Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
135
datasets available to search
ShareScore release 0.9.0
Dataset results
135 results for “Imitation”
The IMITATOR benchmarks library 2.1: A benchmarks library for extended parametric timed automata
<p>We present here the IMITATOR benchmarks library 2.1: A benchmarks library for extended parametric timed automata</p> <p> </p> <p>We present two archives:</p> <p>- one (benchmarks.zip) with the models and the properties</p> <p>- one (full.zip) with the benchmarks and all the results: the expected results, generated PDF and graphics, and a whole standalone Web page (more or less equivalent to <a href="https://www.imitator.fr/static/library.html">www.imitator.fr/static/library.html</a>) summarizing all benchmarks</p> <p> </p> <p>See a full description in the TAP 2021 paper ("<a href="https://link.springer.com/10.1007/978-3-030-79379-1_3">A Benchmarks Library for Extended Parametric Timed Automata</a>")</p>
Evidence for individual vocal recognition in a pair-bonding poison frog, Ranitomeya imitator
<p>Individually distinctive vocalizations are widespread in nature, although the ability of receivers to discriminate these signals has only been explored through limited taxonomic and social lenses. Here, we asked whether anuran advertisement calls, typically studied for their role in territory defense and mate attraction, facilitate recognition and preferential association with partners in a pair-bonding poison frog (<em>Ranitomeya imitator</em>). Combining no- and two-stimulus choice playback experiments, we evaluated behavioral responses of females to male acoustic stimuli. Virgin females oriented to and approached speakers broadcasting male calls independent of caller identity, implying that females are generally attracted to male acoustic stimuli outside the context of a pair bond. When pair-bonded females were presented with calls of a mate and a stranger, they showed significant preference for calls of their mate. Moreover, behavioral responses varied with breeding status: females with eggs were faster to approach stimuli than females that were pair-bonded but did not currently have eggs. Our study suggests a potential role for individual vocal recognition in the formation and maintenance of pair bonds in a poison frog and raises new questions about how acoustic signals are perceived in the context of monogamy and biparental care.</p>
Demonstrations for imitation learning for the paper "Fitting parameters of linear dynamical systems to regularize forcing terms in Dynamical Movement Primitives"
<p>Demonstrations for the coathanger experiment in the paper "Fitting parameters of linear dynamical systems to regularize forcing terms in Dynamical Movement Primitives". https://elib.dlr.de/205110/</p>
Foraging behaviour data for sympatric Ateles geoffroyi, Alouatta palliata, and Cebus imitator
<p><span>Senses form the interface between animals and environments, and their form and function provide a window into the ecology of past and present species. However, research on the senses used during foraging (e.g. smell, vision, touch, taste) by wild terrestrial frugivores is sparse. Here, we combine 26,094 fruit foraging sequences recorded from three wild, sympatric primates (<em>Cebus imitator, Ateles geoffroyi, Alouatta palliata</em>) with data on within- and between-species variation in colour vision, olfaction, taste, and hand anatomy. We hypothesize that dietary and sensory specialization shape foraging behaviours. We find that frugivorous spider monkeys (<em>Ateles geoffroyi</em>) sniff fruits most often, that omnivorous capuchins (<em>Cebus imitator</em>), the species with the highest measure of manual dexterity, uses manual touch most often, and that main olfactory bulb volume is a better predictor of sniffing behaviour than nasal turbinate surface area. We also identify an interaction between colour vision phenotype and use of other senses. Controlling for species, dichromats sniff and bite fruits more often than trichromats, and trichromats use manual touch to evaluate cryptic fruits more often than dichromats. Our findings help reveal how dietary specialization and sensory variation shape foraging behaviours, and inform methods for investigating relationships between behaviour and anatomy.</span></p>
Vocal imitation of synthesised sounds varying in pitch, loudness and spectral centroid
<p>Dataset from the vocal imitation (production) task. Includes the audio stimuli, extracted audio features (for both stimuli and imitations) and extracted parameters, along with participant metadata. Please see the paper for further details. </p> <p>Details of fields in parameter_data.csv:</p> <p>Participant: index from 0-18</p> <p>sex: male/female</p> <p>singer: 1 if participant had been singer for > 5 years, 0 if not</p> <p>feature: feature that the parameter data was extracted for</p> <p>envelope: up/down for ramps, fast(5Hz)/slow(2Hz) for modulations</p> <p>fail: instances where the imitation failed to meet the criteria (see paper for details)</p> <p>rate: ratio of the modulation rate</p> <p>extent: extent of the modulation</p> <p>range: range of the ramp</p> <p>slope: slope of the ramp</p> <p>stimtype: type of stimulus, where single = single features, pitchamps = pitch & loudness combinations, pitchspecs = pitch & spectral centroid combinations</p> <p>stimlabel: label for each stimulus. The letter indicates the feature (p=pitch, a=loudness, s=spectral centroid) and the number indicates the envelope (1 = ramp down, 2 = ramp up, 3 = 5Hz modulation, 4 = 2Hz modulation)</p>
Figure 4 in A new species of Myrmecotypus Pickard-Cambridge spider (Araneae: Corinnidae: Castianeirinae) from the Bolivian orocline, imitating one of the world's most aggressive ants
Figure 4. Myrmecotypus rubrofemoratus new species: Paratype female (CBF). A) Dorsal. B) Lateral (Please note that most hairs are broken off due to storage in ethanol). Scale bar 1 mm.
Figure 3. Myrmecotypus rubrofemoratus new species, genitalia. A in A new species of Myrmecotypus Pickard-Cambridge spider (Araneae: Corinnidae: Castianeirinae) from the Bolivian orocline, imitating one of the world's most aggressive ants
Figure 3. Myrmecotypus rubrofemoratus new species, genitalia. A) Palp male holotype (IBSI-Ara 1507), ventral view. B–C) Epigyne female allotype (IBSI-Ara 1467). B) Ventral. C) Same, cleared.
Figure 5 in A new species of Myrmecotypus Pickard-Cambridge spider (Araneae: Corinnidae: Castianeirinae) from the Bolivian orocline, imitating one of the world's most aggressive ants
Figure 5. Comparison of ant-mimicking Myrmecotypus spider and potential ant model. A–B) Myrmecotypus rubrofemoratus new species female, habitus in life. A) Dorsal. B) Lateral. C–D) Potential ant model Camponotus femoratus (Fabricius, 1804). C) Dorsal. D) Lateral.
Figure 1 in A new species of Myrmecotypus Pickard-Cambridge spider (Araneae: Corinnidae: Castianeirinae) from the Bolivian orocline, imitating one of the world's most aggressive ants
Figure 1. Ecoregion distribution of Myrmecotypus rubrofemoratus new species, according to the regionalization by Navarro and Ferreira (2011). Collection location indicated by red circle, map produced with QGIS (version 2.14.3, http:// www.qgis.org/en/site).
Fig. 1 in A Second Finding Of Vercoia Interrupta Kim & Fujita, 2004 (Crustacea, Decapoda, Crangonidae), A Remarkable Shrimp Imitating Dead Snail Shells
Fig. 1. Vercoia interrupta Kim & Fujita, 2004: A–D, ovigerous female from Balicasag Island, Panglao, Bohol, Philippines; E–G, ovigerous female from Ponson Island, Pilar, Cebu, Philippines. A, recently preserved specimen, lateral view; B, same, dorsal view; C, living shrimp in situ, showing microhabitat; D, same, close-up; E, living shrimp (different individual) in situ, lateral view; F, same, dorsolateral view; G, same, anterodorsal view. (C–G, photographs by Guido Poppe).
Transcriptomes for Ranitomeya imitator and R. variabilis: Evidence for a Parabasalian Gut Symbiote in Egg-Feeding Poison Frog Tadpoles in Peru
<p>This dataset contains the assembled transcriptomes for our paper. The three assemblies are for <em>Ranitomeya imitator, R. variabilis, </em>and a merged assembly of the two species. For methodological details, see the published manuscript.</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> </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> <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_MoCap.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_MoCap.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_MoCap.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_MoCap.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_MoCap.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_MoCap.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_MoCap.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_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>
Evidence for individual vocal recognition in a pair-bonding poison frog, Ranitomeya imitator
Open the record for dataset details and reuse information.
Foraging behaviour data for sympatric Ateles geoffroyi, Alouatta palliata, and Cebus imitator
Open the record for dataset details and reuse information.
Data from: Do people imitate when making decisions? - evidence from a spatial prisoners dilemma experiment
How do people decide which action to take? This question is best answered using Game Theory, which has proposed a series of decision mechanisms that people potentially use. In network simulations, wherein games are repeated and payoff differences can be observed, those mechanisms rely often on imitation of successful behavior. Surprisingly, little to no evidence has been provided about whether, how and what people actually use to alter their actions in that context. By comparing two experimental treatments wherein participants play in a network the iterated Prisoner's Dilemma game, we aim to answer whether more successful actions are imitated. Whereas in the first treatment, participants have the possibility to use payoff differences in making their decision, the second treatment hinders such imitation as no information about the gains is provided. If imitation of the more successful plays a role then there should be a difference in how players switch from cooperation to defection between both treatments. Although, cooperation and payoff levels appear not to be significantly different between both treatments, detailed analysis shows that there are behavioral differences: When confronted with a more successful co-player, the focal player will imitate that behavior as the switching is related to the experienced payoff inequality.
Vocal imitation of percussion sounds: on the perceptual similarity between imitations and imitated sounds
<p>Dataset of the drum sounds and vocal imitations used in the listening study. There are 30 drum sounds, indexed 0-29. The imitations are indexed by imitator (0-13), with imitations of each drum sound in the respective directories. Included is a csv file containing the participant responses from the listening test.</p> <p>NOTE: The BFD drum samples have been made available with the permission of FXpansion Audio UK. Permission is granted for their use in further academic research. Contact SKoT McDonald <skot@fxpansion.com> for further information."</p> <p> </p> <p> </p>
Ukrainian 14-syllable verse in Belarusian poetry: the rhythm of translations and imitations (dataset)
<p>Data and source code accompanying the talk:<br> У. В. Парыцкі. Украінскі 14-складовы верш у беларускай паэзіі: рытміка перакладаў і імітацый // X Міжнародны Кангрэс даследчыкаў Беларусі, Коўна, 01.10.2022 [Vladislav Poritski. Ukrainian 14-syllable verse in Belarusian poetry: the rhythm of translations and imitations // Presented at 10th International Congress of Belarusian Studies, Kaunas, 01.10.2022]</p> <p>The empirical investigation of 14-syllable verse, presented in the talk, is based upon a sample of Ukrainian texts by Taras Shevchenko, their Belarusian translations, and original Belarusian poetry by Yanka Kupala, Yakub Kolas, Piatruś Brouka. The dataset structure is as follows:</p> <ul> <li>./0_plain – plain texts;</li> <li>./1_accentuated – accentuated texts;</li> <li>metadata_shevchenko.tsv, metadata_be_authors.tsv – metadata files describing the texts;</li> <li>make_reports.py – a Python script to generate statistic reports from the accentuated texts;</li> <li>./2_reports – programmatically generated reports;</li> <li>slides.tex – LaTeX source code of the talk's slides, where the reports are embedded as diagrams and tables;</li> <li>slides.pdf – PDF version of the slides.</li> </ul> <p>The directories ./0_plain, ./1_accentuated, ./2_reports are provided in .zip archives.</p> <p>Belarusian translations of Taras Shevchenko's poetry have been taken from the book:<br> Т. Р. Шаўчэнка. Вершы. Паэмы. Мінск: Мастацкая літаратура, 1989.<br> (scan copy available at https://files.knihi.com/Knihi/scanned/Saucenka.Viersy_paemy.djvu)<br> Each poem is stored in a separate .txt file. The file name indicates the number of the poem's first page in the scanned book, e.g.: 021.txt. Same names are used for the respective Ukrainian texts. In each pair of files, such as e.g. ./0_plain/uk/021.txt and ./0_plain/be/021.txt, the texts are aligned line by line. Poem titles in both languages, translator names, and the URLs of Ukrainian source texts are provided in metadata_shevchenko.tsv.</p> <p>Original Belarusian poetry, kept in ./0_plain/be, doesn't require any alignment, and the naming scheme is different. Poem titles, author names, and the URLs of Belarusian source texts are provided in metadata_be_authors.tsv.</p> <p>In all Ukrainian and Belarusian texts, metrically irrelevant lines are discarded, only 14-syllable verse lines are stored, each of them split graphically into 8+6 syllables. Occasional minor violations, i.e. ± one or two syllables, are allowed in the texts but ignored in the statistic reports. No spans shorter than a pair of rhyming 14-syllable lines (or, graphically, a quatraine of 8+6+8+6 syllables) were sampled from polymetric poems.</p> <p>These special characters are used:</p> <ul> <li>"/" to represent line break in the source edition;</li> <li>"//" for section break (next stanza, another character's words);</li> <li>trailing "#" for the inverse of line break: to recover the original 14-syllable line as printed in the source edition, one should remove the newline;</li> <li>leading "#" for mis-aligned lines, e.g. those missing in the Belarusian translation and added hypothetically, in order to restore the alignment.</li> </ul> <p>The procedure of accentuating Ukrainian and Belarusian texts was semi-automatic, using an opportunistic database of word accents crawled from online lexicographic resources: https://slounik.org for Belarusian, https://uk.wiktionary.org and https://slovnyk.ua/nagolos.php for Ukrainian. The database and the accentuator script are not part of this dataset. Although a fair bit of manual supervision was put into ensuring that most accents are accurate, it's likely that some errors still remain, especially in the Ukrainian data, so please be cautious.</p> <p>Accentuated texts in ./1_accentuated/uk and ./1_accentuated/be are lowercased, with all punctuation stripped off. As usual in quantitative study of East Slavic verse (see e.g. https://doi.org/10.12697/smp.2019.6.2.02 for a recent overview), we distinguish between two kinds of stresses: pronouns and certain other function words bear "light" stress, while content words bear "heavy" stress. These are the designations:</p> <ul> <li>"`" for light stress, to the left of the stressed vowel;</li> <li>"'" for heavy stress, to the right of the stressed vowel (note that after consonants, "'" is an apostrophe);</li> <li>"*" for variant heavy stress, as in Ukrainian <em>ба*йду*же</em>;</li> <li>"_" to group clitics together with stressed words, as in Ukrainian <em>і_не_привіта'ла</em>.</li> </ul> <p>In rare exceptional cases, the meter may require to pronounce syllabic consonants, as in Belarusian <em>рэестр</em>. To match pronunciation, we add a vowel in square brackets: <em>рэест[а]р</em>.</p> <p>The reports summarize certain statistic properties of the dataset:</p> <ul> <li>translators.csv – a breakdown of Shevchenko's Belarusian translations into the numbers of lines contributed by each translator. 8+6 are counted as separate lines. Syllable count violations are ignored: a pair of aligned Ukrainian / Belarusian lines is not counted towards the translator's total, if the number of syllables is irrelevant (e.g. 9 and 9) or mismatched (e.g. 8 and 6).</li> <li>be_authors.csv – line counts by author in the original Belarusian poetry. Same counting rules apply, modulo the alignment.</li> <li>rhythm.csv – percentages of accents on each of the 14 syllables in various samples, grouped by author and / or translator. Rows are syllables, columns are samples. Accents in each sample are counted two ways: "min" – only heavy stresses, "max" – all stresses.</li> <li>total_accentuation.csv – average accent counts per line in Shevchenko's Ukrainian texts and Belarusian translations, separately for 8+6, separately for heavy and all stresses.</li> <li>word_boundary.csv – statistics of word boundary positions in 8-syllable 2-word heavy-stressed lines in Shevchenko's Ukrainian texts and Belarusian translations.</li> <li>trochaicity.csv – ratio of stresses that match trochaic metrical template, separately for 8+6, heavy stresses only. Rows are samples: Shevchenko's Ukrainian texts and Belarusian translations, original poetry by three Belarusian authors.</li> </ul> <p>For implementation details, see the source code of make_reports.py.</p> <p>To reproduce report generation, you will need Python. Unzip the archive 1_accentuated.zip and run:<br> python3 make_reports.py</p> <p>To rebuild the slides, you will need LaTeX:<br> xelatex -synctex=1 -interaction=nonstopmode -shell-escape slides.tex<br> If the bibliographic references are not rendered properly, rerun once again.</p>
Fig. 7. Doliops imitator Schultze, 1918 in Type specimens of the genera Doliops Waterhouse, 1841 and Lamprobityle Heller, 1923 (stat. nov.) (Coleoptera: Cerambycidae) and description of two new species deposited in Senckenberg Natural History collections Dresden, Germany
Fig. 7. Doliops imitator Schultze, 1918 (A – dorsal view, B – lateral view, C – labels)
Using machine learning to distinguish between authentic and imitation Jackson Pollock poured paintings: Art images
<p>Jackson Pollock's abstract poured paintings are celebrated for their striking aesthetic qualities. They are also among the most financially valued and imitated artworks, making them vulnerable to high-profile controversies involving Pollock-like paintings of unknown origin. Given the increased employment of artificial intelligence applications across society, we investigate whether established machine learning techniques can be adopted by the art world to help detect imitation Pollocks. The low number of images compared to typical artificial intelligence projects presents a potential limitation for art-related applications. To address this limitation, we develop a machine learning strategy involving a novel image ingestion method which decomposes the images into sets of multi-scaled tiles. Leveraging the power of transfer learning, this approach distinguishes between authentic and imitation poured artworks with an accuracy of 98.9%. The machine also uses the multi-scaled tiles to generate novel visual aids and interpretational parameters which together facilitate comparisons between the machine's results and traditional investigations of Pollock's artistic style.</p>
Vocal Imitation Set v1.1.3 : Thousands of vocal imitations of hundreds of sounds from the AudioSet ontology
<p>The VocalImitationSet is a collection of crowd-sourced vocal imitations of a large set of diverse sounds collected from Freesound (<a href="https://freesound.org/">https://freesound.org/</a>), which were curated based on Google's AudioSet ontology (<a href="https://research.google.com/audioset/">https://research.google.com/audioset/</a>). We expect that this dataset will help research communities obtain a better understanding of human's vocal imitation and build a machine understand the imitations as humans do.</p> <p>See <a href="https://github.com/interactiveaudiolab/VocalImitationSet">https://github.com/interactiveaudiolab/VocalImitationSet</a> for more information about this dataset and its latest updates.</p> <p>For citations, please use this reference:</p> <p>Bongjun Kim, Madhav Ghei, Bryan Pardo, and Zhiyao Duan, "Vocal Imitation Set: a dataset of vocally imitated sound events using the AudioSet ontology," <em>Proceedings of the Detection and Classification of Acoustic Scenes and Events 2018 Workshop (DCASE2018)</em>, Nov. 2018.</p> <p>Contact Info:</p> <p>- Interactive Audio Lab: <a href="http://music.eecs.northwestern.edu/">http://music.eecs.northwestern.edu</a></p> <p>- Bongjun Kim <a href="mailto:bongjun@u.northwestern.edu">bongjun@u.northwestern.edu</a> | <a href="http://www.bongjunkim.com/">http://www.bongjunkim.com</a></p> <p>- Bryan Pardo <a href="mailto:pardo@northwestern.edu">pardo@northwestern.edu</a> | <a href="http://www.bryanpardo.com/">http://www.bryanpardo.com</a></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.