Skip to main content
Log in

Various aspects of interdisciplinarity in research and how to quantify and measure those

Scientometrics Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Adams, J., Loach, T., & Szomszor, M. (2016). Interdisciplinary Research: Methodologies for Identification and Assessment. Digital Research Reports. Digital Science.

    Google Scholar 

  • Allmendinger, J. (2015). Quests for interdisciplinarity: a challenge for the ERA and HORIZON 2020. European Commission.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • Bookstein, A. (1997). Informetric distributions. III. Ambiguity and randomness. JASIS, 48(1), 2–10.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Glänzel, W., & Schubert, A. (2003). A new classification scheme of science fields and subfields designed for scientometric evaluation purposes. Scientometrics, 56(3), 357–367.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Glänzel, W., & Thijs, B. (2012). Using ‘core documents’ for detecting and labelling new emerging topics. Scientometrics, 91(2), 399–416.

    Article  Google Scholar 

  • Glänzel, W., & Thijs, B. (2018). The role of baseline granularity for benchmarking citation impact. The case of CSS profiles. Scientometrics, 116(1), 521–536.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • Klein, J. T. (1990). Interdisciplinarity: History, Theory, and Practice. Wayne State University Press.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Ledford, H. (2015). How to solve the world’s biggest problems. Nature, 525, 208–211.

    Google Scholar 

  • Leydesdorff, L., & Rafols, I. (2011). Indicators of the interdisciplinarity of journals: Diversity, centrality, and citations. Journal of Informetrics, 5(1), 87–100.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • Nichols, L. G. (2014). A topic model approach to measuring interdisciplinarity at the national science foundation. Scientometrics, 100(3), 741–754.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Porter, A. L., Roessner, J. D., Cohen, A. S., & Perreault, M. (2006). Interdisciplinary research: Meaning, metrics and nurture. Research Evaluation, 15(3), 187–195.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Stirling, A. (1994). Diversity and ignorance in electricity supply investment: Addressing the solution rather than the problem. Energy Policy, 22(3), 195–216.

    Article  Google Scholar 

  • Stirling, A. (2007). A general framework for analysing diversity in science, technology and society. Journal of the Royal Society Interface, 4(15), 707–719.

    Article  Google Scholar 

  • Stokols, D., Fuqua, J., Gress, J., et al. (2003). Evaluating transdisciplinary science. Nicotine & Tobacco Research, 5(Suppl. 1), S21–S39.

    Article  Google Scholar 

  • Strauss, B. S. (2019). A physicist’s quest in biology: max Delbrück and complementarity. Genetics, 206(2), 641–650.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Wickson, F., Carew, A. L., & Russell, A. W. (2006). Transdisciplinary research: characteristics, quandaries and quality. Futures, 38(9), 1046–1059.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Article  Google Scholar 

Download references

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.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Wolfgang Glänzel.

Ethics declarations

Conflict of interests

The first author (Wolfgang Glänzel) is the editor-in-chief of Scientometrics, Koenraad Debackere is member of the Distinguished Reviewers Board of Scientometrics.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11192-021-04133-4

Keywords

Navigation