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787 datasets for “machine learning”
- 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 …
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- data.gov.au
Building a Better Geochemical Picture of the Australian Landscape
Geoscience Australia DataUpdated 16 Oct 2025
… Levelled samples were used to train a machine learning algorithm (RandomForests) to generate a suite of predictive geochemical …
- data.gov.au
Revised weathering intensity model of Australia
Geoscience Australia DataUpdated 16 Oct 2025
… Here, I use a random forest decision tree machine learning algorithm to first establish a relationship between field …
- 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
- 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
- Data.gov Catalog
INGENIOUS - Great Basin Regional Dataset Compilation
GBCGE, NBMG, UNRUpdated 6 Mar 2025
This is the regional dataset compilation for the INnovative Geothermal Exploration through Novel Investigations Of Undiscovered Systems (INGENIOUS …
- HTML
- ZIP
- 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
- Data.gov Catalog
JARVIS: Joint Automated Repository for Various Integrated Simulations
National Institute of Standards and TechnologyUpdated 18 Oct 2017
… The Machine-learning section of JARVIS (JARVIS-ML) consists of machine learning prediction tools, trained on JARVIS-DFT …
- TEXT/HTML
- 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
- 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
- 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
- Data.gov Catalog
US EPA EnviroAtlas Meter-Scale Urban Land Cover (MULC) Data Characteristics
U.S. EPA Office of Research and Development (ORD)Updated 1 Jan 2020
Meter-scale Urban Land Cover (MULC), a unique, high resolution (one meter2 per pixel) land cover dataset, has …
- VND.OPENXMLFORMATS-OFFICEDOCUMENT.SPREADSHEETML.SHEET
- 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
… replace the MVP and will be updated regularly. Machine translation was used in the preparation of this document …
- CSV
- Data.gov Catalog
CameraTrapDetectoR Species Model
Agricultural Research ServiceUpdated 21 Nov 2025
CameraTrapDetectoR is an R package that uses deep learning computer vision models to automatically detect, count, and classify …
- ZIP
- 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
- 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
- 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
- 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
- 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
- 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