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

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. REFERENCES [1] S. Afzal, M.M. Hittawe, S. Ghani, T. Jamil, O. Knio, M. Hadwiger, and I. Hoteit. 2019. The State of the Art in Visual Analysis Approaches for Ocean and Atmospheric Datasets. Computer Graphics Forum 38, 3 (2019), 881–907. https://doi.org/10. 1111/cgf.13731 [2] Wael H. Ali, Mohamad H. Mirhi, Abhinav Gupta, Chinmay S. Kulkarni, Corbin Foucart, Manan M. Doshi, Deepak N. Subramani, Chris Mirabito, Patrick J. Haley, and Pierre F. J. Lermusiaux. 2019. SeaVizKit: Interactive Maps for Ocean Visualization. In OCEANS 2019 MTS/IEEE SEATTLE. 1–10. https://doi.org/10.23919/ OCEANS40490.2019.8962794 [3] Natalia Andrienko and Gennady Andrienko. 2006. Exploratory analysis of spatial and temporal data: a systematic approach. Springer Science & Business Media. 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[5] https://www.marine-geo.org/tools/search/entry.php?id=PescaderoBasin_MBARI [6] https://datavis.caltech.edu/ Authors: (1) Adam Coscia, Georgia Institute of Technology, Atlanta, Georgia, USA (acoscia6@gatech.edu); (2) Haley M. Sapers, Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, California, USA (hsapers@caltech.edu); (3) Noah Deutsch, Harvard University Cambridge, Massachusetts, USA (ndeutsch@mde.harvard.edu); (4) Malika Khurana, The New York Times Company, New York, New York, USA (malika.khurana@nytimes.com); (5) John S. Magyar, Division of Geological and Planetary Sciences, California Institute of Technology Pasadena, California, USA (jmagyar@caltech.edu); (6) Sergio A. Parra, Division of Geological and Planetary Sciences, California Institute of Technology Pasadena, California, USA (sparra@caltech.edu); (7) Daniel R. Utter, rwipfler@caltech.edu Division of Geological and Planetary Sciences, California Institute of Technology Pasadena, California, USA (dutter@caltech.edu); (8) John S. Magyar, Division of Geological and Planetary Sciences, California Institute of Technology Pasadena, California, USA (rwipfler@caltech.edu); (9) David W. Caress, Monterey Bay Aquarium Research Institute, Moss Landing, California, USA (caress@mbari.org); (10) Eric J. Martin Jennifer B. Paduan Monterey Bay Aquarium Research Institute, Moss Landing, California, USA (emartin@mbari.org); (11) Jennifer B. Paduan, Monterey Bay Aquarium Research Institute, Moss Landing, California, USA (paje@mbari.org); (12) Maggie Hendrie, ArtCenter College of Design, Pasadena, California, USA (maggie.hendrie@artcenter.edu); (13) Santiago Lombeyda, California Institute of Technology, Pasadena, California, USA (santiago@caltech.edu); (14) Hillary Mushkin, California Institute of Technology, Pasadena, California, USA (hmushkin@caltech.edu); (15) Alex Endert, Georgia Institute of Technology, Atlanta, Georgia, USA (endert@gatech.edu); (16) Scott Davidoff, Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, USA (scott.davidoff@jpl.nasa.gov); (17) Victoria J. Orphan, Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, California, USA (vorphan@caltech.edu). Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Related Work 2 Related Work 3 Methodology 3 Methodology 4 Studying Deep Ocean Ecosystem and 4.1 Deep Ocean Research Goals 4 Studying Deep Ocean Ecosystem and 4.1 Deep Ocean Research Goals 4.2 Workflow and Data 4.2 Workflow and Data 4.3 Design Challenges and User Tasks 4.3 Design Challenges and User Tasks 5 The DeepSea System 5 The DeepSea System 5.1 Map View 5.2 Core View 5.1 Map View 5.2 Core View 5.3 Interpolation View and 5.4 Implementation 5.3 Interpolation View and 5.4 Implementation 6 Usage Scenarios and 6.1 Scenario: Pre-Cruise Planning 6 Usage Scenarios and 6.1 Scenario: Pre-Cruise Planning 6.2 Scenario: On-the-Fly Decision-Making 6.2 Scenario: On-the-Fly Decision-Making 7 Evaluation and 7.1 Cruise Deployment 7 Evaluation and 7.1 Cruise Deployment 7.2 Expert Interviews 7.2 Expert Interviews 7.3 Limitations 7.3 Limitations 7.4 Lessons Learned 7.4 Lessons Learned 8 Conclusions and Future Work, Acknowledgments, and References 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. DeepSee DeepSee 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. Core View DeepSee DeepSee DeepSee 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]. PescaderoBasin_MBARI 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. DeepSee 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. REFERENCES [1] S. Afzal, M.M. Hittawe, S. Ghani, T. Jamil, O. Knio, M. Hadwiger, and I. Hoteit. 2019. The State of the Art in Visual Analysis Approaches for Ocean and Atmospheric Datasets. Computer Graphics Forum 38, 3 (2019), 881–907. https://doi.org/10. 1111/cgf.13731 [2] Wael H. Ali, Mohamad H. Mirhi, Abhinav Gupta, Chinmay S. Kulkarni, Corbin Foucart, Manan M. Doshi, Deepak N. Subramani, Chris Mirabito, Patrick J. Haley, and Pierre F. J. Lermusiaux. 2019. SeaVizKit: Interactive Maps for Ocean Visualization. In OCEANS 2019 MTS/IEEE SEATTLE. 1–10. https://doi.org/10.23919/ OCEANS40490.2019.8962794 [3] Natalia Andrienko and Gennady Andrienko. 2006. Exploratory analysis of spatial and temporal data: a systematic approach. Springer Science & Business Media. [4] Natalia Andrienko, Gennady Andrienko, and Peter Gatalsky. 2003. Exploratory spatio-temporal visualization: an analytical review. Journal of Visual Languages & Computing 14, 6 (2003), 503–541. https://doi.org/10.1016/S1045-926X(03)00046-6 Visual Data Mining. [5] Suyun Bae, Federico Rossi, Joshua Vander Hook, Scott Davidoff, and Kwan-Liu Ma. 2020. A Visual Analytics Approach to Debugging Cooperative, Autonomous Multi-Robot Systems’ Worldviews. In 2020 IEEE Conference on Visual Analytics Science and Technology (VAST). 24–35. https://doi.org/10.1109/VAST50239.2020. 00008 [6] Michael Bostock, Vadim Ogievetsky, and Jeffrey Heer. 2011. D3 data-driven documents. IEEE transactions on visualization and computer graphics 17, 12 (2011), 2301–2309. [7] R.E. Boyatzis. 1998. Transforming Qualitative Information: Thematic Analysis and Code Development. SAGE Publications. [8] R. Cavicchioli, T. Charlton, H. Ertan, S. Mohd Omar, K. S. Siddiqui, and T. J. Williams. 2011. Biotechnological uses of enzymes from psychrophiles. Microbial Biotechnology 4, 4 (2011), 449–460. https://doi.org/10.1111/j.1751-7915.2011. 00258.x [9] Matthew Conlen, Sara Stalla, Chelly Jin, Maggie Hendrie, Hillary Mushkin, Santiago Lombeyda, and Scott Davidoff. 2018. Towards Design Principles for Visual Analytics in Operations Contexts. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (Montreal QC, Canada) (CHI ’18). Association for Computing Machinery, New York, NY, USA, 1–7. https://doi.org/10.1145/3173574.3173712 [10] Cinzia Corinaldesi. 2015. New perspectives in benthic deep-sea microbial ecology. Frontiers in Marine Science 2 (2015). https://doi.org/10.3389/fmars.2015.00017 [11] Michael Correll, Dominik Moritz, and Jeffrey Heer. 2018. Value-suppressing uncertainty palettes. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. 1–11. [12] Roberto Danovaro, Paul V.R. Snelgrove, and Paul Tyler. 2014. Challenging the paradigms of deep-sea ecology. Trends in Ecology & Evolution 29, 8 (2014), 465–475. https://doi.org/10.1016/j.tree.2014.06.002 [13] Robert A. Drebin, Loren Carpenter, and Pat Hanrahan. 1988. Volume Rendering. SIGGRAPH Comput. Graph. 22, 4 (jun 1988), 65–74. https://doi.org/10.1145/ 378456.378484 [14] J Dykes, AM MacEachren, and MJ Kraak. 2005. Exploring Geovisualization. Exploring Geovisualization (2005), 3. [15] Joscha Eirich, Jakob Bonart, Dominik Jäckle, Michael Sedlmair, Ute Schmid, Kai Fischbach, Tobias Schreck, and Jürgen Bernard. 2022. IRVINE: A Design Study on Analyzing Correlation Patterns of Electrical Engines. IEEE Transactions on Visualization and Computer Graphics 28, 1 (2022), 11–21. https://doi.org/10.1109/ TVCG.2021.3114797 [16] Niklas Elmqvist, Andrew Vande Moere, Hans-Christian Jetter, Daniel Cernea, Harald Reiterer, and TJ Jankun-Kelly. 2011. Fluid interaction for information visualization. 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This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv [5] https://www.marine-geo.org/tools/search/entry.php?id=PescaderoBasin_MBARI [6] https://datavis.caltech.edu/ Authors: (1) Adam Coscia, Georgia Institute of Technology, Atlanta, Georgia, USA (acoscia6@gatech.edu); (2) Haley M. Sapers, Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, California, USA (hsapers@caltech.edu); (3) Noah Deutsch, Harvard University Cambridge, Massachusetts, USA (ndeutsch@mde.harvard.edu); (4) Malika Khurana, The New York Times Company, New York, New York, USA (malika.khurana@nytimes.com); (5) John S. Magyar, Division of Geological and Planetary Sciences, California Institute of Technology Pasadena, California, USA (jmagyar@caltech.edu); (6) Sergio A. Parra, Division of Geological and Planetary Sciences, California Institute of Technology Pasadena, California, USA (sparra@caltech.edu); (7) Daniel R. Utter, rwipfler@caltech.edu Division of Geological and Planetary Sciences, California Institute of Technology Pasadena, California, USA (dutter@caltech.edu); (8) John S. Magyar, Division of Geological and Planetary Sciences, California Institute of Technology Pasadena, California, USA (rwipfler@caltech.edu); (9) David W. Caress, Monterey Bay Aquarium Research Institute, Moss Landing, California, USA (caress@mbari.org); (10) Eric J. Martin Jennifer B. Paduan Monterey Bay Aquarium Research Institute, Moss Landing, California, USA (emartin@mbari.org); (11) Jennifer B. Paduan, Monterey Bay Aquarium Research Institute, Moss Landing, California, USA (paje@mbari.org); (12) Maggie Hendrie, ArtCenter College of Design, Pasadena, California, USA (maggie.hendrie@artcenter.edu); (13) Santiago Lombeyda, California Institute of Technology, Pasadena, California, USA (santiago@caltech.edu); (14) Hillary Mushkin, California Institute of Technology, Pasadena, California, USA (hmushkin@caltech.edu); (15) Alex Endert, Georgia Institute of Technology, Atlanta, Georgia, USA (endert@gatech.edu); (16) Scott Davidoff, Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, USA (scott.davidoff@jpl.nasa.gov); (17) Victoria J. Orphan, Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, California, USA (vorphan@caltech.edu). Authors: Authors: (1) Adam Coscia, Georgia Institute of Technology, Atlanta, Georgia, USA (acoscia6@gatech.edu); (2) Haley M. Sapers, Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, California, USA (hsapers@caltech.edu); (3) Noah Deutsch, Harvard University Cambridge, Massachusetts, USA (ndeutsch@mde.harvard.edu); (4) Malika Khurana, The New York Times Company, New York, New York, USA (malika.khurana@nytimes.com); (5) John S. Magyar, Division of Geological and Planetary Sciences, California Institute of Technology Pasadena, California, USA (jmagyar@caltech.edu); (6) Sergio A. Parra, Division of Geological and Planetary Sciences, California Institute of Technology Pasadena, California, USA (sparra@caltech.edu); (7) Daniel R. Utter, rwipfler@caltech.edu Division of Geological and Planetary Sciences, California Institute of Technology Pasadena, California, USA (dutter@caltech.edu); (8) John S. Magyar, Division of Geological and Planetary Sciences, California Institute of Technology Pasadena, California, USA (rwipfler@caltech.edu); (9) David W. Caress, Monterey Bay Aquarium Research Institute, Moss Landing, California, USA (caress@mbari.org); (10) Eric J. Martin Jennifer B. Paduan Monterey Bay Aquarium Research Institute, Moss Landing, California, USA (emartin@mbari.org); (11) Jennifer B. Paduan, Monterey Bay Aquarium Research Institute, Moss Landing, California, USA (paje@mbari.org); (12) Maggie Hendrie, ArtCenter College of Design, Pasadena, California, USA (maggie.hendrie@artcenter.edu); (13) Santiago Lombeyda, California Institute of Technology, Pasadena, California, USA (santiago@caltech.edu); (14) Hillary Mushkin, California Institute of Technology, Pasadena, California, USA (hmushkin@caltech.edu); (15) Alex Endert, Georgia Institute of Technology, Atlanta, Georgia, USA (endert@gatech.edu); (16) Scott Davidoff, Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, USA (scott.davidoff@jpl.nasa.gov); (17) Victoria J. Orphan, Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, California, USA (vorphan@caltech.edu).