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.

中文翻译：

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在 COVID-19 期间使用 Jupyter 笔记本作为实验室练习的替代方法分析生物模型和数据集

Jupyter notebooks 被广泛用于大量科学学科的数据分析。由于 COVID-19 大流行，我使用 Jupyter 笔记本开发了一系列计算练习，以取代通常在我的课程中进行的实验室练习。我的学生之前没有编码知识，因此这些练习以“食谱”格式组织，使用易感-感染-抵抗疾病模型、来自 Lenski 长期进化实验的数据和裂变酵母转录组数据集。尽管互联网连接和在线教学有限，我的学生还是完成了这些计算练习，然后测试了他们自己的假设。因为 Jupyter notebook 可以用文本和图像进行注释，学生笔记本以结构化的科学报告的形式提交评估。这种方法的一个优点是可以轻松复制这些报告中提供的所有计算分析。我的课程中使用的笔记本和完整说明是为其他想要采用这种方法的人提供的。