Data Repository Selection: Criteria That Matter
Bibliographic record
Abstract
Representatives from journals, journal publishers and scholarly communication organisations have come together in the FAIRsharing Community to propose a set of criteria for the identification and selection of those data repositories that accept research data submissions. These repositories can be recommended to researchers when they are preparing to release and publish the data underlying their findings. This work intends to (i) reduce complexity and inconsistencies for researchers in journal data policies, (ii) increase efficiency for data repositories that currently have to work with all individual publishers, and (iii) simplify the process of recommending data repositories by publishers. This work will make the implementation of research data policies more efficient and consistent, which may help to improve approaches to data sharing through the promotion and the use of reliable and sustainable data repositories.
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 | 0.184 | 0.413 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.018 | 0.021 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.029 | 0.018 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.013 |
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".