DeepSee: Making It Easier to Analyze and Explore Ocean Data for Future Research | HackerNoon

Table of Links

Abstract and 1 Introduction

2 Related Work

3 Methodology

4 Studying Deep Ocean Ecosystem and 4.1 Deep Ocean Research Goals

4.2 Workflow and Data

4.3 Design Challenges and User Tasks

5 The DeepSea System

• 5.1 Map View
• 5.2 Core View

5.3 Interpolation View and 5.4 Implementation

6 Usage Scenarios and 6.1 Scenario: Pre-Cruise Planning

• 6.2 Scenario: On-the-Fly Decision-Making

7 Evaluation and 7.1 Cruise Deployment

7.2 Expert Interviews

7.3 Limitations

7.4 Lessons Learned

8 Conclusions and Future Work, Acknowledgments, and References

8 CONCLUSIONS AND FUTURE WORK

In this work, we conducted a design study with a team of domain experts in geology, chemistry, and biology to develop multidimensional visualizations of seabed sediment cores for studying deep ocean microbial ecosystems. We presented DeepSee, an interactive workspace that enables scientists to explore their previous sediment sampling history and decide where new sediment samples may yield the greatest scientific return. By visualizing samples in the context of the environment, bringing together multiple data types in a single interface, and providing capabilities to predict values in unsampled locations, DeepSee offers a new approach to visualizing 3D point samples in fieldwork-driven science.

In the future, we envision adding enhanced data analysis capabilities such as supporting multiple levels of phylogenetic hierarchy as well as aggregating and visualizing hierarchical data in the Core View. Additionally, we aim to integrate new and more sophisticated models for large-scale interpolation over several tens or hundreds of meters, based on new research enabled by DeepSee. The design elements introduced by DeepSee could also extend beyond operator-driven deep ocean sampling into similar domains, including studying remote terrestrial ecosystems as well as interplanetary exploration missions. For example, as autonomous sampling scenarios led by an AI system become possible, we could leverage insights into how human operators work with a system like DeepSee to train future autonomous sampling systems.

ACKNOWLEDGMENTS

Funding for this work was provided by grants from the National Science Foundation including the STC Center For Dark Energy Biosphere Investigations and NSF-OCE 2048666 (to V.J.O.). D.R.U. is a National Science Foundation-Ocean Sciences Postdoctoral Research Fellow (2126631), R.L.W. is an NSF Graduate Research Fellow, and S.A.P. is supported through a grant from the NASA FINESST program (80NSSC22K1336). V.J.O. is a science fellow in the Canadian Institute for Advanced Science (CIFAR) in the Earth 4D program.

Data included in this study was collected during cruise FK181031 on the R/V Falkor supported by the Schmidt Ocean Institute. AUV data have been archived at the Marine Geoscience Data System (MGDS) in the data compilation titled PescaderoBasin_MBARI [5].

The research was carried out at in part at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration (80NM0018D0004). The development of DeepSee was enabled by Data to Discovery[6], a data visualization, art and design research initiative based at NASA Jet Propulsion Laboratory, California Institute of Technology and ArtCenter College of Design; this support is gratefully acknowledged. Finally, we would also like to thank Jasmine Otto for her valuable discussions about this research.

Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

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