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.
234
datasets available to search
ShareScore release 0.9.0
Dataset results
234 results for “Mali”
National Checklists 2017: Mali Species List
Lists of taxa for each country and a few other administrative zones harvested from effechecka using simplified versions of geonames polygons. See <p></p>https://github.com/diatomsRcool/checklists for details<p></p>A list of species from Mali collected using effechecka and geonames polygons
National Checklists 2019: Mali Species List
Lists of taxa for each country and a few other administrative zones harvested from effechecka using simplified versions of geonames polygons. See <p></p>https://github.com/diatomsRcool/checklists for details.<p></p>A list of species from Mali collected using effechecka and geonames polygons
Dataset used for: Effectiveness of Acute Malnutrition Treatment at Health Center and Community Level with a Simplified, Combined Protocol in Mali: An Observational Cohort Study
<p>This dataset contains the variables used in the analysis of the body composition and outcomes of the Acute Malnutrition Treatment at Health Center and Community Level with a Simplified, Combined Protocol in Mali pilot study, from December 2018 to December 2021</p>
qdgc Mali
<p>QDGC tables delivered in geopackage file<br> - - - - - - - - - - - - - - - - - - - - - -<br> QDGC represents a way of making (almost) equal area squares covering a specific area to represent specific qualities of the area covered. The squares themselves are based on the degree squares covering earth. Around the equator we have 360 longitudinal lines , and from the north to the south pole we have 180 latitudinal lines. Together this gives us 64800 segments or tiles covering earth.<br> <br> <br> Within each geopackage file you will find a number of tables with these names:<br> <br> <br> -tbl_qdgc_01<br> -tbl_qdgc_02<br> -tbl_qdgc_03<br> -tbl_qdgc_04<br> -tbl_qdgc_05<br> -etc<br> <br> <br> The attributes for each table are:<br> <br> <br> qdgc Unique Quarter Degree Grid Cell reference string<br> area_reference Country<br> level_qdgc QDGC level<br> cellsize degrees decimal degree for the longitudal and latitudal length of the cell<br> lon_center Longitude center of the cell<br> lat_center Latitudal center of the cell<br> area_km2 Calculated area for the cell<br> geom Geometry<br> <br> <br> Metadata<br> --------<br> Geodata GCS_WGS_1984<br> Datum: D_WGS_1984<br> Prime Meridian: 0<br> <br> <br> Areas are calculated with different versions of Albers Equal Area Conic using the PostGIS function st_area. For the African continent I have used Africa Albers Equal Area Conic which will look like this:<br> - st_area(st_transform(geom, 102022))/1000000)<br> <br> <br> Licensing<br> ---------<br> Creative Commons Attribution 4.0 International<br> <br> <br> Conditions<br> ----------<br> Delivered to the user as-is. No guarantees. If you find errors, please tell me and I will try to fix it.<br> <br> <br> Thankyou<br> --------<br> The work has over the years been supported and receicved advice and moral support from many organisations and stakeholders. Here are some of them:<br> - Tanzania Wildlife Research Institute<br> - Dept of Biology, NTNU, Norway<br> - Norwegian Environment Agency<br> - Eivin Røskaft, Steven Prager, Howard Frederick, Julian Blanc, Honori Maliti, Paul Ramsey<br> <br> <br> References<br> ----------<br> * http://en.wikipedia.org/wiki/QDGC<br> * http://www.mindland.com/wp/projects/quarter-degree-grid-cells/about-qdgc/<br> * http://en.wikipedia.org/wiki/Lambert_azimuthal_equal-area_projection<br> * http://www.safe.com<br> <br> <br> <br> <br> Ragnvald Larsen<br> Trondheim 20th of January, 2021<br> <br> <br> ragnvald@mindland.com<br> www.mindland.com</p>
Infrastructure Climate Resilience Assessment Data Starter Kit for Mali
<p> This starter data kit collects extracts from global, open datasets relating to climate hazards and infrastructure systems. </p> <p> These extracts are derived from global datasets which have been clipped to the national scale (or subnational, in cases where national boundaries have been split, generally to separate outlying islands or non-contiguous regions), using Natural Earth (2023) boundaries, and is not meant to express an opinion about borders, territory or sovereignty. </p> <p> Human-induced climate change is increasing the frequency and severity of climate and weather extremes. This is causing widespread, adverse impacts to societies, economies and infrastructures. Climate risk analysis is essential to inform policy decisions aimed at reducing risk. Yet, access to data is often a barrier, particularly in low and middle-income countries. Data are often scattered, hard to find, in formats that are difficult to use or requiring considerable technical expertise. Nevertheless, there are global, open datasets which provide some information about climate hazards, society, infrastructure and the economy. This "data starter kit" aims to kickstart the process and act as a starting point for further model development and scenario analysis. </p> <p>Hazards:</p> <ul> <li>coastal and river flooding (Ward et al, 2020)</li> <li>extreme heat and drought (Russell et al 2023, derived from Lange et al, 2020)</li> <li>tropical cyclone wind speeds (Russell 2022, derived from Bloemendaal et al 2020 and Bloemendaal et al 2022)</li> </ul> <p>Exposure:</p> <ul> <li>population (Schiavina et al, 2023)</li> <li>built-up area (Pesaresi et al, 2023)</li> <li>roads (OpenStreetMap, 2023)</li> <li>railways (OpenStreetMap, 2023)</li> <li>power plants (Global Energy Observatory et al, 2018)</li> <li>power transmission lines (Arderne et al, 2020)</li> </ul> <p> The spatial intersection of hazard and exposure datasets is a first step to analyse vulnerability and risk to infrastructure and people. </p> <p> To learn more about related concepts, there is a free short course available through the Open University on <a href="https://www.open.edu/openlearncreate/course/view.php?id=11539">Infrastructure and Climate Resilience</a>. This <a href="https://opsis.eci.ox.ac.uk/courses/2-infra-for-resil/">overview of the course</a> has more details. </p> <p> These Python libraries may be a useful place to start analysis of the data in the packages produced by this workflow: </p> <ul> <li> <a href="https://github.com/tomalrussell/snkit"><code>snkit</code></a> helps clean network data </li> <li> <a href="https://github.com/nismod/snail"><code>nismod-snail</code></a> is designed to help implement infrastructure exposure, damage and risk calculations </li> </ul> <p> The <a href="https://github.com/nismod/open-gira"><code>open-gira</code></a> repository contains a larger workflow for global-scale open-data infrastructure risk and resilience analysis. </p> <p> For a more developed example, some of these datasets were key inputs to a regional climate risk assessment of current and future flooding risks to transport networks in East Africa, which has a related online visualisation tool at <a href="https://east-africa.infrastructureresilience.org/">https://east-africa.infrastructureresilience.org/</a> and is described in detail in Hickford et al (2023). </p> <p><strong>References</strong></p> <ul> <li> Arderne, Christopher, Nicolas, Claire, Zorn, Conrad, & Koks, Elco E. (2020). Data from: Predictive mapping of the global power system using open data [Dataset]. In Nature Scientific Data (1.1.1, Vol. 7, Number Article 19). Zenodo. DOI: <a href="https://doi.org/10.5281/zenodo.3628142">10.5281/zenodo.3628142</a> </li> <li> Bloemendaal, Nadia; de Moel, H. (Hans); Muis, S; Haigh, I.D. (Ivan); Aerts, J.C.J.H. (Jeroen) (2020): STORM tropical cyclone wind speed return periods. 4TU.ResearchData. [Dataset]. DOI: <a href="https://doi.org/10.4121/12705164.v3">10.4121/12705164.v3</a> </li> <li> Bloemendaal, Nadia; de Moel, Hans; Dullaart, Job; Haarsma, R.J. (Reindert); Haigh, I.D. (Ivan); Martinez, Andrew B.; et al. (2022): STORM climate change tropical cyclone wind speed return periods. 4TU.ResearchData. [Dataset]. DOI: <a href="https://doi.org/10.4121/14510817.v3">10.4121/14510817.v3</a> </li> <li> Global Energy Observatory, Google, KTH Royal Institute of Technology in Stockholm, Enipedia, World Resources Institute. (2018) Global Power Plant Database. Published on Resource Watch and Google Earth Engine; <a href="http://resourcewatch.org/">resourcewatch.org/</a> </li> <li> Hickford et al (2023) Decision support systems for resilient strategic transport networks in low-income countries – Final Report. Available online: <a href="https://transport-links.com/hvt-publications/final-report-decision-support-systems-for-resilient-strategic-transport-networks-in-low-income-countries">https://transport-links.com/hvt-publications/final-report-decision-support-systems-for-resilient-strategic-transport-networks-in-low-income-countries</a> </li> <li> Lange, S., Volkholz, J., Geiger, T., Zhao, F., Vega, I., Veldkamp, T., et al. (2020). Projecting exposure to extreme climate impact events across six event categories and three spatial scales. Earth's Future, 8, e2020EF001616. DOI: <a href="https://doi.org/10.1029/2020EF001616">10.1029/2020EF001616</a> </li> <li> Natural Earth (2023) Admin 0 Map Units, v5.1.1. [Dataset] Available online: <a href="https://www.naturalearthdata.com/downloads/10m-cultural-vectors/10m-admin-0-details/">www.naturalearthdata.com/downloads/10m-cultural-vectors/10m-admin-0-details</a> </li> <li> OpenStreetMap contributors, Russell T., Thomas F., nismod/datapkg contributors (2023) Road and Rail networks derived from OpenStreetMap. [Dataset] Available at <a href="https://global.infrastructureresilience.org">global.infrastructureresilience.org</a> </li> <li> Pesaresi M., Politis P. (2023): GHS-BUILT-S R2023A - GHS built-up surface grid, derived from Sentinel2 composite and Landsat, multitemporal (1975-2030) European Commission, Joint Research Centre (JRC) PID: <a href="http://data.europa.eu/89h/9f06f36f-4b11-47ec-abb0-4f8b7b1d72ea">data.europa.eu/89h/9f06f36f-4b11-47ec-abb0-4f8b7b1d72ea</a>, doi:10.2905/9F06F36F-4B11-47EC-ABB0-4F8B7B1D72EA </li> <li> Russell, T., Nicholas, C., & Bernhofen, M. (2023). Annual probability of extreme heat and drought events, derived from Lange et al 2020 (Version 2) [Dataset]. Zenodo. DOI: <a href="https://doi.org/10.5281/zenodo.8147088">10.5281/zenodo.8147088</a> </li> <li> Schiavina M., Freire S., Carioli A., MacManus K. (2023): GHS-POP R2023A - GHS population grid multitemporal (1975-2030). European Commission, Joint Research Centre (JRC) PID: <a href="http://data.europa.eu/89h/2ff68a52-5b5b-4a22-8f40-c41da8332cfe">data.europa.eu/89h/2ff68a52-5b5b-4a22-8f40-c41da8332cfe</a>, doi:10.2905/2FF68A52-5B5B-4A22-8F40-C41DA8332CFE </li> <li> Ward, P.J., H.C. Winsemius, S. Kuzma, M.F.P. Bierkens, A. Bouwman, H. de Moel, A. Díaz Loaiza, et al. (2020) Aqueduct Floods Methodology. Technical Note. Washington, D.C.: World Resources Institute. Available online at: <a href="https://www.wri.org/publication/aqueduct-floods-methodology">www.wri.org/publication/aqueduct-floods-methodology</a>. </li> </ul>
Transport Starter Data Kit: Historical socio-transport data for Mali
<p>This Transport Starter Data Kit contains historical annual data (1990–2021) on passenger and freight activity, segregated by mode and fuel. Additionally, historical data on energy intensities, load factors, vehicle stock, population (total, urban, rural, growth), and GDP (total, agriculture, construction, mining, manufacturing, service, energy, growth) are included in the kit, within the 'Data' tab. The historical data can be used as a foundation for transport-energy modelling and/or to identify areas of improvement. This data was verified through consultation with relevant stakeholders before publishing. The definition used for each vehicle mode is found in the 'Definitions' tab, and the description of each data observation status is found in the 'Notes' tab. All data sources are linked where possible.</p>
Figs 1, 2. M in Melting the iceberg: A new Megaselia Rondani species (Diptera: Phoridae) from Mali with the most striking wing ornament
Figs 1, 2. M. guentermuelleri sp. n., frontal setation (1) and left side of hypopygium (2). Scale bar = 0.1 mm.
Figs 3–6 in Melting the iceberg: A new Megaselia Rondani species (Diptera: Phoridae) from Mali with the most striking wing ornament
Figs 3–6. Middle (3) and hind (4) tibiae, hind femur (5) and wing (6) of M. guentermuelleri sp. n. Photographs maY not reflect true colours observed in specimens; bristles oF the tb3 apical comb are enhanced. Scale bars = 0.1 mm.
National Checklists: Mali Species List
Data from: GBIF.org (23 January 2025) GBIF Occurrence Download <a href="https://doi.org/10.15468/dl.vd2ajk" target="_blank" rel="noopener">https://doi.org/10.15468/dl.vd2ajk</a>
500-yr Projections of Thwaites Glacier, Antarctica, with MALI, including glacial isostatic adjustment
<p>This archive contains model code, results, and analysis scripts for<br> reproducing the material presented in the manuscript "Stabilizing effect of<br> bedrock uplift on retreat of Thwaites Glacier, Antarctica, at centennial<br> timescales" by Cameron Book, et al. Questions should be directed to Matt<br> Hoffman (mhoffman@lanl.gov).</p> <p>This archive contains the following directories:</p> <p>|-- MALI_code<br> |-- PIGL_control<br> |-- analysis<br> |-- control<br> |-- run_setup<br> |-- N1<br> |-- N2<br> |-- N3<br> |-- N4<br> `-- PIGL_N3</p> <p><br> 'MALI_code' is a snapshot of the MALI repository used for these simulations,<br> commit 454e0fc8bf384bee1c1560d6c3eaa5fae43bfdda.<br> This commit is present in an older MALI repository that is no longer<br> maintained, at https://github.com/MPAS-Dev/MPAS-Model<br> MALI is currently maintained on Github at https://github.com/MALI-Dev/E3SM<br> Building MALI requires the Albany multiphysics library, which is available<br> at https://github.com/sandialabs/Albany. The simulations presented use Albany<br> master from March 5, 2021.</p> <p>'analysis' contains the scripts used to process the model output and produce<br> the figures and results presented in the manuscript. Filepaths will have to<br> be adjusted to your local layout.</p> <p>'run_setup' is a directory of files and scripts necessary to reproduce the<br> model simulations presented. The GIA model giapy is in the file<br> 'giascript.py'. giapy can also be found on Github at https://github.com/skachuck/giapy</p> <p>'control' is the control run with the GIA model disabled. It corresponds to<br> the run labeled CTRL in the manuscript.</p> <p>'PIGL_control' is the control run using the high melt forcing. It corresponds<br> to the run labeled HM-CTRL in the manuscript.</p> <p>The five run directories included here (N1-N4, PIGL_N3) are the standard<br> ensemble described in the manuscript. (There is a separate archive for the<br> runs briefly mentioned that exclude the elastic response of the lithosphere.)<br> The individual runs have the following correspondence to the manuscript:<br> N1=TYP<br> N2=BEST2<br> N3=VLV-THIN<br> N4=VLV<br> PIGL_N3=HM-VLV-THIN<br> Within each run directory, are the following model output files:<br> globalStats.nc: MALI global, scalar time-series<br> iceload_all.nc: Thwaites Glacier ice load on the GIA grid<br> output_*.nc: MALI spatial output fields, separated by century<br> uplift_GIA_all.nc: GIA output on GIA grid</p>
Figure 1 in Length-weight relationships of 19 fish species from two tropical artificial reservoirs (Manantali and Selingue) in Mali, West Africa
Figure 1. – Situation map (top) and detailed map (bottom) of the two Malian lakes studied: (A) Manantali, (B) Sélingué.
Рис. 1. Карта-схема пунктов сборов Gynaephora (rossii) в Якутии: 1 — о-в КотеΛьный; 2 — о-в СтоΛбовой; 3 — о-в МаΛый Αяховский; 4 — о-в БоΛьшой Αяховский; 5 — п-ов Быковский в устье Αены; 6 — СеΛΛяхская губа, р. СеΛях, низовья Яны; 7 — КоΛымская протока, низовья ИнΔигирки; 8 — озеро ХомоΛох, бассейн р. БёрёΛёх, низовья ИнΔигирки; 9 — о-в Крестовский; 10 — о-в ЧетырехстоΛбовой; 11 — устье р. Энюмчувеем, южное побережье Восточно-Сибирского моря; 12 — хребет СунтарХаята; 13 — р. ÀжеΛинΔа в системе Станового хребта (точками обозначены ранее опубΛикованные точки, треугоΛьниками — новые местообитания) Fig. 1. Chart of Gynaephora (rossii) collection sites in Yakutia: 1 — Kotelny island; 2 — Stolbovoy island; 3 — Maly Lyakhovsky island; 4 — Bolshoi Lyakhovsky island; 5 — Bykovsky peninsula at the mouth of the Lena river; 6 — Sellakhskaya bay, Selyakh river, lower reaches of the Yana river; 7 — Kolymskaya channel, lower reaches of the Indigirka river; 8 — Lake Homolokh, Berelekh river basin, lower reaches of the Indigirka river; 9 — Krestovsky island; 10 — Chetyrekhstolbovoy island; 11 — the mouth of the Enyumchuveem river, southern coast of the East Siberian sea; 12 — Suntar-Khayata ridge; 13 — Gelinda river in the Stanovoy ridge system (dots indicate previously published localities, triangles indicate new localities) in New data on the distribution of the Gynaephora (rossii) species group in Northern Yakutia
Рис. 1. Карта-схема пунктов сборов Gynaephora (rossii) в Якутии: 1 — о-в КотеΛьный; 2 — о-в СтоΛбовой; 3 — о-в МаΛый Αяховский; 4 — о-в БоΛьшой Αяховский; 5 — п-ов Быковский в устье Αены; 6 — СеΛΛяхская губа, р. СеΛях, низовья Яны; 7 — КоΛымская протока, низовья ИнΔигирки; 8 — озеро ХомоΛох, бассейн р. БёрёΛёх, низовья ИнΔигирки; 9 — о-в Крестовский; 10 — о-в ЧетырехстоΛбовой; 11 — устье р. Энюмчувеем, южное побережье Восточно-Сибирского моря; 12 — хребет СунтарХаята; 13 — р. ÀжеΛинΔа в системе Станового хребта (точками обозначены ранее опубΛикованные точки, треугоΛьниками — новые местообитания) Fig. 1. Chart of Gynaephora (rossii) collection sites in Yakutia: 1 — Kotelny island; 2 — Stolbovoy island; 3 — Maly Lyakhovsky island; 4 — Bolshoi Lyakhovsky island; 5 — Bykovsky peninsula at the mouth of the Lena river; 6 — Sellakhskaya bay, Selyakh river, lower reaches of the Yana river; 7 — Kolymskaya channel, lower reaches of the Indigirka river; 8 — Lake Homolokh, Berelekh river basin, lower reaches of the Indigirka river; 9 — Krestovsky island; 10 — Chetyrekhstolbovoy island; 11 — the mouth of the Enyumchuveem river, southern coast of the East Siberian sea; 12 — Suntar-Khayata ridge; 13 — Gelinda river in the Stanovoy ridge system (dots indicate previously published localities, triangles indicate new localities)
Map of Màndén, the Ghana and Mali empires, and Jùlá trade network
<p>Map of Màndén, the Ghana and Mali empires, and Jùlá trade network. Includes original color and an adapted black and white version.</p> <p>Originally appeared in the following:</p> <p>Donaldson, Coleman. 2017. “Clear Language: Script, Register and the N’ko Movement of Manding-Speaking West Africa.” Doctoral Dissertation, Philadelphia, PA: University of Pennsylvania. Philadelphia, PA. <a href="https://repository.upenn.edu/dissertations/AAI10681364/">https://repository.upenn.edu/dissertations/AAI10681364/</a>.</p> <p>Created with data from the following sources:</p> <p>Dalby, David. 1971. “Introduction: Distribution and Nomenclature of the Manding People and Their Language.” In <em>Papers on the Manding</em>, edited by Carleton Hodge, 3:1–13. African Series. Bloomington, IN: Indiana University Publications.</p> <p>Launay, Robert. 1983. <em>Traders Without Trade: Responses to Change in Two Dyula Communities</em>. Cambridge University Press.</p> <p>Simonis, Francis. 2010. <em>L’Afrique soudanaise au Moyen âge: le temps des grands empires (Ghana, Mali, Songhaï)</em>. Marseille: CRDP de l’académie d’Aix-Marseille.</p> <p>--</p> <p>Blog: <a href="https://ajami.hypotheses.org/">https://ajami.hypotheses.org/</a><br> Project: <a href="https://www.manuscript-cultures.uni-hamburg.de/ajami/index_e.html">https://www.manuscript-cultures.uni-hamburg.de/ajami/index_e.html</a></p>
Figure 17 in Two new peculiar species of the genus Siccia Walker from Mali and Ivory Coast (Lepidoptera: Erebidae: Arctiinae)
Figure 17. Type locality of Siccia kravchenkoi sp. n.: the view of the valley from the upland nearby the collecting site by Ouronina (June of 2021).
Figures 1–8 in Two new peculiar species of the genus Siccia Walker from Mali and Ivory Coast (Lepidoptera: Erebidae: Arctiinae)
Figures 1–8. Siccia spp.: adults. Depositories of the specimens: 1 in WIGJ; 2–4 and 6 in ANHRT; 5 in NHMUK (©The Trustees of NHMUK); 7 is after Hacker (2016); 8 in MWM/ZSM.
Fig. 7 in Large palaeophiid and nigerophiid snakes from Paleogene Trans-Saharan Seaway deposits of Mali
Fig. 7. Vertebra of an indeterminate snake CNRST-SUNY 324 from the Eocene Tamaguélelt Formation, Mali; in anterior (A), lateral (B), posterior (C), and ventral (D) views.
Fig. 6 in Large palaeophiid and nigerophiid snakes from Paleogene Trans-Saharan Seaway deposits of Mali
Fig. 6. Vertebra of the nigerophiid snake Amananulam sanogoi gen. et sp. nov. (CNRST-SUNY 426) from the Paleocene Teberemt Formation (phosphate of Mali-19), Mali; in anterior (A), lateral (B), posterior (C), dorsal (D), and ventral (E) views.
Fig. 5 in Large palaeophiid and nigerophiid snakes from Paleogene Trans-Saharan Seaway deposits of Mali
Fig. 5. Anterior (A) and posterior (B) trunk vertebrae of palaeophiid snake Palaeophis colossaeus Rage, 1983 from the Eocene of Tamaguélelt Formation, Mali. A. CNRST-SUNY 309; in anterior (A1), lateral (A2, reversed to facilitate comparisons), posterior (A3), dorsal (A4), and ventral (A5) views. B. CNRST-SUNY 293; in anterior (B1), lateral (B2), posterior (B3), dorsal (B4), and ventral (B5) views.
Fig. 3 in Large palaeophiid and nigerophiid snakes from Paleogene Trans-Saharan Seaway deposits of Mali
Fig. 3. Correlated stratigraphic sections of the Mali-19 and Mali-20 localities that have yielded snake vertebrae.
Fig. 8 in Large palaeophiid and nigerophiid snakes from Paleogene Trans-Saharan Seaway deposits of Mali
Fig. 8. Standard major axis regressions of vertebral metrics on total length in snakes. A. Regression of the width across the prezygapophyses on total length. Palaeophis colossaeus (CNRST-SUNY 290) is plotted as an × B. Regression of the width of the cotyle on total length. Palaeophis colossaeus (CNRST-SUNY 290) is plotted as an ×, Amananulam sanogoi gen. et sp. nov. (CNRST-SUNY 426) as an open triangle, and indeterminate snake CNRST-SUNY 324 as an open circle. Width across prezygapophyses is unavailable in the latter species due to damage.
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.