Visualization has become a ubiquitous component of modern computing and society, transforming how data is analyzed, interpreted, and communicated across disciplines and to the broader public. As early as the late 1980s [7] and again in the early 2000s [2,4-6], the visualization community organized around several articulated grand challenges to guide research and coordinate efforts across academia, industry, and government, e.g., scalable visualization, the role of human perception, and representing uncertainty. These challenge frameworks have played a critical role in establishing visualization as a mature scientific discipline and shaping its funding and research agendas around topics in scientific visualization, information visualization, and visual analytics broadly.

However, we stand at an inflection point. Many of the grand challenges proposed two decades ago have been partially or substantially addressed, while new opportunities have begun to surface due to changes in technology, such as the advent of generative AI, growing attention to developing theoretical foundations grounded in perceptual psychology and cognitive science, and the increased concern for sociotechnical impacts of visualization, such as its use for misinformation campaigns. Importantly, recent years have seen no comprehensive community effort to update or revisit these foundational challenges. Recent grand challenges have come from narrow subfields of visualization [1,3,9,10] or adjacent fields [8]. This may be because visualization has become specialized, perhaps suggesting that field-unifying challenges are not necessary. However, the unification of the VIS conference, which began in 2019 (see https://ieeevis.org/year/2019/governance/restructuring), suggests that it should be viewed as a singular entity instead.

As a result, the time is ripe to reflect on whether we need new grand challenges, and if so, what they should be and in what form they should take. The field continues to produce innovative work, but the absence of a clearly articulated, shared vision has resulted in fragmented efforts that limit the field's ability to coordinate efforts, prioritize impactful problems, and attract sustained funding or interdisciplinary collaboration. Without alignment on strategic priorities, progress can be incremental and dispersed, reducing the field’s potential to address complex, high-impact societal or scientific challenges. A unified grand challenge framework would help focus innovation, increase visibility, and enhance the field's long-term influence.

Overall Organization. We propose to gather a group of international experts, including senior, mid-career, and emerging leaders in visualization, and organize them around three themes:

Past and present. What makes a grand challenge effective? By reflecting on grand challenges past and present, we will seek to form criteria for grand challenges. Through this work, we will identify which challenges were necessary. What made them successful? Were any unsuccessful or did harm to the community (such as by creating silos)?

Future. We will look towards the future, anticipating new areas of disruption that are likely to shape visualization research in the next 10 years. These include areas such as visualization in quantum computing environments, integration with emerging hardware platforms (e.g., AR/VR), and opportunities for visualization to support global-scale issues such as climate change, epidemiology, and other policy issues. What values should we focus on? Is visualization as a community still a useful abstraction, as highlighted by the types of problems we can solve?

Existential Value. In forming communal challenges, we assume that there is value to a unified community. Yet, given visualization’s breadth, this assumption is not a given. We will consider here if such challenges are appropriate: does visualization have field-spanning challenges (echoing the millennium problems in mathematics), or is our scope necessarily more focused on particular technologies or data forms? Can the varied epistemologies and methodological backgrounds (e.g., tool builders, psychologists, and so on) be unified in a coherent way that a challenge might be worthwhile? Is visualization as a community still a useful abstraction, as highlighted by the types of problems we can solve?

Discussion Theme Forming. To complement this top-down approach, we will ask participants to give lightning talks on the first morning of the seminar to highlight their favorite Past/Present grand challenge and Future grand challenge. We use these themes to perform real-time group-driven Affinity Mapping to form sub-area groups that will address these questions and use these answers to identify common threads in the field as a whole.

Outcomes. The primary outcome of this seminar is expected to be a peer-reviewed report that defines new of grand challenges (or a concrete argument on why such a structure is no longer necessary), to be submitted for publication in a high-visibility venue (e.g., IEEE TVCG). This will provide a framework that researchers can use to align their work with strategic priorities, thereby increasing coherence and visibility within the community. Further, we hope the seminar will result in follow-up workshops or panels to sustain momentum and refine the roadmap.

The proposed Shonan Meeting addresses a critical gap in the visualization community: the lack of a current, shared framework for grand challenges. Through retrospective evaluation, present-day analysis, and forward-looking discussion, this meeting will help realign the field's focus and foster the next wave of foundational contributions. The resulting roadmap will serve not only as a reference for researchers but also as a strategic document to support community-building, cross-disciplinary initiatives, and sustained research funding.

[1] Ahrens. "Technology trends and challenges for large-scale scientific visualization." IEEE Computer Graphics and Applications 42.4 (2022): 114-119.

[2] Chen. "Top 10 unsolved information visualization problems." IEEE Computer Graphics and Applications 25.4 (2005): 12-16.

[3] Ens et al. "Grand challenges in immersive analytics." Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems. 2021.

[4] Johnson et al. "NIH-NSF visualization research challenges report." Institute of Electrical and Electronics Engineers, 2005.

[5] Johnson et al. "Visualization and knowledge discovery: Report from the DOE/ASCR workshop on visual analysis and data exploration at extreme scale." Salt Lake City (2007). [6] Johnson. "Top scientific visualization research problems." IEEE Computer Graphics and Applications 24.4 (2004): 13-17.

[7] McCormick, DeFanti, Brown. "Visualization in scientific computing and computer graphics." ACM SIGGRAPH 21. 6 (1987).

[8] Stephanidis et al. "Seven HCI grand challenges." International Journal of Human–Computer Interaction 35.14 (2019): 1229-1269.

[9] Wong et al. "The top 10 challenges in extreme-scale visual analytics." IEEE Computer Graphics and Applications 32.4 (2012): 63-67.

[10] Wu et al. "Grand challenges in visual analytics applications." IEEE Computer Graphics and Applications 43.5 (2023): 83-90.