Cleaning different types of DOI errors found in cited references on Crossref using automated methods
Creators: name: Boente, Ricarda
name: Massari, Arcangelo
name: Santini, Cristian
name: Tural, Deniz
Publisher: name: Zenodo
Year: 2021
Subjects: OpenCitations
open citations
COCI
Crossref
invalid DOIs

description: <strong>Abstract</strong> <br> <strong>Purpose</strong> <br> The purpose of this work is to find an automated process to repair invalid DOI names that have been collected<br> by Silvio Peroni while processing data provided by Crossref (2021). <br> <strong>Design / methodology / approach</strong> <br> The data needed for this research is provided as a CSV list containing more than 1 million invalid cited DOI<br> names. First, to determine an automated process, the errors that characterize the wrong DOI names in the list<br> need to be classified. Concentrating exclusively on the factual errors, such as additional or invalid characters,<br> the DOI names that have become valid in the meantime can be removed. Then, a classification of those factual<br> errors as prefix-, suffix- or other-type errors is proposed. By closer investigation and extension of already<br> existing research in this field, this research classifies regular expressions that can be used to clean the different<br> types of invalid DOI names: for example, by deleting additional strings at the end or the beginning. After the<br> cleanup, the cleaned DOI names are checked for their validity again. <br> <strong>Findings</strong> <br> This research was able to find automated processes based on regular expressions and correct the factual errors<br> belonging to different subclasses. Applying the proposed algorithm to the mentioned dataset, around 16% of<br> the DOI names proved valid afterwards. The largest part of those valid DOIs consists of those made valid by<br> cleaning up suffix errors; however, many DOIs also proved valid without cleaning, being only temporarily<br> invalid. <br> <strong>Research limitations / implications</strong> <br> Checking if the DOI names are valid either consumes a lot of time or a high amount of RAM, since the process<br> should be executed before and after the cleaning. Therefore, the described methods are only applicable on<br> smaller datasets, unless the availability of the necessary resources is ensured. Also, there will always remain<br> DOI names that cannot be made valid using automated processes. In these cases, it is important to find the<br> publishers responsible for the incorrect references, which is done in a separate related project (Cioffi et al.,<br> 2021). <br> <strong>Originality / value</strong> <br> Building on existing research, this study extends and improves regular expressions targeted to clean DOI<br> errors, to enhance the data quality in the COCI dataset. As the COCI project provides open access to reference<br> lists of scientific works, the whole academic community can profit from this improvement in data quality. In<br> addition, the methods submitted could be the base for further research in this field, allowing the correction of<br> DOI name errors in other datasets, too.
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