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2,260 results for “Climatic change”
Figure 1 in Niche evolution and diversification in Middle Eastern stream salamanders (Paradactylodon): vulnerability to future climate change
Figure 1. Study area. The occurrence records of Paradactylodon species with different colors are shown on the map.
Figure 3 in Niche evolution and diversification in Middle Eastern stream salamanders (Paradactylodon): vulnerability to future climate change
Figure 3. Panels (A-D) illustrate the niche overlap values between two species distribution ranges (see table 3), along the
SDM results for 10,590 tree species from "Regional uniqueness of tree species composition and response to forest loss and climate change"
<p>Output from species distribution models (SDMs) with geographic constraints to estimate the spatial distribution of tree species at the global level at a 30-arc second resolution, presented in the publication "Regional uniqueness of tree species composition and response to forest loss and climate change". </p> <h2>Data</h2> <p>This file contains the results for 10,590 tree species. The results for each species are contained in a directory with the species name connected by an underscore. For most species, the directory contains several .tif files that make up the tiles of the distribution maps for that species and a metadata file. The .tif files can be merged with the gdal_merge.py function to obtain a single .tif file per species (see example below). For some species, the directory contains a single .tif file which does not require merging. In all cases, the .tif files contain 9 bands that correspond to the predicted species distribution using climatic variables corresponding to various climate projections from Chelsa 2.1.</p> <h3>Band order</h3> <ol> <li>covariates_1981_2010: average of historical climate measurements from 1981 to 2010</li> <li>covariates_2011_2040_ssp126: average future climate projection for 2011-2040 under shared socioeconomic pathway (SSP) 1.26</li> <li>covariates_2011_2040_ssp370: average future climate projection for 2011-2040 under SSP 3.70</li> <li>covariates_2011_2040_ssp585: average future climate projection for 2011-2040 under SSP 5.85</li> <li>covariates_2041_2070_ssp126: average future climate projection for 2041-2070 under SSP 1.26</li> <li>covariates_2041_2070_ssp370: average future climate projection for 2041-2070 under SSP 3.70</li> <li>covariates_2041_2070_ssp585: average future climate projection for 2041-2070 under SSP 5.85</li> <li>covariates_2071_2100_ssp126: average future climate projection for 2071-2100 under SSP 1.26</li> <li>covariates_2071_2100_ssp370: average future climate projection for 2071-2100 under SSP 3.70</li> <li>covariates_2071_2100_ssp585: average future climate projection for 2071-2100 under SSP 5.85</li> </ol> <h3>Metadata</h3> <p>The metadata contains more information about the bands, as well as the following species-level properties:</p> <ul> <li>nobs: number of spatially distinct occurrence records used in model training</li> <li>precision: precision of binarised model output computed through 3-fold cross-validation</li> <li>threshold: threshold used to binarise probabilistic model output, determined as the threshold maximizing the true skill statistic (TSS) during 3-fold cross-validation</li> <li>f1: F1 score of binarised model output computed through 3-fold cross-validation</li> <li>auc: area under the ROC curve (AUC) of model output computed through 3-fold cross-validation</li> <li>prevalence: prevalence of presences (ie. occurrences records) throughout the training data which consisted of occurrence records and pseudo-absences</li> <li>tss: TSS of binarised model output computed through 3-fold cross-validation</li> <li>recall: recall of binarised model output computed through 3-fold cross-validation</li> <li>nativeness_info: indicates whether reported native countries were available for this species (possible values: "yes" or "no", should be "yes" for all species included)</li> <li>npa: number of pseudo-absences used in model training</li> <li>system:index: species name </li> </ul> <h3>Merging example</h3> <p>For example, the directory Abarema_barbouriana contains files Abarema_barbouriana_0.tif, Abarema_barbouriana_2.tif, ..., Abarema_barbouriana_9.tif and metadata.json. The tiles can be merged with the command "gdal_merge.py -o Abarema_barbouriana_merged.tif Abarema_barbouriana/Abarema_barbouriana_*.tif".</p>
AI in Climate Change
<p><span>Climate change is an urgent issue that must be addressed by individuals, communities, governments, and organizations around the world.<span> </span>Its immediate effects include extreme weather events and unpredictable rainfall, impacting various sectors. Aligned with UN Sustainable Development Goals, addressing climate change is paramount. Despite deteriorating environmental conditions, technology continues to improve. It might be able to withstand and potentially even reduce the effects of climate change on the environment. Artificial intelligence is a technology that has been evolving quickly during the past years. Artificial intelligence is present in many aspects of our daily lives, such as search recommendations on social media. This systematic literature review examines 54 referenced papers, utilizing the Kitchenham approach to validate five research questions. According to the statistics, the Random Forest technique was employed in 18 out of 54 studies to build artificial intelligence for climate change issues. China emerged as the leader in conducting studies on AI's role in addressing climate change challenges. Within climate change research, hydrology stands out as a prominent and extensively discussed aspect. Overall, AI for Climate Change has made considerable improvements, highlighting the significance of continuing study in this specific area. </span></p>
Datasets for the submitted manuscript entitled as "Thermodynamic and Dynamic Changes in Japan Sea Polar Air Mass Convergence Zone and its Associated Heavy Snowfall Under Warmer Climate"
<p>These are datasets for the submitted manuscript entitled as "Thermodynamic and Dynamic Changes in Japan Sea Polar Air Mass Convergence Zone and its Associated Heavy Snowfall Under Warmer Climate". The thirteen cases of WRF domain 2 simulation in historical and PGW experiments were uploaded, respectively. </p>
Climate change has no apparent effect on debris flows in a supply-limited torrent
<h3><strong>This file contains all tree-ring data, growth disturbance data, final debris-flow chronology data and map background data used in the paper "A supply-limited torrent that does not feel the heat of climate change"</strong></h3> <p>Multetta-tree-ring raw data.rwl: Contains the raw measurement data from 761 tree-ring cores from 478 <em>P. mugo</em> trees.</p> <p>Growth Disturbances-Original data.xlsx: Contains detailed information on tree-ring cores, growth disturbances and their intensity. Zone area refers to the zonation (1-4 corresponds to SI-SIV) of the sampled trees in the study area. Tree ID refers to the name of the tree-ring core or cross section/wedge. The third and fourth columns refer to Y/latitude and X/longitude. Last ring refers to the year of the outermost ring and is mostly 2020, which is the year when the fieldwrok was carried out. In some cases, tree-ring cores were broken or they were taken from dead trees, so the year of the outermost ring is not 2020. Oldest ring refers to the year of the innermost available ring. In the age incomplete column, a value of 1 indicates that the oldest ring measured is not the innermost ring of the tree center. Age refers to the age of the trees, which is equals to the value of the last ring minus the value of the oldest ring. The Comments column indicates the wedge and the cross section. From the tenth column, the numbers 2020, 2019, 2018...... refer to different years corresponding to tree rings. Here, all values including 0, 1, 2, 3 and 4 indicate that there is a measured annual ring in the corresponding year. Blank indicates no data. GS refers to growth suppression. CW refers to compression wood. I refers to injury and CT refers to callus tissue. The number 0 means no growth disturbance. Numbers 1-4 mean intensity from weak to strong. For example, in the 2016 column, any core with '2GS' means the tree-ring showed growth suppression in 2016 and the corresponding intensity is 2. The growth disturbance (GDs) statistic is shown at the bottom, including all 1427 GDs, but GDs with intensity 1 were excluded from the analysis. Note: Samples mub74 and mul105 have data from both section and core, so 480 tree-ring series exist. </p> <p>Events-Final definition.xlsx: Contains all tree-ring based reconstructed debris-flow events for each zone (1-4 corresponds to SI-SIV) after careful examination of the spatial distribution of damaged trees. In the process of defining events, years were excluded from the analysis that (1) showed incoherent patterns of damaged trees (e.g. GDs evenly distributed on the cone, probably due to climatic extremes or insect pests), (2) were recorded in historical chronicles as snow avalanche years, or (3) were characterized by high mortality of <em>P. mugo</em> trees, as indicated by low tree-ring index (<1.5 average value) in the event cataster of the Canton of Grisons and the Swiss National Park (Bigler and Rigling, 2013).</p> <p><strong>Figures and the data used in figures are shown as below:</strong></p> <p>Information on tree location and age in fig.1D, GDs and sample depth (At) in fig.3A, reconstructed XXL events in fig.4A, GDs for affected regions (total of 16 regions) in fig.4B, GDs in different years for different zones in fig.S3 and GDs for different affected regions in fig.S4 is from the file: Growth Disturbances-Original data.xlsx.</p> <p>Information on the 56 defined events in fig.3B and the reconstructed debris-flow events in fig.5A, 5B is from the file: Events-Final definition.xlsx. Here, 75 events occurred in 4 zones, but actually all events occurred in 56 different years.</p> <p>The LiDAR DEM background in fig.1B, 1C and the hillshade view of the 2023 LiDAR DEM background in fig.S1, S2 are from Swisstopo (https://map.geo.admin.ch/).</p> <p>The R code used for the repose time pattern analysis in fig.5A, 5B is originally from the previously published paper (Heiser, M. et al., 2023) and is available on GitLab at https://gitlab.com/Rexthor/repose-time-patterns.</p>
Support Materials for Manuscript "Climate change induces rapid growth of dead ice in Asian glaciers" Review
<div>Description of support materials of paper "Climate change induces rapid growth of dead ice in Asian glaciers".</div> <div> </div> <div>The detailed description of files is below:</div> <div> </div> <div>· Total.csv</div> <div>The subregional statistics of HMA dead ice area and mass in 2100 under different SSPs.</div> <div> </div> <div>· Folder ./Inventory</div> <div>Interdecadal potential dead ice inventory in HMA.</div> <div> </div> <div>· Folder ./Individual</div> <div>Interdecadal statistics of individual glaciers' dead ice in HMA for their ablation (unit: Gt), area (unit: km2) and mass (unit: Gt).</div>
Data for "Mitigation strategies can alleviate power system vulnerability to climate change and extreme weather: A case study on the Italian grid"
<p>Data employed for the paper "Mitigation strategies can alleviate power system vulnerability to climate change and extreme weather: A case study on the Italian grid"<br><br>Abstract<br>This study explores compounding impacts of climate change on power system's load and generation, emphasising the need to integrate adaptation and mitigation strategies into investment planning. We combine existing and novel empirical evidence to model impacts on: i) air-conditioning demand; ii) thermal power outages; iii) hydro-power generation shortages. Using a power dispatch and capacity expansion model, we analyse the Italian power system's response to these climate impacts in 2030, integrating mitigation targets and optimising for cost-efficiency at an hourly resolution. We outline different meteorological scenarios to explore the impacts of both average climatic changes and the intensification of extreme weather events. We find that addressing extreme weather in power system planning will require an extra 5-8 GW of photovoltaic (PV) capacity, on top of the 50 GW of the additional solar PV capacity required by the mitigation target alone. Despite the higher initial investments, we find that the adoption of renewable technologies, especially PV, alleviates the power system's vulnerability to climate change and extreme weather events. In fact, renewable energy sources are generally less vulnerable to the impacts of climate change, such as rising temperatures and shifting precipitation patterns, compared to thermal power and hydropower generation. Furthermore, enhancing short-term storage with lithium-ion batteries is crucial to counterbalance the reduced availability of dispatchable hydro generation.</p>
Response of phosphorus burial and post-depositional diagenesis to postglacial climate change in the coastal system
<p><strong>The dataset includes geochemical data comprising major elements, TOC and TN, phosphorus and iron speciation, and EDS analysis results, as used in the manuscript titled "Response of phosphorus burial and post-depositional diagenesis to postglacial climate change in the coastal system".</strong></p>
Supporting Data for "The Vertical Structure of Tropical Temperature Change in Global Storm-Resolving Model Simulations of Climate Change"
<p>Code and netcdf files of processed X-SHiELD and CMIP6 simulations to reproduce the figures of the revised submission of Timothy M. Merlis, Ilai Guendelman, Kai-Yuan Cheng, Lucas Harris, Yan-Ting Chen, Christopher S. Bretherton, Maximilien Bolot, Linjiong Zhou, Alex Kaltenbaugh, Spencer K. Clark, and Stephan Fueglistaler (2024): "The Vertical Structure of Tropical Temperature Change in Global Storm-Resolving Model Simulations of Climate Change".</p> <p> </p>
EPJSOIL SERENA WP3 T3.3 : France climatic and agricultural change modelling dataset (Naizin)
<p>The internal EJP SOIL project SERENA contributed to the evaluation of soil multifunctionality aiming at providing assessment tools for land planning and soil policies at different scales. By co-working with relevant stakeholders, the project provided co-developed indicators and associated cookbooks to assess and map them, to report both on soil degradation, soil-based ecosystem services and their bundles, under actual conditions and for climate and land-use changes, at the regional, national, and European scales. </p> <p>Data aims to explore the effect of climate change according to different climate scenario up to 2050, and to understand the resistance of soil in response to climate change and different type of agricultural management. Additionally, it aims to understand the relations between SES, and the main factors affecting Soil Ecosystem Services (SES) variations. Data has been produced by the SERENA team WP3 T3.3 France using JAVA-STICS 10.0.0 model and the STICSonR package, using the input files specified in the data. Results consit of the yearly data of SES calculated from the daily modeling from STICS. Because of the file size of daily results from STICS, such file are not part of the dataset. Input data and R scripts used are instead provided.</p> <p>Naizin’s soil data come from Walter et al. (1993, 1996, 1998), climatic data were obtained from SAFRAN climatic data provided by Météo-France and were downloaded via the SICLIMA platform developed by AgroClim-INRAE. Plants and fertilizers data come from default dataset from STICS. All input data are available as part of this dataset.</p> <p> </p>
Maps for Soil loss by water from climate change scenarios for Austria
<p><span>The internal EJP SOIL project SERENA contributed to the evaluation of soil multifunctionality aiming at providing assessment tools for land planning and soil policies at different scales. By co-working with relevant stakeholders, the project provided co-developed indicators and associated cookbooks to assess and map them, to report both on soil degradation, soil-based ecosystem services and their bundles, under actual conditions and for climate and land-use changes, at the regional, national, and European scales.</span></p> <p><span>This dataset contains the change of modelled annual soil loss rates for changing R-factor according to RCP4.5 and RPC8.5 climate scenarios, relative to modelled soil loss in the base scenario, using R-factor calculated for the 1990-2021 period. For each climate scenario, four periods were considered: 1991-2020, 2021-2040, 2041-2060 and 2061-2080. The RUSLE-based soil loss calculations were done according to the SERENA/EJP-Soil soil erosion cookbook and are described in the respective project deliverables D3.3 and D3.4.</span></p>
Data for Article "Climate change activity in small and medium-sized Polish towns"
<p>Research Data for Article "Climate change activity in small and medium-sized Polish towns"<br>ZIP file contains:<br>- spreadsheet with anonymised survey data<br>- 10 text files with anonymised interviews with research subjects.<br>The interviews have been edited for clarity (technical parts with no connection to the research have been omitted). The interviews and surveys have been uploaded in the original language.</p>
Triggering Events, Opinion Leader Networks, and Framing Strategies of Climate Change on Chinese Social Media
Open the record for dataset details and reuse information.
Data from: Microhabitat and climatic niche change explain patterns of diversification among frog families
A major goal of ecology and evolutionary biology is to explain patterns of species richness among clades. Differences in rates of net diversification (speciation minus extinction over time) may often explain these patterns, but the factors that drive variation in diversification rates remain uncertain. Three important candidates are climatic niche position (e.g., whether clades are primarily temperate or tropical), rates of climatic niche change among species within clades, and microhabitat (e.g., aquatic, terrestrial, arboreal). The first two factors have been tested separately in several studies, but the relative importance of all three is largely unknown. Here we explore the correlates of diversification among families of frogs, which collectively represent ∼88% of amphibian species. We assemble and analyze data on phylogeny, climate, and microhabitat for thousands of species. We find that the best-fitting phylogenetic multiple regression model includes all three types of variables: microhabitat, rates of climatic niche change, and climatic niche position. This model explains 67% of the variation in diversification rates among frog families, with arboreal microhabitat explaining ∼31%, niche rates ∼25%, and climatic niche position ∼11%. Surprisingly, we show that microhabitat can have a much stronger influence on diversification than climatic niche position or rates of climatic niche change.
Data from: Macroevolutionary consequences of profound climate change on niche evolution in marine mollusks over the past three million years
In order to predict the fate of biodiversity in a rapidly changing world, we must first understand how species adapt to new environmental conditions. The long-term evolutionary dynamics of species' physiological tolerances to differing climatic regimes remain obscure. Here, we unite palaeontological and neontological data to analyse whether species' environmental tolerances remain stable across 3 Myr of profound climatic changes using 10 phylogenetically, ecologically and developmentally diverse mollusc species from the Atlantic and Gulf Coastal Plains, USA. We additionally investigate whether these species' upper and lower thermal tolerances are constrained across this interval. We find that these species' environmental preferences are stable across the duration of their lifetimes, even when faced with significant environmental perturbations. The results suggest that species will respond to current and future warming either by altering distributions to track suitable habitat or, if the pace of change is too rapid, by going extinct. Our findings also support methods that project species' present-day environmental requirements to future climatic landscapes to assess conservation risks.
Data from: Evolutionary potential of a widespread clonal grass under changing climate
Adaptive responses are probably the most effective long-term responses of populations to climate change, but they require sufficient evolutionary potential upon which selection can act. This requires high genetic variance for the traits under selection, and low antagonizing genetic covariances between the different traits. Evolutionary potential estimates are still scarce for long-lived, clonal plants, although these species are predicted to dominate the landscape with climate change. We studied the evolutionary potential of a perennial grass, Festuca rubra, in western Norway, in two controlled environments corresponding to extreme environments in natural populations: cold-dry and warm-wet, the latter being consistent with the climatic predictions for the country. We estimated genetic variances, covariances, selection gradients and response to selection for a wide range of growth, resource acquisition and physiological traits, and compared their estimates between the environments. We showed that the evolutionary potential of F. rubra is high in both environments, and genetic covariances define one main direction along which selection can act with relatively few constraints to selection. The observed response to selection at present is not sufficient to produce genotypes adapted to the predicted climate change under a simple, space for time substitution model. However, the current populations contain genotypes which are pre-adapted to the new climate, especially for growth and resource acquisition traits. Overall, these results suggest that the present populations of the long-lived clonal plant may have sufficient evolutionary potential to withstand long-term climate changes through adaptive responses. We studied the evolutionary potential of a perennial grass, Festuca rubra, in western Norway, in two controlled environments corresponding to extreme environments in natural populations: cold-dry and warm-wet, the latter being consistent with the climatic predictions for the country. We estimated genetic variances, covariances, selection gradients and response to selection for a wide range of growth, resource acquisition and physiological traits, and compared their estimates between the environments. We showed that the evolutionary potential of F. rubra is high in both environments, and genetic covariances define one main direction along which selection can act with relatively few constraints to selection. The observed response to selection at present is not sufficient to produce genotypes adapted to the predicted climate change under a simple, space for time substitution model. However, the current populations contain genotypes which are pre-adapted to the new climate, especially for growth and resource acquisition traits. Overall, these results suggest that the present populations of the long-lived clonal plant may have sufficient evolutionary potential to withstand long-term climate changes through adaptive responses.
Data from: Response of net primary production to land use and climate changes in the middle-reaches of the Heihe River basin
Net primary production (NPP) supplies matter, energy, and services to facilitate the sustainable development of human society and ecosystem. The response mechanism of NPP to land use and climate changes is essential for food security and biodiversity conservation but lacks a comprehensive understanding, especially in arid and semi-arid regions. To this end, taking the middle-reaches of the Heihe River basin (MHRB) as an example, we uncovered the NPP responses to land use and climate changes by integrating multi-source data (e.g., MOD17A3 NPP, land use, temperature, and precipitation) and multiple methods. The results showed that: (1) land use intensity (LUI) increasing, and climate warming and wetting promoted NPP. From 2000 to 2014, the LUI, temperature and precipitation of MHRB increased by 1.46, 0.58 °C and 15.76 mm, respectively, resulting in an increase of 14.62 gC/m2 in annual average NPP. (2) The conversion of low-yield cropland to forest and grassland increased NPP. Although the widespread conversion of unused land and grassland to cropland boosted both LUI and NPP, it was not conducive to ecosystem sustainability and stability due to huge water consumption and human-appropriated NPP. Urban sprawl occupied cropland, forest and grassland, and reduced NPP. (3) Increase in temperature and precipitation generally improved NPP. The temperature decreasing less than 1.2 °C also promoted the NPP of hardy vegetation due to the simultaneous precipitation increasing. However, warming-induced water stress compromised the NPP in arid sparse grassland and deserts. Cropland had greater NPP and NPP increase than natural vegetation due to the irrigation, fertilizers and other artificial inputs it received. Decrease in both temperature and precipitation generally reduced NPP, but the NPP in the well-protection or less-disturbance areas still increased slightly.
Data from: Climate and sea-level changes across a shallow marine Cretaceous–Palaeogene boundary succession in Patagonia, Argentina
Upper Maastrichtian to lower Paleocene, coarse-grained deposits of the Lefipán Formation in Chubut Province, (Patagonia, Argentina) provide an opportunity to study environmental changes across the Cretaceous–Palaeogene (K–Pg) boundary in a shallow marine depositional environment. Marine palynological and organic geochemical analyses were performed on the K–Pg boundary interval of the Lefipán Formation at the San Ramón section. The palynological and organic geochemical records from the San Ramón K–Pg boundary section are characteristic of a highly dynamic, nearshore setting. High abundances of terrestrial palynomorphs, high BIT-index values and the occasional presence of plant fossils are indicative of a large input of terrestrial organic material. The organic-walled dinoflagellate cyst (dinocyst) assemblage is generally dominated by Senegalinium and other peridinioid dinocyst taxa, indicative of high-nutrient conditions and decreased salinities, probably associated with a large fluvial input. The reconstructed sea surface temperatures range from 25°C to 27°C, in accordance with the tropical climate inferred by palynological and megafloral studies. As in the Bajada del Jagüel section, ~500 km north-north-east of San Ramón, peaks of Senegalinium spp. were recorded below and above the K–Pg boundary, possibly related to enhanced runoff resulting from more humid climatic conditions. The lithological, palynological and organic geochemical records suggest the occurrence of a sea-level regression across the K–Pg boundary, resulting in a hiatus directly at the boundary in both sections, followed by a transgression in the Danian.
Data from: Spatially explicit models of dynamic histories: examination of the genetic consequences of Pleistocene glaciation and recent climate change on the American Pika.
A central goal of phylogeography is to identify and characterize the processes underlying divergence. One of the biggest impediments currently faced is how to capture the spatiotemporal dynamic under which a species evolved. Here we described an approach that couples species distribution models (SDMs), demographic and genetic models in a spatiotemporally explicit manner. Analyses of American Pika (Ochotona priniceps) from the sky islands of the central Rocky Mountains of North America are used to provide insights into key questions about integrative approaches in landscape genetics, population genetics and phylogeography. This includes (i) general issues surrounding the conversion of time-specific SDMs into simple continuous, dynamic landscapes from past to current, and (ii) the utility of SDMs to inform demographic models with deme-specific carrying capacities and migration potentials, as well as (iii) the contribution of the temporal dynamic of colonization history in shaping genetic patterns of contemporary populations. Our results support that the inclusion of a spatiotemporal dynamic is an important factor when studying the impact of distributional shifts on patterns of genetic data. Our results also demonstrate the utility of SDMs to generate species-specific predictions about patterns of genetic variation that account for varying degrees of habitat specialization and life-history characteristics of taxa. Nevertheless, the results highlight some key issues when converting SDMs for use in demographic models. Because the transformations have direct affects on the genetic consequence of population expansion by prescribing how habitat heterogeneity and spatiotemporal variation is related to the species-specific demographic model, it is important to consider alternative transformations when studying the genetic consequences of distributional shifts.
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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.