DevLan/vispubs huggingface dataset:
Lactea: Web-Based Spectrum-Preserving Multi-Resolution Visualization of the GAIA Star Catalog
Authors: Reem Alghamdi, Markus Hadwiger, Guido Reina, Alberto Jaspe-Villanueva
Published at EuroVis, 2025

The explosion of data in astronomy has resulted in an era of unprecedented opportunities for discovery. The GAIA mission's catalog, containing a large number of light sources (mostly stars) with several parameters such as sky position and proper motion, is playing a significant role in advancing astronomy research and has been crucial in various scientific breakthroughs over the past decade. In its current release, more than 200 million stars contain a calibrated continuous spectrum, which is essential for characterizing astronomical information such as effective temperature and surface gravity, and enabling complex tasks like interstellar extinction detection and narrow-band filtering. Even though numerous studies have been conducted to visualize and analyze the data in the SciVis and AstroVis communities, no work has attempted to leverage spectral information for visualization in real-time. Interactive exploration of such complex, massive data presents several challenges for visualization. This paper introduces a novel multi-resolution, spectrum-preserving data structure and a progressive, real-time visualization algorithm to handle the sheer volume of the data efficiently, enabling interactive visualization and exploration of the whole catalog's spectra. We show the efficiency of our method with our open-source, interactive, web-based tool for exploring the GAIA catalog, and discuss astronomically relevant use cases of our system.
LayerFlow: Layer-wise Exploration of LLM Embeddings using Uncertainty-aware Interlinked Projections
Authors: Rita Sevastjanova, Robin Gerling, Thilo Spinner, Mennatallah El-Assady
Published at EuroVis, 2025

Large language models (LLMs) represent words through contextual word embeddings encoding different language properties like semantics and syntax. Understanding these properties is crucial, especially for researchers investigating language model capabilities, employing embeddings for tasks related to text similarity, or evaluating the reasons behind token importance as measured through attribution methods. Applications for embedding exploration frequently involve dimensionality reduction techniques, which reduce high-dimensional vectors to two dimensions used as coordinates in a scatterplot. This data transformation step introduces uncertainty that can be propagated to the visual representation and influence users' interpretation of the data. To communicate such uncertainties, we present LayerFlow – a visual analytics workspace that displays embeddings in an interlinked projection design and communicates the transformation, representation, and interpretation uncertainty. In particular, to hint at potential data distortions and uncertainties, the workspace includes several visual components, such as convex hulls showing 2D and HD clusters, data point pairwise distances, cluster summaries, and projection quality metrics. We show the usability of the presented workspace through replication and expert case studies that highlight the need to communicate uncertainty through multiple visual components and different data perspectives.
Mapping Mental Models of Uncertainty to Parallel Coordinates by Probabilistic Brushing
Authors: Gabriel Borrelli, Till Ittermann, Lars Linsen
Published at EuroVis, 2025

Through training and gathered experience, domain experts attain a mental model of the uncertainties inherent in the visual analytics processes for their respective domain. For an accurate data analysis and trustworthiness of the analysis results, it is essential to include this knowledge and consider this model of uncertainty during the analytical process. For multi-dimensional data analysis, Parallel Coordinates are a widely used approach due to their linear scalability with the number of dimensions and bijective (i.e., loss-less) data transformation. However, selections in Parallel Coordinates are typically achieved by a binary brushing operation on the axes, which does not allow the users to map their mental model of uncertainties to their selection. We, therefore, propose Probabilistic Parallel Coordinates as a natural extension of the classical Parallel Coordinates approach that integrates probabilistic brushing on the axes. It supports the interactive modeling of a probability distribution for each parallel coordinate. The selections on multiple axes are combined accordingly. An efficient rendering on a compute shader facilitates interactive frame rates. We evaluated our open-source tool with practitioners and compared it to classical Parallel Coordinates on multiple regression and uncertain selection tasks in user studies.
MatplotAlt: A Python Library for Adding Alt Text to Matplotlib Figures in Computational Notebooks
Authors: Kai Nylund, Jennifer Mankoff, Venkatesh Potluri
Published at EuroVis, 2025

We present MatplotAlt, an open-source Python package for easily adding alternative text to Matplotlib figures. MatplotAlt equips Jupyter notebook authors to automatically generate and surface chart descriptions with a single line of code or command, and supports a range of options that allow users to customize the generation and display of captions based on their preferences and accessibility needs. Our evaluation indicates that MatplotAlt's heuristic and LLM-based methods to generate alt text can create accurate long-form descriptions of both simple univariate and complex Matplotlib figures. We find that state-of-the-art LLMs still struggle with factual errors when describing charts, and improve the accuracy of our descriptions by prompting GPT4-turbo with heuristic-based alt text or data tables parsed from the Matplotlib figure.
Modeling and Measuring the Chart Communication Recall Process
Authors: Anjana Arunkumar, Lace M. K. Padilla, Chris Bryan
Published at EuroVis, 2025

Understanding memory in the context of data visualizations is paramount for effective design. While immediate clarity in a visualization is crucial, retention of its information determines its long-term impact. While extensive research has underscored the elements enhancing visualization memorability, a limited body of work has delved into modeling the recall process. This study investigates the temporal dynamics of visualization recall, focusing on factors influencing recollection, shifts in recall veracity, and the role of participant demographics. Using data from an empirical study (n = 104), we propose a novel approach combining temporal clustering and handcrafted features to model recall over time. A long short-term memory (LSTM) model with attention mechanisms predicts recall patterns, revealing alignment with informativeness scores and participant characteristics. Our findings show that perceived informativeness dictates recall focus, with more informative visualizations eliciting narrative-driven insights and less informative ones prompting aesthetic-driven responses. Recall accuracy diminishes over time, particularly for unfamiliar visualizations, with age and education significantly shaping recall emphases. These insights advance our understanding of visualization recall, offering practical guidance for designing visualizations that enhance retention and comprehension. All data and materials are available at: https://osf.io/ghe2j/.
Multipla: Multiscale Pangenomic Locus Analysis
Authors: Astrid van den Brandt, Emilia Ståhlbom, Fredericus Johannes Maria van Workum, Huub van de Wetering, Claes Lundström, Sandra Smit, Anna Vilanova
Published at EuroVis, 2025

Comparing gene organization across genomic sequences reveals insights into evolutionary and functional diversity among different organisms and varieties. Performing this task across many sequences, such as from a pangenome, is challenging because of the scale, the density of information, and the inherent variation. Often, analyses are centered on a genomic region of interest—a locus that might be associated with a trait or contain genes within the same family or biological pathway. Within these regions, researchers examine the conservation of gene order and orientation across organisms and assess sequence similarity, along with other gene content features such as gene size, to find biological variations or potential errors in the data. Automated methods in comparative genomics struggle to identify meaningful patterns due to varying and often unknown features of interest, leaving manual, time-intensive, and scalability-challenged visualization as the primary alternative. To address these challenges, we present a multiscale design for studying gene organization within pangenomes, developed in close collaboration with domain experts. Our tool, Multipla, enables users to explore organization at multiple levels of detail in a decluttered manner through layout abstractions, semantic zooming, and layouts with flexible distance definitions and feature selections, combining the advantages of manual and automated methods used in practice. We evaluate the design of Multipla through two pangenomic use cases and conclude with lessons learned from designing multiscale views for pangenomic locus analysis.
Necessary but not Sufficient: Limitations of Projection Quality Metrics
Authors: Alister Machado, Michael Behrisch 0001, Alexandru C. Telea
Published at EuroVis, 2025

High-dimensional data analysis often uses dimensionality reduction (DR, also called projection) to map data patterns to human-digestible visual patterns in a 2D scatterplot. Yet, DR methods may fail to show true data patterns and/or create visual patterns that do not represent any data patterns. Projection Quality Metrics (PQMs) are used as objective measures to gauge the above process: the higher a projection's scores in PQMs, the more it is deemed faithful to the data it represents. We show that, while PQMs can be used as exclusion criteria — low values usually mean poor projections — the converse does not always hold. For this, we develop a technique to automatically generate projections that score similar or even higher PQM values than projections created by well-known techniques, but show different, often confusing, visual patterns. Our results show that accepted PQMs cannot be used as an exclusive way to tell whether a projection yields accurate and interpretable visual patterns — in this sense, PQMs play a role akin to that of summary statistics in exploratory data analysis. We also show that not all studied metrics can befooled equally well, suggesting a ranking of metrics in their ability to reliably capture quality.
Nodes, Edges, and Artistic Wedges: A Survey on Network Visualization in Art History
Authors: Michaela Tuscher, Velitchko Filipov, Teresa Kamencek, Raphael Rosenberg, Silvia Miksch
Published at EuroVis, 2025

Art history traditionally relies on qualitative methods. However, the increasing availability of digitized archives has opened new possibilities for research by integrating visual analytics. This survey presents a comprehensive review of the intersection between art history and visual analytics, focusing on network visualization and how it supports researchers in analyzing and understanding complex art historical relationships through nodes (e.g., artists, artworks, institutions) and edges (the relationships between them). We explore how these approaches enable dynamic analysis, offering novel perspectives on artistic influence, stylistic evolution, and social interactions within the art world. Through this, we also examine wedges, a metaphor for the friction often present in art history between individuals and institutions. These tensions, which have historically played a pivotal role in shaping artistic movements, are now better understood through the lens of network visualization, revealing how conflicts and power dynamics influenced the development of art. Through a hierarchical categorization of the literature, we outline saturated problems and research areas as well as ongoing challenges in art historical research. Furthermore, we highlight the potential of visual analytics to bridge the gap between traditional qualitative research and modern computational analysis, offering interactive exploration, temporal analysis, and complex network visualization. We provide a structured foundation for future research in art history, emphasizing the value of network visualization in enriching the understanding of art history.
NODKANT: Exploring Constructive Network Physicalization
Authors: Daniel Pahr, Sara Di Bartolomeo, Henry Ehlers, Velitchko Andreev Filipov, Christina Stoiber, Wolfgang Aigner, Hsiang-Yun Wu, Renata G. Raidou
Published at EuroVis, 2025

Physicalizations, which combine perceptual and sensorimotor interactions, offer an immersive way to comprehend complex data visualizations by stimulating active construction and manipulation. This study investigates the impact of personal construction on the comprehension of physicalized networks. We propose a physicalization toolkit—NODKANT—for constructing modular node-link diagrams consisting of a magnetic surface, 3D printable and stackable node labels, and edges of adjustable length. In a mixed-methods between-subject lab study with 27 participants, three groups of people used NODKANT to complete a series of low-level analysis tasks in the context of an animal contact network. The first group was tasked with freely constructing their network using a sorted edge list, the second group received step-by-step instructions to create a predefined layout, and the third group received a pre-constructed representation. While free construction proved on average more time-consuming, we show that users extract more insights from the data during construction and interact with their representation more frequently, compared to those presented with step-by-step instructions. Interestingly, the increased time demand cannot be measured in users' subjective task load. Finally, our findings indicate that participants who constructed their own representations were able to recall more detailed insights after a period of 10–14 days compared to those who were given a pre-constructed network physicalization. All materials, data, code for generating instructions, and 3D printable meshes are available on https://osf.io/tk3g5/.
Optimizing Staircase Motifs in Biofabric Network Layouts
Authors: Sara Di Bartolomeo, Markus Wallinger, Martin Nöllenburg
Published at EuroVis, 2025

Biofabric is a novel method for network visualization, with promising potential to highlight specific network features. Recent studies emphasize the importance of staircase motifs — equivalent to fans or stars in node-link diagrams — within Biofabric. However, to effectively showcase these motifs, we need to formulate specialized layout algorithms. This paper introduces a method to compute optimal layouts for Biofabric, focusing on maximizing staircase formation. We present an Integer Linear Programming (ILP) model for this task and evaluate its performance in terms of scalability and output quality against a leading heuristic method, Degreecending. Our results demonstrate that the ILP approach identifies significantly more, and often longer, staircases compared to Degreecending, albeit with the trade-off of higher computation times. Our supplemental material, including a full copy of the paper, code, and results, is available on osf.io.
Player-Centric Shot Maps in Table Tennis
Authors: Aymeric Erades, Romain Vuillemot
Published at EuroVis, 2025

Shot maps are popular in many sports as they typically plot events and player positions in the way they are collected, using a pitch or a table as an absolute coordinate system. We introduce a variation of a table tennis shot map that shifts the point of view from the table to the player. This results in a new reference system to plot incoming balls relative to the player's position rather than on the table. This approach aligns with how table tennis tactical analysis is conducted, focusing on identifying empty spaces and weak spots around the players. We describe the motivation behind this work, built through close collaboration with two table tennis experts, and demonstrate how this approach aligns with the way they analyze games to reveal key tactical aspects. We also present the design rationale and the computer vision pipeline used to accurately collect data from broadcast videos. Our findings show that the technique enables capturing insights that were not visible with the absolute coordinate system, particularly in understanding regions that are reachable and those close to the pivot area of the player.
PrismBreak: Exploration of Multi-Dimensional Mixture Models
Authors: Brian Zahoransky, Tobias Günther, Kai Lawonn
Published at EuroVis, 2025

In data science, visual data exploration becomes increasingly more challenging due to the continued rapid increase of data dimensionality and data sizes. To manage complexity, two orthogonal approaches are commonly used in practice: First, data is frequently clustered in high-dimensional space by fitting mixture models composed of normal distributions or Student t-distributions. Second, dimensionality reduction is employed to embed high-dimensional point clouds in a two- or three-dimensional space. Those algorithms determine the spatial arrangement in low-dimensional space without further user interaction. This leaves little room for a guided exploration and data analysis. In this paper, we propose a novel visualization system for the effective exploration and construction of potential subspaces onto which mixture models can be projected. The subspaces are spanned linearly via basis vectors, for which a vast number of basis vector combinations is theoretically imaginable. Our system guides the user step-by-step through the selection process by letting users choose one basis vector at a time. To guide the process, multiple choices are pre-visualized at once on a multi-faceted prism. In addition to the qualitative visualization of the distributions, multiple quantitative metrics are calculated by which subspaces can be compared and reordered, including variance, sparsity, and visibility. Further, a bookmarking tool lets users record and compare different basis vector combinations. The usability of the system is evaluated by data scientists and is tested on several high-dimensional data sets.
Random Access Segmentation Volume Compression for Interactive Volume Rendering
Authors: Max Piochowiak, Florian Kurpicz, Carsten Dachsbacher
Published at EuroVis, 2025

Segmentation volumes are voxel data sets often used in machine learning, connectomics, and natural sciences. Their large sizes make compression indispensable for storage and processing, including GPU video memory constrained real-time visualization. Fast Compressed Segmentation Volumes (CSGV) [PD24] provide strong brick-wise compression and random access at the brick level. Voxels within a brick, however, have to be decoded serially and thus rendering requires caching of visible full bricks, consuming extra memory. Without caching, accessing voxels can have a worst-case decoding overhead of up to a full brick (typically over 32.000 voxels). We present CSGV-R which provide true multi-resolution random access on a per-voxel level. We leverage Huffman-shaped Wavelet Trees for random accesses to variable bit-length encoding and their rank operation to query label palette offsets in bricks. Our real-time segmentation volume visualization removes decoding artifacts from CSGV and renders CSGV-R volumes without caching bricks at faster render times. CSGV-R has slightly lower compression rates than CSGV, but outperforms Neuroglancer, the state-of-the-art compression technique with true random access, with 2× to 4× smaller data sets at rates between 0.648% and 4.411% of the original volume sizes.
Sca2Gri: Scalable Gridified Scatterplots
Authors: Steffen Frey
Published at EuroVis, 2025

Scatterplots are widely used in exploratory data analysis. Representing data points as glyphs is often crucial for in-depth investigation, but this can lead to significant overlap and visual clutter. Recent post-processing techniques address this issue, but their computational and/or visual scalability is generally limited to thousands of points and unable to effectively deal with large datasets in the order of millions. This paper introduces Sca2Gri (Scalable Gridified Scatterplots), a grid-based post-processing method designed for analysis scenarios where the number of data points substantially exceeds the number of glyphs that can be reasonably displayed. Sca2Gri enables interactive grid generation for large datasets, offering flexible user control of glyph size, maximum displacement for point to cell mapping, and scatterplot focus area. While Sca2Gri's computational complexity scales cubically with the number of cells (which is practically bound to thousands for legible glyph sizes), its complexity is linear with respect to the number of data points, making it highly scalable beyond millions of points.
SUPQA: LLM-based Geo-Visualization for Subjective Urban Performance Question-Answering
Authors: Haiwen Huang, Juntong Chen, Changbo Wang, Chenhui Li 0001
Published at EuroVis, 2025

As urbanization accelerates, urban performance has become a growing concern, impacting every aspect of residents' lives. However, urban performance exploration is a tedious and highly subjective process for users. Users need to manually collect and integrate various information, or spend a large amount of time and effort due to the steep learning curves of existing specialized tools. To address these challenges, we introduce SUPQA, a novel approach for urban performance exploration using natural language as input and interactive geographic visualizations as output. Our approach leverages Large Language Models (LLMs) to effectively interpret user intents and quantify various urban performance measures. We integrate progressive navigation and multi-geographic scale analysis in our visualization system, explaining the reasoning process and streamlining users' decision-making workflow. Two usage scenarios and evaluations demonstrate the effectiveness of SUPQA in helping residents and planners acquire desired information more efficiently and enhancing the quality of decision-making.
SurpriseExplora: Tuning and Contextualizing Model-derived Maps with Interactive Visualizations
Authors: Akim Ndlovu, Hilson Shrestha, Evan Peck, Lane Harrison
Published at EuroVis, 2025

People craft choropleth maps to monitor, analyze, and understand spatially distributed data. Recent visualization work has addressed several known biases in choropleth maps by developing new model- and metrics- based approaches (e.g. Bayesian surprise). However, effective use of these techniques requires extensive parameter setting and tuning, making them difficult or impossible for users without substantial technical skills. In this paper we describe SurpriseExplora, which addresses this gap through direct manipulation techniques for re-targeting a Bayesian surprise model's scope and parameters. We present three use cases to illustrate the capabilities of SurpriseExplora, showing for example how models calculated at a national level can obscure key findings that can be revealed through interaction sequences common to map visualizations (e.g. zooming), and how augmenting funnel-plot visualizations with interactions that adjust underlying models can account for outliers or skews in spatial datasets. We evaluate SurpriseExplora through an expert review with visualization researchers and practitioners. We conclude by discussing how SurpriseExplora uncovers new opportunities for sense-making within the broader ecosystem of map visualizations, as well as potential empirical studies with non-expert populations.