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Measuring and visualizing the contributions of Chinese and American LIS research institutions to emerging themes and salient themes

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Abstract

Novelty and salience of the research topics are vital for the competitiveness of research institutions and the development of science and technology. In this study, two novel weighting methods were proposed to differentiate the emergence and salience of research topics. A methodology was constructed to measure and visualize the contributions of research institutions to emerging themes and salient ones. The methods were illustrated with the data of ninety Chinese and American Library and Information Science research institutions collected from the Engineering Compendex and China National Knowledge Infrastructure databases between 2001 and 2012. The contributions of the investigated research institutions to the emerging themes and salient ones were calculated and visualized with the Treemap technique. The institutions were further ranked by their contributions and categorized into four types. The findings can help research institutions evaluate novelty and salience of their research topics, discover research fronts and hotspots and promote their research development.

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References

  • An, L., & Li, G. (2010). Research on salient subjects and their developing trends of foreign library and information science journals. New Technology of Library and Information Service, 26(9), 48–55.

    MathSciNet  Google Scholar 

  • An, L., Yu, C., & Li, G. (2014). Visual topical analysis of Chinese and American Library and Information Science research institutions. Journal of Informetrics, 8(1), 217–233.

    Article  Google Scholar 

  • Apply to join iSchools. (2015). Obtained at http://ischools.org/members/apply-to-join/. Accessed January 18, 2015.

  • Asahi, T., Turo, D., & Shneiderman, B. (1995). Using Treemaps to visualize the analytic hierarchy process. Information Systems Research, 6(4), 357–375.

    Article  Google Scholar 

  • Bederson, B. B., Shneiderman, B., & Wattenberg, M. (2002). Orded and quantum Treemaps: Making effective use of 2D space to display hierarchies. ACM Transactions on Graphics, 21(4), 833–854.

    Article  Google Scholar 

  • Berg, M., Speckmann, B., & Weele, V. (2011). Treemaps with bounded aspect ratio. In Proceedings of the 22nd international symposium on algorithm analysis and computation, Yokohama, Japan, December 5–8, pp. 260–270.

  • Blank, C. D., Pottenger, W. M., Kessler, G. D., Herr, M., Jaffe, H., Roy, S., et al. (2001). CIMEL: Constructive, Collaborative inquiry-based multimedia e-learning. In Proceedings of the Sixth annual conference on innovation and technology in computer science education (ITiCSE), Jun 2001.

  • Blei, D. M., & Lafferty, J. D. (2006). Dynamic topic models. In Proceedings of the 23rd international conference on machine learning (ICML’06). ACM, New York, pp. 113–120.

  • Blei, D. M., & Lafferty, J. D. (2007). A correlated topic model of science. Annals of Applied Statistics, 1(1), 17–35.

    Article  MathSciNet  MATH  Google Scholar 

  • Bruls, M., Huizing, K., & van Wijk, J. J. (2000). Squarified treemaps. In W. de Leeuw & R. van Liere (Eds.), Data visualization 2000: proceedings of joint eurographics and IEEE TCVG symposium on visualization (pp. 33–42). Berlin/Heidelberg: Springer.

    Chapter  Google Scholar 

  • Bruns, A., Burgess, J., Highfield, T., Kirchhoff, L., & Nicolai, T. (2011). Mapping the Australian Networked Public Sphere. Social Science Computer Review, 29(3), 277–287.

    Article  Google Scholar 

  • Cataldi, M., Caro, L. D., & Schifanella, C. (2013). Personalized emerging topic detection based on a term aging model. ACM Transactions on Intelligent Systems and Technology, 5(1), 1–27.

    Article  Google Scholar 

  • Chen, C. (2006). CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature. Journal of the American Society for Information Science and Technology, 57(3), 359–377.

    Article  Google Scholar 

  • Chen, C. (2013). Mapping scientific frontiers: The quest for knowledge visualization (2nd ed.). London: Springer.

    Book  Google Scholar 

  • CNKI. (2014). Obtained at http://ec.cnki.net/cp&fw.html. Accessed December 2, 2014.

  • Engineering Village. (2013). Obtained at http://www.engineeringvillage.com/controller/servlet/Controller?CID=quickSearch&database=1. Accessed January 5, 2013.

  • Fan, Y., & Ma, J. (2012). Review on the LDA-based techniques detection for the field emerging topic. New Technology of Library and Information Service, 12, 58–65.

    Google Scholar 

  • Fan, Y., Ma, J., & Zeng, S. (2013). The analysis of the studies on the field emerging topics based on knowledge mapping. Journal of Intelligence, 32(9), 88–94.

    Google Scholar 

  • Fischer, F., Fuchs, J., & Mansmann, F. (2012). ClockMap: Enhancing circular Treemaps with temporal glyphs for time-series data. In Proceedings of eurographics conference on visualization, pp. 97–101.

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

    Article  Google Scholar 

  • Good, L., Popat, A. C., Janssen, W. C., & Bier, E. (2005). A fluid treemap interface for personal digital libraries. Research and Advanced Technology for Digital Libraries, 3652, 162–173.

    Google Scholar 

  • Griffith, D. A., Cavusgil, S. T., & Xu, S. (2008). Emerging themes in international business research. Journal of International Business Studies, 39(7), 1220–1235.

    Article  Google Scholar 

  • Havre, S., Hetzler, E., Whitney, P., & Nowell, L. (2002). ThemeRiver: Visualizing thematic changes in large document collections. IEEE Transactions on Visualization and Computer Graphics, 8(1), 9–20.

    Article  Google Scholar 

  • Horn, M. S., Tobiasz, M., & Shen, C. (2009). Visualizing biodiversity with Voronoi Treemaps. In Proceedings of sixth annual international symposium on Voronoi diagrams in science and engineering, June 23–26, Copenhagen, Denmark.

  • Hou, S., Tang, J., Zhu, Y., & Yu, F. (2013). The “change” and “constant” of library and information science in 2000–2011: A case of library and information service. Library and Information Service, 57(10), 25–32.

    Google Scholar 

  • iSchools Directory. (2015). Obtained at http://ischools.org/members/directory/. Accessed January 3, 2015.

  • Khan, M. S., Coenen, F., Reid, D., Patel, R., & Archer, L. (2010). A sliding windows based dual support framework for discovering emerging trends from temporal data. Knowledge Based Systems, 23(4), 316–322.

    Article  Google Scholar 

  • Kontostathis, A., Galitsky, L. M., Pottenger, W. M., Roy, S., & Phelps, D. J. (2004). A survey of emerging trend detection in textual data mining. In M. W. Berry (Ed.), Survey of text mining (pp. 185–224). New York: Springer.

    Chapter  Google Scholar 

  • Lai, M., Wang, L., & Li, Y. (2008a). Survey and analysis of the frontiers in information science. Library and Information Service, 52(3), 6–10.

    Google Scholar 

  • Lai, M., Wang, L., Yang, W., & Li, Y. (2008b). The research frontiers of information science: Identification and discussion. Library and Information Service, 52(3), 15–18.

    Google Scholar 

  • Lee, W. H. (2008). How to identify emerging research fields using scientometrics: An example in the field of Information Security. Scientometrics, 76(3), 503–525.

    Article  Google Scholar 

  • Lent, B., Agrawal, R., & Srikant, R. (1997). Discovering trends in text database. In Proceedings of knowledge discovery and data mining (KDD-97), pp. 227–230.

  • Macrofocus. (2013). Treemap. Obtained at http://www.treemap.com/. Accessed April 10, 2013.

  • Malcolm, C., Knighting, K., Forbatm, L., & Kearney, N. (2009). Prioritisation of future research topics for children’s hospice care by its key stakeholders: A Delphi study. Palliative Medicine, 23(5), 398–405.

    Article  Google Scholar 

  • Marghescu, D. (2007). User evaluation of multidimensional data visualization techniques for financial benchmarking. In Proceedings of the European conference on information management and evaluation, pp. 341–356.

  • Ministry of Education. (2012). Catalog of undergraduate majors in universities and colleges. Obtained at http://www.moe.gov.cn/ewebeditor/uploadfile/2012/10/12/20121012084054830.doc. Accessed January 6, 2013.

  • Moed, H. F., Moya-Anegón, F., López-Illescas, C., & Visser, M. (2011). Is concentration of university research associated with better research performance? Journal of Informetrics, 5(4), 649–658.

    Article  Google Scholar 

  • Piwowarski, B., Amini, M. R., & Lalmas, M. (2012). On using a quantum physics formalism for multidocument summarization. Journal of the American Society for Information Science and Technology, 63(5), 865–888.

    Article  Google Scholar 

  • Porter, A. L., & Detampel, M. J. (1995). Technology opportunities analysis. Technological Forecasting and Social Change, 49(3), 237–255.

    Article  Google Scholar 

  • Qiu, J., Wang, X., & Wang, B. (Eds.). (2011). Evaluation report on Chinese graduate education and disciplines. Research center for china science evaluation. Beijing: Kexue Press.

    Google Scholar 

  • Rios-Berrios, M., Sharma, P., Lee, T. Y., Schwartz, R., & Shneiderman, B. (2012). TreeCovery: Coordinated dual treemap visualization for exploring the Recovery Act. Government Information Quarterly, 29(2), 212–222.

    Article  Google Scholar 

  • Rojas, W. A. C., & Villegas, C. J. M. (2013). Graphical representation and exploratory visualization for decision trees in the KDD Process. In Proceedings of the 2nd international conference on integrated information (IC-ININFO 2012), Budapest, Hungary, August 30–September 3, 2012, Vol. 73, pp. 136–144.

  • Schult, R., & Spiliopoulou, M. (2006). Discovering emerging topics in unlabelled text collections. Advances in Databases and Information Systems, 4152, 353–366.

    Article  Google Scholar 

  • Shneiderman, B., & Wattenberg, M. (2001). Ordered Treemap layouts. In Proceedings of the IEEE symposium on information visualization, San Diego, October 22–23, 2001.

  • Small, H. (2006). Tracking and predicting growth areas in science. Scientometrics, 68(3), 595–610.

    Article  Google Scholar 

  • Takahashi, T., Tomioka, R., & Yamanishi, K. (2014). Discovering emerging topics in social streams via link-anomaly detection. IEEE Transactions on Knowledge and Data Engineering, 26(1), 120–130.

    Article  Google Scholar 

  • US News & World Report. (2011). Best graduate school-library and information studies. Obtained at http://grad-schools.usnews.rankingsandreviews.com/best-graduate-schools/search.result/program+top-library-information-science-programs/top-library-information-science-programs+y. Accessed January 15, 2013.

  • Vliegen, R., van der Linden, E., & vanWijk, J. J. (2006). Visualizing business data with generalized Treemaps. IEEE Transactions on Visualization and Computer Graphics, 12(5), 789–796.

    Article  Google Scholar 

  • Wallach, H. M. (2006). Topic modeling: Beyond bag-of-words. In Proceedings of the 23rd international conference on machine learning (ICML’06). ACM, New York, pp. 977–984.

  • Wang, W., Wang, L., & Zhu, H. (2010). Research frontier and hot spots analysis of international informetrics. Journal of Medical Informatics, 31(2), 1–4, 25.

  • Wareham, J. D., Busquets, X., & Austin, R. D. (2009). Creative, convergent, and social: Prospects for mobile computing. Journal of Information Technology, 24(2), 139–143.

    Article  Google Scholar 

  • Wattenberg, M. (2005). A note on space-filling visualizations and space-filling curves. In IEEE symposium on information visualization, October 23–25, 2005, pp. 181–186.

  • Wei, J., & Wei, H. (2011). The analysis of hot topics on digital library by CiteSpace II. Library Journal, 30(4), 70–88.

    Google Scholar 

  • Winston, A. M. (2014). Law firm knowledge management: A selected annotated bibliography. Law Library Journal, 106(2), 175–197.

    Google Scholar 

  • Xiao, M., Chen, J., & Li, G. (2011). Visual analysis of the research on mapping knowledge domains based on CiteSpace. Library and Information Service, 55(6), 91–95.

    Google Scholar 

  • Yang, W., Zhang, L., Li, L., & Du, X. (2008). Research frontiers of information science: A document-based survey. Library and Information Service, 52(3), 11–14.

    Google Scholar 

  • Zhang, Q., & Ma, F. (2007). On paradigm of research knowledge management: A bibliometric analysis. Journal of Management Sciences in China, 12(6), 65–75.

    Google Scholar 

  • Zhou, J. (2011). Document visualization analysis of information visualization based on the CiteSpace II. Information Science, 29(1), 98–101.

    Google Scholar 

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Acknowledgments

We thank the National Social Science Foundation of China (11CTQ025), National Natural Science Foundation of China (71373286) and the China Scholarship Council for financial support. We are also grateful to Qingling Pan, Li Dong and Jiajia Qin for collecting the controlled terms and keywords of the publications by the Chinese and American LIS research institutions in this study. Many thanks go to the anonymous peer reviewer for his or her invaluable suggestion on the revision of this article.

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Authors and Affiliations

  1. School of Information Management, Wuhan University, No. 299 Bayi Road, Wuchang District, Wuhan, 430072, Hubei Province, People’s Republic of China

    Lu An & Xinwen Zhang

  2. College of Computing and Informatics, Drexel University, 3141 Chestnut Street, Philadelphia, PA, 19104, USA

    Xia Lin

  3. School of Information and Safety Engineering, Zhongnan University of Economics and Law, No. 182 Nanhu Ave, East Lake High-Tech Development Zone, Wuhan, 430073, Hubei Province, People’s Republic of China

    Chuanming Yu

Authors
  1. Lu An
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  2. Xia Lin
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  3. Chuanming Yu
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  4. Xinwen Zhang
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Corresponding author

Correspondence to Chuanming Yu.

Appendices

Appendix 1

See Table 8.

Table 8 The list of investigated American research institutions
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Appendix 2

See Table 9.

Table 9 The list of investigated Chinese research institutions
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Appendix 3

See Table 10.

Table 10 Emerging controlled terms and their introduction years
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An, L., Lin, X., Yu, C. et al. Measuring and visualizing the contributions of Chinese and American LIS research institutions to emerging themes and salient themes. Scientometrics 105, 1605–1634 (2015). https://doi.org/10.1007/s11192-015-1640-4

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Keywords

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