String of PURLs – frugal migration and maintenance of persistent identifiers
Bibliographic record
Abstract
FAIR data requires unique and persistent identifiers. Persistent Uniform Resource Locators (PURLs) are one common solution, introducing a mapping layer from the permanent identifier to a target URL that can change over time. Maintaining a PURL system requires long-term commitment and resources, and this can present a challenge for open projects that rely heavily on volunteers and donated resources. When the PURL system used by the Open Biological and Biomedical Ontologies (OBO) community suffered major technical problems in 2015, OBO developers had to migrate quickly to a new system. We describe that migration, the new OBO PURL system that we built, and the key factors behind our design. The OBO PURL system is low-cost and low-maintenance, built on well-established open source software, customized to the needs of the OBO community, and shows how key FAIR principles can be supported on a tight budget.
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 | 0.023 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.009 | 0.019 |
| Open science | 0.005 | 0.015 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 0.012 |
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".