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135 results for “multimodal dataset”
Dataset for "Multimodal Translation in YouTube Shorts from Spanish Football Teams"
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Synthetic Multimodal Dataset using MuJoCo: UR5 Robot Motion
<p>Using the Mujoco environment, we simulated robot trajectory and transitions from one formation<br> to another. Mujoco is a 3D simulator, while Gym serves as an interface to the UR5 robot.<br> The robot has measurement units that allow the acquisition of the angles, positions,<br> and quaternions of the joints and the position of the end-effector. The robot is located on<br> a table with 4 cameras all from the same radius to the center of the robot just<br> rotated by 90° for each of them. Using the described environment, we collected 1999 samples <br> at a rate of 10 samples per second.<br> <br> ################################################################<br> ################################################################<br> <br> camera views:</p> <p> id: 0<br> name: 'camera_0'<br> xmat: array([ 0.70710678, 0.42537261, -0.56485232, -0.70710678, 0.42537261,<br> -0.56485232, 0. , 0.79882181, 0.60156772])<br> xpos: array([-2., -2., 3.])</p> <p> id: 1<br> name: 'camera_1'<br> xmat: array([-0.70710678, 0.42537261, -0.56485232, -0.70710678, -0.42537261,<br> 0.56485232, 0. , 0.79882181, 0.60156772])<br> xpos: array([-2., 2., 3.])</p> <p> id: 2<br> name: 'camera_2'<br> xmat: array([ 0.70710678, -0.42537261, 0.56485232, 0.70710678, 0.42537261,<br> -0.56485232, -0. , 0.79882181, 0.60156772])<br> xpos: array([ 2., -2., 3.])</p> <p> id: 3<br> name: 'camera_3'<br> xmat: array([-0.70710678, -0.42537261, 0.56485232, 0.70710678, -0.42537261,<br> 0.56485232, 0. , 0.79882181, 0.60156772])<br> xpos: array([2., 2., 3.])</p> <p> <br> <br> The camera data is stored as .png files with a size of 256x256 </p> <p><br> <br> ################################################################<br> ################################################################<br> <br> The files angles.pt, angular_velocity.pt, angular_acceleration.pt contains information about<br> the motor data of the joints. The angles, velocity, acceleration is the information about the <br> Motor in each joint in following order:<br> ['base_to_lik', 'base_to_rik', 'elbow_joint', 'shoulder_lift_joint', 'shoulder_pan_joint', 'wrist_1_joint', 'wrist_2_joint', 'wrist_3_joint']<br> <br> <br> ################################################################<br> ################################################################<br> <br> The files pose.pt and quaternion.pt contains information about<br> the body data of the robot. The pose of each element is in following order:<br> ['base', 'base_link', 'box_2_link', 'box_link', 'drop_box', 'ee_link', 'forearm_link', 'left_inner_finger', 'left_inner_knuckle', 'right_inner_finger', 'right_inner_knuckle', 'robotiq_85_base_link', 'shoulder_link', 'upper_arm_link', 'world', 'wrist_1_link', 'wrist_2_link', 'wrist_3_link']<br> <br> <br> ################################################################<br> ################################################################<br> <br> The file action.pt contains information about the used action in the corresponding time-step. <br> The action of each element is in following order:<br> ['forearm_T', 'gripper_motor', 'shoulder_lift_T', 'shoulder_pan_T', 'wrist_1_T', 'wrist_2_T', 'wrist_3_T']<br> <br> </p>
Synthetic Multimodal Dataset using ABB Studio: Single Robot Welding Station
<p>## README</p> <p>### Overview</p> <p>This repository provides the setup and data collected from simulations conducted in RobotStudio for various ABB robot configurations. The simulation, based on Löppenberg et al. (2024), was designed to capture precise sensory data in a controlled environment that mirrors real-world welding conditions.</p> <p>### Simulation Setup</p> <p>The simulation tracks essential parameters of the ABB robot and its end-effector, including:<br>- **Joint Angles**: Real-time angles for each joint.<br>- **TCP Position**: 3D positional coordinates of the Tool Center Point.<br>- **TCP Orientation**: Orientation captured as quaternions to represent rotational states.<br>- **Additional Attributes**: Various other robot-specific details relevant to performance in a welding setup.</p> <p>### Data Collection</p> <p>To generate a high-resolution dataset, data were sampled at intervals between 12 ms and 96 ms. This detailed sampling provides accurate insights into the robot's operation in a simulated welding environment.</p> <p>### Key Parameters</p> <p>The dataset includes the following parameters:</p> <p>- **Camera View**: One camera view with a resolution of 3 x 256 x 256, normalized to the range [0, 1].<br>- **Motor Power (P)**: The power consumed by the robot’s motors, constrained within \([0, P_{\text{max}}]\).<br>- **TCP Speed (v_TCP)**: The velocity of the end-effector, with a maximum cap of 5 m/s.<br>- **TCP Acceleration (a_TCP)**: The acceleration of the TCP, bounded within \([a_{\text{min}}, a_{\text{max}}]\).<br>- **TCP Orientation (q_TCP)**: Quaternion representing orientation, with a normalization condition \(||\mathbf{q}_{\text{TCP}}(t)|| = 1\).<br>- **TCP Position (p_TCP)**: A 3D vector for positional coordinates, with each coordinate constrained within \([p_{\text{min}}, p_{\text{max}}]\).<br>- **Braking Distance (d_brake)**: The distance each joint travels during braking, within bounds \([d_{\text{min}}, d_{\text{max}}]\).<br>- **Holding Position (θ_hold)**: The maintained angles for each joint, constrained to the interval \([-π, π]\).<br>- **Joint Angles (θ)**: The current angles of each joint, also within the interval \([-π, π]\).</p> <p>Frame '02655_frame' is corrupted. That means the corresponding values of the other modality needs to be removed as well.</p> <p><br>### Citation</p> <p>For more details on the simulation methodology, please refer to:<br>- Löppenberg, M., Yuwono, S., Diprasetya, M. R., & Schwung, A. (2024). Dynamic robot routing optimization: State-space decomposition for operations research-informed reinforcement learning. Robotics and Computer-Integrated Manufacturing, 90, 102812.</p>
Synthetic Multimodal Dataset using ABB Studio: Dual Robot Welding Station
<p>## README</p> <p>### Overview</p> <p>This repository provides the setup and data collected from simulations conducted in RobotStudio for various ABB robot configurations. The simulation, based on Diprasetya et al. (2023), was designed to capture precise sensory data in a controlled environment that mirrors real-world welding conditions.</p> <p>### Simulation Setup</p> <p>The simulation tracks essential parameters of the ABB robot and its end-effector, including:<br>- **Joint Angles**: Real-time angles for each joint.<br>- **TCP Position**: 3D positional coordinates of the Tool Center Point.<br>- **TCP Orientation**: Orientation captured as quaternions to represent rotational states.<br>- **Additional Attributes**: Various other robot-specific details relevant to performance in a welding setup.</p> <p>### Data Collection</p> <p>To generate a high-resolution dataset, data were sampled at intervals between 12 ms and 96 ms. This detailed sampling provides accurate insights into the robot's operation in a simulated welding environment.</p> <p>### Key Parameters</p> <p>The dataset includes the following parameters for each robot:</p> <p>- **Camera View**: One camera view with a resolution of 3 x 256 x 256, normalized to the range [0, 1].<br>- **Motor Power (P)**: The power consumed by the robot’s motors, constrained within \([0, P_{\text{max}}]\).<br>- **TCP Speed (v_TCP)**: The velocity of the end-effector, with a maximum cap of 5 m/s.<br>- **TCP Acceleration (a_TCP)**: The acceleration of the TCP, bounded within \([a_{\text{min}}, a_{\text{max}}]\).<br>- **TCP Orientation (q_TCP)**: Quaternion representing orientation, with a normalization condition \(||\mathbf{q}_{\text{TCP}}(t)|| = 1\).<br>- **TCP Position (p_TCP)**: A 3D vector for positional coordinates, with each coordinate constrained within \([p_{\text{min}}, p_{\text{max}}]\).<br>- **Braking Distance (d_brake)**: The distance each joint travels during braking, within bounds \([d_{\text{min}}, d_{\text{max}}]\).<br>- **Holding Position (θ_hold)**: The maintained angles for each joint, constrained to the interval \([-π, π]\).<br>- **Joint Angles (θ)**: The current angles of each joint, also within the interval \([-π, π]\).</p> <p><br>### Citation</p> <p>For more details on the simulation methodology, please refer to:<br>- Diprasetya, M. R., Yuwono, S., Löppenberg, M., & Schwung, A. (2023, July). Integration of ABB Robot Manipulators and Robot Operating System for Industrial Automation. In 2023 IEEE 21st International Conference on Industrial Informatics (INDIN) (pp. 1-7). IEEE.</p>
A Multimodal Dataset for Automatic Edge-AI Cough Detection
<p>Counting the number of times a patient coughs per day is an essential biomarker in determining treatment efficacy for novel antitussive therapies and personalizing patient care. There is a need for wearable devices that employ multimodal sensors to perform accurate, privacy-preserving, automatic cough counting algorithms directly on the device in an edge-AI fashion. To advance this research field, we contribute the first publicly accessible cough counting dataset of multimodal biosignals. The database contains nearly 4 hours of biosignal data, with both acoustic and kinematic modalities, covering 4,300 annotated cough events. Furthermore, several non-cough sounds (i.e. breathing, laughing, and throat clearing), background noises (i.e. music, traffic, bystander coughing) and motion scenarios (i.e. sitting, walking) mimicking daily life activities are also present, which the research community can use to accelerate ML algorithm development.</p> <p>For detailed information about using this dataset to train edge-AI models and example code, please refer to our public Git repository: https://github.com/esl-epfl/edge-ai-cough-count/</p>
PTVD: A Large-Scale Plot-Oriented Multimodal Dataset Based on Television Dramas
<p>Art forms such as movies and television (TV) dramas are reflections of the real world, which have attracted much attention from the multimodal learning community recently. However, existing corpora in this domain share three limitations: <strong>i.</strong> annotated in a scene-oriented fashion, they ignore the coherence within plots; <strong>ii.</strong> their text lacks empathy and seldom mentions situational context; <strong>iii.</strong> their video clips fail to cover long-form relationship due to short duration. To address these fundamental issues, using 1,106 TV drama episodes and 24,875 informative plot-focused sentences written by professionals, with the help of 449 human annotators, we constructed PTVD, the first plot-oriented multimodal dataset in the cinema domain.</p>
The dataset of "Deep Learning-enabled 3D Multimodal Fusion of Cone-Beam CT and Intraoral Mesh Scans for Clinically Applicable Tooth-bone Reconstruction"
<p>The dataset used in the study "Deep Learning-enabled 3D Multimodal Fusion of Cone-Beam CT and Intraoral Mesh Scans for Clinically Applicable Tooth-bone Reconstruction" is available upon request. Please contact the authors of the study or the responsible institution for access to the dataset.</p>
Dataset for Global Reference Seismological Data Sets: Multimode Surface Wave Dispersion
<ul> <li><strong>How fast do surface waves travel globally after any earthquake?</strong></li> <li><strong>Do we get the same information from various measurement techniques?</strong></li> <li><strong>Which features in the Earth are robust and can be resolved by a reference model?</strong></li> </ul> <p>Reference data with uncertainties are useful for improving existing measurement techniques, validating models of interior structure, calculating teleseismic data corrections in local or multiscale investigations and developing a 3-D reference Earth model. This study was done in collaboration with 18 scientists from 16 institutions in 7 countries who actively participated in the <a href="http://rem3d.org">REM3D</a> project. The project assimilated, archived, reconciled and modeled big (>200 million measurements) and diverse <a href="https://globalseismology.princeton.edu/data/surface-waves">surface-wave datasets</a> for global subsurface structure.</p> <p>The reference data set summarizes measurements of dispersion of fundamental-mode surface waves and up to six overtone branches from 44,871 earthquakes recorded on 12,222 globally distributed seismographic stations. Dispersion curves are specified at a set of reference periods between 25 and 250 s to determine propagation-phase anomalies with respect to a reference Earth model. Empirically determined observational uncertainties (1 sigma) for each wave type, branch number and period can be found in Table 3. </p> <p><strong>Summary:</strong></p> <p><strong>[I]</strong> <strong>Reconciled large and diverse catalogues</strong> of Love-wave (49.65 million) and Rayleigh-wave dispersion (177.66 million) from eight groups worldwide.<br> <strong>[II]</strong> Retrieved missing station and earthquake <strong>metadata</strong> in several legacy compilations and codified <strong>scalable formats</strong> to facilitate reproducibility, easy storage and fast I/O on HPC systems.<br> <strong>[III]</strong> <strong>Systematic discrepancies </strong>between raw phase anomalies can be attributed to discrepant theoretical approximations, reference Earth models and processing schemes.<br> <strong>[IV]</strong> <strong>Phase-velocity variations</strong> yielded by the inversion of the summary data set are <strong>highly correlated</strong> (R ≥ 0.8) with those from the quality-controlled contributing data sets, especially for long-wavelength variations (up to degree ∼25) in fundamental-mode dispersion (50–100 s).<br> <strong>[IV]</strong> <strong>Only 2ζ azimuthal variations</strong> in phase velocity of <strong>fundamental-mode Rayleigh waves</strong> are <strong>required</strong>; maps of 2ζ azimuthal variations are highly consistent between catalogues ( R = 0.6–0.8).</p> <p><strong>Feedback/Questions?</strong> Please contact Raj Moulik (<a href="https://rajmoulik.com">rajmoulik.com</a>) at <a href="mailto:moulik@caa.columbia.edu?subject=Query%20from%20Zenodo">moulik@caa.columbia.edu</a> </p> <p><strong>Reference:</strong></p> <p><em>Please cite the following work if you use this data or software.</em></p> <ul> <li>Moulik, P. <em>et al., </em>(2022) Global reference seismological data sets: multimode surface wave dispersion. <em>Geophys J Int</em> <strong>228</strong>, 1808–1849, doi: <a href="https://doi.org/10.1093/gji/ggab418">10.1093/gji/ggab418</a>. <em><a href="https://rajmoulik.com/Publications/Moulik_Reference_Surface_Waves_GJI2022.pdf">pdf</a></em></li> </ul> <p><em>You can also cite the dataset and software from this Zenodo page (Optional).</em></p> <ul> <li> <p>Moulik, P. (2022) Dataset for Global Reference Seismological Data Sets: Multimode Surface Wave Dispersion. In Geophys. J. Int. (v1.0, Vol. 228, pp. 1808–1849). Zenodo. doi: <a href="https://doi.org/10.5281/zenodo.8371228">10.5281/zenodo.8371228</a></p> </li> </ul> <p><strong>HDF5 Container Format</strong></p> <ul> <li><strong>Reference Love waves </strong>(<a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.SW.Love.data.h5">Download All Periods and Branches as Summary.SW.Love.data.h5</a>)</li> <li><strong>Reference Rayleigh waves </strong>(<a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.SW.Rayl.data.h5">Download All Periods and Branches as Summary.SW.Rayl.data.h5</a>)</li> </ul> <p>Summary (reference) data between pairs of 2562 evenly-spaced knot points with an average spacing of 4.33◦. These files store the data in the RSDF HDF5 container format. These can be read using standard HDF5 modules (e.g. h5py) or using <a href="http://avni.globalseismology.org/">AVNI</a>. For example, to read the reference data for fundamental mode R1 waves at 100s into a Pandas Dataframe containing data (df['data']) and a dictionary with the metadata (df['metadata']), and thereafter write contents to an ASCII text file, enter the following in Python:</p> <ul> <li><em>from avni.data.SW import readSWhdf5,writeSWascii</em></li> <li><em>df=readSWhdf5(query='0/100.0/R1/REM3D',hdffile='Summary.SW.Rayl.data.h5',datatype='summary')</em></li> <li><em>writeSWascii(df,'test.txt')</em></li> </ul> <p><strong>ASCII (text) Format</strong></p> <p>These files contain the same reference data as the HDF5 files above but in gzipped ASCII files. The files are named according to the overtone branch, wave type and period as <em>Summary.$overtone.$wave.$period.REM3D.gz</em> Table A1 from the paper describes the various columns in the surface-wave RSDF ASCII format files.</p> <ul> <li><strong>Love waves </strong>(<a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.SW.Love.data.zip">Download All Periods and Branches as Summary.SW.Love.data.zip</a>) <ul> <li>Fundamental Modes <ul> <li>Minor Arc Arrivals (L1) at <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L1.25s.REM3D.gz">25s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L1.27s.REM3D.gz">27s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L1.30s.REM3D.gz">30s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L1.32s.REM3D.gz">32s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L1.35s.REM3D.gz">35s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L1.40s.REM3D.gz">40s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L1.45s.REM3D.gz">45s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L1.50s.REM3D.gz">50s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L1.60s.REM3D.gz">60s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L1.75s.REM3D.gz">75s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L1.100s.REM3D.gz">100s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L1.125s.REM3D.gz">125s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L1.150s.REM3D.gz">150s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L1.175s.REM3D.gz">175s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L1.200s.REM3D.gz">200s</a>, and <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L1.250s.REM3D.gz">250s</a></li> <li>Major Arc Arrivals (L2) at <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L2.150s.REM3D.gz">150s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L2.175s.REM3D.gz">175s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L2.200s.REM3D.gz">200s</a>, and <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L2.250s.REM3D.gz">250s</a></li> <li>Higher Obit Arrivals - L3 at <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L3.150s.REM3D.gz">150s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L3.175s.REM3D.gz">175s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L3.200s.REM3D.gz">200s</a>, and <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L3.250s.REM3D.gz">250s</a>; L4 at <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L4.150s.REM3D.gz">150s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L4.175s.REM3D.gz">175s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L4.200s.REM3D.gz">200s</a>, and <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L4.250s.REM3D.gz">250s</a>; L5 at at <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L5.150s.REM3D.gz">150s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L5.175s.REM3D.gz">175s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L5.200s.REM3D.gz">200s</a>, and <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.L5.250s.REM3D.gz">250s</a>.</li> </ul> </li> <li>I<sup>st</sup> Overtone at <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.1.L1.40s.REM3D.gz">40s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.1.L1.45s.REM3D.gz">45s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.1.L1.50s.REM3D.gz">50s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.1.L1.60s.REM3D.gz">60s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.1.L1.75s.REM3D.gz">75s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.1.L1.100s.REM3D.gz">100s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.1.L1.125s.REM3D.gz">125s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.1.L1.150s.REM3D.gz">150s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.1.L1.175s.REM3D.gz">175s</a>, and <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.1.L1.200s.REM3D.gz">200s</a></li> <li>II<sup>nd</sup> Overtone at <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.2.L1.40s.REM3D.gz">40s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.2.L1.45s.REM3D.gz">45s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.2.L1.50s.REM3D.gz">50s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.2.L1.60s.REM3D.gz">60s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.2.L1.75s.REM3D.gz">75s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.2.L1.100s.REM3D.gz">100s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.2.L1.125s.REM3D.gz">125s</a>, and <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.2.L1.150s.REM3D.gz">150s</a></li> <li>III<sup>rd</sup> Overtone at <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.3.L1.40s.REM3D.gz">40s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.3.L1.45s.REM3D.gz">45s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.3.L1.50s.REM3D.gz">50s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.3.L1.60s.REM3D.gz">60s</a>, and <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.3.L1.75s.REM3D.gz">75s</a></li> <li>IV<sup>th</sup> Overtone at <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.4.L1.40s.REM3D.gz">40s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.4.L1.45s.REM3D.gz">45s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.4.L1.50s.REM3D.gz">50s</a>, and <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.4.L1.60s.REM3D.gz">60s</a></li> <li>V<sup>th</sup> Overtone at <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.5.L1.40s.REM3D.gz">40s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.5.L1.45s.REM3D.gz">45s</a>, and <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.5.L1.50s.REM3D.gz">50s</a></li> </ul> </li> <li><strong>Rayleigh waves </strong>(<a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.SW.Rayl.data.zip">Download All Periods and Branches as Summary.SW.Rayl.data.zip</a>) <ul> <li>Fundamental Modes <ul> <li>Minor Arc Arrivals (R1) at <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R1.25s.REM3D.gz">25s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R1.27s.REM3D.gz">27s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R1.30s.REM3D.gz">30s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R1.32s.REM3D.gz">32s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R1.35s.REM3D.gz">35s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R1.40s.REM3D.gz">40s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R1.45s.REM3D.gz">45s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R1.50s.REM3D.gz">50s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R1.60s.REM3D.gz">60s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R1.75s.REM3D.gz">75s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R1.100s.REM3D.gz">100s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R1.125s.REM3D.gz">125s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R1.150s.REM3D.gz">150s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R1.175s.REM3D.gz">175s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R1.200s.REM3D.gz">200s</a>, and <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R1.250s.REM3D.gz">250s</a></li> <li>Major Arc Arrivals (R2) at <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R2.150s.REM3D.gz">150s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R2.175s.REM3D.gz">175s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R2.200s.REM3D.gz">200s</a>, and <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R2.250s.REM3D.gz">250s</a></li> <li>Higher Obit Arrivals - R3 at <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R3.150s.REM3D.gz">150s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R3.175s.REM3D.gz">175s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R3.200s.REM3D.gz">200s</a>, and <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R3.250s.REM3D.gz">250s</a>; R4 at <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R4.150s.REM3D.gz">150s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R4.175s.REM3D.gz">175s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R4.200s.REM3D.gz">200s</a>, and <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R4.250s.REM3D.gz">250s</a>; R5 at <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R5.150s.REM3D.gz">150s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R5.175s.REM3D.gz">175s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R5.200s.REM3D.gz">200s</a>, and <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.0.R5.250s.REM3D.gz">250s</a>.</li> </ul> </li> <li>I<sup>st</sup> Overtone at <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.1.R1.40s.REM3D.gz">40s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.1.R1.45s.REM3D.gz">45s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.1.R1.50s.REM3D.gz">50s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.1.R1.60s.REM3D.gz">60s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.1.R1.75s.REM3D.gz">75s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.1.R1.100s.REM3D.gz">100s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.1.R1.125s.REM3D.gz">125s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.1.R1.150s.REM3D.gz">150s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.1.R1.175s.REM3D.gz">175s</a>, and <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.1.R1.200s.REM3D.gz">200s</a></li> <li>II<sup>nd</sup> Overtone at <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.2.R1.40s.REM3D.gz">40s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.2.R1.45s.REM3D.gz">45s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.2.R1.50s.REM3D.gz">50s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.2.R1.60s.REM3D.gz">60s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.2.R1.75s.REM3D.gz">75s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.2.R1.100s.REM3D.gz">100s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.2.R1.125s.REM3D.gz">125s</a>, and <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.2.R1.150s.REM3D.gz">150s</a></li> <li>III<sup>rd</sup> Overtone at <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.3.R1.40s.REM3D.gz">40s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.3.R1.45s.REM3D.gz">45s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.3.R1.50s.REM3D.gz">50s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.3.R1.60s.REM3D.gz">60s</a>, and <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.3.R1.75s.REM3D.gz">75s</a></li> <li>IV<sup>th</sup> Overtone at <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.4.R1.40s.REM3D.gz">40s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.4.R1.45s.REM3D.gz">45s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.4.R1.50s.REM3D.gz">50s</a>, and <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.4.R1.60s.REM3D.gz">60s</a></li> <li>V<sup>th</sup> Overtone at <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.5.R1.40s.REM3D.gz">40s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.5.R1.45s.REM3D.gz">45s</a>, and <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.5.R1.50s.REM3D.gz">50s</a></li> <li>VI<sup>th</sup> Overtone at <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.6.R1.40s.REM3D.gz">40s</a>, <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.6.R1.45s.REM3D.gz">45s</a>, and <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Summary.6.R1.50s.REM3D.gz">50s</a></li> </ul> </li> </ul> <p><strong>Other Data Products:</strong></p> <ul> </ul> <ul> <li><strong>ReferenceSW_Moulik2022_Figures(<a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/ReferenceSW_Moulik2022_Figures.zip">.zip</a> or <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/ReferenceSW_Moulik2022_Figures.pdf">.pdf</a>)</strong> - contains all figures from the paper in .png format</li> <li><a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Scatter_Plots.zip"><strong>Scatter_Plots.zip</strong></a> - contains scatter plots similar to Figure 5 in the paper, which compares measurements between two sets of techniques. The files with the suffix *raw.png are comparisons for original raw datasets, while those with the suffix *.clean.png are comparisons after the entire workflow is completed to create the clean datasets (e.g. Figure 13, bottom row).</li> <li><a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Half_cycle.zip"><strong>Half_cycle.zip</strong></a> and <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Cycle_skips.zip"><strong>Cycle_skips.zip</strong></a> - contains list of source-station paths where discrepancies were found between pairs of techniques. Half (±0.9–1.1 · π ) or full-cycle discrepancies (±0.9–1.1 · 2π ) identified in Section 4.5 are used during outlier analysis (Section 5.3) to create the clean summary dataset. Half- and full-cycle discrepancies identified in these files indicate potential polarity reversals and cycle skips respectively. Note that all of these discrepancies have not been checked for specific causes manually. </li> <li><a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/vflip-table.REM3D"><strong>vflip-table.REM3D</strong></a> - an ASCII file containing station names and start/end times where polarity reversal issues have been confirmed through manual analysis. This is in contrast to the automated half-cycle discrepancies identified in <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Half_cycle.zip"><strong>Half_cycle.zip</strong></a> above.</li> <li><a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/M1442"><strong>M1442</strong></a> and <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/B2562"><strong>B2562</strong></a> - Files containing the knot locations of evenly-spaced points on the surface. B2562 has an average knot spacing of 4.33◦ and is used as the underlying grid for the homogenization process to get summary data (Section 5.1). In order to obtain 2-D variations in local phase slowness or velocity, we use 1442 splines with an average knot spacing of 5.77◦ (Section 6.1)</li> <li><a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Cleanhomo.SW.Love.data.h5"><strong>Cleanhomo.SW.Love.data.h5</strong></a> and <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Cleanhomo.SW.Rayl.data.h5"><strong>Cleanhomo.SW.Rayl.data.h5</strong></a> - Clean homogenized data for each research group obtained at the end of our workflow (Figure 2). The ASCII files containing the same data are provided in <a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Cleanhomo.SW.Love.data.zip"><strong>Cleanhomo.SW.Love.data.zip</strong></a> and <strong><a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Cleanhomo.SW.Rayl.data.zip">Cleanhomo.SW.Rayl.data.zip</a>. </strong>The summary dataset listed earlier represents the reconciled measurements, and should be preferred over those from individual groups in most applications.</li> <li><a href="https://zenodo.org/api/files/e41385bf-fc0d-46ff-ac4b-4fb101467c6c/Inversion_Example.zip"><strong>Inversion_Example.zip</strong></a> - Contains an example of a 2D slowness map inversion with 2ζ azimuthal variations using the reference summary dataset at 100s for fundamental-mode minor-arc Rayleigh waves (R1). Also provided are plots for anistropic variation (<em>Anisotropy_Plots</em>), spline coeffients of 1442 evenly-spaced spherical splines (<em>Spline_Coefficients</em>), and corresponding values at every 1X1 degree pixel in extended pixel format (<em>Maps_epix</em>). The aim of this study is to provide dispersion measurements of surface-wave arrivals, not to provide detailed 2D phase velocity/slowness models. </li> </ul> <p><strong>Note about Data Format</strong></p> <p>The underlying philosophy and format of data files are discussed in the <a href="https://globalseismology.princeton.edu/rsdf">reference seismic data format (RSDF) project</a>. Table A1 from the GJI paper describes the various columns in the surface-wave RSDF format files above.</p>
Audio-IMU multimodal cough dataset using wearables
Open the record for dataset details and reuse information.
Loneliness and well-being in Finnish immigrants: A multimodal dataset from wearables and passive data collection
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Dataset for "Output control of dissipative nonlinear multimode amplifiers via spacetime symmetry mapping"
<p>Note: Simulation codes and instructions are available at <a href="https://github.com/joe851642001/MWAT" target="_new" rel="noopener">GitHub - MWAT</a>.</p>
Wheat Breeding Multimodal Dataset
<p>This dataset is a wheat breeding multimodal dataset, including wheat germplasm data, wheat phenotypic data, wheat cultivation technique data, wheat plant protection technique data, wheat seed price data, UAV remote sensing data, and experimental site weather data. The data sources are field acquisition and online public data.</p> <p>The wheat germplasm data comes from the Chinese Crop Germplasm Information Network (https://www.cgris.net/). The data on wheat cultivation technique and wheat plant protection technique come from search engines (Google, Bing, Baidu), and the search terms include "wheat cultivation technique, wheat plant protection technique, 小麦栽培技术, 小麦植保技术". The wheat seed historical price data comes from the National Seed Market Monitoring Information Release Platform (http://202.127.45.18/) - China. UAV remote sensing data is the result of further processing after being obtained from field experiments. Weather data comes from meteorological equipment at various agricultural experimental bases and meteorological observation stations of the China Meteorological Administration.</p> <p>The acquisition and processing of data are described in the relevant part of the manuscript.</p> <p>This dataset will be continuously updated in the future to help breeding work be carried out efficiently and accelerate the breeding process of excellent varieties.</p>
The TUFI dataset used in research paper "Assessing Large Multimodal Models for Urban Floodwater Depth Estimation"
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The DUFI dataset used in research paper "Assessing Large Multimodal Models for Urban Floodwater Depth Estimation"
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UMONS-TAICHI: A multimodal motion capture dataset of expertise in Taijiquan gestures
<p><strong>Presentation</strong></p> <p>UMONS-TAICHI is a large 3D motion capture dataset of Taijiquan martial art gestures (n = 2200 samples) that includes 13 classes (relative to Taijiquan techniques) executed by 12 participants of various skill levels. Participants levels were ranked by three experts on a [0-10] scale. The dataset was captured using two motion capture systems simultaneously: 1) Qualisys, a sophisticated motion capture system of 11 Oqus cameras that tracked 68 retroreflective markers at 179 Hz, and 2) Microsoft Kinect V2, a low-cost markerless sensor that tracked 25 locations of a person’s skeleton at 30 Hz. Data from both systems were synchronized manually. Qualisys data were manually corrected, and then processed to complete any missing data. Data were also manually annotated for segmentation. Both segmented and unsegmented data are provided in this database. The data were initially recorded for gesture recognition and skill evaluation, but they are also suited for research on synthesis, segmentation, multi-sensor data comparison and fusion, sports science or more general research on human science or motion capture. A preliminary analysis has been conducted by Tits et al. (2017) on a part of the dataset to extract morphology-independent motion features for gesture skill evaluation and presented in: “Morphology Independent Feature Engineering in Motion Capture Database for Gesture Evaluation” (<a href="https://doi.org/10.1145/3077981.3078037">https://doi.org/10.1145/3077981.3078037</a>).</p> <p><strong>Processing</strong></p> <p><em><strong>Qualisys</strong></em></p> <p>Qualisys data were processed manually with <a href="http://www.qualisys.com/software/qualisys-track-manager/">Qualisys Track Manager</a>.</p> <p>Missing data (occluded markers) were then recovered with an automatic recovery method: <a href="https://github.com/titsitits/MocapRecovery">MocapRecovery</a>.</p> <p>Data were annotated for gesture segmentation, using the <a href="https://github.com/numediart/ofxMotionMachine">MotionMachine</a> framework (C++ <a href="http://openframeworks.cc/">openFrameworks</a> addon). The code for annotation can be found <a href="https://github.com/numediart/ofxMotionMachine/tree/master/mmTutorial_4_Annotation">here</a>. Annotations were saved as ".lab" files (see Download section).</p> <p><em><strong>Kinect</strong></em></p> <p>The Kinect data were recorded with <a href="https://msdn.microsoft.com/en-us/library/hh855389.aspx">Kinect Studio</a>. Skeleton data were then extracted with <a href="https://www.microsoft.com/en-us/download/details.aspx?id=44561">Kinect SDK</a> and saved into “.txt” files which contain several lines corresponding to each captured frame. Each line contains one integer number (ms), relative to the moment when the frame was captured, followed by 3 x 25 float numbers corresponding to the 3-dimentional locations of the 25 body joints.</p> <p>For more information please visit <a href="https://github.com/numediart/UMONS-TAICHI">https://github.com/numediart/UMONS-TAICHI</a></p> <p>PS: All files can be used with the <a href="https://github.com/numediart/ofxMotionMachine">MotionMachine</a> framework. Please use the parser provided in this github repository for kinect (.txt) data.</p> <p> </p>
Dataset related to article "Evolution of brain injury and neurological dysfunction after cardiac arrest in the rat – a multimodal and comprehensive model."
<p>Excel file related to the article</p>
Supplementary methods and dataset related to the manuscript: Temporal integration of multimodal signals in crocodiles
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OMuSense-23: A Multimodal dataset for contactless breathing pattern recognition and biometric analysis
<p>OMuSense-23 is a multimodal dataset for non-contact biometric and breathing analysis. <br>This database comprises RGBD and mmWave radar data collected from 50 participants. <br>The data capture process involves participants engaging in four breathing pattern activities <br>(normal breathing, reading, guided breathing, and breath holding to simulate apnea) <br>each one performed in three distinct static poses: standing (A), sitting (B), and lying down (C). <br> <br> For citations please refer to the paper:<br> Manuel Lage Cañellas, Le Nguyen, Anirban Mukherjee, Constantino Álvarez Casado,<br> Xiaoting Wu, Nhi Nguyen, Praneeth Susarla, Sasan Sharifipour, Dinesh B. Jayagopi, Miguel Bordallo López,<br> "OmuSense-23: A Multimodal Dataset For Contactless Breathing Pattern Recognition And Biometric Analysis",<br> arXiv:2407.06137, 2024 </p>
Multimodal fish feeding behavior dataset Part 1 (You need download all dataset from Part 2 (https://zenodo.org/records/11060370) and Part 3 (https://zenodo.org/records/11060195))
<p>Multimodal Fish Feeding Intensity Assessment in Aquaculture (The paper you can find on Arxiv: <a href="https://arxiv.org/pdf/2309.05058.pdf" rel="nofollow">https://arxiv.org/pdf/2309.05058.pdf</a>) .</p>
VISEM: A Multimodal Video Dataset of Human Spermatozoa
<p>Real multimedia datasets that contain more than just images or text are rare. Even more so are open multimedia datasets in medicine. Often, clinically related datasets only consist of image or videos. We present a dataset that is novel in two ways. Firstly, it is a multi-modal dataset containing different data sources such as videos, biological analysis data, and participant data. Secondly, it is the first dataset of that kind in the field of human reproduction. It consists of anonymized data from 85 different participants. We hope this dataset will inspire people to apply their knowledge in this important field, generate shareable results in the domain, and ultimately improve human infertility investigation and treatment.</p>
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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.