Data Management for Systematic Reviews: Guidance is Needed
Bibliographic record
Abstract
Data management practices for systematic reviews and other types of knowledge syntheses are variable, with some reviews following open science practices and others with poor reporting practices leading to lack of transparency or reproducibility. Reporting standards have improved the level of detail being shared in published reviews, and also encourage more open sharing of data from various stages of the review process. Similar to project planning or completion of an ethics application, systematic review teams should create a data management plan alongside creation of their study protocol. This commentary provides a brief description of a Data Management Plan Template created specifically for systematic reviews. It also describes the companion LibGuide which was created to provide more detailed examples, and to serve as a living document for updates and new guidance. The creation of the template was funded by the Portage Network.
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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 | 0.574 | 0.774 |
| Meta-epidemiology (narrow) | 0.006 | 0.010 |
| Meta-epidemiology (broad) | 0.019 | 0.018 |
| Bibliometrics | 0.039 | 0.060 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.023 | 0.026 |
| Open science | 0.010 | 0.012 |
| Research integrity | 0.024 | 0.026 |
| Insufficient payload (model declined to judge) | 0.057 | 0.075 |
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".