Retraction Watch: A tool for informing academia about ethical violations in publications

Abstract

Retraction is one of the ways to filter low-quality academic publications. Over the last decade, the number of retracted articles from journals has been steadily growing around the world. Information about such articles can be found both on the websites of the retracting journals and in the databases that index these journals. In 2010, two doctors from the United States, Ivan Oransky and Adam Marcus, created an Internet resource – Retraction Watch (RW), which concentrates information obtained as a result of investigations of ethical misconduct leading to retraction of articles. These investigations are conducted by the RW founders themselves, as well as by volunteers. Currently, RW contains information on over 24,000 retractions. The main violations include serious errors in methodology and calculations that lead to incorrect conclusions, manipulation of figures and data, plagiarism and self-citation, duplication of publications, etc. In its posts, RW considers unintentional mistakes, as well as deliberate fraud in order to obtain a position, degree or other benefit. Retractions can result in the withdrawal of academic degrees, dismissal from work, demotion, loss of authority among colleagues. RW posts are released daily and a cumulative RW issue – at the end of each week. The owners of the resource, having no outside funding, offer to subscribe to a paid resource or provide sponsorship assistance to those interested in maintaining and developing it. However, the resource is publicly available, and everyone can subscribe to mailing lists for free. Recent issues have focused on non-peerreviewed articles about COVID-19 that report erroneous scientific findings with possible serious implications for important decisions. In addition, Retraction Watch accumulates information on academic ethics from other sources. This article aims to describe a variety of ethical violations leading to retraction of articles and the consequences of retractions based on RW data. The sample of posts was drawn mainly from the 2021 RW issues.