Show simple item record

dc.contributor.authorBigelow, Alex
dc.contributor.authorWilliams, Katy
dc.contributor.authorIsaacs, Katherine E.
dc.date.accessioned2021-02-18T22:25:38Z
dc.date.available2021-02-18T22:25:38Z
dc.date.issued2021-02
dc.identifier.citationA. Bigelow, K. Williams and K. E. Isaacs, "Guidelines For Pursuing and Revealing Data Abstractions," in IEEE Transactions on Visualization and Computer Graphics, vol. 27, no. 2, pp. 1503-1513, Feb. 2021, doi: 10.1109/TVCG.2020.3030355.en_US
dc.identifier.issn1077-2626
dc.identifier.doi10.1109/tvcg.2020.3030355
dc.identifier.urihttp://hdl.handle.net/10150/656784
dc.description.abstractMany data abstraction types, such as networks or set relationships, remain unfamiliar to data workers beyond the visualization research community. We conduct a survey and series of interviews about how people describe their data, either directly or indirectly. We refer to the latter as latent data abstractions. We conduct a Grounded Theory analysis that (1) interprets the extent to which latent data abstractions exist, (2) reveals the far-reaching effects that the interventionist pursuit of such abstractions can have on data workers, (3) describes why and when data workers may resist such explorations, and (4) suggests how to take advantage of opportunities and mitigate risks through transparency about visualization research perspectives and agendas. We then use the themes and codes discovered in the Grounded Theory analysis to develop guidelines for data abstraction in visualization projects. To continue the discussion, we make our dataset open along with a visual interface for further exploration.en_US
dc.description.sponsorshipU.S. Department of Defenseen_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_US
dc.rights© 2020 IEEE.en_US
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en_US
dc.subjectCognitive scienceen_US
dc.subjectCreativityen_US
dc.subjectData abstractionen_US
dc.subjectData visualizationen_US
dc.subjectData wranglingen_US
dc.subjectEncodingen_US
dc.subjectGrounded theoryen_US
dc.subjectGuidelinesen_US
dc.subjectInterviewsen_US
dc.subjectSurvey designen_US
dc.subjectVisualizationen_US
dc.titleGuidelines For Pursuing and Revealing Data Abstractionsen_US
dc.typeArticleen_US
dc.identifier.eissn2160-9306
dc.contributor.departmentUniversity of Arizonaen_US
dc.identifier.journalIEEE Transactions on Visualization and Computer Graphicsen_US
dc.description.collectioninformationThis 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.en_US
dc.eprint.versionFinal accepted manuscripten_US
dc.source.journaltitleIEEE Transactions on Visualization and Computer Graphics
dc.source.volume27
dc.source.issue2
dc.source.beginpage1503
dc.source.endpage1513
refterms.dateFOA2021-02-18T22:25:38Z


Files in this item

Thumbnail
Name:
2020_infovis_abstractionGuidel ...
Size:
660.8Kb
Format:
PDF
Description:
Final Accepted Manuscript

This item appears in the following Collection(s)

Show simple item record