ISSN 2186-7437

NII Shonan Meeting Report

No. 193

The Moving Target of Visualization
Software – Closing the Gap between
Research and Application
Christina Gillmann
Takayuki Itoh
Michael Krone
Alexander Lex
Guido Reina
February 12–16, 2024

National Institute of Informatics
2-1-2 Hitotsubashi, Chiyoda-Ku, Tokyo, Japan

The Moving Target of Visualization Software –Closing the Gap between Research andApplication
Organizers:
Christina Gillmann (University of Leipzig, Germany)
Takayuki Itoh (Ochanomizu University, Japan)
Michael Krone (University of Tübingen, Germany)
Alexander Lex (University of Utah, USA)
Guido Reina (University of Stuttgart, Germany)
February 12–16, 2024

Abstract
Visualization, having matured within computer science, is now recognized as avital tool for data analysis across various fields. Its widespread adoption spansscientific research and extends to industry applications, exemplified by platforms
like Microsoft PowerBI and Tableau. Moreover, visualization is a pivotal meansof conveying complex data topics in the news media, such as COVID-19 spreador political poll uncertainty. Despite the proliferation of tools and methods
developed by the academic visualization community, many promising research
prototypes fail to attain broad adoption due to issues of sustainability and extensibility, often tied to the limited lifespan inherent in the academic career cycle.
Compounded by the rapidly evolving software ecosystem, which includes shifts
in execution environments, programming languages, and interaction paradigms,
PhD students face significant challenges in maintaining and advancing theirresearch prototypes amidst their academic pursuits. Recognizing the importanceof this issue to our community, we have organized a series of events to addresspressing concerns and propose solutions to enhance the practical aspects andresearch efficacy within our field. The inaugural event, Shonan Seminar #145
(February 2019), convened industry, national labs, and academia participants, resulting in the identification of nine key concerns and opportunities, subsequently
documented in a publication [22] that delineates the current state and future
directions of our community.

1Background and introduction
Visualization has evolved into a mature subfield of computer science. It has
become widely accepted as an essential data analysis method in diverse fields.
Visualization enables scientific data analysis and is widely adopted in industry,
as the success of business data visualization platforms such as Microsoft PowerBI
or Tableau demonstrates. Visualization is also widely used in the news media
to communicate data about topics as diverse as the spread of COVID-19 or
the uncertainty associated with political polls. Over the years, the academic
visualization community has developed many tools and methods, many of whichhave been widely adopted in diverse application domains. However, many
research prototypes never reach the maturity necessary for broad adoption, eventhough the underlying method has significant merit. These prototypes are often
neither sustainable nor easily extensible for subsequent research projects, andtheir lifespans are often tied to the original author’s academic career. Since Ph.D.
students usually implement research prototypes in academia as part of their
thesis projects, this results in a rather short lifespan. Another aspect that adds tothis problem is that the whole ecosystem around software quickly evolves. This,for example, includes changes in the execution environment (software as well as
hardware), preferred programming languages, external third-party libraries, andinteraction paradigms. Consequently, Ph.D. students would have to invest a lot oftime and effort to keep up with the moving target of developing and maintaining
usable software while also doing actual research at the same time. We foundthis topic to be of great interest to many members of our community, whichis the reason why we have recently organized several related events to discuss
the most pressing issues and propose medium-term solutions that can improve
both the practical aspects of our daily work and the quality and efficiency of ourresearch contributions. The first event in this series was the Shonan Seminar #145 (February 2019, organized by H. Childs, T. Itoh, M. Krone, and G. Reina),
in which the group (consisting of 24 participants from industry, national labs,
and academia) distilled the nine most interesting concerns and opportunities.
We have meanwhile transformed these results into a publication that shows thestate of our community as well as possible future directions [22] and followed
up with additional events and venues, like the establishment of the EuroVis
Workshop VisGap, which has been successful since 2020.

2

Overview of the meeting
In this seminar, we wanted to bring together leading visualization researchersand practitioners to discuss the specific challenges around visualization software.
We identified the following challenges we want to work on during this seminar.
Visualization research usually requires an implementation of the proposed method
or approach for evaluation. However, the visualization community lacks incentives
to publish research software, much less evolving a research prototype into a
usable tool. Furthermore, especially young researchers usually lack the training
and experience to develop sustainable research software. As mentioned above, therequirements for novel approaches are steadily increasing due to data scale andcomplexity, as well as the increasing maturity of the field itself and the influencesfrom other areas, such as machine learning or human-computer interaction.
Consequently, the prototypes usually are not easily extensible for subsequent
research projects. In order to close the gap between research and application,
we want to develop proposals and strategic initiatives to solve these challenges
and to move the whole community forward.
The proposed Shonan Seminar aimed to intensify and advance the discussions
started at Shonan Seminar #145 by elaborating on the unfinished discussions
and examining additional topics. One new aspect beyond the previous seminar
was the discussion of concrete models for incentivizing visualization software
within the research community (e.g., tools papers, which are common and considered essential contributions in other scientific disciplines, mandatory softwareaccompanying submissions, etc.). In addition, we aimed to specify requirements
and changes in the VIS community that need to be fulfilled in order to ease thetranslation of research prototypes into applicable software.

3

Overview of Talks
Uncertainty-aware visual analytics and transferability
Christina Gillmann, University of Leipzig, Germany
The talk briefly introduced the concept of uncertainty-aware visual analytics
and the problems while bringing this into a software. The attempts to do sowhere mainly framed around the ParaView ecosystem that is on the one handflexible, but on the other hand documented insufficiently. Early algorithms
have been integrated into open source software like ITK and shared projects
on GitHub. This was followed by a list of future initiatives that are plannedin the research agenda such as applying for proper funding. Finally, the talkhighlighted the 3 top challenges in open source visualization software that are
the acquisition of proper funding, generalization/standards and accessibility.

Making Sense of our Software Ecosystem and Sustaining it
Guido Reina, University of Stuttgart, Germany
The talk reflected briefly on the richness of the visualization software ecosystem, but also criticized that we are not properly taking advantage of it. This iscaused by several challenges, not the least of which is the diversity of technicalplatforms that are used. We have only been partially successful at creating
software that facilitates composition of more complex approaches from either
minimally coupled building blocks or agreeing on an overarching frameworkor platform we can all make use of. There also is no proper schema or otherformalization that can capture the capabilities and technical implications of theexisting software, so especially fresh Ph.D. candidates have little to go on whenstarting a new project. Finally, sustaining our software in a way that makes it
relevant to practitioners, especially from industry, is an ongoing challenge.

Visualization and Visual Analytics Tools for Biomedical
Application Cases
Michael Krone, University of Tübingen, Germany
In my talk, I briefly introduced myself and showed examples of methods andtools for the visual analysis of biomedical data that I worked on in the past. This
specifically includes the interactive visualization of large, dynamic molecular
structures from simulations, visual analytics tools for biomolecular data in thecontext of biological pathways, and intensive care unit data visualizations. These
applications were either realized within the open-source visualization frameworks
MegaMol [10] and VMD, using game engines, or as web applications. These
platforms allowed for effective rapid prototyping of novel visualization tools.
From my point of view, balancing the time spent on software development andresearch is an important challenge, especially for small research groups. However,building usable visualization research tools is also important for collaborations
with users. This could be partially incentivized by establishing possibilities topublish tools papers, as is common in other communities like bioinformatics.

4Technical Scientific Transfer for Visualization Software
Michael Keckeisen, TWT GmbH Science & Innovation, Germany
In this talk, I explored the significance and complexities surrounding the transfer of scientific knowledge into practical industrial applications, with a focus on
visualization software. Addressing sectors such as automotive, aerospace, energy,
and healthcare, I discussed challenges like user-centric automated personalizationof data visualizations, explainable artificial intelligence through visualization,and the collaborative examination of big data and multidimensional data throughcoupled augmented reality visualizations.
TWT GmbH Science & Innovation is specialized in technical-scientific transfer.
At the interface of informatics, engineering and mathematics, TWT works directlywith customers and partners such as Mercedes-Benz, DaimlerTruck, BMW, Audi,
Porsche, CARIAD, IBM, Bosch, ESA, Airbus and Samsung.
An example of TWT’s scientifc contributions is the GETMe mesh smoothing
method [25].

Diversity of users and platforms
Takayuki Itoh, Ochanomizu University, Japan
This talk first introduced the representative studies of the speaker including
fast isosurface generation, Treemap-like hierarchical data visualization, graphvisualization, and multidimensional data visualization. Then, the speaker mentioned the top three open problems as follows:(1) Addressing novice to expert users by a single software. Preferable visualizations may be different between novice and expert users. Satisfaction of bothtypes of users will make visualization software more robust and general-purpose.
(2) Not only users, but also platforms should be wider. Support of a wide rangeof platforms (mobile, VR/AR, . . . ) by a single software will also be a key pointof visualization software.
(3) From an aspect of the visualization research community, the discipline ofvisualization software development is also an important issue. It should be
more well-defined regarding what is good research, and what are relevant papers
contributions.

Sustainable FAIR VIS Software
Christoph Garth, RPTU Kaiserslautern-Landau, Germany
The talk focuses on two fundamental questions related to visualization
software: 1) How can visualization software adhere to the FAIR principles, which
are rapidly becoming a requirement that researchers have to adhere to obtainfunding and publish; here, especially reusability and reproducability are aspects
that are intrinsic to the VIS community. While containerization and othertechniques increase both, they are not a direct step toward becoming more FAIR.
Other FAIR aspects, such as metadata annotation and accessibility, have notbeen considered to great extent within the community. 2) How can software
libraries be developed and maintained within the community? The open-source
Topology Toolkit (TTK) is a successful example of a software that serves as5

a basis for research in the domain of topological data analysis, with a small
but active community maintaining and developing it. How can such activities
be nurtured and strengthend? It appears that increasing the academic reward
of software activities, e.g. through software publications (in analogy to data
publications), could be beneficial here.

Visualization of Big Complex Networks
Seok-Hee Hong, The University of Sydney, Australia
This talk briefly reviews the state of the art for big complex graph visualization, including the sublinear-time algorithms to address the scalability, as well
as faithful graph drawing to show the ground truth structure of complex graphs.
Based on my experience as a project leader to develop a visual analytic tool
GEOMI for large graph visualization and collaboration with industry and domain
experts in systems biology and social networks, I present the following top threeopen problems for visualization software: (1) closing the gap between theory(research) and practice (tool); (2) designing a unified open source platform,
integrating analysis and visualization; (3) supporting different users, such as
visualization researchers, data scientists, students, and domain experts.

David Laidlaw’s Top Open Problem Examples
David H. Laidlaw, Brown University, United States
Laidlaw identified several challenges regarding software for visualization.
The first was the absence of a mechanism for transferring novel visualization
approaches to potential users. Kitware might serve that purpose, but it could
be more effective. The second challenge is the cost of creating virtual-reality
visualization applications for domain science exploratory data visualizations.
Typically these can take many months or years for each example. Softwarethat speeds this development process could greatly accelerate scientific progress.
A third challenge is the lack of clarity around open research challenges invisualization. A clearer agenda could help focus research and also provide
input to funding agencies about where they might best make investments.
Laidlaw is known for painting-motivated multi-valued visualizations, virtual
reality visualization of bat flight and dinosaur footprint formation, diffusion MRI
visualization and analysis, and a room-sized retinal-resolution virtual reality
display known as the yurt. He worked on many software projects since 1989
when he was one of the creators of the dataflow visualization system AVS. He is
also adept at building campfires and stone walls.

Visual Exploration Tools for Single-Cell Biology
Fritz Lekschas, Ozette Technologies, United States
Fritz Lekschas presented current visual exploration tools for exploring largescale single-cell data and discussed challenges and opportunities in bringing
these software tools closer to the biologists. One such tool, developed at Ozette
Technologies, is a scalable embedding visualization for single-cell data that6

provides a high-resolution overview of the immune cell landscape. Implemented
originally as a purely web-based visualization, Lekschas’ team further improved
the tool in two ways. First, they modularized the visualization tool such that it
can be integrated more easily into other software. Second, they added support
for running the tool as an interactive widget in Jupyter Notebook/Lab todirectly integrate into the biologists workflow. And third, they integrated thetool’s APIs into the Python data ecosystem for simplified re-use with otherdatasets. Fritz Lekschas concluded his talk arguing that similar modularizationand standardization approaches could prove useful for fostering a wider adoptionof visualization software by the data science community.

Visualization of Massive Scientific Data
James Ahrens, Los Alamos National Laboratory, United States
James Ahrens described the state of visualization tools for visualizing massive
scientific data. Ahrens is the founder and design lead of ParaView, a widelyused, open-source, visualization tool for massive data. Ahrens identified futurechallenges for scientific visualization of massive data. The first is the recognition
that science is informed by ensembles of experimental and simulation data.
Existing visualization tools can handle a single massive simulation or experiment
including a time series results and multiple variables but he asked what about
thousands of these? Ahrens noted ensembles are used extensively for machinelearning applications. Ahrens identified the need to capture, represent, process
and visualize ensemble metadata and data. A second challenge he identified
is the integration of machine learning into the visualization process as well as
the need for the visualization community to help visually understand machinelearning results. A third challenge was an idea to re-explore the fundamental
data representation for visualization to support seamlessly transitioning from
1D, 2D, 3D to ND visual representations.

Software Development for Biomedical Image Informatics Examples and Challenges
Katja Bühler, VRVis, Vienna, Austria
Katja Bühler gave an overview of the field of biomedical image informatics,
including AI-based image analysis and semantic enhancement, visualization andintegration of image data, data integration and integrated analysis of image
data across multi-modal imaging and spatial *omics data, and related software
projects. As an example, she presented the Brain* system, which provides
spatial integration, rapid interactive access, visualization, mining, and analysis
capabilities for massive collections of neuroscience data. She concluded with a
discussion of one scientific and two community challenges related to visualization
software: (1) How will AI affect the field in the future? (2) How can we
provide a sustainable software base for research and development that reflects
the state of the art? (3) How can we build a community that works together onstate-of-the-art visualization software for the common good?

7

Challenges in Software Development for Interactive Exploration of Simulation Ensembles
Kresimir Matkovic, VRVis, Vienna, Austria
Kresimir Matkovic gave an overview of the practice of interactive visual
analysis within the realm of simulation ensembles, as well as of the practiceof handling complex data in diverse domains such as engineering, medicine,geology, and ergonomics. He highlighted three key challenges for developers
in modern visualization software: (1.) Rapidly Evolving Technologies:
Keeping up with the fast-paced changes in new (web) technologies is a significant
hurdle. This involves making the right choices amid constant changes and finding
skilled developers familiar with these evolving tools. While the abundance ofsoftware and libraries spurs innovation, it poses the challenge of selecting themost effective tools. (2.) Integration of Research Ideas: Many excellent
ideas from research in visualization remain confined to academic circles andaren’t seamlessly incorporated into standard software. The limited awarenessand involvement of developers outside the visualization research community
worsen this problem. Bridging this gap is essential to unlock the full potential ofthese innovative concepts and encourage collaboration between researchers anddevelopers from diverse backgrounds. (3.) AI for Visualization (AI4Vis):The rise of AI in visualization presents both a formidable challenge and asignificant opportunity. The key question is whether we will effectively use
AI to advance our capabilities. Successfully navigating complexities, fosteringcollaboration, and embracing AI’s transformative potential are crucial steps
toward enhancing and democratizing the field of visualization.

Visual Exploration of Software and Systems
Katherine (Kate) E. Isaacs, The University of Utah, United States
Katherine (Kate) Isaacs presented an overview of visualization and visualization software needs for analyzing software and computing systems. She
emphasized the need to meet users where they are, showing examples of embedding visualizations in both computational notebook and command line interfaces.
She also expressed maintenance issues with visualization software, noting theyeven arise quickly in projects where the domain team were the main developers ofthe visualization. While she noted the strengths of current visualization software
for providing first pass solutions and for prototyping, she noted these tools do
not yet provide good support for charts commonly used in software and systems
visualization, such as interconnected timelines, especially at the scale of the data
these domains generate.

Audience-targeted Visualization Design for Optimal Usability
Kwan-Liu Ma, University of California at Davis, United States
Developing a visualization with maximum usability must take into accountthe purpose of the visualization, the target users, the characteristics of the8data, and the viewing device and environment. Understanding the users is thefirst and the most important task to perform. This task may be done throughextensive interviews, which is then followed by an iterative process of designprototyping-evaluation with the users closely engaged. I show how to design
scientific visualization at extreme-scale that is audience-targeted, task-directedor physically-based, and both computationally and visually scalable for optimal
usability [23, 20, 27]. The resulting visualization can enable scientists to effectively validate their data, uncover previously unseen features, and communicate
their work to others.

The Changing Landscape for Visualization Tools
Cláudio T. Silva, New York University, United States
Over the last two decades, visualization techniques and tools have matured
and are widely used. Many tools have been developed as powerful standalone tools(e.g., ParaView and OpenSpace). While useful and flexible, such comprehensive
tools tend to be fairly complex, and require a steep learning curve on users.
Alternatively, the visualization community has also developed simpler tools,
tailored to particular domains, (e.g., BirdVis and TaxiVis). While easier to use
for their intended purpose, these tools are usually not as flexible, and require
users to transition to other approaches as their needs evolve and shift. In thisshort talk, I discussed how new approaches based on designing “visualization
languages” and “notebook” interfaces might provide a way for users to evolve
their tools more easily and naturally as their needs change.

Software for HPC Scientific Visualization
Kenneth Moreland, Oak Ridge National Laboratory, United States
In this talk I briefly review a history of research to enable large-scale scientificvisualization on high-performance computing (HPC) machinery. The work starts
with parallel rendering to push large polygon structures to rendered images
on large-format displays. The following work moved to fully-featured scientificvisualization by participating in the ParaView application. This software has
been a principal component in delivering visualization research to end users. Morerecent work expands the implementations to use modern accelerator processorsfor visualization processing.
Although creating usable software does add a significant burden additionallyover base research work, such work is beneficial not just to end users but
to researchers themselves. In reviewing the number of references to my past
publications, the references are more correlated to the availability of software
described than the strength of the novel contribution. The lower the barrier tousing software, the more likely other researchers will use that to support theirresearch or build upon your results.

Connecting Visualization Research and Domain Research
Marc Baaden, CNRS Paris, France
It is a challenge to pass on the latest advances in visualization research9to specialist scientists. As the head of a molecular modeling and theoreticalbiochemistry lab, I face this obstacle on a daily basis and have to deal withpractical problems that require a solution. My involvement revolves arounddirecting research efforts towards molecular graphics, interactive simulations andvirtual reality applications, all centered around the development and maintenanceof the UnityMol software.
Using UnityMol, we are exploring novel representations such as HyperBalls,
evaluating the feasibility of using a game engine for development and prototyping
purposes, and striving to facilitate FAIR exchange of visualization experiences.
In my area of work, the pure elements of visualization merge seamlessly withaugmented reality, virtual reality and tangible IoT objects.
In the future, we will explore multi-user collaboration, address the dynamics
of consciousness and presence, incorporate explainable AI into molecular visualization, explore VR in cloud environments, and adapt to the challenges of large
data sets and simulation ensembles.
In my specific subfield, the strengths of the visualization software lie in
its extensive range of functions, its robust code base and its mature stability.
However, interoperability, the lack of metadata for visualization objects, insufficient integration with augmented reality technologies and the lack of multi-user
standards and functionalities are areas for improvement.
Solving the most important unsolved problems requires the creation of asustainable ”business model” for academic software. In addition, developments
need to be seamlessly integrated into data practices to promote FAIR principles and open science amidst the data deluge. Linking these developments toapplication domains remains a critical frontier in our quest for progress.

Broaden participation and create composable software
Dominik Moritz, CMU and Apple, United States
In this introduction talk, Dominik argues that we still have a long way to
go to broaden the diversity of who participates in software development. While
this challenge is not unique to visualization software we should do our part increating a welcoming environment for everyone to publish their visualization
software or contribute to existing projects.
Dominik also presented an overview of the visualization libraries and frameworks he and his groups at CMU and Apple developed (e.g., Mosaic). He argues
for creating not just usable but composable software. By building small building
blocks rather than monoliths, we are more productive with less code, enable
better reuse and comparison, and make our software more useful. To get tocomposable software, we need to value API design and make it part of ourresearch contributions.

Translating Research into PracticeAnamaria (Ana) Crisan, Tableau Research, United States
This talk briefly discussed existing challenges of translating visualization
research and software into industrial products. I highlight several challenges. The
first is the difficulty of integrating visualization research into existing analytic
10

workflows. The integration with commercial workflows can be more challenging
and code artifacts can be difficult to translate directly; opportunities for integrating with open sources tools, existing, for example, with Python and R, aremore fruitful, but still not regularly done. I also highlight alignment challenges
between graduate student incentives and training and the requirements of industry. Finally, I describe the unique regulatory, legal, and legacy environments inindustrial enterprises and how this can hinder the research translation process.

Visualization Software Development for Scientific Workflows
Gerik Scheuermann, Leipzig University, Germany
The talk briefly covered several works on feature and topology-based visualization, mainly tailored towards fluid dynamics, mechanical engineering,and medical application domains. It also covered several software systems likethe Field Analysis with Topological Methods (FAnToM) software for featureand topology based visualizations of scalar, vector, and tensor fields1 [26]. In
addition, the software package OpenWalnut for the visual analysis of multimodal
brain imaging data like DTI, EEG, MEG, HARDI, fMRI, etc.2 [6] Furthermore,
we presented the SARDINE tool for integrating NLP-processed legal documents,
online sensor data, publicly available online resources like weather forecasts,
LIDAR measurements by drones in a GIS-like system for regional planningpurposes3 [1]. Another software tool named LexCube was explained. It shows
data cubes from geo-sciences in an interactive fashion in the browser, but also
as part of a Jupyter notebook with an interactive 3D window4 [24]. The tool
also allows for a physicalization by printing out some cube on a piece of paperthat can be folded into a physical data cube.
The talk finished with some of the challenges that we recognized during
the development of those tools which include how to integrate visualization
software into the workflow of users. This works best, based on our experience,
if the application domain scientists or engineers show a strong interest in thesoftware and come up with the plan of developing such a tool themselves.
Then, we as a visualization group join this effort with our visualization andsoftware development expertise. In this case, our group supports and supported
such developments in LexCube, OpenWalnut, and GeoTemCo5 [15]. Current
experience with this approach is also drawn from showing molecular dynamics
simulations in biochemistry by a web-based tool called MDserv6 [17].

Strategies for Scientific Software Engineering
Alexander Lex, University of Utah, United States
The visualization community has historically focused on large, monolithic,
interactive visualization systems. However, experience has shown that these1 http://www.informatik.uni-leipzig.de/fantom/
2 https://openwalnut.org/
3 https://www.uni-leipzig.de/newsdetail/artikel/projekt-sardine-sollnachnutzung-von-braunkohletagebauen-erleichtern-2022-06-30 (only in German)
4 https://www.lexcube.org/ and https://pypi.org/project/lexcube/
5 http://www.informatik.uni-leipzig.de/geotemco/
6 https://proteinformatics.informatik.uni-leipzig.de/mdsrv

11

systems are hard to maintain and are rarely adopted outside of a narrow setof users. In my talk, I pose the question on whether building such systems
is futile in the first place. I argue that systems of similar complexity thatare developed by commercial entities are usually staffed with dozens or even
hundreds of engineers, product managers, UI/UX designers, etc. As a remedy,
I propose that the community focus on smaller, easier to maintain reusable
components, and provide maintenance and documentation for these. There areseveral prominent libraries that are developed and maintained in this way, suchas D3, Vega, or Matplotlib. While there are challenges with this approach, suchas limited interactivity, these are interesting research topics that the community
can tackle.

In Situ Visualization Tools and Beyond
Gunther H. Weber, Lawrence Berkeley National Laboratory, United States
In my talk I briefly reviewed my previous work on visualization of adaptive
mesh refinement (AMR) data, domain specific visualization tools, topology-based
visualization and parallel data analysis and visualization on high performancecomputing systems. Furthermore, I discussed recent work for the Exascale Computing Project (ECP)—including the integration of algorithms and visualization
approaches into VTK-m and Ascent—and the Scalable In Situ Analysis andVisualization (SENSEI) project. In the future, one of my goals is to develop
visualization methods and software that help understand scientific machinelearning models. Furthermore, I plan to to develop new approaches at the edge
for experimental and observational facilities and for automated laboratories.
In my previous work, I found that visualization tools like VisIt and frameworks
simplify development of new visualization methods and deploying them to wide
audience. Furthermore, there is a growing number of software frameworks in many
languages (C++, Python, JavaScript) that simplify building new visualization
software. However, many challenges remain. Deploying visualization tools
not based on widely available frameworks is often difficult. Securing fundingand “hands on keyboard” to maintain developed software is also often difficult.
Finally, many available frameworks (e.g., Open3D in Python) are often targeted
to non-visualization applications and using them for visualization is often not
ideal.
I see the following (research) challenges for the development of visualization
software: (i) Maintaining visualization software research prototypes in a quickly
changing deployment environment (both funding and reproducibility), (ii) Combining web-based front-ends with HPC back-ends, (iii) Distributed visualization
systems incorporating and synchronizing many mobile devices, (iv) Increasing
the modularity of frameworks (for lightweight linking), and (v) Targeting wider
range of accelerators/hardware (FPGA, neuromorphic computing).

Visualization in the application domains and their softwaredevelopment.
Daniel Wiegreffe, Leipzig University, Germany
The talk briefly introduced different visualization systems for exploring
12

biological data and spatial data. Various aspects of software development werethen discussed, based on the experience gained in the development of thesesystems. It was emphasized that current technologies have made it much easier
to develop platform-independent solutions easily. Nevertheless, there are also
challenges, such as the availability and maintenance of software projects. An
additional topic of discussion was the need for better interoperability between
different software components. In addition, the connection between softwaredevelopment and research in the field of visualization was discussed and how thesustainability of software packages in this field can be improved.

OpenSpace — Astronomy Software for the masses
Alexander Bock, Linköping University, Sweden
This talk focused on the OpenSpace platform, which is an open-source software package originating from the visualization domain and which is designed
to visualize the entire known universe. It is an environment for novel visualization research, a research tool for astronomers, as well as a tool for the publicdissemination of astronomy discoveries. The latter usage is mainly targeted atinteractive planetarium venues. Usage of the same software in all these domains
enables the rapid deployment of research results to the domain scientists andthe general public without the need of format conversions when transitioning
from a “research software” and thus enables a short-circuiting of the knowledge
dissemination pipeline.
This software is based on a second, also open-sourced library called Simple
Graphics Cluster Toolkit (SGCT) that provides a backend for porting arbitrary
graphics application into immersive environments such as stereoscopic dome
theaters, CAVEs, and others.

Visualization Software for Cellular Mesoscale
Ivan Viola, KAUST, Saudi Arabia
The technical aspect presented in the talk were related to MesoCraft, a
procedural modeling platform in which cellular mesoscale structures can beformulated through a set of spatial relationships between individual molecularbuilding blocks. This software has showcased a promising new direction wheredevelopment is performed in C++ with WebGPU as the graphics API, whichallows the software to be deployed as a desktop application on all OS platforms aswell as a web application. The long-standing desire for single code base compiled
either as a local executable or as a client-side web application has become areality. The web application secures an easy deployment for any user, and it
also allows for additional benefits, such as user management, the possibility ofstoring cellular mesoscale models in a central repository that can be shared
among bio-science researchers who together complete a cellular mesoscale modelof an underlying sub-micron biological system.

13

List of Participants• Christina Gillmann, University of Leipzig• Takayuki Itoh, Ochanomizu Univeristy• Michael Krone, University of Tübingen• Alexander Lex, University of Utah
• Guido Reina, University of Stuttgart• James Ahrens, Los Alamos National Laboratory• Marc Baaden, CNRS, Institut de Biologie Physico-Chimique• Alexander Bock, Linköping University• Katja Bühler, VRVis• Anamaria Crisan, Tableau Research• Christoph Garth, University of Kaiserslautern-Landau• Hans Hagen, Technical University Kaiserslautern (RPTU)• Seok-Hee Hong, University of Sydney• Katherine Isaacs, The University of Utah
• Michael Keckeisen, TWT GmbH Science & Innovation• David Laidlaw, Brown University• Steve Legensky, Intelligent Light• Fritz Lekschas, Ozette Technologies• Kwan-Liu Ma, University of Californa Davis• Kresimir Matkovic, VRVis / University of Zagreb• Kenneth Moreland, Oak Ridge National Laboratory• Dominik Moritz, Carnegie Mellon University• Gerik Scheuermann, Leipzig University• Claudio Silva, New York University• Ivan Viola, KAUST• Gunther H. Weber, Lawrence Berkeley National Laboratory• Daniel Wiegreffe, Leipzig University

14Meeting Schedule
Check-in Day: February 11 (Sun)• Welcome Banquet (19:00-20:30)
Day 1: February 12 (Mon)• Opening (9:00-9:30)• Self introduction talk (9:30-12:00, 13:30-14:30)• Problem definition (15:00-18:00)
Day 2: February 13 (Tue)• Discussions (9:30-12:00, 13:30-18:00)• Group Photo Taking (12:00)
Day 3: February 14 (Wed)• Discussions (9:30-12:00)• Excursion and Main Banquet (13:30-21:00)
Day 4: February 15 (Thu)• Discussions (9:30-12:00, 13:30-16:30)• Demo (16:30-18:00)
Day 5: February 16 (Fri)• Report writing (9:00-11:00)• Wrap up (11:00-11:30)

15

Summary of discussions
The following section will present a summary of the discussions for each topic
that has been identified as important in recurrent plenary discussions that would
adjust this ranking based on participants’ interests, coverage, and previous
discussions.

Topic: User interface design, user experience design, andaccessibility of vis software
Participants: Anamaria Crisan, Alex Lex, Ivan Viola, Kwan-Liu Ma, Seok-Hee
Hong, Frtiz Lekschas, Steve Legensky, Takayuki Itoh
Graphical user interfaces (GUIs) serve as the bridge between the user andthe complex world of data. Their primary purpose is to facilitate effective
interaction, interpretation, and manipulation of data, enabling users to derive
meaningful insights. Throughout our discussion sessions, we delved into the needfor continuous adaptation of user interfaces to meet both user requirements andthe evolving technical infrastructure necessary for creating effective GUIs. We
explored this topic from three angles, which also intersected with other topic
areas such as ML/AI in Visualization Software and Common Platforms:• Prioritizing Accessibility. Current visualization software lacks comprehensive support for diverse physical and cognitive capabilities. Addressingthese limitations, as mandated by policies like DOD Section 508, not only
meets regulatory requirements but also enhances our software infrastructure. Creating abstract representations in dashboards can significantly aid
blind/low-vision users, providing not only improved navigation but also
generating rich descriptions for intelligent agents. These abstractions canalso contribute to enhancing data and graphical literacy for users without
vision issues, presenting promising opportunities for further development.
We explored parallels with the classic HCI ’curb cut effect,’ contemplating
its extension to the realm of visualization software development.
• Leveraging Generative AI. The present capabilities of LLM-based
generative AI agents offer opportunities to dynamically adapt user interface
elements in real-time to user needs. This introduces considerations for
multi-modal interactions, such as combining traditional WIMP with voice
and/or text. However, the potential of GenAI is constrained by thecomplexity of user data and workflows. Developing user interfaces forbespoke data and intricate workflows, as seen in scientific research, remains
challenging despite the capabilities of GenAI models.
• Modular, Composable, Adaptable Software. Rapid iteration ofsoftware design and capabilities requires thoughtful architectural considerations. Moving away from monolithic applications to modular components
allows for effective representation of GUI elements, facilitating rapid testingof designs, addressing new needs, and building on prior research techniques.
While the initial investment in a composable architecture may be significant, it accelerates iteration and prototyping in the long run. This themeis aligned with the common platform topic.
16

An overarching theme in our discussion revolved around the challenges ofconducting high-quality user studies. These challenges stem from participant
availability and the necessary investments, both in terms of finances and time.
Addressing these challenges head-on presents opportunities to enhance user
experiences with visualization tools by leveraging emerging technologies andadopting modern software development practices. By embracing these challenges,
we can advance the field and harness the potential of emergent technologies formore effective and user-centric visualization tools.

17Topic: Building Communities & Sustainability
Participants: Dominik Moritz, Christoph Garth, Christina Gillman, AlexanderLex, Alex Bock, Marc Baaden, Kenneth Moreland, Katja Bühler
The ultimate goal of most visualization software is that it is useful to a large
set of users. Hence, success of a project commonly depends on its adoption andthe community that forms around it. In this breakout discussion, we elaborated
how we can build sustainable communities around visualization software.
We analyzed a set of successful communities which participants of our grouphelped co-found, such as the OpenSpace community7 the Vega community8 or
the TTK community9 .
We identified the following best practices:• Rather than building a community, try to join a community.
For example, if there is an existing community related to a new piece ofsoftware, it might be advantageous to leverage that existing communityand cater to their needs as opposed to attempting to start to build one
from scratch.
• Minimize the start-up burden. The most difficult thing for a software
project is to encourage new users to adopt it. To reduce that initial burden,
developers of the project should take all measures to minimize the effort ittakes to get started with the project. For example, good getting started
tutorials are essential, as are ways to reduce the need to install excessive
software packages and dependencies.
• Identify and engage with key stakeholders. As a community develops,
activity on discussion forums and issue trackers will increase. While it might
be tempting to treat all requests equally, successful projects have found it
advantageous to give preferential treatment to key stakeholders, e.g., by
responding to requests or bug reports from them in a more timely manner.
These key stakeholders have the potential to develop into advocates andcontributors for the project, possibly even taking on some of the community
support tasks thus providing the ability for the project to easier scale
beyond its initial set of users.
• Develop trust by your users. For a successful community to develop, it
is critical that users can trust the software project. For example, documentation should be accurate and up-to-date. Also, project leadership should
develop public plans for the future and collect and listen to community
feedback. This environment of openness also includes the communicationof community guidelines, contributor guidelines, and others.
• Provide opportunities for learning Opportunities for learning about
the project are essential for adoption. Primarily, these should be in theform of documentation. Good documentation should follow the Diátaxis approach to documentation authoring10 . These resources should be persistentand searchable.
7 https://www.openspaceproject.com/
8 https://vega.github.io/vega-lite/
9 https://topology-tool-kit.github.io/
10 https://diataxis.fr/

18

Another avenue for learning are tutorials at conferences, such as IEEE
VIS and domain-specific conferences. Tutorials have the potential to give
participants a holistic overview of the project, easing adoption at a latertime while simultaneously exposing a large number of potential users to
the project.
• Provide opportunities for discussions. The ability to ask and answer
questions is critical for the success of a community. Successful projects
tend to employ both real-time discussion forums with tools like Slack andDiscord, as well as asynchronous and searchable methods such as issue
tracking and question and answer forums. An important aspect of thesetools is their indexability, which makes them easier to find for new users
using web search engines.
• Provide opportunities for participation. Finally, community members
might also want to contribute to core aspects of the project. One way to
encourage such participation are Hackatons, where users can contribute toa shared task, moderated by the project leadership.
Other issues discussed include governance models for larger and more mature
projects. An example is the governance model for Vega11 , OpenSpace12 , as well
as models for funding community building, for example donation models13 .
As an outcome, we discussed a position paper or a blog post, but decided toultimately organize a panel at IEEE VIS on the topic.

11 https://github.com/vega/.github
12 https://github.com/OpenSpace/OpenSpace/.github
13 https://godotengine.org/donate/19Topic: Typology
Participants: James Ahrens, Marc Baaden, Katja Bühler, Christina Gillmann,
Michael Krone, Kresimir Matkovic, Guido Reina, Gerik Scheuermann
We based our discussions on the preliminary results of the preceding Shonan
Seminar #145 and the respective publication [22]. One critical observation
was that as a community, we take care of periodically surveying our state-ofthe-art techniques, often accompanying these publications with an interactive
web presence that allows users to select appropriate solutions based on a few
properties of the problem at hand. These catalogs, however, do not include thesystems and libraries we have developed, and especially do not consider any
technical implications or requirements that might be ensue when basing a project
about one or even several of these. The scope and utility of such an endeavor
is two-fold: on the one hand, we gain a better understanding of the properties
and requirements of our software, which also allows relating specific solutions to
one another. On the other hand, a concrete implementation will both serve as aportal into our community for an audience outside our domain, and a practical
tool for ourselves.
We started defining a hierarchical typology of the most important aspects
that categorize visualization software. There was also an extensive discussion
on the technical aspects of making this typology (or ontology) accessible tothe community. The accessibility and ease of use will be an important stepin establishing this typology as a reference model to categorize and compare
visualization software in the future. We finally decided that the best way forward
would be a flat list of the most relevant aspects and their instantiations andre-aggregate the typology hierarchically afterwards to remove current ambiguities
and redundancies. We set up a working repository to start iterate on this after
the seminar.

20Topic: Grand Challenges in Visualization and Funding
Participants: Kenneth Moreland, David Laidlaw, Gerik Scheuermann, Alexander Lex, Alex Bock, Ana Crisan, Christina Gillman, Kate Isaacs, Jim Ahrens,
Hans Hagen, Christoph Garth, Daniel Wiegreffe
A key challenge for visualization software development is funding. However,
because funding is highly heterogeneous in different countries, we did instead
focus on developing arguments for funding visualization research and softwaredevelopment by identifying and describing the grand challenges of visualization
research for the next two decades, updating Chris Johnson’s list from more than
20 years ago [16].
• Visualization Specification / Authoring Creating visualizations isstill difficult to do. While progress has been made in developing GUIs,
tools like Tableau are still difficult to use. A potentially promising avenue
are natural language interfaces for visualization specification.
• AI for Visualization AI and Visualization are both part of the data
analysis pipeline, with great potential for their deep integration throughmethods such as AI and HI (human intelligence) teaming, leveraging thebest of both worlds. Also, there are significant opportunities for combining
language and visualization, for example, by explaining the content andcontext of a visualization in language using LLMs.
• Visualization for AI The development of AI methods can significantly
benefit from visualization approaches. Visualization is critical in model
development and understanding, and in aspects related to accountabilityof decisions made by AI.
• Cognitive Foundations of Visualization and Theory What is agood visualization? Which visual encodings, interaction techniques, andintegrated systems lead to strong insights? While the community has
made progress in understanding low-level aspects of human perception,much work has to be done to understand interactive systems and complex,
combined charts.
• Accessibility Visualization is used to communicate essential insights innews media, education, and science. Yet, low-vision and blind users, or
those with motor or cognitive disabilities are likely to not have equal accessto the information contained in these visualizations. Understanding how
we can make this information accessible to a diverse set of users is an
essential challenge to ensure equitable access.
• Data and Knowledge
Data is an imperfect representation of phenomena of interest. Most of
the time, experts that collect data are aware of its limitations and how tointerpret it for their purposes. However, this knowledge is often lost whendata is stored and repurposed at a later time or analyzed by another person.
In practice, re-use of data is incredibly common, yet this key contextual
knowledge is not preserved. As a community we need to improve our processof documenting knowledge about data, either in the form of metadata, but21

ideally, also “closer to the data”, so that the data can be interpreted withthat critical knowledge even after a hand-off to other stakeholders.
• Scientific Software Development Sustainability and Maintenance, Stewardship of Software• Visualization Literacy & Education• Uncertainty• High-dimensional and spatial data (single cell, lidar, space)• Scalability• Display environments• Integration into common software packages Another challenge is
the integration of visualizations into larger software packages such as R or
SciPy. The lack of interactivity is particularly problematic here, as thiscan often only be achieved using additional components such as Javascript.
However, this makes the development of these applications more complex.
• Funding infrastructure work for research
Funding for infrastructure such as software maintenance can be difficult
to acquire. Different funding agencies have specific criteria for what theyfund, so sometimes you have to do this work pro bono alongside theprojects. However, this does not make it possible to establish a sustainableinfrastructure. A possible solution to these problems is the continuous
promotion of the importance of visualization software for science andsociety, so that there is a stronger demand for stable visualization software.
• Increase awareness of the importance of visualization

22

Topic: Meeting Users Where they Are
Participants: Guido Reina, Hans Hagen, Fritz Lekschas, Seokhee Hong, Kate
Isaacs, Steve Legensky, Michael Krone, Gunther Weber, Gerik Scheuermann,
Kwan-Liu Ma, Ana Crisan, Takayuki Itoh, Daniel Wiegreffe, Alex Lex, Kresimir
Matkovic, Michael KeckeisenThis breakout session featured discussions about meeting the specific needsof both industry professionals and researchers in a diverse range of application
domains and how to address these needs as the visualization community.
A key theme was the importance of the immersion of visualization researchers
within user communities. One example was using formalized programs such as
the Innovative Training Networks (ITN) offered by the European Union. This
program would allow embedding researchers, particularly PhD students, in thesecommunities for periods ranging from a week to a year. These “Fellowship”
programs have already been investigated and have been found useful. A paper byHall et al. [11] describes benefits of immersion in visualization research. These
instruments can be used to embed visualization researchers within the specifictarget domain to gather in-depth knowledge and thus foster information exchange.
VIZBI and Co-located conferences like BioVis and VizSec were highlighted as
positive examples of cross-domain information exchange.
Participants discussed the challenge of integrating visualization tools into existing software ecosystems. The discussion touched upon the use of common tools
like Tableau and software languages like Python and R, alongside more bespoke
solutions commonly employed in the visualization community. However, theseincur significant wind-up costs for creating these demonstrators whereas morecommon software packages, like ParaView, have their own design restrictions.
The widespread use of Python in tooling ecosystems and the ongoing challenges
with interactivity were noted. The discussion ended with considerations forstand-alone web development and the proposal of panels at major conferences
to continue the dialogue on these topics.
Determining where to publish software-based findings was a pivotal topic.
The major opportunities are established venues such as IEEE VIS and the
newly created Journal of Visualization and Interaction (JoVI), or alternatively
publishing at domain-specific journals were suggested strategies. The idea of”Sustainability badges” in the visualization community was also brought up,
requiring further discussion and action. Another idea was to count citations inapplication science community publications and consider awards for adoption ofwork outside the visualization community, similar to the existing test-of-timeawards.
This breakout session “Meeting Users where They are” provided a fruitful
overview of the ongoing challenges and strategies in developing visualization
software for diverse user communities. It highlighted the need for immersion
within user communities, understanding their software ecosystems, selecting
appropriate publication venues, and differentiating between scientific and engineering approaches. The conversation set the stage for ongoing efforts to enhance
software development in visualization.

23

Topic: The Role of AI in Visualization Software
Participants: James Ahrens, Katja Bühler, Anamaria Crisan, Seok-Hee Hong,
Takayuki Itoh, David Laidlaw, Kwan-Liu Ma, Kresimir Matkovic, Michael Keckeisen, Claudio Silva, Gunther Weber, Daniel Wiegreffe, Fritz Lekschas, Ivan Viola
The introduction of neural network based artificial intelligence (in the following denoted simply as AI) is disruptive in every technological aspect where digital
data play a significant role. Visualization research and its software outcome
processes digital data into visual forms that are intuitive to comprehend byhuman users. Visualization establishes a visual dialog interface between thedata and the user with the goal to aid particular use case tasks. Today, AI has
already gained significant transformative influence on visualization research andvisualization software development.
Within two sessions we discussed the state of the art, best practices andfuture opportunities to integrate recent developments of AI models into both,visualization applications and their development processes to more effective,
human-centered visualization and visual analytics pipelines, but also accelerate
and redefine the development process of visualization software itself.
I. AI as an Element of Visualization Applications
Machine Learning for Data Interpretation The Visualization Pipeline is achain of steps where the data is gradually transformed into visual representations.
Along this process, machine learning models assist or perform a fully automatedtask they are trained for. In the stage of data filtering, data can be denoisedusing neural-network inference or certain interesting low-level features can beextracted. Such representations might be already suitable for display. In anothercase, the inference can lead to high-level interpretation into domain-specificobjects through semantic data enhancement e.g. by labeling, segmentation,
feature extraction and data interpretation that is fed back into the visualization
pipeline for display. Visualization can convey these interpretations or serve as aninterface where the domain specialist confirms or rejects the presence of specificfeatures, labeled through inference, within the visual data analysis. Especiallygenerative AI methods have also the potential to support reverse engineering ofdata or models given a specific visualization or parametric model ouput.
AI as an Intelligent Assistant. With the introduction of powerful large
language models, an opportunity arises to utilize such models for various assistive
functions within visualization software:• Conversational Visualization allows a user to formulate natural-language
queries about the data and the large-language model interprets these queries
into specific commands within a visualization software. For example, loosely
formulated geometric transformations can be instantiated into specificactions by way of fine-tuning the model for completion of a specific task.
Furthermore, large-language models can be directly prompted for specificdomain meaning or detailed explanation of the visualized structure. In
this new workflow, the data analyst can converse with a chatbot to exploreand draw conclusions from tabular data, or even medical diagnosis canbe established by conversation between the clinician and the chatbot that
explains why a certain structure has been characterized as a particularpathology.
24• Automatically generated (textual) help functions. When fine-tuning thehelp system of a software with a natural language - formal terminology
pairs, large language models can serve as an effective help system thatguides the user to system’s functionality. For example, ParaView, a wellestablished scientific visualization software is known for a steep learning
curve due to is broad spectrum of flexible functionality. Using ParaView’s
documentation as fine-tuning, a chatbot can be assistive in operating
ParaView through natural language prompts. It can be even used fortraining naive users who may start their training by asking: ”What can I
do with this tool?”. However, all these functionalities need to be used incaution. The implicit neural network knowledge representations might notlead to correct responses and there is always a non-zero probability thatthe chatbot response is simply wrong or incomplete. Therefore, utilizationof these assistive technologies should be used only in non-critical scenarios,
or every outcome has to be scrutinized by respective domain experts who
are ultimately responsible for the completion of specific underlying task.
• Modern multi-modal model architectures that can learn from very diverse
data in a combined way. This might include different time dependent andpotentially spatial data types including user interaction with complex data,
user attention and emotion, data, tasks, app configuration etc. This opens
new opportunities of research on user and task adaptive interfaces including improved interfaces for people with special needs realizing inclusive
interactive interfaces.
• Supporting and acceleration of visual computing workflows can be achievedby learning and predicting interactions. A specific challenge in this context
is the diverse and hierarchical nature of interactions and the related data
modelling.
• In the human-computer interaction research community, the term HumanAI Teaming relates to handling imperfect machine learning outcomes. This
concept of teaming mixes together machine learning for visualization andvisualization for machine learning.
• The lack of participants and time is challenging the systematic evaluationof usability of interactive visual applications with or without Human-AITeaming. The use of AI to mimic real user behavior, for example by using
prediction models to estimate human perception or preference, is an active
field of research.
Deep Learning as Methodological Approach to Visualization The introduction of deep learning as a methodology to replace e.g. traditional rendering
methods or parts of the rendering pipeline caused a rethinking of traditional
approaches computer graphics and visualization research and a wave of novel
solutions. One such example is Differential Rendering: Neural network training
process can be characterized as an optimization process where the numericalchain-rule-based differentiation defines the gradient that is used for optimizer
to update the network parameter values. Instead of using implicit neural representations, differentiable approaches can be used on reverting a well-definedalgorithmic process that is formulated to be continuous, such that either above25

mentioned auto-differentiation can be utilized or analytical gradients can becomputed instead. Visualization can be viewed as such a forward continuous
process and differentiable methods can be used for estimating the visualizationprocess parameters. This way, for example, transfer functions can be estimatedfrom a given set of images and data. In principle, any information can be
estimated from this optimization process, including the particular steps of thevisualization pipeline itself or even the underlying data - along the principle ofthe neural radiance fields, for example. As visual mapping can be formulated asa specific visualization language grammar, it can be possible to revert-engineer
the visualization language, e.g., Vega-Lite, from rendered visualization images.
II. AI assisting the visualization software development process. AI
models supporting coding like Github Copilot 14 are already widely used andadopted by developers, though its specific benefits for the development of specialized visualization software is not so clear. Visualization languages and grammarsof graphics that emerged in the past, adhere to a specific syntax. As such,
this code can be generated by large language models, similar to generation of
any other code syntax. But what are potential promising use cases of AI for
visualization software development also beyond the current state of the art?
During our discussion we identified a couple of promising new use cases andpotential directions novel applications of AI to assist Vis software development:• Increasing interoperability of visualization software by employing AI (LLMs)
for transferring/translating code, between different third party dependencies• Using AI for to automated code / library / dependency reduction• Employing AI to “reverse engineer” visualization code e.g. from publications (published without code):– From pseudo code to code: Presenting the AI pseudo code from a paper
to recreate the code for the visualization in a specified programming
language.
– From visualization to code: Presenting the AI a figure with a specificchart to automatically generate respective code to recreate that chart
for given data.
– From paper to code: Presenting the AI a Vis paper to recreate theunderlying application.
III. Practical challenges and consideration for the integration of AI into
visualization software The group identified several points to be consideredwhen integrating AI into visualization software.
• Tasks where AI can help with have to be carefully chosen• A measurable positive impact, as well as correctness and reliability of the
AI components should be ensured.
• Thinking beyond LLMs might be beneficial including e.g. recent work on
multi modal foundation models
14 https://github.com/features/copilot26• The choice of AI foundation models to be integrated in visualization
software is non-trivial. Considerations include many dimensions including
specialization, accessibility and openness of the models as well as data
privacy.
• Architectural consideration in using AI and Vis in parallel to ensure
efficiency• Interfacing to industry standards for data types is favorable
Concerns in respect to the limits of the usage of AI models were related tothe still very fast evolving field of AI and that the sustainability of solutions
is still not fully given. Also the use of AI models in connection with multi
modal interfaces requires care in respect to ensure determinism, reliability andinterpretability/causality of outcomes.

27Topic: How can visualization software be FAIR?
Participants: Christoph Garth, Jim Ahrens, Alex Bock, Fritz Lekschas, Marc
Baaden, Gerik Scheuermann, Claudio Silva
FAIR requirements have strongly increased in funding calls and publications
outlets. The group identifies two major implications for visualization software:First, as visualization researchers produce software as a primary class of research
artifacts, the question naturally arises how VIS software can be published ina FAIR manner and which requirements one should fulfill to achieve this goal.
A second question is, how can VIS software help domain scientists outside thevisualization field in producing and maintaining FAIR data. Due to scope, thegroup decides to focus on the first issue.
Considering the individual facets of the FAIR principles, the group brainstorms how these can be translated into practical recommendations for VIS
researchers to achieve a gradually increasing degree of FAIRness in software
publishing and dissemination. These recommendations will form the basis for anarticle, targeted at the Visualization Viewpoints category in Computer Graphics& Applications, and an implementable pre-publication checklist for researchers.

28Topic: What, How, and Where to Publish
Participants: Alex Bock, Ana Crisan, Cristophe Garth, Kate Isaacs, Michael
Krone, David Laidlaw, Fritz Lekschas, Ken Moreland, Dominik Moritz, Ivan
Viola, Gunther Weber, Daniel Wiegreffe
We have recognized software tools as an essential part of visualization research,and yet there are several barriers to sharing, reusing, and maintaining them.
We discussed several ways to encourage the sharing, reuse, and maintenance ofvisualization through forms of publication. These included changes to publication
processes and expectations and additional venues in which software contributions
could be recognized. With each of these pointes, we further discussed whatshould be published and how they should be published—what mechanisms andrequirements should each form entail. Finally, we set forth a plan for realizing
the outcomes of our discussion.
Software-focused publications are well established in other communities likebioinformatics and data science. For instance, the Oxford Bioinformatics journal
has a publishing format called Application Notes. An application note paper
entirely focuses on the software tool and comprises only two pages. Such papers
do not present any new scientific results but instead typically provide a shortuse case that demonstrates the utility of the software tool. Similarly, Journalof Open Source Software is a journal that focuses entirely on software-focused
papers. Given its focus, the journal implemented software-specific requirements
such as code archival, tests, or community guidelines. Additionally, authors
have to write a one to two page description of their tool, how it’s filling a gap,
and how it relates to existing software. These existing venues allow us to learn
how a successful software-focused publication tier can be established. But whilevisualization software publication would have much in common with publications
at the above mentioned venues, we recognized that visualization software has
special requirements (e.g., viewing device or user study) which warrants a newformat tailored to the visualization community.
We noted there are several scenarios of visualization software, from generallibraries to be used by the visualization community and beyond to more focusedvisualization software aimed at specific communities. These different types ofsoftware and users suggest different types of validation are needed. Rather thanranking the level of reusability or availability, we agreed that different types ofreusability and availability could be considered to fit these different contexts.
A live demonstration session at visualization conferences would highlightthe potential reuse of software. In the near term, a workshop or application
spotlight-type event may provide the right balance of incentive and additional
work by the authors. Hands-on and bring-your-own-data demonstrations are
something we want to encourage in the longer term.
Archived publications in well-known venues represent strong incentive andrecognition for the work that reusable software entails. In the medium term, there
is strong support for an additional type of short paper that focuses on software
contributions. Leveraging and adapting the mechanisms and requirements ofBioinformatics Application Notes and the Journal of Open Source Software as
described above is a good starting point. We note these short papers should notpreclude papers about the scientific contributions implemented by the software,
but instead recognize the contributions of the software itself.

29

Additionally, we discussed whether traditional full-length papers should
require software artifacts such as source code, executables, or runnable containers.
It was noted that IEEE Transactions on Visualization and Computer Graphics
already has the option of a Replicability Stamp, where software is reviewed after
a paper is accepted for publication. This process has revealed the breadth ofneeds in verifying research software.

30

Topic: Common Platform, Building Blocks / Modularization and Interop / Integration; connecting vis software /systems / building blocks; APIs
Participants: Marc Baaden, Steve Legenski, Kenneth Moreland, Michael Krone,
Guido Reina, Katja Bühler, Ivan Viola, Claudio Silva, Katherine Issacs, SeokHee Hong, Dominik Moritz, Fritz Lekschas, Daniel Wiegreffe, Kwan-Liu Ma,
Anamaria Crisan
While we had initially split the topic into the three more specific subtopics,in the plenary discussion we concluded that the distinction would hinder the
discussion and therefore merged them into a single one to be discussed in multiple
iterations.
During the first iteration of the discussion, we discussed the problem from aworkflow perspective, citing Alteryx or Galaxy and CWL (among many otheropen approaches) and the respectively required building blocks, leading toimportance of proper interfaces and APIs as a valuable contribution. The
lessons learned from practice suggest that the advent of Apache Arrow and its
incarnations and formats like Pandas and Parquet has been a game changer for
its users, allowing for high-performance, zero-copy usage and slicing of massive
data sets. The ensuing question is what such an endeavor would look like for thescientific visualization community, since tables are not necessarily an appropriate
abstraction, and additional features like streaming and hierarchies or LOD levels
are required. The intermediate take-away was that we should invest more time
into designing the APIs and interfaces of our software artifacts, since these efforts
both pay off significantly in practice and represent core take-away messages thatare valuable in publications (see also page 29).
In the subsequent discussions, the audience was slightly biased towards
classical scientific visualization and thus both had a different state of the art
and different views of the subject. The discussion quickly concluded that asingle, common platform is an unattainable panacea. Although standard tools
are established for specific domains, even there the need for customized tools
exists.
The discussion therefore focused more on the general needs of platform
building for visualization tools. What should be the general structure of acommon platform? Should it be one monolithic tool? Such large repositories
allow for well integrated components, efficient interoperability, and simplified
dependencies. But such repositories also have major development burdens andrequire users to adopt large, unruly libraries or applications for even small tasks.
At the other end of the spectrum is to break everything into small, independent
components. Although more agile, it is usually difficult for such tools to interact
with each other, and doing so may require wasteful transformations (cf. thediscussion about Arrow above). Furthermore, dependency management is an
issue, and the use of many tools increases the risk that any one of them will
become unavailable. However, this strategy represents a convenient steppingstone to meeting the user in their own ecosystem, a topic that was also discussed
at this meeting (see page 23). In short, there is no free lunch, and it is anengineering trade-off to determine where to draw component boundaries.
The participants’ experience suggested that attempting to design a specificsolution and foist it upon the community is folly. Instead, the discussion focused31

on broad issues and solutions while pulling in experiences of tools and techniquesthat work best. Rather than try to solve what the “right” level of building blocks
should be, the group recognized that some level of component division is assured
and focused on how such visualization tools might interact. The discussion thenfocused on two main discussion points: a common data model and a common
interaction layer.
Common Data Model Any interesting interaction between visualization
software components inevitably requires sharing data. Doing so is only possible
if components understand each other’s data. Although the base representationof data can usually be trivially broken into tables or arrays, such low levelstructures provide little information about the semantics required to interpretthem.
For inspiration for connecting components together, the group looked at the insitu visualization community [3]. The primary challenge of in situ visualizationis the coupling of a scientific solver code with visualization code, which are
independently constructed and often have conflicting data models. Much thought
as been given on what this interface looks like, particularly with respect to thedata model, and several ideas that have worked to varying degrees.
What the participants found that works particularly well with respect todata sharing is an approach that uses a simple library called Conduit [12].
Conduit provides an object description using a hierarchy of key/value pairs,
where the values may reference an array. The structure is similar to that in many
“self-describing” data stores such as JSON, YAML, HDF5, ADIOS, and others.
However, Conduit is separate from any other data storage or functionality; itprovides a data object that can be passed among software components, and thatis it. Conduit has been adopted by several in situ visualization libraries, such
as Ascent [18], Catalyst [2], and Kombyne [19], to simplify the passing of datafrom simulation to visualization libraries.
The group suggests a similar approach of adopting a lightweight description
object to annotate data as a flexible mechanism for joining disparate components.
Of course, an alternate tool to Conduit could be designed, but the existing
functionality seems well suited for more general application.
Although this approach of adding attributes to arrays is referred to as selfdescribing, this structure alone is not sufficient for software to make use ofsuch data. To make sense of such data, the software components must agree
on a schema for the structure. This provides a specific convention for names
or other attributes that consuming components can use to adapt the data totheir own data models. In situ visualization tools rely on a schema called
Blueprint [12], which provides mechanisms to describe common data structuresof the scientific visualization community. Other visualization communities might
require alternate or expanded schemas. A suggested approach is to observe thedata models of mature tools and use this as a template for the initial structure.
Common Interaction Model Given multiple visualization components thatshare some data, looking into user interaction is the next logical step. Ideally,
states like selection or filtering should be shared or transmitted between components via some mechanism. Again, this hinges on the definition of interfacesor APIs that share a common formulation of interactions and intents and take32

of the technical details. An orthogonal approach to the above would first needto establish the visualization parameters that can sensibly transported fromone component to another. Then it should design an implementation that, forexample, allows to robustly ignore interactions that are irrelevant or not yet
implemented and take part in the rest of the communication. This effectively
describes a generalization of the implementation in Heinemann et al. [14]. A
first internal draft of this is outlined by G. Reina. The connection layer could
be used both for rapid prototyping of coupled visualizations that do not coexist
in the same framework or to facilitate reproducibility. The concept is based onsynchronized arrays of arbitrary objects that can be named to enable mapping
across different data and tools. This would allow to synchronize entities without
requiring access to data beyond that is interesting for each implementation. For
example, one of several isosurfaces could be selected in ParaView, resulting in
its respective ID. This ID could be used to select marks (lines or bars etc.) in avega-lite diagram that depicts metrics or other metadata that is semanticallyconnected to the isosurface. Both tools only need to load and manage data
pertinent to the respective visualization primitives and still contribute to asensible meta system for the analysis of the given dataset. So far, the more
obvious state to share would comprise camera pose and parameters, light source
pose and parameters, transfer functions (both in parametric form and “baked”
into textures), and flags that represent selection and filtering state. The draftenvisions a broker component that mediates between the different running components and would also allow the user to explicitly perform the mapping between
the known entities in all involved components (i.e., one component might call it’Camera1’ while the other uses ’PerspectiveView’ etc.). The obvious drawback ofsuch a solution is that the user is ultimately responsible for connecting entities
the IDs of which are consistent and semantically relevant. It would be interesting
to investigate technical and semantic descriptors that allow for a modicum ofvalidation to simplify usage.

33

Topic: Education of PhD students for visualization softwaredevelopment
Participants: Michael Keckeisen, Krešimir Matković, and Dominik Moritz
Our discussion revolved around the education of graduate students, specifically those pursuing PhDs with a research focus. We emphasize that our focus
is not on undergraduate or master’s level students since they lack a researchorientation.
We advocate for a comprehensive approach to graduate education in visualization software development, integrating research principles, engineering practices,and collaborative learning experiences. By equipping students with these skills,
we aim to foster a new generation of researchers capable of making meaningful
contributions to the field.
More specifically:
The work should be research. Target contributions that generate new human knowledge. Students must develop software capable of demonstrating
research insights, supporting instrumentation, and facilitating adaptability.
Depending on the goals/research type the software can be scrappy or should
be built well. The skills also differ based on the contribution. Students
also come in with different backgrounds. Their skills/excitement shouldbe leveraged.
Follow good engineering practice. They lead to better research and savetime in the end. When starting a project, think about what practices are
the right ones for the research at hand. For example, always do version
control. If needed, have continuous integration, tests (UI and unit tests),build documentation, processes for contributors/code reviews, pull request
templates etc. Building software alone is very different from building with
two, or more people. It’s a good practice from a pedagogical perspective tohave (at least) two students work together. One danger is that sometimes
there is a star student where others just get out of the way. But students
need to learn to work together.
Critical thinking, analysis, and debugging are hard to teach. They are
essential, and should be prioritized.
Students benefit from working together. A challenge when students work
together is when one is not able to articulate their contribution. Then it’sdifficult for them to establish themselves as independent researchers. To
overcome this issue, each student needs to carve out their focus. However,at the same time if you have students who all contribute, each of themcan carve out their own focus. A huge benefit is that credit does not split
50/50 but instead 70/70. The pie grows.
Some different kinds of research that involve software.
1. Code for running a study.
2. Software for an algorithm that needs to be benchmarked. Analysis
scripts. Reproducibility is useful. Contribution: new algorithms, user
studies.
34

3. Doing tool driven research where a holistic software artifact is thecontribution. Reuse of the software is an explicit goal. Contribution:
new workflow that makes a qualitative or quantitative improvement(a) Software that a collaborator used to do some task. Ideas live on
but the code does not.
(b) Software that is sustainably built.
Some ways in which students can learn engineering practices. Classes,
learn from their advisor who enforces certain practices, collaboration
projects with companies that require certain qualities from the artifacts
that are being produced, learn by example from an advisor (copy yourmentors), do a research or class project that involves engineering practices
where students can learn, and internships (e.g. internships at a place withstrong engineering practices).

35

Topic: What are promising technologies to look out for /to adopt right now?
Participants: Ivan Viola, Kresimir Matković, Christoph Garth, Marc Baaden,
Michael Keckeisen
The discussion centered around identifying promising technologies in the realmof visualization that should investigated for adoption, particularly focusing on
the appropriate abstraction level for developing visualization tools and platforms.
This summary organizes the insights from the discussion into multiple themes.
Visualization Languages and Abstraction Levels. The group underscores
the importance of finding the right abstraction level for creating effective visualization tools. There’s a consensus on the need for a ”Vega-Lite for 3D,”
indicating a desire for tools that simplify the creation of complex 3D visualizations without sacrificing power or flexibility. It is identified that there is a gap intools specifically designed for 3D visualization that are as intuitive and powerfulas Vega-Lite is for 2D.
Rendering Technologies. A significant portion of the discussion is dedicatedto rendering technologies, with web-based graphics and WebGPU taking center
stage. WebGPU is lauded for its support of all shader types, compute shadersfor physics and inference, and its portability across platforms, including Android Chrome. This technology, especially when combined with WebAssembly(WASM), is seen as a promising avenue for ”write once, run everywhere” visualization applications while simplifying their development. The success story of
Nanographics, which utilized an abstraction layer for portability across OpenGL,
Vulkan, and WebGPU, is highlighted as an example of effective use of thesetechnologies. There is curiosity about the integration of WebGPU with gameengines like Babylon.js and potential shifts from other engines like three.js toembrace WebGPU.
Game Engines and other Middleware. Game engines such as Unreal arerecognized for their standard features and ease of handling 3D graphics and scenegraphs, which are cumbersome to develop from scratch. The discussion also
touches on Houdini and ytini, particularly their use in rapid prototyping andastrophysics visualization, respectively, showcasing the versatility of game engines
and specialized software in creating high-quality visualizations. The Universal
Scene Description (USD) file format is mentioned as a significant innovation
for scene assembly, alongside the ANARI cross-platform 3D rendering engine
API, which is anticipated to be the foundation for future tools like Paraview.
This suggests a movement towards standardization and cross-compatibility in
3D visualization tools.
Hardware Advancements and Other Notable Technologies. Apple’s
Vision Pro is singled out as a game-changer in hardware, suggesting that new
hardware capabilities could significantly influence the development and deployment of visualization technologies. Additional software technologies discussed
include Kokkos for C++ performance portability, targeting various platforms

36

from CUDA to SYCL, and NeRFs (Neural Radiance Fields) for creating lifelike 3D models from photographic images. These technologies represent the
cutting-edge in performance optimization and realistic rendering, respectively.

37

Topic: AR/VR & Unconventional Displays
Participants: Dominik Moritz, Alexander Bock, Ivan Viola, Gunther Weber,
Takayuki Itoh, Alexander Lex, Daniel Wiegreffe
The group began by discussing the new Apple Vision Pro VR/AR headset.
That led to a list of uses for VR in the context of visualization and some
speculation on challenges for those uses.
Potential uses include:• Visual exploration of scientific data, especially that with intrinsic 3D
geometric relationships, multiple values at each spatial location, variationin time, and scale beyond what can be viewed on a desktop display.
Examples include 3D biological or medical imaging, large simulation results,
planetary-scale remote sensing• Visualization applications that would benefit from very large 2D displays.
• Some 2D visualization approaches, including overdrawing and binning,
may benefit from judicious and understated use of 3D and stereo• Collaborative visualization both remote and co-located.
• Teleoperation.
• Augmented reality for situational awareness in the real world and invisualization applications.
• A super nice office anywhere you go: home, workplace, cafe, park, hotel,
plane.
There are a number of challenges that will influence the success of thesepotential uses.
Development infrastructure. Currently, most VR visualization applications
are custom-developed for a small group of collaborators. Development cantake many months or years, and that limits the number of collaboratorsand the impact that VR/AR visualization can have. More infrastructure
to speed up software development would accelerate progress.
High maintenance costs. Traditionally, high-pixel-count VR visualization
has required a cave or a finicky and expensive headset. Both scenarios
involve substantial maintenance costs. Apple’s new headset may achievesufficient resolution in a relatively low-cost package to allow high-pixelcount VR in a low-maintenance setting.
Travel. In addition to the startup costs of software development, many VR
visualization applications can only run in specific locations. Traveling toa display location, even within a single building, has been a barrier toregular use.
User input. VR applications typically make common input devices, like a keyboard, unusable. Replacement input mechanisms are usually less efficient,making regular use less appealing. Spoken input, virtual tablet interfaces,
and other UI techniques may help but have not yet solved this problem.
38

Collaboration. Synchronizing the virtual world across users so that they canmeaningfully communicate about it is another challenge.
Productive integration. A VR session can be a ”wow” experience, but it
is often difficult to take away more than the ”wow” memory. Better
integration into scientific and professional workflows remains a challenge.

39

Figure 1: A Mosaic-based interface for interactive visual exploration of all 1.8
billion stars in the Gaia star catalog. A high-resolution density map of thesky reveals our Milky Way and satellite galaxies. Stars with higher parallax
values are interactively selected, forming a Hertzsprung-Russell diagram of color
versus stellar magnitude on the right. Mosaic offloads density and histogram
computation to a backing scalable database, and automatically builds optimized
data cube indexes to support interactive linked views.

Visualization Software Showcases
Participants: all participants
Presenters: Alexander Bock, Fritz Lekschas, Steve Legensky, Alexander Lex,
Dominik Moritz, Ivan ViolaIn this plenary session, the participants were given the opportunity to showcase some examples of successful or upcoming visualization software, either bygiving a short presentation or by letting the other participants try out their tool.
The first presenter was Alexander Bock who showed OpenSpace (OpenSpace, [5]),followed by Dominik Moritz presenting Mosaic (see Figure 1). Fritz Lekschas
showed AnyWidget (Figure 3, [21]) and Alexander Lex gave a preview of an
upcoming tool that is not yet published. Steve Legensky showed an example ofa web-based visualization application for monitoring and steering HPC systems.
Ivan Viola gave an overview of a number of web-based molecular visualizationand modeling applications (WebGPU-based Systems for Modeling and Visualization of Cellular Mesoscale) that either resulted from student works or research
projects and gave the other participants the opportunity to try out an intelligent,conversation-based user interface. Below are short descriptions of some of thetools that were presented.
Mosaic [13] is an architecture for greater scalability, extensibility, and interoperability of interactive data views. It decouples data processing from specification
logic: clients publish their data needs as declarative queries that are then managed and automatically optimized by a coordinator that proxies access to ascalable database. Mosaic makes order-of-magnitude performance improvements

40

over existing web-based visualization systems—enabling flexible, real-time visual
exploration of billion+ record datasets. Figure 1 shows an example application
built with Mosaic. You can try this example (albeit on a sample of the data) in
a web browser at uwdata.github.io/mosaic/examples/gaia.html. Mosaic clientscan easily be implemented for existing or new visualizations. Mosaic has thepotential to be an open platform that bridges visualization languages, scalable
visualization, and interactive data systems more broadly.
OpenSpace OpenSpace [5, 4] is a long-standing software package developed by
Linköping University, the American Museum of Natural History, the Universityof Utah, and New York University in collaboration with a large number of digitalscience centers all over the world. The software is designed to serve as a toolfor visualization and astronomy research, while simultaneously functioning as asoftware used for the dissemination of research findings to the general public,
while also spuring interest in STEM fields.
In many ways it can be compared to a custom game engine in which many
modalities of datasets (geolocated raster or vector datasets, spatial pointcloud
data, volumetric simulations, 3D models, and more) can be rendered in asingle coordinate system and thus be placed within their proper context. This
common context allows for seamless transitions from sub-meter resolution onplanetary surfaces all the way to the edge of the universe at the cosmic microwave
background radiation.
The existing plugin infrastructure enables the rapid development of newfeatures into the system and is flexible enough to support the integration of thirdparty rendering tools, such as yt, ViaMD, and others. Using these third-party
softwares prevents the need to reinvent the wheel for every rendering techniquewhile simultaneously providing access to immersive display environments to thesetools which traditionally lack these capabilities.
AnyWidget AnyWidget [21] is a small library developed for the single purpose of making it easier for developers to create interactive widgets for Jupyter
Notebook/Lab and Google Colab. Widgets are a powerful way to embed visualization directly into data science workflows. However, building widgets had
been an error-prone and time-intensive process. AnyWidget makes this process
drastically more efficient by requiring close to no setup. Additionally, it provides
modern tooling such as hot-module reloading know in the web development
community.
For instance, as shown in Figure 3, with AnyWidget we can build a widget
for Observable Plot’s scatter plot requiring only six lines of Python code andtwenty lines of JavaScript code.
Persist—Persistent and Reusable Interactions in Computational Notebooks Computational notebooks, such as Jupyter, support rich data visualization. However, even when visualizations in notebooks are interactive, they
still are a dead end: Interactive data manipulations, such as selections, applying
labels, filters, categorizations, or fixes to column or cell values, could be efficiently
applied in interactive visual components, but interactive components typicallycannot manipulate Python data structures. Furthermore, actions performed in41

Figure 2: A rendering from OpenSpace showing the Earth bathed in its surrounding data, showing the location of star clusters, exoplanetary systems, andplanetary nebulæ.

Figure 3: An example widget for integrating Observable Plot’s scatter plot withPandas DataFrame. The popout in the bottom right shows a fully-featured interactive scatter plot widget developed with AnyWidget by Ozette Technologies [9]that shows a single-cell embedding visualization of immune cells, which wereclustered with FAUST [8].

42Figure 4: The persist workflow of making interactions in Jupyter persistent.
interactive plots are volatile, i.e., they are lost as soon as the cell is re-run, prohibiting reusability and reproducibility. To remedy this, we introduce Persist [7],a family of techniques to capture and apply interaction provenance to enable
persistence of interactions. When interactions manipulate data, we make thetransformed data available in dataframes that can be accessed in downstream
code cells. We implement our approach as a JupyterLab extension that supports
tracking interactions in Vega-Altair plots and in a data table view. Persist canre-execute the interaction provenance when a notebook or a cell is re-executedenabling reproducibility and re-use. The code and documentation is available athttps://github.com/visdesignlab/persist/.
WebGPU-based Systems for Modeling and Visualization of Cellular
Mesoscale WebGPU can now be officially used in most common browsers, forsimple graphics applications but also for advanced applications suitable for aparticular science domain. Presented demos have shown applications developed
at KAUST which fulfill the task of 3D volume visualization of cryo-electron
tomography data, or for procedural modeling of cellular mesoscale models.
Figure 5 depicts the complete, procedurally modeled, and scientifically accurate
ultrastructure of the SARS-CoV-2 virion. Such ultrastructure can be visually
explored by non-expert audience through a conversational visualization interface.

43

Figure 5: Procedurally modeled SARS-CoV-2 virion in a WebGPU-poweredmodeling application.

44

Summary of new findings
In the seminar we made several findings.
The Gap between Visualization and Applications is manifold. In thepresented talks it became clear that all participants of the meeting face the gap
between visualization and applications. Still, the way they experience this gap
is very manifold. It ranges from problems when trying to publish applicationoriented visualization approaches in visualization venues, over missing funding forsoftware development tasks to a lack of recognition for ready-to-use visualization
approaches. A main contribution of the seminar is a list of problems that haveto be solved in the given context.
There exist attempts to close the Gap between visualization andApplications. In the seminar we discussed the current efforts in bridging theexisting gap. They exist in terms of encouraging members of our community
to provide code of their visualization approaches with their papers or the testof time award at the IEEE Vis conference. The participants agreed that weneed take make more use of these offers in terms of bridging the gap between
visualization and applications.
There needs to be more efforts in closing the Gap between visualization and Applications. Although there exist attempts to bridge theconsidered gap, the participants made significant effort in defining and suggesting
new ways to bridge the gap between visualization and applications. These include
mandatory submission of code, novel tracks in the visualization conferences andfurther ways of honoring the extra work that comes with applicability.

45

Identified issues and future directions
In the seminar we identified several issues, that have been brought up in theindividual talks, discussed in break out sessions and roughly formalized in thisreport. The main ongoing task is to formalize the found problems, find potential
solutions for them and make an effort in implementing them into the daily workof visualization researchers. In the seminar different subgroups have formed thatcommitted to continue on various sub-problems in the given context, namely:• A taxonomy of visualization software• An application paper track• Structured funding for software development• Building a community
Therefore, the organizers of the Shonan Seminar aim to continue the work on
this important topic by organizing further events such as the VisGap Workshop
at EuroVis, joint publications on the topic and further seminar series at Shonan
or at similar venues.

46

References[1] Y. Annanias, D. Zeckzer, G. Scheuermann, and D. Wiegreffe. An interactive
decision support system for land reuse tasks. IEEE Computer Graphics andApplications, 42(6):72–83, 2022.
[2] U. Ayachit, A. C. Bauer, B. Boeckel, B. Geveci, K. Moreland, P. O’Leary,
and T. Osika. Catalyst revised: Rethinking the paraview in situ analysis
and visualization API. In High Performance Computing, pages 484–494,
June 2021.
[3] A. C. Bauer, H. Abbasi, J. Ahrens, H. Childs, B. Geveci, S. Klasky, K. Moreland, P. O’Leary, V. Vishwanath, B. Whitlock, and E. W. Bethel. In situ
methods, infrastructures, and applications on high performance computing
platforms. Computer Graphics Forum, 35(3):577–597, June 2016.
[4] A. Bock, E. Axelsson, K. Bladin, J. Costa, G. Payne, M. Territo, J. Kilby,
M. Kuznetsova, C. Emmart, and A. Ynnerman. OpenSpace: An open-source
astrovisualization framework. The Journal of Open Source Software, 2(15),jul 2017.
[5] A. Bock, E. Axelsson, J. Costa, G. Payne, M. Acinapura, V. Trakinski,
C. Emmart, C. Silva, C. Hansen, and A. Ynnerman. OpenSpace: A System
for Astrographics. IEEE Transactions on Visualization and Computer
Graphics, 2019.
[6] S. Eichelbaum, M. Hlawitschka, and G. Scheuermann. Openwalnut: An opensource tool for visualization of medical and bio-signal data. Biomedical Engineering/Biomedizinische Technik, 58(SI-1-Track-G):000010151520134183,
2013.
[7] K. Gadhave, Z. Cutler, and A. Lex. Persist: Persistent and reusableinteractions in computational notebooks. 2024.
[8] E. Greene, G. Finak, L. A. D’Amico, N. Bhardwaj, C. D. Church, C. Morishima, N. Ramchurren, J. M. Taube, P. T. Nghiem, M. A. Cheever, et al.
New interpretable machine-learning method for single-cell data reveals
correlates of clinical response to cancer immunotherapy. Patterns, 2(12),2021.
[9] E. Greene, G. Finak, F. Lekschas, M. Smith, L. A. D’Amico, N. Bhardwaj,
C. D. Church, C. Morishima, N. Ramchurren, J. M. Taube, P. T. Nghiem,
M. A. Cheever, S. P. Fling, and R. Gottardo. Data Transformations for
Effective Visualization of Single-Cell Embeddings, July 2022.
[10] S. Grottel, M. Krone, C. Müller, G. Reina, and T. Ertl. MegaMol - A
Prototyping Framework for Particle-based Visualization. IEEE Transactions
on Visualization and Computer Graphics, 21(2):201–214, 2015.
[11] K. W. Hall, A. J. Bradley, U. Hinrichs, S. Huron, J. Wood, C. Collins,
and S. Carpendale. Design by immersion: A transdisciplinary approach toproblem-driven visualizations. CoRR, abs/1908.00559, 2019.

47

[12] C. Harrison, M. Larsen, B. S. Ryujin, A. Kunen, A. Capps, and J. Privitera.
Conduit: A successful strategy for describing and sharing data in situ. In
EEE/ACM International Workshop on In Situ Infrastructures for Enabling
Extreme-Scale Analysis and Visualization (ISAV), Nov. 2022.
[13] J. Heer and D. Moritz. Mosaic: An architecture for scalable & interoperable
data views. IEEE Transactions on Visualization and Computer Graphics,
2023.
[14] M. Heinemann, S. Frey, G. Tkachev, A. Straub, F. Sadlo, and T. Ertl. Visual
analysis of droplet dynamics in large-scale multiphase spray simulations.
Journal of Visualization, 24(5):943–961, Oct. 2021.
[15] S. Jänicke, C. Heine, and G. Scheuermann. Geotemco: Comparative visualization of geospatial-temporal data with clutter removal based on dynamicdelaunay triangulations. In Computer Vision, Imaging and Computer
Graphics. Theory and Application: 7th International Joint Conference,
VISIGRAPP 2012, Rome, Italy, February 24-26, 2012, Revised Selected
Papers, pages 160–175. Springer, 2013.
[16] C. Johnson. Top scientific visualization research problems. IEEE Computer
Graphics and Applications, 24(4):13–17, 2004.
[17] M. Kampfrath, R. Staritzbichler, G. P. Hernández, A. S. Rose, J. K. Tiemann, G. Scheuermann, D. Wiegreffe, and P. W. Hildebrand. Mdsrv: visual
sharing and analysis of molecular dynamics simulations. Nucleic Acids
Research, 50(W1):W483–W489, 2022.
[18] M. Larsen, E. Brugger, H. Childs, and C. Harrison. Ascent: A Flyweight
In Situ Library for Exascale Simulations. In In Situ Visualization For
Computational Science, pages 255 – 279. Mathematics and Visualization
book series from Springer Publishing, Cham, Switzerland, May 2022.
[19] S. M. Legensky, E. P. Duque, D. A. Amels, B. Whitlock, M. Meyer, A. Gerstenberger, P. Adami, and R. Thümmler. Industrial and biomedical CFD
workflows enhanced via coprocessing for knowledge capture and computational steering. In Eleventh International Conference on Computational
Fluid Dynamics (ICCFD11), July 2022.
[20] K. Ma, I. Liao, J. Frazier, H. Hauser, and H. Kostis. Scientific storytellingusing visualization. IEEE Computer Graphics and Applications, 32(1):12–19,
2012.
[21] T. Manz. anywidget.
[22] G. Reina, H. Childs, K. Matković, K. Bühler, M. Waldner, D. Pugmire,
B. Kozlı́ková, T. Ropinski, P. Ljung, T. Itoh, E. Gröller, and M. Krone. The
moving target of visualization software for an increasingly complex world.
Computers & Graphics, 87, 2020.
[23] F. Sauer, T. Neuroth, J. Chu, and K. Ma. Audience-targeted design considerations for effective scientific storytelling. Comput. Sci. Eng., 18(6):68–76,
2016.

48

[24] M. Söchting, M. D. Mahecha, D. Montero, and G. Scheuermann. Lexcube:
Interactive visualization of large earth system data cubes. IEEE Computer
Graphics and Applications, 2023.
[25] D. Vartziotis and J. Wipper. The GETMe Mesh Smoothing Framework: A
Geometric Way to Quality Finite Element Meshes. CRC Press, 2018.
[26] A. Wiebel, C. Garth, M. Hlawitschka, T. Wischgoll, and G. Scheuermann.
Fantom-lessons learned from design, implementation, administration, anduse of a visualization system for over 10 years. 2009.
[27] Y. C. Ye, F. Sauer, K. Ma, A. Konduri, and J. Chen. A user-centered design
study in scientific visualization targeting domain experts. IEEE Trans. Vis.
Comput. Graph., 26(6):2192–2203, 2020.

49