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Text classification, translation, QA, summarization, dialogue, sentiment analysis, language modeling, text corpora
39,933 datasets
The ASDST Rock Art Pre1750 Model is a raster GIS layer predicting the likelihood of Aboriginal rock art sites across New South Wales prior to European colonization. It was produced by the NSW Department of Planning, Industry and Environment in 2020 at 50-meter resolution. Cell values range from 0 to 1000, indicating low to high relative likelihood.
5.2 GB of simulation data models polarisation vision in honeybees and bumblebees. The dataset, created by George Kolyfetis and last updated in May 2026, incorporates species-specific dorsal rim area anatomy and real sky polarisation images. It evaluates navigational accuracy under matched filter and vector-sum models, with errors below 5° and median errors of ~10° and ~30° respectively.
Macquarie University researchers created a database and GIS workflow to identify corridors of geomorphic river recovery for freshwater streams in the NSW Northern Rivers catchments. The work, funded by an Australian Research Council Linkage project and published in two Open Access papers in 2022, analyzes 13 predefined connection types for conservation and rehabilitation planning. The underlying River Styles database was accessed in January 2021.
The ASDST Artefacts Pre1750 Model is a suite of geospatial raster layers predicting the likelihood of Aboriginal site features across New South Wales, Australia, prior to European colonization. It was developed by the NSW Department of Planning, Industry and Environment, with the current version 7.5 produced in 2020 at 50-meter resolution. The model covers ten distinct feature types, including stone artefacts, rock art, burials, and scarred trees, with cell values ranging from 0 to 1000 indicating relative likelihood.
A 2026 cross-sectional study of 194 Premier and Division One field hockey players in the UK. The dataset, created by Nicholas Dobbin, contains survey responses on non-specific low back pain, player characteristics, injury history, and training factors. It was used to identify factors associated with greater or lesser odds of reporting low back pain.
A geospatial database identifying potential corridors for river recovery in the freshwater streams of New South Wales Southern Rivers catchments. It was created by Macquarie University researchers in 2022 using the NSW River Styles database accessed in January 2021. The data supports systematic analysis for conservation and rehabilitation planning.
Macquarie University researchers created a geospatial database identifying corridors of river recovery for all freshwater stream length in the NSW Central Coast catchments. The database, accessed in January 2021, shows the spatial distribution of thirteen connections based on combinations of conservation, strategic, and high recovery potential targets. The work is published in two Open Access papers and funded by an Australian Research Council Linkage project.
New South Wales data from the Aboriginal Sites Decision Support Tool (ASDST) predicts the likelihood of Aboriginal hearth features in the current landscape. The model, produced by the Department of Planning, Industry and Environment in 2020, is a raster GIS layer at 50-meter resolution covering the entire state. Cell values range from 0 to 1000, indicating relative likelihood rather than probability.
Network contribution area scores identify zones with high potential for surface water flow into river networks in Northern Ireland. The scores were calculated within SAGA GIS using the SciMAP application, based on inputs from the CEH Land Cover 2007 map, Met Office average rainfall data, and a 5-meter Digital Terrain Model. The 'High contribution' category is defined as areas scoring above one standard deviation from the mean.
Network contribution area scores identify land areas contributing significantly to surface water flow into river networks. This geospatial layer was generated using the SciMAP application within SAGA GIS, based on inputs from the CEH Land Cover 2007 map, Met Office average rainfall data, and a 5-meter Digital Terrain Model. The 'High contribution' category boundary is defined statistically as areas scoring above +1 standard deviation from the mean.
Network contribution area scores identify land areas in Northern Ireland with high potential for surface water flow into river networks. These scores were calculated within SAGA GIS using inputs from the CEH Land Cover 2007 map, Met Office average rainfall data, and a 5-meter Digital Terrain Model. The 'High contribution' category is defined as areas scoring above one standard deviation from the mean.
Network contribution area scores were exported from the SciMAP application outputs within SAGA GIS. The scores are based on inputs from CEH Land Cover 2007, Met Office average rainfall, and a 5-meter Digital Terrain Model. The 'High contribution' category boundary was determined by selecting areas with scores greater than +1 standard deviation from the distribution mean.
Network contribution area scores for the Lower Bann region identify land areas most likely to contribute surface flow to the river network. These scores were calculated within SAGA GIS using inputs from the CEH Land Cover 2007 map, Met Office average rainfall data, and a 5-meter Digital Terrain Model. The 'High contribution' category boundary is defined statistically as areas scoring above +1 standard deviation from the mean.
Network contribution area scores were exported from the SciMAP application outputs within SAGA GIS. The scores are based on inputs from CEH Land Cover 2007, Met Office average rainfall, and a 5-meter Digital Terrain Model. The 'High contribution' category boundary was determined by selecting areas scoring above +1 standard deviation of the distribution.
Lough Neagh Network Contribution data delineates areas of high hydrological network contribution in Northern Ireland, UK. The dataset was generated by the SciMAP application within SAGA GIS, using inputs from the CEH Land Cover 2007, Met Office average rainfall, and a 5-meter Digital Terrain Model. High contribution boundaries are defined statistically as areas scoring above +1 standard deviation from the mean.
Network contribution area scores delineate zones of high hydrological connectivity in Northern Ireland's Faughan catchment. This geospatial layer was generated by the SciMAP application within SAGA GIS, integrating CEH Land Cover 2007, Met Office average rainfall, and a 5-meter Digital Terrain Model. The 'High contribution' boundary is defined statistically as areas scoring above one standard deviation from the mean.
Network contribution area scores identify zones of high hydrological connectivity within Northern Ireland's Upper Lough Erne catchment. The scores were calculated using the SciMAP application within SAGA GIS, integrating inputs from CEH Land Cover 2007, Met Office average rainfall, and a 5-meter Digital Terrain Model. Areas classified as 'High Network Contribution' represent the top portion of the score distribution, defined as values exceeding one standard deviation above the mean.
Northern Ireland's Ballinderry River catchment area is analyzed for hydrological network contribution. The dataset identifies high-contribution zones based on land cover, rainfall, and terrain inputs from 2007. It is a processed geospatial layer derived from SciMAP application outputs within SAGA GIS.
Network contribution area scores were exported from the SciMAP application outputs within SAGA GIS. The scores were based on inputs from CEH Land cover 2007, Met Office average rainfall and a 5 meter DTM. The High contribution category boundary was determined by selecting +1 standard deviation of the distribution of the scores, identifying areas with above-average potential for contributing surface flow to river networks.
A geospatial database for all freshwater stream length in the NSW Hunter catchments shows the spatial distribution of thirteen connections based on combinations of conservation, strategic, and high recovery potential targets. It was created by Macquarie University researchers using the NSW River Styles database accessed in January 2021 and published in 2022. The associated workflow allows users to run over 80 different user-defined scenarios to identify corridors of geomorphic river recovery.