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774 results for “glacier”
TermPicks: A century of Greenland glacier terminus data for use inmachine learning applications
<p>TermPicks_V2.zip contains a single shapefile of the TermPicks marine-terminating terminus trace dataset. Version 2 fixes an issue with missing center x and y points and incorrectly labeled data.</p> <p>Centerline_Retreat_V2.zip contains CSV files of the retreats used in Goliber et al. (in prep). V2 fixes missing data (ex. 291.csv).</p> <p>TermPicks_V1_Coverage.kmz can be opened in Google Earth or any GIS software to view the TermPicks IDs and Landsat Coverage figures.</p> <p>TermPicks_IDs.zip contains a single shapefile of the ID schema for the TermPicks dataset.</p> <p>TermPicks+CALFIN_V3.zip contains a single shapefile of the TermPicks marine-terminating terminus trace dataset and the Calving Front Machine (CALFIN): glacial termini dataset. V2 fixes issues of missing data from the previous version due to an indexing error. V3 fixes an issue with missing center x and y points.</p> <p>When using this dataset, please include the following citation is included:</p> <p>Cheng, D., Hayes, W., Larour, E., Mohajerani, Y., Wood, M., Velicogna, I., & Rignot, E. (2021). Calving Front Machine (CALFIN): glacial termini dataset and automated deep learning extraction method for Greenland, 1972–2019. <em>The Cryosphere</em>, <em>15</em>(3), 1663-1675.</p>
500-yr Projections of Thwaites Glacier, Antarctica, with MALI, including glacial isostatic adjustment
<p>This archive contains model code, results, and analysis scripts for<br> reproducing the material presented in the manuscript "Stabilizing effect of<br> bedrock uplift on retreat of Thwaites Glacier, Antarctica, at centennial<br> timescales" by Cameron Book, et al. Questions should be directed to Matt<br> Hoffman (mhoffman@lanl.gov).</p> <p>This archive contains the following directories:</p> <p>|-- MALI_code<br> |-- PIGL_control<br> |-- analysis<br> |-- control<br> |-- run_setup<br> |-- N1<br> |-- N2<br> |-- N3<br> |-- N4<br> `-- PIGL_N3</p> <p><br> 'MALI_code' is a snapshot of the MALI repository used for these simulations,<br> commit 454e0fc8bf384bee1c1560d6c3eaa5fae43bfdda.<br> This commit is present in an older MALI repository that is no longer<br> maintained, at https://github.com/MPAS-Dev/MPAS-Model<br> MALI is currently maintained on Github at https://github.com/MALI-Dev/E3SM<br> Building MALI requires the Albany multiphysics library, which is available<br> at https://github.com/sandialabs/Albany. The simulations presented use Albany<br> master from March 5, 2021.</p> <p>'analysis' contains the scripts used to process the model output and produce<br> the figures and results presented in the manuscript. Filepaths will have to<br> be adjusted to your local layout.</p> <p>'run_setup' is a directory of files and scripts necessary to reproduce the<br> model simulations presented. The GIA model giapy is in the file<br> 'giascript.py'. giapy can also be found on Github at https://github.com/skachuck/giapy</p> <p>'control' is the control run with the GIA model disabled. It corresponds to<br> the run labeled CTRL in the manuscript.</p> <p>'PIGL_control' is the control run using the high melt forcing. It corresponds<br> to the run labeled HM-CTRL in the manuscript.</p> <p>The five run directories included here (N1-N4, PIGL_N3) are the standard<br> ensemble described in the manuscript. (There is a separate archive for the<br> runs briefly mentioned that exclude the elastic response of the lithosphere.)<br> The individual runs have the following correspondence to the manuscript:<br> N1=TYP<br> N2=BEST2<br> N3=VLV-THIN<br> N4=VLV<br> PIGL_N3=HM-VLV-THIN<br> Within each run directory, are the following model output files:<br> globalStats.nc: MALI global, scalar time-series<br> iceload_all.nc: Thwaites Glacier ice load on the GIA grid<br> output_*.nc: MALI spatial output fields, separated by century<br> uplift_GIA_all.nc: GIA output on GIA grid</p>
Natural color composites of VENµs images over South Col Glacier
<p>This datasets contain images of South Col Glacier obtained from VENµs platform from 27 Nov 2017 to 30 Oct 2020. The images are shown on a UTM45\WGS84 projection, and correspond to the band combination 7-4-3. The two stars show remarkable locations.</p>
GPR snow depth survey over Svalbard Glaciers
<p>Dataset contains results of GPR surveys of snowpack performed on five glaciers in Svalbard (Slakbreen, Longyearbreen, Maritbreen, Philipbreen and Holtedahlfonna ). Surveys were performed in March - April 2008, with 800 MHz antenna (Mala ProEx system).</p> <p>Fieldwork was funded by the Svalbard Integrated Arctic Earth Observing System Access project "Snow Observations in Svalabr (SOS)".</p> <p>Dataset consists of following unprocessed files:</p> <p>*.RAD - survey system and antenna control file</p> <p>*.COR - trace number, date, time and poistion</p> <p>*.MRK - reference markers</p> <p>*.RD3 - radarogram (clsed MALA ProEx format)</p>
Supplementary files: A Comparative Study of Active Rock Glaciers Mapped from Geomorphic- and Kinematic-Based Approaches in Daxue Shan, Southeast Tibetan Plateau
<p>Supplement of "A Comparative Study of Active Rock Glaciers Mapped from Geomorphic- and Kinematic-Based Approaches in Daxue Shan, Southeast Tibetan Plateau". The supplementary files provide the outlines and parameters of the rock glaciers inventoried by InSAR-assist kinematic-based approach in the Daxue Shan, Southeast Tibet Plateau. </p> <p>Based on the Sentinel-1A ascending SAR images acquired between 2015 and 2019, we derived a five-year-long LOS mean velocity map of the study area. We then compiled a rock glacier inventory by synergistically interpreting the InSAR-derived surface displacements and geomorphic features based on Google Earth images.</p>
Dataset - Warming-induced monsoon precipitation phase change intensifies glacier mass loss in the southeastern Tibetan Plateau
<p>Materials and data results needed to reproduce the findings of the study published in (PNAS) Proceedings of the National Academy of Sciences of the United States of America:</p> <p>"<em>Warming-induced monsoon precipitation phase change intensifies glacier mass loss in the southeastern Tibetan Plateau"</em>. A. Jouberton, T. E. Shaw, E. Miles, M. McCarthy, S. Fugger, S. Ren, A. Dehecq, W. Yang and F. Pellicciotti</p> <p>It includes the meteorological forcing time-series, an exhaustive list of the model parameters, the outputs of TOPKAPI-ETH, and Matlab scripts allowing to reproduce the figures and compute the numbers given in the main manuscript as well as in the Supplementary Information.</p> <p>---------------</p> <p><strong>Contents</strong> :</p> <p>Folder : "Matlab_scripts"<br> '<strong>Climate_import.m</strong>' : Organizes meteorological forcing and generates Figure S9<br> <strong> 'TOPKAPI_result_import.m' :</strong> Imports TOPKAPI's reference run outputs and prepares them for analysis<br> <strong> 'Experiment_analysis.m' : </strong>Analyses the results of the forcing experiments, generates Figure 4 and Figure S25<br> <strong> 'Main_text_results.m' : </strong>Analyses the results of TOPKAPI's reference runs, generates Figure 1D, FIgure 2 and Figure 3<br> <strong> 'TOPKAPI_validation.m' : </strong>Compares TOPKAPI's reference run results with several validation datasets, generates the figures and performance metrics of the model calibration and validation procedure.<br> <strong> 'Parlung_albedo_regional_analysis.m'</strong>: Computes the mean glacier albedo per elevation band for each glacier within the Southeastern Tibean Plateau and compares it to the albedo of Parlung No.4 glacier.<br> <strong> 'Parlung_GMB_regional_analysis.m'</strong>: Computes the mean glacier mass balance per elevation band for each glacier within the Southeastern Tibean Plateau and compares it to the glacier mass balance of Parlung No.4 glacier.<br> <strong> 'Precipitation_phase_sensitivity_analysis.m'</strong>: Performs a sensitivity analysis on the simulated monsoon snowfall ratio per elevation band and on the attribution of glacier mass loss to precipitation<br> phase change using Monte Carlo simulations.<br> <strong> 'TOPKAPI_MODIS_validation.m'</strong>: Compares the snow cover at Parlung No.4 catchment simulated by TOPKAPI-ETH and observed by MODIS, generates Figure S19.</p> <p> </p> <p>Folder : "Remote_sensing" :</p> <p> Sub-Folder: 'Hugonnet' = Glacier mass balance averaged over 2000-2020 covering the Southeastern Tibetan Plateau, 100m resolution, derived from Hugonnet et al. 2021<br> Sub-Folder: 'MODIS' = contains the snow cover at Parlung No.4 derived from the daily product MOD10A1 version 61, for the period 2000-2018<br> Sub-Folder: 'Regional_glacier_albedo' = contains the annual glacier surface albedo from 2000 to 2020, covering the Southeastern Tibetan Plateau, 500m resolution.<br> Sub-Folder: 'Shapefiles' = contains the Parlung No.4 glacier outlines in 1974 and from the RGI 6.0<br> <strong>'ASTER_Nyainqentanglha_15m_utm.tif'</strong> = ASTER Digital elevation model at 15m resolution covering the Southeastern Tibetan Plateau<br> <strong> 'parlung_mask_1974.mat' </strong>= Parlung No.4 glacier mask as a matlab file<br> <strong> 'dh_ASTER_SRTM_30m.tif' </strong>= Mean elevation change rate from 2000 to 2016 at Parlung No.4 catchment.<br> <strong> 'Geodetic_map.mat'</strong> = Elevation change maps for the periods 1974-2000 and 1974-2014, as a matlab file<br> <strong> 'GMB_geodetic.mat' </strong>= Geodetic mass balance (glacier-wide mean and profile per elevation band) used in Figure S13<br> <strong> 'parlung_30m_catchment_mask.tif'</strong> = Parlung No.4 catchment mask<br> <strong>'parlung_1974_30m_dem.tif' </strong>= DEM of Parlung No.4 catchment, 30 m resolution<br> <strong> 'parlung_1974_30m_gla.tif'</strong> = Parlung No. 4 glacier mask, 30m resolution<br> <strong>'parlung_1974_30m_glah.tif' </strong>= Reconstructed ice thickness of 1975 for Parlung No.4 glacier<br> <strong>'parlung_1974_2000_diff_24m.tif' </strong>= Elevation change from DEM differencing at Parlung No.4 catchment for 1974-2000<br> <strong> 'parlung_1974_2014_diff_24m.tif' </strong>= Elevation change from DEM differencing at Parlung No.4 catchment for 1974-2014<br> <strong> 'Parlung_1974_bedrock_dem_30m.tif' </strong>= Bedrock surface digital elevation model of the catchment, 30m spatial resolution<br> <strong>'RGI_KangriKarpo_100m_utm_id.tif' </strong>= Glacier mask covering the Kangri Karpo mountain region, 100m resolution, with glacier IDs in the attribute table<br> <strong> 'RGI_Nyainqentanglha_100m_utm_id.tif' </strong>= = Glacier mask covering the Southeastern Tibetan Plateau, 100m resolution, with glacier IDs in the attribute table</p> <p> </p> <p>Folder : "TOPKAPI_forcing" :<br> <strong> CCT_AWS4600_extended.csv : </strong>Hourly cloud cover transmissivity from 1975 to 2018 reconstructed at AWSoff location <br> <strong> Climate.mat : </strong>Organizes meteorological forcings, output from the matlab script '<strong>Climate_import.m</strong>'<br> <strong> LR_AWS4600_extended.csv :</strong> Hourly temperature lapse-rates from 1975 to 2018 reconstructed at AWSoff location <br> <strong> Precipitation_AWS4600_extended.csv : </strong>Hourly precipitation from 1975 to 2018 reconstructed at AWSoff location <br> <strong> Ta_AWS4600_extended.csv :</strong> Hourly air temperature from 1975 to 2018 reconstructed at AWSoff location<br> Sub-Folder: 'National_meteorological_stations' = Contains the daily air temperature and precipitation measured at the national meteorological stations of Bomi, Zayu, Zuogong and Basu<br> Sub-Folder: 'Reference_run_inputs' = Contains the input files necessary to run TOPKAPI-ETH to obtain the outputs from which the results of this study are based on.</p> <p> </p> <p>Folder : "TOPKAPI_output":<br> <strong> </strong> Sub-Folder : "Forcing experiment" = organized TOPKAPI outputs from the forcing experiment<br> Sub-Folder :" Reference_run_outputs" = raw TOPKAPI outputs from the reference run (catchment average, spatial and grid cells)<br> Sub-Folder : "Reference_run_results" = organized TOPKAPI outputs from the reference run<br> Sub-Folder : "Snow_ice_cover" = contains TOPKAPI-ETH derived snow cover maps (daily map outputs)<br> Sub-Folder : "Regional_analysis" =<br> 'Alb'= Table containing the mean glacier albedo (2000-2020) per normalized elevation band, for each glacier in the SETP (RGI 6.0)<br> 'GMB'= Table containing the mean glacier mass balance (2000-2020) per normalized elevation band, for each glacier in the SETP (RGI 6.0)<br> 'Hypso_xxm' = Table containing the percentage of glacier area per normalized elevation band, for each glacier in the SETP (RGI 6.0), resolution of 100/500m<br> 'NormEl_100m' = Table containing the elevation per normalized elevation band, for each glacier in the SETP (RGI 6.0), resolution of 100/500m<br> Sub-Folder : "Semi_distributed_outputs" = Precipitation phase and amounts resulting from TOPKAPI-ETH simulation per elevation band, for the reference run and for the Monte Carlo sensitivity analysis</p> <p> </p> <p>Folder : "Validation_data"<br> <strong> 'topkapi.out_reference_discharge2016' </strong>= <strong> </strong>raw TOPKAPI outputs run in 2016 with AWSoff air temperature<br> <strong> </strong><strong> 'master_file_parlung.mat' </strong>=<strong> </strong>matlab structure containing AWS measurements, necessary for running <strong>'TOPKAPI_validation.m'</strong><br> <strong> 'Qdigit.mat' </strong>= Discharge measured at the Parlung No.4 glacier outlet, from Li et al., (2016)<br> <strong> 'Parlung_Q_1970.mat'</strong> = 'Discharge time-series used to run TOPKAPI-ETH (goes back to 1975, but filled with 0 when no measurements are available)</p> <p> </p> <p>In order to run the Matlab scripts, it is recommended to download all folders and gather them into the same folder. Any request about data or questions on how to run the Matlab scripts can be asked to the author of the paper (at achille.jouberton@wsl.ch).</p>
New insights into the decadal variability in glacier volume of a tropical ice-cap explained by the morpho-topographic and climatic context, Antisana, (0°29' S, 78°09' W)
<p>The dataset contains five periods of surface elevation change observed on the Antisana icecap in the inner tropical region. Data were obtained by geodetic observations of aerial photographs and high-resolution satellite images for the study periods: 1956-1965, 1965-1979, 1979-1997, 1997-2009, and 2009-2016.</p>
Ngozumpa Glacier Gokyo ice cliffs photographic surface model
<p>Data files of a photographic surface model of three ice cliffs exposed on the surface of the Ngozumpa Glacier ( 27°57′N, 85°42′E), Nepal, near the village of Gokyo.</p> <p>Details of how the data were produced:</p> <ul> <li>1160 full resolution JPEG images were used to make the difgital surface model (DSM)</li> <li>Photos were taken using Nikon D5000 with 100mm Tamron macro lens on 4th April 2016</li> <li>7 ground control points (GCPs) within the captured scene were used to scale and georeference the DSM</li> <li>GCPs were measured using a Trimble XH6000 with Tornado antenna as rover and a Trimble Geo7X with Zephyr antenna as a local base station</li> <li>Agisoft Photoscan (v1.26) was used to make the DSM using a SfM-MVS workflow</li> <li>Point pairs appearing in only one image pair and havoing a reprojection error grreater than 0.5 pixels were removed</li> <li>Model optimization was done using default parameters of f, b1, b2, cx, cy, k1-4, p1 and p2 and, as the lens distortion plot showed no extreme skewing or distortion, these optimization settings were accepted.</li> <li>The dense point cloud was generated using the ‘High’ quality setting with ‘Aggressive’ point filtering.</li> <li>The dense point cloud was then used directly to generate the DSM and orthomosaic at resolutions of 0.046 and 0.023 m respectively.</li> </ul> <p>Description of files:</p> <ul> <li><strong>GokyoIceCliffs_POINTCLOUD.txt:</strong> dense point cloud including the whole scene covered by the photographs. Format: x,y,z, text file, WGS 84 / UTM zone 45N.</li> <li><strong>GokyoIceCliffs_DSM_0.04.tif:</strong> 0.046 m resolution digital terrain model derived from the dense point cloud covering the area of interest including the 3 sampled ice cliffs. Format: TIFF WGS 84 / UTM zone 45N.</li> <li><strong>GokyoIceCliffs_ORTHO_0.04.tif: </strong>0.023 m resolution orthophoto covering the area of interest including the 3 sampled ice cliffs. Format: TIFF WGS 84 / UTM zone 45N.</li> </ul> <p> </p>
WRF-Chem output supporting manuscript "Sources of Black Carbon Deposition to the Himalayan Glaciers in Current and Future Climates"
<p>Selected output from WRF-Chem v3.6.1 on black carbon deposition, rainfall, and snowfall over Southeast Asia. These files were used to prepare the figures and tables in the manuscript "Sources of Black Carbon Deposition to the Himalayan Glaciers in Current and Future Climates" by these authors. Files are in NetCDF format and contain metadata describing their contents. The naming convention is:</p> <p>YYYY_MM_EXT_daily_12km_dustfix.nc</p> <p>where YYYY_MM is the simulated year and month and the extensions are:</p> <p>NFC - No Further Control emission scenario</p> <p>MIT - Mitigation emission scenario</p> <p>EN - Simulated El Nino year</p> <p>LN - Simulated La Nina year</p> <p> </p>
"Monthly velocity and seasonal variations of the Mont Blanc glaciers derived from Sentinel-2 between 2016-2024" - supplementary materials
<p>The repository contains the supplementary materials to be downloaded relative to the research article:</p> <p>“Monthly velocity and seasonal variations of the Mont Blanc glaciers derived from Sentinel-2 between 2016-2024” </p> <p>https://doi.org/10.5194/egusphere-2023-2771</p> <p>The available files are:</p> <p>-92 raster maps of monthly velocity of the study area.</p> <p>-Shapefiles whith the glacier outlines of the 30 studied glaciers.</p> <p>-Shapefiles of the velocity time series extraction areas.</p> <p>-Velocity time series 2016-2024 of the 30 glaciers from the study. </p>
FIG. 2 in Morphological and phylogenetic data confirm the identity of Prasiola fluviatilis (Prasiolales, Trebouxiophyceae) from glacier streams in the Tianshan Mountains, China
FIG. 2.— Morphology of Prasiola fluviatilis (Sommerfelt) Areschoug ex Lagerstedt: A, B, thalli; C, D, vegetative cells; E, F, cells in the lower part of the blade; G, cells in the upper portion of the thallus; H, uniseriate branches. Scale bars: A, B, 1 cm; C, D, 100 µm; E-H, 20 µm.
FIG. 1 in Morphological and phylogenetic data confirm the identity of Prasiola fluviatilis (Prasiolales, Trebouxiophyceae) from glacier streams in the Tianshan Mountains, China
FIG. 1. — Habitat of Prasiola fluviatilis (Sommerfelt) Areschoug ex Lagerstedt: A, streams under a glacier; B, population of P. fluviatilis.
FIG. 5 in Morphological and phylogenetic data confirm the identity of Prasiola fluviatilis (Prasiolales, Trebouxiophyceae) from glacier streams in the Tianshan Mountains, China
FIG. 5.— Global distribution of Prasiola fluviatilis (Sommerfelt) Areschoug ex Lagerstedt (blue: glacier area [GLIMS and NSIDC 2005, updated 2019]; Ϙ, occurrence records based on morphology; Δ, occurrence records based on morphology and molecular evidence)
APPENDIX 2 in Discovery of Oligocene-aged mammals in Glacier National Park (Kishenehn Formation), Montana
APPENDIX 2. — Dental morphology in occlusal view of CM 87801, Paciculus Cope, 1879 from the Kishenehn Formation.
FIG. 2 in Discovery of Oligocene-aged mammals in Glacier National Park (Kishenehn Formation), Montana
FIG. 2. — Morphology of GLAC 26988, Pronodens transmontanus (Douglass, 1903), from the Kishenehn Formation: A, lateral view of right dentary; B, medial view of right dentary; C, lateral view of left dentary; D, medial view of left dentary; E, line drawing of the occlusal view showing tooth morphology of the left dentary. A photo of the occlusal view of the teeth is showed in Appendix 1. Scale bars: A-D, 2 cm; E, 4 mm.
FIG. 6 in Discovery of Oligocene-aged mammals in Glacier National Park (Kishenehn Formation), Montana
FIG. 6. — Morphology of CM 87801, Paciculus Cope, 1879 from the Kishenehn Formation: A, lateral view of right dentary; B, occlusal view of dentition, m1-2. Abbreviations: Aid, anteroconid; ALac, anterior labial cingulum; ALic, anterior lingual cingulum; Atid, anterolophid; Ecid, ectostylid; Entid, entolophid; Eid, entoconid; Hid, hypoconid; Lc, labial cingulum; Mepid, mesolophid; Mesid, mesostylid; Mid, metaconid; Pc, posterior cingulum; Pid, protoconid; PtiI, protolophid I; PtidII, protolophid II. A photo of the occlusal view of the teeth is showed in Appendix 2. Scale bars: A, 2 mm; B, 1 mm.
FIG. 5 in Discovery of Oligocene-aged mammals in Glacier National Park (Kishenehn Formation), Montana
FIG. 5. — Morphology of GLAC 26987, fossil Miohippus Marsh, 1874 from the Kishenehn Formation: A, lateral view of left dentary; B, medial view of left dentary; C, occlusal view of left dentary; D, lateral view of right dentary; E, medial view of right dentary; F, occlusal view of the right dentary. Scale bar: 2 cm.
FIG. 1 in Discovery of Oligocene-aged mammals in Glacier National Park (Kishenehn Formation), Montana
FIG. 1. — Geographic context of the Oligocene-aged fossils from Glacier National Park: A, detailed map of the Kishenehn basin showing the North Fork and Middle Fork areas, faults in the area (continuous black lines), continental divide (red), national park boundaries (dashed blue line), and water courses (gray). Localities of GLAC 26988 (1), GLAC 26987 (2), and CM 87801 (3) are also showed (modified from Dawson & Constenius 2018). Detailed geographic information is available to qualified researchers from repositories; B, map of western Montana showing the location of five coeval Arikareean-aged deposits bearing leptomerycid and/ or equid fossils: the Kishenehn, Renova (Cabbage Patch beds), and Fort Logan formations as well as the Canyon Ferry Reservoir and the Peterson Creek Local Fauna (in Idaho). Select water bodies, cities, and the continental divide are also displayed. Grey areas represent basins with strata coeval with the Kishenehn Formation. Black box shows location of A; C, location of the state of Montana within the United States with black box showing location of B.
APPENDIX 1 in Discovery of Oligocene-aged mammals in Glacier National Park (Kishenehn Formation), Montana
APPENDIX 1. — Dental morphology in occlusal view of GLAC 26988, Pronodens transmontanus (Douglass, 1903), from the Kishenehn Formation.
FIG. 4 in Discovery of Oligocene-aged mammals in Glacier National Park (Kishenehn Formation), Montana
FIG. 4. — Shape analysis of GLAC 26988 and other leptomerycids from the Arikareean and Hemingfordian: A-C, comparison of the shape of the premolars; D-F, comparison of the shape of the molars. GNP denotes the specimen from the Glacier National Park (Kishenehn Formation); data from the left jaw. Note that the data for Pseudoparablastomeryx Frick, 1937 are in fact a species mean. Each point otherwise represents a specimen. Disparity is showed for Pronodens transmontanus (Douglass, 1903) from the Cabbage Patch beds when four or more specimens were measured. See Tables 1-3 for data. Abbreviations: ant, anterior lophid; Ppf, Pseudoparablastomeryx francescita (Frick, 1937); Pps, P. Pseudoparablastomeryx scotti (Frick, 1937); Ps, Pronodens silberlingi Koerner, 1940; Psp, Pronodens sp.; Pt, P. transmontanus (Douglass, 1903).
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.