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607 results for “wind data”
Wake data documentation for a wind turbine rotor with winglets
<p>This is the documentation of data, measured in a experimental campaign, in which the effects of winglets<br> on a model wind turbine rotor were investigated</p>
Oscillations of Offshore Wind Turbines undergoing Installation II: Filtered and Integrated data - acceleration, velocity, displacement
<p>This is dataset is based on the raw measurement data from <a href="https://zenodo.org/record/5009061">https://zenodo.org/record/5009061</a></p> <p>The data included in the archives are the resampled and high-pass filtered accelerations as well as the velocity and displacement data.</p>
Southern Hemisphere winds, pressure, and temperature over the 20th century from proxy-data assimilation
<p>This archive contains four reconstructions of annually resolved zonal surface wind (us), sea level pressure (psl), and surface temperature (tas) anomalies in the Southern Hemisphere over the period 1900 to 2005 CE. The anomaly reference period is 1961-1990. </p> <p>The reconstructions are generated using the Last Millennium Reanalysis data assimilation framework (Hakim et al., 2016; Tardif et al., 2019). The proxies assimilated come from a global database comprising the PAGES2k database (PAGES2k Consortium, 2017), additional ice core accumulation records (Thomas et al., 2017), and additional coral records (Sanchez et al., 2021). We use four climate models to produce the four reconstructions:</p> <ol> <li>the iCESM Last Millennium Ensemble (“CESM LM”, Brady et al., 2019, Stevenson et al., 2019)</li> <li>the HadCM3 Last Millennium Ensemble (“HadCM3 LM”, Collins et al., 2001)</li> <li>the CESM1 Large Ensemble (“LENS”; Kay et al., 2015)</li> <li>the CESM1 Pacific Pacemaker Ensemble (“PACE”; Schneider and Deser, 2018).</li> </ol> <p>The four reconstructions are named after the prior that is used. The last millennium ensemble of simulations include natural forcings only and the LENS and PACE ensemble of simulations include historical external forcings. For each reconstruction, there are three netCDF files containing the ensemble mean (mean of 100 ensemble members) for each climate field. More details can be found in O'Connor et al. (2021).</p> <p>Please cite O'Connor et al. (2021) when using these datasets. <a href="https://doi.org/10.1029/2021GL095999">https://doi.org/10.1029/2021GL095999</a></p>
Comparison of Large Eddy Simulations against measurements from the Lillgrund offshore wind farm - Manuscript data
<p>Time averaged power and farm inflow velocity for the manuscript "Comparison of Large Eddy Simulations against measurements from the Lillgrund offshore wind farm" for publication in the wind energy science journal. Data is uploaded for the 5 simulation cases covered.</p> <p>'Power' files contain average power production for 48 turbines. First row corresponds to LES data, second row corresponds to SCADA data from the Lillgrund wind farm.</p> <p>'Velocity' files contain inflow mean velocity measurements at the 72 range gate locations. First row corresponds to LES inflow data, second row corresponds to LIDAR inflow data from the Lillgrund wind farm.</p>
Data from: Wind turbines in managed forests partially displace common birds
<p><span>Wind turbines are increasingly being installed in forests, which can lead to land use disputes between climate mitigation efforts and nature conservation. Environmental impact assessments precede the construction of wind turbines to ensure that wind turbines are installed only in managed or degraded forests that are of potentially low value for conservation. It is unknown, nevertheless, if animals deemed of minor relevance in environmental impact assessments are affected by wind turbines in managed forests. We investigated the impact of wind turbines on common forest birds, by counting birds </span><span>along an impact-gradient of wind turbines</span><span> in 24 temperate forests in Hesse, Germany. </span><span>During 860 point counts, we counted 2,231 birds from 45 species. Bird communities were strongly related to forest structure, season and the rotor diameter of wind turbines, but were not related to wind turbine distance. For instance, bird abundance decreased in structure-poor (-38%) and monocultural (-41%) forests with wind turbines, and in young (-36%) deciduous forests with larger and more wind turbines (-24%). Overall, our findings suggest that wind turbines in managed forests partially displace common forest birds. If these birds are displaced to harsh environments, wind turbines might indirectly contribute to a decline of their populations. Yet, forest bird communities are locally more sensitive to forest quality than to wind turbine presence. To prevent further displacement of forest animals, forests of lowest quality for wildlife should be preferred in spatial planning for wind turbines, for instance small and structure-poor monocultures along highways.</span></p>
Enriched Data of Wind Farms (EDWin)
<p>EDWin (Enriched Data of Wind Farms) is a dataset developed to provide information about global wind farms. The dataset is based on OpenStreetMap (OSM) data and has been enriched with additional variables obtained from various databases. The dataset includes two separate data sets, one for global turbines and one for wind farms. As of September 2022, this dataset contains the most recent information available.</p> <p>The datasets have the following structures:</p> <p><strong>Wind Turbine data </strong></p> <p>The data for wind turbines includes 359,947 entries and 12 columns.</p> <table> <thead> <tr> <th>Variable Name</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td>id</td> <td>Key value of the data point</td> </tr> <tr> <td>lon</td> <td>Longitude of the location</td> </tr> <tr> <td>lat</td> <td>Latitude of the location</td> </tr> <tr> <td>country</td> <td>Country where the turbine is located</td> </tr> <tr> <td>continent</td> <td>Continent where the turbine is located</td> </tr> <tr> <td>land cover</td> <td>The type of land on which the turbine is located</td> </tr> <tr> <td>landform</td> <td>The physical features of the land on which the turbine is located</td> </tr> <tr> <td>elevation</td> <td>The altitude of the turbine</td> </tr> <tr> <td>turbine spacing</td> <td>The distance between turbines in the wind farm</td> </tr> <tr> <td>WFid</td> <td>Wind Farm ID</td> </tr> <tr> <td>number of turbines</td> <td>The number of turbines in the wind farm</td> </tr> <tr> <td>shape</td> <td>The rough shape of the wind farm</td> </tr> </tbody> </table> <p> </p> <p><strong>Wind Farm data </strong></p> <p>The data for wind farms includes 20,608 entries and 11 columns.</p> <table> <thead> <tr> <th>Variable Name</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td>WFid</td> <td>Wind Farm ID</td> </tr> <tr> <td>lon</td> <td>Longitude of the location (center of the wind farm)</td> </tr> <tr> <td>lat</td> <td>Latitude of the location (center of the wind farm)</td> </tr> <tr> <td>country</td> <td>Country where the wind farm is located</td> </tr> <tr> <td>continent</td> <td>Continent where the wind farm is located</td> </tr> <tr> <td>land cover</td> <td>The modal value of the land cover for the turbines in the wind farm</td> </tr> <tr> <td>landform</td> <td>The average value of the landform for the turbines in the wind farm</td> </tr> <tr> <td>elevation</td> <td>The average elevation of the turbines in the wind farm</td> </tr> <tr> <td>turbine spacing</td> <td>The average turbine spacing for the turbines in the wind farm</td> </tr> <tr> <td>number of turbines</td> <td>The number of turbines in the wind farm</td> </tr> <tr> <td>shape</td> <td>The rough shape of the wind farm</td> </tr> </tbody> </table> <p> Note that the data for "Country", "Continent", "Land Cover", "Landform", "Elevation" and "Turbine spacing" were collected turbine-specific and later added to the wind farm dataset in an aggregated form. For the categorical variables, the modulus of the respective turbine values was taken, and for numerical variables, the average was calculated. The two variables, number of turbines (i.e. wind farm size) and wind farm shape (i.e. a rough shape of the wind farm), were obtained from the wind farms data and added to the turbine dataset.<br> </p> <p><strong>Sources</strong></p> <p>[1] Open street map. <a href="https://openstreetmap.org/">https://openstreetmap.org/</a>. [Online] Accessed: 2022-10-02.</p> <p>[2] Cutler J. Cleveland, Christopher Morris, Dictionary of Energy (Second Edition), Elsevier, 2015, Pages 638-655, ISBN 9780080968117</p> <p><a href="https://doi.org/10.1016/B978-0-08-096811-7.50023-8">https://doi.org/10.1016/B978-0-08-096811-7.50023-8</a>.</p> <p>[4]<em> </em>Dunnett, S., Sorichetta, A., Taylor, G. <em>et al.</em> Harmonised global datasets of wind and solar farm locations and power. <em>Sci Data</em> <strong>7</strong>, 130 (2020).</p> <p><a href="https://doi.org/10.1038/s41597-020-0469-8">https://doi.org/10.1038/s41597-020-0469-8</a></p> <p>[5] Buchhorn, M. ; Lesiv, M. ; Tsendbazar, N. - E. ; Herold, M. ; Bertels, L. ; Smets, B. Copernicus Global Land Cover Layers-Collection 2. Remote Sensing 2020, 12 Volume 108, 1044. <a href="https://doi.org/10.3390/rs12061044">doi:10.3390/rs12061044</a></p> <p>[6] Theobald, D. M., Harrison-Atlas, D., Monahan, W. B., & Albano, C. M. (2015). Ecologically-relevant maps of landforms and physiographic diversity for climate adaptation planning. PloS one, 10(12), <a href="https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0143619">e0143619</a></p> <p>[7] Global Multi-resolution Terrain Elevation Data 2010 courtesy of the U.S. Geological Survey</p>
Effect of Vertical Shear in the Zonal Wind on Equatorial Electrojet Sidebands: An Observational Perspective Using Swarm and ICON Data
<p>This data set consists of outputs from the EEJ model by Richmond (1973). The data provided is used in the manuscript titled 'Effect of Vertical Shear in the Zonal Wind on Equatorial Electrojet Sidebands: An Observational Perspective Using Swarm and ICON Data' by J. Sreelakshmi et al.</p> <p>Abstract of the manuscript:</p> <p>The wind dynamo in the ionosphere leads to differential motion of ions and electrons, which in turn sets up electric fields and currents. Observations show that daytime lower thermospheric horizontal winds have large vertical gradients. Numerical modelling conducted approximately 50 years ago demonstrated that the zonal wind shears in the ~130-180km altitude range can generate off-equatorial relative minima (dips) in the daytime height-integrated eastward current density, appearing as westward sidebands north and south of the equatorial electrojet (EEJ). This study observationally confirms this connection for the first time by combining Ionospheric CONnection explorer zonal wind profiles and Swarm latitudinal zonal currents. We demonstrate observationally that the magnitude of the EEJ sideband current is proportional to the strength of westward turning winds with altitude in the Pedersen conductivity dominated region. Additional numerical experiments explain the importance of wind shear in different altitude regions in generating the sideband current. This study contributes to the better understanding of the neutral wind effect on local current generation.</p>
Virtual sensors for wind energy applications benchmark study data - preliminary version
<p>Test version of the time series data for the wind energy virtual sensing benchmark study data.</p>
Data example and code used in the publication "Is transport of microplastics different from that of mineral dust? Results from idealized wind tunnel studies"
<p>Background</p> <p>The code labels microspheres and counts them. Further, the code determines which microspheres are independent of microsphere-microsphere collisions by their relative position to the other microspheres in an image. Images were taken with a full-frame visual camera (Sony Alpha 7RII) with a long-distance-microscopy lens (K2 DistaMax).</p> <p>Description of the dataset</p> <ul> <li>image_data_all.zip contains 228 tif-format images taken in a single experiment <ul> <li>the images show borosilicate microspheres with diameters from 63 to 75 µm</li> <li>during the experiment, the microspheres are detached from the substrate and are transported out of the image</li> </ul> </li> <li>functions_particle_labeling.jl contains all necessary functions for particle labeling</li> <li>analysis_protocol.jl is an example, that first determines a color threshold, and then labels all microspheres in all images stored in "image_data_all/substrate_a/image_data_single_experiment"</li> <li>post_processing_visualisation.R is an r-script, that reads the output of analysis_protocol.jl and demonstrates how logistic functions were fitted to the data</li> </ul> <p> </p> <p>We used julia 1.8.5 and R 4.3.0.</p> <p> </p>
Data set from long-term wave, wind and response monitoring of the Bergsøysund Bridge
<p>Wind, wave, displacement and acceleration data have been collected in a measurement campaign on the Bergsøysund Bridge between the years 2014 and 2018. The data set is now available in this open-access research entry, for free access and download. The data is collected in two h5-files (hierachical data format), with sampling rates 2 Hz and 10 Hz, downsampled from the raw sampling rate of 200 Hz. Note that the data has undergone some minimal signal processing and adjustment, in line with that applied to the Hardanger Bridge data described in Fenerci et al. (2021). Tools and examples for import, data visualization and initial analysis are given in the opyndata Python package available on GitHub (Kvåle, 2022). Furthermore, a document briefly describing the hierarchy and structure of the data, is given. For more details on the measurement system and the bridge, it is referred to Kvåle and Øiseth (2017).</p> <p>The updated, copyrighted version of the appended preprint is published by ASCE with the following DOI: <a href="https://doi.org/10.1061/JSENDH.STENG-12095">10.1061/JSENDH.STENG-12095</a></p>
TEAMx-PC22 (TEAMx pre-campaign 2022) – DWD Doppler wind lidar data set (SLXR172)
<p>This dataset contains data measured by DWD with a Doppler Wind Lidar SLXR172 during the TEAMx pre-campaign 2022. More details about TEAMx can be found at <a href="http://www.teamx-programme.org/">http://www.teamx-programme.org</a>.</p> <p><strong>DATA SET DESCRIPTION</strong></p> <p><strong>1. Measurement location and time period </strong></p> <p>Measurements with the SLXR172 were collected at the site of Brannenburg (47.741547 N / 12.122187 E / 456 m MSL) between 15.June – 19 October 2022.</p> <p><strong>2. Measurement setup</strong></p> <p>During the measurement period, two different scanning modes were applied:</p> <p>15. June - 26. July 2022 and 13. August – 19. October 2022 (VAD_CSM).</p> <ul> <li><strong>VAD (velocity-azimuth display) scans in </strong><strong>continuous scanning mode</strong><strong> : </strong>These scans were conducted at an elevation angle of 35°. Azimuth angle interval of the CSM data sampling was about 1.1°. </li> </ul> <p>27.July – 12. August 2022 (VAD_RHI)</p> <ul> <li><strong>VAD scans in step-stare mode: </strong>Step-stare scans were conducted at an elevation angle of 35° and with azimuth steps of 15°.</li> <li><strong>RHI (</strong><strong>range-height indicator) scans into the Inn Valley</strong>; The RHI scans were performed for 10 azimuth angles from 151° to 160° and covered elevation angles from 3° to 51°.</li> </ul> <p> </p> <p><strong><em>3. Data processing, corrections and filter</em></strong></p> <p>For <strong><em>VAD scans in continuous scanning mode</em></strong> the processed wind fields are provided. The data have not been corrected. The data can be filtered using the parameters R<sup>2 </sup>(coefficient of determination), CN (condition number) and NVRAD (number of radial velocities) as described in Päschke (2015):</p> <p>R<sup>2</sup>> 0.95 and CN<10 and NVRAD>12 </p> <p>Please note that in the postprocessing of the VAD CSM scans, the R<sup>2</sup> filter criterion was set to R<sup>2</sup>>0 in order to include all data and therefore might also include scans where the assumptions of homogeneity are not fulfilled. The parameter qwind is therefore not meaningful due to this configuration and should not be used to filter the data. We recommend the use of the above criterion from Päschke.</p> <p>For scans from the <strong><em>VAD scans in step stare mode</em></strong> as well as the <strong><em>RHI scans</em></strong> the raw data files are provided. They have not been corrected nor filtered.</p> <p><strong>4. Data file structure</strong></p> <p>The data are provided in NetCDF format. File names contain date and time information in UTC. The following wildcard characters are used in the file examples below: yyyy - year; mm - month, dd - day; HH - hour, MM - minute, `SS` - second. Files are sorted in monthly folders.</p> <p>The data are provided in two zip-files.</p> <ul> <li>VAD_CSM contains the processed wind fields from 15. June - 26. July 2022 and from 13. August – 19. October 2022)</li> <li>VAD+RHI the raw data files for 27.July – 12. August 2022.</li> </ul> <p>Raw data files of the VAD CSM scans can be provided upon request.</p> <p><strong>5. Contact</strong></p> <p>Contact Katrin.sedlmeier(at)dwd.de.at for any questions regarding the data set.</p> <p><strong>6. References</strong></p> <p>Päschke, E., Leinweber, R., and Lehmann, V.: An assessment of the performance of a 1.5 μm Doppler lidar for operational vertical wind profiling based on a 1-year trial, Atmos. Meas. Tech., 8, 2251–2266, https://doi.org/10.5194/amt-8-2251-2015, 2015.</p>
TEAMx-PC22 (TEAMx pre-campaing 2022) – GeoSphere Austria Doppler wind lidar data
<p><strong>ABSTRACT</strong></p> <p><a href="https://www.geosphere.at/">GeoSpere Austria</a> operated a Doppler lidar (<a href="https://metek.de/product/wind-ranger-100-200/">METEK Wind Ranger 200</a>) during the TEAMx pre-campaign 2022 (TEAMx-PC22) from August 17, 2022 to October 3, 2022 next to the <a href="https://oscar.wmo.int/surface/index.html#/search/station/stationReportDetails/0-20000-0-11130">meteorological station at Kufstein</a>, Austria. The wind lidar data from this campaign is provided here. Standard meteorological data is available on the <a href="https://data.hub.geosphere.at/">GeoSphere Austria data hub</a>.</p> <p>The aim of TEAMx-PC22 was to test new instruments, new instrument configurations and new measurement sites to support the planning of the main TEAMx observational campaign (TOC) in 2024/2025. More details about TEAMx can be found at <a href="http://www.teamx-programme.org">http://www.teamx-programme.org</a> as well as in <a href="https://www.uibk.ac.at/iup/buch_pdfs/10.1520399106-003-1.pdf">Serafin et al. (2020) </a>and in <a href="https://journals.ametsoc.org/view/journals/bams/103/5/BAMS-D-21-0232.1.xml">Rotach et al. (2022)</a>.</p> <p><strong>DATA SET DESCRIPTION</strong></p> <p>The windlidar is operated at Kufstein next to the meteorological station (12.1628°E 47.5753°N 490m asl). 1 VAD scan is measured per second, 100 radial measurements per VAD scan.</p> <p>Provided are daily NetCDF data sets, so-called “averaged files”, i.e. 10min averaged profiles calculated from the instantaneous profiles provided by the operational software of the instrument.</p> <p><strong>Description of variables:</strong></p> <table> <tbody> <tr> <td> <p>lat</p> </td> <td> <p> Latitude</p> </td> </tr> <tr> <td> <p>lon</p> </td> <td> <p> Longitude</p> </td> </tr> <tr> <td> <p>alt</p> </td> <td> <p> Altitude</p> </td> </tr> <tr> <td> <p>height</p> </td> <td> <p> Measuring height</p> </td> </tr> <tr> <td> <p>pitch</p> </td> <td> <p> Tilt towards north arrow</p> </td> </tr> <tr> <td> <p>roll</p> </td> <td> <p> Tilt clockwise looking along north arrow</p> </td> </tr> <tr> <td> <p>heading</p> </td> <td> <p> Azimuth alignment (should be zero)</p> </td> </tr> <tr> <td> <p>time</p> </td> <td> <p> Time stamp (seconds since 01.01.1970 00:00 UTC).</p> </td> </tr> <tr> <td> <p>VEL</p> </td> <td> <p> Wind Velocity (vectorial average)</p> </td> </tr> <tr> <td> <p>VEL_SC</p> </td> <td> <p>Wind Velocity (scalar average)</p> </td> </tr> <tr> <td> <p>DIR</p> </td> <td> <p> Direction (vectorial average)</p> </td> </tr> <tr> <td> <p>U</p> </td> <td> <p> West-East wind component</p> </td> </tr> <tr> <td> <p>V</p> </td> <td> <p> South-North wind component</p> </td> </tr> <tr> <td> <p>W</p> </td> <td> <p> Upward wind component</p> </td> </tr> <tr> <td> <p>SU</p> </td> <td> <p>Standard deviation of U</p> </td> </tr> <tr> <td> <p>SV</p> </td> <td> <p>Standard deviation of V</p> </td> </tr> <tr> <td> <p>SW</p> </td> <td> <p>Standard deviation of W</p> </td> </tr> <tr> <td> <p>SVEL</p> </td> <td> <p>Mean square deviation of radial wind components from fitted values</p> </td> </tr> <tr> <td> <p>DQ</p> </td> <td> <p>Fraction of valid radial components per VAD</p> </td> </tr> <tr> <td> <p>MDT</p> </td> <td> <p>Mean distance to target (Measured distance of focus)</p> </td> </tr> <tr> <td> <p>SNR</p> </td> <td> <p>Signal to noise ratio in dB</p> </td> </tr> <tr> <td> <p>SPW</p> </td> <td> <p>Spectral width (for internal use only)</p> </td> </tr> </tbody> </table> <p> </p> <p>Contact: kathrin.baumann-stanzer@geosphere.at</p>
FarmConners Wind Farm Flow Control Benchmark: Blind Test with CL-WINDCON Wind Tunnel Data
<p>This is the dataset used for running the fourth Blind Test of the FarmConners Wind Farm Flow Control Benchmark. The Blind Test was performed with an extensive dataset gathered while testing a cluster of three scaled wind turbines within a large boundary layer wind tunnel. The experimental dataset has been compared against the predictions provided by 5 different control-oriented wind farm flow models. The resulting comparison is described in the paper "FarmConners Wind Farm Flow Control Benchmark: Blind Test Results, Part 2", by Campagnolo et al, (2023). The dataset consists of:</p> <ol> <li>measurements of the flow within the wake shed by one or two machines, as well as measurements of the power, loads (on the rotating shaft and at tower base), and pitch/yaw/torque actuators states of the three scaled machines. The measurements have been performed under a wide range of inflow and machines operating conditions. The time series of the measured data are provided in the format of Matlab structures saved in .mat files.</li> <li>Predictions provided by the models used by the Blind Test participants</li> <li>Matlab scripts used for comparing the experimental dataset and the numerical predictions provided by the Blind Test participants</li> <li>Additional data provided to the Blind Test participants. This includes a FAST model of the scaled wind turbine and the mapping of the inflow of the empty wind tunnel. </li> </ol>
Data for Potential feeding sites for seabirds and marine mammals reveal large conflicting areas with offshore wind energy development worldwide
<p>This dataset contains;</p> <p>1) The dataset (sample_point_data.Rdata) to develop the Structural Equation model </p> <p>2) The spatial tiff bivariate maps for small-ranged seabird and marine mammals and fish and zooplankton biomass respectively</p> <p>3) The Potential Feeding Sites likelihood map in a tiff format</p> <p>4) The global power density at 200m </p> <p>5) Dataset of risk category and corresponding values and coordinates for spatial representation</p>
Supplementary data for: Comparison of optical flow derivation techniques for retrieving tropospheric winds from satellite image sequences
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Data from: Wind turbines in managed forests partially displace common birds
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Data from: <em>Vespula pensylvanica</em> locate odor sources across diverse natural wind conditions
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Data for: Ocean surface wave slopes and wind-wave alignment observed in Hurricane Idalia
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Year 2011, January to September, wind sensor data, 15 minute intervals, from the Ipswich Bay Yacht Club pier located in Ipswich, MA
Wind sensor measurements (wind speed and wind direction) January to September 2011 at the Ipswich Bay Yacht Club, Ipswich, MA, 15 minute instantaneous measurement.
Year 2010, wind sensor data, 15 minute intervals, from the Ipswich Bay Yacht Club pier located in Ipswich, MA
Wind sensor measurements (wind speed and wind direction) year 2010 at the Ipswich Bay Yacht Club, Ipswich, MA, 15 minute instantaneous measurement.
ScienceDex guides
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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.