Implementing controlled vocabularies and international standards in web services to promote data interoperability: A case study
Bibliographic record
Abstract
Ocean Networks Canada (ONC) is a not-for-profit society that operates and manages innovative cabled observatories which supply continuous power and Internet connectivity to scientific instruments located in coastal, deep-ocean, and Arctic environments. The data from the instruments are archived, quality-controlled, and made freely available to researchers, educators, and the public. The Oceans 2.0 data management system currently contains over 500 terabytes of data collected over 11 years from thousands of sensors. In order to facilitate access to the data, particularly for large datasets and long-time series of high-resolution data, a project was started in 2016 create a comprehensive Application Programming Interface (API) to provide programmatic access to all ONC data products. Interoperability and support for international data exchange are key factors in the requirements and design of the Oceans 2.0 API. In this paper, we discuss how these considerations were taken into account in the requirements and design of the Oceans 2.0 API. In particular, we discuss the 1) the use of controlled vocabularies; 2) support for international web service standards; 3) open data formats for delivery; and 4) common metadata standards. We present our findings as a case study examining the complexity of designing a multi-user, multi-standard system which attempts to strike a balance between usability and support for as wide a user-base as possible.
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 | 0.052 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.013 | 0.019 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".