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76 results for “atmospheric observation”
Atmospheric Halocarbon Observations at Finokalia, Crete, Greece
<p>Atmospheric halocarbon (HFC, HCFC) observations (mole fractions) from the site Finokalia (FKL, 35.34 °N, 25.67 °E, 250 m a.s.l.) on the island of Crete, Greece, covering the period December 2012 to August 2013). The measurements were conducted using a gas chromatograph<br> (Agilent 6890) and:mass spectrometer (Agilent 5973) (GC-MS), coupled to an adsorption desorption system (ADS) for preconcentration of samples from the air (Simmonds et al., 1995).</p> <p>The measurements are described in detail in: Schoenenberger, F., S. Henne, M. Hill, M. K. Vollmer, G. Kouvarakis, N. Mihalopoulos, S. O'Doherty, M. Maione, L. Emmenegger, T. Peter, and S. Reimann (2017), Abundance and Sources of Atmospheric Halocarbons in the Eastern Mediterranean, Atmos. Chem. Phys. Discuss., 2017, 1-46, doi: 10.5194/acp-2017-451.</p> <p>The data format is plain text character-separated and follows that used in the AGAGE community. Further details are given at the AGAGE data archive: http://agage.eas.gatech.edu/data_archive/agage/</p>
Spectral reflectance data of Mercury's surface collected by the Mercury Atmospheric and Surface Composition Spectrometer (MASCS) instrument during orbital observations of the NASA MESSENGER mission between 2011 and 2015 resampled to a [55399 × 396] tabular data format.
<p>MASCS is a three sensor point spectrometer with a spectral coverage from 200 nm to 1450 nm.<br> Single spectra are resamples in to steps to a format useful for our ML application : a datacube with ~400 spectral channel covering the whole surface of Mercury.<br> The final dataset has dimension [N×M] where N is the number of grid cells (360 × 180 = 64, 800) and M is the number of spectral features (396).<br> Due to the incomplete coverage and data filtering, some grid cells are empty.<br> After removing these empty cells, the size of the dataset is [55399 × 396].</p> <p>This specific product is stored as a gzip compressed json, where each element is a grid cell.<br> We are in the process to publish a complete pipeline to produce this product from RAW data on https://github.com/epn-ml/MESSENGER-Mercury-Surface-Cassification-Unsupervised_DLR/ .</p> <p>Spectral reflectance data of Mercury’s surface collected by the Mercury Atmospheric and Surface Composition Spectrometer (MASCS) instrument during orbital observations of the NASA MESSENGER mission between 2011 and 2015.<br> MASCS is a three sensor point spectrometer with a spectral coverage from 200 nm to 1450 nm.<br> Single spectra are resamples in to steps to a format useful for our ML application : a datacube with ~400 spectral channel covering the whole surface of Mercury.<br> The final dataset has dimension [N×M] where N is the number of grid cells (360 × 180 = 64, 800) and M is the number of spectral features (396).<br> Due to the incomplete coverage and data filtering, some grid cells are empty.<br> After removing these empty cells, the size of the dataset is [55399 × 396].</p> <p>0. Pre-filtering<br> We used the most recent dataset that had large-scale photometric corrections and thus was almost free from observation geometry effects.<br> However, extreme geometry are still present and are typically associated with high noise and some residual instrumental effects.<br> Based on our empirical tests, we filtered out observations with an emission/incidence angle ≥80∘.<br> We also calculated the median value per wavelength and per cell grid when constructing the global hyperspectral data cube and filtered out observations falling under the 2nd percentile and above 99.9th percentile to clean some residual geometry effects.<br> With this approach we create an effective noise filter while retaining enough observations to be able to analyse the entirety of the surface of the planet.</p> <p>1. Spectral resmpling<br> Unprocessed MASCS spectra could have 512 or 256 channes, depending on binning.<br> We resampled the data in the spectral dimension to a common wavelength range from 260 nm to 1052 nm with a 4 nm resolution (2 nm spectral sampling), resulting in 396 spectral channels.<br> This approach slightly oversamples the original 4.77 nm spectral resolution and removes some points from the original 200-1050 nm range.<br> The resulting data matrix is expressed in tabular form, with each row representing a single grid cell or pixel on the surface.<br> The elements of each row are the spectral reflectance values from the VIS instrument at 396 (resampled) wavelengths.</p> <p>2. Spatial resmpling<br> The whole dataset of ∼ 5 million spectra is resampled to a planet-wide rectangular grid of 1×1deg in the latitudinal band between ± 80.<br> The cell longitudinal size varies between ∼ 40 km at the equator to a minimum of ∼ 10 km at ±80∘.<br> Thus, the area spanned by each grid cell depends on the latitude. However, the same is true for the acquisition process, where higher spatial resolution is reached near the equator and lower resolution at the poles.</p>
Atmospheric Halocarbon Observations at Beromünster, Switzerland, and Bayesian Inverse Modeling to assess Emissions
<p>Atmospheric halocarbon (CFCs, halons, HCFCs, HFCs, PFCs, SF<sub>6</sub>, NF<sub>3</sub>, HFOs) and carbon monoxide (CO) observations (mole fractions) from the tall tower site at Beromünster, Switzerland (47.2 °N, 8.2 °E, 797 m a.s.l., 212 m a.g.l.), covering the period September 2019 to September 2020. The halocarbon measurements were conducted using a Medusa pre-concentration unit, coupled to gas chromatography (Agilent 6890N) and mass spectrometry (Agilent 5975, GC-MS).</p> <p>For further details see: Miller, B. R., Weiss, R. F., Salameh, P. K., Tanhua, T., Greally, B. R., Mühle, J., and Simmonds, P. G.: Medusa: A Sample Preconcentration and GC/MS Detector System for in Situ Measurements of Atmospheric Trace Halocarbons, Hydrocarbons, and Sulfur Compounds, Anal. Chem., 80, 1536–1545, https://doi.org/10.1021/ac702084k, 2008).</p> <p>The data format follows that used within the AGAGE network (see AGAGE data archive: <a href="http://agage.mit.edu/data/agage-data">http://agage.mit.edu/data/agage-data</a>).</p> <p>Data results for the Bayesian inversion conducted based on the measurement data from Beromünster to assess Swiss halocarbon emissions. Files are provided in netCDF format for the 28 individual substances discussed in (Rust, D. et al., 2022, <em>Swiss halocarbon emissions for 2019 to 2020 assessed from regional atmospheric observations</em>, Atmospheric Chemistry and Physics). Each file contains the a priori and a posteriori emissions as used or calculated in the Bayesian inversion. Data are provided on the grid used in the inversion (irregular longitude/latitude). Metadata are included as netCDF attributes. The netCDF files follow the CF conventions and are readable with any netcdf interface/tool.</p>
Sub-10 nm size-distribution data for "What controls the observed size-dependency of the growth rates of sub-10 nm atmospheric particles?"
<pre>Size-Distribution data from the CERN CLOUD experiment (Kirkby et al., 2011) measured with a DMA-train (Stolzenburg et al., 2017) Data acquired during the CLOUD10 (Fall 2015) and CLOUD12 (Fall 2017) campaigns. Data associated with the publication Kontkane et al. (2022). File name indicates the Experiment number as specified in Table 3, Kontkanen et al. (2022) and the internal CLOUD run numbers as given in Table S1, Kontaknen et al. (2022). Concentration of precursor gases are also given in these two Tables. Exp. 8 only used data from NAIS and is not included in this repository. Header indicates the diameter at which the size-distribution is measured. First column is time column with areadable timestamp in the format %Y-%m-%d %H:%M:%S. Data is dN/dlog_10 dp in unit cm^(-3). Full size-distribution (up to 400 nm) can be obtained from the author upon request. References: Kontkanen et al. (2022), What controls the observed size-dependency of the growth rates of sub-10 nm atmospheric particles?, Environ. Sci.: Atmos., accepted. Kirkby et al. (2011), Role of sulphuric acid, ammonia and galactic cosmic rays in atmospheric aerosol nucleation, Nature, 476, 429-433, http://dx.doi.org/10.1038/nature10343 Stolzenburg et al. (2017), A DMA-train for precision measurement of sub-10nm aerosol dynamics, Atmos. Meas. Tech., 10, 1639-1651, http://www.atmos-meas-tech.net/10/1639/2017/ </pre>
Estimate of the atmospherically-forced contribution to sea surface height variability based on altimetric observations
<p>This repository contains the estimate of the atmospherically-forced contribution to sea level variability described in <a href="http://doi.org/10.1016/j.pocean.2020.102314">Close et al, 2020</a>, and derived from the Ssalto/Duacs altimeter products produced and distributed by the Copernicus Marine and Environment Monitoring Service (CMEMS) (<a href="http://www.marine.copernicus.eu">http://www.marine.copernicus.eu</a>).</p> <p>The files contain successive 5-day averages of sea level anomaly, with the same global coverage and 0.25° grid as the Ssalto/Duacs altimeter products. The estimate is created using a spatial bandpass filter, with cutoff scales of ~1.5° and 10.5°. Zeros in the mask file indicate regions in which it has not been possible to evaluate the quality of the estimate.</p> <p>The cutoff scales applied to the altimetry data were determined through analysis of output from the OceaniC Chaos – ImPacts, strUcture, predicTability (Penduff et al, 2014) experiment, comprising a 50-member ensemble of ocean-sea ice model hindcasts with 0.25° horizontal resolution (<a href="http://doi.org/10.5194/gmd-10-1091-2017">Bessières et al., 2017</a>). The spatiotemporal coherence between the model-based estimates of the atmospherically-forced (ensemble mean) and total simulated sea surface height signals was analysed, and found to exhibit distinct partitioning between the atmospherically-forced and intrinsic contributions in a spatial (but not temporal) sense, thus suggesting that meaningful estimation of the two components can be achieved based on simple spatial filtering. Verification of the method using the model data indicates good accuracy, with a global mean correlation of 0.9 between the estimate based on spatial filtering and the ensemble mean sea surface height. Full details of the methodology and verification may be found in <a href="http://doi.org/10.1016/j.pocean.2020.102314">Close et al, 2020</a>.</p> <p>----</p> <p><strong>References</strong>:</p> <p>Bessières, L., Leroux, S., Brankart, J.-M., Molines, J.-M., Moine, M.-P., Bouttier, P.-A., Penduff, T., Terray, L., Barnier, B., and Sérazin, G., 2017. Development of a probabilistic ocean modelling system based on NEMO 3.5: application at eddying resolution, Geosci. Model Dev., 10, 1091–1106, <a href="https://doi.org/10.5194/gmd-10-1091-2017">doi: 10.5194/gmd-10-1091-2017</a>.</p> <p>Close, S., Penduff, T., Speich, S. and Molines J.-M., 2020. A means of estimating the intrinsic and atmospherically-forced contributions to sea surface height variability applied to altimetric observations. Progr. Oceanogr. <a href="https://doi.org/10.1016/j.pocean.2020.102314">doi: 10.1016/j.pocean.2020.102314</a></p> <p>Penduff, T., Barnier, B. , Terray, L., Bessières, L., Sérazin, G., Grégorio, S., Brankart, J., Moine, M., Molines, J., Brasseur, P., 2014. Ensembles of eddying ocean simulations for climate, CLIVAR Exchanges, Special Issue on High Resolution Ocean Climate Modelling, 19.</p>
Marquette, Michigan Ground Observations and Atmospheric River Dataset
<p>This dataset contains ground-based meteorological observations and accompanying atmospheric river data used in "The influence of atmospheric rivers on cold-season precipitation in the Upper Great Lakes region", Mateling, Pettersen, Kulie, Mattingly, Henderson, and L'Ecuyer, submitted to GRL, in review.</p> <p>The ground-based data contains meteorological data including temperature, wind speed and direction, radar reflectivity and Doppler velocity from a Micro-Rain Radar2 (MRR) and precipitation data from a Precipitation Imaging Package (PIP) hosted at the National Weather Service in Marquette, Michigan (Pettersen, Kulie, et al., 2020; Pettersen, Bliven, et al., 2020; Kulie et al., 2021).</p> <p>The atmospheric river (AR) data contains a flag to identify when an AR is within 100 km of Marquette during a deep cold-season precipitation event. Additionally, the associated integrated water vapor transport (IVT) and motion vectors are within this file. The AR database was created and analyzed in Mattingly et al. (2018). </p>
Atmospheric Distribution of HCN from Satellite Observations and 3-D Model Simulations - TOMCAT data
<p>This repository contains the model data from the paper "Atmospheric Distribution of HCN from Satellite<br> Observations and 3-D Model Simulations" submitted to ACP.</p> <p>The files contains the monthly mean hydrogen cyanide (HCN) mixing ratios modelled using the TOMCAT 3-D offline chemical transport model with a horizontal resolution of 2.8° × 2.8° with 60 hybrid σ-pressure levels from the surface to ~60 km.</p>
Dataset for "Sources and sinks of carbonyl sulfide inferred from atmospheric observations at the Lutjewad tower"
<p>Measurements that are used in "Sources and sinks of carbonyl sulfide inferred from atmospheric observations at the Lutjewad tower”. The dataset includes mole fraction measurements of COS, CO<sub>2</sub>, CO and H<sub>2</sub>O made in Lutjewad (the Netherlands) and Hyytälä (Finland) between 2014 and 2018, and measurements of <sup>222</sup>Rn, SF<sub>6</sub> and meteorological parameters in Lutjewad.</p>
Atmospheric, hydrodynamic and water quality observations from environmental-quality stations, water level sensors, acoustic Doppler velocimeters, and meteorological stations located at the Guadalquivir river estuary (2008 - 2010)
<p>The dataset included in this repository was obtained during the project entitled “Propuesta metodológica para diagnósticar las consecuencias de las actuaciones humanas en el estuario del Guadalquivir” funded by the Autoridad Portuaria de Sevilla (APS), by the Consejería de Innovación, Ciencia y Empresa (Junta de Andalucía), CTM2011-22580, MedEX (CTM2008-04036-E) and PR11-RNM-7722. The data were collected in real time from 2008 until 2010 with a remote monitoring system installed by the Institute of Marine Sciences of Andalusia (ICMAN-CSIC) (Navarro et al., 2011).</p> <p> </p> <p>The environmental quality station recorded turbidity, temperature, conductivity, normalized turbidity, dissolved oxygen, oxygen, oxygen saturation, percentage of oxygen saturation, fluorescence, normalized fluorescence, and salinity every thirty minutes. Current data were measured every 15 minutes by means of acoustic current profilers. The former datasets were obtained at several depths and different locations along the Guadalquivir estuary. Water level sensors recorded the position of the free water surface every 10 minutes at several locations along the Guadalquivir estuary. Wind velocity and direction and solar radiation were measured every 10 minutes in a meteorological station at the mouth of the Guadalquivir estuary.</p> <p>Brief description of dataset.</p> <ul> <li> <p>velocities.csv (in m/s)</p> </li> <li> <p>Turbidity.csv (in Volts), temperature (in Celsius), conductivity (in Siemens/m), normalized turbidity (in FNU), dissolved oxygen (mg/L), oxygen (in Volts), fluorescence (in Volts), normalized fluorescence (in Volts), oxygen saturation (mg/L), percentage of oxygen saturation (%), salinity (in PSU).</p> </li> <li> <p>qual_Salmedina.csv, R_mean (mean radiative flux in W/m²), R_max (max radiative flux in W/m²), Rel_humidity (relative humidity in %), D_mean (wind mean direction in degrees), D_max (wind maximum direction in degrees), D_sig (standard deviation of the wind direction in degrees), V_mean (mean wind velocity in m/s), V_max (maximum wind velocity in m/s), V_sig (standard deviation of the wind velocity in m/s), P_atm_mean (mean atmospheric pressure in mbar), T_mean (mean air temperature in Celsius), T_max (maximum air temperature in Celsius), T_sig (standard deviation of the air temperature in Celsius).</p> </li> <li> <p>Sealevel.csv (in meters)</p> </li> </ul> <p>A wide description of the datasets can be found in Navarro et al (2011).</p> <p>Contact person: infogdfa@ugr.es (or mcobosb@ugr.es)</p>
Seasonal and longitudinal variability in Io's SO2 atmosphere from 22 years of IRTF/TEXES observations
<p>This dataset contains the reduced Io spectra used in the paper "Seasonal and longitudinal variability in Io's SO2 atmosphere from 22 years of IRTF/TEXES observations" (doi: 10.1016/j.icarus.2024.116151). There are 150 spectra, spanning from 2001 to 2023. These spectra are described in Table 1 of the paper.</p> <p>The spectra in the data file are listed in date order. For each spectrum, we first provide the date (YYMMDD format) and the mean Io central longitude at the time of the observation. This is then followed by the spectrum. Column 1 is the wavelength, in units of microns. Column 2 is the Io spectrum, which has been divided by a Callisto spectrum, flattened in order to correct for any residual continuum slope, and then normalized such that the continuum level is 1. </p>
Data for "Constraints on the Observability of Energetic Neutral Atoms from the Magnetosphere-Atmosphere Interactions at Callisto and Europa" by Haynes et al.
<p>Accompanying data products for publication entitled "Constraints on the Observability of Energetic Neutral Atoms from the Magnetosphere-Atmosphere Interactions at Callisto and Europa". The manuscript was submitted to JGR Space Physics shortly after upload.</p> <p>Data includes all simulation outputs that are depicted in this work, both for the AIKEF hybrid model (i.e., Figure 4) and the model used to produce synthetic ENA images (Figures 3, 6, 8, 9, 11, A1, and B1). All other figures in the work are used for illustrative purposes and were not generated with simulation output. </p> <p>Information regarding the organization and file structure can be found in H24_data_readme.txt , as well as which dataset corresponds to which figure. Any inquiries, questions, or comments may be addressed through the email associated with this data publication.</p>
Sources and sinks of carbonyl sulfide inferred from tower and mobile atmospheric observations
<p>These datasets include the results of the combination of STILT simulations with COS and CO2 fluxes datasets as well as the observations at the Lutjewad measurement station (LUT, 53.4235°N, 6.3094°E). Please refer to the ReadMe file for further details.</p>
Supporting data for "Stratospheric Gas-Phase Production Alone Cannot Explain Observations of Atmospheric Perchlorate on Earth" by Chan et al.
<p>Model code, simulation outputs, digitized observation-summary tables, and Python scripts for reproducing the analysis results/ figures presented in "Stratospheric Gas-Phase Production Alone Cannot Explain Observations of Atmospheric Perchlorate on Earth" by Yuk-Chun Chan et al. Please refer to the publication and readme.txt for more information. </p>
Data and code for the paper Atmospheric Observations with E-band Microwave Links – Challenges and Opportunities
<p>Raw and preprocessed data for the paper Atmospheric Observations with E-band Microwave Links – Challenges and Opportunities accepted for publication in the journal Atmospheric Measurement Techniques.</p> <p>The dataset includes total losses (transmitted - received power levels) of commercial microwave links and observations of rainfall, air temperature, and air relative humidity. In addition, theoretical gaseous attenuation calculated from air temperature and relative humidity is provided.</p> <p>Data are stored in semicolon-delimited csv files. Time stamps are in UTC time in the format yyyy-mm-dd HH:MM:SS. Raw data contain not regular time series, preprocessed data contain regular time series at 1-min and 5-min temporal resolution. Metadata are stored in textfiles.</p> <p>Dataset contains also R code for processing and analyzing the data as presented in the paper Atmospheric Observations with E-band Microwave Links – Challenges and Opportunities. The code is in the form of R Markdown files and interactive html notebooks.</p>
Gravity waves in Titan's atmosphere: A comparison between linearized wave model calculations and HASI observations
<p>The data for the article "Gravity waves in Titan's atmosphere: A comparison between linearized wave model calculations and HASI observations" (GWTA). </p> <p> </p> <ol> <li>"Titan_CJP_std_chem.dat" is the background atmosphere data of Titan's atmosphere from Strobel's model. It is used in Figure 1 of the article.</li> <li>"HASI_T_p_rho_vsZ_2008.dat" is the data for Cassini-Huygens observations in Titan's atmosphere. It is used in Figure 1 of the article.</li> <li>"Mma-Program-for-GW-on-Titan.txt" is the main Mathematica program to simulate the gravity waves on Titan.</li> <li>"solutions-fun.rar" is the simulation result. This RAR file includes 174 gravity wave samples simulated with different periods and horizontal wavelengths (can be read from the subfile names after uncompressing). These gravity wave solutions are stored as InterpolatingFunction of Mathematica. The solution describes the gravity wave temperature, velocity, and density perturbations profiles from altitude 300km to 2000km. However, they are plain texts and can easily be read by any software. Figures from 2-10 are based on these data.</li> </ol> <p> </p>
Data for Survival probabilities of atmospheric particles: comparison based on theory, cluster population simulations, and observations in Beijing
<p>Data for<em> Survival probabilities of atmospheric particles: comparison based on theory, cluster population simulations, and observations in Beijing </em>(https://doi.org/10.5194/acp-2022-484)</p> <p>Contact Santeri Tuovinen (santeri.tuovinen@helsinki.fi) for more details.</p>
Large uptake of atmospheric OCS observed at a moist old growth forest: Controls and implications for carbon cycle applications
<p>This repository contains all data for the manuscript by Rastogi et al., titled "<strong>Large uptake of atmospheric OCS observed at a moist old growth forest: Controls and implications for carbon cycle applications</strong>". This manuscript has been accepted for publication in the Journal of Geophysical Research: Biogeosciences</p> <p> </p>
Observational atmospheric angular momentum.
<p>Reanalyses of Earth's angular momentum from ERA data. Data are divided by 1.0e+24.<br>Each row consists of 12 monthly means.<br>Data are given for each of 324 latitudes, starting near the S pole.<br>Data are given for each year from 1960, starting from November 1960 to match model predictions.</p>
Processed data used for JGR publication "Role of Midwater Mixed Waves in the Loop Current Separation Events from A Coupled Ocean-Atmosphere Regional Model and In-Situ Observations"
<p>This is the processed dataset used in the JGR publication "Role of Midwater Mixed Waves in the Loop Current Separation Events from A Coupled Ocean-Atmosphere Regional Model and In-Situ Observations" by Xiao Ge.</p> <p>Please contact the author (gexiao@tamu.edu) for all the original/processed outputs of R-CESM, and use the following original papers as citations.</p> <p>The dataset used in this research includes:</p> <p>1. Loop Current Dynamics 2009-2011: LC_*.nc is the processed (reorganized) data for each in-situ station, * represents their station ID</p> <ul> <li>https://digital.library.unt.edu/ark:/67531/metadc955416/</li> <li>https://www.sciencedirect.com/science/article/pii/S0377026516301348?via%3Dihub</li> <li>https://search.dataone.org/view/%7BBD2513E6-3B34-4B7C-BCB9-3C4ED5E8D0FB%7D</li> </ul> <p>2. Regional Community Earth System Model, R-CESM: <a href="https://zenodo.org/api/records/13932074/draft/files/h.nc/content" target="_blank" rel="noopener noreferrer">h.nc</a> is the bathymetry data of R-CESM; cmpr_*.nc files are provided as examples of the original R-CESM outputs; pvsf_prho_*.nc are the processed (subsampled at the target region and interpolated on potential density layers, derived stream function, potential vorticity, and relative vorticity) R-CESM outputs used in this research; and <a href="https://zenodo.org/uploads/13932074" target="_blank" rel="noopener noreferrer">LC_pv_40hlp_2013.nc</a> is the example of organized processed R-CESM (pvsf_prho_*.nc files) containing potential vorticity and relative vorticity for figures plotting</p> <ul> <li>https://journals.ametsoc.org/view/journals/bams/102/9/BAMS-D-20-0024.1.xml?tab_body=fulltext-display</li> </ul> <p> </p> <p> </p> <p> </p> <p> </p>
Data for Widespread detection of chlorine oxyacids in the Arctic atmosphere: Villum Research Station and Ny-Ålesund observations
<p>The data includes:</p> <p>1) Data for the time series of HClO3 and HClO4 together with relevant data from the Villum Research Station observations.</p> <p>2) Data for the time series of HClO3 from Ny-Ålesund observation.</p> <p>3) Data of the estimated cross-section and photolysis rate of HClO3 and HClO4.</p> <p>Data are also available from the corresponding authors upon request. </p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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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.