Data visualization for biological sciences

In this practical, interactive course, you'll learn to create engaging, scientifically accurate visualizations specifically tailored for biological research. Using programming tools like R and Python, along with graphic editing software such as Inkscape, you'll gain essential skills for clearly and effectively visualizing genomic and biological data.

Some prior programming experience is required for this course. Our aim is not to dive deeply into every coding detail, but rather to equip you with practical skills to adapt existing visualization scripts for your own data. Participants will collaborate in small groups, creating visualizations from provided datasets that cover topics like allele frequencies, mutation patterns, and other genomic data.

Additionally, participants are encouraged to explore AI tools like ChatGPT and Gemini for coding support, troubleshooting, and inspiration, as they work on their visualizations. We'll discuss effective and responsible ways to use such tools, addressing both their advantages and potential pitfalls. Each session concludes with group discussions and instructor-led feedback to help refine visualizations and deepen understanding of best practices, visualization design principles, and publication standards. 

This course is designed for PhD students, postdocs and early-career researchers in biology, bioinformatics, and related fields. While the concepts and skills taught are broadly applicable across the life sciences, our use cases and datasets are primarily drawn from genomics.

Language
English

Registration
Deadline: October 4, 2026
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Scientific organization
Georgios Kallergis
Steven Medina
Philipp Muench

Contact
Carmen Paulmann
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