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Global Pasture Watch - Annual grassland class and extent maps at 30-m spatial resolution (2000—2024)

<h2>Sub-dataset: Dominant grassland class, 2012-2014</h2> <h2>Description</h2> Global annual grassland class and extent for 2000—2024 produced by <a href="https://doi.org/10.1038/s41597-024-04139-6">Parente et al. (2024)</a> within the scope of the <a href="https://landcarbonlab.org/data/global-grassland-and-livestock-monitoring/">Global Pasture Watch initiative</a>. The mapped grassland extent includes any land cover type, which contains at least 30% of dry or wet low vegetation, dominated by grasses and forbs (less than 3 meters) and a: <ul> <li>maximum of 50% tree canopy cover (greater than 5 meters),</li> <li>maximum of 70% of other woody vegetation (scrubs and open shrubland), and</li> <li>maximum of 50% active cropland cover in mosaic landscapes of cropland & other vegetation.</li> </ul> The grassland extent is classified into two classes: <ul> <li>Cultivated grassland: Areas where grasses and other forage plants have been intentionally planted and managed, as well as areas of native grassland-type vegetation where they clearly exhibit active and 'heavy' management for specific human-directed uses, such as directed grazing of livestock.</li> <li>Natural/semi-natural grassland: Relatively undisturbed native grasslands/short-height vegetation, such as steppes and tundra, as well as areas that have experienced varying degrees of human activity in the past, which may contain a mix of native and introduced species due to historical land use and natural processes. In general, they exhibit natural-looking patterns of varied vegetation and clearly ordered hydrological relationships throughout the landscape.</li> <li>Open shrubland (v2-beta): Land on which the vegetation is dominated by low-growing woody plants, characterized by a sparse distribution of shrubs and dominated by woody perennials. Typically covers 50—75% of the area, with significant open ground (with or without herbaceous understory) between them, where shrub canopies are less than 10 meters in diameter, and tree cover is below 10%, meaning they do not form a continuous or semi-continuous canopy.</li> </ul> The dataset is organized in 69 global mosaics (25 years for each time series) in COG (Cloud Optimized GeoTIFF) format, WGS84 Coordinate Systems (EPSG:4326) and pixel size equal to 0.00025 degrees, including: <ul> <li>Probabilities of cultivated grassland (values range from 0–100),</li> <li>Probabilities of natural/semi-natural grassland (values range from 0–100), and</li> <li>Probabilities of open shrubland (values range from 0–100), and</li> <li>Dominant class (0-other land cover, 1-cultivated grassland and 2-natural/semi-natural grassland, 3-open shrubland).</li> </ul> All raster files are in unsigned <code>8-bit integer format</code> and use <code>255</code> as no-data value (pixels ignored by prediction), following an specific naming convention: <ol> <li>Project name: Global Pasture Watch (<code>gpw</code>)</li> <li>Class name: cultivated grassland (<code>cultiv.grassland</code>), natural/semi-natural grassland (<code>nat.semi.grassland</code>), open shrubland (<code>open.shrubland</code>)  and dominant grassland (<code>grassland</code>)</li> <li>Procedure combination: Random Forest (<code>rf</code>), median filter (med.filt) and balanced threshold (<code>bthr</code>).</li> <li>Variable type: probability (<code>p</code>) and factor class (<code>c</code>)</li> <li>Spatial resolution: 30m</li> <li>Begin of time reference: date of first Landsat composite used by the modeling (<code>20240101</code>)</li> <li>End of time reference: date of last Landsat composite used by the modeling (<code>20241231</code>)</li> <li>Spatial extent: global (<code>go</code>)</li> <li>Coordinate system: World Geodetic System 1984, used in GPS (<code>epsg.4326</code>)</li> <li>Version: v2</li> </ol> <h3>Related resources</h3> <ul> <li>Maps of dominant grassland: <a href="http://doi.org/10.5281/zenodo.15646181">2000-2002</a><a href="http://doi.org/10.5281/zenodo.15644486"> 2003-2005</a><a href="http://doi.org/10.5281/zenodo.15644623"> 2006-2008</a><a href="http://doi.org/10.5281/zenodo.15647042"> 2009-2011</a><a href="http://doi.org/10.5281/zenodo.15647681"> 2012-2014</a><a href="http://doi.org/10.5281/zenodo.15648306"> 2015-2017</a><a href="http://doi.org/10.5281/zenodo.15648551"> 2018-2020</a><a href="http://doi.org/10.5281/zenodo.15648751"> 2021-2023</a> <a href="http://doi.org/10.5281/zenodo.15649332">2024</a></li> <li>Probability maps of cultivated grassland: <a href="https://zenodo.org/records/13890401/files/ggc-30m.csv?download=1">2000-2024 (All URLs)</a></li> <li>Probability maps of natural/semi-natural grassland: <a href="https://zenodo.org/records/13890401/files/ggc-30m.csv?download=1">2000-2024 (All URLs)</a></li> <li> <div>Grassland reference samples based on VHR imagery (2000–2024): <a href="https://doi.org/10.5281/zenodo.15631655">GeoPackage files</a></div> </li> <li>Global machine learning models (Random Forest): <a href="https://doi.org/10.5281/zenodo.13952806">Parquet and joblib python files</a></li> <li>Reference sampling design derived by FSCV: <a href="https://doi.org/10.5281/zenodo.11391517">GeoPackage and raster files</a></li> <li>Harmonized reference samples based on existing LULC dataset: <a href="https://doi.org/10.5281/zenodo.13951976">GeoPackage and raster files</a></li> <li>Source code for reproducibility: <a href="https://doi.org/10.5281/zenodo.13952867">GitHub release</a> </li> <li>Mapping feedback tool: <a href="https://geo-wiki.org">GeoWiki</a></li> <li>Data catalogues: <a href="https://stac.openlandmap.org/gpw_ggc-30m/collection.json?.language=en">OpenLandMap STAC</a> <a href="https://global-pasture-watch.projects.earthengine.app/view/ggc-30m">Google Earth Engine</a></li> </ul> Support For questions of bugs/inconsistencies related to the dataset raise a GitHub issue in <a href="https://github.com/wri/global-pasture-watch">https://github.com/wri/global-pasture-watch</a>

Global Pasture Watch - Annual grassland class and extent maps at 30-m spatial resolution (2000—2024)

<h2>Sub-dataset: Dominant grassland class, 2006-2008</h2> <h2>Description</h2> Global annual grassland class and extent for 2000—2024 produced by <a href="https://doi.org/10.1038/s41597-024-04139-6">Parente et al. (2024)</a> within the scope of the <a href="https://landcarbonlab.org/data/global-grassland-and-livestock-monitoring/">Global Pasture Watch initiative</a>. The mapped grassland extent includes any land cover type, which contains at least 30% of dry or wet low vegetation, dominated by grasses and forbs (less than 3 meters) and a: <ul> <li>maximum of 50% tree canopy cover (greater than 5 meters),</li> <li>maximum of 70% of other woody vegetation (scrubs and open shrubland), and</li> <li>maximum of 50% active cropland cover in mosaic landscapes of cropland & other vegetation.</li> </ul> The grassland extent is classified into two classes: <ul> <li>Cultivated grassland: Areas where grasses and other forage plants have been intentionally planted and managed, as well as areas of native grassland-type vegetation where they clearly exhibit active and 'heavy' management for specific human-directed uses, such as directed grazing of livestock.</li> <li>Natural/semi-natural grassland: Relatively undisturbed native grasslands/short-height vegetation, such as steppes and tundra, as well as areas that have experienced varying degrees of human activity in the past, which may contain a mix of native and introduced species due to historical land use and natural processes. In general, they exhibit natural-looking patterns of varied vegetation and clearly ordered hydrological relationships throughout the landscape.</li> <li>Open shrubland (v2-beta): Land on which the vegetation is dominated by low-growing woody plants, characterized by a sparse distribution of shrubs and dominated by woody perennials. Typically covers 50—75% of the area, with significant open ground (with or without herbaceous understory) between them, where shrub canopies are less than 10 meters in diameter, and tree cover is below 10%, meaning they do not form a continuous or semi-continuous canopy.</li> </ul> The dataset is organized in 69 global mosaics (25 years for each time series) in COG (Cloud Optimized GeoTIFF) format, WGS84 Coordinate Systems (EPSG:4326) and pixel size equal to 0.00025 degrees, including: <ul> <li>Probabilities of cultivated grassland (values range from 0–100),</li> <li>Probabilities of natural/semi-natural grassland (values range from 0–100), and</li> <li>Probabilities of open shrubland (values range from 0–100), and</li> <li>Dominant class (0-other land cover, 1-cultivated grassland and 2-natural/semi-natural grassland, 3-open shrubland).</li> </ul> All raster files are in unsigned <code>8-bit integer format</code> and use <code>255</code> as no-data value (pixels ignored by prediction), following an specific naming convention: <ol> <li>Project name: Global Pasture Watch (<code>gpw</code>)</li> <li>Class name: cultivated grassland (<code>cultiv.grassland</code>), natural/semi-natural grassland (<code>nat.semi.grassland</code>), open shrubland (<code>open.shrubland</code>)  and dominant grassland (<code>grassland</code>)</li> <li>Procedure combination: Random Forest (<code>rf</code>), median filter (med.filt) and balanced threshold (<code>bthr</code>).</li> <li>Variable type: probability (<code>p</code>) and factor class (<code>c</code>)</li> <li>Spatial resolution: 30m</li> <li>Begin of time reference: date of first Landsat composite used by the modeling (<code>20240101</code>)</li> <li>End of time reference: date of last Landsat composite used by the modeling (<code>20241231</code>)</li> <li>Spatial extent: global (<code>go</code>)</li> <li>Coordinate system: World Geodetic System 1984, used in GPS (<code>epsg.4326</code>)</li> <li>Version: v2</li> </ol> <h3>Related resources</h3> <ul> <li>Maps of dominant grassland: <a href="http://doi.org/10.5281/zenodo.15646181">2000-2002</a><a href="http://doi.org/10.5281/zenodo.15644486"> 2003-2005</a><a href="http://doi.org/10.5281/zenodo.15644623"> 2006-2008</a><a href="http://doi.org/10.5281/zenodo.15647042"> 2009-2011</a><a href="http://doi.org/10.5281/zenodo.15647681"> 2012-2014</a><a href="http://doi.org/10.5281/zenodo.15648306"> 2015-2017</a><a href="http://doi.org/10.5281/zenodo.15648551"> 2018-2020</a><a href="http://doi.org/10.5281/zenodo.15648751"> 2021-2023</a> <a href="http://doi.org/10.5281/zenodo.15649332">2024</a></li> <li>Probability maps of cultivated grassland: <a href="https://zenodo.org/records/13890401/files/ggc-30m.csv?download=1">2000-2024 (All URLs)</a></li> <li>Probability maps of natural/semi-natural grassland: <a href="https://zenodo.org/records/13890401/files/ggc-30m.csv?download=1">2000-2024 (All URLs)</a></li> <li> <div>Grassland reference samples based on VHR imagery (2000–2024): <a href="https://doi.org/10.5281/zenodo.15631655">GeoPackage files</a></div> </li> <li>Global machine learning models (Random Forest): <a href="https://doi.org/10.5281/zenodo.13952806">Parquet and joblib python files</a></li> <li>Reference sampling design derived by FSCV: <a href="https://doi.org/10.5281/zenodo.11391517">GeoPackage and raster files</a></li> <li>Harmonized reference samples based on existing LULC dataset: <a href="https://doi.org/10.5281/zenodo.13951976">GeoPackage and raster files</a></li> <li>Source code for reproducibility: <a href="https://doi.org/10.5281/zenodo.13952867">GitHub release</a> </li> <li>Mapping feedback tool: <a href="https://geo-wiki.org">GeoWiki</a></li> <li>Data catalogues: <a href="https://stac.openlandmap.org/gpw_ggc-30m/collection.json?.language=en">OpenLandMap STAC</a> <a href="https://global-pasture-watch.projects.earthengine.app/view/ggc-30m">Google Earth Engine</a></li> </ul> Support For questions of bugs/inconsistencies related to the dataset raise a GitHub issue in <a href="https://github.com/wri/global-pasture-watch">https://github.com/wri/global-pasture-watch</a>

Global Pasture Watch - Annual grassland class and extent maps at 30-m spatial resolution (2000—2024)

<h2>Sub-dataset: Dominant grassland class, 2003-2005</h2> <h2>Description</h2> Global annual grassland class and extent for 2000—2024 produced by <a href="https://doi.org/10.1038/s41597-024-04139-6">Parente et al. (2024)</a> within the scope of the <a href="https://landcarbonlab.org/data/global-grassland-and-livestock-monitoring/">Global Pasture Watch initiative</a>. The mapped grassland extent includes any land cover type, which contains at least 30% of dry or wet low vegetation, dominated by grasses and forbs (less than 3 meters) and a: <ul> <li>maximum of 50% tree canopy cover (greater than 5 meters),</li> <li>maximum of 70% of other woody vegetation (scrubs and open shrubland), and</li> <li>maximum of 50% active cropland cover in mosaic landscapes of cropland & other vegetation.</li> </ul> The grassland extent is classified into two classes: <ul> <li>Cultivated grassland: Areas where grasses and other forage plants have been intentionally planted and managed, as well as areas of native grassland-type vegetation where they clearly exhibit active and 'heavy' management for specific human-directed uses, such as directed grazing of livestock.</li> <li>Natural/semi-natural grassland: Relatively undisturbed native grasslands/short-height vegetation, such as steppes and tundra, as well as areas that have experienced varying degrees of human activity in the past, which may contain a mix of native and introduced species due to historical land use and natural processes. In general, they exhibit natural-looking patterns of varied vegetation and clearly ordered hydrological relationships throughout the landscape.</li> <li>Open shrubland (v2-beta): Land on which the vegetation is dominated by low-growing woody plants, characterized by a sparse distribution of shrubs and dominated by woody perennials. Typically covers 50—75% of the area, with significant open ground (with or without herbaceous understory) between them, where shrub canopies are less than 10 meters in diameter, and tree cover is below 10%, meaning they do not form a continuous or semi-continuous canopy.</li> </ul> The dataset is organized in 69 global mosaics (25 years for each time series) in COG (Cloud Optimized GeoTIFF) format, WGS84 Coordinate Systems (EPSG:4326) and pixel size equal to 0.00025 degrees, including: <ul> <li>Probabilities of cultivated grassland (values range from 0–100),</li> <li>Probabilities of natural/semi-natural grassland (values range from 0–100), and</li> <li>Probabilities of open shrubland (values range from 0–100), and</li> <li>Dominant class (0-other land cover, 1-cultivated grassland and 2-natural/semi-natural grassland, 3-open shrubland).</li> </ul> All raster files are in unsigned <code>8-bit integer format</code> and use <code>255</code> as no-data value (pixels ignored by prediction), following an specific naming convention: <ol> <li>Project name: Global Pasture Watch (<code>gpw</code>)</li> <li>Class name: cultivated grassland (<code>cultiv.grassland</code>), natural/semi-natural grassland (<code>nat.semi.grassland</code>), open shrubland (<code>open.shrubland</code>)  and dominant grassland (<code>grassland</code>)</li> <li>Procedure combination: Random Forest (<code>rf</code>), median filter (med.filt) and balanced threshold (<code>bthr</code>).</li> <li>Variable type: probability (<code>p</code>) and factor class (<code>c</code>)</li> <li>Spatial resolution: 30m</li> <li>Begin of time reference: date of first Landsat composite used by the modeling (<code>20240101</code>)</li> <li>End of time reference: date of last Landsat composite used by the modeling (<code>20241231</code>)</li> <li>Spatial extent: global (<code>go</code>)</li> <li>Coordinate system: World Geodetic System 1984, used in GPS (<code>epsg.4326</code>)</li> <li>Version: v2</li> </ol> <h3>Related resources</h3> <ul> <li>Maps of dominant grassland: <a href="http://doi.org/10.5281/zenodo.15646181">2000-2002</a><a href="http://doi.org/10.5281/zenodo.15644486"> 2003-2005</a><a href="http://doi.org/10.5281/zenodo.15644623"> 2006-2008</a><a href="http://doi.org/10.5281/zenodo.15647042"> 2009-2011</a><a href="http://doi.org/10.5281/zenodo.15647681"> 2012-2014</a><a href="http://doi.org/10.5281/zenodo.15648306"> 2015-2017</a><a href="http://doi.org/10.5281/zenodo.15648551"> 2018-2020</a><a href="http://doi.org/10.5281/zenodo.15648751"> 2021-2023</a> <a href="http://doi.org/10.5281/zenodo.15649332">2024</a></li> <li>Probability maps of cultivated grassland: <a href="https://zenodo.org/records/13890401/files/ggc-30m.csv?download=1">2000-2024 (All URLs)</a></li> <li>Probability maps of natural/semi-natural grassland: <a href="https://zenodo.org/records/13890401/files/ggc-30m.csv?download=1">2000-2024 (All URLs)</a></li> <li> <div>Grassland reference samples based on VHR imagery (2000–2024): <a href="https://doi.org/10.5281/zenodo.15631655">GeoPackage files</a></div> </li> <li>Global machine learning models (Random Forest): <a href="https://doi.org/10.5281/zenodo.13952806">Parquet and joblib python files</a></li> <li>Reference sampling design derived by FSCV: <a href="https://doi.org/10.5281/zenodo.11391517">GeoPackage and raster files</a></li> <li>Harmonized reference samples based on existing LULC dataset: <a href="https://doi.org/10.5281/zenodo.13951976">GeoPackage and raster files</a></li> <li>Source code for reproducibility: <a href="https://doi.org/10.5281/zenodo.13952867">GitHub release</a> </li> <li>Mapping feedback tool: <a href="https://geo-wiki.org">GeoWiki</a></li> <li>Data catalogues: <a href="https://stac.openlandmap.org/gpw_ggc-30m/collection.json?.language=en">OpenLandMap STAC</a> <a href="https://global-pasture-watch.projects.earthengine.app/view/ggc-30m">Google Earth Engine</a></li> </ul> Support For questions of bugs/inconsistencies related to the dataset raise a GitHub issue in <a href="https://github.com/wri/global-pasture-watch">https://github.com/wri/global-pasture-watch</a>

Global Pasture Watch - Annual grassland class and extent maps at 30-m spatial resolution (2000—2024)

<h2>Sub-dataset: Dominant grassland class, 2000-2002</h2> <h2>Description</h2> Global annual grassland class and extent for 2000—2024 produced by <a href="https://doi.org/10.1038/s41597-024-04139-6">Parente et al. (2024)</a> within the scope of the <a href="https://landcarbonlab.org/data/global-grassland-and-livestock-monitoring/">Global Pasture Watch initiative</a>. The mapped grassland extent includes any land cover type, which contains at least 30% of dry or wet low vegetation, dominated by grasses and forbs (less than 3 meters) and a: <ul> <li>maximum of 50% tree canopy cover (greater than 5 meters),</li> <li>maximum of 70% of other woody vegetation (scrubs and open shrubland), and</li> <li>maximum of 50% active cropland cover in mosaic landscapes of cropland & other vegetation.</li> </ul> The grassland extent is classified into two classes: <ul> <li>Cultivated grassland: Areas where grasses and other forage plants have been intentionally planted and managed, as well as areas of native grassland-type vegetation where they clearly exhibit active and 'heavy' management for specific human-directed uses, such as directed grazing of livestock.</li> <li>Natural/semi-natural grassland: Relatively undisturbed native grasslands/short-height vegetation, such as steppes and tundra, as well as areas that have experienced varying degrees of human activity in the past, which may contain a mix of native and introduced species due to historical land use and natural processes. In general, they exhibit natural-looking patterns of varied vegetation and clearly ordered hydrological relationships throughout the landscape.</li> <li>Open shrubland (v2-beta): Land on which the vegetation is dominated by low-growing woody plants, characterized by a sparse distribution of shrubs and dominated by woody perennials. Typically covers 50—75% of the area, with significant open ground (with or without herbaceous understory) between them, where shrub canopies are less than 10 meters in diameter, and tree cover is below 10%, meaning they do not form a continuous or semi-continuous canopy.</li> </ul> The dataset is organized in 69 global mosaics (25 years for each time series) in COG (Cloud Optimized GeoTIFF) format, WGS84 Coordinate Systems (EPSG:4326) and pixel size equal to 0.00025 degrees, including: <ul> <li>Probabilities of cultivated grassland (values range from 0–100),</li> <li>Probabilities of natural/semi-natural grassland (values range from 0–100), and</li> <li>Probabilities of open shrubland (values range from 0–100), and</li> <li>Dominant class (0-other land cover, 1-cultivated grassland and 2-natural/semi-natural grassland, 3-open shrubland).</li> </ul> All raster files are in unsigned <code>8-bit integer format</code> and use <code>255</code> as no-data value (pixels ignored by prediction), following an specific naming convention: <ol> <li>Project name: Global Pasture Watch (<code>gpw</code>)</li> <li>Class name: cultivated grassland (<code>cultiv.grassland</code>), natural/semi-natural grassland (<code>nat.semi.grassland</code>), open shrubland (<code>open.shrubland</code>)  and dominant grassland (<code>grassland</code>)</li> <li>Procedure combination: Random Forest (<code>rf</code>), median filter (med.filt) and balanced threshold (<code>bthr</code>).</li> <li>Variable type: probability (<code>p</code>) and factor class (<code>c</code>)</li> <li>Spatial resolution: 30m</li> <li>Begin of time reference: date of first Landsat composite used by the modeling (<code>20240101</code>)</li> <li>End of time reference: date of last Landsat composite used by the modeling (<code>20241231</code>)</li> <li>Spatial extent: global (<code>go</code>)</li> <li>Coordinate system: World Geodetic System 1984, used in GPS (<code>epsg.4326</code>)</li> <li>Version: v2</li> </ol> <h3>Related resources</h3> <ul> <li>Maps of dominant grassland: <a href="http://doi.org/10.5281/zenodo.15646181">2000-2002</a><a href="http://doi.org/10.5281/zenodo.15644486"> 2003-2005</a><a href="http://doi.org/10.5281/zenodo.15644623"> 2006-2008</a><a href="http://doi.org/10.5281/zenodo.15647042"> 2009-2011</a><a href="http://doi.org/10.5281/zenodo.15647681"> 2012-2014</a><a href="http://doi.org/10.5281/zenodo.15648306"> 2015-2017</a><a href="http://doi.org/10.5281/zenodo.15648551"> 2018-2020</a><a href="http://doi.org/10.5281/zenodo.15648751"> 2021-2023</a> <a href="http://doi.org/10.5281/zenodo.15649332">2024</a></li> <li>Probability maps of cultivated grassland: <a href="https://zenodo.org/records/13890401/files/ggc-30m.csv?download=1">2000-2024 (All URLs)</a></li> <li>Probability maps of natural/semi-natural grassland: <a href="https://zenodo.org/records/13890401/files/ggc-30m.csv?download=1">2000-2024 (All URLs)</a></li> <li> <div>Grassland reference samples based on VHR imagery (2000–2024): <a href="https://doi.org/10.5281/zenodo.15631655">GeoPackage files</a></div> </li> <li>Global machine learning models (Random Forest): <a href="https://doi.org/10.5281/zenodo.13952806">Parquet and joblib python files</a></li> <li>Reference sampling design derived by FSCV: <a href="https://doi.org/10.5281/zenodo.11391517">GeoPackage and raster files</a></li> <li>Harmonized reference samples based on existing LULC dataset: <a href="https://doi.org/10.5281/zenodo.13951976">GeoPackage and raster files</a></li> <li>Source code for reproducibility: <a href="https://doi.org/10.5281/zenodo.13952867">GitHub release</a> </li> <li>Mapping feedback tool: <a href="https://geo-wiki.org">GeoWiki</a></li> <li>Data catalogues: <a href="https://stac.openlandmap.org/gpw_ggc-30m/collection.json?.language=en">OpenLandMap STAC</a> <a href="https://global-pasture-watch.projects.earthengine.app/view/ggc-30m">Google Earth Engine</a></li> </ul> Support For questions of bugs/inconsistencies related to the dataset raise a GitHub issue in <a href="https://github.com/wri/global-pasture-watch">https://github.com/wri/global-pasture-watch</a>

opencc-by-4.0Oct 2023View details →

Spatially averaged metocean data at Utsira Nord (UN) and Sørlige Nordsjø II (SN2) with NORA3 (1982-2022)

<h3>Overview</h3>This dataset provides spatially averaged metocean data at Utsira Nord (UN) and Sørlige Nordsjø II (SN2) offshore site. The data span from 1982 to 2022 with a temporal resolution of 1 h and are formatted in NetCDF4. The data are valuable for a range of applications including, but not limited to, offshore engineering, marine renewable energy, climate studies, and environmental monitoring. The mean wind speed and mean direction data are provided at eight altitudes from 10 m to 750 m above sea level.<h3>Data Description</h3>The dataset is organised into NetCDF files with the following variables:<h3>Dataset Variables and Dimensions</h3><h4>Time-Dependent Variables</h4>Wind Direction Variables (Dir_median, Dir_q1, Dir_q99, Dir_q25, Dir_q75): Represent the mean wind direction at different percentiles, and are measured in degrees.Wind Speed Variables (U_median, U_q1, U_q99, U_q25, U_q75): Indicate the mean wind speed at different percentiles, and are measured in metres per second (m/s).Wave Height Variables (hs_median, hs_q1, hs_q99, hs_q25, hs_q75): Denote the significant wave height at different percentiles, and are measured in metres (m).Wave Period Variables (tp_median, tp_q1, tp_q99, tp_q25, tp_q75): Capture the peak wave period at various percentiles, and are measured in seconds (s).Friction Velocity Variables (u_star_median, u_star_q1, u_star_q99, u_star_q25, u_star_q75): Represent the friction velocity at different percentiles, and are measured in metres per second (m/s).Wave Heading Variables (wd_median, wd_q1, wd_q99, wd_q25, wd_q75): Indicate the wave heading at different percentiles, and are measured in degrees.<h4>Static Variables</h4><ul><li>Height Variable (z): Specifies the height above the surface, measured in metres (m). The dataset includes 8 levels: [10, 20, 50, 100, 150, 250, 500, 750].</li></ul><h4>Time Variable</h4><ul><li>Time Variable (time): Represents time in hours since 1970-01-01 00:00:00.</li></ul><h4>Data Dimensions</h4><ul><li>Dimensions:<ul><li>time: 359400</li><li>z: 8</li></ul></li></ul><h4>Data Types</h4><ul><li>The primary data type for these variables is double.</li></ul><h4>Usage</h4>The NetCDF files can be accessed and manipulated using various programming languages that have NetCDF libraries, such as Python, MATLAB, R, and others. The dataset is suitable for both academic research and industrial applications.

opencc-by-4.0Oct 2023View details →

30-m Spatial Resolution Bioclimatic Dataset of 1991-2020 Climate Normals for Hubei Province, the Yangtze River Middle Reaches

Brief Introduction of the Dataset This bioclimatic dataset is the product of research article "Mapping 30-m Resolution Bioclimatic Variables During 1991-2020 Climate Normals for Hubei Province, the Yangtze River Middle Reaches." published in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. The dataset contains 19 30-m resolution average bioclimatic variables during 1991-2020 Climate Normals for Hubei Province (108°21′42″—116°07′50″ E, 29°01′53″—33°6′47″ N), the core region of the Yangtze River middle reaches. The dataset was constructed by statistically downscaling the Climatic Research Unit (CRU) 1-km monthly climate variables (1440 in total), cablirating with ground observation data with 82 weather stations and aggregating based on the defination of 19 bioclimatic variables. The downscaling of four 1-km Climatic Research Unit monthly climate variables including monthly maximum, mean, minimum temperature and precipitation was firstly achieved by random forest model with 30-m resolution terrain and spatial data. Then the interpolation-based geographical differential analysis (GDA) was applied to improve the accuracy of downscaled products based on ground observation data. Finally, the bioclimatic variables were aggregated based on their definitions and averaged for the 30 years. The Yangtze River middle reaches is abundant of forestry, agriculture, biodiversity resources that requires finer bioclimatic data for better understands of these aspects. This dataset will provide higher spatial accuracy, more information and applicability in finer regional studies in the Yangtze River middle reaches.   Description of the 19 Bioclimatic Variables The dataset contains 19 geotiff files in total. File names and the corresponding full name of bioclimatic variables are described as follows: Bio01 Mean annual air temperature (℃) Bio02 Mean diurnal air temperature range (℃) Bio03 Isothermality (%) Bio04 Temperature seasonality (℃) Bio05 Mean daily maximum air temperature of the warmest month (℃) Bio06 Mean daily minimum air temperature of the coldest month (℃) Bio07 Annual range of air temperature (℃) Bio08 Mean daily mean air temperatures of the wettest quarter (℃) Bio09 Mean daily mean air temperatures of the driest quarter (℃) Bio10 Mean daily mean air temperatures of the warmest quarter (℃) Bio11 Mean daily mean air temperatures of the coldest quarter (℃) Bio12 Annual precipitation amount (mm) Bio13 Precipitation amount of the wettest month (mm) Bio14 Precipitation amount of the driest month (mm) Bio15 Precipitation seasonality (%) Bio16 Precipitation amount of the wettest quarter (mm) Bio17 Precipitation amount of the driest quarter (mm) Bio18 Precipitation amount of the warmest quarter (mm) Bio19 Precipitation amount of the coldest quarter (mm)   Others More information related to bioclimatic variables can be found on https://chelsa-climate.org/bioclim/

Energy-Dependent, Self-Adaptive Mesh h(p)-Refinement of a Constraint-Based Continuous Bubnov-Galerkin Isogeometric Analysis Spatial Discretisation of the Multi-Group Neutron Diffusion Equation with Dual-Weighted Residual Error Measures

This repository holds all of the raw data generated by my (Modern) Fortran code for a paper "Energy-Dependent, Self-Adaptive Mesh h(p)-Refinement of a Constraint-Based Continuous Bubnov-Galerkin Isogeometric Analysis Spatial Discretisation of the Multi-Group Neutron Diffusion Equation with Dual-Weighted Residual Error Measures".The (Modern) Fortran code solves the multi-group neutron diffusion equation using a novel IGA-based spatial discretisations.

Investigation of spatial and temporal variability in lower tropospheric ozone from RAL Space UV-Vis satellite products - Dataset

This data set represents a long-term (1996-2017) harmonised record of lower tropospheric ozone (surface - 450 hPa or surface - approximately 6 km) from satellite instruments. These instruments include the Global Ozone Monitoring Experiment (GOME-1, 1996–2002), the SCanning Imaging Absorption spectroMeter for Atmospheric CartograpHY (SCIAMACHY, 2003–2004) and the Ozone Monitoring Instrument (OMI, 2005–2017). These original products were produced by the Rutherford Appleton Laboratory (RAL) Space using the retrieval scheme described by Miles et al., (2015 - doi:10.5194/amt-8-385-2015). Pre-print of accepted manuscript can be found at https://doi.org/10.5194/egusphere-2023-1172.

Efficient PCA denoising of spatially correlated redundant MRI data

MRI data used for the study: "Henriques, Ianus, Novello, Jovicich, Jespersen, Shemesh. Efficient PCA denoising of spatially correlated redundant MRI data. Imaging Neuroscience (In Press)."Preclinical scanner dataAll animal experiments for the collection of these datasets were preapproved by the institutional and national authorities and carried out according to European Directive 2010/63.A mouse brain (C57BL/6J) was extracted via transcardial perfusion with 4% Paraformaldehyde (PFA), immersed in 4% PFA solution for 24 h, washed in Phosphate-Buffered Saline (PBS) solution for at least 24 h, and then placed on a 10 mm NMR tube filled with Flourinert (Sigma Aldrich, Lisbon, PT), which was sealed using paraffin film. The MRI experiments were performed on a 16.4 T Bruker Aeon Ascend scanner (Bruker, Karlsruhe, Germany), interfaced with an Avance IIIHD console, and equipped with a gradient system capable of producing up to 3000 mT/m in all directions. A constant temperature of 37oC was maintained throughout the experiments using the probe's variable temperature capability. Two distinct diffusion-weighted datasets were then acquired using Bruker's standard "Diffusion Tensor Imaging EPI":<ul><li>Dataset1 (MB_exp1.nii and its brain mask MB_exp1_mask.nii): For this dataset, we modulated the amount of spatial correlations by acquiring EPI datasets with parameters optimized to mitigate noise spatial correlations, particularly avoiding k-space undersampling acquisition during EPI's gradient ramps and without using partial Fourier, which minimize regridding.</li><li>Dataset2 (MB_exp2.nii and its brain mask MB_exp2_mask.nii): The second dataset was acquired with identical resolution, number of acquisitions, etc., but with large factors inducing spatial correlations, including k-space sampling during gradient ramps (default Bruker's acquisition and reconstruction procedures for acquisition speed) and with a significant phase partial Fourier factor of 6/8 (note for partial Fourier acquisitions, EPI data is reconstructed with zero-padding, according to the default reconstruction procedures by Bruker's pre-clinical reconstruction software Paravision 6.0.1).</li></ul>All datasets are acquired for the following diffusion-weighted parameters: 30 gradient directions for b-values 1, 2 and 3 ms/μm2 (Δ = 15 ms, δ = 1.5 ms), and 20 consecutive b-value=0 acquisitions - b-values and diffusion gradient directions are saved in files: MB.bval / MB.bvec.Other acquisition parameters: TR/TE = 3000/50 ms, 9 coronal slices, Field of View = 12×12 mm2, matrix size 80×80, in-plane voxel resolution of 150×150 μm2, slice thickness = 0.7 mm, number of averages = 2, number of segments = 1, double sampling acquisition.<ul><li>Gold standard acquisitions for dataset 2 (MB_exp2_20averages.nii): For a gold standard reference, the second dataset was also repeated for 20 averages. Note, since this dataset is aligned to MB_exp2.nii you can use MB_exp2_mask.nii for its brain mask.</li></ul>For all datasets, Spatial drifts in the image domain were first corrected using a sub-pixel registration technique (Guizar-Sicairos et al., 2008). Clinical scanner dataExperiments were approved by the Ethical Committee of the University of Trento and the participant signed an informed consent. MRI data was a acquired for a healthy control (male, 54 years) using a 3T MAGNETOM PRISMA scanner (Siemens Healthcare, Erlangen, Germany) equipped with a 64-channel head-neck RF receive coil. Diffusion MRI data was acquired using a monopolar single diffusion encoding EPI PGSE (Feinberg et al., 2010; Moeller et al., 2010; Xu et al., 2013) along 30 diffusion gradient directions for five non-zero b-values = 1, 2, 3, 4.5 and 6 ms/μm2 (Δ = 39.1 ms, δ = 26.3 ms) and 17 interspersed b-value=0 acquisitions. b-values and diffusion gradient directions are saved in files: HB.bval / HB.bvec. Note, only the masked version of these dataset (HB_masked.nii and its brain mask HB_mask.nii) is provided to guarantee that data privacy standards are met. For noise maps covering all FOV, the noise maps computed as the std of the 5 first repeating unmasked b = 0 acquisitions are provided in file stdS0i.nii.Other acquisition parameters were the following: TR/TE = 4000/80 ms, 63 axial slices, Field of View = 220×220 mm2, matrix size 110×110, isotropic resolution of 2 mm, 6/8 phase partial Fourier, parallel imaging with GRAPPA 2, simultaneous multi-slice factor 3. All diffusion MRI data was reconstructed using zero-padding, which is the default procedure for data acquired with partial Fourier above 70%.

Embodied Spatial Navigation Training in Mild Cognitive Impairment: A Proof-of-Concept Trial

Raw data of included cognitive test and VR data Starting Grant Ricerca Finalizzata, code: SG-2018-12368175

Spatial structure, chemotaxis and quorum sensing shape bacterial biomass accumulation in complex porous media

Dataset associated to the publication"Spatial structure, chemotaxis and quorum sensing shape bacterial biomass accumulation in complex porous media"ByDavid Scheidweiler, Ankur Deep Bordoloi, Wenqiao Jiao, Vladimir Sentchilo, Monica Bollani, Audam Chhun, Philipp Engel and Pietro de AnnaFolder named "Figure_X" contains the original raw data, analysed data and source data for each plot within figure "X" on the manuscript and supplementary information.We do not provide raw data for each replica as one flow&growth experiment consists in 50 large images for a total of about 12 GB per dataset. Thus, we provide here the original data for the Wild Type experiment and the control D-luxS mutant. The data for the replicas and other control experiment can be available upon request.We provide Matlab scripts to read and analyze the original images.

Energy-Dependent, Self-Adaptive Mesh h(p)-Refinement of an Interior-Penalty Scheme for a Discontinuous Galerkin Isogeometric Analysis Spatial Discretisation of the Multi-Group Neutron Diffusion Equation with Dual-Weighted Residual Error Measures

This repository holds all of the raw data generated by my (Modern) Fortran code for a paper "Energy-Dependent, Self-Adaptive Mesh h(p)-Refinement of an Interior-Penalty Scheme for a Discontinuous Galerkin Isogeometric Analysis Spatial Discretisation of the Multi-Group Neutron Diffusion Equation with Dual-Weighted Residual Error Measures".The (Modern) Fortran code solves the multi-group neutron diffusion equation using a novel IGA-based spatial discretisations.

Dataset from: Spatially heterogeneous shifts in vegetation phenology induced by climate change threaten the integrity of the avian migration network

Original data and code for the study: Wei, J., Xu, F., Cole, E. F., Sheldon, B. C., de Boer, W. F., Wielstra, B., Fu, H., Gong, P., & Si, Y. (2024, Accepted). Spatially heterogeneous shifts in vegetation phenology induced by climate change threaten the integrity of the avian migration network. Global Change Biology. The dataset mainly contains data showing the climate change-induced heterogeneous shifts in vegetation phenology and the migration integrity change from 2000 to 2020 for 16 Asian herbivorous waterfowl species. These data were derived from the following resources available in the public domain. The Global Lakes and Wetlands Database is available from “https://www.worldwildlife.org/pages/global-lakes-and-wetlands-database”. The global land cover datasets are available from European Space Agency (ESA) Climate Change Initiative (CCI) products, “https://maps.elie.ucl.ac.be/CCI/viewer/download.php”. The Global Multi-resolution Terrain Elevation Data are available from “https://www.usgs.gov/centers/eros/science/terrain-monitoring-and-modeling”. The Moderate Resolution Imaging Spectroradiometer (MODIS) Terra surface reflectance product is available from “https://modis.gsfc.nasa.gov/data/dataprod/mod09.php”. The bird distribution maps are available from Birdlife International, “https://www.birdlife.org/”. The bird foraging attribute data are available from EltonTraits 1.0, “https://figshare.com”. The bird occurrence data are available from eBird Basic Dataset (EBD), “https://science.ebird.org/en/use-ebird-data/download-ebird-data-products”. The Hackett backbone phylogenetic trees are available from “https://birdtree.org/”. The code contains the R scripts and MATLAB scripts that we used for this study. For details please see the file “Readme.txt”, and the research paper.

openapache2.0Jan 2024View details →

Using RS-DAT to study continental-scale phenology at high-spatial resolution

This repository includes the notebooks employed to calculate and analyze a gridded phenological model, as computed from a set of daily meteorological variables.

opencc-by-4.0Jan 2024View details →

Range expansion is slower and more variable with rapid evolution across a spatial gradient in temperature

Rapid evolution in colonizing populations can alter our ability to predict future range expansions. Recent theory suggests that the dynamics of replicate range expansions are less variable, and hence more predictable, with increased selection at the expanding range front. Here, we test whether selection from environmental gradients across space produces more consistent range expansion speeds, using the experimental evolution of replicate duckweed populations colonizing landscapes with and without a temperature gradient. We found that range expansion across a temperature gradient was slower on average, with range-front populations displaying higher population densities, and genetic signatures and trait changes consistent with directional selection. Despite this, we found that with a spatial gradient range expansion speed became more variable and less consistent among replicates over time. Our results therefore challenge current theory, highlighting that chance can still shape the genetic response to selection to influence our ability to predict range expansion speeds.

opencc-by-4.0May 2023View details →

[Dataset & scripts] to "Spatial scales of kinetic energy in the Arctic Ocean", dataset from Caili Liu

## "Spatial scales of kinetic energy in the Arctic Ocean" Available dataset for each figure (1~9) and figure10 in the main text, including Jupyter notebook scripts (Fig1, Fig2, Fig5, Fig10) and Matlab scripts (Fig3, Fig4, Fig6, Fig7, Fig8, Fig9). ## Description This dataset is as the supplementary to the manuscript "Spatial scales of kinetic energy in the Arctic Ocean", including jupyter notebook scripts and matlab scripts of visualization directly for figures1~9. 1) Jupyter notebook scripts for visualization the MESH and BG are used for visualization, and *.mat are the dataset for Fig1/2/5/10. The load path in the script should be changed to your files accordingly. 2) Matlab scripts for plots All figures/panels are directly produced, but it is composed of panels for Fig7/8/9 additionally.

Library size confounds biology in spatial transcriptomics data

This dataset contains annotated sub-cellular localised spatial measurements from the Visium, Xenium and CosMx platforms. Specifically, it includes datasets analysed in the publication Bhuva et. al, 2023 titled "Library size confounds biology in spatial transcriptomics data". Raw transcript detections are presented. Data is best accessed through the accompanying SubcellularSpatialData R/Bioconductor package. Region files used to annotate individual transcript detections are presented in the form of <a href="https://geojson.org/">GeoJSON</a> files.

opencc-by-4.0Jun 2022View details →

Multifunctional blazed gratings for multiband spatial filtering, retroreflection, splitting, and demultiplexing based on C2 symmetric photonic crystals

These datafiles were generated by MATLAB to create a part of the figures in the paper. The work supported partially by the Narodowe Centrum Nauki (projects nos UMO-2015/17/B/ST3/00118 and UMO-2020/39/I/ST3/02413), TUBITAK (Program No. 2221), and projects UBACyT 20020150100028BA, UBACyT 20020190100108BA and CONICET PIP 11220170100633CO.

opencc-by-4.0Jan 2023View details →

Development of Multiomics in situ Pairwise Sequencing (MiP-Seq) for Single-cell Resolution Multidimensional Spatial Omics

The original data used in the article: Development of Multiomics in situ Pairwise Sequencing (MiP-Seq) for Single-cell Resolution Multidimensional Spatial Omics Delineating the spatial multiomics landscape will pave the way to understanding the molecular basis of physiology and pathology. However, current spatial omics technology development is still in its infancy. Here, we developed a high-throughput targeted in situ sequencing strategy, multiomics in situ pairwise sequencing (MiP-Seq), to efficiently decipher multiplexed DNAs, RNAs, proteins, and small biomolecules at subcellular resolution. MiP-Seq simultaneously sequenced the dual barcode base of padlock probes, dramatically increasing the detection capacity to 10N by N rounds of sequencing. We delineated spatial gene profiles in the hypothalamus using MiP-Seq. Moreover, MiP-Seq was unitized to detect tumor gene mutations and allele-specific expression of parental genes and to differentiate sites with and without the m6A RNA modification at specific sites. MiP-Seq was combined with in vivo Ca2+ imaging and Raman imaging to obtain a spatial multiomics atlas correlated to neuronal activity and cellular biochemical fingerprints. Importantly, we proposed a “signal dilution strategy” to resolve the crowded signals that challenge the applicability of in situ sequencing. Together, our method improves spatial multiomics and precision diagnostics, and facilitates analyzing cell function in connection with gene profiles.