Retractions in India since independence: a multifaceted analysis for 75 years through data carpentry
Abstract
This study aims to explore the nature of article retractions in India for a time frame of 75 years (1947–2021) by developing a comprehensive primary dataset of 1,376 retracted items, and then merging the dataset as developed with an array of external datasets by deploying data carpentry methods and techniques available in OpenRefine, an open-source data wrangling software. This value addition leads to exploring many new angles of study related to retraction in India, like gender distribution, geospatial distribution, institutional distribution, subject distribution in retraction, relations between journal quality and retraction, identification of serial offenders, relations between citation and retraction, and more importantly, reasons for retraction. It discovers many facts about retraction in India and attempts to represent the findings using a few next-generation visualization techniques. The major findings include the following: retraction in India is growing exponentially, and we are now the 4th highest in retraction on a global scale; the majority of the retracted items are published in quality journals in terms of quartile, impact factor, H-Index, and CiteScore; retracted items are distributed in both close-access and open-access source titles; most retracted items are able to fetch citations (mean citation 19.73), including recent citations; a considerable number of retracted items are authored by serial offenders; the retraction map of India includes the majority of the states and union territories; elite educational and research institutes are equally responsible for retraction, along with not-so-known institutes; text manipulation is still the most visible reason for retraction in India, but data manipulation, and image manipulation are increasing rapidly. It also finds that a few cases of retraction are simply due to a lack of awareness on the part of the scholars.
Keyword(s)
Retraction; Research Ethics; Data carpentry
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