DevLan/vispubs huggingface dataset:
DashGuide: Authoring Interactive Dashboard Tours for Guiding Dashboard Users
Authors: Md. Naimul Hoque, Nicole Sultanum
Published at EuroVis, 2025

Dashboard guidance helps dashboard users better navigate interactive features, understand the underlying data, and assess insights they can potentially extract from dashboards. However, authoring dashboard guidance is a time consuming task, and embedding guidance into dashboards for effective delivery is difficult to realize. In this work, we contribute DashGuide, a framework and system to support the creation of interactive dashboard guidance with minimal authoring input. Given a dashboard and a communication goal, DashGuide captures a sequence of author-performed interactions to generate guidance materials delivered as playable step-by-step overlays, a.k.a., dashboard tours. Authors can further edit and refine individual tour steps while receiving generative assistance. We also contribute findings from a formative assessment with 9 dashboard creators, which helped inform the design of DashGuide; and findings from an evaluation of DashGuide with 12 dashboard creators, suggesting it provides an improved authoring experience that balances efficiency, expressiveness, and creative freedom.
DataWeaver: Authoring Data-Driven Narratives through the Integrated Composition of Visualization and Text
Authors: Yu Fu 0010, Dennis Bromley, Vidya Setlur
Published at EuroVis, 2025

Data-driven storytelling has gained prominence in journalism and other data reporting fields. However, the process of creating these stories remains challenging, often requiring the integration of effective visualizations with compelling narratives to form a cohesive, interactive presentation. To help streamline this process, we present an integrated authoring framework and system, DataWeaver, that supports both visualization-to-text and text-to-visualization composition. DataWeaver enables users to create data narratives anchored to data facts derived from "call-out" interactions, i.e., user-initiated highlights of visualization elements that prompt relevant narrative content. In addition to this "vis-to-text" composition, DataWeaver also supports a "text-initiated" approach, generating relevant interactive visualizations from existing narratives. Key findings from an evaluation with 13 participants highlighted the utility and usability of DataWeaver and the effectiveness of its integrated authoring framework. The evaluation also revealed opportunities to enhance the framework by refining filtering mechanisms and visualization recommendations and better support authoring creativity by introducing advanced customization options.
Either Or: Interactive Articles or Videos for Climate Science Communication
Authors: Jeran Poehls, Monique Meuschke, Nuno Carvalhais, Kai Lawonn
Published at EuroVis, 2025

Effective communication of climate science is critical as climate-related disasters become more frequent and severe. Translating complex information, such as uncertainties in climate model predictions, into formats accessible to diverse audiences is key to informed decision-making and public engagement. This study investigates how different teaching formats can enhance understanding of these uncertainties. This study compares two multimodal strategies: (1) a text-image format with interactive components and (2) an explainer video combining dynamic visuals with narration. Participants' immediate and delayed retention (one week) and engagement are assessed to determine which format offers greater saliency.
Embedded and Situated Visualisation in Mixed Reality to Support Interval Running
Authors: Ang Li 0007, Charles Perin, Jarrod Knibbe, Gianluca Demartini, Stephen Viller, Maxime Cordeil
Published at EuroVis, 2025

We investigate the use of mixed reality visualisations to help pace tracking for interval running. We introduce three immersive visual designs to support pace tracking. Our designs leverage two properties afforded by mixed reality environments to display information: the space in front of the user and the physical environment to embed pace visualisation. In this paper, we report on the first design exploration and controlled study of mixed reality technology to support pacing tracking during interval running on an outdoor running track. Our results show that mixed reality and immersive visualisation designs for interval training offer a viable option to help runners (a) maintain regular pace, (b) maintain running flow, and (c) reduce mental task load.
Enhancing Material Boundary Visualizations in 2D Unsteady Flow through Local Reference Frame Transformations
Authors: Xingdi Zhang, Peter Rautek, Thomas Theußl, Markus Hadwiger
Published at EuroVis, 2025

We present a novel technique for the extraction, visualization, and analysis of material boundaries and Lagrangian coherent structures (LCS) in 2D unsteady flow fields relative to local reference frame transformations. In addition to the input flow field, we leverage existing methods for computing reference frames adapted to local fluid features, in particular those that minimize the observed time derivative. Although, by definition, transforming objective tensor fields between reference frames does not change the tensor field, we show that transforming objective tensors, such as the finite-time Lyapunov exponent (FTLE) or Lagrangian-averaged vorticity deviation (LAVD), or the second-order rate-of-strain tensor, into local reference frames that are naturally adapted to coherent fluid structures has several advantages: (1) The transformed fields enable analyzing LCS in space-time visualizations that are adapted to each structure; (2) They facilitate extracting geometric features, such as iso-surfaces and ridge lines, in a straightforward manner with high accuracy. The resulting visualizations are characterized by lower geometric complexity and enhanced topological fidelity. To demonstrate the effectiveness of our technique, we measure geometric complexity and compare it with iso-surfaces extracted in the conventional reference frame. We show that the decreased geometric complexity of the iso-surfaces in the local reference frame, not only leads to improved geometric and topological results, but also to a decrease in computation time.
Euclidean, Hyperbolic, and Spherical Networks: An Empirical Study of Matching Network Structure to Best Visualizations
Authors: Jacob Miller 0001, Dhruv Bhatia, Helen C. Purchase, Stephen G. Kobourov
Published at EuroVis, 2025

We investigate the usability of Euclidean, spherical and hyperbolic geometries for network visualization. Several techniques have been proposed for both spherical and hyperbolic network visualization tools, based on the fact that some networks admit lower embedding error (distortion) in such non-Euclidean geometries. However, it is not yet known whether a lower embedding error translates to human subject benefits, e.g., better task accuracy or lower task completion time. We design, implement, conduct, and analyze a human subjects study to compare Euclidean, spherical and hyperbolic network visualizations using tasks that span the network task taxonomy. While in some cases accuracy and response times are negatively impacted when using non-Euclidean visualizations, the evaluation shows that differences in accuracy for hyperbolic and spherical visualizations are not statistically significant when compared to Euclidean visualizations. Additionally, differences in response times for spherical visualizations are not statistically significant compared to Euclidean visualizations.
FairSpace: An Interactive Visualization System for Constructing Fair Consensus from Many Rankings
Authors: Hilson Shrestha, Kathleen Cachel, Mallak Alkhathlan, Elke A. Rundensteiner, Lane Harrison
Published at EuroVis, 2025

Decisions involving algorithmic rankings affect our lives in many ways, from product recommendations, receiving scholarships, to securing jobs. While tools have been developed for interactively constructing fair consensus rankings from a handful of rankings, addressing the more complex real-world scenario— where diverse opinions are represented by a larger collection of rankings— remains a challenge. In this paper, we address these challenges by reformulating the exploration of rankings as a dimension reduction problem in a system called FairSpace. FairSpace provides new views, including Fair Divergence View and Cluster Views, by juxtaposing fairness metrics of different local and alternative global consensus rankings to aid ranking analysis tasks. We illustrate the effectiveness of FairSpace through a series of use cases, demonstrating via interactive workflows that users are empowered to create local consensuses by grouping rankings similar in their fairness or utility properties, followed by hierarchically aggregating local consensuses into a global consensus through direct manipulation. We discuss how FairSpace opens the possibility for advances in dimension reduction visualization to benefit the research area of supporting fair decision-making in ranking based decision-making contexts.
Fast and Invertible Simplicial Approximation of Magnetic-Following Interpolation for Visualizing Fusion Plasma Simulation Data
Authors: Congrong Ren, Robert Hager, Randy Michael Churchill, Albert Mollén, Seung-Hoe Ku, Choong-Seock Chang, Hanqi Guo 0001
Published at EuroVis, 2025

We introduce a fast and invertible approximation for fusion plasma simulation data represented as 2D planar meshes with connectivities approximating magnetic field lines along the toroidal dimension in deformed 3D toroidal spaces. Scientific variables (e.g., density and temperature) in these fusion data are interpolated following a complex magnetic-field-line-following scheme in the toroidal space represented by a cylindrical coordinate system. This deformation in the 3D space poses challenges for root-finding and interpolation. To this end, we propose a novel paradigm for visualizing and analyzing such data based on a newly developed algorithm for constructing a 3D simplicial mesh within the deformed 3D space. Our algorithm generates a tetrahedral mesh that connects the 2D meshes using tetrahedra while adhering to the constraints on node connectivities imposed by the magnetic field-line scheme. Specifically, we first divide the space into smaller partitions to reduce complexity based on the input geometries and constraints on connectivities. Then, we independently search for a feasible tetrahedralization of each partition, considering nonconvexity. We demonstrate our method with two X-Point Gyrokinetic Code (XGC) simulation datasets on the International Thermonuclear Experimental Reactor (ITER) and Wendelstein 7-X (W7-X), and use an ocean simulation dataset to substantiate broader applicability of our method. An open source implementation of our algorithm is available at https://github.com/rcrcarissa/DeformedSpaceTet.
Fast HARDI Uncertainty Quantification and Visualization with Spherical Sampling
Authors: Tark Patel, Tushar M. Athawale, Timbwaoga A. J. Ouermi, Chris R. Johnson 0001
Published at EuroVis, 2025

In this paper, we study uncertainty quantification and visualization of orientation distribution functions (ODF), which corresponds to the diffusion profile of high angular resolution diffusion imaging (HARDI) data. The shape inclusion probability (SIP) function is the state-of-the-art method for capturing the uncertainty of ODF ensembles. The current method of computing the SIP function with a volumetric basis exhibits high computational and memory costs, which can be a bottleneck to integrating uncertainty into HARDI visualization techniques and tools. We propose a novel spherical sampling framework for faster computation of the SIP function with lower memory usage and increased accuracy. In particular, we propose direct extraction of SIP isosurfaces, which represent confidence intervals indicating spatial uncertainty of HARDI glyphs, by performing spherical sampling of ODFs. Our spherical sampling approach requires much less sampling than the state-of-the-art volume sampling method, thus providing significantly enhanced performance, scalability, and the ability to perform implicit ray tracing. Our experiments demonstrate that the SIP isosurfaces extracted with our spherical sampling approach can achieve up to 8164× speedup, 37282× memory reduction, and 50.2% less SIP isosurface error compared to the classical volume sampling approach. We demonstrate the efficacy of our methods through experiments on synthetic and human-brain HARDI datasets.
Fluidly Revealing Information: A Survey of Un/foldable Data Visualizations
Authors: Mark-Jan Bludau, Marian Dörk, Stefan Bruckner, Christian Tominski
Published at EuroVis, 2025

Revealing relevant information on demand is an essential requirement for visual data exploration. In this state-of-the-art report, we review and classify techniques that are inspired by the physical metaphor of un/folding to reveal relevant information or, conversely, to reduce irrelevant information in data visualizations. Similar to focus+context approaches, un/foldable visualizations transform the visual data representation, often between different granularities, in an integrated manner while preserving the overall context. This typically involves switching between different visibility states of data elements or adjusting the graphical abstraction linked by gradual display transitions. We analyze a literature corpus of 101 visualization techniques specifically with respect to their use of the un/folding metaphor. In particular, we consider the type of data, the focus scope and the effect scope, the number of un/folding states, the transformation type, and the controllability and interaction directness of un/folding. The collection of un/foldables is available as an online catalog that includes classic focus+context, semantic zooming, and multi-scale visualizations as well as contemporary un/foldable visualizations. From our literature analysis, we further extract families of un/folding techniques, summarize empirical findings to date, and identify promising research directions for un/foldable data visualization.
Gridded Visualization of Statistical Trees for High-Dimensional Multipartite Data in Systems Genetics
Authors: Jane Lydia Adams, Robyn L. Ball, Jason A. Bubier, Elissa J. Chesler, Melanie Tory, Michelle A. Borkin
Published at EuroVis, 2025

In systems genetics and other multi-omics research, exploring high-dimensional relationships among molecular and physiological variables across individuals poses significant challenges. We present the Gridded Trees interface, a novel interactive visualization tool designed to facilitate the exploration of conditional inference trees, which are hierarchical models of relationships in these complex datasets. Traditional static tools struggle to reveal patterns in tree-structured data, but the Gridded Trees interface provides interactive, coordinated views, allowing users to navigate between overview and detail, filter data dynamically, and compare molecular-physiological relationships across subgroups. By combining filtering techniques, strip plots, Sankey diagrams, and small multiples, the Gridded Trees interface enhances exploratory data analysis and supports hypothesis generation. In our systems genetics research use case, this tool has revealed significant associations among microbial populations and addiction-related behavioral traits in genetically diverse mice. The Gridded Trees interface suggests broad potential for visualizing hierarchical and multipartite data across domains. A preprint of this paper as well as Supplemental Materials are available on OSF at https://osf.io/9emn5/.
HyperFLINT: Hypernetwork-based Flow Estimation and Temporal Interpolation for Scientific Ensemble Visualization
Authors: Hamid Gadirov, Qi Wu 0015, David Bauer, Kwan-Liu Ma, Jos B. T. M. Roerdink, Steffen Frey
Published at EuroVis, 2025

We present HyperFLINT (Hypernetwork-based FLow estimation and temporal INTerpolation), a novel deep learning-based approach for estimating flow fields, temporally interpolating scalar fields, and facilitating parameter space exploration in spatio-temporal scientific ensemble data. This work addresses the critical need to explicitly incorporate ensemble parameters into the learning process, as traditional methods often neglect these, limiting their ability to adapt to diverse simulation settings and provide meaningful insights into the data dynamics. HyperFLINT introduces a hypernetwork to account for simulation parameters, enabling it to generate accurate interpolations and flow fields for each timestep by dynamically adapting to varying conditions, thereby outperforming existing parameter-agnostic approaches. The architecture features modular neural blocks with convolutional and deconvolutional layers, supported by a hypernetwork that generates weights for the main network, allowing the model to better capture intricate simulation dynamics. A series of experiments demonstrates HyperFLINT's significantly improved performance in flow field estimation and temporal interpolation, as well as its potential in enabling parameter space exploration, offering valuable insights into complex scientific ensembles.
In Situ Workload Estimation for Block Assignment and Duplication in Parallelization-Over-Data Particle Advection
Authors: Zhe Wang 0059, Kenneth Moreland, Matthew Larsen, James Kress, Hank Childs, Guan Li, Guihua Shan, David Pugmire
Published at EuroVis, 2025

Particle advection is a foundational algorithm for analyzing a flow field. The commonly used Parallelization-Over-Data (POD) strategy for particle advection can become slow and inefficient when there are unbalanced workloads, which are particularly prevalent in in situ workflows. In this work, we present an in situ workflow containing workload estimation for block assignment and duplication in a parallelization-over-data algorithm. With tightly coupled workload estimation and load-balanced block assignment strategy, our workflow offers a considerable improvement over the traditional round-robin block assignment strategy. Our experiments demonstrate that particle advection is up to 3X faster and associated workflow saves approximately 30% of execution time after adopting strategies presented in this work.
Instructional Comics for Self-Paced Learning of Data Visualization Tools and Concepts
Authors: Magdalena Boucher, Mashael AlKadi, Benjamin Bach, Wolfgang Aigner
Published at EuroVis, 2025

In this paper, we introduce instructional comics to explain concepts and routines in data visualization tools. As tools for visual data exploration proliferate, there is a growing need for tailored training and onboarding demonstrating interfaces, concepts, and interactions. Building on recent research in visualization education, we detail our iterative process of designing instructional comics for four different types of instructional content. Through a mixed-method eye-tracking study involving 20 participants, we analyze how people engage with these comics when using a new visualization tool, and validate our design choices. We interpret observed behaviors as unique affordances of instructional comics, supporting their use during tasks and complementing traditional instructional methods like video tutorials and workshops, and formulate six guidelines to inform the design of future instructional comics for visualization.
IntelliCircos: A Data-driven and AI-powered Authoring Tool for Circos Plots
Authors: Mingyang Gu, Jiamin Zhu, Qipeng Wang, Fengjie Wang, Xiaolin Wen, Yong Wang 0021, Min Zhu 0005
Published at EuroVis, 2025

Genomics data is essential in biological and medical domains, and bioinformatics analysts often manually create circos plots to analyze the data and extract valuable insights. However, creating circos plots is complex, as it requires careful design for multiple track attributes and positional relationships between them. Typically, analysts often seek inspiration from existing circos plots, and they have to iteratively adjust and refine the plot to achieve a satisfactory final design, making the process both tedious and time-intensive. To address these challenges, we propose IntelliCircos, an AI-powered interactive authoring tool that streamlines the process from initial visual design to the final implementation of circos plots. Specifically, we build a new dataset containing 4396 circos plots with corresponding annotations and configurations, which are extracted and labeled from published papers. With the dataset, we further identify track combination patterns, and utilize Large Language Model (LLM) to provide domain-specific design recommendations and configuration references to navigate the design of circos plots. We conduct a user study with 8 bioinformatics analysts to evaluate IntelliCircos, and the results demonstrate its usability and effectiveness in authoring circos plots.
Interactive Discovery and Exploration of Visual Bias in Generative Text-to-Image Models
Authors: Johannes Eschner, Roberto Labadie Tamayo, Matthias Zeppelzauer, Manuela Waldner
Published at EuroVis, 2025

Bias in generative Text-to-Image (T2I) models is a known issue, yet systematically analyzing such models' outputs to uncover it remains challenging. We introduce the Visual Bias Explorer (ViBEx) to interactively explore the output space of T2I models to support the discovery of visual bias. ViBEx introduces a novel flexible prompting tree interface in combination with zero-shot bias probing using CLIP for quick and approximate bias exploration. It additionally supports in-depth confirmatory bias analysis through visual inspection of forward, intersectional, and inverse bias queries. ViBEx is model-agnostic and publicly available. In four case study interviews, experts in AI and ethics were able to discover visual biases that have so far not been described in literature.
InterChat: Enhancing Generative Visual Analytics using Multimodal Interactions
Authors: Juntong Chen, Jiang Wu 0012, Jiajing Guo, Vikram Mohanty, Xueming Li, Jorge Piazentin Ono, Wenbin He, Liu Ren, Dongyu Liu
Published at EuroVis, 2025

The rise of Large Language Models (LLMs) and generative visual analytics systems has transformed data-driven insights, yet significant challenges persist in accurately interpreting users analytical and interaction intents. While language inputs offer flexibility, they often lack precision, making the expression of complex intents inefficient, error-prone, and time-intensive. To address these limitations, we investigate the design space of multimodal interactions for generative visual analytics through a literature review and pilot brainstorming sessions. Building on these insights, we introduce a highly extensible workflow that integrates multiple LLM agents for intent inference and visualization generation. We develop InterChat, a generative visual analytics system that combines direct manipulation of visual elements with natural language inputs. This integration enables precise intent communication and supports progressive, visually driven exploratory data analyses. By employing effective prompt engineering, and contextual interaction linking, alongside intuitive visualization and interaction designs, InterChat bridges the gap between user interactions and LLM-driven visualizations, enhancing both interpretability and usability. Extensive evaluations, including two usage scenarios, a user study, and expert feedback, demonstrate the effectiveness of InterChat. Results show significant improvements in the accuracy and efficiency of handling complex visual analytics tasks, highlighting the potential of multimodal interactions to redefine user engagement and analytical depth in generative visual analytics.