Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
774
datasets available to search
ShareScore release 0.7.1
Dataset results
774 results for “glacier”
Sliding velocity, water discharge, water pressure, and rainfall time series at Argentière Glacier between 2019 and 2021
<p>Files Description:</p> <p>==================================<br>cavitometer_2019-2021.dat:<br>==================================</p> <p>Contains 30-min sampled values of recorded sliding velocities at the cavitometer.</p> <p>Column 1 = Date<br>Column 2 = Velocity (mm/hour)</p> <p>================================<br>water_discharge_2019-2021.dat:<br>================================</p> <p>Contains 15-min sampled values of recorded water discharge at the glacier outlet.</p> <p>Column 1 = Date<br>Column 2 = Water discharge (m3/s)</p> <p>================================<br>water_pressure_2019-2021.dat:<br>================================</p> <p>Contains 30-min sampled values of recorded water pressure at the borehole BH2.</p> <p>Column 1 = Date<br>Column 2 = Water pressure (bar)</p> <p>================================<br>rainfall_2019-2021.dat:<br>================================</p> <p>Contains 30-min sampled values of recorded rainfall at the meteo station.</p> <p>Column 1 = Date<br>Column 2 = Rainfall (mm w.eq./hour)</p>
Day 13: Lake McDonald Lodge staircase at Glacier
This is the main stairway at the Lake McDonald Lodge at Glacier National Park in Montana. Built in 1913, the lodge is nestled between Lake McDonald and the Going-To-The-Sun Road. Original artwork adorns nearly every wall. Take a peek! 📍 [48.61740, -113.87922](https://scaniver.se/L48.61740,-113.87922) Shot with an iPhone 12 Pro Max and Scaniverse. Note: I couldn't figure out how to get the walls to be transparent when looking into the model, and opaque when looking out. There might be a setting in Sketchfab to fix this. The model looks as it should in Scaniverse (transparent inward, opaque outward). I'm attempting to share #1scanaday for the remainder of 2021. Follow along! Source: Objaverse 1.0 / Sketchfab
Data Products associated with "Stream hydrology controls on ice cliff evolution and survival on debris-covered glaciers"
<p>Data products from the study "Stream hydrology controls on ice cliff evolution and survival on debris-covered glaciers," by Petersen et al, submitted September 2023 to Earth Surface Dynamics.</p>
Siachen Glacier flow velocites from 2018 to 2020
<p>The dataset was created using Sentinel-1/2 and GF-3 satellite data. A total of 168 images were fused, comprising 47 optical images and 121 SAR (Synthetic Aperture Radar) images, to generate the ice flow velocity data for the Siachen Glacier from 2018 to 2020.</p>
A dataset of supraglacial meltwater rivers in the ablation area of Sermeq Avannarleq glacier, Greenland.
<p>Due to the increasingly dramatic global warming, surface meltwater is one of the main causes of the mass balance of Greenland Ice Sheet (GrIS). The detection and the understanding of distribution of supraglacial rivers is of great significance for the studies of glacier dynamics and mass balance. On 24 and 27 October 2021, based on a drone equipped with an RGB sensor, we survey the ablation area of Sermeq Avannarleq Glacier on the northeast of Jakobshavn, and obtained high-resolution orthophoto images of the nearly 40km<sup>2</sup> area of the glacier. Based on these images, the supraglacial rivers were visually interpreted and delineated. This dataset contains 8643 pairs of 256×256 pixels size orthophoto crops and labels.</p>
Data products from "GNSS reflectometry from low-cost sensors for continuous in-situ contemporaneous glacier mass balance and flux divergence"
<p>GNSS, GNSS-IR, and mass balance data from "GNSS reflectometry from low-cost sensors for continuous in-situ contemporaneous glacier mass balance and flux divergence". Contains the following folders and files</p> <ul> <li>GNSS <ul> <li><em>Precise point positioning solution (.pos, etc) using the CSRS-PPP tool for each GNSS system</em></li> </ul> </li> <li>GNSS_basefix <ul> <li><em>Precise point positioning solution (.pos) with base station observations using the Emlid Studio desktop application for GNSS systems AB floating and AB fixed</em></li> </ul> </li> <li>GNSSIR<br> <ul> <li><em>Reflector height solutions for site AB floating, AB fixed, and D floating (see Fig. 7). The filename is the day of year 2023.</em></li> </ul> </li> <li>Monitored Ablation Stake<br> <ul> <li><em>Processed daily and seasonal climatic mass balance height changes (see Fig. 5)</em></li> </ul> </li> </ul>
Animations of iSOSIA glacier model results for Miage Glacier through the Holocene
<p>Animations of the results from a simulaton of Miage Glacier, Italy, through the Holocene (~12 ka to present) made using the iSOSIA glacial landscape evolution model (Egholm et al., 2011). iSOSIA is a glacial landscape evolution model that simulates erosion, transport, and deposition of sediment, and represents the feedbacks between supraglacial debris transport, ice flow and mass balance balance (Rowan et al., 2015; Scherler and Egholm, 2020).</p> <p>The subglacial model domain has a 50 m x 50 m cell size and was defined using the 30-m ASTER GDEM version 2 from which the estimated present-day ice thickness for all glaciers in the catchment with an area greater than 1 km<sup>2</sup> was subtracted (Farinotti et al., 2019). The model was forced using the Temp12k palaeotemperature composite of calibrated records of median annual air temperature for the latitude band 30–60°N which has a time step of 500 years (Kaufman et al., 2020). These data give mean annual air temperature at sea level and were extrapolated to the model domain using a lapse rate of –0.006°C m<sup>–1</sup>. Sediment was produced by hillslope erosion and transported from the catchment headwalls using a non-linear hillslope flux model (Roering et al., 1999).</p> <p> </p> <p>File contents:</p> <p><strong>Miage_icethick.mp4</strong> shows the change in ice thickness during the simulation.</p> <p><strong>Miage_velocity.mp4</strong> shows the change in depth-integrated ice flow (sliding and deformation) during the simulation.</p> <p><strong>Miage_supraglacialdebris.mp4</strong> shows the change in thickness of the sediment layer on the glacier surface during the simulation.</p> <p><strong>Miage_hillslope.mp4</strong> shows the change in sediment production and storage from hillslope erosion during the simulation.</p> <p> </p> <p>References:</p> <p>Egholm DL, Knudsen MF, Clark CD, Lesemann JE. 2011. Modeling the flow of glaciers in steep terrains: The integrated second-order shallow ice approximation (iSOSIA). Journal of Geophysical Research: Earth Surface <strong>116</strong> DOI: 10.1029/2010JF001900</p> <p>Farinotti D, Huss M, Fürst JJ, Landmann J, Machguth H, Maussion F, Pandit A. 2019. A consensus estimate for the ice thickness distribution of all glaciers on Earth. Nature Geoscience <strong>12</strong> : 168–173. DOI: 10.1038/s41561-019-0300-3</p> <p>Kaufman D et al. 2020. A global database of Holocene paleotemperature records. Scientific Data <strong>7</strong> : 115. DOI: 10.1038/s41597-020-0445-3</p> <p>Roering JJ, Kirchner JW, Dietrich WE. 1999. Evidence for nonlinear, diffusive sediment transport on hillslopes and implications for landscape morphology. Water Resources Research <strong>35</strong> : 853–870. DOI: 10.1029/1998WR900090</p> <p>Rowan AV, Egholm DL, Quincey DJ, Glasser NF. 2015. Modelling the feedbacks between mass balance, ice flow and debris transport to predict the response to climate change of debris-covered glaciers in the Himalaya. Earth and Planetary Science Letters <strong>430</strong> : 427–438. DOI: 10.1016/j.epsl.2015.09.004</p> <p>Scherler D, Egholm DL. 2020. Production and Transport of Supraglacial Debris: Insights From Cosmogenic <sup>10</sup>Be and Numerical Modeling, Chhota Shigri Glacier, Indian Himalaya. Journal of Geophysical Research: Earth Surface <strong>125</strong> DOI: 10.1029/2020JF005586</p>
Datasets associated with paper "Five decades of Abramov glacier dynamics reconstructed with multi-sensor optical remote sensing", by Enrico Mattea et al.
<p>This archive contains Digital Elevation Models and orthoimages produced within the study "Five decades of Abramov glacier dynamics reconstructed with multi-sensor optical remote sensing", published on The Cryosphere by Enrico Mattea et al.</p> <p>Assets include derivative products of SPOT satellite scenes, acquired by CNES’s Spot World Heritage Programme, and of Pléiades <span><span><strong>©</strong></span></span>CNES 2015, 2020, 2022, Distribution AIRBUS DS.</p>
Data and code for Recent observations of Thwaites Glacier, West Antarctica are consistent with high rates of loss in next 50 years
<p><span>This repository contains the code and input files needed for running the MITgcm and ISSM experiments in this manuscript, and the relevant outputs from the experiments.</span></p> <p><span>1. STREAMICE</span></p> <p><span>the STREAMICE folder contains the following subfolders:</span></p> <p><span>code: contains all MITgcm source files for calibration experiments that are not included in the MITgcm repository</span></p> <p><span>code_proj: similar to code/ but for projection experiments</span></p> <p><span>model_input: contains subdirectories containing binary inputs and parameter files for all experiments in the manuscript</span></p> <p><span>model_output: output from each projection experiment is contained in a .mat file with the following arrays:</span></p> <ul> <li><span> X, Y: x-and y-coordinates in WGS 84 / Antarctic Polar Stereographic (EPSG:3031)</span></li> <li><span> BED: bed elevation (m)</span></li> <li><span> BETA: \beta described in the manuscript (units: Pa^(1/2) (m/a)^{-1/6})</span></li> <li><span> BGLEN: \overline{B} described in the manuscript (units: Pa^(1/2) a^{-1/6})</span></li> <li><span> SURF: ice surface elevation (m)</span></li> <li><span> THICK: ice thickness (m)</span></li> <li><span> THICK0: initial ice thickness (m)</span></li> <li><span> VX: x-velocity (m/a)</span></li> <li><span> VY: y-velocity (m/a)</span></li> <li><span> times:<span> </span>time in years after 2004</span></li> </ul> <p><span>checkpoint 68y of the MITgcm source code (mitgcm.org) was used for this study.</span></p> <p><span>m1qn3 (https://who.rocq.inria.fr/Jean-Charles.Gilbert/modulopt/optimization-routines/m1qn3/m1qn3.html) was used for optimisation.</span></p> <p><span>details of how to compile and run the forward and adjoint STREAMICE code can be found at https://mitgcm.readthedocs.io/en/latest/</span></p> <p><span>-------------------------------------------</span></p> <p><span>2. ISSM</span></p> <p><span>runme.m contains all ISSM instructions to run the experiments.</span></p> <p><span>output/ contains mat files for all experiments, with array descriptions as above.</span></p> <p><span>ISSM source and documentation can be found at https://issm.jpl.nasa.gov/.</span></p>
Combined CH4, N2O and CO2 fluxes budgets reveal a net carbon sink across a glacier-ocean continuum
<p>This dataset is supplument to a manuscript "Meltwater impacts CH4 and N2O fluxes across a glacier-ocean interface". </p> <p>Dataset includes </p> <ul> <li>timeseries.xlsx <ul> <li>A timeseries dataset conducted at Jökulsárlón Lagoon in Iceland. It includes CH<sub>4</sub> and N<sub>2</sub>O concentration, water flow and environmental variables. </li> </ul> </li> <li>discrete_v3.xlsx <ul> <li>Discrete samples conducted around Jökulsárlón Lagoon in Iceland It includes CH<sub>4</sub> and N<sub>2</sub>O concentration, nutrient. </li> <li>Add lat and lon (updated: 20 Nov 2024) </li> </ul> </li> </ul>
Data products from "An 85-year record of glacier change and impacts on future projections for Kennicott and Root Glaciers, Alaska"
<p>DEMs, orthophotos, glacier outlines, historical velocity, model outputs, and ice-penetrating radar data from "An 85-year record of glacier change and impacts on future projections for Kennicott and Root Glaciers, Alaska". File structure is shown in the readme file.</p>
Frontal ablation estimates for 49 tidewater glaciers in Greenland
<p>This data product contains frontal ablation estimates for 49 selected tidewater glaciers in Greenland from 1987-2020 at 3-monthly resolution. Version 8 of the product is available as merged file for all 49 glaciers as point geometry in NetCDF (FrontalAblationEstimates.nc), Shapefile (FrontalAblationEstimates.shp), and GeoPackage (FrontalAblationEstimates.gpkg) format. The files are stored in the folder FrontalAblationEstimates.zip.</p> <ul> <li><strong>Time</strong> – Midpoint of time intervals on which output data is defined [days since 01/01/1950]</li> <li><strong>Name</strong> – Name of each glacier investigated in this study [unitless]</li> <li><strong>Lat </strong>– Latitude (EPSG:4326) [degrees]</li> <li><strong>Lon</strong> – Longitude (EPSG:4326) [degrees]</li> <li><strong>PolarX</strong> – Polar Stereographic X Coordinate (EPSG:3413) [m]</li> <li><strong>PolarY</strong> – Polar Stereographic Y Coordinate (EPSG:3413) [m]</li> <li><strong>F</strong> – Three-month-average frontal ablation estimates during time intervals [Gt/d]</li> <li><strong>F_Max_U </strong>– Maximum uncertainty over total time period [Gt/d]</li> <li><strong>F_U </strong>–<strong> </strong>three-month-average frontal ablation uncertainty for time intervals [Gt/d]</li> <li><strong>D</strong> – Three-month-average solid ice discharge during time intervals [Gt/d]</li> <li><strong>D_U</strong> – Three-month-average solid ice discharge uncertainty for time interval [Gt/d]</li> <li><strong>TMC</strong> – Terminus mass change (<em>dM/dt</em>) during time intervals [Gt/d]</li> <li><strong>TMC _U</strong> – Three-month-average terminus mass change uncertainty during time intervals [Gt/d]</li> <li><strong>L</strong> – Interpolated terminus change over time (<em>L</em>) [km]</li> <li><strong>Delta_L_W </strong>– Delineation and fjord width uncertainty (δL/<em>δW</em>; constant) [km]</li> <li><strong>H </strong>– Mean ice thickness within the polygon; maximum over the observation period (<em>H</em>) [km]</li> <li><strong>Delta_H</strong> – Ice thickness uncertainty with constant ArcticDEM/AeroDEM uncertainties of 0.1 m and 6 m, respectively (<em>δH</em>) [km]</li> <li><strong>Rho</strong> – Ice density (<em>ρ</em>i; constant) [kg/km<sup>3</sup>]</li> <li><strong>W </strong>– Mean fjord width (<em>W</em>) [km]</li> <li><strong>Bedrock_U </strong>– Mean uncertainty in bedrock topography along the centerline [km]</li> <li><strong>TI </strong>– Time interval over which data are averaged (t2-t1; 90 days for the results presented here) [d]</li> </ul> <p>The shapefile and geopackage files contain the same the same variables as the NetCDF, however the variable Time is formatted differently for ease of use, and the variables Polar X and Polar Y are contained in the field geometry as outlined below:</p> <ul> <li><strong>Time</strong> – Midpoint of time intervals on which output data is defined [yyyy-mm-dd]</li> <li><strong>Geometry</strong> – Point geometry containing X and Y coordinates in Polar Stereographic Coordinate Reference System (EPSG:3413) [m]</li> </ul> <p> </p> <p>The repository also includes figures for each investigated tidewater glacier to show the calculated frontal ablation estimates in relation with terminus positions change. The figures are available in the <em>Plots</em> folder.</p> <p>The frontal ablation estimates have been derived using a processing chain which is described in <em>Fahrner et al. (2025.).</em> The processing chain and further information can been found at <a href="../doi/10.5281/zenodo.8414729">10.5281/zenodo.8414729</a>. </p> <p>---------------------</p> <p>When using this dataset cite the associated publication as well as the dataset. </p> <p>---------------------</p> <p> </p> <p> </p>
Databases and scripts for Calvo, et al. Assessing the effect of glacier runoff changes on basin runoff and agricultural production in the Indus, Amu Darya, and Tarim Interior Basins.
<p>This repository has the necessary script and databases to reproduce the figures and tables presented in Calvo et al. (in press) Assessing the effect of glacier runoff changes on basin runoff and agricultural production in the Indus, Amu Darya, and Tarim Interior Basins. Earth´s Future. Wiley.</p>
GPS data and plotting codes for Remote Sensing paper titled Utilizing Seismic Station Internal GPS for Tracking Surging Glacier Sliding Velocity
<p>Data files (meteorological data, sattelite derived velocity time series, and seismic station GPS data) and plotting codes to reproduce the dataset and plots presented in the paper Gajek et al., Utilizing Seismic Station Internal GPS for Tracking Surging Glacier Sliding Velocity</p>
Additional Visualizations for Glacier Calving Front Delineation on Synthetic Aperture Radar Imagery
<p>Additional visualizations showing the time series of each glacier included in the <a href="https://doi.pangaea.de/10.1594/PANGAEA.940950" target="_blank" rel="noopener">CaFFe</a> dataset and comparing predictions from different deep learning models and human annotations for glacier calving front delineation on Synthetic Aperture Radar imagery.</p>
Supplementary code for: "Historical glacier change on Svalbard predicts doubling of mass loss by 2100"
<p>Code to perform the analysis in:</p> <p>Geyman, E.C., van Pelt, W., Maloof, A.C., Faste Aas, H., and Kohler, J., 2021. "Historical glacier change on Svalbard predicts doubling of mass loss by 2100." Nature.</p> <p>Abstract:</p> <p>The melting of glaciers and ice caps accounts for about one third of current sea level rise, exceeding the mass loss from the more voluminous Greenland or Antarctic Ice Sheets. The Arctic archipelago of Svalbard, which hosts spatial climate gradients that are larger than the expected temporal shifts over the next century, is a natural laboratory to constrain the climate sensitivity of glaciers and predict their response to future warming. Leveraging an archive of historical aerial images from 1936 and 1938, we use structure-from-motion (SfM) photogrammetry to reconstruct the 3D geometry of 1,594 glaciers across Svalbard. We compare these reconstructions to modern ice elevation data to derive the spatial pattern of mass balance over a >70-year timespan, allowing us to see through the noise of annual and decadal variability to quantify how variables such as temperature and precipitation control ice loss. We find a robust temperature dependence of melt rates, whereby a 1°C rise in mean summer temperature corresponds to a decrease in area-normalized mass balance of -0.27 m yr<sup>-1</sup> of water equivalent. Finally, we design a space-for-time substitution to make first-order predictions of 21st century glacier change across Svalbard. Even in the most modest scenario (a ~1.4°C rise in mean summer temperature by 2100), we predict average glacier thinning rates in 2010-2100 of -0.67 m yr<sup>-1</sup>, approximately twice the 1936-2010 rates.</p>
Geodetic mass balance of glaciers in the Manaslu region of Nepal 1970 - 2013
<p>Dataset of the geodetic mass balance and surface elevation changes for the period 1970-2013 for glaciers in the Manaslu area of the Nepal Himalaya. Mean geodetic mass balances are computed from a 1970 DEM generated from CoronaKH4 imagery and 2013 HMA DEM elevations. Analysis was done at 8m spatial resolution.</p> <p>Data formats:</p> <p>Data are provided in two formats:</p> <p>1) shapefile format showing the 1970 glacier outlines for 136 glaciers with valid coverage with a GLIMS ID and mean geodetic mass balance attributes, structured as follows:</p> <p>[POINT_X]: Longitude of the glacier polygon centroid</p> <p>[POINT_Y]: Latitude of the glacier polygon centroid</p> <p>[GLIMSID]: ID of each polygon, generated from the lat long coordinates as per GLIMS standard guidelines</p> <p>[src_image]: image source</p> <p>[src_date]: date of the imagery</p> <p>dh_ma-1: surface elevation change [meters per year]</p> <p>bn_m_weq: geodetic mass balance [ m water equivalent per year]</p> <p>err_dh_ma-1: error term for surface elevation change [meters per year]</p> <p>err_bn : error term for the geodetic mass balance [meters water equivalent per year]</p> <p>area_m2: area of each glacier polygon in m2</p> <p>area_km2: area of each polygon in km2</p> <p>line_type: types of glacier (clean/debris covered)</p> <p>analysis_yr: year of analysis</p> <p> </p> <p>2) excel file containing glacier GLIMS ID, mean elevation change and mean mass balance</p> <p>Coordinate system: UTM datum WGS84 zone 45N</p>
Derived products from "Beyond glacier wide mass balances: parsing seasonal elevation change into spatially-resolved patterns of accumulation and ablation at Wolverine Glacier, Alaska"
<p>Distributed mass balances, emergence velocities, firn compaction rates, and co-registered 10 m resolution DEMs from "Beyond glacier wide mass balances: parsing seasonal elevation change into spatially-resolved patterns of accumulation and ablation at Wolverine Glacier, Alaska" (in review) (Zeller et al., 2022).</p>
SPOT5 (2007), ASTER (2016) and SPOT7 (2018) Digital Elevation Models of Little Kluane Glacier (Yukon Territory)
<p>===========================</p> <p>Authors</p> <p>Etienne BERTHIER</p> <p>LEGOS, Université de Toulouse, CNES, CNRS, IRD, UPS, 31400 Toulouse, France,</p> <p>===========================<br> 1. Collection</p> <p>This collection contains three digital elevation models (DEMs) of "Little Kluane" Glacier (Yukon Territory, Canada) with an horizontal grid spacing of 30 m<br> * SPOT5, 13 September 2007<br> * ASTER, 30 September 2017<br> * SPOT7, 1 October 2018</p> <p>The original SPOT5 DEM was obtained from the SPIRIT project (Korona et al., 2009)<br> ASTER and SPOT7 DEMs have been derived using the Ames Stereo Pipeline (Beyer et al., 2018) from stereo images using the set of correlation of parameters in Deschamps-Berger et al. (2020)</p> <p>All DEMs have been coregistered and bias-corrected to the Copernicus 30 m global DEM following the methods of Berthier & Brun (2019)</p> <p><br> ===========================</p> <p>2. Dataset Acknowledgement</p> <p>* SPOT5 data were obtained thanks to funding from CNES during the fourth international polar year.<br> * SPOT7 data were obtained thanks to DINAMIS Project, for “Dispositif Institutionnel National d’Approvisionnement Mutualisé en Imagerie Satellitaire”, a French platform that acquires and distributes very high resolution Earth satellite imagery for French and foreign institutional users under specific subscription conditions.<br> * ASTER data were provided by NASA (U.S.) and METI (Japan).</p> <p>===========================</p> <p>3. Dataset Attribution</p> <p>This dataset is licensed under a Creative Commons CC BY-NC 4.0 International License (Attribution-NonCommercial).</p> <p><br> ===========================</p> <p>4. Related publication</p> <p>This dataset has been generated for and used in a publication to be submitted to the Journal of Glaciology :<br> Morin, A., Flowers, G. E., Nolan, A., Brinkerhoff, D. J., and Berthier, E.: Exploiting high-slip ?ow regimes to improve bed inference, submitted.<br> </p> <p>===========================</p> <p>5. Collection Location</p> <p>Yukon Territory of Canada<br> Bounding box: WGS 84 / UTM zone 7N</p> <p><north>6771585.730</north><br> <south>6739845.730</south><br> <east>564391.250</east><br> <west>601261.250</west></p> <p><br> ===========================</p> <p>References</p> <p>Berthier, E. and Brun, F.: Karakoram geodetic glacier mass balances between 2008 and 2016: persistence of the anomaly and influence of a large rock avalanche on Siachen Glacier, J Glaciol, 65, 494–507, https://doi.org/10.1017/jog.2019.32, 2019.<br> Beyer, R. A., Alexandrov, O., and McMichael, S.: The Ames Stereo Pipeline: NASA’s Open Source Software for Deriving and Processing Terrain Data, Earth and Space Science, 5, 537–548, https://doi.org/10.1029/2018EA000409, 2018.<br> Deschamps-Berger, C., Gascoin, S., Berthier, E., Deems, J., Gutmann, E., Dehecq, A., Shean, D., and Dumont, M.: Snow depth mapping from stereo satellite imagery in mountainous terrain: evaluation using airborne laser-scanning data, The Cryosphere, 14, 2925–2940, https://doi.org/10.5194/tc-14-2925-2020, 2020.<br> Korona, J., Berthier, E., Bernard, M., Remy, F., and Thouvenot, E.: SPIRIT. SPOT 5 stereoscopic survey of Polar Ice: Reference Images and Topographies during the fourth International Polar Year (2007-2009), ISPRS J. Photogramm., 64, 204–212, https://doi.org/10.1016/j.isprsjprs.2008.10.005, 2009.</p> <p> </p>
SAR image Thwaites glacier
<p>SAR image Thwaites glacier</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.