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11,758 results for “Coastal”
Benthic Chlorophyll of Seagrass Sediment in Virginia Coastal Bays 2008-2021
This data set contains measurements of benthic chlorophyll content in surface sediments in restored Z. marina plots in the Virginia coastal bays. Samples were collected annually during June-July at restored seagrass plots and adjacent bare sediment plots. GPS locations of sampling plots are available in the companion data set VCR11180.
Above- and Below-Ground Biomass and Canopy Height of Seagrass in Virginia Coastal Bays 2007-2021
This data set contains measurements of above and belowground biomass and canopy height in restored Z. marina meadows in the Virginia coastal bays. Samples were collected annually in June-July. GPS locations of sampling plots are available in the companion data set VCR11180.
Organic Matter of Seagrass Sediment in Virginia Coastal Bays 2007-2021
This data set contains measurements of sediment organic matter and bulk density from plots in the restored Z. marina meadows in Hog Island Bay and South Bay, VA. Samples were collected annually in June-July. GPS locations of sampling plots are available in the companion data set VCR11180.
Sediment Carbon and Nitrogen of Seagrass Restoration in Virginia Coastal Bays 2007-2021
This data set contains measurements of sediment carbon and nitrogen content in restored Z. marina meadows in Hog Island Bay and South Bay, VA. Sediments were sampled annually in June-July. GPS locations of sampling plots are available in the companion data set VCR11180.
Carbon and Nitrogen in Seagrass Tissue from Virginia Coastal Bays, 2010-2021
This dataset contains measurements of carbon and nitrogen content of Z. marina tissue sampled in plots in the restored seagrass meadows in Hog Island Bay and South Bay, VA. Samples were collected annually during late June-early July. GPS locations of sampling plots are available in the companion data set VCR11180.
Marsh to Upland: Vegetation Monitoring in Coastal Virginia, 2018-2022
This dataset includes data from vegetation plots along a marsh to upland gradient in three salt-marshes along the Eastern Shore of Virginia. Coastal wetlands serve as vital habitats and provide various ecosystems services. These ecosystems are experiencing novel abiotic conditions driven by anthropogenic change, including changes in salinity and moisture due to sea level rise. A key component of wetland ecosystems are their vegetation communities, which often show a transition from non-woody marsh plants like grasses and sedges to woody shrubs and larger trees in the upland reaches of the wetland. This dataset was collected from three coastal wetlands: Boxtree Preserve (Boxtree), Cushmans Landing (Cushmans), and Mockhorn Wildlife Management Area GATR Tract (GATR). Measurements of emergent vegetation percent cover and tree and shrub characteristics are taken annually at maximum biomass for the plant community, which occurs in August in this system (Moore 2013). At each site sampling occurs along four transects which run through the low marsh, high marsh, transitional area, lowest elevation upland forest, and slightly higher elevation upland forest. Each transect is segmented into five zones based on these plant communities, which roughly correspond to elevation bins of 1m. Each zone contains one sampling location.
Coastal Forest Aboveground Biomass Data at six sites in the Chesapeake Bay and Delaware Bay region, 2021
This dataset contains aboveground biomass measurement and vegetation inventory of 17 coastal forest sites collected during June 1-8 of 2021 across Virginia (n = 6 in Goodwin Island and Phillips Creek), Maryland (n = 4, Monie Bay and Moneystump Swamp) and Delaware (n = 7, Milford Neck and Donas landing). The aboveground biomass was computed with allometric equations and all study sites were located within a narrow elevation range of 0-5m above sea level.
Satellite-based remote sensing of water clarity in the shallow coastal lagoons of Virginia 2013-2021
This dataset contains raw data, analysis products and code for a study of satellite-based estimation of water clarity. The files are: Match-up.csv: In situ Secchi depths collected by the Virginia Coast Reserve Long Term Ecological Research project (VCR LTER), matched with satellite (Landsat-8/Sentinel-2) Secchi depth estimates from 2013-2022 from NASA SeaDAS 8.2. Satellite overpasses occurred +/- 0-1 days within in situ sampling. Valid remote sensing reflectance values (Rrs) from NASA SeaDAS (not masked by quality flags) were recovered at 12 of 17 in situ sampling sites: 6 ocean inlet sites, 2 lagoon site, and 3 mainland tidal creek sites. Therefore, there are 12 in situ sites available for comparison with satellite estimates. compare_L8S2.csv: Satellite data and water clarity estimates from 150 randomly sampled sites across 5 clear day images in the Virginia Coast Reserve, 2021. Satellite data are from Landsat-8 and Sentinel-2 and processed/atmospherically-corrected using NASA SeaDAS 8.2. The Virginia Coast Reserve is a coastal lagoon system located in Virginia, USA, near the southern tip of the Delmarva Peninsula. Due to low nitrogen inputs and frequent exchange with the Atlantic Ocean via inlets between barrier islands, water quality is high relative to many other coastal bays in the United States and worldwide. Spatial_averaging_analysis.csv: Secchi depths at in situ water quality sites at 10 m resolution (Sentinel-2 only), 30 m resolution (Landsat-8 and Sentinel-2), and 90 m resolution (Landsat-8 and Sentinel-2) where there are in situ match-ups. atmocorrect.csv: In situ Secchi depths collected by the Virginia Coast Reserve Long Term Ecological Research project (VCR LTER), matched with satellite (Landsat-8/Sentinel-2) Secchi depth estimates from 2013-2022 from NASA SeaDAS 8.2 and ACOLITE Version 2022022.00. L8_ALL.csv: All Landsat-8 Secchi depth data available between 2013-2021 at in situ water quality sites. S2_ALL.csv: All Sentinel-2 Secchi depth data availab
Oyster and associated fauna counts and lengths from restored and reference reefs in the coastal bays of Virginia, 2005-2019
This dataset has been superceded by Lusk, B., R. Smith, and M.C.N. Castorani. 2024. Oyster fauna lengths, counts, and biomass from restored and reference reefs in Virginia coastal bays, 2005-2023 ver 1. Environmental Data Initiative. https://doi.org/10.6073/pasta/d68de69f29cee5f737313a07f813f245 (Accessed 2024-02-22). which includes additional years and parameters. Oyster and associated reef fauna counts and lengths were sampled at 16 natural reference reefs and 61 restored shell plant reefs located at 18 sites in the Virginia Coast Reserve. Overfishing and disease decimated oyster reefs in the Virginia Coast Reserve in the 1900s. Reference reefs were defined as remnant reefs that naturally recovered in the early 2000s to develop the pronounced vertical structure and multiple oyster size classes that represent the desired endpoint of restoration efforts. Nearly every year since 2003, The Nature Conservancy and Virginia Marine Resource Commission have constructed oyster reefs in intertidal areas in the VCR. To construct the restored reefs, practitioners applied dredged, fossilized oyster shell to intertidal locations chosen for their bottom stability and accessibility (locations lacked oysters prior to construction). Whelk shell supplemented the oyster shell at 9 of the restored reefs.
Reciprocal Transplant Experiment in a Coastal Forest, Nassawadox, VA
In the Mid-Atlantic region, accelerated sea-level rise is provoking rapid shifts in many coastal areas. As salt water intrusion occurs, tidal wetland species are colonizing space opened up by upland dieback and can even invade coastal forest understory prior to said dieback. One such species is the high marsh grass Spartina patens, which is known to exhibit divergent growth forms under the differing conditions of marsh, dune, and swale habitats. It is now colonizing the understory of coastal pine forests experiencing salt water intrusion. I established plots at three sites on the Delmarva peninsula in adjacent marsh and forest areas and conducted a reciprocal transplant experiment to address the following questions: a) How do environmental conditions for S. patens differ between marsh and forest habitats? b) How do S. patens traits differ between these habitats and are these differences a product of plasticity? Our results showed lower salinity and light availability in the forest which corresponded to greater height and leaf area of S. patens plants. Though some traits showed an effect of their habitat of origin, many functional traits exhibited strong phenotypic plasticity in response to environment. This plasticity may prove crucial to the species' resilience to future change.
Invertebrates in a retreating coastal forest near Nassawadox, VA , 2019-2020
Sampling was conducted on the Eastern Shore of Virginia at Brownsville Preserve, part of the Virginia Coast Reserve LTER and adjacent to Upper Phillips Creek (37.463, -75.835). Plots were established in five zones along the forest-to-salt marsh gradient based on vegetation communities and seedling recruitment. The high forest (HF) is characterized by both coniferous and deciduous tree species, with maximum seedling recruitment. The mid forest (MF) contains primarily coniferous trees and shrubs and shows reduced seedling recruitment. The low forest (LF) canopy shows partial mortality due to salt stress and no tree recruitment; the understory contains primarily invasive (Phragmites australis) but also salt marsh (Spartina patens, Distichlis spicata) plant species. The transition zone (TZ) contains mostly dead mature trees, with its understory dominated by S. patens, D. spicata, and P. australis. The high marsh (HM) does not contain trees and is dominated by S. patens and D. spicata. 2019 data was collected September 16-18, 2019. 2020 data was collected September 8-9, 2020.
Forest Transition Experiment - Soil Pore Water Salinity in a Coastal Virginia Forest
A pore water sipper was be used to collect a pore water sample from the top 15 cm of the soil, and was read using a portable refractometer in the field.
Forest Transition Experiment - Vegetation Monitoring on a Coastal Virginia Forest, 2019-2023
This dataset contains data on vegetation (shrubs, trees, non-woody vegetation, seedlings and Phragmites occurrence in permanent plots at the Brownsville Forest near Nassawadox, VA.
Abundance and Size of Seagrass-Associated Fishes in the Virginia Coastal Lagoons, 2019-2024
These data comprise annual summer estimates of the abundance (counts) and size (length) of fishes across restored seagrass meadows of the Virginia coastal lagoons. Fish were collected using a 25-ft (7.62-m) wide beach seine hauled by hand over a 25 m linear swath of the seafloor. Seine hauls were collected in June at 31 sites (1 haul per site). All fish caught in the seine were identified to lowest practical taxonomic level, counted, measured (total length), and released. Data collection began in June 2019 and continues annually (sampling was not carried out in 2020 due to logistical interruptions associated with the COVID-19 pandemic). Data on water temperature, salinity, and conductivity were collected while sampling occurred using a YSI 30 probe. Dissolved oxygen measurements were collected using a YSI ProODO probe. In 2019, these data were collected on at the top and bottom of the water column, but in 2021 and subsequent sampling only one observation (mid-water column) was made. To reconcile this difference for the combined data set, top and bottom environmental measurements from 2019 were averaged. Each fish collection site is co-located with a nearby synoptic site where long-term measurements of seagrass, sediments, and fauna are made. The relationship between site names and coordinates are given in Synoptic_fish_sites.csv. The sites where fish sampling occurred are different and are given by the "fish_sites" column, with coordinates for these sites under the "fish_longitude" and "fish_latitude" columns. Importantly, the coordinates of where sampling occurred will differ slightly between years without a change to the name of the site. Site geographic coordinates for individual years are in the PhysicalSamples.csv file. Sites are separated by at least 300 meters. In 2023, three new sites were added to represent unvegetated areas outside of but near the seagrass meadows. These sites are HI29, SPDR-bare, and SS-bare, and are designed to serve as references for se
Abundance, biomass, and length of seagrass-associated invertebrates in the Virginia coastal lagoons, 2019-2023
These data comprise annual summer estimates of the abundance (counts), biomass (dry mass), and individual lengths of infaunal and epifaunal invertebrates across restored seagrass meadows (eelgrass Zostera marina) of the coastal lagoons of Virginia, USA. Infauna were collected during low tide by hand using cylindrical benthic cores. Epifauna were collected during low tide using cubic weighted throw traps that were sampled with dip nets. Incidentally captured fishes are included in these data. In 2019-2022, 50 sites were sampled, using 3 replicates per sampling method per site. Beginning with 2023 sampling, two additional sites were added that are consistently bare of seagrass (unvegetated seafloor). At sites with patchy areas of seagrass and bare substrate, cores were collected within seagrass only and thus represent seagrass-associated fauna at those sites, rather than a spatially haphazard sample. At the few sites that lack seagrass, cores were collected in bare substrate. All cores were separated by 25 m. Regardless of substrate and seagrass conditions, throw traps were deployed haphazardly and separated by at least 10 m. In the laboratory, invertebrates were first sorted to broad taxonomic groups and later identified to lowest practical taxonomic level and enumerated. Most taxonomic groups were either dried and weighed by taxon or measured as individual length by specimen. Existing data include one table with counts and weights for broad taxonomic groups (2019-2023) and three tables related to lowest practical taxonomic identification (2019-2020), including one for counts and biomass, one for individual lengths, and one for taxonomic information. Data collection began in July 2019 and continues annually in June-July.
Vegetation and physical characteristics of Chesapeake Bay retreating Coastal Forests 2022-2024
This data set contains biomass and physical data across an upland forest to marsh transition. These measurements are taken at 5 sites around the Chesapeake and Delaware Bays. Data is collected at up to 5 ectones across the upland to marsh (High Marsh, Transition Zone, Low, Mid and High Forest). These ecotone definitions follow Smith et al. 2019, https://doi.org/10.6073/pasta/4524c22708628eb7f06d174edae89ff2).
Testing of a benthic incubation chamber design in a Virginia coastal bay, 2023, 2024, and 2025
Benthic incubation chambers enclose a known volume of water overlying a known area to measure water chemistry changes and are typically used to quantify the metabolic activity of benthic organisms or communities. Here, we report data from a series of tests validating a new benthic incubation chamber design. This data includes dissolved oxygen (DO) concentrations and temperatures recorded during three test deployments in South Bay, Virginia during August 2023 (T1_chamber_do_test_data), as well as data from light transmission tests. Light transmission tests included per wavelength transmission of PAR and ultraviolet radiation through the chamber wall (T2_wall_transmission_test) and lid (T3_lid_transmission_test), an in-situ PAR transmission comparison for two different sensor arrangements (side-mounted versus top-mounted) during July 2025 (T4_sensor_shading_test), and an in-situ comparison of natural ambient PAR versus PAR translated through a deployed chamber during March 2024 (T5_in_situ_light_test).
North Carolina Outer Banks, USA Coastal Foredune Sediment Cores - Grain Size Data & Core Log Descriptions
<p>This repository includes sediment core data collected at seven sites along the northern Outer Banks, North Carolina, USA. From north to south, the sites include Pine Island, Corolla Reserve, Duck, the US Army Corps of Engineers Field Research Facility (FRF) North, FRF South, Southern Shores (i.e., Hillcrest Beach Access), and Nags Head (Bonnett St. Beach Access).</p><p>At each site, internal dune sedimentology and stratigraphy were characterized using sediment vibracores, each 1.5–2.2 m long, collected along a cross-shore transect from the dune toe to the dune heel. Coring locations were selected based on dune morphology to capture the stratigraphy of the dune toe, stoss slope, primary dune crest, lee slope, swale, and secondary dune crest, as applicable. Sediment core locations were documented using RTK-GPS and are included in the .kmz file.</p><p>All sediment cores were split, photographed, described for sedimentary structures, texture (as compared to standards), mineralogy, and color (Munsell, 2012). Sediment cores were described using the Modified Burmister System in 10-cm intervals, with additional intervals added as needed to capture stratigraphic units with thicknesses less than 10 cm but greater than 1 cm. Sediment core log descriptions are included in the NOAA_NCDunes_Vibracore_CoreLogs.xlsx data file.</p><p>Sediment size and shape were analyzed from oven-dried samples using a CAMSIZERX2Ⓡ. These data are included in the Dune_Grain_Size_camsizer_outputs.csv data file. Metrics reported for each sample include the following: Site, Core ID, Sample Number, Depth (cm below ground surface), Elevation (m, NAVD88), D2 (mm), D5 (mm), D10 (mm), D16 (mm), D25 (mm), D50 (mm), D75 (mm), D84 (mm), D90 (mm), D95 (mm), D98 (mm), average grain symmetry, average grain sphericity, average grain aspect ratio, percent pebble, percent granule, percent very coarse sand, percent coarse sand, percent medium sand, percent fine sand, percent very fine sand, and percent silt.</p><p><strong>More details regarding these measurements can be found in the following manuscript:</strong></p><p>Davis, E.H., Hein, C.J., Cohn, N., White, A.E., Zinnert, J.C. Differences in internal sedimentologic and biotic structure between natural, managed, and constructed coastal foredunes (in review).</p>
Shoreline series of the Doniños coastal system, NW Iberia (1945-2020): A Geospatial Dataset
<p>This repository stores shoreline data spanning from 1945 to 2020, derived from aerial photography and orthophotos, for the Doniños coastal system in NW Iberia. The shoreline indicator is defined as the boundary between vegetated dunes and bare beach sand. The methodology and dataset are detailed in the following publication:</p> <p><em><strong>Rita González-Villanueva, Martiño Pastoriza, Armand Hernández, Rafael Carballeira, Alberto Sáez, Roberto Bao. "Primary drivers of dune cover and shoreline dynamics: A conceptual model based on the Iberian Atlantic coast." Geomorphology, Volume 423, 2023, 108556, ISSN 0169-555X. <a href="https://doi.org/10.1016/j.geomorph.2022.108556" target="_new">https://doi.org/10.1016/j.geomorph.2022.108556</a>.</strong></em></p> <p>The shoreline dataset is encapsulated in a single GEOJSON file: <code>SHORES_1945_2020.geojson</code>. This dataset encompasses the shorelines mapped from all available aerial data for the Doniños coastal system, on the Galician coast, NW Iberia, from 1945 to 2020. It comprises a total of 15 shorelines. The geospatial layer employs the ETRS89/UTM zone 29N coordinate system (EPSG: 25829).</p> <p><strong>SHORES_1945_2020.geojson</strong>: This layer presents the shorelines, where each feature is a MultiLinestring with the following attributes:</p> <ul> <li><code>objectid</code>: Identifier of the shoreline.</li> <li><code>date</code>: Date of the shoreline capture, in month/year format (mm/yyyy).</li> <li><code>SHAPE_Leng</code>: Length of the mapped shoreline, in meters.</li> <li><code>Source</code>: Source of the original image from which the shoreline was derived, including the Centro Nacional de Información Geográfica (CNIG) and the Centro Cartográfico y Fotográfico del Ejército del Aire (CECAF).</li> <li><code>Type</code>: 'O' signifies an orthophotograph, and 'AP' signifies an aerial photograph.</li> <li><code>GEO_Error</code>: Georeferencing error for each manually georeferenced photograph.</li> <li><code>geometry</code>: The type of geometry used in the file, specified as MultiLineString.</li> <li><code>coordinates</code>: UTM coordinates for each node in the multiline.</li> </ul>
Pan‐Arctic Coastal Settlements and Infrastructure Vulnerable to Coastal Erosion, Sea‐Level Rise, and Permafrost Thaw
<p>The datasets are issued from the combination of records of the ESA EO4PAC and Permafrost_cci and HORIZON 2020 Nunataryuk projects. The EO4PAC project aimed to develop a new generation of geospatial products for the observation of permafrost and associated changes from space with a special focus on the coastal Arctic. Four components were considered in the creation of the datasets:</p> <p>(1) Landsat-7/8 for the detection of coastline changes over the 2000-2020 period (Tanguy et al., 2024).</p> <p>(2) Sentinel-1/2 for the detection and mapping of coastal infrastructures (Bartsch et al. 2024), updating Wang et al. (2021).</p> <p>(3) Permafrost_cci timeseries for retrieval of trends of ground temperature and active layer thickness for the 2000-2020 period (Obu et al. 2021a,b), evaluated based on Martin et al (2023) and CALM et al. (2024).</p> <p>(4) Sea level rise by 2100 (Garner et al. 2022).</p> <p>The respective output provides a consistent mapping of settlements along arctic and permafrost-dominated coasts (2), and associated coastline and permafrost conditions changes during the last 20 years (1, 3). Combined together, an assessment of Arctic infrastructures at risk due to permafrost change (GT, ALT) and coastline erosion was possible, the latter with projections for the years 2030, 2050 and 2100.<a name="_heading=h.jkogrw14ymt"></a></p> <p>References</p> <p>Bartsch, Annett, Pointner, Georg, & Nitze, Ingmar. (2023). Sentinel-1/2 derived Arctic Coastal Human Impact dataset (SACHI) (Version 2) [Data set]. Zenodo. https://zenodo.org/records/10160636.</p> <p>CALM, GTN-P, Wieczorek, M., Heim, B., Streletskiy, D., Bartsch, A., 2024, GTN-P CALM: 34 years of Active Layer Thickness (ALT) across latitudinal and elevational gradients in the Northern Hemisphere [dataset]. PANGAEA, https://doi.pangaea.de/10.1594/PANGAEA.972777</p> <p>Garner, G. G., Hermans, T., Kopp, R. E., Slangen, A. B. A., Edwards, T. L., Levermann, A., et al. (2022). IPCC AR6 sea level projections [Dataset]. Zenodo. <a href="https://doi.org/10.5281/zenodo.6382554">https://doi.org/10.5281/zenodo.6382554</a></p> <p>Martin, Julia; Boike, Julia; Chadburn, Sarah; Zwieback, Simon; Anselm, Norbert; Goldau, Maybrit; Hammar, Jennika; Abramova, Ekatarina N; Lisovski, Simeon; Coulombe, Stéphanie; Dakin, Brampton; Wilcox, Evan James; Giamberini, Mariasilvia; Rader, Fieke; Suominen, Otso; Rudd, Daniel Alexander; Mastepanov, Mikhail; Young, Amanda (2023): T-MOSAiC 2021 myThaw data set [dataset]. PANGAEA, https://doi.org/10.1594/PANGAEA.956039, In: Boike, Julia; Hammar, Jennika; Goldau, Maybrit; Miesner, Frederieke; Anselm, Norbert (2024): Circumarctic seasonal measurements of permafrost parameters (thaw depth, snow depth, vegetation and tree height, water level and soil properties) [dataset publication series]. PANGAEA, https://doi.org/10.1594/PANGAEA.971787</p> <p>Obu, J., Westermann, S., Barboux, C., Bartsch, A., Delaloye, R., Grosse, G., Heim, B., Hugelius, G., Irrgang, A., Kääb, A. M., Kroisleitner, C., Matthes, H., Nitze, I., Pellet, C., Seifert, F. M., Strozzi, T., Wegmüller, U., Wieczorek, M., and Wiesmann, A.: ESA Permafrost Climate Change Initiative (Permafrost_cci): Permafrost active layer thickness for the Northern Hemisphere, v3.0, CEDA, 2021. <a href="https://doi.org/10.5285/29C4AF5986BA4B9C8A3CFC33CA8D7C85">https://doi.org/10.5285/29C4AF5986BA4B9C8A3CFC33CA8D7C85</a></p> <p>Obu, J., Westermann, S., Barboux, C., Bartsch, A., Delaloye, R., Grosse, G., Heim, B., Hugelius, G., Irrgang, A., Kääb, A. M., Kroisleitner, C., Matthes, H., Nitze, I., Pellet, C., Seifert, F. M., Strozzi, T., Wegmüller, U., Wieczorek, M., and Wiesmann, A.: ESA Permafrost Climate Change Initiative (Permafrost_cci): Permafrost active layer thickness for the Northern Hemisphere, v3.0, CEDA, 2021. <a href="https://doi.org/10.5285/29C4AF5986BA4B9C8A3CFC33CA8D7C85">https://doi.org/10.5285/29C4AF5986BA4B9C8A3CFC33CA8D7C85</a></p> <p>Tanguy, R., Bartsch, A., Nitze, I., Irrgang, A., Petzold, P., Widhalm, B., von Baeckmann, C., Boike, J., Martin, J., Efimova, A., Vieira, G., Whalen, D., Heim, B., Wieszorek, M., Grosse, G.: Pan‐Arctic Assessment of Coastal Settlements and Infrastructure Vulnerable to Coastal Erosion, Sea‐Level Rise, and Permafrost Thaw, Earth’s Future, 10.1029/2024EF005013.</p> <p>Wang, S., Ramage, J., Bartsch, A., & Efimova, A. (2021). Population in the Arctic Circumpolar Permafrost Region at settlement level (Version 2) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.4529610" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.4529610</a></p>
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.