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773 datasets for “machine-learning”

  1. data.gov.au

    Application of random forest, generalised linear model and their hybrid methods with geostatistical techniques to count data: Predicting sponge species richness

    Australian Ocean Data NetworkUpdated 11 Jan 2026

    Spatial distribution of sponge species richness (SSR) and its relationship with environment are important for marine ecosystem management …

    • HTML
  2. data.gov.au

    Application of random forest, generalised linear model and their hybrid methods with geostatistical techniques to count data: Predicting sponge species richness

    Australian Ocean Data NetworkUpdated 23 Jan 2026

    Spatial distribution of sponge species richness (SSR) and its relationship with environment are important for marine ecosystem management …

    • HTML
  3. data.gov.au

    Application of random forest, generalised linear model and their hybrid methods with geostatistical techniques to count data: Predicting sponge species richness

    Australian Ocean Data NetworkUpdated 12 Feb 2026

    Spatial distribution of sponge species richness (SSR) and its relationship with environment are important for marine ecosystem management …

    • HTML
  4. data.gov.au

    Application of random forest, generalised linear model and their hybrid methods with geostatistical techniques to count data: Predicting sponge species richness

    Australian Ocean Data NetworkUpdated 5 Mar 2026

    Spatial distribution of sponge species richness (SSR) and its relationship with environment are important for marine ecosystem management …

    • HTML
  5. data.gov.au

    Application of random forest, generalised linear model and their hybrid methods with geostatistical techniques to count data: Predicting sponge species richness

    Australian Ocean Data NetworkUpdated 16 Mar 2026

    Spatial distribution of sponge species richness (SSR) and its relationship with environment are important for marine ecosystem management …

    • HTML
  6. data.gov.au

    Application of random forest, generalised linear model and their hybrid methods with geostatistical techniques to count data: Predicting sponge species richness

    Australian Ocean Data NetworkUpdated 29 Mar 2026

    Spatial distribution of sponge species richness (SSR) and its relationship with environment are important for marine ecosystem management …

    • HTML
  7. data.gov.au

    Application of random forest, generalised linear model and their hybrid methods with geostatistical techniques to count data: Predicting sponge species richness

    Australian Ocean Data NetworkUpdated 4 Apr 2026

    Spatial distribution of sponge species richness (SSR) and its relationship with environment are important for marine ecosystem management …

    • HTML
  8. data.gov.au

    Application of random forest, generalised linear model and their hybrid methods with geostatistical techniques to count data: Predicting sponge species richness

    Australian Ocean Data NetworkUpdated 10 Apr 2026

    Spatial distribution of sponge species richness (SSR) and its relationship with environment are important for marine ecosystem management …

    • HTML
  9. data.gov.au

    Application of random forest, generalised linear model and their hybrid methods with geostatistical techniques to count data: Predicting sponge species richness

    Australian Ocean Data NetworkUpdated 16 Apr 2026

    Spatial distribution of sponge species richness (SSR) and its relationship with environment are important for marine ecosystem management …

    • HTML
  10. data.gov.au

    Application of random forest, generalised linear model and their hybrid methods with geostatistical techniques to count data: Predicting sponge species richness

    Australian Ocean Data NetworkUpdated 5 May 2026

    Spatial distribution of sponge species richness (SSR) and its relationship with environment are important for marine ecosystem management …

    • HTML
  11. data.gov.au

    Application of random forest, generalised linear model and their hybrid methods with geostatistical techniques to count data: Predicting sponge species richness

    Australian Ocean Data NetworkUpdated 5 Jun 2026

    Spatial distribution of sponge species richness (SSR) and its relationship with environment are important for marine ecosystem management …

    • HTML
  12. data.gov.au

    Application of random forest, generalised linear model and their hybrid methods with geostatistical techniques to count data: Predicting sponge species richness

    Australian Ocean Data NetworkUpdated 16 Jun 2026

    Spatial distribution of sponge species richness (SSR) and its relationship with environment are important for marine ecosystem management …

    • HTML
  13. data.gov.au

    Application of random forest, generalised linear model and their hybrid methods with geostatistical techniques to count data: Predicting sponge species richness

    Australian Ocean Data NetworkUpdated 23 Jun 2026

    Spatial distribution of sponge species richness (SSR) and its relationship with environment are important for marine ecosystem management …

    • HTML
  14. data.gov.au

    Application of random forest, generalised linear model and their hybrid methods with geostatistical techniques to count data: Predicting sponge species richness

    Australian Ocean Data NetworkUpdated 27 Jun 2026

    Spatial distribution of sponge species richness (SSR) and its relationship with environment are important for marine ecosystem management …

    • HTML
  15. Data.gov Catalog

    Flow Redirection and Induction in Steady State (FLORIS) Wind Plant Power Production Data Sets

    National Renewable Energy LaboratoryUpdated 2 Jan 2024

    … This dataset is intended to serve as a benchmark against which new artificial intelligence (AI) or machine learning …

    • HTML
    • CSV
  16. GovData Germany

    Tree Species - Sentinel-2 - Germany, 2016

    GDI-DEUpdated 4 Nov 2026

    The Tree Species Germany product provides a map of dominant tree species across Germany for the year 2016 …

    • GEOTIFF
  17. Data.gov Catalog

    Dataset for Evaluating the Impact of Wildfire Smoke on Solar Photovoltaic Production

    Sandia National LaboratoriesUpdated 6 Dec 2023

    This dataset was used to support machine learning model development for wildfire impacts on utility-scale solar in …

    • 121303
    • XLSX
  18. Data.gov Catalog

    Estimated floodplain map for the conterminous United States

    U.S. EPA Office of Research and Development (ORD)Updated 4 Jun 2018

    Understanding the relationship between flood inundation and floodplains is critical for ecosystem and community health and well-being …

    • VND.OPENXMLFORMATS-OFFICEDOCUMENT.SPREADSHEETML.SHEET
  19. Open Government Canada

    Government of Canada AI Register (Minimum Viable Product)

    Treasury Board of Canada Secretariat | Secrétariat du Conseil du Trésor du CanadaUpdated 28 Apr 2026

    The Government of Canada (GC) Artificial Intelligence (AI) Register collects information about the Government of Canada's AI …

    • CSV
    • PDF
  20. Data.gov Catalog

    Figure files for "Modular Autonomous Virtualization System for Two-Dimensional Semiconductor Quantum Dot Arrays" submitted to Physical Review X

    National Institute of Standards and TechnologyUpdated 3 Feb 2025

    … Our method employs machine learning techniques to rapidly extract features from two-dimensional charge stability diagrams. We then …

    • ZIP
    • PLAIN
“machine-learning” datasets · Open Data Finder