Key challenges in epidemiology: embracing open science
Bibliographic record
Abstract
Open science is a movement that fosters research transparency, reproducibility, and equity. Open science has been put forward by numerous stakeholders in the research ecosystem as a key science policy goal, with the United Nations Educational, Scientific, and Cultural Organization creating recommendations on open science and aligning these with UN Sustainability Goals. Open science practices are not standard to epidemiology despite their potential value to the field and especially during disease outbreaks. This article highlights core open science practices, including study registration, open data, code, material, use of reporting guideline, open access publishing, and preprints. It aims to provide readers with the fundamentals about open science, relevant international policy for open science, and the value of implementing open science for epidemiology and society as a whole. It is a practical piece that will provide readers with a starting point to expand their understanding of open science and to identify tools to learn more. The article also highlights the challenges of open science in its implementation and the importance of monitoring open science practices.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.374 | 0.412 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.010 | 0.093 |
| Scholarly communication | 0.041 | 0.082 |
| Open science | 0.008 | 0.036 |
| Research integrity | 0.040 | 0.071 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".