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774 results for “glacier”
Widespread glacier advances across the Tian Shan during Marine Isotope Stage 3 not supported by climate-glaciation simulations
<p>This dataset shows the modeled ice extent over the Tian Shan during the present, MIS 2, MIS 3 interstadial, and MIS 3 stadial based on a 250m resolution of PISM model forced by the outputs from the NorESM1-F climate model.</p>
Glacier model simulations of moraine building forced by interannual variability in climate
<p>A set of 2,000-year simulations of moraine building by a glacier flowing through a synthetic alpine landscape forced by interannual variability in weather imposed on an otherwise stable climate. Moraine relief is shown for a standard deviation in mean annual air temperature (dT) of 0.5°C, 1.5°C, and 3.0°C around a long-term mean of 7.0°C. Simulations were made using the ice-flow model iSOSIA (Egholm et al., 2011, <em>Geomorphology</em>).</p>
Reconstructed, Long-Term Meteorological Forcing and Mass Balance over Glaciers in High Mountain Asia
<p>%%%% Reconstructed_WRF_Forcing_HMA.mat %%%%%%%%<br> A reconstructed, long-term data series of meteorological data for three glaciers in distinct regions of High Mountain Asia. The meteorological forcing consists of hourly Weather Research and Forecasting (WRF) data bias-corrected by high elevation, off-glacier automatic weather station (AWS) data. The variables of the forcing data consist of 2m air temperature ('T' °C), relative humidity ('RH' %), air pressure ('PRESS' hPa), incoming shortwave ('SWIN' Wm-2) and longwave ('LWIN' Wm-2) radiation, precipitation ('PP', mm hr) and wind speed ('FF' m s-1). The period of the timeseries ranges from January 1981 to December 2019. <br> <br> Data are available for the following glaciers (bias-corrected to the given coordinates / elevation of the off-glacier AWS)<br> Yala Glacier, Nepal (28.237°N 85.619°E, 5090 m a.s.l.) - 'AWS Yala Basecamp' (AWS Data available on the ICIMOD RDS)<br> Parlung Glacier Number 4 (29.245°N 96.928°E, 4600 m a.s.l.) - 'AWS4600' (Contact author Wei Yang for AWS data requests)</p> <p>Mugagangqiong Glacier (32.234°N, 87.485°E, 5850 m a.s.l.) - 'AWS5850' (Contact author Wei Yang for AWS data requests)</p> <p> </p> <p>Data are stored as matlab tables in .mat files. Data were created using Matlab version 2020b. </p> <p> </p> <p>%%%% Reconstructed_Mass_Balance_HMA.mat %%%%%%%%<br> <br> Reconstructed cumulative mass balances for the aforementioned glaciers (values in m w.e.)</p> <p> </p> <p> </p> <p> </p> <p> </p>
Pre and post- processing files and scripts for the paper 'The effect of local shear stress on glacier sliding'
<p>The folder contains:</p> <ul> <li>A set of .F90 to be compiled in order to run the simulations with Elmer Ice</li> <li>A python script called <em>newmodel_2a.py</em> for generating the complete analytical model shown in the appendix</li> <li>scripts for post-processing the results. The paths have to be edited. They read the .dat files, extract the main variables: u_b, \tau_b, N and compute C to produce the friction law plots. It also computes the ratio Af/As and produces the respective plots</li> <li>A compressed folder called<em> results</em> with the outputs of the main results. They include the results (.dat file) and the .sif files to run the simulations with FEM software Elmer/Ice. Their names are already included in a small subroutine in the postprocessing script</li> <li>A compressed folder called <em>comparison </em>with the outputs of the comparison between models. They include the results (.dat file) and the .sif files to run the simulations with FEM software Elmer/Ice, as well as the complete solution for the new models (in order to extract \tau_f for the sandpaper model we need the complete flow solution).</li> </ul>
Glacier Bay National Park glacial rock avalanche inventory (1984-2020)
<p>An inventory of supraglacially deposited rock avalanches that occurred in Glacier Bay National Park, Alaska, between 1984 and 2020.</p> <p>Reference: Smith, W.D., Dunning, S.A., Ross, N., Telling, J., Jensen, E.K., Shugar, D.H., Coe, J.A. and Geertsema, M. (2023) Revising supraglacial rock avalanche magnitudes and frequencies in Glacier Bay National Park, Alaska. <em>Geomorphology</em>, doi: <a href="https://doi.org/10.1016/j.geomorph.2023.108591">https://doi.org/10.1016/j.geomorph.2023.108591</a></p>
On-Glacier Air Temperatures for Tsanteleina Glacier, 2015
<p>Tsanteleina_Glacier_Air_Temperature_Data_2015.xlsx<br> %------------------------------------------%<br> Data Generated on 13th May 2022</p> <p>Data Curator: Dr. Thomas Shaw (Swiss Federal Institute, WSL, Switzerland)</p> <p>Data Provider(s): Dr. Thomas Shaw (Swiss Federal Institute, WSL, Switzerland) thomas.shaw@wsl.ch<br> Prof. Benjamin Brock (Northumbria University, Newcastle, UK) benjamin.brock@northumbria.ac.uk</p> <p>Data period: 19th June - 15th September, 2015</p> <p><br> Details:<br> Hourly data are generated for air temperature stations ('T-Loggers') distributed across Tsanteleina Glacier, Italy (45.4812°N, 7.0618°E).<br> Air temperatures (°C) were measured using Tinytag thermistors (accuracy +/- 0.2-0.35°C) housed in naturally ventilated Campbell MET20 / MET21 radiation shields.</p> <p>Some stations fell over at times during the summer season due to differential ablation and tripod stability.<br> Filtering of this data therefore leaves gaps (NaNs) for various stations at different times of the observation period. </p> <p>Off-Glacier air temperatures in the region can be accessed from the platform of the Regione Autonoma Valle d'Aosta:<br> https://presidi2.regione.vda.it/str_dataview_station/3060. </p> <p>Additional details can be found in the article: <br> Shaw, T. E., Brock, B. W., Ayala, A., Rutter, N., & Pellicciotti, F. (2017). <br> Centreline and cross-glacier air temperature variability on an Alpine glacier: assessing temperature distribution methods and their influence on melt model calculations. <br> Journal of Glaciology, 1–16. https://doi.org/10.1017/jog.2017.65</p> <p>Please cite the above article for any usage of the dataset.</p> <p>Data are shared and compiled as part of a wider project to estimate on-glacier air temperatures from off-glacier data<br> For more details on the 'TEMPEST' project, visit: https://tempestglacier.com/</p> <p><br> </p>
Glacier_inventory_debris_cover_ice_thickness_dataset_Chandra_Bhaga_basin_Himalaya
<p>This is a multitemporal glacier dataset over ChandraBhaga basin in western Himalaya which includes glacier inventory (for 251 glaciers with area > 0.5 km<sup>2</sup>) for year 1993, 2000, 2010 and 2019 (.shp files); debris cover for year 1993 and 2019 (.tif files) and ice thickness (.tif files). Details of files are included in data_description.docx. These datasets form part of manuscript submitted to Earth Systems and Science Data.</p>
Terminus Traces for publication 'Ocean-forcing and glacier-specific factors drive differing glacier response across the 69 oN boundary, east Greenland' (Version 1)
<p>Dataset supporting publication 'Brough, S., Carr, J.R., Ross, N., Lea, J.M. (2023) Ocean-forcing and glacier-specific factors drive differing glacier response across the 69 <sup>o</sup>N boundary, east Greenland. J. Geophys. Res. Earth Surf. <a href="https://doi.org/10.1029/2022JF006857">https://doi.org/10.1029/2022JF006857</a>.'</p> <p>This dataset provides GIS ready shapefiles of mapped terminus traces for 24 east Greenland glaciers between 2013 and 2020 for the aforementioned publication. Data are provided in both geographic (EPSG: 4326; WGS84) and projected (EPSG:3413; NSIDC Sea Ice Polar Stereographic North) coordinate systems. As each terminus trace has metadata appended, including the unique path identifier, it is possible to directly and easily identify the original image used in the mapping process. Both shapefiles are compatible for ingestion into the Google Earth Digitisation Tool (GEEDiT) Reviewer (<a href="https://liverpoolgee.wordpress.com/">https://liverpoolgee.wordpress.com/</a>) for reviewing and sub-setting the dataset (see Lea, J.M. [2018] Earth Surf. Dynam. 6, 551–561. <a href="https://doi.org/10.5194/esurf-6-551-2018">https://doi.org/10.5194/esurf-6-551-2018</a>).</p> <p> </p> <p>When using this data product in a publication, please include the following citations:</p> <p>Brough, S., Carr, J.R., Ross, N., Lea, J.M. (2023). Terminus Traces for publication 'Ocean-forcing and glacier-specific factors drive differing glacier response across the 69 <sup>o</sup>N boundary, east Greenland' (Version 1) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.6904219">https://doi.org/10.5281/zenodo.6904219</a>.</p> <p>Brough, S., Carr, J.R., Ross, N., Lea, J.M. (2023) Ocean-forcing and glacier-specific factors drive differing glacier response across the 69 <sup>o</sup>N boundary, east Greenland. J. Geophys. Res. Earth Surf. <a href="https://doi.org/10.1029/2022JF006857">https://doi.org/10.1029/2022JF006857</a>.</p>
Glaciers elevation change over Tibetan Plateau's endorheic basin during 1975-2000
<p>Glacier elevation changes over the endorheic basin of the Tibetan plateau with KH-9 in 1975-2000.</p>
Datasets of surface ice flow speed for Pine Island Glacier, Ferrigno Ice Stream and Leonardo Glacier
<p>Here we make available the datasets used in my MRes dissertation as part of the School of Earth and Environment at the University of Leeds. The research paper presents a novel method to automatically detect speed anomalies in ice velocity datasets.</p>
Abramov glacier data base
<p><strong>Abramov glacier data base</strong></p> <p>Data base compiled by F. Pertziger. Parts of this dataset were published in the Abramov Glacier Data Reference Book (Pertziger, 1996). Please refer to the reference book and to Kronenberg et al. (2021,2022) and Kronenberg (2022) for more details about the data and their acquisition.</p> <p>The mdb file contains monthly mass balance measurements from 165 stake and nine snow pit locations, daily runoff measurements and meteorological observations. Please note, that the reported coordinates are given in a local coordinate system. Coordinates were transferred to WGS84 / UTM Zone 42N as described in Kronenberg et al. (2021) and are available in a separate file.</p> <p><strong>References</strong></p> <p>Kronenberg Marlene. (2022). <em>Changing glacier firn in Central Asia and its impact on glacier mass balance</em>. PhD Thesis. University of Fribourg. https://doi.org/10.51363/unifr.sth.2022.005</p> <p>Kronenberg, M., Machguth, H., Eichler, A., Schwikowski, M., & Hoelzle, M. (2021). Comparison of historical and recent accumulation rates on Abramov Glacier, Pamir Alay. <em>Journal of Glaciology</em>, <em>67</em>(262), 253–268. https://doi.org/10.1017/jog.2020.103</p> <p>Kronenberg, M., Machguth, H., Pelt, W. van, Fiddes, J., Hoelzle, M., & Pertziger, F. (2022). Long-term mass balance and firn modelling for Abramov glacier, Pamir Alay. <em>The Cryosphere Discussions</em>, <em>2021–380</em>, 1–33. https://doi.org/10.5194/tc-2021-380</p> <p>Pertziger, F. I. (1996). <em>Abramov Glacier Data Reference Book: Climate, Runoff, Mass Balance</em>. Central Asian Hydrometeorlogical Institute.</p>
Input and Output files for "Contributions to Streamflow and Sea Level Rise in High Mountain Asia from 2003-2009 Glacier Recession"
<p>All files below were prepared by Collin B. Lawrence. </p> <p>All ARCIDs correspond to the HydroSHEDS Dataset for Asia (Lehner et al., 2008). (http://www.hydrosheds.org/)</p> <p>GLDAS data are from the Global Land Data Assimilation System (Rodell et al., 2004). (https://ldas.gsfc.nasa.gov/gldas/)</p> <p>Q_JJA_(model) contains three columns: 1) ARCID, 2) subsurface and surface runoff for that particular reach, and 3) accumulated subsurface and surface runoff. All flows are in m<sup>3</sup> s<sup>-1</sup>, and are averaged for the months of June, July, and August from the years 2003 – 2009. CLM, MOSAIC, NOAH, and VIC were the subset of models used from the Global Land Data Assimilation System (GLDAS).</p> <p>Q_annual_(model) contains three columns: 1) ARCID, 2) subsurface and surface runoff for that particular reach, and 3) accumulated subsurface and surface runoff. All flows are in m<sup>3</sup> s<sup>-1</sup>, and are yearly averages for the years 2003 – 2009. CLM, MOSAIC, NOAH, and VIC were the subset of models used from the Global Land Data Assimilation System (GLDAS).</p> <p>phi_i_JJA is the accumulated subsurface and surface runoff for the CLM, MOSAIC, NOAH, and VIC model average. The June, July, and August output was averaged over the years 2003 – 2009. Column 1 is ARCID and the accumulated runoff is expressed in m<sup>3</sup> s<sup>-1</sup>.</p> <p>phi_i_JJA_err is the standard error of the model mean in phi_i_JJA.</p> <p>phi_g_JJA contains the accumulated glacier recession flow in m<sup>3</sup> s<sup>-1 </sup>for the months of June, July, August from 2003 - 2009.</p> <p>phi_g_JJA_err is the standard error of the model mean in phi_g_JJA.</p> <p>phi_g_annual contains the annually averaged accumulated glacier recession flow in m<sup>3</sup> s<sup>-1 </sup>for 2003 – 2009.</p> <p>lambda.csv contains the fraction of streamflow from glacier recession.</p>
AGF212 2024: GPR ice thickness measurements of the glaciers Tellbreen and Blekumbreen (Svalbard)
Open the record for dataset details and reuse information.
Data for: Seawater intrusions in the observed grounding zone of Petermann Glacier causes extensive retreat
<p>Understanding grounding line dynamics is critical for projecting glacier evolution and sea level rise. Recent observations from satellite radar interferometry reveal rapid grounding line migration forced by oceanic tides that are several kilometers larger than predicted by hydrostatic equilibrium alone, indicating that the transition from grounded to floating ice is more complex than previously thought. Recent studies suggest that seawater intrusions beneath grounded ice may play a role in glacier dynamics. Here, we investigate their impact on the evolution of Petermann Glacier, Greenland, using an ice sheet model. We compare the model results with observed changes in grounding line position, velocity, and ice elevation between 2010 and 2022. If we exclude seawater intrusions, the model requires anomalously high melt rates to replicate the retreat. Conversely, we match the observed retreat with 3-km-long seawater intrusions with a maximum ice shelf melt rate of 50~m/yr, consistent with observations. We also obtain more realistic glacier speedup and ice thinning when including seawater intrusions in the model. We conclude that seawater intrusions play a critical role in the dynamics of Petermann Glacier. Including them in glacier flow models will make glaciers more sensitive to ocean warming and increase projections of sea level rise.</p>
Temporal morphodynamic evolution of the Glacier d'Otemma proglacial forefield for melt seasons 2020 and 2021: data collection and post-processing
<p><span>The data included in this dataset concern the continuous geomorphic (orthomosaics, DEMs, inundation maps) and sedimentological (grain-size maps) evolution of the Glacier d’Otemma proglacial margin (Southern-Western Swiss Alps) located at an altitude of ca. 2450 m a.s.l. during summer 2020 and 2021. </span></p> <p><span>Data details and formats are available in the pdf document. Further information on data aquisition and post-processing techniques are available in Mancini et al. (2024).</span></p>
Velocity of Greenland's Helheim Glacier controlled both by terminus effects and subglacial hydrology with distinct realms of influence
<p>Description of files contained in this archive:</p> <p><strong> </strong></p> <p>SHAKTI-ISSM model output</p> <p>Winter spin-up</p> <p>Helheim_SHAKTI_N_2_1yr_H10.mat</p> <ul> <li> <p>Winter base state spin-up simulation, final state serves as initial conditions for seasonal simulations</p> </li> </ul> <p><strong> </strong></p> <p>Seasonal hydrology-forced simulations</p> <p>Helheim_big_seasonal_low.mat</p> <ul> <li> <p>Seasonal simulation</p> </li> </ul> <p>Helheim_big_seasonal_firn.mat</p> <ul> <li> <p>Seasonal + firn aquifer simulation</p> </li> </ul> <p>Helheim_big_seasonal_bigmelt.mat</p> <ul> <li> <p>Enhanced melt simulation</p> </li> </ul> <p><strong> </strong></p> <p>Seasonal terminus-forced simulations</p> <p>Helheim_termforce_year2_v3.mat</p> <ul> <li> <p>Termforce simulation</p> </li> </ul> <p>Helheim_termforce_seasonal_v3.mat</p> <ul> <li> <p>Seasonal + termforce simulation</p> </li> </ul> <p>Helheim_termforce_seasonal_firn_v3.mat</p> <ul> <li> <p>Seasonal + firn aquifer + termforce simulation</p> </li> </ul> <p>Helheim_termforce_seasonal_bigmelt2_v3.mat</p> <ul> <li> <p>Enhanced melt + termforce simulation</p> </li> </ul> <p><strong> </strong></p> <p>Sensitivity simulations</p> <p>MOLHO_365d.mat</p> <ul> <li> <p>Winter base state spin-up simulation using MOLHO instead of SSA</p> </li> </ul> <p>A15_365d.mat</p> <ul> <li> <p>Winter base state spin-up simulation with flow law parameter for -15 deg C instead of -10 deg C</p> </li> </ul> <p>Helheim_br1_lr100_1yr.mat</p> <ul> <li> <p>Winter base state spin-up simulation including opening by sliding, final state serves as initial conditions for seasonal simulations</p> </li> </ul> <p>Helheim_br1_lr100_bigmelt.mat</p> <ul> <li> <p>Enhanced melt simulation including opening by sliding</p> </li> </ul> <p><strong><br><br></strong></p> <p>Model setup scripts (to be used with ISSM in MATLAB)</p> <p>runme_Helheim_inversion.m</p> <ul> <li> <p>Script to perform stress balance inversion</p> </li> </ul> <p>Helheim_CG.exp</p> <ul> <li> <p>Model domain coordinates</p> </li> </ul> <p>Greenland_clean.par</p> <ul> <li> <p>Parameter file</p> </li> </ul> <p>merra2_runoff.mat</p> <ul> <li> <p>14-day smoothed meltwater input</p> </li> </ul> <p>runme_Helheim_shaktiissm_startfrominversion_clean.m</p> <ul> <li> <p>Script to perform coupled SHAKTI-ISSM simulation, beginning from a model set up in the inversion script</p> </li> </ul> <p>runme_Helheim_continue_shaktiissm_clean.m</p> <ul> <li> <p>Script to continue a coupled SHAKTI-ISSM simulation, beginning from the end state of a previous SHAKTI-ISSM simulation</p> </li> </ul> <p>runme_Helheim_continue_seasonal_clean.m</p> <ul> <li> <p>Script to run a transient seasonal coupled SHAKTI-ISSM simulation, beginning from the end state of a winter SHAKTI-ISSM spin-up simulation</p> </li> </ul> <p><strong> </strong></p> <p>Plotting scripts</p> <p>load_models_Helheim_termforce.m</p> <ul> <li> <p>Load model output</p> </li> </ul> <p>plot_timeseries_coupled_point_paper_clean.m</p> <ul> <li> <p>Plot time series of effective pressure, velocity, and meltwater input. Also plots scatter plot of velocity vs. effective pressure</p> </li> </ul> <p>plot_pm_logscale.m</p> <ul> <li> <p>Plot difference in velocity compared to winter state with +/- log10 color scale</p> </li> </ul> <p>plot_pm_logscale_N.m</p> <ul> <li> <p>Plot difference in effective pressure compared to winter state with +/- log10 color scale</p> </li> </ul>
Grounding line remote operated vehicle (GROV) exploration of the ice shelf cavity of Petermann Glacier, Greenland
<p>The melting of ice by ocean waters along the periphery of ice sheets is a major physical process driving their evolution in a warming climate. Using the fiber-optic-tethered Grounding line Remote Operated Vehicle (GROV), we explored the ice shelf cavity of Petermann Glacier, in Northwestern Greenland, in May 2023, using a novel interferometric multibeam sonar operating at 117 KHz with 360° viewing capability. The seafloor depth is uniform at 820 m and 200 m deeper than anticipated. At the ice shelf base, we find a succession of terraces interrupted by 20-40 m ice cliffs that have no signature at the surface, but are consistent with double-diffusive convection. The central melt channel deviates by ± 80 m from flotation, is smoother than indicated by the surface, and reveals asymmetric melt. The results demonstrate the fundamental importance of surveying the geometry of ice shelf cavities to document ice-ocean interaction.</p>
Graph neural network emulator for modeling of ice dynamics and calving in the Helheim Glacier, Greenland
<p>These files include the following codes and datasets for developing graph neural network (GNN) emulators for the Ice-sheet and Sea-level System Model (ISSM) for modeling ice sheet dynamics and calving in the Helheim Glacier, Greenland.</p> <ul> <li>ISSM_DGL_Helheim.py: Python file for training GNN models</li> <li>ISSM_CNN_Helheim.py: Python file for training convolutional neural network (CNN) models</li> <li>*.mat: Datasets of the ISSM transient simulation results</li> </ul>
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>
Calculating Supraglacial Debris Properties at Pirámide Glacier, Chile (Data Sets and Codes)
<p>This repository contains the inputs and codes for calculating thermal conductivity and aerodynamic surface roughness length presented in the research paper.</p> <div>Codes:</div> <div> <ol> <li>Functions for thermal conductivity estimations: <ol> <li>RunThermalConductivity_allSites (to calculate CRh or CRi) -> in here choose the parameters to consider (sensor uncertainty, soil moisture, lithological properties) and the method to use (CRh or CRi)</li> <li>ThermalConductivity_CRh</li> <li>ThermalConductivity_CRi</li> <li>Conductivity_Brock (to calculate NYB)</li> <li>Calibrate_deb_par_PIR_k_z0 (calculates optimised value of k (and z0))</li> </ol> </li> <li>Functions for aerodynamic surface roughness lenght: <ol> <li>Runz0 (main) -> in here choose the parameters (d or no d, surface temperature, sensor uncertainty, period of measurements)</li> <li>z0Calc</li> <li>Calibrate_deb_par_PIR_k_z0 -> it calibrates z0 if chosen</li> </ol> </li> </ol> </div> <div> </div> <div>Data:</div> <div> <ol> <li>Ablation_Tower1.mat, Ablation_Tower2.mat and Ablation_Tower3.mat: Timetables containing the automatic distances measured from the ablation stake pictures in m and the calculated ablation in mm w.e.</li> <li>DATA_Piramide.mat: <ol> <li>debtemp: structure containing timetables of thermistor data (°C) for the three sites, where DT1 is the closest to the surface and DT5 the closest to the ice.</li> <li>sm: structure containing timetables of moisture content (m3/m3) data for the three sites, where SM1 is the closest to the surface and SM3 the closest to the ice.</li> <li>trh: structure containing timetables of temperature (°C) and relative humidity (%) data for the three sites, where XX_1, XX_2, XX_3 and XX_4 correspond to the variables at 0.5, 1, 2 and 2.7 m above the surface. TA is the air temperature, RH is the relative humidity and DTA is the dew temperature.</li> <li>wind: structure containing timetables of wind speed (m/s) and direction (°) data for the three sites, where L1, L2, L3 and L4 correspond to 0.5, 1, 2 and 2.7 m above the surface and FF is the wind speed, FFmx is the maximum wind speed, FFstd is the standard deviation, and DIR is the direction.</li> </ol> </li> <li>LithologyData: structure containing the density (kg/m3) and specific heat capacity (J/kg/K) for each site.</li> <li>SM_data: structure containing the moisture content (m3/m3) for the selected period, as well as the mean value for each site</li> <li>Calibration: data required to run the k and z0 calibrations with T&C</li> </ol> </div> <div> </div> <div> </div>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.