Skip to content
Open Data Finder

Search results for simulation data

FiltersPDF

571 datasets for “simulation data”

  1. data.gov.au

    Source Rock Distributions and Petroleum Fluid Bulk Compositional Predictions on the Vulcan Sub-basin, Offshore Western Australia

    Australian Ocean Data NetworkUpdated 5 May 2026

    … model of Fuji et al. (APPEA 2004) were simulated using the PetroMod 3D V.10 modeling software. In …

    • PDF
    • HTML
  2. data.gov.au

    Source Rock Distributions and Petroleum Fluid Bulk Compositional Predictions on the Vulcan Sub-basin, Offshore Western Australia

    Australian Ocean Data NetworkUpdated 4 Jun 2026

    … model of Fuji et al. (APPEA 2004) were simulated using the PetroMod 3D V.10 modeling software. In …

    • PDF
    • HTML
  3. data.gov.au

    Source Rock Distributions and Petroleum Fluid Bulk Compositional Predictions on the Vulcan Sub-basin, Offshore Western Australia

    Australian Ocean Data NetworkUpdated 16 Jun 2026

    … model of Fuji et al. (APPEA 2004) were simulated using the PetroMod 3D V.10 modeling software. In …

    • PDF
    • HTML
  4. data.gov.au

    Source Rock Distributions and Petroleum Fluid Bulk Compositional Predictions on the Vulcan Sub-basin, Offshore Western Australia

    Australian Ocean Data NetworkUpdated 23 Jun 2026

    … model of Fuji et al. (APPEA 2004) were simulated using the PetroMod 3D V.10 modeling software. In …

    • PDF
    • HTML
  5. data.gov.au

    Source Rock Distributions and Petroleum Fluid Bulk Compositional Predictions on the Vulcan Sub-basin, Offshore Western Australia

    Australian Ocean Data NetworkUpdated 27 Jun 2026

    … model of Fuji et al. (APPEA 2004) were simulated using the PetroMod 3D V.10 modeling software. In …

    • PDF
    • HTML
  6. Open Government Canada

    Number of large fires (>200 hectares) - Reference Period (1981-2010)

    Natural Resources Canada | Ressources naturelles CanadaUpdated 7 Feb 2022

    … Future climatic data were simulated using the Canadian Earth System Model version 2 (CanESM2) and downscaled at a …

    • ESRI REST
    • PDF
    • FGDB/GDB
    • KML
    • MXD
    • WMS
  7. 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

    … samples extracted from Geoscience Australia's Marine Samples Database (MARS). The performance of 18 methods (machine learning methods …

    • PDF
    • HTML
  8. 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

    … samples extracted from Geoscience Australia's Marine Samples Database (MARS). The performance of 18 methods (machine learning methods …

    • PDF
    • HTML
  9. 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

    … samples extracted from Geoscience Australia's Marine Samples Database (MARS). The performance of 18 methods (machine learning methods …

    • PDF
    • HTML
  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 7 Mar 2026

    … samples extracted from Geoscience Australia's Marine Samples Database (MARS). The performance of 18 methods (machine learning methods …

    • PDF
    • HTML
  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 25 Mar 2026

    … samples extracted from Geoscience Australia's Marine Samples Database (MARS). The performance of 18 methods (machine learning methods …

    • PDF
    • 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 20 Apr 2026

    … samples extracted from Geoscience Australia's Marine Samples Database (MARS). The performance of 18 methods (machine learning methods …

    • PDF
    • 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 30 Apr 2026

    … samples extracted from Geoscience Australia's Marine Samples Database (MARS). The performance of 18 methods (machine learning methods …

    • 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 14 May 2026

    … samples extracted from Geoscience Australia's Marine Samples Database (MARS). The performance of 18 methods (machine learning methods …

    • 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

    Australian Ocean Data NetworkUpdated 11 Jan 2026

    … samples extracted from Geoscience Australia's Marine Samples Database (MARS). The performance of 18 methods (machine learning methods …

    • 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

    Australian Ocean Data NetworkUpdated 23 Jan 2026

    … samples extracted from Geoscience Australia's Marine Samples Database (MARS). The performance of 18 methods (machine learning methods …

    • 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

    Australian Ocean Data NetworkUpdated 12 Feb 2026

    … samples extracted from Geoscience Australia's Marine Samples Database (MARS). The performance of 18 methods (machine learning methods …

    • 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 5 Mar 2026

    … samples extracted from Geoscience Australia's Marine Samples Database (MARS). The performance of 18 methods (machine learning methods …

    • 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 16 Mar 2026

    … samples extracted from Geoscience Australia's Marine Samples Database (MARS). The performance of 18 methods (machine learning methods …

    • 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 29 Mar 2026

    … samples extracted from Geoscience Australia's Marine Samples Database (MARS). The performance of 18 methods (machine learning methods …

    • PDF
    • HTML
“simulation data” datasets · Open Data Finder