Every year, the IEEE VIS Conference brings together the brightest minds in visualization research, design, and engineering. It’s the go-to event for anyone working on turning data into visual insights—whether through interactive dashboards, advanced graph analysis, or novel storytelling techniques.

Explore the Full Catalog

If you're curious to dig deeper or explore papers on a specific topic, there's a great interactive app created by John Alexis Guerra Gómez that lets you browse all the accepted papers from VIS 2024. You can filter by keyword, institution, author, or topic. Try it out here: IEEE VIS 2024 Interactive Explorer.

Selected Paper Highlights

Below are brief descriptions of some standout papers from this year’s program that I found particularly useful:

✍️ Text in DataViz

From Instruction to Insight: Exploring the Semantic and Functional Roles of Text in Interactive Dashboards

This paper from Tableau research team explores how text functions as a core communicative element in interactive dashboards, presenting a taxonomy of text usage based on a large-scale survey and expert interviews, and offering 12 design heuristics to improve how text guides, contextualizes, and enhances user interaction with data visualizations.

A Qualitative Analysis of Common Practices in Annotations: A Taxonomy and Design Space

This paper analyzes over 1,800 real-world annotated charts to develop a practical design space for chart annotations, revealing common purposes, mechanisms, and data sources used in practice to enhance visual storytelling and data interpretation.

The Effect of Visual Aids on Reading Numeric Data Tables

This paper presents a controlled study on how visual enhancements like color, bars, and zebra striping affect how people read data tables, revealing that while color and bars help with identifying maximum values, simpler designs like striping can be more effective for complex comparisons—offering insights to guide better table design.

🧠 Visual Literacy and Human Perception

Beware of Validation by Eye: Visual Validation of Linear Trends in Scatterplots

This paper investigates how effectively people can visually validate linear regression models in scatterplots, revealing consistent biases — especially toward steeper slopes — and showing that common visualization aids like error lines or confidence intervals do little to improve validation accuracy.

Unmasking Dunning-Kruger Effect in Visual Reasoning and Visual Data Analysis

This paper investigates the Dunning-Kruger Effect in visual reasoning tasks, showing that low performers overestimate and high performers underestimate their abilities, and further links DKE susceptibility to personality traits and interaction behaviors—offering new paths for bias detection and personalized interventions in visualization systems.

Quantifying Emotional Responses to Immutable Data Characteristics and Designer Choices in Data Visualizations

Authors found that data trend, data variance, data density, color palette and chart type all have a certain effect on emotion.

🕸️ Graphs and Network Visualization

Does This Have a Particular Meaning?: Interactive Pattern Explanation for Network Visualizations

This paper introduces an interactive technique for explaining visual patterns in network visualizations, showing that it significantly improves users’ ability to learn unfamiliar visualization designs, recognize data patterns, and understand network science terminology.

Evaluating and extending speedup techniques for optimal crossing minimization in layered graph drawings

This paper enhances the scalability of optimal edge-crossing minimization in layered graph layouts by evaluating and combining nine linear programming techniques, achieving up to 17× speed improvements and offering an open-source Python toolkit to generate more readable layouts for larger graphs.

📊 Statistical Model Evaluation and Validation

VMC: A Grammar for Visualizing Statistical Model Checks

This paper introduces VMC, a framework and R package for designing model check visualizations through four key components—model-generated samples, data transformations, visual encodings, and layout strategies—enabling more systematic and effective validation of statistical models, as demonstrated through canonical examples and expert feedback.

Other

The Language of Infographics: Toward Understanding Conceptual Metaphor Use in Scientific Storytelling

This paper maps Conceptual Metaphor Theory (CMT) to scientific visualization by analyzing visual metaphors in infographics across four domains, creating a taxonomy that reveals common patterns—particularly ontological and orientational metaphors—and introducing a visual tool to explore how metaphors shape the communication of complex scientific ideas.

DataGarden: Formalizing Personal Sketches into Structured Visualization Templates

This paper introduces DataGarden, a visual-first templating system that transforms hand-drawn sketches into reusable visualization templates, enabling users to preserve style and structure while iterating toward full data-driven visualizations without starting from scratch.