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.

:::info This paper is available on arxiv under CC BY 4.0 DEED license.

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:::info Authors:

(1) Adam Coscia, Georgia Institute of Technology, Atlanta, Georgia, USA ([email protected]);

(2) Haley M. Sapers, Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, California, USA ([email protected]);

(3) Noah Deutsch, Harvard University Cambridge, Massachusetts, USA ([email protected]);

(4) Malika Khurana, The New York Times Company, New York, New York, USA ([email protected]);

(5) John S. Magyar, Division of Geological and Planetary Sciences, California Institute of Technology Pasadena, California, USA ([email protected]);

(6) Sergio A. Parra, Division of Geological and Planetary Sciences, California Institute of Technology Pasadena, California, USA ([email protected]);

(7) Daniel R. Utter, [email protected] Division of Geological and Planetary Sciences, California Institute of Technology Pasadena, California, USA ([email protected]);

(8) John S. Magyar, Division of Geological and Planetary Sciences, California Institute of Technology Pasadena, California, USA ([email protected]);

(9) David W. Caress, Monterey Bay Aquarium Research Institute, Moss Landing, California, USA ([email protected]);

(10) Eric J. Martin Jennifer B. Paduan Monterey Bay Aquarium Research Institute, Moss Landing, California, USA ([email protected]);

(11) Jennifer B. Paduan, Monterey Bay Aquarium Research Institute, Moss Landing, California, USA ([email protected]);

(12) Maggie Hendrie, ArtCenter College of Design, Pasadena, California, USA ([email protected]);

(13) Santiago Lombeyda, California Institute of Technology, Pasadena, California, USA ([email protected]);

(14) Hillary Mushkin, California Institute of Technology, Pasadena, California, USA ([email protected]);

(15) Alex Endert, Georgia Institute of Technology, Atlanta, Georgia, USA ([email protected]);

(16) Scott Davidoff, Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, USA ([email protected]);

(17) Victoria J. Orphan, Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, California, USA ([email protected]).

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