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

Paper Title

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

Publication Info

• Topic area: Collaborative immersive visualization and analytics for high-dimensional scientific data.
• Keywords: Collaborative immersive visualization, high-dimensional data, immersive analytics, virtual reality, augmented reality, scientific workflows, multi-user interaction, accessibility, provenance, AI-mediated coordination.

Background and Problem

• Problem / challenge: Existing tools for high-dimensional scientific data visualization are fragmented, desktop-bound, and poorly support real-time, multi-user collaboration. Immersive analytics (IA) systems remain underexplored for collaborative workflows, with challenges in synchronization, accessibility, and integration into established scientific practices.
• Significance: Addressing these challenges can enhance real-time collaboration, mutual awareness, and equitable participation, enabling distributed teams to analyze complex datasets more effectively.
• Motivation and related work: Prior research in IA has focused on individual interactions or short-term evaluations, often neglecting the needs of domain experts in real-world collaborative workflows. Collaborative immersive visualization and analytics (CIVA) remains particularly underdeveloped, with limited understanding of how to integrate it into interdisciplinary, distributed scientific teams.

Solution

• Proposed approach: The study investigates domain experts’ workflows, challenges, and expectations for CIVA through semi-structured interviews, thematic analysis, and design implications for future systems.
• Novelty:
  1. Characterization of current workflows and challenges in high-dimensional data visualization and collaboration.
  2. Identification of domain experts’ expectations and concerns regarding CIVA adoption.
  3. Synthesis of desired features for future CIVA systems, including multi-user annotation, provenance capture, and cross-device integration.
  4. Design implications for scalable, accessible, and collaborative immersive platforms.
• Procedure and key techniques:
  1. Conducted semi-structured interviews with 20 domain experts across diverse fields.
  2. Analyzed transcripts using a hybrid deductive–inductive thematic approach.
  3. Identified four major themes: workflow challenges, adoption perceptions, prospective features, and anticipated risks.
  4. Developed design implications based on findings, mapped to collaborative contexts.

Results

• Concrete findings:
  • Identified major workflow challenges, including fragmented tools, data scale barriers, and lack of shared awareness in immersive environments.
  • Experts envision features like synchronized visualization, multi-modal communication, collaborative provenance, and AI-assisted coordination.
  • Anticipated risks include access inequalities, data privacy restrictions, usability challenges, and shared-understanding issues.
• Advantage over baselines:
  • Provides empirical insights into real-world collaborative practices, addressing gaps in prior IA research.
  • Proposes actionable design implications to integrate CIVA into existing workflows and overcome adoption barriers.
• Experiments / evaluation:
  • Semi-structured interviews with 20 participants across academic, government, and industry sectors.
  • Analysis of workflows, tools, and collaboration practices using thematic coding and affinity diagramming.
• Limitations and future work:
  • Sample skew toward experienced researchers; no direct interaction with immersive systems during interviews.
  • Future work should explore sustainable development models, structured evaluations of collaboration quality, and accessibility in diverse contexts.

Summary

This study examines the potential of collaborative immersive visualization and analytics (CIVA) for high-dimensional scientific data through interviews with 20 domain experts. It identifies key workflow challenges, such as fragmented tools and limited real-time collaboration, and synthesizes desired features like synchronized visualization, AI assistance, and cross-device integration. Anticipated risks include access inequalities, data privacy concerns, and usability barriers. The findings provide actionable design implications for scalable, accessible CIVA systems, positioning immersive collaboration as a transformative infrastructure for interdisciplinary scientific teams.