Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
1,693
datasets available to search
ShareScore release 0.9.0
Dataset results
1,693 results for “droughts”
The South Pacific Drought Atlas (SPaDA)
<p>Instrumental and reconstructed Standardised Precipitation Evapotranspiration Index (SPEI) generated and analysed in the paper 'Extreme events in the multi-proxy South Pacific drought atlas' (Higgins et al., 2023, 10.1007/s10584-023-03585-2).</p>
2021-2022 West False River Emergency Drought Barrier water quality, flow, and fish monitoring
To manage the critically low 2021 water supply for beneficial uses, DWR installed the temporary emergency drought barrier (EDB) on West False River in the Sacramento–San Joaquin Delta (Delta), approximately 5 miles south of Rio Vista, California, in Contra Costa County in June 2021. To monitor the effectiveness and impacts of the EBD, a monitoring program was initiated to track changes in hydrodynamics, water quality, fish, harmful algal blooms, and aquatic weeds in the vicinity of the EDB. The EDB was left in place during the winter of 2021-2022 and removed in fall of 2022. This data set includes all data collected as part of that monitoring program and subsets of ongoing monitoring programs that were used in the final effectiveness report for the EDB.
Interagency Ecological Program: Data for Synthesis for Ecological Impacts of Drought in the Upper San Francisco Estuary, 1975-2021
These data were collected for the special issue on Drought in the Sacramento-San Joaquin Delta published in San Francisco Estuary and Watershed Sciences, Volume 22, Issue 1, March of 2024. Multi-year droughts are important and impactful features of California’s Mediterranean climate and can fundamentally affect the water quality and the ecosystem response of the San Francisco Estuary and the Sacramento-San Joaquin Delta. This data set was assembled from data collected by long-term monitoring programs over the past 46 years (1975-2021) to evaluate how the estuary changed during multi-year droughts. Data include fish, zooplankton, jellyfish, clams, temperature, Secchi depth, and salinity as measured by fish and water quality surveys, as well as nutrients and chlorophyll measured by water quality surveys. These data are accompanied by hydrologic metrics including Delta Inflow, Delta Outflow, the position of the 2-PSU isohaline, residence time, and daily values of various continuous water quality stations located in the Delta.
Sediment organic phosphorus mineralization through extreme drought experiment, Poyang Lake, China, 2022
Sediment samples from Poyang Lake before and after the extreme drought were analyzed by FT ICR-MS, and the samples were named Pre-drought and Post-drought, respectively; initial samples from the simulated drought experiment and samples from the treatment groups at the end of the drought were analyzed by FT ICR-MS, and the samples were named Initial, Light, Light+, respectively. 16S rRNA gene sequence analysis was performed on samples from each bacterial treatment group at the end of the drought and the initial samples, which were named Initial, Micerbe, Microbe+light, respectively.
Climate warming and drought modify galling effects on tall goldenrod
These data and R scripts are from a study of climate change impacts on galling in goldenrod (Solidago altissima), at Kellogg Biological Station Long-Term Ecological Research (KBS LTER) site, Hickory Corners, Michigan, USA during the summers of 2021-2022 (REX 2024). This study set is part of the KBS LTER Rainfall Exclusion eXperiment (REX). Goldenrod plants with and without galls caused by Rhopalomyia solidaginis were exposed to warmed, drought, and warmed x drought treatments, and ambient (no treatment) and irrigated control conditions. Warming was achieved by use of open-top chambers for tall-stature plant communities (Welshofer et al. 2018 MEE) and a 6-week drought was implemented by use of rain-out shelters (Kahmark et al. 2024 Zenodo). L0 data are available upon request; they include the raw data from the KBS LTER REX project. The scripts that are used to clean L0 data and produce L1 data are also available upon request. The L1 data are the result of merged L0 data and are cleaned for typos and use standardized names. L1 data contain plant and gall traits from all treatments. The L2 scripts use the L1 data for statistical analyses and to create figures. Literature cited: Kahmark, K., Jones, M., Bohm, S., Baker, N., & Robertson, G. P. (2024). Rainfall manipulation shelters for agricultural research. Zenodo. https://doi.org/10.5281/zenodo.10607631. Rain Exclusion eXperiment (REX). (2024). https://lter.kbs.msu.edu/research/rainfall-exclusion-experiment/. https://lter.kbs.msu.edu/research/rainfall-exclusion-experiment/. Welshofer KB, Zarnetske PL, Lany NK, Thompson LAE (2018) Open-top chambers for temperature manipulation in taller-stature plant communities. Methods Ecol Evol 9:254–259. https://doi.org/10.1111/2041-210X.12863.
2015 Drought soil biogeochemistry and greenhouse gas emissions study at El Verde
We report the effects of the severe 2015 Caribbean drought on soil moisture, oxygen (O2), temperature, phosphorus (P), iron (Fe), pH, and GHG emissions (CO2 and CH4) across a catena sensor array field outside of El Verde Research Station, Luquillo LTER, Puerto Rico. Seven sensors of each type were installed at 12 cm depth along a ridge to valley catena; the entire catena transect was replicated five times for a total of 105 sensors. Within the sensor field we also installed nine automated gas flux chambers randomly located in each topographic zone (ridge, slope and valley). Soil carbon and nitrogen, extractable phosphorus (P) pools, iron (Fe) species, and pH were sampled before and during the drought as indicators of biogeochemical conditions. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.
Soil Moisture and Temperature following experimental drought in the LEF
We used throughfall exclusion shelters to determine effects of short-term (3 month) drought on trace gas fluxes and nutrient availability in humid tropical forests in Puerto Rico. Exclusion and control plots were replicated within and across three topographic zones (ridge, slope, valley) to account for spatial heterogeneity typical of these ecosystems. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.
Root-associated fungal communities exposed to experimental drought
Plant-associated fungi can ameliorate abiotic stress in their hosts, and changes in these fungal communities can alter plant productivity, species interactions, community structure and ecosystem processes. We investigated the response of root-associated fungi to experimental drought (66% reduction in growing season precipitation) across six North American grassland ecosystem types to determine how extreme drought alters root-associated fungi, and understand what abiotic factors influence root fungal community composition across grassland ecosystems. Next generation sequencing of the fungal ITS2 region demonstrated that drought primarily re-ordered fungal species’ relative abundances within host plant species, with different fungal responses depending on host identity. Grass species that declined more under drought trended toward less community re-ordering of root fungi than species less sensitive to drought. Host identity and grassland ecosystem type defined the magnitude of drought effects on community composition, diversity, and root colonization, and the most important factor affecting fungal composition was plant species identity.
Collection of global datasets for the study of floods, droughts and their interactions with human societies
<p>This is a collection of 134 global and free datasets allowing for spatial (and temporal) analyses of floods, droughts and their interactions with human societies. We have structured the datasets into seven categories: hydrographic baseline, hydrological dynamics, hydrological extremes, land cover & agriculture, human presence, water management, and vulnerability. Please refer to <a href="https://doi.org/10.1002/wat2.1424">Lindersson et al. (2020)</a> for further information about review methodology.</p> <p>The collection is a descriptive list, holding the following information for each dataset: </p> <ul> <li>Category<em> - as structured in Lindersson et al. (2020).</em></li> <li>Sub-category<em>- as structured in Lindersson et al. (2020).</em></li> <li>Abbreviation - <em>official or as specified in Lindersson et al. (2020).</em></li> <li>Title <em>- full title of dataset.</em></li> <li>Product(s)<em> - type of product(s) offered by the dataset.</em></li> <li>Period<em> - time period covered by the dataset, not defined for all datasets.</em></li> <li>Temporal resolution<em> - not defined for static datasets.</em></li> <li>Angular spatial resolution<em> - only defined for gridded datasets.</em></li> <li>Metric spatial resolution <em>- only defined for gridded datasets.</em></li> <li>Map scale</li> <li>Extent<em> - geographic coverage of dataset given in latitude limits.</em></li> <li>Description</li> <li>Creating institute(s)</li> <li>Data type<em> - raster, vector or tabular.</em></li> <li>File format</li> <li>Primary EO type<em> - specifies if the product primarily is based on remote sensing, ground-based data, or a hybrid between remote sensing and ground-based data.</em></li> <li>Data sources<em> - lists the data sources behind the dataset, to the extent this is feasible.</em></li> <li>Data sources also in this table<em> - data sources that are also included as datasets in this collection.</em></li> <li>Intentionally compatible with<em> - defines other datasets in this collection that the dataset is intentinoally compatible with.</em></li> <li>Citation<em> - dataset reference or credit.</em></li> <li>Documentation <em>- dataset documentation.</em></li> <li>Web address<em> - dataset access link.</em></li> </ul> <p>NOTE: Carefully consult the data usage licenses as given by the data providers, to assure that the exact permissions and restrictions are followed.</p>
Transpiration response under drought conditions of sedlings exposed to EMF
<p>Supplementary information of the manuscript: 10.1093/treephys/tpae029</p> <p>Dataset of seedlings exposed to ectomycorrhizal fungi (EMF) containing: transpiration rate and needle water potential time-series, fluorescence, dry biomass, and root morphology traits.</p> <p>Pine seedlings were exposed to EMF and then subject to drought stress for 10 days followed by 14 days of recovery. </p> <p>Transpiration was measured by weight loss, and needle water potential using a pressure chamber</p> <p>Fluorescence measurements were done at the National Plant Phenotyping Infrastructure (NaPPI) facilities at the University of Helsinki</p>
Water surface occurrence and recurrence from the article "Amazon's 2023 Drought: Sentinel-1 Reveals Extreme Rio Negro River Contraction"
<p>This data package contains the 10 m spatial resolution occurrence and recurrence water surface masks from the article "Amazon's 2023 Drought: Sentinel-1 Reveals Extreme Rio Negro River Contraction" . These maps have been produced with Sentinel-1 images (10 m) and a Deep Learning method for image segmentation called U-net, methods and data are fully described in the article. Water surface occurrence is computed for the period 2022-2023 and indicates the percentage of time that a pixel is classified as water (100%: always water, 0%: never water, and values between 0 and 100 indicate seasonality). Water surface recurrence is computed for the period 2022-2023 and indicates the number of times that a pixel was classified as a water surface, i.e., 35 indicates that the pixel was classified 35 times as a water surface during the 2022-2023 period. The total size of the dataset is 158 Mo and is distributed in two Geotiffs, one for the water surface occurence and one for the water surface. When using this dataset, please cite the original article <a href="https://doi.org/10.3390/rs16061056">https://doi.org/10.3390/rs16061056</a></p>
COM-DROUGHTS Drought Indices
<h3><strong>Data Description:</strong></h3> <p>The COM-DROUGHTS Drought Indices dataset consists of daily time series of three drought indicators for meteorological, agricultural, and hydrological drought for 52 lower lying catchments (mean altitude <1500 m asl) in Switzerland for the period 1991-2099. The time series were derived from the newest transiently simulated climate and hydrological scenarios (CH2018, Hydro-CH2018) for Switzerland. Time series are provided on catchment-scale (aggregated).</p> <p>Following Stagge et al. 2015, all years have been harmonized to 365 days by excluding the last day of the year in case of leap years.</p> <p>The time series data is available for 3 representative concentration paths (RCPs, see e.g., Moss et al. 2010 / van Vuuren et al. 2011):<br>- RCP2.6: 8 GCM-RCM model chains<br>- RCP4.5: 16 GCM-RCM model chains<br>- RCP8.5: 20 GCM-RCM model chains</p> <p>Descriptive information is available for:<br>- catchment and climate characteristics (<em>catchment_climate_characteristics.csv</em>)<br>- model calibration and validation statistics (<em>catchment_calibration_validation.csv</em>)<br>- GCM-modelchains and RCP-scenarios (<em>models_scenario_info.csv</em>)</p> <h3><strong>Drought Indices:</strong></h3> <p><strong>1) Meteorological drought: SPI3</strong><br>- represented by the Standardized Precipitation Index (McKee et al. 1993) on a three-monthly basis<br>- derived from the CH2018 climate scenarios for Switzerland (daily mean precipitation aggregated on catchment-area).<br><strong>2) Agricultural drought: ET/PET</strong><br>- represented by the relative evapo(transpi)ration (ET/PET; see e.g., Fuhrer and Jasper 2009) <br>- derived from daily time series of evapotranspiration (ET) and potential evapotranspiration (PET) (output of the Hydro-CH2018 Runoff ensemble / hydrological model simulations; see Muelchi et al. 2021)).<br><strong>3) Hydrological drought: M7Q</strong><br>- represented by the 7-day average streamflow (M7Q)<br>- derived from daily runoff time series (Hydro-CH2018 Runoff ensemble / hydrological model simulations; see Muelchi et al. 2021).</p> <h3><strong>Data Organization:</strong></h3> <p>- Dataset provided as .zip-File<br>- Data files are provided in <em>.csv</em>-Format<br>- A <em>README.txt</em> describes folder, data structure and file-naming convention.<br>- info-files with keys and information on catchments and GCM-RCM chains</p> <p><strong>Drought indices data is structured as follows:</strong><br><strong>> <em><strong>F</strong>older:</em></strong> INDEX_NAME<br>--> containing all data-files: <br>1 File per GCM_RCM model chain containing daily time series for all 52 catchments and index (variations)</p> <p>File-Naming Convention: [INDEX_NAME]_[SCENARIO]_[GCM_RCM_RESOLUTION_SCENARIO].csv<br><br>Example: <em>SPI/SPI_RCP26_KNMI-RACMO_HADGEM_EUR44_RCP26.csv</em></p> <h3><strong>Further reading & methodological details:</strong></h3> <p>The following paper based on this dataset provides more detailed information on the datasets, the underlying methods and associated datasets and literature for further reading. </p> <div> <div>von Matt, C. N., Muelchi, R., Gudmundsson, L., and Martius, O.: Compound droughts under climate change in Switzerland, Nat. Hazards Earth Syst. Sci., 24, 1975–2001, https://doi.org/10.5194/nhess-24-1975-2024, 2024.</div> <div><a href="https://nhess.copernicus.org/articles/24/1975/2024/nhess-24-1975-2024.html">https://nhess.copernicus.org/articles/24/1975/2024/nhess-24-1975-2024.html</a></div> </div> <h3><strong>Funding:</strong></h3> <p>The COM-DROUGHTS project was funded by the Swiss Federal Office for the Environment (FOEN).</p> <h3><strong>References:</strong></h3> <p><strong>1</strong><strong>)</strong> CH2018 Project Team, 2018. CH2018 - Climate Scenarios for Switzerland. National Centre for Climate Services. https://doi.org/doi: 10.18751/Climate/Scenarios/CH2018/1.0<br><strong>2)</strong> Fuhrer, J., Jasper, K., 2009. Bewässerungsbedürftigkeit von Acker- und Grasland im heutigen Klima. Agrarforschung 10, 396–401.<br><strong>3)</strong> McKee, T., Doesken, N., Kleist, J., 1993. THE RELATIONSHIP OF DROUGHT FREQUENCY AND DURATION TO TIME SCALES. Eight conference on applied climatology, 17-22 January 1993, Anaheim, California.<br><strong>4)</strong> Moss, R. H., Edmonds, J. A., Hibbard, K. A., Manning, M. R., Rose, S. K., van Vuuren, D. P., Carter, T. R., Emori, S., Kainuma, M., Kram, T., Meehl, G. A., Mitchell, J. F. B., Nakicenovic, N., Riahi, K., Smith, S. J., Stouffer, R. J., Thomson, A. M., Weyant, J. P., and Wilbanks, T. J.: The next generation of scenarios for climate change research and assessment, Nature, 463, 747–756, https://doi.org/10.1038/nature08823, 2010.<br><strong>5)</strong> Muelchi, R., Rössler, O., Schwanbeck, J., Weingartner, R., Martius, O., 2021. An ensemble of daily simulated runoff data (1981–2099) under climate change conditions for 93 catchments in Switzerland (Hydro-CH2018-Runoff ensemble). Geoscience Data Journal 9, 46–57. https://doi.org/10.1002/gdj3.117<br><strong>6) </strong>Stagge, J. H., Tallaksen, L. M., Gudmundsson, L., Van Loon, A. F., and Stahl, K.: Candidate Distributions for Climatological Drought Indices (SPI and SPEI), International Journal of Climatology, 35, 4027–4040, https://doi.org/10.1002/joc.4267, 2015.<br><strong>7)</strong> van Vuuren, D. P., Edmonds, J., Kainuma, M., Riahi, K., Thomson, A., Hibbard, K., Hurtt, G. C., Kram, T., Krey, V., Lamarque, J.-F., Masui, T., Meinshausen, M., Nakicenovic, N., Smith, S. J., and Rose, S. K.: The representative concentration pathways: an overview, Climatic Change, 109, 5, https://doi.org/10.1007/s10584-011-0148-z, 2011.</p>
Raw Data and Scripts for manuscript submitted to Oikos as 'Early Spring Snowmelt and Summer Droughts Strongly Impair the Resilience of Key Microbial Communities in a Subalpine Grassland Ecosystems'
<p>Raw Data and Scripts for manuscript submitted to PCI as 'Early Spring Snowmelt and Summer Droughts Strongly Impair the Resilience of Key Microbial Communities in Subalpine Grassland Ecosystems'</p>
Raw data: Diversity in root architecture of durum wheat at stem elongation under drought stress
<p>Raw data on above and below ground traits from a greenhouse drought stress experiment with six durum wheat varieties performed at Tuscia University, Viterbo, Italy. Measurements were performed at stem elongation stage; recorded traits: plant shoot length, dry weight, number of leaves and tillers; total root length, root surface area, mean diameter, volume, number of tips, forks, crossings, root dry weight and root angle. Root measurments were performed on the whole root system and the topsoil area (upper 5 cm). </p>
Streamflow drought hazard indicators for monitoring drought risk for human water supply and river ecosystems at the global scale
<p><strong>1) Indicators of streamflow drought hazard (SDHI) as computed by WaterGAP 2.2d (climate data WFDEI-GPCC) for the whole globe except Antarctica, spatial resolution: 0.5°, monthly data for the reference period 1986-2015:</strong></p> <p><strong>Indicators of drought magnitude: </strong>SPI12, SPEI12, SSI1, SSI12, EP1, RDQI1</p> <p><strong>Indicators of drought severity: </strong>CDQI1-Q50, CDQI1-Q80, CDQI1-Q80-HS, CDQI1-WUs, CDQI1-WUs-EFR, CDQI1-Q50_f, CDQI1-Q80_f, CDQI1-WUs_f, CDQI1-WUs-EFR_f, CEP1(20%)_f, CRDQI1(-50%)_f</p> <p><strong>2) WaterGAP grid cell IDs ("arcid") with longitude and latitude </strong>(WaterGAP_ArcID_lon_lat.txt)</p> <p><strong>3) Streamflow observations and SDHI (based on observations) for two GRDC gauging stations: </strong>Input_Figure_2_Little_Colorado_River.txt (station near Cameron) and Input_Figure_2_Danube_River.txt (station at Hofkirchen)</p> <p><strong>4) WaterGAP output: Mean monthly surface water abstractions in km3 per month: </strong>Mean_monthly_WUs_km3_per_month_WFDEI_GPCC_ant_22d_1986_2015.txt</p> <p><strong>5) Input data for computing Pearson correlation between SSI1 based on observations and each of the five indicators SSI1 (simulated), SPI3, SPI6, SPI9, and SPI12.</strong> Indicators computed for 218 out of 220 GRDC gauging stations with continuous streamflow observations between 1986 and 2015. The folder also contains a list of the 220 GRDC station numbers and the related WaterGAP grid cell ID ("arcid").</p> <p> </p>
Dataset – Illuminating snow droughts
<p>This dataset contains snow pack, temperature, and precipitation information from GFDL's SPEAR MED large ensemble over the Western United States between 1914 and 2100 under SSP2-4.5 and SSP5-8.5 scenarios used in our paper <em>Illuminating snow droughts: The future of Western United States snowpack in a high-resolution coupled global climate model.</em> This dataset is connected with repository identified by DOI: 10.5281/zenodo.7130303 and can be found at: https://github.com/Julians42/Snow_Droughts. </p>
Irish Drought Impacts Database v.1.0 (IDID)
<p>The Irish Drought Impacts Database (IDID) presents information from drought related newspaper articles for the island of Ireland, covering the period 1733 – 2019, collected through systematic searching of the Irish Newspaper Archive. These articles were identified, assessed and categorized using a modified version of the classification scheme employed by the European Drought Impact Inventory (EDII) (Stahl et al. 2012). The IDID provides information on the documented temporal and geographical extent of drought events, their socio-economic and political contexts, their consequences and mitigation strategies employed. Temporal information includes the newspaper publication date, timing and duration of drought periods and timing of impacts. Spatial details are provided on three different levels; in addition to Nomenclature of Territorial Units for Statistics Level 2 (Nuts2) regions, county location and, if available, a more localized place name (e.g., town or city name) is also recorded. The database allows the analysis of long-term patterns in drought incidence and impacts as well as offering insights into the impacts of individual drought events over nearly three centuries of Ireland’s history. Spatially specific data provide an opportunity for further exploration of the differential vulnerability of various geographical locations, which may vary depending on biophysical conditions, economic, political and societal context. Textual excerpts offer further insight into the human experience and perception of drought and its impacts. The IDID therefore enables a better understanding of drought, vulnerability, and offers a new open access tool for multi-disciplinary investigation of drought on the island of Ireland.</p>
Examination of the occurrence of drought phenomenon on the basis of the VHI coefficient from NOAA
<p>VHI (Vegetation Health Index) for the period from September 2018 were used for studies related to the occurrence of drought.</p>
Ariaal mother and milk sIgA data from 2006 drought in northern Kenya
<p>Data utilized for the manuscript, "Human milk secretory immunity in relation to maternal nutrition and infant vulnerability in northern Kenya."</p>
Global Soil Moisture Agricultural Drought Index (SMADI)
<p><br> This repository contains the global Soil Moisture Agricultural Drought Index (SMADI) spanning from June 2010 to December 2015 at fortnightly (bi-weekly) rate. The product is gridded in a 0.05deg Climate Modeling Grid.<br> <br> The data are provided in Matlab format (.mat). Each file is a 3600x7200 matrix size, where 3600 is the number of pixels in Latitude coordinates and 7200 to Longitude coordinates, both in the WGS84 system. The center-pixel coordinates are variables "Latitude" and "Longitude". No data values (NaN) correspond to pixels where the retrieval was not possible.<br> <br> <br> SMADI is computed using satellite time series of MODIS Normalized Difference Vegetation Index (NDVI, computed from daily reflectances MOD09CMG v.6), MODIS Land Surface Temperature (LST, product MOD11C1 v.6), and SMOS BEC L3 soil moisture data v2.0 (which corresponds to SMOS L2 v.620, average of ascending and descending orbits). <br> <br> The IGBP land cover (MODIS MOD12C1 product) is used prior to SMADI calculation to select only grassland and cropland/natural vegetation mosaic pixels as representative of the primary agro-ecosystems.</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.