If you are following a project, here are a few general ideas of what to contribute:
- encourage the project owner to think about what data they will use. Consider feasibility:
- is the data already preprocessed?
- How big is the data?
- Are there relevant variable?
- Are there barriers to re-use (ethics committee, data usage agreement, etc).
- what is a feasible primary objective
- list what skills / tools the project owner wants to learn (that is the basis of the CONTRIBUTING.md)
- possibly encourage the project owner to explore alternative tools, and cover multiple areas (preprocessing, stats/machine learning, data vis, code/data sharing)
- make sure the deliverables of the project are well identified
- any good idea (algorithm to try out, cool blog topic to write up on the project, etc)
Here are resources from Mozilla about how to write a good README.md and how to write a good CONTRIBUTING.md (thanks @emdupre for the pointer)
Finally, here are general advice from Mozilla about providing feedback:
- What is Feedback? Actionable criticism or insights
- What’s your mindset when you are preparing to get feedback? Open, trying to understand that person's point of view (and how it could improve the project). Be grateful for the feedback. It takes time and effort to give good feedback so make sure to acknowledge that effort.
- How do you feel when you are giving or preparing to give feedback? Try to view it as a conversation-- trying to be clear and open, both in feedback and in trying to understand how the feedback-ee feels about their work. As directed toward the "item"/"object" and not the person as possible, as focused on improvement and action, not on something that can't be controlled or changed.