DevLan/vispubs huggingface dataset:
VEHICLE: Validation and Exploration of the Hierarchical Integration of Conflict Event Data
Authors: Benedikt Mayer, Kai Lawonn, Karsten Donnay, Bernhard Preim, Monique Meuschke
Published at EuroVis, 2021

The exploration of large‐scale conflicts, as well as their causes and effects, is an important aspect of socio‐political analysis. Since event data related to major conflicts are usually obtained from different sources, researchers developed a semi‐automatic matching algorithm to integrate event data of different origins into one comprehensive dataset using hierarchical taxonomies. The validity of the corresponding integration results is not easy to assess since the results depend on user‐defined input parameters and the relationships between the original data sources. However, only rudimentary visualization techniques have been used so far to analyze the results, allowing no trustworthy validation or exploration of how the final dataset is composed. To overcome this problem, we developed VEHICLE, a web‐based tool to validate and explore the results of the hierarchical integration. For the design, we collaborated with a domain expert to identify the underlying domain problems and derive a task and workflow description. The tool combines both traditional and novel visual analysis techniques, employing statistical and map‐based depictions as well as advanced interaction techniques. We showed the usefulness of VEHICLE in two case studies and by conducting an evaluation together with conflict researchers, confirming domain hypotheses and generating new insights.
VICE: Visual Identification and Correction of Neural Circuit Errors
Authors: Felix Gonda, Xueying Wang, Johanna Beyer, Markus Hadwiger, Jeff W. Lichtman, Hanspeter Pfister
Published at EuroVis, 2021

A connectivity graph of neurons at the resolution of single synapses provides scientists with a tool for understanding the nervous system in health and disease. Recent advances in automatic image segmentation and synapse prediction in electron microscopy (EM) datasets of the brain have made reconstructions of neurons possible at the nanometer scale. However, automatic segmentation sometimes struggles to segment large neurons correctly, requiring human effort to proofread its output. General proofreading involves inspecting large volumes to correct segmentation errors at the pixel level, a visually intensive and time‐consuming process. This paper presents the design and implementation of an analytics framework that streamlines proofreading, focusing on connectivity‐related errors. We accomplish this with automated likely‐error detection and synapse clustering that drives the proofreading effort with highly interactive 3D visualizations. In particular, our strategy centers on proofreading the local circuit of a single cell to ensure a basic level of completeness. We demonstrate our framework's utility with a user study and report quantitative and subjective feedback from our users. Overall, users find the framework more efficient for proofreading, understanding evolving graphs, and sharing error correction strategies.
VisEvol: Visual Analytics to Support Hyperparameter Search through Evolutionary Optimization
Authors: Angelos Chatzimparmpas, Rafael Messias Martins, Kostiantyn Kucher, Andreas Kerren
Published at EuroVis, 2021

During the training phase of machine learning (ML) models, it is usually necessary to configure several hyperparameters. This process is computationally intensive and requires an extensive search to infer the best hyperparameter set for the given problem. The challenge is exacerbated by the fact that most ML models are complex internally, and training involves trial‐and‐error processes that could remarkably affect the predictive result. Moreover, each hyperparameter of an ML algorithm is potentially intertwined with the others, and changing it might result in unforeseeable impacts on the remaining hyperparameters. Evolutionary optimization is a promising method to try and address those issues. According to this method, performant models are stored, while the remainder are improved through crossover and mutation processes inspired by genetic algorithms. We present VisEvol, a visual analytics tool that supports interactive exploration of hyperparameters and intervention in this evolutionary procedure. In summary, our proposed tool helps the user to generate new models through evolution and eventually explore powerful hyperparameter combinations in diverse regions of the extensive hyperparameter space. The outcome is a voting ensemble (with equal rights) that boosts the final predictive performance. The utility and applicability of VisEvol are demonstrated with two use cases and interviews with ML experts who evaluated the effectiveness of the tool.
Visual Analysis of Electronic Densities and Transitions in Molecules
Authors: Talha Bin Masood, Signe Sidwall Thygesen, Mathieu Linares, Alexei I. Abrikosov, Vijay Natarajan, Ingrid Hotz
Published at EuroVis, 2021

The study of electronic transitions within a molecule connected to the absorption or emission of light is a common task in the process of the design of new materials. The transitions are complex quantum mechanical processes and a detailed analysis requires a breakdown of these processes into components that can be interpreted via characteristic chemical properties. We approach these tasks by providing a detailed analysis of the electron density field. This entails methods to quantify and visualize electron localization and transfer from molecular subgroups combining spatial and abstract representations. The core of our method uses geometric segmentation of the electronic density field coupled with a graph‐theoretic formulation of charge transfer between molecular subgroups. The design of the methods has been guided by the goal of providing a generic and objective analysis following fundamental concepts. We illustrate the proposed approach using several case studies involving the study of electronic transitions in different molecular systems.
Visual Analysis of Spatio-temporal Phenomena with 1D Projections
Authors: Max Franke, Henry Martin, Steffen Koch 0001, Kuno Kurzhals
Published at EuroVis, 2021

It is crucial to visually extrapolate the characteristics of their evolution to understand critical spatio‐temporal events such as earthquakes, fires, or the spreading of a disease. Animations embedded in the spatial context can be helpful for understanding details, but have proven to be less effective for overview and comparison tasks. We present an interactive approach for the exploration of spatio‐temporal data, based on a set of neighborhood‐preserving 1D projections which help identify patterns and support the comparison of numerous time steps and multivariate data. An important objective of the proposed approach is the visual description of local neighborhoods in the 1D projection to reveal patterns of similarity and propagation. As this locality cannot generally be guaranteed, we provide a selection of different projection techniques, as well as a hierarchical approach, to support the analysis of different data characteristics. In addition, we offer an interactive exploration technique to reorganize and improve the mapping locally to users' foci of interest. We demonstrate the usefulness of our approach with different real‐world application scenarios and discuss the feedback we received from domain and visualization experts.
Visualization in Astrophysics: Developing New Methods, Discovering Our Universe, and Educating the Earth
Authors: Fangfei Lan, Michael Young, Lauren Anderson, Anders Ynnerman, Alexander Bock 0002, Michelle A. Borkin, Angus G. Forbes, Juna A. Kollmeier, Bei Wang 0001
Published at EuroVis, 2021

We present a state‐of‐the‐art report on visualization in astrophysics. We survey representative papers from both astrophysics and visualization and provide a taxonomy of existing approaches based on data analysis tasks. The approaches are classified based on five categories: data wrangling, data exploration, feature identification, object reconstruction, as well as education and outreach. Our unique contribution is to combine the diverse viewpoints from both astronomers and visualization experts to identify challenges and opportunities for visualization in astrophysics. The main goal is to provide a reference point to bring modern data analysis and visualization techniques to the rich datasets in astrophysics.
Visualizing Carotid Blood Flow Simulations for Stroke Prevention
Authors: Pepe Eulzer, Monique Meuschke, Carsten M. Klingner, Kai Lawonn
Published at EuroVis, 2021

In this work, we investigate how concepts from medical flow visualization can be applied to enhance stroke prevention diagnostics. Our focus lies on carotid stenoses, i.e., local narrowings of the major brain‐supplying arteries, which are a frequent cause of stroke. Carotid surgery can reduce the stroke risk associated with stenoses, however, the procedure entails risks itself. Therefore, a thorough assessment of each case is necessary. In routine diagnostics, the morphology and hemodynamics of an afflicted vessel are separately analyzed using angiography and sonography, respectively. Blood flow simulations based on computational fluid dynamics could enable the visual integration of hemodynamic and morphological information and provide a higher resolution on relevant parameters. We identify and abstract the tasks involved in the assessment of stenoses and investigate how clinicians could derive relevant insights from carotid blood flow simulations. We adapt and refine a combination of techniques to facilitate this purpose, integrating spatiotemporal navigation, dimensional reduction, and contextual embedding. We evaluated and discussed our approach with an interdisciplinary group of medical practitioners, fluid simulation and flow visualization researchers. Our initial findings indicate that visualization techniques could promote usage of carotid blood flow simulations in practice.
What are Table Cartograms Good for Anyway? An Algebraic Analysis
Authors: Andrew M. McNutt
Published at EuroVis, 2021

Unfamiliar or esoteric visual forms arise in many areas of visualization. While such forms can be intriguing, it can be unclear how to make effective use of them without long periods of practice or costly user studies. In this work we analyze the table cartogram—a graphic which visualizes tabular data by bringing the areas of a grid of quadrilaterals into correspondence with the input data, like a heat map that has been “area‐ed” rather than colored. Despite having existed for several years, little is known about its appropriate usage. We mend this gap by using Algebraic Visualization Design to show that they are best suited to relatively small tables with ordinal axes for some comparison and outlier identification tasks. In doing so we demonstrate a discount theory‐based analysis that can be used to cheaply determine best practices for unknown visualizations.
[email protected]: Fostering Visual Exploration of Personal Data on Smartphones Leveraging Speech and Touch Interaction
Authors: Young-Ho Kim, Bongshin Lee, Arjun Srinivasan, Eun Kyoung Choe
Published at CHI, 2021

Most mobile health apps employ data visualization to help people view their health and activity data, but these apps provide limited support for visual data exploration. Furthermore, despite its huge potential benefits, mobile visualization research in the personal data context is sparse. This work aims to empower people to easily navigate and compare their personal health data on smartphones by enabling flexible time manipulation with speech. We designed and developed Data@Hand, a mobile app that leverages the synergy of two complementary modalities: speech and touch. Through an exploratory study with 13 long-term Fitbit users, we examined how multimodal interaction helps participants explore their own health data. Participants successfully adopted multimodal interaction (i.e., speech and touch) for convenient and fluid data exploration. Based on the quantitative and qualitative findings, we discuss design implications and opportunities with multimodal interaction for better supporting visual data exploration on mobile devices.
A Review on Strategies for Data Collection, Reflection, and Communication in Eating Disorder Apps
Authors: Anjali Devakumar, Jay Modh, Bahador Saket, Eric P. S. Baumer, Munmun De Choudhury
Published at CHI, 2021

Eating disorders (EDs) constitute a mental illness with the highest mortality. Today, mobile health apps provide promising means to ED patients for managing their condition. Apps enable users to monitor their eating habits, thoughts, and feelings, and offer analytic insights for behavior change. However, not only have scholars critiqued the clinical validity of these apps, their underlying design principles are not well understood. Through a review of 34 ED apps, we uncovered 11 different data types ED apps collect, and 9 strategies they employ to support collection and reflection. Drawing upon personal health informatics and visualization frameworks, we found that most apps did not adhere to best practices on what and how data should be collected from and reflected to users, or how data-driven insights should be communicated. Our review offers suggestions for improving the design of ED apps such that they can be useful and meaningful in ED recovery.
A Visual Analytics Approach to Facilitate the Proctoring of Online Exams
Authors: Haotian Li 0001, Min Xu, Yong Wang 0021, Huan Wei, Huamin Qu
Published at CHI, 2021

Online exams have become widely used to evaluate students’ performance in mastering knowledge in recent years, especially during the pandemic of COVID-19. However, it is challenging to conduct proctoring for online exams due to the lack of face-to-face interaction. Also, prior research has shown that online exams are more vulnerable to various cheating behaviors, which can damage their credibility. This paper presents a novel visual analytics approach to facilitate the proctoring of online exams by analyzing the exam video records and mouse movement data of each student. Specifically, we detect and visualize suspected head and mouse movements of students in three levels of detail, which provides course instructors and teachers with convenient, efficient and reliable proctoring for online exams. Our extensive evaluations, including usage scenarios, a carefully-designed user study and expert interviews, demonstrate the effectiveness and usability of our approach.
CakeVR: A Social Virtual Reality (VR) Tool for Co-designing Cakes
Authors: Yanni Mei, Jie Li 0064, Huib de Ridder, Pablo César
Published at CHI, 2021

Cake customization services allow clients to collaboratively personalize cakes with pastry chefs. However, remote (e.g., email) and in-person co-design sessions are prone to miscommunication, due to natural restrictions in visualizing cake size, decoration, and celebration context. This paper presents the design, implementation, and expert evaluation of a social VR application (CakeVR) that allows a client to remotely co-design cakes with a pastry chef, through real-time realistic 3D visualizations. Drawing on expert semi-structured interviews (4 clients, 5 pastry chefs), we distill and incorporate 8 design requirements into our CakeVR prototype. We evaluate CakeVR with 10 experts (6 clients, 4 pastry chefs) using cognitive walkthroughs, and find that it supports ideation and decision making through intuitive size manipulation, color/flavor selection, decoration design, and custom celebration theme fitting. Our findings provide recommendations for enabling co-design in social VR and highlight CakeVR’s potential to transform product design communication through remote interactive and immersive co-design.
Can Anthropographics Promote Prosociality?A Review and Large-Sample Study
Authors: Luiz Augusto de Macêdo Morais, Yvonne Jansen, Nazareno Andrade, Pierre Dragicevic
Published at CHI, 2021

Visualizations designed to make readers compassionate with the persons whose data is represented have been called anthropographics and are commonly employed by practitioners. Empirical studies have recently examined whether anthropographics indeed promote empathy, compassion, or the likelihood of prosocial behavior, but findings have been inconclusive so far. This work contributes a detailed overview of past experiments, and two new experiments that use large samples and a combination of design strategies to maximize the possibility of finding an effect. We tested an information-rich anthropographic against a simple bar chart, asking participants to allocate hypothetical money in a crowdsourcing study. We found that the anthropographic had, at best, a small effect on money allocation. Such a small effect may be relevant for large-scale donation campaigns, but the large sample sizes required to observe an effect and the noise involved in measuring it make it very difficult to study in more depth. Data and code are available at https://osf.io/xqae2/.
Collecting and Characterizing Natural Language Utterances for Specifying Data Visualizations
Authors: Arjun Srinivasan, Nikhila Nyapathy, Bongshin Lee, Steven Mark Drucker, John T. Stasko
Published at CHI, 2021

Natural language interfaces (NLIs) for data visualization are becoming increasingly popular both in academic research and in commercial software. Yet, there is a lack of empirical understanding of how people specify visualizations through natural language. We conducted an online study (N = 102), showing participants a series of visualizations and asking them to provide utterances they would pose to generate the displayed charts. From the responses, we curated a dataset of 893 utterances and characterized the utterances according to (1) their phrasing (e.g., commands, queries, questions) and (2) the information they contained (e.g., chart types, data aggregations). To help guide future research and development, we contribute this utterance dataset and discuss its applications toward the creation and benchmarking of NLIs for visualization.
Communicating with Motion: A Design Space for Animated Visual Narratives in Data Videos
Authors: Yang Shi 0007, Xingyu Lan, Jingwen Li, Zhaorui Li, Nan Cao 0001
Published at CHI, 2021

Data videos are a genre of narrative visualization that communicates stories by combining data visualization and motion graphics. While data videos are increasingly gaining popularity, few systematic reviews or structured analyses exist for their design. In this work, we introduce a design space for animated visual narratives in data videos. The design space combines a dimension for animation techniques that are frequently used to facilitate data communication with one for visual narrative strategies served by such animation techniques to support story presentation. We derived our design space from the analysis of 82 high-quality data videos collected from online sources. We conducted a workshop with 20 participants to evaluate the effectiveness of our design space. Qualitative and quantitative feedback suggested that our design space is inspirational and useful for designing and creating data videos.
Comparison of Different Types of Augmented Reality Visualizations for Instructions
Authors: Florian Jasche, Sven Hoffmann, Thomas Ludwig 0005, Volker Wulf
Published at CHI, 2021

Augmented Reality (AR) is increasingly being used for providing guidance and supporting troubleshooting in industrial settings. While the general application of AR has been shown to provide clear benefits regarding physical tasks, it is important to understand how different visualization types influence user’s performance during the execution of the tasks. Previous studies evaluating AR and user’s performance compared different media types or types of AR hardware as opposed to different types of visualization for the same hardware type. This paper provides details of our comparative study in which we identified the influence of visualization types on the performance of complex machine set-up processes. Although our results show clear advantages to using concrete rather than abstract visualizations, we also find abstract visualizations coupled with videos leads to similar user performance as with concrete visualizations.
ConceptScope: Organizing and Visualizing Knowledge in Documents based on Domain Ontology
Authors: Xiaoyu Zhang, Senthil K. Chandrasegaran, Kwan-Liu Ma
Published at CHI, 2021

Current text visualization techniques typically provide overviews of document content and structure using intrinsic properties such as term frequencies, co-occurrences, and sentence structures. Such visualizations lack conceptual overviews incorporating domain-relevant knowledge, needed when examining documents such as research articles or technical reports. To address this shortcoming, we present ConceptScope, a technique that utilizes a domain ontology to represent the conceptual relationships in a document in the form of a Bubble Treemap visualization. Multiple coordinated views of document structure and concept hierarchy with text overviews further aid document analysis. ConceptScope facilitates exploration and comparison of single and multiple documents respectively. We demonstrate ConceptScope by visualizing research articles and transcripts of technical presentations in computer science. In a comparative study with DocuBurst, a popular document visualization tool, ConceptScope was found to be more informative in exploring and comparing domain-specific documents, but less so when it came to documents that spanned multiple disciplines.
Data Animator: Authoring Expressive Animated Data Graphics
Authors: John R. Thompson 0002, Zhicheng Liu 0001, John T. Stasko
Published at CHI, 2021

Animation helps viewers follow transitions in data graphics. When authoring animations that incorporate data, designers must carefully coordinate the behaviors of visual objects such as entering, exiting, merging and splitting, and specify the temporal rhythms of transition through staging and staggering. We present Data Animator, a system for authoring animated data graphics without programming. Data Animator leverages the Data Illustrator framework to analyze and match objects between two static visualizations, and generates automated transitions by default. Designers have the flexibility to interpret and adjust the matching results through a visual interface. Data Animator also supports the division of a complex animation into stages through hierarchical keyframes, and uses data attributes to stagger the start time and vary the speed of animating objects through a novel timeline interface. We validate Data Animator’s expressiveness via a gallery of examples, and evaluate its usability in a re-creation study with designers.
Data Prophecy: Exploring the Effects of Belief Elicitation in Visual Analytics
Authors: Ratanond Koonchanok, Parul Baser, Abhinav Sikharam, Nirmal Kumar Raveendranath, Khairi Reda
Published at CHI, 2021

Interactive visualizations are widely used in exploratory data analysis, but existing systems provide limited support for confirmatory analysis. We introduce PredictMe, a tool for belief-driven visual analysis, enabling users to draw and test their beliefs against data, as an alternative to data-driven exploration. PredictMe combines belief elicitation with traditional visualization interactions to support mixed analysis styles. In a comparative study, we investigated how these affordances impact participants’ cognition. Results show that PredictMe prompts participants to incorporate their working knowledge more frequently in queries. Participants were more likely to attend to discrepancies between their mental models and the data. However, those same participants were also less likely to engage in interactions associated with exploration, and ultimately inspected fewer visualizations and made fewer discoveries. The results suggest that belief elicitation may moderate exploratory behaviors, instead nudging users to be more deliberate in their analysis. We discuss the implications for visualization design.