Identifying and correcting invalid citations due to DOI errors in Crossref data
Bibliographic Data
| ID | 21441776 |
|---|---|
| Authors | Alessia Cioffi (0000-0002-9812-4065, University of Bologna), Sara Coppini (0000-0002-6279-3830, University of Bologna), Arcangelo Massari (0000-0002-8420-0696, University of Bologna), Arianna Moretti (0000-0001-5486-7070, University of Bologna), Silvio Peroni (0000-0003-0530-4305, University of Bologna, corresponding author), Cristian Santini (0000-0001-7363-6737, Karlsruhe Institute of Technology), Nooshin Shahidzadeh Asadi (0000-0003-4114-074X, University of Antwerp) |
| Year | 2022 |
| Volume | 127 |
| Issue | 6 |
| Pages | 3593-3612 |
| Publication date | 2022-06-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Scientometrics (JOURNAL) |
| Journal identifiers | ISSN: 0138-9130 • E-ISSN: 1588-2861 |
| Publisher | Springer Science and Business Media LLC (PUBLISHER) |
| DOI | 10.1007/s11192-022-04367-w |
| OpenAlex | W3214893238 |
| Language | EN |
| Citations received | 2 |
| References cited | 31 |
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. Also, the DOI cleaning mechanism we present could be integrated into the existing process (e.g. in COCI) to add citations by automatically correcting a wrong DOI. This study was run strictly following Open Science principles, and, as such, our research outcomes are fully reproducible
Citation · Data science · Information retrieval · Metadata · Upload · World Wide Web · Computer Science · Data Quality and Management · Research Data Management Practices · Scientific Computing and Data Management
| Citations per year | 1 |
|---|---|
| Citation span | 2024 - 2025 (2) |
| Highly cited | No |