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Sion 1640 - Building 30 (LoD-1)
<u>Coordinates</u>: N/A <br><u>Length</u>: 6.21 m<br><u>Width</u>: 8.38 m<br><u>Height</u>: 10.23 m<br><u>Vertices</u>: 36 <br><u>Primitives</u>: 20 <br><br> The Length, Width, Height, Vertices and Primitives listed above have been derived directly from the OBJ file.<br><br><u>Main Files:</u><br><table><tbody><tr><th>Filename</th><th>.glb</th><th>.obj</th><th>.mtl</th><th>.xml</th></tr><tr><td><a href="https://zenodo.org/api/records/12686323/files/0___LoD1__id-30.obj/content">0___LoD1__id-30.obj</a></td><td></td><td><a href="https://zenodo.org/api/records/12686323/files/0___LoD1__id-30.obj/content">Link</a></td><td></td><td></td></tr><tr><td><a href="https://zenodo.org/api/records/12686323/files/0___LoD1__id-30.glb/content">0___LoD1__id-30.glb</a></td><td><a href="https://zenodo.org/api/records/12686323/files/0___LoD1__id-30.glb/content">Link</a></td><td></td><td></td><td></td></tr><tr><td><a href="https://zenodo.org/api/records/12686323/files/0___LoD1__id-30.mtl/content">0___LoD1__id-30.mtl</a></td><td></td><td></td><td><a href="https://zenodo.org/api/records/12686323/files/0___LoD1__id-30.mtl/content">Link</a></td><td></td></tr><tr><td><a href="https://zenodo.org/api/records/12686323/files/11249928_edm.xml/content">11249928_edm.xml</a></td><td></td><td></td><td></td><td><a href="https://zenodo.org/api/records/12686323/files/11249928_edm.xml/content">Link</a></td></tr><tr><td><a href="https://zenodo.org/api/records/12686323/files/11249928_metsmods.xml/content">11249928_metsmods.xml</a></td><td></td><td></td><td></td><td><a href="https://zenodo.org/api/records/12686323/files/11249928_metsmods.xml/content">Link</a></td></tr></tbody></table><br><br><u>Thumbnails:</u><br><table><tbody><tr><th>Perspective</th><th>1000x1000</th><th>512x512</th><th>256x256</th><th>128x128</th></tr><tr><td>Perspective 1</td><td><a href="https://zenodo.org/api/records/12686323/files/0___LoD1__id-30_perspective_1.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12686323/files/0___LoD1__id-30_perspective_1_512x512.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12686323/files/0___LoD1__id-30_perspective_1_256x256.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12686323/files/0___LoD1__id-30_perspective_1_128x128.png/content">Link</a></td></tr><tr><td>Perspective 2</td><td><a href="https://zenodo.org/api/records/12686323/files/0___LoD1__id-30_perspective_2.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12686323/files/0___LoD1__id-30_perspective_2_512x512.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12686323/files/0___LoD1__id-30_perspective_2_256x256.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12686323/files/0___LoD1__id-30_perspective_2_128x128.png/content">Link</a></td></tr><tr><td>Perspective 3</td><td><a href="https://zenodo.org/api/records/12686323/files/0___LoD1__id-30_perspective_3.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12686323/files/0___LoD1__id-30_perspective_3_512x512.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12686323/files/0___LoD1__id-30_perspective_3_256x256.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12686323/files/0___LoD1__id-30_perspective_3_128x128.png/content">Link</a></td></tr><tr><td>Perspective 4</td><td><a href="https://zenodo.org/api/records/12686323/files/0___LoD1__id-30_perspective_4.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12686323/files/0___LoD1__id-30_perspective_4_512x512.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12686323/files/0___LoD1__id-30_perspective_4_256x256.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12686323/files/0___LoD1__id-30_perspective_4_128x128.png/content">Link</a></td></tr><tr><td>Perspective Top</td><td><a href="https://zenodo.org/api/records/12686323/files/0___LoD1__id-30_perspective_top.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12686323/files/0___LoD1__id-30_perspective_top_512x512.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12686323/files/0___LoD1__id-30_perspective_top_256x256.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12686323/files/0___LoD1__id-30_perspective_top_128x128.png/content">Link</a></td></tr></tbody></table><br><br><br><u>Changelog</u>: <br> - v<a href="https://doi.org/10.5281/zenodo.11487865">0.0.2</a>: Thumbnails added, Description updated with Link Tables.<br> - v<a href="https://doi.org/10.5281/zenodo.12686323">0.0.3</a>: Added XMLs for Europeana Data Model (EDM) and MetsMods.<br>
Trento 1936 - Building 30
<u>Coordinates</u>: N/A <br><u>Length</u>: 49.13 m<br><u>Width</u>: 15.36 m<br><u>Height</u>: 16.32 m<br><u>Points</u>: 40 <br><u>Vertices</u>: 240 <br><u>Primitives</u>: 80 <br><br><u>Main Files:</u><br><table><tbody><tr><th>Filename</th><th>.glb</th><th>.obj</th><th>.xml</th></tr><tr><td><a href="https://zenodo.org/api/records/12686214/files/building_30.obj/content">building_30.obj</a></td><td></td><td><a href="https://zenodo.org/api/records/12686214/files/building_30.obj/content">Link</a></td><td></td></tr><tr><td><a href="https://zenodo.org/api/records/12686214/files/building_30.glb/content">building_30.glb</a></td><td><a href="https://zenodo.org/api/records/12686214/files/building_30.glb/content">Link</a></td><td></td><td></td></tr><tr><td><a href="https://zenodo.org/api/records/12686214/files/11576731_edm.xml/content">11576731_edm.xml</a></td><td></td><td></td><td><a href="https://zenodo.org/api/records/12686214/files/11576731_edm.xml/content">Link</a></td></tr><tr><td><a href="https://zenodo.org/api/records/12686214/files/11576731_metsmods.xml/content">11576731_metsmods.xml</a></td><td></td><td></td><td><a href="https://zenodo.org/api/records/12686214/files/11576731_metsmods.xml/content">Link</a></td></tr></tbody></table><br><br><u>Thumbnails:</u><br><table><tbody><tr><th>Perspective</th><th>1000x1000</th><th>512x512</th><th>256x256</th><th>128x128</th></tr><tr><td>Perspective 1</td><td><a href="https://zenodo.org/api/records/12686214/files/building_30_perspective_1.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12686214/files/building_30_perspective_1_512x512.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12686214/files/building_30_perspective_1_256x256.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12686214/files/building_30_perspective_1_128x128.png/content">Link</a></td></tr><tr><td>Perspective 2</td><td><a href="https://zenodo.org/api/records/12686214/files/building_30_perspective_2.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12686214/files/building_30_perspective_2_512x512.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12686214/files/building_30_perspective_2_256x256.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12686214/files/building_30_perspective_2_128x128.png/content">Link</a></td></tr><tr><td>Perspective 3</td><td><a href="https://zenodo.org/api/records/12686214/files/building_30_perspective_3.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12686214/files/building_30_perspective_3_512x512.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12686214/files/building_30_perspective_3_256x256.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12686214/files/building_30_perspective_3_128x128.png/content">Link</a></td></tr><tr><td>Perspective 4</td><td><a href="https://zenodo.org/api/records/12686214/files/building_30_perspective_4.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12686214/files/building_30_perspective_4_512x512.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12686214/files/building_30_perspective_4_256x256.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12686214/files/building_30_perspective_4_128x128.png/content">Link</a></td></tr><tr><td>Perspective Top</td><td><a href="https://zenodo.org/api/records/12686214/files/building_30_perspective_top.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12686214/files/building_30_perspective_top_512x512.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12686214/files/building_30_perspective_top_256x256.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12686214/files/building_30_perspective_top_128x128.png/content">Link</a></td></tr></tbody></table><br><br><br><u>Changelog</u>: <br> - v<a href="https://doi.org/10.5281/zenodo.12547308">0.0.2</a>: Thumbnails added, Description updated with Link Tables.<br> - v<a href="https://doi.org/10.5281/zenodo.12686214">0.0.3</a>: Added XMLs for Europeana Data Model (EDM) and MetsMods.<br>
Trento 1851 - Building 30
<u>Coordinates</u>: N/A <br><u>Length</u>: 7.56 m<br><u>Width</u>: 13.65 m<br><u>Height</u>: 6.25 m<br><u>Points</u>: 8 <br><u>Vertices</u>: 36 <br><u>Primitives</u>: 12 <br><br><u>Main Files:</u><br><table><tbody><tr><th>Filename</th><th>.glb</th><th>.obj</th><th>.xml</th></tr><tr><td><a href="https://zenodo.org/api/records/12686071/files/building_30.obj/content">building_30.obj</a></td><td></td><td><a href="https://zenodo.org/api/records/12686071/files/building_30.obj/content">Link</a></td><td></td></tr><tr><td><a href="https://zenodo.org/api/records/12686071/files/building_30.glb/content">building_30.glb</a></td><td><a href="https://zenodo.org/api/records/12686071/files/building_30.glb/content">Link</a></td><td></td><td></td></tr><tr><td><a href="https://zenodo.org/api/records/12686071/files/11564418_edm.xml/content">11564418_edm.xml</a></td><td></td><td></td><td><a href="https://zenodo.org/api/records/12686071/files/11564418_edm.xml/content">Link</a></td></tr><tr><td><a href="https://zenodo.org/api/records/12686071/files/11564418_metsmods.xml/content">11564418_metsmods.xml</a></td><td></td><td></td><td><a href="https://zenodo.org/api/records/12686071/files/11564418_metsmods.xml/content">Link</a></td></tr></tbody></table><br><br><u>Thumbnails:</u><br><table><tbody><tr><th>Perspective</th><th>1000x1000</th><th>512x512</th><th>256x256</th><th>128x128</th></tr><tr><td>Perspective 1</td><td><a href="https://zenodo.org/api/records/12686071/files/building_30_perspective_1.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12686071/files/building_30_perspective_1_512x512.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12686071/files/building_30_perspective_1_256x256.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12686071/files/building_30_perspective_1_128x128.png/content">Link</a></td></tr><tr><td>Perspective 2</td><td><a href="https://zenodo.org/api/records/12686071/files/building_30_perspective_2.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12686071/files/building_30_perspective_2_512x512.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12686071/files/building_30_perspective_2_256x256.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12686071/files/building_30_perspective_2_128x128.png/content">Link</a></td></tr><tr><td>Perspective 3</td><td><a href="https://zenodo.org/api/records/12686071/files/building_30_perspective_3.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12686071/files/building_30_perspective_3_512x512.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12686071/files/building_30_perspective_3_256x256.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12686071/files/building_30_perspective_3_128x128.png/content">Link</a></td></tr><tr><td>Perspective 4</td><td><a href="https://zenodo.org/api/records/12686071/files/building_30_perspective_4.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12686071/files/building_30_perspective_4_512x512.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12686071/files/building_30_perspective_4_256x256.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12686071/files/building_30_perspective_4_128x128.png/content">Link</a></td></tr><tr><td>Perspective Top</td><td><a href="https://zenodo.org/api/records/12686071/files/building_30_perspective_top.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12686071/files/building_30_perspective_top_512x512.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12686071/files/building_30_perspective_top_256x256.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12686071/files/building_30_perspective_top_128x128.png/content">Link</a></td></tr></tbody></table><br><br><br><u>Changelog</u>: <br> - v<a href="https://doi.org/10.5281/zenodo.12531335">0.0.2</a>: Thumbnails added, Description updated with Link Tables.<br> - v<a href="https://doi.org/10.5281/zenodo.12686071">0.0.3</a>: Added XMLs for Europeana Data Model (EDM) and MetsMods.<br>
Monthly time series of spatially enhanced relative humidity for Europe at 30 arc seconds resolution (2000 - 2023) derived from ERA5-Land data
<p>Overview:<br>ERA5-Land is a reanalysis dataset providing a consistent view of the evolution of land variables over several decades at an enhanced resolution compared to ERA5. ERA5-Land has been produced by replaying the land component of the ECMWF ERA5 climate reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. Reanalysis produces data that goes several decades back in time, providing an accurate description of the climate of the past.</p> <p>Processing steps:<br>The original hourly ERA5-Land air temperature 2 m above ground and dewpoint temperature 2 m data has been spatially enhanced from 0.1 degree to 30 arc seconds (approx. 1000 m) spatial resolution by image fusion with CHELSA data (V1.2) (<a href="https://chelsa-climate.org/">https://chelsa-climate.org/</a>). For each day we used the corresponding monthly long-term average of CHELSA. The aim was to use the fine spatial detail of CHELSA and at the same time preserve the general regional pattern and fine temporal detail of ERA5-Land. The steps included aggregation and enhancement, specifically: <br>1. spatially aggregate CHELSA to the resolution of ERA5-Land <br>2. calculate difference of ERA5-Land - aggregated CHELSA <br>3. interpolate differences with a Gaussian filter to 30 arc seconds <br>4. add the interpolated differences to CHELSA</p> <p>Subsequently, the temperature time series have been aggregated on a daily basis. From these, daily relative humidity has been calculated for the time period 01/2000 - 12/2023.</p> <p>Relative humidity (rh2m) has been calculated from air temperature 2 m above ground (Ta) and dewpoint temperature 2 m above ground (Td) using the formula for saturated water pressure from Wright (1997):</p> <p><code>maximum water pressure = 611.21 * exp(17.502 * Ta / (240.97 + Ta))</code></p> <p><code>actual water pressure = 611.21 * exp(17.502 * Td / (240.97 + Td))</code></p> <p><code>relative humidity = actual water pressure / maximum water pressure</code></p> <p>The resulting relative humidity has been aggregated to monthly averages.</p> <p>Resultant values have been converted to represent percent * 10, thus covering a theoretical range of [0, 1000].</p> <p>File naming scheme (YYYY = year; MM = month):<br><code>ERA5_land_rh2m_avg_monthly_YYYY_MM.tif</code></p> <p>Projection + EPSG code:<br>Latitude-Longitude/WGS84 (EPSG: 4326)</p> <p>Spatial extent:<br>north: 82:00:30N<br>south: 18N<br>west: 32:00:30W<br>east: 70E</p> <p>Spatial resolution:<br>30 arc seconds (approx. 1000 m)</p> <p>Temporal resolution:<br>Monthly</p> <p>Pixel values:<br>Percent * 10 (scaled to Integer; example: value 738 = 73.8 %)</p> <p>Software used:<br>GDAL 3.2.2 and GRASS GIS 8.0.0/8.3.2</p> <p>Original ERA5-Land dataset license:<br><a href="https://apps.ecmwf.int/datasets/licences/copernicus/">https://apps.ecmwf.int/datasets/licences/copernicus/</a></p> <p>CHELSA climatologies (V1.2):<br>Data used: Karger D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E, Linder, H.P., Kessler, M. (2018): Data from: Climatologies at high resolution for the earth's land surface areas. Dryad digital repository. <a href="http://dx.doi.org/doi:10.5061/dryad.kd1d4">http://dx.doi.org/doi:10.5061/dryad.kd1d4</a><br>Original peer-reviewed publication: Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, P., Kessler, M. (2017): Climatologies at high resolution for the Earth land surface areas. Scientific Data. 4 170122. <a href="https://doi.org/10.1038/sdata.2017.122">https://doi.org/10.1038/sdata.2017.122</a></p> <p>Processed by:<br>mundialis GmbH & Co. KG, Germany (<a href="https://www.mundialis.de/">https://www.mundialis.de/</a>)</p> <p>Reference: Wright, J.M. (1997): Federal meteorological handbook no. 3 (FCM-H3-1997). Office of Federal Coordinator for Meteorological Services and Supporting Research. Washington, DC</p> <p>Data is also available in EU LAEA (EPSG: 3035) projection: <a href="https://doi.org/10.5281/zenodo.7427021">https://doi.org/10.5281/zenodo.7427021</a></p>
Supplementary file 30 in Methylphenidate for attention deficit hyperactivity disorder (ADHD) in children and adolescents - assessment of possible adverse events in non-randomised studies
<p>Supplementary file 30: Proportion of participants on methylphenidate with asthenia and fatigue </p>
Deliverable 1.3 -Vision Catalogue - Encompassing the visions from all 30 countries
<p>This deliverable presents an English translation of the 179 visions elaborated by more than 1000 citizens during the National Citizen Vision Workshops (NCVs), arranged as a part of the CIMULACT project.</p> <p>The main objective of CIMULACT is to add to the relevance and accountability of the European Research and Innovation (R&I) agenda by engaging citizens and multi-actors in the actual formulation of the European Union’s R&I agenda. The NCVs contributed to this process by engaging citizens in formulating their visions for desirable and sustainable futures.</p> <p>Over a three month period (November 2015 until January 2016) 30 NCVs were held in 30 European countries (28 EU member states, as well as Switzerland and Norway). At each NCV 25-42 (36 on average) citizens met for a full day to formulate and debate their visions for a desirable and sustainable future.</p> <p>The visions represent the final product of the NCVs and are the result of a comprehensive and intensive vision building process in each of the participating countries. The visions were originally formulated in the citizens’ national language, but for simplicity all visions have been translated into English. The original visions and national reports from each NCV are to be found elsewhere (Deliverable 1.2 - Collection of national reports on the citizens’ future visions).</p> <p>The present deliverable documents the European citizens’ wishes, needs and demands for a desirable future. The visions enable dialogue between the citizens and the European policy- and decision makers, hereby enhancing Responsible Research and Innovation (RRI) in the European Union.</p> <p>CIMULACT is a three-year project funded by the Horizon 2020 Framework Program of the European Union. The project was kicked-off in June 2015</p> <p> </p>
Coarse fragments % (volumetric) at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution
<p>Coarse fragments % (volumetric) at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution. Based on machine learning predictions from global compilation of soil profiles and samples. Processing steps are described in detail <strong><a href="https://gitlab.com/openlandmap/global-layers/tree/master/soil">here</a></strong>. Antarctica is not included.</p> <p>To access and visualize maps use: <a href="http://www.openlandmap.org/">OpenLandMap.org</a></p> <p>If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels:</p> <ul> <li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/issues">https://gitlab.com/openlandmap/global-layers/issues</a> </li> <li>General questions and comments: <a href="https://disqus.com/home/forums/landgis/">https://disqus.com/home/forums/landgis/</a></li> </ul> <p>All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention:</p> <ul> <li>sol = theme: soil,</li> <li>coarsefrag.vfraction = variable: coarse fragments volumetric fraction,</li> <li>usda.3b1 = determination method: laboratory method code,</li> <li>m = mean value,</li> <li>250m = spatial resolution / block support: 250 m,</li> <li>b10..10cm = vertical reference: 10 cm depth below surface,</li> <li>1950..2017 = time reference: period 1950-2017,</li> <li>v0.2 = version number: 0.2,</li> </ul>
Silt content in % (kg / kg) at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution
<p>Silt content in % (kg / kg) at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution. Based on machine learning predictions from global compilation of soil profiles and samples. Processing steps are described in detail <strong><a href="https://gitlab.com/openlandmap/global-layers/tree/master/soil">here</a></strong>. Antarctica is not included.</p> <p>To access and visualize maps use: <a href="http://www.openlandmap.org/">OpenLandMap.org</a></p> <p>If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels:</p> <ul> <li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/issues">https://gitlab.com/openlandmap/global-layers/issues</a> </li> <li>General questions and comments: <a href="https://disqus.com/home/forums/landgis/">https://disqus.com/home/forums/landgis/</a></li> </ul> <p> </p> <p>All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention:</p> <ul> <li>sol = theme: soil,</li> <li>silt.wfraction = variable: silt weight fraction,</li> <li>usda.3a1a1a = determination method: laboratory method code,</li> <li>m = mean value,</li> <li>250m = spatial resolution / block support: 250 m,</li> <li>b10..10cm = vertical reference: 10 cm depth below surface,</li> <li>1950..2017 = time reference: period 1950-2017,</li> <li>v0.2 = version number: 0.2,</li> </ul>
Fundzeichnung Keramik Groß Zastrow 18 - Blatt 30 Zchng. I
<p>Dateiname - Znr_30_1230_I.tif<br> Dateiformat - TIFF<br> Software - Irfanview<br> Erstellungsdatum - 2018:12:31 14:32</p> <p>ImageWidth - 1378<br> ImageLength - 1631<br> Compression - 1 (None)<br> XResolution - 300.00<br> YResolution - 300.00<br> ResolutionUnit - Inch<br> ColorSpace - Uncalibrated/Unknown (-1)</p> <p><strong>Beschreibung: </strong></p> <p>Profilzeichnung Keramikgefäß</p> <p><strong>Zeichnung ist ein Ausschnitt ("is part of") von <a href="https://zenodo.org/record/2529392">https://zenodo.org/record/2529392</a></strong></p> <p>Die Funde werden im Landesamt für Kultur und Denkmalpflege Mecklenburg-Vorpommern verwahrt.</p> <p>Inventarnummer 2002/1230.367</p>
Soil available water capacity in mm derived for 5 standard layers (0-10, 10-30, 30-60, 60-100 and 100-200 cm) at 250 m resolution
<p>Available Water Capacity (in mm) derived by calculating Water Retention Difference (difference between the field capacity and wilting point; see <a href="https://www.nrcs.usda.gov/wps/portal/nrcs/detail/soils/ref/?cid=nrcs142p2_054247">NRCS Soil Survey Laboratory Methods Manual</a>), and then summing up WRD for all standard layers (0–200 cm). Soil water content (volumetric) in percent for 33 kPa and 1500 kPa suctions predicted at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution is available <a href="https://doi.org/10.5281/zenodo.2609113"><strong>here</strong></a>. These estimates ignore depth to bedrock i.e. existence of any impenetrable layer (total available capacity over the whole land mass is likely about 10–15% smaller). Antarctica is not included.</p> <p>To access and visualize some of the maps use: <a href="http://www.openlandmap.org/">OpenLandMap.org</a></p> <p>If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels:</p> <ul> <li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/issues">https://gitlab.com/openlandmap/global-layers/issues</a></li> <li>General questions and comments: <a href="https://disqus.com/home/forums/landgis/">https://disqus.com/home/forums/landgis/</a></li> </ul> <p>All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention:</p> <ul> <li>sol = theme: soil,</li> <li>available.water.capacity = available water capacity in mm,</li> <li>usda.mm = determination method: Water Retention Difference in mm,</li> <li>m = mean value,</li> <li>250m = spatial resolution / block support: 250 m,</li> <li>b0..10cm = vertical reference: 0-10 cm layer below surface,</li> <li>1950..2017 = time reference: period 1950-2017,</li> <li>v0.1 = version number: 0.1,</li> </ul>
Corresponding spreadsheet to the Paper 'Variability in the assessment of childcare in 30 European countries'
<p>The spreadsheet provides the list of indicators reported by the national experts to assess the quality of child care in the relevant countries along with those gathered from official documents provided by the experts. It has been adopted to the Paper 'Variability in the assessment of childcare in 30 European countries'. </p>
XRD data for Pd EnCat™ 30 recycling
<p>X-ray powder diffraction data for Pd EnCat™ 30. recycling The results refer to the pure catalyst, and its structural changes after each reuse after Suzuki reaction. The data were collected after 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, and 30 reaction cycles, showing depletion of palladium acetate and formation of metallic palladium with plane (1 1 1) and (2 0 0) at 40 and 45 degree 2theta angle.</p>
Data set of detected atmospheric rivers, cyclones, and fronts within the region of 75°N – 82.5°N, 0°E – 30°E and at Ny-Ålesund (Svalbard) for 2017 – 2021
<p>This data set contains times when atmospheric rivers, cyclones, or fronts have been detected within the broader region of 75°N – 82.5°N, 0°E – 30°E and specifically at Ny-Ålesund, Svalbard (78.92308 °N, 11.92108 °E) for the years 2017 to 2021. To this end, the detection methods, as described in Lauer et al. (2023), have been applied to the hourly-resolved ERA5 reanalysis (Hersbach et al., 2020) data. </p> <p>Data set overview</p> <p>Each file contains the times (year, month, day, hour in UTC) when the corresponding weather system, i.e. atmospheric river, cyclone and front, has been detected within the region of 75°N – 82.5°N, 0°E – 30°E. The last column indicates if the weather system was located also over Ny-Ålesund Svalbard (78.92308 °N, 11.92108 °E). </p>
The 30 m land cover dataset for capturing land cover changes induced by ecological restoration from 1990 to 2022 on the Chinese Loess Plateau
<p>Continuous time-series of land cover is critical for attributing runoff, sediment and carbon changes on the Chinese Loess Plateau (CLP). However, current land cover products with annal temporal resolution lack spatial identification accuracy, particularly in capturing authentic changes of cropland, forest and grassland. To address these issues, a 30 m annual land cover dataset was proposed by the Yellow River Conservancy Commission (YRCC_LPLC) for the CLP from 1990 to 2022. Different levels of land cover were classified using different combinations of spectral, monthly and annual temporal and topographic features and Random Forest classifier. Compared to other land cover products (45.64%–73.38%), the accuracy of YRCC_LPLC has a better performance with an overall accuracy of 85.16%. The YRCC_LPLC is capable of capturing not only the explicit spatial variation but also the change direction and change time of land cover, especially for the most critical conversion of cropland into forest and grassland induced by implementation of Grain to Green Program on the CLP.</p>
Seamless 30 meter Sentinel-2 L2A Pan-European seasonal cloudless mosaics from winter 2018 to spring 2020
<p>Seasonal composites of <a href="https://roda.sentinel-hub.com/sentinel-s2-l2a/readme.html">Sentinel-2 L2A</a> imagery created as part of the <a href="https://opendatascience.eu/geo-harmonizer/">Geo-harmonizer project</a>, containing median of the blue, green, red, NIR, SWIR1 and SWIR2 bands, as well as pixel counts per season, produced in the ETRS89-extended / LAEA Europe (<a href="https://epsg.io/3035">EPSG:3035</a>) spatial reference system. Mosaics were produced from winter 2017 to spring 2020, with the imaging intervals per season being:</p> <ul> <li>winter: 02/12 of previous year to 20/03</li> <li>spring: 21/03 to 24/06</li> <li>summer: 25/06 to 12/09</li> <li>fall: 13/09 to 01/12</li> </ul> <p>Seamlessness of the composites was achieved through overlapping pixel averaging weighted by distance from the suborbital track.</p> <p>The data are provided as UINT8 values and were scaled with a common threshold (13712) chosen to minimize compression loss across the dataset. Data at the original (UINT16) scale can be obtained as follows:</p> <p><span>\(x_{\text{uint16}} = 13712 {x_{\text{uint8}} \over 254}\)</span></p> <p>For any additional questions regarding the data please contact the authors at <a href="mailto:multione@multione.hr?subject=S2L2A%20Europe%20mosaics">multione[at]multione.hr</a>.</p>
Data from: Moth species richness and diversity decline in a 30-year time series in Norway, irrespective of species' latitudinal range extent and habitat
<p>Data from:</p> <p>Burner, R., V. Selås, S. Kobro, R. Jacobsen, A. Sverdrup-Thygeson. 2021. Moth species richness and abundance decline in a 30-year time series, irrespective of species’ latitudinal range extent and habitat. <em>Journal of Insect Conservation</em><br> </p> <p>Current contact info for corresponding author: Ryan C. Burner, rburner[at]usgs.gov</p> <p> </p> <p>These data consist of a 30-year time series (1984 to 2013) of moth captures from a single site in southeast Norway, along with trait data for many of the species and climate data for the site. The moths were collected and identified by Sverre Kobro for the entire 30-year period and we are grateful for his efforts. </p> <p> </p> <p>Abstract from manuscript:</p> <p><strong>Introduction</strong></p> <p>Insects are reported to be in decline around the globe, but long-term datasets are rare. The causes of these trends are elusive, with land use change and climate change among the top candidates. Yet if species traits can predict rates of population change, this can help identify underlying mechanisms. If climate change is important, for example, northern species may decline as southern species expand. Land use changes, however, may impact species that rely on certain habitats.</p> <p><strong>Aims and Methods</strong></p> <p>We present 30 years of moth captures (comprising 85,149 individuals of 885 species) from a site in southeastern Norway to test for population trends that are correlated with species traits. We use time series analyses and joint species distribution models combined with local climate and habitat data.</p> <p><strong>Results and Discussion</strong></p> <p>Species richness and abundance declined by 10.1% and 13.8% per decade, respectively. Capture rates declined for 19% of species during this time as well, though 6% have increased. Annual summer weather is correlated with annual rates of abundance change for many species. But, opposite to a general expectation, many species in our study responded negatively to increasing summer temperatures. Surprisingly, neither species’ northern range limits nor the habitat in which their primary food plants grow are strong predictors of their rates of change, or their responses to climatic factors. However, species with more southerly distributions are less likely to be declining. Complex and indirect effects of both land use and climate change may play a role in these declines.</p> <p><strong>Implications for insect conservation</strong></p> <p>Our results provide additional evidence for long-term declines in insect abundance. The multifaceted causes of population changes may limit the ability of species traits to reveal which species are most at risk. </p> <p> </p> <p><strong>ACKNOWLEDGEMENTS</strong></p> <p>Thanks to J. Fjelddalen, who helped with geometrid moth identifications. This project was supported by internal funding from the Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences.</p> <p> </p>
High resolution cropland agreement map (30 m) circa 2020
<p>Accurate and precise measurements of global cropland extent are needed for monitoring the sustainability of agriculture at all scales. Recent advancement in remote sensing and land cover mapping methods have greatly increased the ability to estimate cropland area distribution and trends. Here the FAO presents a map of cropland agreement produced by consolidating information at pixel level from six high-resolutions maps for <em>circa </em>2020. The following six high resolution layers were used: ESRI 10 meter LU/LC, FROM-GLC, GLAD, GLC-FCS30, Globeland30 and Worldcover.</p> <p>Two bands are included in the dataset:</p> <ol> <li>Simple agreement (values between 1 and 6)</li> <li>Detailed agreement (values between 1 and 63)</li> </ol> <p>The map, developed in the Google Earth Engine platform, combines the 6 land cover/cropland layers to show their cropland agreement on pixel level at a spatial resolution of 30 meters. The simple agreement has pixel values that range from 1 (only 1 dataset classifies as cropland) to 6 (all datasets agree on presence of cropland). Pixels with a value of 0 indicate pixels where all datasets agree on absence of cropland. The second band includes a detailed agreement, showing which combination of the 6 datasets classify a pixel as cropland. The overview table (<em>DetailedAgreement_LookupTable.xlsx</em>) shows what the pixel values of this detailed agreement (from 1 to 63) correspond to.</p> <p>The dataset has been uploaded in 16 tiles, in the preview below and in the file "A<em>CroplandAgreement_30m_Tiles.png</em>" the extent of each tile can be found.</p> <p>For more information on FAO statistics on land cover and land use:</p> <p>FAO. 2022. <em>Land use statistics and indicators. Global, regional and country trends, 2000–2020</em>. FAOSTAT Analytical Brief, no. 48. Rome. <a href="https://doi.org/10.4060/cc0963en">https://doi.org/10.4060/cc0963en</a></p> <p>FAO. 2021. <em>Land cover statistics. Global, regional and country trends, 2000–2019</em>. FAOSTAT Analytical Brief Series No. 37. Rome. </p>
The 30 m long-term LAke Water Secchi Depth (SD) dataset (LAWSD30) of China (1985–2020)
<p>Monitoring the water clarity of lakes is essential for the sustainable development of human society. However, existing water clarity assessments in China have mostly focused on lakes with areas > 1 km<sup>2</sup>, and the monitoring periods were mainly in the 21st century. In order to improve the understanding of spatiotemporal variations in lake clarity across China, based on the Google Earth Engine (GEE) cloud platform, a 30 m long-term LAke Water Secchi Depth (SD) dataset (LAWSD30) of China (1985–2020) was developed using Landsat series imagery and a robust water-color-parameter-based SD model. <strong><em>Noted, the LAWSD30 has been updated to 2021 using Landsat data from 2020 to 2022.</em></strong> The details are described in "<em>30 m long-term LAke Water Secchi depth (SD) dataset (LAWSD30) of China (1985–2021)_Readme_V1.1.docx</em>".</p>
CoastSeg: Shoreline data at 30-m spatial resolution for 298 coastal counties of the conterminous USA, in geoJSON format.
<p>Region: 298 coastal counties of the conterminous USA</p> <p>Data fields:</p> <ol> <li>MEAN_SIG_WAVEHEIGHT</li> <li>TIDAL_RANGE</li> <li>CHLOROPHYLL</li> <li>TURBIDITY</li> <li>TEMP_MOISTURE</li> <li>EMU_PHYSICAL</li> <li>REGIONAL_SINUOSITY</li> <li>GHM</li> <li>MAX_SLOPE %</li> <li>OUTFLOW_DENSITY</li> <li>ERODIBILITY</li> <li>LENGTH_GEO</li> <li>ch_label</li> <li>river_label</li> <li>sinuosity_label</li> <li>slope_label</li> <li>tidal_label</li> <li>turbid_label</li> <li>wave_label</li> <li>CSU_Descriptor</li> <li>CSU_ID</li> </ol> <p>The data originally come from https://rmgsc.cr.usgs.gov/outgoing/ecosystems/Global/USGSEsriGlobalCoastalSegmentsv1.mpk</p> <p>The data are described in the following publication</p> <p>Roger Sayre, Suzanne Noble, Sharon Hamann, Rebecca Smith, Dawn Wright, Sean Breyer, Kevin Butler, Keith Van Graafeiland, Charlie Frye, Deniz Karagulle, Dabney Hopkins, Drew Stephens, Kevin Kelly, Zeenatul Basher, Devon Burton, Jill Cress, Karina Atkins, D. Paco Van Sistine, Beverly Friesen, Rebecca Allee, Tom Allen, Peter Aniello, Irawan Asaad, Mark John Costello, Kathy Goodin, Peter Harris, Maria Kavanaugh, Helen Lillis, Eleonora Manca, Frank Muller-Karger, Bjorn Nyberg, Rost Parsons, Justin Saarinen, Jac Steiner & Adam Reed (2019) A new 30 meter resolution global shoreline vector and associated global islands database for the development of standardized ecological coastal units, Journal of Operational Oceanography, 12:sup2, S47-S56, DOI: <a href="https://doi.org/10.1080/1755876X.2018.1529714">10.1080/1755876X.2018.1529714</a></p>
CoastSeg: Shoreline data at 30-m spatial resolution for 2001 coastal provinces or regions of the world, in geoJSON format.
<p>Region: 2001 coastal provinces or regions of the world</p> <p>Data fields:</p> <ol> <li>MEAN_SIG_WAVEHEIGHT</li> <li>TIDAL_RANGE</li> <li>CHLOROPHYLL</li> <li>TURBIDITY</li> <li>TEMP_MOISTURE</li> <li>EMU_PHYSICAL</li> <li>REGIONAL_SINUOSITY</li> <li>GHM</li> <li>MAX_SLOPE %</li> <li>OUTFLOW_DENSITY</li> <li>ERODIBILITY</li> <li>LENGTH_GEO</li> <li>ch_label</li> <li>river_label</li> <li>sinuosity_label</li> <li>slope_label</li> <li>tidal_label</li> <li>turbid_label</li> <li>wave_label</li> <li>CSU_Descriptor</li> <li>CSU_ID</li> </ol> <p>The data originally come from https://rmgsc.cr.usgs.gov/outgoing/ecosystems/Global/USGSEsriGlobalCoastalSegmentsv1.mpk</p> <p>The data are described in the following publication</p> <p>Roger Sayre, Suzanne Noble, Sharon Hamann, Rebecca Smith, Dawn Wright, Sean Breyer, Kevin Butler, Keith Van Graafeiland, Charlie Frye, Deniz Karagulle, Dabney Hopkins, Drew Stephens, Kevin Kelly, Zeenatul Basher, Devon Burton, Jill Cress, Karina Atkins, D. Paco Van Sistine, Beverly Friesen, Rebecca Allee, Tom Allen, Peter Aniello, Irawan Asaad, Mark John Costello, Kathy Goodin, Peter Harris, Maria Kavanaugh, Helen Lillis, Eleonora Manca, Frank Muller-Karger, Bjorn Nyberg, Rost Parsons, Justin Saarinen, Jac Steiner & Adam Reed (2019) A new 30 meter resolution global shoreline vector and associated global islands database for the development of standardized ecological coastal units, Journal of Operational Oceanography, 12:sup2, S47-S56, DOI: <a href="https://doi.org/10.1080/1755876X.2018.1529714">10.1080/1755876X.2018.1529714</a></p> <p>Metadata file (each file listed alongside the bounds in WGS84 latitude/longitude): province_files_bounds.json</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.