Facilitating Open Exchange Of Data And Information
Bibliographic record
Abstract
By broad consensus, Open Data presents great value. However, beyond that simple statement, there are a number of complex, and sometimes contentious, issues that the science community must address. In this review, we examine the current state of the core issues of Open Data with the unique perspective and use cases of the ocean science community: interoperability; discovery and access; quality and fitness for purpose; and sustainability. The topics of Governance and Data Publication are also examined in detail. Each of the areas covered are, by themselves, complex and the approaches to the issues under consideration are often at odds with each other. Any comprehensive policy on Open Data will require compromises that are best resolved by broad community input. In the final section of the review, we provide recommendations that serve as a starting point for these discussions
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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 | 0.167 | 0.272 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.007 | 0.022 |
| Scholarly communication | 0.029 | 0.076 |
| Open science | 0.009 | 0.058 |
| Research integrity | 0.011 | 0.016 |
| Insufficient payload (model declined to judge) | 0.014 | 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".