Appraisal Guidance for the Preservation of Research Data
Bibliographic record
Abstract
Appraisal and selection are key activities necessary for the responsible stewardship of research data. Not all data has long-term research value, and the increasing volume of data produced and published to meet short- and mid-term needs creates a burden on both the repositories storing and maintaining access to the resources and the researchers searching for quality data. Repository appraisal practices, often carried out as part of the curation process at the time of deposit to optimize data for sharing and reuse, need to better address the long-term sustainability of FAIR data practices. This guide is designed to be used alongside a repository’s existing acquisition, collection development, preservation, and deaccessioning policies and other high-level institutional strategy documents to help curators work with researchers and preservation specialists to evaluate research data for long-term preservation.
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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 | 0.282 | 0.470 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.020 | 0.024 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.021 | 0.014 |
| Open science | 0.010 | 0.010 |
| Research integrity | 0.016 | 0.013 |
| Insufficient payload (model declined to judge) | 0.064 | 0.083 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".