"解决交叉参考数据DOI错误导致的引用无效"

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The paper "Identifying and correcting invalid citations due to DOI errors in Crossref data" by Alessia Cioffi and Sara Coppini from the Department of Classical Philology and Italian Studies at the University of Bologna, Italy, addresses the issue of invalid citations caused by DOI errors in Crossref data. The authors highlight the significance of accurate citation information and the impact that incorrect DOIs can have on the reliability of scholarly references. Through their research, Cioffi and Coppini identify common DOI errors in Crossref data and propose methods for detecting and correcting these errors to ensure the integrity of academic citations. They emphasize the importance of maintaining accurate and up-to-date DOI information to facilitate effective scholarly communication and research dissemination. The authors emphasize the role of digital humanities and digital knowledge in addressing this issue and stress the need for collaboration between researchers, publishers, and database managers to improve the quality of citation data. By identifying and rectifying DOI errors in Crossref data, Cioffi and Coppini aim to enhance the credibility and usability of scholarly references, ultimately benefiting the academic community as a whole. Overall, this paper sheds light on the challenges associated with invalid citations due to DOI errors and provides practical solutions for improving the accuracy and reliability of scholarly references. It underscores the importance of meticulous attention to detail in academic publishing and calls for a concerted effort to address and rectify these issues to enhance the overall quality of scholarly communication.

2.1. Input data

A list of invalid cited DOIs was gathered by OpenCitations while processing COCI in the past

two years. Starting from the citation data available in Crossref as of January 2021 (Crossref,

2021), a list of all the citations in Crossref to the invalid DOIs identified during the COCI

processing activities was created and made available online (Peroni, 2021). The data

consists of a CSV file, where each row is a pair of valid citing DOI – invalid cited DOI (see

sample in Table 1). The file contains a total of 1,223,295 DOI-to-DOI invalid citations.

Valid citing DOI

Invalid cited DOI

10.14778/1920841.1920954

10.5555/646836.708343

10.5406/ethnomusicology.59.2.0202

10.2307/20184517

10.1177/1179546820918903

10.3748/wjg.v10.i5.707.

Table 1: Sample of the CSV containing Crossref citations to invalid DOI (Peroni, 2021).

2.2. Identifying publishers responsible for invalid DOIs

G1 processed the input CSV file and created a new dataset, keeping track of each citation’s

validity and the publisher responsible for uploading the related citation data in Crossref. We

queried the DOI.org API (https://doi.org/api/handles/) with the DOI of the cited entity involved

in a citation to check whether the citation was currently valid (i.e. the invalid DOI gathered

via the COCI process has changed its status to valid in the meantime). We assumed that the

citing DOI was correct since Crossref provided it.

Conversely, we used the Crossref API (https://api.crossref.org) to identify publishers of both

the citing and the cited entities involved in each citation by employing their DOIs' prefixes,

i.e. the part of the DOI starting with “10.” and ending before the first “/” (e.g. “10.1016”). In

case a publisher could not be identified in Crossref, e.g. because they do not use Crossref

as their DOI registration agency, we labelled it as “unidentified”.

All the material produced by G1 is available at

https://github.com/open-sci/2020-2021/blob/master/docs/TheLeftovers20/material.md. The

Data Management Plan (Cioffi et al., 2021a), the description of the protocol to gather and

analyse the data (Coppini et al., 2021b), the software implementing the protocol (Coppini et

al., 2021a), and the final data obtained by running the workflow (Cioffi et al., 2021b) are all

available online to enable the reproducibility of the study.

2.3 Classifying and correcting DOIs classes of errors

G2 developed an approach to classify invalid DOIs and clean the related invalid citation

data. The cited invalid DOIs provided in the input data can be either (a) temporarily invalid or

(b) factually invalid. In (a), the cited DOIs were invalid when the COCI ingestion workflow

was executed, and they may have become valid before our analysis. In (b), the DOIs needed

to be corrected due to, for instance, the presence of an additional character. For further

classification of the invalid DOIs, we reused the categorisation presented by Xu et al. (2019),

which split all the DOIs in (b) as follows:

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"解决交叉参考数据DOI错误导致的引用无效"

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