Applying Predictive Analytics on Research Information to Enhance Funding Discovery and Strengthen Collaboration in Project Proposals
Dang Vu Nguyen Hai M.Sc.
Dipl.-Inf. André Langer
Prof. Dr.-Ing. Martin Gaedke
Intelligent Information Management
21th International Conference on Web Engineering
Proceedings of the 21th International Conference on Web Engineering
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 Ph.D. 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 Ph.D. project is to support decision-making process to maximize the funding success rates of universities.
Hai, Dang V. N.; Langer, André; Gaedke, Martin: Applying Predictive Analytics on Research Information to Enhance Funding Discovery and Strengthen Collaboration in Project Proposals. Proceedings of the 21th International Conference on Web Engineering, 2021.