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zenodo52/100

A vertically-resolved atmospheric dust reanalysis for Mars Years 28-29 using Analysis Correction

<p>This is a dataset of meteorological variables for the atmosphere of Mars, obtained by assimilating measurements (retrievals) of atmospheric temperature and dust opacity into a 3-dimensional, time-dependent numerical model of the Martian atmospheric circulation (known as a &ldquo;reanalysis&rdquo;).</p> <p>The observations come from two spacecraft - the Mars Climate Sounder (MCS) instrument on board NASA&rsquo;s Mars Reconnaissance Orbiter (e.g. Kleinboehl et al. 2009) and the Thermal Emission Imaging Spectrometer (THEMIS) on board NASA&rsquo;s Mars Odyssey spacecraft, and cover the period from 21 September 2006 until&nbsp; 5 November 2009 (Mars Years 28:Ls=109.98 - 30:Ls=4.78). MCS observations include profiles of temperature and dust opacity from near the surface up to altitudes of around 80 km obtained from infrared limb-sounding (MCS version 3 retrievals, based on opacities at around 21.6 micron wavelengths), while THEMIS measurements are of column dust opacity in the infrared (centred around 9.3 micron wavelength). Further details can be found on the websites</p> <p>https://pds-geosciences.wustl.edu/missions/odyssey/themis.html,<br> https://atmos.nmsu.edu/data and services/atmospheres data/MARS/aerosols.html</p> <p>The model into which the observations are assimilated is the UK version of Laboratoire de M&eacute;t&eacute;orologie Dynamique Mars Global Circulation Model (LMDMGCM), a 3-dimensional, time-dependent numerical circulation model of the Martian atmosphere and near-surface environment, simulating the changing winds, temperature, pressure and dust content of the atmosphere across the whole planet. The model solves the equations of motion, mass and energy conservation using a spherical harmonic representation in the horizontal and finite difference formulation in the vertical direction, but outputs the data here on a regular longitude-latitude grid with 72 points in longitude, 36 points in latitude and 25 terrain-following sigma levels in the vertical direction (where sigma = pressure/surface pressure) on a stretched vertical grid that extends from the surface to an altitude of approximately 100 km. More details can be found in publications by Forget et al. (1999), Newman et al. (2001), Mulholland et al. (2013).</p> <p>The observations and model are linked by an assimilation scheme, based on the Analysis Correction (AC) algorithm developed by Lorenc et al. (1991) and adapted for Mars by Lewis et al. (2007). Previous reanalyses of Mars observations using this scheme include the MACDA dataset (Montabone et al. 2014) and OPENMars (Holmes et al. 2020). This new dataset, however, makes use of an extension of the AC scheme to enable assimilation of both column integrated dust opacity measurements and dust opacity profiles in the vertical direction (see Ruan et al. 2021). This new dataset therefore provides a more realistic representation of the distribution of dust loading in the Martian atmosphere than previous work, which may also result in improved representation of other meteorological variables, notably temperature.</p> <p>Data are provided as 2D and 3D fields of variables in netCDF format as generated by the numerical model on the (longitude, latitude, sigma) grid at 2-hourly intervals. Each file contains 360 time steps covering 30 Martian days or sols. The variables contained in each file are as follows:</p> <p>&nbsp;Variables and attributes<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 0&nbsp; lon:&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; FLOAT(72) = FLOAT(lon)<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 0&nbsp; long_name: longitude<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 1&nbsp; units: degrees_east<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 1&nbsp; lat:&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; FLOAT(36) = FLOAT(lat)<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 0&nbsp; long_name: latitude<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 1&nbsp; units: degrees_north<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 2&nbsp; sigma:&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; FLOAT(25) = FLOAT(sigma)<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 0&nbsp; long_name: sigma<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 1&nbsp; units: sigma_level = p/ps<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 3&nbsp; soil:&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; FLOAT(18) = FLOAT(soil)<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 0&nbsp; long_name: soil levels (i.e. levels below the surface to represent thermal variations)<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 1&nbsp; units: none<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 4&nbsp; time:&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; FLOAT(360) = FLOAT(time)<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 0&nbsp; long_name: model time<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 1&nbsp; units: days since 00:00:00 (the beginning of the file)<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 5&nbsp; controle:&nbsp;&nbsp;&nbsp; FLOAT(100) = FLOAT(lentable)<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 0&nbsp; long_name: Table of run parameters<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 1&nbsp; description:&nbsp; MGCM run&nbsp;&nbsp;&nbsp; 5.000<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 6&nbsp; Ls:&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; FLOAT(360) = FLOAT(time)<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 0&nbsp; Physics_diagnostic: Solar longitude (such that Ls=0 is northern Spring equinox)<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 1&nbsp; units: deg<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 7&nbsp; tsurf:&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; FLOAT(72,36,360) = FLOAT(lon,lat,time)<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 0&nbsp; Physics_diagnostic: Surface temperature<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 1&nbsp; units: K<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 8&nbsp; ps:&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; FLOAT(72,36,360) = FLOAT(lon,lat,time)<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 0&nbsp; Physics_diagnostic: surface pressure<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 1&nbsp; units: Pa<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 9&nbsp; co2ice:&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; FLOAT(72,36,360) = FLOAT(lon,lat,time)<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 0&nbsp; Physics_diagnostic: co2 ice thickness (column mass density)<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 1&nbsp; units: kg.m-2<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 10&nbsp; fluxsurf_lw: FLOAT(72,36,360) = FLOAT(lon,lat,time)<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 0&nbsp; Physics_diagnostic: fluxsurf_lw (surface infrared radiative flux)<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 1&nbsp; units: W.m-2<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 11&nbsp; fluxsurf_sw: FLOAT(72,36,360) = FLOAT(lon,lat,time)<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 0&nbsp; Physics_diagnostic: fluxsurf_sw (surface visible radiative flux)<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 1&nbsp; units: W.m-2<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 12&nbsp; temp:&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; FLOAT(72,36,25,360) = FLOAT(lon,lat,sigma,time)<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 0&nbsp; Physics_diagnostic: temperature<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 1&nbsp; units: K<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 13&nbsp; u:&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; FLOAT(72,36,25,360) = FLOAT(lon,lat,sigma,time)<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 0&nbsp; Physics_diagnostic: Zonal (east-west) wind<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 1&nbsp; units: m.s-1<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 14&nbsp; v:&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; FLOAT(72,36,25,360) = FLOAT(lon,lat,sigma,time)<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 0&nbsp; Physics_diagnostic: Meridional (north-south) wind<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 1&nbsp; units: m.s-1<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 15&nbsp; rho:&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; FLOAT(72,36,25,360) = FLOAT(lon,lat,sigma,time)<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 0&nbsp; Physics_diagnostic: density<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 1&nbsp; units: kg.m-3<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 16&nbsp; udrag:&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; FLOAT(72,36,360) = FLOAT(lon,lat,time)<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 0&nbsp; Physics_diagnostic: Drag velocity<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 1&nbsp; units: m/s<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 17&nbsp; udragt:&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; FLOAT(72,36,360) = FLOAT(lon,lat,time)<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 0&nbsp; Physics_diagnostic: Threshold velocity for dust lifting<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 1&nbsp; units: m/s<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 18&nbsp; aerosol:&nbsp;&nbsp;&nbsp;&nbsp; FLOAT(72,36,25,360) = FLOAT(lon,lat,sigma,time)<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 0&nbsp; Physics_diagnostic: dust opacity considering layer thickness<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 1&nbsp; units: SI (opacity/m)<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 19&nbsp; taudustvis:&nbsp; FLOAT(72,36,360) = FLOAT(lon,lat,time)<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 0&nbsp; Physics_diagnostic: Dust optical depth<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 1&nbsp; units: SI<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 20&nbsp; q01:&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; FLOAT(72,36,25,360) = FLOAT(lon,lat,sigma,time)<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 0&nbsp; Physics_diagnostic: mix. ratio<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 1&nbsp; units: kg/kg<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 21&nbsp; dqsdevtot:&nbsp;&nbsp; FLOAT(72,36,360) = FLOAT(lon,lat,time)<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 0&nbsp; Physics_diagnostic: dust devil lift rate<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 1&nbsp; units: kg.m-2.s-1<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 22&nbsp; dqsstrtot:&nbsp;&nbsp; FLOAT(72,36,360) = FLOAT(lon,lat,time)<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 0&nbsp; Physics_diagnostic: near surface wind stress dust lifting rate<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 1&nbsp; units: kg.m-2.s-1<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 23&nbsp; dqssedtot:&nbsp;&nbsp; FLOAT(72,36,360) = FLOAT(lon,lat,time)<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 0&nbsp; Physics_diagnostic: dust sedimentation rate<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 1&nbsp; units: kg.m-2.s-1</p> <p>&nbsp;</p>

opencc-by-4.0Oct 2021View details →
zenodo48/100

Coefficients for the SMACPy atmospheric correction algorithm

<p>This dataset stores coefficients used by the updated Simplified Method for Atmospheric Correction - Python (SMACPy). The coefficients are used, together with meteorological data, to remove the effects of atmospheric gases and aerosols from a satellite image.</p> <p>Currently, this is a beta version and more updates to these coefficients may be expected in the future.</p> <p>&nbsp;</p> <p>Coefficients exist for various aerosol types, all defined from the equivalent 6S (<a href="https://doi.org/10.1109/36.581987">10.1109/36.581987</a>) aerosol types:</p> <p>BIOMA: Biomass burning plumes.</p> <p>CONTI: Continental aerosol.</p> <p>DESER: Desert dust aerosol.</p> <p>MARIT: Maritime aerosol, primarily sea salt.</p> <p>STRATO: Stratospheric aerosol.</p> <p>URBAN: Urban pollution aerosol.</p> <p>NOAER: A profile with no aerosol effects, just atmospheric gases and Rayleigh scattering.</p> <p>&nbsp;</p> <p>&nbsp;</p> <p>In this version the following satellites / sensors are supported:</p> <p>NASA Aqua / MODIS</p> <p>NASA Terra / MODIS</p> <p>Fengyun-4A / AGRI</p> <p>GEO-KOMPSAT-2A / AMI</p> <p>GOES-16 / ABI</p> <p>GOES-17 / ABI</p> <p>Himawari-8 / AHI</p> <p>Landsat-8 / OLI</p> <p>Meteosat-8 / SEVIRI (no HRV channel)</p> <p>Meteosat-9 / SEVIRI (no HRV channel)</p> <p>Meteosat-10 / SEVIRI (no HRV channel)</p> <p>Meteosat-11 / SEVIRI (no HRV channel)</p> <p>NOAA-20 / VIIRS (M and I bands)</p> <p>Suomi-NPP / VIIRS (M and I bands)</p> <p>Sentinel-2A / MSI</p> <p>Sentinel-2B / MSI</p> <p>Sentinel-3A / OLCI</p> <p>Sentinel-3B / OLCI</p> <p>Sentinel-3A / SLSTR</p> <p>Sentinel-3B / SLSTR</p> <p>&nbsp;</p>

opencc-by-4.0Dec 2021View details →
zenodo44/100

Water vapor database for atmospheric correction of Landsat imagery

<p>Atmospheric correction is a crucial preprocessing step for the analysis of optical satellite imagery like Landsat. Among the radiance-modifying gases, atmospheric water vapor is spatially and temporally variable, and cannot be measured reliably from the Landsat sensors. As such, atmospheric correction of Landsat data requires spatially and temporally explicit auxiliary information about atmospheric water vapor content.</p> <p>We have compiled a water vapor dataset that can be readily used to perform atmospheric correction of Landsat images. The dataset was generated by a global processing of the MODIS MOD05/MYD05 collection 6.1 products (<a href="https://doi.org/10.1029/2002JD003023">https://doi.org/10.1029/2002JD003023</a>;&nbsp;MODIS Atmosphere L2 Water Vapor Product. NASA MODIS Adaptive Processing System, Goddard Space Flight Center, USA:&nbsp;<a href="https://doi.pangaea.de/10.5067/MODIS/MOD05_L2.006">doi:10.5067/MODIS/MOD05_L2.006</a>,&nbsp;<a href="https://doi.pangaea.de/10.5067/MODIS/MYD05_L2.006">doi:10.5067/MODIS/MYD05_L2.006</a>). The dataset is comprised of daily global water vapor data for February 2000 to December&nbsp;2020&nbsp;for each land-intersecting Worldwide Reference System 2 (WRS-2) scene, as well as a monthly climatology that can be used if no daily value is available.</p> <p>The dataset was generated by the Framework for Operational Radiometric Correction for Environmental monitoring (FORCE v. 2.0 / 3.6, <a href="https://github.com/davidfrantz/force">https://github.com/davidfrantz/force</a>, <a href="https://doi.org/10.3390/rs11091124">https://doi.org/10.3390/rs11091124</a>), which is freely available software under the terms of the GNU General Public License v. &gt;= 3. The water vapor dataset can be readily ingested into the FORCE Level 2 Processing System (Frantz et al. 2016,&nbsp;<a href="https://doi.pangaea.de/10.1109/TGRS.2016.2530856">doi:10.1109/TGRS.2016.2530856</a>) to perform atmospheric correction of Landsat imagery. This dataset is an update of&nbsp;<a href="https://doi.pangaea.de/10.1594/PANGAEA.893109">https://doi.pangaea.de/10.1594/PANGAEA.893109</a> and should be used from now on.</p>

opencc-by-4.0Dec 2017View details →
zenodo36/100

Code and Data for Sturm and Silva (2024) A nudge to the truth: atom conservation as a hard constraint in models of atmospheric composition using an uncertainty-weighted correction

<p>This record contains the Julia photochemical model (https://doi.org/10.5281/zenodo.13385541) output in csv format used for training XGBoost in ProjectionConservationRF.py to emulate ozone photochemical formation. Nonphysical predictions that violate conservation of atoms are corrected using a closed-form, constrained least-squares approach that factors in uncertainty and scale using species-level weights. The file ozoneNOx_visualization.py contains an example and visualization for a smaller system, the primary photolytic cycle from which the Leighton relationship can be derived.</p> <p>The corresponding preprint is available here: <a href="https://doi.org/10.48550/arXiv.2408.16109">https://doi.org/10.48550/arXiv.2408.16109</a></p>

opencc-by-4.0Aug 2024View details →
zenodo32/100

Code for Atmospheric Research publication - Height correction method based on the Monin–Obukhov similarity theory for better prediction of near-surface wind fields

<p>In this repository, we include the source codes for WRF namelist, figures, and height correction used in the Atmospheric Research publication &quot;Height correction method based on the Monin&ndash;Obukhov similarity theory for better prediction of near-surface wind fields&quot;</p> <p>The namelist.wps and namelist.input in WRF namelist are using for making input and running simulation, and Fig scripts in Figure scripts are using for plotting the figures in the paper.</p> <p>hgt_corr in Height correction method is a code to correct&nbsp;the disparity of the 10-m height definition between the model and observation by applying the developed the height correction algorithm based on the Monin-Obukhov similarity theory.</p>

opencc-by-4.0Apr 2022View details →
zenodo32/100

MUA PRR_lidar temperature profiles for atmospheric refraction correction in lunar laser ranging

<p>This PRR lidar temperature data are used for atmospheric refraction corrrection in millimeter-level precision lunar laser ranging.</p>

opencc-by-4.0Nov 2022View details →
zenodo32/100

Wuhan Radiosonde data for LLR atmospheric delay correction study

<p>Wuhan (57494) radiosonde data&nbsp; for studing&nbsp;atmospheric refraction-induced delay correction in lunar laser ranging</p>

opencc-by-4.0Nov 2022View details →
zenodo32/100

Supplementary dataset to the publication by Hieronymi et al.: "Ocean color atmospheric correction methods in view of usability for different optical water types", Frontiers in Marine Science (under review, submitted 22 Dec 2022)

<p>The dataset is an annex to the publication (under review, submitted 22 Dec 2022):</p> <p>Martin Hieronymi, Shun Bi, Dagmar M&uuml;ller, Eike M Sch&uuml;tt, Daniel Behr, Carsten Brockmann, Carole Lebreton, Fran&ccedil;ois Steinmetz, Kerstin Stelzer and Quinten Vanhellemont: &quot;Ocean color atmospheric correction methods in view of usability for different optical water types&quot;, Frontiers in Marine Science.</p> <p>The data were created to compare the results of different atmospheric correction methods for ocean (water) color imagery. The dataset includes ten modified ESA/EUMETSAT Copernicus Sentinel-3 OLCI satellite scenes from optically diverse sea areas worldwide. The NetCDF files are optimized for visualization in the ESA Sentinel Application Platform (SNAP) and especially the Spectrum View. The data include original OLCI Level-1B top-of-atmosphere radiances recorded by the sensor and the results from five different atmospheric correction methods, i.e., spectral remote-sensing reflectance at 16 OLCI bands. The atmospheric correction methods compared are</p> <ol> <li> <p>IPF (Collection 3, the standard method),</p> </li> <li> <p>C2RCC (v1.7 including IPF gains; Brockmann et al. [2016]),</p> </li> <li> <p>A4O (v0.23 (2022-01-19); a novel method by Hieronymi et al.),</p> </li> <li> <p>POLYMER (v4.14 (2021-12-17); Steinmetz et al. [2011]), and</p> </li> <li> <p>ACOLITE-DSF (v2022-10-25.0; Vanhellemont and Ruddick [2021]).</p> </li> </ol> <p>The original flags supplied in each case are also provided.</p> <table> <tbody> <tr> <td> <p><strong># </strong></p> </td> <td> <p><strong>Sensor-Date-UTC</strong></p> </td> <td> <p><strong>Region </strong></p> </td> <td> <p><strong>Special features </strong></p> </td> </tr> <tr> <td> <p>1</p> </td> <td> <p>S3A-20160720-092821</p> </td> <td> <p>Barents Sea</p> </td> <td> <p>High latitudes, bloom of coccolithophores</p> </td> </tr> <tr> <td> <p>2</p> </td> <td> <p>S3A-20160720-093421</p> </td> <td> <p>North Sea, Wadden Sea</p> </td> <td> <p>Moderately to extremely scattering waters, tidal areas, in situ data</p> </td> </tr> <tr> <td> <p>3</p> </td> <td> <p>S3A-20170114-130626</p> </td> <td> <p>South Atlantic Ocean, Rio de la Plata estuary</p> </td> <td> <p>Extremely scattering waters, clear oceanic waters, sun glint, South Atlantic Anomaly</p> </td> </tr> <tr> <td> <p>4</p> </td> <td> <p>S3A-20170527-015236</p> </td> <td> <p>Yellow Sea, East China Sea, Yangtze, Lake Taihu</p> </td> <td> <p>Extremely scattering waters, tidal areas, large rivers, absorbing aerosols, sun glint</p> </td> </tr> <tr> <td> <p>5</p> </td> <td> <p>S3A-20170529-092334</p> </td> <td> <p>Mediterranean Sea</p> </td> <td> <p>Large areas with clear waters, sun glint</p> </td> </tr> <tr> <td> <p>6</p> </td> <td> <p>S3A-20170913-080730</p> </td> <td> <p>Black Sea, Aegean Sea</p> </td> <td> <p>Clear and absorbing waters</p> </td> </tr> <tr> <td> <p>7</p> </td> <td> <p>S3A-20180715-093613</p> </td> <td> <p>North Sea, Baltic Sea</p> </td> <td> <p>Intense bloom of cyanobacteria partly with scum</p> </td> </tr> <tr> <td> <p>8 9</p> </td> <td> <p>S3A-20200601-092517 S3B-20200601-084546</p> </td> <td> <p>North Sea, Baltic Sea</p> </td> <td> <p>Inter-comparison of S3A and S3B with different observation angles, absorbing waters</p> </td> </tr> <tr> <td> <p>10</p> </td> <td> <p>S3B-20200406-093801</p> </td> <td> <p>North Sea, Baltic Sea</p> </td> <td> <p>High OWT diversity</p> </td> </tr> </tbody> </table> <p>&nbsp;</p>

opencc-by-4.0Jan 2023View details →
zenodo32/100

Assessment of the performance of the atmospheric correction algorithm MAJA for Sentinel-2 surface reflectance estimates

<p>Data associated to the paper &quot;Assessment of the performance of the atmospheric correction algorithm MAJA for Sentinel-2 surface reflectance estimates&quot;, Colin, J. et al.</p> <p>Contact: jerome.colin[at]cnrs.fr<br> CESBIO Lab, Toulouse, France</p> <p>Content:<br> - APU_all_sites: APU plots for all the ACIX-II sites<br> - Maja_L2A_noadj_notopo: MAJA Level-2A subsets used to compare against ACIX-II reference reflectances for all sites and time steps<br> - quicklooks_all_sites: quicklooks for all sites</p> <p>&nbsp;</p>

opencc-by-4.0May 2023View details →
zenodo28/100

Measurement Report: The observation of atmospheric HONO by wet-rotating-denuder ion chromatograph in a coastal city: Influencing factors and corrective function

<p>The details are also available upon request from the corresponding author (<a href="mailto:jschen@iue.ac.cn">jschen@iue.ac.cn</a>)</p>

opencc-by-4.0Sep 2023View details →
nasa28/100

DSCOVR EPIC L2 Multi-Angle Implementation of Atmospheric Correction (MAIAC) Version 02

DSCOVR_EPIC_L2_MAIAC_02 is the DSCOVR EPIC L2 Multi-Angle Implementation of Atmospheric Correction (MAIAC) Version 02 data product. Data collection for this product is ongoing. Level 2 Multi-Angle Implementation of Atmospheric Correction (MAIAC) provides an interdisciplinary suite of products for the Deep Space Climate Observatory (DSCOVR) Earth Polychromatic Imaging Camera (EPIC). The current version 2 reports the following products: a) Atmosphere: cloud mask, global aerosol optical depth at 443nm and 551nm, fine mode fraction (over ocean) and spectral aerosol absorption for detected biomass burning or mineral dust aerosols. The absorption information includes single scattering albedo at 443nm, imaginary refractive index at 680nm, and Absorption Angstrom Exponent (AAE) characterizing spectral increase of imaginary refractive index at Vis-UV wavelengths. The absorption information is provided for two effective aerosol layer heights of 1km and 4km generally representing boundary layer and transport mode. b) Land: atmospherically corrected spectral bidirectional reflectance factors (BRF) along with Lambertian surface reflectance, and bidirectional reflectance distribution function (BRDF) for the backscattering view geometries of EPIC. The BRDF is represented by 3 parameters of the Ross-Thick Li-Sparse model. c) Ocean: Water leaving reflectance (non-dimensional) at Ultraviolet-Visible (UV-Vis) bands.The parameters are distributed at 10 km rotated sinusoidal grid and 1 to 2-hour temporal frequency. MAIAC version 02 also provides gap-filled global composite products for Normalized Difference Vegetation Index (NDVI) over land, and water leaving reflectance in 5 UV-Vis bands over global ocean. The composite products represent a weighted running average where the weight of the latest observation is maximized towards the local noon and low aerosol conditions.

restrictednotspecifiedApr 2025View details →
nasa28/100

S-MODE MOSES Level 2 Atmospherically-Corrected Sea Surface Temperature Version 1

This dataset contains airborne sea surface temperature (SST) measurements from the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE). Data were collected approximately 300 km offshore of San Fransisco during a pilot campaign in October 2021, and an intensive operating period (IOP) in Fall 2022. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. The Multiscale Observing System of the Ocean Surface (MOSES) is an aerial observing system that primarily uses a longwave infrared (LWIR) camera to record SST at a resolution of several meters. Individual images are mosaiced together to provide a synoptic map of the sample domain covering approximately 200 km. MOSES is mounted on the B200 aircraft which flies daily surveys of the field domain during deployments. Data are available in netCDF format.

restrictednotspecifiedApr 2025View details →
nasa28/100

BOREAS Follow-On DSP-08 POLDER Atmospherically Corrected Surface Parameters, SSA

This data set contains maps of surface reflectance and ve getation parameters derived from imagery collected by the POLDER instrument over BOREAS conifer tower sites in the Southern Study Area(SSA) during 1994. The POLDER imagery proficed in this data set was collected on June 1, and July 21, 1994 from the NASA C-130 aircraft platform.

restrictednotspecifiedApr 2025View details →
nasa28/100

VIIRS/JPSS1 Atmospherically Corrected Surface Reflectance 6-Min L2 Swath IP 375m, 750m NRT

The VJ109_NRT is a Near Real Time (NRT) Visible Infrared Imager Radiometer Suite (VIIRS) 375 m, 750 m Atmospherically Corrected Surface Reflectance product. The VIIRS surface reflectance products are estimates of surface reflectance in each of the VIIRS reflective bands I1-I3, M1-M5, M7, M8, M10, and M11. The VNP09 Level-2 surface reflectance product contains approximately 6 minutes' worth of data. Surface reflectance for each moderate-resolution (750m) or imagery-resolution (375m) pixel is retrieved separately for the Level-2 products. Surface reflectance is obtained by adjusting top-of-atmosphere reflectance to compensate for atmospheric effects. Corrections are made for the effects of molecular gases, including ozone and water vapor, and for the effects of atmospheric aerosols. The inputs to the surface reflectance algorithm include top-of-atmosphere reflectance for the VIIRS visible bands (VJ102MOD, VJ102IMG), the VIIRS cloud mask and aerosol product, aerosol optical thickness, and atmospheric data obtained from a reanalysis (surface pressure, atmospheric precipitable water, and ozone concentration).All surface reflectance products are produced for daytime conditions only. The product is produced under all atmospheric conditions except for night and oceans. Pixels when not produced are replaced by fill values in the Level-2 and Level-2G products.For more information read Suomi-NPP VIIRS Surface Reflectance User's Guide at https://viirsland.gsfc.nasa.gov/PDF/VIIRS_Surf_Refl_UserGuide_v2.0.pdfor visit VIIRS Land website at https://viirsland.gsfc.nasa.gov/index.html

restrictednotspecifiedApr 2025View details →
nasa28/100

MODIS/Aqua Atmospherically Corrected Surface Reflectance 5-Min L2 Swath 250m, 500m, and 1km

The MODIS/Aqua Atmospherically Corrected Surface Reflectance 5-Min L2 Swath 250m, 500m, 1km (MYD09) product is computed from the MODIS Level 1B land bands 1, 2, 3, 4, 5, 6, and 7 (centered at 648 nm, 858 nm, 470 nm, 555 nm, 1240 nm, 1640 nm, and 2130 nm, respectively). The product is an estimate of the surface spectral reflectance for each band as it would have been measured at ground level if there were no atmospheric scattering or absorption. The surface-reflectance product is the input for product generation for several land products: vegetation Indices (VIs), Bidirectional Reflectance Distribution Function (BRDF), thermal anomaly, snow/ice, and Fraction of Photosynthetically Active Radiation/Leaf Area Index (FPAR/LAI).

restrictednotspecifiedApr 2025View details →
nasa28/100

NPP/VIIRS Atmospherically Corrected Surface Reflectance 6-Min L2 Swath 375m, 750m NRT

The VNP09_NRT is a Near Real Time (NRT) S-NPP/VIIRS 375 m, 750 m Atmospherically Corrected Surface Reflectance product. The NPP/VIIRS surface reflectance products are estimates of surface reflectance in each of the VIIRS reflective bands I1-I3, M1-M5, M7, M8, M10, and M11. The VNP09 Level-2 surface reflectance product contains approximately 6 minutes' worth of data. Surface reflectance for each moderate-resolution (750m) or imagery-resolution (375m) pixel is retrieved separately for the Level-2 products.Surface reflectance is obtained by adjusting top-of-atmosphere reflectance to compensate for atmospheric effects. Corrections are made for the effects of molecular gases, including ozone and water vapor, and for the effects of atmospheric aerosols. The inputs to the surface reflectance algorithm include top-of-atmosphere reflectance for the VIIRS visible bands (VNP02MOD, VNP02IMG), the VIIRS cloud mask and aerosol product (NPP-CMIP_L2), aerosol optical thickness (NPP_VAOTIP_L2, NPP_VAMIP_L2), and atmospheric data obtained from a reanalysis (surface pressure, atmospheric precipitable water, and ozone concentration).All surface reflectance products are produced for daytime conditions only. The product is produced under all atmospheric conditions except for night and oceans. Pixels when not produced are replaced by fill values in the Level-2 and Level-2G products.file_naming_convention = "VNP09_NRT.AYYYYDDD.HHMM.VVV.YYYYDDDHHMMSS.hdf AYYYYDDD = Acquisition Year and Day of Year HHMM = Hour and minute of acquisition VVV = Version number YYYYDDDHHMMSS = Production Date and Time hdf = Hierarchical Data Format (version 4)For more information read Suomi-NPP VIIRS Surface Reflectance User's Guide at https://viirsland.gsfc.nasa.gov/PDF/VIIRS_Surf_Refl_UserGuide_v1.3.pdfor visit VIIRS Land website at https://viirsland.gsfc.nasa.gov/index.html

restrictednotspecifiedMay 2025View details →
nasa28/100

VIIRS/NPP Atmospherically Corrected Surface Reflectance 6-Min L2 Swath 375m, 750m - V2

The VIIRS/NPP Atmospherically Corrected Surface Reflectance 6-Min L2 Swath 375m, 750m product, with short name VNP09, are estimates of surface reflectance in each of the VIIRS reflective bands I1-I3, M1-M5, M7, M8, M10, and M11. The VNP09 contains approximately 6 minutes' worth of data. Surface reflectance for each moderate-resolution (750m) or imagery-resolution (375m) pixel is retrieved separately for the Level-2 products. Surface reflectance is obtained by adjusting top-of-atmosphere reflectance to compensate for atmospheric effects. Corrections are made for the effects of molecular gases, including ozone and water vapor, and for the effects of atmospheric aerosols. All surface reflectance products are produced for daytime conditions only. The product is produced under all atmospheric conditions except for night and oceans.

restrictednotspecifiedApr 2025View details →
nasa28/100

MODIS/Terra Atmospherically Corrected Surface Reflectance 5-Min L2 Swath 250m, 500m, 1km

The MODIS/Terra Atmospherically Corrected Surface Reflectance 5-Min L2 Swath 250m, 500m, 1km (MOD09) product is computed from the MODIS Level 1B land bands 1, 2, 3, 4, 5, 6, and 7 (centered at 648 nm, 858 nm, 470 nm, 555 nm, 1240 nm, 1640 nm, and 2130 nm, respectively). The product is an estimate of the surface spectral reflectance for each band as it would have been measured at ground level if there were no atmospheric scattering or absorption. The surface-reflectance product is the input for product generation for several land products: vegetation Indices (VIs), Bidirectional Reflectance Distribution Function (BRDF), thermal anomaly, snow/ice, and Fraction of Photosynthetically Active Radiation/Leaf Area Index (FPAR/LAI).

restrictednotspecifiedApr 2025View details →
nasa28/100

VIIRS/JPSS1 Atmospherically Corrected Surface Reflectance 6-Min L2 Swath 375m, 750m

The VIIRS/JPSS1 Atmospherically Corrected Surface Reflectance 6-Min L2 Swath 375m, 750m product, with short name VJ109, are estimates of surface reflectance in each of the Visible Infrared Imaging Radiometer Suite (VIIRS) reflective bands I1-I3, M1-M5, M7, M8, M10, and M11. The VJ109 contains approximately 6 minutes' worth of data. Surface reflectance for each moderate-resolution (750m) or imagery-resolution (375m) pixel is retrieved separately for the Level-2 products. Surface reflectance is obtained by adjusting top-of-atmosphere reflectance to compensate for atmospheric effects. Corrections are made for the effects of molecular gases, including ozone and water vapor, and for the effects of atmospheric aerosols. All surface reflectance products are produced for daytime conditions only. The product is produced under all atmospheric conditions except for night and oceans.

restrictednotspecifiedApr 2025View details →
zenodo24/100

Table S1. Detail of comparative RMSE results of the CHL or SD product for each lake after applying the different atmospheric corrections.

<p>Supplementary information about: detail of comparative RMSE results of the CHL or SD product for each lake after applying the different atmospheric corrections.</p>

openOct 2023View details →

ScienceDex guides

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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.

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