Application of machine learning techniques to assess the trends and alignment of the funded research output |
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| Affiliation: | 1. National Research Council Canada, Ottawa, Ontario K1K 2E1 Canada;2. Concordia Institute for Information Systems Engineering (CIISE), Concordia University, Montréal, Québec H3G 2W1 Canada;1. College of Liberal Arts and Sciences, National University of Defense Technology, Changsha, China;2. Department of Mathematics, University of California, Los Angeles, USA;1. University of Hasselt, Belgium;2. University of Antwerp, Faculty of Social Sciences, B-2020, Antwerpen, Belgium;3. Centre for R&D Monitoring (ECOOM) and Dept. MSI, KU Leuven, Leuven, Belgium;1. School of Information Management, Nanjing University, Nanjing 210023, China;2. School of Information Studies, Syracuse University, Syracuse, NY 13244, USA;1. Laboratory for Studies in Research Evaluation, Institute for System Analysis and Computer Science (IASI-CNR), National Research Council, Rome, Italy;2. Nordic Institute for Studies in Innovation, Research and Education, Oslo, Norway;3. University of Rome “Tor Vergata”, Dept of Engineering and Management, Rome, Italy;1. School of Information Management, Wuhan University, China;2. Centre for R&D Monitoring (ECOOM) and Department of MSI, KU Leuven, Belgium;3. School of Information Technology, Shangqiu Normal University, China;1. Sun Yat-sen University, Guangzhou University, Huandong Road, No. 132 Waihuan East Rd., Guangzhou University City, Guangzhou, 510006, China;2. School of Economics and Management, Dalian University, Dalian 116622 Dalian Economic Technological Development Zone, Dalian 116622, China |
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| Abstract: | Research and development activities are regarded as one of the most influencing factors of the future of a country. Large investments in research can yield a tremendous outcome in terms of a country’s overall wealth and strength. However, public financial resources of countries are often limited which calls for a wise and targeted investment. Scientific publications are considered as one of the main outputs of research investment. Although the general trend of scientific publications is increasing, a detailed analysis is required to monitor the research trends and assess whether they are in line with the top research priorities of the country. Such focused monitoring can shed light on scientific activities evolution as well as the formation of new research areas, thus helping governments to adjust priorities, if required. But monitoring the output of the funded research manually is not only very expensive and difficult, it is also subjective. Using structural topic models, in this paper we evaluated the trends in academic research performed by federally funded Canadian researchers during the time-frame of 2000–2018, covering more than 140,000 research publications. The proposed approach makes it possible to objectively and systematically monitor research projects, or any other set of documents related to research activities such as funding proposals, at large-scale. Our results confirm the accordance between the performed federally funded research projects and the top research priorities of Canada. |
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| Keywords: | Text mining Topic modeling Machine learning Funded research Publications Government research priorities Canada |
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