Reproducibility for everyone: a community-led initiative with global reach in reproducible research training
Bibliographic record
Abstract
Reproducibility is a cornerstone of the scientific method and sets apart science from pseudoscience. Unfortunately, a majority of scientists have experienced difficulties in reproducing their own or someone else’s results. This inability to confirm scientific findings negatively impacts individual scientists, funding bodies, academic journals, pharmaceutical drug development and the public’s perception of science. Factors causing irreproducible results can arise from nearly every aspect of the scientific process, and typically reflect a lack of in-depth training in reproducible research practices. Here, we present the Reproducibility for Everyone (R4E) initiative, a collaboration between researchers from diverse scientific disciplines and industry partners who aspire to promote open and reproducible research practices. We have developed a customizable workshop series targeting researchers at all levels and across disciplines. Our workshop series covers the conceptual framework of reproducible research practices followed by an overview of actionable research practices. To date, we have reached more than 2000 researchers through over 25 workshops held at international conferences and local meetings. By incorporating further contributions from the scientific community, we hope to expand this valuable resource for teaching transparent and reproducible research practices. Our initiative demonstrates how a shared set of materials may form the basis for a global initiative to improve reproducibility in science. The workshop materials, including accompanying resources, are available under a CC-BY 4.0 license at www.repro4everyone.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 | 0.246 | 0.160 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.018 | 0.018 |
| Open science | 0.010 | 0.069 |
| Research integrity | 0.009 | 0.014 |
| Insufficient payload (model declined to judge) | 0.018 | 0.010 |
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".