The Fast and the FRDR: Improving Metadata for Data Discovery in Canada
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
The Federated Research Data Repository (FRDR), developed through a partnership between the Canadian Association of Research Libraries’ Portage initiative and the Compute Canada Federation, improves research data discovery in Canada by providing a single search portal for research data stored across Canadian governmental, institutional, and discipline-specific data repositories. While this national discovery layer helps to de-silo Canadian research data, challenges in data discovery remain due to a lack of standardized metadata practices across repositories. In recognition of this challenge, a Portage task group, drawn from a national network of experts, has engaged in a project to map subject keywords to the Online Computer Library Center’s (OCLC) Faceted Application of Subject Terminology (FAST) using the open source OpenRefine software. This paper will describe the task group’s project, discuss the various approaches undertaken by the group, and explore how this work improves data discovery and may be adopted by other repositories and metadata aggregators to support metadata standardization.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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