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Mining patterns of author orders in scientific publications

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  • He, Bing
  • Ding, Ying
  • Yan, Erjia

Abstract

The author order of multi-authored papers can reveal subtle patterns of scientific collaboration and provide insights on the nature of credit assignment among coauthors. This article proposes a sequence-based perspective on scientific collaboration. Using frequently occurring sequences as the unit of analysis, this study explores (1) what types of sequence patterns are most common in the scientific collaboration at the level of authors, institutions, U.S. states, and nations in Library and Information Science (LIS); and (2) the productivity (measured by number of papers) and influence (measured by citation counts) of different types of sequence patterns. Results show that (1) the productivity and influence approximately follow the power law for frequent sequences in the four levels of analysis; (2) the productivity and influence present a significant positive correlation among frequent sequences, and the strength of the correlation increases with the level of integration; (3) for author-level, institution-level, and state-level frequent sequences, short geographical distances between the authors usually co-present with high productivities, while long distances tend to co-occur with large citation counts; (4) for author-level frequent sequences, the pattern of “the more productive and prestigious authors ranking ahead” is the one with the highest productivity and the highest influence; however, in the rest of the levels of analysis, the pattern with the highest productivity and the highest influence is the one with “the less productive and prestigious institutions/states/nations ranking ahead.”

Suggested Citation

  • He, Bing & Ding, Ying & Yan, Erjia, 2012. "Mining patterns of author orders in scientific publications," Journal of Informetrics, Elsevier, vol. 6(3), pages 359-367.
    • Handle: RePEc:eee:infome:v:6:y:2012:i:3:p:359-367
    DOI: 10.1016/j.joi.2012.01.001
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    References listed on IDEAS

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    1. Erjia Yan & Cassidy R. Sugimoto, 2011. "Institutional interactions: Exploring social, cognitive, and geographic relationships between institutions as demonstrated through citation networks," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 62(8), pages 1498-1514, August.
    2. Erjia Yan & Cassidy R. Sugimoto, 2011. "Institutional interactions: Exploring social, cognitive, and geographic relationships between institutions as demonstrated through citation networks," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 62(8), pages 1498-1514, August.
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    6. Bing He & Ying Ding & Chaoqun Ni, 2011. "Mining enriched contextual information of scientific collaboration: A meso perspective," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 62(5), pages 831-845, May.
    7. Kissan Joseph & David N. Laband & Vivek Patil, 2005. "Author Order and Research Quality," Southern Economic Journal, John Wiley & Sons, vol. 71(3), pages 545-555, January.
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    Cited by:

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    2. Jinseok Kim & Jana Diesner, 2014. "A network-based approach to coauthorship credit allocation," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(1), pages 587-602, October.
    3. Kim, Jinseok & Kim, Jinmo, 2015. "Rethinking the comparison of coauthorship credit allocation schemes," Journal of Informetrics, Elsevier, vol. 9(3), pages 667-673.
    4. Zhai, Li & Yan, Xiangbin, 2022. "A directed collaboration network for exploring the order of scientific collaboration," Journal of Informetrics, Elsevier, vol. 16(4).
    5. Xuan Zhen Liu & Hui Fang, 2014. "The impact of publications from mainland China on the trends in alphabetical authorship," Scientometrics, Springer;Akadémiai Kiadó, vol. 99(3), pages 865-879, June.
    6. Prathap, Gangan & Ujum, Ephrance Abu & Kumar, Sameer & Ratnavelu, Kuru, 2021. "Scoring the resourcefulness of researchers using bibliographic coupling patterns," Journal of Informetrics, Elsevier, vol. 15(3).
    7. Carla Mara Hilário & Maria Cláudia Cabrini Grácio & Daniel Martínez-Ávila & Dietmar Wolfram, 2023. "Authorship order as an indicator of similarity between article discourse and author citation identity in informetrics," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(10), pages 5389-5410, October.
    8. Yi Bu & Binglu Wang & Win-bin Huang & Shangkun Che & Yong Huang, 2018. "Using the appearance of citations in full text on author co-citation analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(1), pages 275-289, July.
    9. Siluo Yang & Dietmar Wolfram & Feifei Wang, 2017. "The relationship between the author byline and contribution lists: a comparison of three general medical journals," Scientometrics, Springer;Akadémiai Kiadó, vol. 110(3), pages 1273-1296, March.
    10. Li, Eldon Y. & Liao, Chien Hsiang & Yen, Hsiuju Rebecca, 2013. "Co-authorship networks and research impact: A social capital perspective," Research Policy, Elsevier, vol. 42(9), pages 1515-1530.
    11. Liu, Xuan Zhen & Fang, Hui, 2012. "Modifying h-index by allocating credit of multi-authored papers whose author names rank based on contribution," Journal of Informetrics, Elsevier, vol. 6(4), pages 557-565.
    12. Giovanni Abramo & Ciriaco Andrea D’Angelo & Francesco Rosati, 2013. "Measuring institutional research productivity for the life sciences: the importance of accounting for the order of authors in the byline," Scientometrics, Springer;Akadémiai Kiadó, vol. 97(3), pages 779-795, December.
    13. Abramo, Giovanni & D’Angelo, Ciriaco Andrea & Rosati, Francesco, 2013. "The importance of accounting for the number of co-authors and their order when assessing research performance at the individual level in the life sciences," Journal of Informetrics, Elsevier, vol. 7(1), pages 198-208.

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