This course will focus on issues of reproducibility in research from a broad perspective. It will include an introduction to the differing types of reproducibility and the philosophy that underpins them. The course will look at reproducibility in several contexts, including collecting and communication in experimental research, providing a robust record of computational research, and the limitations and debates around these approaches. We will introduce several tools and approaches to support reproducible research practice, including Jupyter Notebooks, the Open Science Framework, and best practice in research and data management, communication, and open sharing.
There will be five half-day sessions offering a mix of lecture and practical work, particularly information gathering and analysis. The emphasis will be on providing frameworks within which information can be gathered and understood rather than on “fact teaching.”
Proposed level: Beginner to intermediate. Participants should have an interest in reproducibility and may have some experience of implementation in different contexts. Some computer skills will be assumed.
Intended audience: The target audience is researchers seeking a deeper understanding of reproducibility in a variety of contexts, as well as those with a need to support researchers – for example, staff from research offices, libraries, service providers, or publishers. Participants should be seeking an introduction to working toward reproducibility in practice and to the tools that can support them in doing this.
