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151 results for “tree distribution”
Bayesian Analysis of Tree Distributions Across Space and Time in Eastern North America 2010-2011
The distributions of many organisms are spatially autocorrelated, but it is unclear whether including spatial terms in species distribution models (SDMs) improves projections of future species distributions. We provide the first comparative test of a purely spatial SDM, a purely non-spatial SDM, and an SDM that combines spatial and environmental information. Spatial SDMs provided better fits to the calibration data, more accurate predictions of a hold-out validation data set of modern trees, and lower false positive rates at all time periods than non-spatial SDMs. Hindcasted projection of spatial SDMs had higher variance than those of non-spatial SDMs. Overall predictive performance of non-spatial and spatial SDMs varied temporally and as a function of niche overlap. Ecological modelers should include spatial terms in SDMs used for projecting future distributions of species.
Adult baobab trees's distribution map across Sahel
<p><span>The baobab tree (<em>Adansonia digitata</em> <em>L.</em>) is an integral part of rural livelihoods throughout the African continent. However, the combined effects of climate change and increasing global demand for baobab products are currently exerting pressure on the sustainable utilization of these resources. Here we employ sub-meter resolution satellite imagery to identify nearly 3</span><span> million baobab trees in the Sahel, a dryland region of 1.5 million km<sup>2</sup>. This achievement is considered an essential step towards improving valuable woody species' management and monitoring system. To prevent mismanagement of this specific tree species, we aggregated every single adult baobab tree map to<span> 5 <span>× </span>5 km grids. We also classified the baobab trees using the tree crown diameters( small: 3-9m; medium 9m-13m; large: >13m). The baobab tree count map is also available for this three different size classes.</span></span></p>
Presence-Absence Points for Tree Species Distribution Modelling for Europe
<p>The dataset is a collection of presence and absence points for forest tree species for Europe. Each unique combination of longitude, latitude and year was considered as an independent sample. Presence data was obtained from the harmonized tree species occurrence dataset by <a href="https://zenodo.org/record/5524611">Heisig and Hengl (2020)</a> and absence data from the <a href="https://ec.europa.eu/eurostat/web/lucas">LUCAS</a> (in-situ source) dataset.</p> <p>A set of <strong>50</strong> different forest tree species was selected from the harmonized tree species dataset and data lacking a temporal observation was overlaid with yearly forest masks derived from land cover maps produced by <a href="https://zenodo.org/record/4725429">Parente et al. (2021)</a>. We overlaid the points with the probability maps for the classes:</p> <ul> <li>311: Broad-leaved forest,</li> <li>312: Coniferous forest,</li> <li>313: Mixed forest,</li> <li>323: Sclerophyllous forest,</li> <li>324: Transitional woodland-shrub,</li> <li>333: Sparsely vegetated area.</li> </ul> <p>Points were included in the dataset only if the probability value extracted for at least one of the above classes was <strong>≥ 50%</strong> for all the years considered. An additional quality flag was added to distinguish points coming from this operation and the points with original year of observation coming from source datasets.</p> <p>The final dataset contains <strong>4,359,999</strong> observations for and a total of <strong>630 </strong>columns. <br> <br> The first <strong>8 </strong>columns of the dataset contain metadata information used to uniquely identify the points:</p> <ul> <li><strong>id</strong>: unique point identifier,</li> <li><strong>year</strong>: year of observation,</li> <li><strong>postprocess</strong>: quality flag to identify if the temporal reference of an observation comes from the original dataset or is the result of spatiotemporal overlay with forest masks,</li> <li><strong>Tile_ID</strong>: contains the tile id from the eu_tiling_system (30 km grid),</li> <li><strong>easting</strong>: longitude coordinates in Coordinate Reference System ETRS89 / LAEA Europe (= EPSG code 3035),</li> <li><strong>northing</strong>: latitude coordinates in Coordinate Reference System ETRS89 / LAEA Europe (= EPSG code 3035),</li> <li><strong>Atlas_class</strong>: name of the tree species according to the European Atlas of Forest Tree Species or NULL in case of absence point,</li> <li><strong>lc1</strong>: contains original LUCAS land cover class or NULL if it's a presence point.</li> </ul> <p>The remaining columns contain the extracted values of a series of predictor variables (temperature, precipitation, elevation, topographical information, spectral reflectance) useful for species distribution modeling applications. These points were used to model the potential and realized distribution of a series of <strong>16 target species </strong>for the period 2000 - 2020. The approach involved training three ML models to predict probability of presence (<em>i.e.</em> <a href="http://link.springer.com/article/10.1023/A:1010933404324">Random Forest</a>, <a href="http://dl.acm.org/doi/abs/10.1145/2939672.2939785">XGBoost</a>, <a href="https://rss.onlinelibrary.wiley.com/doi/abs/10.2307/2344614">GLM</a>), which served as input to train a linear meta-model (<em>i.e.</em> <a href="http://papers.nips.cc/paper/2014/file/ede7e2b6d13a41ddf9f4bdef84fdc737-Paper.pdf">Logistic regression classifier</a>), responsible for predicting the final probability of presence for each species.</p> <p>The <em>RDS </em>file is created from a data.table object and suitable for fast reading in the R-programming environment. The <em>CSV.GZ</em> file contains records as a table with easting and northing in Coordinate Reference System ETRS89 / LAEA Europe (= EPSG code 3035) and can be fed in a GIS after being unzipped.</p> <p>We provide <em>RDS </em>files for a 30km tile as an example containing raster stacks at 30m resolution of all the covariates included in the regression matrix. You can find the specific geographical location of the tile in Europe using the attached <em>GeoPackage </em>("eu_tiling_system_30km"): open it in QGIS and filter by "ID".</p> <p>In our approach we considered both static and dynamic covariates: dynamic covariates are calculated as averages of a 4 years time window (example: 2004 contains averages from 2002 to 2006). To get the predictions for a specific year, covariates contained in the <em>static</em> RDS file need to be bound with the respective year.</p> <p>To access our predictions (probabilities and uncertainties) produced for the target species access:</p> <ul> <li><strong>Open Data Science Europe viewer: <a href="https://maps.opendatascience.eu">https://maps.opendatascience.eu</a></strong></li> <li>Check the <strong>Related identifiers </strong>section of this repository to access each species individually</li> </ul> <p>If you instead would like to know more about the creation of this dataset and the modeling:</p> <ul> <li><strong>watch</strong> the talk at Open Data Science Workshop 2021 (<a href="https://doi.org/10.5446/55256">TIB AV-PORTAL</a>)</li> <li><strong>access </strong>the repository with our R/Python scripts and follow the instructions (<a href="https://gitlab.com/geoharmonizer_inea/spatial-layers/-/tree/master/veg_tree.species_anv.pnv.eml">GitLab</a>)</li> </ul> <p>A publication describing, in detail, all processing steps, accuracy assessment and general analysis of species distribution maps is available on <a href="https://doi.org/10.7717/peerj.13728">PeerJ</a>. To suggest any improvement/fix use <a href="https://gitlab.com/geoharmonizer_inea/spatial-layers/-/issues">https://gitlab.com/geoharmonizer_inea/spatial-layers/-/issues</a>.</p>
Potential and realized distribution at 30m for Olive tree (Olea europaea) in Europe for 2000 - 2020
<p>Probability and uncertainty maps showing the potential and realized distribution for the olive tree (<em>Olea europaea, L.</em>) for Europe from the dataset prepared by <a href="http://doi.org/10.5281/zenodo.5818021">Bonannella et al. (2022)</a> and predicted using Ensemble Machine Learning (EML). Potential distribution map cover the period 2018 - 2020; realized distribution cover the period 2000 - 2020, split in the following time periods:</p> <ul> <li>2000 - 2002,</li> <li>2002 - 2006,</li> <li>2006 - 2010,</li> <li>2010 - 2014,</li> <li>2014 - 2018,</li> <li>2018 - 2020.</li> </ul> <p>Files are named according to the following naming convention, e.g:</p> <ul> <li>veg_olea.europaea_anv.eml_md_30m_0..0cm_2000..2002_eumap_epsg3035_v0.3</li> </ul> <p>with the following fields:</p> <ul> <li>theme: e.g. <strong>veg</strong>,</li> <li>species code: e.g. <strong>olea.europaea</strong>,</li> <li>species distribution type: e.g. <strong>anv</strong> (= actual natural vegetation),</li> <li>species estimation method: e.g. <strong>eml</strong>,</li> <li>species estimation type: e.g. <strong>md</strong> ( = model deviation),</li> <li>resolution in meters e.g. <strong>30m</strong>,</li> <li>reference depths (vertical dimension): e.g. <strong>0..0cm</strong>,</li> <li>reference period begin end: e.g. <strong>2000..2002</strong>,</li> <li>reference area: e.g. <strong>eumap</strong>,</li> <li>coordinate system: e.g. <strong>epsg3035</strong>,</li> <li>data set version: e.g. <strong>v0.3</strong>.</li> </ul> <p>For each species is then easy to identify probability and uncertainty distribution maps:</p> <ul> <li>veg_olea.europaea_<strong>anv</strong>.eml_<strong>md</strong>: model uncertainty for realized distribution</li> <li>veg_olea.europaea_<strong>anv</strong>.eml_<strong>p</strong>: probability for realized distribution</li> <li>veg_olea.europaea_<strong>pnv</strong>.eml_<strong>md</strong>: model uncertainty for potential distribution</li> <li>veg_olea.europaea_<strong>pnv</strong>.eml_<strong>p</strong>: probability for potential distribution</li> </ul> <p>Files are provided as <a href="https://gdal.org/drivers/raster/cog.html">Cloud Optimized GeoTIFFs</a> and projected in the Coordinate Reference System ETRS89 / LAEA Europe (= EPSG code 3035). Styling files are provided in both <em>SLD</em> and <em>QML</em> format.</p> <p>If you would like to know more about the creation of the maps and the modeling:</p> <ul> <li><strong>watch</strong> the talk at Open Data Science Workshop 2021 (<a href="https://doi.org/10.5446/55256">TIB AV-PORTAL</a>)</li> <li><strong>access </strong>the repository with our R/Python scripts and follow the instructions (<a href="https://gitlab.com/geoharmonizer_inea/spatial-layers/-/tree/master/veg_mapping">GitLab</a>)</li> <li><strong>access </strong>the repository with the training dataset (<a href="https://doi.org/10.5281/zenodo.5818021">Zenodo</a>)</li> <li><strong>read </strong>the tutorial with executable code on our <a href="https://opengeohub.github.io/spatial-prediction-eml/spatiotemporal-ml.html#spatiotemporal-distribution-of-fagus-sylvatica">GitBook</a></li> </ul> <p>A publication describing, in detail, all processing steps, accuracy assessment and general analysis of species distribution maps is available on <a href="https://doi.org/10.7717/peerj.13728">PeerJ</a>. To suggest any improvement/fix use <a href="https://gitlab.com/geoharmonizer_inea/spatial-layers/-/issues">https://gitlab.com/geoharmonizer_inea/spatial-layers/-/issues</a>.</p>
Nairobi_Street_Trees_Distribution_Diversity
<p>Input data and code to accompany the paper:</p> <p>Alice Gerow, Vivian Kathambi, Dexter Locke, Mark Ashton, Craig Brodersen. Street tree communities reflect socioeconomic inequalities and legacy effects of colonial planning in Nairobi, Kenya. Urban Forestry & Urban Greening. <a href="https://doi.org/10.1016/j.ufug.2024.128530">https://doi.org/10.1016/j.ufug.2024.128530</a></p> <p>The input data consists in street tree observations collected during a field survey conducted between June and August 2023 in Nairobi, Kenya. The code includes descriptive tables and plots, statistical tests, and alpha and beta diversity metrics and visualizations used to compare ecological communities across social groups.</p>
Potential tree species distributions from the Last Glacial Maximum in North America
<p>Modern tree distributions modeled under current climate and predicted to past climate.</p> <p>Values of '2' represent presence.</p> <p>The column mark is current presence, while _20000 is 20 ka, _14000 is 14 ka, _13000 is 13 ka, etc.</p> <p>For quick download, the .dbf for each species can be joined to the shapefile (us_can_ecosub). Alternatively, download and use the zipped folder of shapefiles (glac_shapes)..</p>
Distribution and abundance of canopy trees in floodplain forests of the Wisconsin River 1999 - 2001
The Wisconsin River Floodplain Project aimed to identify landscape indicators that are well correlated with specific aspects of ecological function. This is a crucial research need requiring an integrated approach that combines landscape monitoring with field studies. Large river-floodplain systems are among the most diverse and dynamic landscapes in the US, providing many important societal values, but relatively little effort has been devoted to development and testing of landscape indicators for these systems. We developed and tested ecological indicators for large river-floodplain landscapes along reaches of the Wisconsin River to determine which landscape metrics are most useful for monitoring population, community and ecosystem processes in large river-floodplain landscapes. Spatially extensive field sampling was combined with landscape analysis in nine reaches of the Wisconsin River sampling to quantify the ability of landscape indicators to predict ecological variables over broad scales. Landscape indicators were evaluated by their utility for detecting changes in the structure and function of the Wisconsin River floodplain landscape that were related to modification of the natural flow regime, historical land use, and current land-use patterns. Our field studies were concentrated in floodplain forest in nine 12 to 20-km reaches along the lower 400 km of the Wisconsin River.
FIG. 5. — Strict consensus tree from eight most parsimonious trees recovered for Molossus E. Geoffroy, 1805 in Diversity, morphological phylogeny, and distribution of bats of the genus Molossus E. Geoffroy, 1805 (Chiroptera, Molossidae) in Brazil
FIG. 5. — Strict consensus tree from eight most parsimonious trees recovered for Molossus E. Geoffroy, 1805. Numbers above the branches indicate Bootstrap values and bottom numbers indicate Bremer support values.
The Adult Adansonia digitata L. (baobab tree) distribution map derived from very high resolution satelite imagery for 2010s across the Sahel at 1km resolution
<p>The baobab tree (<em>Adansonia digitata</em> <em>L.</em>) is an integral part of rural livelihoods throughout the African continent. However, the combined effects of climate change and increasing global demand for baobab products are currently exerting pressure on the sustainable utilization of these resources. Here we employ sub-meter resolution satellite imagery to identify nearly 3 million baobab trees in the Sahel, a dryland region of 1.5 million km<sup>2</sup>. This achievement is considered an essential step towards improving valuable woody species' management and monitoring system. The map's overall underestimate bias is 0.27. To prevent mismanagement of this specific tree species, we aggregated every single adult baobab tree map to 1 × 1 km grids. We also classified the baobab trees using the tree crown diameters( small: 3-9m; medium 9m-13m; large: >13m). The baobab tree count map is also available for these three different size classes. </p>
TreeGOER Köppen-Geiger Zone Distributions: Observations for 48,129 tree species across the 30 climate zones for 1931-1960, 1961-1990 and 1991-2020 climates
<p><strong>TreeGOER (Tree Globally Observed Environmental Ranges)</strong> is a database that documents the environmental ranges (minimum, maximum, median, mean and 5%, 25%, 75% and 95% quantiles) for 48,129 tree species and for 51 environmental variables, including 38 bioclimatic variables, 8 soil variables and 3 topographic variables. TreeGOER is available from the following Zenodo archives: <a href="https://doi.org/10.5281/zenodo.7922927">https://doi.org/10.5281/zenodo.7922927</a></p> <p>The TreeGOER ranges were calculated after cleaning occurrence records and standardizing species names with the <a href="https://bsapubs.onlinelibrary.wiley.com/doi/10.1002/aps3.11388">WorldFlora</a> R package to <a href="https://onlinelibrary.wiley.com/doi/10.1002/tax.12373">World Flora Online</a> or the <a href="https://www.nature.com/articles/s41597-021-00997-6">World Checklist of Vascular Plants</a> for a global GBIF occurrence download of 44,267,164 occurrences (GBIF.org 2021 <strong>GBIF Occurrence Download</strong> <a href="https://doi.org/10.15468/dl.77gcvq">https://doi.org/10.15468/dl.77gcvq</a>). The process of compilation of TreeGOER with 30 arc-seconds global grid layers, two examples of BIOCLIM applications that investigated the effects of climate change on global tree diversity patterns and R scripts to repeat these analyses have been described by Kindt, R. (2023). <strong>TreeGOER: A database with globally observed environmental ranges for 48,129 tree species</strong>. Global Change Biology 29: 6303–6318. <a href="https://onlinelibrary.wiley.com/doi/10.1111/gcb.16914">https://onlinelibrary.wiley.com/doi/10.1111/gcb.16914</a>.</p> <p>This Zenodo archive documents the occurrence of the same previously compiled and cleaned observations for the TreeGOER across global raster layers that document the 1931-1960, 1961-1990 and 1991-2020 <strong>Köppen-Geiger climate zones</strong>. These global raster layers were created for the following article:</p> <ul> <li>Beck, H. E., T. R. McVicar, N. Vergopolan, A. Berg, N. J. Lutsko, A. Dufour, Z. Zeng, X. Jiang, A. I. J. M. van Dijk, and D. G. Miralles. High-resolution (1 km) Köppen-Geiger maps for 1901–2099 based on constrained CMIP6 projections, Scientific Data 10, 724 (2023). <a href="https://doi.org/10.1038/s41597-023-02549-6">https://doi.org/10.1038/s41597-023-02549-6</a>. The Köppen-Geiger classifcation maps, associated confidence maps, and underpinning monthly near-surface air temperature and precipitation climatologies in netCDF format can all be downloaded <a href="https://doi.org/10.6084/m9.figshare.21789074.v1">here</a>.</li> </ul> <p> </p> <p>For each of the 48,129 tree species, the distribution is given for</p> <ul> <li>Historical climates: number of observations in the 1931-1960, 1961-1990 and 1991-2020 Köppen-Geiger Zone climate zone</li> <li>Mixed climate: number of observations in the 1931-1960 Köppen-Geiger Zone climate zones if observations were before 1961, in the 1961-1990 Köppen-Geiger Zone climate zones if observations were between 1961 and 1990, and in the 1991-2020 Köppen-Geiger Zone climate zones if observations were after 1990</li> <li>Static climate: number of observations if those observations remained in the same Köppen-Geiger Zone climate zones</li> </ul> <p> </p> <p>The development of this data set archive supported by the <strong>Darwin Initiative</strong> to project DAREX001 of <em>Developing a Global Biodiversity Standard certification for tree-planting and restoration</em>, by <strong>Norway’s International Climate and Forest Initiative through the Royal Norwegian Embassy in Ethiopia</strong> to the <em>Provision of Adequate Tree Seed Portfolio</em> project in Ethiopia, by the <strong>Green Climate Fund</strong> through the IUCN-led <em>Transforming the Eastern Province of Rwanda through Adaptation</em> and through the <em>Readiness proposal on Climate Appropriate Portfolios of Tree Diversity for Burkina Faso</em> projects, by the <strong>Bezos Earth Fund</strong> to the <em>Quality Tree Seed for Africa in Kenya and Rwanda</em> project and by the <strong>German International Climate Initiative (IKI)</strong> to the regional tree seed programme on <em>The Right Tree for the Right Place for the Right Purpose in Africa</em>.</p> <p> </p>
High-fidelity simulation of the effects of street trees, green roofs and green walls on the distribution of thermal exposure in Prague-Dejvice
<p>Archive with PALM simulation results. All data were used in paper <a href="https://doi.org/10.1016/j.buildenv.2022.109484">https://doi.org/10.1016/j.buildenv.2022.109484</a></p>
Environment and density-dependency explain the fine-scale distribution of tree recruits before and after thinning
<p>Dataset used in the article "Environment and density-dependency explain the fine-scale distribution of tree recruits before and after thinning" in PeerJ</p>
Fig. 1 in Distribution Pattern, Nest-Tree Features And Breeding Performance Of Population Of The Black Stork, Ciconia Nigra (Ciconiiformes, Ciconiidae), In Northwestern Serbia
Fig. 1. The proportion of tree species picked for nest placement by the Black Stork (Ciconia nigra) in Northwestern Serbia (n = 44).
Figure 2. Mitotype tree and distribution maps for 98 in Integrative taxonomy reveals cryptic diversity in North American Lasius ants, and an overlooked introduced species
Figure 2. Mitotype tree and distribution maps for 98 DNA-barcodes belonging to 7 mitotypes of the ant Lasius niger (blue, n = 70) and 15 mitotypes of L. ponderosae sp. nov. (red, n = 28). The red dashed line delimits the expected natural range of L. ponderosae sp. nov.53 Maps have been created using the free R-package "ggmap" v3.0.0 (https://github.com/dkahle/ggmap) in R v4.1.1. Map tiles by Stamen Design, under CC BY 3.0.
Text-fig. 13. Macrophotographs of the fossil stems from Mhengere. a: large (90 cm diameter) palm tree trunk in situ; b: external view of the outer roots at the base of the trunk; average diameter of single root is 7 mm; c, d: cross-sections of a fragment of trunk showing the random distribution of equal-sized fibre vascular bundles throughout the trunk, the so-called Coccos-type. in Stratigraphy, Chronology And Palaeontology Of The Tertiary Rocks Of The Cheringoma Plateau, Mozambique
Text-fig. 13. Macrophotographs of the fossil stems from Mhengere. a: large (90 cm diameter) palm tree trunk in situ; b: external view of the outer roots at the base of the trunk; average diameter of single root is 7 mm; c, d: cross-sections of a fragment of trunk showing the random distribution of equal-sized fibre vascular bundles throughout the trunk, the so-called Coccos-type.
Supplementary material 1 from: Motloung R, Robertson M, Rouget M, Wilson J (2014) Forestry trial data can be used to evaluate climate-based species distribution models in predicting tree invasions. NeoBiota 20: 31-48. https://doi.org/10.3897/neobiota.20.5778
Current and potential distributions of sixteen species that are not widespread in southern Africa arranged on the basis of their suitable range size : a) Acacia paradoxa, b) A. cultriformis, c) A. falciformis, d) A. pendula, e) A. rubida, f) A. stricta, g) A. retinodes, h) A. fimbriata, i) A. aneura, j) A. viscidula, k) A. acuminata, l) A. adunca, m) A. binervata, n) A. schinoides, o) A. prominens, p) A. mangium. The grey shading indicates areas that SDMs have identified as suitable by SDMs while the white ones are unsuitable.
Figure 7 in Within-tree distribution and seasonal dynamics of Eutetranychus banksi and Euseius stipulatus (Acari: Tetranychidae, Phytoseiidae) on citrus: Implications for the biological control of the pest
Figure 7 Mean proportion of phytoseiids per leaf outside or inside the canopy (a), on the adaxial or abaxial side of the leaves (b), white or red coloured (c), and collected on fruits (d), whenE. banksi occurred or was absent. Capped bars represent ± standard error (SE). Significant differences are denoted with asterisks. Chi square contingency test:P <0.001.
Figure 1 in Within-tree distribution and seasonal dynamics of Eutetranychus banksi and Euseius stipulatus (Acari: Tetranychidae, Phytoseiidae) on citrus: Implications for the biological control of the pest
Figure 1 Mean number ofE. banksi(a–d) and phytoseiid mites (e–h) per leaf or per cm2 of leaves and fruits. Capped bars represent ± standard error (SE). Bars with different letters are significantly different (Wilcoxon rank-sum test).
Figure 5 in Within-tree distribution and seasonal dynamics of Eutetranychus banksi and Euseius stipulatus (Acari: Tetranychidae, Phytoseiidae) on citrus: Implications for the biological control of the pest
Figure 5 (a–d) Representation of the binomial (logit-link) generalized linear models (GLMs) showing the relationship between the proportion
Figure 3 in Within-tree distribution and seasonal dynamics of Eutetranychus banksi and Euseius stipulatus (Acari: Tetranychidae, Phytoseiidae) on citrus: Implications for the biological control of the pest
Figure 3 Seasonal relative abundance of motile forms of phytoseiid species in four (2018) and six (2019) citrus orchards. Percentage of each species per sampling is represented. The summer decline
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.