DevLan/vispubs huggingface dataset:
Do Disease Stories need a Hero? Effects of Human Protagonists on a Narrative Visualization about Cerebral Small Vessel Disease
Authors: Sarah Mittenentzwei, Veronika Weiß, Stefanie Schreiber, Laura A. Garrison, Stefan Bruckner, Malte Pfister, Bernhard Preim, Monique Meuschke
Published at EuroVis, 2023

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.
Don't Peek at My Chart: Privacy-preserving Visualization for Mobile Devices
Authors: Songheng Zhang, Dong Ma 0001, Yong Wang 0021
Published at EuroVis, 2023

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.
Doom or Deliciousness: Challenges and Opportunities for Visualization in the Age of Generative Models
Authors: Victor Schetinger, Sara Di Bartolomeo, Mennatallah El-Assady, Andrew M. McNutt, Matthias Miller, João Paulo Apolinário Passos, Jane Lydia Adams
Published at EuroVis, 2023

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.
Doppler Volume Rendering: A Dynamic, Piecewise Linear Spectral Representation for Visualizing Astrophysics Simulations
Authors: Reem Alghamdi, Thomas Müller 0005, Alberto Jaspe Villanueva, Markus Hadwiger, Filip Sadlo
Published at EuroVis, 2023

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 linearear 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.
Evaluating View Management for Situated Visualization in Web-based Handheld AR
Authors: Andrea Batch, Sungbok Shin, Julia Liu, Peter W. S. Butcher, Panagiotis D. Ritsos, Niklas Elmqvist
Published at EuroVis, 2023

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.
Exploring Interpersonal Relationships in Historical Voting Records
Authors: Gabriel Dias Cantareira, Yiwen Xing, Nicholas Cole, Rita Borgo, Alfie Abdul-Rahman
Published at EuroVis, 2023

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.
Ferret: Reviewing Tabular Datasets for Manipulation
Authors: Devin Lange, Shaurya Sahai, Jeff M. Phillips, Alexander Lex
Published at EuroVis, 2023

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.
FlexEvent: going beyond Case-Centric Exploration and Analysis of Multivariate Event Sequences
Authors: Sanne van der Linden, Bernice M. Wulterkens, Merel van Gilst, Sebastiaan Overeem, Carola van Pul, Anna Vilanova, Stef van den Elzen
Published at EuroVis, 2023

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.
GO-Compass: Visual Navigation of Multiple Lists of GO terms
Authors: Theresa Harbig, Mathias Witte Paz, Kay Nieselt
Published at EuroVis, 2023

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 RE‐VIGO 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 Tue‐Vis Visualization Server as a web application available at https://go‐compass‐tuevis.cs.uni‐tuebingen.de/
Human-Computer Collaboration for Visual Analytics: an Agent-based Framework
Authors: Shayan Monadjemi, Mengtian Guo, David Gotz, Roman Garnett, Alvitta Ottley
Published at EuroVis, 2023

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.
Illustrative Motion Smoothing for Attention Guidance in Dynamic Visualizations
Authors: Johannes Eschner, Peter Mindek, Manuela Waldner
Published at EuroVis, 2023

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 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.
LINGO : Visually Debiasing Natural Language Instructions to Support Task Diversity
Authors: Anjana Arunkumar, Shubham Sharma, Rakhi Agrawal, Sriram Chandrasekaran, Chris Bryan
Published at EuroVis, 2023

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.
Memory-Efficient GPU Volume Path Tracing of AMR Data Using the Dual Mesh
Authors: Stefan Zellmann, Qi Wu 0015, Kwan-Liu Ma, Ingo Wald
Published at EuroVis, 2023

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.
Mini-VLAT: A Short and Effective Measure of Visualization Literacy
Authors: Saugat Pandey, Alvitta Ottley
Published at EuroVis, 2023

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.
ParaDime: A Framework for Parametric Dimensionality Reduction
Authors: Andreas P. Hinterreiter, Christina Humer, Bernhard Kainz, Marc Streit
Published at EuroVis, 2023

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.