Bibliographic record
Abstract
This chapter discusses research data repositories in France with an analysis of the typology of repositories, their scientific domains and their quality. In 2015, most data repositories were located in four countries, namely the United States, Germany, the UK and Canada, which then accounted for 70% of the institutions in the international re3data directory. Several types of repositories can be distinguished, depending on their content, subject matter, governance or institutional affiliation. Re3data distinguishes between two types of devices: the data provider if it offers research data and its metadata, and/or the service provider if it harvests the metadata of the research data from the data providers in order to create value-added services. The ambition of the National Plan for Open Science is “to ensure that the data produced by French public research is progressively structured in accordance with the FAIR principles (Findable, Accessible, Interoperable, Reusable) preserved and, when possible, opened”.
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.
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 | 0.040 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.019 | 0.026 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.027 | 0.018 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".