Guidelines For Pursuing and Revealing Data Abstractions
dc.contributor.author | Bigelow, Alex | |
dc.contributor.author | Williams, Katy | |
dc.contributor.author | Isaacs, Katherine E. | |
dc.date.accessioned | 2021-02-18T22:25:38Z | |
dc.date.available | 2021-02-18T22:25:38Z | |
dc.date.issued | 2021-02 | |
dc.identifier.citation | A. 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.issn | 1077-2626 | |
dc.identifier.doi | 10.1109/tvcg.2020.3030355 | |
dc.identifier.uri | http://hdl.handle.net/10150/656784 | |
dc.description.abstract | Many 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.sponsorship | U.S. Department of Defense | en_US |
dc.language.iso | en | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) | en_US |
dc.rights | © 2020 IEEE. | en_US |
dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en_US |
dc.subject | Cognitive science | en_US |
dc.subject | Creativity | en_US |
dc.subject | Data abstraction | en_US |
dc.subject | Data visualization | en_US |
dc.subject | Data wrangling | en_US |
dc.subject | Encoding | en_US |
dc.subject | Grounded theory | en_US |
dc.subject | Guidelines | en_US |
dc.subject | Interviews | en_US |
dc.subject | Survey design | en_US |
dc.subject | Visualization | en_US |
dc.title | Guidelines For Pursuing and Revealing Data Abstractions | en_US |
dc.type | Article | en_US |
dc.identifier.eissn | 2160-9306 | |
dc.contributor.department | University of Arizona | en_US |
dc.identifier.journal | IEEE Transactions on Visualization and Computer Graphics | en_US |
dc.description.collectioninformation | 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. | en_US |
dc.eprint.version | Final accepted manuscript | en_US |
dc.source.journaltitle | IEEE Transactions on Visualization and Computer Graphics | |
dc.source.volume | 27 | |
dc.source.issue | 2 | |
dc.source.beginpage | 1503 | |
dc.source.endpage | 1513 | |
refterms.dateFOA | 2021-02-18T22:25:38Z |