Analyzing biological models and data sets using Jupyter notebooks as an alternate to laboratory-based exercises during COVID-19.

Abstract

Jupyter notebooks are widely used for data analysis across a large number of scientific disciplines. As a result of the COVID-19 pandemic, I developed a series of computational exercises using the Jupyter notebook to replace the laboratory exercises usually undertaken in my course. My students had no prior coding knowledge and therefore these exercises were structured in a "cookbook" format using the susceptible-infected-resistant model for disease, data from the Lenski long-term evolutionary experiment, and a fission yeast transcriptomic data set. Despite limited internet connectivity and on-line instruction, my students completed these computational exercises and then tested their own hypotheses. Because Jupyter notebooks can be annotated with text and images, student notebooks were submitted for assessment in the form of a structured scientific report. An advantage of this approach was that all the computational analyses presented in these reports could be easily replicated. The notebook and complete instructions used in my course are provided for others who want to adopt this approach.