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.
6
datasets available to search
ShareScore release 0.9.0
Dataset results
6 results for “mocap”
Mocap gait motion samples - Optical marker trajectories
<p>=======================<br> <strong>Summary</strong><br> =======================<br> This database gathers a set of optical marker-based motion capture (mocap) tracking samples. Each sample corresponds to a single acquisition containing the 3D trajectories of a set of markers along a continuous interval of time.</p> <p>Keywords: Motion capture, mocap, Gait analysis, marker tracking.</p> <p>=======================<br> <strong>Data description</strong><br> =======================<br> Several persons wearing small reflective balls (markers) were asked to walk normally while recorded using motion capture cameras. These cameras, previously calibrated, are thought to detect the 2D pixel image position of the markers against the background thanks to IR lightning they are provided with. The XYZ position can be afterwards recovered by means of photogrammetric methods, whereas the tracking is kept across the frames. This data set contains the raw 3D trajectories of the aforementioned markers along the time.</p> <p>The set of markers is the layout proposed by Kadaba, Ramakrishnan, and Wootten, from the Helen Hayes Hospital. More details can be found in 'Dynamics of Human Gait' from Vaughan et al. Check out 'HH_marker_arrangement.png' for a visual description on marker placement.</p> <p>This database was built on purpose to serve as ground truth for the doctoral research "Cloud Point Labeling in Optical Motion Capture Systems", written by Juan L. Jiménez B. and supervised by Prof. Manuel Graña, from the Computational Intelligence Group, University of the Basque Country (UPV/EHU), and expected to be presented by the beginning of 2019.</p> <p>This work has been partially supported by the EC through project CybSPEED funded by the H2020 MSCA-RISE grant agreement Num. 777720.</p> <p>=======================<br> <strong>Equipment</strong><br> =======================<br> The following elements were taken to harvest the data:<br> - Six (6) motion capture cameras model S250e from Natural Point Inc. ( https://optitrack.com/ );<br> - Fifteen (15) passive-reflective optical markers;<br> - One computer running motion capture software from s.t.t.;<br> - A number of other minor accessories as camera wall mounts, Ethernet wires, adhesive tape, coffee and time.</p> <p>=======================<br> <strong>Technical details</strong><br> =======================<br> Database size: 71 recordings between 1.5 and 7 seconds long, summing up more than 20.000 frames<br> Exercise: regular gait motion at different paces<br> People: 14 different persons of different ages and shapes were recorded. No gait pathologies openly acknowledged.<br> Number of markers: 15 (Helen Hayes marker system)<br> Native camera resolution: 800x800 pixels<br> Sampling frequency: 100Hz<br> Cartesian units: millimeters<br> Right handed reference frame, +Y vertical upwards, ground height at Y=~0</p> <p>=======================<br> <strong>Data base format</strong><br> =======================<br> The database is composed by 71 different files, one per gait sequence. Each file is written in plain ASCII, comma separated values (CSV) format. Each line represents an instant of time (frame) containing the 3D Cartesian positions of all the 15 markers with to 2 mantissa digits and dot symbol as decimal separator. A triple 'nan nan nan' instead of actual numbers stands for marker occlusion and consequently no measured position can be given. The coordinates are numerically arranged as follows:<br> <br> X1; Y1; Z1; X2; Y2; Z2; ... Xi; Yi; Zi; ... X15; Y15; Z15;<br> <br> ... where 'i' refers to the i-th marker among the following list:</p> <p> 1- Right ASIS<br> 2- Left ASIS<br> 3- Sacrum<br> 4- Right Femoral wand<br> 5- Left Femoral wand<br> 6- Right Femoral epicondyle<br> 7- Left Femoral epicondyle<br> 8- Right Tibial wand<br> 9- Left Tibial wand<br> 10- Right Malleolus<br> 11- Left Malleolus<br> 12- Right Heel<br> 13- Left Heel<br> 14- Right Metatarsal head II<br> 15- Left Metatarsal head II</p> <p>=======================<br> <strong>Privacy statement</strong><br> =======================<br> The personal privacy of the volunteers, adults of 25 and over, remains protected since no confidential data has been collected at any time.</p> <p>The authors count on the express written consent of the company to use the gathered data in the scope of academic research and as ground truth for the aforementioned PhD Thesis, including its publication under the specified license terms.</p> <p><br> =======================<br> <strong>Further information</strong><br> =======================<br> If you find any mistake or experience any trouble in the interpretation of the data, please do not hesitate to contact us.</p> <p> </p>
JARVIS-MoCap monkey grasping recordings and annotations
<p>12 camera synchronized recording of a rhesus macaque grasping different objects and fruit with his left hand. Included in the dataset are checkerboard calibration recordings, one 12 camera recording and a trainingset containing roughly 3000 manual annotations of 23 hand keypoints.</p>
Mocap video examples for the analysis of Sign Language movements
<p>These mocap videos support my PhD thesis "Extracting human characteristics from motion: the case of identity in Sign Language" carried out from October 2018 to October 2021. The original mocap data is taken from the <a href="https://www.ortolang.fr/market/corpora/mocap1/">MOCAP1</a> corpus of French Sign Language. The videos have been generated using Python code available as part of the <a href="https://github.com/felixbgd/PLmocap">PLmocap</a> library.</p> <p> </p> <p> </p>
Mocap video examples for the analysis of Sign Language motion
<p>These mocap videos support my PhD thesis "Extracting human characteristics from motion: the case of identity in Sign Language" carried out from October 2018 to October 2021. The original mocap data is taken from the <a href="https://www.ortolang.fr/market/corpora/mocap1/">MOCAP1</a> corpus of French Sign Language. The videos have been generated using Python code available as part of the <a href="https://github.com/felixbgd/PLmocap">PLmocap</a> library.</p>
Mocap video examples for the analysis of Sign Language motion
<p>These mocap videos support my PhD thesis "Extracting human characteristics from motion: the case of identity in Sign Language" carried out from October 2018 to October 2021. The original mocap data is taken from the <a href="https://www.ortolang.fr/market/corpora/mocap1/">MOCAP1</a> corpus of French Sign Language. The videos have been generated using Python code available as part of the <a href="https://github.com/felixbgd/PLmocap">PLmocap</a> library.</p>
Mocap video examples for the analysis of Sign Language motion
<p>These mocap videos support my PhD thesis "Extracting human characteristics from motion: the case of identity in Sign Language" carried out from October 2018 to October 2021. The original mocap data is taken from the <a href="https://www.ortolang.fr/market/corpora/mocap1/">MOCAP1</a> corpus of French Sign Language. The videos have been generated using Python code available as part of the <a href="https://github.com/felixbgd/PLmocap">PLmocap</a> library.</p> <p> </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.