Suggest new module topics and target skills

[aprilcs]

Are there target skills that the Reproducibility for Everyone workshop should add to the curriculum? Are there topics that we should cover in future versions of the workshop?

Add your thoughts to this issue!

[aprilcs]

Suggestions from R4E's last Community Calls for new modules and topics (with +1s for those who wanted to vote for a topic):

• Statistics +++++++++
• Hypothesis Testing / p-hacking / "Garden of Forking Paths" / "researcher degrees of freedom" (would need some nuance here and focusing on transparency in reporting over the negatives)
• Data science ++
• The DMPTool (https://dmptool.org/) +++
• Data literacy+++
• Working with big data
• Workflow automation+++++++
• Containers (Docker, Singularity)+ ++++
• Documentation best practice (data, code, protocol, etc. - might need separation?)+ +1+++++
• Versioning +1+++
• Large image sets
• software testing++
• preregistration / registered reports ++
• Code review++
• Automated testing
• Module for new labs - how to use reproducibility tools as a team

[aprilcs]

Target skills needed for reproducibility, collaboratively sorted by order of importance:

1. How to document my methods clearly and completely
2. Write transparent study methods, study plans, instrumentation, and/or protocols
3. How to connect your published paper with data, code, protocol, preprint, preregistration, materials
4. How to report the results of a preregistered study
5. How to register your study
6. Share your study methods / protocols
7. How to handle limitations of sharing (e.g., privacy, data restrictions)
8. Understand your requirements and select a data repository
9. How to select and use preregistration templates for my study
10. Identify different ways of sharing on a spectrum of openness
11. How to advocate for my open practices (e.g., resume building)
12. How to identify your data management requirements and create a data management plan
13. Understand how to submit a registered report paper
14. Organize your project materials
15. How to manage access to data
16. How to document changes to your project
17. Select reporting guidelines for your research
18. How to make null findings discoverable
19. Making data FAIR
20. Create data dictionary or codebook
21. How to select a preprint repository and post a preprint
22. How to conduct a replication
23. Find reproducible protocols and study methods for your research
24. How to choose permission settings for your data
25. How to identify author contributions (and minimize disputes)
26. How to preserve your research
27. Make a reproducible document
28. How to peer review a paper for adherence to open science practices
29. Document your materials (reagents, organisms)
30. Set up a collaborative environment for a research team
31. Select reproducibility tools for researchers
32. How to identify/use an ELN
33. How to design rigorous experiments
34. How to handle with IP and open science practices
35. Understand which journals are okay with preprints
36. How to make physical materials open
37. How to build an open science community within your department or society
38. Create a file structure convention
39. How to organize an OSF Workshop
40. Learn how to advocate trying a new practice to your collaborators
41. How to implement an open science policy in my agency, journal, or department
42. Create a file naming convention
43. How to connect your research tools with a centralized workspace
44. Create a project README file
45. How to get peer review of your study methods and plan
46. How to use scripted data analysis languages
47. Learn to use PIDs to connect the people and things related to my research
48. Develop or select open policies for your community
49. Learn how to assess the current state of openness in your community
50. How to invite, convince and recruit collaborators
51. Create an RMarkdown Document
52. How to run a hackathon
53. Create a Jupyter Notebook

[ha0ye]

More guidance on domain-specific repositories, esp. for funded grants.