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.
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 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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.013 | 0.035 |
| Open science | 0.024 | 0.071 |
| 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".