Ch. 2 What is reproducible science?
2.1 Activity: Thought Exercise
- What does reproducible science mean to you?
- Which tools have you previously used?
- Which tools are you most excited to learn?
2.2 What is reproducible science?
Making entire scientific process transparent (when and as allowable by law, grants).
- Sharing experiment and analysis code
- Detailed methods sections
- Sharing stimulus sets
- Being able to walk through a data analysis start (load raw data) to finish (manuscript analyses) in code
- Following FAIR Principles
- Pre-registering hypotheses and analysis plans (e.g., on OSF)
2.2.1 Benefits of Reproducible Research
Sourced from: https://www.displayr.com/what-is-reproducible-research
- increased likelihood that the research will be correct
- reproducibility makes it easier to check the research
- it is easier to reproduce the research independently
- easier to extend the research
- reusable code and instruction resulting in increased efficiencies
2.3 FAIR Principles
F: Findable: “The first step in (re)using data is to find them. Metadata and data should be easy to find for both humans and computers. Machine-readable metadata are essential for automatic discovery of datasets and services”
A: Accessible: “Once the user finds the required data, she/he/they need to know how they can be accessed, possibly including authentication and authorisation.”
I: Interoperable: “The data usually need to be integrated with other data. In addition, the data need to interoperate with applications or workflows for analysis, storage, and processing.”
R: Reusable: “The ultimate goal of FAIR is to optimise the reuse of data. To achieve this, metadata and data should be well-described so that they can be replicated and/or combined in different settings.”