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Tracing database usage: Detecting main paths in database link networks

Author

Listed:
  • Yu, Qi
  • Ding, Ying
  • Song, Min
  • Song, Sungjeon
  • Liu, Jianhua
  • Zhang, Bin

Abstract

This paper presents a database link network to measure the impact of databases on biological research. To this end, we used the 20,861 full-text articles from PubMed Central in the field of Bioinformatics. We then extracted databases from the methodology sections of these articles and their references. The list of databases was built with The 2013 Nucleic Acids Research Molecular Biology Database Collection (available online), which includes 1512 databases. The database link network was constructed from sets of pairs of databases mentioned in the methodology sections of full-text PubMed Central articles. The edges of the database link network represent the link relationships between two databases. The weight of each edge is determined either by the link frequency of the two databases (i.e., in the link-weighted database link network) or the topic similarity between two databases (i.e., in the similarity-weighted database link network). With the database link network, we analyzed the topological structure and main paths of the database link network to trace the usage, connection, and evolution of databases. We also conducted content analysis by comparing content similarities among the papers citing databases.

Suggested Citation

  • Yu, Qi & Ding, Ying & Song, Min & Song, Sungjeon & Liu, Jianhua & Zhang, Bin, 2015. "Tracing database usage: Detecting main paths in database link networks," Journal of Informetrics, Elsevier, vol. 9(1), pages 1-15.
    • Handle: RePEc:eee:infome:v:9:y:2015:i:1:p:1-15
    DOI: 10.1016/j.joi.2014.10.002
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    References listed on IDEAS

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    1. Hong Huang & James Andrews & Jiang Tang, 2012. "Citation characterization and impact normalization in bioinformatics journals," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 63(3), pages 490-497, March.
    2. C.M. Calero Medina & T.N. Leeuwen, 2012. "Seed journal citation network maps: A method based on network theory," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 63(6), pages 1226-1234, June.
    3. Chang-Ping Hu & Ji-Ming Hu & Sheng-Li Deng & Yong Liu, 2013. "A co-word analysis of library and information science in China," Scientometrics, Springer;Akadémiai Kiadó, vol. 97(2), pages 369-382, November.
    4. C.M. Calero Medina & T.N. van Leeuwen, 2012. "Seed journal citation network maps: A method based on network theory," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 63(6), pages 1226-1234, June.
    5. Karen E. Pettigrew & Lynne (E.F.) McKechnie, 2001. "The use of theory in information science research," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 52(1), pages 62-73.
    6. Hong Huang & James Andrews & Jiang Tang, 2012. "Citation characterization and impact normalization in bioinformatics journals," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 63(3), pages 490-497, March.
    7. Louis Y. Y. Lu & John S. Liu, 2013. "An innovative approach to identify the knowledge diffusion path: the case of resource-based theory," Scientometrics, Springer;Akadémiai Kiadó, vol. 94(1), pages 225-246, January.
    8. Vieira, E.S. & Gomes, J.A.N.F., 2010. "A research impact indicator for institutions," Journal of Informetrics, Elsevier, vol. 4(4), pages 581-590.
    9. Ding, Ying, 2011. "Scientific collaboration and endorsement: Network analysis of coauthorship and citation networks," Journal of Informetrics, Elsevier, vol. 5(1), pages 187-203.
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    Cited by:

    1. Yu, Dejian & Pan, Tianxing, 2021. "Tracing the main path of interdisciplinary research considering citation preference: A case from blockchain domain," Journal of Informetrics, Elsevier, vol. 15(2).
    2. Ning Yang & Zhiqiang Zhang & Feihu Huang, 2023. "A study of BERT-based methods for formal citation identification of scientific data," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(11), pages 5865-5881, November.
    3. Pan, Xuelian & Yan, Erjia & Cui, Ming & Hua, Weina, 2018. "Examining the usage, citation, and diffusion patterns of bibliometric mapping software: A comparative study of three tools," Journal of Informetrics, Elsevier, vol. 12(2), pages 481-493.
    4. Shiyun Wang & Jin Mao & Yujie Cao & Gang Li, 2022. "Integrated knowledge content in an interdisciplinary field: identification, classification, and application," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(11), pages 6581-6614, November.

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