An International Framework to Promote Access to Data
Bibliographic record
Abstract
The emergence of an global cyberinfrastructure is rapidly increasing the ability of scientists to produce, manage, and use data, leading to new understanding and modes of scientific inquiry that depend on broader data access. As research becomes increasingly global, data intensive, and multifaceted, it is imperative to address national and international data access and sharing issues systematically in a policy arena that transcends national jurisdictions. The authors of this Policy Forum summarize key findings of an international group that studied these issues on behalf of the OECD, and argue that an international framework of principles and guidelines for data access is needed to better realize this potential. They provide a framework for locating and analyzing where improvements can be made in data access regimes, and highlight several topics that require further examination to better inform future policies.
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 | 0.094 | 0.072 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.008 | 0.020 |
| Scholarly communication | 0.033 | 0.019 |
| Open science | 0.006 | 0.018 |
| Research integrity | 0.023 | 0.018 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".