Data Science Without Borders¶
For effective collaboration among international groups, such as in the Data Science Without Borders project, it is essential to learn, adopt, and implement best practices in open, reproducible, ethical, and inclusive data science. These curated chapters will provide starting points, helping you navigate a broader set of practices in The Turing Way.
- Overview of Reproducible Research
- Version Control
- Open Research
- Licensing
- Research Data Management
- Data Management Plan
- The FAIR Principles
- Documentation and Metadata
- Data Organisation in Spreadsheets
- Data Curation
- Sharing and Archiving Data
- Checklist
- Code documentation
- Code Testing
- Code Quality
- Code Reviewing Process
- Reusable Code
- Overview of Project Design
- Information Management
- Data Security
- Missing Data Handling
- Risks of Bias in Research
- Communications in Open Source Projects
- Publishing Different Article Types
- Presenting Posters and Conference Talks
- Research Object to capture the Research Life Cycle
- Making Research Objects Citable
- Academic Authorship
- Remote Collaboration
- Getting Started With GitHub
- Maintainers and Reviewers on GitHub
- Organising Remote Meetings
- Organising Coworking Calls or Meetings
- Organising a Conference
- Chairing Events
- Tools for Collaboration
- Guide for Collaboration
- Shared Ownership in Open Source Projects
- Team Manuals
- Facilitating Stakeholder Engagement