Abstract
Interdisciplinary research figures high on today’s policy agendas. This short introduction and overview sketches the complexity of defining and mapping the nature of interdisciplinary research (IDR). The paper focuses on the different approaches to IDR and different methods applied in bibliometric studies that allow measuring it. These methods should not only be able to capture quantitative aspects of IDR but also to monitor evolutionary aspects and help answer the question of whether IDR stimulates collaboration and results in larger impact and visibility. Two specific indicators, variety and disparity, are developed, validated and applied to bibliometric data. They enable the visualization of the interdisciplinary nature of research activities at various levels of analysis (both institutional and individual). And, given the longitudinal character of bibliometric data and databases, both indicators allow for mapping time-dependent phenomena and evolutions. Relevant examples based on the literature and recent results from research conducted at the Leuven bibliometrics group of ECOOM (e.g., Glänzel et al., Proceedings of the 18th International Conference of the International Society of Scientometrics and Informetrics, 453–464, 2021; Huang et al., Proceedings of the 18th International Conference of the International Society of Scientometrics and Informetrics, 533–538, 2021) are given, and concrete proposals for future research are articulated.
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References
Abramo, G., D’Angelo, C. A., & Di Costa, F. (2017). Do interdisciplinary research teams deliver higher gains to science? Scientometrics, 111(1), 317–336.
Abramo, G., D’Angelo, C. A., & Costa, F. D. (2012). Identifying interdisciplinarity through the disciplinary classification of coauthors of scientific publications. JASIST, 63(11), 2206–2222.
Adams, J., Loach, T., & Szomszor, M. (2016). Interdisciplinary Research: Methodologies for Identification and Assessment. Digital Research Reports. Digital Science.
Allmendinger, J. (2015). Quests for interdisciplinarity: a challenge for the ERA and HORIZON 2020. European Commission.
Ba, Z., Cao, Y., Mao, J., et al. (2019). A hierarchical approach to analyzing knowledge integration between two fields – a case study on medical informatics and computer science. Scientometrics, 119(3), 1455–1486.
Bookstein, A. (1997). Informetric distributions. III. Ambiguity and randomness. JASIS, 48(1), 2–10.
Choi, B.C., Pak, A.W. (2006). Multidisciplinarity, interdisciplinarity and transdisciplinarity in health research, services, education and policy: 1. Definitions, objectives, and evidence of effectiveness. Clinical and Investigative Medicine, 29(6), 351–364.
COSEPUP (2004). Facilitating interdisciplinary research. Paper presented at the National academies committee on facilitating interdisciplinary research, committee on science, engineering and public policy (COSEPUP) 2004, Washington, DC, 306 p. Accessible at https://www.nap.edu/download/11153.
Dong, K., Xu, H., Luo, R., Wei, L., & Fang, S. (2018). An integrated method for interdisciplinary topic identification and prediction: a case study on information science and library science. Scientometrics, 115(2), 849–868.
Dou, H. (2017). A catalyst for interdisciplinarity in Science: the patent information. Competitive Intelligence Worldwide’s Interdisciplinary Symposium, Corte, Corsica, July 5–7. Accessible at https://s244543015.onlinehome.fr/ciworldwide/wp-content/uploads/2017/08/informationscience_dou.pdf
Fanelli, D., Glänzel, W. (2013), Bibliometric evidence for a Hierarchy of the Sciences. PLoS ONE, 8(6), Article Number: e66938.
Flinterman, J. F., Teclemariam-Mesbah, R., Broerse, J. E. W., & Buders, J. F. G. (2001). Transdisciplinary: the new challenge for biomedical research. Bulletin of Science, Technology & Society, 21(4), 253–266.
Glänzel, W. (2007), Characteristic scores and scales. A bibliometric analysis of subject characteristics based on long-term citation observation. Journal of Informetrics, 1(1), 92–102
Glänzel, W., Beck, R., Milzow, K., Slipersæter, S., Tóth, G., Kolodziejski, M., Chi, P.S. (2016), Data collection and use in research funding and performing organisations. General outlines and first results of a project launched by Science Europe. Scientometrics, 106(2), 825–835
Glänzel, W., & Czerwon, H. J. (1996). A new methodological approach to bibliographic coupling and its application to the national, regional and institutional level. Scientometrics, 37(2), 195–221.
Glänzel, W., & Schubert, A. (2003). A new classification scheme of science fields and subfields designed for scientometric evaluation purposes. Scientometrics, 56(3), 357–367.
Glänzel, W., Schubert, A., & Czerwon, H. J. (1999). An item-by-item subject classification of papers published in multidisciplinary and general journals using reference analysis. Scientometrics, 44(3), 427–439.
Glänzel, W., Schubert, A., Thijs, B., & Debackere, K. (2009). Subfield-specific normalized relative indicators and a new generation of relational charts: Methodological foundations illustrated on the assessment of institutional research performance. Scientometrics, 78(1), 165–188.
Glänzel, W., & Thijs, B. (2012). Using ‘core documents’ for detecting and labelling new emerging topics. Scientometrics, 91(2), 399–416.
Glänzel, W., & Thijs, B. (2018). The role of baseline granularity for benchmarking citation impact. The case of CSS profiles. Scientometrics, 116(1), 521–536.
Glänzel, W., Thijs, B., Debackere, K. (2019), Citation classes: A distribution-based approach to profiling citation impact for evaluative purposes. In: W. Glänzel, H. Moed, U. Schmoch, M. Thelwall (Eds.), Springer Handbook of Science and Technology Indicators. Springer International Publishing – Berlin, Heidelberg, 335–360
Glänzel, W., Thijs, B., Huang, Y. (2021), Improving the precision of subject assignment for disparity measurement in studies of interdisciplinary research. Proceedings of the 18th International Conference of the International Society of Scientometrics and Informetrics, Leuven University Press, 453–464
Huang, Y., Thijs, B., Glänzel, W. (2021), A framework for measuring the knowledge diffusion impact of interdisciplinary research. Proceedings of the 18th International Conference of the International Society of Scientometrics and Informetrics, Leuven University Press, 533–538
Huutoniemi, K., Klein, J. T., Bruun, H., & Hukkinena, J. (2010). Analyzing interdisciplinarity: typology and indicators. Research Policy, 39(1), 79–88.
Klein, J. T. (1990). Interdisciplinarity: History, Theory, and Practice. Wayne State University Press.
Ko, N., Yoon, J., & Seo, W. (2018). Analyzing interdisciplinarity of technology fusion using knowledge flows of patents. Expert systems with applications, 41(42), 1955–1963.
Lan, G., Katrenko, S., Pan, L., (2015). Analyzing Interdisciplinary Research along multiple dimensions of research impact. ASIS&T METRICS Workshop, St Louis, September 24, 2015. Accessible at https://www.asist.org/SIG/SIGMET/wp-content/uploads/2015/10/sigmet2015_paper_14.pdf
Larivière, V., & Gingras, Y. (2010). On the relationship between interdisciplinarity and scientific impact. JASIST, 61(1), 126–131.
Leahey, E., Beckman, C. M., & Stanko, T. L. (2017). Prominent but less productive, the impact of interdisciplinarity on scientists’ research. Administrative Science Quarterly, 62(1), 105–139.
Ledford, H. (2015). How to solve the world’s biggest problems. Nature, 525, 208–211.
Leydesdorff, L., & Rafols, I. (2011). Indicators of the interdisciplinarity of journals: Diversity, centrality, and citations. Journal of Informetrics, 5(1), 87–100.
Magerman, T., Van Looy, B., & Debackere, K. (2015). Does involvement in patenting jeopardize one’s academic footprint? an analysis of patent-publication pairs in biotechnology. Research Policy, 44, 1702–1713.
Mazzocchi, F. (2019), Scientific research across and beyond disciplines. EMBO Reports, 20: e47682.
Molas-Gallart, J., Rafols, I., & Tang, P. (2014). On the relationship between interdisciplinarity and impact: different modalities of interdisciplinarity lead to different types of impact. Journal of Science Policy and Research Management, 29(2), 69–89.
Mugabushaka, A. M., Kyriakou, A., & Papazoglou, T. (2016). Bibliometric indicators of interdisciplinarity: the potential of the Leinster-Cobbold diversity indices to study disciplinary diversity. Scientometrics, 107(2), 593–607.
Nichols, L. G. (2014). A topic model approach to measuring interdisciplinarity at the national science foundation. Scientometrics, 100(3), 741–754.
NSF (2013), Integrated NSF Support Promoting Interdisciplinary Research and Education (INSPIRE). Accessible at: https://www.nsf.gov/pubs/2013/nsf13518/nsf13518.htm
Porter, A. L., Cohen, A. S., Roessner, J. D., & Perreault, M. (2007). Measuring researcher interdisciplinarity. Scientometrics, 72(1), 117–147.
Porter, A. L., Roessner, J. D., Cohen, A. S., & Perreault, M. (2006). Interdisciplinary research: Meaning, metrics and nurture. Research Evaluation, 15(3), 187–195.
Rafols, I. (2014), Knowledge integration and diffusion: Measures and mapping of diversity and coherence. In: Ding Y., Rousseau R., Wolfram D. (eds), Measuring scholarly impact Springer, Cham. 169–190
Rafols, I., & Meyer, M. (2010). Diversity and network coherence as indicators of interdisciplinarity: case studies in bio-nanoscience. Scientometrics, 82(2), 263–287.
Rousseau, R., Guns, R., Rahman, A. I. M. J., & Engels, T. C. E. (2017). Measuring cognitive distance between publication portfolios. Journal of Informetrics, 11(2), 583–594.
Stirling, A. (1994). Diversity and ignorance in electricity supply investment: Addressing the solution rather than the problem. Energy Policy, 22(3), 195–216.
Stirling, A. (2007). A general framework for analysing diversity in science, technology and society. Journal of the Royal Society Interface, 4(15), 707–719.
Stokols, D., Fuqua, J., Gress, J., et al. (2003). Evaluating transdisciplinary science. Nicotine & Tobacco Research, 5(Suppl. 1), S21–S39.
Strauss, B. S. (2019). A physicist’s quest in biology: max Delbrück and complementarity. Genetics, 206(2), 641–650.
The Royal Society. (2016). Response to the British Academy’s call for evidence on ‘Interdisciplinarity’, Accessible at: https://royalsociety.org/~/media/policy/Publications/2015/29-06-15-rs-response-to-ba-inquiry-interdisciplinarity.pdf.
Thijs, B. (2020), On the added value of networked data and graph embeddings over convolutional neural networks for the classification of scientific publications. Paper presented at the GTM 2020 Virtual Conference, 12 November 2020.
Wang, J., Shapira, P. (2015). Is there a relationship between research sponsorship and publication impact? An analysis of funding acknowledgments in nanotechnology papers. PloS ONE, 10(2), e0117727
Wang, J., Thijs, B., Glänzel, W. (2015). Interdisciplinarity and Impact: Distinct Effects of Variety, Balance and Disparity. Plos One, 10(5): e0127298
Wang, L., Notten, A., & Surpatean, A. (2013). Interdisciplinarity of nano research fields: a keyword mining approach. Scientometrics, 94(3), 877–892.
Wickson, F., Carew, A. L., & Russell, A. W. (2006). Transdisciplinary research: characteristics, quandaries and quality. Futures, 38(9), 1046–1059.
Xu, H., Guo, T., Yue, Z., Ru, L. J., & Fang, S. (2016). Interdisciplinary topics of information science: a study based on the terms interdisciplinarity index series. Scientometrics, 106(2), 583–601.
Yegros-Yegros, A., Rafols, I., D’Este, P. (2015). Does interdisciplinary research lead to higher citation impact? The different effect of proximal and distal interdisciplinarity. PLoS ONE, 10(8), e0135095
Zhang, L., Rousseau, R., & Glänzel, W. (2016). Diversity of references as an indicator for interdisciplinarity of journals: taking similarity between subject fields into account. JASIS, 67(5), 1257–1265.
Zhang, L., Sun, B., Chinchilla-Rodrígue, Z., Chen, L., & Huang, Y. (2018). Interdisciplinarity and collaboration: on the relationship between disciplinary diversity in departmental affiliations and reference lists. Scientometrics, 117(1), 271–291.
Acknowledgements
The research underlying this study is done within the framework of the project “Interdisciplinarity & Impact” (2019-2023) funded by the Flemish Government.
We would like to thank Lin Zhang, Bart Thijs and Ying Huang for inspiring discussions and providing data for this paper as well as the two anonymous reviewers for their advise for improvement of this paper.
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The first author (Wolfgang Glänzel) is the editor-in-chief of Scientometrics, Koenraad Debackere is member of the Distinguished Reviewers Board of Scientometrics.
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Glänzel, W., Debackere, K. Various aspects of interdisciplinarity in research and how to quantify and measure those. Scientometrics 127, 5551–5569 (2022). https://doi.org/10.1007/s11192-021-04133-4
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DOI: https://doi.org/10.1007/s11192-021-04133-4