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3,422 results for “united states”
An ensemble of 48 perturbed-physics model (Noah-MP) estimates of the 1/8° terrestrial water storage over the conterminous United States, 1980–2015 (time-merged version)
<p>This dataset contains the 1980–2015 monthly terrestrial water storage simulated by a 48-member perturbed-physics ensemble configured from the Noah LSM with multi-physics options (Noah‑MP v3.6). Simulation outputs include the total terrestrial water storage and its constituents (snow water equivalent, four-layer soil water content from the surface down to 2 m, and the groundwater storage anomaly). The file name has four parts: the variable collection, the used parameterization, the time period, the suffix, and the compression format.</p> <p>The 48 physics configurations are generated by combining four runoff parameterizations (run1: SIMGM, run2: SIMTOP, run3: NOAHR, run4: BATS), two parameterizations of stomatal conductance (can1: Ball–Berry, can2: Jarvis), three parameterizations of soil moisture stress factor (btr1:NOAHB, btr2: CLM, btr3: SSiB), and two parameterizations of near-surface atmospheric turbulence (tub1: M-O, tub2: Chen97).</p> <p>The simulation domain covers the all of conterminous United States (25°–53°N, 125°–67°W), which is also called the NLDAS-2 testbed (Xia et al., 2012a, b). The simulations were performed at a spatial resolution of 0.125°, which is the same as for NLDAS-2 models. Details of the simulation settings and spin-up run can be found in Section 2.3 of Zheng et al. (2019) and Section 2.2 of Fei et al. (2021).</p>
Multi-model Hydropower Projections for the United States Federal Power Marketing Areas under CMIP5 Climate Change Conditions
<p>This dataset contains an ensemble of monthly hydropower generation projections for the United States Federal Hydropower plants for the periods of 1966-2005 (historical period) and 2011-2050 (future period). The dataset includes the monthly hydropower projections developed in (Kao et al. 2016) based on the Watershed Runoff-Energy Storage (WRES) model and is complemented with another ensemble based on the process-based Water Management Power (WMP) model.</p> <p>The hydrologic projections are estimated through a cascading modeling toolchain that include ten global climate change model projections (ACCESS1-0, BCC-CSM1-1, CCSM4, CMCC-CM, GFDL-ESM2M, MIROC5, MPI-ESM-MR, MRI-CGCM3, NorESM1-M and IPSL-CM5A-LR) under RCP8.5 scenario, which are dynamically downscaled with a regional climate model (RegCM4) ( Pal et al. 2007, Giorgi et al. 2012)), which then inform the Variable Infiltration Capacity (VIC) hydrology model (Liang et al. 1994). The ensemble of hydrologic projections is then informing two processes to translate runoff into hydropower projections. First, WRES models monthly river routing and employs a non-linear statistical approach relating monthly natural flow to hydropower generation, including processes such as spilling. Second, MOSART-WM (Voisin et al. 2013), a large-scale river routing and water management model, provides daily reservoir storage and regulated release at dam locations as well as regulated flow at run-of-the-river power plants. The WMP model then translates reservoir and regulated river dynamics into hydropower projections (Zhou et al. 2018). Those projections are further calibrated to monthly generation provided by the federal utilities. The US federal hydropower plants analyzed in this study include 132 facilities that were built and/or are operated by the US Army Corps of Engineers (USACE), the Bureau of Reclamation (Reclamation), and the International Boundary and Water Commission (IBWC). The electricity generation projected for these hydropower plants were aggregated by four Power Marketing Administrations (PMAs), including Bonneville Power Administration (BPA), Southeastern Power Administration (SEPA), Southwestern Power Administration (SWPA), and Western Area Power Administration (WAPA), and their associate subregions.</p> <p>The two files, <em>SWA9505V2_Gsim_PMA_WRES.mat</em> and <em>SWA9505V2_Gsim_PMA_WMP.mat</em>, represent model outputs from the two hydropower models, WRES and WMP respectively.</p> <p>Each file contains 6 variables:</p> <p>1) “Models”: the 10 global climate models (GCMs).</p> <p>2) “PMA_areas”: the 18 subregions of PMAs as defined in (Kao et al. 2015).</p> <p>3) “PMA_G_mn_6605”: 1966-2005 projected monthly hydropower generation for each PMA sub-regions. Dimension: (12 [months], 40 [years], 18 [subregions], 10 [GCMs]). Unit: MWH.</p> <p>4) “PMA_G_mn_1150”: Same as “PMA_G_mn_6605”, but for 2011-2050 projected hydropower generation.</p> <p>5) “PMA_G_yr_6605”: 1966-2005 projected annual hydropower generation. Dimension: (40 [years], 18 [subregions], 10 [GCMs]) . Unit: MWH.</p> <p>6) “PMA_G_yr_1150”: Same as “PMA_G_yr_6605”, but for 2011-2050 projected hydropower generation.</p> <p>The following journal paper details the method in creating the dataset:</p> <p><strong>Impacts of Climate Change on Subannual Hydropower Generation: A Multi-model Assessment of the United States Federal Hydropower Plants</strong></p> <p><strong>Zhou et al. (2022) Preparing for submission to Environmental Research Letters.</strong></p>
Land use and land cover changes in the contiguous United States from 1630 to 2020
<p>Through integrating multi-source data including high-resolution remote sensing image-based land use and land cover (LULC) data, model-based land use products, and historical land archives, we reconstructed historical LULC at an annual time scale and 1 km x 1 km resolution in the contiguous United States (CONUS) from 1630 to 2020. Compared to other historical LULC datasets, our data can capture the major characters of LULC as well as provide more accurate information with higher spatial and temporal resolution. The LULC data can be used for regional studies in a wide range of topics including LULC impacts on the ecosystem, biodiversity, water resource, carbon and nitrogen cycles, and greenhouse gas emissions.</p>
Canopy gaps facilitate upslope shifts in montane conifers but not in temperate deciduous trees in the Northeastern United States
<p>Many montane tree species are expected to migrate upslope as climate warms, but it is not clear if forest canopy gaps, which can facilitate tree seedling recruitment, serve as an important mechanism driving tree species range shifts. Patterns of tree seedling establishment can inform us about early stages of tree species migrations and are critical to examine in the context of global climate change.</p> <p>We contrasted elevational distributions of tree seedlings both within and outside of forest canopy gaps with the distributions of conspecific adults and saplings across the deciduous-coniferous ecotone on ten mountains in four states of the northeastern United States. We tested if seedling distributions of four dominant tree species (<em>Abies balsamea, Picea rubens, Acer saccharum</em>, and<em> Fagus grandifolia</em>) were shifted upslope of conspecific adult and sapling distributions. We also examined if this shift was facilitated by canopy gaps and what environmental drivers affected species distributions.</p> <p>There was limited seedling recruitment of dominant tree species at the temperate-coniferous ecotone, which we attributed to (i) an observed downslope shift of seedling distributions of the low-elevation deciduous species (<em>Acer saccharum, Fagus grandifoli</em>a) and (ii) an upslope shift in seedling distributions of the high-elevation conifers (<em>Abies balsamea, Picea rubens</em>). The upslope shift of conifer seedlings contrasts with our previous research at these sites which observed downslope shifts of sapling distributions in <em>Picea rubens</em>, suggesting that seedlings may be responding to more recent climate warming. Canopy gaps in high-elevation conifer forests facilitated these upslope shifts by promoting conifer seedling recruitment. However, gaps at lower elevations did not play a significant role in seedling recruitment or the observed downslope shifts of the dominant deciduous species. Climate was the dominant predictor of adult tree distributions whereas both climate and soil were important predictors of seedling distributions.</p> <p><strong><em>Synthesis</em></strong>. Our study illustrates that tree seedlings have the potential for monitoring the early stages of tree species migrations, and particularly so in canopy gaps in high-elevation conifer forests. Further, we stress that species range shifts are sensitive to local scale heterogeneity in light availability (i.e., canopy gaps) and other non-climatic factors.</p>
Herbaceous production lost to tree encroachment in United States rangelands
<p>Data products and modeling code supporting the publication:</p> <p><strong>Herbaceous production lost to tree encroachment in United States rangelands</strong> in the <em>Journal of Applied Ecology</em>.</p> <p>Manuscript DOI: 10.1111/1365-2664.14288</p> <p><strong>Abstract</strong></p> <ol> <li>Rangelands of the United States provide ecosystem services that benefit society and rural economies. Native tree encroachment is often overlooked as a primary threat to rangelands due to the slow pace of tree cover expansion and the positive public perception of trees. Still, tree encroachment fragments these landscapes and reduces herbaceous production, thereby threatening habitat quality for grassland wildlife and the economic sustainability of animal agriculture. </li> <li>Recent innovations in satellite remote sensing permit the tracking of tree encroachment and the corresponding impact on herbaceous production. We analyzed tree cover change and herbaceous production across the western United States from 1990 to 2019.</li> <li>We show that tree encroachment is widespread in U.S. rangelands; absolute tree cover has increased by 50% (77,323 km<sup>2</sup>) over 30 years, with more than 25% (684,852 km<sup>2</sup>) of U.S. rangeland area experiencing tree cover expansion. Since 1990, 302 ± 30 Tg of herbaceous biomass have been lost. Accounting for variability in livestock biomass utilization and forage value reveals that this lost production is valued at between $4.1 - $5.6 billion U.S. dollars.</li> <li>Synthesis and applications: The magnitude of impact of tree encroachment on rangeland loss is similar to conversion to cropland, another well-known and primary mechanism of rangeland loss in the U.S. Prioritizing conservation efforts to prevent tree encroachment can bolster ecosystem and economic sustainability, particularly among privately-owned lands threatened by land-use conversion.</li> </ol> <p><strong>Description</strong></p> <p>This archive contains data products and modeling code for production loss and tree cover change estimates provided in the accompanying refereed publication. The easiest way to view and use these data is in Google Earth Engine:</p> <ul> <li><a href="https://smorford.users.earthengine.app/view/yield-gap">https://smorford.users.earthengine.app/view/yield-gap</a></li> <li><a href="https://code.earthengine.google.com/8ad19c7f7a6e04b377953326b274f98d">https://code.earthengine.google.com/8ad19c7f7a6e04b377953326b274f98d</a></li> </ul> <p>Summary data products are included in the <em>data-products</em> folder, and include links and scripts to download all annual data discussed in the manuscript. The full dataset is roughly 660GB and cannot be achieved on Zonodo as of summer 2022.</p> <p>Similarly, the <em>model</em> directory contains the primary codebase for processing raw tree cover data and running XGBoost modeling training and inference for the production loss model. To recreate the production loss data will require downloading approximately 900GB of biomass data and 250GB of tree cover data; total project size will be approximate 1.8 TB after inference.</p> <p>Data can also be downloaded directly from the University of Montana web servers:</p> <ul> <li><a href="http://rangeland.ntsg.umt.edu/data/rap/rap-vegetation-biomass/v2/">http://rangeland.ntsg.umt.edu/data/rap/rap-vegetation-biomass/v2/</a></li> <li><a href="http://rangeland.ntsg.umt.edu/data/rap/rap-derivatives/yield-gap/v1/">http://rangeland.ntsg.umt.edu/data/rap/rap-derivatives/yield-gap/v1/</a></li> </ul> <p><strong>Journal citation:</strong></p> <p>Morford, S.L., Allred, B.W., Twidwell, D., Jones, M.O., Maestas, J.D., Roberts, C.P. and Naugle, D.E., <em>Accepted</em>. Herbaceous production lost to tree encroachment in United States rangelands. <em>Journal of Applied Ecology</em>, August 2022.</p>
An ensemble of 48 physically perturbed model estimates of the 1/8° terrestrial water budget over the conterminous United States, 1980–2015
<p>This dataset contains the 1980–2015 monthly terrestrial water budget simulated by a 48-member perturbed-physics ensemble configured from the Noah LSM with multi-physics options (Noah‑MP v3.6). Simulation outputs include total evapotranspiration and its constituents (canopy evaporation, soil evaporation, and transpiration), runoff (the surface and subsurface components), as well as terrestrial water storage (snow water equivalent, four-layer soil water content from the surface down to 2 m, and the groundwater storage anomaly). The file name has four parts: the abbreviation for "terrestrial water budget", the used parameterization, the time scale, and the suffix.</p> <p>The 48 physics configurations are generated by combining four runoff parameterizations (run1: SIMGM, run2: SIMTOP, run3: NOAHR, run4: BATS), two parameterizations of stomatal conductance (can1: Ball–Berry, can2: Jarvis), three parameterizations of soil moisture stress factor (btr1:NOAHB, btr2: CLM, btr3: SSiB), and two parameterizations of near-surface atmospheric turbulence (tub1: M-O, tub2: Chen97).</p> <p>The simulation domain covers the all of conterminous United States (25°–53°N, 125°–67°W), which is also called the NLDAS-2 testbed (Xia et al., 2012a, b). The simulations were performed at a spatial resolution of 0.125°, which is the same as for NLDAS-2 models. Details of the simulation settings and spin-up run can be found in Section 2.3 of Zheng et al. (2019) and Section 2.2 of Fei et al. (2021).</p>
A consolidated database of police-reported motor vehicle traffic accidents in the United States for actuarial applications
<p>The parameter estimates along with their 90% confidence intervals obtained for the 20 multinomial logistic regressions are shown here in two presentations. In 'Covariate trends' we show the annual trends for the 20 years of data by type of covariate. In 'Covariates magnitude for each year', we show for each year the magnitude that each covariate has in contrast with the other 23 covariates (the intercept is not included due to scaling issues).</p> <p>All parameter estimates and their confidence intervals can be found in a table format in 'allparameters.csv'.</p>
Random forest climatic modeling of agricultural insurance loss across the inland Pacific Northwest region of the United States
<p>We compared climatic relationships to insurance loss across the inland Pacific Northwest region of the United States, using a design matrix methodology, to identify optimum temporal windows for climate variables by county in relationship to wheat insurance loss due to drought. The results of our temporal window construction for water availability variables (precipitation, temperature, evapotranspiration, and the Palmer drought severity index [PDSI]) identified spatial patterns across the study area that aligned with regional climate patterns, particularly with regards to drought-prone counties of eastern Washington. Using these optimum time-lagged correlational relationships between insurance loss and individual climate variables, along with commodity pricing, we constructed a regression-based random forest model for insurance loss prediction and evaluation of climatic feature importance. Our cross-validated model results indicated that PDSI was the most important factor in predicting total seasonal wheat/drought insurance loss, with wheat pricing and potential evapotranspiration having noted contributions. Our overall regional model had a R<sup>2</sup> of 0.49 and a RMSE of $30.8 million. Model performance typically underestimated annual losses, with moderate spatial variability in terms of performance between counties.</p>
Compilation of Fe and ligand data along the West coast of the United States
<p>Compilation of Fe and ligand data along the West coast of the United States (Compiled by Anh Le-Duy Pham from current available literature as of April 2024)</p> <p> </p> <p><span>USWC_Iron_Ligand_Compilation.xlsx: an excel file containing all the combined dFe and ligand data</span></p> <p><span>USWC_IronLiganddata.mat: a MATLAB file containing all the combined dFe and ligand data</span></p>
FIGURE 16 in Medusapyga LaBonte and Maddison, a New Genus of Anillini (Coleoptera: Carabidae: Trechinae) from the Pacific Northwest of the United States
FIGURE 16. Maximum likelihood tree inferred from the concatenated, eight-gene matrix. Numbers of the branches are maximum likelihood bootstrap percentages. Outgroups omitted. Scale bar 0.1 units, as calculated by IQ-TREE.
FIGURE 12 in Medusapyga LaBonte and Maddison, a New Genus of Anillini (Coleoptera: Carabidae: Trechinae) from the Pacific Northwest of the United States
FIGURE 12. Parameres of Medusapyga chehalis, voucher JRL012. (A) left paramere, (B) right paramere. Scale bar 100 µm.
FIGURE 18 in Medusapyga LaBonte and Maddison, a New Genus of Anillini (Coleoptera: Carabidae: Trechinae) from the Pacific Northwest of the United States
FIGURE 18. Habitats of Medusapyga. (A) Habitat of Medusapyga alsea: USA, Oregon, Benton Co., Prairie Peak, 11 km south of Alsea, 44.2844°N 123.5858°W, 810 m elevation [Type locality] 20 March 2010. (B) Habitat of M. chehalis: USA, Washington, Thurston Co., 11 km NW of Littlerock, approximately 0.4 km SSW of the type locality, 46.9564°N 123.1366°W, 315 m elevation, 2 April 2022.
Figure 6 in Medusapyga LaBonte and Maddison, a New Genus of Anillini (Coleoptera: Carabidae: Trechinae) from the Pacific Northwest of the United States
Figure 6. Female genitalia of Medusapyga alsea from the type locality, ventral view. (A) Overview of voucher V101505. Scale bar 100 µm. (B) Closeup of voucher V101504, showing bifurcate setae on laterotergites. Scale bar 50 µm.
FIGURE 4 in Medusapyga LaBonte and Maddison, a New Genus of Anillini (Coleoptera: Carabidae: Trechinae) from the Pacific Northwest of the United States
FIGURE 4. Left profemora, anterior view. (A) Medusapyga alsea, male. (B) M. alsea, female. (C) M. chehalis, male. (D) M. chehalis, female. Scale bars 100 µm.
FIGURE 1 in Medusapyga LaBonte and Maddison, a New Genus of Anillini (Coleoptera: Carabidae: Trechinae) from the Pacific Northwest of the United States
FIGURE 1. (A) Female of Medusapyga alsea from the type locality. (B) Female of M. chehalis from USA, Washington, Thurston Co., 11 km NW of Littlerock, approximately 0.4 km SSW of the type locality, 46.9564°N 123.1366°W, 315 m elevation. Both specimens were freshly killed, and then posed and photographed on soil from their habitats. Scale bars 1 mm.
United States State Cancer Profiles data extract
<p>State Cancer Profiles (https://statecancerprofiles.cancer.gov) hosts an interactive website for tracking United States cancer incidence and prevalence. The dataset hosted here is an automated scraping from all locales for all demographic variables and diseases. </p> <p>Two tables are available:</p> <ol> <li>Cancer incidence</li> <li>Cancer mortality</li> </ol> <p>The dataset is meant for data mining or for building additional data products based on the state cancer profiles data, but without having to scrape the data yourself. </p>
Data from: Collection methods and distribution modeling for Strepsiptera in the United States
<p>The twisted-wing parasite order (Strepsiptera Kirby, 1813) is difficult to study due to the complexity of strepsipteran life histories, small body sizes, and a lack of accessible distribution data for most species. Here, we present a review of the strepsipteran species known from New York State. We also demonstrate successful collection methods and a survey of species carried out in an old-growth deciduous forest dominated by native New York species (Black Rock Forest, Cornwall, NY) and a private site in the Catskill Mountains (Shandaken, NY). Additionally, we model suitable habitat for Strepsiptera in the United States with species distribution modeling. We base our models on host distributions and climatic variables to inform predictions of where these twisted-wing parasites are likely to be found. With this work, we hope to provide a useful reference for the future collection of Strepsiptera.</p>
Data from: Rates and drivers of carbon emissions from hydropower reservoirs in the southeastern United States
<p>Reservoirs are a significant source of carbon (C) to the atmosphere, but their emission rates vary in space and time. We compared C emissions via diffusive and ebullitive pathways at several stations in six large hydropower reservoirs in the southeastern US that were previously sampled in summer 2012. We found that carbon dioxide (<span>CO<sub>2</sub></span>) diffusion was the dominant flux pathway during 2012 and 2022, with only three exceptions where methane (<span>CH<sub>4</sub></span>) diffusion or <span>CH<sub>4</sub></span> ebullition dominated. <span>CH<sub>4</sub></span> diffusion rates were positively associated with water temperature. However, we found no clear predictors of <span>CH<sub>4</sub></span> ebullition, which had extremely high variability, with rates ranging from 0 to 739 mg C m<sup>-2</sup> day<sup>-1</sup>. For <span>CO<sub>2</sub></span> diffusion, the direction of the flux shifted between 2012 and 2022, where all but three stations across all reservoirs emitted <span>CO<sub>2</sub></span> in summer 2012, but every station sequestered <span>CO<sub>2</sub></span> in summer 2022. Here, indicators of greater algal production were associated with <span>CO<sub>2</sub></span> sequestration, including surface chlorophyll-<em>a</em> concentration, surface dissolved oxygen saturation, and pH. Additional sampling campaigns outside the summer season highlighted the importance of seasonal phenology in primary production on the direction of <span>CO<sub>2</sub></span> diffusive fluxes, which shifted to positive <span>CO<sub>2</sub></span> fluxes by the end of August as productivity decreased. Our results demonstrate the importance of capturing <span>CO<sub>2</sub></span> sequestration in field and modelling measurements and understanding the seasonal drivers of these estimates. Measuring C emissions from multiple pathways in reservoirs and understanding their spatiotemporal responses and variability is vital to reducing uncertainties in global upscaling efforts.</p>
Bumble bee responses to climate and landscapes: Investigating habitat associations and species assemblages across geographic regions in the United States of America
<p><span>Bumble bees are integral pollinators of native and cultivated plant communities, but species are undergoing significant changes in range and abundance on a global scale. Climate change and land cover alteration are key drivers in pollinator declines; however, limited research has evaluated the cumulative effects of these factors on bumble bee<em> </em>assemblages. This study tests bumble bee assemblage (calculated as richness and abundance) responses to climate and land use by <span>modeling </span>species-specific habitat requirements, and assemblage-level responses across geographic regions. <span>We integrated species richness, abundance, and distribution data for 18 bumble bee species with site-specific bioclimatic, landscape composition, and landscape configuration data to evaluate</span> the effects of multiple environmental stressors <span>on bumble bee assemblages throughout</span> 433 agricultural fields in<span> Florida, Indiana, Kansas, Kentucky, Maryland, South Carolina, Utah, Virginia, and West Virginia from 2018 to 2020. Distinct east vs. west groupings emerged when evaluating species-specific habitat associations, prompting a detailed evaluation of bumble bee assemblages by geographic region. Maximum temperature of warmest month and precipitation of driest month had a positive impact on bumble bee assemblages in the Corn Belt/Appalachian/northeast, southeast, and northern plains regions, but a negative impact in the mountain region. Further, </span>forest land cover surrounding agricultural fields was highlighted as supporting more rich and abundant bumble bee assemblages<span>. Overall, climate and land use combine to drive bumble bee assemblages, but how those processes operate is idiosyncratic and spatially contingent across regions. From these findings, we suggested regionally specific management practices to best support rich and abundant bumble bee assemblages in agroecosystems. </span>Results from this study contribute to a better understanding of climate and landscape factors affecting bumble bees and their habitats throughout the USA. </span></p>
Remotely sensed crown nutrient concentrations modulate forest reproduction across the contiguous United States
<p>Global forests are increasingly lost to climate change, disturbance, and human management. Evaluating forests' capacities to regenerate and colonize new habitats has to start with the seed production of individual trees and how it depends on nutrient access. Studies on the linkage between reproduction and foliar nutrients are limited to a few locations and few species, due to the large investment needed for field measurements on both variables. We synthesized tree fecundity estimates from the Masting Inference and Forecasting (MASTIF) network with crown nutrient concentrations from hyperspectral remote sensing at the National Ecological Observatory Network (NEON) across the United States. We evaluated the relationships between seed production and foliar nutrients for 56,544 tree-years from 26 species at individual and community scales. We found a prevalent association between high foliar phosphorous (P) concentration and low individual seed production (ISP) at the continental scale. With-species coefficients to nitrogen (N), potassium (K), calcium (Ca), and magnesium (Mg) are related to species differences in nutrient demand, with distinct biogeographic patterns. Community seed production (CSP) decreased four orders of magnitude from the lowest to the highest foliar P. This first study on hyperspectral imagery indicates promise for future monitoring of reproductive potential. The fact that both ISP and CSP decline at high foliar P levels has immediate applications in improving forest demographic and regeneration models by providing more realistic nutrient effects at multiple scales.</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.