Which methods are the most effective in enabling novice users to participate in ontology creation? A usability study
Affiliation
School of Information, University of ArizonaIssue Date
2021
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Oxford University PressCitation
Zhang, L., Yang, X., Cota, Z., Cui, H., Ford, B., Chen, H.-L., MacKlin, J. A., Reznicek, A., & Starr, J. (2021). Which methods are the most effective in enabling novice users to participate in ontology creation? A usability study. Database, 2021.Journal
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Copyright © The Author(s) 2021. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/).Collection Information
This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.Abstract
Producing findable, accessible, interoperable and reusable (FAIR) data cannot be accomplished solely by data curators in all disciplines. In biology, we have shown that phenotypic data curation is not only costly, but it is burdened with inter-curator variation. We intend to propose a software platform that would enable all data producers, including authors of scientific publications, to produce ontologized data at the time of publication. Working toward this goal, we need to identify ontology construction methods that are preferred by end users. Here, we employ two usability studies to evaluate effectiveness, efficiency and user satisfaction with a set of four methods that allow an end user to add terms and their relations to an ontology. Thirty-three participants took part in a controlled experiment where they evaluated the four methods (Quick Form, Wizard, WebProtégé and Wikidata) after watching demonstration videos and completing a hands-on task. Another think-aloud study was conducted with three professional botanists. The efficiency effectiveness and user confidence in the methods are clearly revealed through statistical and content analyses of participants' comments. Quick Form, Wizard and WebProtégé offer distinct strengths that would benefit our author-driven FAIR data generation system. Features preferred by the participants will guide the design of future iterations. © 2020 The Author(s) 2021. Published by Oxford University Press.Note
Open access journalISSN
1758-0463PubMed ID
34156445Version
Final published versionae974a485f413a2113503eed53cd6c53
10.1093/database/baab035
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Except where otherwise noted, this item's license is described as Copyright © The Author(s) 2021. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/).
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