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Types of DOI errors of cited references in Web of Science with a cleaning method

Author

Listed:
  • Shuo Xu

    (Beijing University of Technology)

  • Liyuan Hao

    (Beijing University of Technology)

  • Xin An

    (Beijing Forestry University)

  • Dongsheng Zhai

    (Beijing University of Technology)

  • Hongshen Pang

    (Library, Shenzhen University)

Abstract

Though the bibliographic databases, such as Web of Science (WoS), largely promote the development of scientometrics and informetrics, these databases are not free of errors. The main purpose of this work is to figure out which types of DOI errors of cited references exist, how often each type of errors occur, and whether it is possible to automatically correct these errors. After careful analysis, several classic DOI errors of cited references, such as prefix-, suffix- and other-type errors, are identified, Then, a cleaning method is put forward on the basis of regular expressions. Experimental results on the bibliographic data in the gene editing field from the WoS database indicate that our cleaning approach can improve largely the quality of DOI names of cited references.

Suggested Citation

  • Shuo Xu & Liyuan Hao & Xin An & Dongsheng Zhai & Hongshen Pang, 2019. "Types of DOI errors of cited references in Web of Science with a cleaning method," Scientometrics, Springer;Akadémiai Kiadó, vol. 120(3), pages 1427-1437, September.
    • Handle: RePEc:spr:scient:v:120:y:2019:i:3:d:10.1007_s11192-019-03162-4
    DOI: 10.1007/s11192-019-03162-4
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    References listed on IDEAS

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    Full references (including those not matched with items on IDEAS)

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    7. Abdelghani Maddi & Lesya Baudoin, 2022. "The quality of the web of science data: a longitudinal study on the completeness of authors-addresses links," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(11), pages 6279-6292, November.
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    9. Raminta Pranckutė, 2021. "Web of Science (WoS) and Scopus: The Titans of Bibliographic Information in Today’s Academic World," Publications, MDPI, vol. 9(1), pages 1-59, March.
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    11. Wang, Feifei & Dong, Jiaxin & Lu, Wanzhao & Xu, Shuo, 2023. "Collaboration prediction based on multilayer all-author tripartite citation networks: A case study of gene editing," Journal of Informetrics, Elsevier, vol. 17(1).
    12. Xu, Shuo & Hao, Liyuan & Yang, Guancan & Lu, Kun & An, Xin, 2021. "A topic models based framework for detecting and forecasting emerging technologies," Technological Forecasting and Social Change, Elsevier, vol. 162(C).
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