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.
18
datasets available to search
ShareScore release 0.9.0
Dataset results
18 results for “Thermal Camera”
Multiple DJI drone flights with thermal camera over Lithuanian forests in winter for wild boar detection
<p>5 Flight over multiple days in the evening time for better thermal conditions for boar detection.</p> <p>Flight were conducted with DJI thermal cameras filmed at the speed of about 5m/s. <br>Flights 1, 2, 4 and 5 were filmed at from 90m height with camera pointing straight down.<br>Flight 3 was filmed at 120m height.</p> <p> </p> <p>Link for Dataset download: <a title="Thermal imaging dataset over Lithuanian forests in winter" href="https://art21-icaerus.s3.eu-central-1.amazonaws.com/Boars.zip" target="_blank" rel="noopener">https://art21-icaerus.s3.eu-central-1.amazonaws.com/Boars.zip</a> </p>
Data from: Saving Bambi from the mower? Using a drone with thermal camera to evaluate a low-tech scaring technique to reduce roe deer fawn mortality during grass harvest
Open the record for dataset details and reuse information.
Estimating density of mountain hares using distance sampling: a comparison of daylight visual surveys, night-time thermal imaging and camera traps
<p><a name="_Hlk58254629"></a></p> <p><a name="_Hlk58254629">Surveying cryptic, nocturnal animals is logistically challenging. Consequently, density estimates may be imprecise and uncertain. Survey innovations mitigate ecological and observational difficulties contributing to estimation variance. Thus, comparisons of survey techniques are critical to evaluate estimates of abundance. We simultaneously compared three methods for observing mountain hare (<i>Lepus timidus</i>) using Distance sampling to estimate abundance. Daylight visual surveys achieved 41 detections, estimating density at 14.3 hares km<sup>-2</sup> (95%CI 6.3–32.5) resulting in the lowest estimate and widest confidence interval. Night-time thermal imaging achieved 206 detections, estimating density at 12.1 hares km<sup>-2 </sup>(95%CI 7.6–19.4). Thermal imaging captured more observations at furthest distances, and detected larger group sizes. Camera traps achieved 3,705 night-time detections, estimating density at 22.6 hares km<sup>-2 </sup>(95%CI 17.1–29.9). Between the methods, detections were spatially correlated, although the estimates of density varied. Our results suggest that daylight visual surveys tended to underestimate density, failing to reflect nocturnal activity. Thermal imaging captured nocturnal activity, providing a higher detection rate, but required fine weather. Camera traps captured nocturnal activity, and operated 24/7 throughout harsh weather, but needed careful consideration of empirical assumptions. </a>We discuss the merits and limitations of each method with respect to the estimation of population density in the field.</p>
LiDAR and thermal data for camera pose estimation using the depth-map correspondence algorithm
<p>Folder and file structure:</p> <ul> <li>lidar_roi.ply : ~360 MB mesh file which is a sub-part of the whole Orlova Chuka scan collected in [1]</li> <li>yyyy-mm-dd total of ~17 GB. All video data including raw data, exported video, digitised xy points and calibration results <ul> <li>2018-08-19</li> <li>2018-08-17</li> <li>2018-08-14</li> <li>2018-07-28</li> <li>2018-07-25</li> <li>2018-07-21</li> </ul> </li> </ul> <p><em>Thermal camera YYYY-MM-DD folder substructure</em>: Each of the yyyy-mm-dd dates is one recording session. Each session folder has the following structure:</p> <ul> <li>avi_files (present on some nights)</li> <li>cave_photos: (present on some nights)</li> <li>mic_and_wall_points</li> <li>tmc_files: (present on some nights) The TMC files is a proprietary format to store thermal camera video data (TeAx GmbH, Germany). on 2018-08-17, only P0000000 is provided as it doesnt' have humans blocking the scene. Each frame can be exported to csv using the ThermoViewer tool, downloadable at: https://thermalcapture.com/thermoviewer-download/</li> <li>video_calibration: results and associated data to get DLT coefficients estimated using the easyWand [2] workflow. <ul> <li>image : csv file with pixel values of the images used for annotations</li> <li>mics : 2D point locations of mics placed on the cave walls</li> <li>other_cave_surface : other points on the cave surface that were pointed at <ul> <li>calibration_output: results from easyWand runs. Choose the highest round number <ul> <li>yyyy-mm-dd_roundX_<wandscore>_cam1Tforms.mat (undistortion files)</li> <li>yyyy-mm-dd_roundX_<wandscore>_cam2Tforms.mat</li> <li>yyyy-mm-dd_roundX_<wandscore>_cam3Tforms.mat</li> <li>yyyy-mm-dd_roundX_<wandscore>_dltCoefs.csv (each column is one camera's DLT coefficients)</li> <li>yyyy-mm-dd_roundX_<wandscore>_easyWandData.mat (easyWand session file)</li> </ul> </li> <li>gravity: (mostly there) video and xy points for a falling object to align the calbiration to gravity. Output from DLTdv7 clicking session.</li> <li>wand: video and xy points of the 'wand' calibration object. Output from DLTdv7 clicking session.</li> <li>camera_intrinsic.txt or thermalcam_camprofiles_profile.txt : the camera instrinsics</li> </ul> </li> </ul> </li> </ul> <ul> <li>alignment_results: ~887 MB zipped folder. <ul> <li>dmcp_experiments: the results of DMCP alignment <ul> <li>round_01 : <em>ignore this folder</em></li> <li>round_03 : <em>ignore this folder</em></li> <li>round_05: here yyyy-mm-dd is short for all other nights. Each yyyy-mm-dd folder has multiple csv files. The 'transform.csv' is the most relevant file, as it holds the transformation matrix to move 3D points from camera triangulations into the LiDAR coordinate system. <ul> <li>2018-07-21--cam0</li> <li>2018-07-21--cam1</li> <li>2018-07-21--cam2</li> <li>yyyy-mm-dd--cam0</li> <li>yyyy-mm-dd--cam1</li> <li>yyyy-mm-dd--cam2</li> <li>...</li> <li>...</li> <li>...</li> <li>2018-08-19--cam0</li> <li>2018-08-19--cam1</li> <li>2018-08-19--cam2</li> </ul> </li> </ul> </li> </ul> </li> </ul> <p> </p> <p>CITATION: If you use this dataset for your research please cite this Zenodo dataset and the accompanying paper.</p> <p>This uploaded dataset is part of the <em>Ushichka</em> dataset [3]. The audio-video system was designed by Holger R. Goerlitz. The LiDAR data was collected by Asparuh Kamburov. Video data collected by Thejasvi Beleyur.</p> <p>References</p> <p>[1] : Kamburov, A., Goerlitz, H. R., Beleyur, T 2018, Geospatial modelling inside the "Orlova Chuka" cave in Bulgaria, <em>non-peer reviewed conference contribution</em>, XXVIII International Symposium on Modern Technologies and Professional Practise in Geodesy and related fields</p> <p>[2]: Theriault, D. H., Fuller, N. W., Jackson, B. E., Bluhm, E., Evangelista, D., Wu, Z., M., Betke & Hedrick, T. L. (2014). A protocol and calibration method for accurate multi-camera field videography. <em>Journal of Experimental Biology</em>, <em>217</em>(11), 1843-1848.</p> <p>[3]: Beleyur Thejasvi, 2021. Theoretical and empirical investigations of echolocation in bat groups, PhD dissertation, University of Konstanz (<a href="http://nbn-resolving.de/urn:nbn:de:bsz:352-2-q41u3qlu1em03">http://nbn-resolving.de/urn:nbn:de:bsz:352-2-q41u3qlu1em03</a>)</p>
Data for: Assessment of the accuracy of counting large ungulate species (red deer Cervus elaphus) with UAV-mounted thermal infrared cameras during night flights
<p>Unmanned Aerial Vehicles (UAVs) are increasingly used in wildlife surveying, including estimation of population densities. It is essential that we evaluate and test new survey methods to guide optimal sampling strategies. This study aimed to assess the accuracy of using a UAV-mounted thermal infrared (TIR) camera to count red deer <em>Cervus elaphus</em> populations, and how this was influenced by flight season, height and velocity, in order to help guide future census design. We flew 57 flights across a captive population of red deer in a 13 ha deer park enclosure of semi-natural habitat, representative of the species' range in northern Germany. Flights and image assessments were performed with no prior knowledge of actual population size. Accuracy was quantified by comparing real population size (known only to deer park staff) and independently estimated population sizes from UAV TIR images. Accuracy was significantly influenced by ecological season (early and late winter, spring and early summer) and height. Across all seasons, lower flights (100 m) performed better than higher ones (120 m), with lower flights in early winter and early summer being on average accurate to within 1% of actual population counts. For the season where we had the largest range of temperatures between flights (late winter) we found that accuracy was highest when temperatures were lowest. Flights were also able to identify all five stags (defined as a male deer ≥2 years old) present in early summer, but not in spring. Deer appeared to avoid the landing/take-off area, but there were no noted behavioural responses to drones flying over animals when at constant height and velocity during surveys. Our results indicate that UAV-mounted TIR camera have the potential to accurately count populations of large ungulate species, but that flight season, height and potentially temperature need to be taken into account to maximise accuracy. This approach has the potential to be scaled up to more accurately estimate densities of wild populations compared to existing approaches.</p>
Prevention of Pressure Wound Development With Infrared Thermal Camera
ClinicalTrials.gov study NCT06219954. IPD Sharing: NO. Countries: 1. Publications: 0.
Estimating density of mountain hares using distance sampling: a comparison of daylight visual surveys, night-time thermal imaging and camera traps
Open the record for dataset details and reuse information.
Data for: Assessment of the accuracy of counting large ungulate species (red deer Cervus elaphus) with UAV-mounted thermal infrared cameras during night flights
Open the record for dataset details and reuse information.
Detecting the presence of nesting nightjar in upland clear-fell using drone-mounted thermal cameras
Open the record for dataset details and reuse information.
A Comparison of Machine-Learning Assisted Optical and Thermal Camera Systems for Beehive Activity Counting
Open the record for dataset details and reuse information.
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>
Data from: Efficacy of spotlights and thermal cameras to detect lions, Panthera leo, and spotted hyenas, Crocuta crocuta, depends on species and management regime
Open the record for dataset details and reuse information.
Thermal camera in walnut site 60 meters
Open the record for dataset details and reuse information.
Raw data: A thermal infrared hyperspectral camera based on a birefringent interferometer
<p>In this repository we publish the raw data for the paper "A thermal infrared hyperspectral camera based on a birefringent interferometer"</p> <p>Data types:</p> <ul> <li> <p><strong>QCLs</strong><br><strong>t5</strong>: motor positions used to sample the interferogram of QCL at 5.263 µm [mm]<br><strong>Int5</strong>: corresponding intensity values of the interferogram of QCL at 5.263 µm as absolute value of the trace of the lock-in detector connected to the MCT [arbitrary counts]<br><strong>t7</strong>: motor positions used to sample the interferogram of QCL at 7.675 µm [mm]<br><strong>Int7</strong>: corresponding intensity values of the interferogram of QCL at 7.675 µm as absolute value of the trace of the lock-in detector connected to the MCT [arbitrary counts]<br><strong>t9</strong>: motor positions used to sample the interferogram of QCL at 9.073 µm [mm]<br><strong>Int9</strong>: corresponding intensity values of the interferogram of QCL at 9.073 µm as absolute value of the trace of the lock-in detector connected to the MCT [arbitrary counts]<br><strong>t9_short</strong>: motor positions used to sample the interferogram of QCL at 9.073 µm (shorter scan with smaller step) [mm]<br><strong>Int9_short</strong>: corresponding intensity values of the interferogram of QCL at 9.073 µm as absolute value of the trace of the lock-in detector connected to the MCT (shorter scan with smaller step) [arbitrary counts]</p> </li> <li> <p><strong>DFG_orig</strong><br><strong>tDFG</strong>: motor positions used to sample the DFG signal [mm]<br><strong>IntDFG</strong>: corresponding intensity values of the DFG signal as absolute value of the trace of the lock-in detector connected to the MCT [arbitrary counts]</p> </li> <li> <p><strong>DFG</strong><br><strong>tDFG</strong>: motor positions used to sample the DFG signal, delay corrected, cropped, and centered [mm]<br><strong>IntDFG</strong>: corresponding intensity values of the DFG signal as absolute value of the trace of the lock-in detector connected to the MCT, low-frequency corrected, inverted and normalized to the mean [arbitrary counts]</p> </li> <li> <p><strong>Kanthal_orig<br>tKanthal</strong>: motor positions used to sample the Kanthal signal [mm]<br><strong>IntKanthal</strong>: corresponding intensity values of the Kanthal signal as absolute value of the trace of the lock-in detector connected to the MCT [arbitrary counts]</p> </li> <li> <p><strong>Kanthal<br>tKanthal</strong>: motor positions used to sample the Kanthal signal [mm]<br><strong>IntKanthal</strong>: corresponding intensity values of the Kanthal signal as absolute value of the trace of the lock-in detector connected to the MCT, linearly detrended, inverted and normalized to the mean [arbitrary counts]</p> </li> <li> <p><strong>Quartz</strong><br><strong>t</strong>: motor positions used to sample the quartz emission [mm]<br><strong>HyperMatrix</strong>: temporal hypercube of the quartz emission containing one bolometer image for every motor position [arbitrary counts]</p> </li> <li> <p><strong>Filters</strong><br><strong>t_Filters</strong>: motor positions used to sample the filters transmitted light [mm]<br><strong>HyperMatrix_Filters</strong>: temporal hypercube of the filters transmitted light containing one bolometer image for every motor position [arbitrary counts]</p> </li> <li> <p><strong>Hotplate</strong><br><strong>t_Hotplate</strong>: motor positions used to sample the hotplate emission [mm]<br><strong>HyperMatrix_Hotplate</strong>: temporal hypercube of the hotplate emission containing one bolometer image for every motor position [arbitrary counts]</p> </li> </ul>
COVID-19 Thales Thermography Triage : Thermal Camera Feasibility Study
ClinicalTrials.gov study NCT04397380. IPD Sharing: NO. Countries: 1. Publications: 0.
Use of Thermal Imaging Camera to Assess Perfusion Before and After Vascular Intervention
ClinicalTrials.gov study NCT06544135. IPD Sharing: NO. Countries: 1. Publications: 0.
Thermal Camera Detection of Ventriculoperitoneal Shunt Flow
ClinicalTrials.gov study NCT03451669. IPD Sharing: NO. Countries: 1. Publications: 0.
Continuous Temperature Measurement by Thermal Imaging Camera
ClinicalTrials.gov study NCT06256978. IPD Sharing: NO. Countries: 0. Publications: 0.
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.