Identifying and correcting invalid citations due to DOI errors in Crossref data
Alessia Cioffi, Sara Coppini, Arcangelo Massari, Arianna Moretti,, Silvio Peroni, Cristian Santini, Nooshin Shahidzadeh Asadi

TL;DR
This paper analyzes invalid DOI citations in Crossref data, identifies error patterns, attributes errors to specific publishers, and proposes automated correction methods to improve citation accuracy.
Contribution
It extends previous taxonomy of DOI errors, introduces advanced regular expressions for correction, and demonstrates a scalable approach for automatic DOI validation and repair.
Findings
Few publishers are responsible for most invalid DOIs.
Enhanced regular expressions can correct more errors than previous methods.
Automated correction can improve citation data quality.
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…
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Taxonomy
TopicsScientific Computing and Data Management · Research Data Management Practices
