The Utility of PIDs in Harvesting Open Data Repositories: Challenges in Operating a National Data Discovery Service
Bibliographic record
Abstract
Lunaris is a national data discovery service for Canada. Providing complete and structured, preferably standards-based, metadata for data records enables Lunaris and other discovery services to effectively find potentially relevant datasets, filter them to concretely identify Canadian datasets, and crosswalk them to an internal metadata schema. We will focus on practical challenges related to our mandate of harvesting Canadian datasets, but our recommendations will be applicable to discovery of other subsets of research data at large. The benefits of persistent identifiers (PIDs) are often discussed in an abstract or aspirational way. Lunaris' experience harvesting repositories reveals concrete scenarios in which PIDs are critical to effectively handling repository metadata. This presentation will outline our procedure for harvesting a new repository and highlight situations in which repositories' use of PIDs and other structured metadata improve this procedure, making explicit recommendations for structured metadata use. We will then discuss the way specific PIDs and other metadata elements enable us to help our users effectively discover records in this cross-repository environment. Finally, we will discuss future work in handling emerging PIDs and metadata standards.
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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 | 0.101 | 0.118 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.013 |
| Science and technology studies | 0.012 | 0.009 |
| Scholarly communication | 0.025 | 0.032 |
| Open science | 0.008 | 0.017 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".