# RSH HackMD
You can use this file to write questions and comments. **To edit click top left edit button.**
- Always write at the very bottom, this is easier for us to watch.
- We will note here URLs that we mention during the session.
- You may comment on, clean up, or answer other people's questions.
- **Be respectful** and form a good community.
What tool/trick you recommend to make your work/research more reproducible?
- Think of your future self, code+take notes so that your future self will understand what you did
- Code with more than one person, joint projects
- Some means to install software libraries needed
- Conda environments!
- Does reproducible also mean completely automated? If others have to be able to run the analysis, it feels like that.
- automation could be bad and not explicitly specify code versions that break an analysis
- As a reviewer: do you ask for the code? Do you review the code?
- I reviewed once a data+software paper and told the editor that I can review the code if they give me 2 weeks more... never heard back :)
- I usually glance at the code. Once found a serious bug.
- How do tasks to make work reproducible change given the scale of the project? LHC analysis groups won't act like a small group of ecologists.
- an example: https://www.nature.com/articles/s41567-018-0342-2
- ReproHack is a great idea: journal-club but for github repro's: try to run the so called "reproducible" code shared by the authors of a paper
- To make our research reproducible, do we have to be using notebooks (Rmarkdown, Jupyter, etc)?
- I personally think the other way around. Full automation (no interaction) -> more reproducible
The Jupyter and R Studio/renv examples (improvements welcome!):
- Containers allow for scaling that virtual environments do not.