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Toward a consensus map of science

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  • Richard Klavans
  • Kevin W. Boyack

Abstract

A consensus map of science is generated from an analysis of 20 existing maps of science. These 20 maps occur in three basic forms: hierarchical, centric, and noncentric (or circular). The consensus map, generated from consensus edges that occur in at least half of the input maps, emerges in a circular form. The ordering of areas is as follows: mathematics is (arbitrarily) placed at the top of the circle, and is followed clockwise by physics, physical chemistry, engineering, chemistry, earth sciences, biology, biochemistry, infectious diseases, medicine, health services, brain research, psychology, humanities, social sciences, and computer science. The link between computer science and mathematics completes the circle. If the lowest weighted edges are pruned from this consensus circular map, a hierarchical map stretching from mathematics to social sciences results. The circular map of science is found to have a high level of correspondence with the 20 existing maps, and has a variety of advantages over hierarchical and centric forms. A one‐dimensional Riemannian version of the consensus map is also proposed.

Suggested Citation

  • Richard Klavans & Kevin W. Boyack, 2009. "Toward a consensus map of science," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 60(3), pages 455-476, March.
    • Handle: RePEc:bla:jamist:v:60:y:2009:i:3:p:455-476
    DOI: 10.1002/asi.20991
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    Cited by:

    1. Harrison, Richard T., 2023. "W(h)ither entrepreneurship? Discipline, legitimacy and super-wicked problems on the road to nowhere," Journal of Business Venturing Insights, Elsevier, vol. 19(C).
    2. Ludo Waltman & Nees Jan Eck, 2012. "A new methodology for constructing a publication-level classification system of science," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 63(12), pages 2378-2392, December.
    3. George Emm Halkos & Nickolaos G. Tzeremes, 2011. "Measuring economic journals’ citation efficiency: a data envelopment analysis approach," Scientometrics, Springer;Akadémiai Kiadó, vol. 88(3), pages 979-1001, September.
    4. 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.
    5. Hyeonchae Yang & Woo-Sung Jung, 2015. "A strategic management approach for Korean public research institutes based on bibliometric investigation," Quality & Quantity: International Journal of Methodology, Springer, vol. 49(4), pages 1437-1464, July.
    6. Cecere, Grazia & Martinelli, Arianna, 2017. "Drivers of knowledge accumulation in electronic waste management: An analysis of publication data," Research Policy, Elsevier, vol. 46(5), pages 925-938.
    7. Carusi, Chiara & Bianchi, Giuseppe, 2019. "Scientific community detection via bipartite scholar/journal graph co-clustering," Journal of Informetrics, Elsevier, vol. 13(1), pages 354-386.
    8. Quirin, Arnaud & Cordón, Oscar & Vargas-Quesada, Benjamín & de Moya-Anegón, Félix, 2010. "Graph-based data mining: A new tool for the analysis and comparison of scientific domains represented as scientograms," Journal of Informetrics, Elsevier, vol. 4(3), pages 291-312.
    9. Richard Klavans & Kevin W Boyack, 2017. "The Research Focus of Nations: Economic vs. Altruistic Motivations," PLOS ONE, Public Library of Science, vol. 12(1), pages 1-27, January.
    10. Zhang, Yi & Shang, Lining & Huang, Lu & Porter, Alan L. & Zhang, Guangquan & Lu, Jie & Zhu, Donghua, 2016. "A hybrid similarity measure method for patent portfolio analysis," Journal of Informetrics, Elsevier, vol. 10(4), pages 1108-1130.
    11. Hofmann, Peter & Keller, Robert & Urbach, Nils, 2019. "Inter-technology relationship networks: Arranging technologies through text mining," Technological Forecasting and Social Change, Elsevier, vol. 143(C), pages 202-213.
    12. Gómez-Núñez, Antonio J. & Batagelj, Vladimir & Vargas-Quesada, Benjamín & Moya-Anegón, Félix & Chinchilla-Rodríguez, Zaida, 2014. "Optimizing SCImago Journal & Country Rank classification by community detection," Journal of Informetrics, Elsevier, vol. 8(2), pages 369-383.
    13. Klavans, Richard & Boyack, Kevin W., 2014. "Mapping altruism," Journal of Informetrics, Elsevier, vol. 8(2), pages 431-447.
    14. Silva, F.N. & Viana, M.P. & Travençolo, B.A.N. & Costa, L. da F., 2011. "Investigating relationships within and between category networks in Wikipedia," Journal of Informetrics, Elsevier, vol. 5(3), pages 431-438.

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