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Measuring diversity in disciplinary collaboration in research teams: An ecological perspective

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  • Arsev U. Aydinoglu
  • Suzie Allard
  • Chad Mitchell

Abstract

This study proposes an alternative and complementary method to bibliometric analysis to measure disciplinary diversity in research teams. Shannon’s entropy index, which is used in ecology to measure biodiversity in habitats, is adapted to measure disciplinary diversity of a research team (habitats become teams, and biodiversity becomes disciplinary diversity). Data come from the National Aeronautics and Space Administration Astrobiology Institute, which funded 14 interdisciplinary virtual research teams in 2012. Authors examined not only team rosters but also the project rosters (167 projects for 2012) of each team to calculate disciplinary diversity. Results suggest that the intended diversity is being achieved for some teams. However, for more than half of the teams, disciplinary diversity scores are lower on the project level compared to the overall team level, which suggests that for these teams, the intended diversity is not being achieved.

Suggested Citation

  • Arsev U. Aydinoglu & Suzie Allard & Chad Mitchell, 2016. "Measuring diversity in disciplinary collaboration in research teams: An ecological perspective," Research Evaluation, Oxford University Press, vol. 25(1), pages 18-36.
    • Handle: RePEc:oup:rseval:v:25:y:2016:i:1:p:18-36.
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    File URL: http://hdl.handle.net/10.1093/reseval/rvv028
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    References listed on IDEAS

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    1. Blaise Cronin, 2001. "Hyperauthorship: A postmodern perversion or evidence of a structural shift in scholarly communication practices?," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 52(7), pages 558-569.
    2. Wagner, Caroline S. & Roessner, J. David & Bobb, Kamau & Klein, Julie Thompson & Boyack, Kevin W. & Keyton, Joann & Rafols, Ismael & Börner, Katy, 2011. "Approaches to understanding and measuring interdisciplinary scientific research (IDR): A review of the literature," Journal of Informetrics, Elsevier, vol. 5(1), pages 14-26.
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    Cited by:

    1. Hajibabaei, Anahita & Schiffauerova, Andrea & Ebadi, Ashkan, 2022. "Gender-specific patterns in the artificial intelligence scientific ecosystem," Journal of Informetrics, Elsevier, vol. 16(2).
    2. Wang, Chun-Chieh & Lin, Jia-Tian & Chen, Dar-Zen & Lo, Szu-Chia, 2023. "A New Look at National Diversity of Inventor Teams within Organizations," Journal of Informetrics, Elsevier, vol. 17(1).
    3. Bethany K Laursen & Nicole Motzer & Kelly J Anderson, 2022. "Pathways for assessing interdisciplinarity: A systematic review," Research Evaluation, Oxford University Press, vol. 31(3), pages 326-343.
    4. Bethany K Laursen & Nicole Motzer & Kelly J Anderson, 2024. "Pathway profiles: Learning from five main approaches to assessing interdisciplinarity," Research Evaluation, Oxford University Press, vol. 32(2), pages 213-227.
    5. Susan Roelofs & Nancy Edwards & Sarah Viehbeck & Cody Anderson, 2019. "Formative, embedded evaluation to strengthen interdisciplinary team science: Results of a 4-year, mixed methods, multi-country case study," Research Evaluation, Oxford University Press, vol. 28(1), pages 37-50.
    6. Berea, Anamaria & Denning, Kathryn & Vidaurri, Monica & Arcand, Kimberly & Oman-Reagan, Michael P. & Bellovary, Jillian & Aydinoglu, Arsev Umur & Lupisella, Mark, 2019. "The Social Sciences Interdisciplinarity for Astronomy and Astrophysics - Lessons from the History of NASA and Related Fields," SocArXiv pfvw2, Center for Open Science.
    7. Yoshi-aki Shimada & Naotoshi Tsukada & Jun Suzuki, 2017. "Promoting diversity in science in Japan through mission-oriented research grants," Scientometrics, Springer;Akadémiai Kiadó, vol. 110(3), pages 1415-1435, March.

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