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164 results for “stratosphere”

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edi56/100

Supporting data for “Climate Intervention through Stratospheric Aerosol Injection may partially mitigate marine heatwaves"

Although climate intervention aims to lower the global average temperature, the potential impact of Stratospheric Aerosol Injection on marine heatwaves (MHW) has not been thoroughly examined. This spatial dataset provides global and regional MHW metrics—such as frequency, maximum intensity, and duration—from the Community Earth System Model, version 2 (CESM2), using the baseline scenario SSP2-4.5, referred to as a no climate intervention scenario, and the ARISE-SAI ensemble. The ARISE-SAI model uses the SSP2-4.5 scenario, introducing stratospheric aerosol injection at approximately 21 km in 2035, aiming to keep global mean surface air temperature near 1.5°C for ARISE-SAI-1.5 and near 1.0°C for ARISE-SAI-1.0 above pre-industrial levels. The dataset includes global MHW properties for the historical period (1990-2009), the current period under SSP2-4.5 emission scenario (2015-2034), and future scenarios under SSP2-4.5, ARISE-SAI-1.5, and ARISE-SAI-1.5 for 2050-2059 and 2060-2069.

openCC (other)Nov 2025View details →

zenodo52/100

TCOM-CFC11 : TOMCAT CTM and Occultation Mesurement based daily zonal mean CFC-11 stratospheric profiles constructed using machine leaning (2000-2023)

<h3>TOMCAT CTM and Occultation measurement-based Stratospheric CFC11 (TCOM-CFC11) profile data data set  </h3> <h3>Sandip S. Dhomse </h3> School of Earth and Envio, University of Leeds, Leeds, UK National Centre for Earth Observations, University of Leeds, Leeds, UK  email: s.s.dhomse@leeds.ac.uk   <h3>Methodology:  TOMCAT simulation is performed at T64L32 resolution for the 2000-2024 time period. Collocated CFC11 (CFCl3)  profiles are divided in five latitude bins: SH polar (90S-50S), SH mid-lat (70S-20S), tropics (40S-40N), NH mid-lat (20N-70N) and NH polar (50N-90N). Initially, model-measurement  differences are calculated for each zonal bins (51 height levels, 10km to 60km). Separate XGBoost regression models are trained for the  differences between TOMCAT and measurements at each level for a given latitude bin. XGBoost model is then used to estimate error corrections for all the TOMCAT grids. Estimated corrections for a given model grid that are added to the original TOMCAT simulated daily (at 1.30 local time) CFC-11 profiles. Height resolved data are then interpolated on 28-pressure levels (300 - 0.1hPa). For overlapping latitude bins, we use averages and then calculate daily zonal mean values.  For more details see attached presentation.</h3> <h3>Dataset also includes two files containing daily mean zonal mean CFC11  profiles on height (10-60 km) and pressure (300-0.1 hPa) levels (8766 days/64 latitudes):</h3> <h3>zmcfc11_TCOM_hlev_T2Dz_2000-2024_V1.1.nc – height level data (10 to 60 km)</h3> <h3>zmcfc11_TCOM_plev_T2Dz_2000-2024_V1.1.nc – pressure level data (300 to 0.1 hPa)</h3> <h3>Daily 3D profiles on height and pressure levels would be made available on request. Xarrays “resample” can be used to get monthly means.</h3> <h3> </h3> <h3> </h3>

opencc-by-4.0Jun 2024View details →

zenodo52/100

The mass of the lowermost stratosphere (LMS): LMS mass calculation and trends in five reanalyses for the time period 1979–2019

Description Python code to calculate the mass of the lowermost stratosphere (LMS) and investigate LMS mass trends with the dynamic linear regression model (DLM, Laine et al. 2014, Alsing 2019) as presented in Weyland et al. (2024). The LMS mass is calculated via a three dimensioal integral, following Appenzeller et al. (1996), given an upper and lower LMS boundary surface (4D pressure fields). Here, the lateral boundary is determined via the intersection of the tropopause with the 350K isentrope (4D pressure field). The upper LMS boundary can be defined by the isentrope according to the potential temperature at the tropical lapse rate tropopause (PPT10mean) or the cold point (PPTcp10mean) or approximated by the 380K isentrope. See Weyland et. al (2024) for further description and context. The mass calculation is performed with calc_LMS_mass.py. The DLM trend analysis is conducted with dlm_LMS_mass.py, using dlm_modules.py. In order to be able to use the provided code, the dlmmc model code has to be downloaded from <a href="https://github.com/justinalsing/dlmmc">https://github.com/justinalsing/dlmmc</a> (Alsing 2019). The neccesary 3D (time, lat, lon) pressure fields to define the LMS boundaries are provided for the time period 1979–20191 from five modern reanalyses: ERA52 (Hersbach et al., 2020), ERA-Interim (Dee et al., 2011), MERRA-2 (Gelaro et al., 2017) and JRA-55 (Kobayashi et al., 2015) and JRA3Q (Kosaka et al., 2024): <ul> <li>lrtp*.nc : <ul> <li>3D (time, lat, lon) pressure, temperature and potential temperature at the WMO lapse rate tropopause for the time period 1979-20191, derived from monthly mean data on pressure levels from the respective reanalysis. The lapse rate detection algorithm closely follows that of Birner et al. (2010), based on the work of Reichler et al. (2003). The lapse rate tropopause can serve as the lower LMS boundary. The potential temperature at the lapse rate tropopause between 10°N-10°S is used to define a &bdquo;dynamic“ upper LMS boundary (PPT10mean).</li> </ul> </li> </ul> <ul> <li>cp*.nc : <ul> <li>3D (time, lat, lon) pressure, temperature and potential temperature at the cold point for the time period 1979–20191 , derived from monthly mean data on pressure levels from the respective reanalysis. The cold point here is defined by the pressure corresponding to a lapse rate of 0K/km. The potential temperature at the cold point between 10°N–10°S is used to define a &bdquo;dynamic“ upper LMS boundary (PPTcp10mean).</li> </ul> </li> </ul> <ul> <li>ppt10mean*.nc : <ul> <li>3D (time, lat, lon) pressure at the isentrope accroding to the potential temperature at the tropical (10°N–10°S) lapse rate tropopause (PPT10mean) for the time period 1979–20191, derived from lrtp*.nc. PPT10mean can be used to define the upper LMS boundary.</li> </ul> </li> </ul> <ul> <li>pptcp10mean*.nc : <ul> <li>3D (time, lat, lon) pressure at the isentrope accroding to the potential temperature at the cold point between 10°N-10°S (PPTcp10mean) for the time period 1979–20191, derived from cp*.nc. PPTcp10mean can be used to define the upper LMS boundary.</li> </ul> </li> </ul> <ul> <li>p380K*.nc : <ul> <li>3D (time, lat, lon) pressure at the 380K isentrope for the time period 1979–20191, derived from monthly mean data on pressure levels from the respective reanalysis. The 380K isentropic pressure field can be used to approximate the upper LMS boundary.</li> </ul> </li> </ul> <ul> <li>p350K*.nc : <ul> <li>3D (time, lat, lon) pressure at the 350K isentrope for the time period 1979–20191, derived from monthly mean data on pressure levels from the respective reanalysis. The 350K isentrope is used to determine the lateral LMS boundaries via its intersection with the tropopause. This intersection approximates the location of the subtropical jet streams and the maximum PV-gradient, marking a transport barrier. It is determined by the sign change of the pressure difference between the tropopause and the 350K isentrope.</li> </ul> </li> </ul> <ul> <li>my_enso_79-19.txt : <ul> <li>Regressor to account for El-Niño/Southern Oscillation for the time period 1979–2019. Source: <a href="https://psl.noaa.gov/enso/mei/">https://psl.noaa.gov/enso/mei/</a>, last accessed: 11 July 2023. The data has been normalized and centered around zero. The use of regressors is optional.</li> </ul> </li> </ul> <ul> <li>my_qbo30_79-19.txt and my_qbo50_79-19.txt : <ul> <li>Regressor to account for the quasi-biennial oscillation at 30 and 50 hPa for the time period 1979–2019. Source: <a href="https://www.geo.fu-berlin.de/met/ag/strat/produkte/qbo/qbo.dat">https://www.geo.fu-berlin.de/met/ag/strat/produkte/qbo/qbo.dat</a>, last accessed: 11 July 2023. The data has been normalized and centered around zero. The use of regressors is optional.</li> </ul> </li> </ul> <ul> <li>my_SAOD_79-19.txt : <ul> <li>Regressor to account for stratospheric (volcanic) aerosol optical depth for the time period 1979-2019. Source: <a href="https://asdc.larc.nasa.gov/project/GloSSAC/GloSSAC_1.0">https://asdc.larc.nasa.gov/project/GloSSAC/GloSSAC_1.0</a>, last accessed: 11 July 2023. The data has been normalized. The use of regressors is optional.</li> </ul> </li> </ul>   For further details see Weyland et al. (2024). 1Note that the ERA-Interim time series ends in 2018 and that the MERRA-2 time series starts in 1980. 2For the time period 2000–2006, the sub-reanalysis ERA5.1 replaces ERA5, correcting the reanalysis for a cold bias in the lower stratosphere (Simmons et al., 2020).   How to use – example: Assuming you are interested in the LMS mass between a lower boundary (-lb, e.g., the lapse rate tropopause) and an upper boundary (-ub, e.g., the 380K isentrope) in ERA5 for the entire Northern hemisphere (-lat=NH) covering the time period 1979-2019:   <ul> <li> Calculate the respective LMS mass timeseries: $ python calc_LMS_mass.py -lb=lrtp_ERA5.nc -ub=p380K_ERA5.nc -latb=p350K_ERA5.nc -lat=NH -fout=LMS_mass_ERA5_lrtp_p380K_NH.nc Isentropic pressure at 350K (-latb) is required to determine the lateral LMS boundary. The LMS mass time series together with an uncertainty estimate is saved to a netCDF file (-fout), e.g. &bdquo;LMS_mass_ERA5_lrtp_p380K_NH.nc“. </li> </ul>   <ul> <li> Perform a DLM trend analysis for your LMS mass time series, here LMS_mass_ERA5_lrtp_p380K_NH.nc (-mf) : Download the DLM model code (dlmmc) from <a href="https://github.com/justinalsing/dlmmc">https://github.com/justinalsing/dlmmc</a> (Alsing 2019) and save the &bdquo;dlmmc“ folder, containing the DLM modules in your working directory. </li> </ul> $ python dlm_lms_mass.py -mf=LMS_mass_ERA5_lrtp_p380K_NH.nc -s=2000 In this example, the DLM will provide 2000 samples (-s) after an additional 1000 warm-up samples. The DLM time series, containing 2000 samples (-s) per time step, is saved to a netCDF file. The name of the output file can be specifyed with -fout. Default is &bdquo;dlm_“ + mf, i.e. &bdquo;dlm_ LMS_mass_ERA5_lrtp_p380K_NH.nc“ in this example. The function dlm_lms_mass.dlm_lms_mass contains an option to visualize the DLM result (plot=True). Furthermore, it can be specified whether the DLM should be run with regressors (use_regressors=True) or without regressors (use_regressors=False). See the DLM documentation (Laine et al. 2014, Alsing 2019) for further options.   Funding: This work was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – TRR 301 – Project-ID 428312742: “The tropopause region in a changing atmosphere”.

opencc-by-4.0Oct 2024View details →

zenodo48/100

SPARC Data Initiative monthly zonal mean composition measurements from stratospheric limb sounders (1978-2018)

The SPARC Data Initiative dataset is the most comprehensive compilation of vertically resolved stratospheric composition measurements to date and consists of four decades of monthly zonal mean climatologies (1978-2018) from a range of satellite limb sounders including LIMS, SAGE I/II/III, HALOE, UARS-MLS, POAMII/III, OSIRIS, SMR, MIPAS, GOMOS, SCIAMACHY, ACE-FTS, ACE-MAESTRO, Aura-MLS, HIRDLS, SMILES, OMPS-LP and SAGE III-ISS. The dataset includes most major long-lived trace gases (O3, H2O, N2O, CH4, CCl3F, and CCl2F2), transport tracers (HF, SF6, HCl, CO, HNO3, NOy), and shorter-lived trace gases important to stratospheric chemistry including nitrogens (NO, NO2, NOx, N2O5,and HNO4), halogens (BrO, ClO, ClONO2 and HOCl), and other minor species (OH, HO2, CH2O, CH3CN). The observations considered have been compiled in units of volume mixing ratio (VMR) and on a common latitude-pressure grid, covering the region from the upper troposphere to the lower mesosphere (300-0.1 hPa) with a latitudinal resolution of 5 degrees.

opencc-by-4.0Nov 2020View details →

zenodo48/100

TCOM-HF : Daily global gap-free stratospheric hydrogen fluoride (HF) profile data set based on TOMCAT CTM and Occultation Measurements

Methodology:  TOMCAT simulation is performed at T64L32 resolution for the 2000-2024 time period. Collocated hydrogen fluoride (HF) profiles are divided in five latitude bins: SH polar (90S-50S), SH mid-lat (70S-20S), tropics (40S-40N), NH mid-lat (20N-70N) and NH polar (50N-90N). Initially, model-measurement  differences are calculated for each zonal bins (51 height levels, 10km to 60km). Note that if enough ACE measurements are not avaliable for a particular level then data is purely based on TOMCAT simulated output field. Separate XGBoost regression models are trained for the  differences between TOMCAT and measurements at each level for a given latitude bin. XGBoost model is then used to estimate error corrections for all the TOMCAT grids.  TOMCAT output sampled at 1.30 pm local time at the equator. Estimated corrections for a given model grid that are added to the original TOMCAT simulated day and night time hydrogen fluoride profiles. Height resolved data are then interpolated on 28-pressure levels (300 - 0.1hPa). For overlapping latitude bins, we use averages and then calculate daily zonal mean values.  For more details see attached presentation. Previous version use both HALOE and ACE data. Here only ACE data is used. Dataset also includes two files containing daily mean zonal mean hydrogen fluoride  profiles on height (10-50 km) and pressure (300-0.1 hPa) levels: zmhf_TCOM_hlev_T2Dz_2000_2024.nc – height level data (10 to 50 km) zmhf_TCOM_plev_T2Dz_2000_2024.nc – pressure level data (300 to 0.1 hPa) Daily 3D profiles on height and pressure levels would be made available on request.

opencc-by-4.0Feb 2023View details →

zenodo48/100

ML-TOMCAT V2.0: Machine-Learning-Based Satellite-Corrected Global Stratospheric Ozone Profile Dataset

MLTOMCAT V2 is 46 years (1979-2024) of gap free ozone profile data sets that is created by correcting biases in a TOMCAT Chemical Transport Model (CTM) simulated ozone profiles. We use Random Forest regression model to correct model biases.  Each file contain monthly mean zonal mean ozone profiles. There are 6 data files. <a href="https://zenodo.org/api/files/0416f9bd-c908-4d3f-9368-47ca2e04d7bd/MLTOMCAT_1979_2020_72_ht_vmr.nc">MLTOMCAT_1979_2024_72_ht_vmr_V2.nc</a> contains ozone profiles on geometric height levels (1 to 60 km)  in mixing ratio units, whereas  <a href="https://zenodo.org/api/files/0416f9bd-c908-4d3f-9368-47ca2e04d7bd/MLTOMCAT_1979_2020_72_ht_vmr.nc">MLTOMCAT_1979_2024_72_ht_nd_V2.nc</a> contains ozone profile in number density units. Similarly,  <a href="https://zenodo.org/api/files/0416f9bd-c908-4d3f-9368-47ca2e04d7bd/MLTOMCAT_1979_2020_72_ht_vmr.nc">MLTOMCAT_1979_2024_72_plev_vmr_V2.nc</a> contains ozone profiles on 43 MLS pressure levels  (1000 to 0.1 hPa)  in mixing ratio units, whereas  <a href="https://zenodo.org/api/files/0416f9bd-c908-4d3f-9368-47ca2e04d7bd/MLTOMCAT_1979_2020_72_ht_vmr.nc">MLTOMCAT_1979_2024_72_plev_nd_V2.nc</a> contains ozone profile in number density units. Please note that data below 300 hPa (~8km) and 1 hPa (~50 km) should be used with caution. There are two straospheric column files ML-TOMCAT-SCO_120ppb_boundary_V2_197901-202412.nc and ML-TOMCAT-SCO_150ppb_boundary_V2_197901-202412.nc Stratospheric column files calculated using 120 ppb and 150 ppb as a chemical ozone boundaries. A manuscript describing MLTOMCAT would be published in EESD (Dhomse et al., 2021).

opencc-by-4.0Jun 2021View details →

zenodo48/100

QBO: monthly zonal stratospheric winds from tropical radiosonde data (mainly Singapore)

Monthly Tropical Stratospheric Zonal Winds from Radiosondes Data Source and Processing: Monthly mean zonal wind data for the tropics are provided as a service for the global QBO and trend analysis communities. The original data source and processing chain were established by the Free University of Berlin (FUB). Currently, the data is processed at the Karlsruhe Institute of Technology (KIT, ROR:04t3en479), Institute of Meteorology and Climate Research (IMK), Germany with the tools developed at FUB. Data Description: The dataset includes monthly mean zonal wind values at pressure levels 100, 90, 80, 70, 60, 50, 45, 40, 35, 30, 25, 20, 15, 12, and 10 hPa, derived from radiosonde observations at four equatorial stations: <ul> <li>Kiribati (Canton Island) - data from 1953 to 1967 (closed)</li> <li>Maldives (Gan Island) - data from 1967 to 1975 (closed)</li> <li>Singapore (Payalebar) - data from 1975 to 1989</li> <li>Singapore (Changi) - data from 1989 onwards</li> </ul> Important Notes: <ul> <li>Values for 100 hPa from October 1967 are solely from Singapore (Changi).</li> <li>Values for 100 hPa before October 1967 are from Kiribati (Canton Island) when available.</li> <li>Detailed information about the radiosonde stations and their periods of operation is provided below.</li> </ul> Additional Information: <ul> <li>Access the data in other formats also published here: <a href="https://www.atmohub.kit.edu/english/807.php" target="_blank" rel="noopener noreferrer">https://www.atmohub.kit.edu/english/807.php</a></li> </ul> Detailed List of Radiosonde Stations: <div> <div> <div> <div> <table> <tbody> <tr> <th>Station Name</th> <th>Location (Lat, Lon)</th> <th>Data Period</th> <th>Pressure Levels (hPa)</th> </tr> <tr> <td>Kiribati (Canton Island)</td> <td>-2.7667, -171.7167</td> <td>1953 - 1967 (closed)</td> <td> Above 100 (until August 1967) 100 (until September 1967) </td> </tr> <tr> <td>Maldives (Gan Island)</td> <td>-0.6933, 73.1556</td> <td>1967 - 1975 (closed)</td> <td>Above 100 (September 1967 to December 1975)</td> </tr> <tr> <td>Singapore (Payalebar)</td> <td>1.3667, 103.9167</td> <td>1975 - 1989</td> <td>Above 100 (January 1976 to May 1989)</td> </tr> <tr> <td>Singapore Upper Air Observatory</td> <td>1.3404, 103.8879</td> <td>1989 - present</td> <td> 100 (October 1967 to May 1989),  All levels from June 1989 </td> </tr> </tbody> </table> </div> </div> </div> </div> <div> </div>

opencc-zeroFeb 2024View details →

zenodo44/100

Stratospheric sudden warmings in an idealized GCM - Zonal mean data

Running an idealized dry General Circulation Model, Stratospheric Sudden Warmings have been generated and analyzed in Jucker, Fueglistaler, Vallis (2014), JGR 119, 11,054, doi:10.1002/2014JD022170. This is the zonal mean data (geopotential height, zonal wind, temperature) produced by 35 independent simulations using the model.

opencc-by-4.0Oct 2014View details →

zenodo44/100

TCOM-HCl : Daily global gap-free stratospheric hydrogen chloride profile data set based on TOMCAT CTM and Occultation Measurements

Methodology:   The TOMCAT simulation was conducted at a T64L32 resolution, consistent with previous work by Dhomse et al. (2021, 2022), covering the period from 2000 to 2024. These simulations utilized ERA-5 reanalysis data. <h3>HCl Profile Processing and Bias Correction</h3> Collocated HCl profiles are organized into five distinct latitude bins: <ul> <li> NH polar: 90∘N - 50∘N </li> <li> NH mid-lat: 20∘N - 70∘N </li> <li> Tropics: 40∘S - 40∘N </li> <li> SH mid-lat: 70∘S - 20∘S </li> <li> SH polar: 90∘S - 50∘S </li> </ul> Initially, differences between TOMCAT and satellite measurements (primarily ACE-FTS data) are calculated for each zonal bin across 51 height levels (ranging from 10,km to 60,km). Separate XGBoost regression models are then trained for these HCl differences at each height level within a given latitude bin. These trained models are subsequently used to estimate HCl bias corrections for all daytime TOMCAT grids (9132 days), specifically sampled at 1:30 PM local time at the equator. This yields grid-specific bias corrections that are applied to the original TOMCAT profiles. Height-resolved HCl profile data are then interpolated onto 28 standard pressure levels (from 300,hPa to 0.1,hPa), using pressure levels directly from the TOMCAT grids. For overlapping latitude bins, values are averaged to ensure smoother fields near boundary regions. <h3>Data Files</h3> The dataset includes two files containing daily mean zonal mean HCl profiles: <ul> <li> <code>zmhcl_TCOM_hlev_T2Dz_2000-2024_V1.1.nc</code>: Contains height level data (10,km to 60,km). </li> <li> <code>zmhcl_TCOM_plev_T2Dz_2000-2024_V1.1.nc</code>: Contains pressure level data (300,hPa to 0.1,hPa). </li> </ul> <h3>Reference Publication</h3> This methodology, incorporating only ACE-FTS data and various minor algorithmic developments, is based on the following publication: Dhomse, S. S. and Chipperfield, M. P.: Using machine learning to construct TOMCAT model and occultation measurement-based stratospheric methane (TCOM-CH4) and nitrous oxide (TCOM-N2O) profile data sets, Earth Syst. Sci. Data, 15, 5105–5120, <a title="null" href="https://doi.org/10.5194/essd-15-5105-2023">https://doi.org/10.5194/essd-15-5105-2023</a>, 2023

opencc-by-4.0Feb 2023View details →

zenodo44/100

TCOM-H2O: TOMCAT CTM and Occultation Measurements based daily zonal stratospheric H2O profile dataset [1991-2021] constructed using machine-learning.

Methodology:   The TOMCAT simulation was conducted at a T64L32 resolution, consistent with previous work by Dhomse et al. (2021, 2022), covering the period from 2000 to 2024. These simulations utilized ERA-5 reanalysis data. <h3>H2O Profile Processing and Bias Correction</h3> Collocated H2O profiles are organized into five distinct latitude bins: <ul> <li> NH polar: 90∘N - 50∘N </li> <li> NH mid-lat: 20∘N - 70∘N </li> <li> Tropics: 40∘S - 40∘N </li> <li> SH mid-lat: 70∘S - 20∘S </li> <li> SH polar: 90∘S - 50∘S </li> </ul> Initially, differences between TOMCAT and satellite measurements (primarily ACE-FTS data) are calculated for each zonal bin across 51 height levels (ranging from 10,km to 60,km). Note that TOMCAT may not accurately capture H2O evolution post-HTHH eruption due to the sparse spatial coverage of ACE measurements, which limits training data. Separate XGBoost regression models are then trained for these H2O differences at each height level within a given latitude bin. These trained models are subsequently used to estimate H2O bias corrections for all daytime TOMCAT grids (9132 days), specifically sampled at 1:30 PM local time at the equator. This yields grid-specific bias corrections that are applied to the original TOMCAT profiles. Height-resolved H2O profile data are then interpolated onto 28 standard pressure levels (from 300,hPa to 0.1,hPa), using pressure levels directly from the TOMCAT grids. For overlapping latitude bins, values are averaged to ensure smoother fields near boundary regions. We acknowledge the inherent dry biases in the original TOMCAT H2O profiles, largely because the TTL entry mixing ratios are based on a simplistic sinusoidal seasonal cycle, which omits the H2O enhancement contributed by tropical convective clouds. <h3>Data Files</h3> The dataset includes two files containing daily mean zonal mean H2O profiles: <ul> <li> <code>zmh2o_TCOM_hlev_T2Dz_2000-2024_V1.1.nc</code>: Contains height level data (10,km to 60,km). </li> <li> <code>zmh2o_TCOM_plev_T2Dz_2000-2024_V1.1.nc</code>: Contains pressure level data (300,hPa to 0.1,hPa). </li> </ul> <h3>Reference Publication</h3> This methodology, incorporating only ACE-FTS data and various minor algorithmic developments, is based on the following publication: Dhomse, S. S. and Chipperfield, M. P.: Using machine learning to construct TOMCAT model and occultation measurement-based stratospheric methane (TCOM-CH4) and nitrous oxide (TCOM-N2O) profile data sets, Earth Syst. Sci. Data, 15, 5105–5120, <a title="null" href="https://doi.org/10.5194/essd-15-5105-2023">https://doi.org/10.5194/essd-15-5105-2023</a>, 2023.

opencc-by-4.0May 2023View details →

zenodo44/100

TCOM-O3: TOMCAT CTM and Occultation Measurements based daily zonal stratospheric ozone profile dataset [1991-2021] constructed using machine-learning

Methodology:  TOMCAT simulation is performed at T64L32 resolution for the 2000-2024 time period. Collocated Ozone (O3) profiles are divided in five latitude bins: SH polar (90S-50S), SH mid-lat (70S-20S), tropics (40S-40N), NH mid-lat (20N-70N) and NH polar (50N-90N). Initially, model-measurement  differences are calculated for each zonal bins (51 height levels, 10km to 60km). Note that if enough ACE measurements are not avaliable for a particular level then data is purely based on TOMCAT simulated output field. Separate XGBoost regression models are trained for the  differences between TOMCAT and measurements at each level for a given latitude bin. XGBoost model is then used to estimate error corrections for all the TOMCAT grids.  TOMCAT output sampled at 1.30 pm local time at the equator. Estimated corrections for a given model grid that are added to the original TOMCAT simulated day and night time ozone profiles. Height resolved data are then interpolated on 28-pressure levels (300 - 0.1hPa). For overlapping latitude bins, we use averages and then calculate daily zonal mean values.  For more details see attached presentation. Previous version use both HALOE and ACE data. Here only ACE data is used (hence starting date is 01 January 2000). PDF file shows comparison between v1.0 and v1.1 as well as TOMCAT data. Dataset also includes two files containing daily mean zonal mean hydrogen fluoride  profiles on height (10-60 km) and pressure (300-0.1 hPa) levels: zmo3_TCOM_hlev_T2Dz_2000_2024.nc – height level data (10 to 60 km) zmo3_TCOM_plev_T2Dz_2000_2024.nc – pressure level data (300 to 0.1 hPa) Daily 3D profiles on height and pressure levels would be made available on request.

opencc-by-4.0Feb 2023View details →

zenodo44/100

TOMCAT CTM and Occultation measurement-based Stratospheric CFC12 (TCOM-CFC12) profile data set

TOMCAT CTM and Occultation measurement-based Stratospheric CFC12 (TCOM-CFC12) profile data set   Sandip S. Dhomse  School of Earth and Enviro, University of Leeds, Leeds, UK National Centre for Earth Observations, University of Leeds, Leeds, UK  email: s.s.dhomse@leeds.ac.uk  Methodology:  TOMCAT simulation is performed at T64L32 resolution for the 2000-2024 time period. Collocated CFC12 (CF2Cl2)  profiles are divided in five latitude bins: SH polar (90S-50S), SH mid-lat (70S-20S), tropics (40S-40N), NH mid-lat (20N-70N) and NH polar (50N-90N). Initially, model-measurement differences are calculated for each zonal bins (51 height levels, 10km to 60km). Separate XGBoost regression models are trained for the differences between TOMCAT and measurements at each level for a given latitude bin. XGBoost model is then used to estimate error corrections for all the TOMCAT grids. Estimated corrections for a given model grid that are added to the original TOMCAT simulated daily (at 1.30 local time) CFC-12 profiles. Height resolved data are then interpolated on 28-pressure levels (300 - 0.1hPa). For overlapping latitude bins, we use averages and then calculate daily zonal mean values.  For more details see attached presentation. Dataset also includes two files containing daily mean zonal mean CFC-12 profiles on height (10-60 km) and pressure (300-0.1 hPa) levels (9132 days/64 latitudes): zmcfc12_TCOM_hlev_T2Dz_2000-2024_V1.1.nc – height level data (10 to 60 km) zmcfc12_TCOM_plev_T2Dz_2000-2024_V1.1.nc – pressure level data (300 to 0.1 hPa) Daily 3D profiles on height and pressure levels would be made available on request. Xarrays “resample” can be used to get monthly means.

opencc-by-4.0Jun 2024View details →

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Dataset for "Reduced ice loss from Greenland under stratospheric aerosol injection"

Dataset for the paper "Reduced ice loss from Greenland under stratospheric aerosol injection" (Journal of Geophysical Research: Earth Surface, 128 (11), e2023JF007112, <a href="https://doi.org/10.1029/2023JF007112">doi: 10.1029/2023JF007112</a>). Please see the README for details. V1.1.1: README and metadata updated. V1.1: Scripts related to the ISIMIP-method downscaling, SEMIC code, as well as configuration and input files for SICOPOLIS and Elmer/Ice added. V1: Results of new simulations that include both atmospheric and oceanic forcing. V0.9.1: Crucial bug fix in the files ElmerIce_MIROC-ESM-CHEM-{RCP85,RCP45,G4}_2D_final.nc (those in V0.9 were faulty). V0.9: Scalar variables: now distinguished between state and flux variables. 2D variables added. V0.5: Scalar variables as functions of time. * * * * * * * Users should cite the original publication when using all or parts of these data.

opencc-by-4.0Oct 2020View details →

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ERA5 dataset for the categorization of Stratospheric Final Warming

This file in HDF5 format includes daily values of the following quantities derived from ERA5 data: <ul> <li>Zonal wind at 60°N – 10 hPa</li> <li>Polar temperature averaged over 80-90°N and 50-10 hPa</li> <li>Amplitude of geopotential wave 1 at 60°N – 10 hPa</li> <li>Zonal-mean meridional heat flux averaged over 45-75°N at 10 hPa</li> </ul> Each data set includes 25933 daily values from January 1n, 1950 to December 31, 2020. They are computed from ERA5 data extracted at 12UT each day and a resolution 2.5 x 2.5 degrees in latitude and longitude. The ERA5 data are provided by ECMWF Copernicus Climate Change Service from their data server <a href="https://confluence.ecmwf.int/display/CKB/How+to+download+ERA5">https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-pressure-levels?tab=form</a>.

opencc-by-4.0Nov 2021View details →

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Data used in: 'Atmospheric impacts of chlorinated very short-lived substances over the recent past – Part 1: Stratospheric chlorine budget and the role of transport' by Bednarz et al. (2022)

Data used in: 'Atmospheric impacts of chlorinated very short-lived substances over the recent past – Part 1: Stratospheric chlorine budget and the role of transport' by Bednarz et al. (2022), which has been accepted for publication in Atmospheric Chemistry and Physics.

opencc-by-4.0Aug 2022View details →

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IMS sulphate aerosol in the stratospheric plume of the January 2022 Tong aeruption

This animation is made using the IMS sulphate aerosol optical depth product (see https://www?doi.org/10.5281/zenodo.7102472) for all day and night orbits of each day between 13 January and 30 April 2022. The indicated times are those of the intersection of the orbits with the equator. The upper chart of each view is a daily composite of the day orbits and the lower chart is a daily composite of the night orbits. When two orbit swaths overlap, the crossing time of the overlapped orbit is indicated in red. Missing orbits are blanked out. Several days are entirely missing between 8 and 14 March.

opencc-by-4.0Oct 2022View details →

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TCOM-COF2: TOMCAT CTM and Occultation measurement-based Stratospheric COF2 profile data set

TCOM-COF2: TOMCAT CTM and Occultation measurement-based Stratospheric TCOM-COF2 profile data set   Sandip S. Dhomse  School of Earth and Enviro, University of Leeds, Leeds, UK National Centre for Earth Observations, University of Leeds, Leeds, UK  email: s.s.dhomse@leeds.ac.uk   Methodology:  TOMCAT simulation is performed at T64L32 resolution for the 2000-2023 time period. Collocated COF2  profiles are divided in five latitude bins: SH polar (90S-50S), SH mid-lat (70S-20S), tropics (40S-40N), NH mid-lat (20N-70N) and NH polar (50N-90N). Initially, model-measurement differences are calculated for each zonal bins (51 height levels, 10km to 60km). Separate XGBoost regression models are trained for the differences between TOMCAT and measurements at each level for a given latitude bin. XGBoost model is then used to estimate error corrections for all the TOMCAT grids. Estimated corrections for a given model grid that are added to the original TOMCAT simulated daily (at 1.30 local time) COF2 profiles. Height resolved data are then interpolated on 28-pressure levels (300 - 0.1hPa). For overlapping latitude bins, we use averages and then calculate daily zonal mean values.  For more details see attached presentation. Dataset also includes two files containing daily mean zonal mean COF2 profiles on height (10-60 km) and pressure (300-0.1 hPa) levels (8766 days/64 latitudes): zmcof2_TCOM_hlev_T2Dz_2000_2023.nc – height level data (10 to 60 km) zmcof2_TCOM_plev_T2Dz_2020_2023.nc – pressure level data (300 to 0.1 hPa) Daily 3D profiles on height and pressure levels would be made available on request. Xarrays “resample” can be used to get monthly means.

opencc-by-4.0Jun 2024View details →

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The SPARC water vapour assessment II: Comparison of annual, semi-annual and quasi-biennial variations in stratospheric and lower mesospheric water vapour observed from satellites

Here we provide a NetCDF data set that contains the amplitudes and phases for the annual, semi-annual and quasi-biennial variations in stratospheric and lower mesospheric water vapour as observed by 30 satellite data sets. In addition, we combine the results from all data sets to provide average amplitudes and the corresponding standard deviations, among other. The content description of the NetCDF file looks as follows: netcdf results.amt-10-1111-2017 { dimensions:     dataset = 30 ;     string_length = 60 ;     latitude = 37 ;     bands = 2 ;     altitude = 59 ; variables:     char dataset_short(string_length, dataset) ;         dataset_short:standard_name = "data set" ;         dataset_short:long_name = "data set name" ;         dataset_short:description = "short label of data set" ;     char dataset_long(string_length, dataset) ;         dataset_long:standard_name = "data set" ;         dataset_long:long_name = "data set name" ;         dataset_long:description = "long label of data set" ;     double latitude(latitude) ;         latitude:standard_name = "latitude" ;         latitude:units = "degree_north" ;         latitude:minimum_value = "-90" ;         latitude:maximum_value = "90" ;         latitude:axis = "Y" ;         latitude:_CoordinateAxisType = "Lat" ;     double latitude_bands(bands, latitude) ;         latitude_bands:units = "degree_north" ;     double altitude(altitude) ;         altitude:standard_name = "altitude" ;         altitude:long_name = "pressure levels" ;         altitude:units = "hPa" ;         altitude:axis = "Z" ;         altitude:_CoordinateAxisType = "Alt" ;     double tropopause(latitude) ;         tropopause:standard_name = "tropopause" ;         tropopause:long_name = "tropopause pressure" ;         tropopause:description = "climatological tropopause pressure based on MERRA reanalysis data 2000 - 2014" ;         tropopause:units = "hPa" ; // global attributes:         :summary = "this file contains the results published in Lossow et al. (2017)" ;         :url = "https://www.atmos-meas-tech.net/10/1111/2017/amt-10-1111-2017.html" ;         :project = "second SPARC water vapour assessment (WAVAS-II)" ;         :creator_name = "Stefan Lossow & Farahnaz Khosrawi" ;         :creator_email = "stefan.lossow@kit.edu & farahnaz.khosrawi@kit.edu" ;         :creator_email_supplemental = "stefan.lossow@yahoo.se & f.khosrawi@gmail.com" ;         :value_for_nodata = "NaN" ;         :date_created = "20190105T112425Z" ; group: AO {   dimensions:       latitude = 37 ;       altitude = 59 ;       dataset = 30 ;   variables:       double amplitude(dataset, altitude, latitude) ;           amplitude:standard_name = "amplitude" ;           amplitude:long_name = "amplitude of the AO variation" ;           amplitude:description = "regression model is given by Eq. (1) in the manuscript; amplitude calculation based on Eq. (2)" ;           amplitude:units = "ppmv" ;       double phase(dataset, altitude, latitude) ;           phase:standard_name = "phase" ;           phase:long_name = "phase of the AO variation" ;           phase:description = "regression model is given by Eq. (1) in the manuscript; phase calculation based on Eq. (3)" ;           phase:units = "month" ;       double offset(dataset, altitude, latitude) ;           offset:standard_name = "offset" ;           offset:long_name = "offset component of the regression model" ;           offset:description = "regression model is given by Eq. (1) in the manuscript; meant for calculation of relative amplitudes" ;           offset:units = "ppmv" ;       double screening(dataset, altitude, latitude) ;           screening:standard_name = "screening" ;           screening:long_name = "screening for the amplitude and phase data" ;           screening:description = "screening matrix for the amplitude and phase data to calculate the standard deviations described in Sect. 3.3; 1 means screening; 0 means no screening" ;           screening:units = "" ;       double phase_difference(dataset, altitude, latitude) ;           phase_difference:standard_name = "phase difference" ;           phase_difference:long_name = "phase difference with respect to the reference data set" ;           phase_difference:reference_data_set_short = "MLS" ;           phase_difference:reference_data_set_long = "Aura/MLS v4.2" ;           phase_difference:description = "phase difference has been adapted so that it not exceeds the [-6,6] months interval by adding +/- 12 months; has been calculated after the screening" ;           phase_difference:units = "month" ;       double amplitude_standard_deviation(altitude, latitude) ;           amplitude_standard_deviation:standard_name = "standard deviation of amplitude" ;           amplitude_standard_deviation:long_name = "standard deviation of amplitude over all data sets" ;           amplitude_standard_deviation:description = "standard deviation calculation based on Eq. (6)" ;           amplitude_standard_deviation:units = "ppmv" ;       double amplitude_mean(altitude, latitude) ;           amplitude_mean:standard_name = "mean amplitude" ;           amplitude_mean:long_name = "mean amplitude over all data sets" ;           amplitude_mean:description = "mean calculation based on Eq. (6)" ;           amplitude_mean:units = "ppmv" ;       double amplitude_relative_standard_deviation(altitude, latitude) ;           amplitude_relative_standard_deviation:standard_name = "relative standard deviation of amplitude" ;           amplitude_relative_standard_deviation:long_name = "relatuve standard deviation of amplitude " ;           amplitude_relative_standard_deviation:description = "relavtive standard deviation calculation based on Eq. (6); uses \"amplitude_mean\" as reference" ;           amplitude_relative_standard_deviation:units = "ppmv" ;       double phase_difference_standard_deviation(altitude, latitude) ;           phase_difference_standard_deviation:standard_name = "standard deviation of phase difference" ;           phase_difference_standard_deviation:long_name = "standard deviation of phase difference over all data sets" ;           phase_difference_standard_deviation:description = "standard deviation calculation based on Eq. (7)" ;           phase_difference_standard_deviation:units = "month" ;       double phase_difference_mean(altitude, latitude) ;           phase_difference_mean:standard_name = "mean of phase difference" ;           phase_difference_mean:long_name = "mean of phase difference over all data sets" ;           phase_difference_mean:description = "mean calculation based on Eq. (7)" ;           phase_difference_mean:units = "month" ;   } // group AO group: SAO {   dimensions:       latitude = 37 ;       altitude = 59 ;       dataset = 30 ;   variables:       double amplitude(dataset, altitude, latitude) ;           amplitude:standard_name = "amplitude" ;           amplitude:long_name = "amplitude of the SAO variation" ;           amplitude:description = "regression model is given by Eq. (4) in the manuscript; amplitude calculation based on Eq. (2)" ;           amplitude:units = "ppmv" ;       double phase(dataset, altitude, latitude) ;           phase:standard_name = "phase" ;           phase:long_name = "phase of the SAO variation" ;           phase:description = "regression model is given by Eq. (4) in the manuscript; phase calculation based on Eq. (3)" ;           phase:units = "month" ;       double offset(dataset, altitude, latitude) ;           offset:standard_name = "offset" ;           offset:long_name = "offset component of the regression model" ;           offset:description = "regression model is given by Eq. (4) in the manuscript; meant for calculation of relative amplitudes" ;           offset:units = "ppmv" ;       double screening(dataset, altitude, latitude) ;           screening:standard_name = "screening" ;           screening:long_name = "screening for the amplitude and phase data" ;           screening:description = "screening matrix for the amplitude and phase data to calculate the standard deviations described in Sect. 3.3; 1 means screening; 0 means no screening" ;           screening:units = "" ;       double phase_difference(dataset, altitude, latitude) ;           phase_difference:standard_name = "phase difference" ;           phase_difference:long_name = "phase difference with respect to the reference data set" ;           phase_difference:reference_data_set_short = "MLS" ;           phase_difference:reference_data_set_long = "Aura/MLS v4.2" ;           phase_difference:description = "phase difference has been adapted so that it not exceeds the [-3,3] months interval by adding +/- 6 months; has been calculated after the screening" ;           phase_difference:units = "month" ;       double amplitude_standard_deviation(altitude, latitude) ;           amplitude_standard_deviation:standard_name = "standard deviation of amplitude" ;           amplitude_standard_deviation:long_name = "standard deviation of amplitude over all data sets" ;           amplitude_standard_deviation:description = "standard deviation calculation based on Eq. (6)" ;           amplitude_standard_deviation:units = "ppmv" ;       double amplitude_mean(altitude, latitude) ;           amplitude_mean:standard_name = "mean amplitude" ;           amplitude_mean:long_name = "mean amplitude over all data sets" ;           amplitude_mean:description = "mean calculation based on Eq. (6)" ;           amplitude_mean:units = "ppmv" ;       double amplitude_relative_standard_deviation(altitude, latitude) ;           amplitude_relative_standard_deviation:standard_name = "relative standard deviation of amplitude" ;           amplitude_relative_standard_deviation:long_name = "relatuve standard deviation of amplitude " ;           amplitude_relative_standard_deviation:description = "relavtive standard deviation calculation based on Eq. (6); uses \"amplitude_mean\" as reference" ;           amplitude_relative_standard_deviation:units = "ppmv" ;       double phase_difference_standard_deviation(altitude, latitude) ;           phase_difference_standard_deviation:standard_name = "standard deviation of phase difference" ;           phase_difference_standard_deviation:long_name = "standard deviation of phase difference over all data sets" ;           phase_difference_standard_deviation:description = "standard deviation calculation based on Eq. (7)" ;           phase_difference_standard_deviation:units = "month" ;       double phase_difference_mean(altitude, latitude) ;           phase_difference_mean:standard_name = "mean of phase difference" ;           phase_difference_mean:long_name = "mean of phase difference over all data sets" ;           phase_difference_mean:description = "mean calculation based on Eq. (7)" ;           phase_difference_mean:units = "month" ;   } // group SAO group: QBO {   dimensions:       latitude = 37 ;       altitude = 59 ;       dataset = 30 ;   variables:       double amplitude(dataset, altitude, latitude) ;           amplitude:standard_name = "amplitude" ;           amplitude:long_name = "amplitude of the QBO variation" ;           amplitude:description = "regression model is given by Eq. (5) in the manuscript; amplitude calculation based on Eq. (2)" ;           amplitude:units = "ppmv" ;       double phase(dataset, altitude, latitude) ;           phase:standard_name = "phase" ;           phase:long_name = "phase of the QBO variation" ;           phase:description = "regression model is given by Eq. (5) in the manuscript; phase is derived as the shift of the QBO regression fit for which the correlation with the Singapore (1N, 104E) winds at 50 hPa maximises" ;           phase:units = "month" ;       double offset(dataset, altitude, latitude) ;           offset:standard_name = "offset" ;           offset:long_name = "offset component of the regression model" ;           offset:description = "regression model is given by Eq. (5) in the manuscript; meant for calculation of relative amplitudes" ;           offset:units = "ppmv" ;       double screening(dataset, altitude, latitude) ;           screening:standard_name = "screening" ;           screening:long_name = "screening for the amplitude and phase data" ;           screening:description = "screening matrix for the amplitude and phase data to calculate the standard deviations described in Sect. 3.3; 1 means screening; 0 means no screening" ;           screening:units = "" ;       double phase_difference(dataset, altitude, latitude) ;           phase_difference:standard_name = "phase difference" ;           phase_difference:long_name = "phase difference with respect to the reference data set" ;           phase_difference:reference_data_set_short = "MLS" ;           phase_difference:reference_data_set_long = "Aura/MLS v4.2" ;           phase_difference:description = "phase difference has been adapted so that it not exceeds the [-14,14] months interval by adding +/- 28 months; has been calculated after the screening" ;           phase_difference:units = "month" ;       double amplitude_standard_deviation(altitude, latitude) ;           amplitude_standard_deviation:standard_name = "standard deviation of amplitude" ;           amplitude_standard_deviation:long_name = "standard deviation of amplitude over all data sets" ;           amplitude_standard_deviation:description = "standard deviation calculation based on Eq. (6)" ;           amplitude_standard_deviation:units = "ppmv" ;       double amplitude_mean(altitude, latitude) ;           amplitude_mean:standard_name = "mean amplitude" ;           amplitude_mean:long_name = "mean amplitude over all data sets" ;           amplitude_mean:description = "mean calculation based on Eq. (6)" ;           amplitude_mean:units = "ppmv" ;       double amplitude_relative_standard_deviation(altitude, latitude) ;           amplitude_relative_standard_deviation:standard_name = "relative standard deviation of amplitude" ;           amplitude_relative_standard_deviation:long_name = "relatuve standard deviation of amplitude " ;           amplitude_relative_standard_deviation:description = "relavtive standard deviation calculation based on Eq. (6); uses \"amplitude_mean\" as reference" ;           amplitude_relative_standard_deviation:units = "ppmv" ;       double phase_difference_standard_deviation(altitude, latitude) ;           phase_difference_standard_deviation:standard_name = "standard deviation of phase difference" ;           phase_difference_standard_deviation:long_name = "standard deviation of phase difference over all data sets" ;           phase_difference_standard_deviation:description = "standard deviation calculation based on Eq. (7)" ;           phase_difference_standard_deviation:units = "month" ;       double phase_difference_mean(altitude, latitude) ;           phase_difference_mean:standard_name = "mean of phase difference" ;           phase_difference_mean:long_name = "mean of phase difference over all data sets" ;           phase_difference_mean:description = "mean calculation based on Eq. (7)" ;           phase_difference_mean:units = "month" ;   } // group QBO }

opencc-by-4.0Jan 2019View details →

zenodo44/100

Loon Stratospheric Electrical Measurements above Thunderstorms

Loon LLC telemetry data containing measurements from a corona current sensor and also aligned with lightning indicators from BCI (CDO, CloudHeight) and the Geostationary Lightning Mapper (GLM).  See README for more details.

opencc-by-4.0Jul 2021View details →

zenodo44/100

Migrating Tides in the Stratosphere from COSMIC Radio Occultation Data

These analyses of the migrating tides in temperature, microwave refractivity, and geopotential in the Earth’s stratosphere are analyzed using GPS radio occultation (RO) data obtained by the COSMIC constellation of six satellites in orbit planes separated by 30° in ascending node. The tides are analyzed monthly, beginning with November 2006 and ending with December 2016. The radio occultation retrievals used as input to the analyses are obtained from the Climate Data Record v1 of the EUMETSAT Radio Occultation Meteorology Satellite Application Facility (ROM SAF; romsaf.org). The reference model that is used for the sake of comparison are the 3-, 6-, 9-, and 12-hr forecasts of the ERA-Interim reanalysis project.

opencc-by-4.0Sep 2021View details →

ScienceDex guides

Understand access before you commit

These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

Compare curated 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.

allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

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.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

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.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

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.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

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.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record