We recently ran a Scientific Practices Workshop, and one of us later collected several links for follow-up materials for the interested. I thought the list of links was a fantastic source of materials, so I post it here:
Why this is important? (New)
Would you like to take the recommended Statistical Rethinking course?
Would you like to learn more about Bayesian statistics?
Would you like to learn more about p-hacking?
Would you like to learn more about research integrity?
Would you like to learn more about reproducibility?
- Five selfish reasons to work reproducibly
- “ask not what you can do for reproducibility; ask what reproducibility can do for you!” (Genome Biology)
- Reproducible research practices masterclass
- Instead of researching reproducibility, just do reproducible research (Simply Statistics)
- We need a statistically rigorous and scientifically meaningful definition of replication (Simply Statistics)
- A guide to open infrastructure for scientific software developers
- If you write R scripts to analyse your data, that’s you
Would you like to pre-register a study?
- Check out: http://aspredicted.org/
Would you like to start using github?
- Software Carpentry guide to Git and GitHub
- A comprehensive guide for novices and advanced git users, recommended
- A Quick Introduction to Version Control with Git and GitHub (PLOS Computational Biology)
- GitHub for everyone
- Get started with git – what is version control
Would you like to learn more about Reproducible Reports?
Or as Matti puts it: “Using RStudio and RMarkdown to achieve happiness and life satisfaction”:
Would you like start using a style guide for your code?
Would you like to join the Peer Review Openness Initiative or learn more about the Open Science Framework?
Would you like to watch scientists try to define a p-value?