocet.com/products/germany-tegut-grocery), \n [famila (179 locations)](https://geolocet.com/products/germany-famila-grocery), \n [FRISTO (206 locations)](https://geolocet.com/products/germany-fristo-grocery)\n\n- **Organic & Specialty Chains:** \n [Alnatura (131 locations)](https://geolocet.com/products/germany-alnatura-grocery), \n [Denns BioMarkt (317 locations)](https://geolocet.com/products/germany-denns-biomarkt-grocery), \n [Mix Markt (199 locations)](https://geolocet.com/products/germany-mix-markt-grocery)\n\n- **Wholesale & Local Formats:** \n [METRO (95 locations)](https://geolocet.com/products/germany-metro-grocery), \n [GLOBUS (85 locations)](https://geolocet.com/products/germany-globus-grocery), \n [Nahkauf (547 locations)](https://geolocet.com/products/germany-nahkauf-grocery), \n [Tante-M (74 locations)](https://geolocet.com/products/germany-tante-m-grocery), \n [K + K Klaas (199 locations)](https://geolocet.com/products/germany-kk-klaas-grocery), \n [Konsum (124 locations)](https://geolocet.com/products/germany-konsum-grocery), \n [HOL'AB! (159 locations)](https://geolocet.com/products/germany-holab-grocery), \n [NP-Markt (195 locations)](https://geolocet.com/products/germany-np-markt-grocery), \n [SPAR (289 locations)](https://geolocet.com/products/germany-spar-grocery), \n [EuroShop (287 locations)](https://geolocet.com/products/germany-euroshop-grocery), \n [MÄC-GEIZ (190 locations)](https://geolocet.com/products/germany-mac-geiz-grocery)\n\n---\n\n## **Technical Specifications & Schema Definitions**\nOptimized for zero-friction schema enforcement when reading via `spark.read.format(\"csv\")`. \n\n* **`GUID`** (`STRING`): Unique global identifier for deterministic primary key mapping.\n* **`Title`** (`STRING`): Official store name and primary brand banner classification.\n* **`Latitude`** (`DOUBLE`): Precise WGS84 latitude coordinate for point-in-polygon & spatial joins.\n* **`Longitude`** (`DOUBLE`): Precise WGS84 longitude coordinate for spatial indexing (H3 / S2).\n* **`Street_No` / `Street`** (`STRING`): Normalized building/house number and street name elements.\n* **`City` / `Postal_Code`** (`STRING`): Core postal addresses and primary municipal settlement names.\n* **`Federal_State`** (`STRING`): High-level regional classification (e.g., Bayern, Nordrhein-Westfalen).\n* **`Admin_level_1` / `_2`** (`STRING`): Local administrative area boundaries and regional district identifiers.\n* **`Gemainde`** (`STRING`): German local municipality classification.\n* **`Population`** (`INTEGER`): Contextual demographic population metric attributed to the location.\n* **`Wheelchair`** (`STRING`): Accessibility flags indicating wheelchair mobility support.\n* **`Popularity_Score`** (`INTEGER`): Relative foot-traffic/rating metric graded on a standardized 1-10 scale.\n* **`Opening_Hours`** (`STRING`): Standard daily operational schedules and structured operating text.\n* **`Phone` / `Website`** (`STRING`): Direct communication data, branch-specific landing URLs, and domain vectors.\n\n---\n\n## **Databricks Workflow & Spatial Analytics Use Cases**\n\n- **Catchment Area Modeling:** Run native Apache Spark SQL spatial functions or python-based `geopandas` routines to construct drive-time isochrones around competitors.\n- **H3 Geospatial Indexing:** Convert the precise WGS84 `Latitude` and `Longitude` fields into Uber H3 spatial hexagons (`h3_longlattocell`) to cross-reference with demographic raster files.\n- **Territory Optimization:** Combine store location points with regional administrative boundary layers to spot gaps in supply chain routing or local market penetration.\n\n---\n\n## **Data Evaluation & Custom Ingestion**\n\n### **Evaluate with a Free Sample Dataset**\nVerify the structure, column types, and coordinate precision before full implementation. A comprehensive schema sample is available for download in CSV/Parquet-friendly formats.\n\n### **Enterprise Delivery & Tailored Formatting**\nWhile natively available as an analysis-ready CSV, we accommodate enterprise-level data architecture needs. Alternative formats—including **GeoJSON, Esri Shapefiles, PostgreSQL dumps, or direct Delta sharing profiles**—can be generated. Reach out to our engineering team at **contact@geolocet.com** for integration support or custom European POI data requests.","terms_of_service":"https://geolocet.com/pages/terms-of-use","documentation_link":"","privacy_policy_link":"https://geolocet.com/pages/privacy-policy","geographical_coverage":"[\"Europe\"]","cost":"PAID","update_frequency":{"interval":1,"unit":"QUARTERLY"},"assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://geolocet.com/pages/terms-of-use"},"provider_summary":{"name":"Geolocet LTD","description":"Geolocet provides premium geospatial data for retail analysis, GIS, and market planning. 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Initial claims measure emerging unemployment and continued weeks claimed measure the number of the insured unemployed.\n\n**Product Details**\n\nIncludes the following tables:\n\n\n- **stat_employ_usa.employ_usa_unemployment_claims_weekly_dol**\n - **Update frequency:** weekly\n - **Source:** DOL\n - **Description:** The claims data contained on the ETA 539 report are used in current economic analysis of unemployment trends in the Nation, and in each state. Initial claims measure emerging unemployment and continued weeks claimed measure the number of the insured unemployed.\n\n\n\n**Data Sources**\n\n\nThe data in this product comes from **DOL**.\n\nDoL, which is short for the Department of Labor contains an agency within known as Employment & Training Administration whose purpose\nit is to contribute to the U.S. Labor Market by providing data and other resources like job training, employement, and income\nmaintenance services.\n\n\n- **Source Homepage:** [DOL ETA](https://www.dol.gov/agencies/eta)\n- **Source Data:** [Department of Labor ETA UI Data](https://oui.doleta.gov/unemploy/csv/ar539.csv)\n- **License:** [CC BY](https://creativecommons.org/2016/02/01/u-s-department-labor-adopts-cc-licensing-policy-department-wide/)\n- **Frequency:** weekly\n\n\n\n\n","terms_of_service":"https://rearc-data-public-assets.s3.amazonaws.com/Rearc_Data_DSA.pdf","documentation_link":"","privacy_policy_link":"https://rearc-data-public-assets.s3.amazonaws.com/Rearc+Privacy+Policy.pdf","embedded_notebook_file_infos":[{"id":"b9dbb250-3ead-4512-8483-0bea05ee4ffe","marketplace_file_type":"EMBEDDED_NOTEBOOK","file_parent":{"parent_id":"f018530a-254d-4fe0-adca-914cc1be1b63","file_parent_type":"LISTING"},"mime_type":"text/html","download_link":"https://marketplace-file-storage-prod.s3-fips.us-east-1.amazonaws.com/EMBEDDED_NOTEBOOK/LISTING_f018530a-254d-4fe0-adca-914cc1be1b63/b9dbb250-3ead-4512-8483-0bea05ee4ffe?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEGgaDnVzLWVhc3QtMS1maXBzIkYwRAIgbC8sWIoYaHR8y7Wq86nPb0pufBl4%2BM4PWpeN%2B5PW9VQCIG7BhAVBDCWTpxFDLeuKYm%2Ff1JLz4iccPMzter0YdhgWKqYECDAQABoMNDE0MzUxNzY3ODI2IgxNAJrHbXaZG20jNfMqgwQyGEb4TsoVOoNvJ%2BaA%2FZ7zeu8hGcHqCeNfblP%2FQo5g6q2aRNtpwtPRKMlhYcIE%2FUQaNT1Lqmyptu32VUQTpzjTTxRVmJrDPkheSCkV06XQ%2FpTMKjU8CC7X1b%2FqYf8CrBwZT7Cv4C5WPjEET38f2A79h0xV6bZgPy28zQWrSeIJ9w6AfZBxiE24%2FVc08zVA0ivCT6lsy7nuU0wcR%2FMg2C9nd1vaMnQ923VpD21CKfVVYcrNWL2b%2FQ%2BNAO%2BxPjPgjiN6UPQbvbvZxkSdhgrGtecIg9NgqGkMcvM5VLTDtdC1kdeZetfOC2obtA%2BT%2F0gy3tIDo6WM5hcQfw743%2FAU9oaGnRBl%2BwoxRRDCZA%2F4jnPdluICSI7NCr6C3HHOe4CSZJjRihczxPA%2FgQczAVjAPs6CrK9N9pIzt0vNJpGUTgg4AoxftTvGfciDREilSctPIcBk6GwB%2Bzt4wbtNCmEPU%2BFD46rc6CWVQmmLwbKOeHw70ipMVFlOc8NlXkEtGO2vu6H44MstSSc6daOVUYUavQSGZ6jGhEjsP2YGz8KDm9XQotB%2FtyVBqiZV01OKqDmmBJA%2FXjUpSJ8M%2FeJXhe5m%2BKcxzNObtoJRI7jci3Tpt08XZyXZ7%2FyIV8H30ya468X3cfkTXrm972uga3z7iXAdubyEHnQIua%2FuvtwOHILi6O5D1Me2MzCPvcnXBjqTAYDoMWBt%2B3AVFuqSs2dmDQNBnvEhXfMgTedKLD5PJHb3aIl15iBkLaNu36GYnVCp9ZU2lSYcTbY5QwNnNeTfJTNy9bKhwzru6Sg%2BslhN%2BTS0zkTn%2BeLMY4tHgPEtEZ2cp%2FCk6GwqswvnYVKHjwO8q%2B1Mx8lc08fVEfarE9dke1GGFqoddx%2Bq%2BZUyHbDlAVfrCmf0yw%3D%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20261110T000135Z&X-Amz-SignedHeaders=host&X-Amz-Expires=86400&X-Amz-Credential=ASIAWA6KKHEJORWBH4N3%2F20261110%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=c6a5b25e3fd9d31148ae72352dbec8fbf2deface8939bdcce7efb5e153159765","created_at":1678741533810,"updated_at":1678741533810,"status":"FILE_STATUS_PUBLISHED"}],"assets":["ASSET_TYPE_DATA_TABLE"]},"provider_summary":{"name":"Rearc","description":"Rearc provides a vast library of data products for Healthcare, Financial Services, and ESG use cases. 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The American Community Survey based on 5-years estimates is, according to U.S Census Bureau, the most reliable, because the samples used are the largest and the data collected cover all country areas, regardless of the population number.\n\n**Description**\nThe U.S. Census Bureau, American Community Survey (ACS) is a nationwide survey designed to provide communities a fresh look at how they are changing. The ACS replaced the decennial census long form in 2010 and thereafter by collecting long form type information throughout the decade rather than only once every 10 years. The American Community Survey produces demographic, social, housing and economic estimates in the form of 1-year, 3-year and 5-year estimates based on population thresholds. The strength of the ACS is in estimating population and housing characteristics. It produces estimates for small areas, including census tracts and population subgroups.\n\nThe main indicators categories and subcategories that can be found in the content of this data package are as follows:\n - General demographics: age, gender (sex) and race/ethnicity\n - Language: language spoken at home and English ability\n - Origins: nativity, place of birth and ancestry\n - Socio-economic demographics: general social and economic characteristics, poverty status\nEvery dataset in this data package has over 100 demographic indicators, each accompanied by the margin of error of the estimated value of the indicator.\n\nDemography has an important role in public health planning and assessment. At the same time demographics plays an important role in the field of health-related research and development. Socio-economic factors are health influencers that are assessed in order to understand better the root causes of health differences among population groups. Personal factors like age, gender and race/ethnicity are directly influencing the risk levels for various diseases. The English ability has an important in relation to health-related communication and health literacy. Place of birth is important because of the different exposures to health risk factors and of differences related to health services (for example immunizations).\n\n**Benefits**\n- Useful for public health specialists and specialized institutions to plan and assess the health services and projects. useful for researchers seeking to improve the methods, regulations, health policies and strategy. the raw version can be found on the author's website, but jsl is offering a clean, standardized and easier to use version, alone or in combination with other datasets found in jsl dataset library. the jsl version was enriched by adding for each zip code tabulation area the geographic coordinates and the land and water surfaces size. the fields descriptions along with the dataset description are again useful for the user to quickly understand the data and the dataset.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [Age and Sex by Zip Code Tabulation Area 2012-2016](https://www.johnsnowlabs.com/marketplace/age-and-sex-by-zip-code-tabulation-area-2012-2016)\n\t- This American Community Survey (ACS) data set identifies age and sex for the population by zip code tabulation areas within the United States. The data includes an estimate of the population by gender and age categories, total estimates as well as these category’s margin of error and margin of error ratio.\n- [Ancestry by Zip Code Tabulation Area 2012-2016](https://www.johnsnowlabs.com/marketplace/ancestry-by-zip-code-tabulation-area-2012-2016)\n\t- This American Community Survey (ACS) data set identifies ancestry by zip code tabulation areas within the United States. Ancestry refers to a person’s ethnic origin or descent, \"roots,\" or heritage, or the place of birth of the person or the person’s parents or ancestors before their arrival in the United States.\n- [Characteristics Language Zip Code Tabulation Area 2012-2016](https://www.johnsnowlabs.com/marketplace/characteristics-language-zip-code-tabulation-area-2012-2016)\n\t- This American Community Survey (ACS) dataset provides characteristics of people by language spoken at home by zip code tabulation areas within the United States. The characteristics of the people include age, nativity and citizenship status, poverty status, educational attainment.\n- [Characteristics by Nativity Zip Code Tabulation Area 2012-2016](https://www.johnsnowlabs.com/marketplace/characteristics-by-nativity-zip-code-tabulation-area-2012-2016)\n\t- This American Community Survey (ACS) data set identifies selected characteristics of the total and native population by zip code tabulation area within the United States. The dataset identifies population by native and foreign-born, including age, sex, language spoken at home, ability to speak English, marital status, educational attainment, income, poverty and citizenship status by zip code tabulation area.\n- [Characteristics by Poverty by Zip Code Tabulation Area 2012-2016](https://www.johnsnowlabs.com/marketplace/characteristics-by-poverty-by-zip-code-tabulation-area-2012-2016)\n\t- This American Community Survey (ACS) dataset identifies selected characteristics of people at specified levels of poverty by zip code tabulation areas within the United States, from 2012 through 2016. The selected characteristics include sex, age, race, living arrangement, educational attainment, nativity and citizenship status, disability status, and work status.\n- [Demographic Housing Estimates Zip Code Tabulation Area 2012-2016](https://www.johnsnowlabs.com/marketplace/demographic-housing-estimates-zip-code-tabulation-area-2012-2016)\n\t- This American Community Survey (ACS) dataset identifies demographic and housing estimates by zip code tabulation areas within the United States, from 2012 through 2016. The dataset identifies sex and age, race and housing units by Zip Code Tabulation Area.\n- [Detailed Race by Zip Code Tabulation Area 2012-2016](https://www.johnsnowlabs.com/marketplace/detailed-race-by-zip-code-tabulation-area-2012-2016)\n\t- This American Community Survey (ACS) dataset identifies race in detail by zip code tabulation areas within the United States, from 2012 through 2016. The races included in this dataset are White, Black or African American, American Indian and Alaskan Native, Asian, Native Hawaiian and other Pacific Islander, and other. The survey also looks at races alone, and two or more races combined.\n- [Language Spoken at Home by Zip Code Tabulation Area 2012-2016](https://www.johnsnowlabs.com/marketplace/language-spoken-at-home-by-zip-code-tabulation-area-2012-2016)\n\t- This American Community Survey (ACS) data set identifies the language spoken at home by zip code tabulation area within the United States, from 2012 through 2016. The dataset identifies languages spoken and how well English is spoken by Zip Code Tabulation Area.\n- [Place of Birth by Zip Code Tabulation Area 2012-2016](https://www.johnsnowlabs.com/marketplace/place-of-birth-by-zip-code-tabulation-area-2012-2016)\n\t- This American Community Survey (ACS) dataset specifies the place of birth for the foreign-born population by zip code tabulation areas within the United States.\n- [Poverty Status by Age and Zip Code Tabulation Area 2012-2016](https://www.johnsnowlabs.com/marketplace/poverty-status-by-age-and-zip-code-tabulation-area-2012-2016)\n\t- This American Community Survey (ACS) dataset identifies poverty status by age and zip code tabulation areas within the United States, from 2012 through 2016.\n- [Poverty Status by Sex Age and Zip Code Tabulation Area 2012-2016](https://www.johnsnowlabs.com/marketplace/poverty-status-by-sex-age-and-zip-code-tabulation-area-2012-2016)\n\t- This American Community Survey (ACS) dataset identifies poverty status by sex, age and zip code tabulation areas within the United States, from 2012 through 2016.\n- [Poverty Status by Zip Code Tabulation Area 2012-2016](https://www.johnsnowlabs.com/marketplace/poverty-status-by-zip-code-tabulation-area-2012-2016)\n\t- This American Community Survey (ACS) dataset identifies poverty status by zip code tabulation areas within the United States, from 2012 through 2016. The poverty characteristics include race, sex, age, educational attainment, employment status, work experience, and poverty level.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated\n- Data Updates\n - Data updates support replace-on-update: outdated foreign keys are deprecated, not deleted\n\n**Our data is curated and enriched by domain experts** \n\n Each dataset is manually curated by our team of doctors, pharmacists, public health & medical billing experts:\n- Field names, descriptions, and normalized values are chosen by people who actually understand their meaning\n- Healthcare & life science experts add categories, search keywords, descriptions and more to each dataset\n- Both manual and automated data enrichment supported for clinical codes, providers, drugs, and geo-locations\n- The data is always kept up to date – even when the source requires manual effort to get updates\n- Support for data subscribers is provided directly by the domain experts who curated the data sets\n- Every data source’s license is manually verified to allow for royalty-free commercial use and redistribution.\n\n**Need Help?**\nIf you have questions about our products, contact us at [info@johnsnowlabs.com](mailto:info@johnsnowlabs.com). \n 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An overview of this approach can be found at: [From General-Purpose LLMs to Verticalized Enterprise Models](https://www.bitext.com/blog/general-purpose-models-verticalized-enterprise-genai/)\n\nThe dataset has the following specifications:\n\n- Use Case: Intent Detection\n- Vertical: Mortgage and Loans\n- 37 intents assigned to 8 categories\n- 36778 question/answer pairs, with approximately 1000 per intent\n- 2141 entity/slot types\n- 10 different types of language generation tags\n\nThe categories and intents are derived from Bitext's extensive experience across various industry-specific datasets, ensuring the relevance and applicability across diverse contexts.\n\n**Dataset Token Count**\n\nThe dataset contains a total of 7.73 million tokens across 'instruction' and 'response' columns. This extensive corpus is crucial for training sophisticated LLMs that can perform a variety of functions including conversational AI, question answering, and virtual assistant tasks in the mortgage and loans domain.\n\n**Fields of the Dataset**\n\nEach entry in the dataset comprises the following fields:\n\n- system_prompt\n- tags\n- instruction: a user request from the Mortgage and Loans domain\n- category: the high-level semantic category for the intent\n- intent: the specific intent corresponding to the user instruction\n- response: an example of an expected response from the virtual assistant\n\n**Categories and Intents**\n\nThe dataset covers a wide range of mortgage and loans-related categories and intents, which are:\n\n- **CONTACT**: contact_agent, customer_service, human_agent\n- **FEES**: check_late_payment_fee, lock_interest_rate\n- **INFORMATION_REQUEST**: borrowing_limit, check_fees, check_loans, compare_loans, estimate_loan_payment\n- **LOAN_APPLICATION**: apply_for_joint_loan, apply_for_loan, consolidate_debt, reapply_for_loan\n- **LOAN_APPLICATION_PROCESS**: change_application, check_application_status, closing, submit_documentation, withdraw_application\n- **LOAN_MODIFICATIONS**: add_coborrower, change_due_date, extend_loan\n- **PAYMENT**: check_loan_terms, check_repayment_methods, make_additional_payments, pay_off_loan, refinance_loan, request_payment_arrangement, split_payment, turn_off_recurring_payments, turn_on_recurring_payments\n- **PERSONAL_INFORMATION**: change_personal_data, change_preferred_bank_account, check_credit_report, check_credit_score, check_loan_details, check_privacy_policy\n\n**Entities**\n\nThe entities covered by the dataset include:\n\n- **{{Customer Support Phone Number}}**, **{{Company Website URL}}**, common with all intents.\n- **{{Email Address}}**, **{{Name}}**, common with most intents.\n- **{{Payment Settings}}**, featured in intents like request_payment_arrangement, change_preferred_bank_account.\n- **{{Billing Preferences}}**, relevant to intents such as turn_on_recurring_payments, turn_off_recurring_payments, split_payment.\n- **{{Payment Options}}**, associated with intents like make_additional_payments,check_repayment_methods.\n- **{{Account Settings}}**, important for intents including change_personal_data, submit_documentation.\n- **{{Application ID}}**, typically present in intents such as change_application, withdraw_application.\n- **{{Loan Account Number}}**, featured in intents like extend_loan, add_coborrower.\n- **{{Loan Details}}**, relevant to intents such as extend_loan, check_loan_details.\n- **{{Application Status}}**, associated with intents like change_application, check_application_status.\n- **{{Interest Rates}}**, important for intents including compare_loans, check_loan_terms.\n\nThis comprehensive list of entities ensures that the dataset is well-equipped to train models that are highly adept at understanding and processing a wide range of mortgage and loans-related queries and tasks.\n\n**Language Generation Tags**\n\nThe dataset includes tags indicative of various language variations and styles adapted for Mortgage and Loans, enhancing the robustness and versatility of models trained on this data. These tags categorize the utterances into different registers such as colloquial, formal, or containing specific mortgage and loans jargon, ensuring that the trained models can understand and generate a range of conversational styles appropriate for different customer interactions in the mortgage and loans sector.\n\n**Language Generation Tags**\n\nThe dataset includes tags that reflect various language variations and styles, crucial for creating adaptable and responsive conversational AI models within the mortgage and loans sector. These tags help in understanding and generating appropriate responses based on the linguistic context and user interaction style.\n\n**Tags for Lexical variation**\n\n- **M - Morphological variation**: Adjusts for inflectional and derivational forms.\n - Example: \"is my account active\", \"is my account activated\"\n- **L - Semantic variations**: Handles synonyms, use of hyphens, and compounding.\n - Example: “what's my balance date\", “what's my billing date”\n\n**Tags for Syntactic structure variation**\n\n- **B - Basic syntactic structure**: Simple, direct commands or statements.\n - Example: \"activate my card\", \"I need to check my balance\"\n- **I - Interrogative structure**: Structuring sentences in the form of questions.\n - Example: “can you show my balance?”, “how do I transfer money?”\n- **C - Coordinated syntactic structure**: Complex sentences coordinating multiple ideas or tasks.\n - Example: “I want to transfer money and check my balance, what should I do?”\n- **N - Negation**: Expressing denial or contradiction.\n - Example: \"I do not wish to proceed with this transaction, how can I stop it?\"\n\n**Tags for language register variations**\n\n- **P - Politeness variation**: Polite forms often used in customer service.\n - Example: “could you please help me check my account balance?”\n- **Q - Colloquial variation**: Informal language that might be used in casual customer interactions.\n - Example: \"can u tell me my balance?\"\n- **W - Offensive language**: Handling potentially offensive language which might occasionally appear in frustrated customer interactions.\n - Example: “I’m upset with these charges, this is ridiculous!”\n\n**Tags for stylistic variations**\n\n- **K - Keyword mode**: Responses focused on keywords.\n - Example: \"balance check\", \"account status\"\n- **E - Use of abbreviations**: Common abbreviations.\n - Example: “acct for account”, “trans for transaction”\n- **Z - Errors and Typos**: Includes common misspellings or typographical errors found in customer inputs.\n - Example: “how can I chek my balance”\n\n**Other tags not in use in this Dataset**\n\n- **D - Indirect speech**: Expressing commands or requests indirectly.\n - Example: “I was wondering if you could show me my last transaction.”\n- **G - Regional variations**: Adjustments for regional language differences.\n - Example: American vs British English: \"checking account\" vs \"current account\"\n- **R - Respect structures - Language-dependent variations**: Formality levels appropriate in different languages.\n - Example: Using “vous” in French for formal addressing instead of “tu.”\n- **Y - Code switching**: Switching between languages or dialects within the same conversation.\n - Example: “Can you help me with my cuenta, please?”\n\nThese tags not only aid in training models for a wide range of customer interactions but also ensure that the models are culturally and linguistically sensitive, enhancing the customer experience in mortgage and loans environments.\n\n**License**\n\nThe `Bitext-mortgage-loans-llm-chatbot-training-dataset` is released under the **Community Data License Agreement (CDLA) Sharing 1.0**. This license facilitates broad sharing and collaboration while ensuring that the freedom to use, share, modify, and utilize the data remains intact for all users.\n\n**Key Aspects of CDLA-Sharing 1.0**\n\n- **Attribution and ShareAlike**: Users must attribute the dataset and continue to share derivatives under the same license.\n- **Non-Exclusivity**: The license is non-exclusive, allowing multiple users to utilize the data simultaneously.\n- **Irrevocability**: Except in cases of material non-compliance, rights under this license are irrevocable.\n- **No Warranty**: The dataset is provided without warranties regarding its accuracy, completeness, or fitness for a particular purpose.\n- **Limitation of Liability**: Both users and data providers limit their liability for damages arising from the use of the dataset.\n\n**Usage Under CDLA-Sharing 1.0**\n\nBy using the `Bitext-mortgage-loans-llm-chatbot-training-dataset`, you agree to adhere to the terms set forth in the CDLA-Sharing 1.0. 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An overview of this approach can be found at: [From General-Purpose LLMs to Verticalized Enterprise Models](https://www.bitext.com/blog/general-purpose-models-verticalized-enterprise-genai/)\n\n**Intended Use**\n\n- **Recommended applications**: This model is designed to be used as the first step in Bitext’s two-step approach to LLM fine-tuning for the creation of chatbots, virtual assistants and copilots for the Telco domain, providing customers with fast and accurate answers about their needs.\n- **Out-of-scope**: This model is not suited for non-telco related questions and should not be used for providing health, legal, or critical safety advice.\n\n**Model Architecture**\n\nThis model utilizes the `MistralForCausalLM` architecture with a `LlamaTokenizer`, ensuring it retains the foundational capabilities of the base model while being specifically enhanced for telco-related interactions.\n\n**Training Data**\n\nThe model was fine-tuned on the [Bitext Telco Dataset](https://huggingface.co/datasets/bitext/Bitext-telco-llm-chatbot-training-dataset) comprising various telco-related intents, including: set_usage_limits, activate_phone, check_mobile_payments, check_signal_coverage, invoices, and more. Totaling 26 intents, and each intent is represented by approximately 1000 examples.\n\nThis comprehensive training helps the model address a broad spectrum of telco-related questions effectively. The dataset follows the same structured approach as our dataset published on Hugging Face as [bitext/Bitext-customer-support-llm-chatbot-training-dataset](https://huggingface.co/datasets/bitext/Bitext-customer-support-llm-chatbot-training-dataset), but with a focus on telco.\n\n**Training Procedure**\n\n**Hyperparameters**\n\n- **Optimizer**: AdamW\n- **Learning Rate**: 0.0002 with a cosine learning rate scheduler\n- **Epochs**: 3\n- **Batch Size**: 4\n- **Gradient Accumulation Steps**: 4\n- **Maximum Sequence Length**: 8192 tokens\n\n**Environment**\n\n- **Transformers Version**: 4.43.4\n- **Framework**: PyTorch 2.3.1+cu121\n- **Tokenizers**: Tokenizers 0.19.1\n\n**Limitations and Bias**\n\n- The model is trained for telco-specific contexts but may underperform in unrelated areas.\n- Potential biases in the training data could affect the neutrality of the responses; users are encouraged to evaluate responses critically.\n\n**Ethical Considerations**\n\nIt is important to use this technology thoughtfully, ensuring it does not substitute for human judgment where necessary, especially in sensitive situations.\n\n**Acknowledgments**\n\nThis model was developed and trained by Bitext using proprietary data and technology.\n\n**License**\n\nThis model, \"Mistral-7B-Telco\", is licensed under the Apache License 2.0 by Bitext Innovations International, Inc. This open-source license allows for free use, modification, and distribution of the model but requires that proper credit be given to Bitext.\n\n**Key Points of the Apache 2.0 License**\n\n- **Permissibility**: Users are allowed to use, modify, and distribute this software freely.\n- **Attribution**: You must provide proper credit to Bitext Innovations International, Inc. when using this model, in accordance with the original copyright notices and the license.\n- **Patent Grant**: The license includes a grant of patent rights from the contributors of the model.\n- **No Warranty**: The model is provided \"as is\" without warranties of any kind.\n\nYou may view the full license text at [Apache License 2.0](http://www.apache.org/licenses/LICENSE-2.0).\n\nThis licensing ensures the model can be used widely and freely while respecting the intellectual contributions of Bitext. 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You can customise the file to only include your selected currencies, your preferred based rate or add extra fields like the reverse rate.\nWe offer six base rate options: USD, GBP, JPY, AUD, CAD, EUR.\n\nHistory is available going back to 1999, hourly history going back to 2016 for both current and obsolete currencies. 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The full product is delivered under a separate agreement.\n\n**Use cases**\n\n- Market sizing and territory planning against a consistent international activity classification.\n- Enriching an internal customer or prospect list with firmographic attributes and a registry-backed identity.\n- Supplier and counterparty due diligence, where the registry source of each record matters.\n- Building a company master with a stable key that joins to our other products.\n\n**Product details**\n\n- Dataset represented: firmographic_sample, 300 companies across 68 countries, 46 columns.\n- Sample fields include CompanyName, Address1, PostCode, City, Country, Phone, Email, Website, NationalID, CompanyRegistrySource, InternationalCode, EmployeesTotal, SalesVolumeDollars and SalesVolumeReliabilityCode.\n- Every column is typed as STRING on purpose: it keeps the leading zero on postal codes and stops an ambiguous date from being reinterpreted on read.\n- A data dictionary is included as column comments inside the schema, where your query tool will show it.\n- This product joins our other listings on RecordID.\n\nFor more details, refer to the embedded notebook.\n\n**Additional Insights**\n\nRead before measuring anything on this sample. The companies in it were selected because they appear across several of our products, which is what lets the sample tables join to each other. That same choice biases the sample towards larger, better documented companies, so fill rates measured here run well above the product: in this sample Website is 32.0% against 6.1% in the full reference sample, and Phone is 49.7% against 27.0%. Ask us for figures on the population you care about and we will send them with their denominator.\n\nCompany data drifts continuously. Counts are measured on a stated date and change month to month.\n\nThis sample has been reduced before publication: one-employee companies are excluded, because their registered address is frequently a private home, and email addresses matching a personal-name pattern have been blanked. The full product contains both.\n\nUpdate cadence: this listing publishes a static sample extracted on 28 July 2026, refreshed when the listing is refreshed. 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It consists of Healthcare Claim for Adjustment, Status Category and Status Codes as well as Healthcare Insurance over Business Process Application Error, Payment Type and Report Type Codes. It also comprises of data about Healthcare Provider Characteristics, Remittance Advice Remark, Services Decision Reason, Inpatient Revenue Crosswalk Codes.\n\n**Description**\nThis data package contains the Centers for Medicare & Medicaid Services (CMS) maintain Place of Service Codes Set used throughout the healthcare industry. These standard codes require all health plans and providers to use standard code sets to populate data elements in each transaction. This standard names the place of service code set currently maintained by CMS as the code set to be used for describing sites of service in such claims. \n\nPlace of Service information is often needed to determine the acceptability of direct billing of Medicare, Medicaid and private insurance services provided by a given provider. Claim Adjustment Reason Codes communicate an adjustment, meaning that they must communicate why a claim or service line was paid differently than it was billed. If there is no adjustment to a claim, then there is no adjustment reason code. Claim Status Category Codes indicate the general category of the status accepted, rejected, additional information requested. Health Care Claim Status Codes convey the status of an entire claim or a specific service line. Claim status codes communicate information about the status of a claim whether it's been received, pended, or paid. The Claim Status transaction is not used as a financial transaction. This Insurance Business Process Application Error Codes list is for use by all users across the insurance and healthcare industry. It gives a summary of business process errors and their corresponding codes. This code list may also be used for responses to inquiries regarding provider participation or registration in a program or plan.\n\nHealth Care Insurance Payment Type Codes are used to identify the type and purpose of the health insurance for the payment amount transmitted. Healthcare Provider Characteristics Codes is intended to provide codified responses to questions presented to a health care provider applying to or registering with an entity and to report the outcome of such application or registration. It may also be used for responses to inquiries regarding provider participation or registration in a program or plan. Healthcare Remittance Advice Remark Codes (RARCs) are used to provide additional explanation for an adjustment already described by a Claim Adjustment Reason Code (CARC) or to convey information about remittance processing for all medical claims. Health Care Services Decision Reason Codes are used to indicate the primary reason for the certification action code assigned as part of a health care services review for all medical claims. \n\nThe objective of the Health Care Services Decision Reason Codes is to create better access to health insurance, limit fraud and abuse and reduce administrative costs in terms of health care services review. The Inpatient Revenue Code Crosswalk is used for an insurance company whether the procedure was performed in the emergency room, operating room or another department. If a revenue code is attached to a supply code, it identifies the equipment and whether the equipment was used in the hospital or taken home by a patient. If a revenue code is attached to a supply code, it identifies the equipment and whether the equipment was used in the hospital or taken home by a patient. This is important because many of the procedures done in the hospital may be done in different areas.\n\n**Benefits**\n- Useful for medical care providers, patients, policymakers and medical research groups and health planners. health care codes lists database is available on centers for medicare & medicaid services website in raw form, whereas a john snow labs (jsl) customer gets it in a clean and normalized form ready to use and create his own custom reports. easy to comprehend for the customers who are unfamiliar with the medical terminologies as all abbreviated terms are replaced with full form, unlike health care codes lists datasets which are full of abbreviations.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [Centers for Medicare and Medicaid Services Place of Service Codes Set](https://www.johnsnowlabs.com/marketplace/centers-for-medicare-and-medicaid-services-place-of-service-codes-set)\n\t- In this dataset the place of service codes and their descriptions have been cited. These codes should be used on professional claims to specify the entity where service(s) were rendered. These Place of Service Codes database have been updated from November 17, 2016.\n- [Health Care Claim Adjustment Reason Codes](https://www.johnsnowlabs.com/marketplace/health-care-claim-adjustment-reason-codes)\n\t- This dataset explains information on Claim adjustment reason codes to communicate an adjustment, meaning that this information communicates why a claim or service line was paid differently than it was billed. If there is no adjustment to a claim/line, then there is no adjustment reason code.\n- [Health Care Claim Status Category Codes](https://www.johnsnowlabs.com/marketplace/health-care-claim-status-category-codes)\n\t- This dataset explains the Claim Status Category Codes that indicate the general category of the status (accepted, rejected, additional information requested, etc.) which is further detailed in the Claim Status Codes.\n- [Health Care Claim Status Codes](https://www.johnsnowlabs.com/marketplace/health-care-claim-status-codes)\n\t- Health Care Claim Status Codes convey the status of an entire claim or a specific service line. Claim status codes communicate information about the status of a claim, i.e., whether it's been received, pended, or paid. The Claim Status transaction is not used as a financial transaction.\n- [Health Care Insurance Business Process Application Error Codes](https://www.johnsnowlabs.com/marketplace/health-care-insurance-business-process-application-error-codes)\n\t- This Insurance Business Process Application Error Codes list is for use by all users across the insurance and healthcare industry. It has information on business process application error codes and their descriptions, status of the codes whether active, to be deactivated or deactivated.\n- [Health Care Insurance Payment Type Codes](https://www.johnsnowlabs.com/marketplace/health-care-insurance-payment-type-codes)\n\t- Healthcare Insurance Payment Type Codes are transmitted in 005010X306, loop 2300, RMR02. They identify the type and purpose of the health insurance for the payment amount transmitted. 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MPPR amount may increase the amount of medically necessary therapy services the beneficiary receives before exceeding the therapy caps. 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Learn how global brands including the NFL, Nike, NHL, Ally Financial, WNBA, and NASCAR use Sports Innovation Lab's data, audiences, and strategic advisory to drive revenue by visiting sportsilab.com or following on LinkedIn, Instagram, and Twitter."},"popularity":997},{"id":"80e838bf-81ca-4628-94ff-a0a831900a50","summary":{"name":"Lexmark Optra IoT Platform","subtitle":"Harness the power of the Internet of Things (IoT) to ingest and operationalize data from connected devices and systems.","setting":{"visibility":"PUBLIC"},"created_at":1727889258560,"updated_at":1753732448957,"categories":["FINANCIAL","HEALTH","MANUFACTURING","RETAIL","TRANSPORTATION_AND_LOGISTICS","ENERGY"],"listingType":"PERSONALIZED","provider_id":"db6e4899-e445-4ad6-bdae-5d64b96b699e"},"detail":{"description":"**Overview**\nLexmark has developed the Optra IoT Platform which simplifies bringing together device and business data. The Optra IoT Platform enables AI skills across data teams and business functions leveraging the Databricks Lakehouse along with Gen AI and AI/ML capabilities.\n\n**Why Lexmark?**\nAs a manufacturer and early adopter of IoT connectivity and IoT data analytics to drive business outcomes, we know how complex IoT is. As a global technology company, we used our expertise to build a secure IoT architecture that has transformed the way we do business. Today, we use it to securely manage more than 1 million printers and devices in over 200,000 locations around the world. We analyze terabytes of device performance data per week. The Optra IoT Platform is in market, at scale and proven with our customers, the world’s largest and most complex organizations in every industry. Lexmark boasts an industry-leading 95% customer renewal rate.\n\n**Accelerate your success with the power of IoT and real-world know-how**\n With the proven Optra IoT Platform, you can harness the power of the Internet of Things (IoT) to operationalize data from your connected devices and grow your business into an intelligent enterprise. \n\n- Enable predictive service and evolve beyond reactive support\n- Optimize asset lifecycle management to improve efficiency and reduce costs\n- Unleash innovation through free-flowing data and insight\n- Drive advanced as-a-service engagements to meet customer expectations\n\n**Customer Stories**\nSee a few examples of how Lexmark has helped transform other enterprises to become a data-centric competitive powerhouse in their industry;\n- Dexis: [Generating Value in 90 Days](https://www.lexmark.com/en_us/solutions/iot-solutions/knowledge-hub/dexis-generating-value-in-90-days-with-optra-iot-from-lexmark.html)\n- Vibenomics: [In-store retail media networks ](https://www.lexmark.com/en_us/knowledge-center/success-stories/success-story-details/vibenomics.html)\n- BAF: [IoT Powered Comfort](https://www.lexmark.com/en_us/knowledge-center/success-stories/success-story-details/big-ass-fans.html)\n\n**Product details**\nThe Optra IoT Platform is unique in that pipelines are built with your metadata. Developers simply provide instructions for building ingestions and refinements. This process improves efficiency by decreasing the time to ingest and transform data, versus having to code an entire program. \n\nAn example of a typical Optra IoT Platform metadata ingestion includes the following simple steps; \n•\tSelect the connected system information\n•\tProvide the location of data (table name)\n•\tChoose the type of ingestion (incremental or full load)\n\n[Optra IoT Platform Overview Video](https://youtu.be/NN4UOh3M5mM?feature=shared)\n[Optra IoT Platform Demonstration](https://media.lexmark.com/www/video/iot/optra/vi_sols_iot_optra-accelerator-demo_en-us.mp4)\n\nOptra Platform has a purpose-built Databricks API Engine that automatically pulls together ingestion and refinement metadata and creates end-to-end data pipelines within Databricks including schedule and event notification management. The Optra Platform protects against unnecessary data pipelines from being created. 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It is necessary to mention that the values are estimated and counted, by using a random sample of fee-for-service Medicare claims.\n\n**Description**\nThis data package contains the Centers for Medicare and Medicaid Services (CMS) is offering through its Public Use Files (PUF), to the external users (researchers, analysts, and other interested professionals), data that can be used to assess longitudinally and transversally Medicare services and their associated costs. Because of the standardization of data, fields titles and of metadata, during the curation and enhancements done by John Snow Labs (JSL) data researchers the datasets based on PUF files are easy to use alone or in combination with other data products available at John Snow Labs. The release by CMS of the Public Use Files was made possible using non-identifiable claims data. Claims are filled usually by doctors or suppliers or by the patient, if doctors or suppliers don’t fill the claim.\n\n**Benefits**\n- Useful for public health specialist, specialized public and private institutions, including educational institutions. cms public use files are designed to offer to researchers and analysts a convenient initial look at data drawn from cms claims records. the data can serve not only analysis about the use of medicare-covered services but also to assess the financial impact for a single service or for multiple services compared. \tthe raw version can be found on author's website, but john snow labs (jsl) is offering a clean, standardized and ready to be used datasets. \tin jsl datasets location, gender, age and other variables data are enhanced and optimized to be ready for use for analysis, charts and along with data from other datasets. the fields descriptions along with the dataset description are again useful for user to quick understand the data and the dataset.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [BSA Durable Medical Equipment Line Items PUF](https://www.johnsnowlabs.com/marketplace/bsa-durable-medical-equipment-line-items-puf)\n\t- This dataset contains the Basic Stand Alone (BSA) Durable Medical Equipment (DME) Line Items with information from 2010 Medicare DME claims. Each record is a line item of a DME claim incurred by a 5% sample of Medicare beneficiaries (each claim is comprised of up to 13 line item services). The dataset can be useful in assessing the utilization of Durable Medical Equipment which can be useful to make estimations for resources planning and to estimate associated costs.\n- [BSA Outpatient Procedures PUF 2010](https://www.johnsnowlabs.com/marketplace/bsa-outpatient-procedures-puf-2010)\n\t- BSA Outpatient Procedures PUF 2010 has information from Medicare outpatient claims. The CMS BSA Outpatient Procedures PUF is a procedure-level file in which each record is a procedure in an outpatient claim incurred by a 5% sample of Medicare beneficiaries. Demographic and claim-related variables most commonly reported/studied in health services research are included in this file.\n- [BSA Prescription Drug Events PUF 2008](https://www.johnsnowlabs.com/marketplace/bsa-prescription-drug-events-puf-2008)\n\t- The dataset BSA Prescription Drug Events (PDE) PUF has information from 2008 Medicare prescription drug claims. The CMS 2008 BSA PDE PUF is an event-level file in which each record is a claim incurred by a 5% sample of Medicare beneficiaries. Demographic and claim-related variables provided in this PUF. The CMS 2008 PDE PUF contains thirteen variables: A primary record (i.e., event) key indexing the events and twelve analytic variables.\n- [BSA Skilled Nursing Facility Beneficiary PUF](https://www.johnsnowlabs.com/marketplace/bsa-skilled-nursing-facility-beneficiary-puf)\n\t- This dataset contains the Basic Stand Alone (BSA) Skilled Nursing Facility (SNF) Public Use File (PUF) data, based on information from Medicare SNF claims. Each record is a beneficiary who had at least one SNF claim from a random 5% sample of Medicare beneficiaries.\n- [Basic Stand Alone Carrier Line Items PUF 2010](https://www.johnsnowlabs.com/marketplace/basic-stand-alone-carrier-line-items-puf-2010)\n\t- BSA Carrier Line Item PUF has information from Medicare Carrier claims. The CMS BSA Carrier Line Items PUF is a line item level file in which each record is a line item of a Carrier claim incurred by a 5% sample of Medicare beneficiaries. The CMS BSA Carrier Line Items PUF originates from a 5% simple random sample of beneficiaries drawn (without replacement) from the 100% Beneficiary Summary File for the reference year.\n- [Basic Stand Alone Home Health Agency Beneficiary PUF](https://www.johnsnowlabs.com/marketplace/basic-stand-alone-home-health-agency-beneficiary-puf)\n\t- This dataset contains the Basic Stand Alone (BSA) Home Health Agency (HHA) Beneficiary Public Use File (PUF) data, based on information from Medicare Skilled Nursing Facility (SNF) claims. Each record is a beneficiary who had at least one HHA claim from a random 5% sample of Medicare beneficiaries.\n- [Basic Stand Alone Hospice Beneficiary PUF](https://www.johnsnowlabs.com/marketplace/basic-stand-alone-hospice-beneficiary-puf)\n\t- This dataset contains the Basic Stand Alone (BSA) Hospice Beneficiary Public Use File (PUF) data, based on information from Medicare hospice claims. Each record is a beneficiary who had at least one hospice claim from a random 5% sample of Medicare beneficiaries.\n- [Basic Stand Alone Inpatient Claims PUF 2008](https://www.johnsnowlabs.com/marketplace/basic-stand-alone-inpatient-claims-puf-2008)\n\t- The dataset the Basic Stand Alone (BSA) Inpatient Public Use Files (PUF) has information from 2008 Medicare inpatient claims. Certain demographic and claim-related variables which are most commonly reported or studied in health services research are included in the dataset. The CMS 2008 BSA Inpatient Claims PUF is a claim-level file in which each record is an inpatient claim incurred by a 5% sample of Medicare beneficiaries.\n- [Chronic Conditions Public Use File](https://www.johnsnowlabs.com/marketplace/chronic-conditions-public-use-file)\n\t- The dataset Chronic Conditions Public Use Files has information from Medicare claims. The 100% Beneficiary Summary File is created annually and contains demographic, entitlement and enrollment data for beneficiaries who were: documented as being alive for some part of the reference year of the Beneficiary Summary File, are entitled to Medicare benefits during the reference year, and enrolled in Medicare Part A and/or Part B for at least one month in the reference year.\n- [Institutional Provider And Beneficiary Summary PUF](https://www.johnsnowlabs.com/marketplace/institutional-provider-and-beneficiary-summary-puf)\n\t- This dataset contains the Basic Stand Alone (BSA) Institutional Provider and Beneficiary Summary (IPBS) Public Use File (PUF) data. Each record summarizes information for a particular institutional provider which can be a hospital, a critical care facility, a skilled nursing facility, a home health agency or hospice or another similar institution providing services to Medicare beneficiaries.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated\n- Data Updates\n - 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The data are disaggregated by the funding agency, country, and geographic region, and health focus area including health current, constant and per capita disbursements in US dollars for specific focus areas including the health sector, HIV/AIDS, tuberculosis, noncommunicable diseases, maternal and child health, and health sector program support.\n\n**Benefits**\n- This data is useful researchers seeking to have a comprehensive analysis of trends in development assistance for health and international disbursements of grants and loans from key agencies by a funding agency, country/geographic region, and health focus area. this can be used for by officials and program managers for potential interventions, setting priority areas for action and evaluating progress\n- policymakers for policy use and development\n- development agencies aid in allocating resources and budget for development.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [Development Assistance for Health 1995 to 2014](https://www.johnsnowlabs.com/marketplace/development-assistance-for-health-1995-to-2014)\n\t- This dataset provides estimates of total health spending and health spending disaggregated by source into government spending, out-of-pocket, prepaid private, and development assistance for health.\n- [Development Assistance for Health Database 1990 to 2016](https://www.johnsnowlabs.com/marketplace/development-assistance-for-health-database-1990-to-2016)\n\t- This dataset provides estimates for 1990-2016 Development Assistance for Health (DAH) which are based on project databases, financial statements, annual reports, Internal Revenue Service (IRS) 990s, and correspondence with agencies.\n- [Development Assistance for Health Faith-Based Organizations 1990-2013](https://www.johnsnowlabs.com/marketplace/development-assistance-for-health-faith-based-organizations-1990-2013)\n\t- This dataset provides the development assistance for health (DAH) provided to faith-based organizations (FBOs), as divided among FBO and non-FBO funding.\n- [Development Assistance for Health Recipient Level 1990 to 2010](https://www.johnsnowlabs.com/marketplace/development-assistance-for-health-recipient-level-1990-to-2010)\n\t- This database only contains Development Assistance for Health (DAH) allocable by country and/or region and does not contain DAH that could not be traced to a specific region or country.\n- [Global Expected Health Spending 2016 to 2040](https://www.johnsnowlabs.com/marketplace/global-expected-health-spending-2016-to-2040)\n\t- This dataset provides projected health spending estimates for 2016 to 2040 for 188 countries. The estimates cover total health spending, and health spending disaggregated by the source into government spending, out-of-pocket, prepaid private, and development assistance for health. GDP and all-sector government spending were extracted for 1980–2015 and used with retrospective health spending estimates for 1995-2015 to forecast GDP, all-sector government spending, and health spending through 2040.\n- [Global HIV AIDS Spending 2000 to 2015](https://www.johnsnowlabs.com/marketplace/global-hiv-aids-spending-2000-to-2015)\n\t- This dataset provides estimates for Human Immunodeficiency Virus/Acquired Immunodeficiency Syndrome (HIV/AIDS) spending for 188 countries for 2000 to 2015. Data is taken from the Institute for Health Metrics and Evaluation (IHME) and the Global Health Data Exchange.\n- [Global Public Health Financing 1995 to 2006](https://www.johnsnowlabs.com/marketplace/global-public-health-financing-1995-to-2006)\n\t- This dataset provides estimates on domestically financed government health expenditures in developing countries and development assistance for health (DAH) to governmental and non-governmental recipients from 1995 to 2006.\n- [Global Retrospective Health Spending 1995 to 2014](https://www.johnsnowlabs.com/marketplace/global-retrospective-health-spending-1995-to-2014)\n\t- This dataset provides retrospective national health spending estimates for 184 countries from 1995-2014. Data is taken from the Institute for Health Metrics and Evaluation (IHME) and the Global Health Data Exchange.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated\n- Data Updates\n - Data updates support replace-on-update: outdated foreign keys are deprecated, not deleted\n\n**Our 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For example, a trend of increasing rental prices and a decreasing number of available properties can signal an opportunity to invest in new commercial builds. Similarly, an increase in industrial rentals can signal the arrival or expansion of a key industry in the area, indicating an opportunity to invest in housing to support this boom. \n\nWith these accurate, localized insights into US and international real estate markets, investors can get a comprehensive view of how their existing assets are performing and where opportunities for new investments lie. They can better predict future market movements, assess asset performance, and become equipped to make more strategic investment decisions.\n\n**Use Cases**\nDiscover the ways real-time, AI-ready residential/commercial/industrial real estate web data can inform your business strategies: \n\n1. **Market Trend Analysis**: Monitor real-time trends in rental prices, vacancy rates, property availability, and other key indicators across relevant markets to stay ahead of market shifts and make informed investment decisions.\n2. **Property Valuation and Pricing Strategy: ** Utilize up-to-date market data on rental prices, purchase prices, and sales history to accurately assess property values and develop competitive pricing strategies for buying, selling, or renting properties.\n3. **Investment Opportunity Identification:** Macrotrends in rental prices, property types, property availability, and other indicators combine to paint a vivid picture of the unique supply and demand economics in different markets. Use these insights to identify opportunities in residential, commercial, and industrial investments. \n4. **Risk Assessment and Management:** Use Nimble’s real estate data to analyze the overall health, market conditions, and potential risks associated with property investments in different regions to enable more effective risk management and strategic planning. \n5. **Competitive Analysis:** Track activities from competing real estate investors and firms in real-time, including new property listings, rental listings, pricing changes, and occupancy rate fluctuations. Use this data to refine your competitive strategies and maintain a market advantage.\n6. **Urban Planning and Development:** Leverage insights on shifting prices, occupancy rates, and property developments broken down by property type to inform urban planning and development projects. Optimize land use and ensure new developments truly meet market demands. \n\n**Included Data Sources**\nOur International Real-Estate Real-Time Data Pipelines provide key data from various public web sources. You can pick and choose different sources, data delivery frequencies, and regions to focus on. Data sources include:\n\n- Maps like Google & Apple Maps\n- Long-term rental sites like Trulia, Apartments.com, & Rent.com\n- Short-term rental platforms like Airbnb & Vrbo\n- Property listing aggregators like Zillow, Redfin, & Realtor.com\n- Census Data\n- Public Databases\n- Government sites like County Assessor, GIS, & Treasury Office sites)\n- Real Estate Transaction Databases\n\n**Key Features**\n- **Real-Time Data:** Get unlimited access to real-time, continuously updated datasets on property listings, rental prices, vacancy rates, and sales trends to ensure you always have the latest market information at your fingertips.\n- **Comprehensive Coverage:** Get dynamically updated structured datasets from public online sources on residential, commercial, and industrial property listings. Includes pricing, availability, market performance, and more to gain a holistic view of different real estate markets on a local, national, and international level. \n- **Actionable Intelligence:** Get structured data that delivers clear, actionable insights tailored for the real estate industry so you can make strategic decisions about property management, investment strategies, and market analysis. \n- **Localized Insights:** Data is segmented by regions, such as countries, cities, and neighborhoods, to provide relevant insights specific to each real estate market, enhancing regional planning and investment strategies.\n- **Customizable Data Pipelines:** Our localized pipelines can be tailored to focus on certain regional markets, property types, market segments, or competitive benchmarks based on your unique business needs. \n- **Scalable Solution:** Our pipelines are capable of processing real estate data at any scale, from local markets to global analyses—ensuring reliable, robust performance no matter how large your data requirements grow.\n\n**Why Choose Nimble’s Dynamic Datasets Over Traditional Static Datasets**\nTraditional static datasets provide snapshots of information frozen in time from a predetermined set of data points and data sources. This means that insights derived from static data are often based on incomplete, outdated, and partially irrelevant data, driving misinformed business decisions. \n\nNimble’s data pipelines deliver real-time dynamic datasets from a wide range of customizable sources, enabling your organization to make decisions based on the freshest, most comprehensive, and most relevant data possible. \n\nKey benefits that make Nimble’s data pipelines superior to static datasets include:\n\n- **Better Relevance & Reduced Data Noise:** High customizability and built-in parsing ensure you only get accurate, clean, and highly relevant data for your specific use case.\n- **Massive Scope:** Our data pipelines deliver real-time data from dozens of different sources so you don’t have to manually combine different datasets. \n- **Better Flexibility:** Easily and quickly add or remove data sources, change collection frequency, tweak target data points, or scale pipelines as your business needs fluctuate. \n- **No Processing:** Our RAG-ready data requires no processing or cleaning.\n- **Instant Insights:** Our dynamic datasets automatically update in real-time to ensure you get the most up-to-date information possible. \n\n**Sample Insights: Residential Rental Data in Georgia (US)**\nExplore Nimble’s Real-Time Residential & Commercial Real Estate Data Pipelines through this sample notebook that analyzes the residential real estate rental market within the US state of Georgia. \n\nThis notebook includes data points like:\n\n- Property Type\n- City\n- Rent Price\n- Days Listed\n- Square Feet\n- Number of Beds/Number of Baths\n- Averages of Different Metrics Across Cities\n\nAnd insights such as: \n\n- Most & Least Popular Cities\n- Change in pricing stability per city over time\n- Home types and availability per city\n- Interactive Map\n- REIT Analytics\n\n…Plus more.","terms_of_service":"https://nimbleway.com/terms-of-use/","documentation_link":"","privacy_policy_link":"https://nimbleway.com/privacy-policy/","embedded_notebook_file_infos":[{"id":"d1f8d3e8-be5b-4de9-9995-690c8695d173","marketplace_file_type":"EMBEDDED_NOTEBOOK","file_parent":{"parent_id":"acc055a0-eac8-44d7-9a04-1153c34c5712","file_parent_type":"LISTING"},"mime_type":"text/html","download_link":"https://marketplace-file-storage-prod.s3-fips.us-east-1.amazonaws.com/EMBEDDED_NOTEBOOK/LISTING_acc055a0-eac8-44d7-9a04-1153c34c5712/d1f8d3e8-be5b-4de9-9995-690c8695d173?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEGgaDnVzLWVhc3QtMS1maXBzIkYwRAIgbC8sWIoYaHR8y7Wq86nPb0pufBl4%2BM4PWpeN%2B5PW9VQCIG7BhAVBDCWTpxFDLeuKYm%2Ff1JLz4iccPMzter0YdhgWKqYECDAQABoMNDE0MzUxNzY3ODI2IgxNAJrHbXaZG20jNfMqgwQyGEb4TsoVOoNvJ%2BaA%2FZ7zeu8hGcHqCeNfblP%2FQo5g6q2aRNtpwtPRKMlhYcIE%2FUQaNT1Lqmyptu32VUQTpzjTTxRVmJrDPkheSCkV06XQ%2FpTMKjU8CC7X1b%2FqYf8CrBwZT7Cv4C5WPjEET38f2A79h0xV6bZgPy28zQWrSeIJ9w6AfZBxiE24%2FVc08zVA0ivCT6lsy7nuU0wcR%2FMg2C9nd1vaMnQ923VpD21CKfVVYcrNWL2b%2FQ%2BNAO%2BxPjPgjiN6UPQbvbvZxkSdhgrGtecIg9NgqGkMcvM5VLTDtdC1kdeZetfOC2obtA%2BT%2F0gy3tIDo6WM5hcQfw743%2FAU9oaGnRBl%2BwoxRRDCZA%2F4jnPdluICSI7NCr6C3HHOe4CSZJjRihczxPA%2FgQczAVjAPs6CrK9N9pIzt0vNJpGUTgg4AoxftTvGfciDREilSctPIcBk6GwB%2Bzt4wbtNCmEPU%2BFD46rc6CWVQmmLwbKOeHw70ipMVFlOc8NlXkEtGO2vu6H44MstSSc6daOVUYUavQSGZ6jGhEjsP2YGz8KDm9XQotB%2FtyVBqiZV01OKqDmmBJA%2FXjUpSJ8M%2FeJXhe5m%2BKcxzNObtoJRI7jci3Tpt08XZyXZ7%2FyIV8H30ya468X3cfkTXrm972uga3z7iXAdubyEHnQIua%2FuvtwOHILi6O5D1Me2MzCPvcnXBjqTAYDoMWBt%2B3AVFuqSs2dmDQNBnvEhXfMgTedKLD5PJHb3aIl15iBkLaNu36GYnVCp9ZU2lSYcTbY5QwNnNeTfJTNy9bKhwzru6Sg%2BslhN%2BTS0zkTn%2BeLMY4tHgPEtEZ2cp%2FCk6GwqswvnYVKHjwO8q%2B1Mx8lc08fVEfarE9dke1GGFqoddx%2Bq%2BZUyHbDlAVfrCmf0yw%3D%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20261110T000135Z&X-Amz-SignedHeaders=host&X-Amz-Expires=86400&X-Amz-Credential=ASIAWA6KKHEJORWBH4N3%2F20261110%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=e1e063a89edaba70fe04b91a281d34e2c2d0ca57ca5c774073dd5d96e8091180","created_at":1742591544732,"updated_at":1742591544732,"display_name":"Georgia US Real Estate Rental Data Notebook","status":"FILE_STATUS_PUBLISHED"}],"geographical_coverage":"[\"Global\"]","cost":"FREE","update_frequency":{"interval":1,"unit":"DAILY"},"collection_granularity":{"interval":1,"unit":"HOURLY"},"data_source":"Public Web Data","assets":["ASSET_TYPE_DATA_TABLE"],"license":""},"provider_summary":{"name":"Nimble","description":"Nimble is building a world where businesses can easily create AI & BI applications using real-time public data. Nimble empowers businesses across industries and the Fortune 500 with seamless data access to anywhere on the public web.\n\nNimble’s novel AI browser technology uses pioneering HTML-trained LLMs to streamline data collection pipelines at any scale with AI-powered data structuring, validation, governance, and compliance mechanisms.\n\nOur mission is to make advanced AI applications accessible to any business, enhancing efficiency and fostering economic growth."},"popularity":1759},{"id":"afcd3174-63bd-4ee2-a406-a1291b5e2dad","summary":{"name":"Denmark 2024 Income Indicators by Parish – Modelled Household Income Data","subtitle":"Parish-level household income indicators for Denmark, modelled from Statistics Denmark data.","setting":{"visibility":"PUBLIC"},"created_at":1762514937936,"updated_at":1762515277492,"categories":["GEOSPATIAL","DEMOGRAPHICS","ECONOMICS","ADVERTISING_AND_MARKETING","RETAIL"],"listingType":"PERSONALIZED","provider_id":"5d3ddc17-a7e8-4794-a760-13587e1de2c6"},"detail":{"description":"Understand household income patterns across Denmark with **parish-level income indicators** derived from official data and advanced statistical modelling by [Geolocet](https://geolocet.com).\n\n### \uD83E\uDDED About the Data\nThe dataset is based on **Statistics Denmark’s official municipality-level income data**, further refined to the **parish level** by the Geolocet data science team. Using robust statistical methods and relevant socioeconomic variables, Geolocet provides a granular, accurate picture of income distribution across the country.\n\n### \uD83D\uDCBE Files Included\nThe dataset package contains two files for flexible analysis:\n\n#### 1️⃣ CSV File (`Denmark_Income_Indicators_Parish.csv`)\nA tabular dataset suitable for direct use in Databricks, Python, R, or spreadsheet applications.\n\n**Fields:**\n- `Parish_ID` — Unique parish identifier \n- `Parish_Name` — Official parish name \n- `Municipality_Name` — Municipality to which the parish belongs \n- `AVG_HInc_B` — Income band code (e.g. *Band 01*, *Band 10*) \n- `AVG_HIncBD` — Income band description (e.g. *Low income*, *High income*) \n- `Population` — Estimated parish population \n- `Data_Source` — Indicates modelled data based on Statistics Denmark \n\n#### 2️⃣ Shapefile (`Denmark_Income_Indicators_Parish.shp`)\nA polygon layer for use in GIS tools (QGIS, ArcGIS, etc.) or geospatial libraries like GeoPandas.\n\n**Attributes:**\n- All CSV fields above, plus geometry field (`geometry`) defining parish boundaries.\n \nThis dataset enables detailed **spatial analysis of wealth, inequality, and purchasing power** — ideal for applications in:\n- Market potential and retail site assessment \n- Socioeconomic segmentation \n- Location intelligence and urban planning \n- Real estate and investment research \n\n### \uD83D\uDCCA Coverage\nComplete national coverage for **all parishes in Denmark**, including Copenhagen, Gentofte, Rudersdal, and other key municipalities.\n\n### \uD83D\uDD17 Explore More\n- \uD83D\uDCD8 **Product Page:** [Denmark Income Indicators at Parish Level](https://geolocet.com/products/denmark-income-indicators-parish) \n- \uD83D\uDCF0 **Read more here:** [Income Distribution Across Danish Parishes](https://geolocet.com/blogs/news/income-distribution-across-danish-parishes)\n\n### \uD83D\uDCC8 Source\nData source: *Statistics Denmark (municipality level)*, modelled to parish level by **Geolocet** using spatial and socioeconomic predictors.\n\n---\n\nGain high-resolution insights into Denmark’s socioeconomic landscape with **Geolocet’s parish-level income dataset**, designed for seamless integration with your location intelligence, demographic, and market analysis workflows.\n\n","terms_of_service":"https://geolocet.com/pages/terms-of-use","documentation_link":"","privacy_policy_link":"https://geolocet.com/pages/privacy-policy","cost":"PAID","data_source":"","assets":["ASSET_TYPE_DATA_TABLE","ASSET_TYPE_MEDIA"],"license":"https://geolocet.com/pages/terms-of-use"},"provider_summary":{"name":"Geolocet LTD","description":"Geolocet provides premium geospatial data for retail analysis, GIS, and market planning. We equip businesses with accurate, ready-to-query data to drive spatial analysis, site selection, and customer segmentation.\n\nCore Offerings:\n\nDemographics: Socioeconomic data covering population, age, and employment across diverse geographies.\n\nPoints of Interest (PoI): Datasets spanning retail, healthcare, and transport for catchment analysis.\n\nAdministrative Boundaries: GIS-ready boundaries, including municipalities and postcodes, for territory management.\n\nBrand Locations: Chain location datasets to monitor store networks and market expansion.\n\nWealth Data: Granular economic indicators like purchasing power to identify high-potential markets.\n\nWe source from trusted providers, delivering highly customizable datasets with comprehensive coverage across Europe and worldwide."},"popularity":220},{"id":"bf5d58c8-0926-4f62-b4b5-d94b450d62d0","summary":{"name":"FDA AEMS Adverse Events Dataset","subtitle":"23M+ FDA drug and device adverse event reports, unified and analysis-ready with built-in PRR signal detection.","setting":{"visibility":"PUBLIC"},"created_at":1783427735817,"updated_at":1784588227997,"categories":["HEALTH","DEMOGRAPHICS","PUBLIC_SECTOR","SCIENCE_AND_RESEARCH"],"listingType":"PERSONALIZED","provider_id":"50cda399-2999-4ead-84e7-6433ed15c0bc"},"detail":{"description":"## Overview\n\nFDA AEMS (Adverse Event Monitoring System) unifies **23M+ U.S. FDA adverse event reports** for both **drugs and medical devices** into a single, analysis-ready schema — with built-in Proportional Reporting Ratio (PRR) signal detection so safety teams move straight from raw reports to actionable signals. Sourced from the FDA's openFDA FAERS (drug) and MAUDE (device) systems and normalized into one consistent set of tables, it removes the work of parsing openFDA JSON and stitching two separate reporting systems together.\n\n**Dataset Size:** 23M+ reports (8.8M drug + 14.9M device) | 17 analysis-ready tables | Coverage 2020–present\n\n[Start Free Trial](https://trial.dataplex-consulting.com) | [Purchase Full Access](https://checkout.dataplex-consulting.com/b/00waEQ1eX6dWh2waC8bQY06)\n\n**Pricing:** $1,295/month with 14-day free trial\n\n## What's Included\n\n- **drug_reports** — Core drug adverse event reports with demographics and outcomes (8.8M+ reports)\n- **drug_reactions** — MedDRA-coded reactions per report (25.9M+ reactions)\n- **drug_substances** — Suspect, concomitant, and interacting substances per report (25.8M+ records)\n- **drug_reaction_associations** — Drug–reaction pair statistics with PRR, 95% CI, and IS_SIGNAL flag\n- **device_reports** — Core device adverse event reports (14.9M+ reports)\n- **device_narratives** — Full-text MDR event descriptions for free-text mining and NLP (34.3M+ narratives)\n- **device_problems** — Standardized device problem codes per report (17.7M+ records)\n- Plus manufacturer rollups, safety trends, device classification, and NDC/RxNorm drug enrichment (**17 tables total**)\n\n## Use Cases\n\n### Pharmaceutical & Biotech\n\n- **Post-market surveillance:** monitor drug and device portfolios in one schema\n- **Competitive intelligence:** benchmark adverse event rates against competitors\n- **Signal detection:** precomputed PRR with confidence intervals surfaces emerging safety concerns\n\n### Healthcare & Research\n\n- **Cross-modal safety analysis:** spanning drugs and devices in a single query\n- **Demographic risk stratification:** by age, sex, and outcome severity\n- **Free-text mining:** 34M+ device narratives ready for NLP\n\n### Consulting & Analytics\n\n- **Market sizing:** quantify the safety burden for any therapeutic area or device class\n- **Due diligence:** adverse-event exposure for M&A and investment decisions\n- **Trend analysis:** pre-built monthly and quarterly aggregates\n\n## Data Quality & Updates\n\n- **Source:** FDA openFDA FAERS (drug) and MAUDE (device) systems, unified into one schema\n- **Coverage:** 2020–present\n- **Signal detection:** Proportional Reporting Ratio (PRR), 95% confidence intervals (Evans 2001), and an IS_SIGNAL flag (PRR ≥ 2, N ≥ 3); published examples filter report_count ≥ 10\n- **Coding:** MedDRA standardized reaction terms; openFDA drug enrichment (NDC, RxNorm RxCUI, UNII, pharmacologic class)\n- **Updates:** source checked daily; Databricks copy refreshed twice weekly\n- **Full lineage:** every event traces back to its source report\n\n## Getting Started\n\n### Start a Free Trial\n\nGet 14 days of access to explore FDA AEMS:\n\n[Start Free Trial](https://trial.dataplex-consulting.com)\n\nTrial includes:\n\n- Full schema access with a seeded sample (1,500 drug reports + 1,500 device reports, joinable across all child tables)\n- Pre-built signal-detection and aggregate tables\n- Databricks notebook with example queries\n\n### Get Full Access\n\nReady for production use? Get unlimited access to all 23M+ reports across all 17 tables, refreshed as FDA publishes:\n\n[Purchase Full Access](https://checkout.dataplex-consulting.com/b/00waEQ1eX6dWh2waC8bQY06)\n\nFull access includes:\n\n- All 23M+ drug and device reports across all 17 tables\n- Twice-weekly refreshes as FDA publishes\n- Precomputed PRR signal detection tables\n- Full historical lineage to source reports\n- Email support from the Dataplex data engineering team\n\n## Documentation & Support\n\n- **Full Documentation:** [docs.dataplex-consulting.com/data-catalog/fda-aems-dataset](https://docs.dataplex-consulting.com/data-catalog/fda-aems-dataset)\n- **Support:** support@dataplex-consulting.com\n\nData provided by Dataplex Consulting | [dataplex-consulting.com](https://dataplex-consulting.com)","terms_of_service":"https://www.dataplex-consulting.com/terms","documentation_link":"https://docs.dataplex-consulting.com/data-catalog/fda-aems-dataset","privacy_policy_link":"https://www.dataplex-consulting.com/privacy","embedded_notebook_file_infos":[{"id":"bbd4c034-348e-4fd5-a8e3-fbfa87100a4b","marketplace_file_type":"EMBEDDED_NOTEBOOK","file_parent":{"parent_id":"bf5d58c8-0926-4f62-b4b5-d94b450d62d0","file_parent_type":"LISTING"},"mime_type":"text/html","download_link":"https://marketplace-file-storage-prod.s3-fips.us-east-1.amazonaws.com/EMBEDDED_NOTEBOOK/LISTING_bf5d58c8-0926-4f62-b4b5-d94b450d62d0/bbd4c034-348e-4fd5-a8e3-fbfa87100a4b?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEGgaDnVzLWVhc3QtMS1maXBzIkYwRAIgbC8sWIoYaHR8y7Wq86nPb0pufBl4%2BM4PWpeN%2B5PW9VQCIG7BhAVBDCWTpxFDLeuKYm%2Ff1JLz4iccPMzter0YdhgWKqYECDAQABoMNDE0MzUxNzY3ODI2IgxNAJrHbXaZG20jNfMqgwQyGEb4TsoVOoNvJ%2BaA%2FZ7zeu8hGcHqCeNfblP%2FQo5g6q2aRNtpwtPRKMlhYcIE%2FUQaNT1Lqmyptu32VUQTpzjTTxRVmJrDPkheSCkV06XQ%2FpTMKjU8CC7X1b%2FqYf8CrBwZT7Cv4C5WPjEET38f2A79h0xV6bZgPy28zQWrSeIJ9w6AfZBxiE24%2FVc08zVA0ivCT6lsy7nuU0wcR%2FMg2C9nd1vaMnQ923VpD21CKfVVYcrNWL2b%2FQ%2BNAO%2BxPjPgjiN6UPQbvbvZxkSdhgrGtecIg9NgqGkMcvM5VLTDtdC1kdeZetfOC2obtA%2BT%2F0gy3tIDo6WM5hcQfw743%2FAU9oaGnRBl%2BwoxRRDCZA%2F4jnPdluICSI7NCr6C3HHOe4CSZJjRihczxPA%2FgQczAVjAPs6CrK9N9pIzt0vNJpGUTgg4AoxftTvGfciDREilSctPIcBk6GwB%2Bzt4wbtNCmEPU%2BFD46rc6CWVQmmLwbKOeHw70ipMVFlOc8NlXkEtGO2vu6H44MstSSc6daOVUYUavQSGZ6jGhEjsP2YGz8KDm9XQotB%2FtyVBqiZV01OKqDmmBJA%2FXjUpSJ8M%2FeJXhe5m%2BKcxzNObtoJRI7jci3Tpt08XZyXZ7%2FyIV8H30ya468X3cfkTXrm972uga3z7iXAdubyEHnQIua%2FuvtwOHILi6O5D1Me2MzCPvcnXBjqTAYDoMWBt%2B3AVFuqSs2dmDQNBnvEhXfMgTedKLD5PJHb3aIl15iBkLaNu36GYnVCp9ZU2lSYcTbY5QwNnNeTfJTNy9bKhwzru6Sg%2BslhN%2BTS0zkTn%2BeLMY4tHgPEtEZ2cp%2FCk6GwqswvnYVKHjwO8q%2B1Mx8lc08fVEfarE9dke1GGFqoddx%2Bq%2BZUyHbDlAVfrCmf0yw%3D%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20261110T000135Z&X-Amz-SignedHeaders=host&X-Amz-Expires=86400&X-Amz-Credential=ASIAWA6KKHEJORWBH4N3%2F20261110%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=b7659afc7518a1fcbd17c5636fbabbe931e7eef463572ee4c65542c1061e5c62","created_at":1784553366092,"updated_at":1784553366092,"display_name":"06_fda_aems_showcase","status":"FILE_STATUS_PUBLISHED"}],"geographical_coverage":"[\"Global\"]","update_frequency":{"interval":3,"unit":"DAILY"},"collection_date_start":1577858400000,"collection_date_end":1774933200000,"assets":["ASSET_TYPE_DATA_TABLE","ASSET_TYPE_NOTEBOOK"],"license":""},"provider_summary":{"name":"Dataplex Consulting & Data Products","description":"Dataplex Consulting & Data Products delivers turnkey, analytics-ready data products that make complex public and commercial data easy to use across modern data platforms. 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We bring deep, hands-on experience supporting both early-stage companies and large enterprises, helping teams build scalable data platforms, improve data reliability, and become more data-driven."},"popularity":180},{"id":"f7dc5f3a-7942-4ee2-be3b-3efeac3502dc","summary":{"name":"KYB Data","subtitle":"Techsalerator's KYB (Know Your Business) Data - Global Business Verification Dataset","setting":{"visibility":"PUBLIC"},"created_at":1789546316608,"updated_at":1789565495310,"categories":["OTHER","FINANCIAL"],"listingType":"PERSONALIZED","provider_id":"cef74d15-013f-4a83-b55c-815ec7a71d40"},"detail":{"description":"**Overview**\nTechsalerator's KYB (Know Your Business) Data is a global business verification dataset covering 430M+ companies and 400+ data points per entity, sourced through direct relationships with 200+ official registries worldwide. It gives financial institutions, fintechs, banks, lenders, insurers, and B2B platforms a single, unified source for verifying business entities, mapping corporate ownership structures (including UBOs), and strengthening compliance and due diligence programs — in any jurisdiction, without stitching together multiple registries or vendors.\n\n**Use cases**\nKYB & onboarding compliance — verify business entities during client and vendor onboarding to meet regulatory and internal requirements.\nAML & financial crime prevention — uncover corporate ownership structures and UBO information to support anti-money laundering programs.\nRisk & due diligence — assess counterparty risk before entering partnerships, contracts, or financial transactions with foreign or domestic entities.\nCorporate ownership investigation — map parent-child company relationships to understand true ownership and control across complex structures.\nRegulatory & sanctions screening — cross-reference business and beneficial ownership data against sanctions and watchlists.\n\n**Product details**\nDatasets represented include Business Entity Data, Parent-Child Corporate Relationships, UBO (Ultimate Beneficial Owner) Data, Registry-Sourced Business Records, and Global Company Coverage.\nSample fields include legal entity name & registration number, entity legal status & incorporation date, parent-subsidiary relationship identifiers, UBO name & ownership percentage, and registry source & jurisdiction code.\n\nFor more details, refer to the embedded notebook.\n\n- Datasets represented include table 1, table 2, and table 3.\n- Sample fields include field 1, field 2, field 3, field 4 and field 5.\n\nFor more details, refer to the embedded notebook.\n\n**Additional Insights**\nKey differentiators: global coverage across 430M+ companies, 400+ data points per entity, UBO-level ownership data, direct relationships with 200+ registries worldwide, and parent-child ownership mapping — built as one unified dataset rather than fragmented, multi-vendor registry lookups.\n\nDelivery: API access for real-time queries, bulk exports (CSV, JSON, TXT, XLS via FTP/SFTP/S3), or custom feeds scoped by registry, region, or field set. Typical setup is 24 hours, with flexible access agreements and subscription options.\n\nAccess: available via customized quote based on registries/regions needed, data fields required, volume/update frequency, and integration method. 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The models provided in this listing are\n\n- [meta_llama_3_70b](https://huggingface.co/meta-llama/Meta-Llama-3-70B)\n- [meta_llama_3_8b](https://huggingface.co/meta-llama/Meta-Llama-3-8B)\n- [meta_llama_3_70b_instruct](https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct)\n- [meta_llama_3_8b_instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct)\n\nThe models are packaged using MLflow’s transformers flavor.\n- [meta_llama_3_70b](https://huggingface.co/meta-llama/Meta-Llama-3-70B), [meta_llama_3_8b](https://huggingface.co/meta-llama/Meta-Llama-3-8B): pretrained generative text model that can be used for further fine-tuning on specific applications.\n- [meta_llama_3_70b_instruct](https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct), [meta_llama_3_8b_instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct): fine-tuned for dialogue-based use cases.\n\nLlama_3 models are licensed under the [Meta Llama 3 Community License, Copyright © Meta Platforms, Inc. All Rights Reserved](https://llama.meta.com/llama3/license/). By installing this listing, you acknowledge and agree to the license.\n\n**Use cases**\n\n[meta_llama_3_70b](https://huggingface.co/meta-llama/Meta-Llama-3-70B), [meta_llama_3_8b](https://huggingface.co/meta-llama/Meta-Llama-3-8B):\n- Fine-tuning on specific applications.\n\n[meta_llama_3_70b_instruct](https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct), [meta_llama_3_8b_instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct):\n- Generating responses to text-based instructions\n- Chatbots and conversational AI systems\n\n\n**Product details**\n\nThe pre-trained model can be used for further fine-tuning on specific applications. The fine-tuned model in this listing can be deployed directly to Databricks Model Serving for immediate use, or loaded for fine-tuning or batch inference use cases. 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The indicators contained in this data package are for the following procedures:\n-\tOutpatient ECG\n-\tInpatient routine ECG\n-\tCardiac Stress Test\n-\tCoronary Angiography\n-\tPercutaneous Transluminal Coronary Angioplasty\n-\tCardiac Echocardiogram\n-\tCarotid Echocardiogram\n-\tImplantable Defibrillator Insertion\n\n**Benefits**\n- Useful to policymakers, healthcare analysts, public health professionals, and other interested parties. the indicators related to cardiovascular procedures covered medicare can be useful to estimate and planning of resources and to evaluate medicare beneficiary population cardiovascular risk level, at hospital referral region, hospital service area, state and/or county. the fields descriptions along with the dataset description are useful for the user to quickly understand the data and the dataset.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [Ambulatory ECG Monitoring per 1000 Medicare Enrollees HRR level 2012](https://www.johnsnowlabs.com/marketplace/ambulatory-ecg-monitoring-per-1000-medicare-enrollees-hrr-level-2012)\n\t- The dataset gives information on Ambulatory Electrocardiography (ECG) monitoring rates of Medicare beneficiaries at Hospital Referral Regions (HRR) for year 2012. Hospitalization rates are the number of discharges that occurred in a definitive time period (the numerator) for a specific population (the denominator).\n- [Cardiac Echocardiogram per 1000 Medicare Enrollees HRR Level 2012](https://www.johnsnowlabs.com/marketplace/cardiac-echocardiogram-per-1000-medicare-enrollees-hrr-level-2012)\n\t- The dataset gives information on Cardiac echocardiogram rates of Medicare beneficiaries at Hospital Referral Regions (HRR) for the year 2012. Hospitalization rates are the counts for the discharges that occurred in a definitive time period (the numerator) for a specific population (the denominator).\n- [Cardiac Stress Testing per 1000 Medicare Enrollees HRR Level 2012](https://www.johnsnowlabs.com/marketplace/cardiac-stress-testing-per-1000-medicare-enrollees-hrr-level-2012)\n\t- The dataset gives information on Cardiac Stress Testing rates of Medicare beneficiaries at Hospital Referral Regions (HRR) for the year 2012. Hospitalization rates represent the counts of the number of discharges that occurred in a definitive time period (the numerator) for a specific population (the denominator).\n- [Carotid Echocardiogram per 1000 Medicare Enrollees HRR Level 2012](https://www.johnsnowlabs.com/marketplace/carotid-echocardiogram-per-1000-medicare-enrollees-hrr-level-2012)\n\t- The dataset gives information on Carotid Echocardiogram rates of Medicare beneficiaries at Hospital Referral Regions (HRR) for the year 2012. Hospitalization rates represent the counts of the number of discharges that occurred in a definitive time period (the numerator) for a specific population (the denominator).\n- [Coronary Angiography per 1000 Medicare Enrollees HRR Level 2012](https://www.johnsnowlabs.com/marketplace/coronary-angiography-per-1000-medicare-enrollees-hrr-level-2012)\n\t- The dataset gives information on Coronary Angiography rates of Medicare beneficiaries at Hospital Referral Regions (HRR) for the year 2012. Hospitalization rates represent the counts of the number of discharges that occurred in a definitive time period (the numerator) for a specific population (the denominator).\n- [Coronary Artery Stenting per 1000 Medicare Enrollees HRR Level 2012](https://www.johnsnowlabs.com/marketplace/coronary-artery-stenting-per-1000-medicare-enrollees-hrr-level-2012)\n\t- The dataset gives information on Coronary Artery Stenting rates of Medicare beneficiaries at Hospital Referral Regions (HRR) for the year 2012. Hospitalization rates represent the counts of the number of discharges that occurred in a definitive time period (the numerator) for a specific population (the denominator).\n- [Implantable Defibrillator Insertion County Levels 2008 to 2012](https://www.johnsnowlabs.com/marketplace/implantable-defibrillator-insertion-county-levels-2008-to-2012)\n\t- The dataset gives information on Implantable Defibrillator Insertion rates of Medicare beneficiaries at County levels from years 2008 to 2012. Hospitalization rates represent the counts of the number of discharges that occurred in a definitive time period (the numerator) for a specific population (the denominator).\n- [Implantable Defibrillator Insertion HRR Levels 2008 to 2012](https://www.johnsnowlabs.com/marketplace/implantable-defibrillator-insertion-hrr-levels-2008-to-2012)\n\t- The dataset gives information on Implantable Defibrillator Insertion rates of Medicare beneficiaries at Hospital Referral Regions (HRR) from years 2008 to 2012. Hospitalization rates are the number of discharges that occurred in a definitive time period (the numerator) for a specific population (the denominator).\n- [Implantable Defibrillator Insertion HSA Levels 2008 to 2012](https://www.johnsnowlabs.com/marketplace/implantable-defibrillator-insertion-hsa-levels-2008-to-2012)\n\t- The dataset gives information on Implantable Defibrillator Insertion rates of Medicare beneficiaries at Hospital Services Area (HSA) levels from years 2008 to 2012. Hospitalization rates are the number of discharges that occurred in a definitive time period (the numerator) for a specific population (the denominator).\n- [Implantable Defibrillator Insertion State Levels 2008 to 2012](https://www.johnsnowlabs.com/marketplace/implantable-defibrillator-insertion-state-levels-2008-to-2012)\n\t- The dataset gives information on Implantable Defibrillator Insertion rates of Medicare beneficiaries at State levels for the year 2008 to 2012. Hospitalization rates are the number of discharges that occurred in a definitive time period (the numerator) for a specific population (the denominator).\n- [Pacemaker Insertion per 1000 Medicare Enrollees HRR Level 2012](https://www.johnsnowlabs.com/marketplace/pacemaker-insertion-per-1000-medicare-enrollees-hrr-level-2012)\n\t- The dataset gives information on Pacemaker Insertion rates of Medicare beneficiaries at Hospital Referral Regions (HRR) for the year 2012. Hospitalization rates represent the counts of the number of discharges that occurred in a definitive time period (the numerator) for a specific population (the denominator).\n- [Percutaneous Transluminal Coronary Angioplasty HRR level 2012](https://www.johnsnowlabs.com/marketplace/percutaneous-transluminal-coronary-angioplasty-hrr-level-2012)\n\t- The dataset gives information on Percutaneous Transluminal Coronary Angioplasty rates of Medicare beneficiaries at Hospital Referral Regions (HRR) for the year 2012. Hospitalization rates are the number of discharges that occurred in a definitive time period (the numerator) for a specific population (the denominator).\n- [Routine ECG per 1000 Medicare Enrollees HRR Level 2012](https://www.johnsnowlabs.com/marketplace/routine-ecg-per-1000-medicare-enrollees-hrr-level-2012)\n\t- The dataset gives information on Routine ECG rates of Medicare beneficiaries at Hospital Referral Regions (HRR) for the year 2012. Hospitalization rates represent the counts of the number of discharges that occurred in a definitive time period (the numerator) for a specific population (the denominator).\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated\n- Data Updates\n - Data updates support replace-on-update: outdated foreign keys are deprecated, not deleted\n\n**Our data is curated and enriched by domain experts** \n\n Each dataset is manually curated by our team of doctors, pharmacists, public health & medical billing experts:\n- Field names, descriptions, and normalized values are chosen by people who actually understand their meaning\n- Healthcare & life science experts add categories, search keywords, descriptions and more to each dataset\n- Both manual and automated data enrichment supported for clinical codes, providers, drugs, and geo-locations\n- The data is always kept up to date – even when the source requires manual effort to get updates\n- Support for data subscribers is provided directly by the domain experts who curated the data sets\n- Every data source’s license is manually verified to allow for royalty-free commercial use and redistribution.\n\n**Need Help?**\nIf you have questions about our products, contact us at [info@johnsnowlabs.com](mailto:info@johnsnowlabs.com). \n ","terms_of_service":"https://www.johnsnowlabs.com/healthcare-data-library-eula/","documentation_link":"https://www.johnsnowlabs.com/marketplace/cardiovascular-conditions-data-package","contact_email":"support@johnsnowlabs.com","privacy_policy_link":"https://www.johnsnowlabs.com/privacy-policy/","embedded_notebook_file_infos":[{"id":"bbb0df05-f9e4-443d-8181-65a6aa18afb5","marketplace_file_type":"EMBEDDED_NOTEBOOK","file_parent":{"parent_id":"9271347a-cc2f-45f8-b735-60295e788255","file_parent_type":"LISTING"},"mime_type":"text/html","download_link":"https://marketplace-file-storage-prod.s3-fips.us-east-1.amazonaws.com/EMBEDDED_NOTEBOOK/LISTING_9271347a-cc2f-45f8-b735-60295e788255/bbb0df05-f9e4-443d-8181-65a6aa18afb5?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEGgaDnVzLWVhc3QtMS1maXBzIkYwRAIgbC8sWIoYaHR8y7Wq86nPb0pufBl4%2BM4PWpeN%2B5PW9VQCIG7BhAVBDCWTpxFDLeuKYm%2Ff1JLz4iccPMzter0YdhgWKqYECDAQABoMNDE0MzUxNzY3ODI2IgxNAJrHbXaZG20jNfMqgwQyGEb4TsoVOoNvJ%2BaA%2FZ7zeu8hGcHqCeNfblP%2FQo5g6q2aRNtpwtPRKMlhYcIE%2FUQaNT1Lqmyptu32VUQTpzjTTxRVmJrDPkheSCkV06XQ%2FpTMKjU8CC7X1b%2FqYf8CrBwZT7Cv4C5WPjEET38f2A79h0xV6bZgPy28zQWrSeIJ9w6AfZBxiE24%2FVc08zVA0ivCT6lsy7nuU0wcR%2FMg2C9nd1vaMnQ923VpD21CKfVVYcrNWL2b%2FQ%2BNAO%2BxPjPgjiN6UPQbvbvZxkSdhgrGtecIg9NgqGkMcvM5VLTDtdC1kdeZetfOC2obtA%2BT%2F0gy3tIDo6WM5hcQfw743%2FAU9oaGnRBl%2BwoxRRDCZA%2F4jnPdluICSI7NCr6C3HHOe4CSZJjRihczxPA%2FgQczAVjAPs6CrK9N9pIzt0vNJpGUTgg4AoxftTvGfciDREilSctPIcBk6GwB%2Bzt4wbtNCmEPU%2BFD46rc6CWVQmmLwbKOeHw70ipMVFlOc8NlXkEtGO2vu6H44MstSSc6daOVUYUavQSGZ6jGhEjsP2YGz8KDm9XQotB%2FtyVBqiZV01OKqDmmBJA%2FXjUpSJ8M%2FeJXhe5m%2BKcxzNObtoJRI7jci3Tpt08XZyXZ7%2FyIV8H30ya468X3cfkTXrm972uga3z7iXAdubyEHnQIua%2FuvtwOHILi6O5D1Me2MzCPvcnXBjqTAYDoMWBt%2B3AVFuqSs2dmDQNBnvEhXfMgTedKLD5PJHb3aIl15iBkLaNu36GYnVCp9ZU2lSYcTbY5QwNnNeTfJTNy9bKhwzru6Sg%2BslhN%2BTS0zkTn%2BeLMY4tHgPEtEZ2cp%2FCk6GwqswvnYVKHjwO8q%2B1Mx8lc08fVEfarE9dke1GGFqoddx%2Bq%2BZUyHbDlAVfrCmf0yw%3D%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20261110T000135Z&X-Amz-SignedHeaders=host&X-Amz-Expires=86400&X-Amz-Credential=ASIAWA6KKHEJORWBH4N3%2F20261110%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=408f463aed6791824e74ccd966e66337ea69c0180eb175bc865ce55ecc37b134","created_at":1682537593537,"updated_at":1682537593537,"display_name":"EDA Notebook","status":"FILE_STATUS_PUBLISHED"}],"assets":["ASSET_TYPE_DATA_TABLE"]},"provider_summary":{"name":"John Snow Labs","description":"John Snow Labs, an AI and NLP for healthcare company, provides state-of-the-art software, models, and data to help healthcare and life science organizations build, deploy, and operate AI projects."},"popularity":296},{"id":"0b4e8468-8b4c-4d14-a4d5-6e7b8f09053d","summary":{"name":"Geolocet | Latest Demographic Data Iceland at Municipality Level","subtitle":"Demographic data for 125 municipalities of Iceland and their population - 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This meticulously compiled dataset, sourced from official statistical sources, including Statistics Iceland, offers a detailed insight into the population landscape of Iceland at the municipality level. \n\nIf you require data at a less granular level, such as an Uber grid or square grid, please don't hesitate to get in touch with us at *contact@geolocet.com*. We understand that different projects may have varying data needs, and we are here to tailor our data to match your specific requirements.\n\n ## **Key Features:**\n---\n* **Population Data Iceland:** Gain access to the latest and most accurate population statistics for Iceland, meticulously organized by municipality .\n\n* **Demographic Insights:** Dive deep into Iceland’s demographic landscape with data on 125 attributes, including: \n* Total Population,\n* Gender,\n* 5-years age bands,\n* Gender split by age,\n* Nationality,\n* Education,\n* Education split by age,\n* Taxable wages,\n* Employed people.\n\nAll demographic attributes are available at the municipal level.\n\n* **Geodemographic Insights:** Unlock geodemographic data Iceland-wide to inform location-based decision-making, marketing strategies, and policy formulation.\n* **Seamless Data Access:** Our data is available for instant download in CSV format. No lengthy sign-up processes or NDAs required on our side – just download the data you need.\n* **Data Samples for Evaluation:** To ensure you're getting exactly what you need, we provide free downloadable samples in CSV format for data evaluation.\n* **Data accuracy:** Our datasets are created using the latest demographics data available in national statistics bureaus, guaranteeing accuracy and reliability.\n* **Best-in-Class Quality:** We guarantee best-in-class quality, backed by over two decades of experience in modeling demographics data and an outstanding quality management process.\n* **Competitive Pricing:** We maintain the lowest prices on the market, ensuring that you get the most cost-efficient demographics data acquisition process.\n---\nAdditionally, for those looking to enrich their analysis or project, we offer:\n* **[Administrative Boundaries](https://geolocet.com/products/iceland-all-available-administrative-boundaries):** If you require geographical boundaries (polygons) for the municipalities of Iceland, consider exploring our Administrative Boundaries product, which seamlessly integrates with this dataset.\n\n* **[Point of Interest (PoI) Data](https://geolocet.com/pages/aggregated-points-of-interest-poi-data):** If you're interested in Points of Interest data, please don't hesitate to contact us for customized datasets to enhance your geographical analysis.\n\n* **[Data Customization](https://geolocet.com/pages/data-services-by-geolocet):** We understand that your project may have specific requirements. Feel free to get in touch with us if you need any modifications or specific data formats such as Excel, JSON, GeoJSON, or Shapefile. We are here to tailor the data to your needs.\n\nIf you're looking for data for Iceland, [look here](https://geolocet.com/pages/iceland) for off-the-shelf or tailor-made datasets to suit your specific requirements.\n\nMake informed decisions, conduct precise research, and unlock the potential of Iceland’s demographic landscape with our comprehensive dataset. Trust in the accuracy and reliability of our official statistical data to drive your projects forward.\nContact us at *contact@geolocet.com* today to access this invaluable resource and explore Iceland census data like never before.\n\n[Geolocet LTD](https://geolocet.com)\n","terms_of_service":"https://geolocet.com/pages/terms-of-use","documentation_link":"","privacy_policy_link":"https://geolocet.com/pages/privacy-policy","geographical_coverage":"[\"Europe\"]","cost":"PAID","data_source":"","assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://geolocet.com/pages/terms-of-use"},"provider_summary":{"name":"Geolocet LTD","description":"Geolocet provides premium geospatial data for retail analysis, GIS, and market planning. 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This meticulously compiled dataset, sourced from official statistical sources, including Central Statistics Office, Ireland, offers a detailed insight into the population landscape of Ireland at the municipality (electoral divisions) level. \n\nIf you require data at a less granular level, such as an Uber grid or square grid, please don't hesitate to get in touch with us at *contact@geolocet.com*. We understand that different projects may have varying data needs, and we are here to tailor our data to match your specific requirements.\n\n ## **Key Features:**\n---\n* **Population Data Ireland:** Gain access to the latest and most accurate population statistics for Ireland, meticulously organized by municipality (electoral divisions).\n\n* **Demographic Insights:** Dive deep into Ireland’s demographic landscape with data on 73 attributes, including: \n* Total population,\n* Average age,\n* Gender,\n* Average age per gender,\n* Age bands,\n* Gender split by age bands,\n* Birthplace,\n* Population 18+ living with parents,\n* Population 18+ living with parents split by gender,\n* Housing stock,\n* Occupants type.\n\n\nAll demographic attributes are available at the municipal (electoral divisions) level.\n\n* **Geodemographic Insights:** Unlock geodemographic data Ireland-wide to inform location-based decision-making, marketing strategies, and policy formulation.\n* **Seamless Data Access:** Our data is available for instant download in CSV format. No lengthy sign-up processes or NDAs required on our side – just download the data you need.\n* **Data Samples for Evaluation:** To ensure you're getting exactly what you need, we provide free downloadable samples in CSV format for data evaluation.\n* **Data accuracy:** Our datasets are created using the latest demographics data available in national statistics bureaus, guaranteeing accuracy and reliability.\n* **Best-in-Class Quality:** We guarantee best-in-class quality, backed by over two decades of experience in modeling demographics data and an outstanding quality management process.\n* **Competitive Pricing:** We maintain the lowest prices on the market, ensuring that you get the most cost-efficient demographics data acquisition process.\n---\nAdditionally, for those looking to enrich their analysis or project, we offer:\n* **[Administrative Boundaries](https://geolocet.com/products/ireland-and-northern-ireland-admin-level-9-electoral-division-ireland):** If you require geographical boundaries (polygons) for the municipalities of Ireland, consider exploring our Administrative Boundaries product, which seamlessly integrates with this dataset.\n\n* **[Point of Interest (PoI) Data](https://geolocet.com/pages/aggregated-points-of-interest-poi-data):** If you're interested in Points of Interest data, please don't hesitate to contact us for customized datasets to enhance your geographical analysis.\n\n* **[Data Customization](https://geolocet.com/pages/data-services-by-geolocet):** We understand that your project may have specific requirements. Feel free to get in touch with us if you need any modifications or specific data formats such as Excel, JSON, GeoJSON, or Shapefile. We are here to tailor the data to your needs.\n\nIf you're looking for data for Ireland, [look here](https://geolocet.com/pages/ireland) for off-the-shelf or tailor-made datasets to suit your specific requirements.\n\n\nMake informed decisions, conduct precise research, and unlock the potential of Ireland's demographic landscape with our comprehensive dataset. Trust in the accuracy and reliability of our official statistical data to drive your projects forward.\nContact us at *contact@geolocet.com* today to access this invaluable resource and explore Ireland census data like never before.\n\n[Geolocet LTD](https://geolocet.com)\n","terms_of_service":"https://geolocet.com/pages/terms-of-use","documentation_link":"","privacy_policy_link":"https://geolocet.com/pages/privacy-policy","geographical_coverage":"[\"Europe\"]","cost":"PAID","data_source":"","assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://geolocet.com/pages/terms-of-use"},"provider_summary":{"name":"Geolocet LTD","description":"Geolocet provides premium geospatial data for retail analysis, GIS, and market planning. 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Lightcast's Profiles database contains workforce information for over a hundred million distinct individuals, providing insight into how people build skills, move between roles and employers, and progress throughout their careers.\n\nLightcast applies machine learning to deduplicate and enrich profile information. Job titles and company names are standardized, skills are extracted and classified, and education information is standardized to make workforce experiences easier to analyze consistently at scale.\n\nThe sample is centered around a primary Profiles table, with supplementary Jobs, Education, and Skills tables that can be joined using the profile identifier. Together, these tables connect profile-level attributes with employment history, educational experience, and skills, allowing users to explore how US Profiles data is structured and queried.\n\nUS Profiles Pseudonymized focuses on professional and workforce attributes without including direct identity and contact information. Employment histories, skills, education, occupations, employers, and geographic attributes enable analysis of workforce composition, talent supply, and career mobility without requiring direct identification of individuals.\n\nThis sample is intended to demonstrate the structure, relationships, geographic coverage, and analytical potential of Lightcast Profiles data. It is not statistically representative of the full Profiles dataset and should not be used to estimate workforce population distributions.\n\nContact Lightcast to learn more about the full Profiles offering and available data fields. For further information, please visit [Lightcast Profiles Methodology.](https://kb.lightcast.io/en/articles/6957504-profiles-methodology)\n\n**Use cases**\n\n*Market Analysis*\nUS Profiles data provides insight into the supply and distribution of talent across occupations, skills, employers, industries, and geographic markets. By analyzing professional experience, education, skills, and location, organizations can explore where specialized talent is concentrated and compare workforce characteristics across American labor markets. These insights support regional talent assessments, location strategy, workforce planning, and a deeper understanding of labor supply.\n\n*Career Pathways & Workforce Mobility*\nExplore how professionals move between occupations, employers, and industries throughout their careers. 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Figures are submitted quarterly on the Quarterly Activity Return (QAR) by National Health Service (NHS) Trusts and Independent sector providers treating NHS patients. Sub-Saharan Public Hospitals Database dataset provides a geocoded inventory of public hospitals across 48 countries and islands of sub-Saharan Africa from 100 different sources. This geo-coded inventory of hospital in Africa is created to enhance access to health services and hospitals.\n\n**Benefits**\n- Capacity planning of hospital. the prediction of hospital bed availability in next 72 hours. optimization of bed occupancy in a hospital. enhancement of the operation process. improved hospital efficiency in the health-care industry.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [Hospital Bed Availability and Occupancy by Sector](https://www.johnsnowlabs.com/marketplace/hospital-bed-availability-and-occupancy-by-sector)\n\t- This dataset provides information on the total number of available and occupied bed days for facility wards open \"overnight\" and \"day only\" by NHS (National Health Service) Trust by Sector.\n- [Hospital Bed Occupancy by Consultant Main Specialty](https://www.johnsnowlabs.com/marketplace/hospital-bed-occupancy-by-consultant-main-specialty)\n\t- This dataset provides information on the total number of occupied bed days for facility wards open \"overnight\" and \"day only\" by consultant main specialty.\n- [Inpatient and Outpatient Related Hospital Activity Data](https://www.johnsnowlabs.com/marketplace/inpatient-and-outpatient-related-hospital-activity-data)\n\t- This dataset contains quarterly hospital activity data relating to inpatients (elective) and outpatients. 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The Patient Safety measures include data for the base and performance periods for six different measures.\n- [New York Medicaid Children Hospitalization Quality by County](https://www.johnsnowlabs.com/marketplace/new-york-medicaid-children-hospitalization-quality-by-county)\n\t- This dataset displays the number of Medicaid Pediatric Quality Indicators (PDI) hospitalizations by county of The Agency for Healthcare Research and Quality (AHRQ). It covers patients from 2 to 17 years old, from 2011 to 2014. Risk-adjusted rates are included.\n- [New York Medicaid Children Hospitalization Quality by Zip Code](https://www.johnsnowlabs.com/marketplace/new-york-medicaid-children-hospitalization-quality-by-zip-code)\n\t- This dataset displays the number of Medicaid Pediatric Quality Indicators (PDI) hospitalizations by zip code of The Agency for Healthcare Research and Quality (AHRQ). It covers patients from 2 to 17 years old, from 2011 to 2014. Risk-adjusted rates are included.\n- [Perinatal Care Early Elective Delivery Data](https://www.johnsnowlabs.com/marketplace/perinatal-care-early-elective-delivery-data)\n\t- This dataset contains different Perinatal Care Early Elective Delivery Data for years 2014 and 2015 from Quality Based Reimbursement (QBR) Program for hospitals in Maryland. 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Derived through extensive multi-party sourcing, this structured, high-quality data originates from:\n- 1st-party proprietary research and direct surveys\n- Regulatory filings from global jurisdictions\n- Direct submissions from fund managers and investors\n- Intelligence sourced through Private Debt Investor events and networks\n- Continuous input from the industry’s most recognised editorial team\n\nEach table is professionally validated through:\n- Multi-stage programmatic and human review\n- Cross-referencing across historical datasets\n- Manual validation by infrastructure specialists\n- Ongoing updates and historical enrichment\n- Normalised schema alignment across institutions, funds, performance, and commitments\n\nThe result is **an actionable dataset built for accuracy, completeness, and reliability** to enhance smarter investing decisions.\n\n\n**What’s included?**\nIn this sample dataset you will receive 10 rows of data across the below categories. \n\n*The industry’s 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This data package helps to look at how communities and hospitals effectively coordinate care for some of the sickest patients and those with hospital discharges after a stay to treat acute or chronic illness.\n\n**Description**\nThis data package contains information on patient hospital discharges as the discharge of patients from the hospital is a complex process that is fraught with challenges and involves over 35 million hospital discharges annually in the United States. The cost of unplanned readmissions is 15 to 20 billion dollars annually. Preventing avoidable readmissions has the potential to profoundly improve both the quality of life for patients and the financial wellbeing of health care systems. \nThe Dartmouth Atlas report reveals striking variation in 30-day readmission rates across hospital referral regions and academic medical centers. Little progress was seen in reducing readmission rates over the five-year period 2004 to 2009. In fact, for some conditions, readmission rates have increased for the nation and for many regions and hospitals.\n\n**Benefits**\n- Useful data package for physicians as it has discharge details of patients from different type of chronic illness by state, region, and county.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [Congestive Heart Failure Hospital Discharge by AMC](https://www.johnsnowlabs.com/marketplace/congestive-heart-failure-hospital-discharge-by-amc)\n\t- This dataset looks at 2013 and 2014 post discharge re-admission and care after patients leave the hospital for an acute or chronic illness by AMC (Academic Medical Centers), Hospital, Region and State.\n- [Congestive Heart Failure Hospital Discharge by County](https://www.johnsnowlabs.com/marketplace/congestive-heart-failure-hospital-discharge-by-county)\n\t- This dataset contains information on Congestive Heart Failure Hospital Discharge by County which looks at 2013 post discharge re-admission and care after patients leave the hospital for an acute or chronic illness.\n- [Congestive Heart Failure Hospital Discharge by Hospital](https://www.johnsnowlabs.com/marketplace/congestive-heart-failure-hospital-discharge-by-hospital)\n\t- This dataset contains information on Congestive Heart Failure Hospital Discharge by Hospital which looks at 2014 post discharge re-admission and care after patients leave the hospital for an acute or chronic illness.\n- [Congestive Heart Failure Hospital Discharge by Region](https://www.johnsnowlabs.com/marketplace/congestive-heart-failure-hospital-discharge-by-region)\n\t- This dataset looks at 2013 and 2014 post discharge re-admission and care after patients leave the hospital for an acute or chronic illness by region. The dataset covers percent readmitted within 30 days of discharge, percent seeing a primary care clinician within 14 days of discharge to home, percent having an ambulatory visit within 14 days of discharge from hospital and percent having an emergency room visit within 30 days of discharge.\n- [Congestive Heart Failure Hospital Discharge by State](https://www.johnsnowlabs.com/marketplace/congestive-heart-failure-hospital-discharge-by-state)\n\t- This dataset contains information on Congestive Heart Failure Hospital Discharge by State which looks at 2013 and 2014 post discharge re-admission and care after patients leave the hospital for an acute or chronic illness.\n- [Medical Hospital Discharge by AMC](https://www.johnsnowlabs.com/marketplace/medical-hospital-discharge-by-amc)\n\t- This dataset provides information on Medical Hospital Discharge by Academic Medical Centers (AMC), which looks at 2014 post-discharge re-admission and care after patients leave the hospital for an acute or chronic illness.\n- [Medical Hospital Discharge by County](https://www.johnsnowlabs.com/marketplace/medical-hospital-discharge-by-county)\n\t- This dataset provides information on Medical Hospital Discharge by county. The dataset covers percent readmitted within 30 days of discharge, percent seeing a primary care clinician within 14 days of discharge to home, percent having an ambulatory visit within 14 days of discharge from hospital and percent having an emergency room visit within 30 days of discharge.\n- [Medical Hospital Discharge by Hospital](https://www.johnsnowlabs.com/marketplace/medical-hospital-discharge-by-hospital)\n\t- This dataset provides information on Medical Hospital Discharge by hospital. The dataset covers percent readmitted within 30 days of discharge, percent seeing a primary care clinician within 14 days of discharge to home, percent having an ambulatory visit within 14 days of discharge from hospital and percent having an emergency room visit within 30 days of discharge.\n- [Medical Hospital Discharge by Region](https://www.johnsnowlabs.com/marketplace/medical-hospital-discharge-by-region)\n\t- This dataset provides information on Medical Hospital Discharge by region. The dataset covers percent readmitted within 30 days of discharge, percent seeing a primary care clinician within 14 days of discharge to home, percent having an ambulatory visit within 14 days of discharge from hospital and percent having an emergency room visit within 30 days of discharge.\n- [Medical Hospital Discharge by State](https://www.johnsnowlabs.com/marketplace/medical-hospital-discharge-by-state)\n\t- This dataset provides information on Medical Hospital Discharge by state. The dataset covers percent readmitted within 30 days of discharge, percent seeing a primary care clinician within 14 days of discharge to home, percent having an ambulatory visit within 14 days of discharge from hospital and percent having an emergency room visit within 30 days of discharge.\n- [Myocardial Infarction Hospital Discharge by AMC](https://www.johnsnowlabs.com/marketplace/myocardial-infarction-hospital-discharge-by-amc)\n\t- This dataset contains information on Myocardial Infarction Hospital Discharge by Academic Medical Centers (AMC) which looks at 2014 post-discharge re-admission and care after patients leave the hospital for an acute or chronic illness.\n- [Myocardial Infarction Hospital Discharge by County](https://www.johnsnowlabs.com/marketplace/myocardial-infarction-hospital-discharge-by-county)\n\t- This dataset contains information on Myocardial Infarction Hospital Discharge by county. The dataset covers percent readmitted within 30 days of discharge, percent seeing a primary care clinician within 14 days of discharge to home, percent having an ambulatory visit within 14 days of discharge from hospital and percent having an emergency room visit within 30 days of discharge.\n- [Myocardial Infarction Hospital Discharge by Hospital](https://www.johnsnowlabs.com/marketplace/myocardial-infarction-hospital-discharge-by-hospital)\n\t- This dataset contains information on Myocardial Infarction Hospital Discharge by hospital which looks at 2014 post-discharge re-admission and care after patients leave the hospital for an acute or chronic illness.\n- [Myocardial Infarction Hospital Discharge by Region](https://www.johnsnowlabs.com/marketplace/myocardial-infarction-hospital-discharge-by-region)\n\t- This dataset contains information on Myocardial Infarction Hospital Discharge by region. The dataset covers percent readmitted within 30 days of discharge, percent seeing a primary care clinician within 14 days of discharge to home, percent having an ambulatory visit within 14 days of discharge from hospital and percent having an emergency room visit within 30 days of discharge.\n- [Myocardial Infarction Hospital Discharge by State](https://www.johnsnowlabs.com/marketplace/myocardial-infarction-hospital-discharge-by-state)\n\t- This dataset contains information on Myocardial Infarction Hospital Discharge by state. The dataset covers percent readmitted within 30 days of discharge, percent seeing a primary care clinician within 14 days of discharge to home, percent having an ambulatory visit within 14 days of discharge from hospital and percent having an emergency room visit within 30 days of discharge.\n- [Pneumonia Hospital Discharge by AMC](https://www.johnsnowlabs.com/marketplace/pneumonia-hospital-discharge-by-amc)\n\t- This dataset contains information on Pneumonia Hospital Discharge by Academic Medical Centers (AMC) . The dataset covers percent readmitted within 30 days of discharge, percent seeing a primary care clinician within 14 days of discharge to home, percent having an ambulatory visit within 14 days of discharge from hospital and percent having an emergency room visit within 30 days of discharge.\n- [Pneumonia Hospital Discharge by County](https://www.johnsnowlabs.com/marketplace/pneumonia-hospital-discharge-by-county)\n\t- This dataset contains information on Pneumonia Hospital Discharge by county. The dataset covers percent readmitted within 30 days of discharge, percent seeing a primary care clinician within 14 days of discharge to home, percent having an ambulatory visit within 14 days of discharge from hospital and percent having an emergency room visit within 30 days of discharge.\n- [Pneumonia Hospital Discharge by Hospital](https://www.johnsnowlabs.com/marketplace/pneumonia-hospital-discharge-by-hospital)\n\t- This dataset contains information on Pneumonia Hospital Discharge by hospital. The dataset covers percent readmitted within 30 days of discharge, percent seeing a primary care clinician within 14 days of discharge to home, percent having an ambulatory visit within 14 days of discharge from hospital and percent having an emergency room visit within 30 days of discharge.\n- [Pneumonia Hospital Discharge by Region](https://www.johnsnowlabs.com/marketplace/pneumonia-hospital-discharge-by-region)\n\t- This dataset contains information on Pneumonia Hospital Discharge by region. The dataset covers percent readmitted within 30 days of discharge, percent seeing a primary care clinician within 14 days of discharge to home, percent having an ambulatory visit within 14 days of discharge to home and percent having an emergency room visit within 30 days of discharge.\n- [Pneumonia Hospital Discharge by State](https://www.johnsnowlabs.com/marketplace/pneumonia-hospital-discharge-by-state)\n\t- This dataset contains information on Pneumonia Hospital Discharge by state. The dataset covers percent readmitted within 30 days of discharge, percent seeing a primary care clinician within 14 days of discharge to home, percent having an ambulatory visit within 14 days of discharge to home and percent having an emergency room visit within 30 days of discharge.\n- [Surgical Hospital Discharge by AMC](https://www.johnsnowlabs.com/marketplace/surgical-hospital-discharge-by-amc)\n\t- This dataset provides information on Surgical Hospital Discharge by Academic Medical Centers (AMC). The dataset covers percent readmitted within 30 days of discharge, percent seeing a primary care clinician within 14 days of discharge to home, and percent having an emergency room visit within 30 days of discharge.\n- [Surgical Hospital Discharge by County](https://www.johnsnowlabs.com/marketplace/surgical-hospital-discharge-by-county)\n\t- This dataset provides information on Surgical Hospital Discharge by county. The dataset covers percent readmitted within 30 days of discharge, percent seeing a primary care clinician within 14 days of discharge to home, and percent having an emergency room visit within 30 days of discharge.\n- [Surgical Hospital Discharge by Hospital](https://www.johnsnowlabs.com/marketplace/surgical-hospital-discharge-by-hospital)\n\t- This dataset provides information on Surgical Hospital Discharge by hospital. The dataset covers percent readmitted within 30 days of discharge, percent seeing a primary care clinician within 14 days of discharge to home, percent having an ambulatory visit within 14 days of discharge from hospital and percent having an emergency room visit within 30 days of discharge.\n- [Surgical Hospital Discharge by Region](https://www.johnsnowlabs.com/marketplace/surgical-hospital-discharge-by-region)\n\t- This dataset provides information on Surgical Hospital Discharge by region. The dataset covers percent readmitted within 30 days of discharge, percent seeing a primary care clinician within 14 days of discharge to home, and percent having an emergency room visit within 30 days of discharge.\n- [Surgical Hospital Discharge by State](https://www.johnsnowlabs.com/marketplace/surgical-hospital-discharge-by-state)\n\t- This dataset provides information on Surgical Hospital Discharge by state. The dataset covers percent readmitted within 30 days of discharge, percent seeing a primary care clinician within 14 days of discharge to home, and percent having an emergency room visit within 30 days of discharge. This particular dataset is on surgical cohort on the state level. Percentage numbers are in % unit.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated\n- Data Updates\n - Data updates support replace-on-update: outdated foreign keys are deprecated, not deleted\n\n**Our data is curated and enriched by domain experts** \n\n Each dataset is 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System identifiers (e.g., hostname, machine_id) are retained only for market traceability and are non-personal.\nFormat\nCSV — lightweight (<0.1 GB), ready for analysis in Python, R, SQL, or Excel.\n\nData Structure Overview\nRows: 64\nColumns: 101\nGranularity: One record per active GPU offer\nDate Range: Snapshot as of 2026-02-05\nUpdate Frequency: Monthly release\n\nThe dataset includes key financial indicators such as Price_Per_GPU, Value_Score, and Signal (BUY_SIGNAL or FAIR_VALUE), providing immediate market intelligence. Each listing tracks hardware specifications (CPU, GPU, memory, NVLink, power, and bandwidth), allowing precise benchmarking of performance versus cost.\nValue Proposition\nFinancial Alpha: Detect GPU supply shifts and compute inflation trends before they are reflected in centralized cloud pricing.\nInstitutional Engineering: Authored by the #1 Kaggle Grandmaster with zero-schema errors and full documentation.\nVerified Market Pricing: Reflects the true market street price of compute, not theoretical list prices.\nMacro Visibility: Forms the missing link between AI model proliferation and compute market economics.\nSummary\nGlobal GPU Spot-Price Ticker transforms decentralized compute pricing into an institutional-grade market signal. With elite engineering, verified provenance, and disciplined monthly publication, it stands as the benchmark dataset for those tracking the real economics of AI infrastructure.","terms_of_service":"https://www.aidc.app/sample-terms-of-service","documentation_link":"https://app.aidatacoop.com/datasets/9e288416-277b-4f5d-9adb-218b606531f9","privacy_policy_link":"https://www.aidc.app/faq","geographical_coverage":"[\"Global\"]","cost":"FREE","update_frequency":{"interval":1,"unit":"MONTHLY"},"collection_granularity":{"interval":1,"unit":"DAILY"},"collection_date_start":1770326949058,"collection_date_end":1770413357227,"data_source":"Data is sourced via transparent APIs from decentralized GPU marketplaces (e.g., Vast.ai, TensorDock). Scripts continuously track spot pricing, hardware specs, and geo data. Monthly releases pass QC checks for pricing accuracy, uptime, and arbitrage. 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Further, sentiment models have typically been very crude, often focusing only on article level sentiment rather than topical sentiment within articles.\n\n[Onclusive’s](https://onclusive.com) media data set is among the most comprehensive and fastest in the world. Onclusive machine reads and analyzes over 200,000,000 articles a month from virtually every corner of the internet, in every language, in close to real time. It also adds a variety of meta data to each article including domain authority and social media amplification. Cutting-edge AI then extracts all the key topics from those articles and applies the most accurate sentiment scoring system to each article at the individual topic level. Because Onclusive has accumulated a database of coverage in excess of 6,000,000,000 articles, our AI has an unparalleled training data set. These articles come from traditional publishers, company websites, and newswire services as opposed to social media. \n\nOnclusive is able to provide this new altdata set because its core customer base is comprised of many of the largest, most demanding companies in the world for which Onclusive provides real time media monitoring services. Onclusive is not just reprocessing other syndicated data sources, it gathers much of the media coverage on its own. On the rare occasions, Onclusive misses an article, its clients make sure the article is added to the database. This new media data set for the investing universe is both the most comprehensive and most sensitive/accurate.\n\n [AltHub](https://althub.com/) is a data analytics platform that helps investors use alternative data to generate performance.\n\nFor any queries reach out to support@althub.com\n\n**Use cases**\n\n- **Enhanced Investment Research**: Investment professionals can use this dataset to conduct in-depth research and analysis on companies, industries, and market trends. By analyzing media coverage and sentiment data, they can gain insights into how news and events impact stock prices and market sentiment.\n\n- **Alternative Data for Investment Strategies**: Alternative data, such as media coverage and sentiment data, is increasingly being used in investment strategies. 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Because Onclusive has accumulated a database of coverage in excess of 6,000,000,000 articles, its AI has an unparalleled training data set."},"popularity":1503},{"id":"4b6b8ccb-ba65-4199-a5b2-67fa6cc22a44","summary":{"name":"EIU | Country Analysis | Insights into 200 countries political, policy and economic outlook with ...","subtitle":"Our Country Analysis service allows you to conduct country market research by looking at global dynamics and ...","share":{"name":"economist_intelligence_un_3a9a096e_8d02_4543_8c41_1870cd98e0fc_samples_share","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1764767908971,"updated_at":1764767908971,"categories":["FINANCIAL","OTHER"],"listingType":"PERSONALIZED","provider_id":"effe68c8-e100-4fa6-b88a-949d53792739"},"detail":{"description":"EIU's Country Analysis provides award-winning forecasts, in-depth analysis and comprehensive data for nearly 200 global markets. This service helps organisations to understand political, policy and economic outlooks so that they can plan and operate effectively. Our experts assess global dynamics, offering a nuanced approach with detailed insights and rigorously researched, non-biased information.\n\nFeatures include global and regional outlooks, daily insights and medium- to long-term country forecasts. We also cover industry analysis across 26 sectors, commodity forecasts, macroeconomic and industry data, proprietary ratings, and regulatory intelligence. Our analysis informs world-leading financial institutions, corporations, governments and academic institutions, helping them to evaluate opportunities, manage risks and anticipate disruptions.","terms_of_service":"https://www.economistgroup.com/results_and_governance/governance/privacy","contact_email":"marketplace@datarade.ai","privacy_policy_link":"https://www.economistgroup.com/results_and_governance/governance/privacy","assets":["ASSET_TYPE_DATA_TABLE"]},"provider_summary":{"name":"Economist Intelligence Unit","description":"Economist Intelligence (EIU) delivers data-led insights at the intersection of macroeconomics and geopolitics, empowering financial, corporate and government leaders to make strategic decisions in a rapidly evolving world.\r\n\r\nTrusted by the world’s largest organisations for over 75 years, Economist Intelligence is known for its award-winning macroeconomic forecasts and independent, actionable insights—enriched by geopolitical analysis. \r\n\r\nOur breadth of coverage across 200+ markets, including 130+ emerging markets, our international Corporate Network, and our global team of subject matter experts allow us to connect and contextualise global trends and events — so that clients are fully informed to take action, with confidence and credibility, in a rapidly evolving world. For more information, please visit www.eiu.com."},"popularity":1243},{"id":"333f585c-4a33-4bd4-8436-4da043fbe8f6","summary":{"name":"Geolocet | Latest Demographic Data Slovakia at Municipality Level","subtitle":"Demographic data for the municipalities (okres) of Slovakia and their their population - age, gender, education,...","setting":{"visibility":"PUBLIC"},"created_at":1705919696456,"updated_at":1752145946564,"categories":["DEMOGRAPHICS","ADVERTISING_AND_MARKETING","GEOSPATIAL","RETAIL","PUBLIC_SECTOR"],"listingType":"PERSONALIZED","provider_id":"5d3ddc17-a7e8-4794-a760-13587e1de2c6"},"detail":{"description":"## **Get instant access to the latest Demographics Data for Slovakia with [Geolocet](https://geolocet.com)**\n\nExplore the richness of Slovakia’s demographic landscape with our Slovakia Census Data. This meticulously compiled dataset, sourced from official statistical sources, including Statistical Office of the Slovak Republic, offers a detailed insight into the population landscape of Slovakia at the municipality (okres) level. \n\nIf you require data at a less granular level, such as an Uber grid or square grid, please don't hesitate to get in touch with us at *contact@geolocet.com*. We understand that different projects may have varying data needs, and we are here to tailor our data to match your specific requirements.\n\n ## **Key Features:**\n---\n* **Population Data Slovakia:** Gain access to the latest and most accurate population statistics for Slovakia, meticulously organized by municipality (okres).\n\n* **Demographic Insights:** Dive deep into Slovakia’s demographic landscape with data on 427 attributes, including: \n* Total population,\n* Gender,\n* 5-years age bands, \n* Gender divided by age,\n* Level of education,\n* Level of education divided by gender,\n* Level of education divided by age,\n* Level of education divided by age and gender,\n* Nationality,\n* Nationality divided by gender.\n\nAll demographic attributes are available at the municipal (okres) level.\n\n* **Geodemographic Insights:** Unlock geodemographic data Slovakia-wide to inform location-based decision-making, marketing strategies, and policy formulation.\n* **Seamless Data Access:** Our data is available for instant download in CSV format. No lengthy sign-up processes or NDAs required on our side – just download the data you need.\n* **Data Samples for Evaluation:** To ensure you're getting exactly what you need, we provide free downloadable samples in CSV format for data evaluation.\n* **Data accuracy:** Our datasets are created using the latest demographics data available in national statistics bureaus, guaranteeing accuracy and reliability.\n* **Best-in-Class Quality:** We guarantee best-in-class quality, backed by over two decades of experience in modeling demographics data and an outstanding quality management process.\n* **Competitive Pricing:** We maintain the lowest prices on the market, ensuring that you get the most cost-efficient demographics data acquisition process.\n---\nAdditionally, for those looking to enrich their analysis or project, we offer:\n* **[Administrative Boundaries](https://geolocet.com/products/slovakia-all-available-administrative-boundaries):** If you require geographical boundaries (polygons) for the municipalities of Slovakia, consider exploring our Administrative Boundaries product, which seamlessly integrates with this dataset.\n\n* **[Point of Interest (PoI) Data](https://geolocet.com/pages/aggregated-points-of-interest-poi-data):** If you're interested in Points of Interest data, please don't hesitate to contact us for customized datasets to enhance your geographical analysis.\n\n* **[Data Customization](https://geolocet.com/pages/data-services-by-geolocet):** We understand that your project may have specific requirements. Feel free to get in touch with us if you need any modifications or specific data formats such as Excel, JSON, GeoJSON, or Shapefile. We are here to tailor the data to your needs.\n\nMake informed decisions, conduct precise research, and unlock the potential of Slovakia demographic landscape with our comprehensive dataset. Trust in the accuracy and reliability of our official statistical data to drive your projects forward.\nContact us at *contact@geolocet.com* today to access this invaluable resource and explore Slovakia census data like never before.\n\n[Geolocet LTD](https://geolocet.com)\n","terms_of_service":"https://geolocet.com/pages/terms-of-use","documentation_link":"","privacy_policy_link":"https://geolocet.com/pages/privacy-policy","geographical_coverage":"[\"Europe\"]","cost":"PAID","data_source":"","assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://geolocet.com/pages/terms-of-use"},"provider_summary":{"name":"Geolocet LTD","description":"Geolocet provides premium geospatial data for retail analysis, GIS, and market planning. We equip businesses with accurate, ready-to-query data to drive spatial analysis, site selection, and customer segmentation.\n\nCore Offerings:\n\nDemographics: Socioeconomic data covering population, age, and employment across diverse geographies.\n\nPoints of Interest (PoI): Datasets spanning retail, healthcare, and transport for catchment analysis.\n\nAdministrative Boundaries: GIS-ready boundaries, including municipalities and postcodes, for territory management.\n\nBrand Locations: Chain location datasets to monitor store networks and market expansion.\n\nWealth Data: Granular economic indicators like purchasing power to identify high-potential markets.\n\nWe source from trusted providers, delivering highly customizable datasets with comprehensive coverage across Europe and worldwide."},"popularity":477},{"id":"6ea7d40a-3431-467b-9198-3fca5d6c51fe","summary":{"name":"Health Plan Selection","subtitle":"This package was designed to offer as main data health plans selection related data, provided by the Office of Enterpr..","share":{"name":"health_plan_selection","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1682356107625,"updated_at":1698965635273,"categories":["HEALTH","SCIENCE_AND_RESEARCH"],"listingType":"STANDARD","provider_id":"f88849f8-9d77-4de2-b4c7-03d55cc3768e"},"detail":{"description":"\n**Overview**\nThis package was designed to offer as main data health plans selection related data, provided by the Office of Enterprise Data and Analytics, part of Centers for Medicare & Medicaid Services (CMS), that cover multiple characteristics for the health/dental insurance consumers from 38 states that that use the HealthCare.gov platform.\n\n**Description**\nAs part of the Obama Administration’s efforts to make healthcare system more transparent, affordable, and accountable, the Office of Enterprise Data and Analytics, within the Centers for Medicare & Medicaid Services (CMS), has prepared public datasets to provide the total number of health plan selections by county for the 38 states that use the HealthCare.gov platform, including the Federally-facilitated Marketplace, State Partnership Marketplaces, and supported State-based Marketplaces. The datasets for county-level plan selection information cut by:\n\n- The type of consumer (consumers auto enrolled or who actively selected a new plan)\n- The household income as percent of the Federal Poverty Level (FPL)\n- Individual and family health/dental plan selection\n- Race/ethnicity\n- The presence or absence of Advanced Premium Tax Credits (APTC)\n- The presence or absence of cost-sharing reductions\n- Metal level or plan policy (Platinum, Gold, Silver, Bronze, Catastrophic)\n\nData were obtained from the Multi-Dimensional Insurance Data Analytics System (MIDAS), which serves as a central repository for capturing, organizing, aggregating, and analyzing CMS’s Marketplace data.\nAs mentioned in the summary information, the data package contains context data related to health professionals (registered nurse, nurse practitioners), public providers enrollment and eligible uninsured people at country level, and, at state level, related to the California’s Medical Service Study Areas (MSSA).\n\n**Benefits**\n- Useful for researchers to assess the health plan selection consumers and influencing factors\n- useful for policy makers and other health insurance interested parties to adjust the health plans according to the population needs and to context changes\n- john snow labs datasets versions are improved through a process of curation and standardization making the datasets easier to use version, alone or in combination with other datasets from other data packages.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [Health Plan by Household Income as Percent of the FPL and County 2015](https://www.johnsnowlabs.com/marketplace/health-plan-by-household-income-as-percent-of-the-fpl-and-county-2015)\n\t- The dataset gives information on the total number of Health plan selection of 2601 counties with respect to the Household Income as Percent of the Federal Poverty Level (FPL).\n- [Individual and Family Health Plan Information PY2017 to PY2018](https://www.johnsnowlabs.com/marketplace/individual-and-family-health-plan-information-py2017-to-py2018)\n\t- This dataset includes 2017 Individual and Family health plans available in the United States where the federal government is operating the Marketplace. It contains plan information for states in the Federally-facilitated Marketplaces and State Partnership Marketplaces: \"Medical plans in the individual market\", \"Medical plans in the small group (SHOP) market\", \"Dental plans in the individual market\" and \"Dental plans in the small group (SHOP) market\".\n- [Medical Service Study Areas Subcity Subcounty Geographical Units 2000](https://www.johnsnowlabs.com/marketplace/medical-service-study-areas-subcity-subcounty-geographical-units-2000)\n\t- The dataset contains information on California’s Medical Service Study Areas (MSSA). MSSAs are sub-city and sub-county geographical units used to organize and display population, demographic and physician data for 2000. Medical Service Study Areas are a geographic analysis unit defined by the California Office of Statewide Health Planning and Development. MSSA are a good foundation for needs assessment analysis, healthcare planning, and healthcare policy development.\n- [Medical Service Study Areas Subcity Subcounty Geographical Units 2010](https://www.johnsnowlabs.com/marketplace/medical-service-study-areas-subcity-subcounty-geographical-units-2010)\n\t- The dataset contains information on California’s Medical Service Study Areas (MSSA). MSSAs are sub-city and sub-county geographical units used to organize and display population, demographic and physician data for 2010. Medical Service Study Areas are a geographic analysis unit defined by the California Office of Statewide Health Planning and Development. MSSA are a good foundation for needs assessment analysis, healthcare planning, and healthcare policy development.\n- [Medical Service Study Areas by Census Tract Detail 2000](https://www.johnsnowlabs.com/marketplace/medical-service-study-areas-by-census-tract-detail-2000)\n\t- The dataset contains information on California’s Medical Service Study Areas (MSSA), at the census tract level for 2000. MSSAs are sub-city and sub-county geographical units used to organize and display population, demographic and physician data. MSSA areas are a geographic analysis unit defined by the California Office of Statewide Health Planning and Development. MSSA are a good foundation for needs assessment analysis, healthcare planning, and healthcare policy development.\n- [Medical Service Study Areas by Census Tract Detail 2013](https://www.johnsnowlabs.com/marketplace/medical-service-study-areas-by-census-tract-detail-2013)\n\t- The dataset contains information on California’s Medical Service Study Areas (MSSA), at the census tract level for 2013. MSSAs are sub-city and sub-county geographical units used to organize and display population, demographic and physician data. Medical Service Study Areas are a geographic analysis unit defined by the California Office of Statewide Health Planning and Development.\n- [Medicare Public Provider Enrollment Data Reassignment Sub File 2016 Q2](https://www.johnsnowlabs.com/marketplace/medicare-public-provider-enrollment-data-reassignment-sub-file-2016-q2)\n\t- The Public Provider Enrollment data for Medicare fee-for-service includes providers who are actively approved to bill Medicare. This dataset contains an enrollment ID crosswalk between those individual providers reassigning their benefits and those providers receiving the reassignment of benefits.\n- [National Sample Survey Of Nurse Practitioners 2012](https://www.johnsnowlabs.com/marketplace/national-sample-survey-of-nurse-practitioners-2012)\n\t- Nurse practitioners play a critical role in extending access to health care by providing a range of primary and specialty care services. The Health Resources and Services Administration (HRSA) conducted the 2012 National Sample Survey of Nurse Practitioners to provide accurate national estimates of the nurse practitioner workforce and to profile their education, certification, and practice patterns.\n- [National Sample Survey of Registered Nurses 2008](https://www.johnsnowlabs.com/marketplace/national-sample-survey-of-registered-nurses-2008)\n\t- The National Sample Survey of Registered Nurses (NSSRN) assists the Health Resources and Services Administration (HRSA), Bureau of Health Professions (BHPr), in carrying out congressional mandates. This is the ninth study and was conducted with registered nurses (RN) who held an active RN license as of March 10, 2008.\n- [Percentage of Estimated Eligible Uninsured People](https://www.johnsnowlabs.com/marketplace/percentage-of-estimated-eligible-uninsured-people)\n\t- The dataset has information on percentage of uninsured population with respect to different population characteristics, including federal poverty level, education, and language. The data here are for outreach targeting purposes only. The number of people with incomes at varying federal poverty levels is based on data from the Census Bureau’s American Community Survey (ACS) data for 2011.\n- [Qualifying Health Plan Selections by APTC and County 2015](https://www.johnsnowlabs.com/marketplace/qualifying-health-plan-selections-by-aptc-and-county-2015)\n\t- The dataset gives information on the total number of Health Plan Selection of 2601 counties with respect to the presence or absence of advanced premium tax credit for the year 2015.\n- [Qualifying Health Plan Selections by CSR and County 2015](https://www.johnsnowlabs.com/marketplace/qualifying-health-plan-selections-by-csr-and-county-2015)\n\t- The dataset gives information on the total number of Health Plan Selection of 2601 counties with respect to the presence or absence of cost-sharing reductions for the year 2015.\n- [Qualifying Health Plan Selections by Metal Level and County 2015](https://www.johnsnowlabs.com/marketplace/qualifying-health-plan-selections-by-metal-level-and-county-2015)\n\t- The dataset gives information on the total number of Health Plan Selection of 2601 counties with respect to different metal level or plan policy for the year 2015. The dataset includes the total number of unique individuals who have a non-canceled plan selection with March 2015 coverage for the 37 states that use the HealthCare.gov platform, including the Federally-facilitated Marketplace, State Partnership Marketplaces and supported State-based Marketplaces.\n- [Qualifying Health Plan Selections by Race or Ethnicity and County 2015](https://www.johnsnowlabs.com/marketplace/qualifying-health-plan-selections-by-race-or-ethnicity-and-county-2015)\n\t- The dataset gives information on the total number of Health Plan Selection of 2601 counties with respect to the race or ethnicity and county for the year 2015. The dataset includes the total number of unique individuals who have a non-canceled plan selection with March 2015 coverage for the 37 states that use the HealthCare.gov platform, including the Federally-facilitated Marketplace, State Partnership Marketplaces and supported State-based Marketplaces.\n- [Qualifying Health Plan Selections by Type of Consumer and County 2015](https://www.johnsnowlabs.com/marketplace/qualifying-health-plan-selections-by-type-of-consumer-and-county-2015)\n\t- The dataset covers the 2601 counties' Health plan selection either the presence or absence of Advanced premium tax credit for 2015. It also includes the total number of unique individuals with non-canceled plan selection for March 2015 of the 37 states that use the HealthCare.gov platform, Federally-facilitated, State-Partnership and supported State-based Marketplaces. Plan selections are from November 15, 2014, to February 15, 2015, plus the special enrollment period from February 22, 2015.\n- [US Delay or Non Receipt of Needed Healthcare](https://www.johnsnowlabs.com/marketplace/us-delay-or-non-receipt-of-needed-healthcare)\n\t- This dataset shows the delay or non-receipt of needed medical care, non-receipt of needed prescription drugs, or non-receipt of needed dental care during the past 12 months due to cost, by selected characteristics for years 1997–2016. Data are based on household interviews of a sample of the civilian noninstitutionalized population.\n- [US Healthcare Visits Statistics](https://www.johnsnowlabs.com/marketplace/us-healthcare-visits-statistics)\n\t- The US Healthcare Visits Statistics dataset includes data about the frequency of healthcare visits to doctor offices, emergency departments, and home visits within the past 12 months in the United States by age, race, Hispanic origin, poverty level, health insurance status, geographic region and other characteristics between 1997 and 2016.\n- [US Uninsured Statistics](https://www.johnsnowlabs.com/marketplace/us-uninsured-statistics)\n\t- This dataset shows the statistics of United States uninsured persons with no health coverage under age 65 by selected characteristics. Data are based on household interviews of a sample of the civilian noninstitutionalized population.\n\n\n**Data Engineering Overview**\n**We deliver high-quality data**\n- Each dataset goes through 3 levels of quality review\n - 2 Manual reviews are done by domain experts\n - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints\n- Data is normalized into one unified type system\n - All dates, unites, codes, currencies look the same\n - All null values are normalized to the same value\n - All dataset and field names are SQL and Hive compliant\n- Data and Metadata\n - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters\n - Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated\n- Data Updates\n - Data updates support replace-on-update: outdated foreign keys are deprecated, not deleted\n\n**Our data is curated and enriched by domain experts** \n\n Each dataset is manually curated by our team of doctors, pharmacists, public health & medical billing experts:\n- Field names, descriptions, and normalized values are chosen by people who actually understand their meaning\n- Healthcare & life science experts add categories, search keywords, descriptions and more to each dataset\n- Both manual and automated data enrichment supported for clinical codes, providers, drugs, and geo-locations\n- The data is always kept up to date – even when the source requires manual effort to get updates\n- Support for data subscribers is provided directly by the domain experts who curated the data sets\n- Every data source’s license is manually verified to allow for royalty-free commercial use and redistribution.\n\n**Need Help?**\nIf you have questions about our products, contact us at [info@johnsnowlabs.com](mailto:info@johnsnowlabs.com). \n 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All results are aggregated and no data pertaining to unique individuals is shared.\n\nBeyond identifying that users are tourists, we can also classify them into different bins by their frequency / dwell time over their estimated number of visits. Using our data and frequency, we can identify: overnight visitors, weekend visits, short-term stays, long-term stays, or frequent holiday visitors.\n\n\nAdditional insights include:\n- Brand index - Learn which major brands and retailers your audience is visiting the most\n- Visitation index - See which other destinations your visitors are visiting the most\n\nData Compliance:\nAll of our Tourism Analyses are fully CCPA compliant and 100% sourced from SDKs (Software Development Kits), the most reliable and consistent mobile data stream with end user consent available with only a 4-5 day delay. This means that our location and device ID data comes from partnerships with over 1,500+ mobile apps. While MAIDs make up the backbone of our Tourism Analyses, individual identifiers or insights are never passed back.\n\nData Quality:\nIn addition to partnering with trusted SDKs, DRAKO has additional screening methods to ensure that our mobile location data is consistent and reliable. This includes data harmonization and quality scoring from all of our partners in order to disregard MAIDs with a low quality score.","terms_of_service":"https://drakomediagroup.com/privacy-policy","contact_email":"support@datarade.ai","privacy_policy_link":"https://drakomediagroup.com/privacy-policy","assets":["ASSET_TYPE_DATA_TABLE"]},"provider_summary":{"name":"DRAKO","description":"Drako Data stands at the forefront of geolocation data solutions, offering value-added services designed to empower our clients' analytics capabilities. We understand the complexities of extracting meaningful insights from raw geolocation data. 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Covering the country’s most granular official statistical units (IRIS), this dataset offers detailed estimates of **average personal income**, **household income**, and **disposable household income**, providing a powerful foundation for **location intelligence, market segmentation, and socio-economic analytics**. \n\nBuilt using official sources from INSEE and enhanced with Geolocet’s proprietary spatial modelling techniques, this dataset is designed for **accuracy**, **consistency**, and smooth integration into analytical workflows like Databricks, GIS platforms, and BI tools.\n\n---\n\n## **Product highlights** \n- **Nation-wide, fine-granular coverage** – All mainland France IRIS zones (≈50,000 units)\n- **Three key income indicators**: \n - Average personal income (€ per person, per year) \n - Average household income (€ per household, per year) \n - Average disposable household income (€ per household, per year) \n- **High spatial precision** – Each IRIS unit typically ~1,370 people \n- **Formats ready for analytics** – Provided in CSV and Shapefile (EPSG:4326) \n\n---\n\n## **Field overview** \nThe dataset contains key fields including, but not limited to: \n- `IRIS_CODE` – Official IRIS zone identifier \n- `MUN_NAME` – Municipality name \n- `REGION_NAME` – Region name \n- `POP_TOT` – Total resident population \n- `HH_TOT` – Total number of households \n- `AVG_PERSONAL_INC` – Average personal income (€ per person/year) \n- `AVG_HH_INC` – Average household income (€ per household/year) \n- `AVG_HH_DISP_INC` – Average household disposable income (€ per household/year) \n- `GEOMETRY` – Polygon geometry for mapping (Shapefile version) \n\nThese structured fields allow seamless integration into geospatial analyses, predictive models, and segmentation workflows.\n\n---\n\n## **Additional Insights** \nThe IRIS-level dataset enables users to move beyond broad municipal averages and dive into **neighbourhood-level income dynamics**—critical for retail site location, audience targeting, credit risk modelling, and public policy planning. For example, city centres, suburban zones and rural regions each display very different income patterns which this data surface clearly. \n\nWant to know more about the average income in the French neighbourhoods? \n\uD83D\uDC49 [Read more](https://geolocet.com/blogs/news/french-personal-and-household-income-data-at-granular-level) \n\n\uD83D\uDCD8 Explore the French dataset on our website: \n\uD83D\uDC49 [France Income Distribution Dataset – IRIS Level](https://geolocet.com/products/france-income-indicators-iris)\n\nInterested in accessing the full version of the dataset? \n\uD83D\uDC49 [Get in touch with Geolocet](https://geolocet.com/pages/contact-us) \n\n---\n\n## **Use cases** \n- Conduct granular **market segmentation** by income band and geography \n- Select optimal **retail or service locations** based on disposable household income \n- Enrich **real-estate investment models** with neighbourhood-level purchasing power data \n- Feed into **machine-learning workflows** for forecasting consumer behaviour or credit risk \n- Enable **public policy & academic research** into income inequality, social mobility and spatial economics \n","terms_of_service":"https://geolocet.com/pages/terms-of-use","documentation_link":"","privacy_policy_link":"https://geolocet.com/pages/privacy-policy","cost":"PAID","data_source":"","assets":["ASSET_TYPE_DATA_TABLE","ASSET_TYPE_MEDIA"],"license":"https://geolocet.com/pages/terms-of-use"},"provider_summary":{"name":"Geolocet LTD","description":"Geolocet provides premium geospatial data for retail analysis, GIS, and market planning. 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This meticulously compiled dataset, sourced from official statistical sources, including MONSTAT, offers a detailed insight into the population landscape of Montenegro at the municipality (општине) level. \n\nIf you require data at a less granular level, such as an Uber grid or square grid, please don't hesitate to get in touch with us at *contact@geolocet.com*. We understand that different projects may have varying data needs, and we are here to tailor our data to match your specific requirements.\n\n ## **Key Features:**\n---\n* **Population Data Montenegro:** Gain access to the latest and most accurate population statistics for Montenegro, meticulously organized by municipality (општине).\n\n* **Demographic Insights:** Dive deep into Montenegro’s demographic landscape with data on 22 attributes, including **total population**, **age**, and **gender** distributions.\n\nAll demographic attributes are available at the municipal (општине) level.\n* **Seamless Data Access:** Our data is available for instant download in CSV format. No lengthy sign-up processes or NDAs required on our side – just download the data you need.\n* **Data accuracy:** Our datasets are created using the latest demographics data available in national statistics bureaus, guaranteeing accuracy and reliability.\n---\nAdditionally, for those looking to enrich their analysis or project, we offer:\n* **[Administrative Boundaries](https://geolocet.com/products/montenegro-all-available-administrative-boundaries):** If you require geographical boundaries (polygons) for the municipalities of Montenegro, consider exploring our Administrative Boundaries product, which seamlessly integrates with this dataset.\n\n* **[Point of Interest (PoI) Data](https://geolocet.com/pages/aggregated-points-of-interest-poi-data):** If you're interested in Points of Interest data, please don't hesitate to contact us for customized datasets to enhance your geographical analysis.\n\n* **[Data Customization](https://geolocet.com/pages/data-services-by-geolocet):** We understand that your project may have specific requirements. Feel free to get in touch with us if you need any modifications or specific data formats such as Excel, JSON, GeoJSON, or Shapefile. We are here to tailor the data to your needs.\n\nMake informed decisions, conduct precise research, and unlock the potential of the Montenegro demographic landscape with our comprehensive dataset. Trust in the accuracy and reliability of our official statistical data to drive your projects forward.\nContact us at *contact@geolocet.com* today to access this invaluable resource and explore Montenegro demographics data like never before.\n\n[Geolocet LTD](https://geolocet.com)\n","terms_of_service":"https://geolocet.com/pages/terms-of-use","documentation_link":"","privacy_policy_link":"https://geolocet.com/pages/privacy-policy","geographical_coverage":"[\"Europe\"]","cost":"FREE","update_frequency":{"interval":1,"unit":"YEARLY"},"assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://geolocet.com/pages/terms-of-use"},"provider_summary":{"name":"Geolocet LTD","description":"Geolocet provides premium geospatial data for retail analysis, GIS, and market planning. We equip businesses with accurate, ready-to-query data to drive spatial analysis, site selection, and customer segmentation.\n\nCore Offerings:\n\nDemographics: Socioeconomic data covering population, age, and employment across diverse geographies.\n\nPoints of Interest (PoI): Datasets spanning retail, healthcare, and transport for catchment analysis.\n\nAdministrative Boundaries: GIS-ready boundaries, including municipalities and postcodes, for territory management.\n\nBrand Locations: Chain location datasets to monitor store networks and market expansion.\n\nWealth Data: Granular economic indicators like purchasing power to identify high-potential markets.\n\nWe source from trusted providers, delivering highly customizable datasets with comprehensive coverage across Europe and worldwide."},"popularity":175},{"id":"5b1292c1-fe1b-4783-92f9-3abd990bbc21","summary":{"name":"Medicare Helpful Contacts Information","subtitle":"This data package comprises of a database of agencies listed by state and topic to provide the individuals with contac..","share":{"name":"medicare_helpful_contacts_information","type":"FULL"},"setting":{"visibility":"PUBLIC"},"created_at":1682356143704,"updated_at":1695691609798,"categories":["HEALTH","SCIENCE_AND_RESEARCH"],"listingType":"STANDARD","provider_id":"f88849f8-9d77-4de2-b4c7-03d55cc3768e"},"detail":{"description":"\n**Overview**\nThis data package comprises of a database of agencies listed by state and topic to provide the individuals with contact information for specific organizations or help them get answers to their Medicare related questions. It also includes a list of suppliers that indicates the supplies carried at that location and the supplier's Medicare participation status.\n\n**Description**\nThis data package contains the Helpful Contacts Information Agency List by Organization is provided by the Centers for Medicare & Medicaid Services (CMS). They provide contact information for specific organizations that can help the individuals with Medicare-related questions. While Helpful Contacts Information Agency List By Topic provides contacts information for specific topics that can help the individuals with Medicare-related questions. CMS gathered the data contained in the supplier directory from the National Supplier Clearinghouse (NSC). This directory helps to provide names, addresses, and contact information for suppliers that provide services or products under the Medicare program throughout the U.S. Medicare’s Durable Medical Equipment, Prosthetics, Orthotics, and Supplies (DMEPOS) Competitive Bidding Program changes the amount Medicare pays for certain DMEPOS items. Under this program, suppliers submit bids to provide certain medical equipment and supplies to people with Medicare living in, or visiting, competitive bidding areas. Medicare uses these bids to set the amount it pays for each item. Qualified, accredited suppliers with winning bids are chosen as Medicare contract suppliers. The program: i). Helps an individual and Medicare save money, ii). Ensures that an individual continue to get quality products from accredited suppliers, and iii). Helps limit fraud and abuse in the Medicare Program. The MED Suppliers Network is a group of durable medical equipment suppliers who have met the quality standards and accreditation requirements set forth by Content Management System (CMS) and were awarded contracts for Competitively Bid items. Also listed on the site are durable medical equipment suppliers across the nation who can supply product to Medicare patients who are not currently affected by Competitive Bidding.\n\n**Benefits**\n- Useful for doctors, patients, medicare suppliers and medical research groups.\n- medicare helpful contacts information database is available on centers for medicare & medicaid services website in raw form, whereas a jsl customer gets it in clean and normalized form ready to use and create his own custom reports. easy to comprehend for the customers who are unfamiliar with the medical terminologies as all abbreviated terms are replaced with full form, centers for medicare & medicaid services original datasets which are full of abbreviations.\n\n**License Information**\nThe use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. \n\n**Included Datasets**\n- [Helpful Contacts Information Agency List By Organization](https://www.johnsnowlabs.com/marketplace/helpful-contacts-information-agency-list-by-organization)\n\t- This dataset comprises of a database of agencies listed by state and topic to provide the individuals with contact information for specific organizations or help them get answers to their Medicare related questions.\n- [Helpful Contacts Information Agency List By Topic](https://www.johnsnowlabs.com/marketplace/helpful-contacts-information-agency-list-by-topic)\n\t- This dataset comprises of a database of agencies listed by state and topic to provide individuals with the contact information for specific organizations or help them get answers to their Medicare related questions.\n- [Indian Health Care Providers](https://www.johnsnowlabs.com/marketplace/indian-health-care-providers)\n\t- This dataset identifies Indian healthcare providers nationally, so that qualified health plan issuers can identify the Indian healthcare provider organizations and sites that are located in the service areas of their Quality Health Providers (QHPs). It includes tribal providers, urban Indian organization providers, and Indian Health Service (IHS) facilities.\n- [Supplier Medicare Participation](https://www.johnsnowlabs.com/marketplace/supplier-medicare-participation)\n\t- This dataset includes a list of Suppliers that indicates the supplies carried at that location and the supplier's Medicare participation status. It comprises of general information about the Medicare suppliers such as their business name, company address, phone numbers and more. 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Hometree’s datasets provide critical early warning capabilities by monitoring global open-source intelligence across multiple languages and regions, delivering actionable insights days or weeks before official government reporting channels.\n\n**Use cases**\nStrategic Intelligence Applications: 
Biological Threat Detection & Assessment: Our multilingual datasets captures emerging biological threats in real-time across conflict zones, politically sensitive areas, and regions with limited official health infrastructure. Intelligence analysts can identify potential bioweapons activity, assess natural outbreak patterns that could destabilize allied nations, and monitor cross-border disease transmission that threatens U.S. interests. With historical data dating to 2021, analysts can establish baseline patterns and detect anomalous biological activity indicative of state or non-state actor involvement.\n\nGeopolitical Risk Analysis: 
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Trust in the accuracy and reliability of our official statistical data to drive your projects forward.\nContact us at *contact@geolocet.com* today to access this invaluable resource and explore Greece census data like never before.\n\n[Geolocet LTD](https://geolocet.com)\n","terms_of_service":"https://geolocet.com/pages/terms-of-use","documentation_link":"","privacy_policy_link":"https://geolocet.com/pages/privacy-policy","geographical_coverage":"[\"Europe\"]","cost":"PAID","update_frequency":{"interval":1,"unit":"YEARLY"},"data_source":"","assets":["ASSET_TYPE_DATA_TABLE"],"license":"https://geolocet.com/pages/terms-of-use"},"provider_summary":{"name":"Geolocet LTD","description":"Geolocet provides premium geospatial data for retail analysis, GIS, and market planning. 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