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: Random versus game trail-based camera trap placement strategy for monitoring terrestrial mammal communities
Open the record for dataset details and reuse information.
Data from: Estimating density for species conservation: comparing camera trap spatial count models to genetic spatial capture-recapture models
Open the record for dataset details and reuse information.
Dataset from: Are we telling the same story? Comparing inferences made from camera trap and telemetry data for wildlife monitoring
Open the record for dataset details and reuse information.
Data from: Estimating animal density without individual recognition using information derivable exclusively from camera traps
Open the record for dataset details and reuse information.
Data from: The challenges of recognising individuals with few distinguishing features: identifying red foxes Vulpes vulpes from camera-trap photos
Open the record for dataset details and reuse information.
Linking camera-trap data to taxonomy: Identifying photographs of morphologically similar chipmunks
Open the record for dataset details and reuse information.
Data from: Accuracy of identifications of mammal species from camera trap images: a northern Australian case study
Open the record for dataset details and reuse information.
Data from: Using camera trapping and hierarchical occupancy modelling to evaluate the spatial ecology of an African mammal community
Open the record for dataset details and reuse information.
Data from: Effectiveness of camera traps for quantifying daytime and nighttime visitation by vertebrate pollinators
Open the record for dataset details and reuse information.
Data from: A camera trap based assessment of climate-driven phenotypic plasticity of seasonal moulting in an endangered carnivore
Open the record for dataset details and reuse information.
Camera trap data of mammals from Baluran National Park
Open the record for dataset details and reuse information.
Data from: Density-dependent space use affects interpretation of camera trap detection rates
<p>Camera-traps (CTs) are an increasingly popular tool for wildlife survey and monitoring. Estimating relative abundance in unmarked species is often done using detection rate as an index of relative abundance, which assumes a positive linear relationship with true abundance. This assumption may be violated if movement behavior varies with density, but the degree to which movement is density-dependent across taxa is unclear. The potential confounding of population-level relative abundance indices by movement depends on how regularly, and by what magnitude, movement rate and home-range size vary with density. We conducted a systematic review and meta-analysis to quantify relationships between movement rate, home range size, and density, across terrestrial mammalian taxa. We then simulated animal movements and CT sampling to test the effect of contrasting movement scenarios on CT detection rates. Overall, movement rate and home range size were negatively correlated with density and positively correlated with one another. The strength of the relationships varied significantly between taxa and populations. In simulations, detection rates were related to true abundance but underestimated change, particularly for slower moving species with small home ranges. In situations where animal space use changes markedly with density, we estimate that up to thirty percent of a true change in abundance may be missed due to the confounding effect of movement, making trend estimation more difficult. The common assumption that movement remains constant across densities is therefore violated across a wide range of mammal species. When studying unmarked species using CT detection rates, researchers and managers should consider that such indices of relative abundance reflect both density and movement. Practitioners interpreting changes in detection rates should be aware that observed differences may be biased low relative to true changes in abundance, and that further information on animal movement may be required to make robust inferences on population trends.</p>
Data from: Under the snow: a new camera trap opens the white box of subnivean ecology
Snow covers the ground over large parts of the world for a substantial portion of the year. Yet very few methods are available to quantify biotic variables below the snow, with most studies of subnivean ecological processes relying on comparisons of data before and after the snow cover season. We developed a camera trap prototype to quantify subnivean small mammal activity. The trap consists of a camera that is attached facing downward from the ceiling of a box, which is designed to function as a snow-free tunnel. We tested it by placing nine traps with passive infrared sensors in a subarctic habitat where snow cover lasted for about 6 months. The traps were functional for the whole winter, permitting continuous data collection of site-specific presence and temporal activity patterns of all three small mammal species present (the insectivorous common shrew, Sorex araneus, the herbivorous tundra vole, Microtus oeconomus, and the carnivorous stoat, Mustela erminea) as well as abiotic conditions (presence/absence of snow cover and subnivean temperature). Based on their successful functioning (only 6% of the photographs appeared empty or were of poor quality, whereas ca 80% were of small mammals and the remaining of birds and invertebrates), we discuss how the new camera trap can enable subnivean studies of small mammal communities. This greatly increases the temporal resolution and extent of data collection and thereby provides unpreceded opportunities to understand population and food web dynamics in ecosystems with snow cover.
Data from: Estimating the intensity of use by interacting predators and prey using camera traps
Understanding how organisms distribute themselves in response to interacting species, ecosystems, climate, human development and time is fundamental to ecological study and practice. A measure to quantify the relationship among organisms and their environments is intensity of use: the rate of use of a specific resource in a defined unit of time. Estimating the intensity of use differs from estimating probabilities of occupancy or selection, which can remain constant even when the intensity of use varies. We describe a method to evaluate the intensity of use across conditions that vary in both space and time. We demonstrate its application on a large mammal community where linear developments and human activity are conjectured to influence the interactions between white‐tailed deer (Odocoileus virginianus) and wolves (Canis lupus) with possible consequences on threatened woodland caribou (Rangifer tarandus caribou). We collect and quantify intensity of use data for multiple, interacting species with the goal of assessing management efficacy, including a habitat restoration strategy for linear developments. We test whether blocking linear developments by spreading logs across a 200‐m interval can be applied as an immediate mitigation to reduce the intensities of use by humans, predator and prey species in a boreal caribou range. We deployed camera traps on linear developments with and without restoration treatments in a landscape exposed to both timber and oil development. We collected a three‐year dataset and employed spatial recurrent event models to analyse intensity of use by an interacting human and large mammal community across a range of environmental and climatic conditions. Spatial recurrent event models revealed that intensity of use by humans influenced the intensity of use by all five large mammal species evaluated, and the intensities of use by wolves and deer were inextricably linked in space and time. Conditions that resist travel on linear developments had a strong negative effect on the intensity of human and large mammal use. Mitigation strategies that resist, or redirect, animal travel on linear developments can reduce the effects of resource development on interacting human and predator–prey interactions. Our approach is easily applied to other continuous time point‐based survey methodologies and shows that measuring the intensity of use within animal communities can help scientists monitor, mitigate and understand ecological states and processes.
Camera trap data: Density dependence of daily activity in three ungulate species
<p><span><span><span><span><span><span><span><span><span><span><span>Daily activity in herbivores reflects a balance between finding food and safety. The safety-in-numbers theory predicts that living in higher population densities increases safety, which should affect this balance. High-density populations are thus expected to show a more even distribution of activity – i.e. spread – and higher activity levels across the day. We tested these predictions for three ungulate species; red deer (<i>Cervus elaphus</i>), roe deer (<i>Capreolus capreolus</i>) and wild boar (<i>Sus scrofa</i>). We used camera traps to measure the level and spread of activity across ten forest sites at the Veluwe, the Netherlands, that widely range in ungulate density. Food availability and hunting levels were included as covariates. Daily activity was more evenly distributed when population density was higher for all three species. Both deer species showed relatively more feeding activity in broad daylight and wild boar during dusk. Activity level increased with population density only for wild boar. Food availability and hunting showed no correlation with activity patterns. These findings indicate that ungulate activity is to some degree density dependent. However, while these patterns might result from larger populations feeling safer as the safety-in-numbers theory states, we cannot rule out that they are the outcome of greater intraspecific competition for food, forcing animals to forage during suboptimal times of the day. Overall, this study demonstrates that wild ungulates adjust their activity spread and level based on their population size.</span></span></span></span></span></span></span></span></span></span></span></p>
Tiwi Island cat density camera-trap data 2017 and 2018
<p>This data was collected as part of the National Environmental Science Program's Threatened Species Recovery Hub (Project 1.1.12 - Mitigating cat impacts on the brush-tailed rabbit-rat). This dataset includes all detections of feral cats recorded on large grids of camera-traps deployed at four locations on the Tiwi Islands. Each of these grids consisted of 70 camera-traps, deployed in 14 rows of five cameras, with each camera spaced ~500 m apart. Camera-traps remained continuously recording for eight weeks. The location of each camera-trap is also provided.</p>
Data for: Estimation of density distribution in unmarked populations using camera traps
<p>Reliable estimates of species distribution and density are essential to ecology. Camera traps have revolutionized wildlife monitoring, and camera-trap data are increasingly used to study animal distribution and density. </p> <p>We propose a general framework and present a statistical model to estimate the distribution and density of species for which individuals lack identifying marks. Numbers recorded at traps allow spatial variation in density to be modelled, while distances of detected animals from the cameras allow correction for missed animals in the detection sector, using distance sampling.</p> <p>We test the model by simulating a camera-trap survey of a population of single animals, and we apply the model to data from a field study of Reeves's muntjac. The simulation indicated that the estimates of population density were unbiased, and the model performed well in depicting spatial variation in density. In the field study, the model estimated that the overall population density of Reeves's muntjac was 4.1 ind/km<sup>2</sup>, and mapped its density distribution across the study area.</p> <p>We provide a method to estimate unmarked species' density distribution using camera-trap data. Application of the model can help investigate the distribution and density of many ground-dwelling solitary animal populations lacking individually recognizable markings. We expect our method to provide an effective means for wildlife monitoring.</p>
Supplementary material 1 from: Thaung R, Frechette J, Luskin MS, Amir Z (2023) Combining Camera Trap Data and Environmental Data to Estimate the Effects of Environmental Gradients on Abundance of the Asian Elephant Elephas maximus in Cambodia. Biodiversity Information Science and Standards 7: e112100. https://doi.org/10.3897/biss.7.112100
Environmental Variables Used in the study
Data from: Estimating the intensity of use by interacting predators and prey using camera traps
Open the record for dataset details and reuse information.
Data from: Under the snow: a new camera trap opens the white box of subnivean ecology
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.