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189 results for “Biophysics”
Eddy covariance 30-minute CO2 fluxes with accompanying biophysical variables from the GCE-LTER flux tower site from January 2014 to December 2017 for sensor Flux1
Eddy covariance (EC) CO2 fluxes from flux sensor set "Flux1" from January 2014 to December 2017 collected over a Spartina alterniflora marsh located on the western side of Sapelo Island bounded by the Duplin River and Barn Creek. EC fluxes were processed in EddyPro 7. Additional biophysical variables included are air temperature, relative humidity, vapor pressure deficit, and water table height from a nearby tidal creek and the marsh platform.
Eddy covariance 30-minute CO2 fluxes with accompanying biophysical variables from the GCE-LTER flux tower site from January 2014 to December 2022 for sensor Flux2
Eddy covariance (EC) CO2 fluxes from sensor set "Flux2" from January 2014 to December 2022 collected over a Spartina alterniflora marsh located on the western side of Sapelo Island bounded by the Duplin River and Barn Creek. EC fluxes were processed in EddyPro 7. Additional biophysical variables included are air temperature, relative humidity, vapor pressure deficit, and water table height from a nearby tidal creek and the marsh platform.
Eddy covariance 30-minute CO2 fluxes with accompanying biophysical variables from the Grand Bay, Mississippi flux tower site from March 2018 to January 2019
Eddy covariance (EC) CO2 fluxes from March 2018 to January 2019 collected over a Juncus roemerianus marsh located in the Grand Bay National Estuarine Research Reserve (NERR) in Mississippi. EC fluxes were processed in EddyPro 7. Additional biophysical variables included are air temperature, relative humidity, vapor pressure deficit, soil temperature, and water table height within the marsh.
Eddy covariance 30-minute CO2 fluxes with accompanying biophysical variables from the GCE-LTER flux tower site from December 2018 to January 2020
Eddy covariance (EC) CO2 fluxes from December 2018 to January 2020 collected over a Spartina alterniflora marsh located on the western side of Sapelo Island bounded by the Duplin River and Barn Creek. EC fluxes were processed in EddyPro 7. Additional biophysical variables included are air temperature, relative humidity, vapor pressure deficit, soil temperature, and water table height from a nearby tidal creek.
LAMASUS NUTS-level biophysical data
<p>This dataset comprises spatial biophysical data regarding aspect, elevation and slope derived from <a href="https://spacedata.copernicus.eu/collections/copernicus-digital-elevation-model">https://spacedata.copernicus.eu/collections/copernicus-digital-elevation-model</a> and topsoil properties derived from <a href="https://esdac.jrc.ec.europa.eu/content/topsoil-physical-properties-europe-based-lucas-topsoil-data">https://esdac.jrc.ec.europa.eu/content/topsoil-physical-properties-europe-based-lucas-topsoil-data</a> to the NUTS regions across Europe. The dataset consists of two files, each corresponding to a different level of NUTS coding (NUTS 2/3).</p> <p>For each file, the following columns are included:</p> <ol> <li>NUTS Code (NUTS): The unique identifier for the NUTS region.</li> <li>Aspect (aspect): The mean aspect in degree for the given region.</li> <li>Elevation (elevation): The mean elevation in meter for the given region.</li> <li>Slope (slope): The mean slope in degree for the given region.</li> <li>- 16. Topsoil (0-20cm) properties: Shares of respective USDA soil textural class derived from clay, silt and sand maps in per cent for the given region.</li> </ol> <p>The spatial dimension is identified by NUTS2/3 (2016) codes.</p> <div> <p>This dataset has been created as part of LAMASUS Project under the scope of Deliverable 3.2 titled "Database on EU policies and payments for agriculture, forest, and other LUM related drivers ". The data is directly linked to the work described on pages 47-49, belonging to section 3.4 Biophysical data. The full text of the deliverable can be accessed via: <a href="https://www.lamasus.eu/wp-content/uploads/LAMASUS_D3.2_policy-and-payment-database.pdf">https://www.lamasus.eu/wp-content/uploads/LAMASUS_D3.2_policy-and-payment-database.pdf.</a></p> <p>Please note that this dataset is intended for research and analysis in the fields of climatology, environmental science, and related disciplines. Users are encouraged to cite this dataset appropriately if utilized in academic or scientific publications.</p> </div>
Data, scripts, and R Notebook for Carneiro et al 2023. Flight performance and wing morphology in the bat Carollia perspicillata: biophysical models and energetics. Integrative Zoology DOI:10.1111/1749-4877.12707
<p>Files provided as supporting information for the paper by Carneiro et al. 2023. Flight performance and wing morphology in the bat <em>Carollia perspicillata</em>: biophysical models and energetics. Integrative Zoology. DOI:10.1111/1749-4877.12707</p> <p>File descriptions</p> <p>ArmTA.txt - Temperature and surface areas for arms of <em>C. perspicillata</em> after flight experiment<br> BodyTA.txt - Temperature and surface areas for body of <em>C. perspicillata</em> after flight experiment<br> HeadTA.txt - Temperature and surface areas for head of <em>C. perspicillata</em> after flight experiment<br> WingTA.txt - Temperature and surface areas for wings (patagium) of <em>C. perspicillata</em> after flight experiment<br> WingMorph.txt - Morphological variables measured in the body and wings of <em>C. perspicillata</em><br> HeatLoss.R - Function to estimate heat loss (Qt)<br> PowFlight.R - Function to estimate minimum power required to fly<br> Script-HeatLoss-FlightPerformance.R - R script with set of analyses performed<br> SupportingInformationFile.docx - R notebook with set of analyses performed, word format<br> SupportingInformationFile.nb.html - R notebook with set of analyses performed, html format<br> SupportingInformationFile.Rmd - R notebook with set of analyses performed (R markdown)</p> <p>For the R scripts (Script-HeatLoss-FlightPerformance.R) and notebook (<br> SupportingInformationFile.Rmd) to work and be compiled, all files need to be copied to the same folder.</p>
Biophysical effects of vegetation cover change from satellite and models
<p>Vegetation cover changes associated with land use and land cover change (LULCC) can perturb the local surface energy balance, which in turn can affect the local climate. Land surface models (LSMs) can be used to simulate such land-climate interactions, but their capacity to model these biophysical effects accurately across the globe remain unclear due to the complexity of the phenomena. This dataset provides idealized simulations from four LSMs (JULES, ORCHIDEE, JSBACH and CLM) that are harmonized with estimations obtained from satellite observations, enabling the inter-comparison and benchmarking of LSM performances and which can serve to identify model limitations and prioritize efforts in model development. The dataset provides the change in latent heat flux, in combined sensible and ground heat flux and in net radiation caused by 15 specific vegetation cover transitions on a 1° by 1° grid at monthly time scale for a synthetic year based on data from 2008 until 2012. The dataset was generated from a collaborative effort lead by JRC within the FP7 LUC4C project (luc4c.eu).</p>
Location, biophysical and agronomic parameters for croplands in Northern Ghana
<p>We present a dataset describing (i) crop locations, (ii) biophysical parameters and (iii) crop yield and biomass was collected in 2020 and 2021 in Ghana, mostly focusing on maize in northern Ghana. The dataset contains repeated multiple measurements of leaf area index (LAI), leaf chlorophyll concentration over a large number of maize fields, as well as associated grain yield, biomass and polygons that delineate the fields.</p>
Dataset for the paper: "Di Felice, L.J.; Ripa, M.; Giampietro, M. Deep Decarbonisation from a Biophysical Perspective: GHG Emissions of a Renewable Electricity Transformation in the EU."
<p>Dataset used for the development of scenarios in the publication "Di Felice, L.J.; Ripa, M.; Giampietro, M. Deep Decarbonisation from a Biophysical Perspective: GHG Emissions of a Renewable Electricity Transformation in the EU. Sustainability 2018, 10, 3685." and used for a case study in "Di Felice L., Dunlop T., Giampietro M., Kovacic Z., Renner A., Ripa M., Velasco-Fernández R. – Report on the Quality Check of the Robustness of the Narrative behind Energy Directives. MAGIC (H2020–GA 689669) Project Deliverable 5.4, 30 November 2018". (link: https://magic-nexus.eu/documents/d54-report-narratives-behind-energy-directives).</p> <p>Sources of other secondary data (from papers, reports) specified in the dataset (under tab "input codes")</p>
Geospatial, biophysical and socioeconomic data for the Athens municipality on a zipcode resolution
<p>A collated dataset from various remote sensing, local and national sources. </p>
A global biophysical typology of mangroves version 3
<p>This dataset in an updated version of:</p> <p>Worthington, T. A. et al. A global biophysical typology of mangroves and its relevance for ecosystem structure and deforestation. Sci. Rep. 10, 14652 (2020)</p> <p>which delineates the world’s mangroves into geomorphic units based on their biophysical setting. Each unit consists of one or more patches of mangrove, grouped based on their proximity to macroscale coastal features, with these features determining their geomorphic class – deltaic, estuarine, lagoonal, and open coast.</p> <p> </p> <p>With the development of an updated mangrove extent timeseries (Global Mangrove Watch (GMW) v3.12), we updated the mangrove biophysical typology<sup>1</sup> to match this new extent. We firstly created an overlay between GMW v3.12 (all years combined to produce a 24-year composite extent) and the mangrove typology (v2.2) and identified those patches that were present in both. These patches were assigned to the same geomorphic class and individual geomorphic unit as the mangrove typology and provided the basis for the updated version. The patches present in the typology v2.2 but not in GMW v3.12 were deleted as they were no longer being mapped as mangrove in the GMW dataset.</p> <p> </p> <p>The patches now being mapped as mangrove in GMW v3.12 but had not been identified as such in the previous extent used to create the typology were then assigned to a geomorphic type and individual geomorphic unit using an iterative approach. Firstly, we identified patches that intersected with a single geomorphic unit and merged those patches to that unit, creating an enlarged unit extent. We repeated this procedure with the enlarged units, again enlarging their extent with patches only intersecting a single unit.</p> <p> </p> <p>We then used a series of distance buffers to identify unassigned patches that were within a certain distance of a single unit. After each step patches that were within the buffer distance of a single geomorphic unit were merged with that unit, and then the buffer was recalculated. The buffer distances were 1000m, 1000m, 1000m, 500m, 250m and 100m. Following the buffer, for the remaining unassigned patches we split them into those whose centroid was ≤ 10,000m from a geomorphic unit and those whose centroid was >10,000m for a geomorphic unit. As some of the patches were close (≤ 10,000m) from multiple geomorphic units, they were manually assessed and their assignment was corrected where necessary.</p> <p> </p> <p>The remaining patches (>10,000m from a geomorphic unit) were then visually assessed and can be split into three groups, 1) those part of large existing geomorphic units (only deltas, estuaries and lagoons) that were merged with that unit, 2) patches near deltas, estuaries and lagoons not mapped in the original GMW dataset, and 3) areas of open coast. The patches near deltas, estuaries and lagoons not mapped in the original GMW dataset resulted in the creation of 81 new geomorphic units. The open coast patches were aggregated into 268 clusters using a distance of 10,000m. Thirty-eight of the clusters were within 10,000m of an original open coast geomorphic unit and were merged with that unit. The remaining 230 were designated as new geomorphic units.</p> <p> </p> <p>We then undertook a process to merge open coast geomorphic units, by finding those small (<1km<sup>2</sup>) open coast geomorphic units that were within 10,000m of a larger one. Repeating the procedure to merge small open coast geomorphic units that were within 10,000m of another small open coast geomorphic unit. The final step was to do a visual assessment of all the units to remove errors. This was based around merging neighbouring geomorphic units of the same class if they represent the same system (e.g., one contiguous estuary or lagoon unit), assessed using high resolution imagery and the fluvial boundaries of the Hydrosheds basins<sup>2</sup>. Manually editing errors at unit boundaries where patches of one unit were surrounded by another unit. Splitting open coast units that overlapped another class of geomorphic unit e.g., an open coast unit with an estuary in the middle of it. Merging open coast units into large extents, particular those of the same section or aspect of the coast, using a distance of 10,000m as an approximate guide and trying not to create extents >100km<sup>2</sup>.</p> <p> </p> <p>A final publication version of the GMW dataset<sup>3</sup> (v3.14) was released <a href="https://zenodo.org/record/6894273">https://zenodo.org/record/6894273</a>, which differed slightly from v3.12. Firstly, a number of small areas of mangrove were removed at the edges of polygons, these were also removed from the typology. Secondly, additional areas of mangrove were mapped in the Persian Gulf, these new areas were merged with existing geomorphic units. These steps resulted in a final dataset ‘Mangrove Typology v3’ consisting of 3983 geomorphic units.</p> <p> </p> <p>1. Worthington, T. A. <em>et al.</em> A global biophysical typology of mangroves and its relevance for ecosystem structure and deforestation. <em>Sci. Rep.</em> <strong>10</strong>, 14652 (2020).</p> <p>2. Lehner, B. & Grill, G. Global river hydrography and network routing: Baseline data and new approaches to study the world’s large river systems. <em>Hydrol. Process.</em> <strong>27</strong>, 2171–2186 (2013).</p> <p>3. Bunting, P. <em>et al.</em> Global mangrove extent change 1996-2020: Global Mangrove Watch version 3.0. <em>Remote Sens.</em> <strong>14</strong>, 3657 (2022).</p> <p> </p>
PLOS Comput. Biol. "Biophysically detailed mathematical models of multiscale cardiac active mechanics": datasets
<p>This repository contains the data accompanying the PLOS Computational Biology paper "<em>Biophysically detailed mathematical models of multiscale cardiac active mechanics</em>", by Francesco Regazzoni, Luca Dedè and Alfio Quarteroni.</p> <p>It contains the following datasets:</p> <ul> <li><strong>steady_state.csv</strong>: steady-state active tension for constant calcium concentration and sarcomere length (Figs. 11, 12, 13 ,14).</li> <li><strong>isometric_twitches.csv</strong>: active tension transients in isometric conditions (Figs. 15, 16, 17).</li> <li><strong>force_velocity_relationship.csv</strong>: force-velocity relationship at different calcium concentrations and sarcomere lenghts (Fig. 18).</li> <li><strong>fast_transient_response.csv</strong>: tension-elongation curve after a fast step in length (Fig. 19).</li> </ul> <p>CSV headers refer to the following variables (and measure units):</p> <ul> <li><strong>Ca</strong> (<em>μM</em>): intracellular calcium concentration.</li> <li><strong>SL</strong> (<em>μm</em>): sarcomere length.</li> <li><strong>active_tension</strong> (<em>kPa</em>): active tension.</li> <li><strong>Delta_L</strong> (<em>nm/hs</em>): step length.</li> <li><strong>velocity</strong> (<em>hs/s</em>): shortening velocity.</li> <li><strong>time</strong> (<em>s</em>): time.</li> </ul>
Silene seeds from the laboratories of the Institute of Biophysics (Academy of Sciences) of Brno (Czech Republic)
<p>Seeds of <em>Silene </em>for the analysis of morphology (Martín Gómez et al.) obtained from the laboratories of the Academy of Sciences of Brno (Czech Republic). Photos contains 40 seeds of:</p> <p><em>Silene acutifolia; S. colpophylla; S. conica; S. diclinis; S. dioica; S. gallica; S. italica; S. latifolia; S. noctiflora; S. nutans; S. otites; S. pendula; S. saxifraga; S. schafta; S. tatarica; S. viscosa; S. vulgaris; S. wolgensis; S. zawadzkii</em></p>
Analysis of insulin glulisine at the molecular level by X-ray crystallography and biophysical techniques
<p>Raw diffraction images for the study:- Gillis, R.B., Solomon, H.V., Govada, L. <em>et al.</em> Analysis of insulin glulisine at the molecular level by X-ray crystallography and biophysical techniques. <em>Sci Rep</em> <strong>11, </strong>1737 (2021). https://doi.org/10.1038/s41598-021-81251-2 </p> <p>PDB code 6GV0.</p>
Databases related to molecular biophysics 09-2022
<p>A survey and comparative analysis of the technical provision of existing databases that provide for archival of data from molecular biophysics experiments and closely related areas has been carried out. The analysis demonstrates that for many methods provided by MOSBRI there is no specialist provision for data archival such as would adequately satisfy FAIR principles. In particular only a small number of databases containing molecular biophysics derived data use standard data formats with sufficiently rich metadata that enables interoperable data reuse. Where relevant molecular biophysics databases do exist, and in the closely related area of structural biology, there is a clear trend toward complete deposition of the experiment life cycle (i.e., primary experimental data, analysis procedure and interpretation), which will need to be incorporated into plans for the future MOSBRI data repository. The survey also highlights many resources that would have to be linked with any future MOSBRI data repository, to ensure findability and interoperability. </p>
A biophysical model of two interacting cortical areas
<p>We present a large-scale, data-driven, biophysically-detailed computational model of two interacting cortical areas, based on data from rodent somatosensory cortex.</p> <p>This model is derived from a previous model (described in two manuscripts: <a href="https://doi.org/10.7554/eLife.99688.1" target="_blank" rel="noopener">anatomy</a>, <a href="https://doi.org/10.1101/2023.05.17.541168" target="_blank" rel="noopener">physiology</a>), but it consists of a reduced setting tailored to the study of inter-areal interactions in cortical sensory processing. Details of the model and initial results can be found <a href="https://doi.org/10.1101/2024.10.13.618022" target="_blank" rel="noopener">here</a>.</p> <h3>Description</h3> <p>The model describes a system of two otherwise isolated cortical areas (X and Y), where area X is a primary sensory and area Y is the first higher-order area in a cortical processing hierarchy. Each area consists of about 200K morphologically-detailed conductance-based neurons, distributed across six cortical layers and of 60 different morphological types and 212 morpho-electrical types.</p> <p>The model incorporates the following connectivity:</p> <ul> <li>Local touch-based connectivity within each area.</li> <li>Thalamocortical innervation from both VPM (core-type) and POm (matrix-type) nuclei to area X.</li> <li>Long-range data-driven projections between both areas with characteristic laminar termination profiles.</li> </ul> <p>Additionally, each area receives nonspecific background noise to exhibit spontaneous activity comparable to experimental recordings of per-layer mean firing rates.</p> <h3>Setup</h3> <p>The model is provided in the <a href="https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1007696" target="_blank" rel="noopener">SONATA</a> format and can be run using <a href="https://github.com/BlueBrain/neurodamus/" target="_blank" rel="noopener">Neurodamus</a>, a simulator frontend for <a href="https://www.neuron.yale.edu/neuron/" target="_blank" rel="noopener">NEURON</a>. Synaptic and ion channel mechanisms specific for <a href="https://github.com/BlueBrain/neurodamus-models/tree/main/neocortex" target="_blank" rel="noopener">neocortical</a> neurons are also required to run this model (build instructions <a href="https://github.com/BlueBrain/neurodamus#build-special-with-mod-files" target="_blank" rel="noopener">here</a>).</p> <p>To setup the model, all files must be placed in the same directory and all the <a href="https://www.nongnu.org/lzip/" target="_blank" rel="noopener">Lzip</a>-compressed TAR archives must be extracted. The total uncompressed size is 219 GB.</p> <pre><code>$ mkdir model_root # download all files into model_root $ cd model_root $ for file in *.tar.lz; do tar -xf $file; done</code></pre> <h3>Simulation</h3> <p>We provide some example configuration files (under<em> </em><strong>example_simulation_configs</strong>) for simulations of spontaneous and evoked activity, as well as some network manipulations (layer-wise pathway blocks and TTX application).</p> <p>In order to run a simulation, copy <strong>simulation_config.json</strong> into a new directory and set the <em>network</em> key to the path of the directory containing the extracted model (optionally, set the <em>output</em> key as well). Instructions for running a simulation can be found <a href="https://github.com/BlueBrain/neurodamus?tab=readme-ov-file#examples" target="_blank" rel="noopener">here</a> and documentation for the simulation configuration file can be found <a href="https://sonata-extension.readthedocs.io/en/latest/sonata_simulation.html" target="_blank" rel="noopener">here</a>.</p> <h3>Analysis</h3> <p>Analysis of model composition and connectivity, as well as of simulation outputs, can be performed using <a href="https://github.com/BlueBrain/snap" target="_blank" rel="noopener">Blue Brain SNAP</a> or by directly accessing the HDF5 files with <a href="https://github.com/BlueBrain/libsonata" target="_blank" rel="noopener">libsonata</a>. Documentation on the SONATA format for all files making up the model can be found <a href="https://sonata-extension.readthedocs.io/en/latest/sonata_overview.html" target="_blank" rel="noopener">here</a>.</p> <h3>Computational resources</h3> <p>Approximate scaling of computational resources is as follows (based on simulations of 5 s biological time running on a cluster with 40 cores @ 2.5 GHz and 376 GB of RAM per node, one MPI process per core, using <a href="https://doi.org/10.3389/fninf.2019.00063" target="_blank" rel="noopener">CoreNEURON</a>):</p> <ul> <li>Memory per process = 1106 GB / N ** 0.87</li> <li>Simulation time = 4278 h / N ** 0.93</li> </ul> <p>For example, running with N = 1000 processes (25 nodes) results in 107 GB memory usage per node and 7h16m simulation time for 5 s biological time. Longer simulations scale approximately linearly in time, taking 13h13m for 10 s biological time and 21h48m for 15 s biological time.</p> <h3>Changelog</h3> <p>v1.0.1<br>Fixed (unused) key "node_sets_file" in example simulation configuration files.</p>
Improving Stability of Tear Film Lipid Layer via Concerted Action of Two Drug Molecules: A Biophysical View
<p>Surface pressure/area isotherms, stress relaxation transients, molecular dynamic simulation parameters of surface films composed of tear lipids and drug molecules.</p>
MOSBRI survey - Biophysical Data Standards and Accessibility
<p>The needs for data standards and formats in molecular biophysics were analysed mainly via a survey focused on data producers and users in the field: Biophysical Data Standards and Accessibility. The questions were focused on identifying the expertise and scientific interests of the respondents, their use of techniques of molecular biophysics, views on the current situation and needs in data formats standardisation and needs for repositories or databases. The data were collected using LimeSurvey technology. Anonymized raw dat, their processing and interpretation included in this dataset. The work was performed as part of the project MOlecular Scale Biophysics Research Infrastructure (MOSBRI).</p> <p> </p>
Data to accompany the publication "Combined biophysical and genetic modelling approaches reveal complementary information about population connectivity of New Zealand green-lipped mussels"
<p>Data to accompany the publication "Combined biophysical and genetic modelling approaches reveal complementary information about population connectivity of New Zealand green-lipped mussels". </p> <p>migrationmatrix14.txt contains the particle tracking matrix, with the total number of particles that migrated from row i to column j (out of a total of 2217864 particles released per population).</p> <p>mussel_microsat_Genepop.txt contains the microsatellite data for each population in Genepop format.</p>
Uncovering circuit mechanisms of current sinks and sources with biophysical simulations of primary visual cortex
<p>Local field potential (LFP) recordings reflect the dynamics of the current source density (CSD) in brain tissue. The synaptic, cellular and circuit contributions to current sinks and sources are ill-understood. We investigated these in mouse primary visual cortex using public Neuropixels recordings and a detailed circuit model based on simulating the Hodgkin-Huxley dynamics of >50,000 neurons belonging to 17 cell types. The model simultaneously captured spiking and CSD responses and demonstrated a two-way dissociation: Firing rates are altered with minor effects on the CSD pattern by adjusting synaptic weights, and CSD is altered with minor effects on firing rates by adjusting synaptic placement on the dendrites. We describe how thalamocortical inputs and recurrent connections sculpt specific sinks and sources early in the visual response, whereas cortical feedback crucially alters them in later stages. These results establish quantitative links between macroscopic brain measurements (LFP/CSD) and microscopic biophysics-based understanding of neuron dynamics and show that CSD analysis provides powerful constraints for modeling beyond those from considering spikes.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.