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Fig. 21 in Two fancy spines and a collar: a taxonomic review of the myrmecomorphic spider genus Mazax O. Pickard-Cambridge, 1898 (Araneae: Corinnidae: Castianeirinae) in South America
Fig. 21. Distribution of the species of the Mazax spinosa group.
Fig. 20 in Two fancy spines and a collar: a taxonomic review of the myrmecomorphic spider genus Mazax O. Pickard-Cambridge, 1898 (Araneae: Corinnidae: Castianeirinae) in South America
Fig. 20. Distribution of the species of the Mazax pax group.
APPENDIX 1 in Paleoclimate estimates for the Paleogene-Neogene in southern South America using fossil leaves as proxies
<p>APPENDIX 1.— Supplementary material including the fossil specimens from the studied sites and the morphological character scores for CLAMP analysis</p><table><thead><tr><th><b>Lower Río Turbio Fm</b></th><th><b>LMA</b></th><th><b>LAA</b></th><th><b>Source</b></th></tr></thead><tbody><tr><th><i>Acrodiclidium flavianum</i></th><td>*</td><td>*</td><td>Hünicken 1967; Vento & Prámparo 2018</td></tr><tr><th><i>Allophylus graciliformis</i></th><td>*</td><td>*</td><td>Hünicken 1967; Vento & Prámparo 2018</td></tr><tr><th><i>Anacardites pichileufensis</i></th><td>*</td><td>*</td><td>Hünicken 1967; Vento & Prámparo 2018</td></tr><tr><th><i>Annona</i> sp.</th><td>*</td><td>*</td><td>Hünicken 1967; Vento & Prámparo 2018</td></tr><tr><th><i>Casearia</i> sp.</th><td>*</td><td>*</td><td>Hünicken 1967; Vento & Prámparo 2018</td></tr><tr><th><i>Cissus pichileufensis</i></th><td>*</td><td>*</td><td>Hünicken 1967; Vento & Prámparo 2018</td></tr><tr><th><i>Cinnamomum neogaea</i></th><td>*</td><td>*</td><td>Hünicken 1967; Vento & Prámparo 2018</td></tr><tr><th><i>Cupania grosse-serrata</i></th><td>*</td><td>-</td><td>Hünicken 1967; Vento & Prámparo 2018</td></tr><tr><th><i>Cupania latifolioides</i></th><td>*</td><td>*</td><td>Hünicken 1967; Vento & Prámparo 2018</td></tr><tr><th><i>Cupania patagonica</i></th><td>*</td><td>*</td><td>Hünicken 1967; Vento & Prámparo 2018</td></tr><tr><th><i>Cupania santacrucensis</i></th><td>*</td><td>*</td><td>Hünicken 1967; Vento & Prámparo 2018</td></tr><tr><th><i>Drimys patagonica</i></th><td>*</td><td>*</td><td>Hünicken 1967; Vento & Prámparo 2018</td></tr><tr><th><i>Drimys</i> sp.</th><td>*</td><td>*</td><td>Hünicken 1967; Vento & Prámparo 2018</td></tr><tr><th><i>Eucalyptus</i> sp.</th><td>*</td><td>*</td><td>Hünicken 1967; Vento & Prámparo 2018</td></tr><tr><th><i>Escallonia</i> sp.</th><td>*</td><td><b>–</b></td><td>Hünicken 1967; Vento & Prámparo 2018</td></tr><tr><th><i>Myrcia</i> sp.</th><td>*</td><td>*</td><td>Hünicken 1967; Vento & Prámparo 2018</td></tr><tr><th><i>Myricaceae</i></th><td>*</td><td>*</td><td>Hünicken 1967; Vento & Prámparo 2018</td></tr><tr><th><i>Myrica hunzikerii</i></th><td>*</td><td>*</td><td>Hünicken 1967; Vento & Prámparo 2018</td></tr><tr><th><i>Nectandra prolifica</i></th><td>*</td><td>*</td><td>Hünicken 1967; Vento & Prámparo 2018</td></tr><tr><th><i>Nothofagus subferruginea</i></th><td>*</td><td>*</td><td>Hünicken 1967; Vento & Prámparo 2018</td></tr><tr><th><i>Nothofagus variabilis</i></th><td>*</td><td>*</td><td>Hünicken 1967; Vento & Prámparo 2018</td></tr><tr><th><i>Nothofagus serrulata</i></th><td>*</td><td>*</td><td>Hünicken 1967; Vento & Prámparo 2018</td></tr><tr><th><i>Nothofagus elongata</i></th><td>*</td><td><b>–</b></td><td>Hünicken 1967</td></tr><tr><th><i>Ocotea menendezi</i></th><td>*</td><td>*</td><td>Hünicken 1967; Vento & Prámparo 2018</td></tr><tr><th><i>Ocotea</i> sp.</th><td>*</td><td>*</td><td>Hünicken 1967; Vento & Prámparo 2018</td></tr><tr><th><i>Persea</i> sp.</th><td>*</td><td>*</td><td>Hünicken 1967; Vento & Prámparo 2018</td></tr><tr><th><i>Psidium liociardensis</i></th><td>*</td><td>*</td><td>Hünicken 1967; Vento & Prámparo 2018</td></tr><tr><th><i>Qualea patagonica</i></th><td>*</td><td>*</td><td>Hünicken 1967; Vento & Prámparo 2018</td></tr><tr><th><i>Schinopsis patagonica</i></th><td>*</td><td>*</td><td>Hünicken 1967; Vento & Prámparo 2018</td></tr><tr><th><i>Styrax</i> sp.</th><td>*</td><td>*</td><td>Hünicken 1967; Vento & Prámparo 2018</td></tr><tr><th><i>Styrax glandulifera</i></th><td>*</td><td>*</td><td>Hünicken 1967; Vento & Prámparo 2018</td></tr><tr><th><i>Tetracera</i> cf. <i>patagonica</i></th><td>*</td><td>*</td><td>Hünicken 1967; Vento & Prámparo 2018</td></tr><tr><th><i>Zyziphus chubutensis</i></th><td>*</td><td>*</td><td>Hünicken 1967; Vento & Prámparo 2018</td></tr><tr><th><i>Malvaceae</i></th><td>*</td><td>*</td><td>Hünicken 1967; Vento & Prámparo 2018</td></tr><tr><th><i>Urticaceae</i></th><td>*</td><td><b>–</b></td><td>Hünicken 1967; Vento & Prámparo 2018</td></tr><tr><th>cf. <i>Pouterlabatia clark</i></th><td>*</td><td>*</td><td>Hünicken 1967; Vento & Prámparo 2018</td></tr><tr><th><i>Paullinia</i> sp.</th><td>*</td><td><b>–</b></td><td>Panti 2018</td></tr><tr><th><i>Banisteriophyllum</i></th><td>*</td><td><b>–</b></td><td>Panti 2018</td></tr></tbody></table>
TABLE 4 in Paleoclimate estimates for the Paleogene-Neogene in southern South America using fossil leaves as proxies
<p>TABLE 4 — Estimated values for the climate variables in the lower Río Turbio Formation member from CLAMP analysis using the CLAMP3B SA and CLAMPSH90 data set.</p><table><thead><tr><th><b>CLAMP Climate parameters</b></th><th><b>Estimates</b></th><th><b>Error</b></th><th><b>Dataset</b></th><th><b>Estimates</b></th><th><b>Error</b></th><th><b>Data set</b></th></tr></thead><tbody><tr><th>Mean annual temperature (°C)</th><td>12.7</td><td>2.1</td><td>CLAMP3BSA</td><td>12.2</td><td>4.83</td><td>CLAMPSH90</td></tr><tr><th>Warm month mean temperature (°C)</th><td>23.2</td><td>3.3</td><td>CLAMP3BSA</td><td>21.5</td><td>7.5</td><td>CLAMPSH90</td></tr><tr><th>Cold month mean temperature (°C)</th><td>3.4</td><td>3.8</td><td>CLAMP3BSA</td><td>4</td><td>4.6</td><td>CLAMPSH90</td></tr><tr><th>Mean Growing Season Precipitation (mm)</th><td>1741</td><td>42.6</td><td>CLAMP3BSA</td><td>1394</td><td>68</td><td>CLAMPSH90</td></tr><tr><th>3-Wettest Months Precipitation (mm)</th><td>784</td><td>19.8</td><td>CLAMP3BSA</td><td>460</td><td>35</td><td>CLAMPSH90</td></tr><tr><th>3-Driest Months Precipitation (mm)</th><td>334</td><td>15.3</td><td>CLAMP3BSA</td><td>160</td><td>22.8</td><td>CLAMPSH90</td></tr><tr><th>Length Growing Season</th><td>7.7</td><td>1.2</td><td>CLAMP3BSA</td><td>7.4</td><td>0.8</td><td>CLAMPSH90</td></tr></tbody></table>
Influence of past and current factors on the beta diversity of coastal lagoon fish communities in South America
<p><strong>Aim: </strong>We aimed to assess the relative influence of past (Quaternary paleodrainage characteristics) and current factors on the beta diversity of freshwater fishes in coastal lagoons and explore the main processes involved.</p> <p><strong>Location:</strong> Atlantic coast of South America. Taxon: Fishes (173 species)</p> <p><strong>Methods:</strong> We built a dataset of species occurrence in 129 lagoons across eight freshwater ecoregions of the world (FEOWs) located between latitudes 0° and 36° and calculated beta diversity (βjac) and its turnover (βjtu) and nestedness (βjne) components. We used a partial Mantel test and multiple regressions on distance matrices to evaluate the importance of past and current factors, and of geographical distance in determining beta diversity. Past variables were those representing the historical freshwater habitat during the last glacial maximum (LGM), and contemporary variables were those related to current habitat.</p> <p><strong>Results:</strong> We found high values of βjac within the FEOWs, with βjtu prevailing over βjne. Both past (paleodrainage) and current (drainage area, salinity, and lagoon area) factors affected species dissimilarity (βjac = 46%) and its components (βjtu = 44% and βjne =20%), although explanation was, in part, shared with geographical distance. Individually, the influence of past factors was prevalent in beta diversity and its components.</p> <p><strong>Main Conclusions:</strong> The results suggest that major changes in the availability of freshwater habitats and connectivity since the Pleistocene must have affected the colonization, extinction and recolonization processes of fishes along the eastern coast of South America. We suggest that the high beta diversity values may result from limited dispersal after extinctions in the LGM and that the dissimilar freshwater fish communities currently seen were formed mainly by heterogeneous subsets of the regional species pool that persisted in landscape refuges during past sea level increases and then recolonized coastal lagoons.</p>
Functional niche constraints on carnivore assemblages (mammalia: carnivora) in the Americas: What facilitates coexistence through space and time?
<p><b>Aim:</b> Mammalian carnivores are among the best studied groups in terms of evolutionary history. However, the effects of species interactions in shaping community assemblages remain poorly understood. We hypothesize that indirect interactions via ecological trait filtering play a key role in structuring carnivoran assemblages, mediate coexistence, and thus should show high functional diversity in space and time at continental scales.</p> <p><b>Location:</b> Americas.</p> <p><b>Taxon:</b> Mammalian carnivores (Mammalia: Carnivora).</p> <p><b>Methods:</b> We followed a macroecological perspective via ecological networks analyses for indirect interactions, and assessed the underlying functional diversity (FD) across space and from the Last Interglacial to the present in the Americas. We analyzed the potential distributions and six ecological traits of 88 species to establish possible mechanisms that enables species to coexist and the underlying diversity patterns. We compared the empirical results with two null models, and two sensitivity analyses.</p> <p><b>Results:</b> Co-occurring carnivore species presented ecological segregation driven mainly by a size ratio (S <sub>R</sub>) relationship, called here the body-size spatial anti-clustering effect. The underlying FD patterns showed low redundancy towards the tropics and the poles during the times evaluated. However, during the LGM, shifts occurred primarily at high latitudes in North America. This shift affected the S <sub>R</sub> relationship and therefore changed functional diversity patterns. These local-to-continental interactions mediated by the S <sub>R</sub> are significant from an ecological and biogeographic perspective, suggesting a robust and consistent trend in which carnivore species of similar size have a lower probability of occupying the same area unless they differentiate in other ecological trait spaces.</p> <p><b>Main conclusions:</b> The S <sub>R</sub> relationship is potentially a primary mechanism limiting carnivore coexistence, reflecting functional filtering. The S <sub>R</sub> tends to be conservative across different ecological trait groups and through time and space. We propose that the body-size spatial anti-clustering effect can directly measure species' coexistence and mediate FD patterns in the Americas.</p>
MIS 5e relative sea-level index points along the Pacific coast of North America
<p>This spreadsheet contains data and metadata on relative sea-level index points and associated ages for the Pacific coast of North America. This is Version 1.1, updated after the peer-review of the associated paper.</p>
AnisoVeg: Anisotropy and Nadir-normalized MODIS MAIAC datasets for satellite vegetation studies in South America
<p><strong>Dataset paper under review.</strong></p> <p><strong>Contact Ricardo Dalagnol (ricds@hotmail.com) for more information.</strong></p> <p> </p> <p><strong>Data:</strong> AnisoVeg: Anisotropy and Nadir-normalized MODIS MAIAC datasets for satellite vegetation studies in South America</p> <p><strong>Scale factor</strong>: 10000</p> <p><em>obs: the no_samples layer does not have scale.</em></p> <p><strong>Coverage:</strong> South America land</p> <p><strong>Time period:</strong> 2000 to 2021 (starting in March 2000)</p> <p><strong>Spatial resolution:</strong> 0.009107388 degree equivalent to ~1 km</p> <p><strong>Temporal resolution:</strong> Monthly</p> <p><strong>Coordinate reference system:</strong> geographic projection, datum WGS-84</p> <p><strong>Processing details summary (more detailed explanation in the paper):</strong></p> <ul> <li>The original MODIS (MAIAC) data were described by Lyasputin et al. 2011 (<a href="https://doi.org/10.1029/2010JD014986">https://doi.org/10.1029/2010JD014986</a>). The daily MODIS (MAIAC) surface reflectance data from collection 6, acquired from Terra and Aqua satellites, are available from the MCD19A1 product (<a href="https://ladsweb.modaps.eosdis.nasa.gov/archive/allData/6/MCD19A1">https://ladsweb.modaps.eosdis.nasa.gov/archive/allData/6/MCD19A1</a>). The Bidirectional Reflectance Distribution Function (BRDF) model parameters are available from MCD19A3 product (<a href="https://ladsweb.modaps.eosdis.nasa.gov/archive/allData/6/MCD19A3">https://ladsweb.modaps.eosdis.nasa.gov/archive/allData/6/MCD19A3</a>)</li> <li>The daily MCD19A1 data at 1 km spatial resolution were normalized using the BRDF parameters and Ross-Thick Li-Sparse (RTLS) model considering (1) a fixed nadir view and a 45 deg. solar zenith angle using the parameters from the MCD19A3 product; and (2) backward and forward scattering. For the nadir (NAD) product, the nadir normalization was taken. For the anisotropy (ANI) product, we calculated the backward minus forward surfaces for each layer, resulting in the ANI product.</li> <li>The daily data were aggregated into monthly composites by the pixel’s median.</li> <li>The tiles that cover the South America were mosaicked and re-projected from sinusoidal to geographic projection</li> <li>The Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) were calculated using standard formulas. The EVI parameters were: C1 = 6, C2 = 7.5, L = 1, G = 2.5</li> </ul> <p><strong>File(s) format:</strong></p> <ul> <li>Zip files - one per year. NAD are the nadir-normalized products and ANI the anisotropy. Inside zip files there are raster files with ".tif" format, one per month window.</li> <li>The filename syntax is "maiac_southamerica_month_PRODUCT_YYYY_MM_LAYER_latlon.tif", where YYYY is the year (e.g. 2000), MM is the month with two digits (e.g. 03 for March), PRODUCT is either nadir or anisotropy, and LAYER can be bands 1-8, EVI and NDVI.</li> <li>There is also the number of samples (no_samples) layers for each date, which can be used to filter composites with a minimum desirable number of daily observations. </li> </ul> <p> </p> <p><strong>Code:</strong> <a href="https://github.com/ricds/maiac_processing">https://github.com/ricds/maiac_processing</a></p> <p> </p> <p><strong>Acknowledgements:</strong> R.D. was supported by Sao Paulo Research Foundation (FAPESP) grants 2015/22987-7 and 2019/21662-8. FHW was supported by FAPESP grant 2015/50484-0. Part of this work was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration (NASA). The funders had no role in the study design, data collection and analysis, including the decision to publish or prepare the manuscript. We thank the MODIS MAIAC team from NASA for providing the freely available MODIS (MAIAC) daily dataset.</p> <p> </p> <p><strong>Auxiliary data of AnisoVeg:</strong></p> <ul> <li>Backscattering data of AnisoVeg: <a href="https://doi.org/10.5281/zenodo.6040299">https://doi.org/10.5281/zenodo.6040299</a> and <a href="https://doi.org/10.5281/zenodo.6040790">https://doi.org/10.5281/zenodo.6040790</a></li> <li>Forward scattering data of AnisoVeg: <a href="https://doi.org/10.5281/zenodo.6048784">https://doi.org/10.5281/zenodo.6048784</a> and <a href="https://doi.org/10.5281/zenodo.6048793">https://doi.org/10.5281/zenodo.6048793</a></li> </ul> <p> </p> <p><strong>Layers available at Google Earth Engine (GEE):</strong></p> <ul> <li>EVI Anisotropy: <a href="https://code.earthengine.google.com/?asset=projects/anisoveg/assets/evi_anisotropy">https://code.earthengine.google.com/?asset=projects/anisoveg/assets/evi_anisotropy</a></li> <li>EVI Nadir: <a href="https://code.earthengine.google.com/?asset=projects/anisoveg/assets/evi_nadir">https://code.earthengine.google.com/?asset=projects/anisoveg/assets/evi_nadir</a></li> </ul> <p>Obs: these require a (free) google earth engine account.</p> <p> </p> <p><strong>Dataset usage</strong>: This dataset is a product of hundreds of hours of coding starting in 2016 with the first author PhD work and then into his Postdoc, and many more hundreds hours of data processing. Data is free to use, but if you use this dataset, please cite the dataset paper or this repository (while paper is under review). Invitation for collaboration are welcomed.</p> <ul> </ul> <p> </p> <p><strong>While the paper is under review, for use of this dataset please cite:</strong></p> <p>Dalagnol, Ricardo; Galvão, Lênio Soares; Wagner, Fabien Hubert; Moura, Yhasmin Mendes; Gonçalves, Nathan; Wang, Yujie; Lyapustin, Alexei; Yang, Yan; Saatchi, Sassan; Aragão, Luiz Eduardo Oliveira e Cruz. (2022). "AnisoVeg: Anisotropy and Nadir-normalized MODIS MAIAC datasets for satellite vegetation studies in South America". (Version v1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.3878879</p>
CCG Starter Kits - Base SAND file for South America- Coal and Natural Gas Scenario
<p>This file is the Base SAND file for South America with coal and natural gas.</p> <p>This is published as part of the MethodsX paper titled <strong>How to put together a Starter Data Kit from scratch? An extensive methodology to compile zero-order energy transition models. </strong>The main goal of the files published for this paper is to develop a set of credible data and an initial investment model for several developing countries.</p>
CCG Starter Kits - Base SAND file for South America
<p>This file is the Base SAND file for South America.</p> <p>This is published as part of the MethodsX paper titled <strong>How to put together a Starter Data Kit from scratch? An extensive methodology to compile zero-order energy transition models. </strong>The main goal of the files published for this paper is to develop a set of credible data and an initial investment model for several developing countries.</p>
Data repository for "Climate change increases the severity and duration of soil water stress in the temperate forest of eastern North America"
<p>Dataset provided for publication in Frontiers in Forests and Global Change : "Climate change increases the severity and duration of soil water stress in the temperate forest of eastern North America".</p>
Data from: Environment but not geography explains genetic variation in the invasive and largely panmictic European starling in North America
Populations of invasive species that colonize and spread in novel environments may differentiate both through demographic processes and local selection throughout the genome. European starlings (Sturnus vulgaris) were introduced to New York in 1890 and subsequently spread throughout North America, becoming one of the most widespread and numerous bird species on the continent. Genome-wide comparisons across starling individuals and populations can identify demographic and/or selective factors that facilitated this rapid and successful expansion. We investigated patterns of genomic diversity and differentiation using reduced-representation genome sequencing (ddRADseq) of 17 starling populations. Consistent with this species' high dispersal rates and rapid expansion history, we found low genome-wide differentiation and few FST outliers even at a continental scale. Despite starting from a founding population of approximately 180 individuals, North American starlings do not seem to have undergone a detectable genetic bottleneck: they have maintained an extremely large effective population size since introduction. We find more than 200 variants that correlate with temperature and/or precipitation. Genotype-environment associations (but not outlier scans) identify these SNPs against a background of negligible genome- and range-wide divergence. Such variants fall in the coding regions of genes associated with metabolism, stress, and neurological function. This evidence for incipient local adaptation in North American starlings suggests that it can evolve rapidly even in wide-ranging and evolutionarily young populations. This survey of genomic signatures of expansion in North American starlings is the most comprehensive to date and complements ongoing studies of world-wide local adaptation in these highly dispersive and invasive birds.
Mycorrhizal colonization and root diameter of native and invasive plants of eastern North America
<p>Arbuscular mycorrhizal colonization (total and arbuscules) were measured in roots of 10 species of eastern North American woody plants. Five were non-native invasive and 5 were native species. Roots were collected from ingrowth cores over three harvests during a single season, from a common garden. Diameter was estimated from published root density and specific root length values for each species.</p>
Figures 1-7 in Confirmation of the existence of Heliophanus apiatus (Araneae: Salticidae) in the San Francisco Bay Area of California, USA, first records for North America
Figures 1-7. Heliophanus apiatus from the San Francisco Bay area of California. 1, Male feeding on Drosphila melanogaster. Note the deep black coloration with four white spots, and the white spots on the black palpi. 2, Female on retreat. Note the brown-black coloration and the lack of white spots or bands on the appendages. 3, Anterior view of female feeding on D. melanogaster, displaying the typical brilliant yellow palpi typical of all instars except mature males. 4, Early instar immature, ~2 mm long. Note the white abdominal markings or bands (not spots) that resemble early instars of Salticus. 5, Same specimen as (4) after the next molt, ~2.5 mm long. Subsequent instars resemble the adult female in coloration. 6, Adult male on edge of planter box. 7, Two immatures on a composite flower.
Figure 8. Asiocnemis colombicus n in New fossil false click-beetles from the Americas (Coleoptera: Eucnemidae)
Figure 8. Asiocnemis colombicus n. sp., holotype, dorsal view.
Figure 6. Thambus woodruffi n in New fossil false click-beetles from the Americas (Coleoptera: Eucnemidae)
Figure 6. Thambus woodruffi n. sp., holotype, dorsal view.
Figure 7. Thambus woodruffi n in New fossil false click-beetles from the Americas (Coleoptera: Eucnemidae)
Figure 7. Thambus woodruffi n. sp., holotype, ventral view.
Figure 5. Neusiokia appalachiensis n in New fossil false click-beetles from the Americas (Coleoptera: Eucnemidae)
Figure 5. Neusiokia appalachiensis n. sp., holotype, ventrolateral view of pronotum and head.
Figure 4. Neusiokia appalachiensis n in New fossil false click-beetles from the Americas (Coleoptera: Eucnemidae)
Figure 4. Neusiokia appalachiensis n. sp., holotype, ventral view of head, pro- and mesothorax.
Figure 1. Neusiokia appalachiensis n in New fossil false click-beetles from the Americas (Coleoptera: Eucnemidae)
Figure 1. Neusiokia appalachiensis n. sp., holotype, ventrolateral view.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.