“For data visualizations” datasets

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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
In 2008, the performance of 14 statistical and mathematical methods for spatial interpolation was compared using samples of …
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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 4 Apr 2026
In 2008, the performance of 14 statistical and mathematical methods for spatial interpolation was compared using samples of …
- PDF
- HTML
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 10 Apr 2026
In 2008, the performance of 14 statistical and mathematical methods for spatial interpolation was compared using samples of …
- PDF
- HTML
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 Apr 2026
In 2008, the performance of 14 statistical and mathematical methods for spatial interpolation was compared using samples of …
- PDF
- HTML
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 May 2026
In 2008, the performance of 14 statistical and mathematical methods for spatial interpolation was compared using samples of …
- PDF
- HTML
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 Jun 2026
In 2008, the performance of 14 statistical and mathematical methods for spatial interpolation was compared using samples of …
- PDF
- HTML
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 17 Jun 2026
In 2008, the performance of 14 statistical and mathematical methods for spatial interpolation was compared using samples of …
- PDF
- HTML
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 Jun 2026
In 2008, the performance of 14 statistical and mathematical methods for spatial interpolation was compared using samples of …
- PDF
- HTML
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 27 Jun 2026
In 2008, the performance of 14 statistical and mathematical methods for spatial interpolation was compared using samples of …
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- HTML
Data.NSW
Foundation for Conservation in the Riverina Bioregion VIS_ID 984
NSW Department of Climate Change, Energy, the Environment and WaterUpdated 3 Oct 2026
"Vegetation coverage derived for the NSW Component of the Riverina Bioregion (IBRA). Data layer was prepared as part …
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- ZIP
data.gov.au
Foundation for Conservation in the Riverina Bioregion VIS_ID 984
NSW Department of Climate Change, Energy, the Environment and WaterUpdated 14 Nov 2025
"Vegetation coverage derived for the NSW Component of the Riverina Bioregion (IBRA). Data layer was prepared as part …
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- ZIP
California Open Data
ROV Surveys of the Mid-depth and Deep Subtidal Ecosystems, California North Coast MPA Baseline Study, 2014 to 2015
California Ocean Protection CouncilUpdated 12 Nov 2026
… were also surveyed for comparison. During the first two years following implementation, benthic visual surveys were conducted using …
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- ZIP
- CSV
- XML
data.gov.au
Smart Pedestrian Project
Transport for NSWUpdated 15 Aug 2025
… The data is stripped of any identification and relayed and collected for analysis by researchers from the University …
- DATA
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- JSON
Data.gov Catalog
Digital Geologic-GIS Map of Salinas Pueblo Missions National Monument and Vicinity, New Mexico (NPS, GRD, GRI, SAPU, SAPU digital map) adapted from a New Mexico Bureau of Geology and Mineral Resources Open-File Geologic Map by Scott, Elrick, Connell, and Karlstrom (2005) and New Mexico Bureau of Geology and Mineral Resources Open-File Geologic Maps by Oviatt (2010, 2011, 2011)
National Park ServiceUpdated 11 Jul 2025
… Users are encouraged to only use the Google Earth data for basic visualization, and to use the GIS …
- PDF
- ZIP
data.gov.au
Regional geology and mineral systems of the Stavely region, western Victoria: Data release 4 - drill core rock property measurements
Geoscience Australia DataUpdated 16 Oct 2025
… for each scanned drill hole is presented, along with visual logs of the petrophysical data. Processed petrophysical data …
- PDF
- DOCX
- ZIP
- HTML
Data.gov Catalog
Digital Geologic-GIS Map of Canyonlands National Park and Vicinity, Utah (NPS, GRD, GRI, CANY, CANY digital map) adapted from a Canyonlands Natural History Association Map by Huntoon, Billingsley and Breed (1982)
National Park ServiceUpdated 20 Feb 2025
… Users are encouraged to only use the Google Earth data for basic visualization, and to use the GIS …
- PDF
- ZIP
data.gov.au
The Heavy Mineral Map of Australia: vision, implementation and progress
Geoscience Australia DataUpdated 16 Oct 2025
… We created a bespoke, cloud-based mineral network analysis (MNA) tool to visualize, explore and discover relationships between …
- PDF
Data.gov Catalog
Digital Geologic-GIS Map of Bryce Canyon National Park and Vicinity, Utah (NPS, GRD, GRI, BRCA, BRCA digital map) adapted from a U.S. Geological Survey Miscellaneous Investigations Series Map by Bowers (1990)
National Park ServiceUpdated 9 Jan 2025
… Users are encouraged to only use the Google Earth data for basic visualization, and to use the GIS …
- PDF
- ZIP
Data.gov Catalog
Digital Geologic-GIS Map of the Pine Ridge Indian Reservation Area, South Dakota (NPS, GRD, GRI, BADL, PRIR digital map) adapted from a U.S. Geological Survey Hydrologic Investigations Atlas map by Ellis and Adolphson (1971)
National Park ServiceUpdated 29 Jun 2023
… Users are encouraged to only use the Google Earth data for basic visualization, and to use the GIS …
- PDF
- ZIP
Data.gov Catalog
Digital Geologic-GIS Map of Fort Laramie National Historic Site and Vicinity, Wyoming (NPS, GRD, GRI, FOLA, FOLA digital map) adapted from a U.S. Geological Survey Bulletin map by McGrew (1963)
National Park ServiceUpdated 15 Nov 2023
… Users are encouraged to only use the Google Earth data for basic visualization, and to use the GIS …
- PDF
- ZIP

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

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

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

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

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

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

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

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

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

Foundation for Conservation in the Riverina Bioregion VIS_ID 984

Foundation for Conservation in the Riverina Bioregion VIS_ID 984

ROV Surveys of the Mid-depth and Deep Subtidal Ecosystems, California North Coast MPA Baseline Study, 2014 to 2015

Smart Pedestrian Project

Regional geology and mineral systems of the Stavely region, western Victoria: Data release 4 - drill core rock property measurements

Digital Geologic-GIS Map of Canyonlands National Park and Vicinity, Utah (NPS, GRD, GRI, CANY, CANY digital map) adapted from a Canyonlands Natural History Association Map by Huntoon, Billingsley and Breed (1982)

The Heavy Mineral Map of Australia: vision, implementation and progress

Digital Geologic-GIS Map of Bryce Canyon National Park and Vicinity, Utah (NPS, GRD, GRI, BRCA, BRCA digital map) adapted from a U.S. Geological Survey Miscellaneous Investigations Series Map by Bowers (1990)

Digital Geologic-GIS Map of the Pine Ridge Indian Reservation Area, South Dakota (NPS, GRD, GRI, BADL, PRIR digital map) adapted from a U.S. Geological Survey Hydrologic Investigations Atlas map by Ellis and Adolphson (1971)

Digital Geologic-GIS Map of Fort Laramie National Historic Site and Vicinity, Wyoming (NPS, GRD, GRI, FOLA, FOLA digital map) adapted from a U.S. Geological Survey Bulletin map by McGrew (1963)

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