Abstract
In academic and industrial research, writing a project proposal is one of the essential but time-consuming activities. Nevertheless, most proposals end in rejection. Moreover, research funding is getting more competitive these days. Funding agencies are increasingly looking for more extensive and more interdisciplinary research proposals. To increase the funding success rate, this PhD project focuses on three open challenges: poor data quality, inefficient funding discovery, and ineffective collaborative team building. We envision a Predictive Analytics-based approach that involves analyzing research information and using statistical and machine learning models that can assure data quality, increase funding discovery efficiency and the effectiveness of collaboration building. Accordingly, the goal of this PhD project is to support decision-making process to maximize the funding success rates of universities.
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Acknowledgements
This PhD project is supported by the project IB20 Fis Heavy/TU Chemnitz/259038, funded by the Saxon State Ministry for Science and Art. In addition, we would like to thank André Langer, Maik Benndorf and Sebastian Heil for their supports during the writing process of this Symposium.
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Vu Nguyen Hai, D., Gaedke, M. (2021). Applying Predictive Analytics on Research Information to Enhance Funding Discovery and Strengthen Collaboration in Project Proposals. In: Brambilla, M., Chbeir, R., Frasincar, F., Manolescu, I. (eds) Web Engineering. ICWE 2021. Lecture Notes in Computer Science(), vol 12706. Springer, Cham. https://doi.org/10.1007/978-3-030-74296-6_37
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