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1,832 results for “Cameras”
Remote camera monitoring and arboreal trapping data for a reintroduced population of red-tailed phascogales (Phascogale calura)
Open the record for dataset details and reuse information.
Brainport, Automated valet parking, RS camera object detection
<p><strong>Scenario description</strong>:</p> <p>RS Camera object detection and publication of the iot message from type AutoPilot.ObjectDetection to the PMS via IoT platforms</p> <p><strong>Session description</strong>:</p> <p>A car drive on the AVP road segment and stop and the RS camera detect the car as obstacle and send the obstacle information to the PMS for free obstacle route calculation for AD-car</p> <p><strong>Datasets descriptions</strong>:</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_DriverVehicleInteraction</strong>: Data extracted from the CAN of the vehicle</p> <p>Dataset Description This dataset contains e.g. throttlestatus, clutchstatus, brakestatus, brakeforce, wipersstatus, steeringwheel for the vehicle</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_DroneAvpCommand</strong>: Data sent from drone</p> <p>Dataset Description This dataset contains route information for a vehicle to a designated parking spot</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_EnvironmentSensorsAbsolute</strong>: Data extracted from the vehicle environment sensors</p> <p>Dataset Description This dataset contains information about detected object, with absolute coordinates</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_EnvironmentSensorsRelative</strong>: Data extracted from the vehicle environment sensors</p> <p>Dataset Description This dataset contains information about detected object, with relative coordinates</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_IotVehicleMessage</strong>: Data sent between all devices, vehicles and services</p> <p>Dataset Description Each sensor data submission is a Message. A Message has an Envelope, a Path, and optionally (but likely) Path Events and optionally Path Media. The envelope bears fundamental information about the individual sender (the vehicle) but not to a level that owner of the vehicle can be identified or different messages can be identified that originate from a single vehicle.</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_ParkingSpotDetection</strong>: Data sent from drone to parkingService</p> <p>Dataset Description This dataset contains informaton about detected parking spots</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_PositioningSystem</strong>: Data from GPS on the vehicle</p> <p>Dataset Description This dataset contains speed, longitude, latitude, heading from the GPS</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_PositioningSystemResampled</strong>: Data from GPS on the vehicle</p> <p>Dataset Description This dataset contains speed,longitude,latitude,heading from the GPS, resampled to 100 milliseconds</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_Vehicle</strong>: Data from the CAN and sensors about the state of the vehicle</p> <p>Dataset Description This dataset contains a.o temperature and battery state of the vehicles</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_VehicleAvpCommand</strong>: Data sent from ParkingService to vehicle</p> <p>Dataset Description This dataset contains route to parkingspot, and some other environmental information</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_VehicleAvpStatus</strong>: Data sent from vehicle to ParkingService</p> <p>Dataset Description This dataset contains information about the current status and parkingstatus of the vehicle</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_VehicleDynamics</strong>: Data from the CAN and sensors about the state of the vehicle</p> <p>Dataset Description This dataset contains a.o accelerations and speedlimit of the vehicle, as observed from the CAN and the external sensors</p>
Data and Code from: Using cameras for precise measurement of two-dimensional plant features: CASS
<p>Computer vision explanation: The code (https://github.com/amy-tabb/CASS, referred to as CASS) takes an image of an object on top of an aruco calibration pattern, calibrates the camera using the detected aruco information as well as EXIF tag information, and undistorts and computes the homography from the current location of the aruco calibration pattern in the image to its location in physical space. Then the image is warped to match the coordinate system of the aruco coordinate system, scaled by a user-selected parameter.</p> <p>This dataset provides examples of properly-formatted input images and accompanying text files, as well as a successful run where the option of writing intermediate results has been selected. Details about how to format the directories is found in the README of https://github.com/amy-tabb/CASS.</p> <ul> <li><code>iphone6</code>. is a directory of input files using the camera of a iPhone 6 cellular phone.</li> <li><code>iphone6_results</code>. is the directory of results created from running CASS on <code>iphone6</code>.</li> <li><code>CanonEOS60D</code>. is a directory of input files using a DSLR camera from Canon, model name EOS 60D.</li> <li><code>CanonEOS60D_results.</code> is the directory of results created from running CASS on <code>CanonEOS60D</code>.</li> </ul> <p>See the <code>Write directory format</code> section of CASS's README for details of all of the files; briefly for this example, <code>warped_ORIGINALFILENAME.jpg</code> is the original image, transformed such that 10 pixels corresponds to 1 millimeter on the two dimensional plane of the calibration pattern.</p> <p>https://github.com/amy-tabb/CASS provides code in C++ for processing on this dataset, as well as as Docker image.</p> <p> </p>
Phenological time lapse images from landscape camera MC130 in Tvärminne Archipelago shore
<p>This record contains phenological time lapse images from camera Tvärminne Archipelago shore. Camera was mounted at landscape view level at location 59.844555, 23.249109(N;E, WGS84).</p> <p>First set of images were taken between 03.12.2015--31.12.2016 (Version 1). Subsequent Versions extend the record with newer images, and the version number indicates the years covered by the record.<br> Cameras were set to fix white balance, brightness automatically adjusted by camera.Image have equal resolution throughout the time series, time indicated in UTC+2. Images are taken half-hourly during fixed day-time period over the year. Gaps in time series and dark images possibly exist.<br> More details on the camera installations and operation history can be found at doi 10.5281/zenodo.777952<br> The cameras were set up and images collected under EU Life+ (LIFE ENV/FI/000409) Monimet project, http://monimet.fmi.fi.<br> For further information contact kristin.bottcher@ymparisto.fi </p>
Allsky images from a camera in Copenhagen, Denmark.
<p>Dark-frame subtracted R G and B images of the night sky from a camera in Copenhagen Denmark. Each file is a tar.gz archive with several files in, each labelled with the Bayer-filter type (R,G or B). Each FITS file in the decompressed archive is the result of subtracting an average darkframe from the average of several sky frames. The dark frames were obtained seconds before the sky image. Each sky image exposure time is 16 seconds. The camera used is a ZWO camera and it obtains its images with a CMOS sensor producing 14-bit RAW images. The FITS files are 16-bit.</p>
Three dimensional dataset combining gait and full body movement of children with autism spectrum disorders collected by Kinect v2 camera
<p><span>To the best of our knowledge, this is the maiden attempt to build a three-dimensional dataset that combines gait and body movement analysis of children with Autism Spectrum Disorders (ASD) in controlled environments for fifty children with autism children and fifty typical children. A 3D dataset includes 3D joints positions, the corresponding skeleton movement video, joints trajectories video captured by Kinect v2, and color videos captured by Samsung Note 9 rear camera. On the other hand, color videos for 9 children suffer from severe autism is also included for scientific benefit. Finally, the dataset includes 700 folders (350 for typical children, 350 for children with ASD) which include 3D files of tracked joints, angles between joints, and skeleton tracking video related to the augmentation of the original dataset based on seven transformations described in the paper.</span></p>
Detection and Estimation of Inundation and Associated Risks Using Traffic and Monitoring Cameras and Image Processing Under Extreme Flooding Conditions
<p>The main objective of this project is to develop an inundation detection and evaluation framework using images from traffic monitoring cameras and reliable flood monitoring under extreme precipitation conditions. This study presents a comparative assessment of image enhancement and segmentation techniques to automatically identify the flash flooding from the low-resolution images taken by traffic-monitoring cameras. Due to inaccurate equipment in severe weather conditions (e.g., raindrops or light refraction on camera lenses), low-resolution images are subject to noises that degrade the quality of information. De-noising procedures are carried out for the enhancement of images by removing different types of noises. After the de-noising, image segmentation is implemented to detect the inundation from the images automatically. In addition, the detection of the inundation using the image segmentation with and without de-noising techniques are compared. The results indicate that among de-noising methods, the Bayes shrink with the thresholding discrete wavelet transform shows the most reliable result. For the image segmentation, the Bayesian segmentation is superior to the others. The results demonstrate that the proposed image enhancement and segmentation methods can be effectively used to identify the inundation from low-resolution images taken in severe weather conditions. A new Bayesian filtering method will be devised and applied to estimate the inundation from low-resolution images that will allow traffic engineers to take preventive or proactive actions to improve the safety of drivers and protect and preserve the transportation infrastructure. This new observation with improved accuracy will enhance our understanding of dynamic urban flooding by filling an information gap in the locations where conventional observations have limitations.</p>
Data from: Camera-based occupancy monitoring at large scales: power to detect trends in grizzly bears across the Canadian Rockies
Monitoring carnivores is critical for conservation, yet challenging because they are rare and elusive. Few methods exist for monitoring wide-ranging species over large spatial and sufficiently long temporal scales to detect trends. Remote cameras are an emerging technology for monitoring large carnivores around the world because of their low cost, non-invasive methodology, and their ability to capture pictures of species of concern that are difficult to monitor. For species without uniquely identifiable spots, stripes, or other markings, cameras collect detection/non-detection data that are well suited for monitoring trends in occupancy as its own independent useful metric of species distribution, as well as an index for abundance. As with any new monitoring method, prospective power analysis is essential to ensure meaningful trends can be detected. Here we test camera-based occupancy models as a method to monitor changes in occupancy of a threatened species, grizzly bears (Ursus arctos), at large landscape scales, across 5 Canadian national parks (~21,000 km2). With n = 183 cameras, the top occupancy model estimated regional occupancy to be 0.79 across all 5 parks. We evaluate the statistical power to detect simulated 5–40% declines in occupancy between two sampling years and test applied questions of how power is affected by the spatial scale of interest (park level vs. regional level), the number of cameras deployed, and duration of camera deployment. We also explore several ecological mechanisms (i.e., spatial patterns) of decline in occupancy, and examine how power changes when focusing only on grizzly bears family groups. As hypothesized, statistical power increased with the number of cameras and with the number of days deployed. Power was unaffected, however, by the ecological mechanisms of decline, indicating that our systematic sampling design can detect a decline regardless of whether occupancy declined due to range edge attrition, ecological trap or other mechanisms. Despite their lower occupancy, power was similarly high for grizzly bear family groups compared to grizzly bears in general. We highlight which study design attributes contributed to high power and we provide advice for establishing cost-effective camera-based programs for monitoring large carnivore occupancy at large spatial scales.
Data from: Through the eye of a Gobi khulan – application of camera collars for ecological research of far-ranging species in remote and highly variable ecosystems
The Mongolian Gobi-Eastern Steppe Ecosystem is one of the largest remaining natural drylands and home to a unique assemblage of migratory ungulates. Connectivity and integrity of this ecosystem are at risk if increasing human activities are not carefully planned and regulated. The Gobi part supports the largest remaining population of the Asiatic wild ass (Equus hemionus; locally called "khulan"). Individual khulan roam over areas of thousands of square kilometers and the scale of their movements is among the largest described for terrestrial mammals, making them particularly difficult to monitor. Although GPS satellite telemetry makes it possible to track animals in near-real time and remote sensing provides environmental data at the landscape scale, remotely collected data also harbors the risk of missing important abiotic or biotic environmental variables or life history events. We tested the potential of animal born camera systems ("camera collars") to improve our understanding of the drivers and limitations of khulan movements. Deployment of a camera collar on an adult khulan mare resulted in 7,881 images over a one-year period. Over half of the images showed other khulan and 1,630 images showed enough of the collared khulan to classify the behaviour of the animals seen into several main categories. These khulan images provided us with: i) new insights into important life history events and grouping dynamics, ii) allowed us to calculate time budgets for many more animals than the collared khulan alone, and iii) provided us with a training dataset for calibrating data from accelerometer and tilt sensors in the collar. The images also allowed to document khulan behaviour near infrastructure and to obtain a day-time encounter rate between a specific khulan with semi-nomadic herders and their livestock. Lastly, the images allowed us to ground truth the availability of water by: i) confirming waterpoints predicted from other analyses, ii) detecting new waterpoints, and iii) compare precipitation records for rain and snow from landscape scale climate products with those documented by the camera collar. We discuss the added value of deploying camera collars on a subset of animals in remote, highly variable ecosystems for research and conservation.
An empirical evaluation of camera trap study design: how many, how long, and when?
1. Camera traps deployed in grids or stratified random designs are a well-established survey tool for wildlife but there has been little evaluation of study design parameters. 2. We used an empirical subsampling approach involving 2225 camera deployments run at 41 study areas around the world to evaluate three aspects of camera trap study design (number of sites, duration and season of sampling) and their influence on the estimation of three ecological metrics (species richness, occupancy, detection rate) for mammals. 3. We found that 25-35 camera locations were needed for precise estimates of species richness, depending on scale of the study. The precision of species-level estimates of occupancy was highly sensitive to occupancy level, with <20 camera sites needed for precise estimates of common (>0.75) species, but more than 150 sites likely needed for rare (<0.25) species. Species detection rates were more difficult to estimate precisely at the grid level due to spatial heterogeneity, presumably driven by unaccounted for habitat variability within the study area. Running a camera at a site for 2 weeks was most efficient for detecting new species, but 3-4 weeks were needed for precise estimates of local detection rate, with no gains in precision observed after 1 month. Metrics for all mammal communities were sensitive to seasonality, with 37-50% of the species at the sites we examined fluctuating significantly in their occupancy or detection rates over the year. This effect was more pronounced in temperate sites, where seasonally sensitive species varied in relative abundance by an average factor of 4-5, and some species were completely absent in one season due to hibernation or migration. 4. We recommend the following guidelines to efficiently obtain precise estimates of species richness, occupancy and detection rates with camera trap arrays: run each camera for 3-5 weeks across 40-60 sites per array. We recommend comparisons of detection rates be model-based and include local covariates to help account for small-scale variation. Furthermore, comparisons across study areas or times must account for seasonality, which had strong impacts on mammal communities in both tropical and temperate sites.
Data from: Accuracy of identifications of mammal species from camera trap images: a northern Australian case study
Camera traps are a powerful and increasingly popular tool for mammal research, but like all survey methods, they have limitations. Identifying animal species from images is a critical component of camera trap studies, yet while researchers recognize constraints with experimental design or camera technology, image misidentification is still not well understood. We evaluated the effects of a species' attributes (body mass and distinctiveness) and individual observer variables (experience and confidence) on the accuracy of mammal identifications from camera trap images. We conducted an Internet‐based survey containing 20 questions about observer experience and 60 camera trap images to identify. Images were sourced from surveys in northern Australia and included 25 species, ranging in body mass from the delicate mouse (Pseudomys delicatulus, 10 g) to the agile wallaby (Macropus agilis, >10 kg). There was a weak relationship between the accuracy of mammal identifications and observer experience. However, accuracy was highest (100%) for distinctive species (e.g. Short‐beaked echidna [Tachyglossus aculeatus]) and lowest (36%) for superficially non‐distinctive mammals (e.g. rodents like the Pale field‐rat [Rattus tunneyi]). There was a positive relationship between the accuracy of identifications and body mass. Participant confidence was highest for large and distinctive mammals, but was not related to participant experience level. Identifications made with greater confidence were more likely to be accurate. Unreliability in identifications of mammal species is a significant limitation to camera trap studies, particularly where small mammals are the focus, or where similar‐looking species co‐occur. Integration of camera traps with conventional survey techniques (e.g. live‐trapping), use of a reference library or computer‐automated programs are likely to aid positive identifications, while employing a confidence rating system and/or multiple observers may lead to a collection of more robust data. Although our study focussed on Australian species, our findings apply to camera trap studies globally.
Data from: Using camera trapping and hierarchical occupancy modelling to evaluate the spatial ecology of an African mammal community
Emerging conservation paradigms have shifted from single to multi-species approaches focused on sustaining biodiversity. Multi-species hierarchical occupancy modelling provides a method for assessing biodiversity while accounting for multiple sources of uncertainty. We analysed camera trapping data with multi-species models using a Bayesian approach to estimate the distributions of a terrestrial mammal community in northern Botswana and evaluate community, group, and species-specific responses to human disturbance and environmental variables. Groupings were based on two life-history traits: body size (small, medium, large and extra-large) and diet (carnivore, omnivore and herbivore). We photographed 44 species of mammals over 6607 trap nights. Camera station-specific estimates of species richness ranged from 8 to 27 unique species, and species had a mean occurrence probability of 0·32 (95% credible interval = 0·21–0·45). At the community level, our model revealed species richness was generally greatest in floodplains and grasslands and with increasing distances into protected wildlife areas. Variation among species' responses was explained in part by our species groupings. The positive influence of protected areas was strongest for extra-large species and herbivores, while medium-sized species actually increased in the non-protected areas. The positive effect of grassland/floodplain cover, alternatively, was strongest for large species and carnivores and weakest for small species and herbivores, suggesting herbivore diversity is promoted by habitat heterogeneity. Synthesis and applications. Our results highlight the importance of protected areas and grasslands in maintaining biodiversity in southern Africa. We demonstrate the utility of hierarchical Bayesian models for assessing community, group and individual species' responses to anthropogenic and environmental variables. This framework can be used to map areas of high conservation value and predict impacts of land-use change. Our approach is particularly applicable to the growing number of camera trap studies world-wide, and we suggest broader application globally will likely result in reduced costs, improved efficiency and increased knowledge of wildlife communities.
Data from: Estimating density for species conservation: comparing camera trap spatial count models to genetic spatial capture-recapture models
Density estimation is integral to the effective conservation and management of wildlife. Camera traps in conjunction with spatial capture-recapture (SCR) models have been used to accurately and precisely estimate densities of "marked" wildlife populations comprising identifiable individuals. The emergence of spatial count (SC) models holds promise for cost-effective density estimation of "unmarked" wildlife populations when individuals are not identifiable. We evaluated model agreement, precision, and survey costs, between i) a fully marked approach using SCR models fit using non-invasive genetic data, and ii) an unmarked approach using SC models fit using camera trap data, for a recovering population of the mesocarnivore fisher (Pekania pennanti). The SCR density estimates ranged from 2.95 to 3.42 (2.18–5.19 95% BCI) fishers 100 km−2. The SC density estimates were influenced by their priors, ranging from 0.95 (0.65–2.95 95% BCI) fishers 100 km−2 for the uninformative model to 3.60 (2.01–7.55 95% BCI) fishers 100 km−2 for the model informed by prior knowledge of a 16 km2 fisher home range. We caution against using strongly informative priors but instead recommend using a range of unweighted prior knowledge. Thin detection data was problematic for both SCR and SC models, potentially producing biased low estimates. The total cost of the genetic survey ($47 610) was two-thirds of the camera trap survey ($77 080), or comparable ($75 746) if genetic sampling effort was increased to include sex and trap-behaviour covariates in SCR models. Density estimation of unmarked populations continues to be a series of trade-offs but as methods improve and integrate, so will our estimates.
Data from: Random versus game trail-based camera trap placement strategy for monitoring terrestrial mammal communities
Camera trap surveys exclusively targeting features of the landscape that increase the probability of photographing one or several focal species are commonly used to draw inferences on the richness, composition and structure of entire mammal communities. However, these studies ignore expected biases in species detection arising from sampling only a limited set of potential habitat features. In this study, we test the influence of camera trap placement strategy on community-level inferences by carrying out two spatially and temporally concurrent surveys of medium to large terrestrial mammal species within Tanzania's Ruaha National Park, employing either strictly game trail-based or strictly random camera placements. We compared the richness, composition and structure of the two observed communities, and evaluated what makes a species significantly more likely to be caught at trail placements. Observed communities differed marginally in their richness and composition, although differences were more noticeable during the wet season and for low levels of sampling effort. Lognormal models provided the best fit to rank abundance distributions describing the structure of all observed communities, regardless of survey type or season. Despite this, carnivore species were more likely to be detected at trail placements relative to random ones during the dry season, as were larger bodied species during the wet season. Our findings suggest that, given adequate sampling effort (> 1400 camera trap nights), placement strategy is unlikely to affect inferences made at the community level. However, surveys should consider more carefully their choice of placement strategy when targeting specific taxonomic or trophic groups.
Data from: Revealing kleptoparasitic and predatory tendencies in an African mammal community using camera traps: a comparison of spatiotemporal approaches
Camera trap data are increasingly being used to characterise relationships between the spatiotemporal activity patterns of sympatric mammal species, often with a view to inferring inter-specific interactions. In this context, we attempted to characterise the kleptoparasitic and predatory tendencies of spotted hyaenas Crocuta crocuta and lions Panthera leo from photographic data collected across 54 camera trap stations and two dry seasons in Tanzania's Ruaha National Park. We applied four different methods of quantifying spatiotemporal associations, including one strictly temporal approach (activity pattern overlap), one strictly spatial approach (co-occupancy modelling), and two spatiotemporal approaches (co-detection modelling and temporal spacing at shared camera trap sites). We expected a kleptoparasitic relationship between spotted hyaenas and lions to result in a positive spatiotemporal association, and further hypothesised that the association between lions and their favourite prey in Ruaha, the giraffe Giraffa camelopardalis and the zebra Equus quagga, would be stronger than those observed with non-preferred prey species (the impala Aepyceros melampus and the dikdik Madoqua kirkii). Only approaches incorporating both the temporal and spatial components of camera trap data resulted in significant associative patterns. The latter were particularly sensitive to the temporal resolution chosen to define species detections (i.e. occasion length), and only revealed a significant positive association between lion on spotted hyaena detections, as well as a tendency for both species to follow each other at camera trap sites, during the dry season of 2013, but not that of 2014. In both seasons, observed spatiotemporal associations between lions and each of the four herbivore species considered provided no convincing or consistent indications of any predatory preferences. Our study suggests that, when making inferences on inter-specific interactions from camera trap data, due regards should be given to the potential behavioural and methodological processes underlying observed spatiotemporal patterns.
Data from: A high-resolution panorama camera system for monitoring colony-wide seabird nesting behaviour
1. Obtaining accurate and representative demographic metrics for animal populations is critical to many aspects of wildlife monitoring and management. However, at remote animal colonies, metrics derived from sequential counts or other continuous monitoring are often subject to logistical, weather and disturbance challenges.The development of remote camera technologies has assisted monitoring, but limitations in spatial and temporal resolution and sample sizes remain. 2. Here we describe the application of a robotic camera system (Gigapan) which takes a tiled sequence of photographs that are automatically stitched together to form high-resolution panoramas. We demonstrate the application of the Gigapan using data collected during field-testing at a shy albatross colony on Albatross Island in northwest Tasmania. 3. We took daily panoramas over five days to estimate mean incubation shift-duration, an indirect measure for foraging trip duration, in an existing study area. Similar numbers of occupied nests could be observed at a distance of ~100m in the Gigapan panoramas compared to ground-based counts (115 and 117 respectively). Of these, birds on 90% of nests visible in the panoramas could be unambiguously identified as marked or unmarked with a small daub of paint throughout the study period and thus a shift change reliably recorded. Gigapan-based shift duration was estimated using a novel instantaneous statistical method and were longer than estimates earlier in the egg brooding period, potentially revealing a new pattern in shift duration. 4. This example field application provides proof-of-concept and demonstration that the relatively low cost Gigapan system provides the spatial advantages of satellite or aerial photos with the detail and temporal replication of land-based camera systems. The Gigapan system can extend or enhance traditional data collection methods, particularly for simultaneous observations, at distance, of the behaviour of many surface nesting colonial seabirds..
Data from: Identifying drivers of spatial variation in occupancy with limited replication camera trap data
Occupancy models are widely used in camera trap studies to analyze species presence, abundance, and geographic distribution, among other important ecological quantities. These models account for imperfect detection using a latent variable to distinguish between true presence/absence and observed detection of a species. Under certain experimental setups, parameter estimation in a latent variable framework can be challenging. Several studies have issued guidelines on the number of independent replicated observations (surveys) needed for each unchanging occupancy field (season) to ensure reliable estimation. In this paper we present a spatio-temporal occupancy model, and show through a simulation study that it can be fit to data obtained from a \textit{single} survey per season, so long as the number of seasons is sufficiently large. We include an application using camera-trap data on the Thomson's gazelle in the Serengeti in Tanzania.
Start 66, model IV camera
The first still camera manufactured in Poland after World War II was the Start I (a prototype was built in 1953). Its design was prepared by a team of engineers of Warszawskie Zakłady Foto-Optyczne: Janusz Jirowec, Tadeusz Lisowski and Jan Matysiak. The goal was to develop a cheap, easy to operate camera ensuring high quality photographs, made solely of Polish materials. The shutter in the device was based on the design of the Soviet Lubitiel camera, which was an unlicensed copy of the pre-war German Voigtländer Brillant camera. The Start 66 camera presented here is the fourth model in the family of devices. It is a twin-lens reflex camera, in which the photographs were made on 120 medium format film with backing paper, in a 6x6 cm size. The camera uses a three-element Emitar lens. Manufacturer: Warszawskie Zakłady Foto-Optyczne, Warsaw, 1967-1978 Inv. No. MIM 358/VI-60 Model prepared on the basis of photogrammetric measurements Licence: CC BY-NC-SA Source: Objaverse 1.0 / Sketchfab
Quarz M camera
The Quarz M is a light camera of compact build, with a detachable handle ("pistol handle"), which houses a compartment for two filters. The Quarz M amateur film camera, manufactured in 1965-1973, is a modification of the Quarz 2 camera, developed for export sales. The Quarz M camera has a built-in spring-type drive, a selenium photometer, and semiautomatic focus. It is also equipped with a non-removable lens with a focal length of 1.9-12.5 mm. The camera enables recording of black and white images without sound, using different tape speeds of 12, 16, 24 or 48 frames per second, and rewinding. The Quarz 2 camera is used by Filip Mosz for documenting daily life (played by Jerzy Stuhr) – the protagonist of the Amator film of 1979, directed by Krzysztof Kieślowski. Manufacturer: Krasnogorsk Mechanical Establishment, 1965 Inv. No.: MIM1355/VI-183 Model prepared on the basis of photogrammetric measurements Licence: CC BY-NC-SA (CC Attribution-NonCommercial-ShareAlike) Source: Objaverse 1.0 / Sketchfab
Diel niche of sympatric small mammals revealed by year-round camera trapping
<p>Data and scripts to reproduce results presented in the research article manuscript.</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.