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

作者：

Highlights：

• The paper provides a quantitative analysis of bibliometric-database errors in the databases Scopus and Web of Science.

• A large corpus of errors in the two databases are collected using an automated procedure.

• Errors are divided in the two macro-categories: (A) pre-existing errors and (B) database mapping errors.

• The analysis reveals lack of correlation between databases, regarding the error classification.

• The description is supported by practical examples concerning a variety of errors in the two databases.

摘要

•The paper provides a quantitative analysis of bibliometric-database errors in the databases Scopus and Web of Science.•A large corpus of errors in the two databases are collected using an automated procedure.•Errors are divided in the two macro-categories: (A) pre-existing errors and (B) database mapping errors.•The analysis reveals lack of correlation between databases, regarding the error classification.•The description is supported by practical examples concerning a variety of errors in the two databases.

论文关键词：Data accuracy,Database error,Omitted citation,Error classification,Phantom citation,Scopus,Web of Science

论文评审过程：Received 9 March 2016, Revised 15 June 2016, Accepted 6 July 2016, Available online 13 September 2016, Version of Record 13 September 2016.

论文官网地址：https://doi.org/10.1016/j.joi.2016.07.003