Collaborating across domains and roles: an interview study of visualization design practices

Journal article

Visualization design study is a widely adopted approach for developing tailored visual solutions to domain-specific problems through close interdisciplinary collaboration. While the visualization community has proposed generalizable frameworks, there is a growing need for domain-aware methodologies that address discipline-specific challenges and refine design study practices. To investigate how domain characteristics and collaborator roles influence the design study process, we conducted interviews with 15 experts, including domain specialists from the humanities, arts, applied sciences, and artificial intelligence, as well as visualization researchers and developers, with direct experience in design studies. Our findings reveal tensions and opportunities that arise from differing expectations, communication styles, and levels of engagement among collaborators at various stages of the design process, including problem formulation, co-design, and evaluation. We highlight how domain-specific norms and role dynamics shape collaboration and influence the trajectory of visualization projects. Based on these insights, we offer practical considerations to help visualization researchers anticipate domain-specific challenges, foster mutual understanding, and adapt their methods accordingly. Our study contributes to ongoing efforts to support more context-sensitive, sustainable, and inclusive design study practices across diverse application domains.

Authors: Xing, Y; Ortoleva, MT; Borgo, R; Abdul-Rahman, A

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: IEEE
• Journal: IEEE Transactions on Visualization and Computer Graphics
• Volume: 32
• Issue: 1
• Pages: 571-581
• Publication date: 2025-11-20
• Acceptance date: 2025-07-15
• DOI: 10.1109/tvcg.2025.3634711
• EISSN: 1941-0506
• ISSN: 1077-2626
• Pmid: 41264445
• Language: English
• Keywords: interview study; natural language processing; artificial intelligence; visualization; interviews; shape; art; design study; iterative methods; collaboration; data visualization; reflection