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5,155 results for “resolution”
Dataset used for "Exploring the ability of the variable-resolution CESM to simulate cryospheric-hydrological variables in High Mountain Asia"
<p><strong>General Info</strong></p> <p>This dataset contains monthly output from two 20-year (1979-1998) variable-resolution (VR) CESM2 simulations (HMA_VR7a and HMA_VR7b). The coupled atmosphere-land simulations were run with a newly generated VR grid that has regional grid refinements up to 7 km over High Mountain Asia. The HMA_VR7b simulation was performed with an updated glacier-cover dataset (<a href="https://doi.org/10.5281/zenodo.7864689">https://doi.org/10.5281/zenodo.7864689</a>) and includes snow and glacier model modifications. Further, monthly output from a globally uniform 1-degree CESM simulation (NE30), used for evaluation of the HMA VR simulations, is also included. The monthly output have been used for analysis and discussion in the paper “<em>Exploring the ability of the variable-resolution CESM to simulate cryospheric-hydrological variables in High Mountain Asia” </em>that is currently under review in the Cryosphere Discussions, <a href="https://tc.copernicus.org/preprints/tc-2022-256/">https://tc.copernicus.org/preprints/tc-2022-256/</a>.</p> <p><strong>Contact</strong></p> <p>René Wijngaard (<a href="mailto:r.r.wijngaard.uu@gmail.com">r.r.wijngaard.uu@gmail.com</a> / <a href="mailto:r.r.wijngaard@uu.nl">r.r.wijngaard@uu.nl</a>) </p> <p><strong>Raw Data</strong></p> <p>Raw monthly and daily unstructured HMA VR model output are available on request. </p> <p><strong>Dataset Contents</strong></p> <pre><code>NE30.tar HMA_VR7a.tar HMA_VR7b.tar </code></pre> <p>These files contain atmosphere (CAM) and land (CLM) model output that are regridded to a 1-degree finite volume (0.9 x 1.25 degrees latitude/longitude) grid. The following variables are included: CLDLIQ, OMEGA, Q, STEND_CLUBB, SWCF, T, Z3, EFLX_LH_TOT, FGR, FIRE, FLDS, FSA, FSDS, FSH, FSM, FSNO, FSM, FSR, H2OSNO, PCT_LANDUNIT, QICE_MELT, QSNOFRZ, RAIN, SNOW, and TSA. </p> <pre><code>SMB_HMA_VR7a.tar SMB_HMA_VR7b.tar</code></pre> <p>These files contain unstructured SMB-related CLM model output (i.e., on the HMA VR grid). The following variables are included: PCT_LANDUNIT, QRUNOFF_ICE, QSNOFRZ_ICE, QSNOMELT_ICE, QSOIL_ICE, RAIN_ICE, and SNOW_ICE.</p>
CBRA: The first multi-annual (2016-2021) and high-resolution (2.5 m) building rooftop area dataset in China derived with Super-resolution Segmentation from Sentinel-2 imagery
<p>Large-scale and up-to-date maps of building rooftop area (BRA) are crucial for addressing policy decisions and sustainable development. In addition, as a fine-grained indicator of human activities, BRA could contribute to urban planning and energy modeling to provide benefits to human well-being. However, existing large-scale BRA datasets, such as those from Microsoft and Google, do not include China, hence there are no full-coverage maps of BRA in China. To this end, we produce the multi-annual China building rooftop area dataset (CBRA) with 2.5 m resolution from 2016-2021 Sentinel-2 images. The CBRA is the first full-coverage and multi-annual BRA data in China. The CBRA achieves good performance with the F1 score of 62.55% (+10.61% compared with the previous BRA data in China) based on 250,000 testing samples in urban areas, and the recall of 78.94% based on 30,000 testing samples in rural areas. </p> <p>The CBRA is organized as GeoTIFF (.tif) raster file format with a single band and GCS_WGS_1984 coordinate system. The pixel values are 0 and 255, with 0 representing the background and 255 representing the building rooftop area. Furthermore, to facilitate the use of the data, the CBRA is split into 215 tiles of spatial grid, named “CBRA_year_E/W**N/S**.tif”, where “year” is the sampling year, the “E/W**N/S**” is the latitude and longitude coordinates found in the upper left corner of the tile data.</p> <p> </p> <p>Version 2.0: In version 1.0, there were empty raster images (because they didn't contain buildings). In version 2.0, these raster images were removed.</p>
Examining the Effects of Resolution Bandwidth when Measuring Compound Radar Waveforms Data
<p>Data used by NTIA/ITS TR-23-566 Examining the Effects of Resolution Bandwidth when Measuring Compound Radar Waveforms.</p>
Active Region Magnetograms for Solar Flare Prediction: Full Resolution Dataset Images for ARs 1708 through 1918
<p>This dataset is the images associated with Dryad dataset https://doi.org/10.5061/dryad.dv41ns23n. These images are consistently sized images of active region magnetograms from the National Aeronautics and Space Administration's (NASA's) Solar Dynamics Observatory (SDO). These data are the full sized images (600x600 pixels) for active regions (ARs) 1708 through 1918 in .fits format.</p>
Active Region Magnetograms for Solar Flare Prediction: Full Resolution Dataset Images for ARs 1919 through 2103
<p>This dataset is the images associated with Dryad dataset https://doi.org/10.5061/dryad.dv41ns23n. These images are consistently sized images of active region magnetograms from the National Aeronautics and Space Administration's (NASA's) Solar Dynamics Observatory (SDO). These data are the full sized images (600x600 pixels) for active regions (ARs) 1919 through 2103 in .fits format.</p>
Active Region Magnetograms for Solar Flare Prediction: Full Resolution Dataset Images for ARs 2284 through 2488
<p>This dataset is the images associated with Dryad dataset https://doi.org/10.5061/dryad.dv41ns23n. These images are consistently sized images of active region magnetograms from the National Aeronautics and Space Administration's (NASA's) Solar Dynamics Observatory (SDO). These data are the full sized images (600x600 pixels) for active regions (ARs) 2284 through 2488 in .fits format.</p>
Active Region Magnetograms for Solar Flare Prediction: Full Resolution Dataset Images for ARs 2104 through 2283
<p>This dataset is the images associated with Dryad dataset https://doi.org/10.5061/dryad.dv41ns23n. These images are consistently sized images of active region magnetograms from the National Aeronautics and Space Administration's (NASA's) Solar Dynamics Observatory (SDO). These data are the full sized images (600x600 pixels) for active regions (ARs) 2104 through 2283 in .fits format.</p>
Active Region Magnetograms for Solar Flare Prediction: Full Resolution Dataset Images for ARs 2489 through 2731
<p>This dataset is the images associated with Dryad dataset https://doi.org/10.5061/dryad.dv41ns23n. These images are consistently sized images of active region magnetograms from the National Aeronautics and Space Administration's (NASA's) Solar Dynamics Observatory (SDO). These data are the full sized images (600x600 pixels) for active regions (ARs) 2489 through 2731 in .fits format.</p>
Data for: Single-gene resolution of diversity-driven overyielding in plant genotype mixtures
<p>In plant communities, diversity often increases productivity and functioning, but the specific underlying drivers are difficult to identify. Most ecological theories attribute positive diversity effects to complementary niches occupied by different species or genotypes. However, the specific nature of niche complementarity often remains unclear, including how it is expressed in terms of trait differences between plants. Here, we use a gene-centred approach to study positive diversity effects in mixtures of natural <em>Arabidopsis </em><em>thaliana</em> genotypes. Using two orthogonal genetic mapping approaches, we find that between-plant allelic differences at the <em>AtSUC8</em> locus are strongly associated with mixture overyielding. <em>AtSUC8</em> encodes a proton-sucrose symporter and is expressed in root tissues. Genetic variation in <em>AtSUC8</em> affects the biochemical activities of protein variants and natural variation at this locus is associated with different sensitivities of root growth to changes in substrate pH. We thus speculate that - in the particular case studied here - evolutionary divergence along an edaphic gradient resulted in the niche complementarity between genotypes that now drives overyielding in mixtures. Identifying such genes important for ecosystem functioning may ultimately allow linking ecological processes to evolutionary drivers, help identify traits underlying positive diversity effects, and facilitate the development of high-performing crop variety mixtures.</p>
A high-resolution global land daily drought index dataset during 1979–2022
<p>A global daily drought index dataset named as daily evapotranspiration deficit index (DEDI) is constructed using daily actual evapotranspiration and potential evapotranspiration data provided by European Centre for Medium-Range Weather Forecasts Reanalysis v5 (ERA5). The DEDI dataset has a spatial grid resolution of 0.25°×0.25° and covers global land areas for the period 1979 to 2022. The DEDI dataset can be a good index for assessing the dry and wet severity in terms of spatial patterns and temporal evolutions when compared to other available daily drought indices. Moreover, the DEDI dataset is also demonstrated to have advantages in detecting ecological or agricultural droughts. The DEDI dataset also appears reasonable and promising in facilitating drought monitoring and early warning from a daily perspective.</p><p>This dataset accompanies the following publication: Zhang, X., Duan, J., Cherubini, F. et al. A global daily evapotranspiration deficit index dataset for quantifying drought severity from 1979 to 2022. Sci Data 10, 824 (2023). https://doi.org/10.1038/s41597-023-02756-1</p>
Active region magnetograms for solar flare prediction: Full resolution dataset
<p>In this dataset, we provide a comprehensive collection of magnetograms from the National Aeronautics and Space Administration's (NASA's) Solar Dynamics Observatory (SDO). The dataset incorporates data from three sources and provides SDO Helioseismic and Magnetic Imager (HMI) magnetograms of solar active regions as well as labels of corresponding flaring activity. This dataset will be useful for image analysis or solar physics research related to magnetic structure, its evolution over time, and its relation to solar flares. The dataset will be of interest to those researchers investigating automated solar flare prediction methods, including supervised and unsupervised machine learning (classical and deep), binary and multi-class classification, and regression. This dataset is a minimally processed, user configurable dataset of consistently sized images of solar active regions that can serve as a benchmark dataset for solar flare prediction research. This dataset consists of full resolution images (see usage notes below).</p>
Data from Zeppenfeld et al. 2023: "Winter storm risk assessment in forests with high resolution gust speed data", Eur. J. For. Res.
<p>Single-tree damage data from winter storm event "Lothar" 1999 in Baden-Wurttemberg, Germany. The data set includes the response (damage or no damage) and covariates for model parametrisation as described in Zeppenfeld et al. (2023).</p>
INEMA: High resolution inventory of atmospheric emissions of Chile
<p><strong>Brief description</strong></p> <p>This study presents the first high-resolution national inventory of anthropogenic emission for Chile (INEMA from spanish Inventario Nacional de EMisiones Antropogénicas). INEMA emission dataset considers emissions for Vehicular, point sources (industrial, energy, and other sectors), residential, forest fires, and agricultural waste burning sectors estimated for 2015–2020 and spatially distributed onto a 0.01°x0.01° high-resolution grid. For all sectors, the pollutants included are CO2, NOx, SO2, CO, VOCs, NH3, PM10, and PM2.5. Also, CH4, N2O, and black carbon (BC) are included for transport, forest fires, agricultural waste burning, and residential sources.</p> <p>Emissions are classified on IPCC categories:</p> <table> <tbody> <tr> <td>Sector</td> <td>IPCC codes</td> </tr> <tr> <td>Energy production</td> <td>1A1</td> </tr> <tr> <td>Industrial Energy consumption</td> <td>1A2</td> </tr> <tr> <td>On road transport energy consumption</td> <td>1A3b</td> </tr> <tr> <td>Comercial energy consumption</td> <td>1A4a</td> </tr> <tr> <td>residential firewood consumption</td> <td>1A4b</td> </tr> <tr> <td>Agriculture energy consumption</td> <td>1a4c</td> </tr> <tr> <td>Industrial processes</td> <td>2</td> </tr> <tr> <td>Agriculture waste burning</td> <td>3F</td> </tr> <tr> <td>Forest fires</td> <td>4A1b.iii</td> </tr> </tbody> </table> <p>This work compiles new activity data and emissions factors and distributes them geographically based on census, Chile´s road network and CONAF information. To consult the main methodological considerations and results of the previous version of INEMA, review the article by Alamos et al.(2022).</p> <p>This inventory should contribute to the design of policies that seek to mitigate climate change and improve air quality by providing policy makers, stakeholders and scientists with qualified scientific spatial explicit emission information.</p> <p><strong>Metadata</strong></p> <p>Each .tar file contain netcdf (.nc) files for each pollutant of the sector and year of the .tar file. Netcdf contains annual total emissions for the pollutant and year indicated per grid cell </p> <p>The emission grid consists of Chilean territory in WGS84 projection (lon-lat) with a spatial resolution of 0.01 * 0.01 degrees (lon x lat). The extension boundaries of the grid are: [(-76-56.3), (-66,-17)]</p> <p>The unit in the .nc files is Gigagrames per year [Gg/year]</p> <p><strong>The dataset is described in </strong></p> <p>Álamos, N., Hunneus, N., Opazo, M., Osses, M., Puja, S., Pantoja, N., Calvo, R., Denier Van Der Gon, H.A.C., Schueftan, A., Reyes, R., High-resolution inventory of atmospheric emissions from transport, industrial, energy, mining and residential activities in Chile. <em>Earth System Science Data</em>, <em>14</em>(1), 361-379. 2022</p> <p> </p>
Biometeorological Dataset for 'Novel algorithms for high resolution prediction of canopy evapotranspiration in grapevine'
<p>A head trained <strong><em>Vitis vinifera</em></strong> L. cv. Zinfandel vine was grafted on St. George rootstock (<em>V. rupestris</em>) then planted in a 1.1 m<sup>3</sup> plastic container filled with Yolo County, CA sourced sandy loam.<br> <br> To estimate evapotranspiration, we measured the wind speed, air temperature and relative humidity in vine canopies by mounting each vine with a suite of research grade sensors. We measured wind speed (units m ᐧ s<sup>-1</sup>) inside the vine canopy using a single needle anemometer (<em>East 30 Sensors</em>; Pullman, WA) that took instantaneous wind speed measurements every 10 seconds and recorded the average of the previous 12 instantaneous measurements for every 2-minute interval.</p> <p>We measured temperature (units <sup>o</sup>C) and relative humidity (units %) using HMP60L sensors (Campbell Scientific; Logan, UT) mounted both inside and outside of each vine canopy and recorded instantaneous measurements at each 2-minute interval. We filtered all biometeorological data using a 3-hour moving average to remove noise without causing any significant over or under-approximation of daily maxima and minima.</p> <p>We automated all data collection using two CR1000 data loggers (<em>Campbell Scientific</em>; Logan, UT), with 1 or 2 vines and associated sensors per logger, using custom CR1 programs. A single 30W solar cell and 12V lead acid battery powered the entire vine-sensor system.</p> <p>This dataset represents all sensor data from a single vine, as measured in August 2020. Columns are named accordingly and include units.</p> <p><strong>Please Note</strong>: The column named 'load_cell_kg' is not named accurately. The values given are in units of millivolts, and need to be translated from millivolts to kilograms. The 2020 calibration coefficient is 0.00330693663 millivolts per kilogram.</p>
Nationwide geospatial dataset of environmental covariates at 1km resolution in Mexico (2015-2020)
<p><strong>Package of 39 covariates, a combination of topographic, climatic, and vegetation derived variables with pixel sizes of 1000 m for the period of 2015 to 2020 from google earth engine (GEE) to assemble a nationwide geospatial dataset of Mexico.</strong><br> <strong>Datasets included WorldClim V1; a set of bioclimatic variables derived from the monthly temperature and rainfall <a href="https://www.zotero.org/google-docs/?tmZL4M">(Hijmans, 2005)</a>; time-series analysis of Landsat images from the Hansen Global Forest Change v1.8 (2000-2020) dataset <a href="https://www.zotero.org/google-docs/?jGj5Rn">(Hansen et al., 2013)</a>; 4-day composite dataset from Moderate Resolution Imaging Spectro-radiometer (MODIS) sensors with fraction of photosynthetic active radiation and leaf area index at 500-m resolution <a href="https://www.zotero.org/google-docs/?t45qy3">(Myneni, Ranga et al., 2015)</a> and the Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Emissivity Database (2000-2008) <a href="https://www.zotero.org/google-docs/?8bVVc9">(Hulley et al., 2009, 2012, 2015; Hulley & Hook, 2008, 2009, 2011; NASA JPL, 2014)</a>. All covariates were resampled to 1000 m. The resampling was done with conventional bilinear interpolation as implemented in GEE.</strong></p>
Source data for paper "Mapping disease regulatory circuits at cell-type resolution from single-cell multiomics data"
<p>Sample-paired scRNA-seq and scATAC-seq data collected from human peripheral blood mononuclear cells with <em>Staphylococcus aureus</em><em> </em>infection. ScATAC-seq data collected from human peripheral blood mononuclear cells with <em>COVID-19</em> infection. </p>
Achieving Nanoscale Resolution with EBSD
<p>Struggling to map nanoscale features in your microstructures with EBSD? Perhaps this video can help... (Case study: indexing the β phase in Ti-6Al-4V).</p> <p>Original research paper: https://doi.org/10.1016/j.matchar.2022.112371</p> <p>Research credits: A. E. Davis, X. Zeng, R. Thomas, J. R. Kennedy, J. Donoghue, A. Gholinia, P. B. Prangnell.</p> <p>Video credits: Produced, written, and performed by Alec E. Davis. Recorded by Alec E. Davis, Jack Donoghue, and Vivek Sahu.</p> <p>This work was supported by grants: Lightform (EPSRC EP/R001715/1), NEWAM (EPSRC EP/R027218/1), and Henry Royce Institute for Advanced Materials (EPSRC EP/R00661X/1, EP/S019367/1, EP/P025021/1, and EP/P025498/1). Alec E. Davis is also appreciated for equipment loan from the @materialsavclub.</p> <p>Commercial EBSD indexing software links:</p> <p>Oxford Instruments: https://www.oxinst.com/blogs/pattern-matching-a-paradigm-shift-for-ebsd EDAX: https://www.edax.com/resources/application-notes/spherical-indexing</p>
Data for: "The effects of DEM resolution on the PyFLOWGO thermorheological lava flow model" paper
<p>PyFLOWGO results for a topographic sensitivity analysis. </p> <p>These are the 5m step size results presented in the publication, additional results upon request. </p>
Confocal microscopy images (CZI files) of human chondrocytes of different resolutions and magnifications with stained nuclei and primary cilia
<p>This dataset is an addition of https://doi.org/10.5281/zenodo.7994589. Here, we have focused on the influence of the magnifications of microscope objectives and image resolution on the results of automated cilia length measurements.</p> <p>1. Methods</p> <p>1.1 Cell culture</p> <p>Human non-degenerative chondrocytes from a 30-year-old male donor (NHAC-kn, CC-2550; LONZA, Walkersville Inc., Walkersville, MD, USA) were used. These chondrocytes were seeded in passage four with a density of 28000 cells/cm<sup>2</sup> on collagen-coated glass coverslips (GG-15-Collagen; Neuvitro Corporation, Camas, WA, USA). The cells were cultivated in 12-well plates (Thermo Fisher Scientific Inc., Waltham, MA, USA) under hypoxic conditions at 37°C, 5% CO<sub>2</sub> and 5% O<sub>2</sub> with different media compositions for three days.<br> The basal medium consisted of Dulbecco’s Modified Eagle Medium (DMEM) (Gibco™) including high glucose (GlutaMAX™), sodium pyruvate supplements (Thermo Fisher Scientific Inc., Waltham, MA, USA), as well as 1% penicillin/streptomycin (Pen/Strep; Thermo Fisher Scientific Inc.), 1% Amphotericin B (Biochrom GmbH, Berlin, Germany), and 50 µg mL<sup>−1</sup> ascorbic acid (Sigma-Aldrich, Merck KGaA, Darmstadt, Germany). To this basal medium, the following supplements were added: ITS with Dexa + IGF-1 + TGF-β1: 1% Insulin-Transferrin-Selenium (ITS+™), 100 nM dexamethasone, 50 ng mL<sup>−1</sup> insulin-like growth factor (IGF)-1 (R&D Systems, Minneapolis, MN, USA) and 50 ng mL<sup>−1</sup> transforming growth factor (TGF)-β1 (Peprotec, Hamburg, Germany).</p> <p>1.2 Immunocytochemistry</p> <p>After three days of cultivation in the different media compositions, the chondrocytes were washed once with phosphate-buffered saline (PBS; Biochrom GmbH, Berlin, Germany) and fixed for 10 min at room temperature (RT) with 4% paraformaldehyde (ROTI ® Histofix, Carl Roth GmbH + Co. KG, Karlsruhe, Germany). After fixation, cells were washed again and permeabilized with 0.2% Triton-X100 (Merck, Darmstadt, Germany) for 10 min. For blocking the unspecific binding sites, cell-seeded coverslips were incubated with bovine serum albumin (BSA; Sigma-Aldrich) with a concentration of 5% in PBS for one hour at RT after another washing step with PBS. To stain the primary cilium, cells were incubated with anti-acetylated α-tubulin (6-11B-1) (RRID: AB 628409) labeled with Alexa Fluor 647 (sc-23950 AF647, Santa Cruz Biotechnology, Dallas, TX, USA) diluted 1:200 in PBS at 4°C overnight. Additionally, the Actin cytoskeleton was stained with Acti-stain 488 Fluorescent Phalloidin (Cytoskeleton, Inc., Denver, CO, USA) diluted 10 in PBS for 30 min at RT. Afterward, cells were washed three times with PBS, and the coverslips were fixed with Fluoroshield™ (Sigma-Aldrich) containing 4’,6-Diamidino-2-phenylindole (DAPI).</p> <p>1.3 Image acquisition</p> <p>Three-dimensional fluorescence images of stained cells were acquired with a ZEISS ELYRA LSM 780 confocal laser scanning microscope (CLSM) (Carl Zeiss AG, Oberkochen, Germany). To find optimal microscopy parameters for automated detection and length measurement of primary cilia, images were recorded using a Plan-Apochromat 63×/1.40 Oil DIC M27 objective (Carl Zeiss AG, Oberkochen, Germany) or an α Plan-Apochromat 100 × /1.46 Oil DIC M27 Elyra objective (Carl Zeiss AG, Oberkochen, Germany) as well as the following resolutions: 1024 × 1024, 2048 × 2048 and 4096 × 4096 pixels resulting in different voxel sizes (see metadata of files).</p>
Gene expression dataset of the Spatially Resolved Single-cell Translatomics at Molecular Resolution
<p>Here are the gene expression datasets of RIBOmap included in "<strong>Spatially Resolved Single-cell Translatomics at Molecular Resolution</strong>" from Zeng et al. Please refer to the README file for more detailed information. </p> <p> </p> <p><strong>Abstract</strong></p> <p>The precise control of mRNA translation is a crucial step in post-transcriptional gene regulation of cellular physiology. However, it remains a major challenge to systematically study mRNA translation at the transcriptomic scale with spatial and single-cell resolution. Here, we report the development of RIBOmap, a three-dimensional (3D) in situ profiling method to detect mRNA translation of thousands of genes simultaneously in intact cells and tissues. By applying RIBOmap to 981 genes in HeLa cells, we revealed a remarkable dependency of translation on cell-cycle stages and subcellular localization. Furthermore, we profiled single-cell translatomes of 5,413 genes in adult mouse brain tissues yielding a spatial cell atlas of 119,173 cells. The pairwise spatial mapping of single-cell translatome and transcriptome in two adjacent mouse brain slices revealed cell-type and brain-region-dependent translational regulation and suggested a translation remodeling during oligodendrocyte lineage maturation. The spatial translatome profiling detected widespread patterns of localized translation in neuronal and glial cells in intact brain tissue networks. Together, RIBOmap presents the first spatially resolved single-cell translatomics technology, accelerating our understanding of protein synthesis in the context of subcellular architecture, cell types, and tissue anatomy.</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.