Daniel Morillo
Sessions
Participants will practice creating a GitHub repository and adding/editing code and text in Rstudio. A few tools will be provided to help them take in these practices and apply them in their everyday workflows. As a result, they are expected to have resources to help them make their research more open and reproducible, including the whole research workflow and not just its outcomes.
Outline:
- Creating a repository in GitHub.
- Cloning the repository locally with Rstudio.
- Tracking changes.
- A few good practices for versioning research projects.
Intended audience: Participants should ideally be proficient in R (or Python); if they are not familiar with these, they can still participate but they will not be able to get the full potential from it. For best use of the workshop, a "BYOP" (Bring your own project) format is encouraged: Participants can use their own data, script(s), notebook(s), and/or computable documents.
Pre-registration and Registered Reports can help diagnose the verifiability of science. However, they cannot inform about when or why research deviated from the intended plan. Moreover, questionable research practices (QRPs) will prevail throughout any research process —even completely honest researchers will make mistakes.
Radical Transparency (RT) is the practice of not only making public research outcomes (research plans, protocols, code, data, results) but also the whole process of developing them, in a “collaborative open-source-like” fashion. While RT may help improve the openness and transparency of science, many questions remain about what it really means and how we can implement it:
What is (not) RT?
Can RT be actually achieved? How, if so?
Is it worth pursuing RT?
How do current/future technologies afford the requirements of RT?
What are the uses (and misuses) of RT?
This unconference intends to address unknowns like these.