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 (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. 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.

  author = {Coscia, Adam and Sapers, Haley M. and Deutsch, Noah and Khurana, Malika and Magyar, John S. and Parra, Sergio A. and Utter, Daniel R. and Wipfler, Rebecca L. and Caress, David W. and Martin, Eric J. and Paduan, Jennifer B. and Hendrie, Maggie and Lombeyda, Santiago and Mushkin, Hillary and Endert, Alex and Davidoff, Scott and Orphan, Victoria J.},  
  title = {DeepSee: Multidimensional Visualizations of Seabed Ecosystems},  
  year = {2024},  
  isbn = {979-8-4007-0330-0/24/05},  
  publisher = {Association for Computing Machinery},  
  address = {New York, NY, USA},  
  url = {},  
  doi = {10.1145/3613904.3642001},  
  booktitle = {Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems},  
  location = {Honolulu, HI, USA},  
  series = {CHI '24}