Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, California, United States of America.
Meta-Research Innovation Center at Stanford (METRICS), Stanford School of Medicine, Stanford, California, United States of America.
PLoS Biol. 2021 Mar 1;19(3):e3001107. doi: 10.1371/journal.pbio.3001107. eCollection 2021 Mar.
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 5 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 (PMC). Our results indicate remarkable improvements in some (e.g., conflict of interest [COI] disclosures and funding disclosures), but not other (e.g., protocol registration and 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.
最近,人们对科学研究的可重复性表示担忧,这促使人们呼吁采取更加开放和透明的研究实践,并对潜在的改进进行长期监测。然而,每周都会有数千篇新的生物医学文章发表,人工映射和监测透明度的变化是不现实的。我们提出了一种开源的自动化方法,用于识别 5 个透明度指标(数据共享、代码共享、利益冲突披露、资金披露和方案注册),并将其应用于 PubMed Central(PMC)上 275 万篇开放获取生物医学文献的所有文章中。我们的研究结果表明,在某些方面(例如,利益冲突[COI]披露和资金披露)取得了显著的改进,但在其他方面(例如,方案注册和代码共享)并没有随着时间的推移而得到改善,同时还绘制了科学领域、国家、期刊和出版商之间的透明度图谱。这项工作促成了一个大型的、集成的、公开可用的数据库的创建,以加速进一步努力监测、理解和促进科学研究的透明度和可重复性。