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585 results for “Camera trap”
MICA - Muskrat and coypu camera trap observations in Belgium, the Netherlands and Germany
<p><em>MICA - Muskrat and coypu camera trap observations in Belgium, the Netherlands and Germany</em> is a camera trap observations dataset published by the <a href="https://inbo.be">Research Institute of Nature and Forest (INBO)</a>. It is part of the <a href="https://lifemica.eu/">LIFE project MICA</a>, in which innovative techniques are tested for a more efficient control of muskrat and coypu populations, both invasive species. The dataset contains camera trap observations of muskrat and coypu, as well as many other observed species.</p> <p>Data in this package are exported from the camera trap management system Agouti (<a href="https://agouti.eu">https://agouti.eu</a>) and formatted as a <a href="https://tdwg.github.io/camtrap-dp/">Camera Trap Data Package (Camtrap DP)</a>.</p> <p><strong>Files</strong></p> <p>Files are structured as a <a href="https://specs.frictionlessdata.io/data-package/">Frictionless Data Package</a>. You can access all data in R via <code>https://zenodo.org/record/5590881/files/datapackage.json</code> using <a href="https://frictionlessdata.github.io/frictionless-r/">frictionless</a>.</p> <ul> <li><strong>datapackage.json</strong>: technical description of the data files.</li> <li><strong>deployments.csv</strong>: camera trap deployments. Includes <code>deploymentID</code>, start, end, location and camera setup information.</li> <li><strong>media.csv</strong>: media files (images/videos) captured by the camera traps. Associated with deployments (<code>deploymentID</code>) and organized in sequences (<code>sequenceID</code>). Includes timestamp and file path.</li> <li><strong>observations.csv</strong>: observations based on the media files. Associated with deployments (<code>deploymentID</code>) and sequences (<code>sequenceID</code>). Observations can mark non-animal events (camera setup, human, blank) or one or more animal observations (<code>observationType</code> = <code>animal</code>) of a certain taxon, count, age, sex, behaviour and/or individual.</li> </ul>
Benchmark datasets for detection and identification of insects from camera trap images with deep learning
<p><strong>Insect benchmark datasets for training, validation and test (train1201.zip, val1201.zip and test1201.zip) with time-lapse images as described in paper:</strong></p> <p><a href="https://www.biorxiv.org/content/10.1101/2022.10.25.513484v1">Bjerge K, Alison J, Dyrmann M, Frigaard C.E., Mann H. M. R., Høye T.T., Accurate detection and identification of insects from camera trap images with deep learning, bioRxiv:10.1101/2022.10.25.513484v1</a></p> <p>Labels in <strong>YOLO format: <a href="https://github.com/ultralytics/yolov5/issues/2293">ultralytics/yolov5: label format</a></strong></p> <p>The annotated training and validation datasets contains insects of nine different species as listed below:</p> <table> <tbody> <tr> <td>0 <em>Coccinellidae septempunctata</em></td> </tr> <tr> <td>1 <em>Apis mellifera</em></td> </tr> <tr> <td>2 <em>Bombus lapidarius</em></td> </tr> <tr> <td>3 <em>Bombus terrestris</em></td> </tr> <tr> <td>4 <em>Eupeodes corolla</em></td> </tr> <tr> <td>5 <em>Episyrphus balteatus</em></td> </tr> <tr> <td>6 <em>Aglais urticae</em></td> </tr> <tr> <td>7 <em>Vespula vulgaris</em></td> </tr> <tr> <td>8 <em>Eristalis tenax</em></td> </tr> </tbody> </table> <p>The test dataset contains additional classes of insects.</p> <table> <tbody> <tr> <td>9 Non-Bombus Anthophila</td> </tr> <tr> <td>10 Bombus spp.</td> </tr> <tr> <td>11 Syrphidae</td> </tr> <tr> <td>12 Fly spp.</td> </tr> <tr> <td>13 Unclear insect</td> </tr> <tr> <td>14 Mixed animals:<br> ——————————<br> Rhopalocera<br> Non-Anthophila Hymenoptera<br> Non-Syrphidae Diptera<br> Non-Conccinalidae Coleoptera<br> Concinellidae<br> Other animals</td> </tr> </tbody> </table> <p><strong>There are two naming conventions for image (.jpg) and label (.txt) files.</strong></p> <p><em>Background images without insects are named</em>:<br> “<strong>X_Seq-YYYYMMDDHHMMSS</strong>-snapshot”.<br> E.g.:<br> Background image: 12_13-20190704172200-snapshot.jpg<br> Empty label file: 12_13-20190704172200-snapshot.txt</p> <p><em>Images annotated with insects are named:</em><br> “<strong>SZ_IP-MonthDate_C_Seq-YYYYMMDDHHMMSS</strong>”.<br> E.g.:<br> Image file: S1_146-Aug23_1_156-20190822133230.jpg<br> Label file: S1_146-Aug23_1_156-20190822133230.txt</p> <p><strong>Abbreviations</strong>:</p> <p><strong>YYYYMMDDHHMMSS </strong>– Capture timestamp with year, month, date, hour, minutes, and second<br> <strong>Seq</strong> – Sequence number created by the motion program to separate images<br> <strong>C</strong> – Identification of two cameras with Id=0 or Id=1 in system identified by <strong>SZ_IP</strong><br> <strong>MonthDate </strong>– Folder name for where the original image were stored in the system<br> <strong>SZ_IP</strong> – Identification of five camera systems: S1_123, S2_146, S3_194, S4_199, S5_187 (Two cameras in each system)<br> <strong>X</strong> – An index number related to a specific camera and folder ensuring unique file names of background images from different camera systems.<br> <br> The important information in a filename is system (<strong>SZ_IP</strong>), camera Id (<strong>C</strong>) and timestamp (<strong>YYYYMMDDHHMMSS</strong>).</p> <p><strong>The three best YOLOv5 models (YOLOv5models.zip) from the paper are available in pytorch format.</strong></p> <p>All models are tested with YOLOv5 release v7.0 (22-11-2022): <a href="https://github.com/ultralytics/yolov5">ultralytics/yolov5: YOLOv5 in PyTorch</a></p> <p><strong>insect1201-bestF1-640v5m.pt</strong>: Model no. 6 in Table 2 (F1=0.912)<br> <strong>insect1201-bestF1-1280v5m6.pt</strong>: Model no. 8 in Table 2 (F1=0.925)<br> <strong>insect1201-bestF1-1280v5m6.pt</strong>: Model no. 10 in Table 2 (F1=0.932)</p> <p><strong>insects-1201val.yaml</strong>: YAML file with label names to train YOLOv5</p> <p><strong>trainInsects-1201m.sh</strong>: Linux bash shell script with parameters to train YOLOv5m6<br> <strong>valInsectsF1-1201.sh</strong>: Linux bash shell script with parameters to validated models</p> <p> </p>
Fig. 1 in Camera traps and genetic identification of faecal samples for detection and monitoring of an endangered ungulate
Fig. 1. Distribution of deployed camera traps showing presence (black circles) and non-detection (purple circles) and genetic sampling locations showing presence (black triangles) and non-detection (purple triangles) of Eld's deer. Inset map shows the location of Chhaeb Wildlife Sanctuary in Cambodia (black rectangle). Background shows proportion of tree cover from WorldCover land cover map (© ESA WorldCover project 2020 / Contains modified Copernicus Sentinel data (2020) processed by ESA WorldCover consortium).
Data from: Holistic monitoring of aquatic and terrestrial vertebrates by camera trapping and aquatic environmental DNA
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Camera trap data suggest uneven predation risk across vegetation types in a mixed farmland landscape
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Combining camera trap surveys and IUCN range maps to improve knowledge of species distributions
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Behavioral “bycatch” from camera trap surveys yields insights on prey responses to human-mediated predation risk
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Using camera traps to estimate site occupancy of invasive Argentine black and white tegus (Salvator merianae) in South Florida
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Data from: Continuous-time spatially explicit capture-recapture models, with an application to a jaguar camera-trap survey
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Listening and watching: do camera traps or acoustic sensors more efficiently detect wild chimpanzees in an open habitat?
<p>1. With one million animal species at risk of extinction, there is an urgent need to regularly monitor threatened species. However, in practice this is challenging, especially with wide-ranging, elusive and cryptic species or those that occur at low density.<br> 2. Here we compare two non-invasive methods, passive acoustic monitoring (n=12) and camera trapping (n=53), to detect chimpanzees (Pan troglodytes) in a savanna-woodland mosaic habitat at the Issa Valley, Tanzania. With occupancy modelling we evaluate the efficacy of each method, using the estimated number of sampling days needed to establish chimpanzee absence with 95% probability, as our measure of efficacy.<br> 3. Passive acoustic monitoring was more efficient than camera trapping in detecting wild chimpanzees. Detectability varied over seasons, likely due to social and ecological factors that influence party size and vocalization rate. The acoustic method can infer chimpanzee absence with less than ten days of recordings in the field during the late dry season, the period of highest detectability, which was five times faster than the visual method.<br> 4. Synthesis and applications: Despite some technical limitations, we demonstrate that passive acoustic monitoring is a powerful tool for species monitoring. Its applicability in evaluating presence/absence, especially but not exclusively for loud call species, such as cetaceans, elephants, gibbons or chimpanzees provides a more efficient way of monitoring populations and inform conservation plans to mediate species-loss.</p>
Sherlock Camera Trap Dataset 9
<p>Part of the camera trap image dataset used for the 'main' Sherlock test. See the base dataset for links to the remaining data. </p>
Sherlock Camera Trap Dataset 7
<p>Part of the camera trap image dataset used for the 'main' Sherlock test. See the base dataset for links to the remaining data. </p>
Sherlock Camera Trap Dataset 8
<p>Part of the camera trap image dataset used for the 'main' Sherlock test. See the base dataset for links to the remaining data. </p>
Sherlock Camera Trap Dataset 6
<p>Part of the camera trap image dataset used for the 'main' Sherlock test. See the base dataset for links to the remaining data. </p>
Sherlock Camera Trap Dataset 4
<p>Part of the camera trap image dataset used for the 'main' Sherlock test. See the base dataset for links to the remaining data. </p>
Sherlock Camera Trap Dataset 5
<p>Part of the camera trap image dataset used for the 'main' Sherlock test. See the base dataset for links to the remaining data. </p>
Sherlock Camera Trap Dataset 3
<p>Part of the camera trap image dataset used for the 'main' Sherlock test. See the base dataset for links to the remaining data. </p>
Sherlock Camera Trap Dataset 2
<p>Part of the camera trap image dataset used for the 'main' Sherlock test. See the base dataset for links to the remaining data. </p>
Locations of black bear (Ursus americanus) reproduction in Nevada from camera-trap data
<p>Understanding factors creating species range boundaries is a fundamental goal of ecology and biogeography. American black bears recolonized the western Great Basin from the Sierra Nevada in the late 1900s but this expansion has not proceeded further into the Great Basin despite the presence of suitable habitat. We deployed 100 camera traps across the occupied range of black bears in the U.S. state of Nevada and tracked bear detections across 3 years. A scent lure was applied in camera trap viewsheds to increase bear detections. We classified detections of bear cubs separately from detections of only adult bears, to serve as an indicator of black bear reproduction occurring at sites. Data are provided in the format necessary for a analysis with multistate occupancy model. Analysis of these data revealed low incidence of reproduction at the periphery of black bear range in the western Great Basin, which likely contributes to range boundary formation.</p>
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>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.