Abstract
Today, governments tackle the science of science policy with quantitative analysis, especially in the USA. This trend promotes the use of objective data by decision-makers in the fields of innovation policy and technology management. These decision-makers seek more reliable research and development at an earlier stage to contribute to the national economy. Under this worldwide trend, there is a high demand for quantitative analysis accompanying complex IT skills from governmental officers and non-IT researchers. The purpose of this research is to extract the destinations of currently growing fields of science by means of tracking citations and to capture signs of state of the art studies. The analysis data are bibliographic data of academic papers retrieved from Web of Science. I provide results for 16 growing topics as of July 2016. I deepen the understanding of the “convolutional neural network” among a lot of topics, and found future application candidates. I provide a methodology to discover future seeds of research and development. Additionally, the analysis system used in this research automatically publishes its results, and always provides the results. This research will contribute to improving strategies around research and development.
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Acknowledgements
This research is performed as a project of TEAM Erusmus Mundus Programme. The resources are supported by Prof. Ichiro Sakata and Prof. Junichiro Mori’s laboratory in the University of Tokyo.
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Iwami, S. Study on the destination of research via knowledge flows. Scientometrics 112, 273–288 (2017). https://doi.org/10.1007/s11192-017-2395-x
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DOI: https://doi.org/10.1007/s11192-017-2395-x