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773 datasets for “machine-learning”

  1. 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 …

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    • HTML
  2. 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 …

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    • HTML
  3. 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 …

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    • HTML
  4. 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 …

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    • HTML
  5. 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 …

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    • HTML
  6. 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
  7. 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 …

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  8. Data.gov Catalog

    Data from: Topographic position index predicts within-field yield variation in a dryland cereal production system

    Agricultural Research ServiceUpdated 21 Nov 2025

    We investigated drivers of sub-field spatial variability in yield for 3 crops (hard red winter wheat, Triticum …

    • CSV
    • TIF
  9. GovData Germany

    DL Surrogates, Training Data Set (8640 samples)

    open.bydataUpdated 14 Nov 2026

    8640 numpy arrays with fluid flow data (tar.bz2) This data set contains 8640 numpy arrays with fluid …

  10. data.gov.au

    Predicting Seabed Mud Content across the Australian Margin: Performance of Machine Learning Methods and their combinations with Ordinary Kriging and Inverse Distance Squared

    Geoscience Australia DataUpdated 17 Oct 2025

    … In this study, we further test the performance of machine learning methods in combination with ordinary kriging (OK …

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  11. data.gov.au

    Predicting Seabed Mud Content across the Australian Margin: Performance of Machine Learning Methods and their combinations with Ordinary Kriging and Inverse Distance Squared

    Geoscience Australia DataUpdated 20 Jan 2026

    … In this study, we further test the performance of machine learning methods in combination with ordinary kriging (OK …

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    • HTML
  12. data.gov.au

    Predicting Seabed Mud Content across the Australian Margin: Performance of Machine Learning Methods and their combinations with Ordinary Kriging and Inverse Distance Squared

    Geoscience Australia DataUpdated 2 Mar 2026

    … In this study, we further test the performance of machine learning methods in combination with ordinary kriging (OK …

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    • HTML
  13. data.gov.au

    Predicting Seabed Mud Content across the Australian Margin: Performance of Machine Learning Methods and their combinations with Ordinary Kriging and Inverse Distance Squared

    Geoscience Australia DataUpdated 7 Mar 2026

    … In this study, we further test the performance of machine learning methods in combination with ordinary kriging (OK …

    • PDF
    • HTML
  14. data.gov.au

    Predicting Seabed Mud Content across the Australian Margin: Performance of Machine Learning Methods and their combinations with Ordinary Kriging and Inverse Distance Squared

    Geoscience Australia DataUpdated 25 Mar 2026

    … In this study, we further test the performance of machine learning methods in combination with ordinary kriging (OK …

    • PDF
    • HTML
  15. data.gov.au

    Predicting Seabed Mud Content across the Australian Margin: Performance of Machine Learning Methods and their combinations with Ordinary Kriging and Inverse Distance Squared

    Geoscience Australia DataUpdated 20 Apr 2026

    … In this study, we further test the performance of machine learning methods in combination with ordinary kriging (OK …

    • PDF
    • HTML
  16. data.gov.au

    Predicting Seabed Mud Content across the Australian Margin: Performance of Machine Learning Methods and their combinations with Ordinary Kriging and Inverse Distance Squared

    Geoscience Australia DataUpdated 30 Apr 2026

    … In this study, we further test the performance of machine learning methods in combination with ordinary kriging (OK …

    • PDF
    • HTML
  17. data.gov.au

    Predicting Seabed Mud Content across the Australian Margin: Performance of Machine Learning Methods and their combinations with Ordinary Kriging and Inverse Distance Squared

    Geoscience Australia DataUpdated 14 May 2026

    … In this study, we further test the performance of machine learning methods in combination with ordinary kriging (OK …

    • PDF
    • HTML
  18. data.gov.au

    Predicting Seabed Mud Content across the Australian Margin: Performance of Machine Learning Methods and their combinations with Ordinary Kriging and Inverse Distance Squared

    Australian Ocean Data NetworkUpdated 11 Jan 2026

    … In this study, we further test the performance of machine learning methods in combination with ordinary kriging (OK …

    • PDF
    • HTML
  19. data.gov.au

    Predicting Seabed Mud Content across the Australian Margin: Performance of Machine Learning Methods and their combinations with Ordinary Kriging and Inverse Distance Squared

    Australian Ocean Data NetworkUpdated 23 Jan 2026

    … In this study, we further test the performance of machine learning methods in combination with ordinary kriging (OK …

    • PDF
    • HTML
  20. data.gov.au

    Predicting Seabed Mud Content across the Australian Margin: Performance of Machine Learning Methods and their combinations with Ordinary Kriging and Inverse Distance Squared

    Australian Ocean Data NetworkUpdated 12 Feb 2026

    … In this study, we further test the performance of machine learning methods in combination with ordinary kriging (OK …

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