Generalist Repositories: Enabling Sharing & Reuse
Bibliographic record
Abstract
This invited presentation, given to the BioImaging North America (BINA) 2025 Community Congress in Montreal, QC, Canada on October 9, 2025, highlights the critical role of generalist repositories in increasing the openness, integrity, and reproducibility of research by accepting data regardless of type or format. To facilitate this, the NIH launched the Generalist Repository Ecosystem Initiative (GREI) to implement consistent metadata models based on DataCite. This collaborative effort ensures NIH-funded data is discoverable, interoperable, and reusable, streamlining compliance with NIH policies and fostering greater cross-repository collaboration.
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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 | 0.066 | 0.116 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.014 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.027 | 0.055 |
| Open science | 0.008 | 0.047 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 0.008 |
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".