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200 results for “SST”
Tropical Pacific SST and wind anomalies generated by a Nonlinear Inverse Model
<p>Tropical Pacific (40S-40N; 120E-50W) sea surface temperature (SST), zonal wind (U) and meridional wind (V) anomalies generated by the Nonlinear Inverse Model described in Martinez-Villalobos et al., 2024 (https://doi.org/10.1038/s41612-024-00675-5). The data consists in 99 realizations (<a href="../api/records/10411023/draft/files/NLIM_output_085.nc/content" target="_blank" rel="noopener noreferrer">NLIM_output_XXX.nc</a>) of 1,000yrs each emulating SST, U, and V monthly anomalies conditions during 1980-2020 (<a href="../api/records/10411023/draft/files/Monthly_obs_1980_2020.nc/content" target="_blank" rel="noopener noreferrer">Monthly_obs_1980_2020.nc</a>) given in a 2.5deg-2.5deg grid. For observations, we used the NOAA Extended Reconstruction SST v5 reanalysis (SST; Huang et al., 2017) and NCEP-NCAR reanalysis (winds; Kalnay et al., 1996) The observed anomalies are calculated as described in Martinez-Villalobos et al., 2024 (https://doi.org/10.1038/s41612-024-00675-5).</p> <p>Given that the stochastic forcing considered is white in time and space (https://doi.org/10.1038/s41612-024-00675-5; Methods, section "Offline simulation of SSH_{12}, PC2, and spatial patterns fron nonlinear inverse model output"), the spatial patterns and lead-lag relationships are better identified using composites. A modification of the methodology that allows for spatially coherent stochastic forcing will be implemented in a future article.</p> <p>When using the data please cite https://doi.org/10.5281/zenodo.10411023 (the data) and Martinez-Villalobos et al., 2024 (https://doi.org/10.1038/s41612-024-00675-5; for the methodology). </p> <p>Any question, please contact Cristian Martinez-Villalobos at his email cristian.martinez.v@uai.cl</p> <p>References</p> <p>Martinez-Villalobos, C., Dewitte, B., Garreaud, R.D. <em>et al.</em> Extreme coastal El Niño events are tightly linked to the development of the Pacific Meridional Modes. <em>npj Clim Atmos Sci</em> <strong>7</strong>, 123 (2024). https://doi.org/10.1038/s41612-024-00675-5</p> <p>Huang, B. et al. Extended Reconstructed Sea Surface Temperature, Version 5 (ERSSTv5): Upgrades, Validations, and Intercomparisons. Journal of Climate 30, 8179–8205 (2017).</p> <p>Kalnay, E. et al. The NCEP/NCAR 40-Year Reanalysis Project. Bulletin of the American Meteorological Society 77, 437–471 (1996).</p> <p> </p>
Historical Sea Surface Temperature (SST) data and thermal stress indices of the Tara Pacific Expedition's coral reef sampling sites, from May 1st 2002 to August 31st 2018.
<p>The Tara Pacific expedition (2016-2018) sampled coral ecosystems at 111 sampling sites around 32 islands in the Pacific Ocean, and sampled the surface of oceanic waters at 249 locations, resulting in the collection of nearly 58,000 samples (Gorsky et al. 2019, Planes et al. 2019, Flores et al. 2020). The expedition was designed to systematically study corals, fish, plankton, and seawater, and included the collection of samples for advanced biogeochemical, molecular, and imaging analysis.</p> <p>Here we provide a high-resolution historical dataset that spans from 2002 to each sites’ sampling date and gives an overview of past climate variability and heatwaves experienced by corals sampled at each site. Ocean skin temperature (11 and 12 µm spectral bands longwave algorithm) was extracted from 1km resolution level-2 MODIS-Aqua and MODIS-Terra from 2002 to the sampling date and from level-2 VIIRS-SNPP from 2012 to the sampling date. Day and night overpasses were used to maximize data recovery. Following recommendations from NASA Ocean Color (OB.DAAC), only SST products of quality 0 and 1 were used. The 9 closest pixels to the sampling sites of each scene were extracted. All the extracted pixels from the 3 satellites were then averaged daily to obtain daily SST averages and standard deviations time series for each sampling site, from 2002 to the sampling date.</p> <p>Each time series was first averaged on a Julian day basis to provide a seasonal average. This yearly seasonal average was triplicated and concatenated into a 3-year seasonal cycle to apply a digital low pass filter on the middle year without generating artifacts. A digital low pass filter (filter order 3, pass band ripple 0.1; “filfilt” function in matlab) with 36 Julian days windows was applied to the concatenated time series to remove high frequency noise. The middle year was then extracted from the concatenated time series to recover the seasonal cycle. The sea surface temperature anomaly was calculated as the SST minus the seasonal cycle over the full time series. Considering the short periods of missing data (mean of the 95th percentile of the duration of consecutive days with missing data: 9.8 ± 4.1 days), the missing values in the SST and SST anomaly time series were linearly interpolated in order to calculate thermal stress indices. The SST anomaly frequency was calculated as the number of days over the past 52 weeks when the SST anomaly is greater than or equal to 1 °C. Thermal stress indices relevant to coral reef health were then calculated using methodology developed for the Coral Reef Temperature Anomaly Database (CoRTAD) data base (Saha et al. 2019). Events of cold temperature accumulation were also reported to cause bleaching and mortality (Lirman et al. 2011; González-Espinosa & Donner 2020), therefore, the same set of indices were calculated for cold stress adapting the CoRTAD method, but using the minimum weekly climatologies.</p> <p>A condensed table containing single values associated with each sampling site was created ('TaraPacific_SST_timeseries_mean_products') extracting the minimum, maximum, sum, averages, standard deviations, and value recorded at the sampling day of each of these indices (detailed in the readme file provided with the dataset 'README_TaraPacific_historical_SST.md'). Additional metrics of the last heating and cooling events as well as the time of recovery is also provided to represent the state of thermal stress at the day of sampling.</p>
Reconstructed SST-NINO3.4 anomalies for the years 850 to 1981
<p>This data set gives values of reconstructed SST-NINO3.4 anomalies for the years 850 to 1981.</p> <p>SST-NINO3.4 is a key ENSO (El Niño Southern Oscillation) index, defined as the spatial average of Sea Surface Temperature (SST) over the NINO3.4 spatial box (170°W-120°W, 5°S-5°N).</p> <p>The reconstructed SST-NINO3.4 anomalies are obtained from a new multiproxy reconstruction based on 45 proxy records obtained from the Past Global Changes 2k database (PAGES 2k Consortium, 2017, 2019) and a so-called Random Forest method. The reconstructed anomalies are relative to the 1870-2014 averaged SST-NINO3.4 obtained from the HadISST product (Rayner et al. 2003). Units are degrees Celsius</p>
MCR LTER: Coral Reef: Optical parameters and SST from SeaWiFS and MODIS, ongoing since 1997 and AVHRR-derived SST from 1985 to 2009
Monthly averages of the Sea Surface Temperature (SST), the Sub-surface chlorophyll-a concentration (Chl), the colored dissolved and detrital organic materials at 443 nm (acdm[443]) and the particulate backscattering coefficient at 443 nm (bbp[443]) around Moorea are obtained or derived from satellite data (SST from AVHRR and MODIS-Aqua; Chl, acdm[443] and bbp[443] from SeaWiFS and MODIS-AQUA). The satellite data are averaged over a 1 month period for geographic areas of 16S-19S/147W-151W (SeaWiFS and MODIS-AQUA) and 15S-20S/145W-155W for AVHRR. This material is based upon work supported by the U.S. National Science Foundation under Grant No. OCE 16-37396 (and earlier awards) as well as a generous gift from the Gordon and Betty Moore Foundation. Research was completed under permits issued by the French Polynesian Government (Délégation à la Recherche) and the Haut-commissariat de la République en Polynésie Francaise (DTRT) (Protocole d'Accueil 2005-2022). This work represents a contribution of the Moorea Coral Reef (MCR) LTER Site.
A global dataset of SST anomaly evolving processes retrieved from remote sensing products (GDSSTAEP V1.0)
<p> The GDSSTAEP includes three datasets and two relationship files with a time range from January 1982 to December 2009. Three datasets formatted in SHP are a dataset of process object-oriented SSTA, named DSPOSSTA, storing SSTA process objects, a dataset of sequence object-oriented SSTA, named DSSOSSTA, storing SSTA sequence objects, and a dataset of variation object-oriented SSTA, named DSVOSSTA, storing SSTA variation objects, respectively. And two relationship files formatted in CSV store the evolving behaviors among sequence objects of SSTA and variation objects of SSTA, respectively. </p> <p> </p>
Eddy Kinetic Energy and SST gradients global datasets and trends. Additionally, this dataset includes ocean basins and ocean processes masks.
<p>This dataset includes the post-processed data used for the paper titled "Mesoscale kinetic energy response to changing oceans". The original data was obtained from AVISO+ SSH altimetry and NOAA optimal interpolated sea surface temperature (OISST):</p> <p>AVISO+ SSH: https://www.aviso.altimetry.fr/en/data/products/sea-surface-height-products/global/gridded-sea-level-heights-and-derived-variables.html</p> <p>NOAA-OISST: https://www.ncdc.noaa.gov/oisst</p> <p>From satellite observations of sea surface height (SSH) and sea surface temperature (SST) over the satellite record (1993 - 2019), EKE and SST gradients are derived. </p> <p>Then the fields are then temporally smoothed using a running average of 12 months. Trends and the significance of each field are finally computed with linear regression and a modified Mann–Kendall test (https://github.com/josuemtzmo/xarrayMannKendall).</p> <p>Geographical regions consist of the following ocean basins: the Southern Ocean, the Indian Ocean, the Pacific Ocean, and the Atlantic ocean. These ocean basins were expert-defined to capture ocean processes at all scales (ocean_basins_and_dynamical_masks.nc).</p> <p>Dynamical regions (Fig. 5d): the Antarctic Circumpolar Current (ACC), the boundary currents and their extensions, the tropics, the subtropical ocean gyres, and the remaining regions (ocean_basins_and_dynamical_masks.nc).</p> <p>Further information and scripts to reproduce the result of the manuscript can be found at: https://github.com/josuemtzmo/EKE_SST_trends</p>
Ocean surface currents, SSH and SST from LLC4320, before and after Lagrangian filtering
<p>This dataset comprises daily snapshots of horizontal velocity, sea surface height and sea surface temperature from LLC4320, a high resolution setup of the MITgcm, in the Agulhas region. We provide the unfiltered data, and the data after Lagrangian filtering as described in Jones, CS, Xiao, Q, Abernathey, RP and Smith, KS <em>Separating balanced and unbalanced flow at the surface of the Agulhas region using Lagrangian filtering (preprint: </em><a href="https://doi.org/10.31223/X5D352">https://doi.org/10.31223/X5D352</a> ). Lagrangian filtering is not applied to the sea surface temperature.</p> <p>This dataset is not the dataset that was used to make the figures in Jones et al. (see <a href="https://doi.org/10.5281/zenodo.6574163">https://doi.org/10.5281/zenodo.6574163</a>), but a separate dataset that is meant to be used in future study. We have decided to make this dataset publicly available because it may be useful for machine learning, or for studies that investigate the dynamical equations that govern the sea surface height and horizontal velocity field.</p> <p>unfilt_u_v_ssh_sst.nc contains unfiltered horizontal velocity, sea surface height and sea surface temperature</p> <p>filt_u_v_ssh.nc contains horizontal velocity and sea surface height after Lagrangian filtering</p> <p>This work was supported by NASA award 80NSSC20K1142.</p>
Mapa Mental: Protección del trabajador desde la normatividad legal vigente en SST
<p>Mapa mental donde se visualiza tres normas en materia de Seguridad y Salud en el Trabajo que le aportan a la Protección del trabajador, y estas son: Ley 1562 de 2012, Decreto 1072 de 2015 y Resolución 0312 de 2019.</p>
SST_front_data: ocean thermal fronts detected by the Cayula and Cornillon SIED algorithm
<p>This dataset includes the post-processed data and a demo MATLAB script used for the paper titled "Global trends of fronts and chlorophyll in a warming ocean"</p> <p><strong>SST_FRONT_data.zip</strong> contains maps of sea surface temperature (SST) fronts detected by the Cayula and Cornillon single image edge detection algorithm over global ocean warming hotspot regions and covering the period 2003-2020. The original data was obtained from NASA OB.DAAC MODIS sea surface temperature (SST) product (MODIS Aqua Level 3 SST MID-IR 8 Day 4km Nighttime V2019.0: https://podaac.jpl.nasa.gov/dataset/MODIS_AQUA_L3_SST_MID-IR_8DAY_4KM_NIGHTTIME_V2019.0?ids=&values=&search=MODIS%20Aqua&provider=POCLOUD). </p> <p>SST_FRONT_data.zip also contains <strong>Fdens_Ffreq_Fstre_example.mlx</strong>, which<strong> </strong>is a MATLAB live script showing how to compute metrics of fronts based on frontal maps: frontal frequency (Ffreq), frontal density (Fdens), and frontal strength (Fstre). </p> <p><strong>Fdens_Ffreq_Fstre_example.pdf</strong> is intended for quick viewing of the script above.</p> <p> </p>
Atmospheric_river_sensitivity_to_local_SST_investigation_datasets
<p>This repository includes all the data used to plot the figures in the main texts and supplements (if applicable) of the following paper:</p> <p> </p> <p>Chen, X. and L. R. Leung, 2020: Response of landfalling atmospheric rivers on the U.S. west coast to local sea surface temperature perturbations, <em>Geophys. Res. Lett.</em></p> <p> </p> <p>The related plotting scripts is available on GitHub: lucas-uw/Chen-2020-GRL</p> <p> </p> <p><em>Should you choose to use this dataset, please cite the above paper where appropriate.</em></p>
SST forcing files and Model Builds for "Inter-basin versus intra-basin sea surface temperature forcing of the Western North Pacific subtropical high's westward extensions"
<p>This repository provides archives of the Community Earth System Model version 2.2.0 (CESM2.2.0) and case directories for the simulations used in the "Inter-basin versus intra-basin sea surface temperature forcing of the Western North Pacific subtropical high's westward extensions" manuscript. The repository includes:</p><ul><li>The original sea surface temperature forcing files used in each experiment (SST_Forcing Files) </li><li>The F2000CLIMO compset model builds forced for each experiment </li></ul>
Climatological global-mean Sea Surface Temperature (SST) in AWI-CM-1-1-MR simulations for CMIP6, in preindustrial, present-day, +2°C, +3°C, and +4°C climates
<p>Daily climatologies of global-mean sea surface temperature (SST, parameter 'tos') free-running simulations performed using the coupled climate models AWI-CM-1-1-MR. The unstructured grid-ocean component FESOM was conservatively remapped to the ERA5 grid. Data was averaged across the 5 ensemble members and temporally averaged over 10-year long time periods: 1850-1859 for preindustrial climate, 2015-2024 for present-day, 2034-2043 for +2°C climate, 2061-2079 for +3°C climate, and 2091-2100 for +4°C climate. </p><p>Data is provided in .nc files, one for each climate.</p>
Output of simulations for "Atmosphere Response to an Oceanic Sub-mesoscale SST Front: A Coherent Structure Analysis": Part 1
<p>This dataset contains the output of the simulations for the paper "Atmosphere Response to an Oceanic Sub-mesoscale SST Front: A Coherent Structure Analysis" doi: [TO BE COMPLETED]. This is part 1. It contains data for the S1 simulation.</p>
Output of simulations for "Atmosphere Response to an Oceanic Sub-mesoscale SST Front: A Coherent Structure Analysis": Part 2
<p>This dataset contains the output of the simulations for the paper "Atmosphere Response to an Oceanic Sub-mesoscale SST Front: A Coherent Structure Analysis" doi: [TO BE COMPLETED]. This is part 2. It contains the remaining of data for the S1 simulation, the data of the reference simulations RefC and RefW, and the data for the sensitivity analysis of the supplementary material.</p>
Daily climatology of 3D ocean currents, SST and SSH based on a 22 year run of the SEA-COFS model
<p>This dataset is a daily climatology created based on a 22 year free run of the SEA-COFS model full domain (EAC 25.25 - 45.55 °S) forced with BARRA-R winds and tides, spanning from January 1994 to September 2016. The model has 30 sigma-stretch vertical levels and an across-shore resolution of 2.5 km (on shelf) – 6 km (off shelf) and 5 km along-shore. For a full description of this version of the model and its validation refer to the following papers:</p> <ul> <li><strong>Li, J, Roughan, M. & Kerry, C.</strong> (2021). <a href="https://doi.org/10.1029/2021GL094115%20">Dynamics of interannual eddy kinetic energy modulations in a Western Boundary Current</a>. <em>Geophysical Research Letters,</em>Vol 48, October 2021.DOI: <a href="https://doi.org/10.1029/2021GL094115">https://doi.org/10.1029/2021GL094115</a> <a href="http://www.oceanography.unsw.edu.au/private/publications/2021/2021GL094115.pdf">[PDF file]</a></li> <li> </li> <li><strong>Li, J, Roughan, M. & Kerry, C.</strong> (2022). <a href="https://doi.org/10.1175/JCLI-D-21-0622.1">Variability and Drivers of Ocean Temperature Extremes in a Warming Western Boundary Current</a>. <em>Journal Of Climate,</em> February 2022. DOI: <a href="https://doi.org/10.1175/JCLI-D-21-0622.1">https://doi.org/10.1175/JCLI-D-21-0622.1</a> <a href="http://www.oceanography.unsw.edu.au/private/publications/2022/Li_2022.pdf">[PDF file]</a></li> </ul> <p>This daily climatology of 365 days was created with NCO tools by taking the mean of the 22 daily average ROMS output files (one for each year) to generate each climatological day. Specific commands used are recorded in the NetCDF file history. Leap year days were excluded since its climatology was computed with only 5 instances.</p> <p>A sample file with the climatological data for January 1st is provided here as an example of the NetCDF format of the dataset. The entire dataset is one file of approximately 62GB and can be provided upon request. </p> <p><strong>NOTE: </strong>The ocean_time variable reflects the dates of the year 1994, but the values of the variables correspond to climatological values computed as described. </p> <p>The ROMS variables below are present in this daily climatology, as well as the S-coordinate stretching curves, grid defining variables and other time independent parameters:</p> <p>AKs = "time-averaged salinity vertical diffusion coefficient" [meter2 seconds-1]</p> <p>AKt = "time-averaged temperature vertical diffusion coefficient" [meter2 seconds-1]</p> <p>AKv = "time-averaged vertical viscosity coefficient" [meter2 seconds-1]</p> <p>bustr = "time-averaged bottom u-momentum stress" [newton meter-2]</p> <p>bvstr = "time-averaged bottom v-momentum stress" [newton meter-2]</p> <p>omega = "time-averaged S-coordinate vertical momentum component" [meter3 second-1]</p> <p>pvorticity = "time-averaged potential vorticity" [meter-1 second-1]</p> <p>pvorticity = "time-averaged 2D potential vorticity" [meter-1 second-1]</p> <p>rho = "time-averaged density anomaly" [kilogram meter-3]</p> <p>rvorticity = "time-averaged relative vorticity, vertical component" [second-1]</p> <p>rvorticity_bar = "time-averaged 2D relative vorticity" [second-1]</p> <p>salt: = "time-averaged salinity" [PSU]</p> <p>shflux = "time-averaged surface net heat flux" [watt meter-2]</p> <p>ssflux = "time-averaged surface net salt flux, (E-P)*SALT" [meter second-1]</p> <p>sustr = "time-averaged surface u-momentum stress" [newton meter-2]</p> <p>svstr = "time-averaged surface v-momentum stress" [newton meter-2]</p> <p>temp = "time-averaged potential temperature" [Celsius]</p> <p>u = "time-averaged u-momentum component" [meter second-1]</p> <p>u_eastward = "time-averaged eastward momentum component at RHO-points" [meter second-1]</p> <p>ubar = "time-averaged vertically integrated u-momentum component" [meter second-1]</p> <p>ubar_eastward = "time-averaged eastward vertically integrated momentum component at RHO-points" [meter second-1]</p> <p>uu = "time-averaged u-momentum times u-momentum" [meter2 second-2]</p> <p>uv = "time-averaged u-momentum times v-momentum" [meter2 second-2]</p> <p>v = "time-averaged v-momentum component" [meter second-1]</p> <p>v_northward = "time-averaged northward momentum component at RHO-points" [meter second-1]</p> <p>vbar = "time-averaged vertically integrated v-momentum component" [meter second-1]</p> <p>vbar_northward = "time-averaged northward vertically integrated momentum component at RHO-points" [meter second-1]</p> <p>vv = "time-averaged v-momentum times v-momentum" [meter2 second-2]</p> <p>w = "time-averaged vertical momentum component" [meter second-1]</p> <p>zeta = "time-averaged free-surface" [meter]</p>
Fig. 2 in Retrieving climate change dependent Sea Surface Temperature (SST) in Southern Turkey by using Landsat thermal imagery
Fig. 2 — The four sections of the sampling site Table 1 — Landsat images features (29) Product Type Pixel size (collected) Pixel size (resampled) Thermal band Landsat 4-5 TM L1 120-meters 30-meters Band 6 Landsat 7 ETM+ L1 60-meters 30-meters Band 6 Landsat 8 OLI/TIRS L1 100-meters 30-meters Band 10/ Band11
Fig. 3 in Retrieving climate change dependent Sea Surface Temperature (SST) in Southern Turkey by using Landsat thermal imagery
Fig. 3 — SST anomalies: a) T1 Cross-section, b) T2 Cross-section, c) T3 Cross-section, and d) T4 Cross-section
Coarse-grained outputs from near-global aqua-planet control run with QOBS SST
<p>This is the coarse-grained CTRL NG-Aqua data described in the following two papers:</p> <p>Narenpitak, P., Bretherton, C. S. & Khairoutdinov, M. F. Cloud and circulation<br> feedbacks in a near-global aquaplanet cloud-resolving model: Cloud Feedbacks in<br> a Near-Global CRM. J. Adv. Model. Earth Syst. 9, 1069–1090 (2017).</p> <p>Bretherton, C. S. & Khairoutdinov, M. F. Convective self-aggregation feedbacks<br> in near-global cloud-resolving simulations of an aquaplanet. Journal of<br> Advances in Modeling Earth Systems 7, 1765–1787 (2015).</p> <p><br> The data are generated using the System for Atmospheric Modeling, and then<br> selected fields are averaged onto (160 km)^2 grid boxes for machine learning<br> purposes.</p>
SST CCI Auxiliary Datasets
<p>These auxiliary datasets are used in the generation of SST CCI ATSR and AVHRR products. Each set of files is contained in a separate tar archive and their application is briefly described below. For more information on the SST CCI algorithms please refer to the publicly available Algorithm Theoretical Basis Document.</p> <p><strong>Retrieval coefficients:</strong></p> <p>ATSR_Retrieval_Coeffs.tar</p> <p>These files contain the retrieval coefficients used to retrieve SST for the ATSR instruments. These are banded by total column water vapour and viewing angle. Coefficients are provided for all applicable retrievals - nadir two channel (n2), nadir three channel (n3), dual-view two channel (d2) and dual-view three channel (d3).</p> <p><strong>Spectral response functions:</strong></p> <p>AATSR_SRF.tar, ATSR2_SRF.tar, ATSR1_SRF.tar, AVHRR_SRF.tar</p> <p>These files contain the spectral response functions for each channel of each ATSR and AVHRR instrument. Note that all the AVHRR instruments are bundled in a single tar file including NOAA6 - NOAA19 and MetopA. All files are in plain text format containing two columns, the first is the wavelength and the second the filter response at that wavelength.</p> <p><strong>Probability Density Functions:</strong></p> <p>ATSR_PDFs.tar, AVHRR_PDFs.tar</p> <p>These are netCDF format files which include spectral and textural PDFs for cloudy conditions and textural PDFs only for clear-sky conditions. Dimensions are defined also as variables which give metadata describing their bounds and bin widths. The PDFs themselves are multi-dimensional variables constrained by the definitions of their given dimensions.</p> <p><strong>Sea Surface Emissivity:</strong></p> <p>ATSR_sse.tar, AVHRR_sse.tar</p> <p>Within SST CCI, the sea surface emissivity is calculated offline, and then passed to RTTOV for use within the radiative transfer calculation (by-passing the internal RTTOV surface emissivity model). These files are in ascii format and constructed in such a way as to be readable by RTTOV.</p> <p><strong>Level 4 Land/Sea Mask</strong></p> <p>mask_ostia20Tnolakes.nc</p> <p>This is a netCDF format file containing the land/sea mask used when generating Level 4 data on the OSTIA grid.</p> <p> </p>
OISSTv2 standardized daily SST in Eastern tropical Pacific
<p>OISSTv2 standardized daily sea surface temperatures (SST) of 11 different 60x60 latitutde x longitude tiles in the eastern tropical Pacific.</p> <p>Time coverage: 1982-2021</p>
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.