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# DeepSee: Multidimensional Visualizations of Seabed Ecosystems

Adam Coscia  0000-0002-0429-9295 Georgia Institute of TechnologyNorth
AvenueAtlantaGeorgiaUSA30332<EMAIL_ADDRESS>,  Haley M. Sapers
0000-0002-1797-1722 Division of Geological and Planetary Sciences, California
Institute of Technology1200 East California
BoulevardPasadenaCaliforniaUSA91125<EMAIL_ADDRESS>,  Noah Deutsch
0009-0005-8303-834X Harvard UniversityMassachusetts
HallCambridgeMassachusettsUSA02138<EMAIL_ADDRESS>,  Malika Khurana
0009-0008-7560-741X The New York Times Company620 Eighth AvenueNew YorkNew
YorkUSA10018<EMAIL_ADDRESS>,  John S. Magyar<EMAIL_ADDRESS>0000-0002-3586-8286 ,  Sergio A. Parra<EMAIL_ADDRESS>0000-0002-2637-7960
,  Daniel R. Utter<EMAIL_ADDRESS>0000-0003-3322-7108 ,  Rebecca L.
Wipfler<EMAIL_ADDRESS>0000-0003-0602-1753 Division of Geological and
Planetary Sciences, California Institute of Technology1200 East California
BoulevardPasadenaCaliforniaUSA91125 ,  David W. Caress<EMAIL_ADDRESS>0000-0002-6596-9133 ,  Eric J. Martin<EMAIL_ADDRESS>0000-0002-2398-4503 ,
Jennifer B. Paduan<EMAIL_ADDRESS>0000-0002-4242-5432 Monterey Bay Aquarium
Research Institute7700 Sandholdt RoadMoss LandingCaliforniaUSA95039 ,  Maggie
Hendrie  0009-0001-1916-3761 ArtCenter College of Design1700 Lida
StreetPasadenaCaliforniaUSA91103<EMAIL_ADDRESS>,  Santiago
Lombeyda<EMAIL_ADDRESS>0000-0003-0684-7889 ,  Hillary Mushkin
<EMAIL_ADDRESS>0000-0002-1967-1790 California Institute of
Technology1200 East California BoulevardPasadenaCaliforniaUSA91125 ,  Alex
Endert  0000-0002-6914-610X Georgia Institute of TechnologyNorth
AvenueAtlantaGeorgiaUSA30332<EMAIL_ADDRESS>,  Scott Davidoff
0000-0002-4417-7268 Jet Propulsion Laboratory, California Institute of
Technology4800 Oak Grove DrivePasadenaCaliforniaUSA91109
<EMAIL_ADDRESS>and  Victoria J. Orphan  0000-0002-5374-6178
Division of Geological and Planetary Sciences, California Institute of
Technology1200 East California BoulevardPasadenaCaliforniaUSA91125
<EMAIL_ADDRESS>
(2024)

###### Abstract.

Scientists studying deep ocean microbial ecosystems use limited numbers of
sediment samples collected from the seafloor to characterize important life-
sustaining biogeochemical cycles in the environment. Yet conducting fieldwork
to sample these extreme remote environments is both expensive and time
consuming, requiring tools that enable scientists to explore the sampling
history of field sites and predict where taking new samples is likely to
maximize scientific return. We conducted a collaborative, user-centered design
study with a team of scientific researchers to develop DeepSee, an interactive
data workspace that visualizes 2D and 3D interpolations of biogeochemical and
microbial processes in context together with sediment sampling history
overlaid on 2D seafloor maps. Based on a field deployment and qualitative
interviews, we found that DeepSee increased the scientific return from limited
sample sizes, catalyzed new research workflows, reduced long-term costs of
sharing data, and supported teamwork and communication between team members
with diverse research goals.

Data visualization, scientific visualization, visual analytics, design study,
deep ocean research.

††journalyear: 2024††copyright: cc††conference: Proceedings of the CHI
Conference on Human Factors in Computing Systems; May 11–16, 2024; Honolulu,
HI, USA††booktitle: Proceedings of the CHI Conference on Human Factors in
Computing Systems (CHI ’24), May 11–16, 2024, Honolulu, HI, USA††doi:
10.1145/3613904.3642001††isbn: 979-8-4007-0330-0/24/05††ccs: Human-centered
computing Scientific visualization††ccs: Human-centered computing Visual
analytics††ccs: Human-centered computing Geographic visualization††ccs: Human-
centered computing Visualization systems and tools††ccs: Applied computing
Earth and atmospheric sciences

##  1\. Introduction

Figure 1. DeepSee presents side-by-side views of 2D geological and biological
landscape maps (A) as well as 2D visualizations and 3D interpolations of
physical, geochemical, and biological parameter gradients in deep sea sediment
cores (B).

The DeepSee web interface. From top to bottom, the page consists of a small
full-width header with three navigation tabs, a larger full-width section with
dropdown menus and buttons, and a main content area with two half-width
columns. The left column labeled (A) shows the Map View with two map types and
user-drawn colored lines as annotations. The right column labeled (B) shows
the Interpolation View with a colored undersea landscape made up of individual
cubes.

Exploration and research on deep-sea benthic ecosystems, ranging from the
expansive abyssal plains to the physico-chemically heterogeneous extreme
environments of hydrothermal vents and methane seeps, has significantly
advanced in the past two decades. These advances arise from massive amounts of
data produced by the development of new technologies for autonomous seafloor
mapping and imaging, physico-chemical sensing, biological measurements, and
sample archival (Martin et al., 2016). Through multidisciplinary teams of
scientists providing complementary data sets on the geology, geochemistry,
macro- and microbiology of these remote ecosystems, deep ocean researchers can
now access 2D topographic maps and color photomosaics of the seafloor,
allowing for the relation of point-source seafloor sample collections (e.g.
sediment cores, rock, animal, and water samples) with their appropriate
environmental context at centimeter to kilometer spatial scales.

However, while 2D maps of spatial locations of samples are valuable, the field
currently lacks visualization tools which extend into the 3rd dimension, i.e.
within the subseafloor. This presents several fundamental data visualization
challenges around exploring associated physico-chemical and biological data of
samples within their spatial context. The samples are sediment cores –
pointwise geospatial and temporal (i.e., multidimensional) data consisting of
hundreds of microbial species and physio-chemical parameters – that are spread
sparsely across several hundred meters of seafloor. Researchers perform
fieldwork to collect these sediment cores from the deep ocean during
expeditions, an expensive and time-consuming task that requires extensive
planning beforehand and usually results in a very limited number of samples.
Seeing prior sampling history to decide where to sample next can help maximize
time spent conducting expensive fieldwork. Further, it is unclear how
scientist’s existing research workflows might change by introducing new
visualization capabilities, both in planning before fieldwork as well as in
tactical decision making in the field as new samples are collected. Our goals
are to develop a system that solves the visualization challenges in exploring
spatial trends between sediment cores in the context of their environment,
deploy the system to measure the impacts on scientists’ workflows conducting
deep ocean fieldwork, and reflect on broader design guidance for visualizing
multidimensional sample data collected during fieldwork expeditions.

We present a collaborative design study (Sedlmair et al., 2012) conducted with
a team of scientific researchers studying deep ocean ecosystems (Sect. 3). We
first characterized their research goals, preexisting workflows and data
practices, leading us to synthesize design challenges and user tasks (Sect. 4)
for a system that can help scientists visualize spatial trends between cores
in context of the environment, integrate core and map data at multiple size
and time scales in a single interface, and increase the scientific return
(long-term usability and value of the data) from the limited number of samples
available by predicting unseen values in unsampled locations.

From our task abstraction, we developed DeepSee (Fig. 1, Sect. 5), an open-
source111DeepSee code: https://github.com/orphanlab/DeepSee, interactive
workspace for scientists to upload sediment core data and map images and see
their sampling history displayed across multiple connected views
simultaneously. Interactive maps of the seafloor between centimeter and
kilometer resolution (Fig. 1A) are labeled with information about previous
dives as well as collected samples. Alongside these maps, DeepSee displays 2D
visualizations that show parameter gradients as a function of depth and
interactive 3D visualizations of data interpolations in the space between
samples (Fig. 1B). The data interpolations can be run in real time, allowing
scientists to “see” below the seafloor and determine the most likely places to
collect high-value samples. To support decision making, DeepSee provides
annotation tools on the maps for taking notes, useful for communicating
findings and planning future dives. Finally, DeepSee is portable and requires
no internet access, empowering scientists to use DeepSee on field expeditions
in remote environments.

To assess the impact of our approach on solving a real-world problem, we
evaluated DeepSee with the same expert collaborators involved in the design
process (Sect. 7). First, we conducted an initial field deployment of DeepSee
aboard a research cruise to test its capabilities and illustrate which user
tasks were accomplished in the field. Then, we interviewed each team member
and collected feedback on the deployment around several areas of deep ocean
fieldwork research that DeepSee impacted. We learned that:

  * (1)

Fluid interaction (Elmqvist et al., 2011) and aggregation of data visualized
at multiple scales can help scientists increase the scientific return of
limited samples by enabling new ways of asking questions about the data;

  * (2)

Integrating 2D and 3D data in the same interface can catalyze new research
workflows by surfacing new potential targets for future sampling and reducing
the long-term cost of data preparation; and

  * (3)

Modular visualizations can help team members with different roles and diverse
research goals to solve specific tasks as they decide where and which samples
to collect.

From these findings, we synthesized principles for designing future
visualization systems in other fieldwork-driven domains:

  * (1)

Prioritize data integration as a user task when designing, helping users track
data being added on the fly in the field;

  * (2)

Visualize physical data in context of the environment, fostering stronger
understanding and communication of insights;

  * (3)

Combine data types to bridge micro and macro scale analysis, increasing
scientific return on limited samples; and

  * (4)

Design interactive visualizations to aid mental modeling, helping scientists
understand and explore complex processes.

Overall, the combination of fluid interaction, tightly integrated data, and
modular visualizations provided deep insight into the data that helped
researchers build a stronger intuition about the spatial distributions of
environmental sample data, ultimately facilitating more informed future sample
selection.

In summary, the contributions of this paper are: (1) a characterization of the
scientific process and design considerations for visualizing deep ocean
ecosystems following a user-centered design process; (2) DeepSee, an
interactive workspace that visualizes the history of deep sea sediment
sampling for selecting future sample site locations; and (3) reflections based
on interviews with scientists that deployed DeepSee discussing the lessons
learned and broader impacts of leveraging data visualizations to support
fieldwork-driven scientific research.

##  2\. Related Work

Scientific Visualization Techniques and Systems.  Drawing inspiration from a
rich history of scientific visualization challenges in analyzing environmental
data (Lipşa et al., 2012; Afzal et al., 2019), DeepSee leverages a combination
of multidimensional- and geo-visualization techniques to help scientists
contextualize and explore multi-field data. To bridge the gap between
understanding global phenomena and local subsets of interest (i.e. “details
within context” (Lipşa et al., 2012)), we utilized selection and details-on-
demand interactions to help users navigate geo-spatiotemporal visualizations
of sampling history at multiple physical and temporal scales (Andrienko et
al., 2003; Andrienko and Andrienko, 2006; Dykes et al., 2005). Once a local
sample subset has been identified, DeepSee presents multi-field visualization
techniques for comparison (Gleicher, 2018) at the data level in both 2D using
juxtaposed bar charts and 3D using basic volume rendering techniques (Drebin
et al., 1988; Song et al., 2006; Hansen and Johnson, 2011). We further
leveraged Value-Suppressing Uncertainty Palettes (Correll et al., 2018) to
represent uncertainty as color for making sense of our simulated data
interpolations in 3D. Our science team collaborators were familiar with GIS
(Geographic Information Systems) such as
ArcGIS222https://www.arcgis.com/index.html in their work, and compared them to
the capabilities in DeepSee. However, while ArcGIS provides state-of-the-art
capabilities for lab-based geo-spatiotemporal data analysis, it lacks out-of-
the-box support for rapid data integration and visualization of point-based
field sample data interpolations in 3D in context of their 2D geolocations. We
aim to bridge this gap in the context of deep ocean fieldwork operations by
combining on-the-fly 3D data interpolation capabilities as samples become
available with fluid interactions (Elmqvist et al., 2011) across side-by-side
2D and 3D views of the data, useful in both planning before fieldwork as well
as for tactical decision making in the field. Our design work seeks to explore
ways of interacting with and combining geovisualization and 3D data
visualizations in a single interface, towards designing future fieldwork data
visualization workflows.

Visualization Systems for Analyzing Oceanographic Data.  Prior work in
developing systems for visually analyzing and predicting ocean data spans
multiple diverse use cases, such as forecasting ocean currents for oil
exploration (Höllt et al., 2013) and comparing multiple geospatial and
temporal ocean simulation models at once (Köthur et al., 2014). One of the
most widely used tools in the community is Ocean Data View (Schlitzer, 2022),
a general-purpose package for exploring multiparameter profile or sequence
oceanographic data. Other tools present more specific task-based
visualizations. For example, Ovis (Höllt et al., 2014) is a visual analytics
framework for investigating multivariate sea surface height data collected
over time, while SeaVizKit (Ali et al., 2019) is a recent toolkit featuring
interactive capabilities for visualizing ocean field data at multiple scales
in the browser, similar to our browser-based approach. However, these systems
and most others focus on visualizing ocean models, vector-based numerical
models of ocean properties such as currents and their circulation. While we
share many of the same challenges including the need for multidimensional
views, quantifying uncertainty and supporting both scientific investigation
and decision making (Ali et al., 2019), our collaborators’ task is to analyze
point-based fieldwork samples collected from deep ocean sediments, which
introduces new challenges in integrating 2D and 3D data as well as supporting
fluid interaction (Elmqvist et al., 2011) between these data at multiple
spatial and temporal scales. Further, these systems introduce design
principles for use during long-term, lab-based strategic analysis of data,
while deep ocean researchers conduct expensive fieldwork that requires support
for on-the-fly decision making. We build on these principles in DeepSee’s
design by additionally supporting short-term, field-based tactical analysis of
data during expeditions. By developing visualizations that can represent
point-based sample data in context of the environment in multiple dimensions,
we aim to learn broad principles for designing future visual analytics systems
that support fieldwork-driven research.

Visualization Design Studies in Fieldwork Contexts.  Data visualization often
engages with a broad range of disciplines through a design study methodology
(Sedlmair et al., 2012), a problem-driven research framework used to
systematically apply visualization techniques to an application area and
evaluate the solution with real users. Examples of visualization design
studies can be found in domains such as energy analysis and modeling (Goodwin
et al., 2013), cybersecurity (Mckenna et al., 2015), and manufacturing (Eirich
et al., 2022). Our design study sought to combine data visualization and
human-centered design in the context of scientific fieldwork operations, or
processes that are time-sensitive, action-oriented, and involve assessing risk
and impact across multiple scenarios (Conlen et al., 2018). There is a growing
body of literature on design principles for building visual analytics systems
in the context of science operations that stresses human-centered design and
information visualization as both the method and outcome, helping scientists
gain insight into their own workflows by seeing the problem through a
different lens (Conlen et al., 2018; Hendrie et al., 2022). For example,
MOSAIC Viewer (Bae et al., 2020) is a visual analytics system that utilized a
human-centered design methodology to help human operators make sense of
multiple robots’ scheduling decisions and synchronization, while reducing the
time and difficulty associated with previous operations. Specifically in
fieldwork-driven ocean science research, DeepSee seeks to expand on human-
centered approaches to geovisualization design (Lloyd and Dykes, 2011) that
put scientists in the driver’s seat for interacting directly with the
algorithms that enable data exploration and analysis.

##  3\. Methodology

In this paper, we followed Sedlmair et al.’s methodological framework for
design studies (Sedlmair et al., 2012). 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 (Goodwin et
al., 2013; Mckenna et al., 2015; Eirich et al., 2022), 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 (Sedlmair et al., 2012) 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 (Sedlmair et
al., 2012). 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.

##  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 (Corinaldesi, 2015). Microorganisms represent primary
drivers of these extreme ecosystems, supporting diverse animal communities
(Jørgensen and Boetius, 2007; Danovaro et al., 2014). 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 (Nealson, 1997; Mason et al.,
2009).

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 (Ohta and Hatada, 2006; Wu et al., 2006; Cavicchioli et
al., 2011). 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 (Hand et al., 2009; Martin and McMinn,
2018; MacKenzie et al., 2022).

###  4.2. Workflow and Data

Figure 2. Region-level data represented as maps, provided by (Paduan et al.,
2022). Maps reveal two important features: (A) geological landscapes, such as
locations of hydrothermal vent mounds and chimneys, visualized by topography
from AUV and low-altitude survey system (LASS) multibeam surveys; and (B)
surface biological and ephemeral “soft” features, such as white patches of
microbial mats growing in hydrothermal fluids, visualized by photomosaics,
acoustic backscatter and LIDAR bathymetry. DeepSee solves a critical challenge
of combining region-, core-, and sample-level data in a single interface using
expressive and interactive visualizations (Sect. 5).

Two photos labeled (A) and (B) show maps of the ocean floor. Image (A) shows a
geological landscape colored by elevation. Image (B) shows a photo mosaic of
the same landscape.

Table 1. A Subset of Core-Level and Sample-Level Data Visualized by DeepSee444The example data is provided by Speth et al. (Speth et al., 2022). | Core (10+ identification and measurement params) | Sample (500+ phys/geo/bio params)
---|---|---
Core ID | Location | Date | Core Fate | Latitude | Longitude | Horizon | Sulfate | Sulfide | Taxa 1 | Taxa 2
NA091_020 | Auka - Matterhorn | 11-01-17 | Geochem | 23.954198 | -108.862394 | 2-3 cm | 22.98 | 5.14 | 0.1358 | 0
NA091_020 | Auka - Matterhorn | 11-01-17 | Geochem | 23.954198 | -108.862394 | 3-4 cm | 17.97 | 4.6 | 50.497 | 0
… | … |  |  |  |  |  | … |  |  |
S0193_PC5 | Auka - Diane’s vent | 11-14-18 | Geochem | 23.954822 | -108.863020 | 1-2 cm | 8.85 | 3.78 | 0.4464 | 37.1574
… | … |  |  |  |  |  | … |  |  |

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 $\approx
30$ 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) (Paduan et al., 2022). 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
4, left) and sample-level (Table 4, 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) (Martin et al., 2016), 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 4, 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 4, 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).

###  4.3. Design Challenges and User Tasks

Table 2. Design Challenges (C) and User Tasks (T)  C1 | Observing spatial trends in sparse, tabular data
---|---
T1 | Correlate taxonomic profiles against geochemistry
T2 | Estimate spheres of influence around a seafloor feature
T3 | Annotate relationships between environmental features
T4 | Compare horizons across multiple cores simultaneously
C2 | Integrating data at multiple scales of size and time
T5 | Contextualize scales between tables and maps
T6 | Select cores across expeditions and date ranges
T7 | View data at multiple spatial resolutions
C3 | Maximizing the scientific value of limited sampling
T8 | Choose sample sites likely to address science team goals
T9 | Compare past geochemical/microbial data with new
T10 | Correlate visual indicators with microorganism data
T11 | Estimate unseen parameters in unsampled locations

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 4 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 4 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 ($\approx 20$) 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.

##  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

Figure 3. The Map View plots cores by latitude/longitude on a map layer to
show the spatial and geographic history of sampling. Users can drill down to
cores of interest (A), explore the map (B), see details on demand (C), switch
maps on the fly (D), draw annotations (E), and select cores (F) to view in the
Core View and Interpolation View.

Six images labeled (A), (B), (C), (D), (E) and (F) each show the Map View with
the same data in different configurations. Image (A) shows the dropdown menus
for filtering data. Image (B) shows a pan and zoom interaction by clicking and
dragging a cursor. Image (C) shows the results of (B) and shows a tooltip with
a cursor hovering on a white dot. Image (D) shows a different map and a
dropdown menu with a cursor selecting a new map name. Image (E) shows the
results of (D) and three colored lines drawn by the user. Image (F) shows a
cursor clicking and dragging over a region of white dots to select them.

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

Figure 4. The Core View arranges sets of horizontal bar charts to compare
parameter values between cores across horizons.

One image shows the Core View. Four horizontal bar charts are aligned
vertically and arranged horizontally side-by-side. They each show measured
sulfide values down the profile of a different core sample.

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 drop-down 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).

###  5.3. Interpolation View

Figure 5. The Interpolation View visualizes parameter values interpolated in
three dimensions (latitude/longitude/depth) between cores. We provide several
reconfiguration interactions (A) to help users make sense of an unfamiliar
data representation. Users can update the view on the fly in several ways:
swapping between standard and Value-Suppressing Color Palettes (VSUPs)
(Correll et al., 2018) (B); changing the interpolation method and/or grid size
(C); and clipping through the interpolation (D). Finally, users can select an
interpolated core to export as JSON (E).

Fourteen images each show the Interpolation View with the same data in
different configurations. One image shows a default configuration of an
isometric rectangular prism made up of smaller colored rectangular prisms
representing interpolated Sulfate values at every latitude, longitude, and
depth coordinate. Six images labeled (A) show three spatially modifications to
the prism with image pairs. The first pair shows elongating one dimension of
the rectangular prism. The second pair shows hiding non-interpolated smaller
rectangular prisms. The third pair shows each smaller rectangular prism
changed into a cylinder. Two images labeled (B) show a value-suppressing
uncertainty palette legend and the palette applied to the color of the smaller
rectangular prisms. Two images labeled (C) show two different interpolation
method results that change the shape of the rectangular prism. Two images
labeled (D) show smaller rectangular prisms hidden when the user clips them
from the view. One image labeled (E) shows a cursor selecting on a column of
smaller rectangular prisms representing a single core.

Figure 6. Two usage scenarios showing how DeepSee helps users make the most of
limited sample data by supporting real-time annotation and interpolation in
the tool, with data from Speth et al. (Speth et al., 2022). In (A), our user
maximized information between map and tabular data to determine where high-
value cores containing both ANME-2c and JS1 are most likely to occur on future
dives. In (B), during an on-the-fly decision-making scenario where the
seafloor changed over time, the Interpolation View created a data-driven
opportunity to correct course by interpolating unseen sulfide values and
helping users find new targets.

Two images labeled (A) and (B) each showing the half-width double column main
content area of DeepSee. Image (A) shows the Map View on the left and the Core
View on the right. The Map View shows colored line annotations and the Core
View shows ANME-2c and JS1 values for two core samples. Image (B) shows the
Map View on the left and the Interpolation View on the right. The Map View
shows colored line annotations and the Interpolation View shows a colored
gradient of sulfide representing high values in the southwest corner
transitioning to low values in the northeast corner.

The Interpolation View (Fig. 5) visualizes sample-level (parameter) data by
interpolating values between cores in three dimensions (latitude, longitude,
and depth) and rendering the resulting data as a three-dimensional object
colored by the parameter value at each point in space. Our visualization
technique can extend to any 3D interpolation method that provides four
attributes ($x$, $y$, $z$, and a value) for each point observation. The
interpolations can be run in real time, allowing the scientists to rapidly
investigate different predictions about the environment below the seafloor
where future sampling might yield the greatest return on investment.

DeepSee renders 3D interpolation data as space-filling volumetric pixels
(voxels) in a 3D space. For example, at an $x$ cm grid size, we represent an
interpolated value at a given latitude/longitude/depth as a rectangular prism
with width/length of $x$ cm and a height of 1 cm, matching the same depth
horizon measurements in real cores. We currently support fast 3D linear
barycentric interpolation and a 3D discretized approximation of Sibson’s
natural-neighbors interpolation (Park et al., 2006) (T11). While the research
scientists expressed concerns that linear and natural-neighbors gradients are
unlikely to reflect spatially heterogeneous in-situ environmental processes,
they were optimistic about the capabilities of visualizing these methods as a
proof-of-concept. Linear interpolations offer a simple explanatory model with
little complexity and no ability to interpolate outside of sampled regions.
Natural-neighbors is a smoother approximation of nearest-neighbors
interpolation with a space-filling property; i.e., outside the space between
observations, values will decay at the rate of the gradient at the boundary.
Additionally, the grid sizes offered were specifically chosen to mirror the
real life size of cores (7 cm diameter) (T7). Because interpolations are
computationally expensive, especially at 7 cm resolution across tens to
hundreds of meters, we provide larger grid sizes in 7 cm increments.

We used the built-in camera system with OrbitControls from Three.js, the
JavaScript library we used to build the Interpolation View (Sect. 5.4). Using
a mouse, users can left click and drag to freely rotate the voxels with a
stationary camera angle, right click and drag to pan the camera in the
latitude/longitude plane, and scroll the mouse wheel to zoom in and out. We
built additional features that allow users to reconfigure the view in several
ways (T7) using buttons (Fig. 5A): (1) extending the height of each voxel from
1 cm to 10 cm, making patterns in depth much easier to see; (2) showing /
hiding all interpolated values and only showing actual sample data; and (3)
changing voxels from rectangular prisms to cylinders at a fixed 7 cm diameter,
mirroring the shape of real life cores. These visual aids helped orient new
users quickly to this projected visualization method.

The research scientists also expressed an interest in seeing where
interpolations were less “certain”. In response, we implemented variations on
the six color palettes from the Core View based on Value-Suppressing
Uncertainty Palettes (VSUPs) (Correll et al., 2018) (Fig. 5B). Where color
palettes apply a color to a given voxel based on a single parameter value,
VSUPs take in an additional uncertainty parameter and suppress the color as a
function of the amount of uncertainty in the value (T11). In DeepSee, we
normalized the linear distances from each voxel to the nearest non-
interpolated voxel as uncertainty; thus, interpolated values farther from a
known sample value will appear suppressed, signaling a lack of confidence in
the interpolation at that location. Similar to the interpolation methods, our
choice of uncertainty measure (distance to nearest sample) worked as a proof
of concept for how to integrate more scientifically accurate measures in the
future. The color palette can be changed on the fly while preserving the
camera angle and visual embellishments.

We provide several on-the-fly capabilities to help users explore
interpolations while maintaining spatial context. Users can change the
interpolation method and grid size (Fig. 5C) while the camera angle and visual
embellishments persist, helping users maintain context and orientation while
immediately seeing changes in gradients (T7). For example, a scientist could
quickly switch between interpolations of taxonomic and geochemical attributes
at various grid sizes to identify relationships between them and find
locations with interesting gradients to sample in future dives (T1, T2, T8).
Users can also clip through the voxels (Fig. 5D) to see patterns in the data
in the interior of the interpolated region. We provide two clipping methods:
(1) clipping individual voxels by their interpolation value outside of a range
chosen by the user using a double-ended range slider; and (2) clipping by
latitude/longitude/depth using a single-ended range slider. In (2), cuts are
taken only from the intersection of all three clipping planes, and we also
ensure users can flip the clipping plane to view cuts from any direction.
Finally, if a user is interested in the exact parameter values at a specific
latitude/longitude, they can hover over the object and select a vertical core
to save (Fig. 5E) (T8). The results are output in JSON as a list of parameter
values at each 1 cm horizon, matching the exact format of a real core sample.
These results can then be further analyzed, e.g., in future dives by
collecting new cores, deriving sample values, and comparing them with the
interpolated values at the same latitude/longitude given by DeepSee (T9).

###  5.4. Implementation

DeepSee is an open-source555DeepSee code: https://github.com/orphanlab/DeepSee
Vue.js Single-Page Application, which provides the UI framework and styling.
We plot cores on top of PNG map backgrounds in the Map View as well as draw
bar charts in the Core View using D3.js (Bostock et al., 2011). To compute
interpolations, we used SciPy’s ND piecewise linear barycentric interpolator
(Virtanen et al., 2020) and a Python implementation of a 3D discretized
approximation for Sibson’s natural-neighbors interpolation (Park et al.,
2006). Then, we used Three.js to create a traditional 3D render scene with
global lighting in the Interpolation View. We also wanted DeepSee to be
accessible while doing fieldwork in remote locations. Thus, we used
Electron.js to create a portable web browser environment as a standalone
desktop executable for both Windows and Mac operating systems that can run
with or without access to the internet.

##  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 $\approx 50$ 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 (Correll et al., 2018). 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.

##  7\. Evaluation

Our goal for evaluation was to validate the effectiveness of DeepSee’s
technical design and visualizations (Sedlmair et al., 2012) in terms of
maximizing scientific return. To do this, we first asked our deep ocean
research collaborators (P$1-5$) 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 (P$1-5$) 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

Figure 7. DeepSee being used shipboard during a cruise, helping the dive team
locate where to sample cores around vents and seeps of the Pescadero Basin in
the Gulf of California.

One photo showing two seated men in a dark, unlit room with digital monitors
and submersible control equipment in front of them. One man is touching the
keyboard of a laptop with the DeepSee interface showing on the screen. The
other man is watching a large digital video monitor that shows a live color
video feed from a submersible camera currently looking at the ocean floor.

Following the evaluation methodology for design studies (Sedlmair et al.,
2012), DeepSee was deployed on an expedition (Parra, 2021) 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 (Paduan et al., 2018) and recently visited in 2018 on the R/V Falkor
during expedition FK181031 (Speth et al., 2022). 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 P$2$
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?” P$3$ 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).
P$4$ 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) P$4$ 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.

###  7.2. Expert Interviews

After the cruise, we conducted individual semi-structured interviews ($1-3$
hours long each) with five domain experts in deep ocean research (P$1-5$).
These experts were the same collaborators involved in the design process and
carried out the initial deployment (Parra, 2021). Very few scientists have
access to the deep ocean sample data we utilized, limiting the number of
potential candidates able to properly evaluate DeepSee. However, in line with
prior design studies (Goodwin et al., 2013; Mckenna et al., 2015; Eirich et
al., 2022), it is common for visualizations that tackle very specific problems
to be validated by only a few users.

Our five collaborators were a team of graduate students, postdocs, and
research scientists broadly interested in fieldwork at the intersection of
geology, geochemistry, and biology in the deep ocean. The multidisciplinary
team has worked together over several years on logistics around planning and
executing cruises, collecting and organizing prior and current cruise data,
and analyzing samples obtained during cruises. Two collaborators self-
identified as female and three self-identified as male. We organized the
interview questions and domain experts’ feedback around three broad questions
we directly asked each collaborator. We then conducted inductive thematic
analysis (Boyatzis, 1998) of the feedback to identify emergent themes that
were discussed amongst all authors.

  * (1)

Research – “How did using DeepSee support your fieldwork research goals?”

  * (2)

Workflow – “How did using DeepSee improve the science return of samples?”

  * (3)

Teamwork – “How did using DeepSee help you with your tasks in your role on the
team?”

(1) In pre-cruise planning, fluid interaction and aggregation between multiple
types and scales of data collected long-term can help researchers visually
discover more insights.

We first explored how visualizations supported fieldwork-driven research. In
pre-cruise planning, several capabilities should be prioritized in system
design, including data aggregation across multiple disparate sources,
comparing multiple data types, fluidly interacting between data scales
(Elmqvist et al., 2011), and keeping humans in the analysis loop through data
simulations. These capabilities can enable serendipitous discoveries from
analyzing data collected over a long period of time. When asked who in the
research community could benefit the most from DeepSee, P$5$ responded: “It’s
really fieldwork driven. If someone has been cataloging a field site for a
decade, like a marine site, I would absolutely recommend it.”

To support these claims, our collaborators provided several examples of
visualizations playing a pivotal role in supporting their research goals. The
team found directly comparing the Map View with the Core View or Interpolation
View helped them discover “holes” in their sampling. For P$4$, this rich
history of sampling helped them familiarize themselves with a new hydrothermal
vent site: “‘Oh, these were hot cores, so this must be a hot area’… I can
identify areas to sample before I [return to a field site].” These features
introduced potential for making serendipitous discoveries in the future, as
P$4$ described while demonstrating how they would use DeepSee: “I didn’t
realize Sulfide was so high in this area! Why is that? …Well, it looks like
based on ANME distributions [in the Core View] …these taxa are in this area
and very abundant!” As a microbial ecology lab, comparing interactions between
geochemical and biological phenomena at multiple scales is also vital. P$3$
specifically felt that DeepSee fit in a role that “enabled spatial thinking
for the middle range of analysis” between the microscopic and global level.
P$2$ described the value of DeepSee in supporting human-in-the-loop sampling
scenarios: “Many times we can’t be [at a site] or make plans in time. Having a
tool like this helps you come up with a plan backed by data and communicate
these plans. We can overcome language barriers. Almost like a virtual reality
simulation!”

(2) When planning fieldwork expeditions, integrating and visualizing 2D/3D
data together can increase the science return on limited samples.

In supporting the team’s research goals, we also found that DeepSee
contributed to fieldwork research workflows in several unique ways. We found
that visualization of complex data during pre-cruise planning was the most
useful to the team. To support this, the ability to visualize data in a single
interface helped experts orient themselves to the field site, perform
gradient-based analysis across the area, and seamlessly explore trends between
2D and 3D. This ultimately increased the long-term value or science return on
using sample data to drive fieldwork research workflows in the future. Beyond
the visualizations, P$4$ also found that preparing data for testing in DeepSee
catalyzed useful data work for the long-term, such as “formatting and
standardizing data collection, and planning on what columns we need.”

P$5$ summarized an example DeepSee workflow used in the lab, highlighting how
questions involved overlaying multiple data types: “We first find a site that
we’re interested in, a vent for example, then zoom in to those samples and
select them. Then, we’ll go to the Core View and look at the geochemical and
microbial taxa values for the cores. We’ll filter out different core types…
then look at the bar charts [in the Core View]. For example, we might see
levels that are higher than normal of ANME, so we switch to a seafloor
photomosaic map [in the Map View] and maybe we don’t see any visible surface
expression. This then leads us to ask what the geochemistry is in this area,
so we switch to Sulfide [in the Core View]… Time is precious, so this [ability
to toggle between data] helps with deciding what data we want to collect from
this site again and which we might not need.” Comparing DeepSee with existing
map tools, P$1$ reported being more comfortable and efficient with ArcGIS, R
and Python, while P$5$ reported they preferred using the Map View to “quickly
orient myself to where samples are and to test the area over where we wanted
to interpolate.” P$4$, in charge of data preparation, summarized both the
startup cost of DeepSee and the payoff for their team in the long run around
catalyzing data aggregation: “There was initially a higher amount of work to
consolidate the data… Once we were past that main push, it was easier to use
DeepSee… Now we can see patterns in the old data that were hidden in an Excel
sheet. If I have a hypothesis and need to show the data to test that
hypothesis, DeepSee makes it possible in just a few clicks.” Analogously, P$5$
described a new use case for DeepSee “as a repository of information… new
researchers that come into the lab can look at [previous] data we’ve collected
from a site right away.”

(3) Simple, intuitive and modular visualizations can help different fieldwork
team members rapidly solve a diversity of specific, directed research tasks.

Finally, because we designed for a single team with a rich diversity of roles
and responsibilities, we investigated how DeepSee impacted communication and
teamwork. We found that the team dynamic can be characterized by many diverse
goals with limited time and resources, where individual wants and needs
illuminate unique use cases that make modular tools like DeepSee valuable.
Having multiple visualizations with simple and intuitive controls helped our
collaborators address specific tasks, such as filtering and selecting specific
sites of interest, performing predictive analysis of potential data trends for
deciding where to sample, and learning a single software instead of multiple
tools.

While one of the lab’s main research goals is to study deep ocean microbial
ecology, every team member has unique questions they want to answer. For
example, P$2$ told us that “DeepSee lets me preselect sample sites in a
meaningful way. I can design samples I would like based on the data we’ve
gotten back.” P$2$ further found value in the predictive capabilities of the
Interpolation View: “If we think a gradient exists in a certain area, we can
fill in the data and see that… DeepSee can maximize time by really targeting
questions about exploration to hone in on specific data when there is a
history of data.” For P$3$, a key improvement was in creating a single
interface for visualizing data, “reducing the need to learn additional
software such as ARCGIS.” During cruises, rapidly changing environments and
unyielding time constraints can create high-pressure situations. P$5$
described a particular cruise environment prior to the initial deployment of
DeepSee: “When we went to Pescadero years ago, the site was dynamic. The
coordinates from the ROV are not very accurate. There’s a lot of stress
aligning previous bathymetry maps to the current location. You’re looking at
previous log books and looking at screens in the control room and talking to
the pilots of the ROV.” Multiple team members agreed that simplicity and
familiarity with DeepSee were critical in such situations.

###  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., P$5$ 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, P$1$
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?” P$1$ 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,
P$4$ 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, P$2$ suggested potential applications of Value-Suppressing Uncertainty
Palettes (VSUPs) (Correll et al., 2018) 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 (Oliver and Webster, 1990)) 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. P$3$ 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. P$3$ 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.

###  7.4. Lessons Learned

Based on the initial deployment of DeepSee (Sect. 7.1), user feedback (Sect.
7.2) and limitations (Sect. 7.3), we synthesized guiding principles for
enhancing the design of future visualization systems that aim to support
fieldwork-driven research.

Prioritize data integration as a user task.  Developing visualizations that
support fieldwork-driven research allowed us to solve interesting constraints
before and during expeditions, including limited computational resources and
reconciling existing data collections with data collected during expeditions.
However, even though we prioritized predictive capabilities and uncertainty
visualizations in DeepSee to address these constraints, we unexpectedly
discovered that not designing for data integration support as a user task
limited the ability for experts to adaptably update hypotheses and make
tactical decisions in the field. It was difficult to track data being added on
the fly, as P$5$ described the challenge they encountered during deployment of
translating a mental decision-making process into using DeepSee: “You might
need to change your hypothesis and your methodology on the fly. These
challenges often happen with the lab advisor in their head making big
decisions… it was hard to incorporate DeepSee into the decision-making
process, especially when it’s in the lab advisor’s head.” A mixed-initiative
approach (Horvitz, 1999) could alleviate time pressure during decision-making
by bringing relevant data to the foreground based users’ interaction history,
bridging the gap in sensemaking between concurrent mental and physical
analysis processes (Pirolli and Card, 2005) to determine and communicate what
to sample. Prioritizing data integration capabilities as a user task when
designing visualization systems for fieldwork-driven research can ensure end-
to-end support for tactical decisions when deploying tools out in the field.

Visualize physical data in context of the environment.  DeepSee fostered new
skills in communicating and conceptualizing complex phenomena using intuitive
data visualization techniques. For example, interpreting 3D data in the
Interpolation View was made easier by representing the data as it looks in
real life, i.e., shaped as a cylinder, or with realistic proportions of height
and area. This allowed P$2$ to think through difficult research questions: “I
think about the structure of the world I am exploring… There is a lot of data
with a lot of dimensions. When we have discrete data points and samples but
the environment is continuous, how do we represent phenomenon? For example,
sampling the seafloor, or other planets, creates discrete measurements of
continuous phenomenon.” Employing direct manipulation helped P$5$ understand
the data more intuitively: “I like being able to physically click around and
associate a map with samples. Before, I had to manually input [markers] with
GIS software. DeepSee automatically visualizes samples [as they are added, so]
now I can click on a sample and see details on demand.” P$2$ felt that showing
the data as it looks in real life helped illuminate patterns in the data that
intuitively showed “holes in sampling that we want to fill in”. The team also
felt the visualization techniques in DeepSee could extend to other
disciplines, such as terrestrial field work integrating drone imagery or
future autonomous sampling missions on other planets. Designing data
visualizations to mirror real-life counterparts can improve people’s ability
to communicate and understand complex scientific phenomena.

Combine data types in new ways to bridge analysis gaps.  For team members such
as P$4$ doing data work, DeepSee made centralizing and aligning data a
priority: “DeepSee forced us to consolidate data from the same cruise and
different cruises into the same format and in one location.” The desire to
overlay multiple data types over different temporal ranges (i.e., region-,
core-, and sample-level data in Sect. 4.2) catalyzed interest in answering new
research questions that were previously time-consuming and/or difficult. For
example, by engaging fluid interaction between multiple levels of data
aggregation in the Map View and Core View, DeepSee enables users to make data-
driven decisions based on complex geochemical and taxonomic data distributed
at the region, core, and sample level over time (Sect. 6.1). This capability
mirrors feedback from our expert interviews (Sect. 7.2) that a key feature of
DeepSee for P$3$ was enabling “spatial thinking for the middle range of
analysis” between the microscopic and global level. Further, combining
multiple data types in a single interface enabled deep ocean researchers to
maximize the scientific value on limited sampling. For example, P$5$ expressed
benefits in their role as scientist of tracking hypotheses and data and their
changes over time when filtering by cruise in the Map View: “How do we know
we’re not reinventing the wheel? DeepSee has really enabled me to ask whether
I’m contributing scientific work that fits in the data we have already
collected…” DeepSee shows the value of designing for and around data
requirements in improving the scientific return 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 workflows by seeing their problems through a different lens (Conlen et
al., 2018). For example, the Map View enabled core sample data tables to be
plotted “live” for building mental models of spatial ecological processes,
rather than only as a “static” output for post-hoc modeling. Because of this,
during the cruise deployment, P$3$ saw increased awareness in colleagues of
the potential for using visualization tools out in the field: “I see a desire
from researchers to use more and different data products live in the field.
Before… we didn’t look at mapping data as much live, we didn’t look at
sequence data as much on the ship during the dive.” In this way, DeepSee
demonstrates potential as a generative tool to think with (Hendrie et al.,
2022). Manipulating physical data at different scales while continuously
maintaining context with the environment between the Map View and
Interpolation View aided researchers in their mental modeling of complex
physical processes, such as how ecological processes might spread under the
sea floor. As a burgeoning PI, P$2$ is excited to use DeepSee as “a resource
for my students to make more informed critical decisions in this line of work.
This tool fills in that gap by giving them a visualization of the field to
help foster intuition.” They elaborated on the effects of using data
visualizations to test ideas live: “I think using DeepSee influenced the way I
teach… [having DeepSee] fundamentally changes the questions we can ask and the
information that is available by changing the way we see the data.” DeepSee
exemplifies interactive visualizations as an opportunity for scientists to
iteratively define and refine questions through “making to know” (Hendrie et
al., 2022; Zimmerman et al., 2007) and for designers to leverage the intuitive
nature of visualization to demonstrate the art of possibility.

##  8\. Conclusions and Future Work

In this work, we conducted a design study with a team of domain experts in
geology, chemistry, and biology to develop multidimensional visualizations of
seabed sediment cores for studying deep ocean microbial ecosystems. We
presented DeepSee, an interactive workspace that enables scientists to explore
their previous sediment sampling history and decide where new sediment samples
may yield the greatest scientific return. By visualizing samples in the
context of the environment, bringing together multiple data types in a single
interface, and providing capabilities to predict values in unsampled
locations, DeepSee offers a new approach to visualizing 3D point samples in
fieldwork-driven science.

In the future, we envision adding enhanced data analysis capabilities such as
supporting multiple levels of phylogenetic hierarchy as well as aggregating
and visualizing hierarchical data in the Core View. Additionally, we aim to
integrate new and more sophisticated models for large-scale interpolation over
several tens or hundreds of meters, based on new research enabled by DeepSee.
The design elements introduced by DeepSee could also extend beyond operator-
driven deep ocean sampling into similar domains, including studying remote
terrestrial ecosystems as well as interplanetary exploration missions. For
example, as autonomous sampling scenarios led by an AI system become possible,
we could leverage insights into how human operators work with a system like
DeepSee to train future autonomous sampling systems.

###### Acknowledgements.

Funding for this work was provided by grants from the National Science
Foundation including the STC Center For Dark Energy Biosphere Investigations
and NSF-OCE 2048666 (to V.J.O.). D.R.U. is a National Science Foundation-Ocean
Sciences Postdoctoral Research Fellow (2126631), R.L.W. is an NSF Graduate
Research Fellow, and S.A.P. is supported through a grant from the NASA FINESST
program (80NSSC22K1336). V.J.O. is a science fellow in the Canadian Institute
for Advanced Science (CIFAR) in the Earth 4D program. Data included in this
study was collected during cruise FK181031 on the R/V Falkor supported by the
Schmidt Ocean Institute. AUV data have been archived at the Marine Geoscience
Data System (MGDS) in the data compilation titled
PescaderoBasin_MBARI666https://www.marine-
geo.org/tools/search/entry.php?id=PescaderoBasin_MBARI. The research was
carried out at in part at the Jet Propulsion Laboratory, California Institute
of Technology, under a contract with the National Aeronautics and Space
Administration (80NM0018D0004). The development of DeepSee was enabled by Data
to Discovery777https://datavis.caltech.edu/, a data visualization, art and
design research initiative based at NASA Jet Propulsion Laboratory, California
Institute of Technology and ArtCenter College of Design; this support is
gratefully acknowledged. Finally, we would also like to thank Jasmine Otto for
her valuable discussions about this research. Any opinions, findings, and
conclusions or recommendations expressed in this material are those of the
author(s) and do not necessarily reflect the views of the National Science
Foundation.

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