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.
8
datasets available to search
ShareScore release 0.9.0
Dataset results
8 results for “melt ponds”
Simulation data for convection in radiatively heated melt ponds on sea ice
<p>This dataset includes data and processing code from simulations of radiatively heated convection in melt ponds on sea ice. </p> <p> </p> <p>Simulation results produced using code written by Andrew Wells and Tom Langton for use with and exploiting examples from the code Dedalus:</p> <p>http://dedalus-project.org/index.html</p> <p> </p> <p>Contact andrew.wells@physics.ox.ac.uk for further details. </p> <p>The data included in this version correspond to figures 3 and S5 , movies S2 and S3, and code described in supporting information in the study:</p> <p><strong>Salinity control of thermal evolution of late summer melt ponds on Arctic sea ice </strong></p> <p>Joo-Hong Kim<sup>1</sup>, Woosok Moon<sup>2,3</sup>, Andrew J. Wells<sup>4</sup>, Jeremy P. Wilkinson<sup>5</sup>, Tom Langton<sup>4</sup>, Byongjun Hwang<sup>6,7</sup>, Mats A. Granskog<sup>8 </sup>and David Rees Jones<sup>9</sup></p> <p><sup>1</sup>Korea Polar Research Institute, Incheon, South Korea</p> <p><sup>2</sup>Nordic Institute for Theoretical Physics, Stockholm, Sweden</p> <p><sup>3</sup>Department of Mathematics, Stockholm University, Stockholm, Sweden</p> <p><sup>4</sup>Atmospheric, Oceanic and Planetary Physics, University of Oxford, Oxford, UK</p> <p><sup>5</sup>British Antarctic Survey, Cambridge, UK.</p> <p><sup>6</sup>Scottish Association for Marine Science, Oban, UK</p> <p><sup>7</sup>University of Huddersfield, Huddersfield, UK</p> <p><sup>8</sup>Norwegian Polar Institute, Fram Centre, Tromsø, Norway</p> <p><sup>9</sup>Dept. of Earth Sciences, University of Oxford, Oxford, UK</p> <p> </p> <p>Citation:</p> <p>Kim, J.-H., Moon, W., Wells, A. J., Wilkinson, J. P., Langton, T., Hwang, B., Granskog, M. A., & Rees Jones, D. W. (2018). Salinity control of thermal evolution of late summer melt ponds on Arctic sea ice. Geophysical Research Letters, 45. https://doi.org/10.1029/2018GL078077</p> <p>Alternative weblink:</p> <p>https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2018GL078077</p> <p> </p> <p>v1: submitted during review of the manuscript.</p> <p>v2: updated with details of accepted publication.</p>
Sea ice, snow and melt pond example data from MOSAiC transect observations
<p>This data set contains in-situ observation of sea ice, snow and melt pond properties from two days in winter (January 23, 2020) and summer (July 7, 2020) during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition. It originates from two sensors:</p> <ol> <li>Broad-band electromagnetic induction sensor (Geophex GEM-2) measuring the combined thickness of the sea ice and snow layers</li> <li>A GPS snow depth probe (Snow-Hydro MagnaProbe) measuring the thickness of the snow layer and melt ponds depth during summer</li> </ol> <p>Both sensors were operated coincidently along transect loops while different loops were used in both days. This data set is a subset of similar weekly activities between October 2019 and September 2020. This publication intends to provide a preview of the data properties and approximate changes between the winter and summer periods. It also has to be noted, that the final data of the EM induction sensor might differ from this release, which is based on a quick-look processing directly after data acquisition.</p> <p><em>GEM-2 data files</em></p> <p>The file format of GEM-2 data is a text file with comma-separated values. Notable parameters are:</p> <ul> <li>‘time’: UTC time</li> <li>‘longitude’: Longitude in degrees east (fill value: 0.0)</li> <li>‘latitude’: Latitude in degrees north (fill value: 0.0)</li> <li>‘`f{frequency}Hz_hcp_{i:Inphase|q:Quadrature}`: total (ice + snow) thickness of the sea ice and snow layers in meter for different channels*</li> </ul> <p>*The channels correspond to the real (Inphase) or imaginary (Quadrature) part of the EM signal at a given frequency. The variable name in the csv file is to be read as `f{frequency}Hz_hcp_{i:Inphase|q:Quadrature}`. It is recommended to use the Inphase component of the 18.325 kHz frequency (variable `f18325Hz_hcp_i`) for analysis.</p> <p><em>MagnaProbe data files</em></p> <p>The file format of MagnaProbe data is a text file with comma-separated values. Notable parameters are:</p> <ul> <li>`timestamp`: Timestamp</li> <li>`longitude_a`: longitude degree in degrees east</li> <li>`longitude_b`: longitude minute</li> <li>`latitude_a`: latitude degree in degrees north</li> <li>`latitude_b`: latitude minute</li> <li>`depthCm`: Snow thickness or melt ponds depth in cm</li> <li>`flag`: flag value indicating the type or measurement*</li> </ul> <p>Flag values are:</p> <ul> <li>-1 : melt pond</li> <li>1 : snow or surface scattering layer depth,</li> <li>2 : mixed surface type when pond water pools at the base of a melting</li> </ul> <p>The filenames follow the naming convention of <sensor>-mosaic-transect-<date>-<device-operations-id>.csv with the device operation id as a unique identifier of the sensor raw data within the MOSAiC project.</p>
Melt pond from aerial photographs of the Healy–Oden Trans Arctic Expedition (HOTRAX)
<p>The dataset contains mapped melt pond zones from aerial photographs which were obtained during helicopter photography flights as part of the Healy–Oden Trans Arctic Expedition (HOTRAX) between 5 August and 30 September 2005. The detection has been done using an original detection algorithm based on machine learning (U-Net). The areas in the images were divided into four classes: sea ice/snow, melt ponds, sub-merged ice and open water. The filenames contain IDs from the original dataset [Perovich et al 2008].</p>
Sea Ice Melt Pond Data from the Canadian Arctic, Version 1
This data set contains observations of albedo, depth, and physical characteristics of melt ponds on sea ice, taken during the summer of 1994. The melt ponds studied were located just south of Cornwallis Island in the Barrow Strait, Nunavat, Canada. The reflectance spectra were acquired with a portable spectrometer. Cloud conditions and ice type were recorded as well.
Arctic Sea Ice Melt Pond Statistics and Maps, 1999-2001, Version 1
Visible band imagery from high-resolution satellites were acquired over four Arctic Ocean sites (three in 1999) during the summers of 1999, 2000, and 2001. The sites were within the median extent of the perennial ice pack. Imagery was analyzed using supervised maximum likelihood classification to derive either two (water and ice) or three (pond, open water, and ice) surface classes. Clouds were masked by hand. The data set consists of tables of pond coverage and size statistics for 500 m square cells within 10 km square images (image resolution is 1 meter), along with the surface type maps called Image Derived Products (IDPs) from which the pond statistics were derived. A total of 101 images over the three summers and four sites were used for pond statistics, out of a total of 1056 images acquired. The images are irregularly spaced in time.Data are stored in Microsoft Excel format and ASCII text, image files are stored as GeoTIFF binary images, browse images in PNG, and JPG image files, and are available from August 1999 and generally for May into September for 2000 and 2001 via FTP.
Data set: Yearly RACMO2.3p2 variables, threshold temperature and Sentinel-2 melt pond volume
<p>Data set of yearly regional atmospheric climate model RACMO2.3p2 data over Antarctica. At the lateral and ocean boundaries the model is forced by ERA5 reanalysis data every 6 hours from 1979-2021. The model is run at 27 km horizontal resolution for the entire Antarctic ice sheet, which constitutes an update of the simulation forced from 1979-2018 by ERA-Interim reported in van Wessem et al., 2018. Upper air relaxation is also active.<br> To simulate the recent past (1950-2014) and the future (2015-2100) we use RACMO2.3p2 at 27 km resolution to dynamically downscale one historical and three future projections emission scenarios (SSP1-2.6, SSP2-4.5 and SSP5-8.5) of the Coupled Model Intercomparison Project Phase 6 (CMIP6). Detailed CESM2 description and latest updates are provided in Danabasoglu et., al 2020 and an evaluation over Greenland in Van Kampenhout et al., 2020.</p> <p>These data are then used to fit exponential and power-law relations of annual total liquid water production (melt + rain) and snow accumulation (snowfall - sublimation) to calculate MoA sensitivity as a function of annual average 2 m temperature. A comparison with Sentinel-2 melt pond observations can then be performed.</p> <p><strong>Data set includes: </strong></p> <p><strong>RACMO2.3p2-ERA5-3H</strong> - Yearly average precipitation, snowfall, snowmelt and sublimation for the period 1979-2021 in mm w.e per year, and yearly average 2 m temperature in K, forced by 3 hourly ERA5 reanalysis data.</p> <p><strong>RACMO2.3p2-hist_r567</strong> - Yearly average precipitation, snowfall, snowmelt and sublimation for the period 1950-2014 in mm w.e per year, and yearly average 2 m temperature in K, forced by 6 hourly CESM2 historical data.</p> <p><strong>RACMO2.3p2-SSP''126,245,585''_r567</strong> - Yearly average precipitation, snowfall, snowmelt and sublimation for the period 2015-2100 in mm w.e per year, and yearly average 2 m temperature in K, forced by 6 hourly CESM2 future scenario (SSP1-2.6, SSP2-4.5, SSP5-8.5) data.</p> <p><strong>TT_ALL.nc </strong>- Antarctic threshold temperature <span class="math-tex">\(T_T\)</span> for MoA = 0.7 as described in the Methods section.</p> <p><strong>dT_ALL.nc </strong>- Uncertainty in Antarctic threshold temperature <span class="math-tex">\(T_T\)</span> for MoA = 0.7 as described in the Methods section.</p> <p><strong>TT_IS.nc </strong>- Antarctic threshold temperature <span class="math-tex">\(T_T\)</span> for MoA = 0.7 of 56 selected ice shelves as described in the Methods section.</p> <p><strong>dT_IS.nc </strong>- Uncertainty in Antarctic threshold temperature <span class="math-tex">\(T_T\)</span> for MoA = 0.7 of 56 selected ice shelves as described in the Methods section.</p> <p><strong>S2_meltpondvolume_v3.nc</strong> - Sentinel-2 melt pond volume as described in the Methods section.</p> <p><strong>Height_latlon_ANT27.nc </strong>- Grids of latitude, longitude, surface elevation (height), land/ice mask (mask2d), grounded land/ice mask (maskgrounded2d), aspect ratio, and surface slope.</p>
ICESat-2 Derived Sea Ice Melt Pond Characteristics from the Density-Dimension Algorithm V003
This data set provides locations and depths of melt ponds in the Multi-Year Arctic Sea Ice Region, calculated from ATLAS/ICESat-2 L2A Global Geolocated Photon Data, Version 5 (ATL03) using an autoadaptive algorithm.
Improved Arctic Melt Pond Fraction Estimation Using Sentinel-2 Imagery
<p>The dataset is part of the "Improved Arctic Melt Pond Fraction Estimation Using Sentinel-2 Imagery" research article.</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.