SA-Score: Measuring Data Sharing Effort through the Lens of Open Science and the FAIR Principles
Bibliographic record
Abstract
This is a submission to the S-Index Challenge for Phase 1, hosted by NIH. This submission was developed by a team of 50+ data experts from leading research institutions, government agencies, and global organizations—including NASA, NIST, the University of California, JHU, MIT, AGU, and the Allen Institute. We span a broad range of disciplines including biohealth, earth, environment, atmospheric, ocean, computer science, chemistry, law, information science, materials, humanities, and economics. Our combined experience includes leadership in developing and implementing data infrastructures, Persistent Identifiers (PIDs), and metadata standards; advancing the FAIR principles; and shaping data sharing policy through global initiatives like the Research Data Alliance, FORCE11, and the Research Software Alliance. Interdisciplinary composition and demonstrated leadership positions this team as the ideal collaborative body to design comprehensive, scalable, and sustainable data (and digital object) sharing solutions that meet the challenges of the 21st century.
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 | 0.056 | 0.221 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.016 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.012 | 0.017 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".