6 FAIR data and materials
7 sub-clusters · 82 referencesStudents will learn about FAIR data (and education/research materials) principles that address Findability, Accessibility, Interoperability, and Reusability; engage with reasons to share data, the initiatives designed to increase scientific openness; as well as of possible privacy and security considerations together with anonymization procedures. There are 7 sub-clusters which aim to further parse the learning and teaching process:
Licenses and reuse
11 / 11Licensing determines how others may access, cite, remix, and redistribute your work. This section orients you to data/code/materials licenses (e.g., CC BY/CC0), data-use agreements, and rights/obligations that shape ethical, legally sound reuse, especially for qualitative and sensitive data
Metadata standards
4 / 4Reusable research starts with good, machine-actionable metadata. This sub-cluster points to field-tested schemas and “minimum information” checklists so teams can capture provenance, methods, and context consistently across datasets, code, and teaching materials.
Repositories
9 / 9Trusted places to deposit datasets, code, and teaching materials so they remain findable, citable, and preserved.
Research data management
9 / 9Introduces the planning and processes for managing research data through its lifecycle, from organizing files and documenting data (so that you and others can understand it later) to storing it securely and preparing it for sharing or archiving. Good RDM underpins the ability to be FAIR.
FAIR principles applied to Education & Training
4 / 4FAIR isn’t only for datasets, syllabi, slides, and assignments can be findable, accessible, interoperable, and reusable too. This section offers institutional and practical roadmaps to make FAIR-by-design teaching materials the default.