Abstract
Recent concerns about the reproducibility of science have led to several calls for more open and transparent research practices and for the monitoring of potential improvements over time. However, with tens of thousands of new biomedical articles published per week, manually mapping and monitoring changes in transparency is unrealistic. We present an open-source, automated approach to identify five indicators of transparency (data sharing, code sharing, conflicts of interest disclosures, funding disclosures and protocol registration) and apply it across the entire open access biomedical literature of 2.75 million articles on PubMed Central. Our results indicate remarkable improvements in some (e.g. conflict of interest disclosures, funding disclosures), but not other (e.g. protocol registration, code sharing) areas of transparency over time, and map transparency across fields of science, countries, journals and publishers. This work has enabled the creation of a large, integrated, and openly available database to expedite further efforts to monitor, understand and promote transparency and reproducibility in science.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
Funding disclosure Funded by National Institutes of Health award HHSN271201800033C. METRICS has also been supported by grants from the Laura and John Arnold Foundation. S.S. has been funded by the Department of Epidemiology and Population Health at Stanford University and as a Scholar of the Stanford Data Science Initiative. In the past 36 months, J.D.W. received research support through the Collaboration for Research Integrity and Transparency from the Laura and John Arnold Foundation and through the Center for Excellence in Regulatory Science and Innovation (CERSI) at Yale University and the Mayo Clinic (U01FD005938).
Competing interest declaration We declare no competing interests.
