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787 datasets for “machine learning”
- data.gov.au
Predicting Seabed Sand Content across the Australian Margin Using Machine Learning and Geostatistical Methods
Australian Ocean Data NetworkUpdated 23 Jan 2026
In this study, we aim to identify the most appropriate methods for spatial interpolation of seabed sand content …
- HTML
- data.gov.au
Predicting Seabed Sand Content across the Australian Margin Using Machine Learning and Geostatistical Methods
Australian Ocean Data NetworkUpdated 12 Feb 2026
In this study, we aim to identify the most appropriate methods for spatial interpolation of seabed sand content …
- HTML
- data.gov.au
Predicting Seabed Sand Content across the Australian Margin Using Machine Learning and Geostatistical Methods
Australian Ocean Data NetworkUpdated 5 Mar 2026
In this study, we aim to identify the most appropriate methods for spatial interpolation of seabed sand content …
- HTML
- data.gov.au
Predicting Seabed Sand Content across the Australian Margin Using Machine Learning and Geostatistical Methods
Australian Ocean Data NetworkUpdated 16 Mar 2026
In this study, we aim to identify the most appropriate methods for spatial interpolation of seabed sand content …
- HTML
- data.gov.au
Predicting Seabed Sand Content across the Australian Margin Using Machine Learning and Geostatistical Methods
Australian Ocean Data NetworkUpdated 29 Mar 2026
In this study, we aim to identify the most appropriate methods for spatial interpolation of seabed sand content …
- HTML
- data.gov.au
Predicting Seabed Sand Content across the Australian Margin Using Machine Learning and Geostatistical Methods
Australian Ocean Data NetworkUpdated 4 Apr 2026
In this study, we aim to identify the most appropriate methods for spatial interpolation of seabed sand content …
- HTML
- data.gov.au
Predicting Seabed Sand Content across the Australian Margin Using Machine Learning and Geostatistical Methods
Australian Ocean Data NetworkUpdated 10 Apr 2026
In this study, we aim to identify the most appropriate methods for spatial interpolation of seabed sand content …
- HTML
- data.gov.au
Predicting Seabed Sand Content across the Australian Margin Using Machine Learning and Geostatistical Methods
Australian Ocean Data NetworkUpdated 16 Apr 2026
In this study, we aim to identify the most appropriate methods for spatial interpolation of seabed sand content …
- HTML
- data.gov.au
Predicting Seabed Sand Content across the Australian Margin Using Machine Learning and Geostatistical Methods
Australian Ocean Data NetworkUpdated 28 Apr 2026
In this study, we aim to identify the most appropriate methods for spatial interpolation of seabed sand content …
- HTML
- data.gov.au
Predicting Seabed Sand Content across the Australian Margin Using Machine Learning and Geostatistical Methods
Australian Ocean Data NetworkUpdated 5 May 2026
In this study, we aim to identify the most appropriate methods for spatial interpolation of seabed sand content …
- HTML
- data.gov.au
Predicting Seabed Sand Content across the Australian Margin Using Machine Learning and Geostatistical Methods
Australian Ocean Data NetworkUpdated 4 Jun 2026
In this study, we aim to identify the most appropriate methods for spatial interpolation of seabed sand content …
- HTML
- data.gov.au
Predicting Seabed Sand Content across the Australian Margin Using Machine Learning and Geostatistical Methods
Australian Ocean Data NetworkUpdated 17 Jun 2026
In this study, we aim to identify the most appropriate methods for spatial interpolation of seabed sand content …
- HTML
- data.gov.au
Predicting Seabed Sand Content across the Australian Margin Using Machine Learning and Geostatistical Methods
Australian Ocean Data NetworkUpdated 23 Jun 2026
In this study, we aim to identify the most appropriate methods for spatial interpolation of seabed sand content …
- HTML
- data.gov.au
Predicting Seabed Sand Content across the Australian Margin Using Machine Learning and Geostatistical Methods
Australian Ocean Data NetworkUpdated 31 Jul 2026
In this study, we aim to identify the most appropriate methods for spatial interpolation of seabed sand content …
- HTML
- Opendatasoft hubLive preview
Original data for "Chapuis et al., 2020. A young age of subspecific divergence in the desert locust Schistocerca gregaria inferred by ABC Random Forest. Molecular Ecology, 29(23): 4542-4558."
CHAPUIS, Marie-PierreUpdated 7 Jan 2021
Lien vers la fiche source Microsatellite genotypic data and R scripts for ABC-RF analyses
- CSV
- JSON
- GEOJSON
- ODS API
- Data.gov Catalog
Closed-loop Autonomous Materials Exploration and Optimization 1.0
National Institute of Standards and TechnologyUpdated 20 Aug 2020
Code and demonstration data for the paper, "On-the-fly closed-loop materials discovery via Bayesian active learning …
- X-ZIP-COMPRESSED
- PLAIN
- GovData Germany
GlobalBuildingMap
open.bydataUpdated 11 Nov 2026
This is the second version of the dataset. The GlobalBuildingMap (GBM) dataset provides the highest resolution and highest …
- data.gov.au
Advancing Radiometric Mapping in Australia: Bridging Gaps and Enhancing Mineral Exploration
Geoscience Australia DataUpdated 24 Jun 2026
This study advances radiometric mapping in Australia by applying machine learning and deep learning to enhance potassium concentration …
- HTML
- Data.gov Catalog
2016–2019 Progress Report: Advancing Artificial Intelligence R&D
NCO NITRDUpdated 20 Nov 2019
This document reports current trends and examples of Federal R&D investments, program information, and activities in artificial …
- data.gov.au
AEM Assist: A national machine learning tool for airborne electromagnetic interpretation and extrapolation
Geoscience Australia DataUpdated 16 Oct 2025
… To address this challenge, we have developed a machine learning workflow to assist with the interpretation of AEM …