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.
1,832
datasets available to search
ShareScore release 0.9.0
Dataset results
1,832 results for “Cameras”
Data from: Efficacy of spotlights and thermal cameras to detect lions, Panthera leo, and spotted hyenas, Crocuta crocuta, depends on species and management regime
<p class="MsoNormal">Accurate abundance estimates can contribute to effective management of large carnivore populations. Lion, <em>Panthera leo</em>, and spotted hyena, <em>Crocuta crocuta</em>, populations are frequently estimated at night by eliciting their approach using broadcasted vocalizations. Spotlights are typically used to observe these species on approach but can disturb animals and adversely affect counts. We compared the efficacy of spotlight with red filters and forward looking infrared (FLIR) thermal monocular to enumerate lions and spotted hyenas in Serengeti National Park (SNP; non-hunted area) and Maswa Game Reserve (MGR; hunted area), Tanzania, during 2015−2017. We established 119 call-in sites in SNP and 20 in MGR and conducted repeated call-ins at 1–2 week intervals. During call-ins we conducted systematic paired counts using both devices. We assessed the influence of device order, species, hunting regime, and land cover on species counts. We found that FLIR was more efficacious for counting hyenas in MGR and spotlight for counting lions in SNP. We found evidence for temporary artificial light disturbance in MGR, as counts were higher when FLIR was used as the second device. Habitat type within 200 m of call-in sites did not influence device performances. Greater spotlight efficacy in SNP is a likely consequence of lower perceived risk and less anthropogenic disturbance compared to MGR. To improve accuracy of counts and subsequent population estimates for lions and spotted hyenas, we recommend consideration of variation in device efficacy, based on species surveyed and management regime.</p>
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>
Camera traps for monitoring insects - supporting information
<p>Insect and pollinator populations are vitally important to the health of ecosystems, food production, and economic stability, but are declining worldwide. New, cheap, and simple monitoring methods are necessary to inform management actions and should be available to researchers around the world.</p> <p>Here we evaluate the efficacy of commercially available, close-focus automated camera traps to monitor insect-plant interactions. We compared two video settings—scheduled and motion-activated—to a traditional human observation method.</p> <p>Our results show that camera traps with scheduled video settings detected more insects overall than humans, but relative performance varied by insect order. Scheduled cameras significantly outperformed motion-activated cameras, detecting more insects of all orders and size classes.</p> <p>We conclude that scheduled camera traps are an effective and relatively inexpensive tool for monitoring interactions between plants and insects of all size classes, and their ease of accessibility and set-up allows for the potential of widespread use. The digital format of video also offers the benefits of recording, sharing, and verifying observations.</p>
Data from: Can the camera lie? A nonpermanent nick in a bottlenose dolphin (Tursiops truncatus)
[No abstract entered]
Supplementary material 1 from: Premate E, Fišer Ž, Kuralt Ž, Pekolj A, Trajbarič T, Milavc E, Hanc Ž, Kostanjšek R (2022) Behavioral observations of the olm (Proteus anguinus) in a karst spring via direct observations and camera trapping. Subterranean Biology 44: 69-83. https://doi.org/10.3897/subtbiol.44.87295
Figure S1
Kodak Camera - 3D Scan
Source: Objaverse 1.0 / Sketchfab
Figure 3 from: Mertens JEJ, Van Roie M, Merckx J, Dekoninck W (2017) The use of low cost compact cameras with focus stacking functionality in entomological digitization projects. ZooKeys 712: 141-154. https://doi.org/10.3897/zookeys.712.20505
Figure 3 - Comparison of image quality between the compact camera (A) and the professional setup (B) with the specimen occupying the same proportion of the frame. A detail is shown below. The compact camera was set up 5 cm from the specimen with the optical zoom at 1×, 29 images (narrow setting) were manually stacked. The professional setup outperforms the compact camera, producing a sharper image when specimens larger than a few centimetres are set to fill the frame optimally.
Figure 4 from: Mertens JEJ, Van Roie M, Merckx J, Dekoninck W (2017) The use of low cost compact cameras with focus stacking functionality in entomological digitization projects. ZooKeys 712: 141-154. https://doi.org/10.3897/zookeys.712.20505
Figure 4 - Images of different taxonomic groups, shot by the compact camera in manual mode (narrow setting). A large fruit-tree tortrix (Archips podana (Lepidoptera - Tortricidae)) B European paper wasp (Polistes dominula (Hymenoptera - Vespidae)), and C common earwig (Forficula auricularia (Dermaptera - Forficulidae)).
Figure 2 from: Mertens JEJ, Van Roie M, Merckx J, Dekoninck W (2017) The use of low cost compact cameras with focus stacking functionality in entomological digitization projects. ZooKeys 712: 141-154. https://doi.org/10.3897/zookeys.712.20505
Figure 2 - Visualization of the variation in image quality, level of detail and proportion of the specimen fitting the frame (insets) at different levels of optical magnification (1–4 times) and distance from the lens (11–5 cm). Every image, shot with the compact camera, is composed of 29 manually stacked images at the narrow setting and cropped to equal dimensions (approx. 1/24 of the original image). Quality and detail improve as lens distance decreases and/or the zoom increases at the cost of reduced depth of field and a smaller portion of the specimen fitting the image frame.
Figure 1 from: Mertens JEJ, Van Roie M, Merckx J, Dekoninck W (2017) The use of low cost compact cameras with focus stacking functionality in entomological digitization projects. ZooKeys 712: 141-154. https://doi.org/10.3897/zookeys.712.20505
Figure 1 - Comparison of the Elytrimitatrix digitized with the professional setup (A shot with the 60 mm macro lens and B with the Canon MP-E 65 mm lens), the compact camera's manual focus stacking mode (C) and internal stacking mode (D). A depicts the whole specimen as would be shot for publication purposes. The red box indicates the section shown in B, C, D and the blue box indicates how the specimen was framed in these three images. Note that the stronger reflections in C, D are the result of a different lighting setup.
ROV-Based Multi-Sensor Dataset: Synchronized Camera and Sonar images taken in the Tropical Waters of the Red Sea, Eilat
<p><strong>Description:</strong></p> <p>This dataset consists of approximately 46,928 synchronized image pairs collected by the Blue-ROV2. The images were captured using a machine-vision camera (IDS UI-3260CP-C-HQ) and a BluePrint Oculus M1200d Forward-Looking Sonar (FLS). Both sensors were installed with the FLS tilted 15 degrees downward to achieve optimal coverage of the terrain and optimal FOV overlap.</p> <p>The data was collected to train and evaluate a comprehensive perception and obstacle avoidance framework.</p> <p> </p> <p><strong>Context:</strong></p> <p>This dataset is the second installment in our collection of synchronized multi-sensor underwater datasets, aimed at enabling advanced research in multi-modal sensor fusion, obstacle detection, and navigation for autonomous underwater vehicles (AUVs). The data was collected using the Blue-ROV2 Remotely Operated Vehicle (ROV) in the tropical waters of the Red Sea, off the coast of Eilat, Israel. This data captures diverse underwater environments and is part of a research project focused on developing fusion models for improved obstacle detection and navigation in AUVs.</p> <p> </p> <p><strong>Content:</strong></p> <p>The data encompasses several sites within the tropical waters of the Red Sea, Eilat, including corals, rocks, shipwrecks, man-made structures, piers, and caves. The ROV platform was operated by divers, ensuring accurate positioning and coverage. Data was acquired at depths ranging from 3 to 12 meters at different times from dawn to dusk.</p> <p><strong> </strong></p> <p><strong>Dataset Composition:</strong><strong><br></strong></p> <div> <table> <tbody> <tr> <td> <p>Site</p> </td> <td> <p>Recording Session</p> </td> <td> <p>Image Pairs</p> </td> <td> <p>Description</p> </td> </tr> <tr> <td> <p>Tropical Site 1</p> </td> <td> <p>20221211_092506</p> <p>20221211_133252</p> </td> <td> <p>10,915</p> <p>7,978</p> </td> <td> <p>Pier, rocks, corals</p> </td> </tr> <tr> <td> <p>Tropical Site 2</p> </td> <td> <p>20221212_095821</p> <p>20221212_141308</p> </td> <td> <p>9,900</p> <p>8,475</p> </td> <td> <p>Man-made structure, </p> <p>rocks, corals</p> </td> </tr> <tr> <td> <p>Tropical Site 3</p> </td> <td> <p>20221213_102542</p> </td> <td> <p>9,390</p> </td> <td> <p>Rocks, corals</p> </td> </tr> <tr> <td> <p>Total</p> </td> <td> </td> <td> <p>46,928 </p> </td> <td> </td> </tr> </tbody> </table> </div> <p><strong> </strong></p> <p>The dataset is organized into separate sessions, each representing a specific dive or experiment. Within each session, data is further categorized into modalities: camera (FLC images), sonar (FLS images), and depth. Each modality directory contains the corresponding data files in PNG format for images and CSV format for depth data.</p> <p><strong> </strong></p> <p>Each modality directory includes:</p> <ul> <li> <p>A `camera.csv` file for the camera modality that maps each image file to its respective timestamp.</p> </li> <li> <p>A `sonar.csv` file for the sonar modality that maps each image file to its respective timestamp.</p> </li> <li> <p>The depth data in `depth.csv` formatted with `timestamp` and `value`.</p> </li> </ul> <p>Additionally, a `samples.json` file documents the relationship between uni-modal and multi-modal samples, enabling easy association of data from different modalities.</p> <p><strong> </strong></p> <p><strong>Technical Details:</strong></p> <ul> <li> <p>Camera: IDS UI-3260CP-C-HQ</p> </li> <ul> <li> <p>Image dimensions: 1936x1216 pixels (downscaled to 968 × 608 for this dataset)</p> </li> <li> <p>Sensor type: Sony IMX249 1/1.2" CMOS</p> </li> <li> <p>Lens: Tamron M112FM06</p> </li> <li> <p>Captured bit depth: 8-bit</p> </li> <li> <p>Frame rate: 5 Hz</p> </li> </ul> <li> <p>Sonar: BluePrint Oculus M1200d</p> </li> <ul> <li> <p>Operating frequency: 1.2 MHz (low frequency mode)</p> </li> <li> <p>Maximum range: 40 m (set to 15 m for this dataset)</p> </li> <li> <p>Horizontal aperture: 130°</p> </li> <li> <p>Vertical aperture: 20°</p> </li> <li> <p>Number of beams: 512</p> </li> <li> <p>Angular resolution: 0.6°</p> </li> <li> <p>Beam separation: 0.25°</p> </li> <li> <p>Image resolution: 544x300 pixels</p> </li> <li> <p>Coordinate system: Polar</p> </li> <li> <p>Frame rate: 5 Hz</p> </li> </ul> <li> <p>Depth: Blue-Robotics Ping2 Sonar Altimeter and Echosounder</p> </li> <ul> <li> <p>Frequency: 115 kHz</p> </li> <li> <p>Source Level: 198 dB re 1µPa @ 1m</p> </li> <li> <p>Beamwidth: 25 degrees</p> </li> <li> <p>Typical Minimum Range: 0.3 m (1 ft)</p> </li> <li> <p>Typical Usable Range: 100 m (328 ft)</p> </li> <li> <p>Range Resolution: 0.5% of range</p> </li> <li> <p>Depth Rating: 300 m (984 ft)</p> </li> <li> <p>Data format: CSV</p> </li> <li> <p>Columns:</p> </li> <ul> <li> <p>timestamp: Unix timestamp (seconds)</p> </li> <li> <p>value: Depth value (meters)</p> </li> </ul> <li> <p>Sample rate: 5 Hz</p> </li> </ul> </ul> <p><strong> </strong></p> <p><strong>Example File Tree Layout:</strong></p> <p>```<br>${session}/<br>${dataset}/<br>camera/<br>camera.csv<br>00000001.png<br>00000002.png<br>…<br>sonar/<br>sonar.csv<br>00000001.png<br>00000002.png<br>…<br>depth/<br>depth.csv<br>samples.json<br>```<strong> <br><br>Example File Content:</strong></p> <p><strong> </strong>camera.csv<br>```<br>timestamp,filename<br>1644234340.181234,00000001.png<br>1644234343.375667,00000002.png<br>```</p> <p><strong> </strong>sonar.csv<br>```<br>timestamp,filename<br>1644234340.181234,00000001.png<br>1644234343.375667,00000002.png<br>```</p> <p><strong> </strong>depth.csv<br>```<br>timestamp,value<br>1644234340.181234,5.4<br>1644234343.375667,6.1<br>```</p> <p><strong> </strong>samples.json</p> <p>```<br>{<br> "samples": [<br> {<br> "camera": [<br> 0<br> ],<br> "depth": [<br> 0<br> ],<br> "sonar": [<br> 0<br> ]<br> },<br> {<br> "camera": [<br> 1<br> ],<br> "depth": [<br> 1<br> ],<br> "sonar": [<br> 1<br> ]<br> }<br>]</p> <p>```</p> <p>By providing synchronized and aligned camera, sonar imagery, and depth data, this dataset enables researchers to explore novel algorithms and techniques for multi-modal sensor fusion in the context of autonomous underwater vehicles operating in the tropical waters of the Red Sea.</p> <p><strong>Acknowledgements</strong></p> <p>The data in this repository is part of the DeeperSense project that received funding from the European Commission, Program H2020-ICT-2020-2 ICT-47-2020, Project Number: 101016958.</p>
Dataset for Study: Negotiating Visibility: Mediating Presence through Zoom Camera Choices in Post-Secondary Students during COVID-19
Open the record for dataset details and reuse information.
Figures 1-4 from: Ribeiro-Silva L, Perrella DF, Biagolini-Jr CH, Zima PVQ, Piratelli AJ, Schlindwein MN, Galetti-Jr PM, Francisco MR (2018) Use of camera traps for detecting nest predation of birds in the Atlantic Forest of Brazil. Zoologia 35: 1-8. https://doi.org/10.3897/zoologia.35.e14678
Figures 1-4 Predators of bird nests recorded with camera traps in an area of Atlantic rainforest. (1) the Collared Forest-falcon, Micrastur semitorquatus, depredating a nest of the White-necked Thrush, Turdus albicollis. (2) the Red-breasted Toucan, Ramphastos dicolorus, depredating a nest of the Ruddy Quail-dove, Geotrygon montana. (3) the Ocelot, Leopardus pardalis, depredating a nest of the Gray-hooded Flycatcher, Mionectes rufiventris. (4) the Gray Slender Mouse Opossum, Marmosops incanus depredating a nest of the Royal Flycatcher, Onychorhynchus swainsoni.
Figure 2 from: Pyšková K, Kauzál O, Storch D, Horáček I, Pergl J, Pyšek P (2018) Carnivore distribution across habitats in a central-European landscape: a camera trap study. ZooKeys 770: 227-246. https://doi.org/10.3897/zookeys.770.22554
Figure 2 Habitat types studied. A wetland B floodplain forest C mixed forest D shrubby grassland (see text for description). Photo credits: Klára Pyšková
Figure 3 from: Pyšková K, Kauzál O, Storch D, Horáček I, Pergl J, Pyšek P (2018) Carnivore distribution across habitats in a central-European landscape: a camera trap study. ZooKeys 770: 227-246. https://doi.org/10.3897/zookeys.770.22554
Figure 3 Habitat preferences of the carnivores studied; the figures are percentages of the total number of standardized daily records as recorded in each habitat.
Figure 1 from: Pyšková K, Kauzál O, Storch D, Horáček I, Pergl J, Pyšek P (2018) Carnivore distribution across habitats in a central-European landscape: a camera trap study. ZooKeys 770: 227-246. https://doi.org/10.3897/zookeys.770.22554
Figure 1 Location of the study area in central Bohemia, western part of the Czech Republic (black rectangle).
Figure 5 from: Pyšková K, Kauzál O, Storch D, Horáček I, Pergl J, Pyšek P (2018) Carnivore distribution across habitats in a central-European landscape: a camera trap study. ZooKeys 770: 227-246. https://doi.org/10.3897/zookeys.770.22554
Figure 5 Circadian activity of fox and marten shown by season, expressed as the percentage of standardized records photographed at daylight and in the night. For badger, a whole-year summary is shown as the significant differences among seasons are due to it not occurring at daylight in winter.
Figure 4 from: Pyšková K, Kauzál O, Storch D, Horáček I, Pergl J, Pyšek P (2018) Carnivore distribution across habitats in a central-European landscape: a camera trap study. ZooKeys 770: 227-246. https://doi.org/10.3897/zookeys.770.22554
Figure 4 Seasonal dynamics shown for the carnivore species commonly occurring in the study area (with > 50 standardized daily records). The data were collected from June 2015 to May 2016, and the seasons are arranged in annual sequence for better illustration of seasonal dynamics. Seasons bearing the same letter are not significantly different from each other, based on linear model testing differences in the total number of records over the three months within the season. Values on top of the bars are percentages of the total number of records for a given species.
Dataset from the Arabidopsis case study for Specim IQ camera
<p>Hyperspectral imaging is a technique used in plant phenotyping which can detect differences in plant traits. Information about the morphology and physiology of plants can be derived by calculating spectral ratios (Vegetation Indices) from hyperspectral datacube.</p> <p>Here we publish the datacube from Arabidopsis plants used as a case study to calculate vegetation indices (NDVI, REIP and PRI) from stressed and non-stressed plants. Files include raw data from IQ camera, analyzed data from ENVI and MatLab software, and visualized in MS Excel. Detailed description of the dataset and methodology used is published in Behmann et al., 2018 (Sensors).</p>
MicroED datasets of hemin and biotin collected on Ceta camera
<p>MicroED datasets of hemin and biotin microcrystals were collected using Talos Arctica (200 kV) with the CMOS camera Ceta. The stage was controlled using SerialEM and diffraction images were independently collected using Velox software. For this reason, only frames with constant rotation speed should be used for data processing.</p> <p>Rotation step (continuous) was ~1.52°/frame and each dataset consisted of ~40 images (tilt range is +/-30°). The calibrated camera length using Al powder was 793.52 mm. Biotin crystals belonged to space group <em>P</em>2<sub>1</sub>2<sub>1</sub>2<sub>1</sub> with a~5.2, b~10.2, c~20.8 Å. ~14 datasets could be merged at ~0.85 Å resolution. Hemin crystals belonged to space group <em>P</em>-1 with a~10.6, b~11.1, c~13.8 Å, α~107.1°, b~99.5°, c~107.9°. ~12 datasets could be merged at ~0.9 Å resolution.</p> <p>Collection conditions:</p> <ul> <li>biotin: gun lens 8.0, spot 11, C2: parallel beam condition, 0.039e/A^2/sec, total ~3.5e/A^2</li> <li>hemin: gun lens 4.0, spot 11, C2: parallel beam condition, 0.095e/A^2/sec, total ~8.6e/A^2</li> </ul> <p>Note:</p> <ul> <li>emd files (hdf5 format) were transparently compressed using h5repack -f SHUF -f GZIP=4 command to reduce file size.</li> <li>EMD file can be processed with DIALS using <a href="https://github.com/keitaroyam/yamtbx/blob/master/dxtbx_formats/FormatEMD.py">this dxtbx format</a> file.</li> <li>Metadata (machine parameters, stage tilt angles etc.) is stored as json format in /Data/Image/*/Metadata in emd file. See <a href="https://github.com/keitaroyam/yamtbx/wiki/EMD-file">here</a> for details.</li> <li>If you want to process data using DIALS, please see <a href="https://github.com/keitaroyam/yamtbx/wiki/Processing-hemin-and-biotin-MicroED-data">the processing note</a>.</li> </ul>
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.