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Data Hunches: Incorporating Personal Knowledge into Visualizations
University of Utah, USA.
University of Utah, USA.ORCID iD: 0000-0001-9419-3402
Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-8971-6245
University of Utah, USA.ORCID iD: 0000-0001-6930-5468
2023 (English)In: IEEE Transactions on Visualization and Computer Graphics, ISSN 1077-2626, E-ISSN 1941-0506, Vol. 29, no 1, p. 504-514Article in journal (Refereed) Published
Abstract [en]

The trouble with data is that it frequently provides only an imperfect representation of a phenomenon of interest. Experts who are familiar with their datasets will often make implicit, mental corrections when analyzing a dataset, or will be cautious not to be overly confident about their findings if caveats are present. However, personal knowledge about the caveats of a dataset is typically not incorporated in a structured way, which is problematic if others who lack that knowledge interpret the data. In this work, we define such analysts' knowledge about datasets as data hunches . We differentiate data hunches from uncertainty and discuss types of hunches. We then explore ways of recording data hunches, and, based on a prototypical design, develop recommendations for designing visualizations that support data hunches. We conclude by discussing various challenges associated with data hunches, including the potential for harm and challenges for trust and privacy. We envision that data hunches will empower analysts to externalize their knowledge, facilitate collaboration and communication, and support the ability to learn from others' data hunches.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2023. Vol. 29, no 1, p. 504-514
National Category
Human Computer Interaction
Identifiers
URN: urn:nbn:se:liu:diva-208631DOI: 10.1109/tvcg.2022.3209451ISI: 000901991800006PubMedID: 36155455Scopus ID: 2-s2.0-85139528255OAI: oai:DiVA.org:liu-208631DiVA, id: diva2:1906815
Funder
Knut and Alice Wallenberg FoundationAvailable from: 2024-10-18 Created: 2024-10-18 Last updated: 2025-05-28Bibliographically approved
In thesis
1. Shifting Perspectives: Conducting Visualization Research with Entanglement Epistemology
Open this publication in new window or tab >>Shifting Perspectives: Conducting Visualization Research with Entanglement Epistemology
2025 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Foundational theories in visualization offer explanations and models for how people use visualizations to interpret data. A recent turn in visualization research has drawn attention to the ways in which these theories have limited explanatory power. Instead, researchers are drawing on alternative theories of knowledge, or epistemology, to address persistent problems in the field. This dissertation joins the conversation, presenting entanglement theory for visualization as an alternative epistemological theory and illustrative case studies that demonstrate how alternative theory in visualization shifts our attention and thus alters visualization research outcomes.

To support this claim, this dissertation presents two contributions that illustrate the productive capacity of feminist theory to contribute to visualization research. The first contribution is a novel theory for visualization transposed from feminist entanglement theory. Entanglement theory for visualization shifts the definitions of data, visualization, and insight toward relational and situated objects that are inseparable from feminist objects of concern: ethics, power, and privilege. Along with new definitions of data, visualization, and insights, we present three ways we mobilize feminist epistemology across visualization as the second contribution. The second contribution consists of three case studies demonstrating how feminist visualization theory drew our attention to previously ignored aspects of visualization design guidelines, the role of knowledge, and ethics in collaborative frameworks. Each case study illustrates the productive nature of approaching visualization research with an alternative epistemology.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2025. p. 65
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 2459
National Category
Human Computer Interaction
Identifiers
urn:nbn:se:liu:diva-214083 (URN)10.3384/9789181181654 (DOI)9789181181647 (ISBN)9789181181654 (ISBN)
Public defence
2025-09-04, Kåkenhus, Room K3, Campus Norrköping, Norrköping, 14:00 (English)
Opponent
Supervisors
Note

Funding: This dissertation was supported in part by the Wallenberg AI, Autonomous Systems and Software Program (WASP) funded by the Knut and Alice Wallenberg Foundation.

Available from: 2025-05-28 Created: 2025-05-28 Last updated: 2025-06-02Bibliographically approved

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Akbaba, DeryaMeyer, Miriah

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