An analysis of retracted papers in Computer Science
Artikel i vetenskaplig tidskrift, 2023

Context The retraction of research papers, for whatever reason, is a growing phenomenon. However, although retracted paper information is publicly available via publishers, it is somewhat distributed and inconsistent. Objective The aim is to assess: (i) the extent and nature of retracted research in Computer Science (CS) (ii) the post-retraction citation behaviour of retracted works and (iii) the potential impact upon systematic reviews and mapping studies. Method We analyse the Retraction Watch database and take citation information from the Web of Science and Google scholar. Results We find that of the 33,955 entries in the Retraction watch database (16 May 2022), 2,816 are classified as CS, i.e., ≈ 8%. For CS, 56% of retracted papers provide little or no information as to the reasons. This contrasts with 26% for other disciplines. There is also some disparity between different publishers, a tendency for multiple versions of a retracted paper to be available beyond the Version of Record (VoR), and for new citations long after a paper is officially retracted (median = 3; maximum = 18). Systematic reviews are also impacted with ≈ 30% of the retracted papers having one or more citations from a review. Conclusions Unfortunately, retraction seems to be a sufficiently common outcome for a scientific paper that we as a research community need to take it more seriously, e.g., standardising procedures and taxonomies across publishers and the provision of appropriate research tools. Finally, we recommend particular caution when undertaking secondary analyses and metaanalyses which are at risk of becoming contaminated by these problem primary studies.

Författare

Martin Shepperd

Brunel University London

Chalmers, Data- och informationsteknik, Interaktionsdesign och Software Engineering

Leila Yousefi

Brunel University London

PLoS ONE

1932-6203 (ISSN) 19326203 (eISSN)

Vol. 18 5 e0285383

Ämneskategorier

Biblioteks- och informationsvetenskap

Datavetenskap (datalogi)

DOI

10.1371/journal.pone.0285383

PubMed

37159472

Mer information

Senast uppdaterat

2023-06-02