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With this work, we explore the unique potential of data visualization on novel foldable mobile devices (foldables). Even though foldables are already commercially available, there is limited knowledge of how to leverage their distinct characteristics for visualization. This gap will only grow as their form factors become increasingly diverse. To address this, we use a two-step approach. First, we present a device-centered design space, structured around physical and usage properties of foldable devices. Second, we introduce a conceptual framework that investigates visualization on foldables from four complementary perspectives: More Displays – distributing multiple views to leverage additional display space; More Shapes – mapping visualizations to spatial fold states; More Interactions – coupling visualization tasks and folding interactions; and More States – enabling responsive visualization through folding. We complement the design space and conceptual framework with a low-fidelity ideation workshop and the subsequent prototyping of interactive artifacts. We reflect on the results and lessons learned from our exploratory, design-driven process and discuss opportunities and challenges, including the gap between our proposed concepts and commercially available foldable devices. By providing conceptual foundations and illustrating the potential of foldables, we hope to inspire and inform the development of future visualization applications for this evolving class of devices.

Details on demand is a common design pattern in visualization design, especially useful when interacting with visually-saturated or small displays. Beyond visualization, another common approach for saturated displays is to incorporate other modalities, such as haptic feedback. While haptic rendering in visualization has primarily targeted accessibility needs, with haptics as a substitute for visual feedback, studies using haptics outside of a visualization context have shown value in experiential factors, such as increased confidence in ambiguous contexts and higher engagement. We explore vibrotactile feedback as a reinforcing information channel for communicating trends in details-on-demand tooltips on touchscreens. We identify preferred parameter configurations for our haptic encoding, informed by a study where participants identified parameter configurations that they perceived to most accurately reflect the dynamics of line charts appearing in tooltips. In a second study, we evaluated participant performance in a pairwise comparison task, finding that incorporating vibrotactile encoding improves involvement without affecting accuracy. We discuss the implications of these findings for future visualization design, and propose directions for applications and future studies.

Visualizations are central to communicating data, yet blind and low-vision (BLV) people often lack support for understanding chart types---knowledge that is essential for interpreting new visualizations and collaborating with sighted peers. Prior work found that BLV individuals viewed example tactile charts as more helpful than text-only approaches and preferred them for learning advanced chart types, particularly for understanding spatial layouts and shapes. Meanwhile, large language models (LLMs) are increasingly used by BLV individuals for chart explanation and question answering (QA), but have been studied primarily for dataset exploration rather than chart-type learning. Existing LLM-based chart QA also shows that users frequently ask about layout and structure, yet struggle with spatial concepts and misdirect questions when mental models are weak. We investigate how LLMs influence chart-type learning and whether tactile learning improves subsequent LLM-supported exploration. We extend our tactile chart learning tools with an LLM chatbot that provides interactive explanations and supports follow-up questions. In an interview study with 12 BLV participants, we compare two learning formats: (1) a tactile chart, a textual explanation, and an LLM chatbot; and (2) a textual explanation and an LLM chatbot. The learning phase was followed by exploration of an unfamiliar dataset using alt text and an LLM. Thematic analysis shows that tactile templates support BLV participants' formation of chart-type mental models, which scaffolds subsequent LLM-mediated data exploration. Text+LLM explanations without tactile support show weaknesses for spatial-reasoning tasks.

Combining refreshable tactile displays (RTDs) with conversational AI offers a promising approach to accessible data visualization for people who are blind or have low vision (BLV). However, it remains an open question how these modalities should be integrated to support accessible data experiences. We address this through a co-design process with three BLV co-designers. Building on our prior Wizard-of-Oz study, we created a conversational tactile data interface (CTDI) that combines an RTD with an LLM-powered conversational agent, refined through four workshops over eight months. In addition to the resulting system, Graphy, we contribute design knowledge and recommendations for CTDIs. Co-designers used touch as the primary sensemaking channel for spatial understanding of the data’s shape, trends, and relationships, reserved the agent for what touch could not resolve (e.g., calculation and analysis), and used the chart on the RTD to verify the agent’s responses. Key findings include: a layered presentation that scaffolds chart exploration through progressive, interactive layers; a feedback grammar that distinguishes user- and agent-initiated tactile feedback; and a sequential interaction pattern—select, confirm, ask, verify—where each step grounds the last.

Group awareness, the ability to perceive the activities of collaborators in a shared space, is a vital mechanism to support effective coordination and joint data analysis in collaborative visualization. We introduce collaborative attention-aware visualizations (CAAVs) that track, record, and revisualize the collective attention of multiple users over time. We implement this concept in HEEDVISION, a standards-compliant WebXR system built with React Three Fiber that runs on modern AR/VR headsets, and complement it with proof-of-concept implementations covering the remaining three quadrants of our design space, varying presentation (embedded vs. separated) and situatedness (world space vs. camera space). Through a mixed-methods exploratory study where pairs of co-located analysts performed visual search tasks in a shared immersive AR environment, we investigate how attention revisualization affects collaborative coordination in immersive analytics. Our results show that CAAVs can improve spatial coordination, search efficiency, and task load distribution in collaborative visual search tasks among collaborators, though benefits vary by context, favoring abstract environments lacking natural landmarks. This work extends attention awareness to multi-user settings and provides empirical evidence for its context-dependent benefits in collaborative visual search tasks within immersive analytics environments.

Anatomical liver resection (ALR) surgery is the most important treatment for liver cancer, yet preoperative planning demands complex, multi-stage clinical reasoning under competing safety constraints. Current 2D desktop tools are not well equipped to support this process, exhibiting three fundamental limitations: reliance on monolithic interfaces that fail to adapt to the distinct cognitive demands of each planning stage; a perceptual bottleneck caused by limited anatomical spatial representation and missing plane-vessel intersection visualization; and an attention bottleneck stemming from fragmented critical safety criteria display across separate views. We present LiverPlan, a stage-adaptive immersive visual analytics framework for ALR planning, grounded in an 8-month collaboration with two expert hepatobiliary surgeons. Decomposing the surgical planning process into three sequential yet cognitively distinct stages, LiverPlan externalizes the cognitive demand of each stage via tailored techniques: (1) context-preserving focus and hue-preserving rendering for anatomical discovery; (2) direct 3D resection plane manipulation coupled with real-time, embedded visual feedback on critical safety criteria during plan refinement; and (3) explicit plane-vessel intersection visualization for anticipatory surgery preparation. A within-subjects study with eight hepatobiliary surgeons against a desktop baseline shows large-effect-size improvements in task completion time, perceived cognitive workload, and system usability on controlled planning tasks. Moreover, our study reveals broader insights: LiverPlan reduces cognitive burden and encourages a shift in surgeons from merely satisfying safety criteria to actively optimizing them, suggesting that explicit visualization of spatial relationships lowers the cognitive barrier to complex surgical planning.

Visualisation is increasingly used to help people explore and reflect on their personal data. However, most work on personal visualisation relies on screen-based solutions that can be hard to integrate into real-world experiences. Immersive visualisation technologies offer solutions to these challenges. In this paper we explore how combining immersive approaches with personal visualisation presents new opportunities for engaging with personal data through situated, affective, and experiential interaction. We examine this emergent space of Immersive Personal Visualisation through a scoping review of 21 studies. We provide a characterisation of existing work in this domain with our analysis revealing five structural trends: an emphasis on late-stage reflective engagement in the personal informatics lifecycle; a prevalence for affective forms of data engagement; a focus on real-time data experiences; widespread use of abstract and metaphorical visual encoding; and a tendency for users to remain passive viewers in implemented systems. Building on these findings, we expose key opportunities for advancing immersive personal visualisation, highlighting the role of interaction design, interpretable visualisation, and lifecycle-spanning engagement. We conclude by outlining a future research agenda for advancing immersive personal visualisation and argue that, as XR technologies become more accessible and pervasive, this emerging space has the potential to reshape how people access, understand, and live with their data.