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5,155 results for “resolution”
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>
EMAG2: Earth Magnetic Anomaly Grid (2-arc-minute resolution) compressed for NumPy
<p>A compressed NumPy version of the <a href="https://www.ngdc.noaa.gov/geomag/emag2.html">EMAG2 (v3)</a> global Earth Magnetic anomaly grid compiled from satellite, ship, and airborne magnetic measurements. The original CSV data was imported, transformed, and saved to a compressed NumPy archive as follows:</p> <pre><code class="language-python">import numpy as np mag_data = np.loadtxt('EMAG2_V3_20170530.csv', delimiter=',', usecols=(2,3,4,5,7)) lon_mask = mag_data[:,0] > 180.0 mag_data[lon_mask,0] -= 360.0 np.savez_compressed('EMAG2_V3_20170530.npz', data=mag_data.astype(np.float32))</code></pre> <p>The NumPy archive (contained in this repository) can be efficiently loaded in Python workflows. It contains the following columns:</p> <ol> <li>Longitude - geographic longitudinal coordinates in decimal degrees (WGS84)</li> <li>Latitude - geographic latitudinal coordinates in decimal degrees (WGS84)</li> <li>SeaLevel - magnetic anomaly value at sea level (nT)</li> <li>UpCont - magnetic anomaly value at continuous 4km altitude (nT)</li> <li>Error - Error estimate (nT)</li> </ol> <p>Code 888 is assigned in certain cells on grid edges where the data source is ambiguous and assigned an error of -888 nT.<br> Code 999 is assigned in cells where no data is reported with the anomaly value assigned 99999 nT and an error of -999 nT.</p> <p><strong>Reference</strong></p> <p>Brian Meyer, Richard Saltus, Arnaud Chulliat (2017): EMAG2: Earth Magnetic Anomaly Grid (2-arc-minute resolution) Version 3. National Centers for Environmental Information, NOAA. Model. doi:10.7289/V5H70CVX</p>
Global monthly water temperature dataset, derived from dynamical 1-D water-energy routing model (DynWat) at 10 km spatial resolution
<pre>Global 10km spatial resolution water temperature dataset at the global scale for all major rivers, lakes and reservoirs. Data are provided at a monthly temporal resolution.</pre> <p>V1.1 update includes a improved version of the model removing some initial spikes related to rapid ice melt and streams that fall dry. The record has been reduced from 1981 tot 2014 to remove potential spinup impacts.</p> <p>The 1960-2010 data from v1.0 can be used for the earlier years.</p> <p>Consistent forcing is used for both time periods to remove potential biases that might occur otherwise.</p>
GRTSmh_diffres: the raster data source GRTSmaster_habitats converted to 9 hierarchical cell address levels at the corresponding lower resolution
<p>The <code>GRTSmh_diffres</code> data source file is a file collection, composed of nine monolayered GeoTIFF files of the <code>INT4S</code> datatype plus a GeoPackage with six polygon layers:</p> <ul> <li> <p>The polygon layers in the GeoPackage are the dissolved, polygonized versions of levels 4 to 9 of the <code>GRTSmh_brick</code> data source (<a href="https://doi.org/10.5281/zenodo.3354403">link</a>). This means that they provide the decimal (i.e. base 10) integer values of these <em>higher hierarchical levels</em> of the GRTS cell addresses of the raw data source <code>GRTSmaster_habitats</code> (<a href="https://doi.org/10.5281/zenodo.2682323">link</a>). Hence, the polygons are typically squares that correspond to the GRTS cell at the specified hierarchical level. The polygon layer is however restricted to the non-<code>NA</code> cells of the original <code>GRTSmaster_habitats</code> raster. Consequently, a part of the polygons is clipped along the Flemish border. Levels 1 to 3 are not provided for the whole of Flanders, because this would inflate the GPKG file. You can look at the <a href="https://github.com/inbo/n2khab-preprocessing/tree/ecadaf5">source code</a> to do such things.</p> </li> <li> <p>The GeoTIFF files provide the respective levels 1 to 9 of the <code>GRTSmh_brick</code> data source in a raster format, at the resolution that corresponds to the GRTS cell at the specified hierarchical level. The presence of <code>NA</code> cells around Flanders at level 0 implies that, with decreasing resolution, the raster's extent increases and larger areas outside Flanders are covered by non-<code>NA</code> cells along the border.</p> </li> </ul> <p>The higher-level ranking numbers (compared to the original level 0) allow spatially balanced samples at lower spatial resolution than that of 32 m, and can also be used for aggregation purposes.</p> <p>See R-code in the GitHub repository <a href="https://github.com/inbo/n2khab-preprocessing/tree/ecadaf54d4a5aa662d0d18fbfe59788732bb7182/src/generate_GRTS_30_GRTSmh_diffres">'n2khab-preprocessing' at commit ecadaf5</a> for the creation from the <code>GRTSmh_brick</code> data source.</p> <p>A reading function to return the data source in a standardized way into the R environment is provided by the R-package <a href="https://inbo.github.io/n2khab/">n2khab</a>.</p> <p>Beware that not all GRTS ranking numbers at the specified level are provided, as the original GRTS raster has been clipped with the Flemish outer borders (i.e., not excluding the Brussels Capital Region).</p>
GRTSmh_brick: the raster data source GRTSmaster_habitats converted to 10 hierarchical cell address levels at the original resolution
<p>The data source file is a 10-layered GeoTIFF file, derived from the raster data source <code>GRTSmaster_habitats</code> (<a href="https://doi.org/10.5281/zenodo.2682323">link</a>). Both GeoTIFFs (<code>GRTSmaster_habitats</code>, <code>GRTSmh_brick</code>) use the <code>INT4S</code> datatype. The <code>GRTSmh_brick</code> data source (resolution 32 m) holds the decimal integer ranking numbers of 10 hierarchical levels of the GRTS cell addresses, including the one from <code>GRTSmaster_habitats</code> (with GRTS cell addresses at the resolution level).</p> <p>See R-code in the GitHub repository <a href="https://github.com/inbo/n2khab-preprocessing/tree/ecadaf54d4a5aa662d0d18fbfe59788732bb7182/src/generate_GRTS_20_GRTSmh_brick">'n2khab-preprocessing' at commit ecadaf5</a> for its creation from the <code>GRTSmaster_habitats</code> data source.</p> <p>A reading function to return the data source in a standardized way into the R environment is provided by the R-package <a href="https://inbo.github.io/n2khab/">n2khab</a>.</p> <p>The higher-level ranking numbers of the RasterBrick allow spatially balanced samples at lower spatial resolution than that of 32 m, and can also be used for aggregation purposes. The provided hierarchical levels correspond to the resolutions vector <code>32 * 2^(0:9)</code> (minimum: 32 meters, maximum: 16384 meters).</p> <p>Beware that not all GRTS ranking numbers are present in the data source, as the original GRTS raster has been clipped with the Flemish outer borders (i.e., not excluding the Brussels Capital Region).</p>
High-Resolution Quantitative Phase Imaging of Plasmonic Metasurfaces with Sensitivity down to a Single Nanoantenna_experimental dataset
<p>This dataset shares the data presented in the paper "Geometric-phase microscopy for high-resolution quantitative phase imaging of plasmonic metasurfaces with sensitivity down to a single nanoantenna" available in open access under <a href="https://doi.org/10.5281/zenodo.3355170">10.5281/zenodo.3355170</a>. The archive contains experimental files titled with references to the figures as they appear in the paper. </p>
Representative Sample Dataset for Resolution-Agnostic Tissue Segmentation in Whole-Slide Histopathology Images
<p>This is a representative sample from the dataset that was used to develop resolution-agnostic convolutional neural networks for tissue segmentation1 in whole-slide histopathology images.</p> <p>The dataset is composed of two parts: <strong>development set</strong> and <strong>dissimilar set</strong>.</p> <p>Sample images from the development set:</p> <ul> <li>breast_hne_00.tif</li> <li>breast_lymph_node_hne_00.tif</li> <li>tongue_ae1ae3_00.tif</li> <li>tongue_hne_00.tif</li> <li>tongue_ki67_00.tif</li> </ul> <p>Sample images from the dissimilar set:</p> <ul> <li>brain_alcianblue_00.tif</li> <li>cornea_grocott_00.tif</li> <li>kidney_cab_00.tif</li> <li>skin_perls_00.tif</li> <li>uterus_vonkossa_00.tif</li> </ul>
Replication Data for: Determination of Intrinsic Effective Fields and Microwave Polarizations by High-Resolution Spectroscopy of Single NV Center Spins
<p>Data repository for: <strong>Determination of Intrinsic Effective Fields and Microwave Polarizations by High-Resolution Spectroscopy of Single NV Center Spins</strong></p> <p><em>Data description.pdf</em> describes the uploaded data.<br> <em>Data.xlsx</em> is the data represented in the paper.<br> <em>Esrfit_Npeak.m</em>, <em>Esrfit_xN.m</em>, <em>GaussianFunc.m</em>, <em>Gaussian_xN_Func.m</em>, <em>Lorentz_Func.m</em>, <em>Lorentz_xN_Func.m</em>, <em>Rabifit_xN.m</em>, <em>Rabi_xN_Func.m</em>, <em>FourierTransformRabi.m</em> are Matlab code files to transform and fit the data.</p>
Source Data and Scripts - MultiMatch: Geometry-Informed Colocalization in Multi-Color Super-Resolution Microscopy
<p>Experimental and simulated STED data and scripts associated with Naas et al. "<em>MultiMatch: Geometry-Informed Colocalization in Multi-Color Super-Resolution Microscopy.</em>" <em>bioRxiv</em> (2024): 2024-02. </p> <p>The MultiMatch Python package and further illustrative examples are available on GitHub repository <a href="https://github.com/gnies/multi_match">https://github.com/gnies/multi_match</a>.</p>
D-PLACE dataset derived from Wessel and Smith 2015 'Global Self-consistent, Hierarchical, High-resolution Geography Database'
<p>Cite the source of the dataset as:</p> <blockquote> <p>Wessel, P., and W. H. F. Smith (1996), A global, self-consistent, hierarchical, high-resolution shoreline database, J. Geophys. Res., 101(B4), 8741–8743, doi:10.1029/96JB00104. Wessel P, Smith, W. H. F. Global Self-consistent, Hierarchical, High-resolution Geography Database (GSHHS) v2.3.4 [Internet]. 2015. Available: https://www.ngdc.noaa.gov/mgg/shorelines/gshhs.html</p> </blockquote>
Ultra-high Resolution Land Use Data Set of Typical Villages in Northeastern Tibetan Plateau
<p>This dataset was collected by a research team during a field investigation in the Hehuang Valley of Qinghai Province from July to August 2022. Using the DJI Mavic2pro equipped with a Hasselblad L1D-20c camera, 55 typical villages were selected in the Hehuang Valley and over 4600 aerial photographs were obtained using drone photogrammetry technology as raw data. Using AgisfphotoScan 1.25 software to synthesize orthophoto images with a spatial resolution of 0.05m. The vector data of human settlement boundaries in villages was extracted through visual interpretation. Based on the object-oriented human-machine interaction interpretation method, 55 typical village land use datasets in 2022 were obtained (including forests, grasslands, forest land, cultivated land, water bodies, roads, unused land, and building land, totaling 8 categories). By establishing 1050 sample points and using confusion matrix analysis, it was found that the overall accuracy of the dataset was 96.86%, with a Kappa coefficient of 0.95. It can accurately reflect the spatial form, land use composition, and surrounding environment of typical villages. Aerial photographs all have longitude, latitude, and altitude information, providing ultra-high resolution data sources for village spatial structure analysis, land use mapping, and analysis work, effectively assisting in the improvement of human housing and rural revitalization strategies.</p>
Using variable-resolution grids to model precipitation from atmospheric rivers around the Greenland ice sheet
<p>This dataset can be used to reproduce the figures created in Waling et al. 2024, "Using variable-resolution grids to model precipitation from atmospheric rivers around the Greenland ice sheet." Each figure has its own script which can be executed.<br><br></p>
GPC/m: Global Precipitation Climatology by Machine Learning; Quasi-global, Daily, and One Degree Spatial Resolution
<p>A precipitation dataset, Global Precipitation Climatology by Machine Learning (ML), GPC/m, is released.</p> <p>This new precipitation dataset has been produced by machine learning, which is daily from 1979 to 2020 (will be to present), 1° × 1° spatial resolution. Three ML methods are used. Data is produced from outgoing longwave radiation (OLR) and atmospheric circulation from reanalysis. You can download this with DOI.</p> <p>This daily precipitation dataset has been produced by machine learning (ML) methods using satellite observations and atmospheric circulations from reanalysis. The quasi-global daily precipitation dataset has been around for 42 years from 1979 to 2020, which will be updated to the present. The spatial resolution is 1° × 1° zonally global and from 40°S to 50°N. The ML methods are supervised learning, and the reference data are estimated precipitation datasets from 2001 to the present. The input data are somewhat modified based on knowledge of the climatological background. Using the trained statistical models, we predict back to 1979, when daily precipitation data was almost unavailable globally. For now, this GPC/m precipitation dataset version is GPC/m-v1-2024. This data will be updated in the future with added value. The purpose of this dataset is a challenge to produce a climatological dataset by reducing artificial gaps as much as possible for discussion of climatology, climate variability, and climate change. This dataset is very useful for statistical analysis, such as composite analysis and correlation analysis. Disadvantages should also be understood in the description paper (Takahashi, 2024c). Also, I hope that this dataset can contribute to improving the current precipitation datasets, which are based on physical or researcher-explaining algorithms.<br><br>To facilitate analysis of the dataset, it is distributed in Network Common Data Form (netCDF) format and the Grid Analysis and Display System (GrADS) format (with control file). If you would like recently updated data, please contact the creator. If it has already been created, it can be distributed.<br><br><em>Added on September 18, 2024.</em><br>More details are in the preprint paper at this link (<a href="https://doi.org/10.48550/arXiv.2409.09639">Takahashi, 2024, https://doi.org/10.48550/arXiv.2409.09639</a>).</p> <p><em>Added on March 4, 2025.</em><br><strong>Alternative Download Options</strong><br>If you experience slow download speeds from Zenodo, alternative mirrors are available for the dataset files.<br><em><span>However, we kindly request you to download the .ctl file from Zenodo for tracking purposes.</span></em><br>Download NetCDF (.nc) or Binary (.bin) from:<br><a href="https://camo.fpark.tmu.ac.jp/gpcm.html">https://camo.fpark.tmu.ac.jp/gpcm.html</a></p>
High-resolution water budget estimates over the Po basin: progress towards digital replicas (OL*, DAg(*), DAs(*), DAgs(*))
<p>NASA LIS output with water budget variables at 0.7 km^2 resolution over the Po river basin (Italy) for 2015-2023. Netcdf files for 8 + 2 experiments, described in De Lannoy et al. (2024, JAMES). Because of storage limitations, this upload contains 7 of the 8 experiments with ERA5. The baseline OL experiment without irrigation is on a separate zenodo link (see below).</p> <p><strong>8 experiments forced with ERA5 meteorology</strong></p> <p>po_ol_hymap_noirr: (OL) open loop simulation, no irrigation modeling --> 10.5281/zenodo.13768739<br>po_ol_hymap_irr: (OL*) open loop simulation, with irrigation modeling</p> <p>po_da_hymap_gamma_noirr: (DAg) data assimilation of Sentinel-1 backscatter (VV) for soil moisture updating, no irrigation modeling<br>po_da_hymap_gamma_irr: (DAg*) data assimilation of Sentinel-1 backscatter (VV) for soil moisture updating, with irrigation modeling</p> <p>po_da_hymap_snd_noirr: (DAs) data assimilation of Sentinel-1 snow depth retrievals, no irrigation modeling<br>po_da_hymap_snd_irr: (DAs*) data assimilation of Sentinel-1 snow depth retrievals, with irrigation modeling</p> <p>po_da_hymap_gamma_snd_noirr: (DAgs) data assimilation of Sentinel-1 backscatter (VV) for soil moisture updating and assimilation of snow depth retrievals, no irrigation modeling<br>po_da_hymap_gamma_snd_irr: (DAgs*) data assimilation of Sentinel-1 backscatter (VV) for soil moisture updating and assimilation of snow depth retrievals, with irrigation modeling</p> <p><strong>2 experiments forced with MERRA2 meteorology </strong></p> <p><strong>--> These are not provided on Zenodo, because we hit the maximum storage limit. Feel free to reach out to the authors and ask for these data.<br></strong></p> <p>po_ol_hymap_noirr_M2: open loop simulation, no irrigation modeling<br>po_ol_hymap_irr_M2: open loop simulation, with irrigation modeling </p>
High-resolution water budget estimates over the Po basin: progress towards digital replicas (OL): open loop without irrigation
<p>NASA LIS output with water budget variables at 0.7 km^2 resolution over the Po river basin (Italy) for 2015-2023. Netcdf files for 8 + 2 experiments, described in De Lannoy et al. (2024, JAMES). Because of storage limitations, this upload only contains the baseline open loop without irrigation (OL), i.e. 1 of the 8 experiments with ERA5. The other 7 experiments are on a separate zenodo link (see below).</p> <p><strong>8 experiments forced with ERA5 meteorology</strong></p> <p>po_ol_hymap_noirr: (OL) open loop simulation, no irrigation modeling <br>po_ol_hymap_irr: (OL*) open loop simulation, with irrigation modeling --> 10.5281/zenodo.13754454</p> <p>po_da_hymap_gamma_noirr: (DAg) data assimilation of Sentinel-1 backscatter (VV) for soil moisture updating, no irrigation modeling --> 10.5281/zenodo.13754454<br>po_da_hymap_gamma_irr: (DAg*) data assimilation of Sentinel-1 backscatter (VV) for soil moisture updating, with irrigation modeling --> 10.5281/zenodo.13754454</p> <p>po_da_hymap_snd_noirr: (DAs) data assimilation of Sentinel-1 snow depth retrievals, no irrigation modeling --> 10.5281/zenodo.13754454<br>po_da_hymap_snd_irr: (DAs*) data assimilation of Sentinel-1 snow depth retrievals, with irrigation modeling --> 10.5281/zenodo.13754454</p> <p>po_da_hymap_gamma_snd_noirr: (DAgs) data assimilation of Sentinel-1 backscatter (VV) for soil moisture updating and assimilation of snow depth retrievals, no irrigation modeling --> 10.5281/zenodo.13754454<br>po_da_hymap_gamma_snd_irr: (DAgs*) data assimilation of Sentinel-1 backscatter (VV) for soil moisture updating and assimilation of snow depth retrievals, with irrigation modeling --> 10.5281/zenodo.13754454</p> <p><strong>2 experiments forced with MERRA2 meteorology </strong></p> <p><strong>--> These are not provided on Zenodo, because we hit the maximum storage limit. Feel free to reach out to the authors and ask for these data.<br></strong></p> <p>po_ol_hymap_noirr_M2: open loop simulation, no irrigation modeling<br>po_ol_hymap_irr_M2: open loop simulation, with irrigation modeling</p>
Supplementary Material for "A comparative high-resolution spectroscopic analysis of in situ and accreted globular clusters"
<p>This is a file containing supplementary material for the paper <em>A comparative high-resolution spectroscopic analysis of in situ and accreted globular clusters.</em> For each star in target globular clusters, it lists crucial information on the linelist analyzed. In particular:</p> <ol> <li>Star ID.</li> <li>Chemical element.</li> <li>Wavelength.</li> <li>log <em>gf</em></li> <li>Excitation potential.</li> <li>Measured equivalent width with uncertaintiy.</li> </ol>
Global 32-4 km variable-resolution mesh for the MPAS-Atmosphere model
<div> <div> <div>This mesh was created by a collaboration between the Department of Energy’s Water Cycle and Climate Extremes Modeling (WACCEM) project and the</div> <div>Mesoscale and Microscale Meteorology (MMM) Laboratory at the National Science Foundation National Center for Atmospheric Research (NSF/NCAR).</div> <div> </div> <div>The mesh contains 1,830,914 horizontal grid cells. The circular refinement region has a radius of approximately 20 degrees. The 4-32km grid-spacing range aims to achieve convection-permitting resolution in the high-resolution domain and resolution sufficient for the jet stream and mid-latitude wave activities (Lu et al., 2015) in the low-resolution domain.</div> <div> </div> <div>The netcdf file "x8.1830914.grid.nc" includes the variables defining the global unstructured grid for the MPAS model as described in the <a href="https://mpas-dev.github.io/files/documents/MPAS-MeshSpec.pdf">MPAS Mesh Specification</a>. Following the other MPAS mesh data, we provide graph.info.part.* files necessary for the Message Passing Interface (MPI) parallelism. For example, a simulation using 1024 MPI ranks will use graph.info.part.1024. For the number of MPI tasks not provided in this dataset, a user needs to create a new partitioning file using the METIS tool and the graph.info file as described in the MPAS user guide (available <a href="https://mpas-dev.github.io/atmosphere/atmosphere_download.html" target="_blank" rel="noopener">here</a>).</div> <div> </div> </div> </div> <div>This data will also be available from the <a href="https://mpas-dev.github.io/atmosphere/atmosphere_meshes.html">MPAS mesh website</a>.</div> <div> </div> <div>The mesh generation is supported by the U.S. Department of Energy Office of Science Biological and Environmental Research (BER) as part of the Regional and Global Model Analysis Program Area. We acknowledge the use of computational resources of the National Energy Research Scientific Computing Center (NERSC). The Pacific Northwest National Laboratory is operated for the Department of Energy by Battelle Memorial Institute under contract DE-AC05-76RL01830.</div>
Vertical cloud radiative heating from the EC-Earth3 PRIMAVERA high-resolution model part 2 (of 2)
<p>The h5 files contain variables used for the article “Vertical structure of cloud radiative heating in the tropics: confronting the EC-Earth v3.3.1/3P model with satellite observations”. The experiment was done with the EC-Earth3 model (https://portal.enes.org/models/earthsystem-models/ec-earth/ec-earth). The model run was done in atmosphere-only mode with prescribed SST for the years 2005 - 2010. Year 2005 and 2006 are considered as spin-up time and are therefore not included. The experiment is described in more detail in the article. </p> <p>The files are named as follows: model-version_resolution_year_month_variable..h5<br> The following variables (named after ECMWF Parameter database) from the model output are included in the dataset:<br> - 107: Top of the atmosphere (TOA) SW radiation (all sky)<br> - 108: TOA SW radiation (clear sky)<br> - 109: TOA SW radiation (cloudy sky)<br> - 110: TOA LW radiation (all sky)<br> - 130: Temperature<br> - 133: Specific humidity<br> - 246: Specific cloud liquid water content<br> - 247: Specific cloud ice water content<br> - 248: Fraction of cloud cover<br> - 54: Pressure</p> <p>The latlon.h5 file contains the latitude and longitude for the dataset. <br> <br> To get the complete dataset for the experiment, see:<br> DOI: 10.5281/zenodo.3958826 for the radiation part of the PRIMAVERA high-resolution data.<br> DOI: 10.5281/zenodo.3960087 for the humidity part of the PRIMAVERA high-resolution data.<br> DOI: 10.5281/zenodo.3947700 for the PRIMAVERA standard-resolution data.<br> DOI: 10.5281/zenodo.3981154 for the V3.3.1 standard-resolution data.<br> DOI: 10.5281/zenodo.4734468 for the python code.</p>
Vertical cloud radiative heating from the EC-Earth3 PRIMAVERA standard-resolution model
<p>The h5 files contain variables used for the article “Vertical structure of cloud radiative heating in the tropics: confronting the EC-Earth v3.3.1/3P model with satellite observations”. The experiment was done with the EC-Earth3 model (https://portal.enes.org/models/earthsystem-models/ec-earth/ec-earth). The model run was done in atmosphere-only mode with prescribed SST for the years 2005 - 2010. Year 2005 and 2006 are considered as spin-up time and are therefore not included. The experiment is described in more detail in the article. </p> <p>The files are named as follows: model-version_resolution_year_month_variable..h5<br> The following variables (named after ECMWF Parameter database) from the model output are included in the dataset:<br> - 107: Top of the atmosphere (TOA) SW radiation (all sky)<br> - 108: TOA SW radiation (clear sky)<br> - 109: TOA SW radiation (cloudy sky)<br> - 110: TOA LW radiation (all sky)<br> - 130: Temperature<br> - 133: Specific humidity<br> - 246: Specific cloud liquid water content<br> - 247: Specific cloud ice water content<br> - 248: Fraction of cloud cover<br> - 54: Pressure</p> <p>The latlon.h5 file contains the latitude and longitude for the dataset. <br> <br> To get the complete dataset for the experiment, see:<br> DOI: 10.5281/zenodo.3958826 for the radiation part of the PRIMAVERA high-resolution data.<br> DOI: 10.5281/zenodo.3960087 for the humidity part of the PRIMAVERA high-resolution data.<br> DOI: 10.5281/zenodo.3947700 for the PRIMAVERA standard-resolution data.<br> DOI: 10.5281/zenodo.3981154 for the V3.3.1 standard-resolution data.<br> DOI: 10.5281/zenodo.4734468 for the python code.</p>
Vertical cloud radiative heating from the EC-Earth3 PRIMAVERA high-resolution model part 1 (of 2)
<p>The h5 files contain variables used for the article “Vertical structure of cloud radiative heating in the tropics: confronting the EC-Earth v3.3.1/3P model with satellite observations”. The experiment was done with the EC-Earth3 model (https://portal.enes.org/models/earthsystem-models/ec-earth/ec-earth). The model run was done in atmosphere-only mode with prescribed SST for the years 2005 - 2010. Year 2005 and 2006 are considered as spin-up time and are therefore not included. The experiment is described in more detail in the article. </p> <p>The files are named as follows: model-version_resolution_year_month_variable..h5<br> The following variables (named after ECMWF Parameter database) from the model output are included in the dataset:<br> - 107: Top of the atmosphere (TOA) SW radiation (all sky)<br> - 108: TOA SW radiation (clear sky)<br> - 109: TOA SW radiation (cloudy sky)<br> - 110: TOA LW radiation (all sky)<br> - 130: Temperature<br> - 133: Specific humidity<br> - 246: Specific cloud liquid water content<br> - 247: Specific cloud ice water content<br> - 248: Fraction of cloud cover<br> - 54: Pressure</p> <p>The latlon.h5 file contains the latitude and longitude for the dataset. <br> <br> To get the complete dataset for the experiment, see:<br> DOI: 10.5281/zenodo.3958826 for the radiation part of the PRIMAVERA high-resolution data.<br> DOI: 10.5281/zenodo.3960087 for the humidity part of the PRIMAVERA high-resolution data.<br> DOI: 10.5281/zenodo.3947700 for the PRIMAVERA standard-resolution data.<br> DOI: 10.5281/zenodo.3981154 for the V3.3.1 standard-resolution data.<br> DOI: 10.5281/zenodo.4734468 for the python code.</p>
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.