Do Institutional Repository Deposit Guidelines Deter Data Discovery?
Bibliographic record
Abstract
Objective – This study uses quantitative methods to determine if the metadata requirements of institutional repositories (IRs) promote data discovery. This question is addressed through an exploration of an international sample of university IRs, including an analysis of the required metadata elements for data deposit, with a particular focus on how these metadata support discovery of research data objects. Methods – The researchers worked with an international universe of 243 IRs. A codebook of 10 variables was developed to enable analysis of the eventual randomly derived sample of 40 institutions. Results – The analysis of our sample IRs revealed that most had metadata standards that offered weak support for data discovery—an unsurprising revelation in view of the fact that university IRs are meant to accommodate deposit and storage of all types of scholarly outputs, only a small percentage of which are research data objects. Most IRs seem to have adopted metadata standards based on the Dublin Core schema, while none of the IRs in our sample used the Data Documentation Initiative metadata that is better suited for deposit and discovery of research datasets. Conclusion – The study demonstrates that while data deposit can be accommodated by the existing metadata requirements of multi-purpose IRs, their metadata practices do little to prioritize data deposit or to promote data discovery. Evidence indicates that data discovery will benefit from additional metadata elements.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | MetaresearchOpen science Domain: Reproducibility · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
| gpt | MetaresearchScholarly communicationOpen science Domain: Reproducibility · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.323 | 0.668 |
| Meta-epidemiology (narrow) | 0.000 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.019 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.021 | 0.026 |
| Open science | 0.006 | 0.015 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".