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55 results for “Dataset augmentation”
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> </td> </tr> <tr> <td> <p>P002_T1_3D_F2_MoCap.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_MoCap.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_MoCap.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_MoCap.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_MoCap.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_MoCap.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_MoCap.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_MoCap.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_MoCap.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_MoCap.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_MoCap.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_MoCap.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_MoCap.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_MoCap.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_MoCap.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_MoCap.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_MoCap.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_MoCap.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_MoCap.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_MoCap.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_MoCap.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_MoCap.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_MoCap.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_MoCap.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_MoCap.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_MoCap.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_MoCap.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_MoCap.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_MoCap.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_MoCap.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_MoCap.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_MoCap.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_MoCap.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_MoCap.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_MoCap.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_MoCap.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_MoCap.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_MoCap.csv</p> </td> <td> 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<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>
Creating speech zones with self-distributing acoustic swarms (Augmented Dataset Part 1 of 2)
<p>Datasets used in the paper: "Creating speech zones with self-distributing acoustic swarms"</p> <p>This deposit contains the <strong>first</strong> part of the augmented dataset containing simulated and real world collected data. The datasets contains 18000 training mixtures of 3-5 speakers, of which 6000 are simulated using PyRoomAcoustics, 6000 are created from synchronized real world recordings in an anechoic chamber, and 6000 are created from synchronized recordings in ordinary reverberant rooms.</p> <p>It also includes a validation set of 500 mixtures from reverberant rooms, and a testing set of 1000 mixtures from reverberant rooms.</p> <p>The source sounds are various utterances from the VCTK dataset. For real world data, the utterances are played over a Rokono Bass+ Mini Speaker. The recordings are captured from an array of 7 microphones, as they are recorded by our robotic swarm as it is distributed across the table. The recorded audio in the real world has been subjected to audio compression and decompression using the Opus Codec to enable multiple simultaneous streams.</p> <p>You must download <strong>both</strong> the first and the second part of this dataset in order to use it properly.</p> <p>To uncompress the two datasets, download both and execute:</p> <p>```cat *.tar.gz.* | tar xvfz -```</p> <p>Please see the Readme for more information. Please see related identifiers for other datasets.</p>
Creating speech zones with self-distributing acoustic swarms (Augmented Dataset Part 2 of 2)
<p>Datasets used in the paper: "Creating speech zones with self-distributing acoustic swarms"</p> <p>This deposit contains the <strong>second</strong> part of the augmented dataset containing simulated and real world collected data. The datasets contains 18000 training mixtures of 3-5 speakers, of which 6000 are simulated using PyRoomAcoustics, 6000 are created from synchronized real world recordings in an anechoic chamber, and 6000 are created from synchronized recordings in ordinary reverberant rooms.</p> <p>It also includes a validation set of 500 mixtures from reverberant rooms, and a testing set of 1000 mixtures from reverberant rooms.</p> <p>The source sounds are various utterances from the VCTK dataset. For real world data, the utterances are played over a Rokono Bass+ Mini Speaker. The recordings are captured from an array of 7 microphones, as they are recorded by our robotic swarm as it is distributed across the table. The recorded audio in the real world has been subjected to audio compression and decompression using the Opus Codec to enable multiple simultaneous streams.</p> <p>You must download <strong>both</strong> the first and the second part of this dataset in order to use it properly.</p> <p>To uncompress the two datasets, download both and execute:</p> <p>```cat *.tar.gz.* | tar xvfz -```</p> <p>Please see the Readme for more information. Please see related identifiers for other datasets.</p>
PoqueiraAR: Dataset and Metrics from Augmented Reality App in Barranco del Poqueira in the Alpujarra Granadina
<p>This dataset is related to an Augmented Reality application implemented in the Sierra Nevada National Park, specifically in the Barranco del Poqueira. This application has been designed to enhance the experience of visitors walking a circular path that connects the three villages of the ravine: Pampaneira, Bubión and Capileira, offering seven points of interest.</p><p>The dataset covers the period from June 2022 to October 2023 and includes information on the number of downloads, download dates, types of devices used to download the application, geographic origin of the devices, attractions visited and most demanded audiovisual resources.</p><p>These data provide an objective view of the effectiveness of the application in improving the visitor experience and its impact on the tourism promotion of Barranco del Poqueira. In addition, they provide relevant information about user preferences, which can guide future updates and improvements in the Augmented Reality application. The application contributes to enrich the visit to these villages and provides useful data for the sustainable management of tourism in this region of Sierra Nevada.</p>
POLAR-Sim: Augmenting NASA's POLAR dataset for data-driven lunar perception and rover simulation
Open the record for dataset details and reuse information.
Data Augmentation at the LHC through Analysis-specific Fast Simulation with Deep Learning: W+jet large test dataset
<p>W+jet events at generator and reconstruction level, used to train analysis-specific generative models.</p> <p>Events are represented as an array of relevant high-level features. Reco objects are matched to Gen objects and a minimal selection is applied to define the generator support in the N-dim space identified by the input features.</p> <p>About 2M events, used for large-scale testing</p> <p>Details in https://arxiv.org/abs/2010.01835</p>
Datasets related to the manuscript 'Hypothalamic deep brain stimulation augments walking after spinal cord injury'
<p>The deposited datasets consist in the following:<br>Supplementary Table 1: All kinematics data and statistical analyses</p> <p>Supplementary Table 2: All differential analyses and statistics for the quantification of transcriptional activity (cFos) and spinal cord-projecting neurons (Rabies) from the contralesional (right) lateral hypothalamus (n = 3 per group).</p> <p> </p>
Data for: A comprehensive dataset of forest above-ground biomass from field observations, machine learning and topographically augmented allometric models over the Kashmir Himalaya
<p>The repository contains observed Above Ground Biomass (AGB) estimates at about 275 sample plots chosen for AGB assessment in the forests of Kashmir Himalaya. The AGB is assessed as a fucntion of dbh using various allometric equations developed specifically for the region. It also contains the AGB for years 1978, 1990, 2000, 2010 and 2021 predicted using topographcally augmeneted multivariate regression model. The extent of forest, delineated using on-screen digitization using Landsat and Sentinel image collection at decadal scale is also provided for the years 1978, 1990, 2000, 2010 and 2021.</p>
Datasets for "effects of augmentation type"
<p>Datasets for ablation study of "effects of augmentation type"</p> <p>"train_no_decl.jsonl", "train_no_api.jsonl" and "train_no_control.jsonl" represent equal-sized datasets that do not contain transformations of declaration, API and control structure, respectively. </p>
Evaluation Dataset: SemOI2 – Building Adaptive And Cost-Effective Recognition Applications With Semantic Augmentation
<p>Evaluation dataset for the paper "SemOI2 – Building Adaptive And Cost-Effective Recognition Applications With Semantic Augmentation", presented at the AAAI-Make conference 2022, to be published in: <em>A. Martin, K. Hinkelmann, H.-G. Fill, A. Gerber, D. Lenat, R. Stolle, F. van Harmelen (Eds.), Proceedings of the AAAI 2022 Spring Symposium on Machine Learning and Knowledge Engineering for Hybrid Intelligence (AAAI-MAKE 2022), Stanford University, Palo Alto, California, USA, March 21–23, 2022.</em></p>
Variable Misuse tool: Dataset for data augmentation (6)
<p>Dataset used for data augmentation in the training phase of the Variable Misuse tool. It contains some source code files extracted from third-party repositories.</p>
Variable Misuse tool: Dataset for data augmentation (4)
<p>Dataset used for data augmentation in the training phase of the Variable Misuse tool. It contains some source code files extracted from third-party repositories.</p>
Training dataset for "A deep learned nanowire segmentation model using synthetic data augmentation"
<p>This image dataset contains synthetic structure images used for training the deep-learning based nanowire segmentation model presented in our work "A deep learned nanowire segmentation model using synthetic data augmentation" to be published in <em>npj Computational materials. </em>Detailed information can be found in the corresponding article.</p>
LFW-Beautified: A Dataset of Face Images with Beautification and Augmented Reality Filters
<p><strong>LFW-Beautified: A Dataset of Face Images with Beautification and Augmented Reality Filters</strong></p> <p><strong>Usage</strong></p> <ul> <li>Download the compressed files (13 in total) and uncompress them</li> <li><strong>Please cite reference 1) below in your publications if you make use of the data of this repository</strong></li> </ul> <p><strong>People & Contact</strong></p> <ul> <li><a href="http://wiki.hh.se/caisr/index.php/Fernando_Alonso-Fernandez">Fernando Alonso-Fernandez</a> (contact person).</li> </ul> <p><strong>References</strong></p> <ol> <li>Hedman, P., Skepetzis, V., Hernandez-Diaz, K., Bigun, J., Alonso-Fernandez, F., "On the Effect of Selfie Beautification Filters on Face Detection and Recognition" <a href="https://github.com/HalmstadUniversityBiometrics/LFW-Beautified/blob/main">https://arxiv.org/abs/2110.08934</a></li> <li>Hedman, P., Skepetzis, V., The Effect of Beautification Filters on Image Recognition: "Are filtered social media images viable Open Source Intelligence?" Master Thesis at Halmstad University, Sweden (Master’s Programme in Network Forensics) <a href="http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-44799">http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-44799</a></li> </ol> <p> </p>
Augmentated DataSet
<p>Correspond to dataset created by researchers of UBB and UCSC members of the SOMOS group research. The data were collected and labeled for us from 2015. Are available in non-augmented and augmented version using Easy Data Augmentation.</p> <p> </p> <div> <table> <tbody> <tr> <td> <p><strong>DataSet</strong></p> </td> <td> <p><strong>Description</strong></p> </td> <td> <p><strong>Word count (original)</strong></p> </td> </tr> <tr> <td>October18</td> <td> <p>Comments related to October 18 social outbreak in Chile.</p> </td> <td> <p>3,336</p> </td> </tr> <tr> <td> <p>Aggressiveness</p> </td> <td> <p>Aggressive comments.</p> </td> <td> <p>33,143</p> </td> </tr> <tr> <td> <p>Teaching Survey (Emotion)</p> </td> <td> <p>Emotional expressions found in feedback from teaching survey.</p> </td> <td> <p> </p> <p>8,645</p> <br> <p> </p> <p> </p> </td> </tr> <tr> <td> <p>Teaching Survey (Aggressiveness)</p> </td> <td> <p>Aggressive comments found in feedback from teaching survey.</p> </td> </tr> <tr> <td> <p>Teaching Survey (Polarity)</p> </td> <td> <p>Good and bad comments found in feedback from teaching survey.</p> </td> </tr> <tr> <td> <p>Teaching Survey (Seriousness)</p> </td> <td> <p>Serious comments found in feedback from teaching survey.</p> </td> </tr> <tr> <td> <p>Headlines newspapers</p> </td> <td> <p>Headlines newspapers labelled as good or bad news.</p> </td> <td> <p>24,245</p> </td> </tr> <tr> <td> <p>Gender-Based Violence</p> </td> <td> <p>Lyrics songs labelled as contains or not contain gender violence.</p> </td> <td> <p>44,024</p> </td> </tr> </tbody> </table> </div>
[Data augmentation in a TTL] - Fictive dataset (27.5M) with up to 5k reactions per template // (13'953 template extracted from USPTO-FULL IBM version)
<p>Full generated fictive dataset, containing 27.5M reactions with up to 5000 reactions per radius 1 reaction template (13'953 reaction templates from USPTO-full, IBM version).</p> <p>Title of the manuscript:</p> <p>"Data augmentation in a Triple Transformer Loop retrosynthesis model"</p> <p>Abstract: </p> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div dir="auto"> <div>Reactions in the US Patent Office (USPTO) are biased towards a few over-represented reaction types, which potentially limits its usefulness for computer-assisted synthesis planning (CASP). To obtain an equilibrated dataset, we applied retrosynthesis templates to USPTO molecules as products (P) to generate starting materials (SM). We then used transformer T2 from our recently reported triple transformer loop (TTL) retrosynthesis model to predict reagents (R) for the SM®P reaction. Finally, we validated the prediction by requesting a high confidence prediction (>95%) for the prediction of P from SM+R by TTL transformer T3. We generated up to 5,000 reactions per template, resulting in 27.5 million validated fictive reactions covering the chemical space of the original UPSTO dataset. To exemplify the use of this dataset, we show that a single-step retrosynthesis transformer model trained with a template equilibrated subset of 1,097,374 fictive reactions outperforms the corresponding model trained on USPTO reactions only.</div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> <div></div>
Dataset: Raiders of the Lost Kek: 3.5 Years of Augmented 4chan Posts from the Politically Incorrect Board
<p>This is the dataset released with the <a href="https://arxiv.org/abs/2001.07487">paper</a> titled: "Raiders of the Lost Kek: 3.5 Years of Augmented 4chan Posts from the Politically Incorrect Board".</p> <p>The dataset is a single <a href="http://ndjson.org/">Newline delimited JSON file</a>. Each line in the file consists of a JSON object which is a full 4chan /pol/ thread. The JSON objects contain all the key/values returned by the <a href="https://github.com/4chan/4chan-API/blob/master/pages/Threads.md">4chan API</a>, along with three additional keys (<em>entities, perspectives</em>, and <em>extracted_poster_id</em>).</p> <p>For each JSON object we complement the data with the list of the named entities we detect for each post, using the <a href="https://spacy.io/api/annotation#named-entities">spaCy </a>Python library. In addition, for each post we add scores returned by the Google’s <a href="https://www.perspectiveapi.com/#/home">Perspective API</a>, and more specifically seven scores in the [0; 1] interval.</p> <p>For the detailed description of every <em>key </em>in the JSON structure, along with the type of the <em>value</em>, please read the readme.pdf file provided with this dataset.</p> <p>If you find our dataset useful, please cite our paper:</p> <blockquote> <pre>@article{papasavva2020raiders, title={Raiders of the Lost Kek: 3.5 Years of Augmented 4chan Posts from the Politically Incorrect Board}, author={Antonis Papasavva, Savvas Zannettou, Emiliano De Cristofaro, Gianluca Stringhini, Jeremy Blackburn}, journal={14th International AAAI Conference On Web And Social Media (ICWSM), 2020}, year={2020} }</pre> </blockquote> <p> </p> <p><strong>How to extract the data:</strong></p> <p>Note that the data is compressed. See the instructions below on how to extract the data:</p> <ul> <li><strong>Linux and Mac</strong></li> </ul> <p>Step 1: Open a terminal window and navigate to the path where the file <em>pol_0616-1119_labeled.tar.zst </em>is located.</p> <p>Step2: Run the following command:</p> <pre><code>unzstd pol_0616-1119_labeled.tar.zst</code></pre> <p>The above command will result in a file named <em>pol_0616-1119_labeled.tar. </em>(in the same directory)</p> <p>Step 3: Again, from your terminal window, run this command:</p> <pre><code>tar -xvf pol_0616-1119_labeled.tar</code></pre> <p>When the above command finishes, you will get (in the same directory) the extracted data - a file named <em>pol_062016-112019_labeled.ndjson.</em></p> <ul> <li><strong>Windows</strong></li> </ul> <p>There are many applications that can be used to extract this data on Windows available online. The authors cannot recommend specific applications. Note that the file is compressed twice so you will need to perform the data extraction twice - once on the downloaded file, and once on the file that was extracted from the downloaded file.</p> <p> </p> <p>Please do not hesitate to contact the author of this study in case you face any problem at: antonis.papasavva@ucl.ac.uk</p>
Dataset: Evaluating Mixed and Augmented Reality: A Systematic Literature Review (2009 – 2019)
<p>A data set of evaluations of mixed and augmented reality in 2009–2019.</p>
Variable Misuse tool: Dataset for data augmentation
<p>Dataset used for data augmentation in the training phase of the Variable Misuse tool. It contains some source code files extracted from third-party repositories.</p>
Variable Misuse tool: Dataset for data augmentation (5)
<p>Dataset used for data augmentation in the training phase of the Variable Misuse tool. It contains some source code files extracted from third-party repositories.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.