Editorial Ten Simple Rules for Better Figures

Scientific visualization is classically defined as the process of graphically displaying scientific data. However, this process is far from direct or automatic. There are so many different ways to represent the same data: scatter plots, linear plots, bar plots, and pie charts, to name just a few. Furthermore, the same data, using the same type of plot, may be perceived very differently depending on who is looking at the figure. A more accurate definition for scientific visualiza-tion would be a graphical interface between people and data. In this short article, we do not pretend to explain everything about this interface; rather, see [1,2] for introductory work. Instead we aim to provide a basic set of rules to improve figure design and to explain some of the common pitfalls. Rule 1: Know Your Audience Given the definition above, problems arise when how a visual is perceived differs significantly from the intent of the conveyer. Consequently, it is impor-tant to identify, as early as possible in the design process, the audience and the message the visual is to convey. The graphical design of the visual should be informed by this intent. If you are making a figure for yourself and your direct collaborators, you can possibly skip a number of steps in the design process, because each of you knows what the figure is about. However, if you intend to publish a figure in a scientific journal, you should make sure your figure is correct and conveys all the relevant information to a broader audi-ence. Student audiences require special care since the goal for that situation is to explain a concept. In that case, you may have to add extra information to make sure the concept is fully understood. Finally, the general public may be the most difficult audience of all since you need to design a simple, possibly ap-proximated, figure that reveals only the most salient part of your research (Figure 1). This has proven to be a difficult exercise [3]