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20 results for “surface melting”
MEaSUREs Greenland Surface Melt Daily 25km EASE-Grid 2.0, Version 1.1.1 (JJA 1980-2022)
<p>This data set offers users a 25 km daily record of surface/near-surface melting on the Greenland Ice Sheet. The presence of melting is determined from brightness temperature data acquired by three satellite-borne microwave radiometers: the Scanning Multichannel Microwave Radiometer (SMMR), the Special Sensor Microwave/Imager (SSM/I), and the Special Sensor Microwave Imager/Sounder (SSMIS).</p> <p>Included in this archive are files for the June-July-August (JJA) summer months during 1980-2022, formatted as a separate file for each year.</p> <p>Version 1.1 includes data for 2021-2022 to supplement the original 1980-2020 dataset from version 1.</p> <p>Version 1.1.1 corrects the 2022 file to include data for 2022-08-24 that was missing in version 1.1.</p>
Data: Contrasting current and future surface melt rates on the ice sheets of Greenland and Antarctica: lessons from in situ observations and climate models
<p>These data accompany the publication "Contrasting current and future surface melt rates on the ice sheets of Greenland and Antarctica: lessons from in situ observations and climate models". The data are organized as follows:</p> <p>- two files with time series (csv) of hourly near-surface climate and surface energy balance values for Neumayer station (ice shelf, East Antarctic ice sheet) and automatic weather station S5 (southwest Greenland ice sheet)</p> <p>- two files (nc) with monthly melt fields from the regional climate model RACMO2.3p2 forced by ERA5 over Greenland (0.05-degree resolution) and Antarctica (0.25-degree resolution)</p> <p>- two files (nc) with annual melt fields from the regional climate model RACMO2.3p2 forced by CESM2 over Greenland (0.1-degree resolution) and Antarctica (0.25-degree resolution) or the historical period (1950-2014)</p> <p>- two files (nc) with annual melt fields from the regional climate model RACMO2.3p2 forced by CESM2 over Greenland (0.1-degree resolution) and Antarctica (0.25-degree resolution) for the future emission scenario SSP5-8.5 (2015-2099)</p>
Antarctic ice sheet daily surface melt detection from ASCAT (2007-2022)
<p>Antarctic ice sheet-wide surface melt detection using enhanced resolution ASCAT C-band radar scatterometer data. Data are daily temporal resolution spanning 2007-2022 and gridded at 4.45 km. Melt detection approach follows Trusel et al., (2012) with updates to threshold and masking procedures. These data were used as a binary melt presence/absence estimate and a predictor in a machine learning-based estimation of Antarctic Peninsula surface meltwater production (https://zenodo.org/record/7995543). </p> <p>Please reach out to Luke Trusel with any questions!</p>
Data set: "Higher Antarctic ice sheet accumulation and surface melt rates revealed at 2 km resolution"
<p>This data set includes the materials required to reproduce the figures and tables presented in the study: "Higher Antarctic ice sheet accumulation and surface melt rates revealed at 2 km resolution". The data consist of:</p><p> </p><p><strong>ASCII files:</strong></p><ol><li><strong>SMB-ANT-Sectors-1979-2021-RACMO2.3p2-ERA5-2km.txt</strong>: time series of Antarctic sector-integrated annual SMB<strong> (Gt per year)</strong> from ERA5-forced RACMO2.3p2 at 27 km, statistically downscaled to 2 km resolution (1979-2021).</li><li><strong>Melt-ANT-Sectors-1979-2021-RACMO2.3p2-ERA5-2km.txt:</strong> time series of Antarctic sector-integrated annual surface melt (Gt per year) from ERA5-forced RACMO2.3p2 at 27 km, statistically downscaled to 2 km resolution (1979-2021).</li><li><strong>Melt-ANT-Sectors-1950-2014-RACMO2.3p2-CESM2-HIST-2km.txt:</strong> time series of Antarctic sector-integrated annual surface melt from CESM2-forced RACMO2.3p2 historical reconstruction (HIST), statistically downscaled to 2 km resolution (1950-2014).</li><li><strong>Melt-ANT-Sectors-2015-2099-RACMO2.3p2-CESM2-SSP126-2km.txt:</strong> time series of Antarctic sector-integrated annual surface melt from CESM2-forced RACMO2.3p2 SSP1-2.6 projection (SSP126), statistically downscaled to 2 km resolution (2015-2099).</li><li><strong>Melt-ANT-Sectors-2015-2099-RACMO2.3p2-CESM2-SSP245-2km.txt:</strong> time series of Antarctic sector-integrated annual surface melt from CESM2-forced RACMO2.3p2 SSP2-4.5 projection (SSP245), statistically downscaled to 2 km resolution (2015-2099).</li><li><strong>Melt-ANT-Sectors-2015-2099-RACMO2.3p2-CESM2-SSP585-2km.txt:</strong> time series of Antarctic sector-integrated annual surface melt from CESM2-forced RACMO2.3p2 SSP5-8.5 projection (SSP585), statistically downscaled to 2 km resolution (2015-2099).</li></ol><p>Antarctic sectors include the Antarctic Peninsula (APIS), the West Antarctic ice sheet (WAIS), the East Antarctic ice sheet (EAIS), the grounded Antarctic ice sheet (AIS), the floating ice shelves (Ice shelves), and the whole of Antarctica (ANT) including both the AIS and Ice shelves. The APIS, WAIS, EAIS and AIS sectors include land ice from neighbouring Antarctic islands.</p><p> </p><p><strong>Netcdf files:</strong></p><ol><li><strong>smb_rec.1979-2021.BN_RACMO2.3p2_ANT27_ERA5-3h.AIS.2km.YY.nc: </strong>map of annual SMB (kg per m² or mm w.e. per year) from ERA5-forced RACMO2.3p2 at 27 km, statistically downscaled to 2 km resolution, covering the whole of Antarctica (1979-2021).</li><li><strong>snowmelt.1979-2021.BN_RACMO2.3p2_ANT27_ERA5-3h.AIS.2km.YY.nc: </strong>map of annual surface melt (kg per m² or mm w.e. per year) from ERA5-forced RACMO2.3p2 at 27 km, statistically downscaled to 2 km resolution, covering the whole of Antarctica (1979-2021).</li><li><strong>snowmelt.1950-2014.BN_RACMO2.3p2_ANT27_CESM2_HIST.AIS.2km.YY.nc: </strong>map of annual surface melt (kg per m² or mm w.e. per year) from CESM2-forced RACMO2.3p2 historical reconstruction (HIST), statistically downscaled to 2 km resolution, covering the whole of Antarctica (1950-2014).</li><li><strong>snowmelt.2015-2099.BN_RACMO2.3p2_ANT27_CESM2_SSP126.AIS.2km.YY.nc: </strong>map of annual surface melt (kg per m² or mm w.e. per year) from CESM2-forced RACMO2.3p2 SSP1-2.6 projection (SSP126), statistically downscaled to 2 km resolution, covering the whole of Antarctica (2015-2099).</li><li><strong>snowmelt.2015-2099.BN_RACMO2.3p2_ANT27_CESM2_SSP245.AIS.2km.YY.nc: </strong>map of annual surface melt (kg per m² or mm w.e. per year) from CESM2-forced RACMO2.3p2 SSP2-4.5 projection (SSP245), statistically downscaled to 2 km resolution, covering the whole of Antarctica (2015-2099).</li><li><strong>snowmelt.2015-2099.BN_RACMO2.3p2_ANT27_CESM2_SSP585.AIS.2km.YY.nc: </strong>map of annual surface melt (kg per m² or mm w.e. per year) from CESM2-forced RACMO2.3p2 SSP5-8.5 projection (SSP585), statistically downscaled to 2 km resolution, covering the whole of Antarctica (2015-2099).</li><li><strong>ANT_masks.2km.nc</strong>: file including the grounded AIS mask (AIS), Antarctic sectors mask (Sectors), floating ice shelves mask (Shelves), surface elevation down-sampled from REMA (Topography), latitude and longitude on the 2 km grid<strong>. </strong>The sector mask includes: 0 – Ocean, 1 – APIS, 2 – WAIS, 3 – EAIS, 4 – APIS islands, 5 – WAIS islands, 6 – EAIS islands, and 7 – ice shelves.<strong> </strong></li></ol><p>The projection used for statistical downscaling is Polar Stereographic South (EPSG:3031) with a spatial resolution of 2 km x 2 km. </p><p> </p><p><strong>Additional data: </strong>The gridded, daily downscaled SMB data sets from the ERA-forced RACMO2.3p2 simulation, and the CESM2-forced RACMO2.3p2 projections under a low-end SSP1-2.6, moderate SSP2-4.5 and high-end SSP5-8.5 warming scenario are freely available from the authors upon request and without conditions (contact: bnoel@uliege.be). Besides SMB, the data sets include total precipitation (snow and rain), snowfall, total melt (snow and ice), runoff, refreezing and retention, drifting snow erosion, and total sublimation (surface and drifting snow) at 2 km horizontal resolution. </p><p> </p><p><strong>Abstract:</strong> Antarctic ice sheet (AIS) mass loss is predominantly driven by increased solid ice discharge, but its variability is governed by surface processes. Snowfall fluctuations control the surface mass balance (SMB) of the grounded AIS, while meltwater ponding can trigger ice shelf collapse potentially accelerating discharge. Surface processes are essential to quantify AIS mass change, but remain poorly represented in climate models typically running at 25-100 km resolution. Here we present SMB and surface melt products statistically downscaled to 2 km resolution for the contemporary climate (1979-2021) and low, moderate and high-end warming scenarios until 2100. We show that statistical downscaling modestly enhances contemporary SMB (3%), which is sufficient to reconcile modelled and satellite mass change. Furthermore, melt strongly increases (46%), notably near the grounding line, in better agreement with in-situ and satellite records. The melt increase persists by 2100 in all warming scenarios, revealing higher surface melt rates than previously estimated.</p>
Aerodynamic Roughness Controlled by Wind Direction, with Implications for Glacial Surface Energy Balance and Melt Rate
<p>This repository includes raw datasets, Python scripts, and output data products associated with the MRes project '<span>Aerodynamic Roughness Controlled by Wind Direction, with Implications for Glacial Surface Energy Balance and Melt Rate</span>', by Josh Abrahams, University of Leeds. </p>
Snow melt onset date estimates derived from CLARA-A2 SAL surface albedo dataset
<p>Snow melt onset date estimates for the Northern Hemisphere, 1982-2015. Derived from the CLARA-A2 SAL surface albedo dataset. Version for manuscript review.</p>
Data set: Statistically parameterizing and evaluating a positive degree-day model to estimate surface melt in Antarctica from 1979 to 2022
<p><strong>Version 2:</strong></p> <p><strong>Updates from version 1: Monthly, daily, and hourly dist-PDD and uni-PDD outputs have been added.</strong></p> <p><strong>https://doi.org/10.5194/tc-17-3667-2023</strong></p> <p> </p> <p>Version 1:</p> <p>This dataset accompanies Zheng et al. (2023): Statistically parameterizing and evaluating a positive degree-day<br> model to estimate surface melt in Antarctica from 1979 to 2022, The Cryosphere.</p> <p>This dataset contains annual PDD model output.</p>
Data for The importance of cloud phase when assessing surface melting in an offline coupled firn model over Ross Ice shelf, West Antarctica
<p>This is the data used in the paper "The importance of cloud phase when assessing surface melting in an offline coupled firn model over Ross Ice shelf, West Antarctica"</p>
Improving surface melt estimation over the Antarctic Ice Sheet using deep learning: a proof of concept over the Larsen Ice Shelf
<p>Hu, Z., Kuipers Munneke, P., Lhermitte, S., Izeboud, M., and van den Broeke, M.: Improving Surface Melt Estimation over Antarctica Using Deep Learning: A Proof-of-Concept over the Larsen Ice Shelf, The Cryosphere Discuss. [preprint], https://doi.org/10.5194/tc-2021-102, in review, 2021.</p> <p>-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p><strong>(1) MLP_model_surface_melt_corr.h5</strong> is the developed MLP model used for correcting RACMO2 surface melt.</p> <p><strong>(2) RACMO2_surface_melt_corr_MLP_AWS14.xlsx </strong>corrected surface melt [mm w.e. per day] from RACMO2 at AWS 14 during austral summers 2001 - 2016. The model inputs are (1) the simulated albedo, (2) the albedo difference between the observed and simulated albedo, (3) air temperature at 2m, (4) incoming shortwave radiation, (5) downwelling longwave radiation, (6) simulated surface melt, (7) Boolean melt flag, (8) surface melt difference to the previous day, and (9) record date as day of the year.</p> <p><strong>(3) RACMO2_surface_melt_corr_MLP_AWS17.xlsx </strong>The same as point 2 but for AWS 17</p> <p><strong>(4) RACMO2_surface_melt_corr_MLP_AWS18.xlsx </strong>The same as point 2 but for AWS 18</p> <p>Note: Data 2-4 are corrected RACMO2 simulations of surface melt at the pixels in RACMO2 27 km grid corresponding to AWS 14, 17, and 18 locations. They are not AWS observations.</p> <p>-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p><strong>Related data set:</strong></p> <p>MODIS/Terra Surface Reflectance Daily L2G Global 1 km and 500 m SIN Grid product is available via the Land Processes Distributed Active Archive Center (LP DAAC) (https://doi.org/10.5067/MODIS/MOD09GA.006, last access: 3 December 2021). MODIS/Terra+Aqua Albedo Daily L3 Global 500 m SIN Grid product is also available via LP DAAC (https://doi.org/10.5067/MODIS/MCD43A3.006, last access: 3 December 2021). Sentinel-1 images are provided by the European Space Agency (ESA) (https://sentinel.esa.int/web/sentinel/sentinel-data-access, last access: 3 December 2021). Automatic weather station observations from AWS 14, 17, and 18 are available via https://doi.pangaea.de/10.1594/PANGAEA.910473 (last access: 3 December 2021). RACMO2 simulations (https://www.projects.science.uu.nl/iceclimate/models/antarctica.php#2-1, last access: 3 December 2021) are provided by van Wessem et al. (2018) which are available on request to the original authors.</p> <p>-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p><strong>You should also refer to and cite the following paper:</strong></p> <p>Hu, Z., Kuipers Munneke, P., Lhermitte, S., Izeboud, M., and van den Broeke, M.: Improving Surface Melt Estimation over Antarctica Using Deep Learning: A Proof-of-Concept over the Larsen Ice Shelf, The Cryosphere Discuss. [preprint], https://doi.org/10.5194/tc-2021-102, in review, 2021.</p>
Ga interaction with ZnO surfaces: diffusion and melt-back etching_experimental dataset
<p>This archive contains the raw unfileterd data to the Pejchal et al., Ga interaction with ZnO surfaces: diffusion and melt-back etching, <a href="https://www.sciencedirect.com/journal/applied-surface-science">Applied Surface Science</a>, <a href="https://www.sciencedirect.com/journal/applied-surface-science/vol/583/suppl/C">Volume 583</a>, 1 May 2022, 152475. </p>
Data: Clouds drive differences in future surface melt over the Antarctic ice shelves (Kittel et al., 2022)
<p>Outputs used in:</p> <p><em>Kittel, C., Amory, C., Hofer, S., Agosta, C., Jourdain, N. C., Gilbert, E., Le Toumelin, L., Gallée, H., and Fettweis, X.: Clouds drive differences in future surface melt over the Antarctic ice shelves, The Cryosphere Discuss. [preprint], https://doi.org/10.5194/tc-2021-263, accepted, 2021.</em></p> <ul> <li>MAR outputs with summer values of melt, surface energy budget components, and cloud properties over the Antarctic ice sheet (1980--2100)</li> <li>Grid file used in MAR simulations</li> </ul> <p>If you need other variables or output frequencies from MAR, write me (c2kittel@gmail.com) and I will be glad to help you. I will also be happy to share the scripts I have developed to analyse the outputs and make the figures in this paper if needed. Please cite the paper if you use these MAR outputs.<br> <br> Data usage notice:</p> <p>If you use any of these results, please acknowledge the work of the people involved in producing them. Acknowledgements should have language similar to the below that contained both informations related to MAR. In order to document MAR scientific impact and enable ongoing support of the model, users are likely encouraged to contact C. Kittel to add their works in the list of MAR-related publications. </p> <p>"We thank C. Kittel and the MAR team which make available the model outputs, as well agencies (F.R.S - FNRS, CÉCI, and the Walloon Region) that provided computational resources for MAR simulations. "</p> <p>You should also refer to and cite the following paper:</p> <p><em>Kittel, C., Amory, C., Hofer, S., Agosta, C., Jourdain, N. C., Gilbert, E., Le Toumelin, L., Gallée, H., and Fettweis, X.: Clouds drive differences in future surface melt over the Antarctic ice shelves, The Cryosphere Discuss. [preprint], https://doi.org/10.5194/tc-2021-263, accepted, 2021.</em></p>
Atomic-resolution imaging of surface and core melting in individual size-selected Au nanoclusters on carbon
<p>Experimental and theoretical data sets for the Nature Communications paper "Atomic-resolution imaging of surface and core melting in individual size-selected Au nanoclusters on carbon". Data structure and formats are described in the README.docx file.</p>
Greenland ice sheet surface melt projections using artifical neural networks
<p>Surface melt projections for the Greenland ice sheet using artificial neural networks.</p> <p>Organized as follows:</p> <p><scenario>/<variable>/<model>/<ensemble_number>.nc</p>
Improving surface melt estimation over the Antarctic Ice Sheet using deep learning: a proof of concept over the Larsen Ice Shelf
<p>Hu, Z., Kuipers Munneke, P., Lhermitte, S., Izeboud, M., and van den Broeke, M.: Improving Surface Melt Estimation over Antarctica Using Deep Learning: A Proof-of-Concept over the Larsen Ice Shelf, The Cryosphere Discuss. [preprint], https://doi.org/10.5194/tc-2021-102, in review, 2021.</p> <p>-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p><strong>(1) MLP_model_surface_melt_corr.h5</strong> is the developed MLP model used for correcting RACMO2 surface melt.</p> <p><strong>(2) RACMO2_surface_melt_corr_MLP_AWS14.xlsx </strong>corrected surface melt [mm w.e. per day] from RACMO2 at AWS 14 during austral summers 2001 - 2016. The model inputs are (1) the simulated albedo, (2) the albedo difference between the observed and simulated albedo, (3) air temperature at 2m, (4) incoming shortwave radiation, (5) downwelling longwave radiation, (6) simulated surface melt, (7) Boolean melt flag, (8) surface melt difference to the previous day, and (9) record date as day of the year.</p> <p><strong>(3) RACMO2_surface_melt_corr_MLP_AWS17.xlsx </strong>The same as point 2 but for AWS 17</p> <p><strong>(4) RACMO2_surface_melt_corr_MLP_AWS18.xlsx </strong>The same as point 2 but for AWS 18</p> <p>Note: Data 2-4 are corrected RACMO2 simulations of surface melt at the pixels in RACMO2 27 km grid corresponding to AWS 14, 17, and 18 locations. They are not AWS observations.</p> <p>-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p><strong>Related data set:</strong></p> <p>MODIS/Terra Surface Reflectance Daily L2G Global 1 km and 500 m SIN Grid product is available via the Land Processes Distributed Active Archive Center (LP DAAC) (https://doi.org/10.5067/MODIS/MOD09GA.006, last access: 3 December 2021). MODIS/Terra+Aqua Albedo Daily L3 Global 500 m SIN Grid product is also available via LP DAAC (https://doi.org/10.5067/MODIS/MCD43A3.006, last access: 3 December 2021). Sentinel-1 images are provided by the European Space Agency (ESA) (https://sentinel.esa.int/web/sentinel/sentinel-data-access, last access: 3 December 2021). Automatic weather station observations from AWS 14, 17, and 18 are available via https://doi.pangaea.de/10.1594/PANGAEA.910473 (last access: 3 December 2021). RACMO2 simulations (https://www.projects.science.uu.nl/iceclimate/models/antarctica.php#2-1, last access: 3 December 2021) are provided by van Wessem et al. (2018) which are available on request to the original authors.</p> <p>-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p><strong>You should also refer to and cite the following paper:</strong></p> <p>Hu, Z., Kuipers Munneke, P., Lhermitte, S., Izeboud, M., and van den Broeke, M.: Improving Surface Melt Estimation over Antarctica Using Deep Learning: A Proof-of-Concept over the Larsen Ice Shelf, The Cryosphere Discuss. [preprint], https://doi.org/10.5194/tc-2021-102, in review, 2021.</p>
First application of artificial neural networks to estimate 21st century Greenland ice sheet surface melt: scripts and models
<p>In this repository you will find the models and the scripts used to generate the journal article: "First application of artificial neural networks to estimate 21st century Greenland ice sheet surface melt."</p> <p>The model.tar contains the script for making a model, in addition to the models used in the journal artcile.</p> <p>The proc.tar contains the scripts used for processing of the CMIP6 data.</p> <p>The plots.tar contains scripts for generating the plots in the journal article, as well as the supplementary information.</p>
Data: Spatially consistent microbial biomass and future cellular carbon release from melting Northern Hemisphere glacier surfaces
<p>Hydrology, flow cytometry and upscaling data for the manuscript "Spatially consistent microbial biomass and future cellular carbon release from melting Northern Hemisphere glacier surfaces". Each .zip file includes an individual 'readme' document.</p>
Mercury's crustal thickness correlates with lateral variations in mantle melt production: Dataset for maps of surface density, degree of partial melting, and crustal thickness
<p>1) DensityGrid.dat = map of density of pore-free surface rocks (Figure 1a)</p> <p>- Units = g/cm3</p> <p>- 720 x 1440 pixels corresponding to 0.25-degree pixels in an equirectangular cylindrical projection with the equator as central parallel.</p> <p>- The first row represents 90 to 89.75 degrees of latitude north; the last row represents 89.75 to 90 degrees of latitude south.</p> <p>- The first column represents 0 to 0.25 degree of longitude; the last column represents 359.75 to 360 degrees of longitude.</p> <p>2) MeltGrid.dat = map of degree of partial melting (Figure S1a)</p> <p>- Units: mass fraction</p> <p>- 720 x 1440 pixels as described above</p> <p>3) CrustalThickness_ModelU0.dat = map of crustal thickness if laterally uniform density, Model U0 (Figure 2a)</p> <p> CrustalThickness_ModelVn.dat = map of crustal thickness if laterally variable density, Model Vn (Figure 2c, Figures S6 to S10)</p> <p>- Units = km</p> <p>- generated with MakeGridDH of SHTOOLS (<a href="https://shtools.github.io/SHTOOLS/">https://shtools.github.io/SHTOOLS/</a>)</p> <p>- 720 x 1440 coordinate points corresponding to 0.25-degree spacing in an equirectangular cylindrical projection with the equator as central parallel.</p> <p>- The first row corresponds to 90 degrees of latitude north; the last row corresponds to 89.75 degrees of latitude south.</p> <p>- The first column corresponds to 0 degree of longitude; the last column corresponds to 359.75 degrees of longitude.</p> <p>Note: Figures refer to the following paper (Geophysical Research Letters, 2020): `Mercury's crustal thickness correlates with lateral variations in mantle melt production’, by M. Beuthe, B. Charlier, O. Namur, A. Rivoldini, and T. Van Hoolst</p>
Assets for "Simulation-Based Inference of Surface Accumulation and Basal Melt Rates of an Antarctic Ice shelf from Isochronal Layers"
<p>Experimental data reported in "Simulation-Based Inference of Surface Accumulation and Basal Melt Rates of an Antarctic Ice shelf from Isochronal Layers".</p><p>Contains processed simulation data for both synthetic and Ekström Ice Shelf experiments. Selected best layers for each Internal Radar Horizons in the respective simulated datasets are contained in "all_layers_final.p", and the respective mass balance parameters for those simulations in "all_mbs_final.p".</p><p>Trained Neural Posterior Estimator (NPE) models for each IRH are found in "inference.p" files, and posterior predictive simulations in "post_predictive.p" files for each posterior respectively. Code to visualise and plot this data is found in https://github.com/mackelab/sbi-ice.</p>
MEaSUREs Greenland Surface Melt Daily 25km EASE-Grid 2.0 V001
This data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, offers users a 25 km daily record of surface/near-surface melting on the Greenland Ice Sheet. The presence of melting is determined from brightness temperature data acquired during a 34 year span by three satellite-borne microwave radiometers: the Scanning Multichannel Microwave Radiometer (SMMR), the Special Sensor Microwave/Imager (SSM/I), and the Special Sensor Microwave Imager/Sounder (SSMIS).
High Mountain Asia Annual 90m Glacier Surface Melt/Freeze Phenology from SAR Imagery V001
This data set contains annual surface melt onset and freeze onset dates across all glaciers in the Hindu Kush Himalayas (HKH) retrieved from time series synthetic aperture radar (SAR) imagery. The data set was based on analysis of C-band Sentinel-1 A/B SAR time series, comprising 32,741 Sentinel-1 A/B SAR images. The duration of annual glacier surface melt was determined for 105,432 mapped glaciers (83,102 km2 glacierized area) during the calendar years 2017-2020.
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.