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277 results for “regional scale”
One-hectare fine-scale dataset of a fynbos plant community in the Cape Floristic Region
<p>Cape fynbos, which forms part of the Cape Floristic Region (CFR) of South Africa, a global biodiversity hotspot, is renowned for its high levels of plant species endemism and diversity. This extraordinary ecosystem, characterised by nutrient-poor soils and fire-adapted vegetation, is a treasure trove of endemic flora. However, this fragile system faces increasing threats from habitat loss, climate change, and invasive species. Pristine fynbos, naturally high in plant diversity and which forms a large part of the CFR, presents an ideal opportunity to gather fine-scale data on community assembly patterns. Most fynbos vegetation surveys use a plot size of about 100 m2, with no spatial structures within plots to demarcate individual subplots. Here, a groundbreaking dataset is presented that fully covers 1-hectare of pristine fynbos, systematically gridded into 50 × 50 subplots, each measuring 2 × 2 m, arranged evenly within a square-shaped survey site. Each plot was assigned a unique Y–X coordinate combination. For each plot, all plant species present were recorded, along with their total percentage covers and maximum height values. Total percentage covers were also recorded for bare soil, rock, and termite mounds. This dataset provides a valuable contribution to the field of fynbos ecology, as well as plant community ecology in general, and establishes a benchmark for future one-hectare surveys of similar fynbos vegetation types, delineating the fine-scale composition and structure of fynbos in the CFR. The dataset will be useful for a wide audience, including community and spatial ecologists, plant and environmental scientists, and biodiversity informaticians and statistical ecologists, offering ideal data for testing new metrics of diversity and compositional turnover. Data in Brief, Volume 59, April 2025, 111334: <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.dib.2025.111334" target="_blank" rel="noreferrer noopener"><span><span>https://doi.org/10.1016/j.dib.2025.111334</span></span></a></p>
The soil province geodatabase of Italy, storing information of soil typological units and broad soil regions at the 1:1,000,000 and 1:10,000,000 scales
<p>The Soil Map of Italy at 1:1,000,000 scale, was the result of the work of Edoardo AC Costantini, Giovanni L'Abate, Roberto Barbetti, Maria Fantappié, Romina Lorenzetti, and Simona Magini affiliated to Research Centre for agrobiology and soil science (CREA-ABP), in collaboration with several regional institutions, universities and other research centers of the CREA - Consiglio per la ricerca in agricoltura e l'analisi dell'economia agraria. The map, was printed by S.EL.CA. of Florence. The map is an informative and educational work of general scientific interest, which updates the previous one edited by prof. Fiorenzo Mancini and collaborators in 1966 both in terms of knowledge and of the adopted methods. It was produced processing of all data within a geographical and soil geodatabase, collected by the CREA-ABP and other institutions collaborating in over ten years of work and using the latest international methods. The soil map shows the distribution of major soils in the country and constitutes a milestone in the process launched in 1999 as part of the project the Soil Map of Italy at a scale of 1: 250,000, funded by MIPAAF and implemented in collaboration with the regional institutions. Both broad soil regions and soil provinces (reference scale 1:10,000,000 and 1:1,000,000) are reported.</p> <p>Most small-scale soil maps report dominant typological units and allow only a partial appraisal of pedodiversity since territories with similar dominant soils can actually possess different pedodiversity. This is particularly true at the national scale, where a great wealth of soil information collected at more detailed scales is generalized.</p> <p>A methodology was set up, which aimed at preserving pedodiversity in upscaling soil maps by using geomatic techniques and the World Reference Base for soil resources (WRB). The main source of information was the soil system geodatabase of Italy, storing information of soil typological units and soilscapes at the 1:500,000 reference scale. Qualitative aggregation of soil taxa followed upscaling rules aimed at (i) maintaining the information about pedogenetic processes and (ii) grouping soilscapes showing recurrent patterns of soil forming processes. The upscaling methodology can be summarized in seven steps as follows: (1) soil forming processes selection, retrieved from soil typological units stored in the national database; (2) upscaling soil systems and creation of broad soil regions at 1:10,000,000 reference scale; (3) semantic upscaling of typological units to form taxa showing different soil forming processes; (4) ranking and associating soil forming processes; (5) geography upscaling of soil systems geometry to form polygons at 1:1,000,000 reference scale, called subregions; (6) ranking subregions according to their extension; (7) naming subregions by ranking the taxa according to the number of soil typological units.</p> <p>The soil subregion map reported 47 map unit and 148 taxa, belonging to 22 reference soil group of WRB and showing from one to four qualifiers. Each map unit had from 2 to 18 taxa, for a total of 317 occurrences. Thirty taxa had 3 or more occurrences, while the remaining took place in one or two subregions only.</p>
Local Governance in Ukraine during the full-scale Russian invasion. – Merged data from online surveys of local self-government authorities by the Congress of Local and Regional Authorities of the Council of Europe in 2022 and Kyiv School of Economics in 2024.
The dataset includes responses from two waves of online surveys targeting local self-government representatives in Ukraine, with a focus on crisis governance during the ongoing Russian war. The first wave was conducted from August 30 to September 20, 2022, by the Congress of Local and Regional Authorities of the Council of Europe, yielding 241 responses (16% of all Ukrainian local communities). The second wave was conducted by Kyiv School of Economics from January 1 to March 12, 2024, with 181 responses (14% of government-controlled municipalities). Data formats include CSV and SAV files, along with an XSL codebook for both waves. The merged dataset comprises 442 responses from small, medium, and large municipalities under varied security conditions, with a total file size of approximately 4 MB.
Regional scale surface of the top of the Variscan basement in some sector of Italy - Supplementary material
<p>The dataset represent the Supplementary material of thew manuscript entitled "Map of the top of the Variscan basement in some sectors of Italy" now under revision.</p> <p>The Supplementary material consist of 9 files:</p> <ul> <li>input data: <ul> <li>dataset_CROP.csv</li> <li>deep_wells.csv</li> <li>domains.geojson</li> <li>thrusts_2.geojson</li> <li>INA_data_point.csv</li> </ul> </li> <li>output data: <ul> <li>INA_depth_1km.csv</li> <li>ONA_OA_ISA_AF_depth_5km.csv</li> <li>INA_contour.geojson</li> <li>ONA_OA_ISA_AF_contour.geojson</li> </ul> </li> </ul>
Lightning Potential Index Using ICON Simulation at the km-scale over the Third Pole Region: ISS-LIS events and ICON-CLM simulated LPI
<p>This dataset contains records of lightning events recorded by the International Space Station (ISS) Lightning Imaging Sensor (LIS) from October 2019 to September 2022 in the Third Pole region. Furthermore, the Icosahedral Nonhydrostatic Weather and Climate Model in Climate Limited-Area Mode (ICON-CLM) was utilized to simulate the hourly Lightning Potential Index (LPI) over the Third Pole region for the same duration. The aforementioned dataset was utilized in the creation of the research article titled "Modeling Lightning Activity in the Third Pole Region: Performance of a km-scale ICON-CLM Simulation" authored by Prashant Singh and Bodo Ahrens. The paper has been submitted to the journal Atmosphere. In CORDEX-FPS-CPTP contribution no. 17 (GUF), you can find more data from ICON-CLM, such as precipitation, CAPE, wind vectors, and more.</p>
Regional scale shear wave velocity profiles for ground response analyses and uncertainties evaluations – the Piedmont Region (NW Italy) Database
<p>The databases provide detailed information for the Piedmont region in Northwest Italy, offering a view of its geological and geophysical characteristics:</p> <ul> <li><strong>Geological-Geomorphological Database</strong>: Includes 13 distinct Geological-Geomorphological Domains (GGD) in shapefile format. It supports spatial analysis and visualization, based on data from the Geological Map of the Piedmont Region at a 1:250,000 scale.</li> <li><strong>Geotechnical Database</strong>: Contains geotechnical data on bedrock depth and texture attributes derived from available logs in CSV format. Georeferenced using UTM coordinates (WGS84 UTM32N), it includes depth values and texture codes (C for clay, G for gravel, S for sand, R for rock, X for not available).</li> <li><strong>Geophysical Database</strong>: Provides data on shear wave velocity (Vs) profiles in CSV format. Georeferenced with UTM coordinates (WGS84 UTM32N), it includes layer interface depth and shear wave velocity above each layer.</li> </ul>
Data supporting manuscript "Regional scaling of sea surface temperature with global warming levels in the CMIP6 ensemble"
<p>Data supporting the results presented in the article Milovac et al: "Regional scaling of sea surface temperature with global warming levels in the CMIP6 ensemble".</p> <p>1. data_raw.tar contains annual and seasonal, global and regional (i.e. over ocean IPCC regions and ocean biomes), mean sea surface and near surface temperatures, calculated for the selected 26 CMIP6 global climate models (GCMs) at low resolution (listed in the file models_low_res.txt) and 1 GCM at high resolution (listed in the file models_high_res.txt). The original files, downloaded from one of the ESGF data centers, were all interpolated onto a common grid with the 1-degree resolution for low-resolution output and the 0.25-degree resolution for high-resolution output. The output was generated using the cdo tool (<a href="https://zenodo.org/record/7112925">https://zenodo.org/record/7112925</a>).</p> <p>2. data_txt.tar contains the results used to obtain all the figures given in the article.</p>
Fig. 1 in New scale insects (Homoptera: Coccinea) from the Cape Floristic Region
Fig. 1. South Africa, Western Cape Prov., banks of the Lower Palmiet River, the type locality of the new species.
Millimeter- to Decimeter-Scale Surface Slope and Roughness of the Moon at the Chang'e-4 Exploration Region
<p>The datasets related to the work of<em> Millimeter- to Decimeter-Scale Surface Slope and Roughness of the Moon at the Chang'e-4 Exploration Region.</em></p> <p>Cite the following references if using the DEM data. Wu, B., Li, Y., Liu, W. C., Wang, Y., Li, F., Zhao, Y., and Zhang, H. (2021), Centimeter-resolution topographic modeling and fine-scale analysis of craters and rocks at the Chang’E-4 landing site, Earth Planet. Sci. Lett., 553, 116666. <a href="https://doi.org/10.1016/j.epsl.2020.116666">https://doi.org/10.1016/j.epsl.2020.116666</a></p> <p>Guo, D., Fa, W., Wu, B., Li, Y., & Liu, Y. (2021). Millimeter- to Decimeter-Scale Surface Slope and Roughness of the Moon at the Chang’e-4 Exploration Region. <em>Geophysical Research Letters</em>, 48, e2021GL094931. <a href="https://doi.org/10.1029/2021GL094931">https://doi.org/10.1029/2021GL094931</a></p>
Data from: harnessing the power of regional baselines for broad-scale genetic stock identification: a multistage, integrated, and cost-effective approach
<p>In mixed-stock fishery analyses, genetic stock identification (GSI) estimates the contribution of each population to a mixture and is typically conducted at a regional scale using genetic baselines specific to the stocks expected in that region. Often these regional baselines cannot be combined to produce broader geographical baselines due to non-overlapping populations and genetic markers. In cases where the mixture contains stocks spanning across a wide area, a broad-scale baseline is created, but often at the cost of resolution. Here, we introduce a new GSI method to harness the resolution capabilities of baselines developed for regional applications in the analysis of mixtures containing individuals from a broad geographic range. This method employs a multistage framework that allows disparate baselines to be used in a single integrated process that produces estimates along with the propagated errors from each stage. All individuals in the mixture sample are required to be genotyped for all genetic markers in the baselines used by this model, but the baselines do not require overlap in genetic markers or populations representing the broad-scale or regional baselines.</p> <p>We demonstrate our integrated multistage GSI model using a synthesized data set made up of Chinook salmon, <em>Oncorhynchus tshawytscha</em>, from the North Bering Sea of Alaska. The data set is designed to be run using R package, Ms.GSI, and it does not represent the composition of the real fishery. The results show an improved accuracy for estimates using an integrated multistage framework, compared to the conventional framework of using separate hierarchical steps. The integrated multistage framework allows GSI of a wide geographic area without first developing a large scale, high-resolution genetic baseline or dividing a mixture sample into smaller regions beforehand. This approach is more cost-effective than updating range-wide baselines with all regionally important markers.</p>
Fig. 5 in Nestedness of stream insects in Subtropical region: importance of inter-annual temporal scale
Fig. 5. NODF nestedness of Chironomidae (Diptera) assemblages in streams of southern Brazil in the summer and winter of 2010, 2011 and 2012. (A) Each line represents a stream independent of intra- and inter-annual factor. (B, C, D) Dotted lines represent winter data and continuous lines represent summer data. In these graphs, the individual information for each stream was grouped to assess intra-annual nestedness.
Fig. 4 in Nestedness of stream insects in Subtropical region: importance of inter-annual temporal scale
Fig. 4. Boxplot of water temperature, dissolved organic carbon (DOC), dissolved oxygen (DO), dissolved total nitrogen (DTN) and precipitation in the summer and winter of 2010, 2011 and 2012 in streams of southern Brazil (only variables with significant differences for each year).
Fig. 2 in Nestedness of stream insects in Subtropical region: importance of inter-annual temporal scale
Fig. 2. Abundance and richness of Chironomidae (Diptera) in streams of southern Brazil in the summer and winter of 2010, 2011 and 2012.
Fig. 1 in Nestedness of stream insects in Subtropical region: importance of inter-annual temporal scale
Fig. 1. Location of sampling sites in southern Brazil (F, Faxinalzinho city; E, Erechim; MR, Marcelino Ramos; TA, TrÊs Arroios).
Fig. 3 in Nestedness of stream insects in Subtropical region: importance of inter-annual temporal scale
Fig. 3. Boxplot of dissolved organic carbon (DOC), dissolved oxygen (DO), dissolved total nitrogen (DTN) and precipitation in streams of southern Brazil in the summer and winter of 2010, 2011 and 2012.
РИС. 2. Места нахождениЯ Amuranodonta kijaensis на территории Хинганского Заповедника, АмурскаЯ обл.: А. Схема расположениЯ лесничеств: 1 – Антоновское, 2 – Лебединское, 3 – Хинганское. B. ТопографическаЯ карта Антоновского вдхр. у пос. Архара. С, D. Топографические карты и спутниковый снимок оЗ. Яценково на территории Антоновского лесничества. E–G. ТопографическаЯ карта и спутниковый снимок оЗ. ПереШеечное на территории Лебединского лесничества. МасШтабные линейки: 20 км (А), 4 км (В, Е), 5 км (С), 1 км (D, G), 2 км (F). FIG. 2. Localities of Amuranodonta kijaensis in the Khingansky Reserve, Amur Region: A. Layout of forestry areas: 1 – Antonovsky, 2 – Lebedinsky, 3 – Khingansky. B. Topographic map of Antonovskoe Reservoir near Arkhara village. C, D. Topographic maps and satellite image of Yatsenkovo lake, Antonovsky forestry. E–G. Topographic map and satellite image of Peresheechnoe lake, Lebedinsky forestry. Scale bars: 20 km (A), 4 km (B, E), 5 km (C), 1 km (D, G), 2 km (F). in Новые данные об охранЯемом пресноводном двустворчатом моллюске Amuranodonta kijaensis Moskvicheva, 1973 (Unionidae, Anodontinae)
РИС. 2. Места нахождениЯ Amuranodonta kijaensis на территории Хинганского Заповедника, АмурскаЯ обл.: А. Схема расположениЯ лесничеств: 1 – Антоновское, 2 – Лебединское, 3 – Хинганское. B. ТопографическаЯ карта Антоновского вдхр. у пос. Архара. С, D. Топографические карты и спутниковый снимок оЗ. Яценково на территории Антоновского лесничества. E–G. ТопографическаЯ карта и спутниковый снимок оЗ. ПереШеечное на территории Лебединского лесничества. МасШтабные линейки: 20 км (А), 4 км (В, Е), 5 км (С), 1 км (D, G), 2 км (F). FIG. 2. Localities of Amuranodonta kijaensis in the Khingansky Reserve, Amur Region: A. Layout of forestry areas: 1 – Antonovsky, 2 – Lebedinsky, 3 – Khingansky. B. Topographic map of Antonovskoe Reservoir near Arkhara village. C, D. Topographic maps and satellite image of Yatsenkovo lake, Antonovsky forestry. E–G. Topographic map and satellite image of Peresheechnoe lake, Lebedinsky forestry. Scale bars: 20 km (A), 4 km (B, E), 5 km (C), 1 km (D, G), 2 km (F).
РИС. 3. Место нахождениЯ Amuranodonta kijaensis в Хабаровском крае: А. Карта-схема краЯ. В. Приустьевый участок р. Амур. С, D. ТопографическаЯ карта и спутниковый снимок с. Чныррах с укаЗанием места сбора. МасШтабные линейки: 200 км (А), 16 км (B), 4 км (C) и 200 м (D). FIG. 3. Locality of Amuranodonta kijaensis in the Khabarovsk Territory: A. Scheme map of the region. B. Amur River mouth area. C, D. Topographic map and satellite image of Chnyrrakh village indicating the collection site. Scale bars: 200 km (А), 16 km (B), 4 km (C), and 200 m (D). in Новые данные об охранЯемом пресноводном двустворчатом моллюске Amuranodonta kijaensis Moskvicheva, 1973 (Unionidae, Anodontinae)
РИС. 3. Место нахождениЯ Amuranodonta kijaensis в Хабаровском крае: А. Карта-схема краЯ. В. Приустьевый участок р. Амур. С, D. ТопографическаЯ карта и спутниковый снимок с. Чныррах с укаЗанием места сбора. МасШтабные линейки: 200 км (А), 16 км (B), 4 км (C) и 200 м (D). FIG. 3. Locality of Amuranodonta kijaensis in the Khabarovsk Territory: A. Scheme map of the region. B. Amur River mouth area. C, D. Topographic map and satellite image of Chnyrrakh village indicating the collection site. Scale bars: 200 km (А), 16 km (B), 4 km (C), and 200 m (D).
РИС. 1. Место нахождениЯ Amuranodonta kijaensis в Зейском районе, АмурскаЯ обл.: А. Карта-схема области. B. Зейское вдхр. С, D. ТопографическаЯ карта и спутниковый снимок Залива в Западной части Зейского вдхр. у пос. Береговой с укаЗанием места сбора. МасШтабные линейки: 300 км (А), 50 км (В), 4 км (С) и 200 м (D). FIG. 1. Locality of Amuranodonta kijaensis in Zeya District, Amur Region: A. Schematic map of the region. B. Zeya Reservoir. C, D. Topographic map and satellite image of the bay in the western part of Zeya Reservoir near Beregovoi village indicating the collection site. Scale bars: 300 km (А), 50 km (В), 4 km (С), and 200 m (D). in Новые данные об охранЯемом пресноводном двустворчатом моллюске Amuranodonta kijaensis Moskvicheva, 1973 (Unionidae, Anodontinae)
РИС. 1. Место нахождениЯ Amuranodonta kijaensis в Зейском районе, АмурскаЯ обл.: А. Карта-схема области. B. Зейское вдхр. С, D. ТопографическаЯ карта и спутниковый снимок Залива в Западной части Зейского вдхр. у пос. Береговой с укаЗанием места сбора. МасШтабные линейки: 300 км (А), 50 км (В), 4 км (С) и 200 м (D). FIG. 1. Locality of Amuranodonta kijaensis in Zeya District, Amur Region: A. Schematic map of the region. B. Zeya Reservoir. C, D. Topographic map and satellite image of the bay in the western part of Zeya Reservoir near Beregovoi village indicating the collection site. Scale bars: 300 km (А), 50 km (В), 4 km (С), and 200 m (D).
РИС. 1. Раковины Digyrcidum и Bithynia иЗ водоемов Тюменской области: A. Digyrcidum bourguignati иЗ оЗ. Кривое, 30.07.2015. B. D. starobogatovi иЗ устьЯ р. Вагай, 06.07.2012. C. Bithynia curta иЗ оЗ. Каракундус, 09.07.2012. D. B. tentaculata иЗ старицы р. ИртыШ, 02.07.2005. E. B. decipiens иЗ оЗ. АрбаШ, 07.07.2012. F. B. producta иЗ р. Ирюм, 15.06.2012. МасШтаб: 1 мм. Фото Н.И. Андреева FIG. 1. Shells of Digyrcidum and Bithynia species of the Tyumen Region waterbodies. A. Digyrcidum bourguignati, Krivoye Lake, 30.07.2015.B. D. starobogatovi, the Vagai River mouth, 06.07.2012. C. Bithynia curta, Karakundus Lake, 09.07.2012. D. B. tentaculata, an oxbow of the Irtysh River, 02.07.2005. E. B. decipiens, Arbash Lake, 07.07.2012. F. B. producta, Iryum River, 15.06.2012. Scale bars: 1 mm. Photos: N.I. Andreyev. in Моллюски семейства Bithyniidae (Mollusca, Gastropoda) Тюменской области
РИС. 1. Раковины Digyrcidum и Bithynia иЗ водоемов Тюменской области: A. Digyrcidum bourguignati иЗ оЗ. Кривое, 30.07.2015. B. D. starobogatovi иЗ устьЯ р. Вагай, 06.07.2012. C. Bithynia curta иЗ оЗ. Каракундус, 09.07.2012. D. B. tentaculata иЗ старицы р. ИртыШ, 02.07.2005. E. B. decipiens иЗ оЗ. АрбаШ, 07.07.2012. F. B. producta иЗ р. Ирюм, 15.06.2012. МасШтаб: 1 мм. Фото Н.И. Андреева FIG. 1. Shells of Digyrcidum and Bithynia species of the Tyumen Region waterbodies. A. Digyrcidum bourguignati, Krivoye Lake, 30.07.2015.B. D. starobogatovi, the Vagai River mouth, 06.07.2012. C. Bithynia curta, Karakundus Lake, 09.07.2012. D. B. tentaculata, an oxbow of the Irtysh River, 02.07.2005. E. B. decipiens, Arbash Lake, 07.07.2012. F. B. producta, Iryum River, 15.06.2012. Scale bars: 1 mm. Photos: N.I. Andreyev.
РИС. 3. Пенисы моллюсков сем. Bithyniidae иЗ водоемов Тюменской области. A. Bithynia tentaculata, B. B. decipiens, C. B. curta, D. B. producta, E. Boreoelona contortrix, F.Opisthorchophorus troscheli, G. O. baudonianus, H. O. abakumovae, I. Boreoelona sibirica, J. Paraelona socialis, K. P. milachevitchi, L. Digyrcidum bourguignati, M. Boreoelona sp., N. Digyrcidum starobogatovi. МасШтабнаЯ линейка 1 мм. Фото: Н.И. Андреев. FIG. 3. The penes of the bithyniid species from Tyumen Region. A. Bithynia tentaculata, B. B. decipiens, C. B. curta, D. B. producta, E. Boreoelona contortrix, F.Opisthorchophorus troscheli, G. O. baudonianus, H. O. abakumovae, I. Boreoelona sibirica, J. Paraelona socialis, K. P. milachevitchi, L. Digyrcidum bourguignati, M. Boreoelona sp., N. Digyrcidum starobogatovi. Scale bars: 1 mm. Photos: N.I. Andreyev. in Моллюски семейства Bithyniidae (Mollusca, Gastropoda) Тюменской области
РИС. 3. Пенисы моллюсков сем. Bithyniidae иЗ водоемов Тюменской области. A. Bithynia tentaculata, B. B. decipiens, C. B. curta, D. B. producta, E. Boreoelona contortrix, F.Opisthorchophorus troscheli, G. O. baudonianus, H. O. abakumovae, I. Boreoelona sibirica, J. Paraelona socialis, K. P. milachevitchi, L. Digyrcidum bourguignati, M. Boreoelona sp., N. Digyrcidum starobogatovi. МасШтабнаЯ линейка 1 мм. Фото: Н.И. Андреев. FIG. 3. The penes of the bithyniid species from Tyumen Region. A. Bithynia tentaculata, B. B. decipiens, C. B. curta, D. B. producta, E. Boreoelona contortrix, F.Opisthorchophorus troscheli, G. O. baudonianus, H. O. abakumovae, I. Boreoelona sibirica, J. Paraelona socialis, K. P. milachevitchi, L. Digyrcidum bourguignati, M. Boreoelona sp., N. Digyrcidum starobogatovi. Scale bars: 1 mm. Photos: N.I. Andreyev.
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.