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.
414
datasets available to search
ShareScore release 0.9.0
Dataset results
414 results for “sea temperature”
Supporting data and code for: Individual plasticity in response to rising sea temperatures contributes to an advancement in green turtle nesting phenology.
Open the record for dataset details and reuse information.
Figure 1 in Assessment of the accuracy of determining the Caspian Sea surface temperature by Landsat-5, -7 satellites based on the measurements of drifters
Figure 1. Drifter device [Lagrangian drifter laboratory, 2024]
New Zealand coastal station sea temperature time series
<p>The following two tables are the daily observations and model output of coastal water temperature analyzed in Souza et al., 2022 (displayed in Figure 13). The first column in both tables are the time in MATLAB datenum format. The subsequent 10 columns are daily temperature values from the following stations with lat/lon information:</p> <p> {'Ahipara' -35.1667 173.1000}<br> {'Leigh' -36.2693 174.7984}<br> {'Moturiki' -37.6330 176.1830}<br> {'Tauranga' -37.6980 176.6160}<br> {'Newplymouth' -39.0500 174.0330}<br> {'Napier' -39.4830 176.9167}<br> {'Evans' -41.3000 174.8000 }<br> {'Lytleton' -43.6330 172.9000 }<br> {'Portobello' -45.8160 170.6500}<br> {'Bluff' -46.6000 168.3000 }</p> <p>Missing data are marked with the value -999. Further observation and station details and quality control procedures are described in the technical data report, Chiswell, S. M. and Grant, B.: New Zealand Coastal Sea Surface Temperature, Tech. rep., National Institute of Water & Atmospheric Research, 2019 </p> <p> </p> <p>We acknowledge the contribution of professional, technical and academic staff from NIWA, University of Otago and University of Auckland who have maintained the coastal SST records and made these available for model validation purposes</p>
Bottom layer temperature and sea level elevation during the Typhoon Lekima
<p>This data is the direct observations of bottom layer temperature and sea level elevations during typhoon Lekima in August 2019.</p>
CRITER 1.0: Sea Surface Temperature Evaluation Datasets
<p>Training and evaluation datasets used in CRITER 1.0: A coarse reconstruction with iterative refinement network for sparse spatio-temporal satellite data .</p>
Planktonic foraminifera faunal data and derived sea-surface temperatures for the Marine Isotope Stage (MIS) 18 to MIS 28 interval of IODP Site U1387, Gulf of Cadiz
<p>Planktonic foraminifera faunal data and sea-surface temperature reconstructions for the early-middle Pleistocene interval between 750 and 1006 kilo years from IODP Site U1387 in the Gulf of Cadiz (southern Portuguese margin). This data was used to reconstruct the paleoecological and paleoclimatical changes at the southern Portuguese margin in Mega et al. (2025), The Early–Middle Pleistocene Transition in the Gulf of Cadiz (NE Atlantic) – an interplay between subtropical gyre and extremely cold surface waters. Clim. Past 21, 919-939, doi: 10.5194/cp-21-919-2025.</p> <p>The data is also available from the world data center Pangaea as a bundled data set:</p> <p>Voelker, Antje H L; Mega, Aline; Rodrigues, Teresa (2025): Planktonic foraminifera faunal data and sea-surface temperatures for the Marine Isotope Stage (MIS) 18 to MIS 28 interval of IODP Site 339-U1387, Gulf of Cadiz [dataset bundled publication]. PANGAEA, <a href="https://doi.org/10.1594/PANGAEA.974451" target="_blank" rel="nofollow noopener">https://doi.org/10.1594/PANGAEA.974451</a></p>
ACCESS-ESM-1.5 net-zero emissions 1000-year long simulations: Seasonal temperature and precipitation gridded data with area-average timeseries of temperature, precipitation and sea ice data
<p>NetCDF files used in analysis shown in King et al. (2024) https://egusphere.copernicus.org/preprints/2024/egusphere-2023-2961/.</p> <p>Seasonal-average mapped 1.5 metre air temperature temperature data are in NetCDFs with "fld_s03i236" in their name. These files are for boreal summer "JJA" or boreal winter "DJF". The "B20__" represents the start year of the net-zero emissions simulation from which these data are drawn. These files each have 1000 time-steps and the data are in native resolution of ACCESS-ESM-1.5: 1.875 degrees longitude by 1.25 degrees latitude.</p> <p>Seasonally-averaged precipitation rate (kg/m2/s) data are in NetCDFs with "fld_s05i216" in their name. Seasons and simulation information follow the same format as the temperature files.</p> <p>The file "ts_gwl.nc" includes several timeseries used in the paper including global-average SST ("TEMP_SURFACE"), global mean ocean temperature ("TEMP_GLOBAL"), and March/September Arctic ("ICEN") and Antarctic ("ICES") sea ice extent.</p> <p>The scripts used to make the Figures are included and run using IDL version 9.0. </p> <p>For any advice on using these files please feel free to contact me: andrew.king@unimelb.edu.au.</p>
Sea-ice, primary productivity and ocean temperatures at the Antarctic marginal zone during late Pleistocene
<p>These data in excel refer to all proxies used to reconstruct late Quaternary sea ice and productivity in in cape Adare, Ross Sea, Antarctica, for the publication in Quaternary Science Reviews, for open access </p>
Ariano-Sánchez et al-Sand temperatures on sea turtle nesting beaches
<p>Dataset of sand temperatures on sea turtle nesting beaches recorded for 2018-2019 from two dark volcanic sand beaches in the Pacific coast of Guatemala, Central America.</p> <p>R Code for analysis of dataset.</p>
PhanSST - A global database of Phanerozoic sea surface temperature proxy data
<p>Beta pre-publication release of the PhanSST database of globally distributed Phanerozoic paleo-sea surface temperature proxy data. The database is currently accepted at Scientific Data.</p>
Data for "Clouds increasingly influence Arctic sea surface temperatures as CO2 rises" part 1
<p>Data for "Clouds increasingly influence Arctic sea surface temperatures as CO2 rises" submitted to Geophysical Research Letters.</p> <p>Data are included from three fully-coupled CESM2 simulations with variable CO2 concentrations: pre-industrial climate ('control'), 424 ppm CO2 ('yr40'), and 1139 ppm CO2 ('yr140'). Data in all files are restricted to 40-90<sup>o</sup>N. Variables include surface temperature (TS), sea ice concentration (ICEFRAC), CALIPSO total cloud fraction (CLDTOT_CAL), total grid cell cloud liquid water path (TGCLDLWP), sea surface temperature (SST), surface downwelling shortwave radiation (FSDS), surface downwelling longwave radiation (FLDS), surface net shortwave radiation (FSNS), clear-sky surface net shortwave radiation (FSNSC), and clear-sky surface downwelling longwave radiation (FLDSC).</p>
Daily surface temperature and current from a 10 members ensemble simulation of June-September 2018 over the South China Sea
<p>This file contains the outputs from an ensemble of 10 members of simulation performed over the South China Sea for summer 2018.</p> <p>Members are numbered from 09 to 18.</p> <p>member_11_daily_surface_tem_u_v_JJAS.nc contains the surface daily temperature and current simulated by member 11</p> <p>grid.nc contains all information about the Arakawa C grid (longitude, latitude, mask, mesh size etc).</p> <p>wstress_surf_2018_JJAS_daily.nc contains the daily wind stress for summer 2018</p>
Sea Surface Temperature Graphs from ERA5
<p>The sea surface temperature graphs were generated from the ERA5 reanalysis product and used in the paper: Graph-Based Deep Learning for Sea Surface Temperature Forecasts, which was accepted at the Tackling Climate Change with Machine Learning Workshop at ICLR 2023.</p>
Identification of Sea Surface Temperature and Sea Surface Salinity Fronts along the California Coast: Application Using Saildrone and Satellite Derived Products
<p>Data and Method described in https://www.mdpi.com/2072-4292/15/2/484, "Identification of Sea Surface Temperature and Sea Surface Salinity Fronts along the California Coast: Application Using Saildrone and Satellite Derived Products"</p> <p> </p>
Reconstructed upper-ocean temperature, salinity, and sea ice at the Last Glacial Maximum
<p>These data accompany the published article</p> <p>Amrhein, D. E., Wunsch, C., Marchal, O., & Forget, G. (2018). A global glacial ocean state estimate constrained by upper-ocean temperature proxies. <em>Journal of Climate</em>, <em>31</em>(19), 8059-8079. <a href="https://doi.org/10.1175/JCLI-D-17-0769.1">https://doi.org/10.1175/JCLI-D-17-0769.1</a></p> <p>Seasonal reconstructions of upper-ocean temperatures, salinity, and sea ice concentrations from Amrhein et al. (2018). Two sets of fields are available: GLACIAL, from the last glacial maximum ocean state estimation, and MODERN, from a similar model configuration but run to quasi-equilibrium under modern forcing conditions. GLACIAL minus MODERN anomaly values are likely to be more robust than standalone values from GLACIAL because of ocean drifts over millennial time scales present in both GLACIAL and MODERN. See publication for further information. See the MITgcm documentation for additional details on model variables.</p> <p>Files in Amrhein_etal_2018_LGM_MODERN_area_heff_seasonal.mat are:</p> <p>lat: Vector of latitudes<br> lon: Vector of longitudes<br> lgm_areai_seas: Monthly maps of fractional sea ice area estimated at the LGM<br> lgm_heffi_seas: Monthly maps of sea ice thickness estimated at the LGM<br> mod_areai_seas: Monthly maps of fractional sea ice area simulated for modern climate<br> mod_heffi_seas: Monthly maps of sea ice thickness simulated for modern climate</p> <p>Files in Amrhein_etal_2018_LGM_MODERN_T_SST_seasonal.mat are:</p> <p>lat: Vector of latitudes<br> lon: Vector of longitudes<br> lgm_Si_seas: Monthly maps of upper-ocean salinity estimated at the LGM<br> lgm_Ti_seas: Monthly maps of upper-ocean temperature estimated at the LGM<br> mod_Si_seas: Monthly maps of upper-ocean salinity simulated for modern climate<br> mod_Ti_seas: Monthly maps of upper-ocean temperature simulated for modern climate</p> <p>A simple plot of, e.g., estimated LGM January upper-ocean temperatures can be made in MATLAB by putting the file in your working directory and running</p> <pre><code>load Amrhein_etal_2018_LGM_MODERN_T_SST_seasonal.mat pcolor(lon,lat,lgm_Si_seas(:,:,1)),shading flat</code></pre> <p>These data are a subset of those generated in the full state estimate. If additional variables are needed, please contact damrhein@ucar.edu and I can update this repository.</p>
A seven-year record of fluctuating core body temperatures of nesting leatherback and hawksbill sea turtles
<p>Sea turtles experience considerable changes in water temperatures during migrations and seasonal movements that will influence their body temperatures. Nothing is known of how sea turtles' core body temperatures vary from season to season at nesting sites. Over seven consecutive seasons we measured the surface temperatures of freshly laid eggs as proxies of core body temperatures of sea turtles using non-contact infrared thermometers. We estimated the temperatures of two species that have contrasting migration patterns - leatherbacks, which are adapted to migrate between tropical breeding sites to cold temperate waters, and hawksbills that are confined to the tropics and sub-tropics. We found considerable year-to-year variations in temperatures in both species (means ranging from 30.4°C to 31.5°C in leatherbacks), hawksbills the more so (28.1°C to 30.3°C). These differences will likely be modified by both natural seasonal variations and anthropogenic changes in global ocean temperatures and resulting changes in currents and water temperatures local to nesting beaches. These previously unrecognised fluctuations in body temperatures of nesting turtles have, it is argued, potential for predicting environmental tolerances, reproductive success, and nest site selection by sea turtles, and contribute to predictions of which rookeries may remain viable or not during future ocean warming.</p>
The sea around China-surface and bottom temperature data
<p>The temperature data in the sea around China, including sea surface temperature and bottom temperature. </p>
Dataset for "On the sensitivity of aerosol-cloud interactions to changes in sea surface temperature in radiative-convective equilibrium"
<p>Dataset for "On the sensitivity of aerosol-cloud interactions to changes in sea surface temperature in radiative-convective equilibrium".</p> <p>All numbers followed by "K" represent the sea surface temperature, and the number that follows them represents aerosol concentration.</p> <p>Abbreviations:</p> <p>prof - profile</p> <p>Adv - Advective</p> <p>CF245 - Cloud Fraction above 245K level</p> <p> cF - cloud fraction</p> <p>cIce - cloud ice</p> <p>CSRH - Clear-sky Radiative heating rate</p> <p>cWtr - cloud water</p> <p>Lat - latent</p> <p>LW - longwave</p> <p>SW - shortwave</p> <p>TOA - top of atmosphere</p> <p>SFC - surface</p> <p>CS - clear sky</p> <p>Temp - temperature</p> <p>tW - total water</p>
Data for sea ice transparency with temperature
<p>This dataset is the result of three temperature experiments for the study of the variation characteristics of sea ice diffuse attenuation with temperature.</p>
NEEM seasonal dO18 data for "Climate information preserved in seasonal water isotope at NEEM: relationships with temperature, circulation and sea ice"
<p>NEEM seasonal dO18 data over the period 1855-2004.</p> <p><strong>Please cite the following reference when using the data:</strong></p> <p>Zheng, M., Sjolte, J., Adolphi, F., Vinther, B. M., Steen-Larsen, H. C., Popp, T. J., and Muscheler, R.: Climate information preserved in seasonal water isotope at NEEM: relationships with temperature, circulation and sea ice, Climate of the Past, 14, 1067-1078, <a href="https://doi.org/10.5194/cp-14-1067-2018">https://doi.org/10.5194/cp-14-1067-2018</a>, 2018.</p> <p> </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.