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385 results for “Environmental factors”
Figure 4. T-S in The spatial and temporal abundance of the spider crab Libinia ferreirae Brito Capello, 1871 (Crustacea, Brachyura) considering different environmental factors
Figure 4. T-S diagrams of the temporal variation in bottom-water temperature and salinity during the sampling period (January 1998 – December 1999) at Ubatumirim, Ubatuba and Mar Virado, São Paulo State, South eastern Brazilian coast. CW – Coastal Water, TW – Tropical Water, SACW – South Atlantic Central Water.
Figure 2 in The spatial and temporal abundance of the spider crab Libinia ferreirae Brito Capello, 1871 (Crustacea, Brachyura) considering different environmental factors
Figure 2. Bottom and surface temperature variation per seasons, areas and stations in 1998 and 1999 in Ubatumirim Bay, Ubatuba Bay and Mar Virado Bay. (BT) Bottom temperature. (ST) Surface temperature.
Figure 6. Libinia ferreirae Brito Capello, 1871 in The spatial and temporal abundance of the spider crab Libinia ferreirae Brito Capello, 1871 (Crustacea, Brachyura) considering different environmental factors
Figure 6. Libinia ferreirae Brito Capello, 1871. Heatmap showing variation in the abundance in relation to phi, along the sampling stations of Ubatumirim (UBM), Ubatuba (UBA) and Mar Virado (MV), São Paulo State littoral. N total – number of individuals, she – sheltered, exp – exposed.
Figure 3 in The spatial and temporal abundance of the spider crab Libinia ferreirae Brito Capello, 1871 (Crustacea, Brachyura) considering different environmental factors
Figure 3. Bottom salinity variation per seasons, areas and stations in 1998 and 1999 in Ubatumirim Bay, Ubatuba Bay and Mar Virado Bay.
Figure 1 in The spatial and temporal abundance of the spider crab Libinia ferreirae Brito Capello, 1871 (Crustacea, Brachyura) considering different environmental factors
Figure 1. Map of the Ubatuba region (North eastern coast of São Paulo State), Brazil, showing the three bays and their respective sampling stations (modified from Fransozo et al. 2013).
Figure 5 in The spatial and temporal abundance of the spider crab Libinia ferreirae Brito Capello, 1871 (Crustacea, Brachyura) considering different environmental factors
Figure 5. Proportions of grain-size classes, central tendency of bottom sediments (phi) and mean values of the organic matter content of the sediment (% OM) for each depth in Ubatumirim, Ubatuba and Mar Virado, São Paulo State littoral, South eastern Brazilian coast. (A) Class A (gravel, very coarse sand, coarse sand, and intermediate sand). (B) Class B (fine and very fine sand). (C) Class C (silt-clay).
Population differences in Chinook salmon (Oncorhynchus tshawytscha) DNA methylation: genetic drift and environmental factors
<p>Local adaptation and phenotypic differences among populations have been reported in many species, though most studies focus on either neutral or adaptive genetic differentiation. With the discovery of DNA methylation, questions have arisen about its contribution to individual variation in and among natural populations. Previous studies have identified differences in methylation among populations of organisms, although most to date have been in plants and model animal species. Here we obtained eyed eggs from eight populations of Chinook salmon (<i>Oncorhynchus tshawytscha</i>) and assayed DNA methylation at 23 genes involved in development, immune function, stress response, and metabolism using a gene-targeted PCR-based assay for next-generation sequencing. Evidence for population differences in methylation was found at eight out of 23 gene loci after controlling for developmental timing in each individual. However, we found no correlation between freshwater environmental parameters and methylation variation among populations at those eight genes. A weak correlation was identified between pairwise DNA methylation dissimilarity among populations and pairwise F<sub>ST</sub> based on 15 microsatellite loci, indicating weak effects of genetic drift or geographic distance on methylation. The weak correlation was primarily driven by two genes, GTIIBS and Nkef. However, single-gene Mantel tests comparing methylation and pairwise F<sub>ST</sub> were not significant after Bonferroni correction. Thus, population differences in DNA methylation are more likely related to unmeasured oceanic environmental conditions, local adaptation, and/or genetic drift. DNA methylation is an additional mechanism that contributes to among population variation, with potential influences on organism phenotype, adaptive potential, and population resilience.</p>
Influence of social and environmental factors for Culex quinquefasciatus distribution in Northeastern Brazil: a risk index
<p>Article data influence of social and environmental factors for Culex quinquefasciatus distribution in Northeastern Brazil: a risk index.</p>
Role of environmental factors in the genetic structure of a highly mobile seabird
<p><strong><span>Aim:</span></strong><span> Environmental features can act as selection pressures and barriers to gene flow between populations. The genetic structuring of highly mobile but philopatric seabirds creates a paradox, and the role of oceanographic and geographic variables is still poorly understood. In this study, we investigate the influence of environmental and geographic variables in the genetic and phenotypic diversity of a pantropical seabird breeding in islands and archipelagos separated by different geographic distances, up to thousand kilometers, and which differ in environmental characteristics.</span></p> <p><strong><span>Location:</span></strong><span> Islands and archipelagos in the southwestern Atlantic Ocean.</span></p> <p><strong><span>Taxon:</span></strong><span> <em>Sula dactylatra</em><span>, Lesson, 1831 (masked booby)</span><em>.</em></span></p> <p><strong><span>Methods:</span></strong><span> </span><span>The population structure of the species was accessed through mitochondrial and nuclear DNA. To test Isolation by Environment (IBE) <em>vs</em>. by Distance (IBD), sea surface temperature, primary productivity, and salinity, as well as isotopic niche based on carbon and nitrogen, and distances between colonies and from the continent, were used. We also tested the correlation between the genetic structure and the morphometry of individuals in each colony.</span></p> <p><strong><span>Results:</span></strong><span> We identified the presence of low genetic structure between populations. Nevertheless, differences were identified between inshore and offshore colonies, with the influence of landscape characteristics of these two types of environment. The morphometric and isotopic niche variations are consistent with this segregation. </span></p> <p><strong><span>Main conclusions:</span></strong><span> Environmental variables of coastal and oceanic environments seem to influence the genetic structure of masked boobies, </span><span>even though it is low in the SW Atlantic Ocean</span><span>, highlighting the role of environmental heterogeneity in shaping biodiversity.</span></p>
Fig. 3 in Comparing Realized and Potential Distributions of the Species of Taurocerastinae (Coleoptera: Geotrupidae) to Examine the Relevance of Dispersal Limitations and Contemporary Environmental Factors
Fig. 3. Predicted potential distributions (red areas) when the conditions of the modeled environmental predictors are extrapolated to a global extent. A) Frickius variolosus, B) Taurocerastes patagonicus.
Fig. 1. Taurocerastinae.A in Comparing Realized and Potential Distributions of the Species of Taurocerastinae (Coleoptera: Geotrupidae) to Examine the Relevance of Dispersal Limitations and Contemporary Environmental Factors
Fig. 1. Taurocerastinae.A) Male Frickius costulatus, B) Male F. variolosus, C) Pair of Taurocerastes patagonicus. Photo A by Guillermo Moreno (used with permission); photos B and C by Mauricio GonzÁlez-Chang.
Fig. 2. A in Comparing Realized and Potential Distributions of the Species of Taurocerastinae (Coleoptera: Geotrupidae) to Examine the Relevance of Dispersal Limitations and Contemporary Environmental Factors
Fig. 2. A) Map showing the geographic occurrences of Frickius costulatus (two green points), F. variolosus (43 red points), and Taurocerastes patagonicus (37 blue points), B) Predicted distributional ranges for the three species, with the green area corresponding to the shared distributional range between the two Frickius species and the yellow area corresponding to the shared distributional area between F. variolosus and T. patagonicus.
Ruinon Landslide, Northern Italy - Evolution and Environmental Factors Through a Virtual Reality Environment - UNITY Dataset
<p><br>This project is part of the thesis titled "Exploring Landslide Evolution and Environmental Factors Through a Virtual Reality Environment", submitted for the MSc. of Geoinformatics Engineering degree at Politecnico di Milano. The development was carried out by Huzaifa Mohammed Khair Khider Abdulaziz and Mohamed Ridaeldin Mukhtar Mohamed during the Academic Year 2023-2024; supervised by Prof. Maria Antonia Brovelli and co-supervised by Dr. Vasil Yordanov.</p> <h2>Video examples of the environment:</h2> <p><a title="Ruinon landslide, Italy - Surrounding area and environmental influencing factors in a VR Environment" href="https://youtu.be/Rpan76VAWFE" target="_blank" rel="noopener">Ruinon landslide, Italy - Surrounding area and environmental influencing factors in a VR Environment</a></p> <p><a href="https://youtu.be/cuq58My4218" target="_blank" rel="noopener">Ruinon landslide, Italy - Landslide evolution over time in the VR Environment.</a></p> <h2>How to use:</h2> <p>To run the project using the Meta Oculus Quest 2, follow these detailed steps:</p> <p> 1. Setting Up Oculus Quest 2 for PC VR</p> <p> A. Install Oculus Software on PC:<br> 1. Download and install the Oculus PC app (Oculus Link) from the official Meta website.<br> 2. Open the Oculus app after installation and sign in to your Oculus account.</p> <p> B. Connecting Oculus Quest 2 to PC:<br> 1. Use a USB-C Cable: Connect the Oculus Quest 2 to your PC using a compatible USB-C cable (Oculus Link cable or any high-quality USB-C cable).<br> 2. Enable Oculus Link:<br> - Once connected, put on your Oculus Quest 2 headset.<br> - In the headset, you’ll see a prompt asking if you want to enable Oculus Link.<br> - Select Enable to connect the headset to the PC.<br> <br> C. Wireless Option (Air Link):<br> 1. Enable Air Link (Optional):If you prefer wireless connectivity, ensure both the PC and Oculus are connected to the same high-speed Wi-Fi network.<br> 2. In the Oculus PC app, go to Settings > Beta and toggle on Air Link.<br> 3. In your headset, go to Settings > Experimental Features and enable Air Link. Pair the headset with the PC following the on-screen instructions.</p> <p> 2. Running the VR Project (VR_Terrain_Visualization.exe):<br> <br> 1. After the Oculus Quest 2 is connected to the PC (either via Oculus Link or Air Link), open Windows Explorer and navigate to the folder containing the project file: VR_Terrain_Visualization.exe.<br> 2. Double-click the VR_Terrain_Visualization.exe file to launch the virtual reality environment.<br> 3. Once the application is running, your Oculus Quest 2 headset will automatically display the project, immersing you in the virtual landslide visualization.<br> <br> 3. Interacting with the VR Environment:<br> <br> - Oculus Quest 2 controllers will allow you to move around, explore the terrain, and interact with different elements of the virtual environment.<br> - Ensure the controllers are working correctly with Unity’s VR features as per the setup.</p> <p> </p>
Fig. 1 in Temperate grassy wetlands of South Africa: Description, classification and explanatory environmental factors
Fig. 1. Distribution map of wetland vegetation plots belonging to temperate grassy wetlands that were included in the present study. The black dots represent historical data and the open dots data newly collected in the current study.
Fig. 2 in Temperate grassy wetlands of South Africa: Description, classification and explanatory environmental factors
Fig. 2. Dendrogram showing the main subdivisions in temperate grassy wetlands. The dendrogram on the right represents the original classification in Sieben et al. (2014) whereas the groups on the left are the simplified version represented in the current paper.
Fig. 3 in Temperate grassy wetlands of South Africa: Description, classification and explanatory environmental factors
Fig. 3. CCA ordination diagrams for temperate grassy wetlands using the same classification as is presented in Fig. 2.
Table 1 in Temperate grassy wetlands of South Africa: Description, classification and explanatory environmental factors
<p><b>Table 1</b> Historical datasets that were included in the present dataset. The shaded references at the bottom are those that only have a few plots that were used.</p><table><tbody><tr><th>Publication/dataset</th><th>Area</th><th>Comments</th><th>No. of plots</th></tr></tbody><tbody><tr><th>Furness (1981)</th><td>Pongola floodplain</td><td>Vegetation plots 10 × 10 m</td><td>26</td></tr><tr><th>Fuls, Bredenkamp & Van Rooyen (1992)</th><td>Pans of Northwestern Free State</td><td>No coordinates, poor environmental data</td><td>4</td></tr><tr><th>Fuls (1993)</th><td>Grasslands Northern Free State</td><td>Vegetation plots of 10 × 10 m</td><td>43</td></tr><tr><th>Bloem, Theron & Van Rooyen (1993)</th><td>Verlorenvlei, Mpumalanga</td><td>Vegetation plots of 10 × 10 m</td><td>13</td></tr><tr><th>Coetzee, Bredenkamp & Van Rooyen (1993)</th><td>Belfast- Wakkerstroom- Barberton- Piet Retief</td><td>No exact coordinates available, unpublished</td><td>18</td></tr><tr><th>Eckhardt, Van Rooyen & Bredenkamp (1993a,b)</th><td>Wetlands Northeastern Free State</td><td>Vegetation plots of 10 × 10 m</td><td>28</td></tr><tr><th>Coetzee, Bredenkamp & Van Rooyen (1994)</th><td>Wetlands of Heidelberg, Witbank, Pretoria area</td><td>Vegetation plots 10 × 10 m</td><td>30</td></tr><tr><th>Guthrie (1996)</th><td>Hlatikhulu, Mooi River, Ntabamhlophe</td><td>Three large wetlands, cover values not in same format</td><td>123</td></tr><tr><th>Eckhardt, Van Rooyen & Bredenkamp (1996)</th><td>Wetlands of Northwestern KwaZulu-Natal</td><td>Vegetation plots of 10 × 10 m</td><td>78</td></tr><tr><th>De Frey (1996)</th><td>South-eastern Mpumalanga</td><td>Vegetation plots 10 × 10 m</td><td>46</td></tr><tr><th>Smit, Bredenkamp, Van Rooyen, van Wyk & Crombrinck (1997)</th><td>Witbank Nature reserve</td><td>Very small amount of wetland plots, mostly terrestrial vegetation</td><td>4</td></tr><tr><th>Cilliers, Schoeman & Bredenkamp (1998)</th><td>Urban wetlands in Potchefstroom</td><td>Three wetlands in detail</td><td>41</td></tr><tr><th>Perkins, Bredenkamp & Granger (2000)</th><td>Southern KwaZulu-Natal</td><td>Vegetation plots of 10 × 10 m</td><td>146</td></tr><tr><th>Burgoyne, Bredenkamp & Van Rooyen (2000)</th><td>Northeastern Mpumalanga</td><td>Vegetation plots large, 20 × 10 m, heterogeneous</td><td>23</td></tr><tr><th>Taylor (2000)</th><td>Mdlanzi Pan, Zululand</td><td>Plots on transects, no wetness data</td><td>6</td></tr><tr><th>Neal (2001)</th><td>Mkhuze floodplains</td><td>Plots on transects, no wetness data</td><td>12</td></tr><tr><th>Goge (2002)</th><td>Eastern shores Lake St. Lucia</td><td>Poor IDs in Swamp Forest</td><td>5</td></tr><tr><th>Venter (2002)</th><td>Mfabeni Swamp, Eastern shores</td><td>One large wetland sampled</td><td>6</td></tr><tr><th>Siebert, Van Wyk, Bredenkamp & Du Plessis (2002)</th><td>Sekhukhuneland</td><td>No coordinates, plots large 20 × 10 m, heterogeneous</td><td>12</td></tr><tr><th>Kotze and O'Connor (2000)</th><td>Middelvlei Wetland, Stellenbosch</td><td>Plots of only 1 × 1 m</td><td>4</td></tr><tr><th>Venter, Bredenkamp, and Grundling (2003)</th><td>Rietvlei in Gauteng</td><td>Many plots in pioneer stage after disturbance</td><td>10</td></tr><tr><th>Malan (2003)</th><td>Cookes Lake recreational area, Mmabatho</td><td>One urban wetland, largely disturbed</td><td>16</td></tr><tr><th>Janecke, DuPreez & Venter (2003)</th><td>Soetdoring Nature Reserve</td><td>Two wetlands</td><td>9</td></tr><tr><th>Cilliers & Bredenkamp (2003)</th><td>Pans in Northwest Province</td><td>Four large pans, many plots with very poor cover</td><td>6</td></tr><tr><th>Cleaver, Brown & Bredenkamp (2004)</th><td>Kamanassie Mountains</td><td>Maybe not all clear wetlands, small springs, plot size variable</td><td>4</td></tr><tr><th>Sieben, Ellery, Garden & Grenfell (2006)</th><td>Wetlands South of Richards Bay</td><td>Unpublished consultancy</td><td>5</td></tr><tr><th>Sieben, Kotze & Morris (2010)</th><td>Maloti-Drakensberg Transfrontier Park</td><td></td><td>147</td></tr><tr><th>Van Aardt (2010)</th><td>Vet River</td><td>No coordinates, poor environmental data</td><td>19</td></tr><tr><th>Collins (2011)</th><td>Free State pans and Valley bottoms</td><td>Soil data and many other variables available</td><td>134</td></tr><tr><th>Corry (2011)</th><td>Western Cape lowlands</td><td>Soil data and many other variables available for most plots</td><td>77</td></tr><tr><th>Pretorius (2011)</th><td>Wetlands Northern Maputaland</td><td>Results not yet published and soil data not yet available</td><td>12</td></tr><tr><th>Cowden, Kotze, Ellery & Sieben (2014)</th><td>Rehabilitated wetlands in KwaZulu-Natal</td><td>Many disturbed sites</td><td>35</td></tr><tr><th>Boucher (1987)</th><td>West Coast</td><td></td><td>3</td></tr><tr><th>Schoultz (2000)</th><td>Mkhuze Swamps</td><td></td><td>1</td></tr><tr><th>Grobler (2009)</th><td>Swamp Forest Kosi Bay</td><td></td><td>2</td></tr></tbody></table>
Table 2 in Temperate grassy wetlands of South Africa: Description, classification and explanatory environmental factors
<p><b>Table 2</b> Environmental variables and their measurement or assessment that have been included in the National Wetland Vegetation database. The variables indicated with an asterisk were not available for all sample plots, only for those plots where a soil sample was taken for analysis.</p><table><tbody><tr><th>Variable</th><th>Type of variable</th><th>Method of measurement</th><th>Method of assessment</th><th>Units</th><th>Transfor-mation</th><th>Abbreviations or categories used in ordination diagram</th></tr></tbody><tbody><tr><th>HGM_type</th><td>Nominal</td><td></td><td>Level 4 of SAWCS classification system (Ollis et al. 2013), Depression, Floodplain, Valley bottom, Valley bottom with channel, Seepage, Channel</td><td>n.a.</td><td></td><td>Depression, Fldplain, VB, VB-wc, Seepage, Channel</td></tr><tr><th>Soil depth</th><td>Nominal</td><td></td><td>Soil augering, subdividing into two categories: deep soils (>50 cm), shallow soils (<50 cm)</td><td>n.a.</td><td></td><td>Soil_dp, Soil_sh</td></tr><tr><th>Wetness</th><td>Index</td><td></td><td>Assessment of soil hydromorphic features following Kotze et al. (1996). Index: 1 = no wetland, 2 = temporary, 3 = temporary/seasonal, 4 = seasonal, 5 = semi-permanent, 6 = permanent</td><td>n.a.</td><td></td><td>Wetness</td></tr><tr><th>Soil texture (1)</th><td>Nominal</td><td></td><td>Feel of soil by touch</td><td>n.a.</td><td></td><td>Peat, Sand, Loam, Clay, Silt, Gravel</td></tr><tr><th>Soil texture (2)</th><td>Ratio*</td><td>Sieving and subdividing into three fractions Clay (<0.002 mm), Silt (0.002–0.05 mm), Sand (0.05–2 mm)</td><td></td><td>mass %</td><td>log</td><td>%Sand, %Clay, %Silt</td></tr><tr><th>Soil organic matter (1)</th><td>Index</td><td></td><td>Checking colour of soil: mineral soils = 1, dark or humic soils = 2, peaty soils = 3</td><td>n.a.</td><td></td><td>Organic</td></tr><tr><th>Soil organic matter (2)</th><td>Ratio*</td><td>Walkley-Black method</td><td></td><td>mass %</td><td>log</td><td>%Carbon</td></tr><tr><th>pH</th><td>Ordinal*</td><td>Water extraction of soil, using pH meter.</td><td></td><td>n.a.</td><td>*</td><td>PH</td></tr><tr><th>Inundation</th><td>Ratio</td><td></td><td>Assessed in field from standing water</td><td>cm</td><td></td><td>Inundation</td></tr><tr><th>Altitude</th><td>Ratio</td><td>using GPS</td><td>Finding locality in Google Earth</td><td>m</td><td></td><td>Altitude</td></tr><tr><th>Slope</th><td>Ratio</td><td></td><td>Assessed in field</td><td>degrees</td><td></td><td>Slope</td></tr><tr><th>Electrical conductivity</th><td>Ratio*</td><td>Water extraction of soil, using conductivity meter</td><td></td><td>mS/cm</td><td>log</td><td>EC</td></tr><tr><th>Nitrogen</th><td>Ratio*</td><td>Sums of measured concentrations of ammonium, nitrite and nitrate, converted towards mol N, converted back into mass N</td><td></td><td>mg/kg</td><td>log</td><td>Nitrogen</td></tr><tr><th>Phosphorus</th><td>Ratio*</td><td>Bray I method</td><td></td><td>mg/kg</td><td>log</td><td>Phosphorus</td></tr><tr><th>Major Cations</th><td>Ratio*</td><td>Using 1:10 water extraction of soil</td><td></td><td>mg/kg</td><td>Standardized, then log</td><td>Na, Ca, K, Mg</td></tr></tbody></table>
Data from: Quantifying effects of environmental and geographical factors on patterns of genetic differentiation
Elucidating the factors influencing genetic differentiation is an important task in biology, and the relative contribution from natural selection and genetic drift has long been debated. In this study, we used a regression-based approach to simultaneously estimate the quantitative contributions of environmental adaptation and isolation by distance on genetic variation in Boechera stricta, a wild relative of Arabidopsis. Patterns of discrete and continuous genetic differentiation coexist within this species. For the discrete differentiation between two major genetic groups, environment has larger contribution than geography, and we also identified a significant environment-by-geography interaction effect. Elsewhere in the species range, we found a latitudinal cline of genetic variation reflecting only isolation by distance. To further confirm the effect of environmental selection on genetic divergence, we identified the specific environmental variables predicting local genotypes in allopatric and sympatric regions. Water availability was identified as the possible cause of differential local adaptation in both geographic regions, confirming the role of environmental adaptation in driving and maintaining genetic differentiation between the two major genetic groups. In addition, the environment-by-geography interaction is further confirmed by the finding that water availability is represented by different environmental factors in the allopatric and sympatric regions. In conclusion, this study found that geographical and environmental factors together created stronger and more discrete genetic differentiation than isolation by distance alone, which only produced a gradual, clinal pattern of genetic variation. These findings emphasize the importance of environmental selection in shaping patterns of species-wide genetic variation in the natural environment.
Data from: Fine-scale genetic structure in a wild bird population: the role of limited dispersal and environmentally-based selection as causal factors
Individuals are typically not randomly distributed in space; consequently ecological and evolutionary theory depends heavily on understanding the spatial structure of populations. The central challenge of landscape genetics is therefore to link spatial heterogeneity of environments to population genetic structure. Here, we employ multivariate spatial analyses to identify environmentally induced genetic structures in a single breeding population of 1174 great tits Parus major genotyped at 4701 single-nucleotide polymorphism (SNP) loci. Despite the small spatial scale of the study relative to natal dispersal we found multiple axes of genetic structure. We built distance-based Moran's eigenvector maps to identify axes of pure spatial variation, which we used for spatial correction of regressions between SNPs and various external traits known to be related to fitness components (avian malaria infection risk, local density of conspecifics, oak tree density and altitude). We found clear evidence of fine-scale genetic structure, with 21, 7 and 9 significant SNPs respectively associated with infection risk by two species of avian malaria (Plasmodium circumflexum and P. relictum) and local conspecific density. Such fine-scale genetic structure relative to dispersal capabilities suggests ecological and evolutionary mechanisms maintain within-population genetic diversity in this population with the potential to drive micro-evolutionary change.
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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.