DevLan/vispubs huggingface dataset:
StreetWeave: A Declarative Grammar for Street-Overlaid Visualization of Multivariate Data
Authors: Sanjana Srabanti, G. Elisabeta Marai, Fabio Miranda 0001
Published at Vis, 2025

The visualization and analysis of street and pedestrian networks are important to various domain experts, including urban planners, climate researchers, and health experts. This has led to the development of new techniques for street and pedestrian network visualization, expanding possibilities for effective data presentation and interpretation. Despite their increasing adoption, there is no established design framework to guide the creation of these visualizations while addressing the diverse requirements of various domains. When exploring a feature of interest, domain experts often need to transform, integrate, and visualize a combination of thematic data (e.g., demographic, socioeconomic, pollution) and physical data (e.g., zip codes, street networks), often spanning multiple spatial and temporal scales. This not only complicates the process of visual data exploration and system implementation for developers but also creates significant entry barriers for experts who lack a background in programming. With this in mind, in this paper, we reviewed 45 studies utilizing street-overlaid visualizations to understand how they are applied in practice. Through qualitative coding of these visualizations, we analyzed three key aspects of street and pedestrian network visualization usage: their analytical purposes, the visualization approaches employed, and the data sources used in their creation. Building on this design space, we introduce StreetWeave, a declarative grammar for designing custom visualizations of multivariate spatial network data across multiple resolutions. We demonstrate how StreetWeave can be used to create various street-overlaid visualizations, enabling effective exploration and analysis of spatial data. StreetWeave is available at urbantk.org/streetweave.
StressDiffVis: Visual Analytics for Multi-Model Stress Comparison
Authors: Jiabao Huang, Zikun Deng, Hanlin Song, Xiang Chen, Shaowu Gao, Yi Cai 0001
Published at Vis, 2025

Structural analysis is essential in modern industrial design, where engineers iteratively refine geometry models based on stress simulations to achieve optimized designs. However, comparing stress distributions across multiple model variants remains challenging due to the complexity of stress fields, which are high-dimensional, unevenly distributed, and dependent on intricate geometric structures. Existing tools primarily support single-model analysis and lack dedicated functionalities for multi-model comparison. As a result, engineers must rely on manual, cognitively demanding visual inspections, making it difficult to systematically identify and interpret stress variations across design iterations. To address these limitations, we propose StressDiffVis, a visual analytics approach that facilitates stress field comparison across multiple structural models. StressDiffVis employs a volumetric representation to encode stress distributions while minimizing occlusion, enabling voxel-wise difference analysis for model comparison. To support localized analysis, we introduce model segmentation, grouping voxels with similar stress patterns across models. StressDiffVis integrates these techniques into an interactive interface with a tree view, organizing models by the iterative design process, and a comparison view, using a matrix layout for detailed comparisons. We demonstrate the effectiveness of StressDiffVis through two case studies illustrating its utility in comparative stress analysis. In addition, expert interviews confirm its potential to enhance engineering workflows.
SynAnno: Interactive Guided Proofreading of Synaptic Annotations
Authors: Leander Lauenburg, Jakob Troidl, Adam Gohain, Zudi Lin, Hanspeter Pfister, Donglai Wei 0001
Published at Vis, 2025

Connectomics, a subfield of neuroscience, aims to map and analyze synapse-level wiring diagrams of the nervous system. While recent advances in deep learning have accelerated automated neuron and synapse segmentation, reconstructing accurate connectomes still demands extensive human proofreading to correct segmentation errors. We present SynAnno, an interactive tool designed to streamline and enhance the proofreading of synaptic annotations in large-scale connectomics datasets. SynAnno integrates into existing neuroscience workflows by enabling guided, neuron-centric proofreading. To address the challenges posed by the complex spatial branching of neurons, it introduces a structured workflow with an optimized traversal path and a 3D mini-map for tracking progress. In addition, SynAnno incorporates fine-tuned machine learning models to assist with error detection and correction, reducing the manual burden and increasing proofreading efficiency. We evaluate SynAnno through a user and case study involving seven neuroscience experts. Results show that SynAnno significantly accelerates synapse proofreading while reducing cognitive load and annotation errors through structured guidance and visualization support. The source code and interactive demo are available at: https://github.com/PytorchConnectomics/SynAnno.
TactiVis: Towards Better Understanding of Team-based Combat Tactics
Authors: Hancheng Zhang, Guozheng Li 0002, Min Lu 0002, Jincheng Li 0004, Chi Harold Liu
Published at Vis, 2025

Team-based combat scenarios are prevalent in various real-world applications like video gaming. Analyzing tactics in these scenarios is essential for gaining insights into game processes and improving combat behaviors. The decision-making data in team-based combat include character actions, movement trajectories, and event sequences. Existing studies face challenges in visualizing and analyzing combat tactics due to the complexity and the multifaceted characteristics of the decision-making data. To address these challenges, we introduce TactiVis, a visual analytics system designed for analyzing combat decision-making behavior. Using MOBA game as a representative case of team-based combat, TactiVis adopts a macro-to-micro tactics visual analytics framework consisting of three stages: match-level analysis, event-level understanding, and character-level comparison. In the TactiVis system, we introduce the v-storyline visualization, which encodes positions along the vertical axis to reveal tactical patterns. Case studies and a usability study demonstrate the utility and usability of TactiVis for helping analysts understand combat patterns and analyze tactics.
Tell Me Without Telling Me: Two-Way Prediction of Visualization Literacy and Visual Attention
Authors: Minsuk Chang, Yao Wang 0018, Huichen Will Wang, Yuanhong Zhou, Andreas Bulling, Cindy Xiong Bearfield
Published at Vis, 2025

Accounting for individual differences can improve the effectiveness of visualization design. While the role of visual attention in visualization interpretation is well recognized, existing work often overlooks how this behavior varies based on visual literacy levels. Based on data from a 235-participant user study covering three visualization tests (mini-VLAT, CALVI, and SGL), we show that distinct attention patterns in visual data exploration can correlate with participants' literacy levels: While experts (high-scorers) generally show a strong attentional focus, novices (low-scorers) focus less and explore more. We then propose two computational models leveraging these insights: Lit2Sal - a novel visual saliency model that predicts observer attention given their visualization literacy level, and Sal2Lit - a model to predict visual literacy from human visual attention data. Our quantitative and qualitative evaluation demonstrates that Lit2Sal outperforms state-of-the-art saliency models with literacy-aware considerations. Sal2Lit predicts literacy with 86% accuracy using a single attention map, providing a time-efficient supplement to literacy assessment that only takes less than a minute. Taken together, our unique approach to consider individual differences in salience models and visual attention in literacy assessments paves the way for new directions in personalized visual data communication to enhance understanding.
TexGS-VolVis: Expressive Scene Editing for Volume Visualization via Textured Gaussian Splatting
Authors: Kaiyuan Tang 0001, Kuangshi Ai, Jun Han 0010, Chaoli Wang 0001
Published at Vis, 2025

Advancements in volume visualization (VolVis) focus on extracting insights from 3D volumetric data by generating visually compelling renderings that reveal complex internal structures. Existing VolVis approaches have explored non-photorealistic rendering techniques to enhance the clarity, expressiveness, and informativeness of visual communication. While effective, these methods often rely on complex predefined rules and are limited to transferring a single style, restricting their flexibility. To overcome these limitations, we advocate the representation of VolVis scenes using differentiable Gaussian primitives combined with pretrained large models to enable arbitrary style transfer and real-time rendering. However, conventional 3D Gaussian primitives tightly couple geometry and appearance, leading to suboptimal stylization results. To address this, we introduce TexGS-VolVis, a textured Gaussian splatting framework for VolVis. TexGS-VolVis employs 2D Gaussian primitives, extending each Gaussian with additional texture and shading attributes, resulting in higher-quality, geometry-consistent stylization and enhanced lighting control during inference. Despite these improvements, achieving flexible and controllable scene editing remains challenging. To further enhance stylization, we develop image-and text-driven non-photorealistic scene editing tailored for TexGS-VolVis and 2D-lift-3D segmentation to enable partial editing with fine-grained control. We evaluate TexGS-VolVis both qualitatively and quantitatively across various volume rendering scenes, demonstrating its superiority over existing methods in terms of efficiency, visual quality, and editing flexibility.
TFZ: Topology-Preserving Compression of 2D Symmetric and Asymmetric Second-Order Tensor Fields
Authors: Nathaniel Gorski, Xin Liang 0001, Hanqi Guo 0001, Bei Wang 0001
Published at Vis, 2025

In this paper, we present a novel compression framework, TFZ, that preserves the topology of 2D symmetric and asymmetric second-order tensor fields defined on flat triangular meshes. A tensor field assigns a tensor—a multi-dimensional array of numbers—to each point in space. Tensor fields, such as the stress and strain tensors, and the Riemann curvature tensor, are essential to both science and engineering. The topology of tensor fields captures the core structure of data, and is useful in various disciplines, such as graphics (for manipulating shapes and textures) and neuroscience (for analyzing brain structures from diffusion MRI). Lossy data compression may distort the topology of tensor fields, thus hindering downstream analysis and visualization tasks. TFZ ensures that certain topological features are preserved during lossy compression. Specifically, TFZ preserves degenerate points essential to the topology of symmetric tensor fields and retains eigenvector and eigenvalue graphs that represent the topology of asymmetric tensor fields. TFZ scans through each cell, preserving the local topology of each cell, and thereby ensuring certain global topological guarantees. We showcase the effectiveness of our framework in enhancing the lossy scientific data compressors SZ3 and SPERR.
The Hue-Man Factor: An Empirical Evaluation of Visualization Perception and Accessibility Across Color Vision Profiles
Authors: Zhuojun Jiang, Anjana Arunkumar, Chris Bryan
Published at Vis, 2025

Color is a powerful tool in data visualization, but for individuals with color vision deficiencies (CVD), hue can become a barrier rather than an aid. In this paper, we examine how real-world visualizations are perceived across vision profiles through three complementary studies. Study 1 assessed how normal vision participants rated 46 visualizations shown in original and simulated red/green colorblind versions. Study 2 collected matched responses from participants with diagnosed CVD. Study 3 involved in-depth interviews exploring how users interpret, adapt to, and evaluate inaccessible designs. Across studies, we find that simulations capture directional perceptual shifts but fail to reflect the interpretive breakdowns and emotional work described by real CVD users. Factor analysis reveals two dominant perceptual dimensions: functional utility and affective experience. While normal vision participants prioritize functional clarity, CVD users rely more on structural cues and emotional resonance, particularly when color is unreliable. Qualitative insights show that perceptual breakdowns occur not only in high-interference charts but also when redundant encoding or layout scaffolding is missing. We synthesize these findings and offer empirically grounded design recommendations to guide inclusive visualization practices. Our results argue that accessibility must go beyond color correction, embracing structural clarity, redundancy, and real-user validation to ensure inclusive visual communication.
The Impact of Visual Segmentation on Lexical Word Recognition
Authors: Matthew Termuende, Kevin Larson, Miguel A. Nacenta
Published at Vis, 2025

When a reader encounters a word in English, they split the word into smaller orthographic units in the process of recognizing its meaning. For example, “rough”, when split according to phonemes, is decomposed as r-ou-gh (not as r-o-ugh or r-ough), where each group of letters corresponds to a sound. Since there are many ways to segment a group of letters, this constitutes a computational operation that has to be solved by the reading brain, many times per minute, in order to achieve the recognition of words in text necessary for reading. In English, the irregular relationships between groups of letters and sounds, and the wide variety of possible groupings make this operation harder than in more regular languages such as Italian. If this segmentation takes a significant amount of time in the process of recognizing a word, it is conceivable that providing segmentation information in the text itself could help the reading process by reducing its computational cost. In this paper we explore whether and how different visual interventions from the visualization literature could communicate segmentation information for reading and word recognition. We ran a series of pre-registered lexical decision experiments with 192 participants that tested five main types of visual segmentations: outlines, spacing, connections, underlines and color. The evidence indicates that, even with a moderate amount of training, these visual interventions always slow down word identification, but each to a different extent (between 32.7ms—color technique—and 70.7ms—connection technique). These findings are important because they indicate that, at least for typical adult readers with a moderate amount of specific training in these visual interventions, accelerating the lexical decision task is unlikely. Importantly, the results also offer an empirical measurement of the cost of a common set of visual manipulations of text, which can be useful for practitioners seeking to visualize alongside or within text without impacting reading performance. Finally, the interaction between typographically encoded information and visual variables presented unique patterns that deviate from existing theories, suggesting new directions for future inquiry.
TiVy: Time Series Visual Summary for Scalable Visualization
Authors: Gromit Yeuk-Yin Chan, Luis Gustavo Nonato, Themis Palpanas, Cláudio T. Silva, Juliana Freire
Published at Vis, 2025

Visualizing multiple time series presents fundamental tradeoffs between scalability and visual clarity. Time series capture the behavior of many large-scale real-world processes, from stock market trends to urban activities. Users often gain insights by visualizing them as line charts, juxtaposing or superposing multiple time series to compare them and identify trends and patterns. However, existing representations struggle with scalability: when covering long time spans, leading to visual clutter from too many small multiples or overlapping lines. We propose TiVy, a new algorithm that summarizes time series using sequential patterns. It transforms the series into a set of symbolic sequences based on subsequence visual similarity using Dynamic Time Warping (DTW), then constructs a disjoint grouping of similar subsequences based on the frequent sequential patterns. The grouping result, a visual summary of time series, provides uncluttered superposition with fewer small multiples. Unlike common clustering techniques, TiVy extracts similar subsequences (of varying lengths) aligned in time. We also present an interactive time series visualization that renders large-scale time series in real-time. Our experimental evaluation shows that our algorithm (1) extracts clear and accurate patterns when visualizing time series data, (2) achieves a significant speed-up (1000×) compared to a straightforward DTW clustering. We also demonstrate the efficiency of our approach to explore hidden structures in massive time series data in two usage scenarios.
TrajLens: Visual Analysis for Constructing Cell Developmental Trajectories in Cross-Sample Exploration
Authors: Qipeng Wang 0003, Shaolun Ruan, Rui Sheng, Yong Wang 0021, Min Zhu 0005, Huamin Qu
Published at Vis, 2025

Constructing cell developmental trajectories is a critical task in single-cell RNA sequencing (scRNA-seq) analysis, enabling the inference of potential cellular progression paths. However, current automated methods are limited to establishing cell developmental trajectories within individual samples, necessitating biologists to manually link cells across samples to construct complete cross-sample evolutionary trajectories that consider cellular spatial dynamics. This process demands substantial human effort due to the complex spatial correspondence between each pair of samples. To address this challenge, we first proposed a GNN-based model to predict cross-sample cell developmental trajectories. We then developed TrajLens, a visual analytics system that supports biologists in exploring and refining the cell developmental trajectories based on predicted links. Specifically, we designed the visualization that integrates features on cell distribution and developmental direction across multiple samples, providing an overview of the spatial evolutionary patterns of cell populations along trajectories. Additionally, we included contour maps superimposed on the original cell distribution data, enabling biologists to explore them intuitively. To demonstrate our system's performance, we conducted quantitative evaluations of our model with two case studies and expert interviews to validate its usefulness and effectiveness.
TrialCompass: Visual Analytics for Enhancing the Eligibility Criteria Design of Clinical Trials
Authors: Rui Sheng, Xingbo Wang 0001, Jiachen Wang 0001, Xiaofu Jin, Zhonghua Sheng, Zhenxing Xu, Suraj Rajendran, Huamin Qu, Fei Wang 0001
Published at Vis, 2025

Eligibility criteria play a critical role in clinical trials by determining the target patient population, which significantly influences the outcomes of medical interventions. However, current approaches for designing eligibility criteria have limitations to support interactive exploration of the large space of eligibility criteria. They also ignore incorporating detailed characteristics from the original electronic health record (EHR) data for criteria refinement. To address these limitations, we proposed TrialCompass, a visual analytics system integrating a novel workflow, which can empower clinicians to iteratively explore the vast space of eligibility criteria through knowledge-driven and outcome-driven approaches. TrialCompass supports history-tracking to help clinicians trace the evolution of their adjustments and decisions when exploring various forms of data (i.e., eligibility criteria, outcome metrics, and detailed characteristics of original EHR data) through these two approaches. This feature can help clinicians comprehend the impact of eligibility criteria on outcome metrics and patient characteristics, which facilitates systematic refinement of eligibility criteria. Using a real-world dataset, we demonstrated the effectiveness of TrialCompass in providing insights into designing eligibility criteria for septic shock and sepsis-associated acute kidney injury. We also discussed the research prospects of applying visual analytics to clinical trials.
Uncertainty-Aware PCA Revisited
Authors: Lukas Friesecke, Christian Braune, Christian Rössl, Holger Theisel
Published at Vis, 2025

Principal Component Analysis (PCA) is perhaps the most popular linear projection technique for dimensionality reduction. We consider PCA under the assumption that the high-dimensional data points are equipped with Gaussian uncertainty. Several approaches to such uncertainty-aware PCA have been developed recently in the visualization community. Since PCA is a discontinuous map, a small uncertainty in the data points can result in a huge uncertainty in the projected points. We show that the uncertainty of the data points also creates uncertainty in the eigenvectors of the covariance matrix that defines the PCA projection. We present a closed-form expression to quantify eigenvector uncertainty. Based on this, we propose a 3D glyph that supports the decision whether existing solutions for uncertainty-aware PCA are sufficient, or whether a more expensive sampling-based approach is required. We apply our approach to several test data sets.