IEEE VIS 2024 Content: Understanding Visualization Authoring Techniques for Genomics Data in the Context of Personas and Tasks

Understanding Visualization Authoring Techniques for Genomics Data in the Context of Personas and Tasks



Astrid van den Brandt - Eindhoven University of Technology, Eindhoven, Netherlands

 Sehi L'Yi - Harvard Medical School, Boston, United States

 Huyen N. Nguyen - Harvard Medical School, Boston, United States

 Anna Vilanova - Eindhoven University of Technology, Eindhoven, Netherlands

 Nils Gehlenborg - Harvard Medical School, Boston, United States

 Download Supplemental Material

 Room: Bayshore II

2024-10-17T17:00:00ZGMT-0600Change your timezone on the schedule page
2024-10-17T17:00:00Z

Exemplar figure, described by caption below — Composite illustration summarizing key results from the two user studies. In Study 1 (n=20), we identified five personas based on interviews, characterized by three dimensions: focus, automation, and audience. In Study 2 (n=13), we collected user preferences across eight tasks (T1--T8) for six common authoring techniques: code-based, example-based, natural language input (NLI), shelf configuration, template-based, and visualization-by-demonstration (VbD).

Fast forward

Full Video

Keywords

User interviews, visual probes, visualization authoring, genomics data visualization

Abstract

Genomics experts rely on visualization to extract and share insights from complex and large-scale datasets. Beyond off-the-shelf tools for data exploration, there is an increasing need for platforms that aid experts in authoring customized visualizations for both exploration and communication of insights. A variety of interactive techniques have been proposed for authoring data visualizations, such as template editing, shelf configuration, natural language input, and code editors. However, it remains unclear how genomics experts create visualizations and which techniques best support their visualization tasks and needs. To address this gap, we conducted two user studies with genomics researchers: (1) semi-structured interviews (n=20) to identify the tasks, user contexts, and current visualization authoring techniques and (2) an exploratory study (n=13) using visual probes to elicit users’ intents and desired techniques when creating visualizations. Our contributions include (1) a characterization of how visualization authoring is currently utilized in genomics visualization, identifying limitations and benefits in light of common criteria for authoring tools, and (2) generalizable design implications for genomics visualization authoring tools based on our findings on task- and user-specific usefulness of authoring techniques. All supplemental materials are available at https://osf.io/bdj4v/.