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 5 THE DEEPSEE SYSTEM From our design challenges and user tasks, we developed DeepSee, an interactive workspace for studying deep ocean ecosystems. Our system tightly integrates three coordinated views. In the Map View (Sect. 5.1), users explore maps at near-centimeter resolution of the ocean floor overlaid with locations of and information about previous dives and sampled cores. After selecting cores of interest, users can compare geochemical, physical, and biological attributes across horizons between cores in the Core View (Sect. 5.2) or analyze interpolated attribute values in 3D between cores in the Interpolation View (Sect. 5.3). 5.1 Map View The Map View (Fig. 3) plots previously sampled cores as a scatter plot using latitude/longitude on top of maps representing geological landscapes (e.g., bathymetry) and surface biological and ephemeral “soft” features (e.g., photomosaics and high-resolution lidar). By visualizing both region-level (map) and core-level (measurement) data simultaneously, users gain more context about spatial trends in the sampling environment by familiarizing themselves with data from previous cruises to help make new inferences about potential future sampling locations. Users start with filtering cores (Fig. 3A) by named location, date range, and core fate (T6). For example, a scientist not familiar with a previous field site could explore names and date ranges to understand when cores were sampled, or a scientist returning to the site could be interested in a specific geological feature or set of cores previously sampled for a follow-up investigation (T9). Then, users navigate the plot by clicking and dragging to pan and zoom (Fig. 3B) (T7). Cores remain the same size at all levels of zoom to avoid screen clutter and help users track them as the view changes. We also provide several details on demand (Fig. 3C). We label the plot with latitude and longitude grid lines and compute the width and height of the current viewport in metric units. This gives users a baseline for the size of the geological and biological features they see on the map as well as the distance between sampled cores at all times (T5). On hover, users can see the named location, fate, date, and exact latitude and longitude of any core. Users can also switch the map on the fly (Fig. 3D) and the boundaries of the viewport will persist, helping users maintain context as the view changes (T5). Once a site of interest has been located, we built an in-situ drawing tool on the map with six colors and undo/redo capabilities (Fig. 3E) to support evidence marshaling through annotation (T3). For example, a user could draw an outline on a bathymetric map around a surface expression of biological activity, write a small note to remember/share their intent, then switch to a seafloor mosaic image to see the same drawn feature projected on the sediment composition (T2). The user naturally concludes their initial exploration by selecting cores (Fig. 3F) to visualize in the Core View or Interpolation View (T8). After clicking and dragging over any number of points, we draw the rectangular convex hull that encases the selected points as a white border with a triangle at the top that always points North (T5). This border is drawn again in the Interpolation View to help orient the user at all times when comparing with the Map View 5.2 Core View The Core View (Fig. 4) arranges columns of horizontal bar charts that visualize a single core-level (measurement) or sample-level (parameter) attribute as individual bars at each horizon of the selected cores in the Map View. This plot enables rapid comparison of attributes in one dimension (depth) to quickly gain insights into biological and geochemical parameter distributions, helping users investigate subsurface gradients or trends among selected cores from a single expedition or multiple expeditions simultaneously. To add a chart of a single attribute for each selected core in the Map View, users search for and select attributes using a dropdown menu. The bar charts show core IDs, fates, dates, and are aligned vertically by depth horizon to facilitate comparison across bar charts along the same horizontal baseline (T4). For example, with a small number of cores selected around a vent in the Map View, a user could use the Core View to ask, “Which cores have high sulfide values? Do all cores with high sulfide also have high relative taxa ‘X’ abundance? If not, what other geochemical parameters are different that might explain this difference?” (T1, T2) leading to predictive analysis such as “As we move further away from this vent, do we see these values increasing or decreasing? Are all orange mats associated with relative taxa ‘X’ abundance?” (T10) We chose horizontal bars to visually mirror the measurement of parameters by depth in the sediment. Where horizons are different sizes, the smallest step size (typically 1 cm) is used. If a core does not have sample data at that resolution (e.g., parameters measured every 3 cm), we duplicate the bar by the number of smallest steps at that scale and repeat the horizon label to make this clear for the user. Users have a choice of six colorblind-friendly palettes (Viridis, Cividis, Greyscale, Inferno, Plasma, Magma). Finally, users can directly deselect cores from this view, to narrow the space of cores for subsequent analysis in the Interpolation View (T8). This paper is available on arxiv under CC BY 4.0 DEED license. 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 5 THE DEEPSEE SYSTEM From our design challenges and user tasks, we developed DeepSee, an interactive workspace for studying deep ocean ecosystems. Our system tightly integrates three coordinated views. In the Map View (Sect. 5.1), users explore maps at near-centimeter resolution of the ocean floor overlaid with locations of and information about previous dives and sampled cores. After selecting cores of interest, users can compare geochemical, physical, and biological attributes across horizons between cores in the Core View (Sect. 5.2) or analyze interpolated attribute values in 3D between cores in the Interpolation View (Sect. 5.3). 5.1 Map View The Map View (Fig. 3) plots previously sampled cores as a scatter plot using latitude/longitude on top of maps representing geological landscapes (e.g., bathymetry) and surface biological and ephemeral “soft” features (e.g., photomosaics and high-resolution lidar). By visualizing both region-level (map) and core-level (measurement) data simultaneously, users gain more context about spatial trends in the sampling environment by familiarizing themselves with data from previous cruises to help make new inferences about potential future sampling locations. Map View Users start with filtering cores (Fig. 3A) by named location, date range, and core fate (T6) . For example, a scientist not familiar with a previous field site could explore names and date ranges to understand when cores were sampled, or a scientist returning to the site could be interested in a specific geological feature or set of cores previously sampled for a follow-up investigation (T9) . Then, users navigate the plot by clicking and dragging to pan and zoom (T6) (T9) (Fig. 3B) (T7). Cores remain the same size at all levels of zoom to avoid screen clutter and help users track them as the view changes. We also provide several details on demand (Fig. 3C). We label the plot with latitude and longitude grid lines and compute the width and height of the current viewport in metric units. This gives users a baseline for the size of the geological and biological features they see on the map as well as the distance between sampled cores at all times (T5). On hover, users can see the named location, fate, date, and exact latitude and longitude of any core. Users can also switch the map on the fly (Fig. 3D) and the boundaries of the viewport will persist, helping users maintain context as the view changes (T5) . (T7). (T5) Once a site of interest has been located, we built an in-situ drawing tool on the map with six colors and undo/redo capabilities (Fig. 3E) to support evidence marshaling through annotation (T3). For example, a user could draw an outline on a bathymetric map around a surface expression of biological activity, write a small note to remember/share their intent, then switch to a seafloor mosaic image to see the same drawn feature projected on the sediment composition (T2). The user naturally concludes their initial exploration by selecting cores (Fig. 3F) to visualize in the Core View or Interpolation View (T8). After clicking and dragging over any number of points, we draw the rectangular convex hull that encases the selected points as a white border with a triangle at the top that always points North (T5). This border is drawn again in the Interpolation View to help orient the user at all times when comparing with the Map View 5.2 Core View The Core View (Fig. 4) arranges columns of horizontal bar charts that visualize a single core-level (measurement) or sample-level (parameter) attribute as individual bars at each horizon of the selected cores in the Map View. This plot enables rapid comparison of attributes in one dimension (depth) to quickly gain insights into biological and geochemical parameter distributions, helping users investigate subsurface gradients or trends among selected cores from a single expedition or multiple expeditions simultaneously. Core View To add a chart of a single attribute for each selected core in the Map View, users search for and select attributes using a dropdown menu. The bar charts show core IDs, fates, dates, and are aligned vertically by depth horizon to facilitate comparison across bar charts along the same horizontal baseline (T4). For example, with a small number of cores selected around a vent in the Map View, a user could use the Core View to ask, “Which cores have high sulfide values? Do all cores with high sulfide also have high relative taxa ‘X’ abundance? If not, what other geochemical parameters are different that might explain this difference?” (T1, T2) leading to predictive analysis such as “As we move further away from this vent, do we see these values increasing or decreasing? Are all orange mats associated with relative taxa ‘X’ abundance?” (T10) We chose horizontal bars to visually mirror the measurement of parameters by depth in the sediment. Where horizons are different sizes, the smallest step size (typically 1 cm) is used. If a core does not have sample data at that resolution (e.g., parameters measured every 3 cm), we duplicate the bar by the number of smallest steps at that scale and repeat the horizon label to make this clear for the user. Users have a choice of six colorblind-friendly palettes (Viridis, Cividis, Greyscale, Inferno, Plasma, Magma). Finally, users can directly deselect cores from this view, to narrow the space of cores for subsequent analysis in the Interpolation View (T8). (T4). (T1, T2) (T10) (T8). This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv 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).

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Bringing Ocean Data to Life with DeepSee's 3D Interpolation

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