Data Policy Standardisation and Implementation Interest Group - RDA P16 2020
Bibliographic record
Abstract
The Research Data Alliance Data Policy Standardisation and Implementation Interest Group is concerned with data policy standardisation in particular journal data policies and funder data policies, which are known to promote and incentivise data sharing by researchers. We call for collaboration across the scholarly publishing and wider research community to drive further implementation and adoption of consistent research data policies. Our meeting objectives for Plenary 16 were two-fold: Journal data policies - continue engaging with publishers and journals, in particular with industry associations such as STM, to promote implementation and adoption of standard research data policies - and planning these initiatives in ways that enables measurement and analysis of the impact of policies. Funder data policies - discuss a potential project to examine funder-publisher policy alignment in collaboration with the Research Funders and Stakeholders Open Research and Data Management Policies IG. Research funders and research publishers are key actors in driving the transition towards a research culture where data and other research outputs are FAIR. However comparatively little work has been done to examine funder-publisher policy alignment. This presents a significant knowledge gap that hinders progress towards more consistent good practice in research data management and, ultimately, the shift towards a FAIR research system. In addition to the presentation slides, the Menti audience poll results are also included here.
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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 | 0.136 | 0.144 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.018 | 0.012 |
| Open science | 0.008 | 0.011 |
| Research integrity | 0.025 | 0.013 |
| Insufficient payload (model declined to judge) | 0.114 | 0.108 |
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".