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”
Acoustic recording under landfast sea ice near glacier
<p>A hydrophone was deployed in February 2022 underneath landfast sea ice in Tempelfjorden, Svalbard. The hydrophone was located approximately 2 km from the glacier. Several major events were recorded by vibrations sensors on the ice next to the hydrophone. Source triangulation identified that the events were coming from the glacier wall. This dataset includes the recording of one of the events (with the event starting at about 0:14), and a sample thereof.</p>
A new inventory of High Mountain Asia surging glaciers derived from multiple elevation datasets since the 1970s
<p>Glacier surging is an unusual undulation instability of ice flow and complete surging glacier inventories are important for regional mass balance studies and assessing glacier-related hazards. Glacier surge events in High Mountain Asia (HMA) are widely reported. Through the estimated elevation changes from multiple DEMs sources that acquired from 1970s to 2020, and morphologic changes from 1986 to 2021, here we present a new surging glacier inventory across HMA. The inventory has incorporated 890 surging and 336 surge-like glaciers, each glacier is assigned with indicators of surging feature and surge possibility. Compared to previous surging glacier inventory in HMA, our inventory is theoretically more complete because of the much longer observation period. This data repository contains the surging glacier inventory and glacier elevation change maps. The inventory is stored in the format of GeoPackage (.gpkg) and ESRI Shapefile format (.shp), which is represented by glacier polygon (from GAMDAM2) or surface point with geometric attributes. The multi-temporal elevation change maps of identified surging glaciers were divided into 1×1° tiles, storing in the format of GeoTiff(*.tif). Detailed description of the dataset including the file contents and attributes information can be found in the metadata file (README.txt).</p>
Orthophoto and DSM Rutor Glacier 2020
<p>Orthophoto and Digital Surface Model (DSM) obtained from the photogrammetric flight over Rutor Glacier in September 2020. Ground Sample Distance (GSD) = 0.5 m (resampled form the 0.2 m original GSD)</p>
The glacier loss day as indicator for extreme glacier melt in 2022
<p>Data and scripts to reproduce the plots in Voordendag, A., Prinz, R., Schuster, L., and Kaser, G.: Brief communication: Brief communication: The Glacier Loss Day as indicator for a record negative glacier mass balance in 2022, The Cryosphere, 2023</p> <p>When using this dataset, please refer to the original publication in addition to this Zenodo repository.</p>
Using Biodiversity Data to Assess Species-Habitat Relationships in Glacier National Park, Montana
Biodiversity surveys are becoming increasingly popular. However, standard analysis techniques for these data have not yet been developed. This paper explores the use of multivariate ordination techniques for assessing species-habitat relationships using biodiversity data. The research was conducted in Glacier National Park, Montana, and birds and butterflies were chosen as the taxonomic groups of study. Biodiversity assessment sites were established through a range of habitats and monitored from 1987 through 1989. Presence/absence sampling over the total number of sampling sites was used to classify species commonness and rarity. Approximately 86% of the historically recorded butterflies and 70% of the historically recorded bird species have been observed in the 3 yr of sampling. During the 3 yr of this study there was a striking continuity of species richness per site. There was also a striking overlap between the sites that support high species diversity and sites that support rare species. Principal components analysis and cluster analysis worked well in discerning species-habitat relationships. Elevation, structural diversity of the site, and moisture were the major factors explaining species distributions. A chi-square analysis also provided some insights into species-habitat relationships, showing birds were more habitat specific than butterflies. Habitat diversity analyses demonstrated a positive but non-significant correlation between remotely sense spectral-class diversity of a site and species richness for both birds and butterflies. Aspect, slope and elevation diversity had a negative or negligible relationship with species richness.
Glacier snow depth measurements, McMurdo Dry Valleys, Antarctica (1993-2023, ongoing)
As part of the Long Term Ecological Research (LTER) project in the McMurdo Dry Valleys of Antarctica, a systematic sampling program has been undertaken to monitor glacial mass balance and meltwater flow. This data package includes snow depth measurements to the surface of six glaciers (Canada, Commonwealth, Hughes, Suess, Howard, and Taylor) in Taylor Valley and one glacier (Adams) in Miers Valley, all of which are located in the McMurdo Dry Valleys of Antarctica. Most measurements began during the 93-94 field season. Adams measurements were established during the 14-15 field season. Measurements are ongoing except at Hughes and Suess Glaciers where monitoring ceased following the 08-09 field season. Monitoring the changes in these measurements over time provides a record of mass balance, and aids in determining the role of glaciers in the polar hydrologic cycle.
Snowpit chemistry from Canada, Commonwealth, and Rhone Glaciers, McMurdo Dry Valleys, Antarctica during the 2000-2001 austral summer
To examine temporal and spatial variability in snow chemistry during the 2000-2001 austral summer, snow samples were collected from the accumulation zones of Canada, Commonwealth, and Rhone Glaciers, located in Taylor Valley in the McMurdo Dry Valleys region of Antarctica. Snowpits were excavated to a depth of 2 meters at each location and samples were collected using a depth interval of 3 cm utilizing clean sampling techniques. Snow density was measured in the field at the time of sample collection. Samples were analyzed for major ions in the Crary Lab at McMurdo Station.
Shapefiles for glacier, stream channel, and watershed boundaries in the McMurdo Dry Valleys, Antarctica (2023)
This data package includes shapefiles for selected glacier, stream watershed, and stream channel boundaries in the McMurdo Dry Valleys region of Antarctica. A combination of satellite imagery and digital elevation models were used to delineate watershed and stream channel outlines, while glaciers were outlined by hand. Watershed boundaries provide an estimate of the overall topographic contributing area for each stream in Fryxell Basin, whereas stream channel boundaries provide a topographic area estimate for stream channel, beyond the wetted margin, for each stream.
Air temperature and solar radiation data for Arikaree Glacier data logger (DP211), 1986 - 2006.
Climatological data were collected from an upper Green Lakes Valley climate station (Arikaree Glacier) throughout the year using an Omnidata DP211 datapod. This instrument has a sample interval of 5 minutes and records averages of those 5-minute readings every 2 hours. Thus, daily totals represent totals of 288 values. Parameters measured were air temperature (averages) and solar radiation (totals).
Terminus lines of the Marr Piedmont Glacier at Palmer Station, Antarctica, from 1963 to 2023.
The Marr Piedmont glacier behind Palmer Station, on Anvers Island, Antarctica, has been in retreat for several decades. Since 1963, the terminus of the glacier has been intermittently mapped, initially with arial imagery, and then with GPS surveys. Annual mappings have occurred from 2017 onwards. This dataset includes both point and line GeoPackage spatial vector files, each of which includes the mapped terminus lines from each available year as individual layers.
Columbia River headwaters - hydroclimate and glacier change, 1977-2017
<p>These data files were used in the production of a manuscript titled "Detecting the effects of sustained glacier wastage on streamflow in variably glacierized catchments" by R.D. Moore, B. Pelto, B. Menounos and D. Hutchinson.</p> <p>cb_shapes - polygons of catchment boundaries for the catchments included in the analysis</p> <p>cq.csv - streamflow and climatic data used in regression and trend analyses within the script "cbt_aug_flow_analysis.r"</p> <p>good_stn.csv - contains station IDs for Water Survey of Canada stations that met selection criteria</p> <p>stn_info.csv - catchment characteristics for Water Survey of Canada stations included in the analysis</p> <p>cbt_metadata.xlsx - full metadata for catchments used in the analysis</p> <p>*.r - R scripts used to conduct analyses and generate figures</p> <p> </p>
Temperature sensitivity of mountain glaciers
<p><strong>Distributed summer air temperatures across mountain glaciers in the south-east Tibetan Plateau: temperature sensitivity and </strong><strong>comparison with existing glacier datasets</strong></p> <p>Thomas E. Shaw<sup>1</sup>, Wei Yang<sup>2,3</sup>, Álvaro Ayala<sup>4</sup>, Claudio Bravo<sup>5</sup>, Chuanxi Zhao<sup>2</sup>, Francesca Pellicciotti<sup>1,6</sup></p> <p> </p> <p><sup>1</sup> Federal Institute for Forest, Snow and Landscape Research (WSL), Birmensdorf, Switzerland</p> <p><sup>2</sup> Key Laboratory of Tibetan Environment Changes and Land Surface Processes, Institute of Tibetan Plateau Research, Chinese Academy of Sciences (CAS), Beijing, China</p> <p><sup>3</sup> CAS Center for Excellence in Tibetan Plateau Earth Sciences, Beijing 100101, China</p> <p><sup>4 </sup>Centre for Advanced Studies in Arid Zones (CEAZA), La Serena, Chile</p> <p><sup>5</sup> School of Geography, University of Leeds, Leeds, UK</p> <p><sup>6 </sup>Department of Geography, Northumbria University, Newcastle, UK</p> <p><em>Corresponding author: Thomas E. Shaw (</em><a href="mailto:thomas.shaw@wsl.ch"><em>thomas.shaw@wsl.ch</em></a><em>)</em></p> <p>Keywords: Air Temperature, Glaciers, Tibetan Plateau, Temperature Sensitivity</p> <p>%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%</p> <p><strong>Dataset provided:</strong></p> <p>'<strong>Climatic_Sensitivity_Mountain_Glaciers.mat'</strong> = Matlab file with data structures for each glacier site. The following sites are:</p> <p>% Parameter set calculated from data on Parlung Glaciers (this study)<br> % Parameter set from Shea and Moore (2010) for Rockies- Canada (Published parameters)<br> % Parameter set from Carturan et al. (2015) for Ortles Cevedale, Italy (k1/k2 data from author)<br> % Parameter set from Shaw et al. (2017) on Tsanteleina Glacier, Italy (Reassesed parameters)<br> % Parameter set calculated from data of Bravo et al., (2019) on South Patagonian Icefield (SPI), Chile<br> % Parameter set calculated from data of Bravo et al., (2017) on Universidad Glacier, Chile<br> % Parameter set calculated from data of Ayala et al., (2015) on Arolla Glacier, Switzerland<br> % Parameter set calculated from data of Ayala et al., (2015) on JuncalNorte Glacier, Chile<br> % Parameter set calculated from data of Troxler et al., (2020) on McCall Glacier, Alaska<br> % Parameter set calculated from data of Greuell and Böhm (1998) on Pasterze Glacier, Austria<br> % Parameter set calculated from data of Rets et al., (2019) on Djankuat Glacier, Russia<br> % Parameter set calculated from data of Pradhananga et al., (2020 In prep) on Peyto Glacier, Canada</p> <p><strong>Variables include:</strong></p> <p>'Name' = name of individual observation station (AWS or Temp/RH 'T-logger')<br> 'Elevation' = Elevation (m a.s.l.) of given observation station<br> 'Flowline' = The distance along the glacier flowline from an upslope summit or crest (m)<br> 'k1' = The climatic sensitivity (ratio) of on-glacier temperatures to changes in the ambient (off-glacier) air temperature below the onset of katabatic onset (following Shea and Moore, 2010)<br> 'k2' = The climatic sensitivity (ratio) of on-glacier temperatures to changes in the ambient (off-glacier) air temperature above the onset of katabatic onset (following Shea and Moore, 2010)<br> 'Tst' = The T* parameter that defines the threshold (off-glacier) temperature for katabatic conditions parameterised following Carturan et al. (2015)<br> 'T1' = The equivalent on-glacier threshold temeprature derived from k1 and Tst<br> 'DataSource' = The citation readout</p> <p><br> <strong>Cited literature</strong><br> Ayala, A., Pellicciotti, F., & Shea, J. (2015). Modeling 2m air temperatures over mountain glaciers: Exploring the influence of katabatic cooling and external warming. Journal of Geophysical Research: Atmospheres, 120, 1–19. https://doi.org/10.1002/2015JD023137.</p> <p><br> Bravo, C., Quincey, D. J., Ross, A. N., Rivera, A., Brock, B. W., Miles, E., & Silva, A. (2019). Air Temperature Characteristics , Distribution , and Impact on Modeled Ablation for the South Patagonia Ice field. Journal of Geophysical Research : Atmospheres, 124, 907–925. https://doi.org/10.1029/2018JD028857</p> <p><br> Bravo, C., Lorlaux, T., Rivera, A., & Brock, B. W. (2017). Assessing glacier melt contribution to streamflow at Universidad Glacier, central Andes of Chile. Hydrology and Earth System Sciences, 21, 3249–3266. https://doi.org/10.5194/hess-21-3249-2017</p> <p><br> Carturan, L., Cazorzi, F., De Blasi, F., & Dalla Fontana, G. (2015). Air temperature variability over three glaciers in the Ortles–Cevedale (Italian Alps): effects of glacier fragmentation, comparison of calculation methods, and impacts on mass balance modeling. The Cryosphere, 9(3), 1129–1146. https://doi.org/10.5194/tc-9-1129-2015</p> <p><br> Greuell, W., & Böhm, R. (1998). 2 m temperatures along melting mid-latitude glaciers , and implications for the sensitivity of the mass balance to variations in temperature. Journal of Glaciology, 44(146), 9–20.</p> <p><br> Rets, E. P., Popovnin, V. V, Toropov, P. A., Smirnov, A. M., Tokarev, I. V, Chizhova, J. N., … Kireeva, M. B. (2019). Djankuat glacier station in the North Caucasus , Russia : a database of glaciological , hydrological , and meteorological observations and stable isotope sampling results during 2007 – 2017. Earth System Science Data, ||, 1463–1481. https://doi.org/https://doi.org/10.5194/essd-11-1463-2019</p> <p><br> Shaw, T. E., Brock, B. W., Ayala, A., Rutter, N., & Pellicciotti, F. (2017). Centreline and cross-glacier air temperature variability on an Alpine glacier: assessing temperature distribution methods and their influence on melt model calculations. Journal of Glaciology, 1–16. https://doi.org/10.1017/jog.2017.65</p> <p><br> Shea, J. M., & Moore, R. D. (2010). Prediction of spatially distributed regional-scale fields of air temperature and vapor pressure over mountain glaciers. Journal of Geophysical Research, 115(D23), D23107. https://doi.org/10.1029/2010JD014351</p> <p><br> Troxler, P., Ayala, Á., Shaw, T. E., Nolan, M., Brock, B. W., & Pellicciotti, F. (2020). Modelling spatial patterns of near-surface air temperature over a decade of melt seasons on McCall Glacier , Alaska. Journal of Glaciology, 1–15. https://doi.org/https://doi.org/10.1017/jog.2020.12</p>
Textured 3D model over Morenci Mine and Shisper glacier using SkySat and PlanetScope satellite imagery
<p>Supplementary material of our research paper entitled "Optimization of optical image geometric modeling, application to topography extraction and topographic change measurements using PlanetScope and SkySat imagery".</p> <p>Flyover animation of 3D model extracted over Morinci Mine using SkySat tri-stereo.</p> <p>Flyover animation of 3D model extracted over Shisper glacier using multi-date PlanetScopeimages.</p> <p> </p>
Shapefiles reporting the boundaries of glaciers analyzed by applying a multifractal approach
<p>We recently investigated the multifractal properties of the perimeters of the Lombardy glaciers in the Italian Alps. We characterized the area and perimeter distributions of the population of glaciers and we showed that the distribution of perimeters exhibits a marked peak, not present in the distribution of areas. We investigated the multifractal spectra of perimeters and we showed that their features are strongly correlated with the area of the glaciers.</p> <p>Here are reported the shapefiles of the boundaries of glaciers of the Lombardia region measured in 2003, 2007, and 2012 that were used for this work.</p>
dh/dt data of the manuscript "Surface Elevation Change of Glaciers Along the Coast of Prudhoe Land, Northwestern Greenland from 1985 to 2018 "
<p>This is a dataset including dt/dt data for periods of T0–T2, T0–T1, and T1–T2, which used in the manuscript "Surface Elevation Change of Glaciers Along the Coast of Prudhoe Land, Northwestern Greenland from 1985 to 2018".</p>
Debris thickness measurements from various glaciers in the Western Alps
<p>Debris thickness measurements collected from seven debris-covered glaciers in Switzerland and Italy in 2019 and 2020. Only data from Miage Glacier was measured in September 2020 all others were measured in August and September in 2019. The RGI Glacier ID is the first cell in each sheet. Location data is reported in decimal degrees. All debris thickness data is in centimeters and elevation data is in meters.</p> <p>Debris was measured by digging through the debris and then directly measuring the debris thickness with a measuring stick. The mean thickness is the most likely average thickness based on 3 measurements within a ~4x4m area. The 'min' measurement is the minimum debris thickness. The 'max' measurement is the absolute maximum estimate of the mean debris thickness, meaning that this estimate is that absolute maximum of the mean debris thickness. This value is determined based on the thickness of the largest non-outlier debris thickness in the area. Three measurements were taken for the mean value and 1 each for the max/min bounds. To save time, sites with mean thicknesses larger than 20 cm the extreme bounds are based on the minimum and maximum measurements within the single hole dug. Elevation data are based on a hand held GPS and this value was only recorded for some debris thickness measurements.</p>
Debris temperature and ablation measurements from the Lirung Debris-Covered Glacier (2013-2014), Nepal
<p>These data are from the debris-covered portion of Lirung Glacier, Langtang Valley located in the Langtang National Park of the Nepal Himalaya that were collected during three field expeditions between September 2013 and April 2014. The data includes debris temperature measurements at two different sites of the glacier in three different seasons of the year 2013 (Monsoon and Winter) and 2014 (Pre-Monsoon). Debris temperature measurements from thermistors located at the surface (Black-colored dirty ice) to up to 40 cm from the surface. Dataset also included the ablation measurement in three seasons at different debris-thicknesses. Below is a brief description of the two different datasets:</p> <p>- Ablation_stake_data_Lirung_glacier_CHAND.csv: ablation stake measurement in the Monsoon and Winter season of 2013 and pre-monsoon season of 2014 from Chand and Kayastha (2018) and Chand et al. (2015).</p> <p>- Debris_temperature_profile_Lirung_Glacier_CHAND.csv: debris temperature measurements (Degree Celcius) where the depth is reported in cm from Chand and Kayastha (2018).</p> <p>----- Citing datasets -----</p> <p>Chand, M. B., and Kayastha, R. B. (2018). Study of thermal properties of supraglacial debris and degree-day factors on Lirung Glacier, Nepal. Sciences in Cold and Arid Regions, 10(5): 357-368 doi:10.3724/SP.J.1226.2018.00357</p> <p>Chand, M. B., Kayastha, R. B., Parajuli, A., and Mool, P. K. (2015). Seasonal variation of ice melting on varying layers of the debris of Lirung Glacier, Langtang Valley, Nepal, Proc. IAHS, 368, 21–26, https://doi.org/10.5194/piahs-368-21-2015</p> <p> </p>
Satellite images of the 17 July 2016 Aru Co glacier collapse
<p>These satellite images were made to visualize the Aru Co glacier avalanche. Some of them were used in these blog posts:</p> <ul> <li>Séries Temporelles (2016, August 25) Sentinel-2A captures a giant ice avalanche in Tibet. http://www.cesbio.ups-tlse.fr/multitemp/?p=8294</li> <li>Séries Temporelles (2016, August 25) Sentinel-2A (and Landsat-8) capture a giant ice avalanche in Tibet http://www.cesbio.ups-tlse.fr/multitemp/?p=8327</li> </ul> <p>Files description:</p> <ul> <li>File 2016-07-21_S2.tif: Sentinel-2A image of the Aru Co glacier avalanche acquired on 21-Jul-2016 (4 days after the event). RGB composite of bands B4,B3,B2 scaled to bytes between 0 and 0.5 from level 1C product (orthorectified top-of-atmosphere reflectances). Format: Geotiff, WGS 84 / UTM zone 44N.</li> <li>File 2016-06-24_L8mos.tif: Landsat-8 image of the Aru Co area acquired on 24-Jun-2016 (23 days before the event). RGB composite of bands B4,B3,B2 scaled to bytes between 0 and 0.5 from level 1C product (orthorectified top-of-atmosphere reflectances). Format: Geotiff, WGS 84 / UTM zone 44N.</li> <li>File anim.gif: animated sequence of both images using the lowest resolution image (Landsat-8)</li> <li>File diff_S2minusL8_band3.tif: difference between the band 3 of the 2016-07-21 Sentinel-2A image and the 2016-06-24 Landsat-8 image after a nearest neighbour resampling of the Sentinel-2 image to the same resolution as the Landsat-8 image (30 m).</li> <li>2016-07-25_S1.tif : Sentinel-1 image of the Aru Co glacier avalanche acquired on 25-Jul-2016 (8 days after the event). VV co-polar band, ascending orbit. The images was pre-processed to backscatter coefficient in decibels after thermal noise removal, radiometric calibration and terrain correction.</li> <li>2016-07-25_S1.tif : Sentinel-1 image of the Aru Co glacier avalanche acquired on 25-Jul-2016 (8 days after the event). VV co-polar band, ascending orbit. The image was pre-processed to backscatter coefficient in decibels after thermal noise removal, radiometric calibration and terrain correction.</li> <li>2016-07-01_S1.tif : Sentinel-1 image of the Aru Co glacier avalanche acquired on 07-Jul-2016 (10 days before the event). VV co-polar band, ascending orbit. The image was pre-processed to backscatter coefficient in decibels after thermal noise removal, radiometric calibration and terrain correction.</li> <li>2016-07-21-01_S1_diff_smoothed_Lee.tif : difference between both Sentinel-1 images after applying a refined Lee filter on the radar intensities</li> </ul> <p>Spatial extent of all the images in WGS 84 UTM 44N and lon/lat coordinates :</p> <p>Upper Left ( 602260.000, 3777670.000) ( 82d 6'32.64"E, 34d 8' 5.67"N)<br> Lower Left ( 602260.000, 3755030.000) ( 82d 6'23.08"E, 33d55'50.74"N)<br> Upper Right ( 640720.000, 3777670.000) ( 82d31'33.86"E, 34d 7'49.56"N)<br> Lower Right ( 640720.000, 3755030.000) ( 82d31'20.72"E, 33d55'34.75"N)</p>
Data for: Glacial and Periglacial Geomorphology of the Drang Drung, Haskira, and Pensilungpa glaciers, Trans-Himalayan Ladakh, India
<p>The geopackage contains the glacial and periglacial geomorphological landforms/features of Drang Drung, Haskira and Pensilungpa glacier valleys mapped from a combination of Planetscope images and Pléiades data (images and digital surface model)</p>
Dataset of micro-roughness, Schmidt hammer and reflectance spectra obtained at Hallstaetter Glacier foreland
<p>The files contain data of micro-roughness (Ra and Rz), raw data of Schmidt hammer rebound-values, and reflectance spectra obtained at Hallstaetter Glacier foreland in July 2022. The data was use in a publication: Dąbski M, Badura I, Kycko M, Grabarczyk A, Matlakowska R, Otto J-C. The Development of Limestone Weathering Rind in a Proglacial Environment of the Hallstätter Glacier. <i>Minerals</i>. 2023; 13(4):530. https://doi.org/10.3390/min13040530. </p><p>Funding provided by National Science Centre, Poland (Preludium Bis-2 2020/39/O/ST10/01068).</p><p>Micro-roughness, rock strength (Schmidt hammer rebound values), and spectral reflectance were obtained in-situ on glacially abraded rock surfaces along a transect from the glacial snout to the outermost moraines from the Little Ice Age, covering circa 172 years of subaerial weathering in the proglacial alpine environment. UAV surveys of the studied area were performed to obtain Digital Elevation Models (DEMs) and allow for detailed comparative studies in the future.</p><p>Test site 1 was very close to the glacier (undergoes weathering for 1–2 years), site 2 was in the zone c. 10 years old, site 3 was in the zone c. 50–51 years old, site 4 was in the zone c. 105–106 years old, and the last one (site 5) was on the LIA moraines, where the duration of weathering is c. 167–172 years. The sites were located on bedrock or boulders embedded in the moraines with distinct traces of glacial abrasion, allowing us to infer that older weathering rind (developed before glacial accumulation) has been eroded. The sites were selected based on their age, homogenous petrography, accessibility, and suitability for micro-roughness measurements. Within each test site, we selected ten specific rock surfaces (c. 100 cm2 each), with clear signs of glacial abrasion, for the measurements of micro-roughness, Schmidt hammer rebound (rock strength), and spectral reflectance. </p><p> </p><p> </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.