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ITGInsight–discovering and visualizing research fronts in the scientific literature

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Abstract

Nowadays, most organizations face the challenge of having to track the latest technological developments so as to discover new technology opportunities and to identify threats in their competitive environment. The capacity to do this relies heavily on the ability to recognize scientific innovation. Hence, monitoring emerging research directions in the scientific literature has become an important task for both researchers and policy makers. Yet the best method of doing so is still a topic of controversy. Our goal is to develop a generic computational framework that can describe a research domain in terms of its research fronts and further track the evolution trends of the knowledge structures behind each research front for the purposes of identifying knowledge innovation. The results show the evolution trends of knowledge structures could lead up to pioneering research. Implemented in ITGInsight, a C# application, the modelling and visualization process incorporates a topic clustering model and a topic evolution model to reveal knowledge structures and their evolution trends. Using the framework in a case study on synthetic biology, we verified the results it produced by consulting the literature and a panel of domain experts. The tool proves to be powerful font of insightful information that would be difficult and time-consuming for researchers and policy makers to gather on their own. Anyone involved in R&D planning, research funds allocation, and technology opportunity analysis will find the framework useful.

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References

  • Barnes, J., & Hut, P. (1986). A hierarchical O(N log N) force-calculation algorithm. Nature, 324(6096), 446–449.

    Article  Google Scholar 

  • Behan, F. M., Iorio, F., Picco, G., Gonçalves, E., Beaver, C. M., Migliardi, G., Santos, R., Rao, Y., & Sassi, F. (2019). Prioritization of cancer therapeutic targets using CRISPR–Cas9 screens. Nature, 568(7753), 511–516.

    Article  Google Scholar 

  • Bowen, A., & Casadevall, A. (2015). Increasing disparities between resource inputs and outcomes, as measured by certain health deliverables, in biomedical research. Proceedings of the National Academy of Sciences, 112(36), 11335–11340.

    Article  Google Scholar 

  • Boyack, K. W., & Klavans, R. (2010). Co-citation analysis, bibliographic coupling, and direct citation: Which citation approach represents the research front most accurately? Journal of the American Society for Information Science and Technology, 61(12), 2389–2404.

    Article  Google Scholar 

  • Breitling, R., & Takano, E. (2015). Synthetic biology advances for pharmaceutical production. Current Opinion in Biotechnology, 35C, 46–51.

    Article  Google Scholar 

  • Breitling, R., Takano, E., & Gardner, T. S. (2015). Judging synthetic biology risks. Science, 347(6218), 107.

    Article  Google Scholar 

  • Casadevall, A., & Fang, F. C. (2014). Causes for the persistence of impact factor mania. Mbio, 5(3), e00064–14.

    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, X., & Liu, L. (2018). Gene Circuits for Dynamically Regulating Metabolism. Trends in Biotechnology, 36(8), 751–754.

    Article  Google Scholar 

  • Chen, J., & Yang, L. (2021). A Bibliometric Review of Volatility Spillovers in Financial Markets: Knowledge Bases and Research Fronts. Emerging Markets Finance and Trade, 57(5), 1358–1379.

    Article  MathSciNet  Google Scholar 

  • Cobo, M. J., Lopez-Herrera, A. G., Herrera-Viedma, E., & Herrera, F. (2012). SciMAT: A New Science Mapping Analysis Software Tool. Journal of the American Society for Information Science and Technology, 63(8), 1609–1630.

    Article  Google Scholar 

  • Fang, Y. S., & Lee, L. S. (2021). Research front and evolution of technology education in Taiwan and abroad: Bibliometric co-citation analysis and maps. International Journal of Technology and Design Education, 1–32.

  • Frantzi, K., Ananiadou, S., & Mima, H. (2000). Automatic recognition of multi-word terms: The C-value/NC-value method. International Journal on Digital Libraries, 3(2), 115–130.

    Article  Google Scholar 

  • Galitsky, L. M., Pottenger, W. M., Roy, S., & Phelps, D. J. (2004). A Survey of Emerging Trend Detection in Textual Data Mining. Springer.

    Google Scholar 

  • García-Aljaro, C., Melado-Rovira, S., Milton, D. L., & Blanch, A. R. (2012). Quorum-sensing regulates biofilm formation in Vibrio scophthalmi. BMC Microbiology, 12, 287.

    Article  Google Scholar 

  • Garg, N., Da Manchan, G., & Kumar, A. (2014). Bacterial quorum sensing: Circuits and applications. Antonie Van Leeuwenhoek, 105(2), 289–305.

    Article  Google Scholar 

  • Han, H. Q., Zhu, D. H., & Wang, X. F. (2011). Technical term extraction method for patent document. Journal of the China Society for Scientific and Technical Information, 30(12), 1280–1285 (in Chinese).

    Google Scholar 

  • He, B. B., Bu, X. L., Zhou, T., Li, S. M., Xu, M. J., & Xu, J. (2018). Combinatory Biosynthesis of Prenylated 4-Hydroxybenzoate Derivatives by Overexpression of the Substrate-Promiscuous Prenyltransferase XimB in Engineered E coli. ACS Synthetic Biology, 7(9), 2094–2104.

    Article  Google Scholar 

  • Huang, M. H., & Chang, C. P. (2014). Detecting research fronts in OLED field using bibliographic coupling with sliding window. Scientometrics, 98(3), 1721–1744.

    Article  Google Scholar 

  • Huang, Y., Zhang, Y., Ma, J., Porter, A. L., Wang, X. F., & Guo, Y. (2016). Generating Competitive Technical Intelligence Using Topical Analysis, Patent Citation Analysis, and Term Clumping Analysis. In Anticipating Future Innovation Pathways Through Large Data Analysis, 153–172. Cham: Springer International.

  • Joseph, J. M., Viswajit, K., Mihe, H., Patrick, S. D., et al. (2021). Model-guided design of mammalian genetic programs. Science Advances, 7(8), eabe9375.

    Article  Google Scholar 

  • Karafyllidis, I. G. (2012). Quantum Gate Circuit Model of Signal Integration in Bacterial Quorum Sensing. Transactions on Computational Biology & Bioinformatics IEEE/ACM, 9(2), 571–579.

    Article  Google Scholar 

  • Keiser, J., & Utzinger, J. (2005). Trends in the core literature on tropical medicine: A bibliometric analysis from 1952–2002. Scientometrics, 62(3), 351–365.

    Article  Google Scholar 

  • Lee, Y., Kim, S. Y., Song, I., Park, Y., & Shin, J. (2014). Technology opportunity identification customized to the technological capability of SMEs through two-stage patent analysis. Scientometrics, 100(1), 227–244.

    Article  Google Scholar 

  • Lee, J., Kim, C., & Shin, J. (2017). Technology opportunity discovery to R&D planning: Key technological performance analysis. Technological Forecasting and Social Change, 119, 53–63.

    Article  Google Scholar 

  • Li, M., & Chu, Y. (2016). Explore the research front of a specific research theme based on a novel technique of enhanced co-word analysis. Journal of Information Science, 43(6), 725–741.

    Article  Google Scholar 

  • Li, X., Jiang, W., Liang, Q., & Qi, Q. (2020). Application of bacterial quorum sensing system in intercellular communication and its progress in synthetic biology. Synthetic Biology Journa, 1(5), 42–57.

    Google Scholar 

  • Liao, S. H., Sun, B. L., & Wang, R. Y. (2003). A knowledge-based architecture for planning military intelligence, surveillance, and reconnaissance. Space Policy, 19(3), 191–202.

    Article  Google Scholar 

  • Liu, J. S., Lu, L., & Lu, W. M. (2015a). Research Fronts in data envelopment analysis. Omega, 58, 33–45.

    Article  Google Scholar 

  • Liu, Z., Yin, Y., Liu, W., & Dunford, M. (2015b). Visualizing the intellectual structure and evolution of innovation systems research: A bibliometric analysis. Scientometrics, 103, 135–158.

    Article  Google Scholar 

  • Liu, Y. Q., Pang, J. H., Cui, Z. C., Wang, X. F., & Gui, J. (2017). An economic method of drawing a technology theme map. Library and Information Service, 61(13), 125–132 (in Chinese).

    Google Scholar 

  • Lucentini, L. (2006). Just what is synthetic biology. Scientist, 20, 36.

    Google Scholar 

  • Ma, V. C., & Liu, J. S. (2016). Exploring the research fronts and main paths of literature: A case study of shareholder activism research. Scientometrics, 109(1), 33–52.

    Article  Google Scholar 

  • Mane, K. K., & Borner, K. (2004). Mapping topics and topic bursts in PNAS. Proceedings of the National Academy of Sciences of the United States of America, 101(1), 5287–5290.

    Article  Google Scholar 

  • Miller, M. B., & Bassler, B. L. (2001). Quorum sensing in bacteria. Annual Review of Microbiology, 55, 165–199.

    Article  Google Scholar 

  • Morris, S. A., Yen, G., Wu, Z., & Asnake, B. (2003). Time line visualization of research fronts. Journal of the American Society for Information Science and Technology, 54(5), 413–422.

    Article  Google Scholar 

  • Morris, S. A., Yen, G., Zheng, W., & Asnake, B. (2014). Time line visualization of research fronts. Journal of the Association for Information Science and Technology, 54(5), 413–422.

    Google Scholar 

  • Nissim, L., Wu, M. R., Pery, E., Binder-Nissim, A., Suzuki, H. I., Stupp, D., Wehrspaun, C., Tabach, Y., Sharp, P. A., & Lu, T. K. (2017). Synthetic RNA-Based Immunomodulatory Gene Circuits for Cancer Immunotherapy. Cell, 171(5), 1138-1150.e15.

    Article  Google Scholar 

  • Noack, A. (2004). An Energy Model for Visual Graph Clustering. Proceedings of the 11th International symposium on Graph Drawing, 29 (12), 425–436.

  • Persson, & Olle. (1994). The Intellectual Base and Research Fronts of JASIS 1986–1990. Journal of the American Society for Information Science, 45(1), 31–38.

    Article  Google Scholar 

  • Pieiro-Chousa, J., López-Cabarcos, M., Romero-Castro, N. M., & Pérez-Pico, A. (2019). Innovation, entrepreneurship and knowledge in the business scientific field: Mapping the research front. Journal of Business Research, 115, 475–485.

    Article  Google Scholar 

  • Ping, X. (2015). Study of international anticancer research trends via co-word and document co-citation visualization analysis. Scientometrics, 105(1), 611–622.

    Article  Google Scholar 

  • Porter, A. L., & Cunningham, S. W. (2005). Tech mining : Exploiting new technologies for competitive advantage. Hoboken, New Jersey: Wiley-Interscience.

    Article  Google Scholar 

  • Price, D. (1965). Networks Of Scientific Papers. Science, 149(3683), 510–515.

    Article  Google Scholar 

  • Roybal, K., Williams, J., Morsut, L., Rupp, L., & Lim, W. (2016). Engineering T Cells with Customized Therapeutic Response Programs Using Synthetic Notch Receptors. Cell, 167(2), 419-432.e416.

    Article  Google Scholar 

  • Shibata, N., Kajikawa, Y., Takeda, Y., & Matsushima, K. (2008). Detecting emerging research fronts based on topological measures in citation networks of scientific publications. Technovation, 28(11), 758–775.

    Article  Google Scholar 

  • Shibata, N., Kajikawa, Y., Takeda, Y., & Matsushima, K. (2009). Comparative study on methods of detecting research fronts using different types of citation. Journal of the American Society for Information Science and Technology, 60(3), 571–580.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Small, H., & Griffith, B. C. (1974). The Structure of Scientific Literatures I: Identifying and Graphing Specialties. Social Studies of Science, 4, 17–40.

    Google Scholar 

  • Strotmann, A., & Zhao, D. (2014). The Knowledge Base and Research Front of Information Science 2006–2010: An Author Cocitation and Bibliographic Coupling Analysis. Journal of the American Society for Information Science and Technology, 65(5), 995–1006.

    Google Scholar 

  • Swofford, C. A., De Ssel, N. V., & Forbes, N. S. (2015). Quorum-sensing Salmonella selectively trigger protein expression within tumors. Proceedings of the National Academy of Sciences of the United States of America, 112(11), 3457–3462.

    Article  Google Scholar 

  • Tian, Z., Wang, Z., Liu, Z., Xiang, H., Liu, J., & Zheng, Q. (2012). Learning to identify core term of knowledge unit from short text. International Conference on Fuzzy Systems & Knowledge Discovery, 1303–1308.

  • Tijssen, R. (2002). Science dependence of technologies: Evidence from inventions and their inventors. Research Policy, 31(4), 509–526.

    Article  Google Scholar 

  • Upham, S. P., & Small, H. (2010). Emerging research fronts in science and technology: Patterns of new knowledge development. Scientometrics, 83(1), 15–38.

    Article  Google Scholar 

  • Wang, J., & Chen, Y. J. (2019). A novelty detection patent mining approach for analyzing technological opportunities. Advanced Engineering Informatics, 42, 100941.

    Article  Google Scholar 

  • Wang, X. W., Wang, Z., & Xu, S. M. (2013). Tracing scientist’s research trends realtimely. Scientometrics, 95(2), 717–729.

    Article  Google Scholar 

  • Wang, X. F., Li, R. R., Ren, S. M., Zhu, D. H., Huang, M., & Qiu, P. J. (2014). Collaboration network and pattern analysis: Case study of dye-sensitized solar cells. Scientometrics, 98(3), 1745–1762.

    Article  Google Scholar 

  • Wang, X. F., Zhang, S., & Liu, Y. Q. (2021a). ITGInsight - Discovering and Visualizing Science, Technology and Innovation Information for Generating Competitive Technological Intelligence. Proceedings of the 1st Workshop on AI + Informetrics (AII2021) co-located with the iConference 2021, 202–219.

  • Wang, X. F., Zhang, S., Liu, Y. Q., Du, J., & Huang, H. (2021). How pharmaceutical innovation evolves: The path from science to technological development to marketable drugs. Technological Forecasting and Social Change, 167(43), 120698.

    Article  Google Scholar 

  • Xie, S., Zhang, J., & Ho, Y. S. (2008). Assessment of world aerosol research trends by bibliometric analysis. Scientometrics, 77(1), 113–130.

    Article  Google Scholar 

  • Yan, B., Lee, T., & Lee, T. (2015). Mapping the intellectual structure of the Internet of Things (IoT) field (2000–2014): A co-word analysis. Scientometrics, 105(2), 1285–1300.

    Article  Google Scholar 

  • Yang, Y., Fu, L., Zhang, J., Hu, L., Xu, M., & Xu, J. (2014). Characterization of the Xiamenmycin Biosynthesis Gene Cluster in Streptomyces xiamenensis 318. Plos One, 9(6), e99537.

    Article  Google Scholar 

  • Ye, Y., Zhang, L., Zhao, X., & Ronald, R. (2012). An Experimental Study on Revealing Dom ain Knowledge Structure by Co-keyword Networks. Journal of the China Society for Scientific and Technical Information, 31(12), 1245–1251.

    Google Scholar 

  • Yi, W., & Di, M. (2016). The research fronts and hotspots on nanotechnology based on journal of vacuum science & technology. Open Journal of Social Sciences, 4(3), 57–65.

    Article  Google Scholar 

  • Yoon, B., Park, I., & Coh, B.-Y. (2014). Exploring technological opportunities by linking technology and products: Application of morphology analysis and text mining. Technological Forecasting & Social Change, 86, 287–303.

    Article  Google Scholar 

  • Yoon, J., Park, H., Seo, W., Lee, J., Coh, B.-Y., & Kim, J. (2015). Technology opportunity discovery (TOD) from existing technologies and products: A function-based TOD framework. Technological Forecasting and Social Change, 100, 153–167.

    Article  Google Scholar 

  • Yu, Y., Zhu, X. N., Bi, C. H., & Zhang, X. L. (2021). Construction of Escherichia coli cell factories. Chinese Journal of Biotechnology, 37(5), 1564–1577 (in Chinese).

    Google Scholar 

  • Zhang, Y., Guo, Y., Wang, X. F., Zhu, D. H., & Porter, A. L. (2013). A hybrid visualisation model for technology roadmapping: Bibliometrics, qualitative methodology and empirical study. Technology Analysis & Strategic Management, 25(6), 707–724.

    Article  Google Scholar 

  • Zhang, Y., Robinson, D., Porter, A. L., Zhu, D. H., Zhang, G. Q., & Lu, J. (2016). Technology roadmapping for competitive technical intelligence. Technological Forecasting and Social Change, 110, 175–186.

    Article  Google Scholar 

  • Zhou, M. Y., Bi, Y. H., Ding, M. Z., & Yuan, Y. J. (2021). One-Step Biosynthesis of Vitamin C in Saccharomyces cerevisiae. Frontiers in Microbiology, 12, 643472.

    Article  Google Scholar 

  • Zhu, D. H., & Porter, A. L. (2002). Automated extraction and visualization of information for technological intelligence. Technological Forecasting and Social Change, 69(5), 495–506.

    Article  Google Scholar 

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Acknowledgements

This work is partly supported by the General Program of the National Natural Science Foundation of China (Grant No.72074020, 71774012). The previous version of this work is published on Artificial Intelligence + Informetrics (AII) 2021 Workshop (Wang et al., 2021a), and the findings and observations present in this paper are those of the authors and do not necessarily reflect the views of the supporters or the sponsors. We are grateful to many scholars and software enthusiasts who provide their valuable opinions and suggestions in the process of ITGInsight design and development. Users could download and install the latest version of ITGInsight from http://en.itginsight.com/download/.

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Authors and Affiliations

  1. School of Management and Economics, Beijing Institute of Technology, Beijing, 100081, China

    Xuefeng Wang & Shuo Zhang

  2. School of Journalism and Publication, Beijing Institute of Graphic Communication, Beijing, 102600, China

    Yuqin liu

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  1. Xuefeng Wang
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  2. Shuo Zhang
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XW conceived and designed the research framework and partially developed the software. YL conceived and designed the analysis and developed the software. SZ wrote the paper and performed the analysis.

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Correspondence to Yuqin liu.

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Wang, X., Zhang, S. & liu, Y. ITGInsight–discovering and visualizing research fronts in the scientific literature. Scientometrics 127, 6509–6531 (2022). https://doi.org/10.1007/s11192-021-04190-9

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