How to accentuate the figures of a scientific paper (my first of series of science data visualization posts!)
After many years of grueling work in the laboratory, fighting with difficult cloning reactions, microscopy settings and Fiji plugins it’s finally time to summarize your data and produce powerful figures for a publication. Hurray! But this is a lot of work! There are 3 main challenges for your scientific visualization:
- Which visual display to choose?
- How to deliver the key message effectively?
- Establishing logic in your figures!
Although this describes the process in a linear way, it in reality is a rather chaotic procedure with lots of going back and forth. And all of us can learn still learn a lot of how to make most use of visual communication.Here, I will explain the steps of the entire process in little steps. As an example, I use a publication of my friend James who studies the origin of life and lipids* [FOOTNOTE ON FAT].
Part 1: Get an overview.
The ultimate goal of figures in publications is to leverage the amazing capabilities of our visual perception and allow readers to take in the data effortlessly – this requires a clear visual language that the reader can rapidly decode. The goal was to make James’ finding about the cholesterol-like role of hopanoids in bacteria more accessible.
- To get a quick overview, I put all figures next to each other, without explanatory text! Then, I determine how much I can already understand that way? Can I grasp the story?
- To assess if the data is presented in a scientific sound and clear way, I check the display-types: were the right display types chosen for this type of data? Are the errors indicated, the axes labeled and intersecting each other in a useful manner?
- Then I look at the color scheme and layout – do they guide the reader to the most important findings? Are labels and fonts consistent?
- Last, I squint my eyes and see if there are imbalances in data presentation, too much white space, too much dark space etc.
What I notice
- There are a couple of structures, many line charts and accompanying bar charts and some image data.
- The orange data stands out in all figures and indeed seems to have been chosen for the key data – it is the molecule of interest to the Saenz group, the cholesterol-analog hopanoid.
- In two parts the color scheme differs: 1. In the schematic drawing of membrane architecture and 2. in the line chart in the lower left hand corner.
- Also, in almost each chart the bars are of different thickness and the layout of the axes changes!
My next step
I take a pen and mark every little detail in the figures that I notice as worth checking. This helps me priorities my work of the make-over and helps me stat focused! More soon!
PS I found that this way of engaging with a publication also works as a fantastic quick way of reviewing a paper – and you might try this approach for one of your future reviews!