FAIR teaching handbook
  • 1. Introduction
  • 2. About this book
    • 2.2. What is FAIR?
    • 2.3. Why make data FAIR?
    • 2.4. Who will find this book useful and why?
    • 2.5. About the authors & facilitators
  • 3. FAIR skills and competences
    • 3.1. The FAIRsFAIR Competence Framework and Body of Knowledge for Higher Education
    • 3.2. FAIR competence profiles for bachelor’s, master’s and doctoral degree levels
      • 3.2.2. Competence profiles
    • 3.3. Learning outcomes
      • 3.3.1. Bachelor level
      • 3.3.2. Master Level
      • 3.3.3. Doctoral Level
  • 4. Teaching and training designs for FAIR
    • 4.2. Elemental phases in course design
      • Step 1: Select or identify learning outcomes
      • Step 2: Select or develop learning experiences
      • Step 3: Select content relevant to the learning outcomes
      • Step 4: Identify or develop assessments to ensure the learning is progressing towards learning outco
      • Step 5: Evaluate course effectiveness
  • 5. FAIR lesson plans
    • Lesson plan 1: FAIR in a nutshell
    • Lesson plan 2: Data management plans (DMP)
    • Lesson plan 3: Documentation
    • Lesson plan 4: Data creation
    • Lesson plan 5: File formats
    • Lesson plan 6: Metadata
    • Lesson plan 7: Data standardisation and ontologies
    • Lesson plan 8: Persistent identifiers (PIDs)
    • Lesson plan 9: Licences, copyright and intellectual property rights (IPR) issues
    • Lesson plan 10: Finding and reusing data
    • Lesson plan 11: Repositories
    • Lesson plan 12: Dealing with confidential, personal, sensitive and private data and ethical aspects
    • Lesson plan 13: Data access
    • Lesson plan 14: FAIR software/citable code
    • Lesson plan 15: Research data management – overview and best practices
    • Lesson plan 16: Data management and governance in industry and research
  • 6. Implementing FAIR
    • 6.2. Arriving at FAIR institutional policies
    • 6.3. Data management planning
    • 6.4. Data processing and documentation
    • 6.5. Support infrastructure
    • 6.6. Data publication
    • 6.7. Data reuse
  • Resources and References
    • Resources
    • Data Stewardship Competence Groups
    • Draft Body of Knowledge
    • Knowledge units and corresponding learning outcomes
    • References
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