Find research datasets worth reusing

Landsat-based Spectral Indices for pan-EU 2000-2022 - Bimonthly predictor (2022-01-01/2022-02-28): Spectral indices

<h2>Data information</h2> This dataset includes widely used indices for 2022-01-01/2022-02-28. It covers key aspects such as vegetation, crops, soil, and water, available bimonthly (i.e. one value per two months). <ul> <li>Normalized Difference Vegetation Index (NDVI) is used to evaluate vegetation health and biomass. It is calculated as <code>(nir - red) / (nir + red)</code> (<a href="https://www.sciencedirect.com/science/article/pii/0034425779900130?via%3Dihub">Tucker, 1979</a>).</li> <li>Normalized Difference Tillage Index (NDTI), also known as Normalized Burn Ratio 2 (NBR2), used in tillage detection, post-fire recovery studies, and soil sealing identification. It is calculated as <code>(swir1 - swir2) / (swir1 + swir2)</code> (<a href="https://www.asprs.org/wp-content/uploads/pers/1997journal/jan/1997_jan_87-93.pdf">Van Deventer et al., 1997</a>).</li> <li>Soil Adjusted Vegetation Index (SAVI) is more effective in areas with sparse vegetation by minimizing the impact of soil brightness on vegetation sensing. It is calculated as <code>((nir - red) / (nir + red + 0.5)) * 1.5</code> (<a href="https://www.sciencedirect.com/science/article/pii/003442578890106X">Huete, 1988</a>).</li> <li>Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) provides a more direct measurement of plant productivity. It is calculated as <code>((ndvi - 0.03) * (0.95 - 0.001)) / (0.96 - 0.03) + 0.001</code> (<a href="https://zslpublications.onlinelibrary.wiley.com/doi/abs/10.1002/rse2.74">Robinson et al., 2018</a>).</li> <li>Normalized Difference Water Index (NDWI) provides insights into water dynamics and climatic characteristics. It is calculated as <code>(nir - swir1) / (nir + swir1)</code> (<a href="https://www.sciencedirect.com/science/article/pii/S0034425796000673">Gao, 1996</a>).</li> <li>Normalized Difference Snow Index (NDSI) helps identify snowy areas. It is calculated as <code>(green - swir1) / (green + swir1)</code> (<a href="https://ieeexplore.ieee.org/abstract/document/1645275/">Salomonson and Appel, 2006</a>).</li> </ul> <h2>As a part of a Data Cube</h2> This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link. <ul> <li>To cite this dataset, refer to the DOI available on the landing page.</li> <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li> </ul> <h2>Support</h2> If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!

Landsat-based Spectral Indices for pan-EU 2000-2022 - Bimonthly predictor (2000-11-01/2000-12-31): Spectral indices

<h2>Data information</h2> This dataset includes widely used indices for 2000-11-01/2000-12-31. It covers key aspects such as vegetation, crops, soil, and water, available bimonthly (i.e. one value per two months). <ul> <li>Normalized Difference Vegetation Index (NDVI) is used to evaluate vegetation health and biomass. It is calculated as <code>(nir - red) / (nir + red)</code> (<a href="https://www.sciencedirect.com/science/article/pii/0034425779900130?via%3Dihub">Tucker, 1979</a>).</li> <li>Normalized Difference Tillage Index (NDTI), also known as Normalized Burn Ratio 2 (NBR2), used in tillage detection, post-fire recovery studies, and soil sealing identification. It is calculated as <code>(swir1 - swir2) / (swir1 + swir2)</code> (<a href="https://www.asprs.org/wp-content/uploads/pers/1997journal/jan/1997_jan_87-93.pdf">Van Deventer et al., 1997</a>).</li> <li>Soil Adjusted Vegetation Index (SAVI) is more effective in areas with sparse vegetation by minimizing the impact of soil brightness on vegetation sensing. It is calculated as <code>((nir - red) / (nir + red + 0.5)) * 1.5</code> (<a href="https://www.sciencedirect.com/science/article/pii/003442578890106X">Huete, 1988</a>).</li> <li>Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) provides a more direct measurement of plant productivity. It is calculated as <code>((ndvi - 0.03) * (0.95 - 0.001)) / (0.96 - 0.03) + 0.001</code> (<a href="https://zslpublications.onlinelibrary.wiley.com/doi/abs/10.1002/rse2.74">Robinson et al., 2018</a>).</li> <li>Normalized Difference Water Index (NDWI) provides insights into water dynamics and climatic characteristics. It is calculated as <code>(nir - swir1) / (nir + swir1)</code> (<a href="https://www.sciencedirect.com/science/article/pii/S0034425796000673">Gao, 1996</a>).</li> <li>Normalized Difference Snow Index (NDSI) helps identify snowy areas. It is calculated as <code>(green - swir1) / (green + swir1)</code> (<a href="https://ieeexplore.ieee.org/abstract/document/1645275/">Salomonson and Appel, 2006</a>).</li> </ul> <h2>As a part of a Data Cube</h2> This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link. <ul> <li>To cite this dataset, refer to the DOI available on the landing page.</li> <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li> </ul> <h2>Support</h2> If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!

Landsat-based Spectral Indices for pan-EU 2000-2022 - Bimonthly predictor (2000-09-01/2000-10-31): Spectral indices

<h2>Data information</h2> This dataset includes widely used indices for 2000-09-01/2000-10-31. It covers key aspects such as vegetation, crops, soil, and water, available bimonthly (i.e. one value per two months). <ul> <li>Normalized Difference Vegetation Index (NDVI) is used to evaluate vegetation health and biomass. It is calculated as <code>(nir - red) / (nir + red)</code> (<a href="https://www.sciencedirect.com/science/article/pii/0034425779900130?via%3Dihub">Tucker, 1979</a>).</li> <li>Normalized Difference Tillage Index (NDTI), also known as Normalized Burn Ratio 2 (NBR2), used in tillage detection, post-fire recovery studies, and soil sealing identification. It is calculated as <code>(swir1 - swir2) / (swir1 + swir2)</code> (<a href="https://www.asprs.org/wp-content/uploads/pers/1997journal/jan/1997_jan_87-93.pdf">Van Deventer et al., 1997</a>).</li> <li>Soil Adjusted Vegetation Index (SAVI) is more effective in areas with sparse vegetation by minimizing the impact of soil brightness on vegetation sensing. It is calculated as <code>((nir - red) / (nir + red + 0.5)) * 1.5</code> (<a href="https://www.sciencedirect.com/science/article/pii/003442578890106X">Huete, 1988</a>).</li> <li>Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) provides a more direct measurement of plant productivity. It is calculated as <code>((ndvi - 0.03) * (0.95 - 0.001)) / (0.96 - 0.03) + 0.001</code> (<a href="https://zslpublications.onlinelibrary.wiley.com/doi/abs/10.1002/rse2.74">Robinson et al., 2018</a>).</li> <li>Normalized Difference Water Index (NDWI) provides insights into water dynamics and climatic characteristics. It is calculated as <code>(nir - swir1) / (nir + swir1)</code> (<a href="https://www.sciencedirect.com/science/article/pii/S0034425796000673">Gao, 1996</a>).</li> <li>Normalized Difference Snow Index (NDSI) helps identify snowy areas. It is calculated as <code>(green - swir1) / (green + swir1)</code> (<a href="https://ieeexplore.ieee.org/abstract/document/1645275/">Salomonson and Appel, 2006</a>).</li> </ul> <h2>As a part of a Data Cube</h2> This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link. <ul> <li>To cite this dataset, refer to the DOI available on the landing page.</li> <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li> </ul> <h2>Support</h2> If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!

Landsat-based Spectral Indices for pan-EU 2000-2022 - Bimonthly predictor (2022-11-01/2022-12-31): Spectral indices

<h2>Data information</h2> This dataset includes widely used indices for 2022-11-01/2022-12-31. It covers key aspects such as vegetation, crops, soil, and water, available bimonthly (i.e. one value per two months). <ul> <li>Normalized Difference Vegetation Index (NDVI) is used to evaluate vegetation health and biomass. It is calculated as <code>(nir - red) / (nir + red)</code> (<a href="https://www.sciencedirect.com/science/article/pii/0034425779900130?via%3Dihub">Tucker, 1979</a>).</li> <li>Normalized Difference Tillage Index (NDTI), also known as Normalized Burn Ratio 2 (NBR2), used in tillage detection, post-fire recovery studies, and soil sealing identification. It is calculated as <code>(swir1 - swir2) / (swir1 + swir2)</code> (<a href="https://www.asprs.org/wp-content/uploads/pers/1997journal/jan/1997_jan_87-93.pdf">Van Deventer et al., 1997</a>).</li> <li>Soil Adjusted Vegetation Index (SAVI) is more effective in areas with sparse vegetation by minimizing the impact of soil brightness on vegetation sensing. It is calculated as <code>((nir - red) / (nir + red + 0.5)) * 1.5</code> (<a href="https://www.sciencedirect.com/science/article/pii/003442578890106X">Huete, 1988</a>).</li> <li>Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) provides a more direct measurement of plant productivity. It is calculated as <code>((ndvi - 0.03) * (0.95 - 0.001)) / (0.96 - 0.03) + 0.001</code> (<a href="https://zslpublications.onlinelibrary.wiley.com/doi/abs/10.1002/rse2.74">Robinson et al., 2018</a>).</li> <li>Normalized Difference Water Index (NDWI) provides insights into water dynamics and climatic characteristics. It is calculated as <code>(nir - swir1) / (nir + swir1)</code> (<a href="https://www.sciencedirect.com/science/article/pii/S0034425796000673">Gao, 1996</a>).</li> <li>Normalized Difference Snow Index (NDSI) helps identify snowy areas. It is calculated as <code>(green - swir1) / (green + swir1)</code> (<a href="https://ieeexplore.ieee.org/abstract/document/1645275/">Salomonson and Appel, 2006</a>).</li> </ul> <h2>As a part of a Data Cube</h2> This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link. <ul> <li>To cite this dataset, refer to the DOI available on the landing page.</li> <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li> </ul> <h2>Support</h2> If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!

Landsat-based Spectral Indices for pan-EU 2000-2022 - Bimonthly predictor (2000-07-01/2000-08-31): Spectral indices

<h2>Data information</h2> This dataset includes widely used indices for 2000-07-01/2000-08-31. It covers key aspects such as vegetation, crops, soil, and water, available bimonthly (i.e. one value per two months). <ul> <li>Normalized Difference Vegetation Index (NDVI) is used to evaluate vegetation health and biomass. It is calculated as <code>(nir - red) / (nir + red)</code> (<a href="https://www.sciencedirect.com/science/article/pii/0034425779900130?via%3Dihub">Tucker, 1979</a>).</li> <li>Normalized Difference Tillage Index (NDTI), also known as Normalized Burn Ratio 2 (NBR2), used in tillage detection, post-fire recovery studies, and soil sealing identification. It is calculated as <code>(swir1 - swir2) / (swir1 + swir2)</code> (<a href="https://www.asprs.org/wp-content/uploads/pers/1997journal/jan/1997_jan_87-93.pdf">Van Deventer et al., 1997</a>).</li> <li>Soil Adjusted Vegetation Index (SAVI) is more effective in areas with sparse vegetation by minimizing the impact of soil brightness on vegetation sensing. It is calculated as <code>((nir - red) / (nir + red + 0.5)) * 1.5</code> (<a href="https://www.sciencedirect.com/science/article/pii/003442578890106X">Huete, 1988</a>).</li> <li>Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) provides a more direct measurement of plant productivity. It is calculated as <code>((ndvi - 0.03) * (0.95 - 0.001)) / (0.96 - 0.03) + 0.001</code> (<a href="https://zslpublications.onlinelibrary.wiley.com/doi/abs/10.1002/rse2.74">Robinson et al., 2018</a>).</li> <li>Normalized Difference Water Index (NDWI) provides insights into water dynamics and climatic characteristics. It is calculated as <code>(nir - swir1) / (nir + swir1)</code> (<a href="https://www.sciencedirect.com/science/article/pii/S0034425796000673">Gao, 1996</a>).</li> <li>Normalized Difference Snow Index (NDSI) helps identify snowy areas. It is calculated as <code>(green - swir1) / (green + swir1)</code> (<a href="https://ieeexplore.ieee.org/abstract/document/1645275/">Salomonson and Appel, 2006</a>).</li> </ul> <h2>As a part of a Data Cube</h2> This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link. <ul> <li>To cite this dataset, refer to the DOI available on the landing page.</li> <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li> </ul> <h2>Support</h2> If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!

Landsat-based Spectral Indices for pan-EU 2000-2022 - Bimonthly predictor (2000-05-01/2000-06-30): Spectral indices

<h2>Data information</h2> This dataset includes widely used indices for 2000-05-01/2000-06-30. It covers key aspects such as vegetation, crops, soil, and water, available bimonthly (i.e. one value per two months). <ul> <li>Normalized Difference Vegetation Index (NDVI) is used to evaluate vegetation health and biomass. It is calculated as <code>(nir - red) / (nir + red)</code> (<a href="https://www.sciencedirect.com/science/article/pii/0034425779900130?via%3Dihub">Tucker, 1979</a>).</li> <li>Normalized Difference Tillage Index (NDTI), also known as Normalized Burn Ratio 2 (NBR2), used in tillage detection, post-fire recovery studies, and soil sealing identification. It is calculated as <code>(swir1 - swir2) / (swir1 + swir2)</code> (<a href="https://www.asprs.org/wp-content/uploads/pers/1997journal/jan/1997_jan_87-93.pdf">Van Deventer et al., 1997</a>).</li> <li>Soil Adjusted Vegetation Index (SAVI) is more effective in areas with sparse vegetation by minimizing the impact of soil brightness on vegetation sensing. It is calculated as <code>((nir - red) / (nir + red + 0.5)) * 1.5</code> (<a href="https://www.sciencedirect.com/science/article/pii/003442578890106X">Huete, 1988</a>).</li> <li>Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) provides a more direct measurement of plant productivity. It is calculated as <code>((ndvi - 0.03) * (0.95 - 0.001)) / (0.96 - 0.03) + 0.001</code> (<a href="https://zslpublications.onlinelibrary.wiley.com/doi/abs/10.1002/rse2.74">Robinson et al., 2018</a>).</li> <li>Normalized Difference Water Index (NDWI) provides insights into water dynamics and climatic characteristics. It is calculated as <code>(nir - swir1) / (nir + swir1)</code> (<a href="https://www.sciencedirect.com/science/article/pii/S0034425796000673">Gao, 1996</a>).</li> <li>Normalized Difference Snow Index (NDSI) helps identify snowy areas. It is calculated as <code>(green - swir1) / (green + swir1)</code> (<a href="https://ieeexplore.ieee.org/abstract/document/1645275/">Salomonson and Appel, 2006</a>).</li> </ul> <h2>As a part of a Data Cube</h2> This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link. <ul> <li>To cite this dataset, refer to the DOI available on the landing page.</li> <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li> </ul> <h2>Support</h2> If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!

Landsat-based Spectral Indices for pan-EU 2000-2022 - Bimonthly predictor (2000-03-01/2000-04-30): Spectral indices

<h2>Data information</h2> This dataset includes widely used indices for 2000-03-01/2000-04-30. It covers key aspects such as vegetation, crops, soil, and water, available bimonthly (i.e. one value per two months). <ul> <li>Normalized Difference Vegetation Index (NDVI) is used to evaluate vegetation health and biomass. It is calculated as <code>(nir - red) / (nir + red)</code> (<a href="https://www.sciencedirect.com/science/article/pii/0034425779900130?via%3Dihub">Tucker, 1979</a>).</li> <li>Normalized Difference Tillage Index (NDTI), also known as Normalized Burn Ratio 2 (NBR2), used in tillage detection, post-fire recovery studies, and soil sealing identification. It is calculated as <code>(swir1 - swir2) / (swir1 + swir2)</code> (<a href="https://www.asprs.org/wp-content/uploads/pers/1997journal/jan/1997_jan_87-93.pdf">Van Deventer et al., 1997</a>).</li> <li>Soil Adjusted Vegetation Index (SAVI) is more effective in areas with sparse vegetation by minimizing the impact of soil brightness on vegetation sensing. It is calculated as <code>((nir - red) / (nir + red + 0.5)) * 1.5</code> (<a href="https://www.sciencedirect.com/science/article/pii/003442578890106X">Huete, 1988</a>).</li> <li>Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) provides a more direct measurement of plant productivity. It is calculated as <code>((ndvi - 0.03) * (0.95 - 0.001)) / (0.96 - 0.03) + 0.001</code> (<a href="https://zslpublications.onlinelibrary.wiley.com/doi/abs/10.1002/rse2.74">Robinson et al., 2018</a>).</li> <li>Normalized Difference Water Index (NDWI) provides insights into water dynamics and climatic characteristics. It is calculated as <code>(nir - swir1) / (nir + swir1)</code> (<a href="https://www.sciencedirect.com/science/article/pii/S0034425796000673">Gao, 1996</a>).</li> <li>Normalized Difference Snow Index (NDSI) helps identify snowy areas. It is calculated as <code>(green - swir1) / (green + swir1)</code> (<a href="https://ieeexplore.ieee.org/abstract/document/1645275/">Salomonson and Appel, 2006</a>).</li> </ul> <h2>As a part of a Data Cube</h2> This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link. <ul> <li>To cite this dataset, refer to the DOI available on the landing page.</li> <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li> </ul> <h2>Support</h2> If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!

Landsat-based Spectral Indices for pan-EU 2000-2022 - Bimonthly predictor (2022-05-01/2022-06-30): Spectral indices

<h2>Data information</h2> This dataset includes widely used indices for 2022-05-01/2022-06-30. It covers key aspects such as vegetation, crops, soil, and water, available bimonthly (i.e. one value per two months). <ul> <li>Normalized Difference Vegetation Index (NDVI) is used to evaluate vegetation health and biomass. It is calculated as <code>(nir - red) / (nir + red)</code> (<a href="https://www.sciencedirect.com/science/article/pii/0034425779900130?via%3Dihub">Tucker, 1979</a>).</li> <li>Normalized Difference Tillage Index (NDTI), also known as Normalized Burn Ratio 2 (NBR2), used in tillage detection, post-fire recovery studies, and soil sealing identification. It is calculated as <code>(swir1 - swir2) / (swir1 + swir2)</code> (<a href="https://www.asprs.org/wp-content/uploads/pers/1997journal/jan/1997_jan_87-93.pdf">Van Deventer et al., 1997</a>).</li> <li>Soil Adjusted Vegetation Index (SAVI) is more effective in areas with sparse vegetation by minimizing the impact of soil brightness on vegetation sensing. It is calculated as <code>((nir - red) / (nir + red + 0.5)) * 1.5</code> (<a href="https://www.sciencedirect.com/science/article/pii/003442578890106X">Huete, 1988</a>).</li> <li>Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) provides a more direct measurement of plant productivity. It is calculated as <code>((ndvi - 0.03) * (0.95 - 0.001)) / (0.96 - 0.03) + 0.001</code> (<a href="https://zslpublications.onlinelibrary.wiley.com/doi/abs/10.1002/rse2.74">Robinson et al., 2018</a>).</li> <li>Normalized Difference Water Index (NDWI) provides insights into water dynamics and climatic characteristics. It is calculated as <code>(nir - swir1) / (nir + swir1)</code> (<a href="https://www.sciencedirect.com/science/article/pii/S0034425796000673">Gao, 1996</a>).</li> <li>Normalized Difference Snow Index (NDSI) helps identify snowy areas. It is calculated as <code>(green - swir1) / (green + swir1)</code> (<a href="https://ieeexplore.ieee.org/abstract/document/1645275/">Salomonson and Appel, 2006</a>).</li> </ul> <h2>As a part of a Data Cube</h2> This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link. <ul> <li>To cite this dataset, refer to the DOI available on the landing page.</li> <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li> </ul> <h2>Support</h2> If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!

Landsat-based Spectral Indices for pan-EU 2000-2022 - Bimonthly predictor (2000-01-01/2000-02-28): Spectral indices

<h2>Data information</h2> This dataset includes widely used indices for 2000-01-01/2000-02-28. It covers key aspects such as vegetation, crops, soil, and water, available bimonthly (i.e. one value per two months). <ul> <li>Normalized Difference Vegetation Index (NDVI) is used to evaluate vegetation health and biomass. It is calculated as <code>(nir - red) / (nir + red)</code> (<a href="https://www.sciencedirect.com/science/article/pii/0034425779900130?via%3Dihub">Tucker, 1979</a>).</li> <li>Normalized Difference Tillage Index (NDTI), also known as Normalized Burn Ratio 2 (NBR2), used in tillage detection, post-fire recovery studies, and soil sealing identification. It is calculated as <code>(swir1 - swir2) / (swir1 + swir2)</code> (<a href="https://www.asprs.org/wp-content/uploads/pers/1997journal/jan/1997_jan_87-93.pdf">Van Deventer et al., 1997</a>).</li> <li>Soil Adjusted Vegetation Index (SAVI) is more effective in areas with sparse vegetation by minimizing the impact of soil brightness on vegetation sensing. It is calculated as <code>((nir - red) / (nir + red + 0.5)) * 1.5</code> (<a href="https://www.sciencedirect.com/science/article/pii/003442578890106X">Huete, 1988</a>).</li> <li>Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) provides a more direct measurement of plant productivity. It is calculated as <code>((ndvi - 0.03) * (0.95 - 0.001)) / (0.96 - 0.03) + 0.001</code> (<a href="https://zslpublications.onlinelibrary.wiley.com/doi/abs/10.1002/rse2.74">Robinson et al., 2018</a>).</li> <li>Normalized Difference Water Index (NDWI) provides insights into water dynamics and climatic characteristics. It is calculated as <code>(nir - swir1) / (nir + swir1)</code> (<a href="https://www.sciencedirect.com/science/article/pii/S0034425796000673">Gao, 1996</a>).</li> <li>Normalized Difference Snow Index (NDSI) helps identify snowy areas. It is calculated as <code>(green - swir1) / (green + swir1)</code> (<a href="https://ieeexplore.ieee.org/abstract/document/1645275/">Salomonson and Appel, 2006</a>).</li> </ul> <h2>As a part of a Data Cube</h2> This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link. <ul> <li>To cite this dataset, refer to the DOI available on the landing page.</li> <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li> </ul> <h2>Support</h2> If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!

opencc-by-4.0Apr 2024View details →

Data and analysis for: "Persistent Spatial Clustering and Predictors of Pediatric La Crosse Virus Neuroinvasive Disease Risk in Eastern Tennessee and Western North Carolina, 2003–2020"

This is the initial release of the data and code corresponding to the manuscript submitted to PLoS Neglected Tropical Diseases. Please refer to the README.md file for a description of the contents of this repository and how to use them. The README file can be opened with a text editor, or viewed directly in the GitHub repository. The data and code are provided within a project directory with a reproducible R package library for ease and accuracy of reproducibility.  Ethics Approval This study was approved by the University of Tennessee, Knoxville Institutional Review Board (UTK IRB-22-07079-XP) and the Tennessee Department of Health Institutional Review Board (TDH IRB 2021-0314). Data provided here is de-identified and aggregated (both temporally and spatially) to protect the privacy of individuals included in the study, in concordance with IRB and Data Use Agreements.

opencc-by-4.0Nov 2021View details →

Predicted times, spatial coordinates of bow shock crossings and shock geometry at Mars from the NASA/MAVEN mission, using spacecraft ephemerides and magnetic field data, with a predictor-corrector algorithm

CHARACTERISTICS Planet: Mars Radius: RM = 3389.5 km (volumetric mean planetary radius) Spacecraft: NASA/Mars Atmosphere and Volatile Evolution (MAVEN) Spacecraft coordinates system: Mars Solar Orbital (MSO) equivalent to Sun-State coordinate system: <ul> <li>+XMSO points towards the Sun from the planet’s centre,</li> <li>+ZMSO towards Mars’ North pole and perpendicular to the orbital plane defined as the XMSO–YMSO plane passing through the centre of Mars,</li> <li>YMSO completes the orthogonal system.</li> </ul> Time span: 01/11/2014 to 30/04/2024 (Mars Years MY32 to MY36 included, part of MY37). Total number N of candidate bow shock crossings in the database: N = 20107 ORIGINAL DATASETS USED The original MAVEN/MAG data repository on which these algorithms were applied is available on NASA's Planetary Data System (PDS) at <a href="https://doi.org/10.17189/1414178">https://doi.org/10.17189/1414178</a>. For this study, 1-Hz magnetic field data was used. METHOD To construct this database from the original datasets above, the predictor and predictor-corrector algorithms used are described in: Simon Wedlund, C., Volwerk, M., Beth, A., Mazelle, C., Möstl, C., Halekas, J., Gruesbeck, J. and Rojas-Castillo, D., (2022), A Fast Bow Shock Location Predictor-Estimator From 2D and 3D Analytical Models: Application to Mars and the MAVEN mission, Journal of Geophysical Research, 127, 1-33, e2021JA029942, <a href="https://doi. org/10.1029/2021JA029942">https://doi. org/10.1029/2021JA029942</a>.  Also available at: <a href="https://doi.org/10.1002/essoar.10507942.1">https://doi.org/10.1002/essoar.10507942.1 </a> and as arXiv e-print: <a href="https://doi.org/10.48550/arXiv.2109.04366">https://doi.org/10.48550/arXiv.2109.04366</a> These algorithms consist of two consecutive steps:  <ol> <li>Predictor geometric algorithm based on J. Gruesbeck's 3D model (<a href="https://doi.org/10.1029/2018JA025366">Gruesbeck et al. 2018</a>) for prediction of Mars bow shock position</li> <li>Corrector algorithm based on magnetic field measurements (magnitude and fluctuations).</li> </ol> REMARK ON VERSIONS From Version 3 onwards, we also provide the angle between the average Interplanetary Magnetic Field (IMF) vector upstream of the shock and the shock normal, noted \(\theta_{Bn}\)(ThetaBn). Assuming a smooth shock surface and the 3D model of Gruesbeck et al. (2018, all points), this gives a first indication of the geometry of the shock, so that: <ul> <li>45∘<θBn<135∘: quasi-perpendicular shock condition</li> <li>θBn≤45∘ and θBn≥135∘: quasi-parallel shock condition</li> </ul> Uncertainty on these angles is estimated to be ± 5º.  From Version 4 onwards, we also added the solar longitude Ls (in degrees). For details, see Simon Wedlund et al. (2022) above, §2.3 pp. 10-12. Note that due to minor adjustments in the code, some of the ThetaBn angles calculated here for the examples of Fig. 6 in Simon Wedlund et al. (2022) may slightly differ from the values quoted in the paper. VARIABLES DESCRIPTION This database contains the following ASCII variables: <ul> <li>Bow shock times in MAVEN's database (1-s resolution): Tbs</li> <li>Mars Solar Orbital coordinates of the shock, in units of Mars radius RM (RM = 3389.5 km): XMSO, YMSO, ZMSO and Euclidean distance \(R_{MSO} = \sqrt{X_{MSO}^2 + Y_{MSO}^2 + Z_{MSO}^2}\) (in RM)</li> <li>Solar Zenith angle in degrees: SZA = \(\tan^{-1}{Y_{MSO}^2+Z_{MSO}^2 \over X_{MSO}^2}\) (in º) </li> <li>Angle between average B-field direction and shock normal assuming a smooth shock surface \(\theta_{Bn}\) (ThetaBn, in º) <ul> <li>45 < ThetaBn <  135 deg: quasi-&perp; shock</li> <li>ThetaBn ≤45 deg & ThetaBn ≥ 135 deg: quasi-|| shock</li> </ul> </li> <li>Solar longitude Ls, in degrees.</li> <li>Flag for crossing: <ul> <li>sheath \(\longrightarrow\) solar wind, flag = 0.</li> <li>solar wind \(\longrightarrow\) sheath, flag = 1.</li> </ul> </li> </ul> WARNING This database is based on an automatic statistical geometrical estimate, further refined by constraints on magnetic field. It is aimed at giving a first approximation of the shock area times in the MAVEN data. It is particularly suited to statistical studies and region identification in the MAVEN datasets. As such, this database should be used as a first indicator of the shock location, and with caution: it CANNOT, and WILL NOT substitute, especially in case studies, for a careful analysis of the full magnetometer and plasma suite bow shock signatures. Moreover, the algorithm is optimised for detecting the first disturbance observed in the magnetic field immediately ahead of the shock's foot (in the foreshock area), and not for the detection of other structures in the shock, such as the shock ramp. The "shock" location is therefore given here with typical uncertainties of about 0.075 RM (with RM = 3389.5 km, i.e., about 250 km in the radial direction). Finally, for multiple shock crossings, the algorithm chooses the first occurrence of the shock starting from the undisturbed solar wind. Current formatting optimised for MATLAB. ACKNOWLEDGEMENTS C. Simon Wedlund and M. Volwerk thank the Austrian Science Fund (FWF) project P32035-N36. C. Möstl thanks the Austrian Science Fund FWF projects P31659-N27, P31521-N27. A. Beth thanks the Swedish National Space Agency (SNSA) and its support with the grant 108/18. This database was notably used to add to the Helio4Cast database which monitors solar wind parameters in the solar system (<a href="https://doi.org/10.6084/m9.figshare.6356420">https://doi.org/10.6084/m9.figshare.6356420</a>). Helio4Cast is available at <a href="http://www.helioforecast.space/icmecat">www.helioforecast.space/icmeca</a>t and <a href="http://www.helioforecast.space/sircat">www.helioforecast.space/sircat</a>.      LICENSE AND RIGHTS This database is shared under a Creative Commons CC-BY-4.0 license. Version 1 (c) Cyril Simon Wedlund @ Space Research Institute of Graz (IWF),                                                              Austrian Academy of Sciences (ÖAW), 2021-09-08 Version 2 (c) CSW @ ÖAW/IWF, 2021-11-30 -- Addition of R_MSO and SZA Version 3 (c) CSW @ ÖAW/IWF, 2022-02-09 -- Addition of ThetaBn Version 4 (c) CSW @ ÖAW/IWF, 2025-03-20 -- Addition of Ls, Bx, By, Bz and Bt.   Contact email:        cyril.simon.wedlund@gmail.com

opencc-by-4.0Jan 2022View details →

Multi-omic machine learning predictor of breast cancer therapy response

H&E slides used in the training dataset described in "Multi-omic machine learning predictor of breast cancer therapy response" published in Nature: <a href="https://www.nature.com/articles/s41586-021-04278-5">https://www.nature.com/articles/s41586-021-04278-5</a> Metadata associated with these images also included in file Slide metadata.xlsx

Landsat-based Spectral Indices for pan-EU 2000-2022 - longterm predictor (2000-2022): Spectral indices P75

<h2>Data Information</h2> This dataset includes long-term P75 values for NDVI, NDWI and BSF. <h2>As a Part of a Data Cube</h2> This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link. <ul> <li>To cite this dataset, refer to the DOI available on the landing page.</li> <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li> </ul> <h2>Support</h2> If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!

Landsat-based Spectral Indices for pan-EU 2000-2022 - longterm predictor (2000-2022): Spectral indices P25

<h2>Data Information</h2> This dataset includes long-term P25 values for NDVI, NDWI and BSF. <h2>As a Part of a Data Cube</h2> This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link. <ul> <li>To cite this dataset, refer to the DOI available on the landing page.</li> <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li> </ul> <h2>Support</h2> If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!

Landsat-based Spectral Indices for pan-EU 2000-2022 - Bimonthly predictor (2000-05-01/2000-06-30): Reflectance bands

<h2>Data Information</h2> This dataset includes seven reflectance bands: blue, green, red, nir, swir1, swir2, and thermal, for the period 2000-05-01/2000-06-30. <h2>As a Part of a Data Cube</h2> This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link. <ul> <li>To cite this dataset, refer to the DOI available on the landing page.</li> <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li> </ul> <h2>Support</h2> If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!

Landsat-based Spectral Indices for pan-EU 2000-2022 - Bimonthly predictor (2022-03-01/2022-04-30): Reflectance bands

<h2>Data Information</h2> This dataset includes seven reflectance bands: blue, green, red, nir, swir1, swir2, and thermal, for the period 2022-03-01/2022-04-30. <h2>As a Part of a Data Cube</h2> This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link. <ul> <li>To cite this dataset, refer to the DOI available on the landing page.</li> <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li> </ul> <h2>Support</h2> If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!

Landsat-based Spectral Indices for pan-EU 2000-2022 - Bimonthly predictor (2000-07-01/2000-08-31): Reflectance bands

<h2>Data Information</h2> This dataset includes seven reflectance bands: blue, green, red, nir, swir1, swir2, and thermal, for the period 2000-07-01/2000-08-31. <h2>As a Part of a Data Cube</h2> This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link. <ul> <li>To cite this dataset, refer to the DOI available on the landing page.</li> <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li> </ul> <h2>Support</h2> If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!

Landsat-based Spectral Indices for pan-EU 2000-2022 - Bimonthly predictor (2022-11-01/2022-12-31): Reflectance bands

<h2>Data Information</h2> This dataset includes seven reflectance bands: blue, green, red, nir, swir1, swir2, and thermal, for the period 2022-11-01/2022-12-31. <h2>As a Part of a Data Cube</h2> This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link. <ul> <li>To cite this dataset, refer to the DOI available on the landing page.</li> <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li> </ul> <h2>Support</h2> If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!

Landsat-based Spectral Indices for pan-EU 2000-2022 - Bimonthly predictor (2000-11-01/2000-12-31): Reflectance bands

<h2>Data Information</h2> This dataset includes seven reflectance bands: blue, green, red, nir, swir1, swir2, and thermal, for the period 2000-11-01/2000-12-31. <h2>As a Part of a Data Cube</h2> This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link. <ul> <li>To cite this dataset, refer to the DOI available on the landing page.</li> <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li> </ul> <h2>Support</h2> If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!

Landsat-based Spectral Indices for pan-EU 2000-2022 - Bimonthly predictor (2000-01-01/2000-02-28): Reflectance bands

<h2>Data Information</h2> This dataset includes seven reflectance bands: blue, green, red, nir, swir1, swir2, and thermal, for the period 2000-01-01/2000-02-28. <h2>As a Part of a Data Cube</h2> This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link. <ul> <li>To cite this dataset, refer to the DOI available on the landing page.</li> <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li> </ul> <h2>Support</h2> If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!