Exploring Collaborative Immersive Visualization & Analytics for High-Dimensional Scientific Data through Domain Expert Perspectives

TL;DR

This paper investigates how domain scientists analyze and collaborate on high-dimensional data and envisions collaborative immersive platforms (CIVA) to support distributed sensemaking. Through 20 semi-structured interviews analyzed with a hybrid deductive-inductive approach, it identifies four themes—workflow challenges, adoption perceptions, prospective features, and usability/ethical risks—and derives five design implications focused on shared history, social translucence, cross-device synchronization, accessibility, and AI-mediated coordination. The findings reveal fragmentation in current toolchains and highlight opportunities for cohesive multi-user collaboration, while foregrounding concerns around access, privacy, comfort, and interpretability. Collectively, the work provides empirical grounding and concrete guidelines for building scalable, inclusive, cross-device immersive environments that enable joint reasoning over high-dimensional scientific data.

Abstract

Cross-disciplinary teams increasingly work with high-dimensional scientific datasets, yet fragmented toolchains and limited support for shared exploration hinder collaboration. Prior immersive visualization and analytics research has emphasized individual interaction, leaving open how multi-user collaboration can be supported at scale. To fill this critical gap, we conduct semi-structured interviews with 20 domain experts from diverse academic, government, and industry backgrounds. Using deductive-inductive hybrid thematic analysis, we identify four collaboration-focused themes: workflow challenges, adoption perceptions, prospective features, and anticipated usability and ethical risks. These findings show how current ecosystems disrupt coordination and shared understanding, while highlighting opportunities for effective multi-user engagement. Our study contributes empirical insights into collaboration practices for high-dimensional scientific data visualization and analysis, offering design implications to enhance coordination, mutual awareness, and equitable participation in next-generation collaborative immersive platforms. These contributions point toward future environments enabling distributed, cross-device teamwork on high-dimensional scientific data.

Figures (9)

• Figure 1: Examples of immersive visualization and analytics platforms across different modalities. (1) STREAM hubenschmid2021stream integrating spatially-aware tablets with AR head-mounted displays for multimodal 3D data interaction. © 2021 ACM. Image reused under CC BY 4.0; (2) ParaView’s XR Interface plugin used in VR with an Oculus Quest 2 headset for exploring high-dimensional scientific data; (3) A CAVE installation at the National Institute of Standards and Technology (NIST) supporting room-scale immersion with HPC-driven datasets; and (4) A conceptual illustration of a SAGE2 marrinan2014sage2 tiled display wall environment depicting collaborative use of images, videos, documents, and 2D/3D data applications across a shared display and personal devices. Generated using GenAI tools and refined by the authors.
• Figure 2: Sequential stages of the user interview process. Generated using GenAI tools and refined by the authors.
• Figure 3: Our thematic analysis resulted in four major themes, each comprising multiple sub-themes.
• Figure 4: Current workflow for high-dimensional scientific data visualization and analytics.
• Figure 5: Provenance swimlanes illustrating a hypothetical collaboration scenario in CIVA, showing branching and merging among three users. Horizontal arrows represent each user’s own workflow progression, while dotted arrows indicate collaborative exchanges. The figure highlights how individual analyses converge into a merged outcome through prospective multi-user provenance tracking.