A modular and community-driven FAIR teaching and training handbook for higher education institutions
Bibliographic record
Abstract
The FAIR principles, providing guidelines to improve the findability, accessibility, interoperability and reusability of research outputs, have become a commonly recognised practice by stakeholders in research and higher education. Although a landscape study undertaken in 2019 showed that universities are well aware of the importance of the FAIR principles and are striving towards the proper integration of FAIR-related content in curricula and teaching, the actual implementation remains a challenge (Stoy et al. 2019). To support higher education institutions in this respect, a group of 40 community experts – brought together by a book sprint organised by the FAIRsFAIR project (https://fairsfair.eu) in June 2020 – created a teaching and training handbook. It comprises tools and information covering different aspects of FAIR- and RDM-related activities. These include: common Body of Knowledge and competence profiles for the bachelor’s, master’s and PhD degree levels, suggesting which knowledge, skills and competences students should acquire in terms of FAIR, learning outcomes matching the competence profiles, specifying what students will be able to do after a course or training on the topic(s) in question, sixteen lesson plans on FAIR- and RDM-related topics, information on course design, guidance on the implementation of the principles in the institutional contex The different components of the handbook can accommodate FAIR implementation at different levels within an institution (e.g. at the faculty level and at the institutional level). The modular design of the handbook provides a framework that can be easily maintained, updated and expanded. We envision the handbook being available in multiple formats. In addition to the project deliverable (Engelhardt et al. 2021) already available on Zenodo, a print publication and a GitBook version will be published. The GitBook version provides the flexibility for future maintenance and contributions by the community beyond the project lifetime. The editorial team intends to review the impact and feedback of the handbook a year after publication. An announcement regarding long-term maintenance and development by a defined community of practice will be made by the time of the conference.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.033 | 0.022 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".