DDI, FAIR, and the Emergent Role of Active Metadata
Bibliographic record
Abstract
This is a CODATA-DDI Alliance webinar for the Research Data Alliance (RDA) Decade of Data celebrations! It is widely accepted that the FAIR data principles highlight the importance of metadata. The Data Documentation Initiative (DDI) community has decades of experience in supporting data production and reuse within the social, behavioural, and economic sciences through the publication and use of detailed, machine-actionable metadata specifications. This webinar explores the lessons that have been learned over that time, and how these lessons can be applied more broadly in the context of FAIR data sharing. The importance of granular metadata to data management practices is clear, but increasingly the ability to leverage metadata in an active capacity – to drive production, management, and dissemination – is growing in importance. With the advent of FAIR, the need for cross-domain exchange of metadata is also growing, and the DDI specifications are evolving to meet that need. More than ever, alignment and coordination among metadata standards and models is needed. This webinar looks at how granular, active metadata can better support research data management both within and across domains, and should be of interest to a broad set of the groups working in the Research Data Alliance (RDA). Speakers: Simon Hodson (CODATA), Amber Leahey (Scholars Portal, Ontario Council of University Libraries), Christophe Dzikowski (INSEE), Arofan Gregory (CODATA/DDI Alliance), joined by Connie Clare (Research Data Alliance).
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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 | 0.046 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.013 | 0.059 |
| Scholarly communication | 0.040 | 0.072 |
| Open science | 0.004 | 0.026 |
| Research integrity | 0.009 | 0.014 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".