Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
1,832
datasets available to search
ShareScore release 0.9.0
Dataset results
1,832 results for “Cameras”
Listening and watching: do camera traps or acoustic sensors more efficiently detect wild chimpanzees in an open habitat?
<p>1. With one million animal species at risk of extinction, there is an urgent need to regularly monitor threatened species. However, in practice this is challenging, especially with wide-ranging, elusive and cryptic species or those that occur at low density.<br> 2. Here we compare two non-invasive methods, passive acoustic monitoring (n=12) and camera trapping (n=53), to detect chimpanzees (Pan troglodytes) in a savanna-woodland mosaic habitat at the Issa Valley, Tanzania. With occupancy modelling we evaluate the efficacy of each method, using the estimated number of sampling days needed to establish chimpanzee absence with 95% probability, as our measure of efficacy.<br> 3. Passive acoustic monitoring was more efficient than camera trapping in detecting wild chimpanzees. Detectability varied over seasons, likely due to social and ecological factors that influence party size and vocalization rate. The acoustic method can infer chimpanzee absence with less than ten days of recordings in the field during the late dry season, the period of highest detectability, which was five times faster than the visual method.<br> 4. Synthesis and applications: Despite some technical limitations, we demonstrate that passive acoustic monitoring is a powerful tool for species monitoring. Its applicability in evaluating presence/absence, especially but not exclusively for loud call species, such as cetaceans, elephants, gibbons or chimpanzees provides a more efficient way of monitoring populations and inform conservation plans to mediate species-loss.</p>
Exakta Varex VX Camera
ID no. MN/AH/63 "Exakta Varex VX Camera" Museum: The Museum of Niepołomice — the Niepołomice Royal Castle http://muzea.malopolska.pl/en/obiekty/-/a/26891/1126982 Digitalisation RDW MIC, Małopolska's Virtual Museums project Source: Objaverse 1.0 / Sketchfab
Camera Limits Demo: Van Gogh - Bedroom in Arles
[Camera Limits](https://help.sketchfab.com/hc/en-us/articles/115003399103-Camera-Limits) constrain the movement of the camera around a model or scene. In this Van Gogh Room, Camera Limits are used to hide the missing parts of the model to the viewer, making the experience more immersive. The [Model Inspector](https://help.sketchfab.com/hc/en-us/articles/115004862686-Inspector) will disable camera limits - click the icon in the bottom-right corner of the viewer, or press 'I' to try it out. *Original model '[Van Gogh Room](https://sketchfab.com/models/311d052a9f034ba8bce55a1a8296b6f9)' published by ruslans3d under a Creative Commons Attribution license.* Source: Objaverse 1.0 / Sketchfab
Camera Lubitel2 Photogrammetry
Photogrammetry reconstruction from sample images and overall cleanup in some areas. https://www.capturingreality.com/free-datasets Source: Objaverse 1.0 / Sketchfab
Speed Graphic Camera
ID no.: MHF 645/I Museum of Photography in Kraków https://muzea.malopolska.pl/en/objects-list/953 Digitalisation: RDW MIC, Virtual Małopolska project Source: Objaverse 1.0 / Sketchfab
Zenit camera
ID no.: MHF 1237/I Museum of Photography in Kraków https://muzea.malopolska.pl/en/objects-list/951 Digitalisation: RDW MIC, Virtual Małopolska project Source: Objaverse 1.0 / Sketchfab
F-21 (Ajax) Camera
ID no.: MHF 1314/I Museum of Photography in Kraków https://muzea.malopolska.pl/en/objects-list/947 Digitalisation: RDW MIC, Virtual Małopolska project Source: Objaverse 1.0 / Sketchfab
Sport Camera
ID no.: MHF 1893/I Museum of Photography in Kraków https://muzea.malopolska.pl/en/objects-list/950 Digitalisation: RDW MIC, Virtual Małopolska project Source: Objaverse 1.0 / Sketchfab
Kodak Startech Camera
Similar to the Kodak Brownie Starflash, this camera was made specifically for close-up dental & medical photography. The Startech outfit included two close-up lenses and a flash shield, presumably to prevent hot shards of glass from becoming embedded in your face, should the bulb explode. c1959 It made 4x4cm images on type No. 127 film rolls. Structured-light scan Private collection Source: Objaverse 1.0 / Sketchfab
Camera - Old
Hey everyone! That's my another work. I did it in 3D program - bltnder. Hope you'll use it! Source: Objaverse 1.0 / Sketchfab
Toulouse Capitole camera tracking dataset
<p>This dataset collects 56 images taken in Place du Capitole in Toulouse (FRA) (https://goo.gl/maps/rWyj8yCGAMP2) and 3 related videos taken while moving around in the place. The aim of the dataset is to test and evaluate the camera tracking algorithms developed for the POPART project (http://www.popartproject.eu/), and help the reproducibility of the experiments.</p> <p>The camera tracking algorithms developed for the POPART project are based on model tracking of an existing 3D reconstruction of the scene. First a collection of still images are taken and a SfM pipeline is used to perform the 3D reconstruction of the scene. As result of this first step, a 3D point cloud is generated. The camera tracking algorithms are then based on camera localization techniques: each frame is individually localized w.r.t. the 3D point cloud using the photometric information (SIFT features) associated to each point. This allows to align the point cloud to the current frame and thus compute the camera pose.</p> <p>The dataset is maintained at https://gitlab.com/simogasp/trackingDataset_TLSCapitole </p>
Daedalus 2 - NS1b - XIX CEA - High sensitivity camera
<p>Video of the PRO-AM colaboration -between the Dep. Astrofísica y CC. de la Atmosfera of the Universidad Complutese de Madrid and Daedalus project, Astroinova Asociation.</p> <p>Mission of an stratospheric balloon.</p> <p>The mission Daedalus 2, launched from Corral de Almaguer (Cuenca/Spain).</p> <p>It was recorded with a High sensitivity camera and a video recorder.</p> <p> </p>
Supplementary material: Modeling and Compensating Temperature-dependent Non-uniformity Noise in IR Microbolometer Cameras
<p><strong>Abstract</strong>: Images rendered by uncooled microbolometer-based infrared (IR) cameras are severely degraded by the spatial non-uniformity (NU) noise. The NU noise imposes a fixed-pattern over the true images, and the intensity of the pattern changes with time due to the temperature instability of such cameras. In this paper, we present a novel model and a compensation algorithm for the spatial NU noise and its temperature-dependent variations. The model separates the NU noise into two components: a constant term, which corresponds to a set of NU parameters determining the spatial structure of the noise, and a dynamic term, which scales linearly with the fluctuations of the temperature surrounding the array of microbolometers. We use a black-body radiator and samples of the temperature surrounding the IR array to offline characterize both the constant and the temperature-dependent NU noise parameters. Next, the temperature-dependent variations are estimated online using both a spatially uniform Hammerstein-Wiener estimator and a pixelwise least mean squares (LMS) estimator. We compensate for the NU noise in IR images from two long-wave IR cameras. Results show an excellent non-uniformity correction performance and a root mean square error of less than 0.25◦C, when array’s temperature varies approximately 15◦C.</p>
Capdigital courtyard camera tracking dataset
<p>This dataset collects 197 images taken in courtyard of Capdigital building (https://goo.gl/maps/1fAw1KqTp9u) in Paris (FRA) and 2 video sequences taken with a camera rig composed of 1 main camera and 2 witness cameras on the side looking outwards. The aim of the dataset is to test and evaluate the camera tracking algorithms developed for the POPART(http://www.popartproject.eu/) project, and help the reproducibility of the experiments. The camera tracking algorithms developed for the POPART project are based on model tracking of an existing 3D reconstruction of the scene. First a collection of still images are taken and a SfM pipeline is used to perform the 3D reconstruction of the scene. As result of this first step, a 3D point cloud is generated.</p> <p>The camera tracking algorithms are then based on camera localization techniques: each frame is individually localized w.r.t. the 3D point cloud using the photometric information (SIFT features) associated to each point. This allows to align the point cloud to the current frame and thus compute the camera pose.</p> <p>The dataset is maintained at https://gitlab.com/simogasp/trackingDataset_CapdigitalOutdoor</p>
Phenological time lapse images from landscape camera MC118 in Paljakka Spruce stand (2015-2016)
<p>This dataset contains phenological time lapse images from x camera Paljakka Spruce stand. Camera was mounted at landscape view level at location 64.677381;28.114014(N;E, WGS84). Images were taken between 29.05.2015--06.07.2016.<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 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 Mikko Peltoniemi (mikko.peltoniemi@luke.fi)</p>
Phenological time lapse images from crown camera MC102 in Tammela Spruce stand (2014-2014)
<p>This dataset contains phenological time lapse images from camera Tammela Spruce stand. Camera was mounted at crown view level at location 60.64598306; 23.80650111(N;E, WGS84). Images were taken between 31.03.2014--18.05.2014.<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 Mikko Peltoniemi (mikko.peltoniemi@luke.fi)</p>
Phenological time lapse images from landscape camera MC117 in Paljakka Spruce stand (2015-2016)
<p>This dataset contains phenological time lapse images from camera Paljakka Spruce stand. Camera was mounted at landscape view level at location 64.677381;28.114014(N;E, WGS84). Images were taken between 27.05.2015--06.07.2016.<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 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 Mikko Peltoniemi (mikko.peltoniemi@luke.fi)</p>
Sherlock Camera Trap Dataset 9
<p>Part of the camera trap image dataset used for the 'main' Sherlock test. See the base dataset for links to the remaining data. </p>
Sherlock Camera Trap Dataset 7
<p>Part of the camera trap image dataset used for the 'main' Sherlock test. See the base dataset for links to the remaining data. </p>
Sherlock Camera Trap Dataset 8
<p>Part of the camera trap image dataset used for the 'main' Sherlock test. See the base dataset for links to the remaining data. </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.