DevLan/vispubs huggingface dataset:
Visualizing Trust: How Chart Embellishments Influence Perceptions of Credibility
Authors: Hayeong Song, Aeree Cho, Cindy Xiong Bearfield, John T. Stasko
Published at Vis, 2025

Effective data visualizations enhance perception, support cognitive processing, and facilitate informed decision-making by aligning with human perceptual strengths. Conversely, poorly designed visualizations can impede comprehension, introduce interpretive bias, and diminish the perceived credibility of the conveyed message. This paper investigates the extent to which visual embellishments influence perceived message credibility in data visualizations. We conducted two crowdsourced experiments to examine both holistic and component-level effects of embellishment. In the first experiment, participants evaluated the relative credibility of plain bar charts versus two embellished variants-cartoon-style and image-style-across topics. Participants provided both comparative judgments and qualitative feedback. In the second experiment, we systematically isolated the influence of specific design elements-color, font, and bar style-on credibility perceptions through controlled variations. Our findings reveal that the impact of embellishments on perceived message credibility is complex and context-dependent. While certain embellishments, such as the use of color and image style bars, enhanced credibility, others-most notably hand-drawn fonts and cartoon-style bars-significantly undermined it. By operationalizing trust through the lens of message credibility, this work offers empirical insight into the design factors that shape viewers' perceptions. We conclude by proposing actionable design guidelines to support the creation of visualizations that are effective for communication and credible.
VizGenie: Toward Self-Refining, Domain-Aware Workflows for Next-Generation Scientific Visualization
Authors: Ayan Biswas 0001, Terece L. Turton, Nishath Rajiv Ranasinghe, Shawn M. Jones, Bradley C. Love, William M. Jones, Aric A. Hagberg, Han-Wei Shen, Nathan DeBardeleben, Earl Lawrence
Published at Vis, 2025

We present VizGenie, a self-improving, agentic framework that advances scientific visualization through large language model (LLM) by orchestrating of a collection of domain-specific and dynamically generated modules. Users initially access core functionalities—such as threshold-based filtering, slice extraction, and statistical analysis—through pre-existing tools. For tasks beyond this baseline, VizGenie autonomously employs LLMs to generate new visualization scripts (e.g., VTK Python code), expanding its capabilities on-demand. Each generated script undergoes automated backend validation and is seamlessly integrated upon successful testing, continuously enhancing the system's adaptability and robustness. A distinctive feature of VizGenie is its intuitive natural language interface, allowing users to issue high-level feature-based queries (e.g., “visualize the skull” or “highlight tissue boundaries”). The system leverages image-based analysis and visual question answering (VQA) via fine-tuned vision models to interpret these queries precisely, bridging domain expertise and technical implementation. Additionally, users can interactively query generated visualizations through VQA, facilitating deeper exploration. Reliability and reproducibility are further strengthened by Retrieval-Augmented Generation (RAG), providing context-driven responses while maintaining comprehensive provenance records. Evaluations on complex volumetric datasets demonstrate significant reductions in cognitive overhead for iterative visualization tasks. By integrating curated domain-specific tools with LLM-driven flexibility, VizGenie not only accelerates insight generation but also establishes a sustainable, continuously evolving visualization practice. The resulting platform dynamically learns from user interactions, consistently enhancing support for feature-centric exploration and reproducible research in scientific visualization.
VolMoVis: Real-Time Volume Generation and Motion Visualization with Dynamic Tomographic Reconstruction
Authors: Gaofeng Deng, Arie E. Kaufman
Published at Vis, 2025

We present VolMoVis, a method for dynamic tomographic reconstruction that supports real-time volume generation and volumetric motion visualization from 2D projections. Visualizing the motion of 3D anatomical structures, such as organs and tumors, is critical for computer-aided interventions. However, conventional 4D volumetric reconstruction methods typically produce a limited set of volumes at discrete phases, suffering from low temporal resolution. Moreover, it often requires extensive segmentation of 3D structures or regions for visualizing volumetric data, making it challenging to segment and visualize dynamic volumes in real-time. To address these challenges, VolMoVis framework employs a continuous implicit neural representation that decomposes the dynamic volumetric data into a static reference volume and a continuous deformation field. This decomposition, along with an efficient deformation network, enables our framework to achieve real-time volume generation and volumetric visualization of continuous anatomical motions. We evaluate VolMoVis on both 4D digital phantoms and real patient datasets, demonstrating its effectiveness for accurate anatomical reconstruction and motion tracking. Furthermore, we highlight its capabilities in real-time simultaneous volume generation and tumor segmentation for visualizing dynamic volumes and 4D tumor tracking, showcasing its potential in image-guided radiation therapy.
VolSegGS: Segmentation and Tracking in Dynamic Volumetric Scenes via Deformable 3D Gaussians
Authors: Siyuan Yao, Chaoli Wang 0001
Published at Vis, 2025

Visualization of large-scale time-dependent simulation data is crucial for domain scientists to analyze complex phenomena, but it demands significant I/O bandwidth, storage, and computational resources. To enable effective visualization on local, low-end machines, recent advances in view synthesis techniques, such as neural radiance fields, utilize neural networks to generate novel visualizations for volumetric scenes. However, these methods focus on reconstruction quality rather than facilitating interactive visualization exploration, such as feature extraction and tracking. We introduce VolSegGS, a novel Gaussian splatting framework that supports interactive segmentation and tracking in dynamic volumetric scenes for exploratory visualization and analysis. Our approach utilizes deformable 3D Gaussians to represent a dynamic volumetric scene, allowing for real-time novel view synthesis. For accurate segmentation, we leverage the view-independent colors of Gaussians for coarse-level segmentation and refine the results with an affinity field network for fine-level segmentation. Additionally, by embedding segmentation results within the Gaussians, we ensure that their deformation enables continuous tracking of segmented regions over time. We demonstrate the effectiveness of VolSegGS with several time-varying datasets and compare our solutions against state-of-the-art methods. With the ability to interact with a dynamic scene in real time and provide flexible segmentation and tracking capabilities, VolSegGS offers a powerful solution under low computational demands. This framework unlocks exciting new possibilities for time-varying volumetric data analysis and visualization.
What is the Color of Serendipity? Investigating the Use of Language Models for Semantically Resonant Color Generation
Authors: Shahreen Salim Aunti, Tanzir Pial, Klaus Mueller 0001
Published at Vis, 2025

Humans inherently connect certain colors with particular concepts in semantically meaningful ways that facilitate visual communication. These colors are known as semantically resonant colors. For instance, we associate “sky” and “ocean” with shades of blue, and “cherry” with red. In this paper, we investigate how language models, including Word2Vec, RoBERTa, GPT-4o mini and the vision language model CLIP generate and represent nuanced semantically resonant colors for diverse concepts. To achieve this, we utilized a large dataset of color names and concepts, tailored models for the structure of each language model, and developed an interactive web interface, Concept2Color, as a use case. Additionally, we conducted experiments and a detailed analysis to assess the ability of these models to generate meaningful colors. Through these experiments, we examined how factors such as model design, training data and context affect the color output. Our findings reveal the capabilities and limitations of language models in processing and generating semantically resonant colors for concepts, thus contributing insights into how they depict semantic color-concept connections. These insights have implications for data visualization, design, and human-computer interaction, where leveraging effective semantic color generation can enhance communication and user experience.
What Makes a Visualization Image Complex?
Authors: Mengdi Chu, Zefeng Qiu, Meng Ling, Shuning Jiang, Robert S. Laramee, Michael Sedlmair, Jian Chen 0006
Published at Vis, 2025

We investigate the perceived visual complexity (VC) in data visualizations using objective image-based metrics. We collected VC scores through a large-scale crowdsourcing experiment involving 349 participants and 1,800 visualization images. We then examined how these scores align with 12 image-based metrics spanning pixel-based and statistic-information-theoretic (clutter), color, shape, and our two new object-based metrics (meaningful-color-count (MeC) and text-to-ink ratio (TiR)). Our results show that both low-level edges and high-level elements affect perceived VC in visualization images; the number of corners and distinct colors are robust metrics across visualizations. Second, feature congestion, a statistical information-theoretic metric capturing color and texture patterns, is the strongest predictor of perceived complexity in visualizations rich in the same continuous color/texture stimuli; edge density effectively explains VC in node-link diagrams. Additionally, we observe a bell-curve effect for texts: increasing TiR initially reduces complexity, reaching an optimal point, beyond which further text increases VC. Our quantification model is also interpretable-enabling metric-based explanations-grounded in the VisComplexity2K dataset, bridging computational metrics with human perceptual responses. The preregistration is available at osf.io/5xe8a. osf.io/bdet6 has the dataset and analysis code.
Write, Rank, or Rate: Comparing Methods for Studying Visualization Affordances
Authors: Chase Stokes, Kylie R. Lin, Cindy Xiong Bearfield
Published at Vis, 2025

A growing body of work on visualization affordances highlights how specific design choices shape reader takeaways from information visualizations. However, mapping the relationship between design choices and reader conclusions often requires labor-intensive crowdsourced studies, generating large corpora of free-response text for analysis. To address this challenge, we explored alternative scalable research methodologies to assess chart affordances. We test four elicitation methods from human-subject studies: free response, visualization ranking, conclusion ranking, and salience rating, and compare their effectiveness in eliciting reader interpretations of line charts, dot plots, and heatmaps. Overall, we find that while no method fully replicates affordances observed in free-response conclusions, combinations of ranking and rating methods can serve as an effective proxy at a broad scale. The two ranking methodologies were influenced by participant bias towards certain chart types and the comparison of suggested conclusions. Rating conclusion salience could not capture the specific variations between chart types observed in the other methods. To supplement this work, we present a case study with GPT-40, exploring the use of large language models (LLMs) to elicit human-like chart interpretations. This aligns with recent academic interest in leveraging LLMs as proxies for human participants to improve data collection and analysis efficiency. GPT-40 performed best as a human proxy for the salience rating methodology but suffered from severe constraints in other areas. Overall, the discrepancies in affordances we found between various elicitation methodologies, including GPT-40, highlight the importance of intentionally selecting and combining methods and evaluating trade-offs.
Your Model Is Unfair, Are You Even Aware? Inverse Relationship Between Comprehension and Trust in Explainability Visualizations of Biased ML Models
Authors: Zhanna Kaufman, Madeline Endres, Cindy Xiong Bearfield, Yuriy Brun
Published at Vis, 2025

Systems relying on ML have become ubiquitous, but so has biased behavior within them. Research shows that bias significantly affects stakeholders' trust in systems and how they use them. Further, stakeholders of different backgrounds view and trust the same systems differently. Thus, how ML models' behavior is explained plays a key role in comprehension and trust. We survey explainability visualizations, creating a taxonomy of design characteristics. We conduct user studies to evaluate five state-of-the-art visualization tools (LIME, SHAP, CP, Anchors, and ELl5) for model explainability, measuring how taxonomy characteristics affect comprehension, bias perception, and trust for non-expert ML users. Surprisingly, we find an inverse relationship between comprehension and trust: the better users understand the models, the less they trust them. We investigate the cause and find that this relationship is strongly mediated by bias perception: more comprehensible visualizations increase people's perception of bias, and increased bias perception reduces trust. We confirm this relationship is causal: Manipulating explainability visualizations to control comprehension, bias perception, and trust, we show that visualization design can significantly (p < 0.001) increase comprehension, increase perceived bias, and reduce trust. Conversely, reducing perceived model bias, either by improving model fairness or by adjusting visualization design, significantly increases trust even when comprehension remains high. Our work advances understanding of how comprehension affects trust and systematically investigates visualization's role in facilitating responsible ML applications.
A Process-Oriented Approach to Analyze Analysts' Use of Visualizations: Revealing Insights into the What, When, and How
Authors: Lisa Zimmermann 0002, Francesca Zerbato, Katerina Vrotsou, Barbara Weber
Published at EuroVis, 2025

Despite Visual Analytics (VA) tools being essential for supporting data analysis, evaluating their use in real-world analytical processes remains challenging. Traditional evaluation methods often overlook the nuanced and evolving nature of analysis processes and are not always suitable for investigating scenarios in which analysts combine multiple tools and visualization types. In this paper, we propose a flexible analysis approach for studying analysts' use of visualizations within and across VA tools. Our qualitative method allows researchers to extract user behavior and cognitive steps from screen recordings and think-aloud data and generate event sequences that capture analytic processes. This enables the analysis of usage patterns from multiple perspectives and levels of granularity and allows for the evaluation of effectiveness measures, such as efficiency and accuracy. We demonstrate our approach in the domain of process mining, where our findings provide insights into the use of existing visualizations, and we reflect on lessons learned from this application.
Accessible Text Descriptions for UpSet Plots
Authors: Andrew M. McNutt, Maggie K. McCracken, Ishrat Jahan Eliza 0001, Daniel Hajas, Jake Wagoner, Nate Lanza, Jack Wilburn, Sarah H. Creem-Regehr, Alexander Lex
Published at EuroVis, 2025

Data visualizations are typically not accessible to blind and low-vision (BLV) users. Automatically generating text descriptions offers an enticing mechanism for democratizing access to the information held in complex scientific charts, yet appropriate procedures for generating those texts remain elusive. Pursuing this issue, we study a single complex chart form: UpSet plots. UpSet Plots are a common way to analyze set data, an area largely unexplored by prior accessibility literature. By analyzing the patterns present in real-world examples, we develop a system for automatically captioning any UpSet plot. We evaluated the utility of our captions via semi-structured interviews with (N=11) BLV users and found that BLV users find them informative. In extensions, we find that sighted users can use our texts similarly to UpSet plots and that they are better than naive LLM usage.
An Interactive Visual Enhancement for Prompted Programmatic Weak Supervision in Text Classification
Authors: Yiming Lin, S. Wei, Huijie Zhang, Dezhan Qu, Jinghan Bai
Published at EuroVis, 2025

Programmatic Weak Supervision (PWS) has emerged as a powerful technique for text classification. By aggregating weak labels provided by manually written label functions, it allows training models on large-scale unlabeled data without the need for costly manual annotations. As an improvement, Prompted PWS incorporates pre-trained large language models (LLMs) as part of the label function, replacing programs coded by experts with natural language prompts. This allows for the more accessible expression of complex and ambiguous concepts. However, the existing workflow does not fully utilize the advantages of Prompted PWS, and the annotators have difficulty in effectively converging their ideas to develop high-quality LFs, and lack support during the iterations. To address this issue, this study improves the existing PWS workflow through interactive visualization. We first propose a collaborative LF development workflow between humans and LLMs, where the large language model assists humans in creating a structured development space for exploration and automatically generates prompted LFs based on human selections. Annotators can integrate their knowledge through informed selection and judgment. Then, we present an interactive visual system that supports efficient development, in-depth exploration, and iteration of LFs. Our evaluation, comprising a quantitative evaluation on the benchmark, a case study, and a user study, demonstrates the effectiveness of our approach.
Benchmarking Visual Language Models on Standardized Visualization Literacy Tests
Authors: Saugat Pandey, Alvitta Ottley
Published at EuroVis, 2025

The increasing integration of Visual Language Models (VLMs) into visualization systems demands a comprehensive understanding of their visual interpretation capabilities and constraints. While existing research has examined individual models, systematic comparisons of VLMs' visualization literacy remain unexplored. We bridge this gap through a rigorous, first-of-its-kind evaluation of four leading VLMs (GPT-4, Claude, Gemini, and Llama) using standardized assessments: the Visualization Literacy Assessment Test (VLAT) and Critical Thinking Assessment for Literacy in Visualizations (CALVI). Our methodology uniquely combines randomized trials with structured prompting techniques to control for order effects and response variability - a critical consideration overlooked in many VLM evaluations. Our analysis reveals that while specific models demonstrate competence in basic chart interpretation (Claude achieving 67.9% accuracy on VLAT), all models exhibit substantial difficulties in identifying misleading visualization elements (maximum 30.0% accuracy on CALVI). We uncover distinct performance patterns: strong capabilities in interpreting conventional charts like line charts (76-96% accuracy) and detecting hierarchical structures (80-100% accuracy), but consistent difficulties with data-dense visualizations involving multiple encodings (bubble charts: 18.6-61.4%) and anomaly detection (25-30% accuracy). Significantly, we observe distinct uncertainty management behavior across models, with Gemini displaying heightened caution (22.5% question omission) compared to others (7-8%). These findings provide crucial insights for the visualization community by establishing reliable VLM evaluation benchmarks, identifying areas where current models fall short, and highlighting the need for targeted improvements in VLM architectures for visualization tasks. To promote reproducibility, encourage further research, and facilitate benchmarking of future VLMs, our complete evaluation framework, including code, prompts, and analysis scripts, is available at https://github.com/washuvis/VisLit-VLM-Eval.
Beyond Entertainment: An Investigation of Externalization Design in Video Games
Authors: Franziska Becker, Rene P. Warnking, Hendrik Brückler, Tanja Blascheck
Published at EuroVis, 2025

This article investigates when and how video games enable players to create externalizations in a diverse sample of 388 video games. We follow a grounded-theory approach, extracting externalizations from video games to explore design ideas and relate them to practices in visualization. Video games often engage players in problem-solving activities, like solving a murder mystery or optimizing a strategy, requiring players to interpret heterogeneous data—much like tasks in the visualization domain. In many cases, externalizations can help reduce a user's mental load by making tangible what otherwise only lives in their head, acting as external storage or a visual playground. Over five coding phases, we created a hierarchy of 277 tags to describe the video games in our collection, from which we extracted 169 externalizations. We characterize these externalizations along nine dimensions like mental load, visual encodings, and motivations, resulting in 13 categories divided into four clusters: quick access, storage, sensemaking, and communication. We formulate considerations to guide future work, looking at tasks and challenges, naming potentials for inspiration, and discussing which topics could advance the state of externalization.
Coupling Guidance and Progressiveness in Visual Analytics
Authors: Ignacio Pérez-Messina, Marco Angelini, Davide Ceneda, Christian Tominski, Silvia Miksch
Published at EuroVis, 2025

Data size and complexity in Visual Analytics (VA)pose significant challenges for VA systems andVA users. Two recent developments address these challenges: progressive VA (PVA) and guidance for VA (GVA). Both share the goal of supporting the analysis flow. PVA primarily considers the system perspective and incrementally generates partial results during long computations to avoid an unresponsive VA system. GVA is primarily concerned with the user perspective and strives to mitigate knowledge gaps during VA activities to prevent the analysis from stalling. Although PVA and GVA share the same goal, it has not yet been studied how PVA and GVA can join forces to achieve it. Our paper investigates this in detail. We structure our research around two questions: How can guidance enhance PVA and how can progressiveness enhance GVA? This leads to two main themes: Guidance for Progressiveness (G4P) and Progressiveness for Guidance (P4G). By exploring both themes, we arrive at a conceptual model of how progressiveness and guidance can work together. We illustrate the practical value of our theoretical considerations in two case studies ofG4P and P4G.