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| Research on Comparison Method of Scientific Funding Layout——Take NSF and EU FP Grants for Instance |
| Chen Ting, Han Tao, Li Zexia, Li Guopeng, Wang Xiaomei |
| National Science Library, Chinese Academy of Sciences, Beijing 100190, China |
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Abstract [Objective] This study targets to improve the ability to discover research layout of funding agencies based on their funding application documents. [Methods] The K-means++ clustering method is proposed to analyse the funding direction and main focus based on multiple sources of funding application documents. [Results] After validation and a case study based on the funding application abstracts from NSF and FP, it is discovered that single-word feature is more accurate than multi-word feature in the K-means++ clustering. If only keep the essential contents of application abstrcts as analysis documents, the accuracy of the K-means++ clustering is significantly improved. [Limitations] Data cleaning of the funding application documents is not fully automated. Adjustment of clustering parameters need to be manually controlled. [Conclusions] The K-means++ clustering of funding application documents is a practicable method by validation and case study. Research layout differences in agencies' funding trends are discovered and could be helpful for scientific management and policy decision.
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Received: 18 December 2014
Published: 25 August 2015
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