DevLan/vispubs huggingface dataset:
Qualitative Study for LLM-assisted Design Study Process: Strategies, Challenges, and Roles
Authors: Shaolun Ruan, Rui Sheng, Xiaolin Wen, Jiachen Wang 0001, Tianyi Zhang, Yong Wang 0021, Tim Dwyer, Jiannan Li
Published at Vis, 2025

Design studies aim to develop visualization solutions for real-world problems across various application domains. Recently, the emergence of large language models (LLMs) has introduced new opportunities to enhance the design study process, providing capabilities such as creative problem-solving, data handling, and insightful analysis. However, despite their growing popularity, there remains a lack of systematic understanding of how LLMs can effectively assist researchers in visualization-specific design studies. In this paper, we conducted a rnulti-stage qualitative study to fill this gap, which involved 30 design study researchers from diverse backgrounds and expertise levels. Through in-depth interviews and carefully-designed questionnaires, we investigated strategies for utilizing LLMs, the challenges encountered, and the practices used to overcome them. We further compiled the roles that LLMs can play across different stages of the design study process. Our findings highlight practical implications to inform visualization practitioners, and also provide a framework for leveraging LLMs to facilitate the design study process in visualization research.
Quantifying Visualization Vibes: Measuring Socio-Indexicality at Scale
Authors: Amy Rae Fox, Michelle Morgenstern, Graham M. Jones, Arvind Satyanarayan
Published at Vis, 2025

What impressions might readers form with visualizations that go beyond the data they encode? In this paper, we build on recent work that demonstrates the socio-indexical function of visualization, showing that visualizations communicate more than the data they explicitly encode. Bridging this with prior work examining public discourse about visualizations, we contribute an analytic framework for describing inferences about an artifact's social provenance. Via a series of attribution-elicitation surveys, we offer descriptive evidence that these social inferences: (1) can be studied asynchronously, (2) are not unique to a particular sociocultural group or a function of limited data literacy, and (3) may influence assessments of trust. Further, we demonstrate (4) how design features act in concert with the topic and underlying messages of an artifact's data to give rise to such ‘beyond-data’ readings. We conclude by discussing the design and research implications of inferences about social provenance, and why we believe broadening the scope of research on human factors in visualization to include sociocultural phenomena can yield actionable design recommendations to address urgent challenges in public data communication.
Reframing Pattern: A Comprehensive Approach to a Composite Visual Variable
Authors: Tingying He, Jason Dykes, Petra Isenberg, Tobias Isenberg 0001
Published at Vis, 2025

We present a new comprehensive theory for explaining, exploring, and using pattern as a visual variable in visualization. Although patterns have long been used for data encoding and continue to be valuable today, their conceptual foundations are precarious: the concepts and terminology used across the research literature and in practice are inconsistent, making it challenging to use patterns effectively and to conduct research to inform their use. To address this problem, we conduct a comprehensive cross-disciplinary literature review that clarifies ambiguities around the use of “pattern” and “texture”. As a result, we offer a new consistent treatment of pattern as a composite visual variable composed of structured groups of graphic primitives that can serve as marks for encoding data individually and collectively. This new and widely applicable formulation opens a sizable design space for the visual variable pattern, which we formalize as a new system comprising three sets of variables: the spatial arrangement of primitives, the appearance relationships among primitives, and the retinal visual variables that characterize individual primitives. We show how our pattern system relates to existing visualization theory and highlight opportunities for visualization design. We further explore patterns based on complex spatial arrangements, demonstrating explanatory power and connecting our conceptualization to broader theory on maps and cartography. An author version and additional materials are available on OSF: osf.io/z7ae2.
RelMap: Reliable Spatiotemporal Sensor Data Visualization via Imputative Spatial Interpolation
Authors: Juntong Chen, Huayuan Ye, He Zhu, Siwei Fu, Changbo Wang, Chenhui Li 0001
Published at Vis, 2025

Accurate and reliable visualization of spatiotemporal sensor data such as environmental parameters and meteorological conditions is crucial for informed decision-making. Traditional spatial interpolation methods, however, often fall short of producing reliable interpolation results due to the limited and irregular sensor coverage. This paper introduces a novel spatial interpolation pipeline that achieves reliable interpolation results and produces a novel heatmap representation with uncertainty information encoded. We leverage imputation reference data from Graph Neural Networks (GNNs) to enhance visualization reliability and temporal resolution. By integrating Principal Neighborhood Aggregation (PNA) and Geographical Positional Encoding (GPE), our model effectively learns the spatiotemporal dependencies. Furthermore, we propose an extrinsic, static visualization technique for interpolation-based heatmaps that effectively communicates the uncertainties arising from various sources in the interpolated map. Through a set of use cases, extensive evaluations on real-world datasets, and user studies, we demonstrate our model's superior performance for data imputation, the improvements to the interpolant with reference data, and the effectiveness of our visualization design in communicating uncertainties.
ReVISit 2: A Full Experiment Life Cycle User Study Framework
Authors: Zach Cutler, Jack Wilburn, Hilson Shrestha, Yiren Ding, Brian C. Bollen, Khandaker Abrar Nadib, Tingying He, Andrew M. McNutt, Lane Harrison, Alexander Lex
Published at Vis, 2025

Online user studies of visualizations, visual encodings, and interaction techniques are ubiquitous in visualization research. Yet, designing, conducting, and analyzing studies effectively is still a major burden. Although various packages support such user studies, most solutions address only facets of the experiment life cycle, make reproducibility difficult, or do not cater to nuanced study designs or interactions. We introduce reVISit 2, a software framework that supports visualization researchers at all stages of designing and conducting browser-based user studies. ReVISit supports researchers in the design, debug & pilot, data collection, analysis, and dissemination experiment phases by providing both technical affordances (such as replay of participant interactions) and sociotechnical aids (such as a mindfully maintained community of support). It is a proven system that can be (and has been) used in publication-quality studies-which we demonstrate through a series of experimental replications. We reflect on the design of the system via interviews and an analysis of its technical dimensions. Through this work, we seek to elevate the ease with which studies are conducted, improve the reproducibility of studies within our community, and support the construction of advanced interactive studies.
Running with Data: a Survey of the Current Research and a Design Exploration of Future Immersive Visualisations
Authors: Ang Li 0007, Charles Perin, Gianluca Demartini, Stephen Viller, Jarrod Knibbe, Maxime Cordeil
Published at Vis, 2025

This work investigates the current research on in-situ visualisations for running: visualisations about data that are referred to during the running activity. We analyse 47 papers from 33 Human-Computer Interaction and Visualisation venues and identify six dimensions of a design space of in-situ running visualisations. Our analysis of this design space highlights an emerging trend: a shift from on-body, peripersonal visualisations (i.e., in the space within direct reach, such as visualisations on a smartwatch or a mobile phone display) towards extrapersonal displays (i.e., in the space beyond immediate reach, such as visualisations in immersive augmented reality displays) that integrate data in the runner's surrounding environment. We explore this opportunity by conducting a series of workshops with 10 active runners in total, eliciting design concepts for running visualisations and interactions beyond conventional 2D displays. We find that runners show a strong interest for visualisation designs that favour more context-aware, interactive, and unobtrusive experiences that seamlessly integrate with their run. These findings inform a set of design considerations for future immersive running visualisations and highlight directions for further research.
SceneLoom: Communicating Data with Scene Context
Authors: Lin Gao, Leixian Shen, Yuheng Zhao, Jiexiang Lan, Huamin Qu, Siming Chen 0001
Published at Vis, 2025

In data-driven storytelling contexts such as data journalism and data videos, data visualizations are often presented alongside real-world imagery to support narrative context. However, these visualizations and contextual images typically remain separated, limiting their combined narrative expressiveness and engagement. Achieving this is challenging due to the need for fine-grained alignment and creative ideation. To address this, we present SceneLoom, a Vision-Language Model (VLM)-powered system that facilitates the coordination of data visualization with real-world imagery based on narrative intents. Through a formative study, we investigated the design space of coordination relationships between data visualization and real-world scenes from the perspectives of visual alignment and semantic coherence. Guided by the derived design considerations, SceneLoom leverages VLMs to extract visual and semantic features from scene images and data visualization, and perform design mapping through a reasoning process that incorporates spatial organization, shape similarity, layout consistency, and semantic binding. The system generates a set of contextually expressive, image-driven design alternatives that achieve coherent alignments across visual, semantic, and data dimensions. Users can explore these alternatives, select preferred mappings, and further refine the design through interactive adjustments and animated transitions to support expressive data communication. A user study and an example gallery validate SceneLoom's effectiveness in inspiring creative design and facilitating design externalization.
SEAL: Spatially-resolved Embedding Analysis with Linked Imaging Data
Authors: Simon Warchol, Grace Guo 0001, Johannes Knittel, Dan Freeman, Usha Shalla, Jeremy Muhlich, Peter K. Sorger, Hanspeter Pfister
Published at Vis, 2025

Dimensionality reduction techniques help analysts make sense of complex, high-dimensional spatial datasets, such as multiplexed tissue imaging, satellite imagery, and astronomical observations, by projecting data attributes into a two-dimensional space. However, these techniques typically abstract away crucial spatial, positional, and morphological contexts, complicating interpretation and limiting insights. To address these limitations, we present SEAL, an interactive visual analytics system designed to bridge the gap between abstract 2D embeddings and their rich spatial imaging context. SEAL introduces a novel hybrid-embedding visualization that preserves image and morphological information while integrating critical high-dimensional feature data. By adapting set visualization methods, SEAL allows analysts to identify, visualize, and compare selections-defined manually or algorithmically-in both the embedding and original spatial views, facilitating a deeper understanding of the spatial arrangement and morphological characteristics of entities of interest. To elucidate differences between selected sets of items, SEAL employs a scalable surrogate model to calculate feature importance scores, identifying the most influential features governing the position of objects within embeddings. These importance scores are visually summarized across selections, with mathematical set operations enabling detailed comparative analyses. We demonstrate SEAL's effectiveness and versatility through three case studies: colorectal cancer tissue analysis with a pharmacologist, melanoma investigation with a cell biologist, and exploration of sky survey data with an astronomer. These studies underscore the importance of integrating image context into embedding spaces when interpreting complex imaging datasets. Implemented as a standalone tool while also integrating seamlessly with computational notebooks, SEAL provides an interactive platform for spatially informed exploration of high-dimensional datasets, significantly enhancing interpretability and insight generation.
Sel3DCraft: Interactive Visual Prompts for User-Friendly Text-to-3D Generation
Authors: Nan Xiang, Tianyi Liang 0002, Haiwen Huang, Shiqi Jiang 0001, Hao Huang, Yifei Huang 0006, Liangyu Chen 0001, Changbo Wang, Chenhui Li 0001
Published at Vis, 2025

Text-to-3D (T23D) generation has transformed digital content creation, yet remains bottlenecked by blind trial-and-error prompting processes that yield unpredictable results. While visual prompt engineering has advanced in text-to-image domains, its application to 3D generation presents unique challenges requiring multi-view consistency evaluation and spatial understanding. We present Sel3DCraft, a visual prompt engineering system for T23D that transforms unstructured exploration into a guided visual process. Our approach introduces three key innovations: a dual-branch structure combining retrieval and generation for diverse candidate exploration; a multi-view hybrid scoring approach that leverages MLLMs with innovative high-level metrics to assess 3D models with human-expert consistency; and a prompt-driven visual analytics suite that enables intuitive defect identification and refinement. Extensive testing and a user study demonstrate that Sel3DCraft surpasses other T23D systems in supporting creativity for designers.
Self-Supervised Continuous Colormap Recovery from a 2D Scalar Field Visualization without a Legend
Authors: Hongxu Liu, Xinyu Chen, Haoyang Zheng, Manyi Li, Zhenfan Liu, Fumeng Yang, Yunhai Wang, Changhe Tu, Qiong Zeng
Published at Vis, 2025

Recovering a continuous colormap from a single 2D scalar field visualization can be quite challenging, especially in the absence of a corresponding color legend. In this paper, we propose a novel colormap recovery approach that extracts the colormap from a color-encoded 2D scalar field visualization by simultaneously predicting the colormap and underlying data using a decoupling-and-reconstruction strategy. Our approach first separates the input visualization into colormap and data using a decoupling module, then reconstructs the visualization with a differentiable color-mapping module. To guide this process, we design a reconstruction loss between the input and reconstructed visualizations, which serves both as a constraint to ensure strong correlation between colormap and data during training, and as a self-supervised optimizer for fine-tuning the predicted colormap of unseen visualizations during inferencing. To ensure smoothness and correct color ordering in the extracted colormap, we introduce a compact colormap representation using cubic B-spline curves and an associated color order loss. We evaluate our method quantitatively and qualitatively on a synthetic dataset and a collection of real-world visualizations from the VIS30K dataset [9]. Additionally, we demonstrate its utility in two prototype applications-colormap adjustment and colormap transfer-and explore its generalization to visualizations with color legends and ones encoded using discrete color palettes.
Set Size Matters: Capacity-Limited Perception of Grouped Spatial-Frequency Glyphs
Authors: Yiran Li 0005, Shan Shao, Peter Baudains, Andrew Isaac Meso, Nicolas S. Holliman, Alfie Abdul-Rahman, Rita Borgo
Published at Vis, 2025

Recent work suggests that shape can encode quantitative data via a mapping between value and spatial frequency (SF). However, the set-size effect when perceiving multiple SF based items remains unclear. While automatic feature extraction has been found to be less affected by set size (number of items in a group), higher-level processes for making perceptual decisions tend to require increased cognitive demand. To investigate the set-size effect on comparing integrated SF based items, we used a risk-based scenario to assess discrimination performance. Participants were asked to discriminate between pairs of maps containing multiple SF glyphs, in which each glyph represents one of four discrete levels (none, low, medium, high), forming an aggregate “risk strength” per map. The set size was also adjusted across conditions, ranging from small (3 items) to large (7 items). Discrimination sensitivity is modeled with a logistic function and response time with a mixed-effect linear model. Results show that smaller set sizes and lower overall strength enable more precise discrimination, with faster response times for larger differences between maps. Incorporating set size and overall strength into the logistic model, we found that these variables both independently and jointly influence discrimination sensitivity. We suggest these results point towards capacity-limited processes rather than purely automatic ensemble coding. Our findings highlight the importance of set size and overall signal strength when presenting multiple SF glyphs in data visualization.
Shifting Expectations for Encoding Rules Mitigates Misinterpretation of Connected Scatterplots
Authors: Wen Xu, Lace M. K. Padilla
Published at Vis, 2025

Connected scatterplots visualize time-series data by connecting the points on a scatterplot based on temporal sequence. Viewers are prone to misinterpret the direction of time in these visualizations, possibly because they encode time with an unexpected rule - along the connected line (TIME IS A LINE) instead of from left to right (RIGHT IS LATER) as conventional in line charts. In this paper, we use the connected scatterplot to illustrate a perspective on visualization comprehension centered around expectations of encoding rules. People have initial expectations of encoding rules for visualizations that can stem from conventional practices or metaphors, and these expectations have been recognized as a potential factor influencing visualization comprehension. We present three preregistered experiments (n = 1429 in total) demonstrating two kinds of design interventions to strengthen the correct expectation for time and testing their effectiveness in reducing errors for understanding realistic connected scatterplots. We found that visual treatments that suppress the incorrect chart-type expectation and directional cues that emphasize the correct expectation both led viewers to expect TIME IS A LINE more. An explicit directional cue (arrows), ideally redundantly encoded with another cue (trace-line effect or animation), was most effective for reducing misinterpretations. Our findings not only provide practical guidelines for designing connected scatterplots but also contribute theoretical insights to inform the design of novel visualizations that challenge interpretability by defying expectations.
Stitching Meaning: Practices of Data Textile Creators
Authors: Sydney K. Purdue, Eduardo Puerta, Enrico Bertini, Melanie Tory
Published at Vis, 2025

Tens of thousands of people have represented data by creating data-encoding textile pieces like blankets, scarves, and more. A prototypical example is the temperature blanket, which represents the weather through rows or blocks of different colors mapped to temperature ranges. While researchers have used fiber arts mediums to create exploratory projects, data visualization and physicalization research has largely not engaged with examples from this enormous and diverse community. We explore the space of data textiles, or fiber arts that encode information, by surveying creators (i.e., data fiber artists) on their projects and processes. We create a corpus of 159 examples of data textiles and present a schema characterizing the data encoding methods used in these projects. We also gather insights into creators' data workflows as well as their motives and discoveries through making with their data. Creators of data textiles use distinct processes to map their data, building fabric from component structures and substructures while using material properties like color and texture. From diverse data-tracking procedures, creators use and relate to data in varied ways. Working on these pieces also contributes to the creators' personal growth and data understanding. Our findings point to new opportunities for visualization, including opportunities to support fiber artists with tools formatted to their needs and opportunities to incorporate concepts from data textiles into other types of visualization (e.g., using texture, structural layouts, colorways).
Story Ribbons: Reimagining Storyline Visualizations with Large Language Models
Authors: Catherine Yeh, Tara Menon, Robin Singh Arya, Helen He, Moira Weigel, Fernanda B. Viégas, Martin Wattenberg
Published at Vis, 2025

Analyzing literature involves tracking interactions between characters, locations, and themes. Visualization has the potential to facilitate the mapping and analysis of these complex relationships, but capturing structured information from unstructured story data remains a challenge. As large language models (LLMs) continue to advance, we see an opportunity to use their text processing and analysis capabilities to augment and reimagine existing storyline visualization techniques. Toward this goal, we introduce an LLM-driven data parsing pipeline that automatically extracts relevant narrative information from novels and scripts. We then apply this pipeline to create Story Ribbons, an interactive visualization system that helps novice and expert literary analysts explore detailed character and theme trajectories at multiple narrative levels. Through pipeline evaluations and user studies with Story Ribbons on 36 literary works, we demonstrate the potential of LLMs to streamline narrative visualization creation and reveal new insights about familiar stories. We also describe current limitations of AI-based systems, and interaction motifs designed to address these issues.