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

2026-02-02

AI Summary

This study addresses the challenges faced by interdisciplinary teams in collaboratively exploring high-dimensional scientific data, which are often hindered by fragmented tools and a lack of shared exploratory mechanisms. Through semi-structured interviews with 20 domain experts from academia, government, and industry, combined with a deductive–inductive hybrid thematic analysis, this work systematically uncovers workflow barriers, adoption willingness, functional expectations, and ethical concerns related to multi-user immersive visualization. The findings distill four key collaboration themes and propose a design direction centered on coordination, mutual awareness, and equitable participation. These insights offer empirical grounding and actionable guidance for the development of next-generation distributed, cross-device collaborative immersive platforms, thereby advancing efficient and inclusive exploration of high-dimensional scientific data.

Technology Category

• Data Mining & Knowledge Management: Mining of Visual, Multimedia & Multimodal Data
• Application Domains: Humanities & Computational Social Science
• Humans and AI: Planning and Decision Support for Human-Machine Teams

Application Category

• Web Mining and Content Analysis: Interdisciplinary science discovery with web data mining
• Security and Privacy: Data transparency and provenance
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization

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.

Problem

• collaborative visualization
• high-dimensional data
• immersive analytics
• multi-user collaboration
• scientific data

Innovation

• collaborative immersive visualization
• high-dimensional scientific data
• multi-user collaboration
• thematic analysis
• cross-disciplinary teamwork