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1,023 results for “Stand”
Pakistan (Gandhāra region). Standing Vaiṣṇāvī.
<p>Pakistan (Gandhāra region). Standing figure of the goddess Vaiṣṇāvī, as photographed in the circa early 20th century.</p>
Van Allen Probes toroidal standing Alfvén wave frequencies
<p>This dataset consists of files containing toroidal standing Alfvén wave frequencies at the Van Allen Probes (RBSP) spacecraft. The frequencies were determined for the fundamental (mode 1) thorough third (mode 3) harmonics using the method described by Takahashi et al. (2021). The files cover the RBSP mission period for which both the fluxgate magnetometer data and the spinfit electric field data are available. Main programs used to generate the data files are also included.</p>
Map of standing water habitat types and regionally important biotopes in Flanders
<p>This map is a combination of the <a href="https://zenodo.org/records/13865531">standardized habitat map of Flanders</a> (version habitatmap_stdized_2023_v1) and <a href="https://zenodo.org/records/14203168">the watersurface map of Flanders</a> (version watersurfaces_2024). It contains standing water Natura 2000 habitat types (2190_a and 31xx) and regionally important biotopes (rbbah) in Flanders.</p> <p>The polygons with 2190_a habitat (dune slack ponds) are generated by selecting all watersurface polygons that overlap with dune habitat polygons (21xx) of the standardized habitat map.</p> <p>For each of the other aquatic habitat types (31xx and rbbah) we select the watersurface polygons that overlap with the selected habitat type polygons of the standardized habitat map. We also select polygons of the standardized habitat map containing standing water types but that do not overlap with polygons of the watersurface map.</p> <p>The <code>watersurfaces_hab.gpkg</code> file is a GeoPackage that contains:</p> <ul> <li><code>watersurfaces_hab_polygons</code>: a spatial layer with the selected polygons that contain standing water habitat types or regionally important biotopes. </li> <li><code>watersurfaces_hab_types</code>: a table with information on standing water habitat types and regionally important biotopes in each watersurface polygon.</li> </ul> <p>The R-code for creating the <code>watersurfaces_hab</code> data source can be found in the GitHub repository <a href="https://github.com/inbo/n2khab-preprocessing/tree/58138a8/src/generate_watersurfaces_hab">'n2khab-preprocessing' at commit 58138a8</a>.</p> <p>A reading function to return the data source in a standardized way into the R environment is provided by the R-package <a href="https://github.com/inbo/n2khab">n2khab</a>.</p>
D4.1. YouCount open data sample from the evaluation – current stand
<p>The H2020 YouCount project runs from February 2021 to January 2024. The overarching objectives are to generate new knowledge and innovations to increase the social inclusion of youth through co-creative youth citizen social science (Y-CSS) and to provide evidence of the actual outcomes of Y-CSS. Multiple case studies—consisting of 10 co-creative Y-CSS projects with young citizen scientists (YCS) aged between about 13-29 years old across nine countries in Europe—will provide knowledge about the positive drivers of social inclusion in general. The cases will further produce knowledge as well as innovations in relation to social participation, social belonging, and citizenship.</p> <p>The YouCount evaluation design for process and outcome evaluation of Y-CSS is a multi-method approach that spans across the whole duration of the project and is carried out by the WP4 of UNIVIE. It therefore is to be classified as current work in progress, as some methods only just have been implemented and will be analyzed in the future, to estimated cross-case comparisons. The deliverable aims at making the research design, as well as the current stand of the evaluative studies, transparent and publicly available. This happens in the spirit of open science, with the goal of doing “Science for and with Society”. Hereby outlined is the theoretical design, the way of carrying it out, and the current stand of each study implementation in the overall project.</p> <p>Moreover, the D4.1 includes open data regarding the outcome methodology (pre-survey questionnaire) and a sav.file with a sample of open data collected from the current pre-survey data. See more details in the report. The attached sav.-file can provide knowledge about the used variables, to estimate occurring answering patterns very roughly, ad to familiarize with the implementation of such a pre-post-survey. However, it is to be noted that this data set is exemplary, anonymized and potentially also not complete yet and must be handled and used accordingly. Due to the relatively low number of participants (yet), this research is to be characterized as early stage research and only depicts a moment in time. This being said, the YouCount project is designed to gather a huge quantity of data that promises a variety of concrete research outputs, so future data samples will be richer for in-depth analyses. At this point, more quantitative as well as qualitative data is needed to estimate real impacts of Y-CSS in all its facettes.</p>
Sārnāth, Uttar Pradesh. Standing Buddha.
<p>Drawing of a standing Buddha from Sārnāth by Markham Kittoe, now in the British Library (WD2977), original sculpture in the British Museum, London, no. 1880-6 (Transferred from the India Museum).</p>
Nepal. Standing Buddha, with inscripton.
<p>Nepal. Standing Buddha, with inscripton, as documented 05/1979 (Collection of the Cleveland Museum of Art)</p>
Soil, climatic, physiographic and stand data in Pinus sylvestris and Pinus halepensis plantations in Spain
<p>This dataset contains information about soil physical, chemical and biochemical, climatic, physiographic and stand parameters of 32 plots belonging to the Spanish National Forest Inventory (SNFI) located in <em>Pinus halepensis</em> Mill. plantations and 35 plots belonging to the Sustainable Forest Management Research Institute (iuFOR; University of Valladolid and INIA) located in <em>Pinus sylvestris </em>L. plantations in Spain.</p> <p>Parameters included in the dataset: <br> Plot: plot identification in the SNFI and iuFOR networks.<br> Species: species present in each plot (1: Pinus sylvestris; 2: Pinus halepensis)<br> Slope: gradient in the plot in percentage.<br> Altitude: elevation of the plot in meters above the sea level<br> Latitude and Longitude: geographical coordinates of the plots in degrees<br> Density: number of trees per hectare in the plot<br> Dg: quadratic mean diameter in centimetersç<br> Hm: mean height in meters of the trees in the plot<br> H0; dominant height in meters of the trees in the plot<br> BA: basal area of the plot in square meters per hectare<br> SI: site index; dominant height of the trees in the plot at the reference age (80 years for Pinus halepensis and 50 years for Pinus sylvestris stands) <br> SQ: the site quality class<br> Age: average age in years of the trees in the plot<br> AW: soil available water in percentage<br> CO: soil coarse particles in percentage<br> Porosity: soil porosity in percentage<br> CLAY: clay content in soil in percentage<br> SILTUS: silt content in soil following the USDA criteria in percentage<br> SILTIS: silt content in soil following the International criteria, in percentage<br> SANDUS: sand content in soil following the USDA criteria, in percentage<br> SANDIS: sand content in soil following the International criteria, in percentage<br> OHT: organic horizon thickness in the plot in centimeters<br> ([C/N]L): the total carbon to total nitrogen ratio in the litter fraction of the organic horizon<br> ([C/N]FH): the total carbon to total nitrogen ratio in the fragmented plus humified fractions of the organic horizon <br> L: amount of litter fraction in the organic horizon in tons per hectare<br> FH: amount of fragmented plus humified fraction in the organic horizon in tons per hectare. <br> pH: soil pH value <br> CEC: cation exchange capacity in soil in centimoles of charge per kilogram of soil (Bascomb, 1964)<br> EOC: amount of easily oxidizable C in soil in percentage (Walkley and Black, 1934)<br> AP: amount of available phosphorus in soil in miligrams per kilogram of soil extracted with anion exchange membranes and determined with colorimetry (Murphy and Riley, 1962)<br> TN: total N in soil in percentage<br> TOC/TN: total organic C to total N ratio in soil<br> Ca, Mg, Na, K: exchangeable calcium, magnesium, sodium and potassium in soil in centimoles of charge per kilogram of soil (Schollenberger and Simon, 1945)<br> WSP: water soluble phenols in soil in micrograms of TAE per gram of soil (Box, 1983)<br> Carbonates: amount of carbonates in soil in percentage (Bundy and Bremner, 1972)<br> React_carb: amount of reactive carbonates in soil in percentage (Bashour and Sayegh, 2007)<br> Gypsum: amount of gypsum in soil in centimoles of charge per kilogram of soil (Richards, 1954)<br> Cu, Fe, Mn, Zn: amount of copper, iron, manganese and zinc in miligrams per kilogram of soil (Lindsay and Norvell, 1978)<br> EA: soil exchangeable acidity in centimoles of charge per kilogram of soil (Bascomb, 1964)<br> Sat: base saturation of soil in percentage <br> AlA, FeA, MnA: amorphous aluminum, iron and manganese (AlA, FeA, MnA) in soil in centimoles of charge per kilogram of soil (Bascomb, 1968)<br> AlM, FeM, MnM: organically bound aluminum, iron and manganese in soil in centimoles of charge per kilogram of soil (Blakemore et al. 1987) <br> AlE: exchangeable aluminum in soil in centimoles of charge per kilogram of soil (Bertsch & Bloom, 1996)<br> AlI: inorganic aluminum in soil in centimoles of charge per kilogram of soil (Mc-Keague et al., 1971)<br> Cmic, Nmic, Pmic: amount of microbial biomass carbon, nitrogen and phosphorus in soil in milligrams per kilogram of soil (Vance et al. 1987)<br> Cmin: amount of mineralizable carbon in soil in milligrams per kilogram of soil (Isermeyer, 1952)<br> Cmin/TOC: mineralizable carbon to total organic carbon ratio <br> Cmic/TOC: microbial biomass carbon to total organic carbon ratio<br> qCO2: microbial metabolic quotient (Cmin/Cmic) in soil in grams per week and gram of soil<br> FDA: fluorescein diacetate hydrolysis reaction (Alef and Nannipieri, 1995) in milliunits per gram of dry soil (nanomoles of fluorescein diacetate produced per gram of soil and minute)<br> DHA: dehydrogenase activity (Casida et al., 1964) in milliunits per gram of dry soil (nanomoles of triphenyl formazan produced per gram of soil and minute)<br> AcPhos, AlkPhos: acid and alkaline phosphatase activity (Tabatabai and Bremner, 1969) in milliunits per gram of dry soil (nanomoles of p-nitrophenol produced per gram of soil and minute)<br> Urease: urease activity in soil (Hofmann, 1963) in milliunits per gram of dry soil (nanomoles of N per gram of soil and minute)<br> Catalase: catalase activity (Tabatabai and Beck, 1971) in milliunits per gram of dry soil (nanomoles of O<sub>2</sub> produced per gram of soil and minute)<br> MAT: mean annual temperature in degrees centigrade (Ninyerola et al., 2005)<br> MMWM: mean maximum temperature of the warmest month in degrees centigrade (Ninyerola et al., 2005)<br> MMCM: mean maximum temperature of the coldest month in degrees centigrade (Ninyerola et al., 2005)<br> MTWM: mean temperature of the warmest month in degrees centigrade (Ninyerola et al., 2005)<br> MTCM: mean temperature of the coldest month in degrees centigrade (Ninyerola et al., 2005)<br> TP: total precipitation in millimeters (Ninyerola et al., 2005)<br> PW, PSP, PSU, PA: winter, spring, summer and autumn precipitation in millimeters (Ninyerola et al., 2005)<br> PET, RET: potential and real evapotranspiration in millimetres (Thornthwaite, 1949 and Thorntwaite and Mather, 1955) <br> Deficit: mean annual hydric deficit in millimeters (Thornthwaite, 1949 and Thorntwaite and Mather, 1955) <br> Surplus: mean annual hydric surplus in millimetres (Thornthwaite, 1949 and Thorntwaite and Mather, 1955) <br> AHI: Annual Hydric Index (Thornthwaite, 1949)<br> Martonne: Martonne index (De-Martonne, 1926)<br> Lang: Lang index (Lang, 1919)</p> <p>Code -999.99 indicates missing values.</p>
Hamlāpurī (हम्लापुरी, Ramtek Tehsil, Nagpur District, Maharashtra). Standing Buddha. Nagpur, Central Museum
<p>Hamlāpurī (हम्लापुरी, Ramtek Tehsil, Nagpur District, Maharashtra). Standing Buddha. Nagpur, Central Museum. High resolution TIFF of http://doi.org/10.5281/zenodo.1473583</p>
Circular seal in a copper alloy engraved with a standing female figure, probably Lakṣmī; inscription at one side.
<p>Circular seal in a copper alloy engraved with a standing female figure, probably Lakṣmī; inscription at one side. British Museum 1897,0528.4.</p>
Global Cluster Test Results: Pathogenic Fungi in Decayed Norway Spruce Stands
<p><strong>Accessing the Results:</strong> Users can retrieve the test results by opening the dataset <code>Global_cluster_test_results.RData</code> in an R session and using the functions inside the script of the same name.</p> <p><strong>Description: </strong>This dataset provides global cluster test results analyzing the spatial distribution of pathogenic fungi in 273 Norway spruce stands in Norway (Lara et al., 2024). The stands, composed mainly of Norway spruce (27% to 100%), also include Scots pine and birch. It focuses on spatial patterns of decayed spruce trees, offering p-values, clustering metrics, and other parameters from statistical analyses.</p> <p><strong>Analysis:</strong> The dataset includes results from three global cluster tests (Tango, 2010):</p> <ul> <li>Tango's Nearest Neighbors (TNN)</li> <li>Tango's Double Exponential Clinal (TCN)</li> <li>Diggle and Chetwynd’s (DC)</li> </ul> <p><strong>Methodology:</strong> Cluster testing employed 1,000 Monte Carlo simulations for each test across all stands to establish null distributions and adjusted p-values, ensuring robust statistical assessments under the random labeling hypothesis: H0: the observed n0 decayed trees are a random sample from the entire sample of size n = n0 + n1 (decayed trees + healthy trees) (Tango, 2010).</p> <p> </p>
Daily temperature data at Adelaide, Australia taken in a Glaisher thermometer stand (1856–1952) and a homogenised daily temperature dataset for Adelaide (1856–2019)
<p>23000_Adelaide_Glaisherstand_Tx_data.tsv and 23000_Adelaide_Glaisherstand_Tn_data.tsv: Daily maximum and minimum temperature observations for Adelaide,South Australia, taken in a Glaisher thermometer stand from November 1856 to July 1947. Data are given in Station Exchange Format (SEF, https://github.com/C3S-Data-Rescue-Lot1-WP3/SEF/wiki).</p> <p>23000_combined_homogenised_data.tsv: A homogenised daily temperature dataset for Adelaide, South Australia, from January 1859 to December 2019. Data are provided in the format Year, Month, Day, Maximum temperature (degrees Celcius), Minimum temperature (degrees Celcius).</p> <p>Images of the data source for File 1 are available from the Australian Meteorological Association at https://www.met-acre.net/MERIT/AMETA.html. </p> <p>The data are shared under Attribution-NonCommercial 4.0 International licence (CC BY-NC 4.0)</p>
Stand structure and tree population dynamic attribute dataset of long abandoned strict forest reserves
<p>We provide an integrated dataset of two consecutive forest inventories, both containing plot-level, and individual tree-level data. The first provides the descriptions and measuring units (or categories) of plot-level variables (Table 1). The plot level table contains 233 records (rows), one for each selected permanent plot of six strict forest reserves located in Hungary. This dataset is georeferenced and contains information on inventories and basic stand structure attributes (Table_1_Plots ESRI shape format). </p> <p>The individual tree-level datasets were acquired by the sampling procedure, detailed in section 2.2. Species, dendrometric attributes, relative crown position, health, and decay status were documented for each tree belonging to the samples. Table 2 provides the descriptions and measuring units (or categories) of tree-level datasets in detail. Furthermore, it provides a tree history classification based on the interpretation of tree status changes. According to a simple scheme of the life and dead history of a tree, it could be classified into four main phases: establishment/regeneration phase; developmental phase; death and gradual decay of the tree trunk; terminated in decomposed/disintegrated state. The main events along these phases are ingrowth regeneration; death of the tree (mortality); disaggregation and decomposition of deadwood. We classify each sampled tree individuals into tree history categories (events and phases, Table 3) that can provide population dynamic aspects at stand level by appropriate tree aggregation functions.</p> <p>Relational link can be set between the plot-level and tree-level datasets based on the unique identification code of the site and sampling plots.</p>
Dataset (81 forest parcels) supplementing the publication "Owner attitudes and landscape parameters drive stand structure and valuable habitats in small-scale private forests of Lower Saxony (Germany)"
<p>The dataset about 81 small-scale private forest parcels contains the answer variables and predictors used in the publication "Owner attitudes and landscape parameters drive stand structure and valuable habitats in small-scale private forests of Lower Saxony (Germany)".</p>
SKY-LAUT – Carriage-based laser scanner data from Austrian forest stands
<p>This dataset contains 3D point clouds, automatically calculated single tree parameters, reference data, and application videos of carriage-based laserscanning in cable yarding operations. Point clouds from 8 cable corridors and 4 scan varaints are provided in .las format in the folder point_clouds.zip. The individual files are labeled with numeric cable corridor IDs and scan variant IDs. According to standard conventions, a .las file contains a header block, variable-length records, and the point cloud data. The .las files can be read, visualized, and processed with common software programs for point cloud processing (e.g., CloudCompare), and they can also be handled with the free statistical software (e.g., R Foundation for Statistical Computing, Vienna, Austria). The reference and algorithm dataset is provided in a comma-separated values (CSV) file (algorithm_results_reference_data.csv ) and contains the manual and automatic measurements of the single-tree attributes. "stand_id" marks the stand (can be stand_1 or stand_2). "cable_corridor" can range from 1 to 8 and "scan_variant" from 1 to 4. "tree_id_ref" is a continuous id for the trees from the reference data collection. "x_ref", "y_ref", "dbh_ref", "h_ref" and "tree_species" are the coordinates, diameter at breast heigths, tree heights and tree species from the reference data collection. "x_cbls", "y_cbls", "dbh_ref" and "h_cbls" are the coordinates, diameter at breast heigth, tree height and tree species from carriage-based laser scanning point clouds and the automatic algorithm. "dist_to_skyline" is the orthogonal distance from the tree to the skyline. "tree_detection" indicates whether a tree was detected "correct", "non" oder "false" by the automatic algorithm.</p>
Soil physical, chemical, and root data from forest stands at Thompson Farm in Durham, NH
In July 2019, quantitative soil pits were excavated alongside replicate power cores from three mature oak-pine forest stands at Thompson Farm (Durham, NH) to characterize soil physical and chemistry properties as well as root depth profiles in control and treatment plots associated with the Thompson Farm DroughtNet project, as well as in the footprint of the Ameriflux tower. Mean total sampling depth across all pits and cores was 84cm, though half of samples reached 100 cm or deeper. Chemical and isotopic analyses for each sample layer include pH, total N, total C, and stable isotope ratios of N and C. Physical data include soil mass, rock volume, bulk density, and soil texture. Oven-dried root mass in each sample layer was recorded in multiple size categories (from pits) or sorted to species (from power cores).
Snag-fall patterns following stand-replacing fire vary with stem characteristics and topography in subalpine forests of Greater Yellowstone
We assessed the stem- and landscape-level drivers of snag persistence and snag-fall mode within the area burned as stand-replacing fire in the 1988 Yellowstone Fires in Yellowstone National Park, Wyoming, USA. Snags were sampled 14-15 years postfire (n = 131) and again in a separate set of plots 34 years postfire (n = 55). Stem characteristics such as species identity (e.g., lodgepole pine, whitebark pine, Engelmann spruce, subalpine fir, and Douglas-fir), diameter at breast height, whether the tree was alive or dead at the time of fire, and the mode of snag-fall (snapping or uprooting) were measured and used to explain patterns of snag persistence and modes of snag-fall. In addition, plot-level environmental variables (e.g., slope, aspect, elevation, stand density) were measured and related to the proportion of stems still standing as snags at 14-15 and 34 years postfire. Data collection is complete and is part of a forthcoming manuscript in revision at Forest Ecology and Management.
Data for: Sparse subalpine forest recovery pathways, plant communities, and carbon stocks 34 years after stand-replacing fire (Greater Yellowstone Ecosystem, Wyoming, USA; 2022)
We assessed postfire forest recovery pathways, stem densities, understory plant communities, and carbon stocks across 55 plots in areas exhibiting sparse and reduced forest recovery 34 years after the 1988 Yellowstone Fires in the Greater Yellowstone Ecosystem, Wyoming, USA. Recovery pathways were identified using plot-level frequency distributions of tree ages and correlated with potentially important biotic and abiotic variables (e.g., elevation, seed source distance). Species- and age-specific stem densities were similarly regressed across environmental factors to determine variability in forest recovery across the sampled landscape. Understory plant communities were sampled in 0.25m-square quadrats and environmental drivers of individual species occurrence and whole compositional shifts were determined. Finally, carbon stock sizes were derived from field measures of tree characteristics, understory cover, and soil combined with regionally derived allometric equations. Data collection is complete and is part of a forthcoming manuscript at Ecological Monographs.
American Goshawk habitat data from nest stands and random points within the Minidoka Ranger District, Sawtooth National Forest, USA
This data supported analysis of American Goshawk (Astur atricapillus) nest stand habitat and was collected within the Minidoka Ranger District of the Sawtooth National Forest in southern Idaho and northern Utah from 2017-2020. The central goal of this research was to develop management tools that demonstrate the utility of conducting analyses at multiple spatial scales as well as using both parametric and machine learning approaches. The stand-level dataset includes variables collected by hand in the field at nest stands and paired random forested sites 300 meters away. It also includes some terrain variables based on remote sensing data. Variables included in the stand-level data table include nest, distance to edge, distance to road, distance to water, division, dominant tree species, canopy closure, Stand Density Index (SDI), Trees per hectare, elevation, slope, Topographic Position Index (TPI), northness, eastness, Diameter at Breast Height (DBH), DBH variance, tree height, tree height variance, and crown depth. We recommend that the stand-level data be used to identify relevant variables and their thresholds for forest managers due to its high resolution. The forest-wide dataset includes only variables collected using various remote sensing datasets at nests and random forested points throghout the Minidoka Ranger District of the Sawtooth National Forest. Variables included in the forest-wide data table include nest, canopy closure, elevation, slope, TPI, northness, eastness, distance to road, distance to water, distance to edge, tree height, and crown depth. We recommend that the forest-wide data be used to identify areas of high suitability for goshawk occupancy across the study area along with sites that could become suitable habitat with management intervention. Latitude and longitude data, while used in our analyses, are excluded from the data tables to protect breeding goshawks from disturbance.
Looking beyond the mean: Drivers of variability in postfire stand development of conifers in Greater Yellowstone
High-severity, infrequent fires in forests shape landscape mosaics of stand age and structure for decades to centuries, and forest structure can vary substantially even among same-aged stands. This variability among stand structures can affect landscape-scale carbon and nitrogen cycling, wildlife habitat availability, and vulnerability to subsequent disturbances. We used an individual-based forest process model (iLand) to ask: Over 300 years of postfire stand development, how does variation in early regeneration densities versus abiotic conditions influence among-stand structural variability for four conifer species widespread in western North America? We parameterized iLand for lodgepole pine (Pinus contorta var. latifolia), Douglas-fir (Pseudotsuga menziesii var. glauca), Engelmann spruce (Picea engelmannii), and subalpine fir (Abies lasiocarpa) in Greater Yellowstone (USA). Simulations were initialized with field data on regeneration following stand-replacing fires, and stand development was simulated under historical climatic conditions without further disturbance. Stand structure was characterized by stand density and basal area. Stands became more similar in structure as time since fire increased. Basal area converged more rapidly among stands than tree density for Douglas-fir and lodgepole pine, but not for subalpine fir and Engelmann spruce. For all species, regeneration-driven variation in stand density persisted for at least 105 years postfire, and for lodgepole pine, early regeneration densities dictated among-stand variation for 203 years. Over time, stands shifted from competition-driven convergence to environment-driven divergence, in which variability among stands was maintained or increased. The relative importance of drivers of stand structural variability differed between density and basal area and among species due to differential species traits, growth rates, and sensitivity to intraspecific competition versus abiotic conditions. Understanding dy
Coarse woody debris volume and mass from line transect inventory from reference stands and inventory plots of the Pacific Northwest, 1997 to 2005
These data can be used to provide an inventory of the volume of coarse woody debris stored in forests.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.