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1,832 results for “Cameras”
Deer GPS and animal-borne camera data showing effects of gray wolves on niche overlap between mule and white-tailed deer in eastern Washington state
<p><span><span><span><span><span><span><span><span><span><span><span>Predators may alter niche overlap between prey species by eliciting divergent anti-predator behavior. Accordingly, we exploited heterogeneous gray wolf (<i>Canis lupus</i>) presence in Washington, USA, to contrast patterns of resource and dietary overlap between mule (<i>Odocoileus hemionus</i>) and white-tailed deer (<i>O. virginianus</i>) at sites with and without resident packs. Mule deer run (stot) in a way that is less effective as a means of fleeing from predators than the galloping gait of white-tailed deer. Consequently, mule deer manage risk from coursing predators like wolves by avoiding encounters, whereas white-tailed deer respond to such predators by exploiting areas where they are most likely to escape pursuit. Thus, under the "refuge partitioning hypothesis" whereby predators reduce prey niche overlap by eliciting use of different refugia, we predicted wolf exposure to (i) decrease resource and dietary overlap between these ungulates, and (ii) induce segregation consistent with each species using different parts of the landscape to reduce their wolf risk. At the home range scale, the ways in which resource overlap diminished in the wolf areas were consistent with the prey species reducing their respective risks, particularly with respect to slope, with mule deer separating from white-tailed deer by seeking steeper areas where wolf encounters are less likely. At the within-home range scale, the manner in which spatial overlap decreased in relation to forest cover was consistent with species-specific risk management, with mule deer avoiding wolf encounters by shifting toward this resource. Reduced resource overlap between the deer in areas occupied by wolves did not correspond with dietary divergence. Our findings suggest that wolf risk mediates spatial but not necessarily dietary overlap between sympatric ungulates, divergent anti-predator behavior is a non-consumptive pathway by which predators can reduce interspecific competition among prey, and use of disparate refugia by prey may not result in dietary divergence.</span></span></span></span></span></span></span></span></span></span></span></p>
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>
Subspecies and Distribution. T.n.napuF.Cuvier,1822—SMyanmar,Thai/MalayPeninsula,islandsoffWMalayPeninsula(Langkawi&Pangkor),Borneo,SSumatra,BangkaI,islandsoffBorneo(Laut&Serasan). T.n.bangue:Chasen&Kloss,1931—BanggiIandBalembanganI,offNBorneo. T.n.bunguranensisMiller,1901—NatunaIs(=Bunguran),oftWBorneo. T.n.neubronneriSody,1931—NSumatra. T.n.nmiasisLyon,1916—NiasI,offWSumatra. T.n.rufulusMiller,1900—TiomanI,offEMalayPeninsula,RiauandLinggaArchipelagos. T. n. terutus Thomas & Wroughton, 1909 — Terutau I, off W Malay Peninsula. The species was recently reconfirmed for Singapore. Maps that include Vietnam, Cambodia, and Laos in the distribution range are based on the earlier assumption that 7. versicolor was a subspecies of 1. napu. Subsequent studies have indicated that 7. versicolor is a distinct species, and that the range of 1. napu therefore does not extend into Cambodia, Laos, and Vietnam. The northern limit on the Thai-Malay peninsula is not well defined. Specimens of 1. napu have been collected from as far north as Bankachon in southern Myanmar (10° 08" N), but despite fairly intensive camera-trapping in Kui Buri National Park, Thailand (12° N), 7. napu has not been photographed there. At the northern margin ofits range, it is generally rare. It has been reported, for example, that during the flooding of the Chiew Larn Reservoir (Surat Thani Province; about 9° N, 98° 45' E), only six 7. napu were rescued compared with 172 71. kanchil. This area is the transition zone from wetter evergreen forest to drier deciduous types, and it might be that 7° napu is not well adapted to the drier forest types towards the northern limit ofits range. There are unconfirmed reports of the species on Java, where it may have been confused with one of the two color morphs of 7. javanicus. As explained in the Taxonomy section, the subspecific status of the populations of several islands remains unclear. in Tragulidae
Subspecies and Distribution. T.n.napuF.Cuvier,1822—SMyanmar,Thai/MalayPeninsula,islandsoffWMalayPeninsula(Langkawi&Pangkor),Borneo,SSumatra,BangkaI,islandsoffBorneo(Laut&Serasan). T.n.bangue:Chasen&Kloss,1931—BanggiIandBalembanganI,offNBorneo. T.n.bunguranensisMiller,1901—NatunaIs(=Bunguran),oftWBorneo. T.n.neubronneriSody,1931—NSumatra. T.n.nmiasisLyon,1916—NiasI,offWSumatra. T.n.rufulusMiller,1900—TiomanI,offEMalayPeninsula,RiauandLinggaArchipelagos. T. n. terutus Thomas & Wroughton, 1909 — Terutau I, off W Malay Peninsula. The species was recently reconfirmed for Singapore. Maps that include Vietnam, Cambodia, and Laos in the distribution range are based on the earlier assumption that 7. versicolor was a subspecies of 1. napu. Subsequent studies have indicated that 7. versicolor is a distinct species, and that the range of 1. napu therefore does not extend into Cambodia, Laos, and Vietnam. The northern limit on the Thai-Malay peninsula is not well defined. Specimens of 1. napu have been collected from as far north as Bankachon in southern Myanmar (10° 08" N), but despite fairly intensive camera-trapping in Kui Buri National Park, Thailand (12° N), 7. napu has not been photographed there. At the northern margin ofits range, it is generally rare. It has been reported, for example, that during the flooding of the Chiew Larn Reservoir (Surat Thani Province; about 9° N, 98° 45' E), only six 7. napu were rescued compared with 172 71. kanchil. This area is the transition zone from wetter evergreen forest to drier deciduous types, and it might be that 7° napu is not well adapted to the drier forest types towards the northern limit ofits range. There are unconfirmed reports of the species on Java, where it may have been confused with one of the two color morphs of 7. javanicus. As explained in the Taxonomy section, the subspecific status of the populations of several islands remains unclear.
Determining the efficacy of camera traps, live capture traps, and detection dogs for locating cryptic small mammal species
<p>Metal box (e.g., Elliott, Sherman) traps and remote cameras are two of the most commonly employed methods presently used to survey terrestrial mammals. However, their relative efficacy at accurately detecting cryptic small mammals has not been adequately assessed. The present study therefore compared the effectiveness of metal box (Elliott) traps and vertically oriented, close range, white flash camera traps in detecting small mammals occurring in the Scenic Rim of eastern Australia. We also conducted a preliminary survey to determine effectiveness of a conservation detection dog (CDD) for identifying presence of a threatened carnivorous marsupial, <i>Antechinus arktos,</i> in present-day and historical locations, using camera traps to corroborate detections. 200 Elliott traps and 20 white flash camera traps were set for four deployments per method, across a site where the target small mammals, including <i>A. arktos</i>, are known to occur. Camera traps produced higher detection probabilities than Elliott traps for all four species. Thus, vertically mounted white flash cameras were preferable for detecting the presence of cryptic small mammals in our survey. The CDD, which had been trained to detect <i>A. arktos</i> scat, indicated in total 31 times when deployed in the field survey area, with subsequent camera trap deployments specifically corroborating <i>A. arktos</i> presence at 100% (3) indication locations. Importantly, the dog indicated twice within Border Ranges National Park, where historical (1980s-1990s) specimen-based records indicate the species was present, but extensive Elliott and camera trapping over the last 5-10 years have resulted in zero <i>A. arktos</i> captures. Camera traps subsequently corroborated <i>A. arktos</i> presence at these sites. This demonstrates that detection dogs can be a highly effective means of locating threatened, cryptic species, especially when traditional methods are unable to detect low-density mammal populations.</p>
Constructing a social-behavioral association network to study management impact on waterbird community ecology using digital video recording cameras
<p>Studying social behavior and species associations in ecological communities is challenging because it is difficult to observe the interactions in the field. Animal behavior is especially difficult to observe when selection of habitat and activities are linked to energy costs of long-distance movement. Migrating communities tend to be resource specific and prefer environments that offer more suitability for coexisting in a shared space and time. Given the recent advances in digital technologies, digital video recording systems are gaining popularity in wildlife research and management. We used digital video recording cameras to study social interactions and species-habitat linkages for wintering waterbird communities in shared habitats. Examining over 8,640 hours of video footages, we built tetrapartite social behavioral association network of wintering waterbirds over habitat (n=5) selection events in sites with distinct management regimes. We analyzed these networks to identify hub species and species role in activity persistence, and to explore the effects of hydrological regime on these network characteristics. Although the differences in network attributes were not significant at treatment level (<i>p</i> = 0.297) in terms of network composition and keystone species composition, our results indicated that network attributes were significantly different (<i>p </i>= 0.000, <i>r<sup>2</sup></i><sup> </sup>= 0.278) at habitat level. There were evidences suggesting that the habitat quality was better at the managed sites, where the formed networks had more species, more network nodes and edges, higher edge density, and stronger intra- and inter-species interactions. In addition, we also calculated the species interaction preference scores (SIPS) and behavioral interaction preference scores (BIPS) of each network. The results showed that species synchronize activities in shared space for temporal niche partitioning in order to avoid or minimize any potential competition for shared space. Our social network analysis (SNA) approach is likely to provide a practical use for ecosystem management and biodiversity conservation.</p>
Global camera trap synthesis highlights the importance of protected areas in maintaining mammal diversity
<p>The establishment of protected areas (PAs) is a central strategy for global biodiversity conservation. While the role of PAs in protecting habitat has been highlighted, their effectiveness at protecting mammal communities remains unclear. We analyzed a global dataset from over 8,671 camera traps in 23 countries on four continents that detected 321 medium- to large-bodied mammal species. We found a strong positive correlation between mammal taxonomic diversity and the proportion of a surveyed area covered by PAs at a global scale (b = 0.39, 95% CI = 0.19, 0.60) and in Indomalaya (b = 0.69, 95% CI = 0.19,1.2), as well as between functional diversity and PA coverage in the Nearctic (b = 0.47, 95% CI = 0.09, 0.85), after controlling for human disturbances and environmental variation. Functional diversity was only weakly (and insignificantly) correlated with PA coverage at the global scale (b =0.22, 95% CI = -0.02, 0.46), pointing to a need to better understand the functional response of mammal communities to protection. Our study provides important evidence of the global effectiveness of PAs in conserving terrestrial mammals and emphasizes the critical role of area-based conservation in a post-2020 biodiversity framework.</p>
Testing the precision and sensitivity of density estimates obtained with a camera-trap method revealed limitations and opportunities
<p>The use of camera traps in ecology helps affordably address questions about the distribution and density of cryptic and mobile species. The Random encounter model (REM) is a camera-trap method that has been developed to estimate population densities using unmarked individuals. However, few studies have evaluated its reliability in the field, especially considering that this method relies on parameters obtained from collared animals (<i>i.e.</i> average speed, in km/h), which can be difficult to acquire at low cost and effort. Our objectives were to (1) assess the reliability of this camera-trap method and (2) evaluate the influence of parameters coming from different populations on density estimates. We estimated a reference density of black bears (<i>Ursus americanus</i>) in Forillon National Park (Québec, Canada) using a spatial capture-recapture estimator based on hair-snag stations. We calculated average speed using telemetry data acquired from four different bear populations located outside our study area and estimated densities using the REM. The reference density, determined with a Bayesian spatial capture-recapture model, was 2.87 individuals/10km<sup>2</sup> [95% CI: 2.41–3.45], which was slightly lower (although not significatively different) than the different densities estimated using REM (ranging from 4.06–5.38 bears/10km<sup>2 </sup>depending on the average speed value used). Average speed values obtained from different populations had minor impacts on REM estimates when the difference in average speed between populations was low. Bias in speed values for slow-moving species had more influence on REM density estimates than for fast-moving species. We pointed out that a potential overestimation of density occurs when average speed is underestimated, i.e. using GPS telemetry locations with large fix-rate intervals. Our study suggests that REM could be an affordable alternative to conventional spatial capture-recapture, but highlights the need for further research to control for potential bias associated with speed values determined using GPS telemetry data.</p>
Validating the use of stereo-video cameras to conduct remote measurements of sea turtles
<p>Stereo-Video Camera Systems (SVCSs) are a promising tool to remotely measure body size of wild animals without the need for animal handling. Here, we assessed the accuracy of SVCSs for measuring straight carapace length (SCL) of sea turtles. To achieve this, we hand captured and measured 63 juvenile, sub-adult, and adult sea turtles across three species, greens, <i>Chelonia mydas </i>(n = 52), loggerheads, <i>Caretta caretta </i>(n = 8), and<i> </i>Kemp's ridley, <i>Lepidochelys kempii </i>(n = 3) in the waters off Eleuthera, The Bahamas and Crystal River, Florida, U.S.A. between May - November 2019. Upon release, we filmed these individuals with the SVCS. We performed photogrammetric analysis to extract stereo SCL measurements (eSCL), which were then compared to the (manual) capture measurements (mSCL). mSCL ranged from 25.9 – 89.2 cm, while eSCL ranged from 24.7 – 91.4 cm. Mean percent bias of eSCL ranged from -0.61% (± 0.11 SE) to -4.46% (± 0.31 SE) across all species and locations. We statistically analyzed potential drivers of measurement error, including distance of the turtle to the SVCS, turtle angle, image quality, turtle size, capture location, and species.Using a linear mixed effects model, we found that the distance between the turtle and the SVCS was the primary factor influencing measurement error. Our research suggests that stereo-video technology enables high-quality measurements of sea turtle body size collected <i>in situ</i> without the need for hand-capturing individuals. This study contributes to the growing knowledge base that SVCS are accurate for body size measurements independent of taxonomic clade.</p>
Supplementary data for camera-based monitoring of Bogong moths
<p>Supplementary data to be used in conjunction with the images available from the following repositories:</p> <p>Cabramurra 2019 dataset: <a href="http://doi.org/10.5281/zenodo.4950570">https://doi.org/10.5281/zenodo.4950570</a>,<br> Ken Green Bogong 2019–2020 dataset: <a href="http://doi.org/10.5281/zenodo.4971714">https://doi.org/10.5281/zenodo.4971714</a>,<br> Mt Kosciuszko 2019–2020 dataset: <a href="http://doi.org/10.5281/zenodo.5039891">https://doi.org/10.5281/zenodo.5039891</a>,<br> Ken Green Bogong 2020–2021 dataset: <a href="https://doi.org/10.5281/zenodo.4972022">https://doi.org/10.5281/zenodo.4972022</a>,<br> Mt Kosciuszko 2020–2021 dataset: <a href="http://doi.org/10.5281/zenodo.5040011">https://doi.org/10.5281/zenodo.5040011</a>,<br> Mt Gingera 2020–2021 dataset: <a href="https://doi.org/10.5281/zenodo.5040018">https://doi.org/10.5281/zenodo.5040018</a>.</p> <p>20210823_14_model.pth (the trained pytorch model used by camfi) is also available from <a href="https://github.com/J-Wall/camfi/releases/download/v2.1.4/20210823_14_model.pth">https://github.com/J-Wall/camfi/releases/download/v2.1.4/20210823_14_model.pth</a> and is included here for posterity.</p> <p> </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>
Linking camera-trap data to taxonomy: Identifying photographs of morphologically similar chipmunks
<p>Remote cameras are a common method for surveying wildlife and recently have been promoted for implementing large-scale regional biodiversity monitoring programs. The use of camera-trap data depends on the correct identification of animals captured in the photographs, yet misidentification rates can be high, especially when morphologically similar species co-occur, and this can lead to faulty inferences and hinder conservation efforts. Correct identification is dependent on diagnosable taxonomic characters, photograph quality, and the experience and training of the observer. However, keys rooted in taxonomy are rarely used for the identification of camera-trap images and error rates are rarely assessed, even when morphologically similar species are present in the study area. We tested a method for ensuring high identification accuracy using two sympatric and morphologically similar chipmunk (<i>Neotamias</i>) species as a case study. We hypothesized that the identification accuracy would improve with use of the identification key, and with observer training, resulting in higher levels of observer confidence and higher levels of agreement among observers. We developed an identification key and tested identification accuracy based on photographs of verified museum specimens. Our results supported predictions for each of these hypotheses. In addition, we validated the method in the field by comparing remote camera data with live-trapping data. We recommend use of these methods to evaluate error rates and to exclude ambiguous records in camera-trap datasets. We urge that ensuring correct and scientifically defensible species identifications is incumbent on researchers and should be incorporated into the camera-trap workflow.</p>
Mechanical Intelligence: Method Comparison Electrosensing and RGB Camera Examples Data
<p>Method Comparison Datasets for "Mechanical Intelligence for Learning Embodied Sensor-Object Relationships" published in Nature Communications [1]. </p> <p>This contains the datasets for method comparisons of ergodic sampling (our approach) and random sampling for the Electrosensory and RGB Camera examples in the paper.</p> <p>[1] Prabhakar, A., Murphey, T. Mechanical intelligence for learning embodied sensor-object relationships. <em>Nat Commun</em> 13, 4108 (2022). https://doi.org/10.1038/s41467-022-31795-2 .</p>
Dataset from: Are we telling the same story? Comparing inferences made from camera trap and telemetry data for wildlife monitoring
<p>Estimating habitat and spatial associations for wildlife is common across ecological studies, and it is well known that individual traits can drive population dynamics and vice versa. Thus, it is commonly assumed that individual- and population-level data should represent the same underlying processes, but few studies have directly compared contemporaneous data representing these different perspectives. We evaluated the circumstances under which data collected from Lagrangian (individual-level) and Eulerian (population-level) perspectives could yield comparable inferences in an effort to understand how scalable information is from the individual to the population. We used Global Positioning System (GPS) collar (Lagrangian) and camera trap (Eularian) data for seven species collected simultaneously in eastern Washington (2018 – 2020) to compare inferences made from different survey perspectives. We fit the respective data streams to resource selection functions (RSFs) and occupancy models and compared estimated habitat- and space-use patterns for each species. Although previous studies have considered whether individual- and population-level data generated comparable information, ours is the first to make this comparison for multiple species simultaneously and to specifically ask whether inferences from the two perspectives differ depending on the focal species. We found general agreement between the predicted spatial distributions for most paired analyses, though specific habitat relationships differed. We hypothesized the discrepancies arose due to differences in statistical power associated with camera and GPS-collar sampling, as well as spatial mismatches in the data. Our research suggests data collected from individual-based sampling methods can capture coarse population-wide patterns for a diversity of species, but results differ when interpreting specific wildlife-habitat relationships.</p>
mmWave Radar and RGB-D Camera Sensor Data for Human Activity Recognition(2)
<p>This is a supplementary dataset, which is linked to https://zenodo.org/record/7088054#.YyVF3ehBwQ8. The dataset is composed of corner radar point cloud data and front radar point cloud data collected from environments existing obstacles between volunteers and sensors. </p>
Data from: A camera trap based assessment of climate-driven phenotypic plasticity of seasonal moulting in an endangered carnivore
<p>For many species, the ability to rapidly adapt to changes in seasonality is essential for long-term survival. In the Arctic, seasonal moulting is a key life history event that provides year-round camouflage and thermal protection. However, increased seasonal variability can lead to phenological mismatch. In this study, we investigated whether winter-white (white morph) and winter-brown (blue morph) Arctic foxes (<em>Vulpes lagopus</em>) could adjust their winter-to-summer moult to match local environmental conditions. We used camera trap images spanning an eight-year period to quantify the timing and rate of fur change in a polymorphic subpopulation in south-central Norway. Seasonal snow cover duration and temperature governed the phenology of the spring moult. We observed a later onset and longer moulting duration with decreasing temperature and longer snow season. Additionally, white foxes moulted earlier than blue in years with shorter periods of snow cover and warmer temperatures. These results suggest that phenotypic plasticity allows Arctic foxes to modulate the timing and rate of their spring moult as snow conditions and temperatures fluctuate. With the Arctic warming at an unprecedented rate, understanding the capacity of polar species to physiologically adapt to a changing environment is urgently needed in order to develop adaptive conservation efforts. Moreover, we provide the first evidence for variations in the moulting phenology of blue and white Arctic foxes. Our study underlines the different intraspecific selective pressures that can exist in populations where several morphs co-occur, and illustrates the importance of integrating morph-based differences in future management strategies of such polymorphic species.</p>
Breathing Rate and Heart Rate Dataset using Integrated mmWave FMCW Radar and Camera Steering System
<p>The presented dataset consists of raw and transformed CWT images of the breathing and heart waveforms obtained from a radar<br> and IP camera setup. The setup is a self-proposed setup with a mmWave radar mounted over an IP camera and can capture<br> the breathing and heart waveforms of the person in the room in front of the system in any orientation. The dataset can be used to estimate the vital signs using any machine learning model and important information about the respiration rate and pulse rate<br> can then be obtained. The dataset is a total of 1280 images- 720 raw and 720 processed. The processed dataset is labeled in six different classes. The first bifurcation is between breath and heart, breath signal is classified as low, normal, and high whereas the heart signal is classified as low, normal, and slightly low. The subject is oriented in differently so that the setup can steer towards the person and capture the breath and heart signals accordingly.</p>
Dataset for "Rainfall intensity estimations based on degradation characteristics of images taken with commercial cameras" (3/3)
<p>Dataset for "Rainfall intensity estimations based on degradation characteristics of images taken with commercial cameras" (3/3)</p> <p>This dataset contains images of Camera 3.</p>
Dataset for "Rainfall intensity estimations based on degradation characteristics of images taken with commercial cameras" (2/3)
<p>Dataset for "Rainfall intensity estimations based on degradation characteristics of images taken with commercial cameras" (2/3)</p> <p>This dataset contains images of Camera 2.</p>
Dataset for "Rainfall intensity estimations based on degradation characteristics of images taken with commercial cameras" (1/3)
<p>Dataset for "Rainfall intensity estimations based on degradation characteristics of images taken with commercial cameras" (1/3)</p> <p>This dataset contains images of Camera 1 and data used for analysis.</p>
Supplemental Video 1 to Vanzella et al paper: A passive, camera-based head-tracking system for real-time, three-dimensional estimation of head position and orientation in rodents
<p>This video shows how the head tracker described in the manuscript "<strong>A passive, camera-based head-tracking system for real-time, three-dimensional estimation of head position and orientation in rodents" </strong>can track in real time the pose of the head of rat engaged in a perceptual discrimination task.</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.