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1,832 results for “Cameras”
Supplements for "Rainfall intensity estimations based on degradation characteristics of images taken with commercial cameras"
<p>Supplements for "Rainfall intensity estimations based on degradation characteristics of images taken with commercial cameras"</p>
Supplementary Information: Video data files: Gray et al. Caught on camera: Ocelot, Leopardus pardalis (Mammalia: Felidae) predation on foam nests of Savage's thin-toed frog, Leptodactylus savagei (Amphibia: Leptodactylidae)
<p>Supplementary Information: Original camera trap footage for Gray, Ibáñez, Barrios & Potvin: Caught on camera: Ocelot, <i>Leopardus pardalis </i>(Mammalia: Felidae) predation on foam nests of Savage's thin-toed frog, <i>Leptodactylus savagei</i> (Amphibia: Leptodactylidae). </p><p> </p>
Open urban mmWave radar and camera vehicle classification dataset for traffic monitoring
<h2><strong>Open urban mmWave radar and camera vehicle classification dataset for traffic monitoring</strong></h2><h3><strong>Description</strong></h3><p>The archive contains a dataset that can be used for multi-sensor-based vehicle detection/classification. Each part of the dataset is divided into four separate subfolders. All the footage was collected from different parts of Tallinn during the late winter and early spring. The dataset contains 8393 frames. Each frame comes with a corresponding annotation in XML and YOLO formats and a JSON file containing mmWave radar point cloud data. </p>
Fig. 2. Polacanthoderes martinezi, camera lucida drawings. A, B in Taxonomy, genetic diversity, and phylogeny of the Antarctic mud dragon, Polacanthoderes (Kinorhyncha: Echinorhagata: Echinoderidae)
Fig. 2. Polacanthoderes martinezi, camera lucida drawings. A, B, holotype female (ZMB 11237), segments 1–11, dorsal view (A) and ventral view (B); C, D, allotype male (ZMB 11238a), segments 10 and 11, dorsal view (C) and ventral view (D). Abbreviations (ac), acicular spine; gco1/2, type-1/2 glandular cell outlet; LA, lateral accessory; LD, laterodorsal; ltas, lateral terminal accessory spine; lts, lateral terminal spine; LV, lateroventral; MD, middorsal; mdp, middorsal placid; ML, midlateral; mvp, midventral placid; pa, papilla; pe, penile spine; (sac), small acicular spine; SD, subdorsal; se, seta; si, sieve plate; ss, sensory spots (tu), tube; VL, ventrolateral; VM, ventromedial. Digits in the abbreviations (except for gco1/2) indicate the corresponding segment number.
TP-K16 closed-circuit television camera
According to the manufacturer's description, the TP-K 16, as "a utility television camera, is a device converting an optical image into its counterpart electric signal which, when sent through a television monitor, is turned into an optical image again". The design of the TP-K 16 was based on an aluminium alloy frame to which housing components and printed circuit boards with transistors, integrated circuits, and capacitors were mounted. Initially, foreign components were used for its production, which were later substituted by Polish parts. A lens by Carl Zeiss from Jena, imported from the German Democratic Republic, was used in the optical system. Images are converted to electrical signals with the use of an analysing tube made by the Japanese Toshiba company – the vidikon 7262A (later substituted by the Polkolor PWM41A vidicon). Manufacturer: Zakłady Kineskopowe Unitra Polkolor, Warsaw, 1980s Inv. No.: MIM700/VI-83 Model prepared on the basis of photogrammetric measurements Licence: CC BY-NC-SA Source: Objaverse 1.0 / Sketchfab
USSR Camera
This is just a USSR Camera «FED-4» Source: Objaverse 1.0 / Sketchfab
iSTAR NCtech camera & Agisoft DepthMaps Mesh
A testcase with only **5 spherical images**, taken with the istar nctech kamera system and processed with Agisoft Metashape 1.6.1 enhanced "**Depth Maps Mesh Algorithm**". Processing time 12 minutes. Compare to Densecloud, See other Sketchfab model Source: Objaverse 1.0 / Sketchfab
Vintage camera
Russan made vintage camera (1980's) Source: Objaverse 1.0 / Sketchfab
Using global remote camera data of a "solitary" species complex to evaluate the drivers of group formation
<p>The social system of animals involves a complex interplay between physiology, natural history, and the environment. Long relied upon discrete categorizations of "social" and "solitary" inhibit our capacity to understand species, and their interactions with the world around them. Here, we use a globally distributed camera trapping dataset to test the drivers of aggregating into groups in a species complex (martens and relatives, family <em>Mustelidae</em>, Order <em>Carnivora</em>) assumed to be obligately solitary. We use a simple quantification, the probability of being detected in a group, that was applied across our globally derived camera trap dataset. Using a series of binomial generalized mixed-effects models applied to a dataset of 16,483 independent detections across 17 countries on four continents we test explicit hypotheses about potential drivers of group formation. We observe a wide range of probabilities of being detected in groups within the "solitary" model system, with the probability of aggregating in groups varying by more than an order of magnitude. We demonstrate that a species' proclivity towards aggregating in groups is underpinned by a range of resource-related factors, primarily the distribution of resources, with increasing patchiness of resources facilitating group formation, as well as interactions between environmental conditions (resource constancy/winter severity) and physiology (energy storage capabilities). Combined these factors explain observed variance in context-dependent tendencies towards grouping. The wide variation in propensities to aggregate with conspecifics observed here highlights how continued failure to recognise complexities in the social behaviours of apparently "solitary" species limits our understanding not only of the individual species, but also the causes and consequences of group formation.</p>
A systematic review of global road ecology camera trap studies that monitored animals' use of wildlife crossings in road-fragmented landscapes
<p>Much research has emphasised the importance of incorporating wildlife crossing-structures in the design of road networks to facilitate connectivity of wildlife crossings in road-fragmented landscapes. Although camera traps have been effective in monitoring wildlife crossing structures, limited studies explore camera trap protocol to monitor wildlife use of crossing structures, particularly in Africa. Our study reviewed and assessed camera trap peer-reviewed research that monitored the use of crossing-structures by wildlife to navigate landscapes fragmented by roads. We found 70 camera trap peer-reviewed publications from 2001 to 2022 that monitored wildlife use of crossing-structures in landscapes intersected by roads, and these were from 22 countries and six continents. The included peer-reviewed studies varied significantly globally, with geographical trends indicating that most studies were conducted in North America. However, the methods used varied considerably between studies, especially in terms of camera trap placement protocol (placement height of camera trap, survey length, and camera multi-shot settings). This showed that camera trap usage for monitoring animal use of crossing structures is still an emerging area of research, and there is a potential for developing a standardised protocol for each type of crossing structure design and size. Future camera trap studies exploring wildlife use of crossing-structures should consider monitoring existing crossing structures (culverts, bridges, and tunnels) as this provides a less costly method of restoring landscape connectivity. We recommend that further research develop a standardised camera trap protocol for monitoring wildlife using crossing-structures to reduce the threats to biodiversity.</p>
Polarisation camera dSTORM datasets of actin in fixed HeLa cells labeled with phalloidin-Alexa Fluor 488
<p>Polarisation camera dSTORM dataset of the actin of fixed HeLa cells labeled with phalloidin-Alexa Fluor 488.</p> <p><strong>Image acquisition was performed as follows:</strong></p> <p>Imaging was performed on a widefield microscope equipped with a polarisation camera (CS505MUP, Thorlabs). The sample was excited using a 488 nm laser at quasi-TIRF, with a measured power density at the image plane of <span>5.04 kW/cm^2</span>. A single long-pass dichroic (Di02-R488, Semrock) was used to seperate fluorescence from the excitation. The emission was filtered using a long-pass (BLP01-488R, Semrock) and a bandpass filter (FF01-582/64, Semrock) before detection. An exposure time of 30 ms was used.</p> <p><strong>Samples were prepared as follows:</strong></p> <p><span>Cell culture: </span>HeLa TDS cells were cultured in DMEM (Gibco, Invitrogen) supplemented with 10 % Fetal Bovine Serum (FBS, Life Technologies), 1 % penicillin/streptomycin (Life Technologies), and 1 % glutamine (Life Technologies) at 37 °C + 5 % CO_2. Cells were periodically tested for mycoplasma contamination and passaged 3 times per week. Cells were plated at low density on high-precision glass coverslips (MatTek, P35G-0.170-14-C) 1 day prior to fixation for dSTORM experiments.</p> <p><span>Labeling:</span> Cells were simultaneously fixed and permeabilized in cytoskeleton buffer (CBS, 10 mM MES, 138 mM KCl, 3 mM MgCl_2, 2 mM EGTA, 4.5 % sucrose w/v, pH 7.4), + 4 % paraformaldehyde (PFA) and 0.2 % Triton for 6 minutes at 37 °C, and further fixed in CBS + 4 % PFA for 14 minutes at 37 °C. Post-fixation, cells were washed x3 in PBST (PBS supplemented with 0.1 % Tween) and permeabilized a second time in PBS + 0.5 % Triton for 5 minutes at room temperature. The samples were then washed 3 times in PBST and blocked for 30 minutes in 5 % BSA. Cells were washed x3 in PBST and then incubated with Alexa Fluor™ 488 Phalloidin (A12379, Invitrogen, 1:50 in PBS) for 1 h in the dark, followed by x2 washes in PBS. Prior to dSTORM imaging, PBS was replaced with dSTORM imaging buffer (base buffer consisting of 0.56 M glucose, 50 mM Tris (pH 8.5), and 10 mM NaCl supplemented with 5 U/mL pyranose oxidase (Sigma, P4234), 10 mM cysteamine (Sigma, 30070), 40 µg/mL catalase (Sigma, C100) and 2 mM cyclooctatetraene (Sigma, 138924).</p>
SiMPL Wildlife Magnets: A camera trap tool for detecting all creatures great and small
<p>Small mammals compose a substantial portion of the seed predators and dispersers, as well as the prey, in many ecosystems. Nevertheless, information on distribution and habitat use is limited, partly because inexpensive, minimally -invasive surveying of small mammals has remained challenging. We created the SiMPL wildlife magnet – a baited camera trap design that allows passive monitoring of wildlife, especially small- to medium-sized mammals. The SiMPL magnet is inexpensive and easy to construct. To evaluate its effectiveness, we conducted a case study using 10 sites operating for two years along an elevation gradient in the White Mountains of the northeastern U.S. We found that the SiMPL wildlife magnet can be used to detect rodents, mesocarnivores, and, to a lesser extent, ungulates. We examined how the detection probability of mammal species changed with the inclusion of a SiMPL wildlife magnet using data from pre- and post-establishment. We found a significant increase in community-level detection probability with the use of SiMPL magnets and for some species, including red squirrels (<em>Tamiasciurus hudsonicus</em>), American marten (<em>Martes americana</em>), and fisher (<em>Pekania pennanti</em>). Moreover, we were able to capture more species with SiMPL magnets than without, including flying squirrels (<em>Glaucomys</em> spp.), various <em>Cricetidae</em> spp., black bears (<em>Ursus americanus</em>), moose (<em>Alces alces</em>), owls and other birds. The SiMPL wildlife magnet is an effective, low-cost method for surveying wildlife communities, especially rodents and mesocarnivores. It addresses the limited range view presented by other techniques for capturing small mammals on camera traps and enables efficient collection of phenology data, including vegetation and snowpack. This tool has several applications, including monitoring species' responses to silvicultural practices and global change.</p>
Polarisation camera TAB-PAINT dataset of alpha-synuclein fibrils using Nile red
<p>Alpha-synuclein fibrils were deposited on PLL-coated and argon plasma-cleaned coverslides. A low concentration of Nile Red (~1 nM) was added for PAINT imaging, and fluorescent beads for post-acquisition drift correction.</p> <p>The data was collected on a fluorescence microscope (Eclipse Ti-U, Nikon) with a polarisation camera (CS505MUP, Thorlabs). The molecules were excited with a 515 nm laser using epi-illumination with a measured power density at the sample plane of 6.08 kW/cm^2. An exposure time of 50 ms was used. The following filters were used: dichroic (Di03-R514-t1, Semrock) and emission filter (FF01-515/LP and FF01-650/200, Semrock). A 60x oil-immersion objective (Plan Apo, 60xA/1.40 Oil, DIC H, inf/0.17 WD 0.21, Nikon) was used for detection.</p>
Polarisation camera dSTORM datasets of actin in fixed HeLa cells labeled with phalloidin-Alexa Fluor 647
<p>Polarisation camera dSTORM dataset of the actin of fixed HeLa cells labeled with phalloidin-Alexa Fluor 647.</p> <p><strong>Image acquisition was performed as follows:</strong></p> <p>Imaging was performed on a widefield microscope equipped with a polarisation camera (CS505MUP, Thorlabs). The sample was excited using a 638 nm laser at quasi-TIRF, with a measured power density at the image plane of 3.51 kW/cm^2. A multiband dichroic (Di03-R405/488/561/635-t1, Semrock) was used to seperate fluorescence from the excitation. The emission was filtered using a long-pass filter (BLP01-635R, Semrock) before detection. An exposure time of 30 ms was used.</p> <p><strong>Samples were prepared as follows:</strong></p> <p>Cell culture: HeLa TDS cells were cultured in DMEM (Gibco, Invitrogen) supplemented with 10 % Fetal Bovine Serum (FBS, Life Technologies), 1 % penicillin/streptomycin (Life Technologies), and 1 % glutamine (Life Technologies) at 37 °C + 5 % CO_2. Cells were periodically tested for mycoplasma contamination and passaged 3 times per week. Cells were plated at low density on high-precision glass coverslips (MatTek, P35G-0.170-14-C) 1 day prior to fixation for dSTORM experiments.</p> <p>Labeling: Cells were simultaneously fixed and permeabilized in cytoskeleton buffer (CBS, 10 mM MES, 138 mM KCl, 3 mM MgCl_2, 2 mM EGTA, 4.5 % sucrose w/v, pH 7.4), + 4 % paraformaldehyde (PFA) and 0.2 % Triton for 6 minutes at 37 °C, and further fixed in CBS + 4 % PFA for 14 minutes at 37 °C. Post-fixation, cells were washed x3 in PBST (PBS supplemented with 0.1 % Tween) and permeabilized a second time in PBS + 0.5 % Triton for 5 minutes at room temperature. The samples were then washed 3 times in PBST and blocked for 30 minutes in 5 % BSA. Cells were washed x3 in PBST and then incubated with Alexa Fluor™ 647 Phalloidin (A22287, Invitrogen, 1:50 in PBS) for 1 h in the dark, followed by x2 washes in PBS. Prior to dSTORM imaging, PBS was replaced with dSTORM imaging buffer (base buffer consisting of 0.56 M glucose, 50 mM Tris (pH 8.5), and 10 mM NaCl supplemented with 5 U/mL pyranose oxidase (Sigma, P4234), 10 mM cysteamine (Sigma, 30070), 40 µg/mL catalase (Sigma, C100) and 2 mM cyclooctatetraene (Sigma, 138924).</p>
Figure 18. Unassigned Emu Bay Shale radiodont setal blades. A, B, SAMA P54822. Body flap and setal blades. A, SAMA P54822a. B, camera lucida drawing incorporating information from counterpart SAMA P54822b. C, SAMA P50287. D, SAMA P43611a in The early Cambrian Emu Bay Shale radiodonts revisited: morphology and systematics
Figure 18. Unassigned Emu Bay Shale radiodont setal blades. A, B, SAMA P54822. Body flap and setal blades. A, SAMA P54822a. B, camera lucida drawing incorporating information from counterpart SAMA P54822b. C, SAMA P50287. D, SAMA P43611a. Scale bars: A, B, D = 10 mm; C = 5 mm.
1000 fps Swimming behavioral videos (high-speed camera, in dorsal view)
Open the record for dataset details and reuse information.
Experiment results for the paper "Uncertainty-Aware Ship Location Estimation using Multiple Cameras in Coastal Areas" to appear in MDM'2024
<p>After decompression, there are 16 folders which corresponding to the 16 multi-camera settings in the paper.</p> <p> </p> <p>Under each folder, there are two files: trajs.csv and trajsGuess.csv.</p> <p> </p> <p>1. trajs.csv contains the trajectories of ships that are located inside the monitored area of the mult-camera setting.</p> <p> The first four columns are MMSI (ship identity), timestamp, lon, and lat.</p> <p> The following columns are the corresponding pixel of the coordinate (lon, lat) in each camera, where (-1,-1) means (lon, lat) is outside the monitored area by a camera.</p> <p> A pixel is a pair of integers. </p> <p> xPos1 and yPos1 are for the 1st camera, and xPos2 and yPos2 are for the 2nd camera, and so on so forth.</p> <p> </p> <p>2. trajsGuess.csv contains the estimated ship locations by using the proposed approach in the paper.</p> <p> There are 6 columns.</p> <p> The 1st column is timestamp.</p> <p> The 2nd column is used to distinguish between the different pixel polygon intersections.</p> <p> The 3rd column and the 4th column can be either a pixel coordinate or a spatial point coordinate in lon/lat.</p> <p> The 5th column is either the cameraID of a pixel, or the order of a boundary point for a spatial polygon. The cameraID starts from 1.</p> <p> The 6th column is the type of the record, which can be</p> <p> pixel,</p> <p> or intersection1 (a polygon),</p> <p> or center1 (center of intersection1),</p> <p> or intersection2 (a polygon),</p> <p> or center2 (center of intersection2).</p> <p> Note that intersection2 and center2 appear rarely in the 6th column.</p>
Leopard and spotted hyena camera trap dataset
<p><span>Human disturbance has the potential to alter competitive interactions, favoring species better able to adapt to areas used by humans. One such species is the spotted hyena (<em>Crocutu crocuta</em>), which has been successful in human dominated areas throughout Africa, competing through kleptoparasitism with other carnivore species (e.g., leopards [<em>Panthera pardus</em>]). In the Udzungwa Mountains, Tanzania, leopard density declines sharply close to human settlements and hyenas are their only competitors. Using camera trap data and a spatio-temporal occupancy model, we assessed the relative dominance of each species through spatial co-occurrence, altered activity patterns and temporary site avoidance. We tested the hypothesis that hyenas gain a competitive advantage over leopards in human-dominated areas due to their relatively higher tolerance for anthropogenic activities. We found that while hyena occupancy was best predicted by prey occupancy and not strongly affected by landscape factors associated with humans, leopards, </span><span>especially male leopards, were </span><span>less likely to be detected close to human settlements</span><span>. Female leopards, which are smaller than males, exhibited activity shifts and temporary site avoidance in response to hyenas, whereas hyenas shifted their activity patterns in response to male leopards. These results suggest that while hyenas may be behaviorally dominant over female leopards, they are subordinate to male leopards. We found that male leopards and hyenas were less </span><span>likely to co-occur closer to people, especially where prey was scarce, suggesting </span><span>subordinance of hyenas to male leopards may be mitigated by human disturbance</span><span>.</span><span> Furthermore, young male leopards shifted their activity patterns to be more diurnal in response to hyena presence, suggesting that dominance relationship between hyenas and leopards develops with age and is probably related to body size. These results indicate that human disturbance has the potential to affect the competitive relationship between leopards and hyenas in the Udzungwa mountains, but that relationships will vary with sex and body size.</span></p>
Zemax Simulation of a USAF-Chart captured by a Plenoptic Camera
<p>Data for https://github.com/lightfieldcamera/Real-Time-Light-Field-Deconvolution</p>
Camera-trapping records of birds and mammals visiting water-filled tree holes in the Calakmul region in southern Mexico
<p>Using camera-traps we documented that 21 bird and 9 mammal species visited water-filled tree holes (dendrotelmata) in the seasonal tropical forest of the Calakmul Biosphere Reserve, in southern Mexico. These species visited dendrotelmata primarily for foraging and drinking. The overall use of dendrotelmata was equally frequent between dry and rainy seasons but drinking behavior increased among birds during the dry season. This dataset includes information on the identity of visiting species, time and date of the visit, behavior of the visiting species, season (rainy/dry) in which the species was recorded, station (dendrotelma) in which the species was recorded, associated temperature and the number of individuals recorded in each visit.</p>
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.