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28,952 results for “Distributed”
Potential and realized distribution at 30m for Austrian pine (Pinus nigra) in Europe for 2000 - 2020
<p>Probability and uncertainty maps showing the potential and realized distribution for the Austrian pine (<em>Pinus nigra</em> J. F. Arnold) for Europe from the dataset prepared by <a href="http://doi.org/10.5281/zenodo.5818021">Bonannella et al. (2022)</a> and predicted using Ensemble Machine Learning (EML). Potential distribution map cover the period 2018 - 2020; realized distribution cover the period 2000 - 2020, split in the following time periods:</p> <ul> <li>2000 - 2002,</li> <li>2002 - 2006,</li> <li>2006 - 2010,</li> <li>2010 - 2014,</li> <li>2014 - 2018,</li> <li>2018 - 2020.</li> </ul> <p>Files are named according to the following naming convention, e.g:</p> <ul> <li>veg_pinus.nigra_anv.eml_md_30m_0..0cm_2000..2002_eumap_epsg3035_v0.3</li> </ul> <p>with the following fields:</p> <ul> <li>theme: e.g. <strong>veg</strong>,</li> <li>species code: e.g. <strong>pinus.nigra</strong>,</li> <li>species distribution type: e.g. <strong>anv</strong> (= actual natural vegetation),</li> <li>species estimation method: e.g. <strong>eml</strong>,</li> <li>species estimation type: e.g. <strong>md</strong> ( = model deviation),</li> <li>resolution in meters e.g. <strong>30m</strong>,</li> <li>reference depths (vertical dimension): e.g. <strong>0..0cm</strong>,</li> <li>reference period begin end: e.g. <strong>2000..2002</strong>,</li> <li>reference area: e.g. <strong>eumap</strong>,</li> <li>coordinate system: e.g. <strong>epsg3035</strong>,</li> <li>data set version: e.g. <strong>v0.3</strong>.</li> </ul> <p>For each species is then easy to identify probability and uncertainty distribution maps:</p> <ul> <li>veg_pinus.nigra_<strong>anv</strong>.eml_<strong>md</strong>: model uncertainty for realized distribution</li> <li>veg_pinus.nigra_<strong>anv</strong>.eml_<strong>p</strong>: probability for realized distribution</li> <li>veg_pinus.nigra_<strong>pnv</strong>.eml_<strong>md</strong>: model uncertainty for potential distribution</li> <li>veg_pinus.nigra_<strong>pnv</strong>.eml_<strong>p</strong>: probability for potential distribution</li> </ul> <p>Files are provided as <a href="https://gdal.org/drivers/raster/cog.html">Cloud Optimized GeoTIFFs</a> and projected in the Coordinate Reference System ETRS89 / LAEA Europe (= EPSG code 3035). Styling files are provided in both <em>SLD</em> and <em>QML</em> format.</p> <p>If you would like to know more about the creation of the maps and the modeling:</p> <ul> <li><strong>watch</strong> the talk at Open Data Science Workshop 2021 (<a href="https://doi.org/10.5446/55256">TIB AV-PORTAL</a>)</li> <li><strong>access </strong>the repository with our R/Python scripts and follow the instructions (<a href="https://gitlab.com/geoharmonizer_inea/spatial-layers/-/tree/master/veg_mapping">GitLab</a>)</li> <li><strong>access </strong>the repository with the training dataset (<a href="https://doi.org/10.5281/zenodo.5818021">Zenodo</a>)</li> <li><strong>read </strong>the tutorial with executable code on our <a href="https://opengeohub.github.io/spatial-prediction-eml/spatiotemporal-ml.html#spatiotemporal-distribution-of-fagus-sylvatica">GitBook</a></li> </ul> <p> </p> <p>A publication describing, in detail, all processing steps, accuracy assessment and general analysis of species distribution maps is available on <a href="https://doi.org/10.7717/peerj.13728">PeerJ</a>. To suggest any improvement/fix use <a href="https://gitlab.com/geoharmonizer_inea/spatial-layers/-/issues">https://gitlab.com/geoharmonizer_inea/spatial-layers/-/issues</a>.</p>
Potential and realized distribution at 30m for Norway spruce (Picea abies) in Europe for 2000 - 2020
<p>Probability and uncertainty maps showing the potential and realized distribution for the Norway spruce (<em>Picea abies, </em>L. H. Karst.) for Europe from the dataset prepared by <a href="http://doi.org/10.5281/zenodo.5818021">Bonannella et al. (2022)</a> and predicted using Ensemble Machine Learning (EML). Potential distribution map cover the period 2018 - 2020; realized distribution cover the period 2000 - 2020, split in the following time periods:</p> <ul> <li>2000 - 2002,</li> <li>2002 - 2006,</li> <li>2006 - 2010,</li> <li>2010 - 2014,</li> <li>2014 - 2018,</li> <li>2018 - 2020.</li> </ul> <p>Files are named according to the following naming convention, e.g:</p> <ul> <li>veg_picea.abies_anv.eml_md_30m_0..0cm_2000..2002_eumap_epsg3035_v0.3</li> </ul> <p>with the following fields:</p> <ul> <li>theme: e.g. <strong>veg</strong>,</li> <li>species code: e.g. <strong>picea.abies</strong>,</li> <li>species distribution type: e.g. <strong>anv</strong> (= actual natural vegetation),</li> <li>species estimation method: e.g. <strong>eml</strong>,</li> <li>species estimation type: e.g. <strong>md</strong> ( = model deviation),</li> <li>resolution in meters e.g. <strong>30m</strong>,</li> <li>reference depths (vertical dimension): e.g. <strong>0..0cm</strong>,</li> <li>reference period begin end: e.g. <strong>2000..2002</strong>,</li> <li>reference area: e.g. <strong>eumap</strong>,</li> <li>coordinate system: e.g. <strong>epsg3035</strong>,</li> <li>data set version: e.g. <strong>v0.3</strong>.</li> </ul> <p>For each species is then easy to identify probability and uncertainty distribution maps:</p> <ul> <li>veg_picea.abies_<strong>anv</strong>.eml_<strong>md</strong>: model uncertainty for realized distribution</li> <li>veg_picea.abies_<strong>anv</strong>.eml_<strong>p</strong>: probability for realized distribution</li> <li>veg_picea.abies_<strong>pnv</strong>.eml_<strong>md</strong>: model uncertainty for potential distribution</li> <li>veg_picea.abies_<strong>pnv</strong>.eml_<strong>p</strong>: probability for potential distribution</li> </ul> <p>Files are provided as <a href="https://gdal.org/drivers/raster/cog.html">Cloud Optimized GeoTIFFs</a> and projected in the Coordinate Reference System ETRS89 / LAEA Europe (= EPSG code 3035). Styling files are provided in both <em>SLD</em> and <em>QML</em> format.</p> <p>If you would like to know more about the creation of the maps and the modeling:</p> <ul> <li><strong>watch</strong> the talk at Open Data Science Workshop 2021 (<a href="https://doi.org/10.5446/55256">TIB AV-PORTAL</a>)</li> <li><strong>access </strong>the repository with our R/Python scripts and follow the instructions (<a href="https://gitlab.com/geoharmonizer_inea/spatial-layers/-/tree/master/veg_mapping">GitLab</a>)</li> <li><strong>access </strong>the repository with the training dataset (<a href="https://doi.org/10.5281/zenodo.5818021">Zenodo</a>)</li> <li><strong>read </strong>the tutorial with executable code on our <a href="https://opengeohub.github.io/spatial-prediction-eml/spatiotemporal-ml.html#spatiotemporal-distribution-of-fagus-sylvatica">GitBook</a></li> </ul> <p>A publication describing, in detail, all processing steps, accuracy assessment and general analysis of species distribution maps is available on <a href="https://doi.org/10.7717/peerj.13728">PeerJ</a>. To suggest any improvement/fix use <a href="https://gitlab.com/geoharmonizer_inea/spatial-layers/-/issues">https://gitlab.com/geoharmonizer_inea/spatial-layers/-/issues</a>.</p>
Potential and realized distribution at 30m for Goat willow (Salix caprea) in Europe for 2000 - 2020
<p>Probability and uncertainty maps showing the potential and realized distribution for the goat willow (<em>Salix caprea, L.</em>) for Europe from the dataset prepared by <a href="http://doi.org/10.5281/zenodo.5818021">Bonannella et al. (2022)</a> and predicted using Ensemble Machine Learning (EML). Potential distribution map cover the period 2018 - 2020; realized distribution cover the period 2000 - 2020, split in the following time periods:</p> <ul> <li>2000 - 2002,</li> <li>2002 - 2006,</li> <li>2006 - 2010,</li> <li>2010 - 2014,</li> <li>2014 - 2018,</li> <li>2018 - 2020.</li> </ul> <p>Files are named according to the following naming convention, e.g:</p> <ul> <li>veg_salix.caprea_anv.eml_md_30m_0..0cm_2000..2002_eumap_epsg3035_v0.3</li> </ul> <p>with the following fields:</p> <ul> <li>theme: e.g. <strong>veg</strong>,</li> <li>species code: e.g. <strong>salix.caprea</strong>,</li> <li>species distribution type: e.g. <strong>anv</strong> (= actual natural vegetation),</li> <li>species estimation method: e.g. <strong>eml</strong>,</li> <li>species estimation type: e.g. <strong>md</strong> ( = model deviation),</li> <li>resolution in meters e.g. <strong>30m</strong>,</li> <li>reference depths (vertical dimension): e.g. <strong>0..0cm</strong>,</li> <li>reference period begin end: e.g. <strong>2000..2002</strong>,</li> <li>reference area: e.g. <strong>eumap</strong>,</li> <li>coordinate system: e.g. <strong>epsg3035</strong>,</li> <li>data set version: e.g. <strong>v0.3</strong>.</li> </ul> <p>For each species is then easy to identify probability and uncertainty distribution maps:</p> <ul> <li>veg_salix.caprea_<strong>anv</strong>.eml_<strong>md</strong>: model uncertainty for realized distribution</li> <li>veg_salix.caprea_<strong>anv</strong>.eml_<strong>p</strong>: probability for realized distribution</li> <li>veg_salix.caprea_<strong>pnv</strong>.eml_<strong>md</strong>: model uncertainty for potential distribution</li> <li>veg_salix.caprea_<strong>pnv</strong>.eml_<strong>p</strong>: probability for potential distribution</li> </ul> <p>Files are provided as <a href="https://gdal.org/drivers/raster/cog.html">Cloud Optimized GeoTIFFs</a> and projected in the Coordinate Reference System ETRS89 / LAEA Europe (= EPSG code 3035). Styling files are provided in both <em>SLD</em> and <em>QML</em> format.</p> <p>If you would like to know more about the creation of the maps and the modeling:</p> <ul> <li><strong>watch</strong> the talk at Open Data Science Workshop 2021 (<a href="https://doi.org/10.5446/55256">TIB AV-PORTAL</a>)</li> <li><strong>access </strong>the repository with our R/Python scripts and follow the instructions (<a href="https://gitlab.com/geoharmonizer_inea/spatial-layers/-/tree/master/veg_mapping">GitLab</a>)</li> <li><strong>access </strong>the repository with the training dataset (<a href="https://doi.org/10.5281/zenodo.5818021">Zenodo</a>)</li> <li><strong>read </strong>the tutorial with executable code on our <a href="https://opengeohub.github.io/spatial-prediction-eml/spatiotemporal-ml.html#spatiotemporal-distribution-of-fagus-sylvatica">GitBook</a></li> </ul> <p>A publication describing, in detail, all processing steps, accuracy assessment and general analysis of species distribution maps is available on <a href="https://doi.org/10.7717/peerj.13728">PeerJ</a>. To suggest any improvement/fix use <a href="https://gitlab.com/geoharmonizer_inea/spatial-layers/-/issues">https://gitlab.com/geoharmonizer_inea/spatial-layers/-/issues</a></p> <p> </p>
Potential and realized distribution at 30m for Cork oak (Quercus suber) in Europe for 2000 - 2020
<p>Probability and uncertainty maps showing the potential and realized distribution for the cork oak (<em>Quercus suber, L.</em>) for Europe from the dataset prepared by <a href="http://doi.org/10.5281/zenodo.5818021">Bonannella et al. (2022)</a> and predicted using Ensemble Machine Learning (EML). Potential distribution map cover the period 2018 - 2020; realized distribution cover the period 2000 - 2020, split in the following time periods:</p> <ul> <li>2000 - 2002,</li> <li>2002 - 2006,</li> <li>2006 - 2010,</li> <li>2010 - 2014,</li> <li>2014 - 2018,</li> <li>2018 - 2020.</li> </ul> <p>Files are named according to the following naming convention, e.g:</p> <ul> <li>veg_quercus.suber_anv.eml_md_30m_0..0cm_2000..2002_eumap_epsg3035_v0.3</li> </ul> <p>with the following fields:</p> <ul> <li>theme: e.g. <strong>veg</strong>,</li> <li>species code: e.g. <strong>quercus.suber</strong>,</li> <li>species distribution type: e.g. <strong>anv</strong> (= actual natural vegetation),</li> <li>species estimation method: e.g. <strong>eml</strong>,</li> <li>species estimation type: e.g. <strong>md</strong> ( = model deviation),</li> <li>resolution in meters e.g. <strong>30m</strong>,</li> <li>reference depths (vertical dimension): e.g. <strong>0..0cm</strong>,</li> <li>reference period begin end: e.g. <strong>2000..2002</strong>,</li> <li>reference area: e.g. <strong>eumap</strong>,</li> <li>coordinate system: e.g. <strong>epsg3035</strong>,</li> <li>data set version: e.g. <strong>v0.3</strong>.</li> </ul> <p>For each species is then easy to identify probability and uncertainty distribution maps:</p> <ul> <li>veg_quercus.suber_<strong>anv</strong>.eml_<strong>md</strong>: model uncertainty for realized distribution</li> <li>veg_quercus.suber_<strong>anv</strong>.eml_<strong>p</strong>: probability for realized distribution</li> <li>veg_quercus.suber_<strong>pnv</strong>.eml_<strong>md</strong>: model uncertainty for potential distribution</li> <li>veg_quercus.suber_<strong>pnv</strong>.eml_<strong>p</strong>: probability for potential distribution</li> </ul> <p>Files are provided as <a href="https://gdal.org/drivers/raster/cog.html">Cloud Optimized GeoTIFFs</a> and projected in the Coordinate Reference System ETRS89 / LAEA Europe (= EPSG code 3035). Styling files are provided in both <em>SLD</em> and <em>QML</em> format.</p> <p>If you would like to know more about the creation of the maps and the modeling:</p> <ul> <li><strong>watch</strong> the talk at Open Data Science Workshop 2021 (<a href="https://doi.org/10.5446/55256">TIB AV-PORTAL</a>)</li> <li><strong>access </strong>the repository with our R/Python scripts and follow the instructions (<a href="https://gitlab.com/geoharmonizer_inea/spatial-layers/-/tree/master/veg_mapping">GitLab</a>)</li> <li><strong>access </strong>the repository with the training dataset (<a href="https://doi.org/10.5281/zenodo.5818021">Zenodo</a>)</li> <li><strong>read </strong>the tutorial with executable code on our <a href="https://opengeohub.github.io/spatial-prediction-eml/spatiotemporal-ml.html#spatiotemporal-distribution-of-fagus-sylvatica">GitBook</a></li> </ul> <p>A publication describing, in detail, all processing steps, accuracy assessment and general analysis of species distribution maps is available on <a href="https://doi.org/10.7717/peerj.13728">PeerJ</a>. To suggest any improvement/fix use <a href="https://gitlab.com/geoharmonizer_inea/spatial-layers/-/issues">https://gitlab.com/geoharmonizer_inea/spatial-layers/-/issues</a></p>
Potential and realized distribution at 30m for Turkey oak (Quercus cerris) in Europe for 2000 - 2020
<p>Probability and uncertainty maps showing the potential and realized distribution for the Turkey oak (<em>Quercus cerris, L.</em>) for Europe from the dataset prepared by <a href="http://doi.org/10.5281/zenodo.5818021">Bonannella et al. (2022)</a> and predicted using Ensemble Machine Learning (EML). Potential distribution map cover the period 2018 - 2020; realized distribution cover the period 2000 - 2020, split in the following time periods:</p> <ul> <li>2000 - 2002,</li> <li>2002 - 2006,</li> <li>2006 - 2010,</li> <li>2010 - 2014,</li> <li>2014 - 2018,</li> <li>2018 - 2020.</li> </ul> <p>Files are named according to the following naming convention, e.g:</p> <ul> <li>veg_quercus.cerris_anv.eml_md_30m_0..0cm_2000..2002_eumap_epsg3035_v0.3</li> </ul> <p>with the following fields:</p> <ul> <li>theme: e.g. <strong>veg</strong>,</li> <li>species code: e.g. <strong>quercus.cerris</strong>,</li> <li>species distribution type: e.g. <strong>anv</strong> (= actual natural vegetation),</li> <li>species estimation method: e.g. <strong>eml</strong>,</li> <li>species estimation type: e.g. <strong>md</strong> ( = model deviation),</li> <li>resolution in meters e.g. <strong>30m</strong>,</li> <li>reference depths (vertical dimension): e.g. <strong>0..0cm</strong>,</li> <li>reference period begin end: e.g. <strong>2000..2002</strong>,</li> <li>reference area: e.g. <strong>eumap</strong>,</li> <li>coordinate system: e.g. <strong>epsg3035</strong>,</li> <li>data set version: e.g. <strong>v0.3</strong>.</li> </ul> <p>For each species is then easy to identify probability and uncertainty distribution maps:</p> <ul> <li>veg_quercus.cerris_<strong>anv</strong>.eml_<strong>md</strong>: model uncertainty for realized distribution</li> <li>veg_quercus.cerris_<strong>anv</strong>.eml_<strong>p</strong>: probability for realized distribution</li> <li>veg_quercus.cerris_<strong>pnv</strong>.eml_<strong>md</strong>: model uncertainty for potential distribution</li> <li>veg_quercus.cerris_<strong>pnv</strong>.eml_<strong>p</strong>: probability for potential distribution</li> </ul> <p>Files are provided as <a href="https://gdal.org/drivers/raster/cog.html">Cloud Optimized GeoTIFFs</a> and projected in the Coordinate Reference System ETRS89 / LAEA Europe (= EPSG code 3035). Styling files are provided in both <em>SLD</em> and <em>QML</em> format.</p> <p>If you would like to know more about the creation of the maps and the modeling:</p> <ul> <li><strong>watch</strong> the talk at Open Data Science Workshop 2021 (<a href="https://doi.org/10.5446/55256">TIB AV-PORTAL</a>)</li> <li><strong>access </strong>the repository with our R/Python scripts and follow the instructions (<a href="https://gitlab.com/geoharmonizer_inea/spatial-layers/-/tree/master/veg_mapping">GitLab</a>)</li> <li><strong>access </strong>the repository with the training dataset (<a href="https://doi.org/10.5281/zenodo.5818021">Zenodo</a>)</li> <li><strong>read </strong>the tutorial with executable code on our <a href="https://opengeohub.github.io/spatial-prediction-eml/spatiotemporal-ml.html#spatiotemporal-distribution-of-fagus-sylvatica">GitBook</a></li> </ul> <p> </p> <p>A publication describing, in detail, all processing steps, accuracy assessment and general analysis of species distribution maps is available on <a href="https://doi.org/10.7717/peerj.13728">PeerJ</a>. To suggest any improvement/fix use <a href="https://gitlab.com/geoharmonizer_inea/spatial-layers/-/issues">https://gitlab.com/geoharmonizer_inea/spatial-layers/-/issues</a>.</p>
Potential and realized distribution at 30m for pedunculate oak (Quercus robur) in Europe for 2000 - 2020
<p>Probability and uncertainty maps showing the potential and realized distribution for the pedunculate oak (<em>Quercus robur, L.</em>) for Europe from the dataset prepared by <a href="http://doi.org/10.5281/zenodo.5818021">Bonannella et al. (2022)</a> and predicted using Ensemble Machine Learning (EML). Potential distribution map cover the period 2018 - 2020; realized distribution cover the period 2000 - 2020, split in the following time periods:</p> <ul> <li>2000 - 2002,</li> <li>2002 - 2006,</li> <li>2006 - 2010,</li> <li>2010 - 2014,</li> <li>2014 - 2018,</li> <li>2018 - 2020.</li> </ul> <p>Files are named according to the following naming convention, e.g:</p> <ul> <li>veg_quercus.robur_anv.eml_md_30m_0..0cm_2000..2002_eumap_epsg3035_v0.3</li> </ul> <p>with the following fields:</p> <ul> <li>theme: e.g. <strong>veg</strong>,</li> <li>species code: e.g. <strong>quercus.robur</strong>,</li> <li>species distribution type: e.g. <strong>anv</strong> (= actual natural vegetation),</li> <li>species estimation method: e.g. <strong>eml</strong>,</li> <li>species estimation type: e.g. <strong>md</strong> ( = model deviation),</li> <li>resolution in meters e.g. <strong>30m</strong>,</li> <li>reference depths (vertical dimension): e.g. <strong>0..0cm</strong>,</li> <li>reference period begin end: e.g. <strong>2000..2002</strong>,</li> <li>reference area: e.g. <strong>eumap</strong>,</li> <li>coordinate system: e.g. <strong>epsg3035</strong>,</li> <li>data set version: e.g. <strong>v0.3</strong>.</li> </ul> <p>For each species is then easy to identify probability and uncertainty distribution maps:</p> <ul> <li>veg_quercus.robur_<strong>anv</strong>.eml_<strong>md</strong>: model uncertainty for realized distribution</li> <li>veg_quercus.robur_<strong>anv</strong>.eml_<strong>p</strong>: probability for realized distribution</li> <li>veg_quercus.robur_<strong>pnv</strong>.eml_<strong>md</strong>: model uncertainty for potential distribution</li> <li>veg_quercus.robur_<strong>pnv</strong>.eml_<strong>p</strong>: probability for potential distribution</li> </ul> <p>Files are provided as <a href="https://gdal.org/drivers/raster/cog.html">Cloud Optimized GeoTIFFs</a> and projected in the Coordinate Reference System ETRS89 / LAEA Europe (= EPSG code 3035). Styling files are provided in both <em>SLD</em> and <em>QML</em> format.</p> <p>If you would like to know more about the creation of the maps and the modeling:</p> <ul> <li><strong>watch</strong> the talk at Open Data Science Workshop 2021 (<a href="https://doi.org/10.5446/55256">TIB AV-PORTAL</a>)</li> <li><strong>access </strong>the repository with our R/Python scripts and follow the instructions (<a href="https://gitlab.com/geoharmonizer_inea/spatial-layers/-/tree/master/veg_mapping">GitLab</a>)</li> <li><strong>access </strong>the repository with the training dataset (<a href="https://doi.org/10.5281/zenodo.5818021">Zenodo</a>)</li> <li><strong>read </strong>the tutorial with executable code on our <a href="https://opengeohub.github.io/spatial-prediction-eml/spatiotemporal-ml.html#spatiotemporal-distribution-of-fagus-sylvatica">GitBook</a></li> </ul> <p> </p> <p>A publication describing, in detail, all processing steps, accuracy assessment and general analysis of species distribution maps is available on <a href="https://doi.org/10.7717/peerj.13728">PeerJ</a>. To suggest any improvement/fix use <a href="https://gitlab.com/geoharmonizer_inea/spatial-layers/-/issues">https://gitlab.com/geoharmonizer_inea/spatial-layers/-/issues</a></p>
Potential and realized distribution at 30m for Scots pine (Pinus sylvestris) in Europe for 2000 - 2020
<p>Probability and uncertainty maps showing the potential and realized distribution for the Scots pine (<em>Pinus sylvestris, L.</em>) for Europe from the dataset prepared by <a href="http://doi.org/10.5281/zenodo.5818021">Bonannella et al. (2022)</a> and predicted using Ensemble Machine Learning (EML). Potential distribution map cover the period 2018 - 2020; realized distribution cover the period 2000 - 2020, split in the following time periods:</p> <ul> <li>2000 - 2002,</li> <li>2002 - 2006,</li> <li>2006 - 2010,</li> <li>2010 - 2014,</li> <li>2014 - 2018,</li> <li>2018 - 2020.</li> </ul> <p>Files are named according to the following naming convention, e.g:</p> <ul> <li>veg_pinus.sylvestris_anv.eml_md_30m_0..0cm_2000..2002_eumap_epsg3035_v0.3</li> </ul> <p>with the following fields:</p> <ul> <li>theme: e.g. <strong>veg</strong>,</li> <li>species code: e.g. <strong>pinus.sylvestris</strong>,</li> <li>species distribution type: e.g. <strong>anv</strong> (= actual natural vegetation),</li> <li>species estimation method: e.g. <strong>eml</strong>,</li> <li>species estimation type: e.g. <strong>md</strong> ( = model deviation),</li> <li>resolution in meters e.g. <strong>30m</strong>,</li> <li>reference depths (vertical dimension): e.g. <strong>0..0cm</strong>,</li> <li>reference period begin end: e.g. <strong>2000..2002</strong>,</li> <li>reference area: e.g. <strong>eumap</strong>,</li> <li>coordinate system: e.g. <strong>epsg3035</strong>,</li> <li>data set version: e.g. <strong>v0.3</strong>.</li> </ul> <p>For each species is then easy to identify probability and uncertainty distribution maps:</p> <ul> <li>veg_pinus.sylvestris_<strong>anv</strong>.eml_<strong>md</strong>: model uncertainty for realized distribution</li> <li>veg_pinus.sylvestris_<strong>anv</strong>.eml_<strong>p</strong>: probability for realized distribution</li> <li>veg_pinus.sylvestris_<strong>pnv</strong>.eml_<strong>md</strong>: model uncertainty for potential distribution</li> <li>veg_pinus.sylvestris_<strong>pnv</strong>.eml_<strong>p</strong>: probability for potential distribution</li> </ul> <p>Files are provided as <a href="https://gdal.org/drivers/raster/cog.html">Cloud Optimized GeoTIFFs</a> and projected in the Coordinate Reference System ETRS89 / LAEA Europe (= EPSG code 3035). Styling files are provided in both <em>SLD</em> and <em>QML</em> format.</p> <p>If you would like to know more about the creation of the maps and the modeling:</p> <ul> <li><strong>watch</strong> the talk at Open Data Science Workshop 2021 (<a href="https://doi.org/10.5446/55256">TIB AV-PORTAL</a>)</li> <li><strong>access </strong>the repository with our R/Python scripts and follow the instructions (<a href="https://gitlab.com/geoharmonizer_inea/spatial-layers/-/tree/master/veg_mapping">GitLab</a>)</li> <li><strong>access </strong>the repository with the training dataset (<a href="https://doi.org/10.5281/zenodo.5818021">Zenodo</a>)</li> <li><strong>read </strong>the tutorial with executable code on our <a href="https://opengeohub.github.io/spatial-prediction-eml/spatiotemporal-ml.html#spatiotemporal-distribution-of-fagus-sylvatica">GitBook</a></li> </ul> <p>A publication describing, in detail, all processing steps, accuracy assessment and general analysis of species distribution maps is available on <a href="https://doi.org/10.7717/peerj.13728">PeerJ</a>. To suggest any improvement/fix use <a href="https://gitlab.com/geoharmonizer_inea/spatial-layers/-/issues">https://gitlab.com/geoharmonizer_inea/spatial-layers/-/issues</a>.</p>
Potential and realized distribution at 30m for Sweet cherry (Prunus avium) in Europe for 2000 - 2020
<p>Probability and uncertainty maps showing the potential and realized distribution for the sweet cherry (<em>Prunus avium, L.</em>) for Europe from the dataset prepared by <a href="http://doi.org/10.5281/zenodo.5818021">Bonannella et al. (2022)</a> and predicted using Ensemble Machine Learning (EML). Potential distribution map cover the period 2018 - 2020; realized distribution cover the period 2000 - 2020, split in the following time periods:</p> <ul> <li>2000 - 2002,</li> <li>2002 - 2006,</li> <li>2006 - 2010,</li> <li>2010 - 2014,</li> <li>2014 - 2018,</li> <li>2018 - 2020.</li> </ul> <p>Files are named according to the following naming convention, e.g:</p> <ul> <li>veg_prunus.avium_anv.eml_md_30m_0..0cm_2000..2002_eumap_epsg3035_v0.3</li> </ul> <p>with the following fields:</p> <ul> <li>theme: e.g. <strong>veg</strong>,</li> <li>species code: e.g. <strong>prunus.avium</strong>,</li> <li>species distribution type: e.g. <strong>anv</strong> (= actual natural vegetation),</li> <li>species estimation method: e.g. <strong>eml</strong>,</li> <li>species estimation type: e.g. <strong>md</strong> ( = model deviation),</li> <li>resolution in meters e.g. <strong>30m</strong>,</li> <li>reference depths (vertical dimension): e.g. <strong>0..0cm</strong>,</li> <li>reference period begin end: e.g. <strong>2000..2002</strong>,</li> <li>reference area: e.g. <strong>eumap</strong>,</li> <li>coordinate system: e.g. <strong>epsg3035</strong>,</li> <li>data set version: e.g. <strong>v0.3</strong>.</li> </ul> <p>For each species is then easy to identify probability and uncertainty distribution maps:</p> <ul> <li>veg_prunus.avium_<strong>anv</strong>.eml_<strong>md</strong>: model uncertainty for realized distribution</li> <li>veg_prunus.avium_<strong>anv</strong>.eml_<strong>p</strong>: probability for realized distribution</li> <li>veg_prunus.avium_<strong>pnv</strong>.eml_<strong>md</strong>: model uncertainty for potential distribution</li> <li>veg_prunus.avium_<strong>pnv</strong>.eml_<strong>p</strong>: probability for potential distribution</li> </ul> <p>Files are provided as <a href="https://gdal.org/drivers/raster/cog.html">Cloud Optimized GeoTIFFs</a> and projected in the Coordinate Reference System ETRS89 / LAEA Europe (= EPSG code 3035). Styling files are provided in both <em>SLD</em> and <em>QML</em> format.</p> <p>If you would like to know more about the creation of the maps and the modeling:</p> <ul> <li><strong>watch</strong> the talk at Open Data Science Workshop 2021 (<a href="https://doi.org/10.5446/55256">TIB AV-PORTAL</a>)</li> <li><strong>access </strong>the repository with our R/Python scripts and follow the instructions (<a href="https://gitlab.com/geoharmonizer_inea/spatial-layers/-/tree/master/veg_mapping">GitLab</a>)</li> <li><strong>access </strong>the repository with the training dataset (<a href="https://doi.org/10.5281/zenodo.5818021">Zenodo</a>)</li> <li><strong>read </strong>the tutorial with executable code on our <a href="https://opengeohub.github.io/spatial-prediction-eml/spatiotemporal-ml.html#spatiotemporal-distribution-of-fagus-sylvatica">GitBook</a></li> </ul> <p> </p> <p>A publication describing, in detail, all processing steps, accuracy assessment and general analysis of species distribution maps is available on <a href="https://doi.org/10.7717/peerj.13728">PeerJ</a>. To suggest any improvement/fix use <a href="https://gitlab.com/geoharmonizer_inea/spatial-layers/-/issues">https://gitlab.com/geoharmonizer_inea/spatial-layers/-/issues</a>.</p>
Raw Data - 3D Printing Temperature Tailors Electrical and Electrochemical Properties through Changing Inner Distribution of Graphite/Polymer
<p>This Data set contains the raw data of the article:</p> <p>3D Printing Temperature Tailors Electrical and Electrochemical Properties through Changing Inner Distribution of Graphite/Polymer, Small, 2021, 17, 2101233.</p> <p>C. Iffelsberger, C. W. Jellett, and M. Pumera*,</p> <p>https://doi.org/10.1002/smll.202101233</p> <p>Related to the MSCA Project: 888797 LoCatSpot</p>
Supporting data of: Hydrography and food distribution during a tidal cycle above a cold-water coral mound
<p>This file contains the raw data and data analyses scripts to:</p> <p>Hydrography and food distribution during a tidal cycle above a cold-water coral mound</p> <p>Evert de Froe, Sandra R. Maier, Henriette G. Horn, George A. Wolff, Sabena Blackbird, Christian Mohn, Mads Schultz, Anna-Selma van der Kaaden, Chiu H. Cheng, Evi Wubben, Britt van Haastregt, Eva Friis Moller, Marc Lavaleye, Karline Soetaert, Gert-Jan Reichart, Dick van Oevelen.</p> <p>Deep Sea Research Part I: Oceanographic Research Papers, 2022,<br> ISSN 0967-0637,<br> https://doi.org/10.1016/j.dsr.2022.103854.<br> <strong>Abstract: </strong>Cold-water corals (CWCs) are important ecosystem engineers in the deep sea that provide habitat for numerous species and can form large coral mounds. These mounds influence surrounding currents and induce distinct hydrodynamic features, such as internal waves and episodic downwelling events that accelerate transport of organic matter towards the mounds, supplying the corals with food. To date, research on organic matter distribution at coral mounds has focussed either on seasonal timescales or has provided single point snapshots. Data on food distribution at the timescale of a diurnal tidal cycle is currently limited. Here, we integrate physical, biogeochemical, and biological data throughout the water column and along a transect on the south-eastern slope of Rockall Bank, Northeast Atlantic Ocean. This transect consisted of 24-hour sampling stations at four locations: Bank, Upper slope, Lower slope, and the Oreo coral mound. We investigated how the organic matter distribution in the water column along the transect is affected by tidal activity. Repeated CTD casts indicated that the water column above Oreo mound was more dynamic than above other stations in multiple ways. First, the bottom water showed high variability in physical parameters and nutrient concentrations, possibly due to the interaction of the tide with the mound topography. Second, in the surface water a diurnal tidal wave replenished nutrients in the photic zone, supporting new primary production. Third, above the coral mound an internal wave (200 m amplitude) was recorded at 400 m depth after the turning of the barotropic tide. After this wave passed, high quality organic matter was recorded in bottom waters on the mound coinciding with shallow water physical characteristics such as high oxygen concentration and high temperature. Trophic markers in the benthic community suggest feeding on a variety of food sources, including phytodetritus and zooplankton. We suggest that there are three transport mechanisms that supply food to the CWC ecosystem. First, small phytodetritus particles are transported downwards to the seafloor by advection from internal waves, supplying high quality organic matter to the CWC reef community. Second, the shoaling of deeper nutrient-rich water into the surface water layer above the coral mound could stimulate diatom growth, which form fast-sinking aggregates. Third, evidence from lipid analysis indicates that zooplankton faecal pellets also enhance supply of organic matter to the reef communities. This study is the first to report organic matter quality and composition over a tidal cycle at a coral mound and provides evidence that fresh high-quality organic matter is transported towards a coral reef during a tidal cycle.</p> <p> </p>
Data accompanying: Impacts into a porous graphite: an investigation on crater formation and ejecta distribution
<p>Reconstructed X-ray tomographies and python analysis scripts for the publication "Impacts into a porous graphite: an investigation on crater formation and ejecta distribution"</p> <p> </p> <p>Uses the spam python toolkit, which can probably be replaced by scipy.ndimage.center_of_mass if needed.</p>
Atmospheric Distribution of HCN from Satellite Observations and 3-D Model Simulations - TOMCAT data
<p>This repository contains the model data from the paper "Atmospheric Distribution of HCN from Satellite<br> Observations and 3-D Model Simulations" submitted to ACP.</p> <p>The files contains the monthly mean hydrogen cyanide (HCN) mixing ratios modelled using the TOMCAT 3-D offline chemical transport model with a horizontal resolution of 2.8° × 2.8° with 60 hybrid σ-pressure levels from the surface to ~60 km.</p>
State level income distributions for net income deciles for the US for historical years (2011-2014) and projections for different SSP scenarios (2015-2100)
<p>This dataset is documented in this manuscript here- https://iopscience.iop.org/article/10.1088/1748-9326/acf9b8/meta</p> <p>Income distributions are a growing area of interest in the examination of equity impacts brought on by climate change and its responses. We project US state level income distributions using a PCA-based approach, applying a downscaled version of the approach employed by Narayan et al. (2022, in-prep). A state-level dataset had to be synthesized and projected based on existing sources. We apply a PC-based model to our derived state-level dataset, employing projected GINI’s from the SSP scenarios. We produce projected income distribution by income decile for three SSPs to year 2100. For the purpose of the projections, we developed a consistent set of tax adjusted net income deciles for all states from 2011 to 2014. This dataset was used for initialization of the projections and for validation.</p> <p>If/when using this dataset, please cite this paper- https://iopscience.iop.org/article/10.1088/1748-9326/acf9b8/meta</p>
Data from: Macro-evolutionary trade-offs as the basis for the distribution of European bats
<p>We have compiled a dataset of life history traits and distribution characteristics of 30 European bat species, based on a literature study of a total of 56 primary and secondary sources. These life history traits are grouped into morphological, physiological and ecological adaptations. </p> <p><em>Physiological adaptations:</em></p> <p>Neonatal mass: the average weight (g) of a newborn pup, measured within five days after birth.</p> <p>Average litter size: the average size of a full-term litter (including stillborn pups) per female.</p> <p>Weaning mass: the weight (g) of a juvenile during its first flight outside the roost.</p> <p>Adult body mass: the average weight (g) of adult bats during the summer (between 1 May and 1 July), excluding pregnant females.</p> <p>Litter mass: neonatal mass * average litter size.</p> <p>Relative mass of neonatal to adult: neonatal mass*100 / adult weight</p> <p>Relative mass of litter to adult: litter mass*100 / adult weight</p> <p>Gestation: the length of gestation period (in days), from fertilisation to birth. When mated during autumn or winter, the sperm (or fertilised egg in <em>M. schreibersii</em>) is stored throughout the winter. On arousal from hibernation in the spring, around mid March, female bats ovulate and gestation begins. In accordance with other researchers (e.g. Altringham 1996, Entwisle <em>et al</em>. 1998), 15 March was used as the start of the gestation period, for statistical reasons we also included <em>M. schreibersii</em>.</p> <p>Weaning: the length of the lactation period (in days) until offspring are fully independent. After the juveniles are capable of flight, mothers continue to give their young nourishment until they are fully independent. Only when no extra nourishment is provided are the offspring considered fully weaned.</p> <p>Reproductive period: gestation + weaning (in days).</p> <p>Average age at first reproduction: The age (in days) at which 75% of the female population becomes sexual mature. Many species reproduce just before or during their first winter (at approximately 80 days old), but in some species the majority of the population postpone their sexual development. Individuals are stated to have become sexual mature if they participate in mating, have been found to be pregnant or inseminated.</p> <p>Observed average age: observed average age of adults in a population at a given time (in years).</p> <p>Longevity: the age (in years) of the oldest observed individual. The longevity can only be obtained by marking and later recapturing individuals. Most recapture data are collected in summer roosts or hibernacula. As not all species show the same fidelity to summer roost sites or can be found in hibernacula that are accessible to humans, this measure is sensitive to the chance of recapture.</p> <p>Minimum hibernation temperature: the minimum temperature (degrees Celsius) at which each species is observed.</p> <p> </p> <p><em>Morphological adaptations</em></p> <p>Length of forearm at birth: the length of the forearm (mm) of a newborn bat, measured between the elbow to the wrist of a folded wing. This is widely accepted as a measurement of size. Although it is not the best reflection of the length of an individual, it can be measured rapidly and accurately under field conditions.</p> <p>Length of forearm adult: The length of the forearm (mm) of an adult bat.</p> <p>Relative length of forearm of a newborn to an adult: (length of the forearm at birth*100)/ Length of forearm adult.</p> <p>Wing span: the length of the wings (m). The distance between the wingtips of a bat with wings extended so the leading edge is straight (including body width).</p> <p>Wing area: The combined area of the two wings (m<sup>2</sup>) including the entire tail membrane and the portion of the body between the wings.</p> <p>Wing loading: the relation between body weight, wing size and gravity (Nm<sup>-2</sup>). This measurement is related to the mean pressure on the wings. Wing loading is the weight (mass, in kg, times gravitational acceleration) divided by the wing area, i.e Wing loading = (weight adult*9.81)/ wing area. The wing load can vary significantly between geometrically similar bats. Because of such allometry, large bats have a higher wing load than smaller bats.</p> <p>Wing aspect ratio: the square of the wingspan divided by the wing area, i.e. Wing aspect ratio = (wingspan)<sup>2</sup> / wing area. This ratio can be interpreted as a measure of the aerodynamic efficiency of flight. A higher aspect ratio usually corresponds with greater aerodynamic efficiency (i.e. a streamlined body) and lower energy use in flight.</p> <p>Flight speed: The speed of flight (m/s). The speed of flight is usually measured in wind tunnel experiments or during radio-tracking.</p> <p> </p> <p><em>Ecological adaptations </em></p> <p>Maximum migration distance: the maximum observed distance (km) between the summer and winter habitat. In contrast to birds, the direction of migration in bats is not determined by the change of the seasons, but by the locations of the hibernacula. This migration distance can only be obtained by capturing, marking and later recapturing individuals. Bats often migrate across national boundaries and gathering recapture data requires international cooperation. The chance of recapture is sensitive to sample effort and local observation methods.</p> <p>Average migration distance: the average distance (km) between the summer and winter habitat. Most species of bats migrate both short and long distances. The same restrictions described for maximum migration distance also apply to this parameter.</p> <p>Echolocation type: the predominant echolocation type used by each bat species. European bats use one or sometimes a combination of the following four types of echolocations: fm-CF-fm, fm-QCF (with the QCF part dominant), FM-qcf (with the FM part dominant) and FM. For statistical reasons both FM-qcf and FM are clustered in the group FM. The FM-qcf and fm-QCF echolocations are both often loud and used to detect distant prey. FM and fm-CF-fm echolocations are softer and bats using these types of echolocation receive more detailed knowledge of their surroundings. Bats primarily use only one type of echolocation, although many can make some slight adjustments to this.</p> <p>Echolocation range: the maximum distance that an echolocating bat can detect a structure or object.</p> <p>Echolocation minimum frequency: the minimum echolocation frequency (MHz) used by each bat species.</p> <p>Echolocation maximum frequency: the maximum echolocation frequency (MHz) used by each bat species.</p> <p>Duration call (ms): the average duration (in ms) of one complete call cycle.</p> <p> </p> <p><em>Distribution parameters</em></p> <p>Northern limit of range: the most northerly observation (in latitude) of each bat species. This measurement includes anecdotal observations and observations of male bats.</p> <p>Northern limit of reproduction range: the most northerly observation (in latitude) of a maternity group. Note: confusion is possible between summer roosts and maternity roosts. Summer roosts are often inhabited by both males and females and less than 70% of the adult females participate in reproduction. Maternity roosts are predominantly occupied by females, and more than 70% of the adult females participate in reproduction.</p> <p>Southern limit of range: the most southerly observation (in latitude) of each bat species. This measurement includes anecdotal observations and observations of male bats.</p> <p>Southern limit of reproduction range: the most southerly observation (in latitude) of a maternity group. The same restrictions described for northern limit of reproduction range also apply to this parameter.</p> <p>Western limit of range: the most western observation (in longitude) of each bat species</p> <p>Eastern limit of range: the most eastern observation (in longitude) of each bat species</p> <p>Night length: The average night length (in hours) during midsummer (21<sup>st</sup> June) at the northern limit of the reproduction range.</p> <p> </p> <p>Sources: 1. Jones et al. 2009, 2. Krapp 2011, 3. Schober & Grimmberger 1997, 4. Norberg & Rayner 1987, 5. Hutterer et al. 2005, 6. Dietz et al. 2009, 7. Supplementary data from Barclay et al. 2004, 8. Wilkinson & South 2002, 9 Jones & Rydell 1994, 10. Norberg 1986, 11. Jones 1994, 12. Baagøe 1987, 13.Fleming & Eby 2003, 14. Neuweiler 2000, 15. Hayssen et al. 1993, 16. Kunz & Kurta 1987, 17. Russo & Jones 2002, 18. Brunet-Rossinni & Austad 2004, 19. Aldridge 1987, 20. Urbańczyk 1991, 21. Nagel & Nagel 1991, 22. Masing & Lutsar 2007, 23. Masing 1983, 24. Gaisler 1970, 25. Norberg 1987, 26. Baydemür & Albayrak 2006, 27. Dietz et al. 2006, 28. Sharifi 2004, 29. Kerth et al. 2001, 30. Schmidt 2005, 31. Smirnov et al. 2008, 32. Verbeek 1998, 33. Pandurkska & Beshkov 1998, 34. Harmata 1969, 35. Sachanowicz & Zub 2002, 36. Arlettaz et al. 2001, 36. Ibáñez et al. 2001, 37. Estók 2007, 38. Lohrl 1936, 39. Kunz & Hood 2000, 40. Happold & Happold 1990, 41. Rydell 1990, 42. Reiter 2004, 43. Ransome 1990, 44. Zahn 1999, 45. Deanesly & Warwick 1939, 46. Racey 1969, 47. Racey & Swift 1981, 48. Racey 1974, 49. Masing 1982, 50. Boyd & Stebbings 1989, 51. Lesiñski 1986, 52. Barak & Yom-tov 1991, 53. Arlettaz et al. 2000, 54. Gaisler et al. 1997, 55, Heise 1989, 56. Papadatou et al. 2009, 57. Unpublished data: own measurements.</p> <p> </p>
Interactive map of distribution of gene fragments indicative of cyanotoxin biosynthesis and cyanotoxins in the European Alps
<p><span>Distribution of cyanotoxins and cyanotoxin biosynthesis genes in Alpine region determined by LC-MS/MS and (q)PCR. Cyanotoxins and cyanotoxin genes are mapped on separate layers, and two basemaps are available (simple and relief). Results can be filtered by location, sample type, water body type, cyanotoxins and cyanotoxin genes. Note that cyanotoxin analyses were not performed on all sampling points.</span></p>
Distribution Map of Festuca dolichophylla (suplemental material-TS1)
<p>The distribution map of <em>Festuca dolichophylla</em> relies on diverse data sources. Geographical coordinates (latitude and longitude) and country initials (countryCode) were extracted from Tropicos, the Gbif repository (up to May 2019), and the iDigBio database (up to July 2021). Additionally, data from other sources, including BMAP Peru (2023), Eduardo-Palomino (2022), Ccora et al. (2019), Arana et al. (2013), Castro (2019), Flores (2017), Gonzales (2017), and Martínez y Pérez (1999), were integrated. The Gbif data points are associated with gbifID numbers for reference. Please note that this compilation provides essential information for understanding the distribution of <em>F. dolichophylla</em> across various regions.</p> <h3>Software</h3> <p>Organized data by geographic coordinates was uploaded to <strong>ArcGIS Pro v. 3.2.0</strong> for map production. Geospatial visualization and mapping were carried out using ArcGIS Pro, allowing us to create the distribution map of <em>F. dolichophylla</em>.</p> <h2>Methods</h2> <div> <p>The dataset for the distribution map of <em>Festuca dolichophylla</em> was meticulously collected from various sources.</p> <ol> <li> <p><strong>Data Collection</strong>:</p> <ul> <li><strong>Tropicos</strong>: Data were extracted from Tropicos until December 2023.</li> <li><strong>Gbif Repository</strong>: Data was sourced from the Gbif repository until May 2019.</li> <li><strong>iDigBio Database</strong>: Additional data points were retrieved from the iDigBio database up to July 2021.</li> <li><strong>Other Sources</strong>: We also incorporated data from various other sources, including BMAP Peru (2023), Eduardo-Palomino (2022), Ccora et al. (2019), Arana et al. (2013), Castro (2019), Flores (2017), Gonzales (2017), and Martínez y Pérez (1999).</li> </ul> </li> <li> <p><strong>Data Organization and Processing</strong>:</p> <ul> <li>All collected data points were meticulously organized by coordinates.</li> <li>We ensured consistency by cross-referencing and validating the data.</li> <li>The dataset was then uploaded to <strong>ArcGIS Pro v. 3.2.0</strong> for map production.</li> <li>Geospatial visualization and mapping were carried out using ArcGIS Pro, allowing us to create the distribution map of <em>F. dolichophylla</em>.</li> </ul> </li> </ol> </div> <h2>Funding</h2> <div> <p>Neotropical Grassland Conservancy, Award: Memorial grant 2020</p> </div>
Initial auralization of a distributed propulsion system equipped with 26 ducted low-speed fans
<p>Illustration of engine noise auralization by DLR Institute of Propulsion Technology obtained with the framework PropNoise, VIOLIN, CORAL. Data associated with the following publication: S. Schade, R. Merino-Martinez, P. Ratei, S. Bartels, R. Jaron and A. Moreau, "<a href="https://doi.org/10.2514/6.2024-3273"><em>Initial Study on the Impact of Speed Fluctuations on the Psychoacoustic Characteristics of a Distributed Propulsion System with Ducted Fans</em></a>", 30th AIAA/CEAS Aeroacoustics Conference, Rome, Italy, 04-07 June, 2024.</p> <p>Selected binaural audio files to illustrate the impact of rotational speed fluctuations on the noise characteristics of a distributed propulsion system equipped with 26 ducted, low-speed fans. Please note that the sound pressure amplitudes are normalized to 110dB for the reference turbofan case and to 90dB for the cases with distributed fans.</p> <p>The corresponding time signals and spectrograms are available in the associated conference paper in Figures 4-6.</p>
Dataset supporting the paper: Deciphering Oxygen Distribution and Hypoxia Profiles in the Tumor Microenvironment: A Data-Driven Mechanistic Modeling Approach
<p>The necessary image files for the paper titled "Deciphering Oxygen Distribution and Hypoxia Profiles in the Tumor Microenvironment: A Data-Driven Mechanistic Modeling Approach"</p>
Data set: Variations in water economy traits in two Sphagnum species across their distribution boundaries
<p><em>Sphagnum</em> trait data collected (2016-2017) across a climatic gradient in Sweden. Trait data for both shoot and canopy traits. Data for <em>Sphagnum cuspidatum</em> and <em>Sphagnum lindbergii</em>. Also contains data on species occurrence records in Sweden and output from speceis distribution modelling. See published paper for more information.</p> <p>Files contain (i) processed data ("calculated_trait_data...cvs"), (ii) raw data ("Campbell_etal_clim_traits_...cvs"), (iii) their readme files, and (iv) R-scripts to run the analyses. Note that you need the files in the zip-file to run the analyses in the R-script. The zip-file contains all raw data (climate, traits, species occurences), MaxEnt output, and raster files from photogrammetry.</p> <p>More info in paper: <a href="https://doi.org/10.1002/ajb2.16347" target="_blank" rel="noopener">https://doi.org/10.1002/ajb2.16347</a></p>
Changes in above- versus belowground biomass distribution in permafrost regions in response to climate warming
<p>Permafrost regions contain approximately half of the carbon stored in land ecosystems and have warmed at least twice as much as any other biome. This warming has influenced vegetation activity, leading to changes in plant composition, physiology, and biomass storage in aboveground and belowground components, ultimately impacting ecosystem carbon balance. Yet, little is known about the causes and magnitude of long-term changes in the above- to belowground biomass ratio of plants (η). Here, we analyzed η values based on 3,013 plots and 26,337 plant-specific measurements representing eight sites across the Tibetan Plateau from 1995 to 2021. Our analysis revealed distinct temporal trends in η for three vegetation types: a 17% increase in alpine wetlands, and a decrease of 26% and 48% in alpine meadows and alpine steppes, respectively. These trends were primarily driven by temperature-induced growth preferences rather than shifts in plant species composition. Our findings indicate that in wetter ecosystems climate warming promotes aboveground plant growth, while in drier ecosystems, such as alpine meadows and alpine steppes, plants allocate more biomass belowground. Four process-based biogeochemical models failed to simulate the observed changes in η, which highlights the importance of improved process understanding of the processes driving the response of biomass distribution to climate warming, which is crucial for predicting the future carbon trajectory of permafrost ecosystems.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.