A multi-perspective analysis of retractions in life sciences

Bhumika Bhatt ()

Scientometrics, 2021, vol. 126, issue 5, No 15, 4039-4054

Abstract: Abstract The aim of this study is to explore trends in retracted publications in life sciences and biomedical sciences over axes like time, countries, journals and impact factors, and topics. Nearly seven thousand publications, which comprise the entirety of retractions visible through PubMed as of August 2019, were used. This work involved sophisticated data collection and analysis techniques to use data from PubMed, Wikipedia, and WikiData, and study it with respect to the above mentioned axes. Importantly, I employ state-of-the-art analysis and visualization techniques from natural language processing (NLP) to understand the topics in retracted literature. To highlight a few results, the analyses demonstrate an increasing rate of retraction over time and noticeable differences in the publication quality (as measured by journal impact factor) among top countries. Moreover, while molecular biology and cancer dominate retractions, we also see a number of retractions not related to biology. The methods and results of this study can be applied to continuously understand the nature and evolution of retractions in life sciences, thus contributing to the health of this research ecosystem.

Keywords: Retracted publications; Natural language processing; Latent Dirichlet allocation; Data mining; Biomedical research (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s11192-021-03907-0

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