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617 results for “Climate models”
Climate model output from a study of tropical cyclones over the Shanghai region under climate change based on a convection-permitting modelling
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Climate biogeography of Arabidopsis thaliana: Linking distribution models and individual variation
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Protea repens whole transcriptome count data for control and drought treatment for 8 populations, climatic data for the 8 populations and phenotypic data collected, and data used for linear mixed models for climate gene expression/trait correlation testing
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The Effects of Climate Downscaling Technique and Observational Dataset on Modeled Ecological Responses: Supporting Data Tables
These data have been prepared as a supplement to Pourmokhtarian et al. (2016; full citation below), where complete details on methods can be found. We evaluated three downscaling methods: the delta method (or the change factor method); monthly quantile mapping (Bias Correction-Spatial Disaggregation, or BCSD); and daily quantile regression (Asynchronous Regional Regression Model, or ARRM). Additionally, we trained outputs from four atmosphere-ocean general circulation models (AOGCMs) (CCSM3, HadCM3, PCM, and GFDL-CM2.1) driven by higher (A1fi) and lower (B1) future emissions scenarios on two sets of observations (1/8th degree resolution grid vs. individual weather station) to generate the high-resolution climate input for the forest biogeochemical model PnET-BGC (8 ensembles of 6 runs). This dataset consists of three files - 1) a zip archive file of all raw daily downscaled AOGCMs (csv format; years 1960-2099; delta method 2012-2099 only) which were used as input for PnET-BGC model, 2) a zip archive file of all PnET-BGC output files for each model run (csv format; years 1000-2100), and 3) a pdf document file that describes the content of the input and output files. Data were also used from the following Hubbard Brook longterm datasests: Daily Streamflow Watershed 6: http://dx.doi.org/10.6073/pasta/727ee240e0b1e10c92fa28641bedb0a3 Chemistry of Streamwater at the Hubbard Brook Experimental Forest, Watershed 6: http://dx.doi.org/10.6073/pasta/2ec152b0ab1d4e64aa40f4aa9bc492ac Daily Precipitation Watershed 6: http://dx.doi.org/10.6073/pasta/17c8ff8b160bf7893ef39f75a02652e5 Daily Maximum/Minimum Temperature Data: http://dx.doi.org/10.6073/pasta/2a4ab5522ce15f28196a6035802b09e8 Daily Solar Radiation Data: http://dx.doi.org/10.6073/pasta/2fa098a5aa191c64e622b253c0fee5af These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest
Climate simulation over the European Alps for the period 1902-2010 produced with the model MAR
<p>This directory contains the netcdf files produced with the MAR model (http://mar.cnrs.fr/; https://gitlab.com/Mar-Group) applied over the European Alps, for the period 1902-2010, based on a spatial resolution of 7km. This data is a downscaling of the ERA20C reanalysis. The list of variables available is described in the file variables.txt, and further information can be found in the file readme.txt</p>
Provisioning forest and conservation science with European tree species distribution models under climate change
<p>Estimating shifts in the current range of forest tree species is crucial for formulating adaptive management strategies such as assisted migration. Ecological niche models have been the most widely used tools to estimate the potential climatic suitability of species worldwide. The reliability of such estimations depends on the model algorithm and the input data such as climate and species occurrence. We developed a dataset of the potential distribution of seven ecologically and economically important tree species of Europe in terms of their climatic suitability with an ensemble approach while accounting for uncertainty due to model algorithms. The distribution models shall be the basis for follow-up studies in forest and conservation science.</p>
Regional Atmospheric Climate Model 2 (RACMO2), version 2.3p2
<p>In the 1990s the KNMI developed in cooperation with the Danish Meteorological Institute the research model RACMO based on the High Resolution Limited Area Model (HIRLAM) numerical weather prediction model. In 1993 UU/IMAU started to modify the model such that it better represented the extreme conditions over glacier surfaces. This first version of RACMO, RACMO1, combined the dynamical core of the HIRLAM model with ECHAM4 physics. The polar modified version of RACMO1 was mainly applied to the Antarctic Ice Sheet.</p> <p>The second version, RACMO2, combines the dynamical core of the HIRLAM model with the European Centre for Medium-range Weather Forecasts (ECMWF) Integrated Forecast System (ISF) physics. RACMO versions 2.0 and 2.1 included HIRLAM version 5.0.6 and ISF cycle CY23r4, while version 2.3 includes HIRLAM version 6.3.7 and cycle CY33r1. Due to the rapid increase in computer capacity over the years, these versions of RACMO have not only been applied to the Greenland and Antarctic Ice Sheets, but also at higher resolution to smaller areas such as Dronning Maud Land and Patagonia.</p> <p>For the RACMO model in general the grids are defined over the equator and then rotated to the area of interest. Grid distance is defined in fraction of degrees, which results in near equidistant grid points as long as the domain is small enough. Note that the domain is thus not on a (polar) stereographic projection plane. In the vertical, the model adopts a system of hybrid sigma levels, which evolve from terrain-following sigma levels close to the surface to pure pressure levels at higher elevation. The actual number of horizontal grid points varies per model run; in most simulations, 40 vertical layers were used.</p> <p>Since RACMO is a regional model, it needs external information at the lateral boundaries and sea surface. At the lateral boundary zone of the model, the temperature, specific humidity, zonal and meridional wind components, and the surface pressure are relaxed towards the fields of a global model every 6 model hours, as are the sea surface temperature and sea ice concentration. RACMO is not forced at the model top. The interior of the model is not nudged towards observations and allowed to evolve freely.</p>
High-resolution future climate data for species distribution models in Europe
<p><strong>Description</strong></p> <p>This dataset contains a set of 13 climatological variables (<code>Variable</code>, <code>VariableName</code>) at a spatial resolution of 1x1km for Europe (nx = 13147, ny = 6071) for historical (<code>ClimatePeriod</code>) and future climate conditions. These variables are a subset of the so-called bioclimatic variables that are often part of global gridded datasets (e.g. <a href="https://worldclim.org/data/bioclim.html">WorldClim</a>, <a href="http://chelsa-climate.org/bioclim/">CHELSA</a>) that have been specifically developed for species distribution modelling and ecological applications.</p> <p>The climatological data correspond to 35-year (<code>Startyear_Endyear</code> = <code>1971_2005</code>) and 30-year (<code>Startyear_Endyear</code> = <code>2041_2070</code>) mean values representing respectively historical and future climate conditions. To account for the future climate conditions, three possible emission scenarios of greenhouse gases as defined by the <a href="https://www.ipcc.ch/">Intergovernmental Panel on Climate Change (IPCC)</a> are used (<code>ClimatePeriod</code> = <code>rcp26</code>, <code>rcp45</code>, <code>rcp85</code>).</p> <p>The complete set of variables (var[1-13]) for which historical and future climate data layers are produced are given below.</p> <p>The source data for the climate layers were assembled from the <a href="https://cordex.org/data-access/">EURO-CORDEX archive</a> (Kotlarski et al., 2014). More specifically, we have used the regional climate model simulations for Europe at a spatial resolution of 12.5x12.5km on which a three-step statistical downscaling approach has been applied:</p> <ol> <li><strong>Processing</strong> (averaging, totals, …) of all available time series of the EURO-CORDEX model experiments (<code>ClimatePeriod</code> = evaluation, historical, rcp) for the climatological variables.</li> <li><strong>Interpolation</strong> of the data layers from the 12.5x12.5km EURO-CORDEX grid to a 1x1km spatial <a href="http://chelsa-climate.org/">CHELSA</a> (Karger et al., 2017) reference grid (see files <code>lat_1km.csv</code> and <code>lon_1km.csv</code>).</li> <li><strong>Calculate differences</strong> between the 1x1km-interpolated variables (<code>Variable</code> = only for var[1-9]) from the evaluation model experiments (or <code>ClimatePeriod</code>) and the corresponding reference bioclimatic CHELSA variables. In order to account for possible biases present in the EURO-CORDEX climate models, these differences (or biases) are then subtracted from the respective 1x1-km-interpolated variables for the historical and rcp model experiments (<code>ClimatePeriod</code>).</li> </ol> <p>The dimensions of the 1x1km grid (excl. the first row and column):</p> <ul> <li>y-dimension = number of columns = 6071</li> <li>x-dimension = number of rows = 13147</li> </ul> <p>The longitudes and latitudes of respectively the southwest and northeast corner of the grid are:</p> <ul> <li>longitude -44.592; latitude 21.991 (southwest corner)</li> <li>longitude 64.967; latitude 72.583 (northeast corner)</li> </ul> <p>The climatological variables are used as input data for the species distribution modelling of Invasive Alien Species for the <a href="https://osf.io/7dpgr/">Tracking Invasive Alien Species (TrIAS)</a> project.</p> <p><strong>Variables</strong></p> <ul> <li><strong>Variable</strong> (VariableName): Unit</li> <li><strong>var1</strong> (AnnualMeanTemperature): °C</li> <li><strong>var2</strong> (AnnualAmountPrecipitation): mm year<sup>-1</sup></li> <li><strong>var3</strong> (AnnualVariationPrecipitation): coefficient of variation</li> <li><strong>var4</strong> (AnnualVariationTemperature): stdev</li> <li><strong>var5</strong> (MaximumTemperatureWarmestMonth): °C</li> <li><strong>var6</strong> (MinimumTemperatureColdestMonth): °C</li> <li><strong>var7</strong> (TemperatureAnnualRange): °C</li> <li><strong>var8</strong> (PrecipitationWettestMonth): mm</li> <li><strong>var9</strong> (PrecipitationDriestMonth): mm</li> <li><strong>var10</strong> (30yrMeanAnnualCumulatedGDDAbove5degreesC): °C days</li> <li><strong>var11</strong> (AnnualMeanPotentialEvapotranspiration): mm day<sup>-1</sup></li> <li><strong>var12</strong> (AnnualMeanSolarRadiation): W m<sup>-2</sup></li> <li><strong>var13</strong> (AnnualVariationSolarRadiation): stdev</li> </ul> <p><strong>Files</strong></p> <ul> <li><strong>varX_VariableName_ClimatePeriod_Startyear_Endyear.csv</strong>: climatological data layers for the 13 variables listed above</li> <li><strong>lon_1km.csv</strong>: longitudes for the 1x1km grid</li> <li><strong>lat_1km.csv</strong>: latitudes for the 1x1km grid</li> </ul>
Data and model output for figures in "Variable particle size distributions reduce the sensitivity of global export flux to climate change"
<p><strong>Associated publication</strong></p> <p>This dataset was used to generate analyses and figures in the following publication:</p> <p>Leung, S., Weber, T., Cram, J. A., & Deutsch, C. Variable particle size distributions reduce the sensitivity of global export flux to climate change. <em>Submitted to Biogeosciences.</em></p> <p><strong>Associated code</strong></p> <p>After downloading this dataset, run the associated MATLAB code at the following link to generate the figures and analyses in the above publication:</p> <p>https://doi.org/10.5281/zenodo.4117382</p>
Model Results for "Groundwater Flow to Gale Crater in an Episodically Warm Climate"
<p>Results from the bous_therm code for "Groundwater Flow to Gale Crater in an Episodically Warm Climate"</p> <p>bous_therm code is at doi:10.5281/zenodo.3779418</p> <p>After extraction, the folder goes in the top directory of the bous_therm repository. It can then be read, analyzed, and plotted by scripts in the repository.</p>
Supplementary material 1 from: Datta A, Schweiger O, Kühn I (2020) Origin of climatic data can determine the transferability of species distribution models. NeoBiota 59: 61-76. https://doi.org/10.3897/neobiota.59.36299
Variable selection using cluster analsys based on Spearman's rank corellation and UPGMA method for agglomeration
CESM1.2 simulation output for: The role of westerly wind bursts during different seasons versus ocean heat recharge in the development of extreme El Niño in a climate model
<p>This is the subset of CESM1.2 model simulation output that was used for analysis and visualization of Yu and Fedorov [2020] (DOI:10.1029/2020GL088381). Please refer to README for details.</p>
Genetic data improves niche model discrimination and alters the direction and magnitude of climate change forecasts
<p>Ecological niche models (ENMs) have classically operated under the simplifying assumptions that there are no barriers to gene flow, species are genetically homogeneous (i.e., no population-specific local adaptation), and all individuals share the same niche. Yet, these assumptions are violated for most broadly distributed species. Here we incorporate genetic data from the widespread riparian tree species narrowleaf cottonwood (<i>Populus angustifolia</i>) to examine whether including intraspecific genetic variation can alter model performance and predictions of climate change impacts. We found that (1) <i>P. angustifolia</i> is differentiated into six genetic groups across its range from México to Canada, and (2) different populations occupy distinct climate niches representing unique ecotypes. Comparing model discriminatory power, (3) all genetically-informed ecological niche models (gENMs) outperformed the standard species-level ENM (3-14% increase in AUC; 1-23% increase in pROC). Furthermore, (4) gENMs predicted large differences among ecotypes in both the direction and magnitude of responses to climate change, and (5) revealed evidence of niche divergence, particularly for the Eastern Rocky Mountain ecotype. (6) Models also predicted progressively increasing fragmentation and decreasing overlap between ecotypes. Contact zones are often hotspots of diversity that are critical for supporting species' capacity to respond to present and future climate change, thus predicted reductions in connectivity among ecotypes is of conservation concern. We further examined the generality of our findings by comparing our model developed for a higher elevation Rocky Mountain species with a related desert riparian cottonwood, <i>P. fremontii</i>. Together our results suggest that incorporating intraspecific genetic information can improve model performance by addressing this important source of variance. gENMs bring an evolutionary perspective to niche modeling and provide a truly "adaptive management" approach to support conservation genetic management of species facing global change.</p>
Evaluating multiple historical climate products in ecological models under current and projected temperatures
<p>Gridded historical climate products (GHCPs) are employed with increasing frequency when modeling ecological phenomena across large scales and predicting ecological responses to projected climate changes. Concurrently, there is an increasing acknowledgement of the need to account for uncertainty when employing climate projections from ensembles of global circulation models (GCMs) and emissions scenarios. Despite the growing usage and documented differences among GHCPs, uncertainty characterization has primarily focused on the roles of GCM and emissions scenario choice, while the consequences of using a single GHCP to make predictions over space and time has received relatively less attention. Here we employ average July temperature data from observations and seven GHCPs to model plant canopy cover and tree basal area across central Alaska, U.S.A. We first compare fit and support of models employing raw observed or GHCP temperature values versus those with an elevation adjustment, finding (1) greater support for, and better fit using elevation-adjusted versus raw temperature models and (2) overall similar fits of elevation-adjusted models employing temperature from observations or GHCPs. Focusing on basal area, we next compare predictions generated by elevation-adjusted models employing GHCP data under current conditions and a warming scenario of current temperatures plus 2 °C, finding good agreement among GHCPs though with between-GHCP differences and variation primarily at middle elevations (~ 1,000 m). These differences were amplified under the warming scenario. Finally, using pooled indices of prediction variation and difference across GHCP models, we identify characteristics of areas most likely to exhibit prediction uncertainty under current and warming conditions. Despite (1) overall good performance of GHCP data relative to observations in models and (2) positive correlation among model predictions, variation in predictions across models—particularly in mid-elevation areas where the position of treeline may be changing—suggests researchers should exercise caution if selecting a single GHCP for use in models. We recommend the use of multiple GHCPs to provide additional uncertainty information beyond standard estimated prediction intervals, particularly when model predictions are employed in conservation planning.</p>
Climate model data from "Changes in local and global climate feedbacks in the absence of interactive clouds: Southern Ocean-climate interactions in two intermediate-complexity models"
<p>This Dataset contains the model output described in the study<br> "Changes in local and global climate feedbacks in the absence of interactive clouds: Southern Ocean-climate interactions in two intermediate-complexity models"<br> by Pfister and Stocker 2020, published in Journal of Climate.</p> <p>The two zip files contain the model output of the two models Bern3D-LPX and LOVECLIM, in folder structures explained below.</p> <p>Bern3D-LPX:</p> <p>The 3 folders contain model simulations tuned to different ECS values (2, 3 and 6 Kelvin).<br> Each folder contains three subfolders corresponding to three simulations: Control, 2xCO2 and 4xCO2.<br> For each simulation, two netcdf model output files are given: a timeseries file for quick overview of various spatially averaged variables (e.g., global mean temperature), and a full output file for local analyses as done in the study.</p> <p>For the main simulations with an ECS of 3 Kelvin, annual mean output is provided for the first 500 years of each simulation. Thereafter, the full output is available only for selected years, which can be read out from the netcdf time dimension or, e.g., the netcdf variable "baseyear".</p> <p>Simulations with an ECS of 2 and 6 Kelvin are only used for Figure 8 and its discussion, therefore their full output file was written with less yearly outputs than the main simulation with ECS=3 Kelvin to reduce data load.</p> <p><br> LOVECLIM:</p> <p>The 2 folders contain the 2xCO2 and 4xCO2 simulations.<br> No separate Control simulations were made, but the first 1000 years of each simulation are unperturbed and used as a control reference (details in Pfister and Stocker 2020, J.Clim.).</p> <p>The two netcdf files for each simulation correspond to atmospheric variables (atmmmyl_cat.nc) and ocean variables (CLIO3m_cat_CO2_2_regridded.nc). Note that the spatial resolution of the atmosphere and ocean component of LOVECLIM are different. Monthly output is provided for the given variables of the full 2000-year-simulations.</p> <p> </p> <p>For a detailed description how these model outputs were analyzed, please refer to Pfister and Stocker 2020, J. Clim.</p> <p> </p> <p> </p>
Database of the ODYM-RECC v2.4 model, used for the Germany case study on material efficiency and climate change mitigation
<p>Database of the ODYM-RECC v2.4 model, used for the Germany case study on material efficiency and climate change mitigation. For the model code, see https://github.com/YaleCIE/RECC-ODYM. The model results archived here were calculated by running the ODYM-RECC scripts of commit no. cb3a388 with the data in this archive.</p>
Combining Satellite Remote Sensing and Climate Data in Species Distribution Models to Improve the Conservation of Iberian White Oaks (Quercus L.)
<p>The Iberian Peninsula hosts a high diversity of oak species, being a hot-spot for the conservation of European White Oaks (Quercus) due to their environmental heterogeneity and its critical role as a phylogeographic refugium. Identifying and ranking the drivers that shape the distribution of White Oaks in Iberia requires that environmental variables operating at distinct scales are considered. These include climate, but also ecosystem functioning attributes (EFAs) related to energy–matter exchanges that characterize land cover types under various environmental settings, at finer scales. Here, we used satellite-based EFAs and climate variables in species distribution models (SDMs) to assess how variables related to ecosystem functioning improve our understanding of current distributions and the identification of suitable areas for White Oak species in Iberia. We developed consensus ensemble SDMs targeting a set of thirteen oaks, including both narrow endemic and widespread taxa. Models combining EFAs and climate variables obtained a higher performance and predictive ability (true-skill statistic (TSS): 0.88, sensitivity: 99.6, specificity: 96.3), in comparison to the climate-only models (TSS: 0.86, sens.: 96.1, spec.: 90.3) and EFA-only models (TSS: 0.73, sens.: 91.2, spec.: 82.1). Overall, narrow endemic species obtained higher predictive performance using combined models (TSS: 0.96, sens.: 99.6, spec.: 96.3) in comparison to widespread oaks (TSS: 0.80, sens.: 92.6, spec.: 87.7). The Iberian White Oaks show a high dependence on precipitation and the inter-quartile range of Normalized Difference Water Index (NDWI) (i.e., seasonal water availability) which appears to be the most important EFA variable. Spatial projections of climate–EFA combined models contribute to identify the major diversity hotspots for White Oaks in Iberia, holding higher values of cumulative habitat suitability and species richness. We discuss the implications of these findings for guiding the long-term conservation of IberianWhite Oaks and provide spatially explicit geospatial information about each oak species (or set of species) relevant for developing biogeographic conservation frameworks.</p>
Supplementary material 1 from: Bustamante RO, Alves L, Goncalves E, Duarte M, Herrera I (2020) A classification system for predicting invasiveness using climatic niche traits and global distribution models: application to alien plant species in Chile. NeoBiota 63: 127-146. https://doi.org/10.3897/neobiota.63.50049
Table S1. Exotic species located in Quadrant 1 (see Figure 3) and impacts on biodiversity, agriculture and cattle raisng
Combining conservation status and species distribution models for planning assisted colonisation under climate change
<p>Effects of climate change are particularly important in the Mediterranean Biodiversity hotspot where rising temperatures and drought are negatively affecting several plant taxa, including endemic species. Assisted Colonisation (AC) represents a useful tool for reducing the effect of climate change on endemic plant species threatened by climate change.</p> <p>We combined SDMs for 188 taxa endemic to Italy with the IUCN red listing range loss threshold under criterion A (30%) to define: a) the number of AC (measured as 2×2 km grid cells that should be occupied by new populations, that is grid cells = new populations) required to fully compensate for predicted range loss and to halt the decline below the 30% of range loss; b) The number of cells necessary to compensate for range loss was calculated as the number of currently occupied cells lost under future climate due to unsuitable conditions. We used two Representative Concentration Pathways, +2.6 and +8.5 W/m2, optimistic and pessimistic scenarios, respectively. Availability of suitable areas for AC was also assessed within the current species distribution and within protected areas.</p> <p>Under the optimistic scenario, no taxa would lose more than 30% of their range and AC would not be required. Under the pessimistic scenario, roughly 90% of taxa showed a cell loss higher than 30%. Eight taxa were predicted to lose >95% of their range. For these species, AC was required from 13 to 16 new populations (= 13 to 16 grid cells) per taxon to cap the range loss at 30%. For currently VU or EN species, an average number of 32 to 35 AC attempts would be necessary to fully compensate for their range loss under a pessimistic scenario. Suitable recipient sites within protected areas falling in their projected range were identified, allowing for short-distance AC.</p> <p>Synthesis. Combining SDMs and red listing thresholds under Criterion A has enabled the strategic planning of multiple-species AC minimising the effort in terms of new populations to be created and maximising the conservation benefit in terms of range loss compensation.</p>
Data to replicate: Forecasting community reassembly using climate-linked spatio-temporal ecosystem models
Ecosystems are increasingly impacted by human activities, altering linkages among physical and biological components. Spatial community reassembly occurs when these human impacts modify the spatial overlap between system components, and there is need for practical tools to forecast spatial community reassembly at landscape scales using monitoring data. To illustrate a new approach, we extend a generalization of empirical orthogonal function (EOF) analysis, which involves a spatio-temporal ecosystem model that approximates coupled physical, biological, and human dynamics. We then demonstrate its application to five trophic levels for the eastern Bering Sea by fitting to multiple, spatially unbalanced datasets measuring physical characteristics (temperature measurements and climate-linked forecasts), primary producers (spring and fall size-fractionated chlorophyll-a), secondary producers (copepods), juveniles (age-0 walleye pollock), adult consumers (five commercially important fishes), human activities (seasonal fishing effort), and mobile predators (seabirds). We identify the spatial niche for each ecosystem component, as well as dominant modes of variability that are highly correlated with a known bottom-up driver of dynamics. We then measure spatial overlap between interacting variables (using Schoener's-D) and identify that age-0 pollock have decreased spatial overlap with copepods and increased overlap with adult pollock during warm years, and also that adult pollock have increased overlap with arrowtooth flounder and decreased overlap with catcher-processor fishing effort during these warm years. Given the warming conditions that are projected for the coming decade, the model forecasts increased prey and competitor overlap involving adult pollock (between age-0 pollock, adult pollock and arrowtooth flounder) and decreased overlap with the copepod forage base and with the catcher-processor fishery during future warming. We recommend that joint species distribution models be extended to incorporate "ecological teleconnections" (correlations between distant locations arising from known mechanisms) arising from behavioral adaptation by mobile animals as well as passive advection of nutrients and planktonic juvenile stages.
ScienceDex guides
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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.