Abstract

This research examines the impact of various funding programs by NSERC on research outcomes. We utilize statistical models and machine learning algorithms trained on the integrated database of researchers’ publications and funding to determine the efficacy of NSERC funding programs. We aim to evaluate the effectiveness of different strategies defined by NSERC through funding programs and analyze the impact of various factors. We seek to enhance our understanding, with the aspiration that it will inform the design of more effective programs in the future.
We compare the results of linear regression, random forest, and neural networks. Then, we perform SHAP analysis to identify the most important features within funding programs. We aim to gain insights into the impact of receiving funding through different programs on research outcomes.
We observed that random forest model outperformed the other models for all dependent variables, i.e., future productivity, quality of the publication, and future co-authorships. Subsequently, we examined the significance of independent variables in predicting dependent variables across the funding programs.
For Canada Research Chairs recipients, the impact of their prior work holds greater importance in shaping research outcomes, underscoring a distinctive emphasis on research excellence within this program. In contrast, the impact of career age is lower compared to other programs. Interestingly, within the Discovery Grants program, career age becomes notably influential in predicting future productivity in favor of young researchers. Furthermore, we found an intriguing exception for researchers with a history of large group collaborations within Discovery Grants, where some experience a negative impact on future collaborations. The award amount plays a more important role in shaping the research outcomes of recipients engaged in strategic projects.
Our findings emphasize the importance of allocating funding programs to researchers whose qualifications are aligned with the programs’ objectives.