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Identifying and correcting invalid citations due to DOI errors in Crossref data

Cioffi, Alessia; Coppini, Sara; Massari, Arcangelo; Moretti, Arianna; Peroni, Silvio ; Santini, Cristian 1; Shahidzadeh Asadi, Nooshin
1 Institut für Angewandte Informatik und Formale Beschreibungsverfahren (AIFB), Karlsruher Institut für Technologie (KIT)

Abstract:

This work aims to identify classes of DOI mistakes by analysing the open bibliographic metadata available in Crossref, highlighting which publishers were responsible for such mistakes and how many of these incorrect DOIs could be corrected through automatic processes. By using a list of invalid cited DOIs gathered by OpenCitations while processing the OpenCitations Index of Crossref open DOI-to-DOI citations (COCI) in the past two years, we retrieved the citations in the January 2021 Crossref dump to such invalid DOIs. We processed these citations by keeping track of their validity and the publishers responsible for uploading the related citation data in Crossref. Finally, we identified patterns of factual errors in the invalid DOIs and the regular expressions needed to catch and correct them. The outcomes of this research show that only a few publishers were responsible for and/or affected by the majority of invalid citations. We extended the taxonomy of DOI name errors proposed in past studies and defined more elaborated regular expressions that can clean a higher number of mistakes in invalid DOIs than prior approaches. The data gathered in our study can enable investigating possible reasons for DOI mistakes from a qualitative point of view, helping publishers identify the problems underlying their production of invalid citation data. ... mehr


Verlagsausgabe §
DOI: 10.5445/IR/1000148730
Veröffentlicht am 15.07.2022
Originalveröffentlichung
DOI: 10.1007/s11192-022-04367-w
Scopus
Zitationen: 4
Web of Science
Zitationen: 2
Dimensions
Zitationen: 3
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Angewandte Informatik und Formale Beschreibungsverfahren (AIFB)
Publikationstyp Zeitschriftenaufsatz
Publikationsmonat/-jahr 06.2022
Sprache Englisch
Identifikator ISSN: 0138-9130, 1588-2861
KITopen-ID: 1000148730
Erschienen in Scientometrics
Verlag Springer-Verlag
Band 127
Heft 6
Seiten 3593–3612
Vorab online veröffentlicht am 09.06.2022
Nachgewiesen in Dimensions
Web of Science
Scopus
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