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Quantitative evaluation of large maps of science

Author

Listed:
  • Richard Klavans

    (SciTech Strategies, Inc.)

  • Kevin W. Boyack

    (Sandia National Laboratories)

Abstract

Summary This article describes recent improvements in mapping the world-wide scientific literature. Existing research is extended in three ways. First, a method for generating maps directly from the data on the relationships between hundreds of thousands of documents is presented. Second, quantitative techniques for evaluating these large maps of science are introduced. Third, these techniques are applied to data in order to evaluate eight different maps. The analyses suggest that accuracy can be increased by using a modified cosine measure of relatedness. Disciplinary bias can be significantly reduced and accuracy can be further increased by using much lower threshold levels. In short, much larger samples of papers can and should be used to generate more accurate maps of science.

Suggested Citation

  • Richard Klavans & Kevin W. Boyack, 2006. "Quantitative evaluation of large maps of science," Scientometrics, Springer;Akadémiai Kiadó, vol. 68(3), pages 475-499, September.
    • Handle: RePEc:spr:scient:v:68:y:2006:i:3:d:10.1007_s11192-006-0125-x
    DOI: 10.1007/s11192-006-0125-x
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    Citations

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    Cited by:

    1. Kevin W. Boyack, 2009. "Using detailed maps of science to identify potential collaborations," Scientometrics, Springer;Akadémiai Kiadó, vol. 79(1), pages 27-44, April.
    2. Ying Huang & Wolfgang Glänzel & Lin Zhang, 2021. "Tracing the development of mapping knowledge domains," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(7), pages 6201-6224, July.
    3. Boyack, Kevin W. & Klavans, Richard, 2008. "Measuring science–technology interaction using rare inventor–author names," Journal of Informetrics, Elsevier, vol. 2(3), pages 173-182.
    4. Juan Pablo Bascur & Suzan Verberne & Nees Jan Eck & Ludo Waltman, 2023. "Academic information retrieval using citation clusters: in-depth evaluation based on systematic reviews," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(5), pages 2895-2921, May.
    5. Marianne Hörlesberger & Ivana Roche & Dominique Besagni & Thomas Scherngell & Claire François & Pascal Cuxac & Edgar Schiebel & Michel Zitt & Dirk Holste, 2013. "A concept for inferring ‘frontier research’ in grant proposals," Scientometrics, Springer;Akadémiai Kiadó, vol. 97(2), pages 129-148, November.
    6. Sun, Xiaoling & Ding, Kun & Lin, Yuan, 2016. "Mapping the evolution of scientific fields based on cross-field authors," Journal of Informetrics, Elsevier, vol. 10(3), pages 750-761.
    7. Nees Jan Eck & Ludo Waltman, 2010. "Software survey: VOSviewer, a computer program for bibliometric mapping," Scientometrics, Springer;Akadémiai Kiadó, vol. 84(2), pages 523-538, August.
    8. Hai-Yun Xu & Zeng-Hui Yue & Chao Wang & Kun Dong & Hong-Shen Pang & Zhengbiao Han, 2017. "Multi-source data fusion study in scientometrics," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(2), pages 773-792, May.
    9. Kevin W. Boyack & Katy Börner & Richard Klavans, 2009. "Mapping the structure and evolution of chemistry research," Scientometrics, Springer;Akadémiai Kiadó, vol. 79(1), pages 45-60, April.
    10. Zehra Taşkın & Arsev U. Aydinoglu, 2015. "Collaborative interdisciplinary astrobiology research: a bibliometric study of the NASA Astrobiology Institute," Scientometrics, Springer;Akadémiai Kiadó, vol. 103(3), pages 1003-1022, June.
    11. Richard Klavans & Kevin W. Boyack, 2008. "Thought leadership: A new indicator for national and institutional comparison," Scientometrics, Springer;Akadémiai Kiadó, vol. 75(2), pages 239-250, May.
    12. Ryo Takahashi & Kenji Kaibe & Kazuyuki Suzuki & Sayaka Takahashi & Kotaro Takeda & Marc Hansen & Michiaki Yumoto, 2023. "New concept of the affinity between research fields using academic journal data in Scopus," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(6), pages 3507-3534, June.
    13. Rongying Zhao & Bikun Chen, 2014. "Applying author co-citation analysis to user interaction analysis: a case study on instant messaging groups," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(2), pages 985-997, November.
    14. Silvia Santini & Vittoria Borghese & Carlo Baggio, 2023. "HBIM-Based Decision-Making Approach for Sustainable Diagnosis and Conservation of Historical Timber Structures," Sustainability, MDPI, vol. 15(4), pages 1-23, February.
    15. Richard Klavans & Kevin W. Boyack, 2010. "Toward an objective, reliable and accurate method for measuring research leadership," Scientometrics, Springer;Akadémiai Kiadó, vol. 82(3), pages 539-553, March.
    16. del Río, Pablo & Kiefer, Christoph P., 2023. "Academic research on renewable electricity auctions: Taking stock and looking forward," Energy Policy, Elsevier, vol. 173(C).
    17. Cathelijn J. F. Waaijer & Cornelis A. Bochove & Nees Jan Eck, 2011. "On the map: Nature and Science editorials," Scientometrics, Springer;Akadémiai Kiadó, vol. 86(1), pages 99-112, January.
    18. van Eck, N.J.P. & Waltman, L., 2009. "VOSviewer: A Computer Program for Bibliometric Mapping," ERIM Report Series Research in Management ERS-2009-005-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    19. 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.
    20. Waltman, Ludo & van Eck, Nees Jan & Noyons, Ed C.M., 2010. "A unified approach to mapping and clustering of bibliometric networks," Journal of Informetrics, Elsevier, vol. 4(4), pages 629-635.

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