The Fabric of Visualization

Marai, G. Elisabeta; Möller, Torsten B.

doi:10.1007/978-3-030-34444-3_1

Cited by 4 publications

(3 citation statements)

References 17 publications

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“…The previous point is also connected to finding the best path to evaluate the effectiveness of the CDM framework. Among others, Marai and Möller propose significance and pragmatic adequacy as evaluation criteria for theoretic contributions to visualization research [24]. While we hope to have motivated the significance of the CDM to the visualization field, its usefulness for visualization practice is yet to be confirmed.…”

Section: Open Issues and Future Research Pathsmentioning

confidence: 92%

Supporting Domain Characterization in Visualization Design Studies With the Critical Decision Method

Cibulski¹,

Dimara²,

Hermawati³

et al. 2022

2022 IEEE 4th Workshop on Visualization Guidelines in Research, Design, and Education (VisGuides)

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While domain characterization has become an integral part of visualization design studies, methodological prescriptions are rare. An underrepresented aspect in existing approaches is domain expertise. Knowledge elicitation methods from cognitive science might help but have not yet received much attention for domain characterization. We propose the Critical Decision Method (CDM) to the visualization domain to provide descriptive steps that open up a knowledge-based perspective on domain characterization. The CDM uses retrospective interviews to reveal expert judgment involved in a challenging situation. We apply it to study three domain problems, reflect on our practical experience, and discuss its relevance to domain characterization in visualization research. We found the CDM's realism and subjective nature to be well suited for eliciting cognitive aspects of high-level task performance. Our insights might guide other researchers in conducting domain characterization with a focus on domain knowledge and cognition. With our work, we hope to contribute to the portfolio of meaningful methods used to inform visualization design and to stimulate discussions regarding prescriptive steps for domain characterization.

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Section: Open Issues and Future Research Pathsmentioning

confidence: 92%

Supporting Domain Characterization in Visualization Design Studies With the Critical Decision Method

Cibulski¹,

Dimara²,

Hermawati³

et al. 2022

2022 IEEE 4th Workshop on Visualization Guidelines in Research, Design, and Education (VisGuides)

View full text Add to dashboard Cite

show abstract

“…The ACD paradigm is an extension of human-centered-design, with emphasis on user activities and workflow. We note that in the ACD paradigm, the value of a tool depends on the value of the activity, not only on the number of people who use the tool (e.g., a tool serving the two researchers who will find a cure for Alzheimer's has no lesser value than a tool serving a larger population who are selecting pet names) [70]. Thus, the ACD paradigm is particularly well suited for tools in scientific research, particularly when we consider the scarcity of trained domain experts, as opposed to the large availability of untrained users, and the importance of slow thinking [49], including scaffolding.…”

Section: Design 41 Collaboration Setting and Design Processmentioning

confidence: 99%

THALIS: Human-Machine Analysis of Longitudinal Symptoms in Cancer Therapy

Floricel¹,

Nipu²,

Biggs³

et al. 2021

Preprint

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Fig. 1. THALIS analysis of longitudinal symptom data. A) Association Rule Diagram panel, showing here association-rule-mining (ARM) relationships among the most frequent late-stage symptoms; rules are represented using bubbles, with size and color encoding the support and lift metrics. B) Symptom trajectory panel-filament plots encode here mean rating values per therapeutic combination, with more frequent observations in the acute stage (left-end) than in the late stage (right-end). C) Sketch of areas affected by the selected symptoms (dry mouth and taste). D) Cohort symptom panel showing via summarization with shade and height the percentile rating distribution. E) Correlation matrix showing associations with the selected symptom. Image cropped and edited for in-print legibility.

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“…As a young and growing field, data visualization has many unresolved, debated, and untested theories, methods, guidelines, and assumptions. However, visualization researchers hardly question or evaluate previously published literature in the field [44,51,58,60]. Meyer & Dykes argue that the field lacks "adequate disciplinary underpinnings" and open dialogue to discuss "methodologies, rigor, and knowledge" [60].…”

Section: Introductionmentioning

confidence: 99%

More Useful Junk? Replicating the Effects of Visual Embellishment on Description and Recall of Charts

Syeda¹,

South²,

Raynor³

et al. 2023

Preprint

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Data visualization is a young and growing field with many unresolved, debated, and untested theories that need to be tested, validated, and strengthened. Replication studies are a common way to achieve this validation by investigating the credibility, rigor, and generalizability of previously published research. The execution of replication studies is crucial to strengthen the field of data visualization and to ensure that the field’s foundations are solid with accurate methods and theories. Although visualization researchers acknowledge the epistemological significance of replications and its necessity to establish trust and reliability, the field has made little progress to support the publication of such studies and importantly provide methods to the community to encourage replications. In this paper, we contribute Vis Repligogy, a novel framework to incorporate replication studies within visualization course curricula that not only teaches students replication and evaluation methodologies but also results in executed replication studies to validate prior work. To validate the feasibility of the framework, we present the results of four replication studies that were conducted in a graduate data visualization course that implemented the Vis Repligogy framework. We also provide a meta-analysis of the four replication studies that replicated and validated, a seminal work in data visualization investigating the effects of visual embellishments on chart interpretability and memorability. Finally, we reflect on our experience of conducting the replication studies through the Vis Repligogy framework and reflect on the experience to provide useful recommendations for future use of the framework. We envision that this framework will encourage instructors to conduct replications in their own courses and will help facilitate more replication studies in the visualization community to support a culture shift where replications are commonplace.

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The Fabric of Visualization

Cited by 4 publications

References 17 publications

Supporting Domain Characterization in Visualization Design Studies With the Critical Decision Method

Supporting Domain Characterization in Visualization Design Studies With the Critical Decision Method

THALIS: Human-Machine Analysis of Longitudinal Symptoms in Cancer Therapy

More Useful Junk? Replicating the Effects of Visual Embellishment on Description and Recall of Charts

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