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The Monterey Police Department located and arrested a man who they say had been seen peeping through hotel room windows.

Man suspected of peeping at least 5 times in Monterey, police say

(KRON) — A man peeped into Monterey hotel rooms for months until he was caught climbing outside yet another hotel in the beachside tourist town, law enforcement authorities said Friday. Michael Clark, 33, of Modesto, was nabbed while Monterey Police Department detectives were conducting ...

Suspected serial peeper preyed on women who stayed in Monterey hotels, police say | KRON4

MONTEREY, Calif. (KION-TV) -- A 33-year-old Modesto man is arrested on multiple charges, including peeping and attempted burglary, for allegedly looking into the windows of occupied hotel rooms on multiple occasions. Monterey Police said the suspect, identified as Michael Clark, was caught ...

Modesto man arrested for allegedly peeping into Monterey hotel windows on multiple occasions – KION546

What If There Was a Right Way to Argue Online?

¿Qué pasaría si existiera una forma correcta de discutir en línea?

Entä jos olisi oikea tapa väitellä verkossa?

E se houbese un xeito correcto de argumentar en liña?

オンラインで議論する正しい方法があったら?

Интернетте дауласудың дұрыс жолы болса ше?

온라인에서 논쟁하는 올바른 방법이 있다면 어떨까?

Ce se întâmplă dacă ar exista o modalitate corectă de a argumenta online?

Что, если бы существовал правильный способ спорить онлайн?

Шта ако је постојао прави начин за свађу на мрежи?

Tänk om det fanns ett rätt sätt att argumentera online?

ብኢንተርነት ክትከራኸረሉ እትኽእል ቅኑዕ መገዲ እንተ ዚህሉኸ፧

Điều gì sẽ xảy ra nếu có một cách đúng đắn để tranh luận trực tuyến?

Have you ever been in the middle of an important task, only for your Mac to dim the screen or go to sleep at the worst possible moment? Whether you’re giving a presentation, running a long download, monitoring real-time data, or just stepping away briefly, there are plenty of situations where you might need your Mac to stay awake. Fortunately, there are multiple ways to prevent your Mac from sleeping — some built-in, and others requiring a little extra help. Let’s explore the best options. Adjust Energy Saver Settings Adjust Energy Saver Settings One way to prevent your Mac from sleeping is to modify the system settings: For macOS Ventura and later: For macOS Ventura and later: Open System Settings → Displays → Advanced
Enable Prevent automatic sleeping when the display is off Open System Settings → Displays → Advanced Enable Prevent automatic sleeping when the display is off For macOS Monterey and earlier: For macOS Monterey and earlier: Go to System Preferences → Energy Saver
Adjust the Turn display off after slider to “Never” Go to System Preferences → Energy Saver Adjust the Turn display off after slider to “Never” This method works well if you only need to keep the system awake while plugged in. However, it doesn’t always prevent applications from detecting inactivity. Use Terminal Command Use Terminal Command For those comfortable with the command line, macOS provides a built-in tool called caffeinate that prevents sleep temporarily. How to use it: Open Terminal
Type: caffeinate -d Open Terminal Type: caffeinate -d This keeps your Mac awake until you close the Terminal window. To stop it, press Control + C or close the Terminal window. You can also use additional options like caffeinate -t 3600 to keep the Mac awake for an hour. External Hardware Solutions Some users opt for external hardware-based mouse jigglers, which are small USB devices that move the cursor automatically. These are plug-and-play solutions that don’t require software installation. While effective, they’re less flexible compared to software options. Third-Party Apps for Keeping Your Mac Awake Sometimes, built-in settings aren’t enough, especially if certain applications check for activity beyond system-level sleep prevention. That’s where third-party apps come in. Mouse Jiggler — For Simulating Activity Mouse Jiggler — For Simulating Activity Unlike traditional sleep prevention apps, Mouse Jiggler works by simulating actual cursor movement, which some applications and policies require to detect activity. When this is useful: When this is useful: • Remote Desktop & Virtual Machines — Apps like Citrix, VMware, and Microsoft RDP may log you out if there’s no mouse or keyboard activity. • Web Applications & Security Portals — Some platforms enforce automatic logouts unless movement is detected. • Live Monitoring Dashboards — Stock trading platforms, analytics dashboards, or real-time monitoring tools often pause or time out without user interaction. • Software Testing & Automation — Developers and IT professionals can use it to simulate user input during testing. Mouse Jiggler also includes Silent Mode, which moves the cursor invisibly to avoid interfering with workflows, along with customizable deactivation options. Download for free from the App Store: https://apps.apple.com/app/id6740313656 Download for free from the App Store https://apps.apple.com/app/id6740313656 Which Method Is Right for You? The best way to keep your Mac awake depends on your needs: ✅ For presentations, sleep prevention or preventing logouts in remote sessions → Mouse Jiggler provides actual movement detection. ✅ For command-line users → Terminal’s caffeinate command is a quick and easy option. ✅ For a plug-and-play solution → A hardware jiggler might be a good choice. Keeping your Mac awake doesn’t have to be complicated. Whether you prefer a built-in option or a third-party tool, there’s a solution that fits your workflow. What’s your preferred way to keep your Mac awake? Let us know in the comments! 🚀

How to Adjust Your Mac's Energy Saver Settings to Keep It Awake For Longer

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

How DeepSee Is Changing the Way Scientists Use Data in the Field

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 7.3 Limitations Several data wrangling and analysis limitations were expressed during the interviews. A common issue discussed was prerequisite knowledge needed to format the data; e.g., P5 faced challenges “inputting DNA sequence information. Knowing how to input data and the centralization of that data is challenging. It’s often that one person ends up inputting data.” While combining multiple data types in a single interface was a design goal, P1 lamented a reduction in dimensionality: “Certain data types might have different data descriptions and having multiple columns and reshaping tables is a challenge... Do I want to try to fit existing data into a certain tool like DeepSee?” P1 also wanted taxonomic data stratified hierarchically, a feature common to many biological analysis systems: “There could be very rare individual units that are technically different taxa. This creates very sparse data where abundance is very small for most of the columns. We needed a way to pick and find different subsets of taxa based on those hierarchies.” Finally, P4 wanted to see “a photo or snapshot of what a core actually looked like. An in-situ photo would allow a person to see what the sea floor looked like, what other cores were around it.” The Interpolation View provided a contextualized way to view the data, as most other similar programs visualize gradients in 2D without spatial context between the sampled cores. However, there was a lack of confidence in the accuracy of the interpolation methods, as natural neighbors and linear gradients are unlikely to reflect spatially heterogeneous in-situ environmental processes. Similarly, distance to the nearest sample as a proxy for uncertainty was not accurate enough to use for in-situ decision making. Still, P2 suggested potential applications of Value-Suppressing Uncertainty Palettes (VSUPs) [11] in the future as the team develops more accurate uncertainty quantification: “We can see where the data is not strong and potentially sample to strengthen our predictions.” Specifically, the researchers noted that the modular nature of DeepSee will make incorporating more sophisticated interpolation methods (e.g., a Gaussian process such as Kriging [36]) and uncertainty measures (e.g., variance in the posterior predictive distribution of the Gaussian process) in the future straightforward. One deployment goal that was not validated during DeepSee’s maiden expedition was whether data could be input in real time to visualize, as many of the relevant geochemical and biological parameters could not be measured while on the cruise, requiring further analysis on land. P3 reflected on this challenge, saying: “We talked about using DeepSee on the fly during the research expedition to load data right away. So far, that’s been impractical. We don’t have geochem or bio data to put in DeepSee while at sea. We also haven’t had a cruise to test whether these challenges were due to personnel on board or based on limitations with the DeepSee interface. Everyone was overextended on the expedition and no one had time to do additional data entry on the computer.” However, the team felt this helped them understand the time and energy allotment needed to use DeepSee for real-time data integration and visualization. P3 further discussed how they envision DeepSee being used on future cruises: “In the future, we’ll have a full team of scientists, and we’ll also have a manned submersible taking cores... We’ll have time to load the day’s cores into DeepSee to help plan for the next day... The important objective is having more people to do the same number of tasks... I could see this becoming one of the evening tasks: take the day’s coordinates and load them into DeepSee.” We synthesize design guidance for supporting real-time decision making shipboard in the next section. 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 7.3 Limitations Several data wrangling and analysis limitations were expressed during the interviews. A common issue discussed was prerequisite knowledge needed to format the data; e.g., P5 faced challenges “inputting DNA sequence information. Knowing how to input data and the centralization of that data is challenging. It’s often that one person ends up inputting data.” While combining multiple data types in a single interface was a design goal, P1 lamented a reduction in dimensionality: “Certain data types might have different data descriptions and having multiple columns and reshaping tables is a challenge... Do I want to try to fit existing data into a certain tool like DeepSee?” P1 also wanted taxonomic data stratified hierarchically, a feature common to many biological analysis systems: “There could be very rare individual units that are technically different taxa. This creates very sparse data where abundance is very small for most of the columns. We needed a way to pick and find different subsets of taxa based on those hierarchies.” Finally, P4 wanted to see “a photo or snapshot of what a core actually looked like. An in-situ photo would allow a person to see what the sea floor looked like, what other cores were around it.” “inputting DNA sequence information. Knowing how to input data and the centralization of that data is challenging. It’s often that one person ends up inputting data.” “Certain data types might have different data descriptions and having multiple columns and reshaping tables is a challenge... Do I want to try to fit existing data into a certain tool like DeepSee?” “There could be very rare individual units that are technically different taxa. This creates very sparse data where abundance is very small for most of the columns. We needed a way to pick and find different subsets of taxa based on those hierarchies.” “a photo or snapshot of what a core actually looked like. An in-situ photo would allow a person to see what the sea floor looked like, what other cores were around it.” The Interpolation View provided a contextualized way to view the data, as most other similar programs visualize gradients in 2D without spatial context between the sampled cores. However, there was a lack of confidence in the accuracy of the interpolation methods, as natural neighbors and linear gradients are unlikely to reflect spatially heterogeneous in-situ environmental processes. Similarly, distance to the nearest sample as a proxy for uncertainty was not accurate enough to use for in-situ decision making. Still, P2 suggested potential applications of Value-Suppressing Uncertainty Palettes (VSUPs) [11] in the future as the team develops more accurate uncertainty quantification: “We can see where the data is not strong and potentially sample to strengthen our predictions.” Specifically, the researchers noted that the modular nature of DeepSee will make incorporating more sophisticated interpolation methods (e.g., a Gaussian process such as Kriging [36]) and uncertainty measures (e.g., variance in the posterior predictive distribution of the Gaussian process) in the future straightforward. Interpolation View “We can see where the data is not strong and potentially sample to strengthen our predictions.” DeepSee One deployment goal that was not validated during DeepSee’s maiden expedition was whether data could be input in real time to visualize, as many of the relevant geochemical and biological parameters could not be measured while on the cruise, requiring further analysis on land. P3 reflected on this challenge, saying: “We talked about using DeepSee on the fly during the research expedition to load data right away. So far, that’s been impractical. We don’t have geochem or bio data to put in DeepSee while at sea. We also haven’t had a cruise to test whether these challenges were due to personnel on board or based on limitations with the DeepSee interface. Everyone was overextended on the expedition and no one had time to do additional data entry on the computer. ” However, the team felt this helped them understand the time and energy allotment needed to use DeepSee for real-time data integration and visualization. P3 further discussed how they envision DeepSee being used on future cruises: “In the future, we’ll have a full team of scientists, and we’ll also have a manned submersible taking cores... We’ll have time to load the day’s cores into DeepSee to help plan for the next day... The important objective is having more people to do the same number of tasks... I could see this becoming one of the evening tasks: take the day’s coordinates and load them into DeepSee.” We synthesize design guidance for supporting real-time decision making shipboard in the next section. “We talked about using DeepSee on the fly during the research expedition to load data right away. So far, that’s been impractical. We don’t have geochem or bio data to put in DeepSee while at sea. We also haven’t had a cruise to test whether these challenges were due to personnel on board or based on limitations with the DeepSee interface. Everyone was overextended on the expedition and no one had time to do additional data entry on the computer. DeepSee “In the future, we’ll have a full team of scientists, and we’ll also have a manned submersible taking cores... We’ll have time to load the day’s cores into DeepSee to help plan for the next day... The important objective is having more people to do the same number of tasks... I could see this becoming one of the evening tasks: take the day’s coordinates and load them into DeepSee.” 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).

What Needs Improvement in DeepSee for Ocean Research? Experts Weigh In

Experts Share How DeepSee Boosted Research Efficiency During Ocean Expeditions

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 7 EVALUATION Our goal for evaluation was to validate the effectiveness of DeepSee’s technical design and visualizations [43] in terms of maximizing scientific return. To do this, we first asked our deep ocean research collaborators (P1 − 5) which user tasks (T) raised in Sect. 4.3 and Table 2 they accomplished during an initial expedition deployment of DeepSee aboard a research vessel in the Gulf of California (Sect. 7.1). After deployment, we conducted semi-structured interviews with the same collaborators (P1 − 5) and collected feedback on how DeepSee broadly affected research outcomes, workflows, and communication between team members (Sect. 7.2). Based on the deployment and interviews, we then discuss limitations of the current implementation (Sect. 7.3) and finally conclude with lessons learned from our design process and the deployment that can be broadly applied to designing future visualization systems in other domains (Sect. 7.4). 7.1 Cruise Deployment Following the evaluation methodology for design studies [43], DeepSee was deployed on an expedition [40] aboard the research vessel R/V Falkor in the Gulf of California (Fig. 7) over the course of 15 days (Expedition FK210922) in 2021. The goal was to help scientists characterize the geochemistry, biology, geology, and ecology of sediment-hosted hydrothermal vents and seeps of the South Pescadero Basin using mapping, ROV operations, and sample collection. This area was first discovered in 2015 [38] and recently visited in 2018 on the R/V Falkor during expedition FK181031 [45]. Overall, DeepSee helped organize collected sample data from cruises into a central database for easy accessibility and comparison of various geochemical and microbiological community data. Before embarking, core data collected in 2018 was processed and loaded into DeepSee to visualize and contextualize each core’s geochemical and biological attributes to one another and to their environment. DeepSee was used in several ways: (1) to familiarize newer scientists who were not aboard the 2018 cruise to the field site through the use of bathymetric maps and sample site locations (T5, T6, T7); (2) to examine the sample data collected and characterize different sediment cores (T3, T4); (3) to correlate different geochemical trends with microbial community data and to form hypotheses as to what was driving changes in microbial communities across the basin (T1, T2); and (4) to identify scientific sites of interest to return to on the 2021 expedition to collect additional samples and examine how the conditions had changed over the course of three years (T8, T9). For example, the Interpolation View supported gradient-based analysis (T10, T11), as P2 described: “I used the Map View to see the spatialization of the cores, then the Interpolation View to see... if we sampled a core in a specific location, how would that change the certainty of the gradient estimation in that area? Where could we sample to add the most information?” P3 described a unique team-based use case of DeepSee that emerged during this period: “In the week before the Pescadero cruise... we all had DeepSee open together to look at sites and trends and to plan where to go. Seeing where the data is sparse, seeing where there is a lot of data, seeing map features without data points, seeing trends in geochemistry to follow out to some distance, looking for particular microorganisms in the sequence data by searching for species. It was quite helpful to have map data and sequence data in one place and see details on demand.” Overall, the scientists were able to test every task we designed DeepSee for during pre-cruise planning. During the cruise, the Map View was used in the ship’s control room as a visual reference by matching up the current dive location to data associated in any area that the ROV had dived previously in 2018 (T5, T6, T7, T8, T9). P4 described their typical workflow in the control room during a dive as well as having DeepSee in parallel: “I was on the side as the data annotator for notes, screenshots, etc. I would pull up reference images and collect notes as the lead researcher would give orders and direct the group. I had DeepSee up and ready to go to answer questions. For example, if we returned to a previous area, we compared the data from before to what was there now. If it was a new area, there was not much to use DeepSee for.” (T3) P4 also mentioned that DeepSee’s dark color scheme helped “keep light to a minimum in the [control room]. I didn’t realize how important that would be until I was in there.” While many data exploration and annotation tasks were performed during the cruise, tasks related to analyzing data collected on the fly for decision making (T1, T2, T4, T10, T11) were not able to be performed. We provide feedback from scientists on the additional support needed to perform in-situ analysis in Sect. 7.3 and synthesize design guidance to address these needs in Sect. 7.4 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 7 EVALUATION Our goal for evaluation was to validate the effectiveness of DeepSee’s technical design and visualizations [43] in terms of maximizing scientific return. To do this, we first asked our deep ocean research collaborators (P1 − 5) which user tasks (T) raised in Sect. 4.3 and Table 2 they accomplished during an initial expedition deployment of DeepSee aboard a research vessel in the Gulf of California (Sect. 7.1). After deployment, we conducted semi-structured interviews with the same collaborators (P1 − 5) and collected feedback on how DeepSee broadly affected research outcomes, workflows, and communication between team members (Sect. 7.2). Based on the deployment and interviews, we then discuss limitations of the current implementation (Sect. 7.3) and finally conclude with lessons learned from our design process and the deployment that can be broadly applied to designing future visualization systems in other domains (Sect. 7.4). 7.1 Cruise Deployment Following the evaluation methodology for design studies [43], DeepSee was deployed on an expedition [40] aboard the research vessel R/V Falkor in the Gulf of California (Fig. 7) over the course of 15 days (Expedition FK210922) in 2021. The goal was to help scientists characterize the geochemistry, biology, geology, and ecology of sediment-hosted hydrothermal vents and seeps of the South Pescadero Basin using mapping, ROV operations, and sample collection. This area was first discovered in 2015 [38] and recently visited in 2018 on the R/V Falkor during expedition FK181031 [45]. Overall, DeepSee helped organize collected sample data from cruises into a central database for easy accessibility and comparison of various geochemical and microbiological community data. Before embarking, core data collected in 2018 was processed and loaded into DeepSee to visualize and contextualize each core’s geochemical and biological attributes to one another and to their environment. DeepSee was used in several ways: (1) to familiarize newer scientists who were not aboard the 2018 cruise to the field site through the use of bathymetric maps and sample site locations (T5, T6, T7) ; (2) to examine the sample data collected and characterize different sediment cores (T3, T4); (3) to correlate different geochemical trends with microbial community data and to form hypotheses as to what was driving changes in microbial communities across the basin (T1, T2) ; and (4) to identify scientific sites of interest to return to on the 2021 expedition to collect additional samples and examine how the conditions had changed over the course of three years (T8, T9). For example, the Interpolation View supported gradient-based analysis (T10, T11), as P2 described: “I used the Map View to see the spatialization of the cores, then the Interpolation View to see... if we sampled a core in a specific location, how would that change the certainty of the gradient estimation in that area? Where could we sample to add the most information?” P3 described a unique team-based use case of DeepSee that emerged during this period: “In the week before the Pescadero cruise... we all had DeepSee open together to look at sites and trends and to plan where to go. Seeing where the data is sparse, seeing where there is a lot of data, seeing map features without data points, seeing trends in geochemistry to follow out to some distance, looking for particular microorganisms in the sequence data by searching for species. It was quite helpful to have map data and sequence data in one place and see details on demand.” Overall, the scientists were able to test every task we designed DeepSee for during pre-cruise planning. (T5, T6, T7) (T1, T2) (T8, T9). (T10, T11), “I used the Map View to see the spatialization of the cores, then the Interpolation View to see... if we sampled a core in a specific location, how would that change the certainty of the gradient estimation in that area? Where could we sample to add the most information?” “In the week before the Pescadero cruise... we all had DeepSee open together to look at sites and trends and to plan where to go. Seeing where the data is sparse, seeing where there is a lot of data, seeing map features without data points, seeing trends in geochemistry to follow out to some distance, looking for particular microorganisms in the sequence data by searching for species. It was quite helpful to have map data and sequence data in one place and see details on demand.” During the cruise, the Map View was used in the ship’s control room as a visual reference by matching up the current dive location to data associated in any area that the ROV had dived previously in 2018 (T5, T6, T7, T8, T9). P4 described their typical workflow in the control room during a dive as well as having DeepSee in parallel: “I was on the side as the data annotator for notes, screenshots, etc. I would pull up reference images and collect notes as the lead researcher would give orders and direct the group. I had DeepSee up and ready to go to answer questions. For example, if we returned to a previous area, we compared the data from before to what was there now. If it was a new area, there was not much to use DeepSee for.” (T3) P4 also mentioned that DeepSee’s dark color scheme helped “keep light to a minimum in the [control room]. I didn’t realize how important that would be until I was in there.” While many data exploration and annotation tasks were performed during the cruise, tasks related to analyzing data collected on the fly for decision making (T1, T2, T4, T10, T11) were not able to be performed. We provide feedback from scientists on the additional support needed to perform in-situ analysis in Sect. 7.3 and synthesize design guidance to address these needs in Sect. 7.4 (T5, T6, T7, T8, T9). “I was on the side as the data annotator for notes, screenshots, etc. I would pull up reference images and collect notes as the lead researcher would give orders and direct the group. I had DeepSee up and ready to go to answer questions. For example, if we returned to a previous area, we compared the data from before to what was there now. If it was a new area, there was not much to use DeepSee for.” “keep light to a minimum in the [control room]. I didn’t realize how important that would be until I was in there.” (T1, T2, T4, T10, T11) 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).

How DeepSee Helped Ocean Researchers Make Smarter Decisions During Their Expedition

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 6 USAGE SCENARIOS In this section, we illustrate how DeepSee can be used to study deep ocean microbial ecology via two example usage scenarios (Fig. 6). 6.1 Scenario: Pre-Cruise Planning A graduate researcher is planning a follow-up cruise with their lab to collect cores at a site visited previously. Based on prior sampling, they want to determine where cores with a high abundance of both ANME-2c archaea and JS1 bacteria might be (Fig. 6A). They start by selecting all cores in the Map View and “ANME-2c” in the Core View, then examining the ANME-2c distribution down each core (T4). Using the Core View instead of the filters in the Map View, they locate four cores with high ANME-2c abundance (T2, T8). They select these cores in the Map View (T6) and find them clustered in pairs of two. Wondering about the interactions between ANME-2c and JS1, they select “JS1” in the Core View and notice that the two cores to the North also have a high abundance of JS1 (T1). Using the menus of the Map View to load a photomosaic map of seafloor imagery on the fly while maintaining the current viewport, they note that this region has a white microbial mat where the cores were taken (T5). They annotate the Map View by drawing circles around the two cores to the North and write a short message: “Area of interest #1” (T3). They then screenshot the image and place the image in a document for future reference (T9). The researcher decides that for the upcoming dive, retrieving cores from areas near these two cores with ANME-2c and JS1 are a high priority for their project, and focus should be on areas with a white microbial mat visible on the seafloor in this region. DeepSee enabled the researcher to maximize the value of limited samples by integrating several different data types (maps, tables, annotations) to make informed decisions about where to sample in the future. 6.2 Scenario: On-the-Fly Decision-Making A chief scientist (PI) is currently on a cruise, working with a team of submersible operators to visit their next sampling location. Unfortunately, once on the seafloor, they discover the site no longer hosts the characteristic white and orange microbial mats and adjacent chemosynthetic clams observed a few years prior. The chief scientist must quickly select another nearby seep sampling site with a high probability of similar microbial composition to collect sediment cores (Fig. 6B). Using DeepSee, the PI quickly loads a photomosaic map in the Map View from the last cruise to this site (T9) and uses the Map View to filter for the local cluster of previous cores collected here (T6). Looking at the spread of previous cores and the photomosaic map together, they realize the submersible is at the far end of the previously sampled area, which is ≈ 50 meters from an area that may still feature microbial activity (T5, T7). To test this hypothesis, they select all of the cores and choose “Sulfide” in the Core View, identifying which cores have measured sulfide gradients which act as a proxy for active seeps (T2, T10). The PI eliminates cores without sulfide concentration data and identifies several cores that span the width of a previous sampling area (T8, T9). Switching to the Interpolation View, they select a natural neighbor interpolation at 77 cm resolution and a VSUP color palette [11]. The PI sees that there is a predicted sharp subsurface gradient towards higher sulfide concentrations heading northeast across the previous sampling area (T11). Switching the predicted attribute to microbial taxa identified at the site, they also observe a similar trend in the relative abundance of a methane-oxidizing archaeon, which are found in areas of methane seepage (T1, T2, T11). Throughout, they annotate the Map View to support their decision-making, document the process, and share the results with the team (T3, T8). The PI decides that the submersible should navigate northwest to the sampled area and sample at, if not past, the location of core collections from the previous cruise located in the region with the highest sulfide concentrations. Having DeepSee’s ability to visualize unseen gradients quickly with limited data in the field can help researchers to make smarter, more data-driven decisions. 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 6 USAGE SCENARIOS In this section, we illustrate how DeepSee can be used to study deep ocean microbial ecology via two example usage scenarios (Fig. 6). 6.1 Scenario: Pre-Cruise Planning A graduate researcher is planning a follow-up cruise with their lab to collect cores at a site visited previously. Based on prior sampling, they want to determine where cores with a high abundance of both ANME-2c archaea and JS1 bacteria might be (Fig. 6A). They start by selecting all cores in the Map View and “ANME-2c” in the Core View, then examining the ANME-2c distribution down each core (T4) . Using the Core View instead of the filters in the Map View, they locate four cores with high ANME-2c abundance (T2, T8) . They select these cores in the Map View (T6) and find them clustered in pairs of two. Wondering about the interactions between ANME-2c and JS1, they select “JS1” in the Core View and notice that the two cores to the North also have a high abundance of JS1 (T1). Using the menus of the Map View to load a photomosaic map of seafloor imagery on the fly while maintaining the current viewport, they note that this region has a white microbial mat where the cores were taken (T5) . They annotate the Map View by drawing circles around the two cores to the North and write a short message: “Area of interest #1” (T3) . They then screenshot the image and place the image in a document for future reference (T9). (T4) (T2, T8) (T6) (T1). (T5) (T3) (T9). The researcher decides that for the upcoming dive, retrieving cores from areas near these two cores with ANME-2c and JS1 are a high priority for their project, and focus should be on areas with a white microbial mat visible on the seafloor in this region. DeepSee enabled the researcher to maximize the value of limited samples by integrating several different data types (maps, tables, annotations) to make informed decisions about where to sample in the future. 6.2 Scenario: On-the-Fly Decision-Making A chief scientist (PI) is currently on a cruise, working with a team of submersible operators to visit their next sampling location. Unfortunately, once on the seafloor, they discover the site no longer hosts the characteristic white and orange microbial mats and adjacent chemosynthetic clams observed a few years prior. The chief scientist must quickly select another nearby seep sampling site with a high probability of similar microbial composition to collect sediment cores (Fig. 6B). Using DeepSee , the PI quickly loads a photomosaic map in the Map View from the last cruise to this site (T9) and uses the Map View to filter for the local cluster of previous cores collected here (T6). Looking at the spread of previous cores and the photomosaic map together, they realize the submersible is at the far end of the previously sampled area, which is ≈ 50 meters from an area that may still feature microbial activity (T5, T7) . To test this hypothesis, they select all of the cores and choose “Sulfide” in the Core View, identifying which cores have measured sulfide gradients which act as a proxy for active seeps (T2, T10). The PI eliminates cores without sulfide concentration data and identifies several cores that span the width of a previous sampling area (T8, T9) . Switching to the Interpolation View, they select a natural neighbor interpolation at 77 cm resolution and a VSUP color palette [11]. The PI sees that there is a predicted sharp subsurface gradient towards higher sulfide concentrations heading northeast across the previous sampling area (T11). Switching the predicted attribute to microbial taxa identified at the site, they also observe a similar trend in the relative abundance of a methane-oxidizing archaeon, which are found in areas of methane seepage (T1, T2, T11) . Throughout, they annotate the Map View to support their decision-making, document the process, and share the results with the team (T3, T8). DeepSee (T5, T7) (T2, T10). (T8, T9) (T11). (T1, T2, T11) (T3, T8). The PI decides that the submersible should navigate northwest to the sampled area and sample at, if not past, the location of core collections from the previous cruise located in the region with the highest sulfide concentrations. Having DeepSee’s ability to visualize unseen gradients quickly with limited data in the field can help researchers to make smarter, more data-driven decisions. 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).

How DeepSee Helps Scientists Plan and Adapt in the Deep Ocean

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

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).

Explore the Ocean Floor in High-Resolution with DeepSee

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 4.3 Design Challenges and User Tasks By understanding the research goals of studying deep ocean ecosystems (Sect. 4.1) as well as scientists’ workflows and data (Sect. 4.2), we synthesized several key design challenges (C). Throughout, we highlight specific user tasks (T) that we aim to support. The challenges and tasks are summarized in Table 2 and integrated into our descriptions of the system (Sect. 5), usage scenarios (Sect. 6) and evaluation (Sect. 7) throughout the rest of the paper. C1 Observing spatial trends in sparse, tabular data. Characterizing the general geochemical and taxonomic data associated within a core is difficult. Using multiple parameters measured for each horizon, scientists seek to understand sediment habitats of vent and seep microorganisms by correlating taxonomic profiles against various geochemical parameters (T1) within a core. For example, the interplay between sulfate and sulfide concentrations (geochemical parameters) could be compared to microbial members that oxidize sulfate to sulfide (biological parameters). With these characterizations, scientists can then estimate spheres of influence around a seafloor feature (T2) to answer specific questions such as “Does community distribution and geochemistry change with increasing distance from the target area of sampled push cores?” Table 1 provides an example of the variety of data associated with a single sediment core, including multiple types of geochemical measurements, relative abundance measurements of microbial taxa, positional coordinates, depth into the seafloor, sediment temperature, etc. Our interface should enable users to explore/highlight trends in their data by annotating relationships between environmental features (T3) as well as showing several sediment horizons down the profile of multiple cores simultaneously (T4), rather than in solidarity. C2 Integrating data at multiple scales of size and time. The context of the environment around a core also includes additional metadata that is difficult to integrate between time scales, size resolutions, and file formats. It is critical to understand relationships between centimeter-scale core data in tables and kilometer-scale seafloor features in maps and images, as well as across scientific research expeditions over long- and short-term scales (T5). For example, rows in Table 1 can span multiple expeditions over several years, sample parameters at each horizon can take several months to measure in the lab, while some environmental metadata can be obtained in real time during a dive. These tables only capture core-level and sample-level data; region-level data (Fig. 2) from image files contains scale, location, and depth information that can contextualize cores in a 3D environment. To integrate these data, our system should allow users to select and view cores across expeditions and date ranges (T6), as well as show tabular core data in relation to map images with associated data at multiple spatial resolutions (T7). C3 Maximizing the scientific value of limited sampling. Biological sampling faces a difficult economy – the deep ocean is remote, requiring expensive ship and ROV time, and typically only a small number of cores (≈ 20) can be processed per dive. Even for teams of experienced scientists, choosing sampling locations most likely to address key goals of the science team (T8) is a challenging problem. For example, during a dive at methane seeps, scientists are limited to what they can see on the surface of the seafloor to make decisions about where to sample. In some locations, the microbes can form microbial “mats” at the seabed, creating visual surface indicators of underlying methane seepage. However, these indicators are like “the tip of the iceberg” with limited ability to predict the diversity of microorganisms within the sediment from the surface. Sampling is also limited by the ROV’s or HOV’s physical capacity to collect and return cores each dive, making it a requirement for scientists to know their data and scientific objectives well. Thus, selecting the best places to sample can be supported by analyzing geochemical or microbial data from past samples (T9) and observing how visual indicators may correlate with subsurface microorganisms (T10). It is essential to provide multiple capabilities within our system for estimating unseen parameters from the data tables in unsampled locations (T11) through spatial association with visual indicators captured by bathymetric and photomosaic map data. 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 4.3 Design Challenges and User Tasks By understanding the research goals of studying deep ocean ecosystems (Sect. 4.1) as well as scientists’ workflows and data (Sect. 4.2), we synthesized several key design challenges (C). Throughout, we highlight specific user tasks (T) that we aim to support. The challenges and tasks are summarized in Table 2 and integrated into our descriptions of the system (Sect. 5), usage scenarios (Sect. 6) and evaluation (Sect. 7) throughout the rest of the paper. C1 Observing spatial trends in sparse, tabular data. Characterizing the general geochemical and taxonomic data associated within a core is difficult. Using multiple parameters measured for each horizon, scientists seek to understand sediment habitats of vent and seep microorganisms by correlating taxonomic profiles against various geochemical parameters (T1) within a core. For example, the interplay between sulfate and sulfide concentrations (geochemical parameters) could be compared to microbial members that oxidize sulfate to sulfide (biological parameters). With these characterizations, scientists can then estimate spheres of influence around a seafloor feature (T2) to answer specific questions such as “Does community distribution and geochemistry change with increasing distance from the target area of sampled push cores?” Table 1 provides an example of the variety of data associated with a single sediment core, including multiple types of geochemical measurements, relative abundance measurements of microbial taxa, positional coordinates, depth into the seafloor, sediment temperature, etc. Our interface should enable users to explore/highlight trends in their data by annotating relationships between environmental features (T3) as well as showing several sediment horizons down the profile of multiple cores simultaneously (T4) , rather than in solidarity. C1 Observing spatial trends in sparse, tabular data. correlating taxonomic profiles against various geochemical parameters (T1) estimate spheres of influence around a seafloor feature (T2) annotating relationships between environmental features (T3) showing several sediment horizons down the profile of multiple cores simultaneously (T4) C2 Integrating data at multiple scales of size and time. The context of the environment around a core also includes additional metadata that is difficult to integrate between time scales, size resolutions, and file formats. It is critical to understand relationships between centimeter-scale core data in tables and kilometer-scale seafloor features in maps and images, as well as across scientific research expeditions over long- and short-term scales ( T5). For example, rows in Table 1 can span multiple expeditions over several years, sample parameters at each horizon can take several months to measure in the lab, while some environmental metadata can be obtained in real time during a dive. These tables only capture core-level and sample-level data; region-level data (Fig. 2) from image files contains scale, location, and depth information that can contextualize cores in a 3D environment. To integrate these data, our system should allow users to select and view cores across expeditions and date ranges (T6) , as well as show tabular core data in relation to map images with associated data at multiple spatial resolutions (T7). C2 Integrating data at multiple scales of size and time. understand relationships between centimeter-scale core data in tables and kilometer-scale seafloor features in maps and images, as well as across scientific research expeditions over long- and short-term scales T5). select and view cores across expeditions and date ranges (T6) show tabular core data in relation to map images with associated data at multiple spatial resolutions (T7). C3 Maximizing the scientific value of limited sampling. Biological sampling faces a difficult economy – the deep ocean is remote, requiring expensive ship and ROV time, and typically only a small number of cores (≈ 20) can be processed per dive. Even for teams of experienced scientists, choosing sampling locations most likely to address key goals of the science team (T8) is a challenging problem. For example, during a dive at methane seeps, scientists are limited to what they can see on the surface of the seafloor to make decisions about where to sample. In some locations, the microbes can form microbial “mats” at the seabed, creating visual surface indicators of underlying methane seepage. However, these indicators are like “the tip of the iceberg” with limited ability to predict the diversity of microorganisms within the sediment from the surface. Sampling is also limited by the ROV’s or HOV’s physical capacity to collect and return cores each dive, making it a requirement for scientists to know their data and scientific objectives well. Thus, selecting the best places to sample can be supported by analyzing geochemical or microbial data from past samples (T9) and observing how visual indicators may correlate with subsurface microorganisms (T10). It is essential to provide multiple capabilities within our system for estimating unseen parameters from the data tables in unsampled locations (T11) through spatial association with visual indicators captured by bathymetric and photomosaic map data. C3 Maximizing the scientific value of limited sampling. choosing sampling locations most likely to address key goals of the science team (T8) analyzing geochemical or microbial data from past samples (T9) observing how visual indicators may correlate with subsurface microorganisms (T10). estimating unseen parameters from the data tables in unsampled locations (T11) 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).

Scientists Develop DeepSee to Solve Data Challenges in Ocean Exploration

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 4.2 Workflow and Data To quantify the distribution of microbial activity in deep sea sediments, microbial ecologists, geochemists and geologists first collect biological and geological samples (often sediment, rock or pore water) from the deep ocean. These samples are then used to examine the geochemical, sedimentological, and biological characteristics of the environment. The sampled cores and associated parameters are both sparse and multidimensional (i.e., cores with a fixed location in 3D, sampled at time points ranging from days to decades). Collecting samples begins with an oceanographic research expedition, where scientists use various human- or robot-operated tools to map the study area and execute sampling dives. For the examples presented in this paper, AUVs (Autonomous Underwater Vehicles) equipped with multibeam sonar collected 1 m lateral resolution bathymetry and acoustic backscatter covering an ≈ 30 km2 area including multiple hydrothermal vent fields. With this context mapping completed, a Low Altitude Survey System (LASS) fielded on an ROV (Remotely Operated Vehicle) then mapped one vent field at much higher resolution, yielding 5 cm resolution bathymetry from a multibeam sonar (Fig. 2A), 1 cm resolution bathymetry from LIDAR, and 5 cm photomosaics from a color stereo camera rig (Fig. 2B) [37]. With these maps, scientists then planned the exploration and execution of sampling dives with ROVs or HOVs (Human Occupied Vehicles) to collect sediment samples from the ocean floor and bring them back to the ship for downstream processing. The samples DeepSee currently visualizes are sediment push cores (PC), hollow polycarbonate tubes 7 cm diameter and 30 cm tall, usually sectioned shipboard into 1 cm or 3 cm tall horizons. There are two types of data attributes: (1) core-level (Table 1, left) and sample-level (Table 1, right). For core-level analysis, there can be more than a dozen separate measurements conducted for a single horizon with a core; considering a core can have between 7-12 horizons, there are over 84 identification data points to interpret per core. For sample-level analysis, each horizon is processed for a wide variety of geochemical parameters, including sulfide, sulfate, iron, and other chemicals associated with metabolism or the state of the environment. Each horizon is also preserved and sequenced for a quantitative measure of how many microbial community members (taxa, e.g., ANME archaea or sulfate-reducing Desulfobacterota) live there. This typically results in over 500 discrete taxonomic, geochemical, or physical parameters per core, used to identify spatial trends in distributions (Sect. 4.3). Finally, cores are often collected in pairs or clusters for different experiments and assigned a core fate descriptor, e.g., “Geochem” or “Live”, which differentiates what type of metadata and parameters are associated with the core. In DeepSee, these data were stratified into three hierarchical levels and visualized in different ways: Region-level data. Region-level data (Fig. 2) are predominantly map-format, including low-altitude survey system (LASS) multi beam amplitude and bathymetry (5 cm) [31], AUV bathymetry (1 m lateral resolution), photo mosaics (5 cm resolution), and LIDAR bathymetry (2 cm). Together, these data types reveal the geological landscape (bathymetry) and surface biological and ephemeral “soft” features (photo mosaics and high-resolution LIDAR). Core-level data. Core-level data (Table 1, left) are unique x,y points within a methane seep where subsurface sediments were sampled. Cores comprise a minimum of 11 parameters stored in a CSV file, including core IDs (e.g., dive number, core number, GPS location), measurements (e.g., temperature, sulfate, sulfide), and associated data (e.g., core type, habitat description). Sample-level data. Sample-level data (Table 1, right) is typically parameters subdivided within cores into horizons of 2-3 cm each. DeepSee treats each horizon (row) as a sample belonging to a core within a seep. Recent cruises have returned cores with up to 257 depth horizons described in CSV format by 21 physicochemical parameters (e.g. pH), 12 geochemical measurements (e.g. methane concentration), and over 500 biological parameters (microbial identity and abundances). This paper is available on arxiv under CC BY 4.0 DEED license. [3] The example data is provided by Speth et al. [45]. 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 4.2 Workflow and Data To quantify the distribution of microbial activity in deep sea sediments, microbial ecologists, geochemists and geologists first collect biological and geological samples (often sediment, rock or pore water) from the deep ocean. These samples are then used to examine the geochemical, sedimentological, and biological characteristics of the environment. The sampled cores and associated parameters are both sparse and multidimensional (i.e., cores with a fixed location in 3D, sampled at time points ranging from days to decades). Collecting samples begins with an oceanographic research expedition , where scientists use various human- or robot-operated tools to map the study area and execute sampling dives. For the examples presented in this paper, AUVs (Autonomous Underwater Vehicles) equipped with multibeam sonar collected 1 m lateral resolution bathymetry and acoustic backscatter covering an ≈ 30 km2 area including multiple hydrothermal vent fields. With this context mapping completed, a Low Altitude Survey System (LASS) fielded on an ROV (Remotely Operated Vehicle) then mapped one vent field at much higher resolution, yielding 5 cm resolution bathymetry from a multibeam sonar (Fig. 2A), 1 cm resolution bathymetry from LIDAR, and 5 cm photomosaics from a color stereo camera rig (Fig. 2B) [37]. With these maps, scientists then planned the exploration and execution of sampling dives with ROVs or HOVs (Human Occupied Vehicles) to collect sediment samples from the ocean floor and bring them back to the ship for downstream processing. The samples DeepSee currently visualizes are sediment push cores (PC) , hollow polycarbonate tubes 7 cm diameter and 30 cm tall, usually expedition push cores (PC) sectioned shipboard into 1 cm or 3 cm tall horizons . There are two types of data attributes: (1) core-level (Table 1, left) and sample-level (Table 1, right). For core-level analysis, there can be more than a dozen separate measurements conducted for a single horizon with a core; considering a core can have between 7-12 horizons, there are over 84 identification data points to interpret per core. For sample-level analysis, each horizon is processed for a wide variety of geochemical parameters , including sulfide, sulfate, iron, and other chemicals associated with metabolism or the state of the environment. Each horizon is also preserved and sequenced for a quantitative measure of how many microbial community members ( taxa , e.g., ANME archaea or sulfate-reducing Desulfobacterota) live there. This typically results in over 500 discrete taxonomic, geochemical, or physical parameters per core, used to identify spatial trends in distributions (Sect. 4.3). Finally, cores are often collected in pairs or clusters for different experiments and assigned a core fate descriptor, e.g., “Geochem” or “Live”, which differentiates what type of metadata and parameters are associated with the core. In DeepSee, these data were stratified into three hierarchical levels and visualized in different ways: horizons parameters chemicals taxa core fate Region-level data. Region-level data (Fig. 2) are predominantly map-format, including low-altitude survey system (LASS) multi beam amplitude and bathymetry (5 cm) [31], AUV bathymetry (1 m lateral resolution), photo mosaics (5 cm resolution), and LIDAR bathymetry (2 cm). Together, these data types reveal the geological landscape (bathymetry) and surface biological and ephemeral “soft” features (photo mosaics and high-resolution LIDAR). Region-level data. Core-level data. Core-level data (Table 1, left) are unique x,y points within a methane seep where subsurface sediments were sampled. Cores comprise a minimum of 11 parameters stored in a CSV file, including core IDs (e.g., dive number, core number, GPS location), measurements (e.g., temperature, sulfate, sulfide), and associated data (e.g., core type, habitat description). Core-level data. Sample-level data. Sample-level data (Table 1, right) is typically parameters subdivided within cores into horizons of 2-3 cm each. DeepSee treats each horizon (row) as a sample belonging to a core within a seep. Recent cruises have returned cores with up to 257 depth horizons described in CSV format by 21 physicochemical parameters (e.g. pH), 12 geochemical measurements (e.g. methane concentration), and over 500 biological parameters (microbial identity and abundances). Sample-level data. 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 [3] The example data is provided by Speth et al. [45]. 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).

Bridging Gaps in Deep Ocean Research with Data Visualization Tools

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 4 STUDYING DEEP OCEAN ECOSYSTEMS As the first step in the design study methodology, we characterized the needs of deep ocean researchers when visualizing multidimensional environmental data collected during field expeditions. What makes this challenging is addressing the size and complexity of data collected in a single interface, where researchers can correlate processes at several size scales, explore spatial trends in context of the environment, and test hypotheses where samples are sparse and limited. DeepSee addresses these challenges specifically in studying deep ocean ecosystems by integrating multiple coordinated views that both present data at multiple size scales simultaneously and visualize geochemical gradients and variation in microbial taxa in the context of the environment in both 2D and 3D. This can help researchers decide on new sampling locations that maximize scientific return. 4.1 Deep Ocean Research Goals Broadly, our collaborators seek to advance an understanding of deep ocean microbial ecosystems. The deep ocean is Earth’s largest biome and is home to a variety of habitats that host rich, complex chemosynthetic ecosystems including hydrothermal vents, cold seeps, gas hydrates, whale falls, and carbonate platforms [10]. Microorganisms represent primary drivers of these extreme ecosystems, supporting diverse animal communities [12, 25]. Importantly, these deep ocean microbial ecosystems have been observed to facilitate key environmental processes, including the transformation of greenhouse gases such as methane and carbon dioxide, movement of energy between trophic levels, and general cycling of elements, such as sulfur and metals, making these ecosystems critical drivers on the global ecological scale [32, 34]. Clearly defining the nature, role, and impact of microorganisms in deep ocean ecosystems is critical for connecting deep ocean processes to biogeochemical cycling and diversity globally. There is increased importance of temporal and spatial monitoring with exploitation of deep ocean resources (e.g. deep sea mining) or in response to environmental change. Characterizing these extreme environments and the microbial adaptations required to persist and carry out key ecological processes is also advantageous for applications in the industrial world, where yet-to-be discovered molecular machinery and compounds may be applied to a variety of unsolved problems in medicine, agriculture, and environmental science [8, 35, 47]. Similarly, advances in planetary exploration have also revealed similar environments to those found in Earth’s deep oceans; further exploration of Earth’s deep ocean microbial ecosystems can therefore further shape our understanding of how life can adapt to the similarly extreme conditions that may exist beyond Earth [19, 29, 30]. 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 4 STUDYING DEEP OCEAN ECOSYSTEMS As the first step in the design study methodology, we characterized the needs of deep ocean researchers when visualizing multidimensional environmental data collected during field expeditions. What makes this challenging is addressing the size and complexity of data collected in a single interface, where researchers can correlate processes at several size scales, explore spatial trends in context of the environment, and test hypotheses where samples are sparse and limited. DeepSee addresses these challenges specifically in studying deep ocean ecosystems by integrating multiple coordinated views that both present data at multiple size scales simultaneously and visualize geochemical gradients and variation in microbial taxa in the context of the environment in both 2D and 3D. This can help researchers decide on new sampling locations that maximize scientific return. 4.1 Deep Ocean Research Goals Broadly, our collaborators seek to advance an understanding of deep ocean microbial ecosystems. The deep ocean is Earth’s largest biome and is home to a variety of habitats that host rich, complex chemosynthetic ecosystems including hydrothermal vents, cold seeps, gas hydrates, whale falls, and carbonate platforms [10]. Microorganisms represent primary drivers of these extreme ecosystems, supporting diverse animal communities [12, 25]. Importantly, these deep ocean microbial ecosystems have been observed to facilitate key environmental processes, including the transformation of greenhouse gases such as methane and carbon dioxide, movement of energy between trophic levels, and general cycling of elements, such as sulfur and metals, making these ecosystems critical drivers on the global ecological scale [32, 34]. Clearly defining the nature, role, and impact of microorganisms in deep ocean ecosystems is critical for connecting deep ocean processes to biogeochemical cycling and diversity globally. There is increased importance of temporal and spatial monitoring with exploitation of deep ocean resources (e.g. deep sea mining) or in response to environmental change. Characterizing these extreme environments and the microbial adaptations required to persist and carry out key ecological processes is also advantageous for applications in the industrial world, where yet-to-be discovered molecular machinery and compounds may be applied to a variety of unsolved problems in medicine, agriculture, and environmental science [8, 35, 47]. Similarly, advances in planetary exploration have also revealed similar environments to those found in Earth’s deep oceans; further exploration of Earth’s deep ocean microbial ecosystems can therefore further shape our understanding of how life can adapt to the similarly extreme conditions that may exist beyond Earth [19, 29, 30]. 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).

On Studying the Deep Ocean Ecosystem

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 3 METHODOLOGY In this paper, we followed Sedlmair et al.’s methodological framework for design studies [43]. Each phase of the study resulted in a contribution, namely: (1) a characterization of the problem domain via design challenges and user tasks; (2) a novel and validated visualization design; and (3) reflections on the process and lessons learned for improving visualization and interaction design guidelines. We connect these phases throughout the paper by illustrating how our synthesized challenges and tasks informed DeepSee’s design and how DeepSee’s success in addressing the challenges and tasks is reflected in our evaluation. To the best of our knowledge, the problem we describe in Sect. 4 is unique to our collaborators and has not been addressed by other researchers so far. Thus, we posit that a design study based on active participation from collaborators in both the design and evaluation phases is necessary to successfully support domain experts in their highly specialized tasks. We began by establishing a close collaboration between visualization researchers and domain experts, to properly assess the impact of our approach on solving a real-world problem. In line with prior design studies [15, 18, 33], our team comprised all authors and included visualization experts in human-centered computing, interaction design, scientific data visualization and art, as well as scientists and researchers with expertise in environmental microbiology, geochemistry and geology. We developed DeepSee through several user-centered design methodologies including contextual inquiry, mixed-fidelity prototyping, user testing, and design iteration. Over an intensive ten-week period, we worked closely together to develop a shared understanding of the importance and aims of their scientific research (Sect. 4.1), characterize data and workflows (Sect. 4.2), and identify data visualization challenges and user tasks (Sect. 4.3) that would guide our system design. Our process started with contextual inquiry, combining observation and interviews with our domain collaborators to deeply understand their existing tools and workflows, discuss unmet needs and pain points, and identify key technical constraints. After two weeks, we began mixed-fidelity prototyping, starting with paper prototypes, wireframes and small interactive prototypes in code, before moving on to progressively higher-fidelity prototypes. These prototypes were iteratively discussed with the science team over the next seven weeks through semi-structured user testing. Using these prototypes as stimuli, we deepened our understanding of the problem space through on-going contextual inquiry. During this time, systems diagrams and conceptual workflows were also created to help us validate our understanding of the problem space in collaboration with the science team. As a result of this approach, we progressively uncovered insights that guided each subsequent design iteration, leading us to generate a working prototype described in Sect. 5 that addressed our collaborators’ tasks. DeepSee was then deployed in the wild [43] to assess whether our approach solves a real problem – bridging gaps in supporting visual analysis of samples during field research. Deploying DeepSee with the same experts that we designed with allowed us to assess whether domain experts are indeed helped by the new solution, as well as reflect on and refine principles for visualization and interaction design guidelines [43]. Specifically, we investigated the impact of introducing data visualizations into both planning before fieldwork as well as tactical decision making in the field. We learned that visualizing data at multiple scales, integrating 2D and 3D data in the same interface, and modularizing visualizations positively impacted scientists planning workflows the most, and that additional support for integrating data on the fly could better enable in-situ decision making. Methodological details of our evaluation and how we synthesized these lessons learned are provided in Sect. 7. 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 3 METHODOLOGY In this paper, we followed Sedlmair et al.’s methodological framework for design studies [43]. Each phase of the study resulted in a contribution, namely: (1) a characterization of the problem domain via design challenges and user tasks; (2) a novel and validated visualization design; and (3) reflections on the process and lessons learned for improving visualization and interaction design guidelines. We connect these phases throughout the paper by illustrating how our synthesized challenges and tasks informed DeepSee’s design and how DeepSee’s success in addressing the challenges and tasks is reflected in our evaluation. To the best of our knowledge, the problem we describe in Sect. 4 is unique to our collaborators and has not been addressed by other researchers so far. Thus, we posit that a design study based on active participation from collaborators in both the design and evaluation phases is necessary to successfully support domain experts in their highly specialized tasks. We began by establishing a close collaboration between visualization researchers and domain experts, to properly assess the impact of our approach on solving a real-world problem. In line with prior design studies [15, 18, 33], our team comprised all authors and included visualization experts in human-centered computing, interaction design, scientific data visualization and art, as well as scientists and researchers with expertise in environmental microbiology, geochemistry and geology. We developed DeepSee through several user-centered design methodologies including contextual inquiry, mixed-fidelity prototyping, user testing, and design iteration. Over an intensive ten-week period, we worked closely together to develop a shared understanding of the importance and aims of their scientific research (Sect. 4.1), characterize data and workflows (Sect. 4.2), and identify data visualization challenges and user tasks (Sect. 4.3) that would guide our system design. Our process started with contextual inquiry, combining observation and interviews with our domain collaborators to deeply understand their existing tools and workflows, discuss unmet needs and pain points, and identify key technical constraints. After two weeks, we began mixed-fidelity prototyping, starting with paper prototypes, wireframes and small interactive prototypes in code, before moving on to progressively higher-fidelity prototypes. These prototypes were iteratively discussed with the science team over the next seven weeks through semi-structured user testing. Using these prototypes as stimuli, we deepened our understanding of the problem space through on-going contextual inquiry. During this time, systems diagrams and conceptual workflows were also created to help us validate our understanding of the problem space in collaboration with the science team. As a result of this approach, we progressively uncovered insights that guided each subsequent design iteration, leading us to generate a working prototype described in Sect. 5 that addressed our collaborators’ tasks. DeepSee was then deployed in the wild [43] to assess whether our approach solves a real problem – bridging gaps in supporting visual analysis of samples during field research. Deploying DeepSee with the same experts that we designed with allowed us to assess whether domain experts are indeed helped by the new solution, as well as reflect on and refine principles for visualization and interaction design guidelines [43]. Specifically, we investigated the impact of introducing data visualizations into both planning before fieldwork as well as tactical decision making in the field. We learned that visualizing data at multiple scales, integrating 2D and 3D data in the same interface, and modularizing visualizations positively impacted scientists planning workflows the most, and that additional support for integrating data on the fly could better enable in-situ decision making. Methodological details of our evaluation and how we synthesized these lessons learned are provided in Sect. 7. 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).

Making Field Research Easier with DeepSee’s Data Visualization

How DeepSee Bridges Gaps in Ocean Research Visualization for Fieldwork Decisions

How DeepSee Helps Us See Deeper into the Oceans than Ever Before

Wie DeepSee uns hilft, tiefer in die Ozeane zu blicken als je zuvor

Cómo DeepSee nos ayuda a ver los océanos más profundamente que nunca

DeepSee-k nola laguntzen digun inoiz baino ozeanoetan sakonago ikusten

डीपसी किस तरह से हमें महासागरों में पहले से कहीं अधिक गहराई तक देखने में मदद करता है

Kako nam DeepSee pomaže da vidimo dublje u oceane nego ikada prije

Hogyan segít a DeepSee mélyebbre látni az óceánokba, mint valaha

DeepSee がこれまで以上に海の奥深くまで見えるようにする方法

როგორ გვეხმარება DeepSee უფრო ღრმად ჩავიხედოთ ოკეანეებში, ვიდრე ოდესმე

DeepSee нь далай тэнгисийн гүнийг урьд урьдынхаас илүү гүнзгий харах бидэнд хэрхэн тусалдаг вэ?

ډیپ سی څنګه له موږ سره مرسته کوي چې د پخوا په پرتله بحرونو ته ژور وګورو

Como o DeepSee nos ajuda a enxergar mais profundamente os oceanos do que nunca

Hur DeepSee hjälper oss att se djupare ner i haven än någonsin tidigare

Beyond Text Embeddings: Addressing the Gaps in RAG Applications for Structured Data Queries

Акрамя тэкставых убудоўванняў: ухіленне прабелаў у праграмах RAG для запытаў структураваных даных

Über Texteinbettungen hinaus: Behebung der Lücken in RAG-Anwendungen für strukturierte Datenabfragen

Más allá de las incrustaciones de texto: cómo abordar las deficiencias en las aplicaciones RAG para consultas de datos estructurados

Izvan ugrađivanja teksta: rješavanje nedostataka u RAG aplikacijama za upite strukturiranih podataka

Melampaui Penyematan Teks: Mengatasi Kesenjangan dalam Aplikasi RAG untuk Kueri Data Terstruktur

テキスト埋め込みを超えて: 構造化データクエリの RAG アプリケーションのギャップに対処する

Verder dan tekst-embeddings: de hiaten in RAG-toepassingen voor gestructureerde dataquery's aanpakken

Texto Embeddings nisqamanta aswan karun: RAG Yanapakuykunapi Estructurado Willay Tapuykunapaq Ch'usaq kaqkunata allichay

Përtej përfshirjeve të tekstit: Adresimi i boshllëqeve në aplikacionet RAG për pyetjet e strukturuara të të dhënave

Ғайр аз ҷойгиркунии матн: Бартараф кардани камбудиҳо дар барномаҳои RAG барои дархостҳои сохтории додаҳо

ኪኖ ጽሑፍ ምትእትታው፡ ኣብ ኣፕሊኬሽናት ራግ ንዝተዋደደ ሕቶታት ዳታ ንዘሎ ክፍተታት ምፍታሕ

Vượt ra ngoài nhúng văn bản: Giải quyết các khoảng trống trong ứng dụng RAG cho truy vấn dữ liệu có cấu trúc

Authors:
(1) Ruochu Yang;
(2) Chad Lembke;
(3) Fumin Zhang;
(4) Catherine Edwards. Table of Links Abstract and Intro Anomaly Detection Algorithm Experimental Evaluation Conclusion and References IV. CONCLUSION In this paper, we apply an anomaly detection algorithm to four real glider missions supported by the Skidaway Institute of Oceanography in the University of Georgia and the University of South Florida. On one side of generality, the algorithm is capable of detecting anomalies like remora attachment and shark hit in diverse real-world deployments based on high-resolution DBD data. On the other side of realtime performance, we simulate the online detection on subsetted SBD data. It utilizes generic data of glider trajectory and heading angle to estimate glider speed and flow speed. Anomalies can be identified by comparing the estimated glider speed with the normal speed range. False alarms can be minimized by comparing the algorithm-estimated flow speed with the glider-estimated flow speed. The algorithm achieves real-time estimation through a model-based framework by continuously updating estimates based on ongoing deployment feedback. Future work will enhance estimation accuracy by incorporating large amount of glider data into a data-driven framework. It is also worth taking into account the impact of the anomaly on the estimated flow speed, aiding in the process of determining false alarms. REFERENCES [1] F. Zhang, D. M. Fratantoni, D. A. Paley, J. M. Lund, and N. E. Leonard, “Control of coordinated patterns for ocean sampling,” International Journal of Control, vol. 80, no. 7, pp. 1186–1199, 2007. [2] D. A. Paley, F. Zhang, and N. E. Leonard, “Cooperative control for ocean sampling: The glider coordinated control system,” IEEE Transactions on Control Systems Technology, vol. 16, no. 4, pp. 735– 744, 2008. [3] M. Hou, S. Cho, H. Zhou, C. R. Edwards, and F. Zhang, “Bounded cost path planning for underwater vehicles assisted by a time-invariant partitioned flow field model,” Frontiers in Robotics and AI, vol. 8, 2021. [4] J. Nicholson and A. Healey, “The present state of autonomous underwater vehicle (auv) applications and technologies,” Marine Technology Society Journal, vol. 42, no. 1, pp. 44–51, 2008. [5] O. Schofield, J. Kohut, D. Aragon, L. Creed, J. Graver, C. Haldeman, J. Kerfoot, H. Roarty, C. Jones, D. Webb et al., “Slocum gliders: Robust and ready,” Journal of Field Robotics, vol. 24, no. 6, pp. 473– 485, 2007. [6] M. J. Stanway, B. Kieft, T. Hoover, B. Hobson, D. Klimov, J. Erickson, B. Y. Raanan, D. A. Ebert, and J. Bellingham, “White shark strike on a long-range auv in monterey bay,” in OCEANS 2015-Genova. IEEE, 2015, pp. 1–7. [7] J. E. McCosker and R. N. Lea, “White shark attacks upon humans in california and oregon, 1993-2003,” Proceedings-California Academy Of Sciences, vol. 57, no. 12/24, p. 479, 2006. [8] L. G. Baehr, “Swimming with sharks: An underwater robot learns how to track great whites,” Oceanus, vol. 50, no. 2, pp. 42–49, 2013. [9] J. J. Gertler, Fault detection and diagnosis in engineering systems. CRC press, 2017. [10] J. Chen and R. J. Patton, Robust model-based fault diagnosis for dynamic systems. Springer Science & Business Media, 2012, vol. 3. [11] K. Aslansefat, G. Latif-Shabgahi, and M. Kamarlouei, “A strategy for reliability evaluation and fault diagnosis of autonomous underwater gliding robot based on its fault tree,” International Journal of Advances in Science Engineering and Technology, vol. 2, no. 4, pp. 83–89, 2014. [12] X. Wang, “Active fault tolerant control for unmanned underwater vehicle with sensor faults,” IEEE Transactions on Instrumentation and Measurement, vol. 69, no. 12, pp. 9485–9495, 2020. [13] B. Y. Raanan, J. Bellingham, Y. Zhang, B. Kieft, M. J. Stanway, R. McEwen, and B. Hobson, “A real-time vertical plane flight anomaly detection system for a long range autonomous underwater vehicle,” in OCEANS 2015 - MTS/IEEE Washington, 2015, pp. 1–6. [14] E. Anderlini, C. A. Harris, G. Salavasidis, A. Lorenzo, A. B. Phillips, and G. Thomas, “Autonomous detection of the loss of a wing for underwater gliders,” in 2020 IEEE/OES Autonomous Underwater Vehicles Symposium (AUV). IEEE, 2020, pp. 1–6. [15] G. Fagogenis, V. De Carolis, and D. M. Lane, “Online fault detection and model adaptation for underwater vehicles in the case of thruster failures,” in 2016 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2016, pp. 2625–2630. [16] Y.-s. Sun, X.-r. Ran, Y.-m. Li, G.-c. Zhang, and Y.-h. Zhang, “Thruster fault diagnosis method based on gaussian particle filter for autonomous underwater vehicles,” International Journal of Naval Architecture and Ocean Engineering, vol. 8, no. 3, pp. 243–251, 2016. [17] A. Caiti, F. Di Corato, F. Fabiani, D. Fenucci, S. Grechi, and F. Pacini, “Enhancing autonomy: Fault detection, identification and optimal reaction for over—actuated auvs,” in OCEANS 2015-Genova. IEEE, 2015, pp. 1–6. [18] V. Asalapuram, I. Khan, and K. Rao, “A novel architecture for condition based machinery health monitoring on marine vessels using deep learning and edge computing,” in 2019 IEEE International Symposium on Measurement and Control in Robotics (ISMCR). IEEE, 2019, pp. C1–3. [19] P. Wu, C. A. Harris, G. Salavasidis, I. Kamarudzaman, A. B. Phillips, G. Thomas, and E. Anderlini, “Anomaly detection and fault diagnostics for underwater gliders using deep learning,” in OCEANS 2021: San Diego–Porto. IEEE, 2021, pp. 1–6. [20] Z. Bedja-Johnson, P. Wu, D. Grande, and E. Anderlini, “Smart anomaly detection for slocum underwater gliders with a variational autoencoder with long short-term memory networks,” Applied Ocean Research, vol. 120, p. 103030, 2022. [21] V. Chandola, A. Banerjee, and V. Kumar, “Anomaly detection: A survey,” ACM computing surveys (CSUR), vol. 41, no. 3, pp. 1–58, 2009. [22] W. Yassin, N. I. Udzir, Z. Muda, and M. N. Sulaiman, “Anomaly-based intrusion detection through k-means clustering and naives bayes classification,” International Conference on Computing and Informatics, p. 49, 2013. [23] K. Szwaykowska and F. Zhang, “Controlled lagrangian particle tracking: error growth under feedback control,” IEEE Transactions on Control Systems Technology, vol. 26, no. 3, pp. 874–889, 2017. [24] S. Cho, F. Zhang, and C. R. Edwards, “Learning and detecting abnormal speed of marine robots,” International Journal of Advanced Robotic Systems, vol. 18, no. 2, p. 1729881421999268, 2021. [25] R. Yang, M. Hou, C. Lembke, C. Edwards, and F. Zhang, “Anomaly detection of underwater gliders verified by deployment data,” in 2023 IEEE Underwater Technology (UT). IEEE, 2023, pp. 1–10. [26] Y. Ruochu, M. Hou, C. Lembke, C. Edwards, and F. Zhang, “Real-time autonomous glider navigation software,” arXiv preprint arXiv:2304.13096, 2023. [27] X. Liang, W. Wu, D. Chang, and F. Zhang, “Real-time modelling of tidal current for navigating underwater glider sensing networks,” Procedia Computer Science, vol. 10, pp. 1121–1126, 2012. [28] O. Schofield, J. Kohut, D. Aragon, L. Creed, J. Graver, C. Haldeman, J. Kerfoot, H. Roarty, C. Jones, D. Webb, and S. Glenn, “Slocum gliders: Robust and ready,” Journal of Field Robotics, vol. 24, no. 6, pp. 473–485, 2007. This paper is available on arxiv under CC 4.0 license. Authors: (1) Ruochu Yang; (2) Chad Lembke; (3) Fumin Zhang; (4) Catherine Edwards. Authors: Authors: (1) Ruochu Yang; (2) Chad Lembke; (3) Fumin Zhang; (4) Catherine Edwards. Table of Links Abstract and Intro Abstract and Intro Anomaly Detection Algorithm Anomaly Detection Algorithm Experimental Evaluation Experimental Evaluation Conclusion and References Conclusion and References IV. CONCLUSION In this paper, we apply an anomaly detection algorithm to four real glider missions supported by the Skidaway Institute of Oceanography in the University of Georgia and the University of South Florida. On one side of generality, the algorithm is capable of detecting anomalies like remora attachment and shark hit in diverse real-world deployments based on high-resolution DBD data. On the other side of realtime performance, we simulate the online detection on subsetted SBD data. It utilizes generic data of glider trajectory and heading angle to estimate glider speed and flow speed. Anomalies can be identified by comparing the estimated glider speed with the normal speed range. False alarms can be minimized by comparing the algorithm-estimated flow speed with the glider-estimated flow speed. The algorithm achieves real-time estimation through a model-based framework by continuously updating estimates based on ongoing deployment feedback. Future work will enhance estimation accuracy by incorporating large amount of glider data into a data-driven framework. It is also worth taking into account the impact of the anomaly on the estimated flow speed, aiding in the process of determining false alarms. REFERENCES [1] F. Zhang, D. M. Fratantoni, D. A. Paley, J. M. Lund, and N. E. Leonard, “Control of coordinated patterns for ocean sampling,” International Journal of Control, vol. 80, no. 7, pp. 1186–1199, 2007. [2] D. A. Paley, F. Zhang, and N. E. Leonard, “Cooperative control for ocean sampling: The glider coordinated control system,” IEEE Transactions on Control Systems Technology, vol. 16, no. 4, pp. 735– 744, 2008. [3] M. Hou, S. Cho, H. Zhou, C. R. Edwards, and F. Zhang, “Bounded cost path planning for underwater vehicles assisted by a time-invariant partitioned flow field model,” Frontiers in Robotics and AI, vol. 8, 2021. [4] J. Nicholson and A. Healey, “The present state of autonomous underwater vehicle (auv) applications and technologies,” Marine Technology Society Journal, vol. 42, no. 1, pp. 44–51, 2008. [5] O. Schofield, J. Kohut, D. Aragon, L. Creed, J. Graver, C. Haldeman, J. Kerfoot, H. Roarty, C. Jones, D. Webb et al., “Slocum gliders: Robust and ready,” Journal of Field Robotics, vol. 24, no. 6, pp. 473– 485, 2007. [6] M. J. Stanway, B. Kieft, T. Hoover, B. Hobson, D. Klimov, J. Erickson, B. Y. Raanan, D. A. Ebert, and J. Bellingham, “White shark strike on a long-range auv in monterey bay,” in OCEANS 2015-Genova. IEEE, 2015, pp. 1–7. [7] J. E. McCosker and R. N. Lea, “White shark attacks upon humans in california and oregon, 1993-2003,” Proceedings-California Academy Of Sciences, vol. 57, no. 12/24, p. 479, 2006. [8] L. G. Baehr, “Swimming with sharks: An underwater robot learns how to track great whites,” Oceanus, vol. 50, no. 2, pp. 42–49, 2013. [9] J. J. Gertler, Fault detection and diagnosis in engineering systems. CRC press, 2017. [10] J. Chen and R. J. Patton, Robust model-based fault diagnosis for dynamic systems. Springer Science & Business Media, 2012, vol. 3. [11] K. Aslansefat, G. Latif-Shabgahi, and M. Kamarlouei, “A strategy for reliability evaluation and fault diagnosis of autonomous underwater gliding robot based on its fault tree,” International Journal of Advances in Science Engineering and Technology, vol. 2, no. 4, pp. 83–89, 2014. [12] X. Wang, “Active fault tolerant control for unmanned underwater vehicle with sensor faults,” IEEE Transactions on Instrumentation and Measurement, vol. 69, no. 12, pp. 9485–9495, 2020. [13] B. Y. Raanan, J. Bellingham, Y. Zhang, B. Kieft, M. J. Stanway, R. McEwen, and B. Hobson, “A real-time vertical plane flight anomaly detection system for a long range autonomous underwater vehicle,” in OCEANS 2015 - MTS/IEEE Washington, 2015, pp. 1–6. [14] E. Anderlini, C. A. Harris, G. Salavasidis, A. Lorenzo, A. B. Phillips, and G. Thomas, “Autonomous detection of the loss of a wing for underwater gliders,” in 2020 IEEE/OES Autonomous Underwater Vehicles Symposium (AUV). IEEE, 2020, pp. 1–6. [15] G. Fagogenis, V. De Carolis, and D. M. Lane, “Online fault detection and model adaptation for underwater vehicles in the case of thruster failures,” in 2016 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2016, pp. 2625–2630. [16] Y.-s. Sun, X.-r. Ran, Y.-m. Li, G.-c. Zhang, and Y.-h. Zhang, “Thruster fault diagnosis method based on gaussian particle filter for autonomous underwater vehicles,” International Journal of Naval Architecture and Ocean Engineering, vol. 8, no. 3, pp. 243–251, 2016. [17] A. Caiti, F. Di Corato, F. Fabiani, D. Fenucci, S. Grechi, and F. Pacini, “Enhancing autonomy: Fault detection, identification and optimal reaction for over—actuated auvs,” in OCEANS 2015-Genova. IEEE, 2015, pp. 1–6. [18] V. Asalapuram, I. Khan, and K. Rao, “A novel architecture for condition based machinery health monitoring on marine vessels using deep learning and edge computing,” in 2019 IEEE International Symposium on Measurement and Control in Robotics (ISMCR). IEEE, 2019, pp. C1–3. [19] P. Wu, C. A. Harris, G. Salavasidis, I. Kamarudzaman, A. B. Phillips, G. Thomas, and E. Anderlini, “Anomaly detection and fault diagnostics for underwater gliders using deep learning,” in OCEANS 2021: San Diego–Porto. IEEE, 2021, pp. 1–6. [20] Z. Bedja-Johnson, P. Wu, D. Grande, and E. Anderlini, “Smart anomaly detection for slocum underwater gliders with a variational autoencoder with long short-term memory networks,” Applied Ocean Research, vol. 120, p. 103030, 2022. [21] V. Chandola, A. Banerjee, and V. Kumar, “Anomaly detection: A survey,” ACM computing surveys (CSUR), vol. 41, no. 3, pp. 1–58, 2009. [22] W. Yassin, N. I. Udzir, Z. Muda, and M. N. Sulaiman, “Anomaly-based intrusion detection through k-means clustering and naives bayes classification,” International Conference on Computing and Informatics, p. 49, 2013. [23] K. Szwaykowska and F. Zhang, “Controlled lagrangian particle tracking: error growth under feedback control,” IEEE Transactions on Control Systems Technology, vol. 26, no. 3, pp. 874–889, 2017. [24] S. Cho, F. Zhang, and C. R. Edwards, “Learning and detecting abnormal speed of marine robots,” International Journal of Advanced Robotic Systems, vol. 18, no. 2, p. 1729881421999268, 2021. [25] R. Yang, M. Hou, C. Lembke, C. Edwards, and F. Zhang, “Anomaly detection of underwater gliders verified by deployment data,” in 2023 IEEE Underwater Technology (UT). IEEE, 2023, pp. 1–10. [26] Y. Ruochu, M. Hou, C. Lembke, C. Edwards, and F. Zhang, “Real-time autonomous glider navigation software,” arXiv preprint arXiv:2304.13096, 2023. [27] X. Liang, W. Wu, D. Chang, and F. Zhang, “Real-time modelling of tidal current for navigating underwater glider sensing networks,” Procedia Computer Science, vol. 10, pp. 1121–1126, 2012. [28] O. Schofield, J. Kohut, D. Aragon, L. Creed, J. Graver, C. Haldeman, J. Kerfoot, H. Roarty, C. Jones, D. Webb, and S. Glenn, “Slocum gliders: Robust and ready,” Journal of Field Robotics, vol. 24, no. 6, pp. 473–485, 2007. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv available on arxiv

Real-Time Anomaly Detection in Underwater Gliders: Conclusion and References

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