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6 results for “distance from camera”
Dataset of Detection Distances to Small Bodies using Spacecraft Cameras
<p>The dataset contains the detection distances to small bodies (in kilometres) considering three different spacecraft camera setups for the full list of known objects by the Minor Planet Center catalogue (https://www.minorplanetcenter.net). A separate ASCII file has been created per each considered phase angle.</p> <p>The generation of the dataset as well as the simulation settings are detailed in the following paper:</p> <p>Franzese, Hein, Modelling Detection Distances to Small Bodies Using Spacecraft Cameras, <em>Modelling</em> <strong>2023</strong>, <em>4</em>(4), 600-610; <a href="https://doi.org/10.3390/modelling4040034">https://doi.org/10.3390/modelling4040034</a></p> <p>The columns of the dataset are as follows:</p> <ol> <li>Object: The object's numerical identifier.</li> <li>MPC Designation: The object designation of the Minor Planet Center.</li> <li>Name: The object name, if available.</li> <li>HP Cam & rp: Object detection distance in km considering the high-performance camera and the object at perihelion</li> <li>HP Cam & ra: Object detection distance in km considering the high-performance camera and the object at aphelion</li> <li>MP Cam & rp: Object detection distance in km considering the medium performance camera and the object at perihelion</li> <li>MP Cam & ra: Object detection distance in km considering the medium performance camera and the object at aphelion</li> <li>LP Cam & rp: Object detection distance in km considering the low-performance camera and the object at perihelion</li> <li>LP Cam & ra: Object detection distance in km considering the low-performance camera and the object at aphelion.</li> </ol> <p>Note that the detection distances refer to the following phase angles: 0 deg, 15 deg, 30 deg, 60 deg, and 90 deg.</p>
Data belonging to "Successful invasion: camera trap distance sampling reveals higher density for invasive raccoon dog compared to native mesopredators"
<p>Data files (comma separated text files) containing the camera data (CameraData) containing the information on camera trap placements in the various sites and their operation time in days and aperture, the distance sampling data (DistanceData) containing the information on the species and distance detected for each 1s time interval in front of each camera, and the trigger data (TriggerData) containing the time stamps for the pictures taken of each species with each camera, collected in the years 2020 and 2021 in southern Finland. The repository further contains an R script "distanceSamplingScript" which uses the reposited above-described files for analysis reported in the publication "Successful invasion: camera trap distance sampling reveals higher density for invasive raccoon dog compared to native mesopredators" https://doi.org/10.1007/s10530-024-03323-4. The R script has been confirmed to run in R version 4.3.3 using packages "activity" vs 1.3.4 and "Distance" vs 1.0.9</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>
Estimating density of mountain hares using distance sampling: a comparison of daylight visual surveys, night-time thermal imaging and camera traps
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Data from: Distance and size matters: a comparison of six wildlife camera traps and their usefulness for wild birds
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Data from: Distance sampling with camera traps
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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.