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

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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).

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