DevLan/vispubs huggingface dataset:
Uncertainty in Science is Malleable. Advocating for User-Agency in Defining Uncertainty in Visualizations: a Case Study in Geology
Authors: Vanessa Peña Araya, Consuelo Martínez Fontaine, Xiang Wei, Guillaume Delpech, Anastasia Bezerianos
Published at CHI, 2025

Uncertainty is inherent in science built on previous results. In geoscience, for instance, researchers analyzing volcanic deposits assess the uncertainty around past deposit classifications. To aid this assessment, we followed a design by immersion approach to co-design uncertainty visualizations. We observed that besides visualizing it, it is challenging even to define what constitutes uncertainty, as how researchers understand and process uncertainty evolves. This motivated us to reach other members of the community to better understand how they integrate uncertainty in their work. Informed by a series of interviews, we first redesigned our visualization system and then introduced it as a technology probe to a broader community of geoscientists. Our results highlight that uncertainty in science is malleable and that visualization systems should be designed with this malleability in mind. Through a set of design implications, we advocate for visualizations that promote user agency and flexibility in defining and processing uncertainty.
Understanding Marine Scientist Software Tool Use
Authors: Matthew Lakier, Andrew Irwin, Daniel Vogel 0001
Published at CHI, 2025

Marine science researchers are heavy users of software tools and systems such as statistics packages, visualization tools, and online data catalogues. Following a constructivist grounded theory approach, we conduct a semi-structured interview study of 23 marine science researchers and research supports within a North American university, to understand their perceptions of and approaches towards using both graphical and code-based software tools and systems. We propose the concept of fragmentation to represent how various factors lead to isolated pockets of views and practices concerning software tool use during the research process. These factors include informal learning of tools, preferences towards doing things from scratch, and a push towards more code-based tools. Based on our findings, we suggest design priorities for user interfaces that could more effectively help support marine scientists make and use software tools and systems.
Unveiling High-dimensional Backstage: A Survey for Reliable Visual Analytics with Dimensionality Reduction
Authors: Hyeon Jeon, Hyunwook Lee, Yun-Hsin Kuo, Taehyun Yang, Daniel Archambault, Sungahn Ko, Takanori Fujiwara, Kwan-Liu Ma, Jinwook Seo
Published at CHI, 2025

Dimensionality reduction (DR) techniques are essential for visually analyzing high-dimensional data. However, visual analytics using DR often face unreliability, stemming from factors such as inherent distortions in DR projections. This unreliability can lead to analytic insights that misrepresent the underlying data, potentially resulting in misguided decisions. To tackle these reliability challenges, we review 133 papers that address the unreliability of visual analytics using DR. Through this review, we contribute (1) a workflow model that describes the interaction between analysts and machines in visual analytics using DR, and (2) a taxonomy that identifies where and why reliability issues arise within the workflow, along with existing solutions for addressing them. Our review reveals ongoing challenges in the field, whose significance and urgency are validated by five expert researchers. This review also finds that the current research landscape is skewed toward developing new DR techniques rather than their interpretation or evaluation, where we discuss how the HCI community can contribute to broadening this focus.
Visualization Guardrails: Designing Interventions Against Cherry-Picking in Interactive Data Explorers
Authors: Maxim Lisnic, Zach Cutler, Marina Kogan, Alexander Lex
Published at CHI, 2025

The growing popularity of interactive time series exploration platforms has made data visualization more accessible to the public. However, the ease of creating polished charts with preloaded data also enables selective information presentation, often resulting in biased or misleading visualizations. Research shows that these tools have been used to spread misinformation, particularly in areas such as public health and economic policies during the COVID-19 pandemic. Post hoc fact-checking may be ineffective because it typically addresses only a portion of misleading posts and comes too late to curb the spread. In this work, we explore using visualization design to counteract cherry-picking, a common tactic in deceptive visualizations. We propose a design space of guardrails—interventions to expose cherry-picking in time-series explorers. Through three crowd-sourced experiments, we demonstrate that guardrails, particularly those superimposing data, can encourage skepticism, though with some limitations. We provide recommendations for developing more effective visualization guardrails.
VisUnit: Literate Visualisation Studies Assembled from Reusable Test-Suites
Authors: Radu Jianu, Aidan Slingsby, Dany Laksono, Mershack Okoe
Published at CHI, 2025

We make four contributions to lower the overhead of conducting visualisation user studies and promote the reuse and extension of their materials. (i) A declarative Javascript specification lets experimenters describe how studies are assembled from tested visualisations, datasets, tasks and chosen evaluation strategies. (ii) A VisUnit library translates these into sequences of visual stimuli and delivers them to participants. We move away from monolithic evaluation stimuli typical of previous work and construct studies around three ingredients – visual encodings, datasets, and tasks – that can be developed independently and recombined flexibly. (iii) This paves the way for developing benchmark data+tasks test-suites as independent, reusable resources to support multiple studies. (iv) Structuring user studies as "literate" visualisation notebooks brings together in the open all ingredients necessary for replication and scrutiny: formal design specification; underlying materials; participant-facing views; and narratives justifying design and supporting reuse.
What-if Analysis for Business Professionals: Current Practices and Future Opportunities
Authors: Sneha Gathani, Zhicheng Liu, Peter J. Haas, Çagatay Demiralp
Published at CHI, 2025

What-if analysis (WIA) is essential for data-driven decision-making, allowing users to assess how changes in variables impact outcomes and explore alternative scenarios. Existing WIA research primarily supports the workflows of data scientists and analysts, and largely overlooks business professionals who engage in WIA through non-technical means. To bridge this gap, we conduct a two-part user study with 22 business professionals across marketing, sales, product, and operations roles. The first study examines their existing WIA practices, tools, and challenges. Findings reveal that business professionals perform many WIA techniques independently using rudimentary tools due to various constraints. We then implement representative WIA techniques in a visual analytics prototype and use it as a probe to conduct a follow-up study evaluating business professionals' practical use of the techniques. Results show that these techniques improve decision-making efficiency and confidence while underscoring the need for better support in data preparation, risk assessment, and domain knowledge integration. Finally, we offer design recommendations to enhance future business analytics systems.
ZuantuSet: A Collection of Historical Chinese Visualizations and Illustrations
Authors: Xiyao Mei, Yu Zhang 0043, Chaofan Yang, Rui Shi, Xiaoru Yuan
Published at CHI, 2025

Historical visualizations are a valuable resource for studying the history of visualization and inspecting the cultural context where they were created. When investigating historical visualizations, it is essential to consider contributions from different cultural frameworks to gain a comprehensive understanding. While there is extensive research on historical visualizations within the European cultural framework, this work shifts the focus to ancient China, a cultural context that remains underexplored by visualization researchers. To this aim, we propose a semi-automatic pipeline to collect, extract, and label historical Chinese visualizations. Through the pipeline, we curate ZuantuSet, a dataset with over 71K visualizations and 108K illustrations. We analyze distinctive design patterns of historical Chinese visualizations and their potential causes within the context of Chinese history and culture. We illustrate potential usage scenarios for this dataset, summarize the unique challenges and solutions associated with collecting historical Chinese visualizations, and outline future research directions.
"I Came Across a Junk": Understanding Design Flaws of Data Visualization from the Public's Perspective
Authors: Xingyu Lan, Yu Liu
Published at Vis, 2024

The visualization community has a rich history of reflecting upon visualization design flaws. Although research in this area has remained lively, we believe it is essential to continuously revisit this classic and critical topic in visualization research by incorporating more empirical evidence from diverse sources, characterizing new design flaws, building more systematic theoretical frameworks, and understanding the underlying reasons for these flaws. To address the above gaps, this work investigated visualization design flaws through the lens of the public, constructed a framework to summarize and categorize the identified flaws, and explored why these flaws occur. Specifically, we analyzed 2227 flawed data visualizations collected from an online gallery and derived a design task-associated taxonomy containing 76 specific design flaws. These flaws were further classified into three high-level categories (i.e., misinformation, uninformativeness, unsociability) and ten subcategories (e.g., inaccuracy, unfairness, ambiguity). Next, we organized five focus groups to explore why these design flaws occur and identified seven causes of the flaws. Finally, we proposed a research agenda for combating visualization design flaws and summarize nine research opportunities.
"It's a Good Idea to Put It Into Words": Writing 'Rudders' in the Initial Stages of Visualization Design
Authors: Chase Stokes, Clara Hu, Marti A. Hearst
Published at Vis, 2024

Written language is a useful tool for non-visual creative activities like composing essays and planning searches. This paper investigates the integration of written language into the visualization design process. We create the idea of a ‘writing rudder,’ which acts as a guiding force or strategy for the designer. Via an interview study of 24 working visualization designers, we first established that only a minority of participants systematically use writing to aid in design. A second study with 15 visualization designers examined four different variants of written rudders: asking questions, stating conclusions, composing a narrative, and writing titles. Overall, participants had a positive reaction; designers recognized the benefits of explicitly writing down components of the design and indicated that they would use this approach in future design work. More specifically, two approaches — writing questions and writing conclusions/takeaways — were seen as beneficial across the design process, while writing narratives showed promise mainly for the creation stage. Although concerns around potential bias during data exploration were raised, participants also discussed strategies to mitigate such concerns. This paper contributes to a deeper understanding of the interplay between language and visualization, and proposes a straightforward, lightweight addition to the visualization design process.
2D Embeddings of Multi-dimensional Partitionings
Authors: Marina Evers, Lars Linsen
Published at Vis, 2024

Partitionings (or segmentations) divide a given domain into disjoint connected regions whose union forms again the entire domain. Multi-dimensional partitionings occur, for example, when analyzing parameter spaces of simulation models, where each segment of the partitioning represents a region of similar model behavior. Having computed a partitioning, one is commonly interested in understanding how large the segments are and which segments lie next to each other. While visual representations of 2D domain partitionings that reveal sizes and neighborhoods are straightforward, this is no longer the case when considering multi-dimensional domains of three or more dimensions. We propose an algorithm for computing 2D embeddings of multi-dimensional partitionings. The embedding shall have the following properties: It shall maintain the topology of the partitioning and optimize the area sizes and joint boundary lengths of the embedded segments to match the respective sizes and lengths in the multi-dimensional domain. We demonstrate the effectiveness of our approach by applying it to different use cases, including the visual exploration of 3D spatial domain segmentations and multi-dimensional parameter space partitionings of simulation ensembles. We numerically evaluate our algorithm with respect to how well sizes and lengths are preserved depending on the dimensionality of the domain and the number of segments
A Deixis-Centered Approach for Documenting Remote Synchronous Communication around Data Visualizations
Authors: Chang Han, Katherine E. Isaacs
Published at Vis, 2024

Referential gestures, or as termed in linguistics, deixis, are an essential part of communication around data visualizations. Despite their importance, such gestures are often overlooked when documenting data analysis meetings. Transcripts, for instance, fail to capture gestures, and video recordings may not adequately capture or emphasize them. We introduce a novel method for documenting collaborative data meetings that treats deixis as a first-class citizen. Our proposed framework captures cursor-based gestural data along with audio and converts them into interactive documents. The framework leverages a large language model to identify word correspondences with gestures. These identified references are used to create context-based annotations in the resulting interactive document. We assess the effectiveness of our proposed method through a user study, finding that participants preferred our automated interactive documentation over recordings, transcripts, and manual note-taking. Furthermore, we derive a preliminary taxonomy of cursor-based deictic gestures from participant actions during the study. This taxonomy offers further opportunities for better utilizing cursor-based deixis in collaborative data analysis scenarios.
A General Framework for Comparing Embedding Visualizations Across Class-Label Hierarchies
Authors: Trevor Manz, Fritz Lekschas, Evan Greene, Greg Finak, Nils Gehlenborg
Published at Vis, 2024

Projecting high-dimensional vectors into two dimensions for visualization, known as embedding visualization, facilitates perceptual reasoning and interpretation. Comparing multiple embedding visualizations drives decision-making in many domains, but traditional comparison methods are limited by a reliance on direct point correspondences. This requirement precludes comparisons without point correspondences, such as two different datasets of annotated images, and fails to capture meaningful higher-level relationships among point groups. To address these shortcomings, we propose a general framework for comparing embedding visualizations based on shared class labels rather than individual points. Our approach partitions points into regions corresponding to three key class concepts—confusion, neighborhood, and relative size—to characterize intra- and inter-class relationships. Informed by a preliminary user study, we implemented our framework using perceptual neighborhood graphs to defne these regions and introduced metrics to quantify each concept. We demonstrate the generality of our framework with usage scenarios from machine learning and single-cell biology, highlighting our metrics' ability to draw insightful comparisons across label hierarchies. To assess the effectiveness of our approach, we conducted an evaluation study with fve machine learning researchers and six single-cell biologists using an interactive and scalable prototype built with Python, JavaScript, and Rust. Our metrics enable more structured comparisons through visual guidance and increased participants' confdence in their fndings
A Large-Scale Sensitivity Analysis on Latent Embeddings and Dimensionality Reductions for Text Spatializations
Authors: Daniel Atzberger, Tim Cech, Willy Scheibel, Jürgen Döllner, Michael Behrisch 0001, Tobias Schreck
Published at Vis, 2024

The semantic similarity between documents of a text corpus can be visualized using map-like metaphors based on twodimensional scatterplot layouts. These layouts result from a dimensionality reduction on the document-term matrix or a representation within a latent embedding, including topic models. Thereby, the resulting layout depends on the input data and hyperparameters of the dimensionality reduction and is therefore affected by changes in them. Furthermore, the resulting layout is affected by changes in the input data and hyperparameters of the dimensionality reduction. However, such changes to the layout require additional cognitive efforts from the user. In this work, we present a sensitivity study that analyzes the stability of these layouts concerning (1) changes in the text corpora, (2) changes in the hyperparameter, and (3) randomness in the initialization. Our approach has two stages: data measurement and data analysis. First, we derived layouts for the combination of three text corpora and six text embeddings and a grid-search-inspired hyperparameter selection of the dimensionality reductions. Afterward, we quantified the similarity of the layouts through ten metrics, concerning local and global structures and class separation. Second, we analyzed the resulting 42 817 tabular data points in a descriptive statistical analysis. From this, we derived guidelines for informed decisions on the layout algorithm and highlight specific hyperparameter settings. We provide our implementation as a Git repository at hpicgs/Topic-Models-and-DimensionalityReduction-Sensitivity-Study and results as Zenodo archive at DOI:10.5281/zenodo.12772898
A Multi-Level Task Framework for Event Sequence Analysis
Authors: Kazi Tasnim Zinat, Saimadhav Naga Sakhamuri, Aaron Sun Chen, Zhicheng Liu 0001
Published at Vis, 2024

Despite the development of numerous visual analytics tools for event sequence data across various domains, including but not limited to healthcare, digital marketing, and user behavior analysis, comparing these domain-specific investigations and transferring the results to new datasets and problem areas remain challenging. Task abstractions can help us go beyond domain-specific details, but existing visualization task abstractions are insufficient for event sequence visual analytics because they primarily focus on multivariate datasets and often overlook automated analytical techniques. To address this gap, we propose a domain-agnostic multi-level task framework for event sequence analytics, derived from an analysis of 58 papers that present event sequence visualization systems. Our framework consists of four levels: objective, intent, strategy, and technique. Overall objectives identify the main goals of analysis. Intents comprises five high-level approaches adopted at each analysis step: augment data, simplify data, configure data, configure visualization, and manage provenance. Each intent is accomplished through a number of strategies, for instance, data simplification can be achieved through aggregation, summarization, or segmentation. Finally, each strategy can be implemented by a set of techniques depending on the input and output components. We further show that each technique can be expressed through a quartet of action-input-output-criteria. We demonstrate the framework's descriptive power through case studies and discuss its similarities and differences with previous event sequence task taxonomies
A Practical Solver for Scalar Data Topological Simplification
Authors: Mohamed Kissi, Mathieu Pont, Joshua A. Levine, Julien Tierny
Published at Vis, 2024

This paper presents a practical approach for the optimization of topological simplification, a central pre-processing step for the analysis and visualization of scalar data. Given an input scalar field f and a set of "signal" persistence pairs to maintain, our approaches produces an output field g that is close to f and which optimizes (i) the cancellation of "non-signal" pairs, while (ii) preserving the "signal" pairs. In contrast to pre-existing simplification algorithms, our approach is not restricted to persistence pairs involving extrema and can thus address a larger class of topological features, in particular saddle pairs in three-dimensional scalar data. Our approach leverages recent generic persistence optimization frameworks and extends them with tailored accelerations specific to the problem of topological simplification. Extensive experiments report substantial accelerations over these frameworks, thereby making topological simplification optimization practical for real-life datasets. Our approach enables a direct visualization and analysis of the topologically simplified data, e.g., via isosurfaces of simplified topology (fewer components and handles). We apply our approach to the extraction of prominent filament structures in three-dimensional data. Specifically, we show that our pre-simplification of the data leads to practical improvements over standard topological techniques for removing filament loops. We also show how our approach can be used to repair genus defects in surface processing. Finally, we provide a C++ implementation for reproducibility purposes
A Qualitative Analysis of Common Practices in Annotations: A Taxonomy and Design Space
Authors: Md Dilshadur Rahman, Ghulam Jilani Quadri, Bhavana Doppalapudi, Danielle Albers Szafir, Paul Rosen 0001
Published at Vis, 2024

Annotations play a vital role in highlighting critical aspects of visualizations, aiding in data externalization and exploration, collaborative sensemaking, and visual storytelling. However, despite their widespread use, we identified a lack of a design space for common practices for annotations. In this paper, we evaluated over 1,800 static annotated charts to understand how people annotate visualizations in practice. Through qualitative coding of these diverse real-world annotated charts, we explored three primary aspects of annotation usage patterns: analytic purposes for chart annotations (e.g., present, identify, summarize, or compare data features), mechanisms for chart annotations (e.g., types and combinations of annotations used, frequency of different annotation types across chart types, etc.), and the data source used to generate the annotations. We then synthesized our findings into a design space of annotations, highlighting key design choices for chart annotations. We presented three case studies illustrating our design space as a practical framework for chart annotations to enhance the communication of visualization insights. All supplemental materials are available at https://shorturl.at/bAGM1.