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The sum of it all: Revealing collaboration patterns by combining authorship and acknowledgements

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  • Paul-Hus, Adèle
  • Mongeon, Philippe
  • Sainte-Marie, Maxime
  • Larivière, Vincent

Abstract

Acknowledgments are one of many conventions by which researchers publicly bestow recognition towards individuals, organizations and institutions that contributed in some way to the work that led to publication. Combining data on both co-authors and acknowledged individuals, the present study analyses disciplinary differences in researchers’ credit attribution practices in collaborative context. Our results show that the important differences traditionally observed between disciplines in terms of team size are greatly reduced when acknowledgees are taken into account. Broadening the measurement of collaboration beyond co-authorship by including individuals credited in the acknowledgements allows for an assessment of collaboration practices and team work that might be closer to the reality of contemporary research, especially in the social sciences and humanities.

Suggested Citation

  • Paul-Hus, Adèle & Mongeon, Philippe & Sainte-Marie, Maxime & Larivière, Vincent, 2017. "The sum of it all: Revealing collaboration patterns by combining authorship and acknowledgements," Journal of Informetrics, Elsevier, vol. 11(1), pages 80-87.
    • Handle: RePEc:eee:infome:v:11:y:2017:i:1:p:80-87
    DOI: 10.1016/j.joi.2016.11.005
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    References listed on IDEAS

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    5. Adèle Paul-Hus & Nadine Desrochers & Rodrigo Costas, 2016. "Characterization, description, and considerations for the use of funding acknowledgement data in Web of Science," Scientometrics, Springer;Akadémiai Kiadó, vol. 108(1), pages 167-182, July.
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    Cited by:

    1. Revet, Karine & Bodas-Freitas, Isabel Maria & Chollet, Barthélemy & D'Este, Pablo, 2023. "Exploring resource seeking in a scientific collaboration network and its effect on scientists' knowledge creation," INGENIO (CSIC-UPV) Working Paper Series 202311, INGENIO (CSIC-UPV), revised 15 May 2024.
    2. Katherine W. McCain, 2024. "Collaboration at the phylum level: coauthorship and acknowledgment patterns in the world of the water bears (phylum Tardigrada)," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(10), pages 6089-6125, October.
    3. 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.
    4. Alberto Baccini & Eugenio Petrovich, 2022. "Normative versus strategic accounts of acknowledgment data: The case of the top-five journals of economics," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(1), pages 603-635, January.
    5. Katherine W. McCain, 2018. "Beyond Garfield’s Citation Index: an assessment of some issues in building a personal name Acknowledgments Index," Scientometrics, Springer;Akadémiai Kiadó, vol. 114(2), pages 605-631, February.
    6. Li, Heyang & Wu, Meijun & Wang, Yougui & Zeng, An, 2022. "Bibliographic coupling networks reveal the advantage of diversification in scientific projects," Journal of Informetrics, Elsevier, vol. 16(3).
    7. Wen Lou & Jiangen He & Lingxin Zhang & Zhijie Zhu & Yongjun Zhu, 2023. "Support behind the scenes: the relationship between acknowledgement, coauthor, and citation in Nobel articles," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(10), pages 5767-5790, October.
    8. Pengfei Jia & Weixi Xie & Guangyao Zhang & Xianwen Wang, 2023. "Do reviewers get their deserved acknowledgments from the authors of manuscripts?," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(10), pages 5687-5703, October.
    9. Adèle Paul-Hus & Adrián A Díaz-Faes & Maxime Sainte-Marie & Nadine Desrochers & Rodrigo Costas & Vincent Larivière, 2017. "Beyond funding: Acknowledgement patterns in biomedical, natural and social sciences," PLOS ONE, Public Library of Science, vol. 12(10), pages 1-14, October.
    10. Shanwu Tian & Xiurui Xu & Ping Li, 2021. "Acknowledgement network and citation count: the moderating role of collaboration network," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(9), pages 7837-7857, September.
    11. Min Song & Keun Young Kang & Tatsawan Timakum & Xinyuan Zhang, 2020. "Examining influential factors for acknowledgements classification using supervised learning," PLOS ONE, Public Library of Science, vol. 15(2), pages 1-21, February.

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