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Beyond Garfield’s Citation Index: an assessment of some issues in building a personal name Acknowledgments Index

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  • Katherine W. McCain

    (Drexel University)

Abstract

To study patterns of personal acknowledgments in life sciences research and assess the feasibility of a formal Personal Acknowledgments Index, two successive 5-year (1995–1999, 2000–2004) sets of original research articles on zebrafish (Danio rerio) were scanned for acknowledgment statements thanking individuals for various “gifts” of research materials, services, and interpersonal communication. Text areas mined included “Materials and Methods” (M&M) and various text locations of “Acknowledgments” (ACK). Acknowledgment statements were coded using a detailed Personal Acknowledgments Classification. Including the M&M sections increased the number of unique personal names, primarily in classes 1a (experimental animals) and 1b (research materials)—with a few highly acknowledged researchers adding substantially to their tallies. The challenges of locating personal acknowledgment statements, harvesting and disambiguating personal names, determining the level of detail useful in characterizing the nature of the “gifts,” and assessing the level of interest in the potential user community are discussed.

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  • 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.
    • Handle: RePEc:spr:scient:v:114:y:2018:i:2:d:10.1007_s11192-017-2598-1
    DOI: 10.1007/s11192-017-2598-1
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    Cited by:

    1. Adèle Paul-Hus & Nadine Desrochers, 2019. "Acknowledgements are not just thank you notes: A qualitative analysis of acknowledgements content in scientific articles and reviews published in 2015," PLOS ONE, Public Library of Science, vol. 14(12), pages 1-13, December.
    2. 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.
    3. Heo, Go Eun & Ko, Young Soo & Xie, Qing & Song, Min, 2023. "High acknowledgement index: Characterizing research supporters with factors of acknowledgement affecting paper citation counts," Journal of Informetrics, Elsevier, vol. 17(4).
    4. 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.
    5. Nina Smirnova & Philipp Mayr, 2023. "A comprehensive analysis of acknowledgement texts in Web of Science: a case study on four scientific domains," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(1), pages 709-734, January.

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