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Abstract
Aim
Article retraction is a measure taken by journals or authors where there is evidence of research misconduct or error, redundancy, plagiarism or unethical research. Recently, the retraction of scientific publications has been on the rise. In this survey, we aimed to describe the characteristics and distribution of retracted articles and the reasons for retractions.
Methods
We searched retracted articles on the PubMed database and Retraction Watch website from 1980 to February 2016. The primary outcomes were the characteristics and distribution of retracted articles and the reasons for retractions. The secondary outcomes included how article retractions were handled by journals and how to improve the journal practices toward article retractions.
Results
We included 1,339 retracted articles. Most retracted articles had six authors or fewer. Article retraction was most common in the USA (26%), Japan (11%) and Germany (10%). The main reasons for article retraction were misconduct (51%, n = 685) and error (14%, n = 193). There were 66% (n = 889) of retracted articles having male senior or corresponding authors. Of the articles retracted after August 2010, 63% (n = 567) retractions were reported on Retraction Watch. Large discrepancies were observed in the ways that different journals handled article retractions. For instance, articles were completely withdrawn from some journals, while in others, articles were still available with no indication of retraction. Likewise, some retraction notices included a detailed account of the events that led to article retraction, while others only consisted of a statement indicating the article retraction.
Conclusion
The characteristics, geographic distribution and reasons for retraction of published articles involving human research participants were examined in this survey. More efforts are needed to improve the consistency and transparency of journal practices toward article retractions.
Supplementary material
Table S1 Search strategy on PubMed
Acknowledgments
We would like to acknowledge Nancy Toma for her unconditional support on data abstraction. This study received no funding.
Author contributions
All authors made substantial contributions to conception and design, acquisition of data, or analysis and interpretation of data; took part in drafting the article or revising it critically for important intellectual content; gave final approval of the version to be published; and agree to be accountable for all aspects of the work.
Disclosure
The authors report no conflicts of interest in this work.