Scientific communication is heavily focused on the use of text as opposed to visuals. Writing text is, of course, important: it has the power to structure thoughts, clarify complex topics, and transfer research findings to peers. Any scientist can relate to the hours (and hours, and hours….) of work spent on writing, re-writing, editing, revising manuscripts and grant proposals.
In contrast, visual communication is not getting the same amount of attention. And this is truly a missed opportunity, because visual communication is a rich and powerful toolbox, filled with many types of figures, images, graphs, charts, infographics and tables, all conveying information in their own way.
In my ideal world, visuals get the same respect as a means of communicating science as text currently does. Because: visual communication also has the power to structure thoughts, clarify complex topics, and transfer research findings to peers. Just as manuscripts are being text-edited in detail, going back-and-forth between researchers and their peers and supervisors, passing through many rounds of revisions: well, the same should be true for scientific visuals.
Overlooking the importance of visual communication is a problem that occurs on many levels in the academic-pipeline:
- Education - Rarely do young, newly starting scientists receive basic training or guidance on visual communication
- Acceptance - Visuals are not fully embraced for their exploratory nor explanatory power
- Tools - Visuals are often created using default settings of the software that is being used by the researcher, which are not always optimized for communicative strength
- Incentive - There is little to no incentive for scientists to spend more time and attention on their visuals when publishing their results. The only check that scientific visuals get before being printed in the literature is often: ‘please upload in .TIFF or .PDF’.
To illustrate that last point, I looked at the Author's Style Guide of the journal Magnetic Resonance in Medicine, the journal where I would publish my work in back in the days when I was still doing MRI research. It is interesting to note that out of the 13 pages, only 6 sentences are explicitly given design advice when it comes to the figures:
The little bit of information that is provided in the Style Guide is, surely, useful. For example, under the section Line charts, the advice is:
'Do not shade the graph's background area'.
Ok. That's good advice, for clarity reasons, and applies to charts other than line charts as well.
However, when it comes to line charts specifically, there are many, many more pieces of advise that can be given. What about the lines themselves: how do you determine line thickness? When is it ok/not ok to use markers in addition to the lines? When should you use color in line charts? And what colors are best to convey the message? Have you tried direct-labeling to see if you can get rid of the separate legend? How to best deal with gridlines? What aspect-ratio of the figure would best convey the message that your lines are telling?
And: is the line chart even the best chart to represent your data in this particular figure?
This brings back the memory of a line chart I once created, in 2012. It was accepted without any revisions by the reviewers (as opposed to the accompanying text...). Please note I am only using it here to show how line charts should not be designed:
Figure 1. A line chart from a publication in 2012. This chart was accepted without any asks for revision. And it is a good example of how not to design line charts.
Sprinkhuizen et al. Magn Reson Mater Phy (2012) 25:33–39
With that, I am thinking this could become a series of posts where I redesign my own scientific visuals....
Because nothing is more convincing than actually seeing it for yourself, right?
About the author
Sara Maria Sprinkhuizen, PhD
I am a physicist who fell in love with MRI scanners, which launched my path into health care. After finalizing my PhD in MRI physics (Utrecht University, the Netherlands) I moved to Boston for a post-doc (Harvard Medical School, USA). Only after I left academia did I discover the rich and amazing world of data visualization. The last years I have worked as a data analyst and visualizer, teaching workshops to scientists on data viz.
