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45 results for “glacier dataset”
Dataset from Mannerfelt et al., (2024): Dynamic LIA advances hastened the demise of small valley glaciers in central Svalbard
<h1>Data from Bolterdalen and Foxdalen, Svalbard</h1> <p>This repository contains geospatial data from 1914 to 2019 of the valleys Bolterdalen and Foxdalen on Svalbard, associated with (and explained further in) Mannerfelt et al., (2024); <a href="https://cdnsciencepub.com/doi/10.1139/as-2024-0024">https://cdnsciencepub.com/doi/10.1139/as-2024-0024</a>.</p> <p><br>Its contents are:</p> <ul> <li>Shapefiles of geomorphological features</li> <li>Glacier outlines</li> <li>DEMs of the glaciers and their forefields</li> <li>Orthomosaics of the glaciers and their forefields</li> <li>Interpreted GPR measurements; interpolated and as point data.</li> </ul> <p>The 2009/2011 DEM has no associated orthomosaic in the repository. This is available as a WMTS service from the Norwegian Polar Institute:</p> <p>https://geodata.npolar.no/arcgis/rest/services/Basisdata/NP_Ortofoto_Svalbard_WMTS_25833/MapServer/WMTS/1.0.0/WMTSCapabilities.xml</p> <p>For more info (or there is an issue with the link), visit: <a href="https://geodata.npolar.no/">https://geodata.npolar.no/</a></p> <p> </p> <p><strong>NOTE</strong>: One file is slightly misnamed; "<em>Rieperbreen_forefield_ortho_2019.tif</em>" was collected in the autumn of 2017.</p> <p><br>To cite the dataset, please cite the associated paper:</p> <p>Mannerfelt, E. S., Hodson, A. J., Håkansson, L., and Lovell, H. (2024). Dynamic LIA advances hastened the demise of small valley glaciers in central Svalbard. Arctic Science.</p>
Regional Assessments of Glacier Mass Change (RAGMAC) experiment dataset
<p>This repository contains the dataset that was distributed to the participants of the Regional Assessments of Glacier Mass Change (RAGMAC) working<br>group of the International Association of Cryospheric Science (IACS, 2023).</p> <p>In short, the dataset is divided in 5 study sites: Hintereisferner (Austrian Alps - HEF_AT), Grosser Aletschgletscher (Swiss Alps - ALE_CH), Vestre Svartisen ice cap (Scandes, Norway - VES_NO), Baltoro glacier (Karakoram, Pakistan - BAL_PK), and the Northern Patagonian Icefield (Andes, Chile - NPI_CL).</p> <p>For each site, we provide series of Digital Elevation Models (DEMs) derived from SRTM, ASTER and TanDEM-X sensors. Additionally, we provide glacier outlines extracted from the Randolph Glacier Inventory version 6, for all glaciers in the study area, and selected glaciers for which participants were required to calculate the geodetic mass balance.</p> <p>Additionally, we provide the airborne DEMs that were used as reference data to evaluate the spaceborne DEMs. These are available for the cases HEF_AT, ALE_CH and VES_NO, all included in the single zip file “validation_data.zip”.</p> <p>For details on the dataset, experiment and any reference to this dataset, please refer to the following publication: Piermattei et al. (2024) "Observing glacier elevation changes from spaceborne optical and radar sensors – an inter-comparison experiment using ASTER and TanDEM-X data" The Cryosphere, DOI: <a href="https://doi.org/10.5194/egusphere-2023-2309">10.5194/egusphere-2023-2309</a> (to be updated upon final acceptance).</p> <p><strong>NOTE: </strong>As of July 2024, the TanDEM-X DEMs cannot be publicly shared due to license restrictions. We are discussing future opportunities to share the data in a future version of this repository.</p> <p> </p>
Dataset for 'Warmer Oceans may Accelerate the Marine-to-Land Transition of a West Greenland Outlet Glacier'
<p>Monthly hydrographic records from 2008-2023, collected from the standard station GF10 (64°36.6N, 51°57.5 W; maximum depth: 576 m) as part of Greenland Climate Research Centre “Long-term standard hydrographic fjord research program”. Note that the date values reflect the last day of each month, not necessarily the date that the measurements were obtained.</p> <p>Please cite this reference if dataset were used in any way.</p> <p>Correspondence to John Mortensen at jomo@natur.gl</p>
Dataset associated with article: Self-sufficient seismic boxes for monitoring glacier seismology in Greenland
<p>Dataset associated with article: Self-sufficient seismic boxes for monitoring glacier3 seismology in Greenland</p> <p>Contains:<br> - Seismic data of both SG-boxes and regular geophones from Gornergletscher fieldtest, 2021( Seismic_Data_Gorner_Fieldtest.zip) <br> --> SG-box data naming: GO"station_number"SG <br> --> Geophone data naming: GO"station_number"GP<br> <br> - Weather data Gornergletscher fieldtest from Monte Rosa, Meteo Swiss Weather station ( Weather_data_Gorner_Fieldtest_2021.zip) <br> --> 1hr wind averages <br> --> 1hr temperature averages<br> <br> - MSR logger data from SG-boxes from Gornergletscher fieldtest, 2021. Every 5 min these log battery power, tilt (along three axes, temperature and humidity inside the box and light strength on two sides of the SG-box. ( MSR_logger_data_SGboxes_Gorner_Fieldtest.zip )<br> <br> - Seismic data of SG box (Sensor code BSM) next to weather station first acquisition Greenland 2021 ( Seismic_Data_SG_Box_first_acquisition_Greenland_2021.zip) <br> --> .pri0 is East component, .pri1 is North component, .pri2 is Vertical component.<br> <br> - Weather data from weather station next to SG-box (sensor code BSM) during first acquisition Greenland 2021 ( Weather_station_data_Greenland_2021.zip) <br> --> The weather station logs a value every two hours.</p> <p> </p>
Dataset for "Bias correction and statistical modeling of variable oceanic forcing of Greenland outlet glaciers" by Verjans et al.
<p>Code and data products associated with "Bias correction and statistical modeling of variable oceanic forcing of Greenland outlet glaciers" by Verjans V., Robel A., Thompson A. F., and Seroussi H.</p> <p>Please see readme file for all the information.</p> <p>Contact: vverjans3@gatech.edu</p> <p>Author: Vincent Verjans</p>
Toy glacier elevation change dataset
<p>Dataset for conference paper: 'Beyond Intuition, a Framework for Applying GPs to Real-World Data' at the ICML Workshop tructured Probabilistic Inference and Generative Modelling, 2023 (Honolulu, Hawaii, USA).</p> <p><a href="https://arxiv.org/abs/2307.03093">https://arxiv.org/abs/2307.03093</a></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>
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>
Dataset for: Evaluation of low-cost Raspberry Pi sensors for photogrammetry of glacier calving fronts
<p>Points clouds of Fjallsjökull calving front, as derived by a Raspberry Pi and a Unoccupied Aerial Vehicle. For each sensor, eight sub-sections are analysed. Point clouds are provided in .las format. Each sub-section is generated within its own spatial reference (matching that of the other sensor to allow for comparison). </p>
Dataset: Quantification of post-glacier bedrock surface erosion in the European Alps using 10Be and optically stimulated luminescence exposure dating
<p>This contains the dataset associated with the publication titled "Quantification of post-glacier bedrock surface erosion in the European Alps using 10Be and optically stimulated luminescence exposure dating" published in Earth Surface Dynamics (2022).</p>
Datasets on multiparameter glacier change dynamics for the Jankar Chhu Watershed, Lahaul Himalaya, India
<p>Characterization of glacier changes in the surface area, terminus, equilibrium line altitude (ELA), elevation, and velocity was worked out for the Jankar Chhu Watershed (JCW) of Lahaul Himalaya using freely available satellite remote sensing data and the limited number of field observations. We studied changes using Corona (1971), Landsat (1993‒2017), Sentinel 2A (2016), the SRTM Digital Elevation Model (DEM; 2000), and the global TanDEM‒X DEM (2014). Our results showed that changes in glacier area (‒14.7 ± 4.3 km²), terminus (‒4.7 ± 0.4 m a¯¹), and ELA (~ 20 m rise) between 1971 and 2016 are smaller than previously reported. Glacier lake area increased by ~0.3 km² during 1971‒2016. An intricate pattern of mass changes across the JCW was observed, with surface lowering on an average of ‒0.7 ± 0.4 m a¯¹ which equates to a geodetic mass balance of ‒0.6 ± 0.4 m w.e. a¯¹ during 2000‒14. The computed glacier surface velocities (1971‒2017) reveal nearly stagnant debris-covered ablation zone but the dynamically active main trunk. The present study provides valuable insights into the recent multiparameter glacier variations, which are of critical importance to assess the future glacier dynamics on a regional scale in areas like the present one.</p>
Dataset: Meltwater discharge, suspended sediment and bedload flux, air temperature and precipitation at the Otemma Glacier terminus (2022 melt season)
<p>This dataset extends existing timeseries from the Otemma Glacier terminus on meltwater discharge (Müller and Miesen, 2022), suspended sediment and bedload flux (Mancini et al., 2023a), and air temperature and precipitation (Müller, 2022) into the 2022 melt season. The methodologies used for data collection were consistent with those used in previous years (see included technical report). Water and sediment flux measurements were performed at gauging station GS1 (aka Station 1) located in the main proglacial river, approx. 0.35 downstream of the glacier terminus (Müller and Miesen, 2022; Mancini et al., 2023a). Air temperature and precipitation data were collected at the "Glacier snout station" adjacent to GS1 (Müller, 2022). Data collection specifics for the 2022 melt season are provided in the <code>report2022.pdf</code> file.</p> <p><strong>DATA:</strong><br><code>river.csv</code> - water and sediment flux timeseries from GS1 with columns:<br>year: yyyy<br>frac_day: fractional day of the year<br>dt: measurement interval [mins]<br>Qw: meltwater discharge [m3/s]<br>Qss: suspended sediment flux [kg/s]<br>Qb: bedload flux [kg/s]<br>Q<em>xx</em>_lo or Q<em>xx</em>_hi: the lower and upper uncertainty bounds for Qw, Qss, Qb [kg/s]<br><em>Extended data for 2020 and 2021 melt seasons also included*</em></p> <p><code>weather.csv</code> - air temperature and precipitation timeseries spanning 18 Nov 2021 to 16 Aug 2022 from 'Glacier snout station' with columns:<br>datetime: local time (UTC+01 with DST) [yyyy-mm-dd hh:mm:ss] <br>temperature: air temperature [°C]<br>precipitation: liquid or solid precipitation [mm w.eq.]</p> <p><em>*Extended flux datasets are also provided for 2020 and 2021 given the difference in scope between Mancini et al. (2023b) and Jenkin et al. (2024, submitted for review). The datasets in Mancini et al. (2023b) have been post-processed for comparative analysis between two different gauging stations (upstream and downstream), while in this work, the most temporally complete timeseries of subglacial sediment export at the upstream gauging station was required. Therefore, the data provided here for 2020 and 2021 include: (i) a short extension of the timeseries, (ii) the removal of a suspended sediment clipping threshold, and (iii) filling of gaps in the 2020 suspended sediment timeseries with real measured data.</em></p>
Thwaites Glacier thins and retreats fastest where ice-shelf channels intersect its grounding zone, dataset+code
<p>Thwaites Glacier thins and retreats fastest where ice-shelf channels intersect its grounding zone, updated dataset+code submitted for publication in The Cryosphere. Dataset includes all data produced in this study, including velocity maps derived from speckle tracking of Sentinel 1 images, maps of rates of ice shelf change and the annual mosaic digital surface models from which they were derived, maps of the basal conditions at Thwaites glacier, shapefiles and masks of the annual hydrostatic boundary (grounding line proxy position), and shapefiles of all hydrostatic boundary features, ice shelf basal channels and surface depressions, intermediate polygons used to filter the features, and digital surface model strips with registration data included as attributes. </p>
Air temperature and near-surface meteorology datasets on three Swiss glaciers - Extreme 2022 Summer
<p>%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%<br> GLACIER METEOROLOGICAL DATA<br> SWISS ALPS -2022<br>%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%<br>Data gathered and structured by Thomas Shaw (WSL, Switzerland (until Oct 2022)).</p><p>On and off-glacier meteorological data were gathered and analysed as part of a Marie-Curie project 'TEMPEST' (tempestglacier.com).<br>The dataset consists of hourly low-cost AWS (Davis Vantage Pro2) and simple temperature ('T-')logger (Onset TidBitv2) sensor records on three glaciers in the Swiss Alps (Canton Valais).</p><p>The glaciers are:<br>Haut Glacier d'Arolla (45.967°N, 7.526°E)<br>Glacier d'Otemma (45.956°N, 7.454°E)<br>Glacier du Corbassière (45.975°N, 7.303°E)</p><p>Data are provided in individual Excel files per glacier that contain all hourly data for the sub-period of comparison (11 August-18 September, 2022).<br>Data are quality controlled and checked for obvious errors. Any uncertain values are set to NaN.<br>Air temperature data at 'T-Logger' stations were corrected for heating errors using the comparison of measurements in artificially (AWS) and naturally ventilated (T-Logger) radiation shields on Arolla and Corbassiere glaciers.<br>A multiple linear regression model was applied to estimate these differences at all T-Loggers on all glaciers as a function of incoming shortwave radiation (MeteoSwiss station-derived) and wind speed (measured at AWS).</p><p>Each Excel file contains a 'META' tab for simple metadata related to station locations (latitude 'LAT' (°), longitude 'LON' (°), elevation 'ELE' (m a.s.l.) and flowpath length 'FPL' (m)) and a 'DATA' tab for the hourly data. <br>Suffixes to the station names in each column provide the variable measured at that site:<br>'TA' - 2m air temperature (°C)<br>'TA_Hi' - Maximum air temperature for timestep (°C)<br>'TA_Lo' - Minimum air temperature for timestep (°C)<br>'RH' - 2m relative humiditiy (%)<br>'FF' - Wind speed (m s^-1)<br>'FF_Hi' - Maximum wind speed for timestep (m s^-1)<br>'FF_Lo' - Minimum wind speed for timestep (m s^-1)<br>'DIR' - Wind direction (°)<br>'DEW' - Dewpoint temperature (°C)<br>'PRESS' - Air pressure (mbar)<br>'CHILL' - Calculated wind chill temperature (°C)<br>'Heat_idx' - Calculated heat index (°C)<br>'THSW' - A calculated index that uses humidity and temperature like for the Heat Index, but also includes the heating effects of sunshine and the cooling effects of wind (like Wind Chill) to calculate an apparent temperature of what it "feels" like out in the shade</p><p>Wind speeds and direction measured at off-glacier sites 'OG' are for the lower off-glacier station ('OG_Low'). </p><p>-------------------------</p><p> </p><p>This work was funded by the EU Horizon 2020 Marie Skłodowska-Curie Actions Grant 101026058.<br> </p>
Dataset for: Fast retreat of Pope, Smith, and Kohler glaciers in West Antarctica observed by satellite interferometry
Open the record for dataset details and reuse information.
Comparison of turbulent structures and energy fluxes over exposed and debris-covered glacier ice: Datasets
<p>This repository contains data of near surface data of turbulence conditions measured simultaneously over exposed ice and a 0.08 m thick supraglacial debris cover on Suldenferner, a small glacier in the Italian Alps. It is related to the following publication:</p> <p>Nicholson, L. and Stiperski, I. (2020) Comparison of turbulent structures and energy fluxes over exposed and debris-covered glacier ice. Journal of Glaciology.</p> <p><strong>The repository contains the following files</strong></p> <p>(1) Overview figure of the locations of the installed weather stations collecting data used in the analysis, and images of the eddy covariance installations </p> <ul> <li><strong>Filename:</strong> overview.tif</li> </ul> <p>(2) 30 minute average meteorological data from the automatic weather station (AWS) on the debris-covered glacier surface with ca. 0.09 m thick debris cover.</p> <ul> <li><strong>Filename:</strong> aws.csv</li> <li><strong>Location:</strong> 46.496 °N / 10.569 °E / ~2625 m</li> <li><strong>Time period:</strong> 11.08.2015 13:30 – 14.08.2015 21:00</li> <li><strong>Variables - unit:</strong> listed in variables&units.pdf</li> </ul> <p>(3) 5 minute data from the eddy covariance station on the clean ice glacier surface.</p> <ul> <li><strong>Filename:</strong> ecci.csv</li> <li><strong>Location:</strong> 46.498°N /10.560°E / ~ 2780 m</li> <li><strong>Time period:</strong> 11.08.2015 13:42 – 14.08.2015 20:57</li> <li><strong>Variables - unit:</strong> listed in variables&units.pdf</li> </ul> <p>(4) 5 minute data from the eddy covariance station on the debris-covered glacier surface with ca. 0.08 m thick debris cover.</p> <ul> <li><strong>Variables - unit:</strong> listed in variables&units.pdf</li> <li><strong>Location:</strong> 46.495 °N / 10.572 °E / ~ 2600 m</li> <li><strong>Time period:</strong> 11.08.2015 13:42 – 14.08.2015 20:57</li> <li><strong>Variables - unit:</strong> listed in variables&units.pdf</li> </ul> <p>(5) A list of the variables and units used in the datafiles.</p> <ul> <li><strong>Filename:</strong> variables&units.pdf</li> </ul> <p> </p> <p>The<strong> instrumentation locations</strong> can be seen in the overview figure. </p> <p>The <strong>automatic weather station </strong>consists of a Kipp and Zonen CNR1 4-way radiation sensor, a shielded Vaisala HMP45c temperature and relative humidity sensor, and a Young 05103 anemometer. 30-minute averages and standard deviations of variables were recorded by a Campbell C3000 datalogger. Temperature and relative humidity are also sampled at 30-minute intervals allowing the vapor pressure to be calculated at this interval. </p> <p>The <strong>eddy covariance</strong><strong> instrumentation</strong> was identical at both stations (ecci and ecdc) and consisted of two segmented masts drilled into the ice with sensors mounted at a height of 1.6 m on a cross arm spanning the vertical masts. A CSAT 3D sonic anemometer and KH20 hygrometer sampling data at a frequency of 20Hz were mounted parallel to the surface and facing obliquely across-glacier at a bearing of 255° so as to capture both up and downglacier winds. A shielded Vaisala HMP45 was installed on the EC mast to record 1-minute averages of air temperature, relative humidity and vapor pressure. Data were recorded using Campbell Scientific CR1000 data loggers with compact flash card storage modules. Power was provided by 60Ah deep cycle batteries connected to 20 W solar panels. </p> <p>The <strong>data provided</strong> here is gap-filled data. The eddy covariance station over debris-covered ice was installed on 10 August 2015, and the one over clean ice was installed on 11 August 2015. At ecdc, a faulty solar panel regulator resulted in this station losing power on 15 August. At ecci two instrument failures occurred; the Vaisala instrument on the afternoon of 12 August and the KH20 at the end of 14 August. Air pressure was not recorded at any of the three stations. Missing data was filled on the basis of multiple regression transformation of data measured at nearby stations, as described fully in the publication. Pressure data were filled in with data from the nearby Madritsch weather station, operated by the Autonomous Province of Bozen. Missing temperature and vapor pressure data spanning 12-14 August at the eddy station over clean ice was filled in with data from the glacier automatic weather station.</p> <p><strong>Thanks</strong> are due to the Gutgsell family at the Hintergrathütte for continued support of our research activities and (in alphabetical order) Michael Adamer, Federico Covi, Costanza del Gobbo, Lukas Hammerer, Irmgard Juen, Marius Massimo, Kristin Richter, Reto Stauffer and Anna Wirbel for assistance in the field. Permission to work on Suldenferner is granted by Stelvio National Park. This research was funded by the Austrian Science Fund Grant numbers V309, P28521 and T781-N32.</p> <p> </p>
Grounding line datasets of Totten and Moscow University Glaciers 2017-2021
<p>The grounding line (GL) datasets in 2017-2021 of Totten and Moscow University Glaciers located in Wilkes Land were mapped from different remote sensing methods, including ICESat and ICESat-2 satellite altimetry, and Sentinel-1a/b Differential Interferometry Synthetic Aperture Radar (DInSAR).</p>
Randolph Glacier Inventory - A Dataset of Global Glacier Outlines, Version 2
The Randolph Glacier Inventory (RGI) is a global set of glacier outlines; it is intended as a snapshot of the world’s glaciers. This data set provides a single outline for each glacier and is produced in coordination with the Global Land Ice Measurements from Space (GLIMS) initiative. The RGI is not suitable for measuring glacier-by-glacier rates of area change, but can be used to estimate glacier volumes, rates of elevation change at regional and global scales, and cryospheric responses to climatic forcing.Glacier mapping data are contributed to both GLIMS and the RGI from the glaciological community. RGI is produced by the Working Group on the Randolph Glacier Inventory and Infrastructure for Glacier Monitoring, a body of the International Association of Cryospheric Sciences (IACS). This data set is updated approximately annually.Glacier outlines are distributed as Shapefiles. Hypsometric data (CSV files) and gridded auxiliary data (GeoTIFFs) are also available. All RGI data are packaged globally and by region, with regions based upon the Global Terrestrial Network for Glaciers.
Randolph Glacier Inventory - A Dataset of Global Glacier Outlines, Version 4
The Randolph Glacier Inventory (RGI) is a global set of glacier outlines; it is intended as a snapshot of the world’s glaciers. This data set provides a single outline for each glacier and is produced in coordination with the Global Land Ice Measurements from Space (GLIMS) initiative. The RGI is not suitable for measuring glacier-by-glacier rates of area change, but can be used to estimate glacier volumes, rates of elevation change at regional and global scales, and cryospheric responses to climatic forcing.Glacier mapping data are contributed to both GLIMS and the RGI from the glaciological community. RGI is produced by the Working Group on the Randolph Glacier Inventory and Infrastructure for Glacier Monitoring, a body of the International Association of Cryospheric Sciences (IACS). This data set is updated approximately annually.Glacier outlines are distributed as Shapefiles. Hypsometric data (CSV files) and gridded auxiliary data (GeoTIFFs) are also available. All RGI data are packaged globally and by region, with regions based upon the Global Terrestrial Network for Glaciers.
Randolph Glacier Inventory - A Dataset of Global Glacier Outlines, Version 7
The Randolph Glacier Inventory (RGI) is a global set of glacier outlines intended as a snapshot of the world’s glaciers outside of ice sheets. It provides a single outline for each glacier from approximately the year 2000, as well as a set of attributes and other relevant auxiliary information. Glacier outlines are distributed as Shapefiles. Hypsometric data and attributes (CSV files) and metadata (json) are also available. All RGI data are packaged both globally and by region (as defined by the Global Terrestrial Network for Glaciers (GTN-G) Glacier Regions).The RGI is not suitable for measuring glacier-by-glacier rates of area change. However, it can be used to estimate glacier volumes; rates of elevation change at regional and global scales; and glacier responses to climatic forcing.RGI version 7.0 was developed by the “Working Group on the Randolph Glacier Inventory (RGI) and its role in future glacier monitoring” of the International Association of Cryospheric Sciences (IACS). The glaciological community contributes glacier mapping data to the Global Land Ice Measurements from Space (GLIMS) database. A subset of the glacier outlines in GLIMS are then extracted and reprocessed to produce the RGI.See the RGI documentation under "User Guide" (below) for more information.
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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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.