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Regional and Historical Variation in Garlic Mustard Distribution in Western Massachusetts 2006-2007
The susceptibility of a site to invasion by nonnative species depends on its current ecological features and its historical land use. Certain environments might be more conducive to an invasive plant’s success, and several recent studies have shown that former agricultural sites are more susceptible to invasion than sites that have been continuously wooded. We studied the invasive herb garlic mustard (Alliaria petiolata), at roadside forested edges. Site selection was stratified by two regions with distinct ecological characteristics (the Connecticut River Valley and the Berkshire Valley in Massachusetts), and two historical land uses (wooded versus cleared in 1830).
Regional Distribution and Abundance of Eastern Hemlock in Eastern North America 2010
We developed comprehensive maps on the distribution and abundance of hemlock for the purposes of mapping the host distribution, and modeling the spread, of the hemlock woolly adelgid. Multiple statistical models were used to map the distribution of hemlock. Hemlock occurrence data were taken from the FIA database and multiple environmental predictors were gathered from various databases as described in methods. The raster map depicts the predicted abundance of hemlock m2 basal area per hectare) across its range in eastern North America.
Habitatquarries: distribution of underground marl quarries in the Flemish Region and border areas, with the Flemish distribution of Natura 2000 habitat type 8310
<p><strong>General</strong></p> <p>The data source is a geospatial collection of polygons that correspond with the presence or absence of the Natura 2000 Annex I habitat type 8310 (Caves not open to the public) in the Flemish Region (and border areas), Belgium. </p> <p>The dataset contains all known, not collapsed, underground marl quarries in Flanders. Several of these quarries have their entrance in or run underground to the neighboring regions/countries.</p> <p>In general, different polygons represent different quarry units with their own internal climatic environment. Units that cross Flemish borders have been split into separate polygons. Exceptionally they may overlap if such units are situated above each other. </p> <p>For safety reasons, the dataset only contains the contour of the quarries, and no details like floor plans or entrances. For admission to research the indoor climate, please contact the Quarries and Safety Department of the municipality of Riemst (<a href="https://www.riemst.be/nl/wonen/groeven">https://www.riemst.be/nl/wonen/groeven</a>; <a href="mailto:mike.lahaye@riemst.be">mike.lahaye@riemst.be</a>).</p> <p>The data source is produced, owned and administered by the Research Institute for Nature and Forest (INBO, a scientific institute of the Flemish government).</p> <p> </p> <p><strong>Technical aspects</strong></p> <p>The data source is a GeoPackage that contains:</p> <ul> <li> <p>a spatial polygon layer ‘<code>habitatquarries</code>’ in the Belgian Lambert 72 coordinate reference system (EPSG-code <a href="https://epsg.io/31370">31370</a>);</p> </li> <li> <p>a non-spatial table ‘<code>extra_references</code>’ with site-specific bibliographic references.</p> </li> </ul> <p>The data source has been based on an unpublished shapefile used in De Saeger & Lahaye (2019) and on a BibTeX bibliography file. See R-code in the GitHub repository <a href="https://github.com/inbo/n2khab-preprocessing/tree/c0821eb/src/generate_habitatquarries">'n2khab-preprocessing' at commit c0821eb</a> for the creation.</p> <p>A reading function to return <code>habitatquarries</code> (this data source) in a standardized way into the R environment is provided by the R-package <a href="https://inbo.github.io/n2khab/">n2khab</a>.</p> <p>The attributes of the spatial polygon layer ‘<code>habitatquarries</code>’ are: </p> <ul> <li> <p><code>polygon_id</code>: a unique number per polygon; </p> </li> <li> <p><code>unit_id</code>: a unique number for each quarry unit. Quarry units consisting of several polygons (= partly outside the Flemish region) have a number greater than 100;</p> </li> <li> <p><code>name</code>: name of the site;</p> </li> <li> <p><code>habitattype</code>: either:</p> <ul> <li> <p><code>8310</code> (habitat type 8310)</p> </li> <li> <p><code>gh</code> (no Natura 2000 type)</p> </li> <li> <p>missing (outside of the Flemish Region);</p> </li> </ul> </li> <li> <p><code>extra_reference</code>: extra reference with more information.</p> </li> </ul> <p>The non-spatial table <code>extra_references</code> provides the bibliography referred to by the spatial attribute <code>extra_reference</code>. It was derived from a BibTeX bibliography file by using the R-package <a href="https://docs.ropensci.org/bib2df">bib2df</a>, and it is back-convertible into one (see R-package <a href="https://inbo.github.io/n2khab/">n2khab</a>). The original bibliography file is also available in the above linked ‘n2khab-preprocessing’ repository.</p>
Geosci. Model Dev. paper data for Flipo et al., "Regional coupled surface-subsurface hydrological model fitting based on a spatially distributed minimalist reduction of frequency-domain discharge data"
<p>Data and associated user guide, as part of the paper :</p> <p>Flipo N., Gallois N., Schuite J. Regional coupled surface-subsurface hydrological model fitting based on a spatially distributed minimalist reduction of frequency-domain discharge data, Geoscientific Model Development.</p> <p>In consistency with the “Code and data availability” sub-section of the paper, all data necessary for the reproduction of<br> Figs. 7, 8c, 8d, 9, 10 and 11 are here provided.</p>
Changes in above- versus belowground biomass distribution in permafrost regions in response to climate warming
<p>Permafrost regions contain approximately half of the carbon stored in land ecosystems and have warmed at least twice as much as any other biome. This warming has influenced vegetation activity, leading to changes in plant composition, physiology, and biomass storage in aboveground and belowground components, ultimately impacting ecosystem carbon balance. Yet, little is known about the causes and magnitude of long-term changes in the above- to belowground biomass ratio of plants (η). Here, we analyzed η values based on 3,013 plots and 26,337 plant-specific measurements representing eight sites across the Tibetan Plateau from 1995 to 2021. Our analysis revealed distinct temporal trends in η for three vegetation types: a 17% increase in alpine wetlands, and a decrease of 26% and 48% in alpine meadows and alpine steppes, respectively. These trends were primarily driven by temperature-induced growth preferences rather than shifts in plant species composition. Our findings indicate that in wetter ecosystems climate warming promotes aboveground plant growth, while in drier ecosystems, such as alpine meadows and alpine steppes, plants allocate more biomass belowground. Four process-based biogeochemical models failed to simulate the observed changes in η, which highlights the importance of improved process understanding of the processes driving the response of biomass distribution to climate warming, which is crucial for predicting the future carbon trajectory of permafrost ecosystems.</p>
The impact of 11 May 2024 super geomagnetic storm on the plasma distribution over the Indian equatorial/low latitude ionospheric region
<p>The file contains the data set and the software for the generation the plots used in the manuscript " The impact of 11 May 2024 super geomagnetic storm on the plasma distribution over the Indian equatorial/low latitude ionospheric region".</p>
Data from: Seagrasses in coastal wetlands of the Algarve region (southern Portugal): past and present distribution and extent
<p>These datasets support the scientific article "Seagrasses in coastal wetlands of the Algarve region (southern Portugal): past and present distribution and area extent" published in 2025 (Journal of Sea Research, 205, 102580; <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.seares.2025.102580" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.seares.2025.102580</a>). It contains detailed data on the distribution and area extent of intertidal and subtidal seagrass meadows in the four main wetlands of the Algarve region (Southern Portugal): Ria de Alvor, Arade Estuary, Ria Formosa, Guadiana Estuary.</p> <p>The data is composed by 5 datasets with the following variables:</p> <p><strong>1) data_field_points.csv</strong></p> <p>Contains data points based on field surveys.</p> <ul> <li>dataset_id [character] - Unique identifier for the data set.</li> <li>data_id [character] - Unique identifier for the data point.</li> <li>wetland [character] - Wetland full name, in Portuguese: Ria de Alvor, Estuário do Arade, Ria Formosa, Estuário do Guadiana.</li> <li>wetland_slug [character] - Short name of the wetland for coding purposes: alvor, arade, riaformosa, guadiana.</li> <li>quadrat_id [character] - Name of the quadrat as recorded in the field.</li> <li>photo_id [character] - Name of the pictured associated to the observation.</li> <li>sampling_id - Name of the observation as recorded in the field.</li> <li>date [date] - Date of observation (YYYY-MM-DD).</li> <li>year [integer] - Year of sample collection (YYYY).</li> <li>month [integer] - Month of sample collection (MM).</li> <li>latitude [numeric] - The geographic latitude (in decimal degrees, WGS84) of data point.</li> <li>longitude [numeric] - The geographic longitude (in decimal degrees, WGS84) of data point.</li> <li>habitat_class [factor] - Type of habitat: unvegetated, seagrass intertidal, seagrass subtidal, seagrass unknown, salt marsh low, caulerpa.</li> <li>species [factor] - Vegetation species: No vegetation, <em>Zostera noltei</em>, <em>Zostera marina</em>, <em>Cymodocea nodosa</em>, unspecified species, <em>Caulerpa prolifera</em>, <em>Sporobolus maritimus</em>.</li> <li>notes [character] - Any relevant notes on the data compilation.</li> <li>method [factor] - Method used for the observation: boat and camera, boat and snorkelling, kayak and camera, on foot.</li> <li>survey_area [character] - Number of the survey area.</li> <li>site [character] - Name of the site.</li> <li>observers [character] - Name of the researcher(s) who collected the data.</li> </ul> <p> </p> <p><strong>2) data_compilation_records.csv</strong></p> <p>Contains information on the records (i.e. sources) screened during the systematic review for the compilation od seagrass occurrence data.</p> <ul> <li>record_id [character] - unique id for the compiled records.</li> <li>short_citation [character] - short citation of the record, with author and publication year.</li> <li>included [boolean] - whereas the record was used to extract data or informacion.</li> <li>record_type [factor] - type of record: journal article, book or book chapter, PhD or MSc thesis, report, others.</li> <li>publication_year [integer] - year of the publication of the record, YYYY.</li> <li>title_record [character] - title of the record.</li> <li>link [character] - link to access the record, if available (DOI, handle, others URLs).</li> <li>full_citation [character] - full citation of the record, with authors, publication year, title, etc.</li> </ul> <p> </p> <p><strong>3) data_compilation_points_raw.csv</strong></p> <p>Contains data points of seagrass occurrence based on the systematic review. This is the original raw file with all the compiled points.</p> <ul> <li>data_id [character] - Unique identifier for the data point (same as used in data_compilation_clean.csv).</li> <li>included [boolean] - whether the data point was kept in the clean dataset or not: 1, the point has been validated and it is included in the final dataset; 0, the point is excluded due to unprecise location (on land, open ocean, etc.).</li> <li>reason_exclusion [character] - Reason to exclude the data point from the clean dataset.</li> <li>record_id [character] - unique id for the compiled record from where data was extracted (same as in data_compilation_records.csv).</li> <li>short_citation [character] - Short reference (author(s) and year) (same as in data_compilation_records.csv).</li> <li>wetland [character] - Wetland full name, in Portuguese: Ria de Alvor, Estuário do Arade, Ria Formosa, Estuário do Guadiana.</li> <li>wetland_slug [character] - Short name of the wetland for coding purposes: alvor, arade, riaformosa, guadiana.</li> <li>latitude [numeric] - The geographic latitude (in decimal degrees, WGS84) of data point.</li> <li>longitude [numeric] - The geographic longitude (in decimal degrees, WGS84) of data point.</li> <li>year [integer] - Year of sample collection (YYYY).</li> <li>month [integer] - Month of sample collection (MM).</li> <li>year_precision [character] - Precision of the year registred: exact, after, before, or aproximately.</li> <li>habitat_class [factor] - Type of seagrass habitat: seagrass intertidal, seagrass subtidal, or seagrass unknown.</li> <li>species [factor] - Dominant seagrass species: <em>Zostera noltei</em>, <em>Zostera marina</em>, <em>Cymodocea nodosa</em>, unspecified.</li> <li>collection_code [character] - The name identifying the data set or collection from which the record was derived.</li> <li>catalogue_number [character] - An identifier for the record within the data set or collection.</li> <li>original_id [character] - An identifier given to the occurrence at the time it was recorded (specimen collector's number or site collection).</li> <li>duplicated [boolean] - whether the data point was flagged as duplicated or not.</li> </ul> <p> </p> <p><strong>4) data_compilation_points_clean.csv</strong></p> <p>Contains data points of seagrass occurrence based on the systematic review. This is the clean file after elimitating duplicates and points with unprobable or unprecise location.</p> <ul> <li>data_id [character] - Unique identifier for the data point (same as used in data_compilation_raw.csv).</li> <li>record_id [character] - unique id for the compiled record from where data was extracted (same as in data_compilation_records.csv).</li> <li>short_citation [character] - Short reference (author(s) and year) (same as in data_compilation_records.csv).</li> <li>wetland [character] - Wetland full name, in Portuguese: Ria de Alvor, Estuário do Arade, Ria Formosa, Estuário do Guadiana.</li> <li>wetland_slug [character] - Short name of the wetland for coding purposes: alvor, arade, riaformosa, guadiana.</li> <li>latitude [numeric] - The geographic latitude (in decimal degrees, WGS84) of data point.</li> <li>longitude [numeric] - The geographic longitude (in decimal degrees, WGS84) of data point.</li> <li>year [integer] - Year of sample collection (YYYY).</li> <li>month [integer] - Month of sample collection (MM).</li> <li>year_precision [character] - Precision of the year registred: exact, after, before, or aproximately.</li> <li>habitat_class [factor] - Type of seagrass habitat: seagrass intertidal, seagrass subtidal, or seagrass unknown.</li> <li>species [factor] - Dominant seagrass species: <em>Zostera noltei</em>, <em>Zostera marina</em>, <em>Cymodocea nodosa</em>, unspecified.</li> <li>collection_code [character] - The name identifying the data set or collection from which the record was derived.</li> <li>catalogue_number [character] - An identifier for the record within the data set or collection.</li> <li>original_id [character] - An identifier given to the occurrence at the time it was recorded (specimen collector's number or site collection).</li> </ul> <p> </p> <p><strong>5) data_compilation_extent.csv</strong></p> <p>Contains area extent data of seagrass meadows based on the systematic review.</p> <ul> <li>data_id [character] - Unique identifier for the data.</li> <li>record_id [character] - unique id for the compiled record from where data was extracted.</li> <li>short_citation [character] - Short reference (author(s) and year).</li> <li>wetland [character] - Wetland full name, in Portuguese: Ria de Alvor, Estuário do Arade, Ria Formosa, Estuário do Guadiana.</li> <li>wetland_slug [character] - Short name of the wetland for coding purposes: alvor, arade, riaformosa, guadiana.</li> <li>value [boolean] - whether the data extracted from the record is a extent value (i.e., a value of area covered by seagrasses).</li> <li>polygon [boolean] - whether the data extracted from the record is a polygon.</li> <li>year [integer] - Year of sample collection (YYYY).</li> <li>month [integer] - Month of sample collection (MM).</li> <li>year_precision [character] - Precision of the year registred: exact, after, before, or aproximately.</li> <li>habitat_class [factor] - Type of seagrass habitat: seagrass intertidal, seagrass subtidal, or seagrass unknown.</li> <li>species [factor] - Dominant seagrass species: <em>Zostera noltei</em>, <em>Zostera marina</em>, <em>Cymodocea nodosa</em>, unspecified.</li> <li>area_source [numeric] - The area extent given in the record.</li> <li>area_source_cover [factor] - The cover of the wetland for the compiled extent from the record means: total, partial or unknown.</li> <li>area_gis [numeric] - The area extent obtained using GIS.</li> <li>area_gis_cover [factor] - The cover of the wetland for the obtained extent from GIS means: total, partial or unknown.</li> <li>notes [character] - Any relevant notes on the data compilation.</li> </ul>
Size fractionation for total Chl a within the surface layer and calculated size distribution of total Chl a from discrete bottle samples collected during CCE LTER process cruises in the CCE region, 2006 - 2024 (ongoing).
Water for size fractionation of chlorophyll a is sampled from ~10m depth (surface layer) in the CCE study area. The size distribution of total chlorophyll a is determined by filtering water though filters of differing pore sizes. These are then extracted in acetone and analyzed fluorometrically with Turner Designs 10-AU Fluorometer on CCE Process cruises (since 2006, ongoing). Chlorophyll a and taxon-specific pigments (chlorophylls and carotenoids) are qualitatively and quantitatively characterized in the lab onshore by several size fractions (< 1µm to > 20µm) utilizing High Performance Liquid Chromatography (HPLC) analysis. The samples analyzed within the CCE region are used to develop a metric for phytoplankton community structure that can be used to monitor its state and changes thereof over time.
Maharashtra, region of ancient Vidarbha show distribution of known archaeological sites and coin finds.
<p>Maharashtra, region of ancient Vidarbha show distribution of known archaeological sites and coin finds.</p>
Maharashtra, region of ancient Vidarbha show distribution of key copper-plate charters of the Vakataka period.
<p>Maharashtra, region of ancient Vidarbha show distribution of key copper-plate charters of the Vakataka period.</p> <p> </p>
Figure 4 in Inter-oceanic comparison of planktonic copepod ecology (vertical distribution, abundance, community structure, population structure and body size) between the Okhotsk Sea and Oyashio region in autumn
Figure 4. Copepod species composition (centre) and copepodid stage structures of the dominant species (left: Oyashio region, right: Okhotsk Sea). All data are integrated means of a 0– 500 m water column based on the IONESS samples in the Oyashio region (St. 19) and Okhotsk Sea (St. OK24) from October to November 1996. Error bars for the copepodid stage indicate standard deviations of each daily duplicate.
Figure 3 in Inter-oceanic comparison of planktonic copepod ecology (vertical distribution, abundance, community structure, population structure and body size) between the Okhotsk Sea and Oyashio region in autumn
Figure 3. Vertical distribution of zooplankton biovolume in the Oyashio region (upper panels) and Okhotsk Sea (lower panels) from September to December in 1996–1998. Note that the biovolume axes are not the same between panels. Tc: thermocline.
Fig. 9 in Distribution, systematics and nomenclature of the three taxa of Common Stonechats (Aves, Passeriformes, Muscicapidae, Saxicola) that breed in the Caucasian region
Fig. 9. Main types of variations of tail colouration of males in Saxicola maura variegatus. A, 5 to 10 mm; B, 11 to 20 mm; C, 21 mm and more.
Fig. 8 in Distribution, systematics and nomenclature of the three taxa of Common Stonechats (Aves, Passeriformes, Muscicapidae, Saxicola) that breed in the Caucasian region
Fig. 8. Adult males in breeding plumage, ventral (A–C) and dorsal (D–E) view. A, D, Saxicola maurus armenicus, coll. No. 28292/109, Azerbaijan, Nakhichevan Autonomous Republic, Dzul'fa Distr., NW slopes of Ilan-Dag Mt., 39°08.78′ N, 45°40.47′ E, 1,160 m a.s.l., 12 June 1974, Yu.A. Volnenko leg. (NMNH); B, E, Saxicola maurus variegatus, coll. No. 28005/106, Azerbaijan, Ismailli Distr., vicinity of Ismailly, 40°46.99′ N, 48°06.73′ E, 540 m a.s.l., 2 July 1973, V.M. Loskot leg. (NMNH); C, F, S. m. variegatus, coll. No. 173387/208-2002, Russia, Rostov Prov., Don River Delta, floodplain at mouth of Aksay River, Starodon'e Lake, 47°17.00′ N, 40°15.10′ E, 1 m a.s.l., 2 May 1997, G.B. Bakhtadze leg. (ZIN).
Fig. 7 in Distribution, systematics and nomenclature of the three taxa of Common Stonechats (Aves, Passeriformes, Muscicapidae, Saxicola) that breed in the Caucasian region
Fig. 7. Subadult females in spring plumage, ventral (A, B) and dorsal (C, D) view. A, C, Saxicola maurus armenicus, coll. No. 136211, Iraq, Wasit Governorate, Bagsaya ruins, 32°53.73′ N, 46°27.60′ E, 95 m a.s.l., 17 March 1914, P.V. Nesterov leg. (ZIN); B, D, Saxicola maurus variegatus, coll. No. 136212, the same locality, collector and collection, 16 March 1914.
Fig. 4 in Distribution, systematics and nomenclature of the three taxa of Common Stonechats (Aves, Passeriformes, Muscicapidae, Saxicola) that breed in the Caucasian region
Fig. 4. First year male of Saxicola rubicola rubicola in fresh autumnal plumage. Holotype of Saxicola torquata amaliae Buturlin, 1929. ZMMU, coll. No. R-13488, Russia, Republic Severnaya Osetiya – Alaniya, vicinity of Vladikavkaz, 42°59.99′ N, 44°38.53′ E, 750 m a.s.l., 13 Oct. 1919, L.B. Beme leg. Ventral (A) and dorsal (B) view.
Fig. 5 in Distribution, systematics and nomenclature of the three taxa of Common Stonechats (Aves, Passeriformes, Muscicapidae, Saxicola) that breed in the Caucasian region
Fig. 5. Females in fresh autumnal plumage, ventral (A, B) and dorsal (C, D) view. A, C, Saxicola maurus armenicus, coll. No. 136219, Iran, West Azerbaijan Prov., Vezne River valley, 36°34.51′ N, 45°10.80′ E, 1,400 m a.s.l., 18 July 1914, ad., P.V. Nesterov leg. (ZIN); B, D, Saxicola maurus variegatus, coll. No. R-97651, Russia, Krasnodar Terr., vicinity of Krasnodar, 45°16.60′ N, 38°05.39′ E, 3 m a.s.l., 10 Aug. 1973, 1-st year, A.M. Peklo leg. (ZMMU).
Fig. 3 in Distribution, systematics and nomenclature of the three taxa of Common Stonechats (Aves, Passeriformes, Muscicapidae, Saxicola) that breed in the Caucasian region
Fig. 3. Males in fresh autumnal plumage, ventral (A–C) and dorsal (D–F) view. A, D, Saxicola maurus armenicus, coll. No. 136248, Iran, West Azerbaijan Prov., Vezne River valley, 36°34.51′ N, 45°10.80′ E, 1,400 m a.s.l., 22 July 1914, ad., P.V. Nesterov leg. (ZIN); B–F, Saxicola maurus variegatus, Georgia, Kakhetiya, vicinity of Lagodekhi, 41°48.28′ N, 46°16.56′ E, 380 m a.s.l., L.A. Portenko leg. (ZIN): coll. No. 163316/425- 974, 19 Sept. 1953, ad. (B, E) and coll. No. 163315/425-974, 21 Sept. 1953, 1-st year (C, F).
Fig. 2 in Distribution, systematics and nomenclature of the three taxa of Common Stonechats (Aves, Passeriformes, Muscicapidae, Saxicola) that breed in the Caucasian region
Fig. 2. Birds in nesting plumage. A, Saxicola maurus armenicus, coll. No. 28126/108, Azerbaijan, Nakhichevan Autonomous Republic, Dzul'fa Distr., NW slopes of Ilan-Dag Mt., 39°08.78′ N, 45°40.47′ E, 1,160 m a.s.l., 12 June 1974, female, Yu.A. Volnenko leg. (NMNH); B, Saxicola maurus variegatus, coll. No. 137879, Russia, Kabardino-Balkar Republic, Prokhladnyy (= Prokhladnaya Vill.), 43°45.11′ N, 44°05.79′ E, 180 m a.s.l., 2 July 1883, male, K.N. Rossikov leg. (ZIN).
Figure 3 in On the distribution and taxonomy of bats of the Myotis mystacinus morphogroup from the Caucasus region (Chiroptera: Vespertilionidae)
Figure 3. Bivariate plot of the examined samples of the Myotis mystacinus morphogroup from the Caucasus region: tibia length (LTib) against the thumb length (LPol). For explanations see Figure 2.
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.