DevLan/vispubs huggingface dataset:
GALE: Leveraging Heterogeneous Systems for Efficient Unstructured Mesh Data Analysis
Authors: Guoxi Liu, Thomas Randall, Rong Ge 0002, Federico Iuricich
Published at Vis, 2025

Unstructured meshes present challenges in scientific data analysis due to irregular distribution and complex connectivity. Computing and storing connectivity information is a major bottleneck for visualization algorithms, affecting both time and memory performance. Recent task-parallel data structures address this by precomputing connectivity information at runtime while the analysis algorithm executes, effectively hiding computation costs and improving performance. However, existing approaches are CPU-bound, forcing the data structure and analysis algorithm to compete for the same computational resources, limiting potential speedups. To overcome this limitation, we introduce a novel task-parallel approach optimized for heterogeneous CPU-GPU systems. Specifically, we offload the computation of mesh connectivity information to GPU threads, enabling CPU threads to focus on executing the visualization algorithm. Following this paradigm, we propose GPU-Aided Localized data structurE (GALE), the first open-source CUDA-based data structure designed for heterogeneous task parallelism. Experiments on two 20-core CPUs and an NVIDIA V100 GPU show that GALE achieves up to $2.7\times$ speedup over state-of-the-art localized data structures while maintaining memory efficiency.
GhostUMAP2: Measuring and Analyzing (r,d)-Stability of UMAP
Authors: Myeongwon Jung, Takanori Fujiwara, Jaemin Jo
Published at Vis, 2025

Despite the widespread use of Uniform Manifold Approximation and Projection (UMAP), the impact of its stochastic optimization process on the results remains underexplored. We observed that it often produces unstable results where the projections of data points are determined mostly by chance rather than reflecting neighboring structures. To address this limitation, we introduce $(r,d)$-stability to UMAP: a framework that analyzes the stochastic positioning of data points in the projection space. To assess how stochastic elements—specifically, initial projection positions and negative sampling—impact UMAP results, we introduce “ghosts“, or duplicates of data points representing potential positional variations due to stochasticity. We define a data point's projection as $(r,d)$-stable if its ghosts perturbed within a circle of radius $r$ in the initial projection remain confined within a circle of radius $d$ for their final positions. To efficiently compute the ghost projections, we develop an adaptive dropping scheme that reduces a runtime up to 60% compared to an unoptimized baseline while maintaining approximately 90% of unstable points. We also present a visualization tool that supports the interactive exploration of the $(r,d)$-stability of data points. Finally, we demonstrate the effectiveness of our framework by examining the stability of projections of real-world datasets and present usage guidelines for the effective use of our framework.
GoFish: a Grammar of More Graphics!
Authors: Josh Pollock, Arvind Satyanarayan
Published at Vis, 2025

Visualization grammars from ggplot2 to Vega-Lite are based on the Grammar of Graphics (GoG), our most comprehensive formal theory of visualization. The GoG helped expand the expressive gamut of visualization by moving beyond fixed chart types and towards a design space of composable operators. Yet, the resultant design space has surprising limitations, inconsistencies, and cliffs---even seemingly simple charts like mosaics, waffles, and ribbons fall out of scope of most GoG implementations. To author such charts, visualization designers must either rely on overburdened grammar developers to implement purpose-built mark types (thus reintroducing the issues of typologies) or drop to lower-level frameworks. In response, we present GoFish: a declarative visualization grammar that formalizes Gestalt principles (e.g., uniform spacing, containment, and connection) that have heretofore been complected in GoG constructs. These graphical operators achieve greater expressive power than their predecessors by enabling recursive composition: they can be nested and overlapped arbitrarily. Through a diverse example gallery, we demonstrate how graphical operators free users to arrange shapes in many different ways while retaining the benefits of high-level grammars like scale resolution and coordinate transform management. Recursive composition naturally yields an infinite design space that blurs the boundary between an expressive, low-level grammar and a concise, high-level one. In doing so, we point towards an updated theory of visualization, one that is open to an innumerable space of graphic representations instead of limited to a fixed set of good designs.
Graphical Perception of Icon Arrays versus Bar Charts for Value Comparisons in Health Risk Communication
Authors: Jade Kandel, Jiayi Liu, Arran Zeyu Wang, Chin Tseng, Danielle Albers Szafir
Published at Vis, 2025

Visualizations support critical decision making in domains like health risk communication. This is particularly important for those at higher health risks and their care providers, allowing for better risk interpretation which may lead to more informed decisions. However, the kinds of visualizations used to represent data may impart biases that influence data interpretation and decision making. Both continuous representations using bar charts and discrete representations using icon arrays are pervasive in health risk communication, but express the same quantities using fundamentally different visual paradigms. We conducted a series of studies to investigate how bar charts, icon arrays, and their layout (juxtaposed, explicit encoding, explicit encoding plus juxtaposition) affect the perception of value comparison and subsequent decision-making in health risk communication. Our results suggest that icon arrays and explicit encoding combined with juxtaposition can optimize for both accurate difference estimation and perceptual biases in decision making. We also found misalignment between estimation accuracy and decision making, as well as between low and high literacy groups, emphasizing the importance of tailoring visualization approaches to specific audiences and evaluating visualizations beyond perceptual accuracy alone. This research contributes empirically-grounded design recommendations to improve comparison in health risk communication and support more informed decision-making across domains.
GSCache: Real-Time Radiance Caching for Volume Path Tracing using 3D Gaussian Splatting
Authors: David Bauer, Qi Wu 0015, Hamid Gadirov, Kwan-Liu Ma
Published at Vis, 2025

Real-time path tracing is rapidly becoming the standard for rendering in entertainment and professional applications. In scientific visualization, volume rendering plays a crucial role in helping researchers analyze and interpret complex 3D data. Recently, photorealistic rendering techniques have gained popularity in scientific visualization, yet they face significant challenges. One of the most prominent issues is slow rendering performance and high pixel variance caused by Monte Carlo integration. In this work, we introduce a novel radiance caching approach for path-traced volume rendering. Our method leverages advances in volumetric scene representation and adapts 3D Gaussian splatting to function as a multi-level, path-space radiance cache. This cache is designed to be trainable on the fly, dynamically adapting to changes in scene parameters such as lighting configurations and transfer functions. By incorporating our cache, we achieve less noisy, higher-quality images without increasing rendering costs. To evaluate our approach, we compare it against a baseline path tracer that supports uniform sampling and next-event estimation and the state-of-the-art for neural radiance caching. Through both quantitative and qualitative analyses, we demonstrate that our path-space radiance cache is a robust solution that is easy to integrate and significantly enhances the rendering quality of volumetric visualization applications while maintaining comparable computational efficiency.
Here's what you need to know about my data: Exploring Expert Knowledge's Role in Data Analysis
Authors: Haihan Lin, Maxim Lisnic, Derya Akbaba, Miriah Meyer, Alexander Lex
Published at Vis, 2025

Data-driven decision making has become a popular practice in science, industry, and public policy. Yet data alone, as an imperfect and partial representation of reality, is often insufficient to make good analysis decisions. Knowledge about the context of a dataset, its strengths and weaknesses, and its applicability for certain tasks is essential. Analysts are often not only familiar with the data itself, but also have data hunches about their analysis subject. In this work, we present an interview study with analysts from a wide range of domains and with varied expertise and experience, inquiring about the role of contextual knowledge. We provide insights into how data is insufficient in analysts' workflows and how they incorporate other sources of knowledge into their analysis. We analyzed how knowledge of data shaped their analysis outcome. Based on the results, we suggest design opportunities to better and more robustly consider both knowledge and data in analysis processes.
How do Data Journalists Design Maps to Tell Stories?
Authors: Arlindo Gómes 0001, Emilly Brito, Luiz Morais, Nivan Ferreira
Published at Vis, 2025

Maps are essential to news media as they provide a familiar way to convey spatial context and present engaging narratives. However, the design of journalistic maps may be challenging, as editorial teams need to balance multiple aspects, such as aesthetics, the audience's expected data literacy, tight publication deadlines, and the team's technical skills. Data journalists often come from multiple areas and lack a cartography, data visualization, and data science background, limiting their competence in creating maps. While previous studies have examined spatial visualizations in data stories, this research seeks to gain a deeper understanding of the map design process employed by news outlets. To achieve this, we strive to answer two specific research questions: what is the design space of journalistic maps? and how do editorial teams produce journalistic map articles? To answer the first one, we collected and analyzed a large corpus of 462 journalistic maps used in news articles from five major news outlets published over three months. As a result, we created a design space comprised of eight dimensions that involved both properties describing the articles' aspects and the visual/interactive features of maps. We approach the second research question via semi-structured interviews with four data journalists who create data-driven articles daily. Through these interviews, we identified the most common design rationales made by editorial teams and potential gaps in current practices. We also collected the practitioners' feedback on our design space to externally validate it. With these results, we aim to provide researchers and journalists with empirical data to design and study journalistic maps.
HypoChainer: A Collaborative System Combining LLMs and Knowledge Graphs for Hypothesis-Driven Scientific Discovery
Authors: Haoran Jiang, Shaohan Shi, Yunjie Yao, Chang Jiang 0001, Quan Li 0002
Published at Vis, 2025

Modern scientific discovery faces challenges in integrating the rapidly expanding and diverse knowledge required for exploring novel knowledge in biology. While traditional hypothesis-driven research has proven effective, it is constrained by human cognitive limitations, knowledge complexity, and the high costs of trial-and-error experimentation. Deep learning models, particularly graph neural networks (GNNs), have accelerated scientific progress. However, the vast predictions generated make manual selection for experimental validation impractical. Attempts to leverage large language models (LLMs) for filtering predictions and generating novel hypotheses have been impeded by issues such as hallucinations and the lack of structured knowledge grounding, which undermine their reliability. To address these challenges, we propose HypoChainer, a collaborative visualization framework that integrates human expertise, LLM-driven reasoning, and knowledge graphs (KGs) to enhance scientific discovery visually. HypoChainer operates through three key stages: (1) Contextual Exploration: Domain experts employ retrieval-augmented LLMs (RAGs) and visualizations to extract insights and research focuses from vast GNN predictions, supplemented by interactive explanations for in-depth understanding; (2) Hypothesis Construction: Experts iteratively explore the KG information relevant to the predictions and hypothesis-aligned entities, gaining knowledge and insights while refining the hypothesis through suggestions from LLMs; and (3) Validation Selection: Predictions are prioritized based on the refined hypothesis chains and KG-supported evidence, identifying high-priority candidates for validation. The hypothesis chains are further optimized through visual analytics of the retrieval results. We evaluated the effectiveness of HypoChainer in hypothesis construction and scientific discovery through a case study and expert interviews.
InsightChaser: Enhancing Visual Reasoning of Sports Tactical Visualization with Visual-Text Linking
Authors: Ziao Liu, Wenshuo Zhao, Xiao Xie, Anqi Cao, Yihong Wu 0003, Hui Zhang 0051, Yingcai Wu
Published at Vis, 2025

In sports analytics, tactical visualization is widely used to convey valuable insights. However, due to the complex domain knowledge and contextual information involved in tactical visualizations, it is challenging for users to connect high-level tactical insights to corresponding visual patterns. This requires users to engage in a reasoning process to interpret insights within game contexts, which remains insufficiently supported in existing visual-text linking studies. In this work, we propose InsightChaser, a novel approach to bridge tactical insights and soccer visualizations through visual-text linking and visual reasoning enhancement. InsightChaser constructs knowledge graphs to represent both visual elements and contextual game information. Integrating large language models (LLMs), our approach retrieves relevant visual elements and establishes explicit links with insights. Moreover, InsightChaser utilizes LLMs to enhance these visual-text links by providing reasoning explanations and visual effects. We further develop an interactive visualization system that supports navigation and explanation of enhanced visual-text links. Users can explore linked tactical insights interactively and reason through enhanced visual explanations. We conduct two case studies using real-world soccer data and a user study to demonstrate the effectiveness of our approach.
Interactive Composition Operators An Alternative Approach for Selecting Linear Embedding Parameters
Authors: Dirk J. Lehmann, Kai Michael Blum, Manuel Rubio-Sánchez, Konrad Simon
Published at Vis, 2025

Linear embeddings support interactive visual exploration by mapping high-dimensional (nD) data into a two-dimensional space. Despite their popularity, selecting meaningful projection parameters remains a key challenge due to the infinite 2$n$-dimensional parameter space. Once an informative projection is found, users often seek similar ones that emphasize specific items differently while preserving global structure. For instance: Do clusters become outliers under slight changes? Can grouped items separate-or merge-through parameter adjustments? Which changes to the embedding parameters lead to such projections - and do they exist at all? Answering these questions efficiently is critical for effective visual search. Yet, current methods-such as projection tours or manual parameter tuning-are time-consuming and risk overlooking important views, including those of specific interest. We propose Composition Operators, a mathematical foundation for a novel set-of-point manipulation concept for linear embeddings-such as Star Coordinates-as an alternative approach to selecting informative embedding parameters in a more controllable manner with respect to the desired outcome. Users specify item-based constraints on the projection result; the corresponding 2n parameters are then derived automatically, eliminating the need to exhaustively search the entire parameter space to get a similar outcome. Neither the embedding space nor the set of parameters is altered - only the mechanism for navigating and selecting parameters is redefined. We provide closed-form solutions for this and demonstrate our interactive prototype on nD datasets from the UCI repository.
Interactive Hybrid Rice Breeding with Parametric Dual Projection
Authors: Changjian Chen, Pengcheng Wang, Fei Lyu 0007, Zhuo Tang, Li Yang 0012, Long Wang, Yong Cai, Feng Yu, Kenli Li 0001
Published at Vis, 2025

Hybrid rice breeding crossbreeds different rice lines and cultivates the resulting hybrids in fields to select those with desirable agronomic traits, such as higher yields. Recently, genomic selection has emerged as an efficient way for hybrid rice breeding. It predicts the traits of hybrids based on their genes, which helps exclude many undesired hybrids, largely reducing the workload of field cultivation. However, due to the limited accuracy of genomic prediction models, breeders still need to combine their experience with the models to identify regulatory genes that control traits and select hybrids, which remains a time-consuming process. To ease this process, in this paper, we proposed a visual analysis method to facilitate interactive hybrid rice breeding. Regulatory gene identification and hybrid selection naturally ensemble a dual-analysis task. Therefore, we developed a parametric dual projection method with theoretical guarantees to facilitate interactive dual analysis. Based on this dual projection method, we further developed a gene visualization and a hybrid visualization to verify the identified regulatory genes and hybrids. The effectiveness of our method is demonstrated through the quantitative evaluation of the parametric dual projection method, identified regulatory genes and desired hybrids in the case study, and positive feedback from breeders.
Investigating the Effects of Augmented Reality on Message Credibility When Visualizing Environmental Impacts
Authors: Aymeric Ferron, Ambre Assor, Pierre Dragicevic, Yvonne Jansen
Published at Vis, 2025

Augmented reality (AR) has increasingly been used to communicate environmental impacts, offering greater engagement than conventional displays. However, its effect on message credibility-how much people believe in the content of the communication-remains unclear. In a preregistered study, we compared the perceived credibility of environmental information presented via visualizations on an AR headset or a desktop display. We created display-specific visual encodings (3D concrete for AR, 2D bar charts for desktop) and added two control conditions to cross display and encoding. We found no difference in message credibility between AR and desktop, though concrete AR was rated most engaging. Supplementary material is available at https://osf.io/n4p5c/.
Locally Adapted Reference Frame Fields using Moving Least Squares
Authors: Julio Rey Ramirez, Peter Rautek, Tobias Günther, Markus Hadwiger
Published at Vis, 2025

The detection and analysis of features in fluid flow are important tasks in fluid mechanics and flow visu alization. One recent class of methods to approach this problem is to first compute objective optimal reference frames, relative to which the input vector field becomes as steady as possible. However, existing methods either optimize locally over a fixed neighborhood, which might not match the extent of interesting features well, or perform global optimization, which is costly. We propose a novel objective method for the computation of optimal reference frames that automatically adapts to the flow field locally, without having to choose neighborhoods a priori. We enable adaptivity by formulating this problem as a moving least squares approximation, through which we determine a continuous field of reference frames. To incorporate fluid features into the computation of the reference frame field, we introduce the use of a scalar guidance field into the moving least squares approximation. The guidance field determines a curved manifold on which a regularly sampled input vector field becomes a set of irregularly spaced samples, which then forms the input to the moving least squares approximation. Although the guidance field can be any scalar field, by using a field that corresponds to flow features the resulting reference frame field will adapt accordingly. We show that using an FTLE field as the guidance field results in a reference frame field that adapts better to local features in the flow than prior wo rk. However, our moving least squares framework is formulated in a very general way, and therefore other types of guidance fields could be used in the future to adapt to local fluid features.
MisVisFix: An Interactive Dashboard for Detecting, Explaining, and Correcting Misleading Visualizations using Large Language Models
Authors: Amit Kumar Das 0002, Klaus Mueller 0001
Published at Vis, 2025

Misleading visualizations pose a significant challenge to accurate data interpretation. While recent research has explored the use of Large Language Models (LLMs) for detecting such misinformation, practical tools that also support explanation and correction remain limited. We present MisVisFix, an interactive dashboard that leverages both Claude and GPT models to support the full workflow of detecting, explaining, and correcting misleading visualizations. MisVisFix correctly identifies 96% of visualization issues and addresses all 74 known visualization misinformation types, classifying them as major, minor, or potential concerns. It provides detailed explanations, actionable suggestions, and automatically generates corrected charts. An interactive chat interface allows users to ask about specific chart elements or request modifications. The dashboard adapts to newly emerging misinformation strategies through targeted user interactions. User studies with visualization experts and developers of fact-checking tools show that MisVisFix accurately identifies issues and offers useful suggestions for improvement. By transforming LLM-based detection into an accessible, interactive platform, MisVisFix advances visualization literacy and supports more trustworthy data communication.