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”
AEROARMS Visual servoing with actively movable camera
<p>This dataset includes a library of elementary behaviours, namely atomic tasks to be assigned to a dual arm aerial manipulator in a priority order. The devised approach, developed within the EU funded research project AEROARMS (AErial RObotic system integrating multiple ARMS and advanced manipulation capabilities for inspection and maintenance) funded by the European Commission under Horizon 2020 Program (grant agreement No. 644271), is based on a set-based inverse kinematics control [1]. For each elementary task the Jacobian matrix and the task function are provided.</p> <p>The code has been developed in C++ under ROS environment and it has been experimentally tested on the AEROARMS dual-arm prototype developed by the University of Seville constituted by an under-actuated multi-rotor with two arms, each of them characterized by 4 DOFs. The total number of DOFs of the system is 12, while the amount of implemented tasks required a total number of 23 DOFs. This is possible since the different tasks are never activated all together but only a subset of them, based on the state of the system. More in detail, the following equality tasks (i.e. tasks characterized by a specific desired value) have been implemented: </p> <p>• Position and orientation trajectory tracking of left arm end-effectors. Such a tasks requires 6 DOFs.</p> <p>• Center of mass, this task is aimed at ensuring that the center of mass of the dual-arm system is, as much as possible, aligned with that of the UAV, in such a way to avoid to destabilize the flight and reduce the power consumption. Such a task requires 1 DOFs.</p> <p>• Field of View of the end effector of the right arm, equipped with a micro camera.</p> <p>As concerns the set-based tasks (i.e. tasks whose desired value is represented by an interval) the following have been implemented:</p> <p>• Joint limits: for each joint, upper and lower limits are set in order to avoid its mechanical limits. Such a task requires 1 DOF for each joint of the arms, thus the total number of required DOFs is 8.</p> <p>• Virtual wall between the two arms: to avoid collisions between the two arms, a virtual wall is implemented in order to delimit their working spaces. Such a task requires 2 DOF.</p> <p>• Virtual wall between the arms and the vehicle: to avoid collisions between the arms and the vehicle, virtual walls are implemented in order to delimit their working spaces. Such a task requires 1 DOF for each arm, thus the total number of required DOFs is 2.</p> <p>• Manipulability, aimed at keeping the manipulators far enough from singular configurations, at which the structure loses mobility. Such a task requires 1 DOF.</p> <p>The equality tasks are always active, while the set-based tasks are active only when the task variable is close to the domain border. The experimental results are reported in the paper:</p> <p>E. Cataldi, F. Real, A. Suarez, P.A. Di Lillo, F. Pierri, G. Antonelli, F. Caccavale, G. Heredia, A. Ollero, Set-based Inverse Kinematics Control of an Anthropomorphic Dual Arm Aerial Manipulator, 2019 IEEE International Conference on Robotics and Automation (ICRA), Montreal, Canada.</p> <p>The following data and files are included:</p> <p>• The ROS code: ros_code_set_based.zip, that includes the nodes ros_aeroarms_set_based_seville and lib_multitasks that can be tested in simulation with the simulator V-Rep.</p> <p>• The model of a dual arm aerial manipulator, developed in V-Rep.</p> <p>• The experimental data both in the .mat format for reading with Matlab and in the .bag format for ROS.</p> <p>• The experimental results in ASCII files.</p> <p>[1] S. Moe, G. Antonelli, A. R. Teel, K. Y. Pettersen e J. Schrimpf, «Set-Based Tasks within the Singularity-Robust Multiple Task-Priority Inverse Kinematics Framework: General Formulation, Stability Analysis, and Experimental Results,» Frontiers in Robotics and AI , vol. 3, n. 16, 2016.</p>
Sample raw images from cryogenically cooled Large Field Camera chip 0 at Palomar Observatory
<p>Bias, dark, flat and science images from chip 0 of the LFC at Palomar Observatory, taken by Erik Tollerud</p>
Occupancy Sensing and Activity Recognition with Cameras and Wireless Sensors
<p>This dataset contains human activity data from a wireless sensing system, which includes a Doppler motion sensor and a wireless network. The Doppler sensor is a low-cost dual Doppler sensor modified from a commercial-off-the-shelf range-controlled radar, which operates at 5.8 GHz with two directional antennas. The wireless network uses four IEEE 802.15.4 radio nodes (CC2531 from TI) to create a mesh network to measure the RSS between each pair of radio nodes operating on the 16 frequency channels at 2.4 GHz.</p> <p>For the activity experiment, we recruited human subjects to perform 42 trials of four activities (each one with two minutes duration): (1) walking in a room (10 trials), (2) sitting in a chair (10 trials), (3) lying on a bed (12 trials), and (4) body turning on a bed (10 trials). For the walking activity, the human subjects walk along different paths at different locations in the room. For the lying on bed activity, we ask human subjects to breathe normally on bed with three orientations facing upwards, right and left. Finally, for the turning on bed case, human subjects turn their bodies from one side to the other on bed with random time intervals. We also recorded two-minute data of the empty room case before and after each human subject trial. Note that each data file name has its corresponding activity in it, so it is pretty self-explanatory. </p>
Helheim Terminus Camera Images 2009-2015
<p>Images collected by two cameras positioned close to the terminus of Helheim Glacier, South East Greenland between 2009 and 2015.</p>
Figure 3. Camera trap 2 in Evidence of presence of Marbled Cat Pardofelis marmorata (Martin, 1837) in Neora Valley National Park, Central Himalaya, India
Figure 3. Camera trap 2 recording the second individual.
Fig. 1 in Camera Trapping The Indochinese Tiger, Panthera Tigris Corbetti, In A Secondary Forest In Peninsular Malaysia
Fig. 1. Map of FJB and infra red sensored camera locations.
Figure. Camera traps points (l) in study area. in Camera trapping of medium and large-sized mammals in western Black Sea deciduous forests in Turkey
Figure. Camera traps points (l) in study area.
ENDGAME - Laboratory Experiment 2024-01-16 Exp. 003 - High Speed Camera data
<p>Shock-tube experiments in combination with high speed Schlieren shadow photography. </p> <p>The shocktube setup consists of a high-pressure reservoir connected with a cylindrical tube through a diaphragm pulse valve which allows a fast release of pressurized gas into the ambient pressure tube. The high-pressure reservoir is filled with compressed air at a given overpressure with respect to ambient pressure (up to 8 bar). The pipe was either empty (i.e. with air at ambient conditions) or filled with a given amount of small particles. We adopted pipes an inner diameter of 4 cm and different lengths (20, 30 and 80 cm). We also considered different volumes of pressurized gas (~650 cm3 and ~1550 cm3). A pressure sensor has been placed at the vent of the conduit to record pressure variations during experiments. Images from the high speed camera were collected at a frame rate of 30000 fps.</p> <p>When the valve is open, a jet flow is produced, with shock and acoustic waves propagating in the atmosphere, which become visible due to the high speed Schlieren shadow photography.</p>
Preliminary data and analysis from ultrafast calcium or voltage imaging recordings at 8-bits resolution using a Kinetix camera
<p><span>This dataset was obtained from brain slices of the mouse. Data are from transversal hippocampal slices from </span><span>30-40 postnatal days old C57Bl6 mice (of both genders), stained with the Ca<sup>2+</sup> indicator Fluo-4 AM; or from layer-5 pyramidal neurons loaded intracellularly either with the Ca<sup>2+</sup> indicator Oregon Green BAPTA-5N or with the voltage sensitive dye JPW1114. <span> </span>Details are in the Read_me file. This dataset cannot be used for publications without permission of the contact person.</span></p>
4th International Workshop on Camera Traps, AI, and Ecology - Photos
Open the record for dataset details and reuse information.
PM_110106_Antique_Photo_camera
<u>File Name</u>: PM_110106_Antique_Photo_camera.jpg <br><u>Sublocation</u>: None <br><u>Location</u>: None <br><u>Province</u>: None <br><u>Country</u>: None <br><u>Header</u>: Antiek fototoestel, Kodak, SIX-20 Brownie C, film 620, 1933-1941; Privécollectie Paul M.R; Maeyaert; <br><u>Description</u>: Photo camera Kodak, SIX-20 Brownie C, film 620, 1933-1941 Private collection Paul M. R Maeyaert <br><u>Keywords</u>: Antique photo camera/cinema, Cultural heritage, Museum/private collection, Thematic, privé, privé collecties <br><br><u>Author</u>: Photo: Paul M.R. Maeyaert <br><u>Copyright</u>: Paul M.R. Maeyaert <br>
PM_110131_Antique_Photo_camera
<u>File Name</u>: PM_110131_Antique_Photo_camera.jpg <br><u>Sublocation</u>: None <br><u>Location</u>: None <br><u>Province</u>: None <br><u>Country</u>: None <br><u>Header</u>: Antiek fototoestel, Kodak, Retina; Privécollectie Paul M.R; Maeyaert; <br><u>Description</u>: Photo camera Kodak, Retina Private collection Paul M. R Maeyaert <br><u>Keywords</u>: Antique photo camera/cinema, Cultural heritage, Museum/private collection, Thematic, privé, privé collecties <br><br><u>Author</u>: Photo: Paul M.R. Maeyaert <br><u>Copyright</u>: Paul M.R. Maeyaert <br>
Optic flow and odometry data from intelrealsense camera
<p>Insects rely on the perception of image motion, or optic flow, to estimate their velocity relative to nearby objects. This information provides important sensory input for avoiding obstacles. However, certain behaviors, such as estimating the absolute distance to a landing target, accurately measuring absolute distance travelled, and estimating the ambient wind speed require decoupling optic flow into its component parts: absolute ground velocity and distance to nearby objects. Behavioral experiments suggest that insects perform these calculations, but their mechanism for doing so remains unknown. Here we present a novel algorithm that combines the geometry of dynamic forward motion with known features of insect visual processing to provide a hypothesis for how insects might \textit{directly} estimate absolute ground velocity from a combination of optic flow and acceleration information. Our robotics-inspired-biology approach reveals three critical requirements. First, absolute ground velocity can only be directly estimated from optic flow during times of active acceleration and deceleration. Second, spatial pooling of optic flow across a receptive field helps to alleviate the effects of noise and/or low resolution visual systems. Third, averaging velocity estimates from multiple receptive fields further helps to reject noise. Our algorithm provides a hypothesis for how insects might estimate absolute velocity from vision during active maneuvers, and also provides a theoretical framework for designing fast analog circuitry for efficient state estimation that can be applied to insect-sized robots. </p>
Karioi Predator Camera Trap
<p>The Karioi Predator camera trap videos was provided by a New Zealand regional council taken from 2018-2020 in the Mount Karioi region by motion-activated cameras. The raw videos files consist of 2,101 thirty-second clips of videos captured using motion-triggered cameras deployed in the native forests of New Zealand. </p> <p>This dataset contains crops of various predator species resized to 224x224, This dataset is organized by folder, and contains five classes namely cats, rats, stoats, possums, and empty (false positives)</p>
Fig. A1. A in The first recorded activity pattern for the Sunda stink-badger Mydaus javanensis (Mammalia: Carnivora: Mephitidae) using camera traps
Fig. A1. A pair of Sunda stink-badgers Mydaus javanensis photo-
FIGURE 3 in COMPUTER-AIDED DRAWING SYSTEM - SUBSTITUTE FOR CAMERA LUCIDA Ekaterina A. S and Dmitry D. V
FIGURE 3: Setting a live image from the camera as the background for drawing (software used for this example are VLC media player and GIMP graphic software). Since the overlay mode image cannot be reproduced in a screenshot, we photographed the computer screen using a digital camera instead of making the combined images from incomplete screenshots: A – media player showing the image; B – screenshot taken from A and pasted to the new file in the drawing software. The overlay mode is active: part of the video frame is visible through a replica of the media player's window where it overlaps with an original window of the media player, the rest of the replica is dark but is nevertheless filled with overlay color. Both arrows point to the area filled with overlay color; C – media player is closed, the whole area filled with overlay color looks dark; D – same as C, but the media player was not in overlay mode when the screenshot was taken. There is no overlay color on the screen. See text for further explanations.
FIGURE 2 in COMPUTER-AIDED DRAWING SYSTEM - SUBSTITUTE FOR CAMERA LUCIDA Ekaterina A. S and Dmitry D. V
FIGURE 2: Settings of VLC media player. Those important for correct and convenient work in overlay mode are indicated by arrows. This dialog can be found under the 'Tools> Preferences' menu.
FIGURE 1 in COMPUTER-AIDED DRAWING SYSTEM - SUBSTITUTE FOR CAMERA LUCIDA Ekaterina A. S and Dmitry D. V
FIGURE 1: An example of a working place with the complete drawing apparatus ready to use. C-camera, T-pen tablet
FIGURE 4 in COMPUTER-AIDED DRAWING SYSTEM - SUBSTITUTE FOR CAMERA LUCIDA Ekaterina A. S and Dmitry D. V
FIGURE 4: The procedure of setting the video camera as a source of live capture image in VLC media player: A – click 'Open capture device' under 'Media' menu; B – choose the video camera ('Video device name'), set 'Audio device name' to 'None' and set the horizontal pixel size of the video image — it must be chosen from the predefined list specific for particular video camera or, alternatively, left blank. In the latter case the video, most probably, will have the lowest possible resolution. The bottom arrow points to the command line string which is generated by VLC media player and which can be used to automate the procedure of setting the video camera parameters; this command line is not required for the method described in this paper; C – setting the live capture image as a desktop background for convenient drawing.
Data from: Predicting bushmeat biomass from species composition captured by camera traps: implications for locally-based wildlife monitoring
<p>The 'StatAnalysis.zip' contains the data and model files. We used it for the four analyses below.</p> <p>First, we estimated population densities, the mean body mass and camera-trap capture rates of five main bushmeat targets in a rainforest of southeast Cameroon: Peters's duikers (<em>Cephalophus callipygus</em>), bay duikers (<em>C. dorsalis</em>), blue duikers (<em>Philantomba monticola</em>), brush-tailed porcupines (<em>Atherurus africanus</em>) and Emin's pouched rats (<em>Cricetomys emini</em>). Second, on the basis of the density and body mass estimates, we estimated bushmeat biomass—the total biomass of the five bushmeat species—and its spatial variation. Third, we calculated six bushmeat indicators based on the capture rate estimates. Lastly, we examined the correlation between bushmeat biomass and the indicators.</p> <p>The ZIP file consists of 16 R script files, three CSV files (in the 'data' subfolder) and 135 stan files (in the 'stan' subfolders). It also has two empty folders, 'figure' and 'res', where the figures and R objects of model results will be stored following the analyses. Please see the document 'README.txt' before performing the analysis. This text file gives the ZIP file structure and brief descriptions of the files.</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.