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12,632 results for “fish”
Cortisol in fish scales remains stable during extended periods of storage.
<p>Dataset to accompany the manuscript: <a href="https://doi.org/10.1093/conphys/coae065">https://doi.org/10.1093/conphys/coae065</a></p> <p>The dataset contains two data files describing cortisol concentrations in the scales of adult salmon and a "Read Me" file that explains the data structure and source. </p>
Coping with Collapse: Functional Robustness of Coral-Reef Fish Network to Simulated Cascade Extinction
<p>Data set, codes and results related to the article "Coping with Collapse: Functional Robustness of Coral-Reef Fish Network to Simulated Cascade Extinction", accepted in the periodic Global Change Biology. Stored are the full results of site occupancy models fitted to fish data, with coral and turf algae cover as predictor variables (results published in Luza et al. 2022, Scientific Reports), and the results of the present article. The RData also contains site coordinates, and the fish traits used in trait-based analyzes.</p>
Detailed insight into gillnet catches: fish directivity and micro distribution
<p>This dataset contains data for gillnets that were deployed in Římov reservoir, South Bohemia, Czech Republic (48°50'55.0"N 14°29'14.0"E). The sampling dates were recorded from July 30 to August 2, 2019. This experiment was conducted to test the bias of gillnets in relation to fish direction capture. To determine if this is a random pattern or if it follows a directional pattern. The dataset includes various terms such as eventID, eventDate, country, countryCode, geodeticDatum, decimalLatitude, decimalLongitude, coordinateUncertaintyInMeters, habitat, waterBody, locality, DEIMS.iD, basisOfRecord, minimumDepthInMeters, maximumDepthInMeters, samplingEffort, samplingProtocol, dynamicProperties, occurrenceStatus, organismQuantity, organismQuantityType, measurementValue, measurementUnit, measurementType, measurementRemarks, organismRemarks, acceptedNameUsageID, scientificName, taxonRank, class, order, family.</p>
Model projection of the effect of climate change and fishing pressure on key species of the South East Asia Seas
<p>The dataset contain Projection from the Size-Spectra Bioclimatic Envelop Model (SS-DBEM), this work was part of the GCRF Blue communities Programme (www.blue-communities.org). The model provides distribution and abundance and/or biomass of fish and other species of commercial interest under climate change and fishing pressure. The model outputs are yearly abundance/biomass on a 0.5-by-0.5 degree grid, covering the period from 2000 to 2098. Further description of the model and relevant references are listed in the following file: Guide-fish-model-output-use.docx</p> <p>The model was run under two climate scenario: RCP4.5 and RCP8.5, with different combinations of fishing pressure expressed as the Maximum Sustainable Yield (MSY) for the following values: 0 (no fishing, climate change alone will cause variation in fish biomass), 1 (sustainable fishing), 2, 3 (overfishing), and, 4 (overfishing with destructive practice). The intent is not to reproduce current fishing level but to provide a range of scenarios with which the future of fisheries can be explored.</p> <p>We projected fish species that were identified as key in the South East Asia seas region by our regional partners.The full list is provided in document: Fish-list-modelguide.xlsx</p> <p>There are 4 zip files that contain the model outputs of in either abundance (number of fish) or biomass grams of fish) for the two climate scenario. For example Biomass-RCP45.zip will contain model outputs in biomass for projections under RCP4.5 and all MSY. within the zip files are .csv files of the outputs for each species under the 5 MSY (0 to 4), the individual file names identify the species (identified by a 6digit code), the output provided (abundance or biomass), the RCP (8.5 or 4.5), and the MSY (0, 1, 2, 3, or 4). For example the file labelled 600107-Abundance-rcp85-msy4.csv contains the outputs for species 600107 (Skipjack tuna, <em>Katsuwonnus pelamis</em>), as abundance, under RCP8.5 with MSY4. Headers indicate what is in each column (latitude, longitude and year).</p> <p> </p> <p>Note: some knowledge of Python, R, or a similar software is recommended to ensure easy of use.</p>
CT Fishing Report Occurrences
<p>Dataset of gamefish occurrences as compiled from the Connecticut Fishing Report (2006-2018) and Trophy Fish Report (2009-2018), both published by the Connecticut Department of Energy and Environmental Protection. Compiled as thesis project by Rebecca Hedreen for a Masters of Science in Biology from Southern Connecticut State University, with advisor Dr. Sean Grace.</p>
Sampling metadata for the publication: "Deep-sea sponge derived environmental DNA analysis reveals demersal fish biodiversity of a remote Arctic ecosystem "
<p>Meta data of sampling location, time and depth of eDNA samples used in the study: "Deep-sea sponge derived environmental DNA analysis reveals demersal fish biodiversity of a remote Arctic ecosystem". As well as taxonomic identification of sponges, their microbial abundance and growth form.</p>
HIdden fishing activity hotspots in the Adriatic Sea in 2019
<p>The animated image shows the unreported fishing activity hotspots in the Adriatic in 2019. The datasets report the fishing activity ban areas in the Adriatic over the months and the reported, unreported, total, unreported/total fishing hour distributions across the months estimated by our workflow for vessel data processing (Coro et al., 2023).</p>
The raw data of Souma, Katano, Doi et al. "Comparing environmental DNA with whole pond survey to estimate the total biomass of fish species in ponds" in Freshwater Biology
<p>The raw data of Souma, Katano, Doi, Takahara, and Minamoto. "Comparing environmental DNA with whole pond survey to estimate the total biomass of fish species in ponds" in Freshwater Biology.</p>
fish larvae abundance as a function of oceanographic variables in GoM deep waters
<p>We describe the larval occurrence and abundance of six fish species with contrasting life histories and examine their relationship with oceanographic variables during two seasons in the deep-water region (>1000 m) of the southern Gulf of Mexico based on 12 cruises (2011-2018). Given that <em>Caranx crysos</em> adults are neritic, larval presence close to the continental shelf indicates offshore cross-shelf transport to oceanic waters, which likely leads to mortality. Generalized additive models indicated <em>C. crysos</em> abundance was not related with oceanographic variables, while that of Auxis spp. (with neritic and oceanic adults) was related to wind speed, sea surface temperature and height and surface chlorophyll a. The mesopelagic <em>Benthosema suborbitale</em>, <em>Notolychnus valdiviae</em> and <em>Bregmaceros atlanticus</em> were more abundant and broadly distributed, and higher abundance was found in conditions indicative of higher nutrient availability and productivity, suggesting greater feeding success and survival. The distribution of the epi- and mesopelagic <em>Cubiceps pauciradiatus</em> extended through the southern Gulf of Mexico, and was related to wind speed, SST, stratification and chlorophyll a. Our results suggest that the abundance of the neritic species in oceanic waters could be mediated by regional cross-shelf transport, while that of oceanic species is linked with productivity.</p>
Climate change threats to the global functional diversity of freshwater fish
<p>This dataset provides supplementary information for the paper entitled "Climate change threats to the global functional diversity of freshwater fish".</p> <p> </p> <p><strong>Fish trait data</strong></p> <p>fish_traits_removed.csv<br> - species with missing trait values were removed<br> - species coverage: 3,792</p> <p>fish_traits_imputed.csv<br> - missing trait values were imputed<br> - species coverage: 11,425</p> <p>Traits<br> - HLrel = relative head length<br> - BDrel = relative body depth<br> - Troph = trophic level<br> - K = relative growth rate</p> <p><br> <strong>Geospatial data</strong></p> <p>Files<br> Data under the assumption of no dispersal<br> - SR.tif: species richness<br> - FRic.tif: functional richness<br> - FEve.tif: functional evenness<br> - FDiv.tif: functional divergence<br> - FRic_loss.tif: functional richness loss<br> - FEve_loss.tif: functional evenness loss<br> - FDiv_loss.tif: functional divergence loss</p> <p>Data under the assumption of maximal dispersal<br> - SR_dispersal.tif: species richness<br> - FRic_dispersal.tif: functional richness<br> - FEve_dispersal.tif: functional evenness<br> - FDiv_dispersal.tif: functional divergence<br> - FRic_loss_dispersal.tif: functional richness loss<br> - FEve_loss_dispersal.tif: functional evenness loss<br> - FDiv_loss_dispersal.tif: functional divergence loss</p> <p>Layers<br> - imp_*: missing trait values were imputed<br> - rem_*: species with missing trait values were removed<br> - *_hist: historical reference scenario<br> - *_1p5: warming level of 1.5°C<br> - *_2p0: warming level of 2.0°C<br> - *_3p2: warming level of 3.2°C<br> - *_4p5: warming level of 4.5°C</p> <p>Spatial resolution: 0.08333333, 0.08333333 (x, y)<br> Spatial extent: -180, 180, -60, 85 (xmin, xmax, ymin, ymax)<br> Coordinate reference system: WGS84</p>
Fisheries independent trawl survey data of fish biomass on North American and European oceanic shelves.
<p>Publicly available scientific bottom trawl survey data, primarily sampling demersal commercial species, were obtained from the Northeast Pacific and North Atlantic shelf regions in 2021. The final dataset contains approx. 197,000 unique tows and includes data from 1970 to 2019 (166,000 tows between 1980-2015). For each tow in each survey, we selected all teleost and elasmobranch species and obtained species weight. We corrected these weights for differences in sampling area (in km2) and trawl gear catchability.</p> <p>The data processing scripts and individual survey data can be found on Github (DOI: 10.5281/zenodo.7992482). The data processing scripts are modified based on earlier work from Pinsky et al. (2013) and Maureaud et al. (2019).</p> <p>If the correction for gear catchability is not important, it is recommended to use the FishGlob database (DOI: 10.5281/zenodo.7484547).</p> <p>Please contact Daniel van Denderen (pdvd@aqua.dtu.dk) for questions.</p> <p><strong>Column names</strong><br> Haul_id: unique haul identifyer<br> Survey_Region: survey name or name of ecoregion (depending on survey)<br> Gear: gear information (only included for northeast Atlantic region). Gear information is available for other regions. See original survey description (sources in manuscript).<br> Year: sampling year<br> Month: sampling month<br> Longitude: longitude (EPSG:4326)<br> Latitude: latitude (EPSG:4326)<br> Swept_area: estimate of swept area of survey gear (only included for northeast Atlantic region)<br> Bottom_depth: bottom depth in meters (as recorded in the survey data)<br> Family: taxonomic family of the teleost/elasmobranch<br> Name: species name (or higher taxonomic grouping)<br> kg_km2: wet weight (kilogram) per unit of swept area (km2)<br> kg_km2_corrected: wet weight (kilogram) per unit of swept area (km2) corrected for trawl gear catchability<br> F_type: fish type (demersal or pelagic)<br> Trophic_lev: Species-specific trophic level information</p> <p><br> <strong>Data uncertainties</strong><br> Data have predominantly been analysed at the community level and between 1980 and 2015. Any species-specific inferences may need further checking.</p> <p>To reduce the effect of potential outlying biomass estimates, it is recommended to remove all individual observations 1.5 times less/greater than the interquantile range per survey and year based on log10-transformed biomass values.</p> <p>Please contact Daniel van Denderen (pdvd@aqua.dtu.dk) for any comments/questions.</p>
European river typologies fail to capture trends in diatom, fish, and macrophyte community composition
<p>This repository contains files related to the publication: "European river typologies fail to capture trends in diatom, fish, and macrophyte community composition".</p> <p> </p> <p> </p>
Dataset - Drying out fish ponds, for an entire growth season, as an agroecological practice: maintaining primary producers for fish production and biodiversity conservation
<p>This dataset is based on samples taken from fish ponds in the Dombes region between 2007 and 2014. It includes sediment and water physio-chemistry data, as well as primary producer diversity, benthic invertebrate density and fish yield for 85 different ponds. All these data are linked to the distance to the last dry-out, a major practice in extensive fish farming in this region.</p> <p>There are two .tab and .csv files:<br> One containing the dataset<br> One containing the description of the different variables (Metadata)</p>
A gonad photographs dataset for fish of commercial interest
<p>This dataset was established during a one year project under the IFREMER (Institut Français de Recherche pour l’Exploitation de la Mer) for the harmonisation of maturity data acquisition methods for bony fish of commercial interest, with the help of scientific campaign CGFS, EVHOE, IBTS and ACCOBIOM (Auber et al., 2021, Laffargue et al.,1987, Le Roy et al., 1988).</p> <p>This dataset contains 4133 standardised gonad’s macroscopic photos of 61 species of fish of commercial interests collected along the European coastal water and the Caribbean Sea. The scale used throughout this project is the ICES maturity scale “WKASMSF” (ICES, 2018). To have more details about the photography process used for photos in this database, check the “Fish gonads’ photography protocol” from Le Meleder et al. (2022).</p> <p>This dataset is associated with a GitHub page hosting tools to generate maturity identification forms for fish of commercial interest. To have more detail about identification forms files and have the latest update, check the GitHub page “MaturityScaleTools” (<a href="https://github.com/LM-Anna/MaturityScaleTools">LM-Anna/MaturityScaleTools: Maturity scale tools to identify visual maturity phases (github.com)</a>).</p> <p>This dataset is meant to be enriched with time. Photos may be added to complete the missing maturity phases for every species of the world. To have more details about the dataset or to add new photos, please contact annalemeleder@orange.fr or <a href="mailto:laurent.dubroca@ifremer.fr">laurent.dubroca@ifremer.fr</a>.</p> <p> </p> <p><strong>Images:</strong></p> <ul> <li> <p><strong>Photo_MATURITY.zip</strong> : archive in zip format of 4133 macroscopic photographs of gonads (.JPG; 2Mo-6Mo; sRGB; 1080p). Each photo was taken with the same camera (OLYMPUS / Tough F2.0), on the same white background, with homogeneous lighting to avoid glints from overexposure. Since there are no duplicated photos’ names because all photos were taken with the same camera, names correspond to the one generated by the camera. Photos are sorted under three levels of directories :</p> <ul> <li> <p><strong>First level :<em> species’ scientific name</em></strong> (Example : <em>Dicentrarchus labrax</em>) : there are currently 61 different species listed</p> </li> <li> <p><strong>Second level :<em> F or M</em> </strong>: the sex, with F from females and M for male</p> </li> <li> <p><strong>Third level : <em>A, B, C, D, E or F</em> </strong>: the maturity phases of the ICES 2018 scale. In each folder are assigned the corresponding gonadic photos.</p> </li> </ul> </li> </ul> <p> </p> <p><strong>Data frames:</strong></p> <ul> <li> <p><strong>photo_mat.xlsx</strong> (13 columns / 4133 rows): data table (Excel format) listing all photos in the Photo_MATURITY database, as well as the data associated with the photos. The data table is presented as followed, for each photo :</p> <ul> <li> <p>Name : Name of the photo</p> </li> <li> <p>Type : Type of gonad photo (INT = inside without organs, INT ORG = inside with organs, EXT = outside, EXT OUV = outside and open, FLUANT = fluent)</p> </li> <li> <p>sppeng : English vernacular name of the species or species group established for identification forms</p> </li> <li> <p>Species : Scientific name of the species or species group established for identification guides</p> </li> <li> <p>Sex : Sex of the fish (M = male, F = female)</p> </li> <li> <p>phase ID : visually estimated maturity phase (ICES WKASMSF scale : A, B, C, D, E or F)</p> </li> <li> <p>Link : Link to the photo, to change depending on your path to the downloaded dataset =LIEN_HYPERTEXTE(« (Your path to the dataset)\Photo_MATURITE\« &H<sub>n</sub>& »\« &E<sub>n</sub>& »\« &F<sub>n</sub>& »\« &A<sub>n</sub>& ».JPG »)*</p> </li> <li> <p>spplatTRUE : Scientific name of the species without taking species groups into account</p> </li> <li> <p>sppengTRUE : English vernacular name of the species without taking species groups into account</p> </li> <li> <p>Date : Date the photo was added to the dataset (the year correspond to the year the photo was took)</p> </li> <li> <p>Campaign : Survey during which the photo was taken</p> </li> <li> <p>Area : Geographical area (ICES or not) where the scientific survey occurred (Caribbean sea = Caribbean waters area, IVb-c = ICES area for the IBTS campaign, NA = unknown area, VIId = ICES area for NourManche campaign, VIId/VIIe = ICES area for CGFS campaign, VIIg/VIIj/VIIh/VIIIa-b = ICES area for EVHOE campaign)</p> </li> <li> <p>Commentary : Comments about the photo.</p> </li> </ul> </li> </ul> <p> </p> <p><strong>CAUTION</strong> : When using this database, please make sure to modify the link to the photos in the “Link” column with the link where you downloaded the Photo_MATURITY.zip file, and to check if it works by clicking it.</p> <p> </p> <p>*<sub>n</sub> = row number</p>
Data from: Identifying priority areas for spatial management of mixed fisheries using ensemble of multi-species distribution models. Panzeri D. et al., 2023, Fish and Fisheries
<p>Panzeri D.<sup>1</sup>, Russo T., Arneri E., Carlucci R., Cossarini G., Isajlović I., Krstulović Šifner S., Manfredi C., Masnadi F., Reale M., Scarcella G., Solidoro C., Spedicato M.T., Vrgoč N., W. Zupa, Libralato S<sup>2</sup>.</p> <p><sup>1 </sup>dpanzeri@ogs.it<br> <sup>2 </sup>slibralato@ogs.it</p> <p>Spatial fisheries management is widely used to reduce overfishing, rebuild stocks, and protect biodiversity. However, the effectiveness and optimization of spatial measures depend on accurately identifying ecologically meaningful areas, which can be difficult in mixed fisheries. To apply a method generally to a range of target species, we developed an ensemble of species distribution models (e-SDM) that combines general additive models, generalized linear mixed models, random forest, and gradient-boosting machine methods in a training and testing protocol. The e-SDM was used to integrate density indices from two scientific bottom trawl surveys with the geopositional data, relevant oceanographic variables from the three-dimensional physical-biogeochemical operational model, and fishing effort from the vessel monitoring system. The determined best distributions for juveniles and adults are used to determine hot spots of aggregation based on single or multiple target species. We applied e-SDM to juvenile and adult stages of 10 marine demersal species representing 60% of the total demersal landings in the central areas of the Mediterranean Sea. Using the e-SDM results, hot spots of aggregation and grounds potentially more selective were identified for each species and for the target species group of otter trawl and beam trawl fisheries. The results confirm the ecological appropriateness of existing fishery restriction areas and support the identification of locations for new spatial management measures.</p> <p>Data (csv) for Panzeri et al. 2023</p> <p>1. <a href="https://zenodo.org/api/files/0b1b7af4-6a3b-481d-8d5f-57cf02d20eaa/Ensemble_density_F%26F_D.Panzeri_et_al_2023.csv">Ensemble_density_F&F_D.Panzeri_et_al_2023.csv: CSV file with density values (column pred) in terms of number of individuals (log N/km2) for each species (column sp) and life stage (column age) for each grid cell (X = longitude and Y = latitude).</a> </p> <p>2. <a href="https://zenodo.org/api/files/0b1b7af4-6a3b-481d-8d5f-57cf02d20eaa/Ensemble_density_F%26F_D.Panzeri_et_al_2023.csv">Getis_hotspot_F&F_D.Panzeri_et_al_2023.csv: CSV file with Getis ord Gi* values (column Gi) derived from the previous file 1, developed for each species and life stage for each grid cell (X = longitude and Y = latitude).</a></p> <p>3. <a href="https://zenodo.org/api/files/0b1b7af4-6a3b-481d-8d5f-57cf02d20eaa/Ensemble_density_F%26F_D.Panzeri_et_al_2023.csv">Multispecies_HotSpot_F&F_D.Panzeri_et_al_2023.csv: Frequency map expressed as the number of species for each grid cell (column freq) that has the hotspot (previous file 2) above the third quartile.</a></p> <p> </p> <p> </p>
Data from: Genome-wide selection components analysis in a fish with male pregnancy
Open the record for dataset details and reuse information.
Kelp forest fish communities environmental DNA samples from Santa Barbara Channel
The dataset in this package is the processed fish community structure inferred from 12S eDNA metabarcoding in the Santa Barbara Channel. 49 water samples were collected across 11 sites in 2017 and the taxa were identified to the highest resolution possible. The raw DNA sequence has been archived in the Sequence Read Archive (SRA) database (https://www.ncbi.nlm.nih.gov/sra) under the accession number PRJNA667508. This dataset is used to support manuscript: Lamy, T., Pitz, K.J., Chavez, F.P. et al. Environmental DNA reveals the fine-grained and hierarchical spatial structure of kelp forest fish communities. Sci Rep 11, 14439 (2021). https://doi.org/10.1038/s41598-021-93859-5
SBC LTER Darwin Core Archive: Kelp Forest Reef Fish Abundance
These data describe the abundance of reef fish as part of the Santa Barbara Coastal LTER program (SBC LTER) to track long-term patterns in kelp forest reef species abundance and diversity. The study began in 2000 in the Santa Barbara Channel, California, USA, and the time series is ongoing and updated approximately annually. Abundances of all taxa of resident kelp forest fish encountered along permanent transects are recorded at nine reef sites located along the mainland coast of the Santa Barbara Channel and at two sites on the north side of Santa Cruz Island. These sites reflect several oceanographic regimes in the channel and vary in distance from sources of terrestrial runoff. In these surveys, fish were counted in either a 40x2m benthic quadrat, or in the water parcel 0-2m off the bottom over the same area. This dataset is formatted as a Darwin Core Archive (DwC-A, occurrence core). All taxa are counted (using an open species list), and abundances are zero-filled for each taxon not encountered. This is a derived data product and less-processed data may be available. See http://sbc.lternet.edu for more information and source data, which may include additional measurements, and http://sbc.marinebon.edu for processing notes.
California Department of Fish and Wildlife Enhanced Large Fish Study, San Francisco Estuary, California, 2023 Gillnet Survey
The Enhanced Large Fish Study (ELFS) was established and included in the Interagency Ecological Program (IEP) work plan in 2023 to fill some of the monitoring gap of fishes in the San Francisco Estuary (SFE), California. The fish monitoring of the IEP prior to ELFS primarily consisted of trawl- and seine-based surveys, which generally capture small and/or juvenile fishes due to survey gear and methodologies. In order to more effectively sample the large/adult fish portion of SFE fish communities, the ELFS uses American Fisheries Society experimental gillnets, plus the inclusion of the optional "large fish panel," to sample the waters of the SFE. The experimental gillnets measure 24.4m in length, 1.8m in depth, and includes eight 3m length panels with stretch mesh measurements of 76.2mm, 114.3mm, 50.8mm, 88.9mm, 38.1mm, 127.0mm, 63.5mm, and 101.6mm. The optional large fish panel measures 9.1m by 1.8m in depth and includes three 3m length panels with stretch mesh measurements of 152.4, 177.8, and 203.2mm. The ELFS conducted its first year of sampling in the North Delta, California, in 2023, and will be expanded to the greater Delta and Suisun Bay and Marsh in future sampling years. The ELFS is currently funded as a special study to inform future monitoring, with funding contracted through fiscal year 2026-2027.
Concentrations of cyanotoxins in fresh water and fish
This dataset accompanies the publication Flores, N.M., T.R. Miller, and J.D. Stockwell. Accepted. A global analysis of the relationship between cyanotoxins in water and fish. Frontiers in Marine Science. doi: 10.3389/fmars.2018.00030 Cyanobacteria, the primary bloom-forming organisms in fresh water, elicit a spectrum of problems in lentic systems. The most immediate concern for people and animals are cyanobacterial toxins, which have been detected at variable concentrations in water and fish around the world. Cyanotoxins can transfer through food webs, potentially increasing the risk of exposure to people who eat fish from affected waters, yet little is known about how cyanotoxins fluctuate in wild fish tissues. We collated existing studies on cyanotoxins in fish and fresh water from lakes around the world into a global dataset to test the hypothesis that cyanotoxin concentrations in fish increase with water toxin concentrations. We limited our quantitative analysis to microcystins because data on other cyanotoxins in fish were sparse, but we provided a qualitative summary of other cyanotoxins reported in wild, freshwater fish tissues. We found a positive relationship between intracellular microcystin in water samples and microcystin in fish tissues that had been analyzed by assay methods (enzyme-linked immunosorbent assay and protein phosphatase inhibition assay). We expected microcystin to be found in increasingly higher concentrations from carnivorous to omnivorous to planktivorous fishes. We found, however, that omnivores generally had the highest tissue microcystin concentrations. Additionally, we found contrasting results for the level of microcystin in different tissue types depending on the toxin analysis method. Because microcystin and other cyanotoxins have the potential to impact public health, our results underline the current need for comprehensive and uniform detection methods for the analysis of cyanotoxins in complex matrices.
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.