DeepSee:多维可视化海底生态系统

ArXiv Pub Date : 2024-03-07 DOI:10.1145/3613904.3642001
Adam Joseph Coscia, H. Sapers, Noah Deutsch, Malika Khurana, J. Magyar, Sergio A. Parra, Daniel R. Utter, R.L. Wipfler, D. Caress, Eric J. Martin, J. Paduan, M. Hendrie, S. Lombeyda, H. Mushkin, A. Endert, Scott Davidoff, V. Orphan
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引用次数: 0

摘要

研究深海微生物生态系统的科学家利用从海底采集的数量有限的沉积物样本来描述环境中重要的维持生命的生物地球化学循环。然而,在这些极端偏远的环境中进行实地采样既昂贵又耗时,这就需要有工具能让科学家探索实地采样点的采样历史,并预测在哪些地方采集新样本有可能获得最大的科学回报。我们与科研人员团队合作开展了一项以用户为中心的设计研究,以开发 DeepSee,这是一个交互式数据工作区,可将生物地球化学和微生物过程的二维和三维插值与沉积物取样历史叠加在二维海底地图上,实现可视化。通过实地部署和定性访谈,我们发现 DeepSee 提高了有限样本量的科学回报,催化了新的研究工作流程,降低了数据共享的长期成本,并支持了具有不同研究目标的团队成员之间的团队合作与交流。
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DeepSee: Multidimensional Visualizations of Seabed Ecosystems
Scientists studying deep ocean microbial ecosystems use limited numbers of sediment samples collected from the seafloor to characterize important life-sustaining biogeochemical cycles in the environment. Yet conducting fieldwork to sample these extreme remote environments is both expensive and time consuming, requiring tools that enable scientists to explore the sampling history of field sites and predict where taking new samples is likely to maximize scientific return. We conducted a collaborative, user-centered design study with a team of scientific researchers to develop DeepSee, an interactive data workspace that visualizes 2D and 3D interpolations of biogeochemical and microbial processes in context together with sediment sampling history overlaid on 2D seafloor maps. Based on a field deployment and qualitative interviews, we found that DeepSee increased the scientific return from limited sample sizes, catalyzed new research workflows, reduced long-term costs of sharing data, and supported teamwork and communication between team members with diverse research goals.
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