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236 results for “motor learning”
Motor sequence learning
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Experimental data for the motor learning study performed: "Promoting Motor Variability During Robotic Assistance Enhances Motor Learning of Dynamic Tasks"
<p>The dataset contains the kinematic data and the questionnaire responses for a robot-assisted motor learning study performed in the Motor Learning and Neurorehabilitation Laboratory at University of Bern. The details of the study are described in [doi: 10.3389/fnins.2020.600059]. The kinematic data for each participant is stored as a data frame inside a “pickle” (serialized python object) file. The questionnaire responses are stored as a “csv” file. The variables inside the files are explained in “DataframeVariableDescription.rtf”. For questions, please contact oezhan.oezen@artorg.unibe.ch or L.MarchalCrespo@tudelft.nl.</p>
Experimental data for the motor learning study performed: "Towards functional robotic training: Motor learning of dynamic tasks is enhanced by haptic rendering but hampered by robotic assistance"
<p>The dataset contains the kinematic data and the questionnaire responses for a robot-assisted motor learning study performed in the Motor Learning and Neurorehabilitation Laboratory at the University of Bern. The details of the study are described in [doi: ]. The kinematic data for each participant is stored as a data frame inside a “pickle” (serialized python object) file. The questionnaire responses and population metrics are stored as “CSV” files. The variables inside the files are explained in “DataframeVariableDescription.rtf”. For questions, please contact oezhan.oezen@artorg.unibe.ch or L.MarchalCrespo@tudelft.nl.</p>
Current Harmonics Minimization of PMSM Based on Iterative Learning Control and Neural Networks: Motor Data
<p>The provided motor data corresponds to an electrical machine with 24 stator slots and 16 poles. As is common in electrical machines, this motor generates unwanted flux and current harmonics. However, the accompanying paper presents an effective solution to suppress these harmonics through the combined use of Iterative Learning Control (ILC) and Neural Networks (NNs).</p> <p>The ILC method demonstrates proficient compensation for harmonics during operations with constant speed and current reference values. Additionally, Neural Networks are trained with data derived from ILC, proving to be highly effective in suppressing harmonics even during transient operation. The simulation model used in the study is based on flux and torque maps, dependent on dq-currents and the electrical angle. These maps are obtained from Finite Element Method (FEM) simulations of an interior permanent magnet synchronous machine (IPM) and are openly published here, intended to facilitate other researchers in making direct comparisons with their own methodologies.</p> <p>Simulation results presented in the paper confirm that the integration of ILC and NNs leads to superior elimination of current harmonics during transient operations compared to using ILC alone.<br> If you use the provided maps and motor data, kindly cite the associated paper for reference: https://doi.org/10.3390/machines11080784, https://www.mdpi.com/2075-1702/11/8/784</p>
Dataset for the Article entitled "Dissociable effects of practice variability on learning motor and timing skills"
<p>Dataset collected for studying the effect of the amount and the schedule of task variability on motor and timing learning.</p>
Relevance of predictive and postdictive error information in the course of motor learning.
<p>Dataset associated with the following publication:</p> <p>Maurer, LK, Joch, M, Hegele, M, Maurer, H, & Müller, H (2021). Relevance of predictive and postdictive error information in the course of motor learning. Neuroscience doi:10.1016/j.neuroscience.2021.05.007</p>
Dataset for the assessment of presence and performance in an augmented reality environment for motor imitation learning: a case-study on violinists.
<p><strong>Data description</strong></p> <ol> <li>Repository structure</li> </ol> <p>The repository contains several zip files, which contain a set of individual data files. We describe the general content of every zip file and supplement this information with a table describing the individual data files in the zip file. An overview of the different zip-files is given in Table 1.</p> <p> </p> <table> <tbody> <tr> <td> <p>Zip-files in repository</p> </td> <td> <p>description</p> </td> </tr> <tr> <td> <p>Labeled_MoCap_Data.zip</p> </td> <td> <p>Labeled motion capture data</p> </td> </tr> <tr> <td> <p>Joint_Angle_Data.zip</p> </td> <td> <p>Joint angles extracted from mocap data</p> </td> </tr> <tr> <td> <p>Analyzed_Data.zip</p> </td> <td> <p>Filtered and analyzed mocap data</p> </td> </tr> <tr> <td> <p>Audio_Data.zip</p> </td> <td> <p>Audio data participants</p> </td> </tr> <tr> <td> <p>Questionnaire_Data.zip</p> </td> <td> <p>Questionnaire data</p> </td> </tr> <tr> <td> <p>Scores.zip</p> </td> <td> <p>Scores played by participants</p> </td> </tr> <tr> <td> <p>Avatar_Data.zip</p> </td> <td> <p>All data collected for the avatars</p> </td> </tr> </tbody> </table> <p> Table 1: overview of different zip-files in repository.</p> <p> </p> <ol> <li> </li> </ol> <p>Csv format with labeled MoCap Data, including data labels. Every column is a data stream from a marker. Every marker has 3 data streams, referring to the x, y, and z coordinates of the marker position. In addition, the violin (3-4 markers) and the violin bow (3 markers) are labelled as well. An overview of the different labels and their meaning is given in Table 2. One data file per participant (P001-P011), per trial (T1-T4), per condition (2D/3D) is presented. Additionally, the data type (MoCap), and the performed fragment (F1-F4) are given in the filename. An example of a file name is e.g., ‘P001_T1_2D_F1_MoCap.csv’ for a participant (see Table 2).</p> <p> </p> <table> <tbody> <tr> <td> <p>File Name</p> </td> <td> <p>Labeled_MoCap_Data.zip</p> </td> </tr> <tr> <td> <p>Content</p> </td> <td> <p>Participant</p> </td> <td> <p>Trial</p> </td> <td> <p>Condition</p> </td> <td> <p>Piece</p> </td> <td> <p>Data Type</p> </td> </tr> <tr> <td> <p>P001_T1_2D_F2_MoCap.csv</p> </td> <td> <p>1</p> </td> <td> <p>1</p> </td> <td> <p>2</p> </td> <td> <p>2</p> </td> <td> <p>Labeled MoCap Data</p> </td> </tr> <tr> <td> <p>P001_T2_2D_F2_MoCap.csv</p> </td> <td> <p>1</p> </td> <td> <p>2</p> </td> <td> <p>2</p> </td> <td> <p>2</p> </td> </tr> <tr> <td> <p>P001_T3_2D_F2_MoCap.csv</p> </td> <td> <p>1</p> </td> <td> <p>3</p> </td> <td> <p>2</p> </td> <td> <p>2</p> </td> </tr> <tr> <td> <p>P001_T4_2D_F2_MoCap.csv</p> </td> <td> <p>1</p> </td> <td> <p>4</p> </td> <td> <p>2</p> </td> <td> <p>2</p> </td> </tr> <tr> <td> <p>P001_T1_3D_F1_MoCap.csv</p> </td> <td> <p>1</p> </td> <td> <p>1</p> </td> <td> <p>3</p> </td> <td> <p>1</p> </td> </tr> <tr> <td> <p>P001_T2_3D_F1_MoCap.csv</p> </td> <td> <p>1</p> </td> <td> <p>2</p> </td> <td> <p>3</p> </td> <td> <p>1</p> </td> </tr> <tr> <td> <p>P001_T3_3D_F1_MoCap.csv</p> </td> <td> <p>1</p> </td> <td> <p>3</p> </td> <td> <p>3</p> </td> <td> <p>1</p> </td> </tr> <tr> <td> <p>P001_T4_3D_F1_MoCap.csv</p> </td> <td> <p>1</p> </td> <td> <p>4</p> </td> <td> <p>3</p> </td> <td> <p>1</p> </td> </tr> <tr> <td> <p>P002_T1_2D_F1_MoCap.csv</p> </td> <td> <p>2</p> </td> <td> <p>1</p> </td> <td> <p>2</p> </td> <td> <p>1</p> </td> </tr> <tr> <td> <p>P002_T2_2D_F1_MoCap.csv</p> </td> <td> <p>2</p> </td> <td> <p>2</p> </td> <td> <p>2</p> </td> <td> <p>1</p> </td> </tr> <tr> <td> <p>P002_T3_2D_F1_MoCap.csv</p> </td> <td> <p>2</p> </td> <td> <p>3</p> </td> <td> <p>2</p> </td> <td> <p>1</p> </td> </tr> <tr> <td> <p>P002_T4_2D_F1_MoCap.csv</p> </td> <td> <p>2</p> </td> <td> <p>4</p> </td> <td> <p>2</p> </td> <td> <p>1</p> 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</td> </tr> <tr> <td> <p>P008_T2_2D_F4_MoCap.csv</p> </td> <td> <p>8</p> </td> <td> <p>2</p> </td> <td> <p>2</p> </td> <td> <p>4</p> </td> </tr> <tr> <td> <p>P008_T3_2D_F4_MoCap.csv</p> </td> <td> <p>8</p> </td> <td> <p>3</p> </td> <td> <p>2</p> </td> <td> <p>4</p> </td> </tr> <tr> <td> <p>P008_T4_2D_F4_MoCap.csv</p> </td> <td> <p>8</p> </td> <td> <p>4</p> </td> <td> <p>2</p> </td> <td> <p>4</p> </td> </tr> <tr> <td> <p>P008_T1_3D_F3_MoCap.csv</p> </td> <td> <p>8</p> </td> <td> <p>1</p> </td> <td> <p>3</p> </td> <td> <p>3</p> </td> </tr> <tr> <td> <p>P008_T2_3D_F3_MoCap.csv</p> </td> <td> <p>8</p> </td> <td> <p>2</p> </td> <td> <p>3</p> </td> <td> <p>3</p> </td> </tr> <tr> <td> <p>P008_T3_3D_F3_MoCap.csv</p> </td> <td> <p>8</p> </td> <td> <p>3</p> </td> <td> <p>3</p> </td> <td> <p>3</p> </td> </tr> <tr> <td> <p>P008_T4_3D_F3_MoCap.csv</p> </td> <td> <p>8</p> </td> <td> <p>4</p> </td> <td> <p>3</p> </td> <td> <p>3</p> </td> </tr> <tr> <td> <p>P009_T1_2D_F1_MoCap.csv</p> </td> <td> <p>9</p> </td> <td> <p>1</p> </td> <td> <p>2</p> </td> <td> <p>1</p> </td> </tr> <tr> <td> <p>P009_T2_2D_F1_MoCap.csv</p> </td> <td> <p>9</p> </td> <td> <p>2</p> </td> <td> <p>2</p> </td> <td> <p>1</p> </td> </tr> <tr> <td> <p>P009_T3_2D_F1_MoCap.csv</p> </td> <td> <p>9</p> </td> <td> <p>3</p> </td> <td> <p>2</p> </td> <td> <p>1</p> </td> </tr> <tr> <td> <p>P009_T4_2D_F1_MoCap.csv</p> </td> <td> <p>9</p> </td> <td> <p>4</p> </td> <td> <p>2</p> </td> <td> <p>1</p> </td> </tr> <tr> <td> <p>P009_T1_3D_F2_MoCap.csv</p> </td> <td> <p>9</p> </td> <td> <p>1</p> </td> <td> <p>3</p> </td> <td> <p>2</p> </td> </tr> <tr> <td> <p>P009_T2_3D_F2_MoCap.csv</p> </td> <td> <p>9</p> </td> <td> <p>2</p> </td> <td> <p>3</p> </td> <td> <p>2</p> </td> </tr> <tr> <td> <p>P009_T3_3D_F2_MoCap.csv</p> </td> <td> <p>9</p> </td> <td> <p>3</p> </td> <td> <p>3</p> </td> <td> <p>2</p> </td> </tr> <tr> <td> <p>P009_T4_3D_F2_MoCap.csv</p> </td> <td> <p>9</p> </td> <td> <p>4</p> </td> <td> <p>3</p> </td> <td> <p>2</p> </td> </tr> <tr> <td> <p>P010_T1_2D_F2_MoCap.csv</p> </td> <td> <p>10</p> </td> <td> <p>1</p> </td> <td> <p>2</p> </td> <td> <p>2</p> </td> </tr> <tr> <td> <p>P010_T2_2D_F2_MoCap.csv</p> </td> <td> <p>10</p> </td> <td> <p>2</p> </td> <td> <p>2</p> </td> <td> <p>2</p> </td> </tr> <tr> <td> <p>P010_T3_2D_F2_MoCap.csv</p> </td> <td> <p>10</p> </td> <td> <p>3</p> </td> <td> <p>2</p> </td> <td> <p>2</p> </td> </tr> <tr> <td> <p>P010_T4_2D_F2_MoCap.csv</p> </td> <td> <p>10</p> </td> <td> <p>4</p> </td> <td> <p>2</p> </td> <td> <p>2</p> </td> </tr> <tr> <td> <p>P010_T1_3D_F1_MoCap.csv</p> </td> <td> <p>10</p> </td> <td> <p>1</p> </td> <td> <p>3</p> </td> <td> <p>1</p> </td> </tr> <tr> <td> <p>P010_T2_3D_F1_MoCap.csv</p> </td> <td> <p>10</p> </td> <td> <p>2</p> </td> <td> <p>3</p> </td> <td> <p>1</p> </td> </tr> <tr> <td> <p>P010_T3_3D_F1_MoCap.csv</p> </td> <td> <p>10</p> </td> <td> <p>3</p> </td> <td> <p>3</p> </td> <td> <p>1</p> </td> </tr> <tr> <td> <p>P010_T4_3D_F1_MoCap.csv</p> </td> <td> <p>10</p> </td> <td> <p>4</p> </td> <td> <p>3</p> </td> <td> <p>1</p> </td> </tr> <tr> <td> <p>P011_T1_2D_F4_MoCap.csv</p> </td> <td> <p>11</p> </td> <td> <p>1</p> </td> <td> <p>2</p> </td> <td> <p>4</p> </td> </tr> <tr> <td> <p>P011_T2_2D_F4_MoCap.csv</p> </td> <td> <p>11</p> </td> <td> <p>2</p> </td> <td> <p>2</p> </td> <td> <p>4</p> </td> </tr> <tr> <td> <p>P011_T3_2D_F4_MoCap.csv</p> </td> <td> <p>11</p> </td> <td> <p>3</p> </td> <td> <p>2</p> </td> <td> <p>4</p> </td> </tr> <tr> <td> <p>P011_T4_2D_F4_MoCap.csv</p> </td> <td> <p>11</p> </td> <td> <p>4</p> </td> <td> <p>2</p> </td> <td> <p>4</p> </td> </tr> <tr> <td> <p>P011_T1_3D_F3_MoCap.csv</p> </td> <td> <p>11</p> </td> <td> <p>1</p> </td> <td> <p>3</p> </td> <td> <p>3</p> </td> </tr> <tr> <td> <p>P011_T2_3D_F3_MoCap.csv</p> </td> <td> <p>11</p> </td> <td> <p>2</p> </td> <td> <p>3</p> </td> <td> <p>3</p> </td> </tr> <tr> <td> <p>P011_T3_3D_F3_MoCap.csv</p> </td> <td> <p>11</p> </td> <td> <p>3</p> </td> <td> <p>3</p> </td> <td> <p>3</p> </td> </tr> <tr> <td> <p>P011_T4_3D_F3_MoCap.csv</p> </td> <td> <p>11</p> </td> <td> <p>4</p> </td> <td> <p>3</p> </td> <td> <p>3</p> </td> </tr> </tbody> </table> <p>Table 2: content and file structure of Labeled_MoCap_Data.zip.</p> <p> </p> <ol> <li> </li> </ol> <p>Csv format with joint angles, including data labels. Every column is a data stream from a joint. Every joint has a varying number of data streams, depending on the calculated angles. In addition to joint angles, the angles of the instrument relative to the body are given as well, the distances of the bow to the bridge, and to distances of the bow to the strings, respectively. An overview of the different labels and their meaning is given in Table 3. One data file per participant (P001-P011), per trial (T1-T4), per condition (2D/3D) is presented. Additionally, the data type (JointAngles), and the performed fragment (F1-F4) are given in the filename. An example of a file name is e.g., ‘P001_T1_2D_F1_JointAngles.csv’ for a participant (see Table 3).</p> <p> </p> <table> <tbody> <tr> <td> <p>File Name</p> </td> <td> <p>Joint_Angle_Data.zip</p> </td> </tr> <tr> <td> <p>Content</p> </td> <td> <p>Participant</p> </td> <td> <p>Trial</p> </td> <td> <p>Condition</p> </td> <td> <p>Piece</p> </td> <td> <p>Data Type</p> </td> </tr> <tr> <td> <p>P001_T1_2D_F2_JointAngles.csv</p> </td> <td> <p>1</p> </td> <td> <p>1</p> </td> <td> <p>2</p> </td> <td> <p>2</p> </td> <td> <p>Joint Angle Data</p> </td> </tr> <tr> <td> <p>P001_T2_2D_F2_JointAngles.csv</p> </td> <td> <p>1</p> </td> <td> <p>2</p> </td> <td> <p>2</p> </td> <td> <p>2</p> </td> </tr> <tr> <td> <p>P001_T3_2D_F2_JointAngles.csv</p> </td> <td> <p>1</p> </td> <td> <p>3</p> </td> <td> <p>2</p> </td> <td> <p>2</p> </td> </tr> <tr> <td> <p>P001_T4_2D_F2_JointAngles.csv</p> </td> <td> <p>1</p> </td> <td> <p>4</p> </td> <td> <p>2</p> </td> <td> <p>2</p> </td> </tr> <tr> <td> <p>P001_T1_3D_F1_JointAngles.csv</p> </td> <td> <p>1</p> </td> <td> <p>1</p> </td> <td> <p>3</p> </td> <td> <p>1</p> </td> </tr> <tr> <td> <p>P001_T2_3D_F1_JointAngles.csv</p> </td> <td> <p>1</p> </td> <td> <p>2</p> </td> <td> <p>3</p> </td> <td> <p>1</p> </td> </tr> <tr> <td> <p>P001_T3_3D_F1_JointAngles.csv</p> </td> <td> <p>1</p> </td> <td> <p>3</p> </td> <td> <p>3</p> </td> <td> <p>1</p> </td> </tr> <tr> <td> <p>P001_T4_3D_F1_JointAngles.csv</p> </td> <td> <p>1</p> </td> <td> <p>4</p> </td> <td> <p>3</p> </td> <td> <p>1</p> </td> </tr> <tr> <td> <p>P002_T1_2D_F1_JointAngles.csv</p> </td> <td> <p>2</p> </td> <td> <p>1</p> </td> <td> <p>2</p> </td> <td> <p>1</p> </td> </tr> <tr> <td> <p>P002_T2_2D_F1_JointAngles.csv</p> </td> <td> <p>2</p> </td> <td> <p>2</p> </td> <td> <p>2</p> </td> <td> <p>1</p> </td> </tr> <tr> <td> <p>P002_T3_2D_F1_JointAngles.csv</p> </td> <td> <p>2</p> </td> <td> <p>3</p> </td> <td> <p>2</p> </td> <td> <p>1</p> </td> </tr> <tr> <td> <p>P002_T4_2D_F1_JointAngles.csv</p> </td> <td> <p>2</p> </td> <td> <p>4</p> </td> <td> <p>2</p> </td> <td> <p>1</p> </td> </tr> <tr> <td> <p>P002_T1_3D_F2_JointAngles.csv</p> </td> <td> <p>2</p> </td> <td> <p>1</p> </td> <td> <p>3</p> </td> <td> <p>2</p> </td> </tr> <tr> <td> <p>P002_T2_3D_F2_JointAngles.csv</p> </td> <td> <p>2</p> </td> <td> <p>2</p> </td> <td> <p>3</p> </td> <td> <p>2</p> </td> </tr> <tr> <td> <p>P002_T3_3D_F2_JointAngles.csv</p> </td> <td> <p>2</p> </td> <td> <p>3</p> </td> <td> <p>3</p> </td> <td> <p>2</p> </td> </tr> <tr> <td> <p>P002_T4_3D_F2_JointAngles.csv</p> </td> <td> <p>2</p> </td> <td> <p>4</p> </td> <td> <p>3</p> </td> <td> <p>2</p> </td> </tr> <tr> <td> <p>P003_T1_2D_F3_JointAngles.csv</p> </td> <td> <p>3</p> </td> <td> <p>1</p> </td> <td> <p>2</p> </td> <td> <p>3</p> </td> </tr> <tr> <td> <p>P003_T2_2D_F3_JointAngles.csv</p> </td> <td> <p>3</p> </td> <td> <p>2</p> </td> <td> <p>2</p> </td> <td> <p>3</p> </td> </tr> <tr> <td> <p>P003_T3_2D_F3_JointAngles.csv</p> </td> <td> <p>3</p> </td> <td> <p>3</p> </td> <td> <p>2</p> </td> <td> <p>3</p> </td> </tr> <tr> <td> <p>P003_T4_2D_F3_JointAngles.csv</p> </td> <td> <p>3</p> </td> <td> <p>4</p> </td> <td> <p>2</p> </td> <td> <p>3</p> </td> </tr> <tr> <td> <p>P003_T1_3D_F4_JointAngles.csv</p> </td> <td> <p>3</p> </td> <td> <p>1</p> </td> <td> <p>3</p> </td> <td> <p>4</p> </td> </tr> <tr> <td> <p>P003_T2_3D_F4_JointAngles.csv</p> </td> <td> <p>3</p> </td> <td> <p>2</p> </td> <td> <p>3</p> </td> <td> <p>4</p> </td> </tr> <tr> <td> <p>P003_T3_3D_F4_JointAngles.csv</p> </td> <td> <p>3</p> </td> <td> <p>3</p> </td> <td> <p>3</p> </td> <td> <p>4</p> </td> </tr> <tr> <td> <p>P003_T4_3D_F4_JointAngles.csv</p> </td> <td> <p>3</p> </td> <td> <p>4</p> </td> <td> <p>3</p> </td> <td> <p>4</p> </td> </tr> <tr> <td> <p>P004_T1_2D_F1_JointAngles.csv</p> </td> <td> <p>4</p> </td> <td> <p>1</p> </td> <td> <p>2</p> </td> <td> <p>1</p> </td> </tr> <tr> <td> <p>P004_T2_2D_F1_JointAngles.csv</p> </td> <td> <p>4</p> </td> <td> <p>2</p> </td> <td> <p>2</p> </td> <td> <p>1</p> </td> </tr> <tr> <td> <p>P004_T3_2D_F1_JointAngles.csv</p> </td> <td> <p>4</p> </td> <td> <p>3</p> </td> <td> <p>2</p> </td> <td> <p>1</p> </td> </tr> <tr> <td> <p>P004_T4_2D_F1_JointAngles.csv</p> </td> <td> <p>4</p> </td> <td> <p>4</p> </td> <td> <p>2</p> </td> <td> <p>1</p> </td> </tr> <tr> <td> <p>P004_T1_3D_F2_JointAngles.csv</p> </td> <td> <p>4</p> </td> <td> <p>1</p> </td> <td> <p>3</p> </td> <td> <p>2</p> </td> </tr> <tr> <td> <p>P004_T2_3D_F2_JointAngles.csv</p> </td> <td> <p>4</p> </td> <td> <p>2</p> </td> <td> <p>3</p> </td> <td> <p>2</p> </td> </tr> <tr> <td> <p>P004_T3_3D_F2_JointAngles.csv</p> </td> <td> <p>4</p> </td> <td> <p>3</p> </td> <td> <p>3</p> </td> <td> <p>2</p> </td> </tr> <tr> <td> <p>P004_T4_3D_F2_JointAngles.csv</p> </td> <td> <p>4</p> </td> <td> <p>4</p> </td> <td> <p>3</p> </td> <td> <p>2</p> </td> </tr> <tr> <td> <p>P005_T1_2D_F3_JointAngles.csv</p> </td> <td> <p>5</p> </td> <td> <p>1</p> </td> <td> <p>2</p> </td> <td> <p>3</p> </td> </tr> <tr> <td> <p>P005_T2_2D_F3_JointAngles.csv</p> </td> <td> <p>5</p> </td> <td> <p>2</p> </td> <td> <p>2</p> </td> <td> <p>3</p> </td> </tr> <tr> <td> <p>P005_T3_2D_F3_JointAngles.csv</p> </td> <td> <p>5</p> </td> <td> <p>3</p> </td> <td> <p>2</p> </td> <td> <p>3</p> </td> </tr> <tr> <td> <p>P005_T4_2D_F3_JointAngles.csv</p> </td> <td> <p>5</p> </td> <td> <p>4</p> </td> <td> <p>2</p> </td> <td> <p>3</p> </td> </tr> <tr> <td> <p>P005_T1_3D_F4_JointAngles.csv</p> </td> <td> <p>5</p> </td> <td> <p>1</p> </td> <td> <p>3</p> </td> <td> <p>4</p> </td> </tr> <tr> <td> <p>P005_T2_3D_F4_JointAngles.csv</p> </td> <td> <p>5</p> </td> <td> <p>2</p> </td> <td> <p>3</p> </td> <td> <p>4</p> </td> </tr> <tr> <td> <p>P005_T3_3D_F4_JointAngles.csv</p> </td> <td> <p>5</p> </td> <td> <p>3</p> </td> <td> <p>3</p> </td> <td> <p>4</p> </td> </tr> <tr> <td> <p>P005_T4_3D_F4_JointAngles.csv</p> </td> <td> <p>5</p> </td> <td> <p>4</p> </td> <td> <p>3</p> </td> <td> <p>4</p> </td> </tr> <tr> <td> <p>P006_T1_2D_F1_JointAngles.csv</p> </td> <td> <p>6</p> </td> <td> <p>1</p> </td> <td> <p>2</p> </td> <td> <p>1</p> </td> </tr> <tr> <td> <p>P006_T2_2D_F1_JointAngles.csv</p> </td> <td> <p>6</p> </td> <td> <p>2</p> </td> <td> <p>2</p> </td> <td> <p>1</p> </td> </tr> <tr> <td> <p>P006_T3_2D_F1_JointAngles.csv</p> </td> <td> <p>6</p> </td> <td> <p>3</p> </td> <td> <p>2</p> </td> <td> <p>1</p> </td> </tr> <tr> <td> <p>P006_T4_2D_F1_JointAngles.csv</p> </td> <td> <p>6</p> </td> <td> <p>4</p> </td> <td> <p>2</p> </td> <td> <p>1</p> </td> </tr> <tr> <td> <p>P006_T1_3D_F2_JointAngles.csv</p> </td> <td> <p>6</p> </td> <td> <p>1</p> </td> <td> <p>3</p> </td> <td> <p>2</p> </td> </tr> <tr> <td> <p>P006_T2_3D_F2_JointAngles.csv</p> </td> <td> <p>6</p> </td> <td> <p>2</p> </td> <td> <p>3</p> </td> <td> <p>2</p> </td> </tr> <tr> <td> <p>P006_T3_3D_F2_JointAngles.csv</p> </td> <td> <p>6</p> </td> <td> <p>3</p> </td> <td> <p>3</p> </td> <td> <p>2</p> </td> </tr> <tr> <td> <p>P006_T4_3D_F2_JointAngles.csv</p> </td> <td> <p>6</p> </td> <td> <p>4</p> </td> <td> <p>3</p> </td> <td> <p>2</p> </td> </tr> <tr> <td> <p>P007_T1_2D_F4_JointAngles.csv</p> </td> <td> <p>7</p> </td> <td> <p>1</p> </td> <td> <p>2</p> </td> <td> <p>4</p> </td> </tr> <tr> <td> <p>P007_T2_2D_F4_JointAngles.csv</p> </td> <td> <p>7</p> </td> <td> <p>2</p> </td> <td> <p>2</p> </td> <td> <p>4</p> </td> </tr> <tr> <td> <p>P007_T3_2D_F4_JointAngles.csv</p> </td> <td> <p>7</p> </td> <td> <p>3</p> </td> <td> <p>2</p> </td> <td> <p>4</p> </td> </tr> <tr> <td> <p>P007_T4_2D_F4_JointAngles.csv</p> </td> <td> <p>7</p> </td> <td> <p>4</p> </td> <td> <p>2</p> </td> <td> <p>4</p> </td> </tr> <tr> <td> <p>P007_T1_3D_F3_JointAngles.csv</p> </td> <td> <p>7</p> </td> <td> <p>1</p> </td> <td> <p>3</p> </td> <td> <p>3</p> </td> </tr> <tr> <td> <p>P007_T2_3D_F3_JointAngles.csv</p> </td> <td> <p>7</p> </td> <td> <p>2</p> </td> <td> <p>3</p> </td> <td> <p>3</p> </td> </tr> <tr> <td> <p>P007_T3_3D_F3_JointAngles.csv</p> </td> <td> <p>7</p> </td> <td> <p>3</p> </td> <td> <p>3</p> </td> <td> <p>3</p> </td> </tr> <tr> <td> <p>P007_T4_3D_F3_JointAngles.csv</p> </td> <td> <p>7</p> </td> <td> <p>4</p> </td> <td> <p>3</p> </td> <td> <p>3</p> </td> </tr> <tr> <td> <p>P008_T1_2D_F4_JointAngles.csv</p> </td> <td> <p>8</p> </td> <td> <p>1</p> </td> <td> <p>2</p> </td> <td> <p>4</p> </td> </tr> <tr> <td> <p>P008_T2_2D_F4_JointAngles.csv</p> </td> <td> <p>8</p> </td> <td> <p>2</p> </td> <td> <p>2</p> </td> <td> <p>4</p> </td> </tr> <tr> <td> <p>P008_T3_2D_F4_JointAngles.csv</p> </td> <td> <p>8</p> </td> <td> <p>3</p> </td> <td> <p>2</p> </td> <td> <p>4</p> </td> </tr> <tr> <td> <p>P008_T4_2D_F4_JointAngles.csv</p> </td> <td> <p>8</p> </td> <td> <p>4</p> </td> <td> <p>2</p> </td> <td> <p>4</p> </td> </tr> <tr> <td> <p>P008_T1_3D_F3_JointAngles.csv</p> </td> <td> <p>8</p> </td> <td> <p>1</p> </td> <td> <p>3</p> </td> <td> <p>3</p> </td> </tr> <tr> <td> <p>P008_T2_3D_F3_JointAngles.csv</p> </td> <td> <p>8</p> </td> <td> <p>2</p> </td> <td> <p>3</p> </td> <td> <p>3</p> </td> </tr> <tr> <td> <p>P008_T3_3D_F3_JointAngles.csv</p> </td> <td> <p>8</p> </td> <td> <p>3</p> </td> <td> <p>3</p> </td> <td> <p>3</p> </td> </tr> <tr> <td> <p>P008_T4_3D_F3_JointAngles.csv</p> </td> <td> <p>8</p> </td> <td> <p>4</p> </td> <td> <p>3</p> </td> <td> <p>3</p> </td> </tr> <tr> <td> <p>P009_T1_2D_F1_JointAngles.csv</p> </td> <td> <p>9</p> </td> <td> <p>1</p> </td> <td> <p>2</p> </td> <td> <p>1</p> </td> </tr> <tr> <td> <p>P009_T2_2D_F1_JointAngles.csv</p> </td> <td> <p>9</p> </td> <td> <p>2</p> </td> <td> <p>2</p> </td> <td> <p>1</p> </td> </tr> <tr> <td> <p>P009_T3_2D_F1_JointAngles.csv</p> </td> <td> <p>9</p> </td> <td> <p>3</p> </td> <td> <p>2</p> </td> <td> <p>1</p> </td> </tr> <tr> <td> <p>P009_T4_2D_F1_JointAngles.csv</p> </td> <td> <p>9</p> </td> <td> <p>4</p> </td> <td> <p>2</p> </td> <td> <p>1</p> </td> </tr> <tr> <td> <p>P009_T1_3D_F2_JointAngles.csv</p> </td> <td> <p>9</p> </td> <td> <p>1</p> </td> <td> <p>3</p> </td> <td> <p>2</p> </td> </tr> <tr> <td> <p>P009_T2_3D_F2_JointAngles.csv</p> </td> <td> <p>9</p> </td> <td> <p>2</p> </td> <td> <p>3</p> </td> <td> <p>2</p> </td> </tr> <tr> <td> <p>P009_T3_3D_F2_JointAngles.csv</p> </td> <td> <p>9</p> </td> <td> <p>3</p> </td> <td> <p>3</p> </td> <td> <p>2</p> </td> </tr> <tr> <td> <p>P009_T4_3D_F2_JointAngles.csv</p> </td> <td> <p>9</p> </td> <td> <p>4</p> </td> <td> <p>3</p> </td> <td> <p>2</p> </td> </tr> <tr> <td> <p>P010_T1_2D_F2_JointAngles.csv</p> </td> <td> <p>10</p> </td> <td> <p>1</p> </td> <td> <p>2</p> </td> <td> <p>2</p> </td> </tr> <tr> <td> <p>P010_T2_2D_F2_JointAngles.csv</p> </td> <td> <p>10</p> </td> <td> <p>2</p> </td> <td> <p>2</p> </td> <td> <p>2</p> </td> </tr> <tr> <td> <p>P010_T3_2D_F2_JointAngles.csv</p> </td> <td> <p>10</p> </td> <td> <p>3</p> </td> <td> <p>2</p> </td> <td> <p>2</p> </td> </tr> <tr> <td> <p>P010_T4_2D_F2_JointAngles.csv</p> </td> <td> <p>10</p> </td> <td> <p>4</p> </td> <td> <p>2</p> </td> <td> <p>2</p> </td> </tr> <tr> <td> <p>P010_T1_3D_F1_JointAngles.csv</p> </td> <td> <p>10</p> </td> <td> <p>1</p> </td> <td> <p>3</p> </td> <td> <p>1</p> </td> </tr> <tr> <td> <p>P010_T2_3D_F1_JointAngles.csv</p> </td> <td> <p>10</p> </td> <td> <p>2</p> </td> <td> <p>3</p> </td> <td> <p>1</p> </td> </tr> <tr> <td> <p>P010_T3_3D_F1_JointAngles.csv</p> </td> <td> <p>10</p> </td> <td> <p>3</p> </td> <td> <p>3</p> </td> <td> <p>1</p> </td> </tr> <tr> <td> <p>P010_T4_3D_F1_JointAngles.csv</p> </td> <td> <p>10</p> </td> <td> <p>4</p> </td> <td> <p>3</p> </td> <td> <p>1</p> </td> </tr> <tr> <td> <p>P011_T1_2D_F4_JointAngles.csv</p> </td> <td> <p>11</p> </td> <td> <p>1</p> </td> <td> <p>2</p> </td> <td> <p>4</p> </td> </tr> <tr> <td> <p>P011_T2_2D_F4_JointAngles.csv</p> </td> <td> <p>11</p> </td> <td> <p>2</p> </td> <td> <p>2</p> </td> <td> <p>4</p> </td> </tr> <tr> <td> <p>P011_T3_2D_F4_JointAngles.csv</p> </td> <td> <p>11</p> </td> <td> <p>3</p> </td> <td> <p>2</p> </td> <td> <p>4</p> </td> </tr> <tr> <td> <p>P011_T4_2D_F4_JointAngles.csv</p> </td> <td> <p>11</p> </td> <td> <p>4</p> </td> <td> <p>2</p> </td> <td> <p>4</p> </td> </tr> <tr> <td> <p>P011_T1_3D_F3_JointAngles.csv</p> </td> <td> <p>11</p> </td> <td> <p>1</p> </td> <td> <p>3</p> </td> <td> <p>3</p> </td> </tr> <tr> <td> <p>P011_T2_3D_F3_JointAngles.csv</p> </td> <td> <p>11</p> </td> <td> <p>2</p> </td> <td> <p>3</p> </td> <td> <p>3</p> </td> </tr> <tr> <td> <p>P011_T3_3D_F3_JointAngles.csv</p> </td> <td> <p>11</p> </td> <td> <p>3</p> </td> <td> <p>3</p> </td> <td> <p>3</p> </td> </tr> <tr> <td> <p>P011_T4_3D_F3_JointAngles.csv</p> </td> <td> <p>11</p> </td> <td> <p>4</p> </td> <td> <p>3</p> </td> <td> <p>3</p> </td> </tr> </tbody> </table> <p>Table 3: content and file structure of Joint_Angle_Data.zip.</p> <p> </p> <ol> <li> </li> </ol> <p>Time series of filtered and analyzed data. The distances of the bow to the bridge and frog, are filtered so that only bow strokes with a certain bowing length and a certain loudness level are retained. The resulting collection of regions-of-interest (ROIs) is then analyzed for movement smoothness (as assessed with the SPARC index), and a comparison is made between the profile of avatar bowing movements and participant bowing movements by means of the Procrustes distance. These data are presented as csv files with 4 columns: SPARC index per ROI, Procrustes distance between the bow movement of the avatar and participant, index of start and end of each ROI. One data file per participant (P001-P011), per trial (T1-T4), per condition (2D/3D) is presented. Additionally, the data type (AnalyzedData), and the performed fragment (F1-F4) are given in the filename. An example of a file name is e.g., ‘P001_T1_2D_F1_ AnalyzedData.csv’ for a participant (see Table 4).</p> <p> </p> <table> <tbody> <tr> <td> <p>File Name</p> </td> <td> <p>Analyzed_Data.zip</p> </td> </tr> <tr> <td> <p>Content</p> </td> <td> <p>Participant</p> </td> <td> <p>Trial</p> </td> <td> <p>Condition</p> </td> <td> <p>Piece</p> </td> <td> <p>Data Type</p> </td> </tr> <tr> <td> <p>P001_T1_2D_F2_AnalyzedData.csv</p> </td> <td> <p>1</p> </td> <td> <p>1</p> </td> <td> <p>2</p> </td> <td> <p>2</p> </td> <td> <p>Analyzed Data</p> </td> </tr> <tr> <td> <p>P001_T2_2D_F2_AnalyzedData.csv</p> </td> <td> <p>1</p> </td> <td> <p>2</p> </td> <td> <p>2</p> </td> <td> <p>2</p> </td> </tr> <tr> <td> <p>P001_T3_2D_F2_AnalyzedData.csv</p> </td> <td> <p>1</p> </td> <td> <p>3</p> </td> <td> <p>2</p> </td> <td> <p>2</p> </td> </tr> <tr> <td> <p>P001_T4_2D_F2_AnalyzedData.csv</p> </td> <td> <p>1</p> </td> <td> <p>4</p> </td> <td> <p>2</p> </td> <td> <p>2</p> </td> </tr> <tr> <td> <p>P001_T1_3D_F1_AnalyzedData.csv</p> </td> <td> <p>1</p> </td> <td> <p>1</p> </td> <td> <p>3</p> </td> <td> <p>1</p> </td> </tr> <tr> <td> <p>P001_T2_3D_F1_AnalyzedData.csv</p> </td> <td> <p>1</p> </td> <td> <p>2</p> </td> <td> <p>3</p> </td> <td> <p>1</p> </td> </tr> <tr> <td> <p>P001_T3_3D_F1_AnalyzedData.csv</p> </td> <td> <p>1</p> </td> <td> <p>3</p> </td> <td> <p>3</p> </td> <td> <p>1</p> </td> </tr> <tr> <td> <p>P001_T4_3D_F1_AnalyzedData.csv</p> </td> <td> <p>1</p> </td> <td> <p>4</p> </td> <td> <p>3</p> </td> <td> <p>1</p> </td> </tr> <tr> <td> <p>P002_T1_2D_F1_AnalyzedData.csv</p> </td> <td> <p>2</p> </td> <td> <p>1</p> </td> <td> <p>2</p> </td> <td> <p>1</p> </td> </tr> <tr> <td> <p>P002_T2_2D_F1_AnalyzedData.csv</p> </td> <td> <p>2</p> </td> <td> <p>2</p> </td> <td> <p>2</p> </td> <td> <p>1</p> </td> </tr> <tr> <td> <p>P002_T3_2D_F1_AnalyzedData.csv</p> </td> <td> <p>2</p> </td> <td> <p>3</p> </td> <td> <p>2</p> </td> <td> <p>1</p> </td> </tr> <tr> <td> <p>P002_T4_2D_F1_AnalyzedData.csv</p> </td> <td> <p>2</p> </td> <td> <p>4</p> </td> <td> <p>2</p> </td> <td> <p>1</p> </td> </tr> <tr> <td> <p>P002_T1_3D_F2_AnalyzedData.csv</p> </td> <td> <p>2</p> </td> <td> <p>1</p> </td> <td> <p>3</p> </td> <td> <p>2</p> </td> </tr> <tr> <td> <p>P002_T2_3D_F2_AnalyzedData.csv</p> </td> <td> <p>2</p> </td> <td> <p>2</p> </td> <td> <p>3</p> </td> <td> <p>2</p> </td> </tr> <tr> <td> <p>P002_T3_3D_F2_AnalyzedData.csv</p> </td> <td> <p>2</p> </td> <td> <p>3</p> </td> <td> <p>3</p> </td> <td> <p>2</p> </td> </tr> <tr> <td> <p>P002_T4_3D_F2_AnalyzedData.csv</p> </td> <td> <p>2</p> </td> <td> <p>4</p> </td> <td> <p>3</p> </td> <td> <p>2</p> </td> </tr> <tr> <td> <p>P003_T1_2D_F3_AnalyzedData.csv</p> </td> <td> <p>3</p> </td> <td> <p>1</p> </td> <td> <p>2</p> </td> <td> <p>3</p> </td> </tr> <tr> <td> <p>P003_T2_2D_F3_AnalyzedData.csv</p> </td> <td> <p>3</p> </td> <td> <p>2</p> </td> <td> <p>2</p> </td> <td> <p>3</p> </td> </tr> <tr> <td> <p>P003_T3_2D_F3_AnalyzedData.csv</p> </td> <td> <p>3</p> </td> <td> <p>3</p> </td> <td> <p>2</p> </td> <td> <p>3</p> </td> </tr> <tr> <td> <p>P003_T4_2D_F3_AnalyzedData.csv</p> </td> <td> <p>3</p> </td> <td> <p>4</p> </td> <td> <p>2</p> </td> <td> <p>3</p> </td> </tr> <tr> <td> <p>P003_T1_3D_F4_AnalyzedData.csv</p> </td> <td> <p>3</p> </td> <td> <p>1</p> </td> <td> <p>3</p> </td> <td> <p>4</p> </td> </tr> <tr> <td> <p>P003_T2_3D_F4_AnalyzedData.csv</p> </td> <td> <p>3</p> </td> <td> <p>2</p> </td> <td> <p>3</p> </td> <td> <p>4</p> </td> </tr> <tr> <td> <p>P003_T3_3D_F4_AnalyzedData.csv</p> </td> <td> <p>3</p> </td> <td> <p>3</p> </td> <td> <p>3</p> </td> <td> <p>4</p> </td> </tr> <tr> <td> <p>P003_T4_3D_F4_AnalyzedData.csv</p> </td> <td> <p>3</p> </td> <td> <p>4</p> </td> <td> <p>3</p> </td> <td> <p>4</p> </td> </tr> <tr> <td> <p>P004_T1_2D_F1_AnalyzedData.csv</p> </td> <td> <p>4</p> </td> <td> <p>1</p> </td> <td> <p>2</p> </td> <td> <p>1</p> </td> </tr> <tr> <td> <p>P004_T2_2D_F1_AnalyzedData.csv</p> </td> <td> <p>4</p> </td> <td> <p>2</p> </td> <td> <p>2</p> </td> <td> <p>1</p> </td> </tr> <tr> <td> <p>P004_T3_2D_F1_AnalyzedData.csv</p> </td> <td> <p>4</p> </td> <td> <p>3</p> </td> <td> <p>2</p> </td> <td> <p>1</p> </td> </tr> <tr> <td> <p>P004_T4_2D_F1_AnalyzedData.csv</p> </td> <td> <p>4</p> </td> <td> <p>4</p> </td> <td> <p>2</p> </td> <td> <p>1</p> </td> </tr> <tr> <td> <p>P004_T1_3D_F2_AnalyzedData.csv</p> </td> <td> <p>4</p> </td> <td> <p>1</p> </td> <td> <p>3</p> </td> <td> <p>2</p> </td> </tr> <tr> <td> <p>P004_T2_3D_F2_AnalyzedData.csv</p> </td> <td> <p>4</p> </td> <td> <p>2</p> </td> <td> <p>3</p> </td> <td> <p>2</p> </td> </tr> <tr> <td> <p>P004_T3_3D_F2_AnalyzedData.csv</p> </td> <td> <p>4</p> </td> <td> <p>3</p> </td> <td> <p>3</p> </td> <td> <p>2</p> </td> </tr> <tr> <td> <p>P004_T4_3D_F2_AnalyzedData.csv</p> </td> <td> <p>4</p> </td> <td> <p>4</p> </td> <td> <p>3</p> </td> <td> <p>2</p> </td> </tr> <tr> <td> <p>P005_T1_2D_F3_AnalyzedData.csv</p> </td> <td> <p>5</p> </td> <td> <p>1</p> </td> <td> <p>2</p> </td> <td> <p>3</p> </td> </tr> <tr> <td> <p>P005_T2_2D_F3_AnalyzedData.csv</p> </td> <td> <p>5</p> </td> <td> <p>2</p> </td> <td> <p>2</p> </td> <td> <p>3</p> </td> </tr> <tr> <td> <p>P005_T3_2D_F3_AnalyzedData.csv</p> </td> <td> <p>5</p> </td> <td> <p>3</p> </td> <td> <p>2</p> </td> <td> <p>3</p> </td> </tr> <tr> <td> <p>P005_T4_2D_F3_AnalyzedData.csv</p> </td> <td> <p>5</p> </td> <td> <p>4</p> </td> <td> <p>2</p> </td> <td> <p>3</p> </td> </tr> <tr> <td> <p>P005_T1_3D_F4_AnalyzedData.csv</p> </td> <td> <p>5</p> </td> <td> <p>1</p> </td> <td> <p>3</p> </td> <td> <p>4</p> </td> </tr> <tr> <td> <p>P005_T2_3D_F4_AnalyzedData.csv</p> </td> <td> <p>5</p> </td> <td> <p>2</p> </td> <td> <p>3</p> </td> <td> <p>4</p> </td> </tr> <tr> <td> <p>P005_T3_3D_F4_AnalyzedData.csv</p> </td> <td> <p>5</p> </td> <td> <p>3</p> </td> <td> <p>3</p> </td> <td> <p>4</p> </td> </tr> <tr> <td> <p>P005_T4_3D_F4_AnalyzedData.csv</p> </td> <td> <p>5</p> </td> <td> <p>4</p> </td> <td> <p>3</p> </td> <td> <p>4</p> </td> </tr> <tr> <td> <p>P006_T1_2D_F1_AnalyzedData.csv</p> </td> <td> <p>6</p> </td> <td> <p>1</p> </td> <td> <p>2</p> </td> <td> <p>1</p> </td> </tr> <tr> <td> <p>P006_T2_2D_F1_AnalyzedData.csv</p> </td> <td> <p>6</p> </td> <td> <p>2</p> </td> <td> <p>2</p> </td> <td> <p>1</p> </td> </tr> <tr> <td> <p>P006_T3_2D_F1_AnalyzedData.csv</p> </td> <td> <p>6</p> </td> <td> <p>3</p> </td> <td> <p>2</p> </td> <td> <p>1</p> </td> </tr> <tr> <td> <p>P006_T4_2D_F1_AnalyzedData.csv</p> </td> <td> <p>6</p> </td> <td> <p>4</p> </td> <td> <p>2</p> </td> <td> <p>1</p> </td> </tr> <tr> <td> <p>P006_T1_3D_F2_AnalyzedData.csv</p> </td> <td> <p>6</p> </td> <td> <p>1</p> </td> <td> <p>3</p> </td> <td> <p>2</p> </td> </tr> <tr> <td> <p>P006_T2_3D_F2_AnalyzedData.csv</p> </td> <td> <p>6</p> </td> <td> <p>2</p> </td> <td> <p>3</p> </td> <td> <p>2</p> </td> </tr> <tr> <td> <p>P006_T3_3D_F2_AnalyzedData.csv</p> </td> <td> <p>6</p> </td> <td> <p>3</p> </td> <td> <p>3</p> </td> <td> <p>2</p> </td> </tr> <tr> <td> <p>P006_T4_3D_F2_AnalyzedData.csv</p> </td> <td> <p>6</p> </td> <td> <p>4</p> </td> <td> <p>3</p> </td> <td> <p>2</p> </td> </tr> <tr> <td> <p>P007_T1_2D_F4_AnalyzedData.csv</p> </td> <td> <p>7</p> </td> <td> <p>1</p> </td> <td> <p>2</p> </td> <td> <p>4</p> </td> </tr> <tr> <td> <p>P007_T2_2D_F4_AnalyzedData.csv</p> </td> <td> <p>7</p> </td> <td> <p>2</p> </td> <td> <p>2</p> </td> <td> <p>4</p> </td> </tr> <tr> <td> <p>P007_T3_2D_F4_AnalyzedData.csv</p> </td> <td> <p>7</p> </td> <td> <p>3</p> </td> <td> <p>2</p> </td> <td> <p>4</p> </td> </tr> <tr> <td> <p>P007_T4_2D_F4_AnalyzedData.csv</p> </td> <td> <p>7</p> </td> <td> <p>4</p> </td> <td> <p>2</p> </td> <td> <p>4</p> </td> </tr> <tr> <td> <p>P007_T1_3D_F3_AnalyzedData.csv</p> </td> <td> <p>7</p> </td> <td> <p>1</p> </td> <td> <p>3</p> </td> <td> <p>3</p> </td> </tr> <tr> <td> <p>P007_T2_3D_F3_AnalyzedData.csv</p> </td> <td> <p>7</p> </td> <td> <p>2</p> </td> <td> <p>3</p> </td> <td> <p>3</p> </td> </tr> <tr> <td> <p>P007_T3_3D_F3_AnalyzedData.csv</p> </td> <td> <p>7</p> </td> <td> <p>3</p> </td> <td> <p>3</p> </td> <td> <p>3</p> </td> </tr> <tr> <td> <p>P007_T4_3D_F3_AnalyzedData.csv</p> </td> <td> <p>7</p> </td> <td> <p>4</p> </td> <td> <p>3</p> </td> <td> <p>3</p> </td> </tr> <tr> <td> <p>P008_T1_2D_F4_AnalyzedData.csv</p> </td> <td> <p>8</p> </td> <td> <p>1</p> </td> <td> <p>2</p> </td> <td> <p>4</p> </td> </tr> <tr> <td> <p>P008_T2_2D_F4_AnalyzedData.csv</p> </td> <td> <p>8</p> </td> <td> <p>2</p> </td> <td> <p>2</p> </td> <td> <p>4</p> </td> </tr> <tr> <td> <p>P008_T3_2D_F4_AnalyzedData.csv</p> </td> <td> <p>8</p> </td> <td> <p>3</p> </td> <td> <p>2</p> </td> <td> <p>4</p> </td> </tr> <tr> <td> <p>P008_T4_2D_F4_AnalyzedData.csv</p> </td> <td> <p>8</p> </td> <td> <p>4</p> </td> <td> <p>2</p> </td> <td> <p>4</p> </td> </tr> <tr> <td> <p>P008_T1_3D_F3_AnalyzedData.csv</p> </td> <td> <p>8</p> </td> <td> <p>1</p> </td> <td> <p>3</p> </td> <td> <p>3</p> </td> </tr> <tr> <td> <p>P008_T2_3D_F3_AnalyzedData.csv</p> </td> <td> <p>8</p> </td> <td> <p>2</p> </td> <td> <p>3</p> </td> <td> <p>3</p> </td> </tr> <tr> <td> <p>P008_T3_3D_F3_AnalyzedData.csv</p> </td> <td> <p>8</p> </td> <td> <p>3</p> </td> <td> <p>3</p> </td> <td> <p>3</p> </td> </tr> <tr> <td> <p>P008_T4_3D_F3_AnalyzedData.csv</p> </td> <td> <p>8</p> </td> <td> <p>4</p> </td> <td> <p>3</p> </td> <td> <p>3</p> </td> </tr> <tr> <td> <p>P009_T1_2D_F1_AnalyzedData.csv</p> </td> <td> <p>9</p> </td> <td> <p>1</p> </td> <td> <p>2</p> </td> <td> <p>1</p> </td> </tr> <tr> <td> <p>P009_T2_2D_F1_AnalyzedData.csv</p> </td> <td> <p>9</p> </td> <td> <p>2</p> </td> <td> <p>2</p> </td> <td> <p>1</p> </td> </tr> <tr> <td> <p>P009_T3_2D_F1_AnalyzedData.csv</p> </td> <td> <p>9</p> </td> <td> <p>3</p> </td> <td> <p>2</p> </td> <td> <p>1</p> </td> </tr> <tr> <td> <p>P009_T4_2D_F1_AnalyzedData.csv</p> </td> <td> <p>9</p> </td> <td> <p>4</p> </td> <td> <p>2</p> </td> <td> <p>1</p> </td> </tr> <tr> <td> <p>P009_T1_3D_F2_AnalyzedData.csv</p> </td> <td> <p>9</p> </td> <td> <p>1</p> </td> <td> <p>3</p> </td> <td> <p>2</p> </td> </tr> <tr> <td> <p>P009_T2_3D_F2_AnalyzedData.csv</p> </td> <td> <p>9</p> </td> <td> <p>2</p> </td> <td> <p>3</p> </td> <td> <p>2</p> </td> </tr> <tr> <td> <p>P009_T3_3D_F2_AnalyzedData.csv</p> </td> <td> <p>9</p> </td> <td> <p>3</p> </td> <td> <p>3</p> </td> <td> <p>2</p> </td> </tr> <tr> <td> <p>P009_T4_3D_F2_AnalyzedData.csv</p> </td> <td> <p>9</p> </td> <td> <p>4</p> </td> <td> <p>3</p> </td> <td> <p>2</p> </td> </tr> <tr> <td> <p>P010_T1_2D_F2_AnalyzedData.csv</p> </td> <td> <p>10</p> </td> <td> <p>1</p> </td> <td> <p>2</p> </td> <td> <p>2</p> </td> </tr> <tr> <td> <p>P010_T2_2D_F2_AnalyzedData.csv</p> </td> <td> <p>10</p> </td> <td> <p>2</p> </td> <td> <p>2</p> </td> <td> <p>2</p> </td> </tr> <tr> <td> <p>P010_T3_2D_F2_AnalyzedData.csv</p> </td> <td> <p>10</p> </td> <td> <p>3</p> </td> <td> <p>2</p> </td> <td> <p>2</p> </td> </tr> <tr> <td> <p>P010_T4_2D_F2_AnalyzedData.csv</p> </td> <td> <p>10</p> </td> <td> <p>4</p> </td> <td> <p>2</p> </td> <td> <p>2</p> </td> </tr> <tr> <td> <p>P010_T1_3D_F1_AnalyzedData.csv</p> </td> <td> <p>10</p> </td> <td> <p>1</p> </td> <td> <p>3</p> </td> <td> <p>1</p> </td> </tr> <tr> <td> <p>P010_T2_3D_F1_AnalyzedData.csv</p> </td> <td> <p>10</p> </td> <td> <p>2</p> </td> <td> <p>3</p> </td> <td> <p>1</p> </td> </tr> <tr> <td> <p>P010_T3_3D_F1_AnalyzedData.csv</p> </td> <td> <p>10</p> </td> <td> <p>3</p> </td> <td> <p>3</p> </td> <td> <p>1</p> </td> </tr> <tr> <td> <p>P010_T4_3D_F1_AnalyzedData.csv</p> </td> <td> <p>10</p> </td> <td> <p>4</p> </td> <td> <p>3</p> </td> <td> <p>1</p> </td> </tr> <tr> <td> <p>P011_T1_2D_F4_AnalyzedData.csv</p> </td> <td> <p>11</p> </td> <td> <p>1</p> </td> <td> <p>2</p> </td> <td> <p>4</p> </td> </tr> <tr> <td> <p>P011_T2_2D_F4_AnalyzedData.csv</p> </td> <td> <p>11</p> </td> <td> <p>2</p> </td> <td> <p>2</p> </td> <td> <p>4</p> </td> </tr> <tr> <td> <p>P011_T3_2D_F4_AnalyzedData.csv</p> </td> <td> <p>11</p> </td> <td> <p>3</p> </td> <td> <p>2</p> </td> <td> <p>4</p> </td> </tr> <tr> <td> <p>P011_T4_2D_F4_AnalyzedData.csv</p> </td> <td> <p>11</p> </td> <td> <p>4</p> </td> <td> <p>2</p> </td> <td> <p>4</p> </td> </tr> <tr> <td> <p>P011_T1_3D_F3_AnalyzedData.csv</p> </td> <td> <p>11</p> </td> <td> <p>1</p> </td> <td> <p>3</p> </td> <td> <p>3</p> </td> </tr> <tr> <td> <p>P011_T2_3D_F3_AnalyzedData.csv</p> </td> <td> <p>11</p> </td> <td> <p>2</p> </td> <td> <p>3</p> </td> <td> <p>3</p> </td> </tr> <tr> <td> <p>P011_T3_3D_F3_AnalyzedData.csv</p> </td> <td> <p>11</p> </td> <td> <p>3</p> </td> <td> <p>3</p> </td> <td> <p>3</p> </td> </tr> <tr> <td> <p>P011_T4_3D_F3_AnalyzedData.csv</p> </td> <td> <p>11</p> </td> <td> <p>4</p> </td> <td> <p>3</p> </td> <td> <p>3</p> </td> </tr> </tbody> </table> <p>Table 4: content and file structure of Analyzed_Data.zip.</p> <p> </p> <ol> <li> </li> </ol> <p>Wav-files are presented per participant (P001-P011), per trial (T1-T4), per condition (2D/3D). Audio files contain 2 tracks (left and right microphone), i.e., they are stereo recordings. Additionally, the data type (Audio), and the performed fragment (F1-F4) are given in the filename. An example of a file name is e.g., ‘P001_T1_2D_F1_Audio.wav’ for a participant (see Table 5).</p> <p> </p> <table> <tbody> <tr> <td> <p>File Name</p> </td> <td> <p>Audio_Data.zip</p> </td> </tr> <tr> <td> <p>Content</p> </td> <td> <p>Participant</p> </td> <td> <p>Trial</p> </td> <td> <p>Condition</p> </td> <td> <p>Piece</p> </td> <td> <p>Data Type</p> </td> </tr> <tr> <td> <p>P001_T1_2D_F2_Audio.csv</p> </td> <td> <p>1</p> </td> <td> <p>1</p> </td> <td> <p>2</p> </td> <td> <p>2</p> </td> <td> <p>Audio Data</p> </td> </tr> <tr> <td> <p>P001_T2_2D_F2_Audio.csv</p> </td> <td> <p>1</p> </td> <td> <p>2</p> </td> <td> <p>2</p> </td> <td> <p>2</p> </td> </tr> <tr> <td> <p>P001_T3_2D_F2_Audio.csv</p> </td> <td> <p>1</p> </td> <td> <p>3</p> </td> <td> <p>2</p> </td> <td> <p>2</p> </td> </tr> <tr> <td> <p>P001_T4_2D_F2_Audio.csv</p> </td> <td> <p>1</p> </td> <td> <p>4</p> </td> <td> <p>2</p> </td> <td> <p>2</p> </td> </tr> <tr> <td> <p>P001_T1_3D_F1_Audio.csv</p> </td> <td> <p>1</p> </td> <td> <p>1</p> </td> <td> <p>3</p> </td> <td> <p>1</p> </td> </tr> <tr> <td> <p>P001_T2_3D_F1_Audio.csv</p> </td> <td> <p>1</p> </td> <td> <p>2</p> </td> <td> <p>3</p> </td> <td> <p>1</p> </td> </tr> <tr> <td> <p>P001_T3_3D_F1_Audio.csv</p> </td> <td> <p>1</p> </td> <td> <p>3</p> </td> <td> <p>3</p> </td> <td> <p>1</p> </td> </tr> <tr> <td> <p>P001_T4_3D_F1_Audio.csv</p> </td> <td> <p>1</p> </td> <td> <p>4</p> </td> <td> <p>3</p> </td> <td> <p>1</p> </td> </tr> <tr> <td> <p>P002_T1_2D_F1_Audio.csv</p> </td> <td> <p>2</p> </td> <td> <p>1</p> </td> <td> <p>2</p> </td> <td> <p>1</p> </td> </tr> <tr> <td> <p>P002_T2_2D_F1_Audio.csv</p> </td> <td> <p>2</p> </td> <td> <p>2</p> </td> <td> <p>2</p> </td> <td> <p>1</p> </td> </tr> <tr> <td> <p>P002_T3_2D_F1_Audio.csv</p> </td> <td> <p>2</p> </td> <td> <p>3</p> </td> <td> <p>2</p> </td> <td> <p>1</p> </td> </tr> <tr> <td> <p>P002_T4_2D_F1_Audio.csv</p> </td> <td> <p>2</p> </td> <td> <p>4</p> </td> <td> <p>2</p> </td> <td> <p>1</p> </td> </tr> <tr> <td> <p>P002_T1_3D_F2_Audio.csv</p> </td> <td> <p>2</p> </td> <td> <p>1</p> </td> <td> <p>3</p> </td> <td> <p>2</p> </td> </tr> <tr> <td> <p>P002_T2_3D_F2_Audio.csv</p> </td> <td> <p>2</p> </td> <td> <p>2</p> </td> <td> <p>3</p> </td> <td> <p>2</p> </td> </tr> <tr> <td> <p>P002_T3_3D_F2_Audio.csv</p> </td> <td> <p>2</p> </td> <td> <p>3</p> </td> <td> <p>3</p> </td> <td> <p>2</p> </td> </tr> <tr> <td> <p>P002_T4_3D_F2_Audio.csv</p> </td> <td> <p>2</p> </td> <td> <p>4</p> </td> <td> <p>3</p> </td> <td> <p>2</p> </td> </tr> <tr> <td> <p>P003_T1_2D_F3_Audio.csv</p> </td> <td> <p>3</p> </td> <td> <p>1</p> </td> <td> <p>2</p> </td> <td> <p>3</p> </td> </tr> <tr> <td> <p>P003_T2_2D_F3_Audio.csv</p> </td> <td> <p>3</p> </td> <td> <p>2</p> </td> <td> <p>2</p> </td> <td> <p>3</p> </td> </tr> <tr> <td> <p>P003_T3_2D_F3_Audio.csv</p> </td> <td> <p>3</p> </td> <td> <p>3</p> </td> <td> <p>2</p> </td> <td> <p>3</p> </td> </tr> <tr> <td> <p>P003_T4_2D_F3_Audio.csv</p> </td> <td> <p>3</p> </td> <td> <p>4</p> </td> <td> <p>2</p> </td> <td> <p>3</p> </td> </tr> <tr> <td> <p>P003_T1_3D_F4_Audio.csv</p> </td> <td> <p>3</p> </td> <td> <p>1</p> </td> <td> <p>3</p> </td> <td> <p>4</p> </td> </tr> <tr> <td> <p>P003_T2_3D_F4_Audio.csv</p> </td> <td> <p>3</p> </td> <td> <p>2</p> </td> <td> <p>3</p> </td> <td> <p>4</p> </td> </tr> <tr> <td> <p>P003_T3_3D_F4_Audio.csv</p> </td> <td> <p>3</p> </td> <td> <p>3</p> </td> <td> <p>3</p> </td> <td> <p>4</p> </td> </tr> <tr> <td> <p>P003_T4_3D_F4_Audio.csv</p> </td> <td> <p>3</p> </td> <td> <p>4</p> </td> <td> <p>3</p> </td> <td> <p>4</p> </td> </tr> <tr> <td> <p>P004_T1_2D_F1_Audio.csv</p> </td> <td> <p>4</p> </td> <td> <p>1</p> </td> <td> <p>2</p> </td> <td> <p>1</p> </td> </tr> <tr> <td> <p>P004_T2_2D_F1_Audio.csv</p> </td> <td> <p>4</p> </td> <td> <p>2</p> </td> <td> <p>2</p> </td> <td> <p>1</p> </td> </tr> <tr> <td> <p>P004_T3_2D_F1_Audio.csv</p> </td> <td> <p>4</p> </td> <td> <p>3</p> </td> <td> <p>2</p> </td> <td> <p>1</p> </td> </tr> <tr> <td> <p>P004_T4_2D_F1_Audio.csv</p> </td> <td> <p>4</p> </td> <td> <p>4</p> </td> <td> <p>2</p> </td> <td> <p>1</p> </td> </tr> <tr> <td> <p>P004_T1_3D_F2_Audio.csv</p> </td> <td> <p>4</p> </td> <td> <p>1</p> </td> <td> <p>3</p> </td> <td> <p>2</p> </td> </tr> <tr> <td> <p>P004_T2_3D_F2_Audio.csv</p> </td> <td> <p>4</p> </td> <td> <p>2</p> </td> <td> <p>3</p> </td> <td> <p>2</p> </td> </tr> <tr> <td> <p>P004_T3_3D_F2_Audio.csv</p> </td> <td> <p>4</p> </td> <td> <p>3</p> </td> <td> <p>3</p> </td> <td> <p>2</p> </td> </tr> <tr> <td> <p>P004_T4_3D_F2_Audio.csv</p> </td> <td> <p>4</p> </td> <td> <p>4</p> </td> <td> <p>3</p> </td> <td> <p>2</p> </td> </tr> <tr> <td> <p>P005_T1_2D_F3_Audio.csv</p> </td> <td> <p>5</p> </td> <td> <p>1</p> </td> <td> <p>2</p> </td> <td> <p>3</p> </td> </tr> <tr> <td> <p>P005_T2_2D_F3_Audio.csv</p> </td> <td> <p>5</p> </td> <td> <p>2</p> </td> <td> <p>2</p> </td> <td> <p>3</p> </td> </tr> <tr> <td> <p>P005_T3_2D_F3_Audio.csv</p> </td> <td> <p>5</p> </td> <td> <p>3</p> </td> <td> <p>2</p> </td> <td> <p>3</p> </td> </tr> <tr> <td> <p>P005_T4_2D_F3_Audio.csv</p> </td> <td> <p>5</p> </td> <td> <p>4</p> </td> <td> <p>2</p> </td> <td> <p>3</p> </td> </tr> <tr> <td> <p>P005_T1_3D_F4_Audio.csv</p> </td> <td> <p>5</p> </td> <td> <p>1</p> </td> <td> <p>3</p> </td> <td> <p>4</p> </td> </tr> <tr> <td> <p>P005_T2_3D_F4_Audio.csv</p> </td> <td> <p>5</p> </td> <td> <p>2</p> </td> <td> <p>3</p> </td> <td> <p>4</p> </td> </tr> <tr> <td> <p>P005_T3_3D_F4_Audio.csv</p> </td> <td> <p>5</p> </td> <td> <p>3</p> </td> <td> <p>3</p> </td> <td> <p>4</p> </td> </tr> <tr> <td> <p>P005_T4_3D_F4_Audio.csv</p> </td> <td> <p>5</p> </td> <td> <p>4</p> </td> <td> <p>3</p> </td> <td> <p>4</p> </td> </tr> <tr> <td> <p>P006_T1_2D_F1_Audio.csv</p> </td> <td> <p>6</p> </td> <td> <p>1</p> </td> <td> <p>2</p> </td> <td> <p>1</p> </td> </tr> <tr> <td> <p>P006_T2_2D_F1_Audio.csv</p> </td> <td> <p>6</p> </td> <td> <p>2</p> </td> <td> <p>2</p> </td> <td> <p>1</p> </td> </tr> <tr> <td> <p>P006_T3_2D_F1_Audio.csv</p> </td> <td> <p>6</p> </td> <td> <p>3</p> </td> <td> <p>2</p> </td> <td> <p>1</p> </td> </tr> <tr> <td> <p>P006_T4_2D_F1_Audio.csv</p> </td> <td> <p>6</p> </td> <td> <p>4</p> </td> <td> <p>2</p> </td> <td> <p>1</p> </td> </tr> <tr> <td> <p>P006_T1_3D_F2_Audio.csv</p> </td> <td> <p>6</p> </td> <td> <p>1</p> </td> <td> <p>3</p> </td> <td> <p>2</p> </td> </tr> <tr> <td> <p>P006_T2_3D_F2_Audio.csv</p> </td> <td> <p>6</p> </td> <td> <p>2</p> </td> <td> <p>3</p> </td> <td> <p>2</p> </td> </tr> <tr> <td> <p>P006_T3_3D_F2_Audio.csv</p> </td> <td> <p>6</p> </td> <td> <p>3</p> </td> <td> <p>3</p> </td> <td> <p>2</p> </td> </tr> <tr> <td> <p>P006_T4_3D_F2_Audio.csv</p> </td> <td> <p>6</p> </td> <td> <p>4</p> </td> <td> <p>3</p> </td> <td> <p>2</p> </td> </tr> <tr> <td> <p>P007_T1_2D_F4_Audio.csv</p> </td> <td> <p>7</p> </td> <td> <p>1</p> </td> <td> <p>2</p> </td> <td> <p>4</p> </td> </tr> <tr> <td> <p>P007_T2_2D_F4_Audio.csv</p> </td> <td> <p>7</p> </td> <td> <p>2</p> </td> <td> <p>2</p> </td> <td> <p>4</p> </td> </tr> <tr> <td> <p>P007_T3_2D_F4_Audio.csv</p> </td> <td> <p>7</p> </td> <td> <p>3</p> </td> <td> <p>2</p> </td> <td> <p>4</p> </td> </tr> <tr> <td> <p>P007_T4_2D_F4_Audio.csv</p> </td> <td> <p>7</p> </td> <td> <p>4</p> </td> <td> <p>2</p> </td> <td> <p>4</p> </td> </tr> <tr> <td> <p>P007_T1_3D_F3_Audio.csv</p> </td> <td> <p>7</p> </td> <td> <p>1</p> </td> <td> <p>3</p> </td> <td> <p>3</p> </td> </tr> <tr> <td> <p>P007_T2_3D_F3_Audio.csv</p> </td> <td> <p>7</p> </td> <td> <p>2</p> </td> <td> <p>3</p> </td> <td> <p>3</p> </td> </tr> <tr> <td> <p>P007_T3_3D_F3_Audio.csv</p> </td> <td> <p>7</p> </td> <td> <p>3</p> </td> <td> <p>3</p> </td> <td> <p>3</p> </td> </tr> <tr> <td> <p>P007_T4_3D_F3_Audio.csv</p> </td> <td> <p>7</p> </td> <td> <p>4</p> </td> <td> <p>3</p> </td> <td> <p>3</p> </td> </tr> <tr> <td> <p>P008_T1_2D_F4_Audio.csv</p> </td> <td> <p>8</p> </td> <td> <p>1</p> </td> <td> <p>2</p> </td> <td> <p>4</p> </td> </tr> <tr> <td> <p>P008_T2_2D_F4_Audio.csv</p> </td> <td> <p>8</p> </td> <td> <p>2</p> </td> <td> <p>2</p> </td> <td> <p>4</p> </td> </tr> <tr> <td> <p>P008_T3_2D_F4_Audio.csv</p> </td> <td> <p>8</p> </td> <td> <p>3</p> </td> <td> <p>2</p> </td> <td> <p>4</p> </td> </tr> <tr> <td> <p>P008_T4_2D_F4_Audio.csv</p> </td> <td> <p>8</p> </td> <td> <p>4</p> </td> <td> <p>2</p> </td> <td> <p>4</p> </td> </tr> <tr> <td> <p>P008_T1_3D_F3_Audio.csv</p> </td> <td> <p>8</p> </td> <td> <p>1</p> </td> <td> <p>3</p> </td> <td> <p>3</p> </td> </tr> <tr> <td> <p>P008_T2_3D_F3_Audio.csv</p> </td> <td> <p>8</p> </td> <td> <p>2</p> </td> <td> <p>3</p> </td> <td> <p>3</p> </td> </tr> <tr> <td> <p>P008_T3_3D_F3_Audio.csv</p> </td> <td> <p>8</p> </td> <td> <p>3</p> </td> <td> <p>3</p> </td> <td> <p>3</p> </td> </tr> <tr> <td> <p>P008_T4_3D_F3_Audio.csv</p> </td> <td> <p>8</p> </td> <td> <p>4</p> </td> <td> <p>3</p> </td> <td> <p>3</p> </td> </tr> <tr> <td> <p>P009_T1_2D_F1_Audio.csv</p> </td> <td> <p>9</p> </td> <td> <p>1</p> </td> <td> <p>2</p> </td> <td> <p>1</p> </td> </tr> <tr> <td> <p>P009_T2_2D_F1_Audio.csv</p> </td> <td> <p>9</p> </td> <td> <p>2</p> </td> <td> <p>2</p> </td> <td> <p>1</p> </td> </tr> <tr> <td> <p>P009_T3_2D_F1_Audio.csv</p> </td> <td> <p>9</p> </td> <td> <p>3</p> </td> <td> <p>2</p> </td> <td> <p>1</p> </td> </tr> <tr> <td> <p>P009_T4_2D_F1_Audio.csv</p> </td> <td> <p>9</p> </td> <td> <p>4</p> </td> <td> <p>2</p> </td> <td> <p>1</p> </td> </tr> <tr> <td> <p>P009_T1_3D_F2_Audio.csv</p> </td> <td> <p>9</p> </td> <td> <p>1</p> </td> <td> <p>3</p> </td> <td> <p>2</p> </td> </tr> <tr> <td> <p>P009_T2_3D_F2_Audio.csv</p> </td> <td> <p>9</p> </td> <td> <p>2</p> </td> <td> <p>3</p> </td> <td> <p>2</p> </td> </tr> <tr> <td> <p>P009_T3_3D_F2_Audio.csv</p> </td> <td> <p>9</p> </td> <td> <p>3</p> </td> <td> <p>3</p> </td> <td> <p>2</p> </td> </tr> <tr> <td> <p>P009_T4_3D_F2_Audio.csv</p> </td> <td> <p>9</p> </td> <td> <p>4</p> </td> <td> <p>3</p> </td> <td> <p>2</p> </td> </tr> <tr> <td> <p>P010_T1_2D_F2_Audio.csv</p> </td> <td> <p>10</p> </td> <td> <p>1</p> </td> <td> <p>2</p> </td> <td> <p>2</p> </td> </tr> <tr> <td> <p>P010_T2_2D_F2_Audio.csv</p> </td> <td> <p>10</p> </td> <td> <p>2</p> </td> <td> <p>2</p> </td> <td> <p>2</p> </td> </tr> <tr> <td> <p>P010_T3_2D_F2_Audio.csv</p> </td> <td> <p>10</p> </td> <td> <p>3</p> </td> <td> <p>2</p> </td> <td> <p>2</p> </td> </tr> <tr> <td> <p>P010_T4_2D_F2_Audio.csv</p> </td> <td> <p>10</p> </td> <td> <p>4</p> </td> <td> <p>2</p> </td> <td> <p>2</p> </td> </tr> <tr> <td> <p>P010_T1_3D_F1_Audio.csv</p> </td> <td> <p>10</p> </td> <td> <p>1</p> </td> <td> <p>3</p> </td> <td> <p>1</p> </td> </tr> <tr> <td> <p>P010_T2_3D_F1_Audio.csv</p> </td> <td> <p>10</p> </td> <td> <p>2</p> </td> <td> <p>3</p> </td> <td> <p>1</p> </td> </tr> <tr> <td> <p>P010_T3_3D_F1_Audio.csv</p> </td> <td> <p>10</p> </td> <td> <p>3</p> </td> <td> <p>3</p> </td> <td> <p>1</p> </td> </tr> <tr> <td> <p>P010_T4_3D_F1_Audio.csv</p> </td> <td> <p>10</p> </td> <td> <p>4</p> </td> <td> <p>3</p> </td> <td> <p>1</p> </td> </tr> <tr> <td> <p>P011_T1_2D_F4_Audio.csv</p> </td> <td> <p>11</p> </td> <td> <p>1</p> </td> <td> <p>2</p> </td> <td> <p>4</p> </td> </tr> <tr> <td> <p>P011_T2_2D_F4_Audio.csv</p> </td> <td> <p>11</p> </td> <td> <p>2</p> </td> <td> <p>2</p> </td> <td> <p>4</p> </td> </tr> <tr> <td> <p>P011_T3_2D_F4_Audio.csv</p> </td> <td> <p>11</p> </td> <td> <p>3</p> </td> <td> <p>2</p> </td> <td> <p>4</p> </td> </tr> <tr> <td> <p>P011_T4_2D_F4_Audio.csv</p> </td> <td> <p>11</p> </td> <td> <p>4</p> </td> <td> <p>2</p> </td> <td> <p>4</p> </td> </tr> <tr> <td> <p>P011_T1_3D_F3_Audio.csv</p> </td> <td> <p>11</p> </td> <td> <p>1</p> </td> <td> <p>3</p> </td> <td> <p>3</p> </td> </tr> <tr> <td> <p>P011_T2_3D_F3_Audio.csv</p> </td> <td> <p>11</p> </td> <td> <p>2</p> </td> <td> <p>3</p> </td> <td> <p>3</p> </td> </tr> <tr> <td> <p>P011_T3_3D_F3_Audio.csv</p> </td> <td> <p>11</p> </td> <td> <p>3</p> </td> <td> <p>3</p> </td> <td> <p>3</p> </td> </tr> <tr> <td> <p>P011_T4_3D_F3_Audio.csv</p> </td> <td> <p>11</p> </td> <td> <p>4</p> </td> <td> <p>3</p> </td> <td> <p>3</p> </td> </tr> </tbody> </table> <p>Table 5: content and file structure of Audio_Data.zip.</p> <p> </p> <ol> <li> </li> </ol> <p>The results of 5 standardized questionnaires are presented: the Makransky Multimodal Presence Questionnaire (the social presence subset or MPQS and the physical presence subset or MPQP), the Witmer Presence Questionnaire (WPQ), the Immersive Tendencies Questionnaire (ITQ), the Musical Sophistication Index (MSI), and the Sense of Musical Agency Questionnaire (SOMA). Additionally, demographic data (DQ) were collected, along with some open questions (OQ). The answers to the questionnaires are organized in 3 csv files: ‘MB.csv’, containing answers to the questionnaires presented before the first session (ITQ, MSI and some DQ); and ‘C1.csv’ and ‘C2.csv’, containing the answers to the questionnaires presented before and after each session (MPQS, MPQP, WPQ, SOMA, some DQ and some OQ) in the first and second condition, respectively. A csv file named ‘Legend.csv’ indicates the codes of all the questions, and where the answers to the questions can be found (see Table 6). Since some participants answered in Dutch, all responses were translated to English before adding them to the repository.</p> <p> </p> <table> <tbody> <tr> <td> <p>File Name</p> </td> <td> <p>Questionnaire_Data.zip</p> </td> </tr> <tr> <td> <p>Content</p> </td> </tr> <tr> <td> <p>C1.csv</p> </td> <td> <p>questionnaires and answeres of condition 1</p> </td> </tr> <tr> <td> <p>C2.csv</p> </td> <td> <p>questionnaires and answeres of condition 2</p> </td> </tr> <tr> <td> <p>MB.csv</p> </td> <td> <p>questionnaires and answers related to musical background</p> </td> </tr> <tr> <td> <p>Legend.csv</p> </td> <td> <p>questions and question codes</p> </td> </tr> </tbody> </table> <p>Table 6: content and file structure of Questionnaire_Data.zip.</p> <p> </p> <ol> <li> </li> </ol> <p>The scores which were played by both the avatar and the participants are provided, with the correct bowings and articulations. The fragment and the violin section are indicated in the filename. E.g., ‘First_Violin_F2.pdf’, contains the scores of fragment F2, as played by the first violins. See Table 7 for an overview of the file structure and the content.</p> <p> </p> <table> <tbody> <tr> <td> <p>File Name</p> </td> <td> <p>Scores.zip</p> </td> </tr> <tr> <td> <p>Content</p> </td> </tr> <tr> <td> <p>First_Violin_F1.pdf</p> </td> <td> <p>Fragment F1 as played by the first violin section</p> </td> </tr> <tr> <td> <p>First_Violin_F2.pdf</p> </td> <td> <p>Fragment F2 as played by the first violin section</p> </td> </tr> <tr> <td> <p>Second_Violin_F3.pdf</p> </td> <td> <p>Fragment F3 as played by the second violin section</p> </td> </tr> <tr> <td> <p>Second_Violin_F4.pdf</p> </td> <td> <p>Fragment F4 as played by the second violin section</p> </td> </tr> </tbody> </table> <p>Table 7: content and file structure of Scores.zip.</p> <p> </p> <ol> <li> </li> </ol> <p>This directory contains files in csv format with labeled MoCap Data, including data labels. Every column is a data stream from a marker. Every marker has 3 data streams, referring to the x, y, and z coordinates of the marker position. In addition, the violin (3-4 markers) and the violin bow (3 markers) are labelled as well. One data file per avatar (First Violin or Second Violin) is presented. Additionally, the data type (MoCap), and the performed fragment (F1-F4) are given in the filename. An example of a file name is e.g., ‘First_Violin_F2_MoCap.csv’ for an avatar (see Table 8).</p> <p>Additionally, the directory contains files in csv format with joint angles, including data labels. Every column is a data stream from a joint. Every joint has a varying number of data streams, depending on the calculated angles. In addition to joint angles, the angles of the instrument relative to the body are given as well, the distances of the bow to the bridge, and the distances of the bow to the strings, respectively. One data file per avatar (First Violin or Second Violin) is presented. Additionally, the data type (JointAngles), and the performed fragment (F1-F4) are given in the filename. An example of a file name is e.g., ‘Second_Violin_F3_JointAngles.csv’ for an avatar (see Table 8).</p> <p>Finally, this directory contains wav-files per avatar (First Violin or Second Violin). Audio files contain 2 tracks (left and right microphone), i.e., they are stereo recordings. Additionally, the data type (Audio), and the performed fragment (F1-F4) are given in the filename. An example of a file name is e.g., ‘First_Violin_F1_Audio.wav’ for an avatar (see Table 8).</p> <p> </p> <table> <tbody> <tr> <td> <p>File Name</p> </td> <td> <p>Avatar_Data.zip</p> </td> </tr> <tr> <td> <p>Content</p> </td> </tr> <tr> <td> <p>First_Violin_F1_MoCap.csv</p> </td> <td> <p>MoCap data of the first violin avatar, playing fragment F1</p> </td> </tr> <tr> <td> <p>First_Violin_F1_JointAngles.csv</p> </td> <td> <p>Joint angle data of the first violin avatar, playing fragment F1</p> </td> </tr> <tr> <td> <p>First_Violin_F1_Audio.wav</p> </td> <td> <p>Audio data of the first violin avatar, playing fragment F1</p> </td> </tr> <tr> <td> <p>First_Violin_F2_MoCap.csv</p> </td> <td> <p>MoCap data of the first violin avatar, playing fragment F2</p> </td> </tr> <tr> <td> <p>First_Violin_F2_JointAngles.csv</p> </td> <td> <p>Joint angle data of the first violin avatar, playing fragment F2</p> </td> </tr> <tr> <td> <p>First_Violin_F2_Audio.wav</p> </td> <td> <p>Audio data of the first violin avatar, playing fragment F2</p> </td> </tr> <tr> <td> <p>Second_Violin_F3_MoCap.csv</p> </td> <td> <p>MoCap data of the second violin avatar, playing fragment F3</p> </td> </tr> <tr> <td> <p>Second_Violin_F3_JointAngles.csv</p> </td> <td> <p>Joint angle data of the second violin avatar, playing fragment F3</p> </td> </tr> <tr> <td> <p>Second_Violin_F3_Audio.wav</p> </td> <td> <p>Audio data of the second violin avatar, playing fragment F3</p> </td> </tr> <tr> <td> <p>Second_Violin_F4_MoCap.csv</p> </td> <td> <p>MoCap data of the second violin avatar, playing fragment F4</p> </td> </tr> <tr> <td> <p>Second_Violin_F4_JointAngles.csv</p> </td> <td> <p>Joint angle data of the second violin avatar, playing fragment F4</p> </td> </tr> <tr> <td> <p>Second_Violin_F4_Audio.wav</p> </td> <td> <p>Audio data of the second violin avatar, playing fragment F4</p> </td> </tr> </tbody> </table> <p>Table 8: content and file structure of Avatar_Data.zip.</p> <p> </p>
Neural ensemble reactivation in REM and SWS coordinate with muscle activity to promote rapid motor skill learning
<p>Neural activity patterns of recent experiences are reactivated during sleep in structures critical for memory storage, including hippocampus and neocortex. This reactivation process is thought to aid memory consolidation. Although synaptic rearrangement dynamics following learning involve an interplay between slow-wave sleep (SWS) and rapid eye movement sleep (REM), most physiological evidence implicates SWS directly following experience as a preferred window for reactivation. Here we show that reactivation occurs in both REM and SWS, and that coordination of REM and SWS activation on the same day is associated with rapid learning of a motor skill. We performed 6-hour recordings from cells in rats' motor cortex as they were trained daily on a skilled reaching task. In addition to SWS following training, reactivation occurred in REM, primarily during the pre-task rest period, and REM and SWS reactivation occurred on the same day in rats that acquired the skill rapidly. Both pre-task REM and posttask SWS activation were coordinated with muscle activity during sleep, suggesting a functional role for reactivation in skill learning. Our results provide the first demonstration that reactivation in REM sleep occurs during motor skill learning, and that coordinated reactivation in both sleep states on the same day, although at different times, is beneficial for skill learning.</p>
Local and global predictors of synapse elimination during motor learning
<p>During learning, synaptic connections between excitatory neurons in the brain display considerable dynamism, with new connections being added and old connections eliminated. Synapse elimination offers an opportunity to understand the features of synapses that the brain deems dispensable. However, with limited observations of synaptic activity and plasticity <i>in vivo</i>, the features of synapses subjected to elimination remain poorly understood. Here, we examined the functional basis of synapse elimination in the apical dendrites of L2/3 neurons in the primary motor cortex throughout motor learning. We found no evidence that synapse elimination is facilitated by other local forms of plasticity, or that they reflect less active inputs. Instead, eliminated synapses display asynchronous activity with nearby synapses, suggesting functional synaptic clustering is a critical component of synapse survival. In addition, eliminated synapses show delayed activity timing with respect to postsynaptic output. Thus, synaptic inputs that fail to be co-active with their neighboring synapses or are mistimed with neuronal output are targeted for elimination.</p>
Cholecystokinin facilitates motor skill learning by modulating neuroplasticity in the motor cortex
<p>Cholecystokinin (CCK) is an essential modulator for neuroplasticity in sensory and emotional domains. Here, we investigated the role of CCK in motor learning using a single pellet reaching task in mice. Mice with a knockout of <em>cck</em> gene (CCK<sup>-/-</sup>) or blockade of CCK-B receptor (CCKBR) showed defective motor learning ability; the success rate of retrieving reward remained at the baseline level compared to the wildtype mice with significantly increased success rate. We observed no long-term potentiation (LTP) upon high-frequency stimulation (HFS) in the motor cortex of CCK<sup>-/-</sup> mice, indicating a possible association between motor learning deficiency and neuronal plasticity in the motor cortex. In vivo calcium imaging demonstrated that the deficiency of CCK signalling disrupted the refinement of population neuronal activity in the motor cortex during motor skill training. Anatomical tracing revealed direct projections from CCK-expressing neurons in the rhinal cortex to the motor cortex. Inactivating the CCK neurons in the rhinal cortex using chemogenetic methods significantly suppressed motor learning, and intraperitoneal application of CCK4, a tetrapeptide CCK agonist, rescued the motor learning deficits of CCK<sup>-/-</sup> mice. In summary, our results suggest that CCK, which could be provided from the rhinal cortex, enables neuroplasticity in the motor cortex leading to motor skill learning.</p>
Tabular data of striatal motor learning experiments in LRRK2 KI mice
<p>The tabular data of the behavioral experiments described in the manuscript titled "<strong>R1441C and G2019S LRRK2 knockin mice have distinct striatal molecular, physiological, and behavioral alterations."</strong></p>
Cell-type specific responses to associative learning in the primary motor cortex
<p>The primary motor cortex (M1) is known to be a critical site for movement initiation and motor learning. Surprisingly, it has also been shown to possess reward-related activity, presumably to facilitate reward-based learning of new movements. However, whether reward-related signals are represented among different cell types in M1, and whether their response properties change after cue-reward conditioning remains unclear. Here, we performed longitudinal <i>in vivo</i> two-photon Ca<sup>2+</sup> imaging to monitor the activity of different neuronal cell types in M1 while mice engaged in a classical conditioning task. Our results demonstrate that most of the major neuronal cell types in M1 showed robust but differential responses to both cue and reward stimuli, and their response properties undergo cell-type specific modifications after associative learning. PV-INs' responses became more reliable to the cue stimulus, while VIP-INs' responses became more reliable to the reward stimulus. PNs only showed robust response to the novel reward stimulus, and they habituated to it after associative learning. Lastly, SOM-IN responses emerged and became more reliable to both conditioned cue and reward stimuli after conditioning. These observations suggest that cue- and reward-related signals are represented among different neuronal cell types in M1, and the distinct modifications they undergo during associative learning could be essential in triggering different aspects of local circuit reorganization in M1 during reward-based motor skill learning.</p>
Dataset for "A Double Dissociation between Savings and Long-Term Memory in Motor Learning"
<p>Data for "A Double Dissociation between Savings and Long-Term Memory in Motor Learning"<br> <br> For analysis code and any updates, please visit https://github.com/AlkisMH/Savings_vs_Long_Term_Memory<br> <br> For any questions, please contact Alkis Hadjiosif (alkis [at] seas [dot] harvard [dot] edu; ahadjiosif [at] gmail [dot] com)</p> <p>These .mat files contain data collected for Experiments 1-4 and S1, used to reproduce Figures 1-6 and S1 in the paper. Data for each experiment are in the form of a Matlab structure, with each field consisting of a [Num_trials x Num_participants] matrix.</p> <p>The fields are:</p> <p><strong>TN:</strong> Trial Number</p> <p><strong>VF:</strong> Whether visual feedback was given during the trial (1: online visual feedback; 2: no visual feedback)</p> <p><strong>Rotation:</strong> The visuomotor rotation imposed on each trial (in degrees). CCW is positive, CW is negative.</p> <p><strong>Wait:</strong> Whether, right before the trial, a wait time was imposed (1) or not (0). Trials immediately following breaks are indicated by (2).</p> <p><strong>Instruction:</strong> Whether an instruction was given for the trial (1) or not (0). Only present in Experiment 3. Note that Experiment 3 includes:</p> <p>-> Two specific instruction trials during learning and and two during relearning, before and after the 1-minute wait ("Move your hand to the center of the target","Move your hand to the far end of the target")</p> <p>-> Six random instruction trials before initial learning and six before relearning ("Move your hand to the left/right/near/far end of the target"), to familiarize participants with the instruction process</p> <p><strong>ITI:</strong> Time (in seconds) since the last trial (regardless of direction).</p> <p><strong>theta_target:</strong> Target direction (in degrees) relative to the 12 o'clock position. Only included for Experiment 4: target is always at 12 o'clock for Experiments 1-3.</p> <p><strong>theta:</strong> = reaching direction relative to theta_target, measured 150ms into the movement. Note that this is flipped based on the rotation sign so that adaptation towards the imposed visuomotor rotation is always positive.</p> <p><strong>theta_end:</strong> = reaching direction relative to theta_target, measured at the end of movement. Only included for Experiment 4: it is to be used for the no visual feedback blocks in Experiment 4.</p> <p>Note for <strong>Experiment 4</strong>: the last two blocks (last 114 trials) were done on day 2.</p>
Strategy-based motor learning decreases the post-movement β power
<p>This publication contains the following data, in connection with the manuscript entitled <em>"Strategy-based motor learning decreases the post-movement </em><em>β</em><em> power"</em>, by <em>Korka et al. (2023)</em>:</p> <p>1- raw EEG and kinematics data files (for these, please see Version 1);</p> <p>2 - task and stimulation files;</p> <p>3 - analysis files.</p> <p><strong>Task and stimulation files</strong>:</p> <p>- 2 experimental file formats, each corresponding to one condition: .exp</p> <p>- each .exp file calls on different scenarios (.sce) files:</p> <ul> <li>“train1” and “train2” correspond to task instructions regarding the experimental set-up and the general task (please see instructions in the <em>Supplementary Material</em> associated with the paper).</li> <li>“COMPENSATE_DEMO” and “IGNORE_DEMO” correspond to demonstrating the rotation in each condition; the rotation is constantly switched on for the duration of these training blocks.</li> <li>“COMPENSATE_TRAIN” and “IGNORE_TRAIN” correspond to training for the actual experiment including a probabilistic rotation (i.e., same as in the experimental blocks, but fewer trials).</li> <li>Finally, “COMPENSATE” and “IGNORE” scenarios represent the actual experimental blocks for each condition.</li> </ul> <p><strong>Analysis files</strong></p> <p>-main analysis files:</p> <ul> <li>ecEEG_kinematics – processes the relevant kinematics parameters by reading in the raw kinematics datafiles.</li> <li>ecEEG_eeg_preproc – reads in and preprocesses EEG raw data files; main steps steps include: filtering, segmenting the data into epochs (and matching each epoch with the kinematics data storing movement parameters), rejection of epochs containing artifacts based on visual inspection, rejection of channels containing extreme amplitudes, Independent Component Analysis (ICA) for detecting eye- and muscle-related artifacts, interpolation of channels, automated trial rejection after ICA.</li> <li>ecEEG_eeg_freq and ecEEG_eeg_freq_baseline – computes time-frequency analyses for the PMBR data and baseline (inter-trial-interval) data, respectively + re-references data to a common average.</li> <li>ecEEG_eeg_freq_2 – excludes marked trials based on task performance, kinematics parameters, and EEG rejection criteria + organizes the data from all participants into a unique structure (for PMBR and ITI data, in turn).</li> <li>ecEEG_change_plots – produces the EEG figures displayed in the manuscript + exports data for statistics.</li> </ul> <p>- auxiliary analysis files:</p> <ul> <li>ecEEG_ica – if called on, computes the ICA</li> <li>VMB_neighbours – defines neighbors for each channel, which are necessary for interpolation</li> <li>ecEEG_trialreject – automated trial rejection based on kurtosis</li> <li>topoplotbasic_ml2012_varchans – called on for creating the topographic plots when visualizing the ICA components</li> <li>ecEEG_eeg_trial_rej_15thresh – used to determine the number of trials in which participants did not follow the task instructions (see manuscript for details)</li> </ul>
Supplementary Videos for MEMTrack: A deep learning-based approach to micro- motor tracking in dense fibrous environments
<ul><li><strong>Video S1: </strong>Video shows the movement of representative bacterial biomotors from each of the four motility subpopulations in collagen. Video slowed down by 2x</li><li><strong>Video S2:</strong> Videos show ground truth annotation (red) by manual tracking and MEMTrack-enabled automated detection and tracking of bacteria in collagen. All scale bars are 20 µm and timestamp indicates s:ms. Video slowed down by 2x</li><li><strong>Video S3: </strong>Videos show ground truth annotation (red) by manual tracking and MEMTrack-enabled automated detection and tracking of bacteria in aqueous media. All scale bars are 20 µm and timestamp indicates s:ms. Video slowed down by 2x</li></ul>
Novel Brain Signal Feedback Paradigm to Enhance Motor Learning After Stroke
ClinicalTrials.gov study NCT02856035. IPD Sharing: YES. Countries: 1. Publications: 1.
Modulating Interaction of Motor Learning Networks in Rehabilitation of Stroke
ClinicalTrials.gov study NCT03086551. IPD Sharing: NO. Countries: 1. Publications: 3.
Role of Sleep on Motor Learning in Parkinson's Disease and Healthy Older Adults
ClinicalTrials.gov study NCT04144283. IPD Sharing: YES. Countries: 1. Publications: 6.
Motor Learning in Individuals With Lower Limb Loss and Chronic Diabetes
ClinicalTrials.gov study NCT03989063. IPD Sharing: NO. Countries: 1. Publications: 1.
Robot-Assisted Therapy and Motor Learning: An Active Learning Program for Stroke
ClinicalTrials.gov study NCT02747433. IPD Sharing: NO. Countries: 1. Publications: 1.
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.