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25 results for “postprocessing”
A uniaxial hysteretic superelastic constitutive model applied to additive manufactured lattices - data and postprocessing tools
<p>This data set contains all result data obtained during the implementation of an uniaxial hysteretic superelastic constitutive model and its application to additive manufactured lattices.</p> <p>Furthermore, it contains all ABAQUS .inp files, the implemented subroutine of the hysteretic superelastic constitutive model, diagrams generated from the data, as well as postprocessing tools for generating the diagrams.</p>
Re-postprocessed POSYDON v1.0 dataset compatible with code release v2.0.0-pre1
<p>This dataset includes the downsampled data from the single- and binary-star models grids, as well as the trained classification and interpolation models, initially released with POSYDON v1 (see <a href="https://ui.adsabs.harvard.edu/abs/2023ApJS..264...45F/abstract">Fragos et al. 2023</a>), re-postprocessed to be compatible with POSYDON code release <strong>v2.0.0-pre1</strong> (see <a href="https://ui.adsabs.harvard.edu/abs/2024arXiv241102376A/abstract">Andrews et al. 2024</a>). This data release included the fix for reverse mass transfer introduced by <a href="https://ui.adsabs.harvard.edu/abs/2024A%26A...683A.144X/abstract">Xing et al. (2023)</a>. </p> <p>If you use this dataset, please cite the following papers:<br><a href="https://ui.adsabs.harvard.edu/abs/2023ApJS..264...45F/abstract">Fragos et al. (2023), The Astrophysical Journal Supplement Series, Volume 264, Issue 2, id.45, 46 pp.</a><br><a href="https://ui.adsabs.harvard.edu/abs/2024A%26A...683A.144X/abstract">Xing et al. (2023), Astronomy & Astrophysics, Volume 683, id.A144, 17 pp.</a><br><a href="https://ui.adsabs.harvard.edu/abs/2024arXiv241102376A/abstract">Andrews et al. (2024), eprint arXiv:2411.02376</a><br><br></p>
RACMO regional climate model data, postprocessed for winter precipitation and winter temperature
<p>This contains statistics of winter precipitation and winter temperature derived from the 16 model ensemble by RACMO2. In addition to the GCM driven runs, also a PGW (pseudo global warming) set is given. Data is used for a paper to be submitted.</p> <p>Reference on the RACMO2 runs: Aalbers EE, Lenderink G, van Meijgaard E, van den Hurk BJJM (2018) Local-scale changes in mean and heavy precipitation in Western Europe, climate change or internal variability? Climate Dynamics 50:4745–4766. <a href="https://doi.org/10.1007/s00382-017-3901-9">https://doi.org/10.1007/s00382-017-3901-9</a></p>
EUPPBench postprocessing benchmark dataset - gridded data - Part III
<p>The EUMETNET EUPPBench postprocessing benchmark gridded data is an analysis-ready dataset to perform benchmarks of different postprocessing methods on a common dataset.</p> <p>This dataset is using the <a href="https://zarr.dev/">Zarr</a> format. Please look at the <a href="https://zarr.readthedocs.io/en/stable/">Zarr documentation</a> to see how to load and access the data.</p> <p>The documentation of the dataset is available on <a href="https://eupp-benchmark.github.io/EUPPBench-doc/">https://eupp-benchmark.github.io/EUPPBench-doc/</a> .</p> <p>The official way to download the dataset is through the <a href="https://github.com/ecmwf/climetlab">climetlab</a> <a href="https://github.com/EUPP-benchmark/climetlab-eumetnet-postprocessing-benchmark">EUMETNET postprocessing benchmark plugin</a>.</p> <p>This Zenodo repository aims to preserve the dataset by providing long-term storage.</p> <p>Please read the LICENSE file for more information on the data licenses.</p> <p><strong>Installation procedure</strong></p> <p>Download the 3 parts of the dataset</p> <ol> <li> <a href="https://doi.org/10.5281/zenodo.7429236">EUPPBench-gridded.part.z01</a></li> <li> <a href="http://Remark You might also be interested by the gridded data part of this dataset also available on Zenodo here: https://doi.org/10.5281/zenodo.7428239">EUPPBench-gridded.part.z02</a></li> <li> <a href="https://doi.org/10.5281/zenodo.7429917">EUPPBench-gridded.part.zip</a></li> </ol> <p>in a given folder, and on a Linux (or mac OS) terminal, and still in this folder, enter the following commands</p> <pre><code class="language-bash">zip -FF EUPPBench-gridded.part.zip --out EUPPBench-gridded.zip rm EUPPBench-gridded.part.* unzip EUPPBench-gridded.zip </code></pre> <p>This will unpack the dataset. You need at least 250Gb of free space on your disk to perform this operation.</p> <p><strong>Citation</strong></p> <p>If you use this dataset for a publication, please cite the dataset article:</p> <ul> <li>Demaeyer, J., Bhend, J., Lerch, S., Primo, C., Van Schaeybroeck, B., Atencia, A., Ben Bouallègue, Z., Chen, J., Dabernig, M., Evans, G., Faganeli Pucer, J., Hooper, B., Horat, N., Jobst, D., Merše, J., Mlakar, P., Möller, A., Mestre, O., Taillardat, M., and Vannitsem, S.: The EUPPBench postprocessing benchmark dataset v1.0, Earth Syst. Sci. Data Discuss. [preprint], <a href="https://doi.org/10.5194/essd-2022-465">https://doi.org/10.5194/essd-2022-465</a>, in review, 2023.</li> </ul> <p><strong>Remark</strong></p> <p>You might also be interested by the station data part of this dataset also available on Zenodo here: <a href="https://doi.org/10.5281/zenodo.7708362">https://doi.org/10.5281/zenodo.7708362</a>.</p>
EUPPBench postprocessing benchmark dataset - gridded data - Part II
<p>The EUMETNET EUPPBench postprocessing benchmark gridded data is an analysis-ready dataset to perform benchmarks of different postprocessing methods on a common dataset.</p> <p>This dataset is using the <a href="https://zarr.dev/">Zarr</a> format. Please look at the <a href="https://zarr.readthedocs.io/en/stable/">Zarr documentation</a> to see how to load and access the data.</p> <p>The documentation of the dataset is available on <a href="https://eupp-benchmark.github.io/EUPPBench-doc/">https://eupp-benchmark.github.io/EUPPBench-doc/</a> .</p> <p>The official way to download the dataset is through the <a href="https://github.com/ecmwf/climetlab">climetlab</a> <a href="https://github.com/EUPP-benchmark/climetlab-eumetnet-postprocessing-benchmark">EUMETNET postprocessing benchmark plugin</a>.</p> <p>This Zenodo repository aims to preserve the dataset by providing long-term storage.</p> <p>Please read the LICENSE file for more information on the data licenses.</p> <p><strong>Installation procedure</strong></p> <p>Download the 3 parts of the dataset</p> <ol> <li> <a href="https://doi.org/10.5281/zenodo.7429236">EUPPBench-gridded.part.z01</a></li> <li> <a href="https://doi.org/10.5281/zenodo.7429420">EUPPBench-gridded.part.z02</a></li> <li> <a href="https://doi.org/10.5281/zenodo.7429917">EUPPBench-gridded.part.zip</a></li> </ol> <p>in a given folder, and on a Linux (or mac OS) terminal, and still in this folder, enter the following commands</p> <pre><code class="language-bash">zip -FF EUPPBench-gridded.part.zip --out EUPPBench-gridded.zip rm EUPPBench-gridded.part.* unzip EUPPBench-gridded.zip </code></pre> <p>This will unpack the dataset. You need at least 250Gb of free space on your disk to perform this operation.</p> <p> </p> <p><strong>Citation</strong></p> <p>If you use this dataset for a publication, please cite the dataset article:</p> <ul> <li>Demaeyer, J., Bhend, J., Lerch, S., Primo, C., Van Schaeybroeck, B., Atencia, A., Ben Bouallègue, Z., Chen, J., Dabernig, M., Evans, G., Faganeli Pucer, J., Hooper, B., Horat, N., Jobst, D., Merše, J., Mlakar, P., Möller, A., Mestre, O., Taillardat, M., and Vannitsem, S.: The EUPPBench postprocessing benchmark dataset v1.0, Earth Syst. Sci. Data Discuss. [preprint], <a href="https://doi.org/10.5194/essd-2022-465">https://doi.org/10.5194/essd-2022-465</a>, in review, 2023.</li> </ul> <p><strong>Remark</strong></p> <p>You might also be interested by the station data part of this dataset also available on Zenodo here: <a href="https://doi.org/10.5281/zenodo.7708362">https://doi.org/10.5281/zenodo.7708362</a>.</p>
EUPPBench postprocessing benchmark dataset - gridded data - Part I
<p>The EUMETNET EUPPBench postprocessing benchmark gridded data is an analysis-ready dataset to perform benchmarks of different postprocessing methods on a common dataset.</p> <p>This dataset is using the <a href="https://zarr.dev/">Zarr</a> format. Please look at the <a href="https://zarr.readthedocs.io/en/stable/">Zarr documentation</a> to see how to load and access the data.</p> <p>The documentation of the dataset is available on <a href="https://eupp-benchmark.github.io/EUPPBench-doc/">https://eupp-benchmark.github.io/EUPPBench-doc/</a> .</p> <p>The official way to download the dataset is through the <a href="https://github.com/ecmwf/climetlab">climetlab</a> <a href="https://github.com/EUPP-benchmark/climetlab-eumetnet-postprocessing-benchmark">EUMETNET postprocessing benchmark plugin</a>.</p> <p>This Zenodo repository aims to preserve the dataset by providing long-term storage.</p> <p>Please read the LICENSE file for more information on the data licenses.</p> <p><strong>Installation procedure</strong></p> <p>Download the 3 parts of the dataset</p> <ol> <li><a href="https://doi.org/10.5281/zenodo.7429236">EUPPBench-gridded.part.z01</a></li> <li><a href="https://doi.org/10.5281/zenodo.7429420">EUPPBench-gridded.part.z02</a></li> <li><a href="https://doi.org/10.5281/zenodo.7429917">EUPPBench-gridded.part.zip</a></li> </ol> <p>in a given folder, and on a Linux (or mac OS) terminal, and still in this folder, enter the following commands</p> <pre><code class="language-bash">zip -FF EUPPBench-gridded.part.zip --out EUPPBench-gridded.zip rm EUPPBench-gridded.part.* unzip EUPPBench-gridded.zip</code></pre> <p>This will unpack the dataset. You need at least 250Gb of free space on your disk to perform this operation.</p> <p><strong>Citation</strong></p> <p>If you use this dataset for a publication, please cite the dataset article:</p> <ul> <li>Demaeyer, J., Bhend, J., Lerch, S., Primo, C., Van Schaeybroeck, B., Atencia, A., Ben Bouallègue, Z., Chen, J., Dabernig, M., Evans, G., Faganeli Pucer, J., Hooper, B., Horat, N., Jobst, D., Merše, J., Mlakar, P., Möller, A., Mestre, O., Taillardat, M., and Vannitsem, S.: The EUPPBench postprocessing benchmark dataset v1.0, Earth Syst. Sci. Data Discuss. [preprint], <a href="https://doi.org/10.5194/essd-2022-465">https://doi.org/10.5194/essd-2022-465</a>, in review, 2023.</li> </ul> <p> </p> <p><strong>Remark</strong></p> <p>You might also be interested by the station data part of this dataset also available on Zenodo here: <a href="https://doi.org/10.5281/zenodo.7708362">https://doi.org/10.5281/zenodo.7708362</a>.</p>
Data set for the ensemble postprocessing of 2m surface temperature forecasts in Germany for 24 hours lead time
<p>Full data set for the ensemble postprocessing of 2m surface temperature forecasts at 462 observation stations in Germany for 24 hours lead time in the years 2015-2020. The data set is provided in .Rdata format supported by the statistical software <a href="https://www.r-project.org">R</a>. The ensemble forecasts are retrieved from <a href="https://www.ecmwf.int">ECMWF</a> and the observation data from the <a href="https://opendata.dwd.de/climate_environment/CDC/observations_germany/climate/hourly/air_temperature/historical/BESCHREIBUNG_obsgermany_climate_hourly_tu_historical_de.pdf">German Weather Service</a> (<a href="https://www.dwd.de/">DWD</a>). <br> <br> For more information about the data set see: <a href="https://github.com/jobstdavid/paper_gamvinereg">https://github.com/jobstdavid/paper_gamvinereg</a></p>
Data set for the ensemble postprocessing of 2m surface temperature forecasts in Germany for five different lead times
<p>Full data set for the ensemble postprocessing of 2m surface temperature forecasts at 462 observation stations in Germany for the lead times 24, 48, 72, 96 and 120 hours in the years 2015-2020. The data set is provided in .RData format supported by the statistical software <a href="https://www.r-project.org">R</a>. The ensemble forecasts are retrieved from <a href="https://www.ecmwf.int">ECMWF</a> and the observation data from the <a href="https://opendata.dwd.de/climate_environment/CDC/observations_germany/climate/hourly/air_temperature/historical/BESCHREIBUNG_obsgermany_climate_hourly_tu_historical_de.pdf">German Weather Service</a> (<a href="https://www.dwd.de/">DWD</a>). <br> <br> For more information about the data set see: <a href="https://github.com/jobstdavid/paper_tsEMOS">https://github.com/jobstdavid/paper_tsEMOS</a></p>
All-optical Supercontinuum Switching (data and postprocessing script)
<p>Data archive for the article</p> <p>"All-optical Supercontinuum Switching"</p> <p>Oliver Melchert (1,2,3), Carsten Brée (4), Ayhan Tajalli (2), Alexander Pape (2), Rostislav Arkhipov (5), Stephanie Willms (1,2), Ihar Babushkin (1,2), Dmitry Skryabin (6), Günter Steinmeyer (7,8), Uwe Morgner (1,2,3), and Ayhan Demircan (1,2,3)</p> <p>1. Cluster of Excellence PhoenixD, Welfengarten 1, 30167, Hannover, Germany<br> 2. Institute of Quantum Optics, Leibniz University Hannover, Welfengarten 1 30167, Hannover, Germany<br> 3. Hannover Centre for Optical Technologies, Nienburgerstr. 17, 30167, Hannover, Germany<br> 4. Weierstraß Institute for Applied Analysis and Stochastics, Mohrenstraße 39, 10117 Berlin, Germany<br> 5. St. Petersburg State University, Universitetskaya nab. 7/9, St. Petersburg 199034, Russia<br> 6. Department of Physics, University of Bath, Bath, BA2 7AY, UK<br> 7. Max-Born-Institute (MBI), Max-Born-Str. 2a, 12489 Berlin<br> 8. Institut für Physik, Humboldt-Universität zu Berlin, Newtonstraße 15, 12489 Berlin, Germany</p> <p>This repository contains data from an experiment and numerical simulations, allowing to reproduce a draft version of Figure 2 of the article.</p>
Data for DynVarMIP postprocessing in CMIP6
<p>These netCDF files have been used to develop and test a diagnostic tool to compute the Transformed Eulerian Mean (TEM) variables. The calculation is done following the recommendations given in Gerber and Manzini, GMD, (2016).</p> <p>Data for January 2020 derived from the ERA5 reanalysis are provided, on pressure levels close to native levels and with reduced horizontal resolution. Note that ERA5 is distributed under the Copernicus <a href="https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf">licence</a>.</p> <p>More details will be made available in due course.</p>
data postprocessing for the EMEP local city source contribution
<p>- First edition by A. Valdebenito (Norwegian Meteorological Institute)</p> <p>similar codes were used in the country source contribution calculations: https://doi.org/10.5194/gmd-13-1787-2020</p> <p>-updated for the analysis and the publication by M. Pommier</p> <p> </p>
Re-postprocessed POSYDON v1.0 dataset, assuming super-Eddington accretion, compatible with code release v2.0.0-pre1
<p>This dataset includes the downsampled data, as well as the trained classification and interpolation models, from the CO-HMS_RLO and CO-HeMS grids calculated with moderately super-Eddington accretion and conservative mass transfer model, at solar metallicity. This dataset is described in <a href="https://ui.adsabs.harvard.edu/abs/2024arXiv240700200X/abstract">Xing et al. (2024)</a>. The resolution and stellar and binary physics assumptions follow <a href="https://ui.adsabs.harvard.edu/abs/2023ApJS..264...45F/abstract">Fragos et al. 2023</a>, but the dataset is re-postprocessed to be compatible with POSYDON code release <strong>v2.0.0-pre1</strong> (see <a href="https://ui.adsabs.harvard.edu/abs/2024arXiv241102376A/abstract">Andrews et al. 2024</a>). </p> <p>If you use this dataset, please cite the following papers:</p> <p><a href="https://ui.adsabs.harvard.edu/abs/2024arXiv240700200X/abstract">Xing et al. (2024), eprint arXiv:2407.00200</a><br><a href="https://ui.adsabs.harvard.edu/abs/2023ApJS..264...45F/abstract">Fragos et al. (2023), The Astrophysical Journal Supplement Series, Volume 264, Issue 2, id.45, 46 pp.</a><br><a href="https://ui.adsabs.harvard.edu/abs/2024arXiv241102376A/abstract">Andrews et al. (2024), eprint arXiv:2411.02376</a></p>
Postprocessed trajectory output for ice cloud microphysics - ICON and CLaMS-Ice models
<p>Postprocessed output from a trajectory module implemented in ICON v 2.3.0. The trajectories track density, temperature, pressure, specific humidity, cloud ice mass and number mixing ratios, cloud liquid mass and number mixing ratios, graupel mass and number mixing ratios, and ice sedimentation mass and number mixing ratios both into and out of the parcel. They are initiated over the Sichuan basin and allowed to flow for 51 hours westward during which the cross India into the Arabian Sea. This trajectory output is also used to run an offline microphysics box model, CLaMS-Ice. qih-Nih* files contain histograms of ice mass mixing ratio (qi) and ice crystal number concentration (Ni); het-hom-pre* files contain process tendencies from heterogeneous nucleation, homogeneous nucleation, and preexisting ice in CLaMS-Ice; qippmvNi-TRHi* files contain qi and Ni versus a range of cirrus temperatures and a range of supersaturations with respect to ice; qi_ppmv_abs* and Ni_abs* files contain probability distributions of qi and Ni differences over absolute time; and qi_ppmv_norm* and Ni_norm* files contain probability distributions of qi and Ni differences over normalized time. Suffixes in all cases indicate the cloud microphysical setup that the trajectory values were used to run with 1M = one-moment scheme, 2M = two-moment scheme, Tf = temperature fluctuation parameterization in CLaMS-Ice simulations, and noSHflux = no pseudo-mixing tendency included in CLaMS-Ice simulations.</p>
Postprocessed output from VR-CESM with refinement over the greater Greenland area and a CAM-SE control experiment
<p>This dataset contains post-processed monthly output from two VR-CESM simulations that were performed with CAM5.4 and CLM5, with refinement patches over the greater Greenland area. Data from a standard, quasi-uniform CAM-SE simulations is included as well. These are the data that are analysed and discussed by our paper in The Cryosphere, <a href="https://www.the-cryosphere-discuss.net/tc-2018-257/">https://www.the-cryosphere-discuss.net/tc-2018-257/</a>.</p> <p>The postprocessing involved (A) regridding from the respective unstructured CAM grids to regular latitude-longitude grids to allow for easy plotting and comparison, (B) vertical interpolation of some atmospheric fields to constant pressure levels, and (C) averaging in time to compute climatological means and variances.</p> <p><strong>Contact</strong><br> Leo van Kampenhout (L.vankampenhout@uu.nl)</p> <p><strong>Raw data</strong><br> The raw, ungridded monthly timeseries data are currently available on NCAR's HPSS tape system, under file path /home/lvank/archive_pp, and can be requested through the contact person. There exists also daily data for selected variables.</p> <p><strong>Dataset contents</strong></p> <pre><code>Global_Uniform.tar Global_VR28.tar Global_VR55.tar</code></pre> <p>Atmospheric CAM output regridded to a global regular 1 degree latitude-longitude grid. Variables are PHIS, T200, T500, T700, Z200, Z500, Z700.</p> <pre><code>Greenland_0.25_Uniform.tar Greenland_0.25_VR28.tar Greenland_0.25_VR55.tar</code></pre> <p>Atmospheric CAM output and CLM land model output regridded to a 0.25x0.25 degree grid stretching from 40N - 90N and 100W - 0W. Variables are FLDS, FLNS, FSDS, FSNS, H2OSNO, LHFLX, SHFLX, PHIS, PRECC, PRECL, PRECSC, PRECSL, TGCLDCWP, TREFHT, TSOI_10CM, TS, U10.</p> <pre><code>acab_c2b8_UNI_fdm.004_timmean.nc acab_c2b8_VRGRN_28.005_timmean.nc acab_c2b8_VRGRN_55.005_timmean.nc</code></pre> <p>Downscaled SMB on the 4km CISM grid, in meters of ice equivalent. These files represent time means over the period 1980-1999.</p> <pre><code> cism_thickness.nc </code></pre> <p>CISM ice thickness and ice elevation at 4 km. Note that the ice sheet was non-evolving in our simulations, so both these fields are constant in time.</p>
GRBoondi_PostProcessing_TestData
<p>Test data for GRBoondi's post-processing routines</p>
Long-term climate impacts of large stratospheric water vapor perturbations: Postprocessed WACCM data (Part 3)
Open the record for dataset details and reuse information.
Long-term climate impacts of large stratospheric water vapor perturbations: Postprocessed MiMA data (Part 2)
Open the record for dataset details and reuse information.
ece_iasi_postprocessed_output
<p>The dataset contains monthly averaged clear-sky radiances computed over the ocean starting from the initial grib output of the EC-Earth climate model.</p> <p> </p>
Postprocessed data for "Tracing the rain formation pathways in numerical simulations of deep convection"
<p>This dataset is associated with the journal paper "Tracing the rain formation pathways in numerical simulations of deep convection".</p> <p>There are six .npz files containing data for the 5 simulations (CTRL, CTRLrfix, K13, CTRL800, K13800)<br> and one npz file containing data for constructing the rain pdfs of simulation CTRL (Figure 6).</p> <p>This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52‐07NA27344 IM Release number LLNL-MI-843623</p>
EUPPBench postprocessing benchmark dataset - station data
<p>The EUMETNET EUPPBench postprocessing benchmark station data is an analysis-ready dataset to perform benchmarks of different postprocessing methods on a common dataset.</p> <p>This dataset is using the <a href="https://zarr.dev/">Zarr</a> format. Please look at the <a href="https://zarr.readthedocs.io/en/stable/">Zarr documentation</a> to see how to load and access the data.</p> <p>The documentation of the dataset is available on <a href="https://eupp-benchmark.github.io/EUPPBench-doc/">https://eupp-benchmark.github.io/EUPPBench-doc/</a> .</p> <p>The official way to download the dataset is through the <a href="https://github.com/ecmwf/climetlab">climetlab</a> <a href="https://github.com/EUPP-benchmark/climetlab-eumetnet-postprocessing-benchmark">EUMETNET postprocessing benchmark plugin</a>.</p> <p>This Zenodo repository aims to preserve the dataset by providing long-term storage.</p> <p>Please read the LICENSE file for more information on the data licenses.</p> <p><strong>Installation procedure</strong></p> <p>Download the the dataset in a given folder, and on a Linux (or mac OS) terminal, and still in this folder, enter the following command</p> <pre><code class="language-bash">unzip EUPPBench-stations.zip rm EUPPBench-stations.zip</code></pre> <p>This will unpack the dataset. You need at least 35Gb of free space on your disk to perform this operation.</p> <p> </p> <p><strong>Citation</strong></p> <p>If you use this dataset for a publication, please cite the dataset article:</p> <p>Demaeyer, J., Bhend, J., Lerch, S., Primo, C., Van Schaeybroeck, B., Atencia, A., Ben Bouallègue, Z., Chen, J., Dabernig, M., Evans, G., Faganeli Pucer, J., Hooper, B., Horat, N., Jobst, D., Merše, J., Mlakar, P., Möller, A., Mestre, O., Taillardat, M., and Vannitsem, S.: The EUPPBench postprocessing benchmark dataset v1.0, Earth Syst. Sci. Data Discuss. [preprint], <a href="https://doi.org/10.5194/essd-2022-465">https://doi.org/10.5194/essd-2022-465</a>, in review, 2023.</p> <p> </p> <p><strong>Remark</strong></p> <p>You might also be interested by the gridded data part of this dataset also available on Zenodo here: <a href="https://doi.org/10.5281/zenodo.7429236">https://doi.org/10.5281/zenodo.7429236</a> .</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.