Skip to main content
Log in

Diversity and network coherence as indicators of interdisciplinarity: case studies in bionanoscience

  • Published:
Scientometrics Aims and scope Submit manuscript

Abstract

The multidimensional character and inherent conflict with categorisation of interdisciplinarity makes its mapping and evaluation a challenging task. We propose a conceptual framework that aims to capture interdisciplinarity in the wider sense of knowledge integration, by exploring the concepts of diversity and coherence. Disciplinary diversity indicators are developed to describe the heterogeneity of a bibliometric set viewed from predefined categories, i.e. using a top-down approach that locates the set on the global map of science. Network coherence indicators are constructed to measure the intensity of similarity relations within a bibliometric set, i.e. using a bottom-up approach, which reveals the structural consistency of the publications network. We carry out case studies on individual articles in bionanoscience to illustrate how these two perspectives identify different aspects of interdisciplinarity: disciplinary diversity indicates the large-scale breadth of the knowledge base of a publication; network coherence reflects the novelty of its knowledge integration. We suggest that the combination of these two approaches may be useful for comparative studies of emergent scientific and technological fields, where new and controversial categorisations are accompanied by equally contested claims of novelty and interdisciplinarity.

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.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Notes

  1. In this study interdisciplinarity refers to all these types of cross-disciplinary research.

  2. Hereafter we will use only the term similarity, which is the one commonly used in bibliometrics.

  3. Simpson's diversity is defined as (1-Simpson Index), where the Simpson index is the commonly used measure of concentration (also known as Herfindahl-Hirschman Index in disciplines such as economics).

  4. Stirling index has become known in ecology literature as Rao's quadratic entropy (Rao 1982).

  5. One example could be ‘Nanoscience&Nanotechnology’ (N&N) from the ISI categorisation: according to Leydesdorff’s and Rafols’ metric (2009), N&N has a distance of only 0.0354 with ‘Materials Science, multidisciplinary’, whereas the distance between the latter and a relatively related field, such as ‘Physics, applied’, is 0.1916.

  6. Matrices of knowledge flows among disciplines are another way to present interdisciplinarity. E.g. Bourke and Butler (1998), calculated the number of publications from discipline-based departments associated to discipline-based journals. These matrices can then be used to compute similarity measures.

  7. Other publications use measures of diversity in bibliometrics, to examine not the diversity of disciplines, but diversity/concentration of research in institutions (e.g. Rousseau 2000).

  8. If the initial bibliometric set is large enough for statistical purposes, diversity can be computed directly from the SCs of the references.

  9. Simpson I and Stirling ∆, by definition, satisfy this condition. Variety N and Shannon H are normalised by dividing by their maximum values, N max and ln(N max), respectively, with N max being the total number of ISI SCs.

  10. Although co-citation analysis is the most extended technique to measure similarities between publications, it is impractical for our purposes for two reasons: first, it cannot be for used for recently published papers, due to lack of citations; second, it reflects similarities in the audience, rather than in the knowledge sources.

  11. Details of the scale invariance test are presented below.

    Network size

    10

    51

    255

    637

    1275

    Mean linkage strength

    0.022

    0.023

    0.024

    0.025

    0.024

    Standard deviation per network

    0.045

    0.046

    0.047

    0.047

    0.046

    Network realisations

    10

    9

    7

    1

    1

    Standard deviation over realisations

    0.007

    0.004

    0.002

    From a network of 1,275 publications on kinesin research, random subnetworks of different sizes were extracted. Mean linkage strength and standard deviation were computed for each. For small networks, multiple realisations were carried out to minimise statistical fluctuations.

  12. In one case, Noji 1997, we had to set the threshold at 0.025 in order to keep the network connected.

  13. More qualitative insights are described in Rafols and Meyer (2007) and Rafols (2007).

  14. This might explain, in part, the large difference between the SC distribution of ref-of-refs in Table 4 and the distribution of references among four selected disciplines reported in Rafols and Meyer (2007).

  15. The historical anecdote is that Paul D. Boyer, the author of this long review, was awarded the Nobel Prize precisely in 1997, thanks, in part, to the evidence provided by Noji and co-authors on his model of ATPase as a rotary motor.

  16. Two caveats apply to Fig. 8: (i) on average 30% of the references were published in Multidisciplinary Sciences journals; (ii) about 25% of the references are published in journals that are attributed to at least two SCs (which is why the publication SCs cannot be presented in one unique network).

  17. This is an inference from the qualitative interviews. Without quantitative benchmarks from other areas of science, the position of the case studies on the disciplinary diversity axis cannot be determined.

  18. The only processed input needed is the SC similarity matrix used to create the science map and compute Stirling ∆. This is available as a Pajek input file (Leydesdorff and Rafols 2009): http://users.fmg.uva.nl/lleydesdorff/map06/data.xls.

References

  • Ahlgren, P., Jarneving, B., & Rousseau, R. (2003). Requirement for a cocitation similarity measure, with special reference to Pearson’s Correlation Coefficient. Journal of the American Society for Information Science and Technology, 54(6), 550–560.

    Article  Google Scholar 

  • Barjak, F. (2006). Team diversity and research collaboration in life sciences teams: Does a combination of research cultures pay off?. Olten, Switzerland: University of Applied Sciences Northwestern.

    Google Scholar 

  • Batagelj, V., & Mrvar, A. (2008). Pajek. Program for large network analysis. http://vlado.fmf.uni-lj.si/pub/networks/pajek/ Accessed 15-01-2008.

  • Bordons, M., Morillo, F., & Gómez, I. (2004). Analysis of cross-disciplinary research through bibliometric tools. In: H. F. Moed, W. Glänzel, & U. Schmoch (Eds.), Handbook of quantitative science and technology research (pp. 437–456). Dordrecht: Kluwer.

    Google Scholar 

  • Bourke, P., & Butler, L. (1998). Institutions and the map of science: Matching university departments and fields of research. Research Policy, 26, 711–718.

    Article  Google Scholar 

  • Boyack, K. W., Klavans, R., & Börner, K. (2005). Mapping the backbone of science. Scientometrics, 64(3), 351–374.

    Article  Google Scholar 

  • Braam, R. R., Moed, H. F., & van Raan, A. F. J. (1991). Mapping of science by combined co-citation and word analysis I. Structural aspects. Journal of the American Society for Information Science, 42(4), 233–251.

    Article  Google Scholar 

  • Egghe, L., & Rousseau, R. (2003). A measure for the cohesion of weighted networks. Journal of the American Society for Information Science and Technology, 54(3), 193–202.

    Article  MathSciNet  Google Scholar 

  • Havemann, F., Heinz, M., Schmidt, M., & Glaser, J. (2007). Measuring diversity of research in bibliographic-coupling networks. In: Torres-Salinas & H. Moed (Eds.), 11th International conference of the international society for scientometrics and informetrics (pp. 860–861). Madrid.

  • Hellsten, I., Lambiotte, R., Scharnhorst, A., & Ausloos, M. (2007). Self-citation, co-authorships and keywords: A new approach to scientists’ field mobility? Scientometrics, 72(3), 469–486.

    Article  Google Scholar 

  • Hollingsworth, J. R. (2006). A path-dependent perspective on institutional and organizational factors shaping major scientific discoveries. In: J. Hage & M. Meeus (Eds.), Innovation, science, and institutional change (pp. 423–442). Oxford: Oxford University Press.

    Google Scholar 

  • Kiss, I. Z., Broom, M., Craze, P., & Rafols, I. (submitted). Can epidemic model describe the diffusion of topics across disciplines? Available at http://www.sussex.ac.uk/spru/irafols.

  • Klavans, R., & Boyack, K. (2008). A map of science. Retrieved on January 30th 2008, from http://mapofscience.com/

  • Klein, J. T. (2000). A conceptual vocabulary of interdisciplinary science. In: P. Weingart & N. Stehr (Eds.), Practising interdisciplinarity (pp. 3–24). Toronto: University of Toronto Press.

    Google Scholar 

  • Leydesdorff, L. (2007). Betweenness centrality as an indicator of the interdisciplinarity of scientific journals. Journal of the American Society for Information Science and Technology, 58(9), 1303–1319.

    Article  Google Scholar 

  • Leydesdorff, L., & Rafols, I. (2009). A global map of science based on the ISI subject categories. Journal of the American Society for Information Science and Technology, 60(2), 348–362.

    Article  Google Scholar 

  • Leydesdorff, L., & Zhou, P. (2007). Nanotechnology as a field of science: Its delineation in terms of journals and patents. Scientometrics, 70(3), 693–713.

    Article  Google Scholar 

  • Meyer, M., & Persson, O. (1998). Nanotechnology–Interdisciplinarity, patterns of collaboration and differences in application. Scientometrics, 42, 195–205.

    Article  Google Scholar 

  • Morillo, F., Bordons, M., & Gomez, I. (2001). An approach to interdisciplinarity through bibliometric indicators. Scientometrics, 51(1), 203–222.

    Article  Google Scholar 

  • Morillo, F., Bordons, M., & Gómez, I. (2003). Interdisciplinarity in science: A tentative typology of disciplines and research areas. Journal of the American Society for Information Science and Technology, 54(13), 1237–1249.

    Article  Google Scholar 

  • Moya-Anegón, F. de, Vargas-Quesada, B., Chinchilla-Rodríguez, Z., Corera-Álvarez, E., Munoz-Fernández, F. J., & Herrero-Solana, V. (2007). Visualizing the marrow of science. Journal of the American Society for Information Science and Technology, 58(14), 2167–2179.

    Article  Google Scholar 

  • Moya-Anegón, F., Vargas-Quesada, B., Herrero-Solana, V., Chinchilla-Rodríguez, Z., Corera-Álvarez, E., & Munoz-Fernández, F. J. (2004). A new technique for building maps of large scientific domains based on the cocitation of classes and categories. Scientometrics, 61(1), 129–145.

    Article  Google Scholar 

  • National Academies. (2005). Facilitating interdisciplinary research. Washington, DC: National Academies Press.

    Google Scholar 

  • Nesta, L., & Saviotti, P. P. (2005). Coherence of the knowledge base and the firm’s innovative performance: Evidence from the U.S. pharmaceutical industry. Journal of Industrial Economics, 8(1), 123–142.

    Article  Google Scholar 

  • Otte, E., & Rousseau, R. (2002). Social network analysis: A powerful strategy, also for the information sciences. Journal of Information Science, 28(6), 441–453.

    Article  Google Scholar 

  • Persson, O. (2008) Bibexcel. A tool-box programme for bibliometric analysis. http://www.umu.se/inforsk/Bibexcel/ Accessed 01-02-2008.

  • Porter, A. L., & Chubin, D. E. (1985). An indicator of cross-disciplinary research. Scientometrics, 8, 161–176.

    Article  Google Scholar 

  • 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). Interdiscipinary research: Meaning, metrics and nurture. Research Evaluation, 15, 187–196.

    Article  Google Scholar 

  • Purvis, A., & Hector, A. (2000). Getting the measure of biodiversity. Nature, 405, 212–219.

    Article  Google Scholar 

  • R Development Core Team. (2007). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL http://www.R-project.org

  • Rafols, I. (2007). Strategies for knowledge acquisition in bionanotechnology: Why are interdisciplinary practices less widespread than expected? Innovation; The European Journal of Social Sciences, 20(4), 395–412.

    Article  Google Scholar 

  • Rafols, I., & Leydesdorff, L. (2009). Content-based and algorithmic classifications of journals: Perspectives on the dynamics of scientific communication and indexer effects. Journal of the American Society for Information Science and Technology. doi:10.1002/asi.21086.

  • Rafols, I., & Meyer, M. (2007). How cross-disciplinary is bionanotechnology? Explorations in the specialty of molecular motors. Scientometrics, 70(3), 633–650.

    Article  Google Scholar 

  • Rao, C. R. (1982). Diversity and dissimilarity coefficients: A unified approach. Theoretical Population Biology, 21, 24–43.

    Article  MATH  MathSciNet  Google Scholar 

  • Rousseau, R. (2000). Concentration and evenness measures as macro-level scientometric indicators. In: J. Gua-hua (Ed.), Research and university evaluation (pp. 72–89). Beijing: Red Flag Publishing House. (In Chinese).

    Google Scholar 

  • Sanz-Menéndez, L., Bordons, M., & Zulueta, M. A. (2001). Interdisciplinarity as a multidimensional concept: Its measure in three different research areas. Research Evaluation, 10(1), 47–58.

    Article  Google Scholar 

  • Scharnhorst, A. (1998). Citation-networks, science landscapes and evolutionary strategies. Scientometrics, 43(1), 95–106.

    Article  MathSciNet  Google Scholar 

  • Schmidt, M., Gläser, J., Havemann, F., & Heinz, M. (2006). A methodological study for measuring the diversity of science. In: Proceedings international workshop on webometrics, informetrics and scientometrics & seventh COLLNET meeting, Nancy (France).

  • Schummer, J. (2004). Multidisciplinarity, Interdisciplinarity, and patterns of research collaboration in nanoscience and nanotechnology. Scientometrics, 59, 425–465.

    Article  Google Scholar 

  • Small, H. G. (1973). Co-citation in the scientific literature: A new measure of the relationship between two documents. Journal of the American Society for Information Science, 24(4), 265–269.

    Article  Google Scholar 

  • Small, H. G. (1977). A co-citation model of a scientific specialty: A longitudinal study of collagen research. Social studies of science, 7(2), 139–166.

    Article  Google Scholar 

  • Stirling, A. (1998). On the economics and analysis of diversity. SPRU Electronic Working Paper. http://www.sussex.ac.uk/Units/spru/publications/imprint/sewps/sewp28/sewp28.pdf Accessed 01-04-2006.

  • 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 

  • van den Besselaar, P., & Heimeriks, G. (2001). Disciplinary, multidisciplinary, interdisciplinary: Concepts and indicators. In: M. Davis & C. S. Wilson (Eds.), Proceedings of the 8th international conference on scientometrics and informetrics—ISSI 2001 (pp. 705–716). Sydney: University of New South-Wales.

  • van den Besselaar, P., & Leydesdorff, L. (1994). Mapping change in scientific specialties: A scientometric reconstruction of the development of Artificial intelligence. Journal of the American Society for Information Science, 47(6), 415–436.

    Article  Google Scholar 

  • van Leeuwen, T., & Tijssen, R. (2000). Interdisciplinary dynamics of modern science: Analysis of cross-disciplinary citation flows. Research Evaluation, 9(3), 183–187.

    Article  Google Scholar 

  • van Raan, A. F. J., & van Leeuwen, T. (2002). Assessment of the scientific basis of interdisciplinarity, applied research. Application of bibliometric methods in Nutrition and Food Research. Research Policy, 31, 611–632.

    Article  Google Scholar 

  • Weingart, P., & Stehr, N. (Eds.). (2000). Practising interdisciplinarity. Toronto: University of Toronto Press.

    Google Scholar 

  • Zitt, M. (2005). Facing diversity of science: A challenge for bibliometric indicators. Measurement, 3(1), 38–49.

    Google Scholar 

Download references

Acknowledgements

This research was supported by an EU postdoctoral Marie-Curie Fellowship to IR, and the Daiwa Anglo-Japanese Foundation. We benefited from discussions with J. Gläser, L. Leydesdorff, F. Morillo, A.L. Porter, and SPRU colleagues S. Katz, R. Kempener, W.E. Steinmueller and A. Stirling.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ismael Rafols.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Rafols, I., Meyer, M. Diversity and network coherence as indicators of interdisciplinarity: case studies in bionanoscience. Scientometrics 82, 263–287 (2010). https://doi.org/10.1007/s11192-009-0041-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11192-009-0041-y

Keywords

Navigation