In this lesson we will discuss different methods and tools for better reproducibility in research. We will demonstrate how version control, workflow engines and containers can be used to develop reproducible workflows.
This demonstration will use Python snippets but all tools work equally well for other programming languages.
Why should research be reproducible?
What factors influence reproducibility?
|14:05||Creating a reproducible workflow||
How can we create a reproducible workflow?
How do we repeat an experiment with different data one year later?
|14:25||Containers for reproducible research||
How to capture the environment under which experiment was made?
How do you communicate different versions of dependencies you need?
|14:45||Workflow management and tracking tools||
What are scientific workflow management systems all about?
Can such tools be adopted to your research?