The Turing Way
1. Introduction
2. Reproducibility
2.1 Why reproducibility is important
2.2 Why you should care
2.3 Definitions
2.4 Resources
3. Open Research
3.1 Open data
3.2 Open source software
3.3 Open hardware
3.4 Open access
3.5 Open notebooks
3.6 Open scholarship
3.7 Resources
4. Version Control
5. Licensing
5.1 Software licenses
5.2 Data licenses
6. Collaborating on GitHub/GitLab
6.1 README and Project Communication
6.2 Roadmapping
6.3 Getting Contributors
6.4 Checklist and Bibliography
7. Community Communications for Open Source Projects
7.1 Issue Tracking
7.2 Communication Channels
7.3 Further Reading
8. Credit for reproducible research
9. Research Data Management
9.1 The FAIR principles and practices
9.2 Storage and backup
9.3 Data organisation in spreadsheets
9.4 Documentation and metadata
9.5 Sharing and archiving data
9.6 Personal Stories
9.7 Resources
10. Reproducible Environments
10.1 Choosing a tool
10.2 Conda
10.3 YAML
10.4 Binder
10.5 Virtual machines
10.6 Containers
10.7 Checklist
10.8 Resources
11. Code quality
12. Testing
13. Reviewing
13.1 How this will help you and why this is useful
13.2 Best Practice
13.3 Typical Workflows
13.4 Checklists, what to learn next and bibliography
14. Continuous Integration
15. Reproducible Research with Make
16. Research Compendia
17. Risk Assessment
17.1 Long Read on Risk Assessment
17.2 Summary
18. BinderHub
19. Remote Collaboration
19.1 Bootstrapping an Online Community
19.2 Establishing Protocols
19.3 Managing Resources
19.4 Tool Choices
19.5 Leadership and Management
19.6 Well-being of your members
19.7 Case Studies
19.8 Perceived Pros and Cons
19.9 Checklist of checklists
20. Personal Experience: Diving into Leadership
21. Glossary
22. Five book preview
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