Mandated data archiving greatly improves access to research data
Bibliographic record
Abstract
The data underlying scientific papers should be accessible to researchers both now and in the future, but how best can we ensure that these data are available? Here we examine the effectiveness of four approaches to data archiving: no stated archiving policy, recommending (but not requiring) archiving, and two versions of mandating data deposition at acceptance. We control for differences between data types by trying to obtain data from papers that use a single, widespread population genetic analysis, structure . At one extreme, we found that mandated data archiving policies that require the inclusion of a data availability statement in the manuscript improve the odds of finding the data online almost 1000‐fold compared to having no policy. However, archiving rates at journals with less stringent policies were only very slightly higher than those with no policy at all. We also assessed the effectiveness of asking for data directly from authors and obtained over half of the requested datasets, albeit with ~8 d delay and some disagreement with authors. Given the long‐term benefits of data accessibility to the academic community, we believe that journal‐based mandatory data archiving policies and mandatory data availability statements should be more widely adopted.—Vines, T. H., Andrew, R. L., Bock, D. G., Franklin, M. T., Gilbert, K. J., Kane, N. C., Moore, J‐S., Moyers, B. T., Renaut, S., Rennison, D. J., Veen, T., Yeaman, S. Mandated data archiving greatly improves access to research data. FASEB J. 27, 1304–1308 (2013). www.fasebj.org
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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 | 0.459 | 0.736 |
| Meta-epidemiology (narrow) | 0.001 | 0.004 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.013 | 0.021 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.029 | 0.028 |
| Open science | 0.009 | 0.020 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.008 | 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; 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".