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Accuracy of funding information in Scopus: a comparative case study

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  • Weishu Liu

    (Zhejiang University of Finance and Economics)

Abstract

As an emerging bibliographic database, Scopus is increasingly used in academic research and evaluation practice. Compared with Web of Science, its data quality/reliability is still relatively underexplored. By using the author’s twenty-six English papers published during 2014 and 2019, this case study probes the accuracy of funding information in Scopus and shows that the accuracy of funding information collected by Web of Science is better than that of Scopus. Some obvious errors in funding acknowledgement text and funding agency fields still exist in Scopus. Therefore, Scopus needs to optimize the funding acknowledgement text identification method and improve the funding agency extraction and standardization strategy.

Suggested Citation

  • Weishu Liu, 2020. "Accuracy of funding information in Scopus: a comparative case study," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(1), pages 803-811, July.
    • Handle: RePEc:spr:scient:v:124:y:2020:i:1:d:10.1007_s11192-020-03458-w
    DOI: 10.1007/s11192-020-03458-w
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    References listed on IDEAS

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    1. Erjia Yan & Chaojiang Wu & Min Song, 2018. "The funding factor: a cross-disciplinary examination of the association between research funding and citation impact," Scientometrics, Springer;Akadémiai Kiadó, vol. 115(1), pages 369-384, April.
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    8. Jue Wang & Philip Shapira, 2011. "Funding acknowledgement analysis: an enhanced tool to investigate research sponsorship impacts: the case of nanotechnology," Scientometrics, Springer;Akadémiai Kiadó, vol. 87(3), pages 563-586, June.
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    12. Weishu Liu & Li Tang & Guangyuan Hu, 2020. "Funding information in Web of Science: an updated overview," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(3), pages 1509-1524, March.
    13. Junwen Zhu & Weishu Liu, 2020. "A tale of two databases: the use of Web of Science and Scopus in academic papers," Scientometrics, Springer;Akadémiai Kiadó, vol. 123(1), pages 321-335, April.
    14. 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.
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    Cited by:

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    3. Jordi Ardanuy & Llorenç Arguimbau & Ángel Borrego, 2022. "Social sciences and humanities research funded under the European Union Sixth Framework Programme (2002–2006): a long-term assessment of projects, acknowledgements and publications," Palgrave Communications, Palgrave Macmillan, vol. 9(1), pages 1-13, December.
    4. Manoj Kumar Verma & Daud Khan & Mayank Yuvaraj, 2023. "Scientometric assessment of funded scientometrics and bibliometrics research (2011–2021)," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(8), pages 4305-4320, August.
    5. Ghirelli, Corinna & Havari, Enkelejda & Meroni, Elena Claudia & Verzillo, Stefano, 2023. "The Long-Term Causal Effects of Winning an ERC Grant," IZA Discussion Papers 16108, Institute of Labor Economics (IZA).
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    7. Hui Li & Weishu Liu, 2020. "Same same but different: self-citations identified through Scopus and Web of Science Core Collection," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(3), pages 2723-2732, September.
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    13. Xiaoling Huang & Lei Wang & Weishu Liu, 2023. "Identification of national research output using Scopus/Web of Science Core Collection: a revisit and further investigation," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(4), pages 2337-2347, April.
    14. Emilio Abad-Segura & Mariana-Daniela González-Zamar & Eloy López-Meneses & Esteban Vázquez-Cano, 2020. "Financial Technology: Review of Trends, Approaches and Management," Mathematics, MDPI, vol. 8(6), pages 1-37, June.
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    17. Tanja Mihalic & Sahar Mohamadi & Abbas Abbasi & Lóránt Dénes Dávid, 2021. "Mapping a Sustainable and Responsible Tourism Paradigm: A Bibliometric and Citation Network Analysis," Sustainability, MDPI, vol. 13(2), pages 1-22, January.
    18. Qianqian Jin & Hongshu Chen & Ximeng Wang & Tingting Ma & Fei Xiong, 2022. "Exploring funding patterns with word embedding-enhanced organization–topic networks: a case study on big data," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(9), pages 5415-5440, September.
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