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76 results for “ENSO”
Dataset for "Holocene hydroclimatic variability in the tropical Pacific explained by changing ENSO diversity."
<p>This repository contains the tropical Pacific sea surface temperature and global precipitation data from the CESM1 time slice experiments, which were used for the analysis presented in Karamperidou & DiNezio (2022), Nature Communications (https://www.nature.com/articles/s41467-022-34880-8)</p> <p> </p> <p>From Karamperidou & DiNezio (2022):</p> <p>“To assess the response of ENSO flavors to orbital forcing over the past 12,000 years (12ka), we use a suite of time-slice experiments in 3ka intervals with version 1 of the Community Earth System Model (CESM1). Each experiment is 400-600 years long and was run until the surface climate and oceanic processes controlling tropical climate, such as the depth of the thermocline in the equatorial Pacific or the Atlantic Meridional Overturning Circulation (AMOC), have reached equilibrium. All simulations exhibit minimal drift in global mean surface temperature (less than 0.05<sup>o</sup>C per century), tropical mean surface temperature (less than 0.04<sup>o</sup>C per century), the depth of the equatorial thermocline in the Pacific (less than 0.3m per century), and the strength of the AMOC (less than 0.25 Sv per century) during the periods used in the analyses. With the exception of the 12 ka BP interval which includes ice sheet changes and lower greenhouse gases, the primary forcing in the 0, 3, 6, and 9 ka BP intervals is changes in Earth's precession, and each simulation branched off its preceding one, starting from 0ka sequentially through the Holocene. The maximum TOA energetic imbalance does not exceed 0.45 Wm<sup>-2</sup>, which is much smaller than the imposed radiative forcing.”</p> <p> </p> <p> </p> <p>Karamperidou, C., DiNezio, P.N. Holocene hydroclimatic variability in the tropical Pacific explained by changing ENSO diversity. <em>Nat Commun</em> <strong>13</strong>, 7244 (2022). https://doi.org/10.1038/s41467-022-34880-8</p>
Data in support of 'ENSO influences subsurface marine heatwave occurrence in the Kuroshio Extension'
<p>Data in support of 'Chandler M, Sprintall J, Zilberman NV. (2025). ENSO influences subsurface marine heatwave occurrence in the Kuroshio Extension. <em>Journal of Geophysical Research: Oceans</em>. <a href="https://doi.org/10.1029/2025JC022899" target="_blank" rel="noopener">https://doi.org/10.1029/2025JC022899</a>'</p> <p> </p> <p>There are 2 netCDF files:</p> <ol> <li>p40tem1211_2312.nc</li> <li>synthetic_T_10day_px40_kuroshio_chandler2024.nc</li> </ol> <p><strong>p40tem1211_2312.nc </strong>contains the temperature sections from <a href="https://www-hrx.ucsd.edu/px40.html">HR-XBT transect PX40</a> objectively mapped onto a 10-m depth grid and a 0.1° longitudinal grid. <em>[LONGITUDE; LATITUDE; DEPTH; TIME; TEM]</em></p> <p><strong>synthetic_T_10day_px40_kuroshio_chandler2024.nc</strong> contains the synthetic temperature anomaly time series between the surface and 800-m deep at the western end of transect PX40 over the period from January-1993 to April-2023, as well as the temperature annual cycle needed for reconstructing the full synthetic temperature time series. <em>[time; depth; longitude; latitude; T_prime; T_ann]</em></p> <p> </p> <p>There is 1 MATLAB file:</p> <ol> <li>px40_synthetic_T.m</li> </ol> <p><strong>px40_synthetic_T.m</strong> is the MATLAB script used to produce the synthetic temperature anomaly time series saved in synthetic_T_10day_px40_kuroshio_chandler2024.nc.</p> <p> </p> <p>There is 1 Julia file:</p> <ol> <li>px40_synthetic_T_julia.jl</li> </ol> <p><strong>px40_synthetic_T_julia.jl</strong> is a Julia implementation of the MATLAB script px40_synthetic_T.m.</p> <p> </p> <p>There is 1 R file:</p> <ol> <li>px40_synthetic_T_R.R</li> </ol> <p><strong>px40_synthetic_T_R.R</strong> is an R implementation of the MATLAB script px40_synthetic_T.m.</p> <p> </p> <p><code>Version history:</code><br><code>v1.0.0 First uploaded (25-November-2024)</code><br><code>v1.0.1 Julia script uploaded (18-January-2025)</code><br><code>v1.0.2 R script uploaded (28-January-2025)</code><br><code>v1.1.0 Updated description of synthetic_T_10day_px40_kuroshio_chandler2024.nc to include reference to accepted publication (21-August-2025)</code></p>
Supplemental Figures for "On the comparative utility of entropic learning versus deep learning for long-range ENSO prediction"
<p>Supplemental figures for the paper "On the comparative utility of entropic learning versus deep learning for long-range ENSO prediction".</p>
Understanding ENSO dynamics through the exploration of past climates
<p>The palaeoclimate record shows that significant changes in ENSO characteristics took place during the Holocene. Exploring these changes, using both data and models, provides a means of understanding ENSO dynamics. Previous modelling studies have suggested a mechanism whereby changes in the Earth’s orbital geometry explain the strengthening of ENSO over the Holocene. Decreasing summer insolation over the Asian landmass resulted in a weakening of the Asian monsoon system. This led to a weakening of the easterly trade winds in the western Pacific, creating conditions more favourable for El Niño development. To explore this hypothesised forcing mechanism, we use a climate system model to conduct a suite of simulations of the climate of the past 8,000 years. In the early Holocene, we find that the Asian summer monsoon system is intensified, resulting in an amplification of the easterly trade winds in the western Pacific. The stronger trade winds represent a barrier to the eastward propagation of westerly wind bursts, therefore inhibiting the onset of El Niño events. The fundamental behaviour of ENSO remains unchanged, with the major change over the Holocene being the influence of the background state of the Pacific on the susceptibility of the ocean to the initiation of El Niño events.</p>
DATA (part 2): Response of Global SSTs and ENSO to the Atlantic and Pacific Meridional Overturning Circulations
<p>Data used for the peer-reviewed article published in the Journal of Climate, titled: "Response of Global SSTs and ENSO to the Atlantic and Pacific Meridional Overturning Circulations."</p> <p>The publication is available at: https://journals.ametsoc.org/view/journals/clim/aop/JCLI-D-21-0172.1/JCLI-D-21-0172.1.xml.</p> <p>The software developed for the data herein is available at: https://github.com/mariajmolina/climatico.</p>
DATA (part 1): Response of Global SSTs and ENSO to the Atlantic and Pacific Meridional Overturning Circulations
<p>Data used for the peer-reviewed article published in the Journal of Climate, titled: "Response of Global SSTs and ENSO to the Atlantic and Pacific Meridional Overturning Circulations."</p> <p>The publication is available at: https://journals.ametsoc.org/view/journals/clim/aop/JCLI-D-21-0172.1/JCLI-D-21-0172.1.xml.</p> <p>The software developed for the data herein is available at: https://github.com/mariajmolina/climatico.</p>
SAM-ENSO Index (SEI)
<p>Note: This v.2 updates the SEI data up to the 01.03.2024 as new SAM and ENSO data became available.</p> <p>The SAM-ENSO climate index (SEI) provided here combines the Southern Anular Mode (SAM) index (Marshall, 2003) with the Oceanic Niño Index (ONI) (Bamston et al., 1997; Huang et al., 2016), taking into account that their opposing phases reinforce each other in their overlapping effects on the wind field around Antarctica (Fogt et al., 2011; McKee et al., 2011; Stammerjohn et al., 2008).</p> <p>\(SEI = {SAM \over std(SAM)} - {ONI \over std(ONI)}\)</p> <p>The SEI has been first employed in Llanillo et al. (2023).</p> <p> </p> <p><strong>References:</strong></p> <p>Bamston, A. G., Chelliah, M., & Goldenberg, S. B. (1997). Documentation of a highly enso-related sst region in the equatorial pacific: Research note. <em>Atmosphere - Ocean</em>, <em>35</em>(3), 367–383. https://doi.org/10.1080/07055900.1997.9649597</p> <p>Fogt, R. L., Bromwich, D. H., & Hines, K. M. (2011). Understanding the SAM influence on the South Pacific ENSO teleconnection. <em>Climate Dynamics</em>, <em>36</em>(7), 1555–1576. https://doi.org/10.1007/s00382-010-0905-0</p> <p>Huang, B., Thorne, P. W., Smith, T. M., Liu, W., Lawrimore, J., Banzon, V. F., Zhang, H. M., Peterson, T. C., & Menne, M. (2016). Further exploring and quantifying uncertainties for extended reconstructed sea surface temperature (ERSST) version 4 (v4). <em>Journal of Climate</em>, <em>29</em>(9), 3119–3142. https://doi.org/10.1175/JCLI-D-15-0430.1</p> <p>Llanillo, P.J., Kanzow, T., Janout, M. and Rohardt, G. (2023): The Deep-Water Plume in the northwestern Weddell Sea, Antarctica: Mean state, seasonal cycle and interannual variability influenced by climate modes. <em>JGR-Oceans (accepted).</em></p> <p>Marshall, G. J. (2003). Trends in the Southern Annular Mode from observations and reanalyses. <em>Journal of Climate</em>, <em>16</em>(24), 4134–4143. https://doi.org/10.1175/1520-0442(2003)016<4134:TITSAM>2.0.CO;2</p> <p>McKee, D. C., Yuan, X., Gordon, A. L., Huber, B. A., & Dong, Z. (2011). Climate impact on interannual variability of Weddell Sea Bottom Water. <em>Journal of Geophysical Research: Oceans</em>, <em>116</em>(5), 1–17. https://doi.org/10.1029/2010JC006484</p> <p>Stammerjohn, S. E., Martinson, D. G., Smith, R. C., Yuan, X., & Rind, D. (2008). Trends in Antarctic annual sea ice retreat and advance and their relation to El Niño–Southern Oscillation and Southern Annular Mode variability. <em>Journal of Geophysical Research</em>, <em>113</em>(C3), C03S90. https://doi.org/10.1029/2007JC004269</p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p>
On the validity of foraminifera-based ENSO reconstructions
<p>Video of model run. Calculated oxygen isotope values of three species of planktonic foraminifera (<span class="math-tex">\( \delta^{18}O_c\)</span>) using the Foraminifera as modeled entities (FAME) module and Ocean Reanalysis data temperature and salinity data. Upper panel represents the Oceanic Nino Index (ONI) used to determine the ocean state for each monthly time step, lower panels the <span class="math-tex">\( \delta^{18}O_c\)</span> for each individual species of planktonic foraminifera (<em>G. ruber</em>; <em>G. sacculifer</em> and <em>N. dutertrei</em>).</p>
Data and code for gmd-2023-113 "Parameter estimation for ocean background vertical diffusivity coefficients in the Community Earth System Model (v1.2.1) and its impact on ENSO forecast"
<p>Data and code for the paper "Parameter estimation for ocean background vertical diffusivity coefficients in the Community Earth System Model (v1.2.1) and its impact on ENSO forecast"</p> <p>includes: </p> <p>The model is Community Earth System Model (v1.2.1) (provided by www.cesm.ucar.edu)</p> <p>Data assimilation code is initially provided by Data Assimilation Research Testbed (DART) (https://dart.ucar.edu/), some modifications are made to enable parameter estimation function of ocean background vertical diffusivity coefficients. And the programs and scripts for deal with OISST and EN4 profiles are also developed.</p> <p>The parameter sensitivity experiment results are saved as <a href="https://zenodo.org/api/files/6e5b4bfa-61cf-44bc-8e15-e62fab467ea6/sensitive2008-2012.nc">sensitive2008-2012.nc</a> and <a href="https://zenodo.org/api/files/6e5b4bfa-61cf-44bc-8e15-e62fab467ea6/sensitive2008-2012salt.nc">sensitive2008-2012salt.nc</a> for temperature and salinity, respectively. And the python script to draw the results is </p> <p>The state estimation results are provided as <a href="https://zenodo.org/api/files/6e5b4bfa-61cf-44bc-8e15-e62fab467ea6/Temp_05-17.nc">Temp_05-17.nc</a> and <a href="https://zenodo.org/api/files/6e5b4bfa-61cf-44bc-8e15-e62fab467ea6/Temp_05-17.nc">Salt_05-17.nc</a> for temperature and salinity, respectively.</p> <p>The parameter estimation results are provided as <a href="https://zenodo.org/api/files/6e5b4bfa-61cf-44bc-8e15-e62fab467ea6/PE_Temp_05-17.nc">PE_Temp_05-17.nc</a> and <a href="https://zenodo.org/api/files/6e5b4bfa-61cf-44bc-8e15-e62fab467ea6/PE_Temp_05-17.nc">PE_Salt_05-17.nc</a> for temperature and salinity, respectively.</p> <p>the estimated paremeter ensemble is saved in <a href="https://zenodo.org/api/files/6e5b4bfa-61cf-44bc-8e15-e62fab467ea6/parameters.nc">parameters.nc</a></p> <p>the python script for comparing the SE and PE results is <a href="https://zenodo.org/api/files/6e5b4bfa-61cf-44bc-8e15-e62fab467ea6/plot_analysis.py">plot_analysis.py</a></p> <p>the nino3.4 indices computed by the forecast experiment is saved in <a href="https://zenodo.org/api/files/6e5b4bfa-61cf-44bc-8e15-e62fab467ea6/fcst_correlation.nc">fcst_correlation.nc</a></p> <p> </p>
GFDL CM2.1 Partially-Coupled Simulations Data for "Understanding Lead Times of Warm-Water-Volumes to ENSO Sea Surface Temperature Anomalies"
<p>GFDL CM2.1 partially-coupled idealized simulations:</p> <p>Two sets of idealized experiments with prescribed EP and CP ENSO SST anomaly patterns. Each set of experiments has a prescribed idealized sinusoidal ENSO oscillation with periodicities of 48, 36, and 24 months, respectively.</p> <p>For the details please refer to our paper;<br> Zhao, S., Jin, F.-F., & Stuecker, M. F. (2021). Understanding Lead Times of Warm Water Volumes to ENSO Sea Surface Temperature Anomalies. <em>Geophysical Research Letters</em>, <em>48</em>(19), e2021GL094366. <a href="https://doi.org/10.1029/2021GL094366">https://doi.org/10.1029/2021GL094366</a></p> <p> </p> <p> </p> <p> </p>
Data provided in manuscript Mid-Holocene rainfall seasonality and ENSO dynamics over the southwestern Pacific
<p>Here we provide datasets of trace elements (LA-ICP-MS), carbon and oxygen stable isotopes, and greyscale values extracted from stalagmite C132 from Niue Island, covering the mid-Holocene (6.4 to 5.4 ka BP). The dataset includes the speleothem 230Th dates, and layer counting.</p>
Three-dimensional water exchanges in the shelf circulation system of the Northern South China Sea under climatic modulation from ENSO
<p>Three-dimensional water exchanges in the shelf circulation system of the Northern South China Sea under climatic modulation from ENSO</p>
Data & code repository for "A re-appraisal of the ENSO response to volcanism with paleoclimate data assimilation"
<p>This repository includes the data and code that can be used to reproduce the figures for the paper entitled <em>A re-appraisal of the ENSO response to volcanism with paleoclimate data assimilation</em>.</p>
Numerical experiments of ENSO and Arctic sea ice
<p>The data named "CTRL_40yrs_output.nc", “EXP_ALL_40yrs_R.nc”, “EXP_SIC_40yrs.nc”, “EXP_SST_40yrs_R.nc” are the output of last 40 model years for CTRL, EXP_ALL, EXP_SIC, and EXP_SST, which are run for totally 60 model years based on CAM5.</p>
Heat stress at Uva Island during the 1982-83, 1997-98 and 2015-16 ENSOs
<p><strong>Heat stress at Uva Island during the 1982-83, 1997-98 and 2015-16 ENSOs</strong></p> <p><strong>Description</strong>: This repository obtains and analyzes SST data for Uva Island reef to compare the intensity of heat stress during the past three major ENSOs.</p> <p><strong>Content</strong>:</p> <p>A.Temperature_DHW.Rmd: This script uses the temperature data in the <strong>B.Temperature_data</strong> folder to calculate the accumulated heat stress (DHW) at Uva Island reef, based on three different temperature data sources (OISST, CRW and In situ sensors). Figures resulting from running the script are saved in the <strong>C.Outputs</strong> folder.</p> <p>B.Temperature_data: Directory containing data files in .csv format with the temperature data for Uva Island reef from different sources</p> <ul> <li> <p><strong>B.Temperature_data/Uva_CRW_MMM_1985-2012.csv:</strong> Coral Reef Watch MMM (5km products v3.1) for the closest pixel to Uva Island reef</p> </li> <li> <p><strong>B.Temperature_data/Uva_Daily_CRW_5km_1985-2016.csv:</strong> Coral Reef Watch SST data (5km products v3.1) for the closest pixel to Uva Island reef</p> </li> <li> <p><strong>B.Temperature_data/Uva_Daily_InSitu_1997-2018.csv:</strong> In situ temperature data obtained from 1997-2018 at Uva Island reef ("Gardineroseris city') ~ 3m depth</p> </li> <li> <p><strong>B.Temperature_data/Uva_Daily_OISST_1982-2016.csv:</strong> Optimal Interpolation SST data (V2) for the closest pixel to Uva Island reef</p> </li> <li> <p><strong>B.Temperature_data/BanderasBay_Daily_OISST_1982-2016.csv:</strong> Optimal Interpolation SST data (V2) for the closest pixel to Uva Island reef</p> </li> </ul> <p>C.Outputs: Directory containing figure files related with temperature and DHW included on the paper.</p> <ul> <li>Figure 1a and 3a</li> <li>Figure S6</li> <li>Figure S7</li> <li>Figure S8</li> <li>Figure S9</li> <li>Figure S5b</li> </ul> <p>D.SST_nc: Directory containing scripts used to download and extract OISST and CRW SST data from .nc files. You do not have to run the code in the <strong>D.SST_nc</strong> folder to produce the data analysis in the paper (this can be achieved by running <strong>A.Temperature_DHW.Rmd</strong> using the temperature data already provided in <strong>B.Temperature_data</strong>). However, you could use these scripts to retrieve and extract the SST data by yourself.The outputs of these scripts are already included in <strong>B.Temperature_data/Uva_CRW_MMM_1985-2012.csv:</strong>, <strong>B.Temperature_data/Uva_Daily_CRW_5km_1985-2016.csv</strong>" and <strong>B.Temperature_data/Uva_Daily_OISST_1982-2016.csv</strong>. By running the scripts inside this folder you will create those files again in the "<strong>D.SST_nc</strong>" folder.</p> <p>You would need ~ 123GB of space for CRW SST data and ~17GB for OISST data</p> <ul> <li> <p><strong>D.SST_nc/1.Get_OISST_data.sh:</strong> This script downloads 1981-2016 NOAA OI SST V2 High Resolution data from ftp://ftp.cdc.noaa.gov/Datasets/noaa.oisst.v2.highres/</p> </li> <li> <p><strong>D.SST_nc/2.Get_CoralTemp_data.sh:</strong> This script downloads NOAA CRW 5km SST data (CoralTemp v3.1) (fftp://ftp.star.nesdis.noaa.gov/pub/sod/mecb/crw/data/coraltemp/v1.0/nc/) and the 1985-2012 climatology (ftp://ftp.star.nesdis.noaa.gov/pub/sod/mecb/crw/data/5km/v3.1/climatology/nc/ct5km_climatology_v3.1.nc). It could also download the already calculated DHW values (ftp://ftp.star.nesdis.noaa.gov/pub/sod/mecb/crw/data/5km/v3.1/nc/v1.0/daily/dhw/) directly from NOAA CRW 5km products. However, these lines are commented by default since we performed these calculations ourselves based on the CRW SST data (CoralTemp) and MMM.</p> </li> <li> <p><strong>D.SST_nc/3.Extract_SST_Uva.Rmd:</strong> This script extracts the SST data from OISST and CRW datasets for Uva Island. It also extracts the already calculated climatology (MMM) from CRW 5km products. CRW DHW values from NOAA CRW 5km products can be extracted as well. However, these lines are commented by default since we performed these calculations ourselves based on the CRW SST data (CoralTemp) and MMM.</p> </li> </ul>
Data for 'Improved predictability of the Indian Ocean Dipole using seasonally modulated ENSO forcing forecasts'
<p>Abstract of the associated paper: Despite recent progress in seasonal forecast development, the predictive skill for the Indian Ocean Dipole (IOD) remains typically limited to a lead time of one season or less in both dynamical and empirical models. Here we develop a simple stochastic-dynamical model (SDM) to predict the IOD using seasonally modulated El Niño-Southern Oscillation (ENSO) forcing together with a seasonal modulation of the Indian Ocean coupled ocean-atmosphere feedback. The SDM, with either observed or forecasted ENSO forcing, exhibits generally higher skill and longer lead times for predicting IOD events than the operational Climate Forecast System Version 2 and the SINTEX system. These results affirm our hypothesis that operational IOD predictability beyond persistence is largely controlled by ENSO predictability and the signal-to-noise ratio of the system. Therefore, potential future ENSO improvements in models should also translate to more skillful IOD predictions.</p>
HadGEM3-GC31-MM year-long ENSO, QBO, and sea-ice simulations
<p>HadGEM3-GC31-MM year-long (June to May) simulations of two ENSO, two QBO, and two sea-ice states. The ENSO states are neutral ENSO and El Niño, the QBO states are QBO westerly and easterly, and the sea-ice states are present day and future sea-ice (following the<a href="https://gmd.copernicus.org/articles/12/1139/2019/"> PAMIP protocol</a>). There are 150 ensemble members per experiment. HadGEM3 is run in an atmosphere only configuration. The variables making up this dataset are zonal wind (ua), sea level pressure (psl), surface temperature (ts), precipitation (pr), atmospheric temperature (ta), geopotential height (zg), vertical Eliassen-Palm flux (epfz), meridional Eliassen-Palm flux (epfy) and Eliassen-Palm flux divergence (epfd). Zonal means are given for ua, ta, zg, epfz, epfy, and epfd. Both daily and monthly version of zonal wind are provided.</p> <p>This data is uploaded to support the paper "Nonlinear response of the extratropics to tropical climate variability" published in Geophysical Research Letters and in support of the paper "Interdependent extratropical atmospheric responses to Arctic sea-ice loss, QBO and ENSO" submitted to Journal of Climate.</p>
MITgcm simulations to study ENSO in warmer climates
Open the record for dataset details and reuse information.
Matlab codes implementing the XDROM+ data-driven ENSO forecast model and some analysis of it
<p>This is the BEST forecast model of large scale features of ENSO as of today, beating (Zhao et al. Nature 2024).</p> <p>This archive is supplementary to a comment article concerning (Zhao et al. Nature 2024) intended as a "Matters Arising" piece to be submitted to Nature (https://www.researchsquare.com/article/rs-5336072/v1). Given that i criticise also the handling editor and 3 reviewers of (Zhao et al. Nature 2024) calling their incompetence out, do not be surprised if you have to look for the paper in some other journal instead. Oh well, integrity is above all else, no?! On that note, may I interest you in a bit of sci-fi? https://www.linkedin.com/pulse/crime-punishment-bit-differently-tamas-bodai-g4cvf/?trackingId=WdQkSNjgSuyxlyWVLRonrw%3D%3D</p>
Data for "Strong El Niño events lead to robust multi-year ENSO predictability"
<p>The full raw data, intermediate steps, and final results for all three experiments used in Lenssen et al. (202X), "Strong El Niño events lead to robust multi-year ENSO predictability," as submitted to GRL. Each zip file contains the raw data, intermediate steps, and final results for model analog forecast issued to predict that model or observational record. All data has been gridded to a common 2x2 grid using nco following the include `mygrid_2x2` file.</p><p>Archived codebase for this data available at <a href="https://zenodo.org/doi/10.5281/zenodo.10045615">https://zenodo.org/doi/10.5281/zenodo.10045615</a><br>Live codebase at <a href="https://github.com/nlenssen/LongLeadENSO/">https://github.com/nlenssen/LongLeadENSO/</a></p>
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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.