Creating Figures

Eye-catching figures and fonts do not only facilitate communication but improve the experience of listening to a presentation or reading a paper! Mastering the skill of creating visually appealing figures directly from your code editor (Python, MatLab, R) not only saves you time but also promotes reproducibility in your analyses!

Generating Figures

Various resources are available for learning how to create figures from your data. Below, we’ll highlight those tailored to the three main programs used by lab members: Python, R, and Matlab. (note: if you are new to programming, check the Technical Help section, we highlight resources to learn how to code)

Importantly, there are notions that you should always keep in mind regardless of the specific software being used for generating figures. In this paper, their authors highlight ten fundamental considerations when creating figures for scientific communication.

MatLab Resources

This website contains useful information on how to visualize and generate figures using MATLAB:

As a member of the Donders Insitute, you can download MATLAB on your office PC free of charge. Simply reach out to the Technical Group (TG) or refer to the institute’s intranet page for assistance. If you prefer to install it on your personal computer, you can obtain an educational license using your university email. The TG can assist you with this process as well.

Python Resources

You can find a large range of tutorials for data visualization using Python online. Here, we will mention resources that have been useful for members of the lab.

The two main visualization libraries in Python are the following:

  • MatPlotLib - Widely used, and includes a large range of visualization options. Their website provides tutorials as well as sample code.
  • SeaBorn - Visualization library based on matplotlib. Elegant and user friendly. Increasingly popular in the Neuro-AI community. Includes sample code and tutorials for different kinds of plots.

R Resources

You can find a wide range of tutorials for data visualization using R. We will mention the most common libraries and direct you to websites that provide tutorials to master their usage.

  • GGPlot2 - Library commonly used in the field of statistics to generate diverse plots. User friendly and flexible to generate a wide variety of plots. Their website provides extensive explanations and code examples.

If you do not feel confident to use these skills on your own or simply by looking at the sample code. These tutorials are great to introduce you to these Python and R libraries:

Important to note is that these two resources are mostly paid (CodeAcademy has some free options). Ask lab members how to get free access.

Editing Figures for Manuscripts

Small tweaks might be needed in your code generated figures before they are publication ready. You might also wonder where you can generate images visualizing your experimental procedure. Here is software that will do both of these jobs well for you:

  • Adobe Illustrator (paid software) How to get it? You can have it installed on your office PC (just ask the TG). If you wish to have it on your own computer, check surfspot (https://www.surfspot.nl/). Sign up with your RU account on surfspot, and you’ll be able to get Adobe Creative Cloud service by paying only ~20 euros per year. How to start from scratch? There are numerous YouTube tutorials available online. Additionally, keep an eye on the graduate school of the Radboud University as they often offer workshops on generating figures using Illustrator.

  • Inkscape (free software) How to get it? You can have directly download it from their website. How to start from scratch? Inkscape developers have provided extensive tutorials for their users.

Remember that journals will have speicific guidelines for figures, you can find examples here:

Useful Resources

Here are a list of resources that might be useful when generating figures: