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.

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Converting unstructured data, i.e. data coded in a format which is not structured in a predefined way, such as PDF, into structured data, i.e. clearly defined types of data organised in a structure, has several advantages. One of the most positive effects of this conversion is that data becomes easier to search, both for humans and for algorithms. Even if there are many tools which have this objective, through a systematic review of the existing literature it is possible to understand whether there is a software whose features allow it to have better performances than the others in order to carry out a specific task in this context. This protocol shows the methodology followed in order to make a systematic review of the literature regarding the software dedicated to the extraction and manipulation of references from papers in PDF file format. Thus, the objective of this research, which is reflected on the flow of the literature review methodology, is to retrieve the most suitable software for the specified purpose, i.e.retrieving and manipulating citations from PDF files.