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”
Seismic monitoring of Hans glacier (Svalbard) using dedicated local network
<p>Seismic dataset registered during monitoring of Hans glacier (Svalbard) using dedicated local network in Hornsund 10/2017-04/2018 carried by Wojciech Gajek and coworkers financed by an internal grant of Institute of Geophysics Polish Academy of Sciences.</p> <p>Dataset can be used for analyzing the glacier seismicity. More on that topic in Svalbard can be find in Seismology chapter of SESS 2019 report <a href="https://sios-svalbard.org/SESS_Issue2">https://sios-svalbard.org/SESS_Issue2</a></p> <p>Project log in ResearchGate:</p> <p><a href="https://www.researchgate.net/project/Seismic-monitoring-of-Hans-glacier-Svalbard-using-dedicated-local-network">https://www.researchgate.net/project/Seismic-monitoring-of-Hans-glacier-Svalbard-using-dedicated-local-network</a></p> <p> </p> <p>The data includes seismic records (3C) from the temporary seismic network. It is advised to take into the processing also the permanent station HSPB.<br> Data is packed as a zip archive. Its structure is SDS, compatible with ObsPy query system.<br> The structure includes HSPB but HSPB data is not there due to limited file space here (its publicly available eg in Orpheus).</p> <p> </p> <p>Other files are:<br> coordinates,<br> map<br> data availability chart<br> my presentation from ESC Malta with preliminary results<br> photos from field installation<br> data conditioning report</p> <p>Have fun.</p> <p>You can contact me via researchgate:</p> <p><a href="https://www.researchgate.net/profile/Wojciech_Gajek">https://www.researchgate.net/profile/Wojciech_Gajek</a></p>
Topography extraction and topographic change measurements using PlanetScope data: Shisper Glacier (Pakistan)
<p>The study area is located in the north flank of Hunza Valley in the Central Karakoram. Shisper glacier covers ~53.7 km2 at an elevation range of 2567-611 m a.s.l. It is a surge-type glacier, which has recently gained the attention of the scientific community and disaster response agencies during its surge. In 2018, the glacier surged beyond the confluence with the outlet stream of Mochwar glacier. The blockage resulted in the creation of a lake, which then drained causing a GLOF (Glacial Lake Outbreak Flood) and has recently begun reforming. The melt water from the two glaciers feeds hydropower plants in the Hunza valley and is a major source of fresh water for agriculture. Glacier-related hazards threaten both the town of Hassanabad and the Karakoram Highway, the only paved road through the mountain range. Here we show the potential of CubSat data to monitor such glaciers, which are not easily accessible to field observation and their potential impact on power generation, water resources and infrastructure.</p> <p>We use multi-date L1B DOVE-C PlanetScope data to extract two DEMs in 2017 and 2019 over the study area in order to compute the elevation difference caused by the glacier surge.</p> <p>Supplementary material for our paper: Optimization of optical image geometric modeling, application to topography extraction and topographic change measurements using PlanetScope and SkySat imagery.</p>
Landslides from Space - Glacier Bay Landslide, Alaska USA (28th June 2016)
<p>On 28th June 2016 seismometer recorded an event with a magnitude of 5.2. Later it was visually confirmed that this was caused by a landslide and not an earthquake.</p> <p>The pre-event acquisition is from 5th February 2016 (Sentinel-2) and the post-event acquisition is from 29th September 2016 (Sentinel-2).<br> <br> <em>Contains modified Copernicus Sentinel data (2016)</em></p>
Model data for "Recent irreversible retreat phase of Pine Island Glacier"
<p>Model inputs and outputs for the experiments in Reed et al., 2023 "Recent irreversible retreat phase of Pine Island Glacier".</p>
Carbon outwelling and uptake along a tidal glacier-lagoon-ocean continuum
<p>Data_Jokulsarlon2022: Excel file containing raw data collected at Jökulsárlón Glacial Lagoon in September 2022. </p><p>The data set includes parameters measured in the surface water of our spatial survey and timeseries. This includes temperature and salinity, oxygen concentration, nutrients (total dissolved nitrogen, phosphate, silica), dissolved organic carbon, photosynthetic pigments (chlorophyll a and fucoxanthin) and carbonate species (total alkalinity and dissolved inorganic carbon), as well as atmospheric data (temperature and wind speed).</p>
Orthophoto and DSM Rutor Glacier 2021
<p>Orthophoto and Digital Surface Model (DSM) obtained from the photogrammetric flight over Rutor Glacier in September 2021. Ground Sample Distance (GSD) = 0.5 m (resampled form the 0.2 m original GSD)</p>
Calving Front Dataset for Marine-Terminating Glaciers in Svalbard 1985-2023
<p>Svalbard has experienced increased climate variability as a result of global warming, leading to significant mass loss in its marine-terminating glaciers over recent decades. Nevertheless, the mechanisms driving this mass loss remain less understood, primarily due to a limited understanding of calving dynamics. Here we present a new high-resolution calving front dataset of 149 marine-terminating glaciers in Svalbard, comprising 124919 glacier calving front positions during the period of 1985-2023. This dataset was generated using a novel automated deep learning framework and multiple optical and SAR satellite images from Landsat, Terra-ASTER, Sentinel-2, and Sentinel-1 satellite missions.</p> <p>The information regarding the glacier calving front terminal traces, glacier centrelines, glacier domains, fjord masks and the along-centreline glacier calving front change time series is consolidated into a single Geopackage file named "Svalbard_Calving_Front_Product.gpkg." The specific file structure for this data file is detailed in Table 1, and the feature attribute table for the different data layers recorded in this data file can be found in Table 2.</p> <p>Furthermore, we have included spatial distribution map plots of the glacier calving front traces and line plots depicting the time series of calving front changes for each individual glacier. These plots are provided in .PNG file format and can be accessed within the Figures folder.</p> <p>Table 1. The layer structure of the Svalbard calving front data product.</p> <table> <tbody> <tr> <td> <p><strong>Layer Name</strong></p> </td> <td> <p><strong>Details</strong></p> </td> </tr> <tr> <td> <p>traces</p> </td> <td> <p>Line geometries recording the terminal traces of all the glaciers (EPSG:3995).</p> </td> </tr> <tr> <td> <p>centrelines</p> </td> <td> <p>Line geometries recording the glacier centrelines used in calving front change estimation (EPSG:3995).</p> </td> </tr> <tr> <td> <p>domains</p> </td> <td> <p>Polygon geometries recording the glacier domains (EPSG:3995).</p> </td> </tr> <tr> <td> <p>fjord_masks</p> </td> <td> <p>Polygon geometries recording the fjord masks (EPSG:3995).</p> </td> </tr> <tr> <td> <p>front_change_time_series</p> </td> <td> <p>Point geometries recording the along-centreline glacier calving front change time series (EPSG:4326).</p> </td> </tr> </tbody> </table> <p> </p> <p>Table 2. The feature attribute table of the data layer.</p> <table> <tbody> <tr> <td> <p><strong>Data Field</strong></p> </td> <td> <p><strong>Description</strong></p> </td> </tr> <tr> <td> <p>Glacier</p> </td> <td> <p>The Randolph Glacier Inventory (RGI) version 6 (RGI Consortium, 2017) glacier id.</p> </td> </tr> <tr> <td> <p>Sensor</p> </td> <td> <p>The satellite platform used in mapping glacier calving front, including “Landsat”, “Terra-ASTER”, “Sentinel2” and “Sentinel1”.</p> </td> </tr> <tr> <td> <p>ImageId</p> </td> <td> <p>The image id of the satellite image used in mapping the glacier calving front.</p> </td> </tr> <tr> <td> <p>DateString</p> </td> <td> <p>The datetime string of the satellite image in the format of “YYYYMMDD”.</p> </td> </tr> <tr> <td> <p>CFL_Change</p> </td> <td> <p>The calving front location (CFL) changes in meters along the glacier centreline in relation to the earliest calving front location in the time series.</p> </td> </tr> <tr> <td> <p>glacier_lat</p> </td> <td> <p>The latitude of the glacier location (WGS84 coordinate system).</p> </td> </tr> <tr> <td> <p>glacier_lon</p> </td> <td> <p>The longitude of the glacier location (WGS84 coordinate system).</p> </td> </tr> </tbody> </table>
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>
Output from the Glacier Energy and Mass Balance (GEMB v1.0) forced with 3-hourly ERA5 fields and gridded to 10km, Greenland and Antarctica 1979-2024
<p>These model output of firn air content (FAC) and surface mass balance (SMB) are from version 1.0 of the open-source Glacier Energy and Mass Balance model. GEMB is a column model of ice sheet and glacier surface-atmospheric energy and mass exchange as well as firn state. GEMB has been integrated into the open-source Ice-Sheet and Sea-level System Model which can be downloaded at https://issm.jpl.nasa.gov/. Here, GEMB is forced with 3-hourly ERA5 output from 1979 through end of 2024. For Greenland and its periphery, the ERA5 surface temperature and downwelling longwave radiation forcing are spatially bias-corrected for each month. All values are adjusted by the difference between the RACMO2.3 and the ERA5 1980-2015 monthly means. The GEMB output is bilinearly interpolated onto a 10km grid, from the native ISSM grid, and the output is given as 5-day output or as monthly.</p>
Data from Glacier Model Intercomparison Project Phase 3 (GlacierMIP3)
<p>This dataset presents the data from the third phase of <a href="https://climate-cryosphere.org/glaciermip/">GlacierMIP</a> (GlacierMIP3: Equilibration of glaciers under different climate states). It includes regional glacier volume and area projections as submitted by the glacier modelling groups. Additionally, it features post-processed and aggregated data derived from GlacierMIP3, or in combination with other studies, which is used for the analyses and visualisations presented in the following manuscript: </p> <p><em>Zekollari*, H., Schuster*, L., Maussion, F., Hock, R., Marzeion, B., Rounce, D. R., Compagno, L., Fujita, K., Huss, M., James, M., Kraaijenbrink, P. D. A., Lipscomb, W. H., Minallah, S., Oberrauch, M., Van Tricht, L., Champollion, N., Edwards, T., Farinotti, D., Immerzeel, W., Leguy, G., Sakai, A. (under review): Glacier preservation doubled by limiting warming to 1.5°C. Preprint available at <a href="https://doi.org/10.31223/X51T5W">https://doi.org/10.31223/X51T5W</a>, 2024.</em><br><em>*Harry Zekollari and Lilian Schuster contributed equally to this dataset and the manuscript above.<br><br></em>If you use the data, please cite this Zenodo dataset and the above study. <em><br></em><br>More info in <em>README_data.pdf</em>. For information about the GlacierMIP3 experimental design, please refer to the<em> GlacierMIP3_protocol.pdf</em>. The code used to generate the postprocessed data and to conduct the analyses for the manuscript mentioned above is available at <a href="https://github.com/GlacierMIP/GlacierMIP3]">https://github.com/GlacierMIP/GlacierMIP3</a>.<br><br></p> <p>To assist potential data users, we have included a jupyter notebook (gmip3_data_example_use_cases.ipynb) that guides you through some simple use cases. This notebook can be directly run when clicking on this <a href="https://drive.google.com/file/d/1xbhXZwT3sQydAGi8rSjEXRKdKhFosW6d/view?usp=sharing">link</a>. Please note that you will need to log in to your Google account and, if you haven't already done so, install Google Colaboratory. The data will then be automatically downloaded to your account.</p> <p>We may adapt the data structure and improve the documentation during the review phase. If you have any questions or suggestions, please contact us (lilian.schuster@uibk.ac.at, harry.zekollari@vub.be).</p> <p>----<br>difference version v2 to v1.0: only the files <em>README_data.pdf</em> and the <em>lowess*_regional_glacier_temp_ch.csv</em> were changed according to the resubmission of the manuscript</p>
MEaSUREs ITS_LIVE Sentinel-1 Image-Pair Glacier and Ice Sheet Surface Velocities: Version 2 (Greenland Sample Products)
<p>We provide 21 sample products of MEaSUREs ITS_LIVE Sentinel-1 Image-Pair Glacier and Ice Sheet Surface Velocities: Version 2 in three test regions of Greenland Ice Sheet. The full archive of version 2 ITS_LIVE products (including image pair maps, data cubes and mosaics) from Sentinel-1 as well as other optical sensors (Landsat-4/5/6/7/8 and Sentinel-2) can be found at the ITS_LIVE project website: <a href="https://its-live.jpl.nasa.gov/">https://its-live.jpl.nasa.gov</a>.</p> <p><strong>Sensor</strong>: Sentinel-1A/B</p> <p><strong>Processor</strong>: <a href="https://github.com/isce-framework/isce2">ISCE</a>v2.4.1 (topsApp -> <a href="https://github.com/leiyangleon/Geogrid">Geogrid</a>v1.4.0 -> <a href="https://github.com/nasa-jpl/autoRIFT">autoRIFT</a>v1.4.0)</p> <p><strong>Project</strong>: NASA MEaSUREs project <a href="https://its-live.jpl.nasa.gov">ITS_LIVE</a></p> <p><strong>Region 1</strong> (69.13N, 50.88W; Jakobshavn Isbræ Glacier): 7 ascending image pairs</p> <p><strong>Region 2</strong> (77.61N, 42.79W; central north of interior Greenland): 3 ascending image pairs</p> <p><strong>Region 3</strong> (72.48N, 35.87W; central south of interior Greenland): 10 descending image pairs and 1 ascending image pair</p> <p>This serves as a supplementary dataset for the companion journal article submitted to Earth System Science Data (to appear).</p> <p> </p> <p><strong>Acknowledgement</strong>: This effort was funded by the NASA MEaSUREs program in contribution to the Inter-mission Time Series of Land Ice Velocity and Elevation (ITS_LIVE) project (<a href="https://its-live.jpl.nasa.gov/">https://its-live.jpl.nasa.gov/</a>) and through Alex Gardner’s participation in the NASA NISAR Science Team.</p>
LGM-Lateglacial 3D ice surface reconstructions of the Dora Baltea glacier system (western Italian Alps)
<p>3D ice surface configurations of six LGM-Lateglacial ice stages of the Dora Baltea glacier system (western Italian Alps).</p> <p>Ice-configurations were obtained by combining existing and new chronological constraints from glacial and postglacial landforms/deposits from the Dora Baltea catchment into 2D and 3D ice surface reconstructions, similar to the approach of the GlaRe ArcGIS toolbox (Pellitero et al., 2016).</p> <p>Mean position of the study area: 45.7412/7.3978 (°N/°E, WGS84)</p>
Understanding monsoon controls on the energy and mass balance of glaciers in the Central and Eastern Himalaya (Data Sets and Codes)
<p>This repository contains AWS datasets for the modelling periods considered in the analysis presented in the research paper, together with ablation measurements, pre-processed forcing data, T&C model codes, outputs and scripts for analysing outputs. When previously published elsewhere, references and links to the full, original datasets are provided under References.</p> <p>Matlab scripts for executing the T&C model are provided and should work stand-alone on any machine with a Matlab version 2019b or later installed.</p>
Mechanism of landslide induced by glacier-retreat on the Tungnakvíslarjökull area, Iceland
<p><strong>Introduction</strong></p> <p>This repository contains the data used for the study of the slope instability of Tungnakvíslarjökull, Iceland, described in Lacroix et al. (submitted). Specifically, the repository contains three time series in Tungnakvíslarjökull:</p> <ol> <li> <p>Time series of Digital Elevation Models (DEMs) from ASTER, 2000-2020.</p> </li> <li> <p>Time series of horizontal ground displacements, 1999-2019.</p> </li> <li> <p>Time series of earthquakes, 1995-2019.</p> </li> </ol> <p>Finally, we provide the map of the rate of elevation difference and the map of horizontal ground displacements for the whole period 2000-2019, as shown in Figure 1 of Lacroix et al. (submitted).</p> <p>The data and methods used for the elaboration of this data repository are described in detail in Lacroix et al. (submitted). In this repository we also provide a short summary and overview of the data and methods used.</p> <p><strong>Data</strong></p> <p>A total of 160 ASTER scenes were used to produce the time series of DEMs. A series of images from SPOT1, Landsat-7, ASTER and Landsat-8 was used in order to produce the horizontal ground displacements maps. The South-Iceland Lowlands (SIL) network (Jóndsdóttir et al., 2007) was obtained from Veðurstofan Íslands (www.vedur.is). Table 1 provides an overview of these data.</p> <table> <caption>Table1: Data used for the creation of this repository</caption> <tbody> <tr> <td>Application</td> <td>Platforms</td> <td>Acquisition dates</td> </tr> <tr> <td>DEM</td> <td>ASTER</td> <td>160 scenes from 2000-10-16 to 2020-08-27. <p>Format for the date is YYYYMMDD</p> </td> </tr> <tr> <td>Horizontal ground displacement</td> <td>SPOT1</td> <td>1987-08-05</td> </tr> <tr> <td> </td> <td>Landsat-7</td> <td>1999-07-26, 2000-08-20, 2001-09-24, 2002-07-09</td> </tr> <tr> <td> </td> <td>ASTER</td> <td>2003-08-04, 2004-09-18, 2007-08-15, 2011-08-10, 2013-07-24, 2014-08-18, 2016-08-07</td> </tr> <tr> <td> </td> <td>Landsat-8</td> <td>2014-08-12, 2015-09-16, 2016-08-24, 2017-08-20, 2018-09-14, 2019-08-10</td> </tr> <tr> <td>Seismicity</td> <td>SIL network</td> <td>370491 events recorded between 1995-2019 in the Mýrdalsjökull (S-Iceland) area and surroundings</td> </tr> </tbody> </table> <p><strong>Methods</strong></p> <p>The DEMs were created using the Ames StereoPipeline (ASP, Shean et al., 2016) with the same setup as used in Brun et al., (2017). Each DEM was then co-registered to a lidar DEM acquired in 2010 (Jóhannesson et al., 2013), using the co-registration methods from Berthier et al. (2007), and adding an across-track fifth-degree polynomial correction (Gardelle et al., 2013). The stack of elevations obtained from the DEM time series was linearly fitted in order to produce the map of elevation difference (file name 20000101_20210101_30x30m_UTM27N_DHDT_Lacroixetal2022.tif) of the period 2000-2020.</p> <p>The horizontal ground displacement maps were created using the offset tracking methodology described in Bontemps et al. (2018), consisting of: (1) pairwise image correlation using Mic-Mac (Rupnik et al., 2017), (2) masking of areas with low correlation coefficients (3) correction of co-registration bias by subtracting the mean values of the NS and EW displacement fields and (4) pixelwise fit of the horizontal ground displacements by least squares, using the time interval between measurements as weights and obtaining the full horizontal ground displacement for the analyzed period (file name 19990726_20200101_15x15m_UTM27N_HGD_Lacroixetal2022.tif)</p> <p>The time series of earthquakes obtained from the SIL network was filtered, and 2089 earthquakes with depth <5 km and magnitude <1.7 were used in this study and data repository (file name 19950814_20181118_SILvedur_time_lon_lat_dep_mag.txt).</p> <p><strong>Acknowledgements</strong></p> <p>We thank Bryndís Brandsdóttir for providing the seismic data used in this repository. E.B. and P.L. acknowledge the support from the French Space Agency (CNES) through the TOSCA, PNTS, SWH and ISIS programs.</p> <p><strong>Dataset attribution</strong></p> <p>This dataset is licensed under a Creative Commons CC BY 4.0 International License.</p> <p><strong>Dataset Citation</strong></p> <p>Lacroix, P., Belart, J.M.C., Berthier, E., Sæmundsson, Þ., Jónsdóttir, K.: Data Repository: Mechanism of landslide induced by glacier-retreat on the Tungnakvíslarjökull area, Iceland. Dataset distributed on Zenodo: 10.5281/zenodo.6388069</p>
Automatic weather station data from the debris-covered Kennicott Glacier, Alaska (May-Aug 2019)
<p>Data from an automatic weather station installed on the debris-covered<strong> Kennicott Glacier</strong> in the Wrangell St. Elias National Park, Alaska, USA, spanning most of the 2019 ablation season (28 May - 22 August 2019).</p> <p>Brief description of the uploaded datasets:</p> <ul> <li><em><strong>KEN_2019_60.csv</strong></em>: Hourly aggregated measurements of air temperature, relative humidity, wind speed and direction, atmospheric pressure, incoming and reflected shortwave radiation, incoming and outgoing longwave radiation and precipitation</li> <li><em><strong>KEN_2019_AWS_Metadata.pdf</strong></em>: Metedata file describing the station location, sensor setup and measurements</li> <li><em><strong>KEN_2019_debris.csv</strong></em>: Debris properties assumed to be potentially applicable for this site (not measured but taken from literature)</li> <li><strong><em>KEN_2019_VAL_debristemperature.csv</em></strong>: Hourly time series of temperature measurements at known depths within the debris cover close to the station location</li> <li><strong><em>KEN_2019_VAL_subdebrismelt.csv</em></strong>: Monthly stake measurements of surface height change (sub-debris ice melt) close to the station location spanning the ablation season</li> </ul> <p>We recorded additional meteorological data at the weather station (e.g. vertical profiles of wind speed/direction, relative humidity and temperature; 3D ultrasonic anemometer) as well as subdebris melt, dGPS-surface change and temperature profiles at additional transects for the same period. Please contact the authors in case of interest in any additional data.</p>
Dataset: Halving of Swiss glacier volume since 1931 observed from terrestrial image photogrammetry
<p>This is supplementary data for the article currently in review for The Cryosphere, titled "Halving of Swiss glacier volume since 1931 observed from terrestrial image photogrammetry".</p> <p><a href="https://doi.org/10.5194/tc-2022-14">See the preprint here</a></p> <p> </p>
Data set used in glacier algae and filamentous cyanobacteria models
<p>This is a data set for using the glacier algae and filamentous cyanobacteria models (Onuma et al., 2022). The content is as below.</p> <p>- data: observed data (bio-volume, cell count, mineral weight, EC, pH and meteorological conditions) on the bare ice surface in Qaanaaq Ice Cap (CSV files). And, model input and output data (CSV files). About the detailed information on each file, please see the readme files.</p> <p>- python: programs for the visualization (python scripts)</p> <p>- figure: png files created by the python scripts</p> <p>The codes of the glacier algae model can be downloaded below.<br> https://github.com/YukihikoOnuma/SnowAlgaeModel<br> <br> The article regarding the models is as below.<br> https://doi.org/10.1017/jog.2022.76</p>
Digital Elevation Models of Tweedsmuir Glacier 1950-2018
<p>This dataset contains digital elevation models (DEMs) of Tweedsmuir Glacier, British Columbia, Canada between 1950 and 2018. DEMs from 1950, 1969, 1974, and 1987 were created using Structure-from-Motion photogrammetry in Agisoft Metashape by Meghan A. Sharp. DEMs from 2000, 2007, 2010, and 2018 were acquired from open-source satellite sources (see "Amplification of Surface Topography during Surges of Tweedsmuir Glacier" for sources). All DEMs have been co-registered to ArcticDEM (Porter et al., 2018) using Shean et al (2016)'s open-source tool <em>demcoreg</em>.</p> <p> </p>
UAV Laser Scanning surveys of the lake terminating glacier Fjallsjokull in SE Iceland, captured in July, 2021.
<p>This dataset consists of 5 separate laser scanning surveys performed between the 8th and 15th July, 2021. Two surveys were conducted in the morning and afternoon of the 8th and the 9th, and then only the morning of the 15th. The point clouds have been cleaned to remove erroneous points. The point clouds were processed using the methods and code available at <a href="https://github.com/christomsett/Direct_Georeferencing">Direct_Georeferencing</a>. All point clouds are georeferenced in the projected WGS 1984 UTM 28N system, and provided in the widely used compressed 'laz' format. An accuracy assessment of the data showed that all surveys were consistent to within 0.1 m of each other, apart from the second flight (afternoon) on the 8th July. Any users of this data should be aware of its limitations in a challenging cryospheric environment. </p>
Glacier runoff projections and their multiple sources of uncertainty in the Patagonian Andes (40-56°S)
<p>This dataset contains the catchment scale results of the study: "<strong>Unravelling the sources of uncertainty in glacier runoff projections in the Patagonian Andes (40–56° S)</strong>". The results are disaggregated in the following files (for more details, please read the README file):</p> <p><em>- basins_boundaries.zip:</em> Contains the polygons (in .shp format) of the studied catchments. Each catchment is identified by its "basin_id".</p> <p><em>- dataset_historical.csv: </em>Summarises the historical conditions of each glacier at the catchment scale (area, volume and reference climate).</p> <p><em>- dataset_future.csv: </em>Summarises the future glacier climate drivers and their impacts at the catchment scale. </p> <p><em>- dataset_signatures.csv: </em>Summarises the glacio-hydrological signatures of each glacier at the catchment scale.</p> <p><strong>Citation (preprint under review): </strong></p> <p>- Aguayo, R., Maussion, F., Schuster, L., Schaefer, M., Caro, A., Schmitt, P., Mackay, J., Ultee, L., Leon-Muñoz, J., and Aguayo, M.: Assessing the glacier projection uncertainties in the Patagonian Andes (40–56° S) from a catchment perspective, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2023-2325, 2023.</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.