A Survey On Annotations In Data Visualization: Empirical Insights And Applications

A Survey On Annotations In Data Visualization: Empirical Insights And Applications
Md Dilshadur Rahman, Bhavana Doppalapudi, Ghulam Jilani Quadri, and Paul Rosen
To appear in IEEE Transactions on Computer Graphics and Visualization, 2025

Abstract

Annotations are widely used in information visualization to guide attention, clarify patterns, and support interpretation. We present a comprehensive survey of 191 research papers describing empirical studies, tools, techniques, and systems that incorporate annotations across various visualization contexts. Based on a structured analysis, we characterize annotations by their types, generation methods, and targets, and examine their use across four primary application domains: user engagement, storytelling, collaboration, and exploratory data analysis. We also discuss key trends, practical challenges, and open research directions. These findings offer a foundation for designing more effective annotation systems and advancing future research on annotation in visualization. An interactive web resource detailing the surveyed papers is available at https://shape-vis.github.io/annotation star/.

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Citation

Md Dilshadur Rahman, Bhavana Doppalapudi, Ghulam Jilani Quadri, and Paul Rosen. A Survey On Annotations In Data Visualization: Empirical Insights And Applications. To appear in IEEE Transactions on Computer Graphics and Visualization, 2025.

Bibtex


@article{rahman2025survey,
  title = {A Survey on Annotations in Data Visualization: Empirical Insights and
    Applications},
  author = {Rahman, Md Dilshadur and Doppalapudi, Bhavana and Quadri, Ghulam Jilani and
    Rosen, Paul},
  journal = {To appear in IEEE Transactions on Computer Graphics and Visualization},
  year = {2025},
  abstract = {Annotations are widely used in information visualization to guide
    attention, clarify patterns, and support interpretation. We present a comprehensive
    survey of 191 research papers describing empirical studies, tools, techniques, and
    systems that incorporate annotations across various visualization contexts. Based on a
    structured analysis, we characterize annotations by their types, generation methods, and
    targets, and examine their use across four primary application domains: user engagement,
    storytelling, collaboration, and exploratory data analysis. We also discuss key trends,
    practical challenges, and open research directions. These findings offer a foundation
    for designing more effective annotation systems and advancing future research on
    annotation in visualization. An interactive web resource detailing the surveyed papers
    is available at https://shape-vis.github.io/annotation star/.}
}