Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
107
datasets available to search
ShareScore release 0.9.0
Dataset results
107 results for “Camera trap data”
Data from: Camera traps reveal seasonal variation in activity and occupancy of the Alpine mountain hare (Lepus timidus varronis)
<p>Mountain hare is a cold-adapted species threatened by climate change, but despite its emblematic nature, our understanding of the causes of population decline remains limited. Camera traps are increasingly used in ecology as a tool for monitoring animal populations at large spatial and temporal scales. In mountain environments where field work is constrained by difficult access and harsh conditions, camera traps constitute a promising tool for surveying rare and elusive species such as the mountain hare. Our study explored the use of camera traps as a tool for studying seasonal habitat occupancy and daily activity patterns of the mountain hare, in order to carry out long-term monitoring of populations. We installed 46 camera traps along elevation gradients in the Mont-Blanc massif (France) from January 2018 to June 2022. We measured habitat variables at each camera trap site in order to define vegetation composition and habitat structure. We performed multi-season and single-season occupancy models to respectively describe habitat occupancy of the mountain hare throughout the year and identify the environmental variables influencing mountain hare presence during the breeding season. Mountain hares occupy coniferous forest in winter, and then switch to mixed areas of shrubland and grassland above treeline in spring and the beginning of summer. In spring, occupancy probability of the mountain hare increases with relative cover of mixed low shrub and herbaceous layer (i.e. the 10-40 cm vegetation layer), suggesting a link to food resources and protection from predation. Our results also confirm the nocturnal and crepuscular activity of the mountain hare during the breeding season, and strictly nocturnal activity in winter. Our results demonstrate the efficiency of camera traps as tools for monitoring mountain hare habitat occupancy in mountain environments and underline the importance of diverse habitat mosaics for the preservation of the species.</p>
Data from: using camera traps and N-mixture models to estimate population abundance: model selection really matters
<p>Estimating the abundance or density of wildlife populations is a critical part of species conservation and management, but estimates can vary greatly in precision and accuracy according to the data collection and statistical methods, sampling and ecological variation, and sample size. N-mixture models are a common method which has been applied to a wide range of taxa for estimating population abundance from non-invasive data representing the distribution of the species. We used population estimates from an aerial survey of moose and videos from camera traps to assess the sensitivity of N-mixture models to ecological conditions, the spatial scale at which they were measured, the criteria used to define independent detections, and model choice based on the common statistical criterion of parsimony. The most parsimonious N-mixture models were considerably biased, producing implausibly large and considerably imprecise estimates of the abundance of moose. Most of the other models produced estimates of abundance that were ecologically realistic and relatively accurate. The accuracy of population estimates produced by N-mixture models were not overly sensitive to the formulation of models, the scale at which ecological conditions were measured, or the criteria used to define independent detection and by extension sample size. Our results suggest that parsimony was a poor measure of the predictive accuracy of the population estimates produced with the N-mixture model. Collecting and processing data from the aerial survey was less expensive and took less time, but data from camera traps can provide valuable information on behavior of the target species as well as insights into multiple species in the community.</p>
Data from: Shooting area of infrared camera traps affects recorded taxonomic richness and abundance of ground-dwelling invertebrates
<p>Ground-dwelling invertebrates are vital for soil biodiversity and function maintenance. Contemporary biodiversity assessment necessitates novel and automatic monitoring methods because of the threat of sharp reductions in soil biodiversity in farmlands worldwide. Using infrared camera traps (ICTs) is an effective method for assessing richness and abundance of ground-dwelling invertebrates. However, the influence that the shooting area of ICTs has on the diversity of ground-dwelling invertebrates has not been strongly considered during survey design. In this study, data from 6 ICTs with two shooting areas (A1, 38.48 cm<sup>2</sup>; A2, 400 cm<sup>2</sup>) were used to investigate ground-dwelling invertebrates in a farm in a city on the Eastern Coast of China from 20:00 on July 31 to 00:00 on September 29, 2022. Over the course of 59 days and 1,420 h, invertebrates within 9 taxa, 2,447 individuals, and 112,909 ind./m<sup>2</sup> were observed from 222,912 images. Our results show that ICTs with relatively large shooting areas recorded relatively high taxonomic richness and abundance of total ground-dwelling invertebrates, relatively high abundance of the dominant taxon, and relatively high daily and hourly abundance of most taxa. The shooting areas of ICTs significantly affected the recorded taxonomic richness and abundance of ground-dwelling invertebrates throughout the experimental period and at fine temporal resolutions. Overall, these results suggest that the shooting areas of ICTs should be considered when designing experiments, and ICTs with relatively large shooting areas are more favorable for monitoring the diversity of ground-dwelling invertebrates. This study further provides an automatic tool and high-quality data for biodiversity monitoring and protection in farmlands.</p>
Using by-catch camera trapping data for estimating the population size of spotted hyena (Crocuta crocuta)
<p>Spotted hyenas (<em>Crocuta crocuta</em>) are an important carnivore species whose dual role of scavenger and predator is vital to trophic energy flows of systems in which they are found. Where populations of spotted hyenas are small, the environment has few cleaners and carcasses can remain unprocessed. Despite being largely characterized as scavengers, spotted hyenas actively hunt and take down live prey and at high densities can have depressing effects on fragile or choice ungulate populations. In addition, they can alter the structure and composition dynamics of the carnivore guild through direct conflict or indirectly through competition for food and space. Despite their importance to ecosystem function and balance, reliable estimates of spotted hyena densities are rare. This is because unlike lions and leopards, spotted hyenas are generally not regarded as a charismatic species and, as such, survey resources, which are costly, are seldom solely allocated towards surveying them. Nonetheless, being able to confidently estimate spotted hyena numbers is important for the effective management of carnivore and herbivore populations whose dynamics they influence.</p>
Data availability: Random encounter model is a reliable method for estimating population density of multiple species using camera traps
<p>Data of the paper entitled "Random encounter model is a reliable method for estimating population density of multiple species using camera traps" published on Remote Sensing in Ecology and Conservation</p>
Camera trap data used for assessment of movement patterns by injured moose
<p>The data were collected from camera trap recordings of animals on a remote trail in northern British Columbia, Canada, from 2017-2020. The data were used for an assessment of the general fauna composition of the area; and also for assessing movement patterns of moose - especially those with obvious leg injuries. The data set includes 4562 observations - that is, instances of an animal recorded on a given video. For further description see the paper "<strong><span>Performance of Wild Animals with "Broken" Traits: </span></strong><strong><span>Movement Patterns in Nature of Moose That Have Leg Injuries" published in Ecology and Evolution.</span></strong></p>
Temporal data from camera trap captures of raccoons (Procyon lotor) and coyote (Canis latrans) across urban-rural gradient Michigan 2015-2020
<p>Temporal data and trap success for raccoons (<em>Procyon lotor</em>) and coyotes (<em>Canis latrans</em>) across an urban-rural gradient in Michigan, from 2015 to 2020. These data are associated with the article "Temporal refuges of a subordinate carnivore vary across rural-urban gradient" in the journal Ecology and Evolution. </p>
Data belonging to "Successful invasion: camera trap distance sampling reveals higher density for invasive raccoon dog compared to native mesopredators"
<p>Data files (comma separated text files) containing the camera data (CameraData) containing the information on camera trap placements in the various sites and their operation time in days and aperture, the distance sampling data (DistanceData) containing the information on the species and distance detected for each 1s time interval in front of each camera, and the trigger data (TriggerData) containing the time stamps for the pictures taken of each species with each camera, collected in the years 2020 and 2021 in southern Finland. The repository further contains an R script "distanceSamplingScript" which uses the reposited above-described files for analysis reported in the publication "Successful invasion: camera trap distance sampling reveals higher density for invasive raccoon dog compared to native mesopredators" https://doi.org/10.1007/s10530-024-03323-4. The R script has been confirmed to run in R version 4.3.3 using packages "activity" vs 1.3.4 and "Distance" vs 1.0.9</p>
Repository of camera trap data recorded during three pilot studies of the Amsterdamse Waterleidingduinen
<p>Three camera trap data packages (https://camtrap-dp.tdwg.org/) of data collected as part of pilot studies carried out in the Amsterdamse Waterleidingduinen. The pilots were aimed at determining how different types of camera deployment (e.g. regular vs. wide lens, various heights, inside/outside exclosures) might influence species detections, and how to deploy autonomous wildlife monitoring networks. Two pilots were conducted in herbivore exclosures and mainly detected European rabbits (Oryctolagus cuniculus) and red fox (Vulpes vulpes). The third pilot was conducted outside exclosures, with the European fallow deer (Dama dama) being most prevalent. Across all three pilots, a total of 47,597 images were annotated using the Agouti platform. All annotations were verified and quality-checked by a human expert. A total of 2,779 observations of 20 different species (including humans) were observed using 11 wildlife cameras during 2021–2023. The raw image files (excluding humans), image metadata, deployment metadata and observations from each pilot are shared using the Camtrap DP open standard and the extended data publishing capabilities of GBIF to increase the findability, accessibility, interoperability, and reusability of this data. The data are freely available and can be used for developing artificial intelligence (AI) algorithms that automatically detect and identify species from wildlife camera images.</p> <p><a name="_Hlk161305518"></a>The repository contains a data package in <a href="https://camtrap-dp.tdwg.org/">Camtrap DP format </a> for each of the three pilots. Camtrap DP is an open standard for the exchange and archiving of camera trap data using a standardized data structure. Each data package consists of the following resources:</p> <p>· <strong>datapackage.json:</strong> Contains metadata about the data package and camera trap project from which the data originates. Describes taxonomic, temporal, and spatial extent.</p> <p>· <strong>deployments.csv:</strong> Table of individual camera trap deployments, detailing exact location and times active of each camera deployment.</p> <p>· <strong>media.csv: </strong>Table detailing every image in the data package. Lists the filenames and paths of images within the data package.</p> <p>· <strong>observations.csv:</strong> Table of observations of species (or lack thereof) derived from the images.</p> <p>· <strong>e</strong><strong>vents.csv: </strong>Table linking observation events to media.</p> <p>· <strong>media folder:</strong> Folder containing a subfolder for each deployment, which contains the raw images from that deployment.</p> <p>Some additional notes, specific to these datasets:</p> <p>· The deployment table contains “deployment tags”, which specify extra information about the deployment, formatted as key:value pairs, separated by pipes (‘|’). Of particular interest for these datasets are the tags that state lens angle, specify habitat type and identify paired cameras (e.g. to assess differences in species detections between cameras with regular and wide lens, respectively).</p> <p>· In all three pilots, most observations are linked to sequences of images recorded within 120 seconds of each other. Hence, observations in these datasets are generally linked to an “event” (i.e. a sequence of images) rather than to an individual media file. We have added an events table to more easily link observation events and the media items that make up that event. This is an extension of the camera trap DP standard.</p> <p>· All annotations were verified and checked by a human expert, even in cases where an observation is listed as being made by an AI algorithm.</p> <p>· Whether or not an image is included in the data package is indicated by the ‘filePublic’ column in the media table. All raw images are included except for those where humans were detected. Images in which humans were detected have a ‘filePublic’ value of FALSE. Although the current location of these files within the Agouti platform (<a href="https://www.agouti.eu/">https://www.agouti.eu/</a>) is recorded in the ‘filePath’ column, these files cannot be accessed. The ‘fileName’ of these filles is the original filename they possessed when uploaded to Agouti.</p> <p>· Where ‘filePublic’ is TRUE, the `filePath` given is relative to the root of the data package (e.g. ‘media/<deployment>’) and the `fileName` of the file is the current name of the file within the data package (‘<mediaID>.JPG’). </p> <p>More details about individual metadata fields in the Camtrap DP format can be found on <a href="https://camtrap-dp.tdwg.org/">https://camtrap-dp.tdwg.org/</a>.</p> <p> </p> <p> </p>
Remote camera monitoring and arboreal trapping data for a reintroduced population of red-tailed phascogales (Phascogale calura)
<p>Effective monitoring methods are required to evaluate the success of wildlife reintroduction programs. To improve the threat status of the Vulnerable red-tailed phascogale (<em>Phascogale calura</em>), the Australian Wildlife Conservancy reintroduced the species to a fenced reserve at Mt. Gibson Wildlife Sanctuary. After trialing a variety of post-release monitoring methods, remote camera monitoring and arboreal trapping with an extensive period of pre-luring provided the most information with which to evaluate the success of the reintroduction. To date, reintroduced red-tailed phascogales have increased in both occupancy and population size following releases which began at Mt. Gibson in 2017. Other managers of red-tailed phascogale populations may find the described methods useful, particularly in the context of multi-species reintroductions where trap saturation can reduce capture rates of smaller species, such as phascogales.</p>
Long term bearded pig camera trap data across the SAFE landscape.
<p><strong>Description: </strong></p> <p>Data on camera trap surveys and capture events for bearded pigs across the SAFE landscape from 2011-2017.<br> Data was collected by Dr Oliver Wearn from 2011 to 2014, by Phil Chapman from 2015 to 2016 and by Charlie Davison in 2017.<br> Used to assess how bearded pigs are responding to land-use change in Sabah. NB: These data are a subset of the full SAFE Project core mammal trapping data, but include additional details about bearded pig social structure and abundances.</p> <p><strong>Project: </strong>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/173"><strong>Group Dynamics of Bornean Bearded Pigs: the advantages of behavioural plasticity in changeable landscapes.</strong></a></p> <p><strong>XML metadata: </strong>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=312">here</a></p> <p><strong>Files: </strong>This consists of 1 file: DavisonBeardedPigs.xlsx</p> <p><strong>DavisonBeardedPigs.xlsx</strong></p> <p>This file contains dataset metadata and 2 data tables:</p> <ol> <li> <p><strong>Deployments</strong> (described in worksheet Deployments)</p> <p>Description: Data relating to all random camera trap deployments</p> <p>Number of fields: 5</p> <p>Number of data rows: 833</p> <p>Fields:</p> <ul> <li><strong>TrapID</strong>: Camera placement point on SAFE project core grids (Field type: Location)</li> <li><strong>Date.On</strong>: Date survey started (Field type: Date)</li> <li><strong>Date.Off</strong>: Date survey ended (Field type: Date)</li> <li><strong>CTNs</strong>: Length of survey (camera trap nights). Zero if camera was faulty. (Field type: Numeric)</li> <li><strong>Landuse</strong>: Land-use type (Field type: Categorical)</li> </ul> </li> <li> <p><strong>Records</strong> (described in worksheet Records)</p> <p>Description: Data relating to all camera trap records of bearded pigs across all land-uses, and humans and domestic dogs in Oil palm; data generated from individual camera trap images</p> <p>Number of fields: 13</p> <p>Number of data rows: 4236</p> <p>Fields:</p> <ul> <li><strong>TrapID</strong>: Camera placement location on SAFE project core grids (Field type: Location)</li> <li><strong>Date</strong>: Date of photo capture (Field type: Date)</li> <li><strong>Time</strong>: Time of photo capture (Field type: Time)</li> <li><strong>Ambient.Temp</strong>: Temperature at the time of photo capture (Field type: Numeric)</li> <li><strong>Moon.Phase</strong>: Moonphase at time of photo capture (Field type: Categorical)</li> <li><strong>Species</strong>: Identity of the individual(s) (Field type: Taxa)</li> <li><strong>Soc.Str</strong>: Social structure (Field type: Categorical)</li> <li><strong>Sex</strong>: Sex of the individual (Field type: Categorical)</li> <li><strong>No.Individuals</strong>: Number of individuals in survey (Field type: Abundance)</li> <li><strong>No.Juveniles</strong>: Number of adults in survey (Field type: Abundance)</li> <li><strong>No.Subadults</strong>: Number of subadults in survey (Field type: Abundance)</li> <li><strong>No.Adults</strong>: Number of juveniles in survey (Field type: Abundance)</li> <li><strong>Land-use</strong>: Land use type (Field type: Categorical)</li> </ul> </li> </ol> <p><strong>Date range: </strong>2011-04-30 to 2018-04-01</p> <p><strong>Latitudinal extent: </strong>4.6350 to 4.7538</p> <p><strong>Longitudinal extent: </strong>116.9472 to 117.6253</p> <p><strong>Taxonomic coverage: </strong><br> All taxon names are validated against the GBIF backbone taxonomy. If a dataset uses a synonym, the accepted usage is shown followed by the dataset usage in brackets. Taxa that cannot be validated, including new species and other unknown taxa, morphospecies, functional groups and taxonomic levels not used in the GBIF backbone are shown in square brackets.</p> <p>Animalia<br>  - Chordata<br>  -  - Mammalia<br>  -  -  - Artiodactyla<br>  -  -  -  - Suidae<br>  -  -  -  -  - <em>Sus</em><br>  -  -  -  -  -  - <em>Sus barbatus</em><br>  -  -  - Carnivora<br>  -  -  -  - Canidae<br>  -  -  -  -  - <em>Canis</em><br>  -  -  -  -  -  - <em>Canis lupus</em><br>  -  -  -  -  -  -  - <em>Canis lupus familiaris</em><br>  -  -  - Primates<br>  -  -  -  - Hominidae<br>  -  -  -  -  - <em>Homo</em><br>  -  -  -  -  -  - <em>Homo sapiens</em></p> <p> </p>
Data from: Predicting bushmeat biomass from species composition captured by camera traps: implications for locally-based wildlife monitoring
<p>The 'StatAnalysis.zip' contains the data and model files. We used it for the four analyses below.</p> <p>First, we estimated population densities, the mean body mass and camera-trap capture rates of five main bushmeat targets in a rainforest of southeast Cameroon: Peters's duikers (<em>Cephalophus callipygus</em>), bay duikers (<em>C. dorsalis</em>), blue duikers (<em>Philantomba monticola</em>), brush-tailed porcupines (<em>Atherurus africanus</em>) and Emin's pouched rats (<em>Cricetomys emini</em>). Second, on the basis of the density and body mass estimates, we estimated bushmeat biomass—the total biomass of the five bushmeat species—and its spatial variation. Third, we calculated six bushmeat indicators based on the capture rate estimates. Lastly, we examined the correlation between bushmeat biomass and the indicators.</p> <p>The ZIP file consists of 16 R script files, three CSV files (in the 'data' subfolder) and 135 stan files (in the 'stan' subfolders). It also has two empty folders, 'figure' and 'res', where the figures and R objects of model results will be stored following the analyses. Please see the document 'README.txt' before performing the analysis. This text file gives the ZIP file structure and brief descriptions of the files.</p>
Northern Nevada wildlife and topography: Camera trapping data set for 14 mammal species collected from 100 sampling sites in northwestern Nevada
<p>Camera traps are one of the most common field techniques for surverying terrestrial mammal communities and thus, much work has gone into understanding how different factors influence species detection at camera trap locations. However, the effect of fine-scale topography, such as terrain slope and position, on wildlife detection has not been explicitly quantified despite strong effects of topography on animal movement in mountainous regions. This data set contains weekly detection non-detection data for 14 mammal species from 100 camera traps sites monitored for 28 months (June 2018 - September 2020) in northwestern Nevada, U.S.A. This sampling extent was split into 3 month sampling seasons, exclusive of winter (Dec, Jan, Feb) and spring 2020, when data were sparse. In addition to species detection data, that dataset includes topographic variables at cameras sites: 1) terrain slope, calculated in R package raster from a 10m digital elevation model and 2) Topographic position index averaged across three buffer sizes around points 270m, 810m, and 2430m. The land cover variables proportion mixed conifer and proportion pinyon-juniper woodland within a 5000m buffer of sites are also included. Both are derived from the USDA/US DOI Landfire 2016 dataset. The luring variable indicates whether attractant was applied at a site during a given week, the effect of which was assumed to last for a month after the last application. </p>
Mammalian Camera Trap Data; Northwest Arkansas
<p>The human footprint is rapidly expanding, and wildlife habitat is continuously being converted to human residential properties. Surviving wildlife that reside in developing areas are displaced to nearby undeveloped areas. However, some animals can co-exist with humans and acquire the necessary resources (food, water, shelter) within the human environment. This may be particularly true when development is low intensity, as in residential suburban yards. Yards are individually managed "greenspaces" that can provide a range of food (e.g., bird feeders, compost, gardens), water (bird baths and garden ponds), and shelter resources (e.g., brush-piles, outbuildings) and are surrounded by varying landscape cover. To evaluate which residential landscape and yard features influence the richness and diversity of mammalian herbivores and mesopredators; we deployed wildlife game cameras in 46 residential yards in summer 2021 and 96 yards in summer 2022. We found that mesopredator diversity had a negative relationship with fences and was positively influenced by the number of bird feeders present in a yard. Mesopredator richness increased with the amount of forest within 400m of the camera. Herbivore diversity and richness were positively correlated to the area of forest within 400m surrounding yard and by garden area within yards, respectively. Our results suggest that while landscape does play a role in the presence of wildlife in a residential area, homeowners also have agency over the richness and diversity of mammals occurring in their yards based on the features they create or maintain on their properties.</p>
Camera trap grey squirrel photograph data
<p>Effective wildlife population management requires an understanding of the abundance of the target species. <span>In the UK, the increase in numbers and range of the non-native invasive grey squirrel </span><em>Sciurus</em> <em>carolinensis</em><span> poses a substantial threat to the existence of the native red squirrel <em>S. vulgaris</em>, to tree health, and to the forestry industry. Reducing the number of grey squirrels is crucial to mitigate their impacts.</span><span> </span></p> <p>Camera traps are increasingly used to estimate animal abundance, and methods have been developed that do not require the identification of individual animals. Most of these methods have been focussed on medium to large mammal species with large range sizes and may be unsuitable for measuring local abundances of smaller mammals that have variable detection rates and hard-to-measure movement behaviour.</p> <p>The aim of this study was to develop a practical and cost-effective method, based on a camera trap index, that could be used by practitioners to estimate target densities of grey squirrels in woodlands to provide guidance on the numbers of traps or contraceptive feeders required for local grey squirrel control.</p> <p><span>Camera traps were deployed in ten independent woods of between 6 and 28 ha in size. An index, calculated from the number of grey squirrel photographs recorded per camera per day had a strong linear relationship (<em>R<sup>2</sup></em> = 0.90) with the densities of squirrels removed in trap and dispatch operations. From different time filters tested, a 5 minute filter was applied, where photographs of squirrels recorded on the same camera within 5 minutes of a previous photograph were not counted. There were no significant differences between the number of squirrel photographs per camera recorded by three different models of camera, increasing the method's practical application.</span></p> <p><span>This study demonstrated that a camera index could be used to inform the number of feeders or traps required for grey squirrel </span><span>management through culling or contraception. Results could be obtained within six days without requiring expensive equipment or a high level of technical input. This method can easily be adapted to other rodent or small mammal species, making it widely applicable to other wildlife management interventions.</span></p>
Data from: Evaluating predator control using two non-invasive population metrics: a camera trap activity index and density estimation from scat genotyping
<p>Includes datasets from the Wimmera and Mallee, Victoria, Australia:</p> <p>- Fox camera trap data used to model activity</p> <p>- Fox scat SECR capture and trap files used to model density</p>
Data from: Using camera traps to estimate habitat preferences and occupancy patterns of vertebrates in boreal wetlands
<p><span>Wetlands are a critical habitat for boreal mammals and birds that rely on them for breeding, foraging, and resting. However, wetlands in boreal regions are increasingly experiencing natural and human pressures. These impacts can lead to a reduction in the availability of wetland habitats </span><span>for boreal mammals and birds that rely on wetlands for breeding, foraging, and resting. To inform management and conservation, camera traps provide an opportunity to survey mammals and birds to investigate their habitat preferences. We aimed to evaluate the effect of habitat features on the occupancy of mammals and birds in boreal wetlands. We used a multispecies occupancy model to estimate the habitat associations of 11 mammals and 45 avian species detected at 50 sampling ponds </span><span>during the summers of 2018 and 2019 </span><span>in Northern Quebec. Our results indicate that certain mammals, such as Red Fox and River Otters, and birds including </span>the American Pipit, Common Raven, Hooded Merganser, and Greater Yellowlegs <span>showed a preference for peatland ponds, whereas the </span>Common Grackle preferred <span>beaver ponds. We found few effects of distance to roads, and no effect of amount of forest cover on species occupancy. The occupancy of 27% of mammals and 24% of birds decreased with increasing latitude. These findings offer valuable insights for informing conservation initiatives focused on the preservation of wetlands in northern Quebec. By discerning the specific types of ponds preferred by each species, conservationists can strategically ensure the preservation and proper management of these habitats, thereby enhancing their conservation efforts.</span></p>
Camera trap data of small mammals at experimental dishes
Open the record for dataset details and reuse information.
Northern Nevada wildlife and topography: Camera trapping data set for 14 mammal species collected from 100 sampling sites in northwestern Nevada
Open the record for dataset details and reuse information.
Data from: Shooting area of infrared camera traps affects recorded taxonomic richness and abundance of ground-dwelling invertebrates
Open the record for dataset details and reuse information.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.