DevLan/vispubs huggingface dataset:
Tasks and Visual Abstractions for 3D Chromatin Representation
Authors: Adam Rychlý, Jan Byska, Barbora Kozlíková, Katarína Furmanová
Published at EuroVis, 2025

The spatial organization of chromatin fiber directly influences its function. However, the high visual complexity of chromatin spatial models makes the understanding of the structure extremely challenging. Therefore, genomic researchers still primarily rely on indirect analysis of chromatin through 2D views, missing the advantages that 3D visualization can offer. In this paper, we first analyze the task space of genomic research and identify biological domain tasks that can benefit from dedicated spatial representations. We organize these tasks into four categories: tasks related to structural features, additional meta-data, structural relationships, and comparative tasks. We analyze these tasks in terms of their complexity, co-dependence, and potential benefits of 3D-based solutions. Secondly, we present four newly designed visual representations of chromatin 3D structure, focused on enhancing the understanding of structural features and solving relationships tasks. These include the hierarchical nature of spatial chromatin sub-units, their visual abstractions, spatial interactions, and a cumulative representation of chromatin dynamic behavior. We also include feedback from four domain researchers and discuss future steps necessary to make spatial representations valid and valuable part of genomic research.
The Geometry of Color in the Light of a Non-Riemannian Space
Authors: Roxana Bujack, Emily N. Stark, Terece L. Turton, Jonah M. Miller, David H. Rogers 0001
Published at EuroVis, 2025

We formalize Schrödinger's definitions of hue, saturation, and lightness, building on the foundational idea from Helmholtz that these perceptual attributes can be derived solely from the perceptual metric. We identify three shortcomings in Schrödinger's approach and propose solutions to them. First, to encompass the Bezold-Brücke effect, we replace the straight-line definition of stimulus quality between a color and black with the geodesic path in perceptual color space. Second, to model diminishing returns in color perception, we employ a non-Riemannian perceptual metric, which introduces a potential ambiguity in defining lightness, but our experiments show that this ambiguity is inconsequential. Third, we provide a geometric definition of the neutral axis as the closest color to black within each equal-lightness surface—a definition feasible only in a non-Riemannian framework. Collectively, our solutions provide the first comprehensive realization of Helmholtz's vision: formal geometric definitions of hue, saturation, and lightness derived entirely from the metric of perceptual similarity, without reliance on external constructs.
Towards a Better Evaluation of 3D CVML Algorithms: Immersive Debugging of a Localization Model
Authors: Tica Lin, Jun Yuan, Kevin Miao, Tigran Katolikyan, Isaac Walker, Marco Cavallo
Published at EuroVis, 2025

As advancements in robotics, autonomous driving, and spatial computing continue to unfold, a growing number of Computer Vision and Machine Learning (CVML) algorithms are incorporating three-dimensional data into their frameworks. Debugging these 3D CVML models often requires going beyond traditional performance evaluation methods, necessitating a deeper understanding of an algorithm's behavior within its spatio-temporal context. However, the lack of appropriate visualization tools presents a significant obstacle to effectively exploring 3D data and spatial features in relation to key performance indicators (KPIs). To address this challenge, we explore the application of Immersive Analytics (IA) methodologies to enhance the debugging process of 3D CVML models. Through in-depth interviews with eight CVML engineers, we identify common tasks and challenges faced during the development of spatial algorithms, and establish a set of design principles for creating tools tailored to spatial model evaluation. Building on these insights, we propose a novel immersive analytics system for debugging an indoor localization algorithm. The system is built using web technologies and integrates WebXR to enable fluid transitions across the reality-virtuality continuum. We conduct a qualitative study with six CVML engineers using our system on Apple Vision Pro, observing their analytical workflow as they debug an indoor localization sequence. We discuss the advantages of employing immersive analytics in the model evaluation workflow, emphasizing the role of seamlessly integrating 2D and 3D visualizations across varying levels of immersion to facilitate more effective model assessment. Finally, we reflect on the implementation trade-offs and discuss the generalizability of our findings for future efforts in immersive 3D CVML model debugging.
Uncertainty-Aware Visualization of Biomolecular Structures
Authors: Anna Sterzik, Christina Gillmann, Michael Krone, Kai Lawonn
Published at EuroVis, 2025

Molecular structure visualization is fundamental to molecular biology, aiding in understanding complex biological processes. While advancements in molecular visualization have greatly improved the representation of these structures, inherent uncertainties—such as inaccuracies in atomic positions or variability in secondary structure classifications—impact the accuracy of the visualizations. Uncertainty-aware visualization (UAV) emerged as a response to these challenges, integrating uncertainty into visual representations to improve data interpretation and decision-making. Despite extensive work on both molecular and uncertainty visualization (UV), there is a lack of comprehensive surveys addressing the intersection of these two fields. This paper provides a state-of-the-art review of UAV approaches for biomolecular structures. We propose a classification schema that organizes existing methods based on the type of molecule visualized, the manifestation of uncertainty, and the mapping of uncertainty to a visual representation. Using this framework, we identified research gaps and areas for future exploration in uncertainty-aware biomolecular structure visualization.
Viewpoint Optimization for 3D Graph Drawings
Authors: Simon van Wageningen, Tamara Mchedlidze, Alexandru C. Telea
Published at EuroVis, 2025

Graph drawings using a node-link metaphor and straight edges are widely used to represent and understand relational data. While such drawings are typically created in 2D, 3D representations have also gained popularity. When exploring 3D drawings, finding viewpoints that help understanding the graph's structure is crucial. Finding good viewpoints also allows using the 3D drawings to generate good 2D graph drawings. In this work, we tackle the problem of automatically finding high-quality viewpoints for 3D graph drawings. We propose and evaluate strategies based on sampling, gradient descent, and evolutionary-inspired meta-heuristics. Our results show that most strategies quickly converge to high-quality viewpoints within a few dozen function evaluations, with meta-heuristic approaches showing robust performance regardless of the quality metric.
VISLIX: An XAI Framework for Validating Vision Models with Slice Discovery and Analysis
Authors: Xinyuan Yan, Xiwei Xuan, Jorge Piazentin Ono, Jiajing Guo, Vikram Mohanty, Arvind Kumar Shekar, Liang Gou, Bei Wang 0001, Liu Ren
Published at EuroVis, 2025

Real-world machine learning models require rigorous evaluation before deployment, especially in safety-critical domains like autonomous driving and surveillance. The evaluation of machine learning models often focuses on data slices, which are subsets of the data that share a set of characteristics. Data slice finding automatically identifies conditions or data subgroups where models underperform, aiding developers in mitigating performance issues. Despite its popularity and effectiveness, data slicing for vision model validation faces several challenges. First, data slicing often needs additional image metadata or visual concepts, and falls short in certain computer vision tasks, such as object detection. Second, understanding data slices is a labor-intensive and mentally demanding process that heavily relies on the expert's domain knowledge. Third, data slicing lacks a human-in-the-loop solution that allows experts to form hypothesis and test them interactively. To overcome these limitations and better support the machine learning operations lifecycle, we introduce VISLIX, a novel visual analytics framework that employs state-of-the-art foundation models to help domain experts analyze slices in computer vision models. Our approach does not require image metadata or visual concepts, automatically generates natural language insights, and allows users to test data slice hypothesis interactively. We evaluate VISLIX with an expert study and three use cases, that demonstrate the effectiveness of our tool in providing comprehensive insights for validating object detection models.
Visually Assessing 1-D Orderings of Contiguous Spatial Polygons
Authors: Julius Rauscher, Frederik L. Dennig, Udo Schlegel, Daniel A. Keim, Johannes Fuchs 0001
Published at EuroVis, 2025

One-dimensional orderings of spatial entities have been researched in many contexts, e.g. spatial indexing structures or visualizations for spatiotemporal trend analysis. While plenty of studies have been conducted to evaluate orderings of point-based data, polygonal shapes, despite their different topological properties, have received less attention. Existing measures to quantify errors in projections or orderings suffer from generic neighborhood definitions and over-simplification of distances when applied to polygonal data. In this work, we address these shortcomings by introducing measures that adapt to a varying neighborhood size depending on the number of contiguous neighbors and thus, address the limitations of existing measures for polygonal shapes. To guide experts in determining a suitable ordering, we propose a user-steerable visual analytics prototype capable of locally and globally inspecting ordering errors, investigating the impact of geographic obstacles, and comparing ordering strategies using our measures. We demonstrate the effectiveness of our approach through a use case and conducted an expert study with 8 data scientists as a qualitative evaluation of our approach. Our results show that users are capable of identifying ordering errors, comparing ordering strategies on a global and local scale, as well as assessing the impact of semantically relevant geographic obstacles.
VizTA: Enhancing Comprehension of Distributional Visualization with Visual-Lexical Fused Conversational Interface
Authors: Liangwei Wang 0001, Zhan Wang 0001, Shishi Xiao, Le Liu 0008, Fugee Tsung, Wei Zeng 0004
Published at EuroVis, 2025

Comprehending visualizations requires readers to interpret visual encoding and the underlying meanings actively. This poses challenges for visualization novices, particularly when interpreting distributional visualizations that depict statistical uncertainty. Advancements in LLM-based conversational interfaces show promise in promoting visualization comprehension. However, they fail to provide contextual explanations at fine-grained granularity, and chart readers are still required to mentally bridge visual information and textual explanations during conversations. Our formative study highlights the expectations for both lexical and visual feedback, as well as the importance of explicitly linking these two modalities throughout the conversation. The findings motivate the design of VizTA, a visualization teaching assistant that leverages the fusion of visual and lexical feedback to help readers better comprehend visualization. VizTA features a semantic-aware conversational agent capable of explaining contextual information within visualizations and employs a visual-lexical fusion design to facilitate chart-centered conversation. A between-subject study with 24 participants demonstrates the effectiveness of VizTA in supporting the understanding and reasoning tasks of distributional visualization across multiple scenarios.
Voronoi Cell Interface-Based Parameter Sensitivity Analysis for Labeled Samples
Authors: Ruben Bauer, Marina Evers, Quynh Quang Ngo, Guido Reina, Steffen Frey, Michael Sedlmair
Published at EuroVis, 2025

Varying the input parameters of simulations or experiments often leads to different classes of results. Parameter sensitivity analysis in this context includes estimating the sensitivity to the individual parameters, that is, to understand which parameters contribute most to changes in output classifications and for which parameter ranges these occur. We propose a novel visual parameter sensitivity analysis approach based on Voronoi cell interfaces between the sample points in the parameter space to tackle the problem. The Voronoi diagram of the sample points in the parameter space is first calculated. We then extract Voronoi cell interfaces which we use to quantify the sensitivity to parameters, considering the class label information of each sample's corresponding output. Multiple visual encodings are then utilized to represent the cell interface transitions and class label distribution, including stacked graphs for local parameter sensitivity. We evaluate the approach's expressiveness and usefulness with case studies for synthetic and real-world datasets.
When Dimensionality Reduction Meets Graph (Drawing) Theory: Introducing a Common Framework, Challenges and Opportunities
Authors: Fernando V. Paulovich, Alessio Arleo, Stef van den Elzen
Published at EuroVis, 2025

In the vast landscape of visualization research, Dimensionality Reduction (DR) and graph analysis are two popular subfields, often essential to most visual data analytics setups. DR aims to create representations to support neighborhood and similarity analysis on complex, large datasets. Graph analysis focuses on identifying the salient topological properties and key actors within network data, with specialized research investigating how such features could be presented to users to ease the comprehension of the underlying structure. Although these two disciplines are typically regarded as disjoint subfields, we argue that both fields share strong similarities and synergies that can potentially benefit both. Therefore, this paper discusses and introduces a unifying framework to help bridge the gap between DR and graph (drawing) theory. Our goal is to use the strongly math-grounded graph theory to improve the overall process of creating DR visual representations. We propose how to break the DR process into well-defined stages, discuss how to match some of the DR state-of-the-art techniques to this framework, and present ideas on how graph drawing, topology features, and some popular algorithms and strategies used in graph analysis can be employed to improve DR topology extraction, embedding generation, and result validation. We also discuss the challenges and identify opportunities for implementing and using our framework, opening directions for future visualization research.
"Housing Diversity Means Diverse Housing": Blending Generative AI into Speculative Design in Rural Co-Housing Communities
Authors: Hongyi Tao, Dhaval Vyas
Published at CHI, 2025

In response to various environmental and societal challenges, co-housing has emerged to support social cohesion, grassroots innovation and ecological regeneration. Co-housing communities typically have smaller personal spaces, closer neighbourly relationships, and engage in more mutually supportive sustainable practices. To understand such communities' motivations and visions, we developed a speculative design tool that harnesses Generative Artificial Intelligence (GenAI) to facilitate the envisioning of alternative future scenarios that challenge prevailing values, beliefs, lifestyles, and ways of knowing in contemporary society. Within the context of co-housing communities, we conducted a participatory design study with participants in co-creating their future communities. This paper unpacks implications and also reflects on the co-design approach employing GenAI. Our main findings highlight that GenAI, as a catalyst for imagination, empowers individuals to create visualisations that pose questions through a plural and situated speculative discourse.
"I can run at night!": Using Augmented Reality to Support Nighttime Guided Running for Low-vision Runners
Authors: Yuki Abe, Keisuke Matsushima, Kotaro Hara, Daisuke Sakamoto, Tetsuo Ono
Published at CHI, 2025

Dark environment challenges low-vision (LV) individuals to engage in running by following sighted guide—a Caller-style guided running—due to insufficient illumination, because it prevents them from using their residual vision to follow the guide and be aware about their environment. We design, develop, and evaluate RunSight, an augmented reality (AR)-based assistive tool to support LV individuals to run at night. RunSight combines see-through HMD and image processing to enhance one's visual awareness of the surrounding environment (e.g., potential hazard) and visualize the guide's position with AR-based visualization. To demonstrate RunSight's efficacy, we conducted a user study with 8 LV runners. The results showed that all participants could run at least 1km (mean = 3.44 km) using RunSight, while none could engage in Caller-style guided running without it. Our participants could run safely because they effectively synthesized RunSight-provided cues and information gained from runner-guide communication.
"Piecing Data Connections Together Like a Puzzle": Effects of Increasing Task Complexity on the Effectiveness of Data Storytelling Enhanced Visualisations
Authors: Mikaela Elizabeth Milesi, Paola Mejia-Domenzain, Laura Brandl, Vanessa Echeverría, Yueqiao Jin, Dragan Gasevic, Yi-Shan Tsai, Tanja Käser, Roberto Martínez-Maldonado
Published at CHI, 2025

The emerging concept of data storytelling (DS) suggests that enhancing visualisations with annotations and narratives can make complex data more insightful than conventional visualisations. Previous works found that DS-enhanced visualisations are more effective than conventional visualisations for simple tasks like identifying key data points or the main message. However, no previous work has explored the extent to which DS enhancements influence task completion across different levels of cognitive complexity. We address this gap by presenting the results of a study where 128 participants completed tasks based on four visualisations (two line charts and two choropleth maps, either with or without DS elements) spanning a range of complexity based on Bloom's taxonomy, which has been applied in data visualisation to categorise tasks hierarchically from lower to higher-order thinking. Results suggest that while DS-enhanced visualisations effectively support lower-order tasks (finding data points and understanding insights), they don't necessarily aid the correct completion of higher-order tasks (application, analysis, evaluation and creation). However, DS enhancements improve how efficiently participants complete complex tasks.
A Novel Lens on Metacognition in Visualization
Authors: Mengyu Chen, Andrew Yang, Seungchan Min, Kristy A. Hamilton, Emily Wall 0001
Published at CHI, 2025

Metacognition, or the awareness and regulation of one's own cognitive processes, allows individuals to take command of their learning and decision making in various contexts. In tasks that require problem-solving and adaptive learning, individuals with heightened metacognitive awareness tend to outperform others, as they are better equipped to regulate cognition, leading to more effective processes. On the other hand, visualization research facilitates exploration and decision making with data. We posit that metacognitive frameworks that examine how individuals think about their own thinking processes can likewise enhance visualization processes. In this paper, we review metacognition literature from the cognitive and learning science to identify opportunities in visualization to improve people's ability to reason with data. We propose the use of a metacognitive framework, serving as a starting point to inspire future research to improve visualization practices and outcomes.
A Placebo Concert: The Placebo Effect for Visualization of Physiological Audience Data during Experience Recreation in Virtual Reality
Authors: Xiaru Meng, Yulan Ju, Christopher Changmok Kim, Yan He, Giulia Barbareschi, Kouta Minamizawa, Kai Kunze, Matthias Hoppe 0003
Published at CHI, 2025

A core use case for Virtual Reality applications is recreating real-life scenarios for training or entertainment. Promoting physiological responses for users in VR that match those of real-life spectators can maximize engagement and contribute to more co-presence. Current research focuses on visualizations and measurements of physiological data to ensure experience accuracy. However, placebo effects are known to influence performance and self-perception in HCI studies, creating a need to investigate the effect of visualizing different types of data (real, unmatched, and fake) on user perception during event recreation in VR. We investigate these conditions through a balanced between-groups study (n=44) of uninformed and informed participants. The informed group was provided with the information that the data visualizations represented previously recorded human physiological data. Our findings reveal a placebo effect, where the informed group demonstrated enhanced engagement and co-presence. Additionally, the fake data condition in the informed group evoked a positive emotional response.
Accessibility for Whom? Perceptions of Mobility Barriers Across Disability Groups and Implications for Designing Personalized Maps
Authors: Chu Li, Rock Yuren Pang, Delphine Labbé, Yochai Eisenberg, Maryam Hosseini, Jon E. Froehlich
Published at CHI, 2025

Today's mapping tools fail to address the varied experiences of different mobility device users. This paper presents a large-scale online survey exploring how five mobility groups—users of canes, walkers, mobility scooters, manual wheelchairs, and motorized wheelchairs—perceive sidewalk barriers and differences therein. Using 52 sidewalk barrier images, respondents evaluated their confidence in navigating each scenario. Our findings (N=190) reveal variations in barrier perceptions across groups, while also identifying shared concerns. To further demonstrate the value of this data, we showcase its use in two custom prototypes: a visual analytics tool and a personalized routing tool. Our survey findings and open dataset advance work in accessibility-focused maps, routing algorithms, and urban planning.
Ad-Blocked Reality: Evaluating User Perceptions of Content Blocking Concepts Using Extended Reality
Authors: Christopher Katins, Jannis Strecker, Jan Hinrichs, Pascal Knierim, Bastian Pfleging, Thomas Kosch
Published at CHI, 2025

Inspired by the concepts of diminishing reality and ad-blocking in browsers, this study investigates the perceived benefits and concerns of blocking physical, real-world content, particularly ads, through Extended Reality (XR). To understand how users perceive this concept, we first conducted a user study (N = 18) with an ad-blocking prototype to gather initial insights. The results revealed a mixed willingness to adopt XR blockers, with participants appreciating aspects such as customizability, convenience, and privacy. Expected benefits included enhanced focus and reduced stress, while concerns centered on missing important information and increased feelings of isolation. Hence, we investigated the user acceptance of different ad-blocking visualizations through a follow-up online survey (N = 120), comparing six concepts based on related work. The results indicated that the XR ad-blocker visualizations play a significant role in how and for what kinds of advertisements such a concept might be used, paving the path for future feedback-driven prototyping.