Making sense of data was long been the exclusive domain of research and trained data analysts. Meanwhile data have becoming an important part of daily life and society. Not only do we produce data everyday as we browse online, communicate online, and buy online but we do need to understand data about our world in order to make informed decisions in our daily lifes. Journalists have started building informing about evidence in data and building their stories on data: demographics, political alliances, voting polls, economy, etc. Data is becoming an essential part of daily life and the ability of correctly interpreting and understanding data can have impact from an individual level (e.e., energy consumption, nutrition), over society (e.g., voting behaviour and economics), to global (e.g., climate, world demographics).

One efficient and well studied and popular way to make data understandable is visualization. Using visualization effectively in a communication is meant to help people better understanding data and make informed decisions. Therefore, we need to understand how effective communication with visualization works; how to make visualization understandable, how to make visualization engaging, when to use visualization, which visualization to use, and which media are available to tell compelling stories with data.

This topic investigates the genre of comics, or sequential art, for visual storytelling with data (visualizations). Comics provide rich expressiveness and freedom of design, yet have developed and adopted specific reading conventions. Comics do not require interaction nor dynamic media. They can be printed and embedded in books as well as scientific articles. The idea is to explore the design space and visual affordances of comics (e.g, panels, sequence, word+picture, narration, etc) and provide respective adoptions tailored to data-driven storytelling. On the other side, we want to understand where static story-telling falls short and what can be done.

The goals of this research can vary from tools for the creation and analysis of comics to the quantitative analysis of specific examples, design principles, and human factors.


Below are listed important references in the field, serving for inspiration and examples.

  1. The Emerging Genre of Data Comics: Benjamin Bach, Nathalie Henry Riche, Hanspeter Pfister, Sheelagh Carpendale, IEEE Computer Graphics and Applications, 2017
  2. Telling Stories about Dynamic Networks with Graph Comics, ACM Human Factors in Computing Systems, 2016
  3. Edward Segel and Jeffrey Heer. 2010. Narrative Visualization: Telling Stories with Data. IEEE Transactions on Visualization and Computer Graphics
  4. Z. Zhao, R. Marr, and N. Elmqvist. 2015. Data Comics: Sequential Art for Data-Driven Storytelling. Technical Report. Human Computer Interaction Lab, University of Maryland.
  5. Bongshin Lee, Nathalie Henry Riche, Petra Isenberg, Sheelagh Carpendale: More than Telling a Story: A Closer Look at the Process of Transforming Data into Visually Shared Stories, Computer Graphics and Applications, 2015
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