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160 results for “Runoff”
Ancillary data for article "The general formulation for runoff components estimation and attribution at mean annual time scale"
<p>The mean annual (1960-1990) precipitation and estimated model parameters wetting potential (Wp), vaporization potential (Vp) and upper limit of <span>the portion remaining after precipitation (</span>Up) of 312 catchments over China.</p>
Glacier runoff projections and their multiple sources of uncertainty in the Patagonian Andes (40-56°S)
<p>This dataset contains the catchment scale results of the study: "<strong>Unravelling the sources of uncertainty in glacier runoff projections in the Patagonian Andes (40–56° S)</strong>". The results are disaggregated in the following files (for more details, please read the README file):</p> <p><em>- basins_boundaries.zip:</em> Contains the polygons (in .shp format) of the studied catchments. Each catchment is identified by its "basin_id".</p> <p><em>- dataset_historical.csv: </em>Summarises the historical conditions of each glacier at the catchment scale (area, volume and reference climate).</p> <p><em>- dataset_future.csv: </em>Summarises the future glacier climate drivers and their impacts at the catchment scale. </p> <p><em>- dataset_signatures.csv: </em>Summarises the glacio-hydrological signatures of each glacier at the catchment scale.</p> <p><strong>Citation (preprint under review): </strong></p> <p>- Aguayo, R., Maussion, F., Schuster, L., Schaefer, M., Caro, A., Schmitt, P., Mackay, J., Ultee, L., Leon-Muñoz, J., and Aguayo, M.: Assessing the glacier projection uncertainties in the Patagonian Andes (40–56° S) from a catchment perspective, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2023-2325, 2023.</p>
Model-based runoff in Northwestern Patagonia (41-46°S)
<p><strong>Dataset: </strong></p> <p>The present dataset includes model-based runoff data for each of the 896 catchments with a surface area > 5 km2 draining into the coastal zone of Northwestern Patagonia (41-46°S). The data were generated using the Variable Infiltration Capacity (VIC) model. VIC is a semi-distributed, physically based hydrological model that simulates snow accumulation and melt, evapotranspiration, canopy interception, surface runoff, baseflow and other hydrological processes at sub-daily time steps. The model was forced with gridded meteorological data from PMET-sim and ERA5-Land for the period 1980-2020. The VIC model was calibrated (1985-2004) and validated (2005-2020) in 43 catchments using a split-sample approach. The calibration was performed using the Shuffled Complex Evolution algorithm included in the SPOTPY framework. The modelling approach achieved adequate performance of hydrological fluxes with modified Kling-Gupta efficiencies of 0.75 ± 0.12 and 0.64 ± 0.23 in the calibration and validation phases (daily timestep), respectively. <br><br>The file details are as follows <br><br>- basins_NP_metadata.csv: basin attributes including basin ID, area (in km2), name, location, mean elevation and climate attributes.<br>- basins_NP_shapefile.zip: Zip file containing the shp file of all basins in the study area ()<br>- basins_NP_historical_runoff.csv: Daily time series of runoff data (in m3/s). Each column in the .csv file represents a basin.</p> <p><br><strong>Citation: </strong></p> <p>A preprint is in preparation and will be added here.</p> <p><strong>Version history:</strong></p> <ul> <li>v1.0: First public released</li> <li>v1.1: Corrected NAs in some basins + minor changes</li> </ul>
Data sets used for: Urban runoff velocity measurement with consumer-grade surveillance cameras and surface structure image velocimetry
<p>Original videos and reference bulk velocity and water depth data sets used to develop the study: <em>Urban runoff velocity measurement with consumer-grade surveillance cameras and surface structure image velocimetry.</em></p> <p>The reference bulk velocity and water depth data sets were obtained with the Nivus OFR Radar and Nivus NivuCompact sensors, respectively.</p>
The Application of a Snowpack Runoff Decision Support System for Rain-on-Snow Events Dataset
<p>This work was funded by the State of Nevada - Department of Transportation award No. P296-22-803 and UCAR COMET Outreach Program SUBAWD004566. </p>
Plant cover on 2 x 2 meter rainfall runoff plots in grassland and creosotebush communities at the Jornada Basin LTER, 1989-1990
This data package contains plant cover measurements collected at natural rainfall-runoff plots installed on lands managed by the Chihuahuan Desert Rangeland Research Center (CDRRC) and the Jornada Experimental Range (JER) during the early years of Jornada Basin LTER (LTER-I and II). The purpose of this study was to quantify plant cover and phenology in grassland and creosote bush in runoff study plots, including those that included a termite removal treatment. Plant cover was measured on 20 2 x 2 meter hydrology runoff plots located in zones dominated by either creosotebush or grassland using sampling quadrats divided into 10 x 10 cm units. The number of 10x10cm squares occupied each by plant species was counted and scaled up to determine the total percent cover of each species per plot. Data recorded includes plant species codes, raw cover, percent cover, height of vegetation, and phenology. This study was completed in October 1990.
Rainfall runoff and sediment deposition from 2 x 2 meter plots in grassland and creosotebush communities at the Jornada Basin LTER, 1982-1994
This data package contains data from natural rainfall-runoff plots installed on lands managed by the Chihuahuan Desert Rangeland Research Center (CDRRC) and the Jornada Experimental Range (JER) during the early years of Jornada Basin LTER (LTER-I and II). The purpose of this study was to measure infiltration and runoff rates related to rain event duration and intensity, and soil and organic movement in creosotebush and grassland areas. In addition, some plots were treated with chlordane to remove termites. Nine hydrology runoff plots in the creosotebush sites (Larrea tridentata) were established in 1983 and 12 black grama (Bouteloua eriopoda) grassland runoff plots were established in 1989. Each plot was 2 × 2 square meters and surrounded on 3 sides by a metal frame. Rainfall was collected using a 10-cm diameter PVC pipe and a 120-liter tub. There are 4074 plot-events or observations in the data set. The data set includes the date of the rainfall event, relative cover of vegetation, sediment concentration in runoff (mg/l), precipitation (mm), deposited sediment, and percent carbon. This study began in fall 1982 and was completed in fall 1994.
Rainfall runoff water chemistry from 2 x 2 meter plots in grassland and creosotebush communities at the Jornada Basin LTER, 1988-1990
This data package contains data from natural rainfall-runoff plots installed on lands managed by the Chihuahuan Desert Rangeland Research Center (CDRRC) and the Jornada Experimental Range (JER) during the early years of Jornada Basin LTER (LTER-I and II). The purpose of this study was to analyze dissolved chemicals in surface runoff after precipitation events from creosotebush and grassland areas. In addition, some plots were treated with chlordane to remove termites. Nine hydrology runoff plots in the creosotebush sites (Larrea tridentata) were established in 1983 and 12 black grama (Bouteloua eriopoda) grassland runoff plots were established in 1989. Chemical analyses began in 1988 (1989 for grassland plots). Each plot was 2 × 2 square meters and surrounded on 3 sides by a metal frame. At the lower slope of the plot is a trough that collects the runoff for analysis. The data set includes the date of sampling, plot IDs, volume (L) of surface water runoff collected, and concentrations of F, Cl, NO3, SO4, NH4, Ca, Mg, Na, K, total N, and total P in mg/L. This study was completed in fall 1990.
Gridded reconstruction of monthly runoff for Northwest Russia
<p>Developed reconstructions of monthly runoff for Northwest Russia -- BASE and SOTA -- are the part of the manuscript "The influence of regional hydrometric data incorporation on the accuracy of gridded reconstruction of monthly runoff" by G. Ayzel, L. Kurochkina, and S. Zhuravlev which was submitted in the Special Issue on “Hydrological Data: Opportunities and Barriers” of the Hydrological Sciences Journal (http://explore.tandfonline.com/cfp/est/hydrological-science-data).</p>
LSTM-REG: regional gridded runoff dataset for northwest Russia based on Long Short-Term Memory (LSTM) networks
<p>LSTM-REG: regional gridded runoff dataset for northwest Russia.</p> <p>Model: LSTM.</p> <p>Geographical domain: 25–57°E; 55–70°N.</p> <p>Spatial resolution: 0.5°x0.5°.</p> <p>Temporal resolution: daily.</p> <p>Period: 1980-2016.</p>
GR4J-REG: regional gridded runoff dataset for northwest Russia based on GR4J model and nearest-neighbor regionalization technique
<p>GR4J-REG: regional gridded runoff dataset for northwest Russia.</p> <p>Model: GR4J.</p> <p>Regionalization technique: Nearest-neighbor.</p> <p>Geographical domain: 25–57°E; 55–70°N.</p> <p>Spatial resolution: 0.5°x0.5°.</p> <p>Temporal resolution: daily.</p> <p>Period: 1979-2016.</p>
Regional Revised River Runoff Reanalysis (R5): historical and projected river runoff data set for the northwest of the European part of Russia
<p>This data set presents a uniform spatio-temporal assessment of projected river runoff for the northwest of the European part of Russia, which is based on two hydrological models (GR4J-REG and LSTM-REG), four General Circulation models (GFDL-ESM2M, HadGEM2-ES, IPSL-CM5A, and MIROC5), and three Representative Concentration Pathways (RCP2.6, RCP6.0, and RCP8.5). Each of the 24 gridded runoff data sets has daily temporal and 0.5° spatial resolution. They cover the geographical domain of 25–57° East and 55–70° North, and the temporal period from 2006 (2007 for LSTM-REG) to 2099.</p>
GDRD: gridded daily runoff dataset for the Nadym, Pur, and Taz river basins
<p>Historical variations in daily runoff for large Russian Arctic domain of the Nadym, Pur and Taz basins were calculated. Robustness estimation of proposed dataset for monthly runoff predictions in ungauged basins shows high efficiency on historical observations period. The dataset is open and could be freely distributed.</p>
Data: Constraining Ice Slab Thickness at the Onset of Visible Surface Runoff from the Greenland Ice Sheet
<h1>Data repository associated with the manuscript 'Constraining Ice Slab Thickness at the Onset of Visible Surface Runoff from the Greenland Ice Sheet', Nicolas Jullien, Andrew J. Tedstone, Horst Machguth, (under review in the Journal of Glaciology)</h1> <h2> </h2> <h2>Introduction:</h2> <p>We provide a short description of each file present in this data repository, and flag to the corresponding reference when applicable. Please cite the appropriate references when using these data.</p> <h2> </h2> <h2>Data:</h2> <h3>In this repository:</h3> <ul> <li>'Ice_Layer_Output_Thicknesses_Likelihood_2010_2018_jullienetal2021_modified.csv'. Modified 2010-2018 ice slabs thickness retrievals from Jullien et al., (2023) where ice slabs thickness > 16 m thick and < 1 m thick are retained, and flight-lines not holding ice slab were set to hold an ice content of 0 m thick.</li> <li>'master_maps.zip'. Raster files. Surface hydrology connectivity map over the Greeland Ice sheet, first presented in Tedstone and Machguth (2022). The easiest way to handle this dataset is to use the 'master_map_GrIS_mean.vrt' file.</li> <li>'MARv.3.14_MoA_2000_2012.nc'. Melt over accumulation from 2000 to 2012 extracted from MARv3.14. See file '<a title="melt_over_accumulation_calculations.py" href="https://github.com/jullienn/IceSlabs_SurfaceRunoff/blob/main/melt_over_accumulation_calculations.py">melt_over_accumulation_calculations.py</a>' in the code repository for post processing analysis.</li> <li>'RunoffLimits.zip'. '.csv' files. Maximum visible runoff limits in 2012 and 2019, sorted for each boxes generated by Tedstone and Machguth (2022). Each '.csv' file stores the data points coordinates (Geographical Reference System: WGS 84 / NSIDC Sea Ice Polar Stereographic North (EPSG:3413)) of the maximum visible runoff limit retrievals after filtering out the outliers. The maximum visible runoff limits where first presented in Tedstone and Machguth (2022).</li> </ul> <h3>Used in this study but from other datasets:</h3> <ul> <li>The ice slabs extent and ice slabs thickness were first presented in Jullien et al., (2023), and are accessible at: https://zenodo.org/records/7505426</li> <li>The radargrams displayed in Fig. 5c-f were first presented in Jullien et al., (2023), and are accessible at: https://zenodo.org/records/7505426. The following files were used: <ul> <li>'L1_may12_03_1_aggregated.pickle'</li> <li>'L1_may12_03_2_aggregated.pickle'</li> <li>'20100508_01_114_115_Depth_CORRECTED.pickle'</li> <li>'20140424_01_002_004_Depth_CORRECTED.pickle'</li> <li>'20180427_01_170_172_Depth_CORRECTED.pickle'</li> </ul> </li> <li>The surface topography present in Fig. 5g are 10 m resolution mosaics from the ArcticDEMv3 (Porter et al., 2018), and accessible at: https://data.pgc.umn.edu/elev/dem/setsm/ArcticDEM/mosaic/v3.0/</li> <li>The winter time strain rates map displayed in Fig. 5h were first presented in Poinar and Andrews (2021), and are accessible at: https://ubir.buffalo.edu/xmlui/handle/10477/82127</li> </ul> <p> </p> <h2>References:</h2> <p>Jullien, N., Tedstone, A. J., Machguth, H., Karlsson, N. B., & Helm, V. (2023). Greenland Ice Sheet Ice Slab Expansion and Thickening. <em>Geophysical Research Letters</em>, <em>50</em>(10), e2022GL100911. https://doi.org/10.1029/2022GL100911</p> <p>Poinar, K., & Andrews, L. C. (2021). Challenges in predicting Greenland supraglacial lake drainages at the regional scale. <em>The Cryosphere</em>, <em>15</em>(3), 1455–1483. https://doi.org/10.5194/tc-15-1455-2021</p> <p>Porter, C., Morin, P., Howat, I., Noh, M.-J., Bates, B., Peterman, K., Keesey, S., Schlenk, M., Gardiner, J., Tomko, K., Willis, M., Kelleher, C., Cloutier, M., Husby, E., Foga, S., Nakamura, H., Platson, M., Wethington, M., Jr., Williamson, C., … Bojesen, M. (2018). <em>ArcticDEM, Version 3</em> (Version V1) [dataset]. Harvard Dataverse. https://doi.org/10.7910/DVN/OHHUKH</p> <p>Tedstone, A. J., & Machguth, H. (2022). Increasing surface runoff from Greenland’s firn areas. <em>Nature Climate Change</em>. https://doi.org/10.1038/s41558-022-01371-z</p>
Dataset to: Terrestrial runoff is an important source of biological ice-nucleating particles in Arctic marine systems
<p>The dataset contains supplementary information to the manuscript "Terrestrial runoff is an important source of biological ice-nucleating particles in Arctic marine systems"</p> <p>The file <a href="https://zenodo.org/api/records/14988900/draft/files/INP_data_all_samples.csv/content" target="_blank" rel="noopener noreferrer">INP_data_all_samples.csv</a> contains information on the ice nucleation measurements for all samples presented.</p> <p>The file "<a href="https://zenodo.org/api/records/14044414/draft/files/Significant_taxa_list_16S.xlsx/content" target="_blank" rel="noopener noreferrer">Significant_taxa_list_16S.xlsx</a>" contains a list of the bacterial taxa that significantly correlated with the concentration of INPs observed in the samples, while the file <a href="https://zenodo.org/api/records/14044414/draft/files/Significant_taxa_list_18S.xlsx/content" target="_blank" rel="noopener noreferrer">Significant_taxa_list_18S.xlsx</a> contains the same information for the microalgae. </p> <p>The relative abundance of the taxa in each sample is indicated in the columns "F" to "T". </p>
Glaciological data (point mass balance, SWE, snow depth, bulk snow density, modelled runoff) from Werenskioldbreen (Svabard) 2009-2020
<p>This repository contains supporting data associated to the manuscript to <em>Earth System Science Data: </em></p> <p><strong>Ignatiuk D., Błaszczyk M., Budzik T., Grabiec M., Jania J., Kondracka M., Laska M., Małarzewski Ł., Stachnik Ł. A decade of glaciological and meteorological observations in the High Arctic (Werenskioldbreen, Svalbard)</strong></p> <p>In 2009-2020, 9 ablation stakes were installed on the Werenskioldbreen.<strong> </strong>Based on the data collected, the following glaciological variables are available for Werenskioldbreen: annual and seasonal point ablation and accumulation, snow cover depth, bulk snow density and SWE (snow water equivalent) at the measuring points and modelled total runoff from the surface ablation. </p>
An ensemble of 48 perturbed-physics model (Noah-MP) estimates of the 1/8° runoff over the conterminous United States, 1980–2015
<p>This dataset contains the 1980–2015 monthly evapotranspiration 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 surface and subsurface runoff. The file name has four parts: the variable collection, 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>
Prediction of runoff characteristics in ungauged basins in Central Europe with machine learning – files
<p><em><strong>English</strong></em></p> <p>This are the shapefiles accompanying the paper: Klingler et al. (2022), Prediction of runoff characteristics in ungauged basins with machine learning, published in the journal Österreichische Wasser- und Abfallwirtschaft: <a href="https://doi.org/10.1007/s00506-022-00891-4">https://doi.org/10.1007/s00506-022-00891-4</a></p> <p>The basic idea was to train a machine learning model with observed runoff characteristics of the hydrological years 2003 - 2017 (LamaH_observations, 859 features) and 90 different catchment characteristics to be able to predict runoff characteristics in unobserved catchments (OWK_predictions, 9533 features).</p> <p>We provide two shapefiles to download:<br> <strong>1) LamaH_observations</strong>, which contains attributes for 6 different runoff characteristics calculated from observed runoff timeseries from the LamaH-CE dataset (https://doi.org/10.5194/essd-13-4529-2021).<br> <strong>2)</strong> <strong>OWK_predictions</strong>, which includes additionally to the predicted 6 runoff characteristics also attributes for uncertainty quantification.<br> All attributes of the shapefiles are described in the associated metadata (.qmd files).</p> <p><strong>Disclaimer:</strong> We have created the shapefiles with care and checked the outputs for plausibility. By downloading the data, you agree that we nor the provider of the used source datasets (e.g. observed runoff time series) cannot be liable for the data provided.</p> <p><strong>License:</strong> This work is licensed with CC BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0/). This means that you may freely use and modify the data (even for commercial purposes). But you have to give appropriate credit (associated ÖWAV paper, version of dataset), indicate if and what changes were made and distribute your work under the same public license as the original.</p> <p><strong>Contact:</strong> If you find any errors in the dataset or have any further questions, feel free to send us an email: info@baseflow.ai</p> <p>-------------</p> <p><strong><em>Deutsch</em></strong></p> <p>Dies sind die beiden Shapefiles, welche dem folgenden Fachartikel zugehörig sind: Klingler et al. (2022), Vorhersage von hydrologischen Abflusskennwerten in unbeobachteten Einzugsgebieten mit Machine Learning, veröffentlicht im Journal Österreichische Wasser- und Abfallwirtschaft: <a href="https://doi.org/10.1007/s00506-022-00891-4">https://doi.org/10.1007/s00506-022-00891-4</a></p> <p>Der Ansatz hinter dieser Arbeit war ein Machine Learning Modell mit beobachteten Abflusskennwerten der hydrologischen Jahre 2003 - 2017 (LamaH_observations, 859 Features) und 90 verschiedenen Einzugsgebietseigenschaften zu trainieren, um anschließend diese Abflusskennwerte in unbeobachteten Einzugsgebieten vorherzusagen (OWK_predictions, 9533 Features).</p> <p>Wir bieten zwei Shapefiles zum Download an:<br> <strong>1)</strong> <strong>LamaH_observations</strong>, welche Attribute für 6 verschiedene Abflusskennwerten (MJHQ, MQ, MJNQ, MJNQ7, Q95, Q98) enthält, die aus beobachteten Abflusszeitreihen aus dem LamaH-CE-Datensatz berechnet wurden (https://doi.org/10.5194/essd-13-4529-2021).<br> <strong>2)</strong> <strong>OWK_predictions</strong>, welche zusätzlich zu den vorhergesagten 6 Abflusskennwerten auch Attribute zur Quantifizierung der Unsicherheit enthält.<br> Alle Attribute der Shapefiles sind in den zugehörigen Metadaten (.qmd-Dateien) beschrieben.</p> <p><strong>Haftungsausschluss:</strong> Wir haben die Shapefiles mit Sorgfalt erstellt und die Ergebnisse auf Plausibilität geprüft. Mit dem Herunterladen der Daten erklären Sie sich damit einverstanden, dass weder wir noch der Anbieter der verwendeten Quelldatensätze (zB. beobachtete Abflusszeitreihen) für die bereitgestellten Daten haften.</p> <p><strong>Lizenz:</strong> Diese Arbeit ist lizenziert mit CC BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0/). Dies bedeutet, dass Sie die Daten frei verwenden und verändern dürfen (auch für kommerzielle Zwecke). Sie müssen jedoch eine entsprechende Quellenangabe machen (zugehöriger ÖWAV-Artikel, Version des Datensatzes), angeben ob und welche Änderungen vorgenommen wurden, und Ihre Arbeit unter der gleichen Lizenz wie das Original veröffentlichen.</p> <p><strong>Kontakt:</strong> Wenn Sie Fehler im Datensatz finden oder weitere Fragen haben, können Sie uns gerne eine E-Mail schicken: info@baseflow.ai</p>
Annual river runoff in the Xinjiang Uygur Autonomous Region, China.
<p>The annual river runoff in the Xinjiang Uygur Autonomous Region, China during the period 1980-2010.</p>
An ensemble of 48 perturbed-physics model (Noah-MP) estimates of the 1/8° runoff over the conterminous United States, 1980–2015 (time-merged version)
<p>This dataset contains the 1980–2015 monthly evapotranspiration 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 surface and subsurface runoff. 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>
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.