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Assessing researcher interdisciplinarity: a case study of the University of Hawaii NASA Astrobiology Institute

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  • Michael Gowanlock

    (University of Hawaii)

  • Rich Gazan

    (University of Hawaii)

Abstract

In this study, we combine bibliometric techniques with a machine learning algorithm, the sequential information bottleneck, to assess the interdisciplinarity of research produced by the University of Hawaii NASA Astrobiology Institute (UHNAI). In particular, we cluster abstract data to evaluate Thomson Reuters Web of Knowledge subject categories as descriptive labels for astrobiology documents, assess individual researcher interdisciplinarity, and determine where collaboration opportunities might occur. We find that the majority of the UHNAI team is engaged in interdisciplinary research, and suggest that our method could be applied to additional NASA Astrobiology Institute teams in particular, or other interdisciplinary research teams more broadly, to identify and facilitate collaboration opportunities.

Suggested Citation

  • Michael Gowanlock & Rich Gazan, 2013. "Assessing researcher interdisciplinarity: a case study of the University of Hawaii NASA Astrobiology Institute," Scientometrics, Springer;Akadémiai Kiadó, vol. 94(1), pages 133-161, January.
    • Handle: RePEc:spr:scient:v:94:y:2013:i:1:d:10.1007_s11192-012-0765-y
    DOI: 10.1007/s11192-012-0765-y
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    References listed on IDEAS

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    Cited by:

    1. Froese, Anna & Woiwode, Hendrik & Suckow, Silvio, 2019. "Mission Impossible? Neue Wege zu Interdisziplinarität: Empfehlungen für Wissenschaft, Wissenschaftspolitik und Praxis," Discussion Papers, Research Group Science Policy Studies SP III 2019-601, WZB Berlin Social Science Center.
    2. Finn Valentin & Maria Theresa Norn & Lars Alkaersig, 2016. "Orientations and outcome of interdisciplinary research: the case of research behaviour in translational medical science," Scientometrics, Springer;Akadémiai Kiadó, vol. 106(1), pages 67-90, January.
    3. Erin Leahey & Sondra N. Barringer & Misty Ring-Ramirez, 2019. "Universities’ structural commitment to interdisciplinary research," Scientometrics, Springer;Akadémiai Kiadó, vol. 118(3), pages 891-919, March.
    4. Zehra Taşkın & Arsev U. Aydinoglu, 2015. "Collaborative interdisciplinary astrobiology research: a bibliometric study of the NASA Astrobiology Institute," Scientometrics, Springer;Akadémiai Kiadó, vol. 103(3), pages 1003-1022, June.
    5. Xuefeng Wang & Zhinan Wang & Ying Huang & Yun Chen & Yi Zhang & Huichao Ren & Rongrong Li & Jinhui Pang, 2017. "Measuring interdisciplinarity of a research system: detecting distinction between publication categories and citation categories," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(3), pages 2023-2039, June.
    6. Zuo, Zhiya & Zhao, Kang, 2018. "The more multidisciplinary the better? – The prevalence and interdisciplinarity of research collaborations in multidisciplinary institutions," Journal of Informetrics, Elsevier, vol. 12(3), pages 736-756.
    7. Richard Heidler & Olof Hallonsten, 2015. "Qualifying the performance evaluation of Big Science beyond productivity, impact and costs," Scientometrics, Springer;Akadémiai Kiadó, vol. 104(1), pages 295-312, July.
    8. Sander Zwanenburg & Maryam Nakhoda & Peter Whigham, 2022. "Toward greater consistency and validity in measuring interdisciplinarity: a systematic and conceptual evaluation," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(12), pages 7769-7788, December.
    9. Ryo Takahashi & Kenji Kaibe & Kazuyuki Suzuki & Sayaka Takahashi & Kotaro Takeda & Marc Hansen & Michiaki Yumoto, 2023. "New concept of the affinity between research fields using academic journal data in Scopus," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(6), pages 3507-3534, June.

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