In public R&D projects, to improve the decision-making process and ensure the sustainability of public investment, it is indispensable to effectively evaluate the project performance. Currently, public R&D project management departments and various academic databases have accumulated a large number of project-related data. In view of this, we propose a data-driven performance evaluation framework for public R&D projects. In our framework, we collect structured and unstructured data related to completed projects from multiple websites. Then, these data are cleaned and fused to form a unified dataset. We train a project performance evaluation model by extracting the project performance information implicit in the dataset based on multi-classification supervised learning algorithms. When facing a new project that needs to be evaluated, its performance can be automatically predicted by inputting the characteristic information of the project into our performance evaluation model. Our framework is validated based on the project data of the National Natural Science Foundation of China (NSFC) in terms of four performance measures (i.e., Accuracy, Recall, Precision, F1 score). In addition, we provide a case study that applies our framework to evaluate the project performance in the logistics and supply chain area of NSFC. In conclusion, this paper contributes to the body of knowledge in sustainability by developing a data-driven method that equips the decision-maker with an automated project performance evaluation tool to make sustainable project decisions.
Getting Lucky in Ontology Search: A Data-Driven Evaluation Framework for Ontology Ranking
