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5,784 results for “Density”
Results of "Storm Time Data Assimilation in the Thermosphere Ionosphere with TIDA" CHAMP, GRACE-A, and GRACE-B neutral density data assimilation into CTIPe for 2003 Halloween Storms
<p># README</p> <p>Results for the article "Storm Time Neutral Density Assimilation in the Thermosphere Ionosphere with<br> TIDA".</p> <p>There are three storms presented here:</p> <p>1. 2003 storm: October 26-30, 2003<br> 2. 2004 storm: July 26-30, 2004<br> 3. 2002 storm: September 27 - October 2, 2002.</p> <p>For each of these three storms, there are four runs. For each storm, we've done a run<br> assimilating all satellites, and then three more assimilating each satellite individually and<br> comparing against the others.</p> <p>Each directory name before underscore identifies the date the run was<br> started. After the underscore identifies the date assimilated.</p> <p>This readme uses the notation that in curly brackets the satellites assimilated are given.</p> <p>- a stands for GRACE-A<br> - b stands for GRACE-B<br> - c stands for CHAMP</p> <p>The runs are summarized below:</p> <p>1. 2003 storm: {a, b, c}: 2021-12-29T1259_...<br> 3. 2003 storm: {a}: 2022-01-01T1654_...<br> 4. 2003 storm: {b}: 2022-01-02T1423_...<br> 2. 2003 storm: {c}: 2021-12-29T2342_...</p> <p>5. 2004 storm: {a, b, c}: 2022-01-03T1614_...<br> 6. 2004 storm: {a}: 2022-01-04T1027_...<br> 7. 2004 storm: {b}: 2022-01-04T2144_...<br> 8. 2004 storm: {c}: 2022-01-05T0502_...</p> <p>9. 2002 storm: {a, b, c}: 2022-01-02T2025_...<br> 10. 2002 storm: {a}: 2022-01-05T1056_...<br> 11. 2002 storm: {b}: 2022-01-05T1854_...<br> 12. 2002 storm: {c}: 2022-01-06T1017_...</p> <p>## Example result directory</p> <p>2021-12-29T1259_d2003-10-27<br> ├── density_champ_density.csv<br> ├── density_grace-a_density.csv<br> ├── density_grace-b_density.csv<br> └── inputs<br> ├── reference_2003-10-27_input.txt<br> └── special_2003-10-27_input.txt<br> <br> 1 directory, 5 files</p> <p>## Zenodo doesn't support directories</p> <p>So, the file structure has been flattened in the following way:</p> <p>Before: ./aaa/bbb/ccc.png</p> <p>After: ./aaa-bbb-ccc.png</p> <p>https://unix.stackexchange.com/~/45659</p> <p> </p>
Soil bulk density [10x kg/m3] for continental Europe at 30 m spatial resolution for period 2000-2020: Open Soil Data Cube for Europe
<p>Predictions are based on the 3D Ensemble Machine Learning framework, as implemented in the R environment for statistical computing (Hengl & MacMillan, 2019; Hengl, et al., 2021). For each pixel we provide prediction errors as 1 standard deviation in either log or the original variable scale.</p> <p>The short description of currently available soil properties: db_od = bulk density over dry [kg/m3 ⨉ 10];</p> <p>Soil properties were predicted at fixed depths:</p> <p> Surface soil = s0..0cm,<br> Subsoil 1 = s30..30cm,<br> Subsoil 2 = s60..60cm,<br> Subsoil 3 = s100..100cm.</p> <p>To produce estimates for depth intervals e.g. 0–30 cm, 0–100 cm best use the trapezoidal rule formula.</p> <p>Periods: 2000 (2000–2003), 2004 (2004–2007), 2008 (2008–2011), 2012 (2012–2015), 2016 (2016–2019), 2020;</p> <p>The bulk density maps are also provided in 10 kg / m-cubic to reduce total data size; to convert values to kg / m-cubic multiply by 10 e.g. 120 = 1200 kg / m-cubic = 1.2 t / m-cubic.</p>
diFUME Population Density V0.1
<p>Description:</p> <p>Annual statistics per city block on residential population (by age group) and workplace employees (<a href="https://www.basleratlas.ch/">https://www.basleratlas.ch/</a> ) are used to derive maps of annual night-time and daytime building-scale population density (inhabitants per m2) for weekdays and weekends. The spatial resampling of the population is based on the assumption of proportionality between building inhabitants and building volume (estimated as mean building height×building plan area, derived by land cover and DSM products). Considering the building type, building volume is separated to residential volume and workplace volume, so that population is redistributed between night-time, daytime, workdays and weekends.</p> <p> </p> <p>Data specifications:</p> <p>CRS: EPSG:32632 - WGS 84 / UTM zone 32N - Projected</p> <p>Spatial Extent: 392120.0,5266860.0 : 395160.0,5269840.0</p> <p>Temporal Extent: 2018 - 2020</p> <p>Units: meters</p> <p>Width: 608</p> <p>Height: 596</p> <p>Bands: 1</p> <p>Pixel Size: 5,-5</p> <p>Data type: Float32 - Thirty two bit floating point</p> <p>GDAL Driver Description: GTiff</p> <p>GDAL Driver Metadata: GeoTIFF</p>
Results of "Ensemble Kalman Filter for the Thermosphere Ionosphere", CHAMP neutral density assimilation into CTIPe for March 20, 2007
<p>These data are the result of assimilating neutral density measurements from the CHAMP satellite on March 20, 2007 into the CTIPe model and and comparison of results with observations made by the GRACE satellite. Data assimilation is performed in three configurations: Configuration (i) is ds, state correction. Configuration (ii) is dfds, both input estimatation and state correction. Configuration (iii) is df, estimation of model inputs only.</p> <p>This data is associated with the following publication:</p> <blockquote> <p>Codrescu S., M.V. Codrescu, and M. Fedrizzi (2018), An Ensemble Kalman Filter for the Thermosphere-Ionosphere, Space Weather, 16, doi:<a href="http://dx.doi.org/10.1002/2017SW001752" title="Link to external resource: 10.1002/2017SW001752">10.1002/2017SW001752</a>.</p> </blockquote> <p> </p>
GLOBAL SNAPSHOT Physician Distribution and Density of Physicians per 1000 population - Worldwide 2021
<p>The chart presents the most up-to-date data (2021) available for 49 of the world’s 195 countries, focusing on the total number of physicians and the number of physicians per 1000 population(1). The countries are categorized into four income groups based on World Bank classifications, which are updated annually on July 1st each year(2).</p> <p>Only 25% of the countries present current data. This information is critical for decision-making for healthcare planning and policy development. Equally crucial, is for researchers to have comparable data to propose initiatives, to establish benchmarks and for crafting holistic strategies to gauge and advance progress in healthcare systems globally.</p> <p>Data sources: UnData <a href="https://data.un.org/">https://data.un.org/</a></p> <p>Visualization tools used: RAWGraphs <a href="https://www.rawgraphs.io/">https://www.rawgraphs.io/</a>, MS PowerPoint and Microsoft Excel</p> <p>Intended Audience: Academics and Researchers; Students and Educators; Healthcare Administrators and Policy Makers; Non-Governmental Organizations</p> <p>The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.</p> <p>The NNLM Data Visualization Challenge happens through work funded by the National Institutes of Health's National Library of Medicine, grant number U24LM013751</p> <p> </p> <p>References:</p> <p>1. United Nations, Department of Economic and Social Affairs. 10 Health Personnel. In: Statistical Yearbook. 66th issue (2023). New York: United Nations; 2023. (ST/ESA/STAT/SER.S/42). [Dataset available at UnData] <a href="https://data.un.org/_Docs/SYB/CSV/SYB66_154_202310_Health%20Personnel.csv">https://data.un.org/_Docs/SYB/CSV/SYB66_154_202310_Health%20Personnel.csv</a></p> <p>2 World Bank. World Bank Country and Lending Groups. World Bank Data Help Desk [Internet]. [cited 2024 Apr 5]. Available from:<a href="https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and-lending-groups"> https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and-lending-groups</a></p>
Direct observation of electron density reconstruction at the metal-insulator transition in NaOsO3
<p>Open access data set for manuscript "Direct observation of electron density reconstruction at the metal- insulator transition in NaOsO3" published in Physical Review B, 98, 115116 (2018)</p>
Soil bulk density (fine earth) 10 x kg / m-cubic at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution
<p>Soil bulk density (fine earth) 10 x kg / m<sup>3</sup> at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution. Processing steps are described in detail <strong><a href="https://gitlab.com/openlandmap/global-layers/tree/master/soil">here</a></strong>. Antarctica is not included.</p> <p>To access and visualize maps use: <a href="http://www.openlandmap.org/">OpenLandMap.org</a></p> <p>If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels:</p> <ul> <li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/issues">https://gitlab.com/openlandmap/global-layers/issues</a> </li> <li>General questions and comments: <a href="https://disqus.com/home/forums/landgis/">https://disqus.com/home/forums/landgis/</a></li> </ul> <p>All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention:</p> <ul> <li>sol = theme: soil,</li> <li>bulkdens.fineearth = variable: soil bulk density,</li> <li>usda.4a1h = determination method: laboratory method code,</li> <li>m = mean value,</li> <li>250m = spatial resolution / block support: 250 m,</li> <li>b10..10cm = vertical reference: 10 cm depth below surface,</li> <li>1950..2017 = time reference: period 1950-2017,</li> <li>v0.2 = version number: 0.2,</li> </ul>
Generalization of a density-dependent ecosystem function in dominant aquatic macroinvertebrates
<div> <div> <p>This Zenodo record contains the supporting data and code for the publication 'Generalization of a density-dependent ecosystem function in dominant aquatic macroinvertebrates', published in Oikos (<a title="DOI to publication" href="https://doi.org/10.1111/oik.10774">https://doi.org/10.1111/oik.10774</a>). The data are described in detail in the corresponding publication. The data archive contains a ReadMe file, two text files with the empirical data, and a corresponding R script for analysis. All required data to reproduce the full analysis from the original publication are provided.</p> <p>In order to reproduce the analysis and figures, run <code>DensityDependenceAnalysis20240319.R</code>. Make sure that your working directory is the actual folder containing the data files <code>Data_Field.txt</code> and <code>Data_Lab.txt</code>. If run in Rstudio, this should happen automatically. Else this is easily achieved by (re)starting R (or R Studio) by double-clicking the R script file from the folder. The script will produce all the figures from the paper, organized in a folder <code>AnalysisYYYYMMDD</code> and two subfolders <code>CheckFigs</code> and <code>SuppFigs</code>. Figures are prepared as pixel graphics (PNG).</p> </div> </div>
Dataset of "Black Titanium Oxide/Activated TaS2 Flakes Photoelectrode for Plasmon Assisted Hydrogen Evolution at Neutral pH at High Current Density"
<p>Nanotubular structure of black titania with sputtered gold and incorporation of 3R-TaS2 self-activated flakes for high current density and neutral pH usage for hydrogen evolution reaction. Dataset consists of electrochemical data (LSV, EIS, CA), x-ray difractograms, Raman spectra, SEM images with EDX mapping, UV-vis spectra, DEMS records, ICP-MS records, XPS spectra and compositional analysis and BET records.</p>
Dataset of the paper Zeitler et al. (2021) : Scale factors of the thermospheric density - a comparison of SLR and accelerometer solutions
<p>The dataset consists of two .h5 files. "Dataset_DOGSOC_GROOPS.h5" contains the 12-hour thermospheric density scale factors of the satellites Starlette, Stella, and Larets of Chapter 4.2. Each path includes a file with three columns. The first column contains the time vector in JD2000.0. The second column and third column contain the scale factor time series (unfiltered, smoothed with a 10-day moving average filter). The following scale factor time series are available:</p> <ul> <li>DOGSOC/starlette</li> <li>DOGSOC/stella</li> <li>DOGSOC/larets</li> <li>GROOPS/starlette</li> <li>GROOPS/stella</li> <li>GROOPS/larets</li> </ul> <p> </p> <p>"Dataset_SLR_ACC.h5" contains the 12-hour thermospheric density scale factors from SLR measurements (DOGS-OC) to the satellites Starlette, WESTPAC, Stella, and Larets and from accelerometer measurements of the satellites GRACE and CHAMP of Chapter 4.1. Each path includes a file with three columns. Again, the first column contains the time vector in JD2000.0, and columns 2 and 3 contain the thermospheric density scale factors (unfiltered, smoothed with a 10-day moving average fitler). The following scale factor time series are available:</p> <ul> <li>ACC/CHAMP</li> <li>ACC/GRACE</li> <li>SLR/starlette</li> <li>SLR/westpac</li> <li>SLR/stella</li> <li>SLR/larets</li> </ul> <p>Further information about the data can be found in the file "description_of_datasets_v1.txt" or in the paper Zeitler et al. (2021): Scale factors of the thermospheric density - a comparison of SLR and accelerometer solutions. Journal of Geophysical Research: Space Physics.</p>
Relative density variations of common vole population based on index transect, Septfontaines - Le Souillot, France (1990-2000)
<p>Transects were walked from village to village along a transect line. Common vole (<em>Microtus arvalis</em>) activity indices were recorded in every ten pace interval from October 1990 to April 2000. In 2014, the geographical coordinates of each interval has been computed by spatial interpolation based on georeferenced maps. Therefore, users must be aware that individual locations of intervals are unprecise, but not the general bearing of the transect in the landscape and interval succession. See articles published for reference and more details.</p> <p>During the same time span, small mammmals (including common voles) were sampled using live-trapping, see <a href="https://doi.org/10.5281/zenodo.6997316">10.5281/zenodo.6997316</a></p> <p><strong>FILE DESCRIPTION:</strong></p> <p><a href="https://zenodo.org/record/7544358/files/db.txt?download=1">db.txt </a>index transect file</p> <ul> <li>name: transect name</li> <li>date: on eight digits, '19921014' reads 14/10/1992</li> <li>ID: interval ID = number (within a given transect at a given date)</li> <li>Habitat: (indicative) the habitat category crossed. Just mentioned when passing from one category to the other; the following intervals are assumed to belong to this habitat</li> <li>ma1: number of <em>Microtus</em> holes; A, 1-5 holes; B, 6-10 holes; C > 10 holes</li> <li>ma2: answered only if A, B, or C are defined in ma1; NA, not answered (ma1 not defined), 0, zero faeces, 1 some faeces or fresh indices (runways with grass freshly cut, etc.); 2 many faeces in heaps</li> <li>long: longitude (WGS84)</li> <li>lat: latitude (WGS84)</li> </ul> <p><a href="https://zenodo.org/record/7544358/files/StudyAreaBoundingBox.kml?download=1">StudyAreaBoundingBox.kml</a> Bounding box of the study area.</p>
Supplementary CIF files for "Shedding Light on the Enigmatic TcO2 ⋅ xH2O Structure with Density Functional Theory and EXAFS Spectroscopy"
<p>Optimized geometries from the paper "Shedding Light on the Enigmatic TcO2 ⋅ <em>x</em>H2O Structure with Density Functional Theory and EXAFS Spectroscopy" (<a href="https://doi.org/10.1002/chem.202202235">https://doi.org/10.1002/chem.202202235</a>), provided in CIF format.</p> <p>All structures were fully optimized (lattice vectors and atomic coordinates) using AMS/BAND (<a href="https://www.scm.com/">https://www.scm.com/</a>) with the PBE density functional, scalar relativistic effects (ZORA), and numerical atomic orbitals (NAOs) augmented with a triple-zeta polarized (TZP) set of Slater-type basis functions. For the chains, D3 dispersion corrections were also included.</p> <p> </p>
High-Throughput Density Functional Theory Screening of Double Transition Metal MXene Precursors
<p>This dataset contains density functional theory results on a set of double-transition metal MXene precursors</p>
Palmyra Atoll soil and/or wood density sampling locations used in the carbon storage analysis
This dataset provided soil and/or wood density sampling locations and values from Palmyra Atoll (2016 and 2019). Soil samples were extracted to measure organic carbon content associated with different vegetation communities in Palmyra. Wood samples were collected to measure basic wood density values for dominant woody vegetation types found in Palmyra to calculate aboveground carbon values.
Density-dependent effects of exotic brook trout on aquatic communities in mountain lakes revealed by environmental DNA and morphological taxonomy
Invasion of non-native fishes threatens freshwater biodiversity worldwide. Yet, detailed estimates of population demography for invasive species, that estimate population size and body size of the invasive species, are rarely integrated in evaluating aquatic community responses. Our study capitalized on detailed brook trout population demographic data collected for a replicated whole lake ecosystem experiment involving experimental harvesting of exotic brook trout in nine mountain lakes. We applied environmental DNA (eDNA) metabarcoding and morphological taxonomy to examine the response of crustacean zooplankton and macroinvertebrate communities to gradients in brook trout effective density and lake elevation. Density-dependent effects of brook trout on crustacean zooplankton and macroinvertebrate communities were detected even decades after their first introductions (between 1926 and 1980). However, they were moderated by environmental factors such as elevation, lake maximum depth and dissolved organic carbon. Elevation was important in structuring crustacean zooplankton and macroinvertebrate community composition. While there were differences in explanatory variables when describing communities characterized by eDNA metabarcoding and morphological taxonomy, the principal environmental factors that structured the communities were similar. Our paper highlights persisting density-dependent impacts of exotic trout on invertebrate communities even decades after first introduction, and it considers the conservation implications for lake restoration.
Kawe Gidaa-naanaagadawendaamin Manoomin Tribal-University Research Collaborative, University of Minnesota, Manoomin / Psiη (Wild Rice) Density Survey for Northern Minnesota and Wisconsin Waters
Wild Rice (Ojibwemowin: Manoomin; Dakodiapi: Psiŋ; Latin: Zizania palustris) abundance, harvest, and water level data, across the upper Great Lakes region collected by tribal organizations.
Predator effects on metamorphosis: The effects of scaring versus thinning at high prey densities.
Organisms with complex life cycles face the challenge of when to switch between habitats and foraging strategies over ontogeny in ways that improve their fitness. Metamorphosis is a well-studied life history event in animals and ecologists have spent decades trying to understand how the size at and time to metamorphosis are altered by natural stressors such as competition and predation. The challenges in interpreting the effects of predators on metamorphic decisions include the need to compare predator species that pose different levels of risk, compare the roles of predators inducing fear versus thinning of the density of prey, and examine prey life history traits and behavior over ontogeny. We addressed these challenges in a mesocosm experiment in which we introduced a high initial density of hatchling Northern Leopard Frogs (Rana pipiens) and exposed them to three different species of caged predators (to induce three different levels of fear), three rates of hand-thinning (to mimic the thinning effect of each predator), or three species of lethal predators (to cause induction and thinning). Under these initial high densities, we found that caged predators had no effects on tadpole activity, growth, and development. This outcome was likely due to the high density of tadpoles causing high competition, which can inhibit anti-predator responses. High rates of hand thinning caused decreased tadpole activity, greater mass, and faster development. Interestingly, lethal predators caused phenotypic changes that were largely in line with the hand thinning effects alone. These results suggest that at high initial prey densities, the thinning process of predation appears plays a much more important role in prey metamorphosis than induction from predatory chemical cues.
Influence of cockle bioturbation on microphytobenthic primary producers: habitat and density-dependent effect
The purpose of this study was to better understand the non-trophic interactions of benthic macrofauna, especially through their bioturbation activity, on microphytobenthos (MPB), which remain poorly studied and understood. For this purpose, a mesocosm experiment was performed, using the common cockles Cerastoderma edule. This species plays a key role in coastal ecosystem, especially impacting sediment characteristics and biogeochemistry through their bioturbation, including sediment reworking and bioirrigation. For the first time, bioturbation rates, biogeochemical fluxes at the sediment-water interface and MPB biomass and photosynthetic variables were measured at the same time. The effect of cockles density and sediment type were also investigated. This mesocosm experiment took place at the marine station of Arcachon. Experimental units consisted of PVC tubes filled with 2 types of sediment (medium sand or fine sand; samples in Arachon Bay and Baie des Veys in France). Then, 4 density of cockles were added in triplicate (0, 288, 720 and 1,297 ind. m-2), for each sediment type. The whole design was repeated twice, because in one of them luminophores were added at the top to measure sediment reworking, and could interfere with fluorescence measurement of MPB variables. All experimental units were incubated 6 days into a big tank, with artificial tide and light. Dissolved tracers were added into the natural seawater (close system) to measure bioirrigation rates of cockles. After 6 days, regarding experimental units with luminophores, they were slices and porewater was extracted to quantify bioturbation rates. Regarding units without luminophores, surface biomass and photosynthetic parameters of MPB were first measured in all unit with an IMAGING PAM. Then, oxygen and nutrient fluxes at the sediment water interface were measured using incubations. And finally, the first centimeter was sliced to measure total MPB biomass. This study demonstrated that bioturbation intens
SGS-LTER Ecosystem Stress Area - long-term density dataset following nutrient enrichment stress on the Central Plains Experimental Range in Nunn, Colorado, USA 1975-2011, ARS Study Number 3 (Reformatted to the ecocomDP Design Pattern)
This data package is formatted as an ecocomDP (Ecological Community Data Pattern). For more information on ecocomDP see https://github.com/EDIorg/ecocomDP. This Level 1 data package was derived from the Level 0 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-sgs/520/8. The abstract below was extracted from the Level 0 data package and is included for context: This data package was produced by researchers working on the Shortgrass Steppe Long Term Ecological Research (SGS-LTER) Project, administered at Colorado State University. Long-term datasets and background information (proposals, reports, photographs, etc.) on the SGS-LTER project are contained in a comprehensive project collection within the Digital Collections of Colorado (http://digitool.library.colostate.edu/R/?func=collections&collection_id=3429). The data table and associated metadata document, which is generated in Ecological Metadata Language, may be available through other repositories serving the ecological research community and represent components of the larger SGS-LTER project collection. Water, nitrogen, and water-plus-nitrogen at levels beyond the range normally experience by shortgrass steppe communities were applied from 1971 through 1975, plant densities were sampled through 1977, and then sampling resumed in 1982, with sampling frequencies changing from annually to every other year. The initial sampling from 1970 to 1974 showed that the water and water plus nitrogen treatments had the strongest effect on plant community structure, both treatments increased biomass, and exotic weed species were noted on the water plus nitrogen treatment. Later sampling from 1982 to 1991 showed a ten-fold increase in exotic weed species on the water plus nitrogen plots as compared to the controls (Milchunas and Lauenroth 1995), a community change that has persisted on this site due to a chronic elevation of soil nitrogen caused by a plant tissue/soil organic matter feedback mec
Gloeotrichia echinulata density at four nearshore sites in Lake Sunapee, NH, USA from 2005-2016
Surface densities of Gloeotrichia echinulata, a filamentous colonial cyanobacterium, were collected at four nearshore sites in Lake Sunapee, NH, USA from 2005-2016. Lake Sunapee is a large (1667 hectare surface area), oligotrophic, north temperate lake used for drinking water and recreation with a primarily forested watershed and a moderately-developed shoreline. Samples were collected approximately weekly at Herrick Cove South and Newbury mid-July to September in 2005 and at Herrick Cove South late June to mid-September in 2006. Data collection at Herrick Cove South, Newbury, South of the Fells, and Sunapee Harbor occurred from mid-June to mid-September in 2007-2008 and from May to October in 2009-2016.
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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)
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.