This dataset contains 35 of 39 taxonomies that were the result of a systematic review. The systematic review was conducted with the goal of identifying taxonomies suitable for semantically annotating research data. A special focus was set on research data from the hybrid societies domain.

The following taxonomies were identified as part of the systematic review:

Filename

Taxonomy Title

acm_ccs

ACM Computing Classification System [1]

amec

A Taxonomy of Evaluation Towards Standards [2]

bibo

A BIBO Ontology Extension for Evaluation of Scientific Research Results [3]

cdt

Cross-Device Taxonomy [4]

cso

Computer Science Ontology [5]

ddbm

What Makes a Data-driven Business Model? A Consolidated Taxonomy [6]

ddi_am

DDI Aggregation Method [7]

ddi_moc

DDI Mode of Collection [8]

n/a

DemoVoc [9]

discretization

Building a New Taxonomy for Data Discretization Techniques [10]

dp

Demopaedia [11]

dsg

Data Science Glossary [12]

ease

A Taxonomy of Evaluation Approaches in Software Engineering [13]

eco

Evidence & Conclusion Ontology [14]

edam

EDAM: The Bioscientific Data Analysis Ontology [15]

n/a

European Language Social Science Thesaurus [16]

et

Evaluation Thesaurus [17]

glos_hci

The Glossary of Human Computer Interaction [18]

n/a

Humanities and Social Science Electronic Thesaurus [19]

hcio

A Core Ontology on the Human-Computer Interaction Phenomenon [20]

hft

Human-Factors Taxonomy [21]

hri

A Taxonomy to Structure and Analyze Human–Robot Interaction [22]

iim

A Taxonomy of Interaction for Instructional Multimedia [23]

interrogation

A Taxonomy of Interrogation Methods [24]

iot

Design Vocabulary for Human–IoT Systems Communication [25]

kinect

Understanding Movement and Interaction: An Ontology for Kinect-Based 3D Depth Sensors [26]

maco

Thesaurus Mass Communication [27]

n/a

Thesaurus Cognitive Psychology of Human Memory [28]

mixed_initiative

Mixed-Initiative Human-Robot Interaction: Definition, Taxonomy, and Survey [29]

qos_qoe

A Taxonomy of Quality of Service and Quality of Experience of Multimodal Human-Machine Interaction [30]

ro

The Research Object Ontology [31]

senses_sensors

A Human-Centered Taxonomy of Interaction Modalities and Devices [32]

sipat

A Taxonomy of Spatial Interaction Patterns and Techniques [33]

social_errors

A Taxonomy of Social Errors in Human-Robot Interaction [34]

sosa

Semantic Sensor Network Ontology [35]

swo

The Software Ontology [36]

tadirah

Taxonomy of Digital Research Activities in the Humanities [37]

vrs

Virtual Reality and the CAVE: Taxonomy, Interaction Challenges and Research Directions [38]

xdi

Cross-Device Interaction [39]

We converted the taxonomies into SKOS (Simple Knowledge Organisation System) representation. The following 4 taxonomies were not converted as they were already available in SKOS and were for this reason excluded from this dataset:

1) DemoVoc, cf. http://thesaurus.web.ined.fr/navigateur/ available at https://thesaurus.web.ined.fr/exports/demovoc/demovoc.rdf

2) European Language Social Science Thesaurus, cf. https://thesauri.cessda.eu/elsst/en/ available at https://zenodo.org/record/5506929

3) Humanities and Social Science Electronic Thesaurus, cf. https://hasset.ukdataservice.ac.uk/hasset/en/ available at https://zenodo.org/record/7568355

4) Thesaurus Cognitive Psychology of Human Memory, cf. https://www.loterre.fr/presentation/ available at https://skosmos.loterre.fr/P66/en/

References

[1] “The 2012 ACM Computing Classification System,” ACM Digital Library, 2012. https://dl.acm.org/ccs (accessed May 08, 2023).

[2] AMEC, “A Taxonomy of Evaluation Towards Standards.” Aug. 31, 2016. Accessed: May 08, 2023. [Online]. Available: https://amecorg.com/amecframework/home/supporting-material/taxonomy/

[3] B. Dimić Surla, M. Segedinac, and D. Ivanović, “A BIBO ontology extension for evaluation of scientific research results,” in Proceedings of the Fifth Balkan Conference in Informatics, in BCI ’12. New York, NY, USA: Association for Computing Machinery, Sep. 2012, pp. 275–278. doi: 10.1145/2371316.2371376.

[4] F. Brudy et al., “Cross-Device Taxonomy: Survey, Opportunities and Challenges of Interactions Spanning Across Multiple Devices,” in Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, in CHI ’19. New York, NY, USA: Association for Computing Machinery, Mai 2019, pp. 1–28. doi: 10.1145/3290605.3300792.

[5] A. A. Salatino, T. Thanapalasingam, A. Mannocci, F. Osborne, and E. Motta, “The Computer Science Ontology: A Large-Scale Taxonomy of Research Areas,” in Lecture Notes in Computer Science 1137, D. Vrandečić, K. Bontcheva, M. C. Suárez-Figueroa, V. Presutti, I. Celino, M. Sabou, L.-A. Kaffee, and E. Simperl, Eds., Monterey, California, USA: Springer, Oct. 2018, pp. 187–205. Accessed: May 08, 2023. [Online]. Available: http://oro.open.ac.uk/55484/

[6] M. Dehnert, A. Gleiss, and F. Reiss, “What makes a data-driven business model? A consolidated taxonomy,” presented at the European Conference on Information Systems, 2021.

[7] DDI Alliance, “DDI Controlled Vocabulary for Aggregation Method,” 2014. https://ddialliance.org/Specification/DDI-CV/AggregationMethod_1.0.html (accessed May 08, 2023).

[8] DDI Alliance, “DDI Controlled Vocabulary for Mode Of Collection,” 2015. https://ddialliance.org/Specification/DDI-CV/ModeOfCollection_2.0.html (accessed May 08, 2023).

[9] INED - French Institute for Demographic Studies, “Thésaurus DemoVoc,” Feb. 26, 2020. https://thesaurus.web.ined.fr/navigateur/en/about (accessed May 08, 2023).

[10] A. A. Bakar, Z. A. Othman, and N. L. M. Shuib, “Building a new taxonomy for data discretization techniques,” in 2009 2nd Conference on Data Mining and Optimization, Oct. 2009, pp. 132–140. doi: 10.1109/DMO.2009.5341896.

[11] N. Brouard and C. Giudici, “Unified second edition of the Multilingual Demographic Dictionary (Demopaedia.org project),” presented at the 2017 International Population Conference, IUSSP, Oct. 2017. Accessed: May 08, 2023. [Online]. Available: https://iussp.confex.com/iussp/ipc2017/meetingapp.cgi/Paper/5713

[12] DuCharme, Bob, “Data Science Glossary.” https://www.datascienceglossary.org/ (accessed May 08, 2023).

[13] A. Chatzigeorgiou, T. Chaikalis, G. Paschalidou, N. Vesyropoulos, C. K. Georgiadis, and E. Stiakakis, “A Taxonomy of Evaluation Approaches in Software Engineering,” in Proceedings of the 7th Balkan Conference on Informatics Conference, in BCI ’15. New York, NY, USA: Association for Computing Machinery, Sep. 2015, pp. 1–8. doi: 10.1145/2801081.2801084.

[14] M. C. Chibucos, D. A. Siegele, J. C. Hu, and M. Giglio, “The Evidence and Conclusion Ontology (ECO): Supporting GO Annotations,” in The Gene Ontology Handbook, C. Dessimoz and N. Škunca, Eds., in Methods in Molecular Biology. New York, NY: Springer, 2017, pp. 245–259. doi: 10.1007/978-1-4939-3743-1_18.

[15] M. Black et al., “EDAM: the bioscientific data analysis ontology,” F1000Research, vol. 11, Jan. 2021, doi: 10.7490/f1000research.1118900.1.

[16] Council of European Social Science Data Archives (CESSDA), “European Language Social Science Thesaurus ELSST,” 2021. https://thesauri.cessda.eu/en/ (accessed May 08, 2023).

[17] M. Scriven, Evaluation Thesaurus, 3rd Edition. Edgepress, 1981. Accessed: May 08, 2023. [Online]. Available: https://us.sagepub.com/en-us/nam/evaluation-thesaurus/book3562

[18] Papantoniou, Bill et al., The Glossary of Human Computer Interaction. Interaction Design Foundation. Accessed: May 08, 2023. [Online]. Available: https://www.interaction-design.org/literature/book/the-glossary-of-human-computer-interaction

[19] “UK Data Service Vocabularies: HASSET Thesaurus.” https://hasset.ukdataservice.ac.uk/hasset/en/ (accessed May 08, 2023).

[20] S. D. Costa, M. P. Barcellos, R. de A. Falbo, T. Conte, and K. M. de Oliveira, “A core ontology on the Human–Computer Interaction phenomenon,” Data Knowl. Eng., vol. 138, p. 101977, Mar. 2022, doi: 10.1016/j.datak.2021.101977.

[21] V. J. Gawron et al., “Human Factors Taxonomy,” Proc. Hum. Factors Soc. Annu. Meet., vol. 35, no. 18, pp. 1284–1287, Sep. 1991, doi: 10.1177/154193129103501807.

[22] L. Onnasch and E. Roesler, “A Taxonomy to Structure and Analyze Human–Robot Interaction,” Int. J. Soc. Robot., vol. 13, no. 4, pp. 833–849, Jul. 2021, doi: 10.1007/s12369-020-00666-5.

[23] R. A. Schwier, “A Taxonomy of Interaction for Instructional Multimedia.” Sep. 28, 1992. Accessed: May 09, 2023. [Online]. Available: https://eric.ed.gov/?id=ED352044

[24] C. Kelly, J. Miller, A. Redlich, and S. Kleinman, “A Taxonomy of Interrogation Methods,”

Licensing

This dataset is adapted from the Group Affect and Performance dataset which is released under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license. https://creativecommons.org/licenses/by-nc/4.0/

Description

This dataset contains the audio files containing manually annotated cases of overlapped utterances, classified into True Interruptions and False Interruptions. It is derived from the Group Affect and Performance dataset created by the University of the Fraser Valley, Canada. Original conversation transcripts and audio files have been supplied for context. The Group Affect and Performance dataset provides a rich source of interruptions and overlapped utterances in general, yielding 200 True Interruptions from 355 instances of overlapped utterances in the 14 Group meetings which were annotated.

Structure

This dataset is structured into three parts:

1. data.json contains a list of all instances of overlapped utterances, classified into ‘interruption’ and ‘non-interruption’ corresponding to True and False Interruptions respectively. Each instance is uniquely identified by the Group in which it occurred, the speaker and the starting time of the utterance.

2. The 'audio' directory contains the audio of each instance of overlapped utterances corresponding to those found in data.json. The naming convention of the files is as such: ‘Group [group number]: [utterance start time] - [utterance end time].wav’.

3. Also included is a copy of the original dataset which includes the full audio and transcript. This allows the full meeting to be heard and any context for interruptions to be evaluated.

Note that directories 2. and 3. can be accessed by unzipping audio-and-transcripts.zip.

Data Collection Protocol

Of paramount importance to our process are the definitions of an overlapped utterance and a True Interruption. A False Interruption is simply an overlapped utterance which is not a True Interruption. These definitions directly impact the dataset; for overlapped utterance it informs which data points are included in our dataset and for True Interruption it informs the classes assigned to each sample.

In defining an overlapped utterance, our primary aim is to create an overarching class encompassing interruptions and all instances that could be deemed a True Interruption. For this reason, we omit cases where the timing misplaced speech and early-onset responses.

An overlapped utterance is defined as an instance where one interlocutor provides speech or noise during another interlocutor’s speech, creating an overlap that may be deemed a possible interruption when considering its timing alone. For this reason we omit cases of where the timing indicates misplaced speech or early-onset responses.

Our definition of True Interruption is an instance where an interrupting party intentionally attempts to take over a turn of the conversation from an interruptee and, in doing so, creates an overlap in speech.

As previously mentioned, due to the ‘intent’ part of this definition, we avoid cases of misplaced speech and early-onset responses. The former is enforced by not considering cases of overlapped speech which begin within 300ms of each other since this is an estimate for the average human reaction time of articulating a vowel in response to a speech stimuli. The latter is enforced by not considering speech starting within the last 10% of first utterance in the overlapped speech. Note that this approach fails to filter out all cases of misplaced speech, so we manually remove the remaining instances.

Methodology

Three main steps were taken to produce this dataset:

1. Parsing the transcripts for cases of overlapping speech

2. Manually annotating these cases per our protocol and adding them to data.json

3. Extracting audio samples from data.json and adding them to the audio folder

If you use this dataset, please cite the following paper:

Doyle, D.; Şerban, O. Interruption Audio & Transcript: Derived from Group Affect and Performance Dataset. Data 2024, 9, 104. https://doi.org/10.3390/data9090104

@article{data9090104,

AUTHOR = {Doyle, Daniel and Şerban, Ovidiu},

TITLE = {Interruption Audio & Transcript: Derived from Group Affect and Performance Dataset},

JOURNAL = {Data},

VOLUME = {9},

YEAR = {2024},

NUMBER = {9},

ARTICLE-NUMBER = {104},

URL = {https://www.mdpi.com/2306-5729/9/9/104},

ISSN = {2306-5729},

DOI = {10.3390/data9090104}

}