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.

翻译：跨学科团队日益频繁地处理高维科学数据集，然而碎片化的工具链及对协同探索支持的不足阻碍了有效合作。既往的沉浸式可视化与分析研究多聚焦于个体交互，对于如何大规模支持多用户协作仍缺乏深入探讨。为填补这一关键空白，我们对来自学术界、政府机构及产业界不同背景的20位领域专家进行了半结构化访谈。通过演绎-归纳混合主题分析法，我们识别出四个以协作为核心的主题：工作流挑战、采纳认知、预期功能特性，以及可预见的可用性与伦理风险。研究发现揭示了当前生态系统如何干扰团队协调与共识建立，同时凸显了促进高效多用户参与的潜在机遇。本研究为高维科学数据可视化与分析的协作实践提供了实证见解，并为下一代协同沉浸式平台提出了旨在增强协调性、相互感知与公平参与的设计启示。这些成果为构建支持分布式、跨设备的高维科学数据团队协作的未来环境指明了方向。