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.
62
datasets available to search
ShareScore release 0.7.1
Dataset results
62 results for “climate indices”
Long-term climate indices (SPEI and scPDSI) derived from monthly meteorology data collected at USHCN stations in the northern Chihuahuan Desert of the United States, 1911-2021
Drought indices — Standardized Precipitation Evapotranspiration Index (SPEI) and the self-calibrating Palmer Drought Severity Index (scPDSI) —where derived from 9 United States Historical Climate Network (USHCN) stations on the Chihuahuan Desert in North America for this dataset. USHCN is a subset of the NOAA Cooperative Observer Program (COOP) Network, which consists of selected sites based on spatial coverages and completeness of data. Monthly precipitation depths, minimum, maximum and mean temperature were pulled from the dataset. These drought indices were derived using the SPEI package and scPDSI packages in R. Potential evapotranspiration was also calculated in R using the Thornthwaite method. All 9 sites are within the bounds of the Chihuahuan Desert in the state of New Mexico, with a single site (EL PASO) in the state of Texas.
Database of indicators to evaluate the contribution of urban nature-based solutions to climate change adaptation, biodiversity conservation, and social justice
<p>Supplementary data used within the publication: Goodwin, S., Olazabal, M., Castro, A. J., & Pascual, U. (2024). Measuring the contribution of nature-based solutions beyond climate adaptation in cities. <em>Global Environmental Change</em>, <em>89</em>, 102939. <a href="https://doi.org/10.1016/j.gloenvcha.2024.102939">https://doi.org/10.1016/j.gloenvcha.2024.102939</a>. Please also cite this paper when citing this database.</p> <div> <div>Within this database, you can find a list of indicators used to evaluate the contribution of a collection of 74 nature-based solutions (NbS) to climate change adaptation and related biodiversity and social justice challenges in cities. This list of indicators may be useful to those working in cities to provide inspiration for similar indicators they may wish to use to evaluate NbS in their city. This collection of NbS was drawn from previous work published in <em>Nature Sustainability</em> <a href="https://rdcu.be/c4tjk">here</a>.</div> <div> </div> </div> <p><em>The project that gave rise to these results received the support of a fellowship from the “la Caixa” Foundation (ID 100010434). The fellowship code is “LCF/BQ/DI20/11780006”. Marta Olazabal’s research is funded by the European Union (ERC, IMAGINE adaptation, 101039429). This research is further supported by María de Maeztu Excellence Unit 2023-2027 (ref. CEX2021-001201-M), funded by the Ministerio de Ciencia, Innovación y Universidades/Agencia Estatal de Investigación (AEI) (Spain) (MCIN/AEI/10.13039/501100011033/); and by the Basque Government through the BERC 2022-2025 program. </em></p> <p><em>Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Council Executive Agency. Neither the European Union nor the granting authority can be held responsible for them.</em></p>
Zonal Statistics of Climate Indicators from ERA5-Land for Brazilian Municipalities, 2023
<p>Climate indicators are used in several statistical models for many research areas and are specially important for modelling Climate Sensitive Diseases (CSD) incidence. Those models usually adopts a lattice structure, where its data is aggregated at administrative boundaries (e.g. disease incidence), but climate indicators are usually presented in a continuous regular grid format.</p> <p>To make climate indicators compatible with lattice structures, zonal statistics may be adopted. Zonal statistics are descriptive statistics calculated using a set of cells that spatially intersects a given spatial boundary. For each boundary in a map, statistics like average, maximum value, minimum value, standard deviation, and sum are obtained to represent the cell's values that intersect the boundary.</p> <p>This dataset present zonal statistic of climate indicators computed from Copernicus ERA5-Land daily aggregates for the Brazilian municipalities, for the year of 2023.</p>
Riverine Flood Insurance assessment indicators under climate and socio-economic change
<p>Expected annual river flood damages, flood insurance premiums, and insurance penetration rates, for EU-regions (NUTS2) and under future climatic and socio-economic conditions (RCP-SSP combinations).</p>
Zonal Statistics of Climate Indicators from ERA5-Land for Brazilian Municipalities, 1950-2022
<p>Climate indicators are used in several statistical models for many research areas and are specially important for modelling Climate Sensitive Diseases (CSD) incidence. Those models usually adopts a lattice structure, where its data is aggregated at administrative boundaries (e.g. disease incidence), but climate indicators are usually presented in a continuous regular grid format.</p><p>To make climate indicators compatible with lattice structures, zonal statistics may be adopted. Zonal statistics are descriptive statistics calculated using a set of cells that spatially intersects a given spatial boundary. For each boundary in a map, statistics like average, maximum value, minimum value, standard deviation, and sum are obtained to represent the cell's values that intersect the boundary.</p><p>This dataset present zonal statistic of climate indicators computed from Copernicus ERA5-Land daily aggregates for the Brazilian municipalities, from 1950 to 2022.</p><p> </p><p> </p>
STARS4Water climate indicators - tier 1
<p>This dataset was generated as part of the Horizon Europe project STARS4Water, which is dedicated to collecting and providing access to existing and newly developed datasets for future water resources management. The data entails a set of indicators for assessing climate risks and impacts on integrated water resources systems. More datasets will be added later-on in the project. This first tier is a set of indicators that are directly derived from readily available datasets from e.g. the Copernicus Climate Data Store. The future projections are simplified by projecting them onto (sub)catchments and by reducing the number<br>of scenarios. This makes them easier to handle by river basin managers and for usages in dashboards. </p>
Indicators of Global Climate Change 2024
<p>This release contains the indicators of global climate change updated to the end of 2024. Datasets included are:</p> <ul> <li>Attribution of historical warming 1850-2024</li> <li>Earth's energy imbalance 1971-2024</li> <li>Effective radiative forcing 1750-2024</li> <li>Global mean surface temperature anomalies 1850-2024</li> <li>Global temperature extreme anomalies 1950-2024</li> <li>Greenhouse gas concentrations 1750-2024</li> <li>Greenhouse gas emissions 1750-2023</li> <li>Remaining carbon budgets in 0.1°C increments</li> <li>Sea level rise 1880-2024 (corrected time bounds)</li> </ul>
CMIP6 Climate Change indicators
<p>Paneuropean maps of climate change indicators (e.g. heating degree days) for different climate scenarios (historical, SSP1-2.6, SSP2-4.5, SSP5-8.5) and time horizons (reference, short time-horizon, medium time-horizon, long time-horizon) derived from CMIP6 climate data. This v2 includes the metadata.</p>
The data used for "Exploring how differences in dust particle size distribution and complex refractive indices affect dust direct radiative fluxes using the CAS-FGOALS-SPRINTARS global climate model"
<p>These data are used for " Exploring how differences in dust particle size distribution (PSD) and complex refractive indices (CRI) affect direct radiative effect (DRE) using the CAS-FGOALS-SPRINTARS global climate model ". </p> <p>(1) AS83+OPAC: The control experiment, dust PSD is the original AS83, and the generic CRI is from OPAC. </p> <p>(2) BFT22+OPAC: Same as the control experiment, but the PSD is updated to use BFT22.</p> <p>(3) BFT22+DB: Same as the experiment BFT22+OPAC, but the generic OPAC CRI is replaced by nine regionally dependent DB CRIs.</p> <p>(4) BFT22+DB strong abs: Same as the experiment BFT22+DB, but the generic CRI consists of 10% percentile real and 90% percentile imaginary parts and no regional dependencies.</p> <p>(5) BFT22+DB weak abs: Same as the experiment BFT22+DB, but the generic CRI consists of 90% percentile real and 10% percentile imaginary parts and no regional dependencies.</p> <p>All experiments mentioned above are run for 5 years (2010-2014). The annual average simulation results are stored here.</p> <p><strong>Note:</strong> AS83 represents the dust PSD scheme from d'Almeida and Schütz. (1983). BFT22 represents the new dust PSD developed by Meng et al. (2022) based on the improved brittle fragmentation theory. OPAC: the Optical Properties for Aerosols and Clouds dataset, DB: the CRIs from Di Biagio et al. (2017, 2019).</p> <p><strong>References</strong></p> <p>d'Almeida, G. A., & Schütz, L. (1983). Number, Mass and Volume Distributions of Mineral Aerosol and Soils of the Sahara. <em>Journal of Applied Meteorology and Climatology</em>,<em> 22</em>(2), 233-243. https://doi.org/https://doi.org/10.1175/1520-0450(1983)022<0233:NMAVDO>2.0.CO;2</p> <p>Di Biagio, C., Formenti, P., Balkanski, Y., Caponi, L., Cazaunau, M., Pangui, E., et al. (2019). Complex refractive indices and single-scattering albedo of global dust aerosols in the shortwave spectrum and relationship to size and iron content. <em>Atmospheric Chemistry and Physics</em>,<em> 19</em>(24), 15503-15531. https://doi.org/10.5194/acp-19-15503-2019</p> <p>Di Biagio, C., Formenti, P., Balkanski, Y., Caponi, L., Cazaunau, M., Pangui, E., et al. (2017). Global scale variability of the mineral dust long-wave refractive index: a new dataset of in situ measurements for climate modeling and remote sensing. <em>Atmospheric Chemistry and Physics</em>,<em> 17</em>(3), 1901-1929. https://doi.org/10.5194/acp-17-1901-2017</p> <p>Meng, J., Huang, Y., Leung, D. M., Li, L., Adebiyi, A. A., Ryder, C. L., et al. (2022). Improved Parameterization for the Size Distribution of Emitted Dust Aerosols Reduces Model Underestimation of Super Coarse Dust. Geophysical Research Letters, 49(8), e2021GL097287, https://doi.org/https://doi.org/10.1029/2021GL097287</p>
Data and codes related to the article: Renard et al. A Hidden Climate Indices Modeling Framework for Multi-Variable Space-Time Data. Water Resources Research.
<p>This package contains data and codes related to the article:</p> <p>B. Renard, M. Thyer, D. McInerney, D. Kavetski, M. Leonard and S. Westra. A Hidden Climate Indices Modeling Framework for Multi-Variable Space-Time Data. <em>Water Resources Research</em>.</p> <p><strong>R scripts</strong></p> <p>The main computations of the paper have been performed using a computing code named <a href="https://github.com/STooDs-tools">STooDs</a>, which is called using the bash script launchpad.sh.</p> <p>The R scripts in this package only perform pre-processing (create configuration files) and post-processing (analyze results) steps.</p> <ul> <li>Funk.R: a set of functions called by other scripts.</li> <li>1_defineModel.R: define the model to be inferred and create STooDs configuration files in <em>dataset_XXX/runs.</em></li> <li>2_analyzeResults.R: analyze the outputs of STooDs runs.</li> <li>3_crossValidation.R: analyze the outputs of cross-validation experiments in <em>dataset_XV</em> and <em>dataset_XV_1971-1990</em>.</li> </ul> <p><strong>Data</strong></p> <p>Data for the 3 cases (full dataset and 2 cross-validation experiments) are located in folders <em>dataset_XXX/data</em>.</p> <ul> <li>dat.txt: raw dataset in text format.</li> <li>dataset.RData: dataset in RData format.</li> <li>DMI.txt, NINO.txt, SAM.txt: 3 standard climate indices.</li> <li>spaceP.txt, spaceQ.txt, spaceT.txt: properties of Precipitation (P), Streamflow (Q) and Temperature (T) stations.</li> <li>[only for cross-validation experiments] validation.RData: left-out data used for validation.</li> </ul> <p> </p> <p> </p>
Local Indicators of Climate Change Impacts reported by the Tuareg of Illizi (Algeria)
<p>The dataset reports the Local Indicators of Climate Change Impacts mentioned during 19 interviews and 3 focus groups with members of the Tuareg community of Illizi (Algeria). The dataset includes reports events important to the local timeline and observations of atmospheric changes including seasonal, precipitation, temperature, and biodiversity changes.</p>
Text-fig. 1. Modern vegetation proxies as delivered by the Drudge 1 and 2 tools for Parschlug. Left column results from KovarEder et al. (2021) based on the floristic spectrum published by Kovar-Eder et al. (2004). The other three columns result from three variants using the enlarged floristic spectrum herein. Differences between variants 1–3 from this study are caused by differences in assignment of some taxa and morphotypes (see Appendix 1). European vegetation formations: Formation C – Subarctic, boreal and nemoral-montane open woodlands as well as subalpine and oro-Mediterranean vegetation; Formation D – Mesophytic and hygromesophytic coniferous and mixed broad-leaved-coniferous forests; Formation F – Mesophytic broadleaved deciduous and mixed broadleaved/conifer forests; Formation G – Thermophilous mixed deciduous broadleaved forests; Formation J – Mediterranean sclerophyllous forests and scrub; Formation K – Xerophytic coniferous forests, coniferous woodland and scrub. East Asian vegetation types: MCF China, Japan – Montane Coniferous Forests China, Honshu, Yakushima; BLDF N and NE Provinces, China – Broad-leaved Deciduous Forests of the Northern and Northeastern Provinces (China); BLDF Upper Yangtze, Honshu – Broad-leaved Deciduous Forest, Upper Yangtze Provinces, Mt. Emei, and Honshu; MMF China – Mixed Mesophytic Forest, Lower Yangtze Provinces; BLEF China, Japan – Broad-leaved Evergreen Forests, China, Japan; Meili Snow Mt. high altitude SCL and BLF, China – Meili Snow Mt., Sclerophyllous and broad-leaved forest zone (2,580-3,650 m alt.). (Designations of European vegetation formations follow Bohn et al. (2004) and Asian ones follow Kovar-Eder et al. (2021). in Floristic, Vegetation And Climate Assessment Of The Early/Middle Miocene Parschlug Flora Indicates A Distinctly Seasonal Climate
Text-fig. 1. Modern vegetation proxies as delivered by the Drudge 1 and 2 tools for Parschlug. Left column results from KovarEder et al. (2021) based on the floristic spectrum published by Kovar-Eder et al. (2004). The other three columns result from three variants using the enlarged floristic spectrum herein. Differences between variants 1–3 from this study are caused by differences in assignment of some taxa and morphotypes (see Appendix 1). European vegetation formations: Formation C – Subarctic, boreal and nemoral-montane open woodlands as well as subalpine and oro-Mediterranean vegetation; Formation D – Mesophytic and hygromesophytic coniferous and mixed broad-leaved-coniferous forests; Formation F – Mesophytic broadleaved deciduous and mixed broadleaved/conifer forests; Formation G – Thermophilous mixed deciduous broadleaved forests; Formation J – Mediterranean sclerophyllous forests and scrub; Formation K – Xerophytic coniferous forests, coniferous woodland and scrub. East Asian vegetation types: MCF China, Japan – Montane Coniferous Forests China, Honshu, Yakushima; BLDF N and NE Provinces, China – Broad-leaved Deciduous Forests of the Northern and Northeastern Provinces (China); BLDF Upper Yangtze, Honshu – Broad-leaved Deciduous Forest, Upper Yangtze Provinces, Mt. Emei, and Honshu; MMF China – Mixed Mesophytic Forest, Lower Yangtze Provinces; BLEF China, Japan – Broad-leaved Evergreen Forests, China, Japan; Meili Snow Mt. high altitude SCL and BLF, China – Meili Snow Mt., Sclerophyllous and broad-leaved forest zone (2,580-3,650 m alt.). (Designations of European vegetation formations follow Bohn et al. (2004) and Asian ones follow Kovar-Eder et al. (2021).
Variable species establishment in response to microhabitat indicates different likelihoods of climate-driven range shifts
<p>Climate change is causing geographic range shifts globally, and understanding the factors that influence species' range expansions is crucial for predicting future biodiversity changes. A common, yet untested, assumption in forecasting approaches is that species will shift beyond current range edges into new habitats as they become macroclimatically suitable, even though microhabitat variability could have overriding effects on local population dynamics. We aim to better understand the role of microhabitat in range shifts in plants through its impacts on establishment by Q1) examining microhabitat variability along large macroclimatic (i.e., elevational) gradients, Q2) testing which of these microhabitat variables explain plant recruitment and seedling survival, and Q3) predicting microhabitat suitability beyond species range limits. We transplanted seeds of 25 common tree, shrub, forb, and graminoid species across and beyond their current elevational ranges in the Washington Cascade Range, USA, along a large elevational gradient spanning a broad range of macroclimates. Over five years, we recorded recruitment, survival, and microhabitat (i.e., high resolution soil, air, and light) characteristics rarely measured in biogeographic studies. We asked whether microhabitat variables correlate with elevation, which variables drive species establishment, and whether microhabitat variables important for establishment are already suitable beyond leading range limits. We found that only 30% of microhabitat parameters covaried with elevation. We further observed extremely low recruitment and moderate seedling survival, and these were generally only weakly explained by microhabitat. Moreover, species and life stages responded in contrasting ways to soil biota, soil moisture, temperature, and snow duration. Microhabitat suitability predictions suggest that distribution shifts are likely to be species-specific, as different species have different suitability and availability of microhabitat beyond their present ranges, thus calling into question low-resolution macroclimatic projections that will miss such complexities. We encourage further research on species responses to microhabitat and including microhabitat in range shift forecasts.</p>
Text-fig. 3. CA climate charts for the Monte Tondo and Tossignano floras, showing climatic ranges of the Nearest Living Relatives of the fossil taxa with respect to MAT. Right-hand positoned large bold figures and shaded areas in each case indicate the Coexistence Interval, with the number of overlapping taxa being at a maximum. in Palaeoenvironmental Analysis Of The Messinian Macrofossil Floras Of Tossignano And Monte Tondo (Vena Del Gesso Basin, Romagna Apennines, Northern Italy)
Text-fig. 3. CA climate charts for the Monte Tondo and Tossignano floras, showing climatic ranges of the Nearest Living Relatives of the fossil taxa with respect to MAT. Right-hand positoned large bold figures and shaded areas in each case indicate the Coexistence Interval, with the number of overlapping taxa being at a maximum.
Linked collectors and determiners for: Potential indicator species of climate changes occurring in Québec, Part 1: the small brown lacewing fly Micromus posticus (Walker) (Neuroptera: Hemerobiidae).
Natural history specimen data linked to collectors and determiners held within, "Potential indicator species of climate changes occurring in Québec, Part 1: the small brown lacewing fly Micromus posticus (Walker) (Neuroptera: Hemerobiidae)". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/8260db39-8776-4ad6-bd80-46e4e1168bf7">https://bionomia.net/dataset/8260db39-8776-4ad6-bd80-46e4e1168bf7</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/8260db39-8776-4ad6-bd80-46e4e1168bf7">https://gbif.org/dataset/8260db39-8776-4ad6-bd80-46e4e1168bf7</a>. Formatted as a Frictionless Data package.
MED-GOLD Indicators for the Wine pilot over Douro Valley based on High Resolution Climate projections
<p>Indicators of interest for the Wine sector over the Douro Valley using high resolution climate projections.</p> <ol> <li>GDD (Growing Degree Days) - summation of daily differences between daily temperature averages and 10 for the period April-October</li> <li>GST (Growing Season Temperature) - average of daily average temperatures for the period April-October</li> <li>SprR (Spring Rain) - Precipitation accumulated between 21st April-to 21st June,</li> <li>HarvR (Harvest Rain)-Precipitation accumulated between 21 August and 21 October</li> <li>SU35 -number of days with temperature higher than 35°C for the period April-October,</li> <li>WSDI (Warm Spell Duration Index) -days with at least 6 consecutive days when the daily temperature maximum exceeds its 90th percentile for the period April-October.</li> </ol> <p>The results are based on an sub-ensemble of five RCMs from the EURO-CORDEX modelling experiment which have been statistically downscaled to 1km x1km horizontal resolution using the PTHRES gridded dataset as the reference dataset. More details can be found in Raül Marcos-Matamoros, (2018). Report on the methodology followed to implement the wine pilot services. Zenodo. https://doi.org/10.5281/zenodo.4543337</p> <p>Datasets computed by National Observatory of Athens, in collaboration with SOGRAPE VINHOS S.A. in the framework of the European MED-GOLD project, funded from the European Union's Horizon 2020 Research and Innovation programme under Grant agreement No.776467</p> <p> </p> <p> </p>
Indicators and metrics in local climate adaptation plans
<p>This dataset gathers information related to indicators and metrics collected from 11 local climate adaptation plans in worldwide cities - Athens (Greece), Auckland (USA), Barcelona (Spain), Glasgow (UK), Istanbul (Turkey), Lima (Peru), Los Angeles (USA), Montreal (Canada), Nagoya (Japan), New York City (USA), Portland (USA), Tokyo (Japan) and Vancouver (Canada). The dataset describes the use and characteristics of adaptation indicators and metrics across climate adaptation-related planning documents. Although the sample is relatively small, it is a reflection of the global embryonic stage of adaptation metrics practice.</p> <p>The results of the analysis of this database have been published in:</p> <p>Goonesekera, S. M., & Olazabal, M. (2022). Climate adaptation indicators and metrics: State of local policy practice. <em>Ecological Indicators</em>, <em>145</em>, 109657. <a href="https://doi.org/10.1016/j.ecolind.2022.109657">https://doi.org/10.1016/j.ecolind.2022.109657</a> (OPEN ACCESS)</p>
Variable species establishment in response to microhabitat indicates different likelihoods of climate-driven range shifts
Open the record for dataset details and reuse information.
Data of monthly climate variables and drought indices within continental Chile for 1981-2023
<p>The dataset contains derived climatic data from ERA-5 at monthly frequency for continental Chile. The variables are:</p><ul><li>Precipitation (pre)</li><li>Minimum temperature (tas_min)</li><li>Mean temperature (tas)</li><li>Maximum temperature (tas_max)</li><li>Reference evapotranspiration (pet)</li><li>Snow water eqivalent (swe)</li><li>Soil volumetric water content at 1m depth (sm)</li></ul><p>Besides, the dataset contains the follwoing derived drough indices:</p><ul><li>Standardized Precipitation Index (SPI) for 1, 3, 6, 12, 24, and 36 months (spi_1<i> to </i>spi<i>_</i>36)</li><li>Standardized Precipitation Evapotranspiration Index (SPEI) for 1, 3, 6, 12, 24, and 36 months (spei_1<i> to </i>spei<i>_</i>36)</li><li>Evaporative Demand Drought Index (EDDI) for 1, 3, 6, 12, 24, and 36 months (eddi_1<i> to eddi_</i>36)</li><li>Anomaly of cumulative soil moisture at 1m depth (zcSM) for 1, 3, 6, 12, 24, and 36 months (zcsm_1<i> to zcsm_</i>36)</li><li>Anomaly of cumulative NDVI (zcNDVI) for 1, 3, and 6 months (zcndvi<i>1 to zcndvi</i>6)</li><li>Snow Water Equivalent Index (SWEI)</li></ul><p> </p>
Indicators used to calculate the Vulnerability of European wine PDOs to climate change
<p>Dataset of the indicators used to define the Vulnerability of European wine PDO to climate change. The database includes the values of Exposure, Sensitivity and Adaptive Capacity for each PDO region. In the case of Exposure we also included the values for the indicators of Cool Night Iindex, Dryness Index and Huglin index. In the case of Adaptive Capacity we also included all the fifteen indicators used for the assessment of financial, natural, physical, social and human capacity. The classification in one of the Vulnerability classes is included in the related field.</p> <p>Please refer to the following article when citing the dataset:</p> <p>Tscholl, S., Candiago, S., Marsoner, T., Fraga, H., Giupponi, C., & Egarter Vigl, L.Climate resilience of European wine regions. Nat Commun 15, 6254 (2024). https://doi.org/10.1038/s41467-024-50549-w</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.