Accuracy of Author Names in Bibliographic Data Sources: An Italian Case Study

We investigate the accuracy of how author names are reported in bibliographic records excerpted from four prominent sources: WoS, Scopus, PubMed, and CrossRef. We take as a case study 44,549 publications stored in the internal database of Sapienza University of Rome, one of the largest universities in Europe. While our results indicate generally good accuracy for all bibliographic data sources considered, we highlight a number of issues that undermine the accuracy for certain classes of author names, including compound names and names with diacritics, which are common features to Italian and other Western languages

Evaluation of unique identifiers used as keys to match identical publications in Pure and SciVal:a case study from health science

Unique identifiers (UID) are seen as an effective key to match identical publications across databases or identify duplicates in a database. The objective of the present study is to investigate how well UIDs work as match keys in the integration between Pure and SciVal, based on a case with publications from the health sciences. We evaluate the matching process based on information about coverage, precision, and characteristics of publications matched versus not matched with UIDs as the match keys. We analyze this information to detect errors, if any, in the matching process. As an example we also briefly discuss how publication sets formed by using UIDs as the match keys may affect the bibliometric indicators number of publications, number of citations, and the average number of citations per publication.  The objective is addressed in a literature review and a case study. The literature review shows that only a few studies evaluate how well UIDs work as a match key. From the literature we identify four error types: Duplicate digital object identifiers (DOI), incorrect DOIs in reference lists and databases, DOIs not registered by the database where a bibliometric analysis is performed, and erroneous optical or special character recognition. The case study explores the use of UIDs in the integration between the databases Pure and SciVal. Specifically journal publications in English are matched between the two databases. We find all error types except erroneous optical or special character recognition in our publication sets. In particular the duplicate DOIs constitute a problem for the calculation of bibliometric indicators as both keeping the duplicates to improve the reliability of citation counts and deleting them to improve the reliability of publication counts will distort the calculation of average number of citations per publication. The use of UIDs as a match key in citation linking is implemented in many settings, and the availability of UIDs may become critical for the inclusion of a publication or a database in a bibliometric analysis

A Novel Approach for Estimating the Omitted-Citation Rate of Bibliometric Databases With an Application to the Field of Bibliometrics

One of the most significant inaccuracies of bibliometric databases is that of omitted citations, namely, missing electronic links between a paper of interest and some citing papers, which are (or should be) covered by the database. This paper proposes a novel approach for estimating a database’s omitted-citation rate, based on the combined use of 2 or more bibliometric databases. A statistical model is also presented for (a) estimating the “true” number of citations received by individual papers or sets of papers, and (b) defining an appropriate confidence interval. The proposed approach could represent a first step towards the definition of a standard for evaluating the accuracy level of databases

Empirical analysis and classification of database errors in Scopus and Web of Science

In the last decade, a growing number of studies focused on the qualitative/quantitative analysis of bibliometric-database errors. Most of these studies relied on the identification and (manual) examination of relatively limited samples of errors. Using an automated procedure, we collected a large corpus of more than 10,000 errors in the two multidisciplinary databases Scopus and Web of Science (WoS), mainly including articles in the Engineering-Manufacturing field. Based on the manual examination of a portion (of about 10%) of these errors, this paper provides a preliminary analysis and classification, identifying similarities and differences between Scopus and WoS. The analysis reveals interesting results, such as: (i) although Scopus seems more accurate than WoS, it tends to forget to index more papers, causing the loss of the relevant citations given/obtained, (ii) both databases have relatively serious problems in managing the so-called Online-First articles, and (iii) lack of correlation between databases, regarding the distribution of the errors in several error categories. The description is supported by practical examples concerning a variety of errors in the Scopus and WoS databases