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34 results for “water color”
LAGOS - Chlorophyll, TP, and water color summer epilimnetic concentrations and lake and catchment data for inland lakes in WI, MI, NY, and ME – a subset of lake data from LAGOSLimno v.1.040.1
This dataset includes lake total phosphorus (TP), true water color, and chlorophyll a (CHLa) concentrations from summer, epilimnetic water samples and is a subset of the larger LAGOS database (Lake multi-scaled geospatial and temporal database, described in Soranno et al. 2015). LAGOS compiles multiple, individual lake water chemistry datasets into an integrated database. We accessed LAGOSLIMNO version 1.040.0 for lake water chemistry data and LAGOSGEO version 1.02 for lake catchment geographic data. In the LAGOSLIMNO database, lake water chemistry data were collected from individual state agency sampling and volunteer programs designed to monitor lake water quality. Water chemistry analyses follow standard lab methods. In the LAGOSGEO database geographic data were collected from national scale geographic information systems (GIS) data layers. Lake catchments, defined as 'The area of land that drains directly into a lake, and into all upstream-connected, permanent streams to that lake exclusive of any upstream lake watersheds for lakes greater than or equal to 10 ha that are connected via permanent streams', were delineated for lakes greater than or equal to 4 ha. Lake-stream connectivity type was assigned to lakes greater than or equal to 4 ha using GIS tools that use the National Hydrology Dataset (See Soranno et al. 2015 for LAGOS geographic processing steps). A subset of lake and geographic data was created to examine spatial variation in TP and water color relationships with CHLa across broad geographic extents using spatially-varying coefficient models with a Bayesian framework. Lakes were selected that had complete records for summer epilimnetic total TP, true water color, and CHLa. In addition we selected lakes with surface area greater than or equal to 4 ha and less than 10,000 ha to exclude very small and very large lakes from the analyses. The resulting dataset includes 838 lakes in Wisconsin, Michigan, New York, and Maine with 7395 observations. The majo
North Temperate Lakes LTER Regional Survey Water Color Scans 2015 - current
The Northern Highlands Lake District (NHLD) is one of the few regions in the world with periodic comprehensive water chemistry data from hundreds of lakes spanning almost a century. Birge and Juday directed the first comprehensive assessment of water chemistry in the NHLD, sampling more than 600 lakes in the 1920s and 30s. These surveys have been repeated by various agencies and we now have data from the 1920s (UW), 1960s (WDNR), 1970s (EPA), 1980s (EPA), 1990s (EPA), and 2000s (NTL). The 28 lakes sampled as part of the Regional Lake Survey have been sampled by at least four of these regional surveys including the 1920s Birge and Juday sampling efforts. These 28 lakes were selected to represent a gradient of landscape position and shoreline development, both of which are important factors influencing social and ecological dynamics of lakes in the NHLD. This long-term regional dataset will lead to a greater understanding of whether and how large-scale drivers such as climate change and variability, lakeshore residential development, introductions of invasive species, or forest management have altered regional water chemistry. Color is measured in water samples that are filtered in the field through 0.45 um nucleopore membrane filters. A spectrophotometer is used to quantify color in the lab as absorbance (unitless) at 1 nm intervals between the wavelengths of 200 and 800 nm. Absorbance data are considered suspect for values greater than 2.
FUI Water Color product of inland waters in China at 30-m in 2015
<p>The first 30-meters FUI water color product of China. The product was developed using time-series Landsat 8 imagery and FUI water color retrieval method. Taking into account the huge amount of computational and storage space required for the national-scale water color mapping, the high-performance Google Earth Engine (GEE) cloud-based platform was introduced to support the computation. First, a cloud-free composite in China for the summer of 2015 was generated using time-series Landsat-8 imagery and the Best-Available-Pixel (BAP) compositing algorithm. Then, the first 30-merters FUI water color product of China was developed using the generated BAP composite and the Google Earth Engine computing platform. The first 30-meters FUI water color product can promote the understanding of the water color of water bodies in China, and provide very important information for preserving and restoring inland water quality.</p> <p>The details of the product is described in "<a href="https://zenodo.org/api/files/59060333-b9fc-45ad-b381-3b05a866de6c/FUI_WaterColor_2015China_Readme_V1.1.docx?versionId=44bb5bbb-407f-4dc8-b183-fa1a3843488f">FUI_WaterColor_2015China_Readme_V1.1.docx</a>".</p>
Lake morphometry mediates the relationship between water color and fish biomass in small boreal lakes
<p>The data are for an analysis of the influence of water color and lake depth on fish biomass small (1-10 ha) lakes in boreal Sweden.</p> <p>AllBorealLakes.csv contains a list of surface areas (variable name hectares, given in hectares) for all lakes greater or equal to 1 hectare surface area in the boreal zone of Sweden. The original lake census comes from the Swedish government (Nisell et al. 2007) and lakes within the boreal zone were extracted based on the boreal zone boundary of Olson et al. (2001). There is also a lake ID number (FID_vivan_) used in the extraction.</p> <p> </p> <p>SmallBorealLakes.csv contains a list of surface areas (variable name hectares, given in hectares) for all lakes greater or equal to 1 hectare surface area and less than or equal to 10 hectares in the boreal zone of Sweden. The original lake census comes from the Swedish government (Nisell et al. 2007) and lakes within the boreal zone were extracted based on the boreal zone boundary of Olson et al. (2001). There is also a lake ID number (FID_vivan_) used in the extraction.</p> <p> </p> <p>SNILLE_ms_data.csv contains data on fish biomass for 16 small boreal lakes. The geographic coordinates (Northing and Easting) are based on the Swedish Grid, see: http://www.lantmateriet.se. Lake surface areas based on the Swedish lake census (Nisell et al. 2007). Mean depth (meters) is based on echo sounding with an integrated GIS (Lowrance m52i). Volumes were calculated by calculating a triangulated irregular network and then mean depth subsequently calculated as volume divided by surface area. kd is the vertical light extinction coefficient (m^-1). We calculated <em>k</em><sub>d</sub> from the slope of the linear regression of the logarithm of photosynthetically active radiation (measured with LI-COR LI-193 spherical quantum sensor) versus measurement depth (measured in approximately 0.5 meter intervals over the deepest part of the lake). The shallowest measure was excluded from the calculation. The values in the table are the average of kd calculated from three visits to each lake (once each approximately in June, July, and August 2014). kd is an indicator of colored dissolved organic carbon and water color (brownness) in this region and there is relatively little contribution of phytoplankton or inorganic particulate. CPUE Catch-per-unit-effort (kg wet weight / net) is an indicator of fish biomass. For each lake, we set 8 multi mesh gill nets (Nordic 12 nets, 30 x 1.5 m; Mesh sizes: 5, 6.25, 8, 10, 12.5, 15.5, 19.5, 24, 29, 35, 43, 55 mm) over one night (approximately 12 hours) in August 2014. Four nets were deployed in the littoral zone perpendicular to the shoreline. These nets were approximately equally spaced. Two floating nets were deployed across the deepest point of the pelagic zone, and two benthic nets were set in the hypolimnion near the deepest point of the lake. Net-specific catches were averaged with weighting based on the relative extent of the different habitat types (see Karlsson et al. 2015). Specifically, the profundal nets were assumed to represent the total hypolimnetic volume and the pelagic nets were assumed to represent the volume above the hypolimnion. The volume of the littoral nets was calculated by subtracting the volume of the pelagic and profundal habitats from the total lake volume. These weighted CPUE values are given in the file. Species identified through gill netting are abbreviated as: P for European perch (<em>Perca fluviatilis</em>), R for common roach (<em>Rutilus rutilus</em>), N for northern pike (<em>Esox lucius</em>), B for burbot (<em>Lota lota</em>)</p> <p>Boreal_Area_kd_data.csv contains a list of estimated vertical light extinction coefficients (kd, m^-1) for lakes in boreal Sweden. Specifically, the values are based on water chemistry data from a national water quality survey conducted in Sweden every five years. Lake surface water (0.5 m) was sampled from above the deepest part of the lake during early autumn when the water column is mixed. Water quality analyses were performed using standard limnological techniques (detailed methods available on the internet at: http://www.slu.se/en/departments/aquatic-sciences-assessment/laboratories/geochemicallaboratory/water-chemical-analyses/) by a certified water analysis laboratory at the Swedish University of Agricultural Sciences. The data are freely available on the Internet at http://www.slu.se/vatten-miljo. Absorbance at 420 nm (D) which is a metric of water color (brownness) was used to calculate absorption coefficients per meter (a, m-1) from the initial measurement: a = (D * 2.303) / L. where L is the optical path length in meters, 0.05 in the case of the monitoring data. We then estimated kd (m^-1) based on the calibration curve reported by Seekell et al. (2015): = kd = 0.3121 + 0.1327a. These values were associated with surface areas from the Swedish lake census (Nisell et al. 2007) using a identification number common to both the Swedish water chemistry and lake census datasets. Finally, the file was trimmed to only include lakes with surface areas greater or equal to 1 hectare and less than or equal to 10 hectares.</p> <p>References:</p> <ul> <li>Nisell, J., A. Lindsjö, and J. Temnerud (2007), Rikstäckande virtuellt vattendrags nätverk för flödesbaserad modellering VIVAN, [In Swedish], Rapport 2007:17, Institutionen för miljöanalys, SLU.</li> <li>Olson DM, Dinerstein E, Wikramanayake ED, Burgess ND, Powell GVN, Underwood EC, D’amico JA, Itoua I, Strand HE, Morrison JC, Loucks CJ, Allnutt TF, Ricketts TH, Kura Y, Lamoreux JF, Wettengel WW, Hedao P, Kassem KR (2001) Terrestrial ecoregions o the world: A new map of life on Earth. <em>BioScience</em> 51:933-938.</li> <li> <p>Karlsson J, Bergström AK, Byström P, Gudasz C, Rodriguez P, Hein C (2015) Terrestrial organic matter input suppresses biomass production in lake ecosystems. <em>Ecology</em> 96:2870-2876. doi: 10.1890/15-0515.1</p> </li> <li> <p>Seekell DA, Lapierre JF, Karlsson J (2015) Trade-offs between light and nutrient availability across gradients of dissolved organic carbon concentration in Swedish lakes: Implications for patterns in primary production. <em>Canadian Journal of Fisheries and Aquatic Sciences</em> 72:1663-1671. doi: 10.1139/cjfas-2015-0187</p> </li> </ul>
Texas 2022 water clarity and color (FLAMe and Sentinel-2)
Water clarity and color were determined for six reservoirs using rapid spatial surveys from a sensor equipped boat and concurrent Sentinel-2 satellite imagery across Texas during drought conditions between the months of July and August 2022. From west to east, these systems include Red Bluff Reservoir, O.H. Ivie Lake, Lake Arrowhead, Lake Brownwood, Lake Waco, and Lake Bonham. For the water year leading up to the sampling dates, the precipitation ranged from 182 mm in Red Bluff Reservoir to 1036 mm in Lake Bonham. A total of 254 km of boat path were covered across the six reservoirs with a mean boat speed of 19.17 km/h. The data for this study covers three spatial approaches 1) along the boat path 2) longitudinal transects from dam to river arm and 3) whole system. For the boat path, data variables include turbidity measured continuously with a YSI EXO2 sonde, Secchi disk depth predicted from the turbidity values, normalized difference turbidity index (NDTI), and dominant wavelength. For both the longitudinal transects and whole system data, variables include the two remotely derived measures of clarity and color, NDTI and dominant wavelength. Data is also categorized by zone as either "arm" (reservoir arm) or "body" (main body) determined by a 4m depth threshold to compare between zones.
FUI Water Color product of inland waters in China at 30-m in 2015
<p>The first 30-meters FUI water color product of China. The product was developed using time-series Landsat 8 imagery and FUI water color retrieval method. Taking into account the huge amount of computational and storage space required for the national-scale water color mapping, the high-performance Google Earth Engine (GEE) cloud-based platform was introduced to support the computation. First, a cloud-free composite in China for the summer of 2015 was generated using time-series Landsat-8 imagery and the Best-Available-Pixel (BAP) compositing algorithm. Then, the first 30-merters FUI water color product of China was developed using the generated BAP composite and the Google Earth Engine computing platform. The first 30-meters FUI water color product can promote the understanding of the water color of water bodies in China, and provide very important information for preserving and restoring inland water quality.</p>
FIGURE 3 in Water column use by reef fishes of different color patterns
FIGURE 3 | Coloration of reef fish species by their position in the water column (benthic, n = 35; demersal, n = 35; pelagic, n = 30). Homogeneous refers to the presence of a moderately homogenous non-silvering color pattern without large contrasting patches (typical of background matching); patches refer to the presence of contrasting contour breaks patches (typical of disruptive coloration); stripes refer to the presence of highly contrasting regular stripes (e.g., black and white stripes, typical of motion-dazzle strategy), and silvering to fishes with silvery homogenous body coloration.
FIGURE 2 in Water column use by reef fishes of different color patterns
FIGURE 2 | Phylogeny of the 100 species used in this study generated from data in the Open Tree of Life. Branch lengths represent phylogenetic distance and were estimated by the Grafen's method. Color bars denote the water column use (blue shades) and coloration pattern (red shades) we attributed to them.
FIGURE 4 in Water column use by reef fishes of different color patterns
FIGURE 4 | Results of the Bayesian statistical analysis showing the difference in the proportion of coloration types between positions in the water column. Points denote the mode; thick and thin lines denote 67% and 95% credible intervals. Comparisons based on the expected values of the posterior predictive distribution. The analysis indicated that presence of contrasting contour breaks patches is more frequent in benthic than in demersal and pelagic species; and that silvering is more frequent in pelagic species than in demersal and benthic species.
FIGURE 1 in Water column use by reef fishes of different color patterns
FIGURE 1 | Species exemplifying the color patterns used in this study. A. Large contrasting patterns typical of disruptive coloration in Hippocampus reidi (~ 13 cm of total length, TL); B. Silvery bodies in Haemulon aurolineatum Cuvier, 1830 (~ 18 cm TL); C. Contrasting stripes typical of motion-dazzle in Elacatinus figaro Sazima, Moura & Rosa, 1997 (~ 3 cm TL); D. Homogeneous coloration in adult female Parablennius pilicornis (Cuvier, 1829) (~ 6 cm TL). Photographs by Gualter Pedrini.
Data from: Multi-scale landscape and wetland drivers of lake total phosphorus and water color
We quantified relationships between local wetland cover in the riparian lake buffer and lake total phosphorus (TP) and water color (color) using multilevel mixed-effects models that also incorporate landscape features such as hydrogeomorphology and land use at broad regional scales to determine 1) Within regions, are local wetland relationships with TP and color affected by interactions with local land use or hydrogeomorphic variables? 2) Across regions, are local wetland relationships with TP and color different? And if so, 3) Are differences in local wetland relationships with TP and color a result of cross-scale interactions? We answered these questions by analyzing TP, color, and multi-scaled landscape data for 1790 North temperate lakes. We found that local wetland-TP and wetland-color relationships were not affected by local-scale interactions; we found that local wetland-TP and wetland-color relationships were different across regions; and these differences were related to cross-scale interactions with regional landscape characteristics. For example, regional human land-use affected local wetland-TP relationships such that in regions with high amounts of agriculture, local wetlands were associated with decreased lake TP. However, in regions with low amounts of agriculture, local wetlands were associated with increased lake TP. In contrast, regional hydrogeomorphic characteristics influenced local wetland-color relationships such that in regions with high groundwater contribution, the strength of local wetland relationships were weak. Regional landscape setting influences local wetland relationships with TP and color through cross-scale interactions and lake TP and color are controlled by both local-scale wetland extent and regional-scale landscape variables.
Distribution. Now restricted to the Channel Country of SW Queensland and the Lake Eyre Basin in NE South Australia. Descriptive notes. Head-body 95-120 mm, tail 105-160 mm, ear 23-29 mm, hindfoot 32-37 mm; weight 30-50 g. The Fawn Hopping Mouse has body form typical of hopping mice, with very long hindfeet, long tail with distal brush of longer hairs, very long ears, and large protruberant eyes. Dorsal fur is of variable color, from pale pinkish fawn to gray; ventral fur white. Unlike most other hopping mice, it has no throat pouch, but males have a glandular area of naked skin on the chest. Habitat. Occurs in low shrublands and tussock grasslands on stony ("gibber") plains and claypans. Shows marked habitat segregation from the Dusky Hopping Mouse (N. fuscus), which is closely associated with sandy substrates. Food and Feeding. The Fawn Hopping Mouse is mostly granivorous, but also eats other plant material (stems, leaves) and occasionally invertebrates. It uses succulent, salt-adapted plants around edges of claypans as a source of water. Breeding. Reproduction is probably largely opportunistic and aseasonal, with high reproductive output from near-continuous breeding after periods of high rainfall; reported littersize is 1-5, most commonly three; gestation period 38-43 days for nonlactating females. Females may mature later than other hopping mice, with reproductive maturity reached at about six months. Activity patterns. Terrestrial and nocturnal. Fawn Hopping Mice shelter during day in burrow systems that are typically simpler and shallower than those of other hopping mice. Movements, Home range and Social organization. Fawn Hopping Mice generally live singly or in small groups; typically uncommon within range, but population density may increase by an order of magnitude following periods of high rainfall. Status and Conservation. Classified as Near Threatened on The IUCN Red List. The Fawn Hopping Mouse has shown marked decline in range (estimated at greater than 50%), and presumably population size, since European settlement of Australia. This is mostlikely due to predation by the introduced house cat and Red Fox (Vulpes vulpes), and to habitat degradation associated with pastoralism. Bibliography. Brazenor (1934), Burbidge et al. (2008), Finlayson (1939), Gould (1853), Jackson & Groves (2015), Murray et al. (1999), Ogilby (1892), Thomas (1921h), Van Dyck & Strahan (2008), Waite (1898), Watts & Aslin (1981), Woinarski et al. (2014), Wood Jones (1925). in Muridae
Distribution. Now restricted to the Channel Country of SW Queensland and the Lake Eyre Basin in NE South Australia. Descriptive notes. Head-body 95-120 mm, tail 105-160 mm, ear 23-29 mm, hindfoot 32-37 mm; weight 30-50 g. The Fawn Hopping Mouse has body form typical of hopping mice, with very long hindfeet, long tail with distal brush of longer hairs, very long ears, and large protruberant eyes. Dorsal fur is of variable color, from pale pinkish fawn to gray; ventral fur white. Unlike most other hopping mice, it has no throat pouch, but males have a glandular area of naked skin on the chest. Habitat. Occurs in low shrublands and tussock grasslands on stony ("gibber") plains and claypans. Shows marked habitat segregation from the Dusky Hopping Mouse (N. fuscus), which is closely associated with sandy substrates. Food and Feeding. The Fawn Hopping Mouse is mostly granivorous, but also eats other plant material (stems, leaves) and occasionally invertebrates. It uses succulent, salt-adapted plants around edges of claypans as a source of water. Breeding. Reproduction is probably largely opportunistic and aseasonal, with high reproductive output from near-continuous breeding after periods of high rainfall; reported littersize is 1-5, most commonly three; gestation period 38-43 days for nonlactating females. Females may mature later than other hopping mice, with reproductive maturity reached at about six months. Activity patterns. Terrestrial and nocturnal. Fawn Hopping Mice shelter during day in burrow systems that are typically simpler and shallower than those of other hopping mice. Movements, Home range and Social organization. Fawn Hopping Mice generally live singly or in small groups; typically uncommon within range, but population density may increase by an order of magnitude following periods of high rainfall. Status and Conservation. Classified as Near Threatened on The IUCN Red List. The Fawn Hopping Mouse has shown marked decline in range (estimated at greater than 50%), and presumably population size, since European settlement of Australia. This is mostlikely due to predation by the introduced house cat and Red Fox (Vulpes vulpes), and to habitat degradation associated with pastoralism. Bibliography. Brazenor (1934), Burbidge et al. (2008), Finlayson (1939), Gould (1853), Jackson & Groves (2015), Murray et al. (1999), Ogilby (1892), Thomas (1921h), Van Dyck & Strahan (2008), Waite (1898), Watts & Aslin (1981), Woinarski et al. (2014), Wood Jones (1925).
Machine Learning Constructs Color Features to Accelerate Development of Long-Term Continuous Water Quality Monitoring
<p>This is a machine learning method for predicting the concentration of colored pollutants based on RGB and kmeans methods. This dataset includes raw images of pollutants as well as characteristic data of pollutants, as well as code for the model. You can see the contents of the zip file for details.</p>
FIGURE 5 in Alpheus luiszapatai, a new species of rare and colorful deep water alpheid shrimp (Crustacea: Decapoda: Alpheidae) from Arusí, Chocó Department, Pacific Coast of Colombia
FIGURE 5. Alpheus luiszapatai sp. nov. holotype, female CL 22.9 mm, Arusí, Chocó, Pacific coast of Colombia (USNM 1468999): A, major cheliped, external view; B, same, mesial view; C, merus detail; D, minor cheliped, external view; E, same, mesial view; F, merus detail; G, fixed finger and dactylus, mesial view, detail, H, dactylus, external view. Scales A, B, D, E = 5 mm, C, F, G = 1 mm, H = 2 mm.
FIGURE 3 in Alpheus luiszapatai, a new species of rare and colorful deep water alpheid shrimp (Crustacea: Decapoda: Alpheidae) from Arusí, Chocó Department, Pacific Coast of Colombia
FIGURE 3. Alpheus luiszapatai sp. nov. holotype, female CL 22.9 mm, Arusí, Chocó, Pacific coast of Colombia (USNM 1468999): A, anterior part of the carapace and cephalic appendices, dorsal view; B, same, lateral view; C, same, infero-lateral view; D, first antennular segment, lateral view; E, left second pereopod, lateral view; F, right third pereopod, lateral view; G, left third pereopod, detail of distal propodus and dactyl, mesial view; H, left fifth pereopod, detail of distal propodus and dactylus, mesial view; I, telson and uropods, dorsal view. Scales A, B, E, F, I = 5 mm, C = 2 mm, D = 2.5 mm, G, H = 1mm.
FIGURE 2 in Alpheus luiszapatai, a new species of rare and colorful deep water alpheid shrimp (Crustacea: Decapoda: Alpheidae) from Arusí, Chocó Department, Pacific Coast of Colombia
FIGURE 2. Alpheus luiszapatai sp. nov. holotype, female CL 22.9 mm, Arusí, Chocó, Pacific coast of Colombia (USNM 1468999): habitus, lateral view. Scale = 10 mm.
FIGURE 1 in Alpheus luiszapatai, a new species of rare and colorful deep water alpheid shrimp (Crustacea: Decapoda: Alpheidae) from Arusí, Chocó Department, Pacific Coast of Colombia
FIGURE 1. Map of type locality of Alpheus luiszapatai sp. nov., Arusí, Chocó, Pacific coast of Colombia.
FIGURE 4 in Alpheus luiszapatai, a new species of rare and colorful deep water alpheid shrimp (Crustacea: Decapoda: Alpheidae) from Arusí, Chocó Department, Pacific Coast of Colombia
FIGURE 4. Alpheus luiszapatai sp. nov. holotype, female CL 22.9 mm, Arusí, Chocó, Pacific coast of Colombia (USNM 1468999): Left side mouthparts, lateral view: A, left mandible; B first Maxilla; C, second maxilla; D, first maxilliped; E, second maxilliped; F, third maxilliped. Scales A-E = 1 mm, F = 5 mm.
Figs. 1–5. Oxybleptes kiteleyi. 1 in Report of Two Rove Beetle Species (Coleoptera: Staphylinidae), Oxybleptes kiteleyi Smetana, 1982 and Omalium rivulare (Paykull, 1789), Collected in Colored Water Traps
Figs. 1–5. Oxybleptes kiteleyi. 1) Male. Photograph by filiperibeiro via iNaturalist, used under a CC BY-NC 4.0 license; 2) Female, presumably calling males from a nearby swarm. Photograph by alisonnetta via iNaturalist, used under a CC BY-NC 4.0 license; 3) Range of O. kiteleyi. Open symbols represent state-level records without finer locality information. A female specimen from peninsular Florida reported by Frank et al. (2005) that may be O. kiteleyi but needs confirmation is not shown; 4) Males crawling out of the pink kiddie pool after landing and becoming trapped in the water; 5) The pink kiddie pool in situ, with MJS's daughter helping collect beetles.
Figs. 6–8. Omalium rivulare. 6 in Report of Two Rove Beetle Species (Coleoptera: Staphylinidae), Oxybleptes kiteleyi Smetana, 1982 and Omalium rivulare (Paykull, 1789), Collected in Colored Water Traps
Figs. 6–8. Omalium rivulare. 6) Dorsal habitus of collected individual; 7) Range of O. rivulare. Open symbols represent state- and province-level records without finer locality information; 8) Individuals caught in the water and on the sides of the orange five-gallon bucket.
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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.