Program

Abstract: The visualization community regards visualization literacy as a necessary skill. Yet, despite the recent increase in research into visualization literacy by the education and visualization communities, we lack practical and time-effective instruments for the widespread measurements of people’s comprehension and interpretation of visual designs. We present Mini-VLAT, a brief but practical visualization literacy test. The Mini-VLAT is a 12-item short form of the 53-item Visualization Literacy Assessment Test (VLAT). The Mini-VLAT is reliable (coefficient omega = 0.72) and strongly correlates with the VLAT. Five visualization experts validated the Mini-VLAT items, yielding an average content validity ratio (CVR) of 0.6. We further validate Mini-VLAT by demonstrating a strong positive correlation between study participants’ Mini-VLAT scores and their aptitude for learning an unfamiliar visualization using a Parallel Coordinate Plot test. Overall, the Mini-VLAT items showed a similar pattern of validity and reliability as the 53-item VLAT. The results show that Mini-VLAT is a psychometrically sound and practical short measure of visualization literacy.

Presenter: Saugat Pandey

Abstract: Exploratory analysis of the chemical space is an important task in the field of cheminformatics. For example, in drug discovery research, chemists investigate sets of thousands of chemical compounds in order to identify novel yet structurally similar synthetic compounds to replace natural products. Manually exploring the chemical space inhabited by all possible molecules and chemical compounds is impractical, and therefore presents a challenge. To fill this gap, we present ChemoGraph, a novel visual analytics technique for interactively exploring related chemicals. In ChemoGraph, we formalize a chemical space as a hypergraph and apply novel machine learning models to compute related chemical compounds. It uses a database to find related compounds from a known space and a machine learning model to generate new ones, which helps enlarge the known space. Moreover, ChemoGraph highlights interactive features that support users in viewing, comparing, and organizing computationally identified related chemicals. With a drug discovery usage scenario and initial expert feedback from a case study, we demonstrate the usefulness of ChemoGraph.

Presenter: Bharat Kumar Kale

Abstract: Analyzing stenoses of the internal carotids – local constrictions of the artery – is a critical clinical task in cardiovascular disease treatment and prevention. For this purpose, we propose a self-contained pipeline for the visual analysis of carotid artery geometries. The only inputs are computed tomography angiography (CTA) scans, which are already recorded in clinical routine. We show how integrated model extraction and visualization can help to efficiently detect stenoses and we provide means for automatic, highly accurate stenosis degree computation. We directly connect multiple sophisticated processing stages, including a neural prediction network for lumen and plaque segmentation and automatic global diameter computation. We enable interactive and retrospective user control over the processing stages. Our aims are to increase user trust by making the underlying data validatable on the fly, to decrease adoption costs by minimizing external dependencies, and to optimize scalability by streamlining the data processing. We use interactive visualizations for data inspection and adaption to guide the user through the processing stages. The framework was developed and evaluated in close collaboration with radiologists and neurologists. It has been used to extract and analyze over 100 carotid bifurcation geometries and is built with a modular architecture, available as an extendable open-source platform.

Presenter: Pepe Eulzer

Presenter: Ivan Viola

Abstract: We present a novel approach for rendering volumetric data including the Doppler effect of light. Similar to the acoustic Doppler effect, which is caused by relative motion between a sound emitter and an observer, light waves also experience compression or expansion when emitter and observer exhibit relative motion. We account for this by employing spectral volume rendering in an emission-absorption model, with the volumetric matter moving according to an accompanying vector field, and emitting and attenuating light at wavelengths subject to the Doppler effect. By introducing a novel piecewise linear representation of the involved light spectra, we achieve accurate volume rendering at interactive frame rates. We compare our technique to rendering with traditional point-based spectral representation, and demonstrate its utility using a simulation of galaxy formation.

Presenter: Reem Alghamdi

Abstract: A common way to render cell-centric adaptive mesh refinement (AMR) data is to compute the dual mesh and visualize that with a standard unstructured element renderer. While the dual mesh provides a high-quality interpolator, the memory requirements of the dual mesh data structure are significantly higher than those of the original grid, which prevents rendering very large data sets. We introduce a GPU-friendly data structure and a clustering algorithm that allow for efficient AMR dual mesh rendering with a competitive memory footprint. Fundamentally, any off-the-shelf unstructured element renderer running on GPUs could be extended to support our data structure just by adding a gridlet element type in addition to the standard tetrahedra, pyramids, wedges, and hexahedra supported by default. We integrated the data structure into a volumetric path tracer to compare it to various state-of-the-art unstructured element sampling methods. We show that our data structure easily competes with these methods in terms of rendering performance, but is much more memory-efficient.

Presenter: Stefan Zellmann

Abstract: Histopathology research quickly evolves thanks to advances in whole slide imaging (WSI) and artificial intelligence (AI). However, existing WSI viewers are tailored either for clinical or research environments, but none suits both. This hinders the adoption of new methods and communication between the researchers and clinicians. The paper presents xOpat, an open-source, browser-based WSI viewer that addresses these problems. xOpat supports various data sources, such as tissue images, pathologists’ annotations, or additional data produced by AI models. Furthermore, it provides efficient rendering of multiple data layers, their visual representations, and tools for annotating and presenting findings. Thanks to its modular, protocol-agnostic, and extensible architecture, xOpat can be easily integrated into different environments and thus helps to bridge the gap between research and clinical practice. To demonstrate the utility of xOpat, we present three case studies, one conducted with a developer of AI algorithms for image segmentation and two with a research pathologist.

Presenter: Jiří Horák

Abstract: Design studies are an integral method of visualization research with hundreds of instances in the literature. Although taught as a theory, the practical implementation of design studies is often excluded from visualization pedagogy due to the lengthy time commitments associated with such studies. Recent research has addressed this challenge and developed an expedited design study framework, the Design Study “Lite” Methodology (DSLM), which can implement design studies with novice students within just 14 weeks. The framework was developed and evaluated based on five semesters of in-person data visualization courses with 30 students or less and was implemented in conjunction with Service-Learning (S-L). With the growth and popularity of the data visualization field—and the teaching environment created by the COVID-19 pandemic—more academic institutions are offering visualization courses online. Therefore, in this paper, we strengthen and validate the epistemological foundations of the DSLM framework by testing its (1) adaptability to online learning environments and conditions and (2) scalability to larger classes with up to 57 students. We present two online implementations of the DSLM framework, with and without Service-Learning (S-L), to test the adaptability and scalability of the framework. We further demonstrate that the framework can be applied effectively without the S-L component. We reflect on our experience with the online DSLM implementations and contribute a detailed retrospective analysis using thematic analysis and grounded theory methods to draw valuable recommendations and guidelines for future applications of the framework. This work verifies that DSLM can be used successfully in online classes to teach design study methodology. Finally, we contribute novel additions to the DSLM framework to further enhance it for teaching and learning design studies in the classroom. The preprint and supplementary materials for this paper can be found at https://osf.io/6bjx5/.

Presenter: Uzma Haque Syeda

Abstract: Modelling the dynamics of a growing financial environment is a complex task that requires domain knowledge, expertise and access to heterogeneous information types. Such information can stem from several sources at different scales, complicating the task of forming a holistic impression of the financial landscape, especially in terms of the economical relationships between firms. Bringing this scattered information into a common context is, therefore, an essential step in the process of obtaining meaningful insights about the state of an economy. In this paper, we present Sabrina 2.0, a Visual Analytics (VA) approach for exploring financial data across different scales, from individual firms up to nation-wide aggregate data. Our solution is coupled with a pipeline for the generation of firm-to-firm financial transaction networks, fusing information about individual firms with sector-to-sector transaction data and domain knowledge on macroscopic aspects of the economy. Each network can be created to have multiple instances to compare different scenarios. We collaborated with experts from finance and economy during the development of our VA solution, and evaluated our approach with seven domain experts across industry and academia through a qualitative insight-based evaluation. The analysis shows how Sabrina 2.0 enables the generation of insights, and how the incorporation of transaction models assists users in their exploration of a national economy.

Abstract: The use of good-quality data to inform decision making is entirely dependent on robust processes to ensure it is fit for purpose. Such processes vary between organisations, and between those tasked with designing and following them. In this paper we report on a survey of 53 data analysts from many industry sectors, 24 of whom also participated in in-depth interviews, about computational and visual methods for characterizing data and investigating data quality. The paper makes contributions in two key areas. The first is to data science fundamentals, because our lists of data profiling tasks and visualization techniques are more comprehensive than those published elsewhere. The second concerns the application question “what does good profiling look like to those who routinely perform it?,” which we answer by highlighting the diversity of profiling tasks, unusual practice and exemplars of visualization, and recommendations about formalizing processes and creating rulebooks.

Abstract: OWL is a powerful language to formalize terminologies in an ontology. Its main strength lies in its foundation on description logics, allowing systems to automatically deduce implicit information through logical reasoning. However, since ontologies are often complex, understanding the outcome of the reasoning process is not always straightforward. Unlike already existing tools for exploring ontologies, our visualization tool Evonne is tailored towards explaining logical consequences. In addition, it supports the debugging of unwanted consequences and allows for an interactive comparison of the impact of removing statements from the ontology. Our visual approach combines (1) specialized views for the explanation of logical consequences and the structure of the ontology, (2) employing multiple layout modes for iteratively exploring explanations, (3) detailed explanations of specific reasoning steps, (4) cross-view highlighting and colour coding of the visualization components, (5) features for dealing with visual complexity and (6) comparison and exploration of possible fixes to the ontology. We evaluated Evonne in a qualitative study with 16 experts in logics, and their positive feedback confirms the value of our concepts for explaining reasoning and debugging ontologies.

Abstract: We present ComBiNet, a visualization, query, and comparison system for exploring bipartite multivariate dynamic social networks. Historians and sociologists study social networks constructed from textual sources mentioning events related to people, such as marriage acts, birth certificates, and contracts. We model this type of data using bipartite multivariate dynamic networks to maintain a representation faithful to the original sources while not too complex. Relying on this data model, ComBiNet allows exploring networks using both visual and textual queries using the Cypher language, the two being synchronized to specify queries using the most suitable modality; simple queries are easy to express visually and can be refined textually when they become complex. These queries are used for applying topological and attribute-based selection on the network. Query results are visualized in the context of the whole network and over a geographical map for geolocalized entities. We also present the design of our interaction techniques for querying social networks to visually compare the selections in terms of topology, measures, and attribute distributions. We validate the query and comparison systems by showing how they have been used to answer historical questions and by explaining how they have been improved through a usability study conducted with historians.

Abstract: Edge-Path bundling is a recent edge bundling approach that does not incur ambiguities caused by bundling disconnected edges together. Although the approach produces less ambiguous bundlings, it suffers from high computational cost. In this paper, we present a new Edge-Path bundling approach that increases the computational speed of the algorithm without reducing the quality of the bundling. First, we demonstrate that biconnected components can be processed separately in an Edge-Path bundling of a graph without changing the result. Then, we present a new edge bundling algorithm that is based on observing and exploiting a strong relationship between Edge-Path bundling and graph spanners. Although the worst case complexity of the approach is the same as of the original Edge-Path bundling algorithm, we conduct experiments to demonstrate that the new approach is 5–256 times faster than Edge-Path bundling depending on the dataset, which brings its practical running time more in line with traditional edge bundling algorithms.

Abstract: Euler diagrams are a popular technique to visualize set-typed data. However, creating diagrams using simple shapes remains a challenging problem for many complex, real-life datasets. To solve this, we propose RectEuler: a flexible, fully-automatic method using rectangles to create Euler-like diagrams.
We use an efficient mixed-integer optimization scheme to place set labels and element representatives (e.g., text or images) in conjunction with rectangles describing the sets. By defining appropriate constraints, we adhere to well-formedness properties and aesthetic considerations. If a dataset cannot be created within a reasonable time or at all, we iteratively split the diagram into multiple components until a drawable solution is found. Redundant encoding of the set membership using dots and set lines improves the readability of the diagram. Our web tool lets users see how the layout changes throughout the optimization process and provides interactive explanations. For evaluation, we perform quantitative and qualitative analysis across different datasets and compare our method to state-of-the-art Euler diagram generation methods.

Presenter: Patrick Paetzold

Abstract: We present a methodology for making sense of the communicative role of data visualizations in journalistic storytelling and share findings from surveying water-related data stories. Data stories are a genre of long-form journalism that integrate text, data visualization, and other visual expressions (e.g., photographs, illustrations, videos) for the purpose of data-driven storytelling. In the last decade, a considerable number of data stories about a wide range of topics have been published worldwide. Authors use a variety of techniques to make complex phenomena comprehensible and use visualizations as communicative devices that shape the understanding of a given topic. Despite the popularity of data stories, we, as scholars, still lack a methodological framework for assessing the communicative role of visualizations in data stories. To this extent, we draw from data journalism, visual culture, and multimodality studies to propose an interpretative framework in six stages. The process begins with the analysis of content blocks and framing elements and ends with the identification of dimensions, patterns, and relation- ships between textual and visual elements. The framework is put to the test by analyzing 17 data stories about water-related issues. Our observations from the survey illustrate how data visualizations can shape the framing of complex topics

Presenter: Manuela Garreton

Abstract: When individuals encounter new information (data), that information is incorporated with their existing beliefs (prior) to form a new belief (posterior) in a process referred to as belief updating. While most studies on rational belief updating in visual data analysis elicit beliefs immediately after data is shown, we posit that there may be critical movement in an individual’s beliefs when elicited immediately after data is shown v. after a temporal delay (e.g., due to forgetfulness or weak incorporation of the data). Our paper investigates the hypothesis that posterior beliefs elicited after a time interval will “decay” back towards the prior beliefs compared to the posterior beliefs elicited immediately after new data is presented. In this study, we recruit 101 participants to complete three tasks where beliefs are elicited immediately after seeing new data and again after a brief distractor task. We conduct (1) a quantitative analysis of the results to understand if there are any systematic differences in beliefs elicited immediately after seeing new data or after a distractor task and (2) a qualitative analysis of participants’ reflections on the reasons for their belief update. While we find no statistically significant global trends across the participants beliefs elicited immediately v. after the delay, the qualitative analysis provides rich insight into the reasons for an individual’s belief movement across 9 prototypical scenarios, which includes (i) decay of beliefs as a result of either forgetting the information shown or strongly held prior beliefs, (ii) strengthening of confidence in updated beliefs by positively integrating the new data and (iii) maintaining a consistently updated belief over time, among others. These results can guide subsequent experiments to disambiguate when and by what mechanism new data is truly incorporated into one’s belief system.

Presenter: Alireza Karduni

Abstract: Authors use various media formats to convey disease information to a broad audience, from articles and videos to interviews or documentaries. These media often include human characters, such as patients or treating physicians, who are involved with the disease. While artistic media, such as hand-crafted illustrations and animations are used for health communication in many cases, our goal is to focus on data-driven visualizations. Over the last decade, narrative visualization has experienced increasing prominence, employing storytelling techniques to present data in an understandable way. Similar to classic storytelling formats, narrative medical visualizations may also take a human character-centered design approach. However, the impact of this form of data communication on the user is largely unexplored. This study investigates the protagonist’s influence on user experience in terms of engagement, identification, self-referencing, emotional response, perceived credibility, and time spent in the story. Our experimental setup utilizes a character-driven story structure for disease stories derived from Joseph Campbell’s Hero’s Journey. Using this structure, we generated three conditions for a cerebral small vessel disease story that vary by their protagonist: (1) a patient, (2) a physician, and (3) a base condition with no human protagonist. These story variants formed the basis for our hypotheses on the effect of a human protagonist in disease stories, which we evaluated in an online study with 30 participants. Our findings indicate that a human protagonist exerts various influences on the story perception and that these also vary depending on the type of protagonist.

Presenter: Sarah Mittenentzwei

Abstract: Data visualizations have been widely used on mobile devices like smartphones for various tasks (e.g., visualizing personal health and financial data), making it convenient for people to view such data anytime and anywhere. However, others nearby can also easily peek at the visualizations, resulting in personal data disclosure. In this paper, we propose a perception-driven approach to transform mobile data visualizations into privacy-preserving ones. Specifically, based on human visual perception, we develop a masking scheme to adjust the spatial frequency and luminance contrast of colored visualizations. The resulting visualization retains its original information in close proximity but reduces visibility when viewed from a certain distance or farther away. We conducted two user studies to inform the design of our approach (N=16) and systematically evaluate its performance (N=18), respectively. The results demonstrate the effectiveness of our approach in terms of privacy preservation for mobile data visualizations.

Presenter: Songheng Zhang

Abstract: We propose Composite Parallel Coordinates, a novel parallel coordinates technique to effectively represent the interplay of component alternatives in a system. It builds upon a dedicated data model that formally describes the interaction of components. Parallel coordinates can help decision-makers identify the most preferred solution among a number of alternatives. Multi-component systems require one such multi-attribute choice for each component. Each of these choices might have side effects on the system’s operability and performance, making them co-dependent. Common approaches employ complex multi-component models or involve back-and-forth iterations between single components until an acceptable compromise is reached. A simultaneous visual exploration across independently modeled but connected components is needed to make system design more efficient. Using dedicated layout and interaction strategies, our Composite Parallel Coordinates allow analysts to explore both individual properties of components as well as their interoperability and joint performance. We showcase the effectiveness of Composite Parallel Coordinates for co-dependent multi-attribute choices by means of three real-world scenarios from distinct application areas. In addition to the case studies, we reflect on observing two domain experts collaboratively working with the proposed technique and communicating along the way.

Abstract: Handling emergencies requires efficient and effective collaboration of medical professionals. To analyze their performance, in an application study, we have developed VisCoMET, a visual analytics approach displaying interactions of healthcare personnel in a triage training of a mass casualty incident. The application scenario stems from social interaction research, where the collaboration of teams is studied from different perspectives. We integrate recorded annotations from multiple sources, such as recorded videos of the sessions, transcribed communication, and eye-tracking information. For each session, an informationrich timeline visualizes events across these different channels, specifically highlighting interactions between the team members. We provide algorithmic support to identify frequent event patterns and to search for user defined event sequences. Comparing different teams, an overview visualization aggregates each training session in a visual glyph as a node, connected to similar sessions through edges. An application example shows the usage of the approach in the comparative analysis of triage training sessions, where multiple teams encountered the same scene, and highlights discovered insights. The approach was evaluated through feedback from visualization and social interaction experts. The results show that the approach supports reflecting on teams’ performance by exploratory analysis of collaboration behavior while particularly enabling the comparison of triage training sessions.

Presenter: Carina Liebers

Abstract: In many domains, multivariate event sequence data is collected focused around an entity (the case). Typically, each event has multiple attributes, for example, in healthcare a patient has events such as hospitalization, medication, and surgery. In addition to the multivariate events, also the case (a specific attribute, e.g., patient) has associated multivariate data (e.g., age, gender, weight). Current work typically only visualizes one attribute per event (label) in the event sequences. As a consequence, events can only be explored from a predefined case-centric perspective. However, to find complex relations from multiple perspectives (e.g., from different case definitions, such as doctor), users also need an event- and attribute-centric perspective. In addition, support is needed to effortlessly switch between and within perspectives. To support such a rich exploration, we present FlexEvent: an exploration and analysis method that enables investigation beyond a fixed case-centric perspective. Based on an adaptation of existing visualization techniques, such as scatterplots and juxtaposed small multiples, we enable flexible switching between different perspectives to explore the multivariate event sequence data needed to answer multi-perspective hypotheses. We evaluated FlexEvent with three domain experts in two use cases with sleep disorder and neonatal ICU data that show our method facilitates experts in exploring and analyzing real-world multivariate sequence data from different perspectives.

Presenter: Sanne van der Linden

Abstract: Real-world event sequences are often complex and heterogeneous, making it difficult to create meaningful visualizations using
simple data aggregation and visual encoding techniques. Consequently, visualization researchers have developed numerous
visual summarization techniques to generate concise overviews of sequential data. These techniques vary widely in terms of
summary structures and contents, and currently there is a knowledge gap in understanding the effectiveness of these techniques.
In this work, we present the design and results of an insight-based crowdsourcing experiment evaluating three existing visual
summarization techniques: CoreFlow, SentenTree, and Sequence Synopsis. We compare the visual summaries generated by
these techniques across three tasks, on six datasets, at six levels of granularity. We analyze the effects of these variables on
summary quality as rated by participants and completion time of the experiment tasks. Our analysis shows that Sequence
Synopsis produces the highest-quality visual summaries for all three tasks, but understanding Sequence Synopsis results also
takes the longest time. We also find that the participants evaluate visual summary quality based on two aspects: content and
interpretability. We discuss the implications of our findings on developing and evaluating new visual summarization techniques.

Presenter: Kazi Tasnim Zinat

Abstract: How do we ensure the veracity of science? The act of manipulating or fabricating scientific data has led to many high-profile fraud cases and retractions. Detecting manipulated data, however, is a challenging and time-consuming endeavor. Automated detection methods are limited due to the diversity of data types and manipulation techniques. Furthermore, patterns automatically flagged as suspicious can have reasonable explanations. Instead, we propose a nuanced approach where experts analyze tabular datasets, e.g., as part of the peer-review process, using a guided, interactive visualization approach.
In this paper, we present an analysis of how manipulated datasets are created and the artifacts these techniques generate. Based on these findings, we propose a suite of visualization methods to surface potential irregularities.
We have implemented these methods in Ferret, a visualization tool for data forensics work. Ferret makes potential data issues salient and provides guidance on spotting signs of tampering and differentiating them from truthful data.

Presenter: Devin Lange

Abstract: Despite the tremendous advances in machine learning (ML), training with imbalanced data still poses challenges in many real‐world applications. Among a series of diverse techniques to solve this problem, sampling algorithms are regarded as an efficient solution. However, the problem is more fundamental, with many works emphasizing the importance of instance hardness. This issue refers to the significance of managing unsafe or potentially noisy instances that are more likely to be misclassified and serve as the root cause of poor classification performance.This paper introduces HardVis, a visual analytics system designed to handle instance hardness mainly in imbalanced classification scenarios. Our proposed system assists users in visually comparing different distributions of data types, selecting types of instances based on local characteristics that will later be affected by the active sampling method, and validating which suggestions from undersampling or oversampling techniques are beneficial for the ML model. Additionally, rather than uniformly undersampling/oversampling a specific class, we allow users to find and sample easy and difficult to classify training instances from all classes. Users can explore subsets of data from different perspectives to decide all those parameters, while HardVis keeps track of their steps and evaluates the model’s predictive performance in a test set separately. The end result is a well‐balanced data set that boosts the predictive power of the ML model. The efficacy and effectiveness of HardVis are demonstrated with a hypothetical usage scenario and a use case. Finally, we also look at how useful our system is based on feedback we received from ML experts.

Abstract: The visual analytics community has long aimed to understand users better and assist them in their analytic endeavors. As a result, numerous conceptual models of visual analytics aim to formalize common workflows, techniques, and goals leveraged by analysts. While many of the existing approaches are rich in detail, they each are specific to a particular aspect of the visual analytic process. Furthermore, with an ever-expanding array of novel artificial intelligence techniques and advances in visual analytic settings, existing conceptual models may not provide enough expressivity to bridge the two fields. In this work, we propose an agent-based conceptual model for the visual analytic process by drawing parallels from the artificial intelligence literature. We present three examples from the visual analytics literature as case studies and examine them in detail using our framework. Our simple yet robust framework unifies the visual analytic pipeline to enable researchers and practitioners to reason about scenarios that are becoming increasingly prominent in the field, namely mixed-initiative, guided, and collaborative analysis. Furthermore, it will allow us to characterize analysts, visual analytic settings, and guidance from the lenses of human agents, environments, and artificial agents, respectively.

Presenter: Shayan Monadjemi

Abstract: Currently, the methodological and technical developments in visual analytics, as well as the existing theories, are not sufficiently grounded by empirical studies that can provide an understanding of the processes of visual data analysis, analytical reasoning and derivation of new knowledge by humans. We conducted an exploratory empirical study in which participants analysed complex and data‐rich visualisations by detecting salient visual patterns, translating them into conceptual information structures and reasoning about those structures to construct an overall understanding of the analysis subject. Eye tracking and voice recording were used to capture this process. We analysed how the data we had collected match several existing theoretical models intended to describe visualisation‐supported reasoning, knowledge building, decision making or use and development of mental models. We found that none of these theoretical models alone is sufficient for describing the processes of visual analysis and knowledge generation that we observed in our experiments, whereas a combination of three particular models could be apposite. We also pondered whether empirical studies like ours can be used to derive implications and recommendations for possible ways to support users of visual analytics systems. Our approaches to designing and conducting the experiments and analysing the empirical data were appropriate to the goals of the study and can be recommended for use in other empirical studies in visual analytics.

Abstract: Historical records from democratic processes and negotiation of constitutional texts are a complex type of data to navigate due to the many different elements that are constantly interacting with one another: people, timelines, different proposed documents, changes to such documents, and voting to approve or reject those changes. In particular, voting records can offer various insights about relationships between people of note in that historical context, such as alliances that can form and dissolve over time and people with unusual behavior. In this paper, we present a toolset developed to aid users in exploring relationships in voting records from a particular domain of constitutional conventions. The toolset consists of two elements: a dataset visualizer, which shows the entire timeline of a convention and allows users to investigate relationships at different moments in time via dimensionality reduction, and a person visualizer, which shows details of a given person’s activity in that convention to aid in understanding the behavior observed in the dataset visualizer. We discuss our design choices and how each tool in those elements works towards our goals, and how they were perceived in an evaluation conducted with domain experts.

Presenter: Gabriel Dias Cantareira

Abstract: Sub‐national governments across the United States implement a variety of policies to address large societal problems and needs. Many policies are picked up or adopted in other states. This process is called policy diffusion and allows researchers to analyse and compare the social, political, and contextual characteristics that lead to adopting certain policies, as well as the efficacy of these policies once adopted. In this paper, we introduce PDViz, a visual analytics approach that allows social scientists to dynamically analyse the policy diffusion history and underlying patterns. It is designed for analysing and answering a list of research questions and tasks posed by social scientists in prior work. To evaluate our system, we present two usage scenarios and conduct interviews with domain experts in political science. The interviews highlight that PDViz provides the result of policy diffusion patterns that align with their domain knowledge as well as the potential to be a learning tool for students to understand the concept of policy diffusion.

Abstract: During training sessions, sailors rely on feedback provided by the coaches to reinforce their skills and improve their performance. Nowadays, the incorporation of sensors on the boats enables coaches to potentially provide more informed feedback to the sailors. A common exercise during practice sessions, consists of two boats of the same class, sailing side by side in a straight line with different boat handling techniques. Coaches try to understand which techniques are that make one boat go faster than the other. The analysis of the obtained data from the boats is challenging given its multi-dimensional, time-varying and spatial nature. At present, coaches only rely on aggregated statistics reducing the complexity of the data, hereby losing local and temporal information. We describe a new domain characterization and present a visualization design that allows coaches to analyse the data, structuring their analysis and explore the data from different perspectives. A central element of the tool is the glyph design to intuitively represent and aggregate multiple aspects of the sensor data. We have conducted multiple user studies with naive users, sailors and coaches to evaluate the design and potential of the overall tool.

Abstract: Anticipation skill is important for elite racquet sports players. Successful anticipation allows them to predict the actions of the opponent better and take early actions in matches. Existing studies of anticipation behaviors, largely based on the analysis of in-lab behaviors, failed to capture the characteristics of in-situ anticipation behaviors in real matches. This research proposes a data-driven approach for research on anticipation behaviors to gain more accurate and reliable insight into anticipation skills. Collaborating with domain experts in table tennis, we develop a complete solution that includes data collection, the development of a model to evaluate anticipation behaviors, and the design of a visual analytics system called Tac-Anticipator. Our case study reveals the strengths and weaknesses of top table tennis players’ anticipation behaviors. In a word, our work enriches the research methods and guidelines for visual analytics of anticipation behaviors.

Presenter: Jiachen Wang

Abstract: Univariate visualizations like histograms, rug plots, or box plots provide concise visual summaries of distributions. However, each individual visualization may fail to robustly distinguish important features of a distribution, or provide sufficient information for all of the relevant tasks involved in summarizing univariate data. One solution is to juxtapose or superimpose multiple univariate visualizations in the same chart, as in Allen et al.’s “raincloud plots.” In this paper I examine the design space of raincloud plots, and, through a series of simulation studies, explore designs where the component visualizations mutually “defend” against situations where important distribution features are missed or trivial features are given undue prominence. I suggest a class of “defensive” raincloud plot designs that provide good mutual coverage for surfacing distributional features of interest.

Presenter: Michael Correll

Abstract: Visualizations have played a crucial role in helping quantum computing users explore quantum states in various quantum computing applications. Among them, Bloch Sphere is the widely-used visualization for showing quantum states, which leverages angles to represent quantum amplitudes. However, it cannot support the visualization of quantum entanglement and superposition, the two essential properties of quantum computing. To address this issue, we propose VENUS, a novel visualization for quantum state representation. By explicitly correlating 2D geometric shapes based on the math foundation of quantum computing characteristics, VENUS effectively represents quantum amplitudes of both the single qubit and two qubits for quantum entanglement. Also, we use multiple coordinated semicircles to naturally encode probability distribution, making the quantum superposition intuitive to analyze. We conducted two well-designed case studies and an in-depth expert interview to evaluate the usefulness and effectiveness of VENUS. The result shows that VENUS can effectively facilitate the exploration of quantum states for the single qubit and two qubits.

Presenter: Shaolun Ruan

Abstract: Exploring high-dimensional data is a common task in many scientific disciplines. To address this task, two-dimensional embeddings, such as tSNE and UMAP, are widely used. While these determine the 2D position of data items, effectively encoding the first two dimensions, suitable visual encodings can be employed to communicate higher-dimensional features. To investigate such encodings, we have evaluated two commonly used glyph types, namely flower glyphs and star glyphs. To evaluate their capabilities for communicating higher-dimensional features in two-dimensional embeddings, we ran a large set of crowd-sourced user studies using real-world data obtained from data.gov. During these studies, participants completed a broad set of relevant tasks derived from related research. This paper describes the evaluated glyph designs, details our tasks, and the quantitative study setup before discussing the results. Finally, we will present insights and provide guidance on the choice of glyph encodings when exploring high-dimensional data.

Abstract: One of the fundamental problems in neurobiological research is to understand how neural circuits generate behaviors in response to sensory stimuli. Elucidating such neural circuits requires anatomical and functional information about the neurons that are active during the processing of the sensory information and generation of the respective response, as well as an identification of the connections between these neurons. With modern imaging techniques, both morphological properties of individual neurons as well as functional information related to sensory processing, information integration and behavior can be obtained. Given the resulting information, neurobiologists are faced with the task of identifying the anatomical structures down to individual neurons that are linked to the studied behavior and the processing of the respective sensory stimuli. Here, we present a novel interactive tool that assists neurobiologists in the aforementioned task by allowing them to extract hypothetical neural circuits constrained by anatomical and functional data. Our approach is based on two types of structural data: brain regions that are anatomically or functionally defined, and morphologies of individual neurons. Both types of structural data are interlinked and augmented with additional information. The presented tool allows the expert user to identify neurons using Boolean queries. The interactive formulation of these queries is supported by linked views, using, among other things, two novel 2D abstractions of neural circuits. The approach was validated in two case studies investigating the neural basis of vision-based behavioral responses in zebrafish larvae. Despite this particular application, we believe that the presented tool will be of general interest for exploring hypotheses about neural circuits in other species, genera and taxa.

Abstract: We present an approach for the visual analysis of multi-omics data obtained using high-throughput methods.
The term “omics” denotes measurements of different types of biologically relevant molecules, like the products of gene transcription (transcriptomics) or the abundance of proteins (proteomics).
Current popular visualization approaches often only support analyzing each of these omics separately.
This, however, disregards the interconnectedness of different biologically relevant molecules and processes.
Consequently, it describes the actual events in the organism suboptimally or only partially.
Our visual analytics approach for multi-omics data provides a comprehensive overview and details-on-demand by integrating the different omics types in multiple linked views.
To give an overview, we map the measurements to known biological pathways and use a combination of a clustered network visualization, glyphs, and interactive filtering.
To ensure the effectiveness and utility of our approach, we designed it in close collaboration with domain experts and assessed it using an exemplary workflow with real-world transcriptomics, proteomics, and lipidomics measurements from mice.

Presenter: Nicolas Brich

Abstract: Analysis pipelines in genomics, transcriptomics, and proteomics commonly produce lists of genes, e.g., differentially expressed genes. Often these lists overlap only partly or not at all and contain too many genes for manual comparison. However, using background knowledge, such as the functional annotations of the genes, the lists can be abstracted to functional terms. One approach is to run Gene Ontology (GO) enrichment analyses to determine over- and/or underrepresented functions for every list of genes. Due to the hierarchical structure of the Gene Ontology, lists of enriched GO terms can contain many closely related terms, rendering the lists still long, redundant, and difficult to interpret for researchers.

In this paper, we present GO-Compass (Gene Ontology list comparison using Semantic Similarity), a visual analytics tool for the dispensability reduction and visual comparison of lists of GO terms. For dispensability reduction, we adapted the REVIGO algorithm, a summarization method based on the semantic similarity of GO terms, to perform hierarchical dispensability clustering on multiple lists.
In an interactive dashboard, GO-Compass offers several visualizations for the comparison and improved interpretability of GO terms lists. The hierarchical dispensability clustering is visualized as a tree, where users can interactively filter out dispensable GO terms and create flat clusters by cutting the tree at a chosen dispensability. The flat clusters are visualized in animated treemaps and are compared using a correlation heatmap, UpSet plots, and bar charts.

With two use cases on published datasets from different omics domains, we demonstrate the general applicability and effectiveness of our approach.
In the first use case, we show how the tool can be used to compare lists of differentially expressed genes from a transcriptomics pipeline and incorporate gene information into the analysis. In the second use case using genomics data, we show how GO-Compass facilitates the analysis of many hundreds of GO terms. For qualitative evaluation of the tool, we conducted feedback sessions with five domain experts and received positive comments.
GO-Compass is part of the TueVis Visualization Server as a web application available at https://go-compass-tuevis.cs.uni-tuebingen.de/

Presenter: Theresa Harbig

Abstract: Developing applicable clinical machine learning models is a difficult task when the data includes spatial information, for example, radiation dose distributions across adjacent organs at risk. We describe the co-design of a modeling system, DASS, to support the hybrid human-machine development and validation of predictive models for estimating long-term toxicities related to radiotherapy doses in head and neck cancer patients. Developed in collaboration with domain experts in oncology and data mining, DASS incorporates human-in-the-loop visual steering, spatial data, and explainable AI to augment domain knowledge with automatic data mining. We demonstrate DASS with the development of two practical clinical stratification models and report feedback from domain experts. Finally, we describe the design lessons learned from this collaborative experience.

Presenter: Andrew Wentzel

Abstract: Existing work on visualizing multivariate graphs is primarily concerned with representing the attributes of nodes. Even though edges are the constitutive elements of networks, there have been only few attempts to visualize attributes of edges. In this work, we focus on the critical importance of edge attributes for interpreting network visualizations and building trust in the underlying data. We propose ‘unfolding of edges’ as an interactive approach to integrate multivariate edge attributes dynamically into existing node-link diagrams. Unfolding edges is an in-situ approach that gradually transforms basic links into detailed representations of the associated edge attributes. This approach extends focus+context, semantic zoom, and animated transitions for network visualizations to accommodate edge details on-demand without cluttering the overall graph layout. We explore the design space for the unfolding of edges, which covers aspects of making space for the unfolding, of actually representing the edge context, and of navigating between edges. To demonstrate the utility of our approach, we present two case studies in the context of historical network analysis and computational social science. For these, web-based prototypes were implemented based on which we conducted interviews with domain experts. The experts’ feedback suggests that the proposed unfolding of edges is a useful tool for exploring rich edge information of multivariate graphs.

Presenter: Mark-Jan Bludau

Abstract: Retrieving charts from a large corpus is a fundamental task that can benefit numerous applications such as visualization recommendations.The retrieved results are expected to conform to both explicit visual attributes (e.g., chart type, colormap) and implicit user intents (e.g., design style, context information) that vary upon application scenarios. However, existing example-based chart retrieval methods are built upon non-decoupled and low-level visual features that are hard to interpret, while definition-based ones are constrained to pre-defined attributes that are hard to extend. In this work, we propose a new framework, namely WYTIWYR (What-You-Think-Is-What-You-Retrieve), that integrates user intents into the chart retrieval process. The framework consists of two stages: first, the Annotation stage disentangles the visual attributes within the bitmap query chart; and second, the Retrieval stage embeds the user’s intent with customized text prompt as well as query chart, to recall targeted retrieval result. We develop a prototype WYTIWYR system leveraging a contrastive language-image pre-training (CLIP) model to achieve zero-shot classification, and test the prototype on a large corpus with charts crawled from the Internet. Quantitative experiments, case studies, and qualitative interviews are conducted. The results demonstrate the usability and effectiveness of our proposed framework.

Presenter: Shishi XIAO

Abstract: Modern visualization software and programming libraries have made data visualization construction easier for everyone. However, the extent of accessibility design they support for blind and low-vision people is relatively unknown. It is also unclear how they can improve chart content accessibility beyond conventional alternative text and data tables. To address these issues, we examined the current accessibility features in popular visualization tools, revealing limited support for the standard accessibility methods and scarce support for chart content exploration. Next, we investigate two promising accessibility approaches that provide off-the-shelf solutions for chart content accessibility: structured navigation and conversational interaction. We present a comparative evaluation study and discuss what to consider when incorporating them into visualization tools.

Presenter: Nam Wook Kim

Abstract: ParaDime is a framework for parametric dimensionality reduction (DR). In parametric DR, neural networks are trained to embed high-dimensional data items in a low-dimensional space while minimizing an objective function. ParaDime builds on the idea that the objective functions of several modern DR techniques result from transformed inter-item relationships. It provides a common interface for specifying these relations and transformations and for defining how they are used within the losses that govern the training process. Through this interface, ParaDime unifies parametric versions of DR techniques such as metric MDS, t-SNE, and UMAP. It allows users to fully customize all aspects of the DR process. We show how this ease of customization makes ParaDime suitable for experimenting with interesting techniques such as hybrid classification/embedding models and supervised DR. This way, ParaDime opens up new possibilities for visualizing high-dimensional data.

Presenter: Andreas Hinterreiter

Abstract: As visualization makes the leap to mobile and situated settings, where data is increasingly integrated with the physical world using mixed reality, there is a corresponding need for effectively managing the immersed user’s view of situated visualizations. In this paper we present an analysis of view management techniques for situated 3D visualizations in handheld augmented reality: a shadowbox, a world-in-miniature metaphor, and an interactive tour. We validate these view management solutions through a concrete implementation of all techniques within a situated visualization framework built using a web-based augmented reality visualization toolkit, and present results from a user study in augmented reality accessed using handheld mobile devices.

Presenter: Andrea Batch

Abstract: 3D animations are an effective method to learn about complex dynamic phenomena, such as mesoscale biological processes. The animators’ goals are to convey a sense of the scene’s overall complexity while, at the same time, visually guiding the user through a story of subsequent events embedded in the chaotic environment. Animators use a variety of visual emphasis techniques to guide the observers’ attention through the story, such as highlighting, halos — or by manipulating motion parameters of the scene. In this paper, we investigate the effect of smoothing the motion of contextual scene elements to attract attention to focus elements of the story exhibiting high-frequency motion. We conducted a crowdsourced study with 108 participants observing short animations with two illustrative motion smoothing strategies: geometric smoothing through noise reduction of contextual motion trajectories and visual smoothing through motion blur of \rev{context} items. We investigated the observers’ ability to follow the story as well as the effect of the techniques on speed perception in a molecular scene. Our results show that moderate motion blur significantly improves users’ ability to follow the story. Geometric motion smoothing is less effective but increases the visual appeal of the animation. However, both techniques also slow down the perceived speed of the animation. We discuss the implications of these results and derive design guidelines for animators of complex dynamic visualizations.

Presenter: Johannes Eschner

Abstract: Augmented Reality (AR) provides new ways for situated visualization and human-computer interaction in physical environments. Current evaluation procedures for AR applications rely primarily on questionnaires and interviews, providing qualitative means to assess usability and task solution strategies. Eye tracking extends these existing evaluation methodologies by providing indicators for visual attention to virtual and real elements in the environment. However, the analysis of viewing behavior, especially the comparison of multiple participants, is difficult to achieve in AR.
Specifically, the definition of areas of interest (AOIs), which is often a prerequisite for such analysis, is cumbersome and tedious with existing approaches.
To address this issue, we present a new visualization approach to define AOIs, label fixations, and investigate the resulting annotated scanpaths. Our approach utilizes automatic annotation of gaze on virtual objects and an image-based approach that also considers spatial context for the manual annotation of objects in the real world. Our results show, that with our approach, eye tracking data from AR scenes can be annotated and analyzed flexibly with respect to data aspects and annotation strategies.

Presenter: Seyda Öney

Abstract: The distribution of visual attention can be evaluated using eye tracking, providing valuable insights into usability issues and interaction patterns. However, when used in real, augmented, and collaborative environments, new challenges arise that go beyond desktop scenarios and purely virtual environments. Toward addressing these challenges, we present a visualization technique that provides complementary views on the movement and eye tracking data recorded from multiple people in real-world environments. Our method is based on a space-time cube visualization and a linked 3D replay of recorded data. We showcase our approach with an experiment that examines how people investigate an artwork collection. The visualization provides insights into how people moved and inspected individual pictures in their spatial context over time. In contrast to existing methods, this analysis is possible for multiple participants without extensive annotation of areas of interest. Our technique was evaluated with a think-aloud experiment to investigate analysis strategies and an interview with domain experts to examine the applicability in other research fields.

Presenter: Nelusa Pathmanathan

Abstract: Understanding the behavior of deep reinforcement learning agents is a crucial requirement throughout their development.
Existing work has addressed the identification of observable behavioral patterns in state sequences or analysis of isolated internal representations; however, the overall decision-making of deep-learning RL agents remains opaque.
To tackle this, we present VISITOR, a visual analytics system enabling the analysis of entire state sequences, the diagnosis of singular predictions, and the comparison between agents.

A sequence embedding view enables the multiscale analysis of state sequences, utilizing custom embedding techniques for a stable spatialization of the observations and internal states.

We provide multiple layers: (1) a state space embedding, highlighting different groups of states inside the state-action sequences, (2) a trajectory view, emphasizing decision points, (3) a network activation mapping, visualizing the relationship between observations and network activations, (4) a transition embedding, enabling the analysis of state-to-state transitions. The embedding view is accompanied by an interactive reward view that captures the temporal development of metrics, which can be linked directly to states in the embedding. Lastly, a model list allows for the quick comparison of models across multiple metrics. Annotations can be exported to communicate results to different audiences.

Our two-stage evaluation with eight experts confirms the effectiveness in identifying states of interest, comparing the quality of policies, and reasoning about the internal decision-making processes.

Presenter: Yannick Metz

Abstract: Cross-task generalization is a significant outcome that defines mastery in natural language understanding. Humans show a remarkable aptitude for this, and can solve many different types of tasks, given definitions in the form of textual instructions and a small set of examples. Recent work with pre-trained language models mimics this learning style: users can define and exemplify a task for the model to attempt as a series of natural language prompts or instructions. While prompting approaches have led to higher cross-task generalization compared to traditional supervised learning, analyzing ‘bias’ in the task instructions given to the model is a difficult problem, and has thus been relatively unexplored. For instance, are we truly modeling a task, or are we modeling a user’s instructions? To help investigate this, we develop LINGO, a novel visual analytics interface that supports an effective, task-driven workflow to (1) help identify bias in natural language task instructions, (2) alter (or create) task instructions to reduce bias, and (3) evaluate pre-trained model performance on debiased task instructions. To robustly evaluate LINGO, we conduct a user study with both novice and expert instruction creators, over a dataset of 1,616 linguistic tasks and their natural language instructions, spanning 55 different languages. For both user groups, LINGO promotes the creation of more difficult tasks for pre-trained models, that contain higher linguistic diversity and lower instruction bias. We additionally discuss how the insights learned in developing and evaluating LINGO can aid in the design of future dashboards that aim to minimize the effort involved in prompt creation across multiple domains.

Presenter: Anjana Arunkumar

Abstract: Generative text-to-image models (as exemplified by DALL-E, MidJourney, and Stable Diffusion) have recently made enormous technological leaps, demonstrating impressive results in many graphical domains—from logo design to digital painting to photographic composition. However, the quality of these results has led to existential crises in some fields of art, leading to questions about the role of human agency in the production of meaning in a graphical context. Such issues are central to visualization, and while these generative models have yet to be widely applied in visualization, it seems only a matter of time until their integration is manifest. Seeking to circumvent similar ponderous dilemmas, we attempt to understand the roles that generative models might play across visualization. We do so by constructing a framework that characterizes what these technologies offer at various stages of the visualization workflow, augmented and analyzed through semi-structured interviews with 21 experts from related domains. Through this work, we map the space of opportunities and risks that might arise in this intersection, identifying doomsday prophecies and delicious low-hanging fruits that are ripe for research.

Presenter: Victor Schetinger

Abstract: Task-incremental learning (Task-IL) aims to enable an intelligent agent to continuously accumulate knowledge from new learn- ing tasks without catastrophically forgetting what it has learned in the past. It has drawn increasing attention in recent years, with many algorithms being proposed to mitigate neural network forgetting. However, none of the existing strategies is able to completely eliminate the issues. Moreover, explaining and fully understanding what knowledge and how it is being forgotten during the incremental learning process still remains under-explored. In this paper, we propose KnowledgeDrift, a visual analytics framework, to interpret the network forgetting with three objectives: (1) to identify when the network fails to memorize the past knowledge, (2) to visualize what information has been forgotten, and (3) to diagnose how knowledge attained in the new model interferes with the one learned in the past. Our analytical framework first identifies the occurrence of forgetting by tracking the task performance under the incremental learning process and then provides in-depth inspections of drifted information via various levels of data granularity. KnowledgeDrift allows analysts and model developers to enhance their understanding of network forgetting and compare the performance of different incremental learning algorithms. Three case studies are conducted in the paper to further provide insights and guidance for users to effectively diagnose catastrophic forgetting over time.

Presenter: Ziwei Li