How DeepSee is changing the way scientists use data in the field

Links table

One summary and introduction

2 related works

3 Methodology

4 Study of the deep ocean ecosystem and 4.1 Objectives of deep ocean research

4.2 Workflow and data

4.3 Design challenges and user tasks

5 Deep sea system

• 5.1 Map view
• 5.2 Basic Offer

5.3 Display Interpolation and 5.4 Implementation

6 Usage and scenario 6.1: Pre-trip planning

• 6.2 Scenario: Immediate 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

7.4 Lessons learned

Based on the initial publication of Deep Sea (Section 7.1), user feedback (Section 7.2), and limitations (Section 7.3), we have compiled guidelines to enhance the design of future visualization systems aimed at supporting fieldwork-based research.

Prioritize data integrity as the user’s task. Developing visualizations that support fieldwork-based research has allowed us to resolve interesting limitations before and during expeditions, including limited computational resources and reconciliation of existing datasets with data collected during expeditions. However, although we prioritized predictive capabilities and perceptions of uncertainty in… Deep Sea To address these limitations, we unexpectedly discovered that not designing to support data integration as a user task limits the ability of experts to adaptively update hypotheses and make tactical decisions in the field. It was difficult to keep track of the data being added quickly, with P5 describing the challenge they faced during deployment of translating mental decision making into use Deep Sea: “You may need to change your hypothesis and methodology quickly. These challenges often occur when a lab consultant is making big decisions… It was difficult to integrate DeepSee into the decision-making process, especially when it’s in the lab consultant’s head.” Mixed initiative approach [22] It can alleviate the time pressure during decision making by bringing relevant data to users’ interaction history to the fore, bridging the gap in understanding meaning between simultaneous mental and physical analysis processes. [41] To identify and communicate what should be sampled. Prioritizing data integration capabilities as a user task when designing visualization systems for fieldwork-based research can ensure comprehensive support for tactical decisions when deploying tools in the field.

Visualize physical data in the context of the environment. Deep Sea Foster new skills in communicating and visualizing complex phenomena using intuitive data visualization techniques. For example, interpreting 3D data in an interpolation view is made easier by representing the data as it appears in real life, i.e. as a cylinder, or with realistic height and area ratios. This allowed P2 to think about challenging research questions: “I think about the structure of the world I’m exploring…there’s a lot of data with many dimensions. When we have discrete data points and samples but the environment is continuous, how do we model the phenomenon? For example, sampling the seafloor, or other planets, creates Discrete measurements of a continuous phenomenon. Using direct processing helped P5 understand the data more intuitively: “I love being able to actually click and link the map to the samples. Previously, I had to enter data manually [markers] With geographic information systems programs. DeepSee automatically visualizes samples [as they are added, so] Now I can click on a sample and see the details when ordering.” P2 felt that displaying the data as it appears in real life helped shed light on the patterns in the data that it showed intuitively “The gaps in the samples we want to fill.”. The team also felt the visualization techniques Deep Sea It could extend to other disciplines, such as terrestrial fieldwork integrating drone imagery or future autonomous sampling missions on other planets. Designing data visualizations to reflect their real-life counterparts can improve people’s ability to communicate and understand complex scientific phenomena.

Combine data types in new ways to fill analysis gaps. For team members like P4 who do data work, Deep Sea Make data centralization and harmonization a priority: “DeepSee forced us to combine data from the same cruise and different cruises in the same format and in one place.” The desire to overlay multiple data types at different time scales (i.e., district, core, and sample-level data in Section 4.2) has stimulated interest in answering new research questions that were previously time-consuming and/or difficult. For example, by engaging seamless interaction between multiple levels of data aggregation in the map view and the base view, Deep Sea It enables users to make data-driven decisions based on complex geochemical and taxonomic data distributed at the area, core and sample levels over time (Section 6.1). This capability reflects feedback from our expert interviews (Section 7.2) and is a key feature of it Deep Sea For P3 it was empowering “Spatial thinking for middle range analysis” Between the microscopic and global levels. Furthermore, combining multiple data types into a single interface has enabled deep-ocean researchers to maximize the scientific value of limited sampling. For example, P5 expressed the benefits of his role as a scientist in tracking hypotheses and data and their changes over time when filtering by trip in a map view: “How do we know we’re not reinventing the wheel? DeepSee has really enabled me to question whether I’m contributing scientific work that matches the data we’ve already collected…” Deep Sea Demonstrates the value of designing for and around data requirements in improving the scientific yield and longevity of visualization tools.

Design interactive visualizations to aid mental modeling. Interactive visualizations as a research tool can help scientists gain insight into their own workflow by seeing their problems through a different lens [9]. For example, View map Enable basic sample data tables to be drawn “live” to build mental models of spatial-ecological processes, and not just as “static” outputs for subsequent modeling. That’s why, during the cruise deployment, P3 saw a growing awareness among colleagues that visualization tools could be used in the field: “I see a desire from researchers to use more and different data products in this area. “Before… we weren’t looking at mapping data directly, and we weren’t looking at sequence data as much on board while diving.” that way, Deep Sea It demonstrates potential as a generative tool for thinking about it [21]. Processing physical data at different levels while constantly maintaining context with the environment between the map view and the interpolation view has helped researchers mentally model complex physical processes, such as how environmental processes propagate beneath the seafloor. As a thriving PI, P2 is excited to use Deep Sea like “A resource for my students to make more informed, critical decisions in this type of work. This tool bridges that gap by giving them a visualization of the field to help enhance intuition.” They demonstrated the effects of using data visualizations to directly test ideas: “I think using DeepSee has influenced the way I teach… [having DeepSee] It fundamentally changes the questions we can ask and the information available by changing the way we see data. Deep Sea Embodies interactive visualizations as an opportunity for scientists to define and iteratively improve questions by “acting on knowledge.” [21, 48] Designers can take advantage of the intuitive nature of visualization to demonstrate the art of possibility.