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150 results for “Cube”
Amsterdam - Cube 1525
<u>Coordinates</u>: N/A <br><u>Length</u>: 24.23 m<br><u>Width</u>: 26.52 m<br><u>Height</u>: 16.89 m<br><u>Vertices</u>: 7306 <br><u>Primitives</u>: 6675 <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>.mtl</th><th>.obj</th><th>.zip</th><th>.glb</th><th>.xml</th></tr><tr><td><a href="https://zenodo.org/api/records/12687927/files/textures.zip/content">textures.zip</a></td><td></td><td></td><td><a href="https://zenodo.org/api/records/12687927/files/textures.zip/content">Link</a></td><td></td><td></td></tr><tr><td><a href="https://zenodo.org/api/records/12687927/files/Cube_1525.mtl/content">Cube_1525.mtl</a></td><td><a href="https://zenodo.org/api/records/12687927/files/Cube_1525.mtl/content">Link</a></td><td></td><td></td><td></td><td></td></tr><tr><td><a href="https://zenodo.org/api/records/12687927/files/Cube_1525.obj/content">Cube_1525.obj</a></td><td></td><td><a href="https://zenodo.org/api/records/12687927/files/Cube_1525.obj/content">Link</a></td><td></td><td></td><td></td></tr><tr><td><a href="https://zenodo.org/api/records/12687927/files/Cube_1525.glb/content">Cube_1525.glb</a></td><td></td><td></td><td></td><td><a href="https://zenodo.org/api/records/12687927/files/Cube_1525.glb/content">Link</a></td><td></td></tr><tr><td><a href="https://zenodo.org/api/records/12687927/files/11253216_metsmods.xml/content">11253216_metsmods.xml</a></td><td></td><td></td><td></td><td></td><td><a href="https://zenodo.org/api/records/12687927/files/11253216_metsmods.xml/content">Link</a></td></tr><tr><td><a href="https://zenodo.org/api/records/12687927/files/11253216_edm.xml/content">11253216_edm.xml</a></td><td></td><td></td><td></td><td></td><td><a href="https://zenodo.org/api/records/12687927/files/11253216_edm.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/12687927/files/Cube_1525_perspective_1.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12687927/files/Cube_1525_perspective_1_512x512.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12687927/files/Cube_1525_perspective_1_256x256.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12687927/files/Cube_1525_perspective_1_128x128.png/content">Link</a></td></tr><tr><td>Perspective 2</td><td><a href="https://zenodo.org/api/records/12687927/files/Cube_1525_perspective_2.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12687927/files/Cube_1525_perspective_2_512x512.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12687927/files/Cube_1525_perspective_2_256x256.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12687927/files/Cube_1525_perspective_2_128x128.png/content">Link</a></td></tr><tr><td>Perspective 3</td><td><a href="https://zenodo.org/api/records/12687927/files/Cube_1525_perspective_3.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12687927/files/Cube_1525_perspective_3_512x512.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12687927/files/Cube_1525_perspective_3_256x256.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12687927/files/Cube_1525_perspective_3_128x128.png/content">Link</a></td></tr><tr><td>Perspective 4</td><td><a href="https://zenodo.org/api/records/12687927/files/Cube_1525_perspective_4.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12687927/files/Cube_1525_perspective_4_512x512.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12687927/files/Cube_1525_perspective_4_256x256.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12687927/files/Cube_1525_perspective_4_128x128.png/content">Link</a></td></tr><tr><td>Perspective Top</td><td><a href="https://zenodo.org/api/records/12687927/files/Cube_1525_perspective_top.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12687927/files/Cube_1525_perspective_top_512x512.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12687927/files/Cube_1525_perspective_top_256x256.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12687927/files/Cube_1525_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.11483913">0.0.2</a>: Thumbnails added, Description updated with Link Tables.<br> - v<a href="https://doi.org/10.5281/zenodo.12687927">0.0.3</a>: Added XMLs for Europeana Data Model (EDM) and MetsMods.<br>
Accompanying Dataset migr_asyappctzm for Efficient Analytical Queries on Semantic Web Data Cubes
<p>This dataset shows how the Eurostat data cube in the orginal publicatin is modelled in QB4OLAP.</p> <p>This data is based on statistical data about asylum applications to the European Union, provided by Eurostat on</p> <p><a href="http://ec.europa.eu/eurostat/web/products-datasets/-/migr_asyappctzm">http://ec.europa.eu/eurostat/web/products-datasets/-/migr_asyappctzm</a></p> <p>Further data has been integrated from: https://github.com/lorenae/qb4olap/tree/master/examples</p>
A global land-use data cube 1992-2020 based on the Human Appropriation of Net Primary Production: Dataset 2
<p>This dataset is part of the LUIcube, a global dataset on land-use at 30 arcsecond spatial resolution. The LUIcube includes information on area, the change in NPP due to land conversions (HANPP<sub>luc</sub>), the harvested NPP (including losses, HANPP<sub>harv</sub>), and the NPP remaining in ecosystems after harvest (NPP<sub>eco</sub>) for 32 land-use classes in annual time-steps from 1992 to 2020. A detailed description of the LUIcube is available in the accompanying publication.</p> <p>The layers of land-use areas are provided in square kilometers (km²) per grid cell. All NPP flows are provided in tC/yr per grid cell. Adding HANPP<sub>harv</sub> to NPP<sub>eco</sub> results in the actual NPP available before harvest (NPP<sub>act</sub>=NPP<sub>eco</sub>+HANPP<sub>harv</sub>), and adding HANPP<sub>luc</sub> to NPP<sub>act</sub> results in the potential NPP available in the hypothetical absence of land use (NPP<sub>pot</sub>=NPP<sub>act</sub>+HANPP<sub>luc</sub>) for the given land-use class. Area-intensive values (in gC/m²/yr) can be calculated by dividing the NPP flows by the area of the respective land-use class per grid cell. HANPP in % of NPP<sub>pot</sub> can be calculated by summing up HANPP<sub>harv</sub> and HANPP<sub>luc</sub> and dividing it by NPP<sub>pot</sub>. Areas and NPP flows of land-use classes can be aggregated to calculate their overall HANPP. </p> <p>This Zenodo repository provides data on following land-use classes: grazing land characterized by open wooded lands (GL-owl)</p>
Figure 1. Necker cube depth illusion (Adapted from [http://en.wikipedia.org/wiki/Necker_cube])-Gestalt Processing in Human-Robot Interaction: A Novel Account for Autism Research
<p>Often the term Gestalt is used interchangeably with the term “emergent whole” (Johansson,<br> 1998). The emergence of a cognitive Gestalt structure adds dynamical and psychophysical forces,<br> which are different from the static notion of the “emergent whole”. An eminent example for the<br> dynamic nature of the emergent process is the Necker cube, which cannot be perceived as static, but<br> rotates in front of our eyes to the complete exhaustion of the eye gazing process (figure 1).</p>
Space time cube for precipitation derived from 24h hours of metereological radar data
<p>The animation depicts a 24h space-time cube derived from radar-metereologic data recorded by the x-band radar station of the South African Weather Service for the Liebenbergvlei in the Freestate, South Africa on December 31 2001. The temporal resolution (z) is 5 Minutes, starting on 2001-12-31 00:00:00 hours, ending on 2001-12-31 23:55:00 hours. Spatial resulution please see. The spatial resolution (xy) is 1km, covering a radius of 200km from the radar station. Tempospatial zones of weak precispitation are colored in blue, zones of severe precipitation are colored in yellow.</p> <p>Data processing was done in GRASS v6.x, visualisation was done in Paraview.</p>
SeasFire Cube: A Global Dataset for Seasonal Fire Modeling in the Earth System
<p>The <strong>SeasFire Cube</strong> is a scientific datacube for seasonal fire forecasting around the <strong>globe</strong>. Apart from seasonal fire forecasting, which is the aim of the SeasFire project, the datacube can be used for several other tasks. For example, it can be used to model teleconnections and memory effects in the earth system. Additionally, it can be used to model emissions from wildfires and the evolution of wildfire regimes.<br> <br> It has been created in the context of the <a href="https://seasfire.hua.gr/">SeasFire project</a>, which deals with "<em>Earth System Deep Learning for Seasonal Fire Forecasting</em>" and <strong>is funded by the European Space Agency (ESA) </strong> in the context of ESA Future EO-1 Science for Society Call.<br> <br> It contains <strong>21 years</strong> of data (2001-2021) in an <strong>8-days</strong> time resolution and <strong>0.25 degrees grid</strong> resolution. It has a diverse range of seasonal fire drivers. It expands from atmospheric and climatological ones to vegetation variables, socioeconomic and the target variables related to wildfires such as burned areas, fire radiative power, and wildfire-related CO2 emissions.</p> Datacube properties <table><tbody><tr> <th> <p><strong>Feature</strong></p> </th> <th> <p><strong>Value</strong></p> </th> </tr> </tbody><tbody> <tr> <td> <p>Spatial Coverage</p> </td> <td> <p>Global</p> </td> </tr> <tr> <td> <p>Temporal Coverage</p> </td> <td> <p>2001 to 2021</p> </td> </tr> <tr> <td> <p>Spatial Resolution</p> </td> <td> <p>0.25 deg x 0.25 deg</p> </td> </tr> <tr> <td> <p>Temporal Resolution</p> </td> <td> <p>8 days</p> </td> </tr> <tr> <td> <p>Number of Variables</p> </td> <td> <p>54</p> </td> </tr> <tr> <td> <p>Tutorial Link </p> </td> <td> <p><a href="https://github.com/SeasFire/seasfire-datacube">https://github.com/SeasFire/seasfire-datacube</a></p> </td> </tr> </tbody> </table> <table> <tbody><tr> <th>Full name</th> <th>DataArray name</th> <th>Unit</th> <th>Contact *</th> </tr> </tbody><tbody> <tr> <th>Dataset: <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-pressure-levels?tab=overview">ERA5 Meteo Reanalysis Data</a></th> <td> </td> <td> </td> <td> </td> </tr> <tr> <th>Mean sea level pressure</th> <td>mslp</td> <td>Pa</td> <td>NOA</td> </tr> <tr> <th>Total precipitation</th> <td>tp</td> <td>m</td> <td>MPI</td> </tr> <tr> <th>Relative humidity</th> <td>rel_hum</td> <td>%</td> <td>MPI</td> </tr> <tr> <th>Vapor Pressure Deficit</th> <td>vpd</td> <td>hPa</td> <td>MPI</td> </tr> <tr> <th>Sea Surface Temperature</th> <td>sst</td> <td>K</td> <td>MPI</td> </tr> <tr> <th>Skin temperature</th> <td>skt</td> <td>K</td> <td>MPI</td> </tr> <tr> <th>Wind speed at 10 meters</th> <td>ws10</td> <td>m*s-2</td> <td>MPI</td> </tr> <tr> <th>Temperature at 2 meters - Mean</th> <td>t2m_mean</td> <td>K</td> <td>MPI</td> </tr> <tr> <th>Temperature at 2 meters - Min</th> <td>t2m_min</td> <td>K</td> <td>MPI</td> </tr> <tr> <th>Temperature at 2 meters - Max</th> <td>t2m_max</td> <td>K</td> <td>MPI</td> </tr> <tr> <th>Surface net solar radiation</th> <td>ssr</td> <td>MJ m-2</td> <td>MPI</td> </tr> <tr> <th>Surface solar radiation downwards</th> <td>ssrd</td> <td>MJ m-2</td> <td>MPI</td> </tr> <tr> <th>Volumetric soil water level 1</th> <td>swvl1</td> <td>m3/m3</td> <td>MPI</td> </tr> <tr> <th> <table> <tbody> <tr> <th>Volumetric soil water level 2</th> </tr> </tbody> </table> </th> <td>swvl2</td> <td>m3/m3</td> <td>MPI</td> </tr> <tr> <th>Volumetric soil water level 3</th> <td>swvl3</td> <td>m3/m3</td> <td>MPI</td> </tr> <tr> <th>Volumetric soil water level 4</th> <td>swvl4</td> <td>m3/m3</td> <td>MPI</td> </tr> <tr> <th>Land-Sea mask</th> <td>lsm</td> <td>0-1</td> <td>NOA</td> </tr> <tr> <th>Dataset: Copernicus <p><a href="http://cds.climate.copernicus.eu/cdsapp#!/dataset/cems-fire-historical?tab=overview">CEMS</a></p> </th> <td> </td> <td> </td> <td> </td> </tr> <tr> <th>Drought Code Maximum</th> <td>drought_code_max</td> <td>unitless</td> <td>NOA</td> </tr> <tr> <th>Drought Code Average</th> <td>drought_code_mean</td> <td>unitless</td> <td>NOA</td> </tr> <tr> <th>Fire Weather Index Maximum</th> <td>fwi_max</td> <td>unitless</td> <td>NOA</td> </tr> <tr> <th>Fire Weather Index Average</th> <td>fwi_mean</td> <td>unitless</td> <td>NOA</td> </tr> <tr> <th>Dataset: <a href="http://confluence.ecmwf.int/display/CKB/CAMS%3A+Global+Fire+Assimilation+System+%28GFAS%29+data+documentation">CAMS: Global Fire Assimilation System (GFAS)</a></th> <td> </td> <td> </td> <td> </td> </tr> <tr> <th>Carbon dioxide emissions from wildfires</th> <td>cams_co2fire</td> <td>kg/m²</td> <td>NOA</td> </tr> <tr> <th>Fire radiative power</th> <td>cams_frpfire</td> <td>W/m²</td> <td>NOA</td> </tr> <tr> <th>Dataset: <a href="https://climate.esa.int/en/projects/fire/data/">FireCCI - European Space Agency’s Climate Change Initiative</a></th> <td> </td> <td> </td> <td> </td> </tr> <tr> <th>Burned Areas from Fire Climate Change Initiative (FCCI)</th> <td>fcci_ba</td> <td>ha</td> <td>NOA</td> </tr> <tr> <th>Valid mask of FCCI burned areas</th> <td>fcci_ba_valid_mask</td> <td>0-1</td> <td>NOA</td> </tr> <tr> <th><br> Fraction of burnable area</th> <td>fcci_fraction_of_burnable_area</td> <td>%</td> <td>NOA</td> </tr> <tr> <th>Number of patches</th> <td>fcci_number_of_patches</td> <td>N</td> <td>NOA</td> </tr> <tr> <th>Fraction of observed area</th> <td>fcci_fraction_of_observed_area</td> <td>%</td> <td>NOA</td> </tr> <tr> <th>Dataset: Nasa MODIS <a href="https://lpdaac.usgs.gov/products/mod11c1v006/">MOD11C1</a>, <a href="https://lpdaac.usgs.gov/products/mod13c1v006/">MOD13C1</a>, <a href="https://lpdaac.usgs.gov/products/mcd15a2hv006/">MCD15A2</a></th> <td> </td> <td> </td> <td> </td> </tr> <tr> <th>Land Surface temperature at day</th> <td>lst_day</td> <td>K</td> <td>MPI</td> </tr> <tr> <th>Leaf Area Index</th> <td>lai</td> <td>m²/m²</td> <td>MPI</td> </tr> <tr> <th>Normalized Difference Vegetation Index</th> <td>ndvi</td> <td>unitless</td> <td>MPI</td> </tr> <tr> <th>Dataset: Nasa SEDAC <a href="https://sedac.ciesin.columbia.edu/data/set/gpw-v4-population-density-adjusted-to-2015-unwpp-country-totals-rev11">Gridded Population of the World (GPW), v4</a></th> <td> </td> <td> </td> <td> </td> </tr> <tr> <th>Population density</th> <td>pop_dens</td> <td>persons per square kilometers</td> <td>NOA</td> </tr> <tr> <th>Dataset: <a href="http://www.globalfiredata.org/data.html">Global Fire Emissions Database (GFED)</a></th> <td> </td> <td> </td> <td> </td> </tr> <tr> <th>Burned Areas from GFED (large fires only)</th> <td>gfed_ba</td> <td>hectares (ha)</td> <td>MPI</td> </tr> <tr> <th>Valid mask of GFED burned areas</th> <td>gfed_ba_valid_mask</td> <td>0-1</td> <td>NOA</td> </tr> <tr> <th>GFED basis regions</th> <td>gfed_region</td> <td>N</td> <td>NOA</td> </tr> <tr> <th>Dataset: <a href="http://gwis.jrc.ec.europa.eu/apps/country.profile/downloads">Global Wildfire Information System (GWIS)</a></th> <td> </td> <td> </td> <td> </td> </tr> <tr> <th>Burned Areas from GWIS</th> <td>gwis_ba</td> <td>ha</td> <td>NOA</td> </tr> <tr> <th>Valid mask of GWIS burned areas</th> <td>gwis_ba_valid_mask</td> <td>0-1</td> <td>NOA</td> </tr> <tr> <th>Dataset: <a href="https://psl.noaa.gov/data/climateindices/list/">NOAA Climate Indices</a></th> <td> </td> <td> </td> <td> </td> </tr> <tr> <th>Arctic Oscillation Index</th> <td>oci_ao</td> <td>unitless</td> <td>NOA</td> </tr> <tr> <th>Western Pacific Index</th> <td>oci_wp</td> <td>unitless</td> <td>NOA</td> </tr> <tr> <th>Pacific North American Index</th> <td>oci_pna</td> <td>unitless</td> <td>NOA</td> </tr> <tr> <th>North Atlantic Oscillation</th> <td>oci_nao</td> <td>unitless</td> <td>NOA</td> </tr> <tr> <th>Southern Oscillation Index</th> <td>oci_soi</td> <td>unitless</td> <td>NOA</td> </tr> <tr> <th>Global Mean Land/Ocean Temperature</th> <td>oci_gmsst</td> <td>unitless</td> <td>NOA</td> </tr> <tr> <th>Pacific Decadal Oscillation</th> <td>oci_pdo</td> <td>unitless</td> <td>NOA</td> </tr> <tr> <th>Eastern Asia/Western Russia</th> <td>oci_ea</td> <td>unitless</td> <td>NOA</td> </tr> <tr> <th>East Pacific/North Pacific Oscillation</th> <td>oci_epo</td> <td>unitless</td> <td>NOA</td> </tr> <tr> <th>Nino 3.4 Anomaly</th> <td>oci_nino_34_anom</td> <td>unitless</td> <td>NOA</td> </tr> <tr> <th>Bivariate ENSO Timeseries</th> <td>oci_censo</td> <td>unitless</td> <td>NOA</td> </tr> <tr> <th>Dataset: <a href="https://www.esa-landcover-cci.org/">ESA CCI</a></th> <td> </td> <td> </td> <td> </td> </tr> <tr> <th>Land Cover Class 0 - No data</th> <td>lccs_class_0</td> <td>%</td> <td>NOA</td> </tr> <tr> <th>Land Cover Class 1 - Agriculture</th> <td>lccs_class_1</td> <td>%</td> <td>NOA</td> </tr> <tr> <th>Land Cover Class 2 - Forest</th> <td>lccs_class_2</td> <td>%</td> <td>NOA</td> </tr> <tr> <th>Land Cover Class 3 - Grassland</th> <td>lccs_class_3</td> <td>%</td> <td>NOA</td> </tr> <tr> <th>Land Cover Class 4 - Wetlands</th> <td>lccs_class_4</td> <td>%</td> <td>NOA</td> </tr> <tr> <th>Land Cover Class 5 - Settlement</th> <td>lccs_class_5</td> <td>%</td> <td>NOA</td> </tr> <tr> <th>Land Cover Class 6 - Shrubland</th> <td>lccs_class_6</td> <td>%</td> <td>NOA</td> </tr> <tr> <th>Land Cover Class 7 - Sparse vegetation, bare areas, permanent snow and ice</th> <td>lccs_class_7</td> <td>%</td> <td>NOA</td> </tr> <tr> <th>Land Cover Class 8 - Water Bodies</th> <td>lccs_class_8</td> <td>%</td> <td>NOA</td> </tr> <tr> <th>Dataset: <a href="https://ecoregions.appspot.com/">Biomes</a></th> <td> </td> <td> </td> <td> </td> </tr> <tr> <th>Dataset: Calculated</th> <td> </td> <td> </td> <td> </td> </tr> <tr> <th>Grid Area in square meters</th> <td>area</td> <td>m²</td> <td>NOA</td> </tr> </tbody> </table> <p>*The datacube specifications (temporal, spatial resolution, chunk size) have been set up by the Max Planck Institut (MPI) team. For the variables that the contact is MPI, Lazaro Alonso (lalonso <at> bgc-jena.mpg.de) has led the efforts to collect and process them. For the variables that the contact is NOA, Ilektra Karasante (ile.karasante <at> noa.gr) has led the efforts to collect and process them.</p>
Parihaka Annotated Seismic Dataset - Slices from Cube (inlines and crosslines)
<p><strong>Parihaka Seismic Dataset</strong></p> <p> </p> <p><strong>Description - Images</strong></p> <p><strong>crosslines:</strong> TIFF image data, little-endian, direntries=10, height=1006, bps=134, compression=none, PhotometricIntepretation=RGB, width=590</p> <p><strong>inlines:</strong> TIFF image data, little-endian, direntries=10, height=1006, bps=134, compression=none, PhotometricIntepretation=RGB, width=781</p> <p> </p> <p><strong>Description - Labels (Annotations)</strong></p> <p><strong>crosslines:</strong> PNG image data, 590 x 1006, 8-bit grayscale, non-interlaced</p> <p><strong>inlines:</strong> PNG image data, 781 x 1006, 8-bit grayscale, non-interlaced</p> <p> </p> <p> </p> <p>Acknowledgements:<br><br>New Zealand Petroleum and Minerals (NZPM) for providing data<br>https://www.nzpam.govt.nz/</p> <p>https://geodata.nzpam.govt.nz/</p> <p> </p> <p>The training labels for this volume have been provided by Chevron U.S.A. Inc</p> <p> </p> <p>The original data is from: 2020 SEG Annual Meeting Machine Learning Interpretation Workshop by Susan Stamm (Sep 3, 2020). Available at: https://public.3.basecamp.com/p/JyT276MM7krjYrMoLqLQ6xST</p>
A global land-use data cube 1992-2020 based on the Human Appropriation of Net Primary Production: Dataset 1
<p>This dataset is part of the LUIcube, a global dataset on land-use at 30 arcsecond spatial resolution. The LUIcube includes information on area, the change in NPP due to land conversions (HANPP<sub>luc</sub>), the harvested NPP (including losses, HANPP<sub>harv</sub>), and the NPP remaining in ecosystems after harvest (NPP<sub>eco</sub>) for 32 land-use classes in annual time-steps from 1992 to 2020. A detailed description of the LUIcube is available in the accompanying publication.</p> <p>The layers of land-use areas are provided in square kilometers (km²) per grid cell. All NPP flows are provided in tC/yr per grid cell. Adding HANPP<sub>harv</sub> to NPP<sub>eco</sub> results in the actual NPP available before harvest (NPP<sub>act</sub>=NPP<sub>eco</sub>+HANPP<sub>harv</sub>), and adding HANPP<sub>luc</sub> to NPP<sub>act</sub> results in the potential NPP available in the hypothetical absence of land use (NPP<sub>pot</sub>=NPP<sub>act</sub>+HANPP<sub>luc</sub>) for the given land-use class. Area-intensive values (in gC/m²/yr) can be calculated by dividing the NPP flows by the area of the respective land-use class per grid cell. HANPP in % of NPP<sub>pot</sub> can be calculated by summing up HANPP<sub>harv</sub> and HANPP<sub>luc</sub> and dividing it by NPP<sub>pot</sub>. Areas and NPP flows of land-use classes can be aggregated to calculate their overall HANPP.</p> <p>This Zenodo repository provides data on following land-use classes: unused productive wilderness areas (WILD-core); productive wilderness areas that are sporadically used at very low intensity (WILD-periphery); unused unproductive wilderness areas (WILD-nps); forestry areas, mainly coniferous (FO-con); forestry areas, mainly non-coniferous (FO-ncon); settlements, urban areas and infrastructure (BU-builtup)</p> <p> </p>
A global land-use data cube 1992-2020 based on the Human Appropriation of Net Primary Production: Dataset 6
<p>This dataset is part of the LUIcube, a global dataset on land-use at 30 arcsecond spatial resolution. The LUIcube includes information on area, the change in NPP due to land conversions (HANPP<sub>luc</sub>), the harvested NPP (including losses, HANPP<sub>harv</sub>), and the NPP remaining in ecosystems after harvest (NPP<sub>eco</sub>) for 32 land-use classes in annual time-steps from 1992 to 2020. A detailed description of the LUIcube is available in the accompanying publication.</p> <p>The layers of land-use areas are provided in square kilometers (km²) per grid cell. All NPP flows are provided in tC/yr per grid cell. Adding HANPP<sub>harv</sub> to NPP<sub>eco</sub> results in the actual NPP available before harvest (NPP<sub>act</sub>=NPP<sub>eco</sub>+HANPP<sub>harv</sub>), and adding HANPP<sub>luc</sub> to NPP<sub>act</sub> results in the potential NPP available in the hypothetical absence of land use (NPP<sub>pot</sub>=NPP<sub>act</sub>+HANPP<sub>luc</sub>) for the given land-use class. Area-intensive values (in gC/m²/yr) can be calculated by dividing the NPP flows by the area of the respective land-use class per grid cell. HANPP in % of NPP<sub>pot</sub> can be calculated by summing up HANPP<sub>harv</sub> and HANPP<sub>luc</sub> and dividing it by NPP<sub>pot</sub>. Areas and NPP flows of land-use classes can be aggregated to calculate their overall HANPP.</p> <p>This Zenodo repository provides data on following land-use classes: cropland used for production of potatoes (CL-POTA); sweet potatoes and yams (CL-SWPY); and rest of crops (CL-REST)</p>
A global land-use data cube 1992-2020 based on the Human Appropriation of Net Primary Production: Dataset 4
<p>This dataset is part of the LUIcube, a global dataset on land-use at 30 arcsecond spatial resolution. The LUIcube includes information on area, the change in NPP due to land conversions (HANPP<sub>luc</sub>), the harvested NPP (including losses, HANPP<sub>harv</sub>), and the NPP remaining in ecosystems after harvest (NPP<sub>eco</sub>) for 32 land-use classes in annual time-steps from 1992 to 2020. A detailed description of the LUIcube is available in the accompanying publication.</p> <p>The layers of land-use areas are provided in square kilometers (km²) per grid cell. All NPP flows are provided in tC/yr per grid cell. Adding HANPP<sub>harv</sub> to NPP<sub>eco</sub> results in the actual NPP available before harvest (NPP<sub>act</sub>=NPP<sub>eco</sub>+HANPP<sub>harv</sub>), and adding HANPP<sub>luc</sub> to NPP<sub>act</sub> results in the potential NPP available in the hypothetical absence of land use (NPP<sub>pot</sub>=NPP<sub>act</sub>+HANPP<sub>luc</sub>) for the given land-use class. Area-intensive values (in gC/m²/yr) can be calculated by dividing the NPP flows by the area of the respective land-use class per grid cell. HANPP in % of NPP<sub>pot</sub> can be calculated by summing up HANPP<sub>harv</sub> and HANPP<sub>luc</sub> and dividing it by NPP<sub>pot</sub>. Areas and NPP flows of land-use classes can be aggregated to calculate their overall HANPP. </p> <p>This Zenodo repository provides data on following land-use classes: cropland used for production of wheat (CL-WHEA); maize (CL-MAIZ); soybean (CL-SOYB)</p>
A global land-use data cube 1992-2020 based on the Human Appropriation of Net Primary Production: Dataset 5
<p>This dataset is part of the LUIcube, a global dataset on land-use at 30 arcsecond spatial resolution. The LUIcube includes information on area, the change in NPP due to land conversions (HANPP<sub>luc</sub>), the harvested NPP (including losses, HANPP<sub>harv</sub>), and the NPP remaining in ecosystems after harvest (NPP<sub>eco</sub>) for 32 land-use classes in annual time-steps from 1992 to 2020. A detailed description of the LUIcube is available in the accompanying publication.</p> <p>The layers of land-use areas are provided in square kilometers (km²) per grid cell. All NPP flows are provided in tC/yr per grid cell. Adding HANPP<sub>harv</sub> to NPP<sub>eco</sub> results in the actual NPP available before harvest (NPP<sub>act</sub>=NPP<sub>eco</sub>+HANPP<sub>harv</sub>), and adding HANPP<sub>luc</sub> to NPP<sub>act</sub> results in the potential NPP available in the hypothetical absence of land use (NPP<sub>pot</sub>=NPP<sub>act</sub>+HANPP<sub>luc</sub>) for the given land-use class. Area-intensive values (in gC/m²/yr) can be calculated by dividing the NPP flows by the area of the respective land-use class per grid cell. HANPP in % of NPP<sub>pot</sub> can be calculated by summing up HANPP<sub>harv</sub> and HANPP<sub>luc</sub> and dividing it by NPP<sub>pot</sub>. Areas and NPP flows of land-use classes can be aggregated to calculate their overall HANPP.</p> <p>This Zenodo repository provides data on following land-use classes: cropland used for production of millet (CL-MILL); barley (CL-BARL); sorghum (CL-SORG); rice (CL-RICE)</p>
A global land-use data cube 1992-2020 based on the Human Appropriation of Net Primary Production: Dataset 7
<p>This dataset is part of the LUIcube, a global dataset on land-use at 30 arcsecond spatial resolution. The LUIcube includes information on area, the change in NPP due to land conversions (HANPP<sub>luc</sub>), the harvested NPP (including losses, HANPP<sub>harv</sub>), and the NPP remaining in ecosystems after harvest (NPP<sub>eco</sub>) for 32 land-use classes in annual time-steps from 1992 to 2020. A detailed description of the LUIcube is available in the accompanying publication.</p> <p>The layers of land-use areas are provided in square kilometers (km²) per grid cell. All NPP flows are provided in tC/yr per grid cell. Adding HANPP<sub>harv</sub> to NPP<sub>eco</sub> results in the actual NPP available before harvest (NPP<sub>act</sub>=NPP<sub>eco</sub>+HANPP<sub>harv</sub>), and adding HANPP<sub>luc</sub> to NPP<sub>act</sub> results in the potential NPP available in the hypothetical absence of land use (NPP<sub>pot</sub>=NPP<sub>act</sub>+HANPP<sub>luc</sub>) for the given land-use class. Area-intensive values (in gC/m²/yr) can be calculated by dividing the NPP flows by the area of the respective land-use class per grid cell. HANPP in % of NPP<sub>pot</sub> can be calculated by summing up HANPP<sub>harv</sub> and HANPP<sub>luc</sub> and dividing it by NPP<sub>pot</sub>. Areas and NPP flows of land-use classes can be aggregated to calculate their overall HANPP.</p> <p>This Zenodo repository provides data on following land-use classes: cropland used for production of cassava (CL-CASS); sugarcane (CL-SUGC); sugarbeet (CL-SUGB); cotton (CL-COTT); fruits and vegetables (CL-VEFR)</p>
A global land-use data cube 1992-2020 based on the Human Appropriation of Net Primary Production: Dataset 8
<p>This dataset is part of the LUIcube, a global dataset on land-use at 30 arcsecond spatial resolution. The LUIcube includes information on area, the change in NPP due to land conversions (HANPP<sub>luc</sub>), the harvested NPP (including losses, HANPP<sub>harv</sub>), and the NPP remaining in ecosystems after harvest (NPP<sub>eco</sub>) for 32 land-use classes in annual time-steps from 1992 to 2020. A detailed description of the LUIcube is available in the accompanying publication.</p> <p>The layers of land-use areas are provided in square kilometers (km²) per grid cell. All NPP flows are provided in tC/yr per grid cell. Adding HANPP<sub>harv</sub> to NPP<sub>eco</sub> results in the actual NPP available before harvest (NPP<sub>act</sub>=NPP<sub>eco</sub>+HANPP<sub>harv</sub>), and adding HANPP<sub>luc</sub> to NPP<sub>act</sub> results in the potential NPP available in the hypothetical absence of land use (NPP<sub>pot</sub>=NPP<sub>act</sub>+HANPP<sub>luc</sub>) for the given land-use class. Area-intensive values (in gC/m²/yr) can be calculated by dividing the NPP flows by the area of the respective land-use class per grid cell. HANPP in % of NPP<sub>pot</sub> can be calculated by summing up HANPP<sub>harv</sub> and HANPP<sub>luc</sub> and dividing it by NPP<sub>pot</sub>. Areas and NPP flows of land-use classes can be aggregated to calculate their overall HANPP.</p> <p>This Zenodo repository provides data on following land-use classes: cropland used for production of beans (CL-BEAN); other pulses (CL-OPUL); groundnuts (CL-GROU); bananas and plantains (CL-BANP)</p>
A global land-use data cube 1992-2020 based on the Human Appropriation of Net Primary Production: Dataset 9
<p>This dataset is part of the LUIcube, a global dataset on land-use at 30 arcsecond spatial resolution. The LUIcube includes information on area, the change in NPP due to land conversions (HANPP<sub>luc</sub>), the harvested NPP (including losses, HANPP<sub>harv</sub>), and the NPP remaining in ecosystems after harvest (NPP<sub>eco</sub>) for 32 land-use classes in annual time-steps from 1992 to 2020. A detailed description of the LUIcube is available in the accompanying publication.</p> <p>The layers of land-use areas are provided in square kilometers (km²) per grid cell. All NPP flows are provided in tC/yr per grid cell. Adding HANPP<sub>harv</sub> to NPP<sub>eco</sub> results in the actual NPP available before harvest (NPP<sub>act</sub>=NPP<sub>eco</sub>+HANPP<sub>harv</sub>), and adding HANPP<sub>luc</sub> to NPP<sub>act</sub> results in the potential NPP available in the hypothetical absence of land use (NPP<sub>pot</sub>=NPP<sub>act</sub>+HANPP<sub>luc</sub>) for the given land-use class. Area-intensive values (in gC/m²/yr) can be calculated by dividing the NPP flows by the area of the respective land-use class per grid cell. HANPP in % of NPP<sub>pot</sub> can be calculated by summing up HANPP<sub>harv</sub> and HANPP<sub>luc</sub> and dividing it by NPP<sub>pot</sub>. Areas and NPP flows of land-use classes can be aggregated to calculate their overall HANPP.</p> <p>This Zenodo repository provides data on following land-use classes: cropland used for production of other oilcrops (CL-OOIL); coffee (CL-COFF); fodder crops (CL-FODD)</p>
Fornax3D Planetary Nebulae Catalouge: MUSE emission residual cubes
<p>The residual data cubes that contain the residual emission lines, focused on the [OIII] 5007 Angstrom region, resulting from passing MUSE data cubes through the GIST pipeline ( < v.2).</p> <p>Header contains all the information to run in the MUSE_PNe_fitting pipeline, which was developed to run on these residual data cubes. The full MUSE cubes can be obtained from the ESO data portal.</p>
Arctic cubed sphere configuration of MITgcm with 2-km resolution
<p>This repository includes a regional Arctic configuration of the Massachusetts Institute of Technology general circulation model (MITgcm,<a href="https://doi.org/10.1029/96JC02775">Marshall et al., 1997</a>; <a href="http://mitgcm.org/public/docs.html">MITgcm Group, 2017</a>) with a horizontal resolution of 2 km, which is described in <a href="https://doi.org/10.5194/tc-14-93-2020">Hutter & Losch (2020)</a>. It is based on a regional Arctic configuration (<a href="https://doi.org/10.1175/JPO-D-11-040.1">Nguyen et al., 2012</a>) of the MITgcm, which represents the Northern face of a global cubed sphere configuration. The number of vertical layers is reduced to 16, with the first 5 layers covering the uppermost 120 m to decrease the computational cost associated with the ocean model component. The Refined Topography dataset 2 (RTopo-2) (<a href="https://doi.org/10.1594/PANGAEA.856844">Schaffer and Timmermann, 2016</a>) is used as bathymetry for the entire model domain. The lateral boundary conditions are taken from the globally optimized ECCO-2 simulations (Menemenlis et al., 2008). The configuration is designed to use the 3-hourly Japanese 55-year Reanalysis (JRA-55, <a href="https://doi.org/10.2151/jmsj.2015-001">Kobayashi et al., 2015</a>) with a spatial resolution of 0.5625° for surface boundary conditions. The ocean temperature and salinity are initialized on 1 January, 1992, from the World Ocean Atlas 2005 (Locarnini et al., 2006; Antonov et al., 2006). The initial conditions for sea ice are taken from the Polar Science Center (<a href="http://doi.org/10.1029/2001JC001041">Zhang et al., 2003</a>). Ocean and sea ice parameterizations and parameters are directly taken from <a href="https://doi.org/10.1029/2010JC006573">Nguyen et al. (2011)</a>, with the ice strength P=2.264×104Nm−2. The configuration uses the classical discrimination of two ice classes: thin and thick ice (<a href="https://doi.org/10.1175/1520-0485(1979)009%3C0815:ADTSIM%3E2.0.CO;2">Hibler, 1979</a>). The momentum equations are solved by an iterative method and line successive relaxation (LSR) of the linearized equations following <a href="https://doi.org/10.1029/96JC03744">Zhang and Hibler (1997)</a>. In each time step (<span class="math-tex">\(\Delta\)</span>t=120 s), 10 nonlinear steps are made and the linear problem is iterated until an accuracy of 10e−5 is reached or 500 iterations are performed. With this configuration, simulations were run from 1 January 1992 to 31 December 2012. We also provide pickup files to restart the simulation in 2012.</p> <p>To reference this configuration please use the citation of this repository and reference to the paper describing the configuration:</p> <p>Hutter, N. and Losch, M.: Feature-based comparison of sea ice deformation in lead-permitting sea ice simulations, The Cryosphere, 14, 93–113, <a href="https://doi.org/10.5194/tc-14-93-2020">https://doi.org/10.5194/tc-14-93-2020</a>, 2020.</p>
Supplementary Information Materials for the G-Cubed submission by Zakharov et al.
<p>This is an upload for the purposes of review at the G-Cubed journal by AGU. The supporting information is provided for the MGL opal-CT, as well as the results of the SIMS and EMPA measurements. The Secondary Ion Probe Mass Spectrometry (SIMS) measurements are included as the .xslx table (Data Set S1) with analytical conditions, raw measurements and VSMOW-calibrated values. The Electron Microprobe (EMPA) analyses are provided in the .xslx file (Data Set S2). The Data Set S2 is separated by tabs for individual sample. Images feature the analyzed areas, including petrographic image, reflected light and the SIMS points.</p>
Low temperature magnetic properties of variably oxidized natural and synthetic siderite Dekkers et al., 2023, G-cubed
<p>The data magnetometric measurements performed on natural and synthetic iron carbonates presented in Dekkers et al. (2023) submitted to G-cubed. Details on the samples and methods can be found in the manuscript and the Additional Supporting Information</p> <p> </p>
Cube Plenoptic Dataset
<p>This dataset contains a test scene acquired with a raytrix R8 camera.</p> <p>Calibration images and subaperture views with their parameters are provided.<br> The focal of the main lens was 35mm, the aperture F/4 and the focus distance 700mm.<br> The nearest object is at 690mm and the farthest at 810mm (from sensor).</p>
HiSS-Cube: A scalable framework for Hierarchical Semi-Sparse Cube that preserves uncertainties
<p>This dataset is used for our framework HiSS-Cube, available at <a href="https://github.com/nadvornikjiri/HiSS-Cube">GitHub</a>. </p> <p>It includes the data folder, the generated HDF5 file (SDSS_cube_gzip.h5) and a contiguous stream export in FITS that can be visualized for example in TOPCAT (SDSS_cutout_export.fits).</p> <p>The data folder contains spectra and images from the SDSS DR14. The documentation for these can be found on the <a href="https://data.sdss.org/datamodel/files/BOSS_PHOTOOBJ/frames/RERUN/RUN/CAMCOL/frame.html">Frame</a> and <a href="https://data.sdss.org/datamodel/files/BOSS_SPECTRO_REDUX/RUN2D/spectra/PLATE4/spec.html">Spectra</a> pages, respectively.</p> <p>The SDSS_cube_gzip.h5 file contains a copy of the data ingested from the data folder optimized for both visualization and stream-lined contiguous access required for example by machine learning algorithms. The purpose is to visualize or run machine learning on combined spectra and images.</p> <p>The SDSS_cube_export.fits contains joined spectra with their respective image cutouts flattened to a table where every row represents one image pixel or spectral "pixel". To visualize these in TOPCAT, choose the 3D Cube plot and RA for X axis, Dec for Z axis and Wavelength or Time for Y axis. Go to the Form tab and choose the "aux" where you can enter either the Mean or Sigma axis as auxiliary.</p> <p> </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.