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1,293 results for “thickness”
Ice thickness at Green Lake 4, 1984 - ongoing.
Thickness of seasonal ice cover in high alpine lakes can be influenced by local environmental change, including changes in temperature and winter precipitation. Increased winter precipitation can increase the insulating effects of snow cover on lake surfaces or increase water storage in the lake’s catchment, both of which may decrease ice thickness (Caine 2002). To assess long-term trends in seasonal ice thickness, as well as sensitivity to environmental drivers, measurements of ice thickness are taken approximately monthly throughout the winter at Green Lake 4.
Sea ice thickness, snow depth, and sea ice freeboard in lagoon sites along the Alaska Beaufort Sea coast, 2019-ongoing
Physical parameters related to snow and sea ice have implications for lagoon circulation, sea-air heat exchange, and underwater light regimes. To understand these relationships and their greater effect on ecosystem function, the Beaufort Lagoon Ecosystem LTER (BLE LTER) uses in situ methods to assess snow depth, ice freeboard, and ice thickness in select water bodies across the Beaufort Sea coast (Elson Lagoon, Simpson Lagoon, Kaktovik Lagoon, Jago Lagoon, and Stefansson Sound). Sea ice thickness is the distance from sea ice bottom to top, not including snow. Freeboard, determined in the same drilled hole, is the distance from the surface of the water to the top of the ice, not including snow cover. These measurements are made annually, close to maximum ice thickness (typically April).
Thickness map of the Patagonian Icefields
<p>Ice thickness field for the Patagonian icefields relying on mass-conservation approach, which assimilates both glacier retreat data as well as an abundant record of direct thickness measurements. The thickness map has a time stamp of 2000. This map is provided together with error estimates and the basal topography beneath the icefields based on c-SRTM (v2.1) (Farr, T. et al. The Shuttle Radar Topography Mission. Reviews of Geophysics 45 (2007), http://dx.doi.org/10.1029/2005RG000183.)</p>
Correspondence of the natural oscillation frequencies of perforated plates depending on the type of holes, plate material and thickness, type of fixing (CCCS or CSCS)
<p>The method involved the analysis of oscillations of base plates: solid non-perforated and with round holes, as well as perforated plates with holes of complex geometry in the form of a five-petal epicycloid.</p> <p>As a result of the modeling (Abaqus), the natural oscillations frequencies of the studied plates were obtained depending on the type of perforation, material, thickness and type of their fixing. The use of different materials (steel and aluminium) showed an insignificant influence on the natural oscillation frequency of the plates. It was found that the plate thickness has the greatest influence (31.85– 33.35%), the following are the hole parameters: partition width between holes; pitch between hole centers.</p> <p>Analysis of the results showed that the natural vibrations of plates with holes of complex geometry differ by up to 7% compared to plates with basic round holes. </p>
Imaging Temperature and Thickness of Thin Planar Liquid Water Jets in Vacuum - data
<p>Data set pertaining to the article "Imaging Temperature and Thickness of Thin Planar Liquid Water Jets in Vacuum", published in <em>Struct. Dyn.</em> 10, 034901 (2023), <a href="https://doi.org/10.1063/4.0000188" target="_blank" rel="noopener">https://doi.org/10.1063/4.0000188 </a>.</p> <p>The following data are provided:</p> <table> <tbody> <tr> <td>(zip-)file/Folder</td> <td>Description</td> <td>Format</td> <td>Extension</td> </tr> <tr> <td>IR_images/calibration_data/vacuum</td> <td> <p>Snapshots from a thermographic movie of our flat jet running in vacuum, at thirty different background temperature. (A snapshot shown in Fig. 3a, rhs.)</p> </td> <td> <p>temperature values per camera pixel (°C), 640 row * 480 columns, semicolon-separated ascii data</p> </td> <td>.asc</td> </tr> <tr> <td>IR_images/calibration_data/1atm</td> <td>As above, for our flat jet running in atmosphere. (Three snapshots shown in Fig. 2a.)</td> <td>as above</td> <td>.asc</td> </tr> <tr> <td>IR_images/calibration_data/chipnozzle</td> <td>As above, for a flat jet produced from a chip nozzle, and running in atmosphere.</td> <td>as above</td> <td>.asc</td> </tr> <tr> <td>IR_images/raw_data</td> <td>As above, for various conditions of the flat jet environment as detailed in table exp_settings.csv.</td> <td>as above</td> <td>.asc</td> </tr> <tr> <td>IR_video</td> <td>Two thermographic movies recorded of our flat jet at varied conditions of the jet environment detailed in table chamber_pressure.pdf.</td> <td>Radiographic image stream, suitable for opening with free software Optris Pix Connect.</td> <td>.ravi</td> </tr> <tr> <td>FJ_cooling_2D.mph</td> <td>Input file for 2D finite element simulation of our flat jet.</td> <td>Input file suitable for Comsol software, proprietary format.</td> <td>.mph</td> </tr> <tr> <td>Y_Z_Temp_Comsol.txt</td> <td>Ascii representation of our simulated temperature profile (Fig. S7 (SI)).</td> <td>List of (y,z,T) tupels, with (y,z) in m and T in °C.</td> <td>.txt</td> </tr> </tbody> </table> <p> </p> <p>In case you have any questions regarding this data set please contact: Uwe Hergenhahn, uhe@fhi.mpg.de .</p>
IISD Experimental Lakes Area: Ice Phenology and Thickness, 1969-2025 (partial start/end years).
The IISD Experimental Lakes Area (IISD-ELA) Ice Phenology and Thickness data package provides two types of ice data from multiple lakes in northwestern Ontario, Canada. Ice phenology is the timing of when lakes freeze over in the fall and thaw out in the spring, as an ice-on and ice-off date each year, and the days of ice duration for each winter. Ice thickness data includes total ice thickness as well as the complex breakdown of individual layers (snow, slush, white ice, black ice). This data package includes both tabular data and metadata files for each type of ice data. The ice phenology table consists of a row for each ice-on date, ice-off date, and ice duration period for each lake, including the associated sampling method and any comments. The ice thickness table has a row for each measurement of a frozen lake on a specific date, including the total ice thickness and the thickness of individual layers as column values for each row. Metadata in this data package include a table of location coordinates and record counts, and an information sheet for ice phenology and one for ice thickness. The table of coordinates and counts is useful to know where the lakes and specific sampling sites are located and as an overview of data availability per lake (Lake 239 has the longest and most consistent data record, for both ice phenology and thickness). The two info sheets provide additional metadata details about the datasets, including background and uses of the datasets, a data dictionary, diagrams, lookup tables, descriptions of methods, and additional references. For data about lake depth, size, and volume, please consult the most recent version of our bathymetry data package: https://portal.edirepository.org/nis/revisionbrowse?scope=edi&identifier=1276 This data package is ongoing—updates will be provided as data are collected from these lakes in subsequent years. If data are not present for a lake you are interested in from IISD-ELA, please get in touch with us. The
Lake Mendota water temperature secchi depth snow depth ice thickness and meterological conditions 1894 - 2007
Data for water temperature at different depth and different frequencies assembled from various sources by Dale Roberson. A table with additional parameters collected at the same time is also provided for dates when available. These parameters are weather observations, secchi depth, snow and ice depths.
ICESat-2 monthly gridded winter Arctic sea ice thickness
<p>Monthly gridded (winter only) Arctic sea ice thickness estimates from ICESat-2 derived using ATL10 freeboards (https://nsidc.org/data/atl10) together with snow depth and density estimates from the NASA Eulerian Snow on Sea Ice Model (NESOSIM, https://github.com/akpetty/NESOSIM). Along-track data (from the three strong beams) are binned to the 25 km x 25 km NSIDC polar stereographic projection (EPSG:3411). The full processing chain is described in Petty et al., (2020) (code available at https://github.com/akpetty/ICESat-2-sea-ice-thickness) including several updates as detailed below.</p> <p>Temporal range: November 2018 - April 2019, October 2019 to April 2020.</p> <p>Data: A single netCDF file is included for each month. Variables include:</p> <ul> <li>Sea ice freeboard (from ATL10)</li> <li>Snow depth (redistributed NESOSIM)</li> <li>Snow density (redistributed NESOSIM)</li> <li>Bulk sea ice density</li> <li>Sea ice type (from OSI SAF)</li> <li>Sea ice thickness uncertainty</li> <li>Mean day of month in a given grid cell</li> <li>Number of freeboard segments in a given grid cell.</li> </ul> <p>A summary of the differences between the version 1 and version 2 winter Arctic sea ice thickness estimates are being presented at AGU 2020 and prepared for publication.</p> <p>Key changes from version 1 (Petty et al., 2020) to version 2 include:</p> <ul> <li>Use of release 003 ATL10 freeboards. A detailed assessment of the freeboard changes from release 002 to release 003 is provided in Kwok et al., (2020).</li> <li>Upgrade to NESOSIM v1.1: CloudSat scaling of ERA5 snowfall, a new atmospheric wind loss term, calibration against recent OIB snow depths, an extended Arctic Ocean domain and various bug fixes (<a href="https://github.com/akpetty/NESOSIM">https://github.com/akpetty/NESOSIM</a>).</li> <li>Use of all three strong beams (instead of just strong beam #1).</li> </ul> <p>The data have also been made available on a Google Cloud bucket to enable rapid data analysis from any cloud-based analytics platform: <em>gs://sea-ice-thickness-data/v2/</em></p>
DebDaB: A database of supraglacial debris thickness and physical properties
<p><strong>DebDaB: A database of supraglacial debris thickness and physical properties</strong></p> <p>DebdaB is a database of measured and reported physical properties and thickness of supraglacial debris that is openly available and open to community submissions.</p> <p>The majority of the database (90%) is compiled from 172 sources in the literature, and the remaining 10% has not been published before. DebDaB contains 8,286 data entries for supraglacial debris thickness, of which 1,852 entries also include sub-debris ablation rates, 167 data entries of thermal conductivity of debris, 157 of aerodynamic surface roughness length, 77 of debris albedo, 56 of debris emissivity and 37 of debris porosity. The data are distributed over 83 glaciers in 13 regions in the Global Terrestrial Network for Glaciers. </p> <p>This is version 2 of the dataset, corresponding to the revised version of the database after peer-review of its accompanying "Data descriptor manuscript" submitted for publication to the scientific journal "Earth System Science Data (ESSD)" from Copernicus Publications. The preprint is available at <a href="https://doi.org/10.5194/essd-2024-559">https://doi.org/10.5194/essd-2024-559 </a></p> <p>DebDaB version 2 consists of the following files:</p> <ul> <li>DebDaB_v2.zip : The actual DebDaB database, provided as a navigable Open Document Spreadsheet (.ods) with spreadsheet tabs for each of the debris properties. Additionally, the database is also provided as separate .csv files for each debris property, and as a GeoPackage (.gpkg). </li> <li>Readme_files.zip: A .txt file for each of the debris property tabs, describing all the fields in each tab. </li> <li>Templates_for_data_submission.zip: Templates (.csv files and additionally .xlsx files) for data submission for each of the debris properties in DebDaB. Data submissiosn to DebDaB should be sent to debriscoveredglaciers@ista.ac.at. </li> <li>DebDaB_data_sources.pdf: List of DebDaB sources from published literature. </li> <li>DebDaB_data_sources.bib: BibTeX list of DebDaB sources from published literature. </li> <li>Manuscript_codes.zip: The codes to download and process the data to generate the figures for data descriptor manuscript on ESSD.</li> </ul> <p>The data descriptor manuscript is in open review stage at: <a href="https://essd.copernicus.org/preprints/essd-2024-559/">https://essd.copernicus.org/preprints/essd-2024-559/ </a></p> <p><strong>DebDaB is open to new data submissions</strong>, and therefore future data submissions of previously unpublished data to DebDaB will entail co-authorship on the DebDaB database on Zenodo. </p> <p>According to the authors’ understanding of FAIR principles, authors of published literature and published data, that:</p> <ul> <li>Correct existing data within DebDaB, in case of errors</li> <li>Send the raw data from digitised figures</li> <li>Submit additional data that was previously unavailable (for example, accurate coordinates or additional data or metadata which is not already available)</li> </ul> <div>will have the right to be added as co-authors on the database in Zenodo. The authors are working to reevaluate their policies to conform to changes or unusual circumstances in authorship contributions, and are happy to involve eager people in the core team.</div> <div> </div> <div><strong>How to submit data: </strong>Please use the templates provided in the database files for data submissions and send it to debriscoveredglaciers@ista.ac.at. Authors who submit data will be asked to fill in a form regarding authorship contributions. </div> <p><strong>Important note on citations:</strong> DebDaB data users must cite the data descriptor manuscript (Fontrodona-Bach et al. 2025), the DebDaB zenodo repository<br>(Groeneveld et al., 2025), <strong>and the original data sources</strong> when using the database, given that DebDaB is mostly<br>a compilation of previously published data. To facilitate the citations of original data sources, each of the data entries in DebDaB contains the corresponding<br>original reference and corresponding DOI.</p> <p><strong>Manuscript citation:</strong> Fontrodona-Bach, A., Groeneveld, L., Miles, E., McCarthy, M., Shaw, T., Melo Velasco, V., and Pellicciotti, F.: DebDaB: A database of supraglacial debris thickness and physical properties, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2024-559, in review, 2025.</p> <p><strong>Zenodo citation:</strong> Groeneveld, L., Fontrodona-Bach, A., Miles, E., McCarthy, M., Melo Velasco, V., Shaw, T., Pellicciotti, F., Bauder, A., Buri, P., Kneib, M., Kumar, A., Mishra, A., & Petersen, L. (2025). DebDaB: A database of supraglacial debris thickness and physical properties (Version v2) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.14514803" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.</a><a href="https://doi.org/10.5281/zenodo.14224835" target="_blank" rel="noopener">14224835</a></p> <p><strong>Original data sources citation:</strong> See <em>DebDaB_data_sources.pdf</em> or <em>DebDaB_data_sources.bib</em></p> <p>The authors acknowledge the Debris-Covered Glaciers Working Group (DCGWG) from the International Association of Cryospheric Sciences (IACS) for setting the stage and drawing together the debris-covered glaciers community to focus on broader needs transcending a specific research topic, and starting the zenodo community on debris-covered glaciers, where this database is hosted. </p> <p><strong>Author contributions: </strong>The following spreadsheet states the contribution of each of the co-authors on the database: <br><a href="https://docs.google.com/spreadsheets/d/1nTieH_ZkwqnUpHQMYn7bygEcV5RzX4DJuqZ_qd-_PzE/edit?usp=sharing" target="_blank" rel="noopener">Author contributions statement (click here)</a></p> <p>A description of what each contribution field means is below:</p> <ul> <li><em>Conceptualisation:</em> This refers to the original idea and shaping of the database and is therefore closed.</li> <li><em>Data curation:</em> The data managers of DebDaB. Primarily the quality checks and curation done to all the collected published and unpublished data. It may also include authors who have compiled a lot of measurements from sources the authors did not have, and merged them into DebDaB, or if someone else takes on the role of ingesting/homogenizing data in the future.</li> <li><em>Data collection: </em>Field measurements as well as scouring past literature that the authors have missed, digitising sources, or advocating for old missing data sources to be entered into DebDaB.</li> <li><em>Formal analysis:</em> In the case of methods being applied to derive debris property values from other measurements, such as the case for surface roughness and thermal conductivity.</li> <li><em>Supervision/funding: </em>This refers to funding provided for the generation of DebDaB itself, but also funding for the data collection (measurements). </li> </ul>
Gridded active layer thickness across northern permafrost regions from 2003 to 2020
<ol><li>This dataset provides the annual gridded active layer thickness (ALT) across northern permafrost regions (NPR) at 1 km resolution for the period 2003–2020. The ALT in permafrost regions refers to the upper layer of soil that thaws and refreezes annually as a result of seasonal temperature variations. It is a critical parameter in permafrost studies because it determines the depth to which plant roots can penetrate and influences various ecological and engineering processes. This dataset was produced based on the relationship between available ALT site measurements (2966 site-years) and satellite observations of annual gridded predictors, including vegetation, temperature, soil, and topography, using a Random Forest (RF) approach. The annual ALT map for the NPR was generated using the ensemble mean of ALT from the ten best RF predictions. Extensive uncertainty analysis was also conducted. </li><li>The dataset can be viewed at https://liuzh833.users.earthengine.app/view/altv1</li></ol>
Snow depth, snow water equivalent, ice thickness in Fuglebekken and Revdalen catchments collected in the SnowPilot campaign in Spring 2022
<p>File SnowPilot_snowdepth_along_the_GPR_profile_2022 contains snow depth measurements taken along the GPR profile performed during the SIOS SnowPilot campaign in Spring 2022. File SnowPilot_snowdepth_swe_2022 contains depth, snow water equivalent and basal ice thickness. Snowpits were dug on GPR profile crossings in the Fuglebekken and Revdalen catchments in the Hornsund fiord, Spitsbergen catchment. Snow density was measured with an IG PAS snow tube, and snow depth and basal ice (ice forming on the ground surface) thickness were measured with an avalanche probe. Point locations measured. with handheld GPR reciever.</p>
Data associated with following publication: "In situ optical sub-wavelength thickness control of porous anodic aluminum oxide"
<p>Data associated with following publication: "In situ optical sub-wavelength thickness control of porous anodic aluminum oxide" (DOI: <a href="https://doi.org/10.3762/bjnano.15.12" target="_blank" rel="noopener">https://doi.org/10.3762/bjnano.15.12</a>)</p>
Database of pyroclastic cover deposit thickness measurements (PT-Cam) in peri-volcanic areas of Campania (Italy)
<p>In an eruptive event, tephra deposits (i.e. ash and pumice) disperse in the atmosphere and deposit on the ground surface according to the speed and direction of the wind. Because the geotechnical and hydraulic properties of the unconsolidated pyroclastic fall deposits usually differ from the bedrock, their spatial thickness significantly influences geomorphological and hydrogeological processes such as landscape evolution, erosion, landslide, and hillslope hydrogeology.</p> <p>The PT-Cam database presents the thickness of tephra deposits (i.e. the unconsolidated materials over the bedrock) in Campania region (Italy), measured through in-situ investigations of some territories around the Somma-Vesuvius, Campi Flegrei, Roccamonfina, and Ischia volcanoes during the last decades. The measurements were conducted with probing tests, dynamic penetration tests, trenches, man-made pits, seismic surveys, and outcrops.</p> <p>Explanation for database attribute:</p> <ul> <li>CODE: identification code of the measurement;</li> <li>z: measured thickness expressed in cm;</li> <li>type_z: thickness type (i.e. if investigation has reached to the bedrock the type is “total” otherwise it is “partial”);</li> <li>type_investigation: method of in-situ investigation;</li> <li>locality: municipality to which the measurement point belongs;</li> <li>Longitude, Latitude: km coordinates in UTM WGS 84 system.</li> </ul>
Lake ice thickness and density measurements, McMurdo Dry Valleys, Antarctica (1989-2025, ongoing)
As part of the McMurdo Dry Valleys Long Term Ecological Research (LTER) project, ice thickness and density are measured annually for several lakes located throughout this region of Antarctica. Ice thickness is measured from the bottom of the ice cover to the piezometric water level and to the top of the ice cover. Because most limnological sampling holes are drilled in low-lying areas, it was thought that ice thickness measurements were biased. Therefore, random ice thickness measurements were performed in a 1000 m2 sampling grid on each lake in past years. Statistical analyses by Dr. Priscu have shown that measurements from three ice holes are representative of the ice thickness measured by this grid system.
Length, width, perimeter, and sediment thickness of ice-rafted sediment deposits, Rowley, MA.
Following a historic bomb cyclone (Winter Storm Grayson) in January of 2018, a large volume of ice-rafted sediment was patchily deposited on the surface of salt marshes in the Great Marsh, MA. In May of 2018, twenty patches of ice-rafted sediments and paired reference sites (i.e., no sediment deposition) were delineated. In May 2018, August 2018, and August 2019, samples were collected to examine how ice-rafted sediments affected vegetation, infauna, and epifauna recovery over time. This specific dataset focuses on the length, width, perimeter, and sediment thickness measurements of each ice-rafted sediment deposit. This dataset is complete and please see our publication (https://doi.org/10.1007/s12237-021-01023-z) for more information.
Estimating TOF from ultrasound signal over bare steel sample of 5 mm thickness
<p>These data have been obtained using the CEIT ultrasound testbed exciting a PZT piezoelectric sensor with a +/-15 volts pulse located over a 5 mm bare steel sample. The testbed receives the ultrasound response and estimates the TOF measuring the distance between two consecutive echoes. The aim is to develop an embedded system to measure the loss of thickness produced by corrosion.</p>
Ice thickness and bed topography of all Scandinavian glaciers and ice caps
<p>Files showing the ice thickness (m) and subglacial bed elevation (m) for all Scandinavian (i.e. Norwegian and Swedish) glaciers and ice caps. Coordinate system is epsg:25833</p> <p>Related publication which should be referenced when this data is used is </p> <div> <div> <div>Frank T, van Pelt W. Ice volume and thickness of all Scandinavian glaciers and ice caps. <em>Journal of Glaciology</em>. Published online 2024:1-34. doi:10.1017/jog.2024.25 <div> </div> </div> </div> </div>
PEATGRIDS: Mapping global peat thickness and carbon stock via digital soil mapping approach, dataset
<p>PEATGRIDS: a dataset containing the first peat thickness and carbon stock maps estimated over peatlands area across the globe at ~1 km x ~1 km resolution. Carbon stock was calculated across all depths of the predicted peat thickness, multiplied by peat bulk density (BD) and carbon content (CC) across five depths: 0-15 cm, 15-30 cm, 30-60 cm, 60-100 cm, and 100-200 cm. Mapping effort was performed using quantile random forest regression based on remotely sensed data and environmental covariates, including topography, climate, soil properties, and land cover. The maps cover areas potentially as peatlands according to the UNEP's global peatland map obtained from the <a title="Global Peat Database" href="https://greifswaldmoor.de/global-peatland-database-en.html" target="_blank" rel="noopener">Global Peat Database</a>. We may update this dataset in the future, please consider using the latest version. </p> <p>Note: This version (2.0.1) clarifies the metric units for carbon stock per area in the previous version (2.0). </p>
Radiance and Cloud Optical Thickness from Large Eddy Simulations over the Sulu Sea
<p>This repository contains the data files to accompany the paper "Segmentation-Based Multi-Pixel Cloud Optical Thickness Retrieval Using a Convolutional Neural Network". Please cite the paper as follows:</p> <p>Nataraja, V., Schmidt, S., Chen, H., Yamaguchi, T., Kazil, J., Feingold, G., Wolf, K., and Iwabuchi, H.: Segmentation-Based Multi-Pixel Cloud Optical Thickness Retrieval Using a Convolutional Neural Network, Atmos. Meas. Tech. Discuss. [preprint], https://doi.org/10.5194/amt-2022-45, in review, 2022.</p> <p>The 6 HDF5 files were generated using a tool called EaR<sup>3</sup>T developed by Hong Chen using Large Eddy Simulations over the Sulu Sea (Yamaguchi et al., 2019). Each hdf5 file contains 6 fields: </p> <p>cot_inp_3d: COT Input: column integrated COT directly from LES data;</p> <p>rad_mca_1d: MCARaTS 1D Radiance: radiance calculated from COT Input using MCARaTS in IPA mode;</p> <p>rad_mca_3d: MCARaTS 3D Radiance: radiance calculated from COT Input using MCARaTS in 3D mode;</p> <p>rad_ret_1d: Radiance from Input COT: radiance calculated from COT Input using a pre-calculated COT vs Radiance relationship;</p> <p>cot_ret_1d: COT from MCARaTS 1D Radiance: COT obtained from MCARaTS 1D Radiance using a pre-calculated COT vs Radiance relationship;</p> <p>cot_ret_3d: COT from MCARaTS 3D Radiance: COT obtained from MCARaTS 3D Radiance using a pre-calculated COT vs Radiance relationship.</p> <p> </p>
Global map of elastic thickness on Venus
<p>This map is Figure 14 from Anderson, F. S., and S. E. Smrekar (2006), Global mapping of crustal and lithospheric thickness on Venus, J. Geophys. Res., 111, E08006, doi:10.1029/2004JE002395. The location labels have been removed. Estimates of elastic thickness have an error of ±10 to 15 km. Caveats in Anderson and Smrekar (2006) should be carefully understood prior to use. No value of elastic thickness was obtained in areas in white. </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.