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773 datasets for “machine-learning”

  1. data.gov.uk

    ATR-FTIR transmission spectra for natural and man-made textile fibres

    Environmental Information Data CentreUpdated 13 Nov 2026

    This data resource contains 160 attenuated total reflectance Fourier transform infrared (ATR-FTIR) transmission spectra acquired from 137 …

    • ZIP
  2. Data.gov Catalog

    Airfoil Computational Fluid Dynamics - 2k shapes, 25 AoA's, 3 Re numbers

    National Renewable Energy Laboratory (NREL)Updated 2 Jan 2024

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

    • HTML
  3. data.gov.au

    Surficial and deep earth material prediction from geochemical compositions - a spatial predictive model

    Geoscience Australia DataUpdated 18 Nov 2025

    … The model is based on a combination of geostatistical simulation and machine learning approaches. A random forest predictive …

    • HTML
  4. Data.gov Catalog

    Aboveground Biomass Estimates for Salt Marsh for the Contiguous United States, 2020

    ORNL_DAACUpdated 5 Nov 2026

    … Estimates were generated by XGBoost machine learning regression. Salt marsh extent was classified using an ensemble of XGBoost …

    • ISO
    • PNG
    • ZIP
    • PDF
    • HTML
    • BIN
  5. Humanitarian Data Exchange

    Buildings of Nepal

    OSGeo NepalUpdated 9 May 2026

    … Combines crowd-sourced OpenStreetMap data with machine-learning building detections from Microsoft, Google, and Esri (via Overture Maps).

    • GEOPACKAGE
    • SHP
    • JSON
    • HTML
  6. Data.gov Catalog

    Airfoil Computational Fluid Dynamics - 9k shapes, 2 AoA's

    National Renewable Energy Laboratory (NREL)Updated 2 Jan 2024

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

    • HTML
  7. Data.gov Catalog

    Data and Code from: Smart vision-based monitoring system for AI-driven moth population estimation using camera-equipped trap imaging

    Agricultural Research ServiceUpdated 22 Aug 2025

    … Artificial intelligence (AI) and machine learning can provide the models necessary for accurate identification and population-counting within …

    • JPG
    • M
    • XLSX
    • DOCX
  8. Open Government Canada

    Digital Soil Mapping

    Government of Ontario | Gouvernement de l'OntarioUpdated 13 May 2026

    ## Purpose The Ministry of Agriculture, Food and Agribusiness (OMAFA) is responsible for Ontario’s provincial soil maps and …

    • ESRI REST
    • GEOTIF
    • HTML
  9. Socrata networkLive preview

    Pierce County Sentiment

    KyleUpdated 13 Feb 2020

    … Meltwater’s Natural Language Processing model is supported by AI and machine learning algorithms. Using this model, we …

    • CSV
    • JSON
    • SODA API
  10. Humanitarian Data Exchange

    Future Displacement Forecasts

    Danish Refugee CouncilUpdated 15 Jul 2026

    … displacement (IDPs, asylum seekers and refugees) one to three years into the future based on machine learning model.

    • XLSX
    • CSV
  11. Opendatasoft hubLive preview

    Physics-based Simulations of 3D Wave Propagation with Source Variability: HEMEW^S-3D

    Lehmann, FannyUpdated 19 Sept 2024

    Lien vers la fiche source The HEMEW^S-3D database contains 30,000 simulation results of the 3D …

    • CSV
    • JSON
    • GEOJSON
    • ODS API
  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

    Geoscience Australia DataUpdated 18 Nov 2025

    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

    Geoscience Australia DataUpdated 8 Jan 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

    Geoscience Australia DataUpdated 20 Jan 2026

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

    • HTML
  15. data.gov.au

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

    Geoscience Australia DataUpdated 2 Mar 2026

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

    • HTML
  16. data.gov.au

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

    Geoscience Australia DataUpdated 7 Mar 2026

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

    • HTML
  17. data.gov.au

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

    Geoscience Australia DataUpdated 25 Mar 2026

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

    • HTML
  18. data.gov.au

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

    Geoscience Australia DataUpdated 20 Apr 2026

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

    • HTML
  19. data.gov.au

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

    Geoscience Australia DataUpdated 30 Apr 2026

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

    • HTML
  20. 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 20 Dec 2025

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

    • HTML
“machine-learning” datasets · Open Data Finder