The FAIR Funding Model: Providing a Framework for Research Funders to Drive the Transition toward FAIR Data Management and Stewardship Practices
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A growing number of research funding organizations (RFOs) are taking responsibility to increase the scientific and social impact of research output. Also reusable research data are recognized as relevant output for gaining impact. RFOs are therefore promoting FAIR research data management and stewardship (RDM) in their research funding cycle. However, the implementation of FAIR RDM still faces important obstacles and challenges. To solve these, stakeholders work together to develop innovative tools and practices. Here we elaborate on the role of RFOs in developing a FAIR funding model to support the FAIR RDM in the funding cycle, integrated with research community specific guidance, criteria and metadata, and enabling automatic assessments of progress and output from RDM. The model facilitates to create research data with a high level of FAIRness that are meaningful for a research community. To fully benefit from the model, RFOs, research institutions and service providers need to implement machine actionability in their FAIR RDM tools and procedures. As many stakeholders still need to get familiar with “human actionable” FAIR data practices, the introduction of the model will be stepwise, with an active role of the RFOs in driving FAIR RDM processes as effectively as possible.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.009 | 0.030 |
| Open science | 0.021 | 0.017 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it