CORTEXA
← Browse
arxivcs.HC2026-07-31

Data Visualization Style Guides in Practice: Why They Emerge, How They Work, and When They Bend

Alvitta Ottley, Jonathan Schwabish

Visualization style guides play a crucial role in shaping how data is interpreted and trusted, yet they often receive little scrutiny in their creation and use. Understanding their impact requires looking beyond the specific rules that style guides prescribe and examining how they function within organizations to coordinate visual work, manage trade-offs, and support judgment under real constraints. Analyzing interviews with nine authors of twenty-six style guides across journalism, government, industry, and the public sector, we reveal how these guides reflect the specific challenges of their organizations, including consistency, training, governance, and accountability. Our study highlights the tensions between standardization and flexibility, guidance and discretion, and automation and human oversight. We propose PRISM, a socio-technical framework that characterizes visualization style guides by their Purpose, Rules & Mechanisms, Institutional Enforcers, and Situated Flexibility. We show that publicly available style guides expose only a subset of this system. By viewing style guides as socio-technical systems, we enrich the research on design guidance and offer practical insights for those who create and rely on these guides in critical environments.

View free PDFSource page

Related papers

arxivcs.HC2026-07-23

Charting the Moral Universe: Capturing Virtues and Values of Data Visualization Practice

Chloe Hudson Prock, Enrico Bertini, Michael Correll

What do we value in our visualizations, and in the people who design them? Despite a growing body of work on critical data visualization, the conception of what it is to do ethical data visualization work can often be narrow (for instance, holding that our ethical duties are disc…

View free PDFSource page
arxivcs.HCcs.CV2026-07-16

Skeleton: Visual Authoring of Non-visual Data Experiences

Frank Elavsky, Chieri Nnadozie, Lucas Nadolskis, Patrick Carrington, Dominik Moritz

When sighted practitioners author accessible data visualizations, they build navigation structures (the nodes, edges, and input bindings that govern how assistive technologies traverse an interface) entirely in code, with no visual representation. Without a representation to reac…

View free PDFSource page
arxivcs.HCcs.PL2026-07-22

Flint: A Semantics-Driven Data Visualization Intermediate Language

Yunhai Wang, Kecheng Lu, Junhao Chen, Alper Sarikaya, Chenglong Wang

We present Flint, an intermediate language that enables authors to create high-quality visualizations from concise, semantics-driven specifications without explicitly configuring low-level parameters such as scales, axes, and formatting. Unlike prior systems that infer default co…

View free PDFSource page
arxivcs.HC2026-06-29

Debugging as Evidence-Driven Reasoning: Visualization Opportunities in Data-Intensive Programming

Yongbo Chen, Yan Zhu, Rebecca Faust

Visualization has been recognized as a valuable means of supporting debugging by externalizing runtime behavior that would otherwise remain hidden or scattered. However, most visual debugging research has focused on traditional software development settings, leaving the distinct…

View free PDFSource page