In an era characterized by an unprecedented volume of data, designing visualizations requires effectively representing complex data in a manner that is both interpretable and meaningful to users while ensuring that visual encodings are accurate, clear, and free from potential biases or misrepresentations. Designers must further consider users' diverse cognitive and perceptual abilities and the context in which visualizations are displayed. Creating such designs requires significant insights into a broad range of knowledge from diverse sources, including the theoretical foundations of visualization, empirical (e.g., perceptual/cognitive) studies, design studies, applications, and others. However, as a community, we are inundated with a fragmented knowledge of how to optimally design visualizations, leading all of us, especially those who do not regularly read visualization research, to rely heavily on intuition.

There is an urgent need for methodical, evidence-based guidance to inform the creation of effective and engaging data visualizations. Best practices, namely design guidelines and recommendations, hold a significant role. They serve as invaluable resources for designers, educators, communicators, and decision-makers, offering a structured framework to optimize data communication, informed decision-making, and the mitigation of misinformation's spread. However, best practices alone are an incomplete solution.

The challenge within this research area is the vast and complex design space, encompassing various chart types, color schemes, interaction techniques, layout choices, and many others. The sheer diversity of possibilities makes it challenging to distill comprehensive guidelines capable of covering every conceivable scenario. To make matters worse, research papers in this domain are frequently difficult to generalize and synthesize into existing knowledge. The contextual and domain-specific nature of the studies introduces significant hurdles when attempting to consolidate diverse research findings into a unified set of actionable principles. What proves effective for one type of visualization may not necessarily apply in other contexts, leading to potential disparities among research outcomes. The challenge is so widespread that it intersects with four out of six topical areas for research papers at IEEE VIS: Theoretical & Empirical, Applications, Representations & Interaction, and Analytics & Decisions.

In this Dagstuhl Seminar, we will address this challenge by discussing the sources of guidelines and recommendations (e.g., theoretical frameworks, controlled experiments, qualitative studies, design studies, and practitioners expertise), how to integrate guidelines and recommendations into systems and designs (e.g., challenges with generalization and the synthesis of research), and the utility of guidelines and recommendations (e.g., issues of education and literacy, communication and misinformation, role in decision making, and how to automatically apply recommendations). Our goal is to explore the challenging problem of converging this foundational knowledge, design recommendations, and best practices into technical, sociotechnical, and theoretical frameworks that provide actionable insights that serve the practical needs of both the visualization community and the broader public and propose a course of action for the visualization community to address the problem more directly.

Creative Commons BY 4.0

Evanthia Dimara, Miriah Meyer, Ghulam Jilani Quadri, and Paul Rosen