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1,328 results for “motion”
IMU and marker-based optical motion capture from a humanoid robot
<p>The motion capture contains walking trials from the lower body of the humanoid robot Reem-C from Pal Robotics (Barcelona, Spain). Seven IMUs were attached on the foot, lower leg, upper leg and pelvis segments. IMU data was collected at 100 Hz. Moreover, the robot motion was captured with a marker-based optical system (Qualisys AB, Göteborg, Sweden) at 150 Hz. The focus of the dataset was mainly walking. There are three trials, each with a length of about 6.5 minutes.<br>The dataset contains the definition of the skeleton (segment lengths and coordinate locations), the actual IMU readings and the pose or kinematics from the optical system.</p>
Full Body Motion Capture of Single Individuals Following External Perturbations from Different Directions
<p>This dataset is composed of C3D files corresponding to full body motion of participants undergoing external perturbation at shoulder height with different sensory conditions. The temporal force profiles of the perturbations are also available.</p> <p>The following experiment received ethical approval from an ethics committee and all participants signed an informed consent form relative to the processing of their data. <br>The experiments were carried on 21 healthy young adults (10 females, 11 males). All were between 20 and 38 yo with a mean age of 27.2 (std: 4.2). Mean mass was 70.2 (std: 12.1) kg and height was 1.74 (std: 0.08) m. </p> <p>Participants motion was recorded using 45 reflective markers and a 23 Qualisys camera system (200Hz). <br>The markers were placed on participants following standardised anatomical landmarks. <br>The output signal of the force sensor was processed using a Butterworth low pass filter with a 5Hz cutoff frequency without phase shift. <br>The force sensor was synchronised with the motion capture software.<br>Tree reflective markers were also placed along the pole in order to retrieve the exact direction of the perturbations. </p>
Kuopio gait dataset: motion capture, inertial measurement and video-based sagittal-plane keypoint data from walking trials
<p>This dataset contains motion capture (3D marker trajectories, ground reaction forces and moments), inertial measurement unit (wearable Movella Xsens MTw Awinda sensors on the pelvis, both thighs, both shanks, and both feet), and sagittal-plane video (anatomical keypoints identified with the OpenPose human pose estimation algorithm) data.<br>The data is from 51 willing participants and collected in the HUMEA laboratory in the University of Eastern Finland, Kuopio, Finland, between 2022 and 2023. All trials were conducted barefoot.</p> <p>The file structure contains an Excel file containing information of the participants, data folders under each subject (numbered 01 to 51), and a MATLAB script.</p> <p>The Excel file has the following data for the participants:</p> <ul> <li><strong>ID</strong>: ID of the participants from 1 to 51</li> <li><strong>Age</strong>: age of the participant in years</li> <li><strong>Gender</strong>: biological sex as M for male, F for female</li> <li><strong>Leg</strong>: the participant's dominant leg, identified by asking which foot the participant would use to kick a football; R for right, L for left</li> <li><strong>Height</strong>: height of the participant in centimeters</li> <li><strong>Invalid_trials</strong>: list of invalid trials in the motion capture data (MOCAP) data, usually classified as such because the participant did not properly step on the middle force plate</li> <li><strong>IAD</strong>: inter-asis distance in millimeters, the distance between palpated left and right anterior superior iliac spine, measured with a caliper</li> <li><strong>Left_knee_width</strong>: width of the left knee from medial epicondyle to lateral epicondyle in millimeters, palpated and measured with a caliper</li> <li><strong>Right_knee_width</strong>: same as above for the right knee</li> <li><strong>Left_ankle width</strong>: width of the left ankle from medial malleolus to lateral malleolus in millimeters, palpated and measured with a caliper</li> <li><strong>Right_ankle_width</strong>: same as above for the right ankle</li> <li><strong>Left_thigh_length</strong>: the distance between the greater trochanter of the left femur and the lateral epicondyle of the left femur in millimeters, palpated and measured with a measuring tape</li> <li><strong>Right_thigh_length</strong>: same as above for the right thigh</li> <li><strong>Left_shank_length</strong>: the distance between the medial epicondyle of the femur and the medial malleolus of the tibia in millimeters, palpated and measured with a measuring tape</li> <li><strong>Right_shank_length</strong>: same as above for the right shank</li> <li><strong>Mass</strong>: mass in kilograms, measured on a force plate just before the walking measurements</li> <li><strong>ICD</strong>: inter-condylar distance of the knee of the dominant leg, measured from low-field MRI</li> <li><strong>Left_knee_width_mocap</strong>: distance between reflective MOCAP markers on the medial and lateral epicondyles of the knee in millimeters, measured from a static standing trial; -1 for missing (subject did not have those markers)</li> <li><strong>Right_knee_width_mocap</strong>: same as above for the right knee</li> </ul> <p>The folders under each subject (folders numbered 01 to 51) are as follows:</p> <ul> <li><strong>imu</strong>: "Raw" inertial measurement unit (IMU) data files that can be read with Xsens Device API (included in Xsens MT Manager 4.6, which may be unavailable these days, not sure). You won't need this if you use the data in the imu_extracted folder.</li> <li><strong>imu_extracted</strong>: IMU data extracted from those data files using the Xsens Device API, so you don't have to. <ul> <li>The data is saved as MATLAB structs where the fields are named as a sensor ID (e.g., "B42D48"). The sensor IDs and their corresponding IMU locations are as follows: <ul> <li>pelvis IMU: B42DA3</li> <li>right femur IMU: B42DA2</li> <li>left femur IMU: B42D4D</li> <li>right tibia IMU: B42DAE</li> <li>left tibia IMU: B42D53</li> <li>right foot IMU: B42D48</li> <li>left foot IMU: B42D51 (except for subjects 01 and 02, where left foot IMU has the ID B42D4E)</li> </ul> </li> <li>Some of the data are just zeros as they couldn't be read from these sensors, but under each sensor, the fields "calibratedAcceleration", "freeAcceleration", "time", "rotationMatrix", and "quaternion" contain usable data. <ul> <li>time: Contains time stamps of the measurement at each frame recorded at 100 Hz, so if you remove the first value from all values in the time vector and divide the result by 100, you will get the time in seconds from the beginning of the walking trial.</li> <li>calibratedAcceleration and freeAcceleration: Contain triaxial acceleration data from the accelerometers of the IMU. freeAcceleration is just calibratedAcceleration without the effect of Earth's gravitational acceleration.</li> <li>rotationMatrix: Orientations of the IMU as rotation matrices.</li> <li>quaternion: Orientations of the IMU as quaternions.</li> </ul> </li> </ul> </li> <li><strong>openpose</strong>: Trajectories of the keypoints identified from sagittal plane video frames, saved as json files. <ul> <li>The keypoints are from the BODY_25 model of OpenPose (https://cmu-perceptual-computing-lab.github.io/openpose/web/html/doc/md_doc_02_output.html).</li> <li>Each frame in the video has its own json file.</li> <li>You can use the function in the script "OpenPose_to_keypoint_table.m" in the root folder to read the keypoint trajectories and confidences of all frames in a walking trial into MATLAB tables. The function takes as argument the path to the folder containing the json files of the walking trial.</li> </ul> </li> <li>Note that some subjects (11, 14, 37, 49) do not have keypoint and IMU data.</li> </ul> <p>The folders under each subject are divided into three ZIP archives with 17 subjects each.</p> <p>The script "OpenPose_to_keypoint_table.m" is a MATLAB script for extracting keypoint trajectories and confidences from JSON files into tables in MATLAB.</p> <p><br><strong>Publication in Data in Brief</strong>: <a href="https://doi.org/10.1016/j.dib.2024.110841" target="_blank" rel="noopener">https://doi.org/10.1016/j.dib.2024.110841</a></p> <p><br><strong>Contact</strong>: Jere Lavikainen, jere.lavikainen@uef.fi</p>
Benchmark for classifying presence of coral and camera motion in underwater
<p>Benchmark for classifying presence of coral in underwater videos, and camera motion that would be necessary for 3d reconstruction of coral. Videos are collected from the YouTube-8M dataset.</p>
Dataset for "Effect of the atomic structure of complexions on the active disconnection mode during shear-coupled grain boundary motion"
<p>This repository contains the data of the simulations and theoretical<br>calculations of the paper "Effect of the atomic structure of complexions on the active disconnection mode during shear-coupled grain boundary motion".</p>
Shoulder kinematics derived from radiographic and optical motion analysis
<p>This dataset contains torso/arm, scapula, and humerus kinematics from subjects performing a variety of static poses and dynamic activities. The humerus and scapula were imaged at 100 Hz using a biplane fluoroscopy/dynamic stereoradiography system. Then, 3D models of the humerus and scapula were constructed from each subject’s CT scan. Model-based markerless tracking ascertained the 3D position and orientation of each bone model by semi-automatically aligning digitally reconstructed radiographs against each frame of the radiographic recordings. The kinematics of the torso and arm were measured using skin marker motion capture and co-calibrated spatially and temporally to the radiography system.</p> <p>This repository contains an expanded release of data found in doi:10.5281/zenodo.7542486 and doi:10.5281/zenodo.10972005. The rationale to provide a new repository is that this release, and forthcoming releases, will follow a new format that provides more granular data for past and ongoing studies from our laboratory. These studies may include motion analysis data from healthy controls, pathologic subjects, and those after surgical intervention.</p> <p>v1.1 now contains transforms from Vicon to biplane fluoro coordinate systems.</p> <p> </p>
Context-Aware Activity Recognition in Logistics (CAARL) – A optical marker-based Motion Capture Dataset
<p><strong>CAARL </strong>is a freely accessible logistics-dataset for human activity recognition, which contains human movement and context information from two subjects. The context information includes the positions of objects such as two picking carts, a packaging table, different racks, a base and three entrances.</p> <p>In the ’Innovationlab Hybrid Services in Logistics’ at TU Dortmund University, two picking and one packing scenarios were recorded using an optical marker based motion capture system. Each subject and object is equipped with several markers. 140 minutes of human movements have been labelled and categorised into 8 activity classes and 19 binary coarse-semantic descriptions, also called attributes. The labelled human movements are synchronised with the context information. They have exactly the same sampling rate (same start and end).</p> <p>The oMoCap data is in csv format. Further formats (e.g. C3D) are available on request.</p> <p>CAARL is based on the set-up and scenarios of the LARa dataset, which contains only human movements. Information about LARa can be found in the dataset and the associated paper:</p> <ul> <li>Dataset: “Logistic Activity Recognition Challenge (LARa) – A Motion Capture and Inertial Measurement Dataset”, Zenodo 2020, DOI: <a href="https://doi.org/10.5281/zenodo.3862782">10.5281/zenodo.3862782</a></li> <li>Paper: “LARa: Creating a Dataset for Human Activity Recognition in Logistics Using Semantic Attributes”, Sensors 2020, DOI: <a href="https://doi.org/10.3390/s20154083">10.3390/s20154083</a></li> </ul> <p> </p> <p><strong>If you use the CAARL dataset for research, please cite the following paper: “Context-Aware Human Activity Recognition in Industrial Processes”, Sensors 2021, DOI: <a href="https://doi.org/10.3390/s22010134">10.3390/s22010134</a></strong></p>
Deformation composite of the RADARSAT Geophysical Processor System (RGPS) Lagrangian motion data
<p>Deformation composite constructed from the Lagrangian RADARSAT Geophysical Processor System (RGPS) Lagrangian motion data for January-February-March, 1997 to 2008. The nominal temporal and spatial scales for the composite data are T<sup>*</sup> = 3 days, and L<sup>*</sup> = 10 km. This data is analyzed and compared with model deformation statistics in Bouchat et al., Sea Ice Rheology Experiment (SIREx), Part I: Scaling and statistical properties of sea-ice deformation fields, Journal of Geophysical Research: Oceans (2022).</p> <p>The original RGPS Lagrangian motion data set consists in lists of trajectories (time and positions records) for points that are tracked in sequential synthetic aperture radar (SAR) images. The trajectories are organized in different “streams”, corresponding to different initial satellite passes over which a set of tracked points were initialized. For all streams, the trajectories are initialized on a uniform 10 km x 10 km grid at the beginning of the winter in November. Each tracked point can therefore be assigned to <em>(i,j)</em> indices corresponding to its initialization location on the grid. As time increases and the position records are updated, the tracked points are no longer uniformly separated, but their assigned <em>(i,j)</em> indices do not change. The trajectory records are updated when the tracking algorithm detects the tracked points in a new SAR image. The update interval is therefore not always the same for all points, nor is it always on the same time/day within a given stream as the tracking algorithm may be unsuccessful for certain images/points. Moreover, the multiple streams can overlap spatially, such that more than one trajectory can be assigned to the same<em> (i,j)</em> indices. Computing strain rates directly from the original RGPS Lagrangian motion product therefore results in deformation estimates that can span a wide range of spatio-temporal scales, that are not temporally coherent across all streams, and that can also be spatially redundant. The goal of constructing a deformation composite from the original RGPS Lagrangian motion product is to generate a coherent set of non-overlapping Lagrangian deformation estimates at fixed time intervals and with a uniform spatial scale that can be used for statistical analysis.</p> <p>The RGPS Lagrangian deformation composite is constructed using the weighted-average pre-processing method described in Bouchat & Tremblay (2020) and Hutter et al. (2020) and summarized here. For each stream separately, we first define quadrilateral Lagrangian cells assigned to the <em>(i,j)</em> indices by combining records from the <em>(i,j),</em> <em>(i+1,j)</em>, <em>(i,j+1)</em>, and <em>(i+1, j+1)</em> available Lagrangian trajectories. For each <em>(i,j) </em>cell, we then compute the Lagrangian strain rates if, between any two update times, the cell's records have: (i) simultaneous (plus or minus 3 hours) start and end times for all fours corners, (ii) an average time interval for all corners that corresponds to the nominal temporal resolution of T<sup>*</sup>= 3 days, and (iii) an area at the start time that corresponds to the nominal spatial resolution of L<sup>*</sup>= 10 km. The strain rates, the cell area, and the start and end times used to compute the cell's strain rates are also assigned to the <em>(i,j) </em>indices. Then, to create the composite deformation estimates at the same fixed start and end dates for all cells, we average the strain rate and area records at each <em>(i,j)</em> indices in fixed 3-day periods starting on January 1st, using the overlapping time between their start/end date interval with the fixed 3-day periods as weight. For visualization purposes only, we also average the cells' corners' starting positions from all records overlapping with the fixed 3-day interval and use these averaged positions as approximate coordinates for the composite deformation cells. Finally, all streams are spatially combined into a single strain rate composite. In the case of spatial overlap between two or more streams, we keep the cells that have the longest time coverage and discard the other ones.</p> <p> </p> <p>There is one netCDF file per year. Data are organized in matrices where the <em>(i,j)</em> indices are the Lagrangian cells identifier. This allows us to keep track of neighbouring cells for the scaling analysis. See below for more information on what variables are included in the files and their structure. </p> <p> </p> <p><strong>1. Variables included</strong></p> <ul> <li><em>(x1,y1), (x1,y2), (x3,y3), (x4,y4)</em>: Average positions of the composite cells' corners. Used for visualization only (deformations should not be computed using these positions) - (meters);</li> <li><em>A</em>: Composite cells' area - (meters squared);</li> <li><em>dudx, dudy, dvdx, dvdy</em>: Composite cell's velocity derivatives (strain rates/deformation) - (1/seconds);</li> <li><em>d_dudx, d_dudy, d_dvdx, d_dvdy</em>: Trajectory error on the composite cells' velocity derivatives - (1/seconds);</li> <li><em>time</em>: Day of year.</li> </ul> <p><strong>*Note:</strong> The composite cells were removed if their average position was within 100 km from land. Before comparing the deformation statistics with sea-ice models, one should only keep cells available in both the model and the RGPS composite.</p> <p> </p> <p><strong>2. Variable structure</strong></p> <p>All variables (except <em>time</em>) are matrices with axes (<em>it, i, j </em>), where <em>it</em> is the time stamp/iteration and<em> i,j </em>are the cells identifiers. See below for how the cells are defined: </p> <p> |--------------------------------------------------------------><sub> <strong>j-axis</strong> </sub> <br> | <br> | <strong>(</strong><strong>x1_ij,y1_ij</strong><strong>)</strong> <strong>o</strong> --------------------<strong>o</strong> <strong>(</strong><strong>x2_ij,y2_ij</strong><strong>)</strong> <br> | | | <br> | | <strong>A_ij or dudx_ij</strong> | <strong> </strong><br> | | | <br> | <strong>(</strong><strong>x4_ij,y4_ij</strong><strong>) </strong><strong>o</strong> ------------------- <strong>o</strong> <strong>(</strong><strong>x3_ij,y3_ij</strong><strong>)</strong> <br> | <br> |<br> V<sub><strong>i-axis</strong></sub> </p> <p> </p> <p> </p> <p> </p> <p><strong>References:</strong><br> Bouchat, A., & Tremblay, B. (2020). Reassessing the Quality of Sea-Ice Deformation Estimates Derived From the RADARSAT Geophysical Processor System and Its Impact on the Spatiotemporal Scaling Statistics. Journal of Geophysical Research: Oceans, 125(8), https://doi.org/10.1029/2019JC015944</p> <p>Hutter, N. and Losch, M.: Feature-based comparison of sea ice deformation in lead-permitting sea ice simulations, The Cryosphere, 14, 93–113, https://doi.org/10.5194/tc-14-93-2020, 2020.</p> <p>The original RGPS Lagrangian Motion data set can be accessed here: https://asf.alaska.edu/data-sets/derived-data-sets/seaice-measures/sea-ice-measures-data-products/</p>
Visual-inertial input datasets for SLAM applications containing extreme and human-like motion patterns
<p>Recorded datasets in compressed rosbag format, which contain visual and IMU sensor information that are bearing high resemblance to the movement of a human player with a handheld AR-capable device.</p> <p>For machine learning training and validation tasks, separate dataset are available containing motion patterns in a wide range from steady camera image to extremely challenging movements.</p>
Motion Capture Benchmark of Industrial Tasks for Ergonomic Assessment and European Historic Crafts
<p><strong>General Info:</strong></p> <p>This benchmark provides motion capture (MoCap) files in .bvh form. The recordings were done in the span of May 2019 to January 2020 for the needs of the <a href="https://collaborate-project.eu/"><strong>CoLLaboratE</strong></a> and <a href="http://www.mingei-project.eu/"><strong>MINGEI</strong></a> H2020 projects<strong> </strong>funded by the European Commission. The tasks included are:</p> <ul> <li>TV assembling</li> <li>Airplane component manufacturing</li> <li>High ergonomic hazard motions </li> <li>Silk-Weaving</li> <li>Glassblowing</li> <li>Mastic Cultivation</li> </ul> <p>The TV assembly and airplane component manufacturing tasks were recorded in real-world conditions inside the factory during the actual production of the items. The high ergonomic hazard motions were recorded in a controlled lab environment and serve as baseline/prototype motions for ergonomic risk assessment.</p> <p>The silk-weaving, glassblowing, and mastic cultivation data sets were created, corresponding to movements performed by skilled craftsmen and mastic farmers. These data sets were produced in order to extract the expert's gestural knowledge and analyze their dexterity while doing their crafts.</p> <p><strong>Naming Convention:</strong></p> <p>All files in this benchmark follow a strict naming convention to allow for easier parsing by scripts. The names have a total of 12 or 13 characters that convey the following information:</p> <ul> <li>The first three or fours characters label the <strong>recording session </strong>(e.g., LAB, PLN, GBBC, MCSN, etc.)</li> <li>The next three characters label the <strong>subject number </strong>(e.g., S01, S02, S03, etc.)</li> <li>The next three characters label the <strong>posture or gesture number </strong>(e.g., P01, P02, G01, G02, etc.)</li> <li>The final three characters label the <strong>repetition number </strong>(e.g., R01, R02, R03, etc.)</li> </ul> <p>For example, LABS02P03R01 denotes a lab recording of the second subject, performing the third posture for the first time.</p> <p><strong>Recording Sessions:</strong></p> <p>There are six recording sessions in this benchmark, the ergonomic risk motion recorded in the lab (denoted as "<strong>LAB</strong>"), the construction of an airplane component (denoted as "<strong>PLN</strong>"), and the assembling and packaging of TVs (denoted as "<strong>TV*</strong>"), the silk weaving (denoted as "<strong>SW*</strong>"), glassblowing (denoted as "<strong>GB*</strong>"), and mastic cultivation (denoted as "<strong>MC*</strong>").</p> <p>The postures are the following:</p> <p><strong>LAB:</strong></p> <ul> <li><strong>Standing:</strong> <ul> <li><strong>P01</strong>: The subject stays in I-pose</li> <li><strong>P02:</strong> The subject rotates his/her torso to the left as far the person can</li> <li><strong>P03: </strong>The subject will laterally bend his/her torso to the left for 6 seconds</li> <li><strong>P04</strong>: The subject bends more than 20° but less than 60°</li> <li><strong>P05:</strong> The subject bends more than 20° but less than 60° while rotating and laterally bending the torso to the left</li> <li><strong>P06: </strong>The subject stretches his/her arms, and bends forward more than 20° but less than 60° while rotating and laterally bending the torso to the left</li> <li><strong>P07</strong>: The subject bends more than 60°</li> <li><strong>P08:</strong> The subject bends more than 60° while rotating and laterally bending the torso to the left</li> <li><strong>P09: </strong>The subject stretches his/her arms, and bends forward more than 60° while rotating and laterally bending the torso to the left</li> <li><strong>P10:</strong> The subject upright, raises the elbows above the shoulder level with the forearms bent 90° (</li> <li><strong>P11</strong>: The subject raises the elbows above the shoulder level with the forearms bent 90° while rotating and laterally bending the torso to the left</li> <li><strong>P12:</strong> The subject raises the elbows above the shoulder level with the arms stretched while rotating and laterally bending the torso to the left</li> <li><strong>P13:</strong> The subject upright, raises the hands above the head</li> <li><strong>P14: </strong>The subject raises the hands above the head with the arms stretched while rotating and laterally bending the torso to the left</li> </ul> </li> <li><strong>Sitting on a chair:</strong> <ul> <li><strong>P15: </strong>The subject sits upright</li> <li><strong>P16:</strong> The subject bends forward more than 60°</li> <li><strong>P17:</strong> The subject bends forward more than 60° while rotating and laterally bending the torso to the left</li> <li><strong>P18:</strong> The subject stretches the arms, and bends forward more than 60° while rotating and laterally bending the torso to the left</li> <li><strong>P19:</strong> The subject raises the hands above the head with arms stretched</li> <li><strong>P20:</strong> The subject raises the hands above the head with the arms stretched while rotating and laterally bending the torso to the left</li> </ul> </li> <li><strong>Kneeling:</strong> <ul> <li><strong>P21:</strong> The subject stays upright</li> <li><strong>P22:</strong> The subject rotates the torso to the left as far he/she can</li> <li><strong>P23: </strong>The subject will laterally bend the torso to the left for 6 seconds</li> <li><strong>P24:</strong> The subject bends more than 60°</li> <li><strong>P25:</strong> The subject bends more than 60° while rotating and laterally bending the torso to the left</li> <li><strong>P26:</strong> The subject stretches the arms, and bends forward more than 60° while rotating and laterally bending the torso to the left</li> <li><strong>P27: </strong>The subject upright, raises the elbows to the shoulder level with the arms stretched</li> <li><strong>P28:</strong> The subject raises the elbows to the shoulder level with the arms stretched while rotating and laterally bending the torso to the left</li> </ul> </li> </ul> <p>The TV assembling tasks are further divided. The subtasks are: packing the TVs on a stack for shipping (denoted as "<strong>TVP</strong>" for medium-sized TVs and "<strong>TVL</strong>" for larger TVs), placing assembling and placing electronic circuit boards on the chassis (denoted as "<strong>TVB</strong>"), and screwing the boards on the TV chassis (denoted as "<strong>TV_</strong>"). Each task is comprised of a number of postures. </p> <p><strong>TV Assembling:</strong></p> <ul> <li><strong>Assembling the board and placing it on the TV chassis (TVB):</strong> <ul> <li><strong>P01: </strong>Reaching high, above the shoulder level, to pick one component</li> <li><strong>P02: </strong>Reaching low, below the knee level, to pick up the second component</li> <li><strong>P03: </strong>Connecting the components and placing the board on the chassis to be screwed</li> </ul> </li> <li><strong>Screwing an electrical circuit board on the TV chassis (TV_) :</strong> <ul> <li><strong>P01: </strong>A screw is placed on a power tool and it is being screwed on the chassis. The process is repeated four times</li> </ul> </li> <li><strong>Preparing TVs for Shipping (TVP & TVL):</strong> <ul> <li><strong>P01: </strong>Placing TVs on a wooden pallet (bottom level)</li> <li><strong>P02:</strong> Preparing to wrap the bottom level with a membrane</li> <li><strong>P03:</strong> Wrapping the bottom level</li> <li><strong>P04:</strong> Placing TVs on top of the bottom level (second level)</li> <li><strong>P05:</strong> Placing TVs on top of the second level (third level)</li> <li><strong>P06: </strong>Wrapping the second level with a plastic membrane</li> <li><strong>P07:</strong> Wrapping the third level with a plastic membrane</li> <li><strong>P08:</strong> Placing TVs on top of the third level (fourth level)</li> <li><strong>P09:</strong> Wrapping the fourth level with a plastic membrane</li> </ul> </li> </ul> <p><strong>Riveting of an airplane floater (PLN):</strong></p> <ul> <li><strong>P01:</strong> Rivet with the pneumatic hammer.</li> <li><strong>P02:</strong> Prepare the pneumatic hammer and grab rivets. </li> <li><strong>P03:</strong> Place the bucking bar to counteract the incoming rivet.</li> </ul> <p>The tasks recorded for silk weaving, glassblowing, and mastic cultivation data sets were segmented by gestures (e.g., G01, G02, etc.) . The tasks recorded for these three data sets are the following:</p> <p><strong>Silk weaving (SW*):</strong></p> <ul> <li>The creation of the punch cards <strong>(SWPC)</strong>.</li> <li>Preparation of the beam <strong>(SWPB)</strong>.</li> <li>Wrapping of the beam <strong>(SWWB)</strong>.</li> <li>Jacquard weaving with small loom <strong>(SWSL)</strong>.</li> <li>Jacquard weaving with medium size loom <strong>(SWML)</strong>.</li> <li>Jacquard weaving with large loom <strong>(SWLL)</strong>.</li> </ul> <p><strong>Glassblowing (GB*):</strong></p> <ul> <li>Beak cutting <strong>(GBBC)</strong>.</li> <li>Blowing and shaping <strong>(GBBS)</strong>.</li> <li>Cervix refining <strong>(GBCR)</strong>.</li> <li>Cord laying <strong>(GBCL)</strong>.</li> <li>Finish details <strong>(GBFD)</strong>.</li> <li>Handle laying <strong>(GBHL)</strong>.</li> <li>Transfer to punty <strong>(GBTP)</strong>.</li> <li>Leg and foot laying <strong>(GBLF)</strong>.</li> </ul> <p><strong>Mastic Cultivation (MC*):</strong></p> <ul> <li>Scrapping with new tool <strong>(MCSN)</strong>.</li> <li>Scrapping with old tool <strong>(MCSO)</strong>.</li> <li>Sweeping <strong>(MCSW)</strong>.</li> <li>Dusting <strong>(MCDU)</strong>.</li> <li>Embroidery A <strong>(MCEA)</strong>.</li> <li>Embroidery B <strong>(MCEB)</strong>.</li> <li>Embroidery with an axe <strong>(MCEX)</strong>.</li> <li>Gathering <strong>(MCGA)</strong>.</li> <li>Harvesting <strong>(MCHA)</strong>.</li> <li>Wiping <strong>(MCWI)</strong>.</li> <li>Shifting A <strong>(MCSA)</strong>.</li> <li>Shifting B <strong>(MCSB)</strong>.</li> <li>Cleaning with the wind <strong>(MCCW).</strong></li> </ul> <p>The motion capture files were processed and segmented with a 3D character animation software (MotionBuilder, Autodesk Inc., San Rafael, CA. USA) and exported to Biovision Hierarchy (BVH) files.</p>
Reverse Total Shoulder Arthroplasty Alters Humerothoracic, Scapulothoracic, and Glenohumeral Motion During Weighted Scaption
<p>This dataset contains scapula and humerus kinematics from 10 healthy subjects, and 10 subjects post-operative to reverse total shoulder arthroplasty, performing scapular plane abduction (scaption) with and without a 2.2 kg (5 lb) handheld weight. The humerus and scapula were imaged at 100 Hz using a biplane fluoroscopy system. 3D models of the humerus and scapula were constructed from each subject’s CT scan. Model-based markerless tracking ascertained the 3D position and orientation of each bone model by semi-automatically aligning digitally reconstructed radiographs against each frame of the biplane fluoroscopy recordings. The kinematics of the bones are presented relative to each subject's torso. These data are available for download to aid researchers and clinicians in characterizing non-pathologic and reverse total shoulder arthroplasty motion during scapular plane abduction with and without a 5 lb handheld weight.</p>
Dataset for the article "Spatially coherent diffusion of human RNA Pol II depends on transcriptional state rather than chromatin motion" by Roman Barth and Haitham Shaban
<p>The data set comprises all raw microscopy images and DFCC analyses as presented in </p> <p><strong>Spatially coherent diffusion of human RNA Pol II depends on transcriptional state rather than chromatin motion</strong></p> <p>by Roman Barth and Haitham Shaban, published in Nucleus (https://doi.org/10.1080/19491034.2022.2088988)</p> <p>There are two folders for RNAPII and DNA each, one for the raw images and one for the processed DFCC data, supplied as .mat files.</p> <p>Every folder contains three sub-folders containing the data for the conditions: +Serum, -Serum, and +DRB.</p>
SIGMOID-TR: A Simulated Ground Motion Dataset for Turkey
<p><strong>SIGMOID-TR: A Simulated Ground Motion Dataset for Turkey</strong></p> <p>A simulated ground motion dataset for ten earthquake scenarios in Turkey (seven real, three hypothetical earthquakes).</p> <p>Each scenario consists of 180 combinations for input parameters, and ground motions are simulated at 104 sites for each set of combinations, resulting in 18720 simulated ground motions for each earthquake and 187200 motions in total.</p>
Buoyancy and Brownian motion of plastics in aqueous media: Predictions and implications for density separation and aerosol internal mixing state (Data Underlying Figures)
<p>Data underlying figures in A. Bain 'Buoyancy and Brownian motion of plastics in aqueous media: Predictions and implications for density separation and aerosol internal mixing state' RSC Environmental Science: Nano, 2022. </p> <p>CA = citric acid<br> NaCl = sodium chloride<br> AS = ammonium sulfate</p> <p>rho = difference in density (g/cm^3)<br> Rh = % relative humidity<br> radius is in micrometers<br> Pe0 are the calculated dimensionless Peclet numbers<br> </p>
Visual and Auditory vection stimuli reduce motion sickness
<p>This is the raw data and the full data set from all of our participants included in the analysis for this experiment. </p> <p>The raw data represents the data recorded throughout the experience. Motion Sickness scores, performance on reading task, performance on attention task. </p> <p>While the full data set (Final1) additionally includes questionnaire data (SSQ, NASA TLX, IPQ,...) as well as demographic data of the participants. </p>
Supplement - Structure from Motion Raster Data
<p>We created orthorectified images and digital elevation models using Agisoft Metashape, a photogrammetric processing software application that uses SfM. We followed the workflow outlined in Bywater-Reyes and Pratt-Sitaula (2022). Once processed, orthorectified imagery and Digital Elevation Models (DEMs) were exported to ArcGIS Pro for additional analysis. Data collection metadata and postprocessing outcomes can be found in this repository. </p>
Leap Motion Hand Gestures for Interaction with 3D Virtual Music Instruments (LMHGIf3DVMI)
<p>The aim of the dataset is to investigate machine learning real-time gesture recognizer captured with a Leap Motion sensor to control the performance of a virtual 3D musical instrument. The dataset includes from 10-15 samples for each of the 8 gesture classes collected from 10 participants (5 female and 5 male) using the Leap Motion sensor.</p> <p> </p>
Dataset: Feedback contribution to surface motion perception in the human early visual cortex
<p><strong>Dataset</strong></p> <p>Dataset accompanying the manuscript "Feedback contribution to surface motion perception in the human early visual cortex" (<a href="https://doi.org/10.1101/653626">biorxiv</a>).</p> <p><strong>Description</strong></p> <p>fMRI data are arrange by subject (following BIDS convention). For each subject, there are subfolders for anatomical and functional MRI data.</p> <p>├── sub-01<br> │ ├── anat<br> │ │ └── ...<br> │ ├── func<br> │ │ └── ...<br> │ ├── func_se<br> │ │ └── ...<br> │ └── func_se_op<br> │ └── ...</p> <p>The subfolder 'anat' contains four images from the MP2RAGE sequence (among these, T1 and proton-density weighted images). The subfolder 'func' contains the functional data (GE EPI, T2* weighted) from the main experiment (i.e. the data from which the haemodynamic response was estimated, and on which statistical analysis was performed). The subfolders 'func_se' and 'func_se_op' contain SE EPI images with opposite phase encode polarity that were used for distortion correction. Moreover, for each image/timeseries there is a json file with metadata.</p> <p>Anatomical images have been masked anteriorly (defaced). Functional images are in coronal oblique orientation, covering early visual cortex.</p> <p>The folder 'stimuli' contains information on the stimuli used for retinotopic mapping, including timecourse models used for population receptive field mapping. (These files are included here because of their relatively large file size, which would make distribution via a git repository impractical.) The software used for the presentation of retinotopic mapping stimuli (and for the corresponding analysis) is available on <a href="https://github.com/ingo-m/pyprf">github</a>.</p> <p>For example videos of the main experimental stimuli, see <a href="https://doi.org/10.5281/zenodo.2583017">zenodo.2583017</a>. If you would like to reproduce the experimental stimuli, the respective PsychoPy code can be found on <a href="https://github.com/ingo-m/PacMan/tree/master/stimuli/experiment">github</a>.</p> <p>The exact timing of events during the experiments (rest & stimulus blocks, target events) can be found in FSL-style design matrices ("3 column format") on <a href="https://github.com/ingo-m/PacMan/tree/master/analysis/FSL_MRI_Metadata">github.com/ingo-m/PacMan/tree/master/analysis/FSL_MRI_Metadata</a>.</p> <p><strong>Analysis</strong></p> <p>The analysis pipeline makes use of several MRI software packages (such as SPM and FSL for preprocessing, and CBS tools for cortical depth sampling). In order to facilitate reproducibility, the entire analysis was containerised using docker. Because of licensing issues, the docker images with the third-party software cannot be directly made available. However, the docker files and detailed instructions for the creation of the docker images are available on <a href="https://github.com/ingo-m/PacMan/tree/master/docker">github</a>.</p> <p>If you would like to reproduce the analysis, the first step will be to create the docker images (which provide an exact copy of the system environment that was used to conduct the published analysis). There are two docker images, one for the main analysis (motion correction, distortion correction, GLM fitting; named "dockerimage_pacman_jessie"), and another one for the depth sampling (named "dockerimage_cbs"). Detailed instructions on how to create the docker images can be found <a href="https://github.com/ingo-m/PacMan/blob/master/docker/Info_Prepare_PacMan_Image_Jessie.txt">here</a> and <a href="https://github.com/ingo-m/PacMan/blob/master/docker/Info_Prepare_CBS_Image.txt">here</a>.</p> <p>Once you set up the docker images, the analysis can be run automatically. For each subject, there is one parent script for the main analysis (e.g. <a href="http://github.com/ingo-m/PacMan/blob/master/analysis/20180118/metascript_01.sh">~/analysis/20180118/metascript_01.sh</a> for subject 20180118) and a separate script for the depth sampling (e.g. <a href="https://github.com/ingo-m/PacMan/blob/master/analysis/20180118/metascript_03.sh">~/analysis/20180118/metascript_03.sh</a>). The only manual adjustments you should have to perform to reproduce the analysis is to change the file paths in the first section of these scripts ('pacman_anly_path' is the parent directory containing the analysis code, i.e. the git repository, and 'pacman_data_path' is the parent directory containing the MRI data). The main analysis (metascript_01.sh) should take about 24 h per subject on a workstation with 12 cores, and the depth sampling (metascript_02.sh) about 2 h. The analysis can be run on consumer-grade hardware, but some parts of the analysis may not run with less than 16 GB of RAM (recommended: 32 GB).</p> <p>Visualisations (e.g. cortical depth profiles and signal timecourses) and group-level statistical tests are implemented in <a href="https://github.com/ingo-m/py_depthsampling/tree/PacMan">py_depthsampling</a>.</p> <p><strong>Further resources</strong></p> <p>Please refer to the research paper for more details: <a href="https://doi.org/10.1101/653626">https://doi.org/10.1101/653626</a></p> <p>The analysis pipeline can be found on <a href="https://github.com/ingo-m/PacMan">https://github.com/ingo-m/PacMan</a></p> <p>A separate repository contains the code used for visualisation of depth-sampling results: <a href="https://github.com/ingo-m/py_depthsampling/tree/PacMan">https://github.com/ingo-m/py_depthsampling/tree/PacMan</a></p> <p>Free & open source software package for population receptive field mapping: <a href="https://github.com/ingo-m/pyprf">https://github.com/ingo-m/pyprf</a></p> <p> </p>
Data of the Tiangong-1's rotational motion estimated from SLR
<p>Updated v1.1.0 in Sep. 2019.</p> <p>==============================================</p> <p>Data for the Tiangong-1 estimated rotational motion. The detailed method is presented in Tiangong-1’s Accelerated Self-spin Before Reentry.</p> <p>https://doi.org/10.1186/s40623-019-0996-8</p> <p>The data can provide an important basis for improvement of aerodynamic model or models for other effects.</p> <p> </p>
Deliverable 1.1.1.1 BEL-Float project | Dataset containing the results of numerical simulations (motions, forces) of the operational performance analysis - Input files
<p>This dataset contains the parent input used to generate the simulation files of the DeepCwind OC4 semi-submersible combined with the 5MW NREL turbine for various wind and wave conditions. The basis of the OpenFAST input files are taken from <a href="https://github.com/OpenFAST/r-test/tree/main/glue-codes/openfast/5MW_OC4Semi_WSt_WavesWN">OpenFAST r-test GitHub repository (5MW_OC4Semi_WSt_WavesWN)</a> and adapted to simulate various wind and wave conditions. The turbulent wind field as the input to the InflowWind module is generated using <a href="https://www.nrel.gov/wind/nwtc/turbsim.html">TurbSim</a>. The simulations are performed on a modified version of OpenFAST v3.5.3 to which adaptation to the code is made to extract additional Morison drag output up to 16 cylindrical members. This adapted code is <a href="https://github.com/abkpribadi/openfast/tree/Morison_additional_output">uploaded on GitHub as a branch from a forked OpenFAST repository</a>. In total there are 1152 simulation results consists of 768 irregular waves and 384 regular waves cases. The complete dataset is divided into 9 sub-datasets, see "Related work" section. A report describing this dataset will be made available on BEL-Float project website by November 2024: https://www.owi-lab.be/bel-float.</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.