Two links in the research data life cycle: collaboration between a university and long-term repository
Bibliographic record
Abstract
This story describes a collaboration between a university and a national data repository. More specifically, it involves the Research Information Services (RIS) at Radboud University (RU) and the long-term data repository at DANS (hence: the Repository), both in The Netherlands. A substantial part of the digital research data produced at RU are deposited at the Repository, which publishes and preserves them. Originally, the Repository was developed and implemented to provide individual researchers in the Netherlands with a trustworthy digital repository. It was designed for self-archiving, putting the data producer – the researcher – in charge of depositing the files and entering the metadata. In recent years, DANS witnessed a gradual shift towards institutional deposits.The RU has a home-built Current Research Information System (CRIS) called METIS, which is also used as a portal for archiving datasets in the Repository. RIS staff take care that their researchers prepare the data well for deposit and curate each dataset. Using the SWORD protocol the data is forwarded to the Repository, where a Repository data manager inspects and publishes the data. The Repository preserves them in the long run.
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 | 0.094 | 0.149 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.017 | 0.014 |
| Scholarly communication | 0.044 | 0.057 |
| Open science | 0.006 | 0.054 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.019 | 0.009 |
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".