Repository Features to Help Researchers: An invitation to a dialogue
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 group of publishers came together to discuss how we could reduce the complexity and inconsistency provided in publisher's advice to researchers when selecting an appropriate data repository. It is a shared goal among publishers and other stakeholders to increase repository use – which remains far from optimal – and we assume that helping researchers find a suitable repository more easily will help achieve this.<br> To address this a list of features has been created and it is intended only as a framework within which publishers can make recommendations to researchers, not as a way to restrict which repositories researchers may choose for their data. Our intention is that the features we highlight will act to initiate engagement and collaboration among publishers, repositories and the RPOs, government and funders that ultimately make the policies around Open Research. As we start this conversation, it is important that we act together with other stakeholders to raise awareness of the challenges involved around FAIR data and to prevent any perverse consequences. From the RDA FAIRsharing WG point of view, the ultimate objective is to map repository features across all existing initiatives, and to identify a common core set of metadata fields that all stakeholders want to see in registry of repositories. The FAIRsharing registry in particular is agnostic as to the selection process of standards, repositories and policies, as part of its commitment to working with and for all stakeholder groups.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it