Demo Data from EDBT 2026 / reviewers in the wild / expert
Cole Nussbaumer Knaflic
dblp:328/7120
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2022
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Computer graphics and multimedia
1 paper |
Visualization and visual analytics · 100% | |
| Human-computer interaction and pervasive computing
1 paper |
Usability and user experience research · 100% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Visualization and visual analytics › visualization design
visualization design guidelines |
0.6 | 1 | 2022 | Declutter and Focus: Empirically Evaluating Design Guidelines for Effective Data Communication · IEEE Trans. Vis. Comput. Graph. 2022 |
Usability and user experience research › evaluation methodology
empirical evaluation |
0.6 | 1 | 2022 | Declutter and Focus: Empirically Evaluating Design Guidelines for Effective Data Communication · IEEE Trans. Vis. Comput. Graph. 2022 |
Methods — techniques the papers use, named apart from their topics
redrawing task · 1.1controlled experiment · 1.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2022 | Declutter and Focus: Empirically Evaluating Design Guidelines for Effective Data CommunicationabstractData visualization design has a powerful effect on which patterns we see as salient and how quickly we see them. The visualization practitioner community prescribes two popular guidelines for creating clear and efficient visualizations: declutter and focus. The declutter guidelines suggest removing non-critical gridlines, excessive labeling of data values, and color variability to improve aesthetics and to maximize the emphasis on the data relative to the design itself. The focus guidelines for explanatory communication recommend including a clear headline that describes the relevant data pattern, highlighting a subset of relevant data values with a unique color, and connecting those values to written annotations that contextualize them in a broader argument. We evaluated how these recommendations impact recall of the depicted information across cluttered, decluttered, and decluttered+focused designs of six graph topics. Undergraduate students were asked to redraw previously seen visualizations, to recall their topics and main conclusions, and to rate the varied designs on aesthetics, clarity, professionalism, and trustworthiness. Decluttering designs led to higher ratings on professionalism, and adding focus to the design led to higher ratings on aesthetics and clarity. They also showed better memory for the highlighted pattern in the data, as reflected across redrawings of the original visualization and typed free-response conclusions, though we do not know whether these results would generalize beyond our memory-based tasks. The results largely empirically validate the intuitions of visualization designers and practitioners. The stimuli, data, analysis code, and Supplementary Materials are available at https://osf.io/wes9u/. Kiran Ajani, Elsie Lee-Robbins, Cindy Xiong Bearfield, Cole Nussbaumer Knaflic, William Kemper, Steven Franconeri |
IEEE Trans. Vis. Comput. Graph. | 4 |