DeepSee: Multidimensional Visualizations of Seabed Ecosystems
Adam Coscia, Haley M. Sapers, Noah Deutsch, Malika Khurana, John S. Magyar, Sergio A. Parra, Daniel R. Utter, Rebecca L. Wipfler, David W. Caress, Eric J. Martin, Jennifer B. Paduan, Maggie Hendrie, Santiago Lombeyda, Hillary Mushkin, Alex Endert, Scott Davidoff, Victoria J. Orphan
Caltech • ArtCenter • NASA JPL • MBARI • Georgia Tech
ACM conference on Human Factors in Computing Systems (ACM CHI), 2024
DeepSee teaser

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. Through a collaborative design study with a team of scientific researchers, we developed DeepSee, an interactive data workspace that visualizes 2D and 3D interpolations of biogeochemical processes in context together with sediment sampling history overlaid on 2D seafloor maps. Based on a user-centered design process, qualitative interviews and a field deployment, 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 competing research goals.

Coming soon!