Delivering Scientific Influence Analysis as a Service on Research Grants Repository

Research grants have played an important role in seeding and promoting fundamental research projects worldwide. There is a growing demand for developing and delivering scientific influence analysis as a service on research grant repositories. Such analysis can provide insight on how research grants help foster new research collaborations, encourage cross-organization collaborations, influence new research trends, and identify technical leadership. This article presents the design and development of a grant-based scientific influence analysis service, coined as
GImpact
. It takes a graph-theoretic approach to design and develop the scientific influence analysis algorithms over a real research-grant repository with three original contributions. First, we model the scientific influence analysis problem as a graph-based analysis problem by constructing heterogeneous graphs from the grants dataset, including mining the dataset to identify and extract important features and represent such features as a research grants information network. Second, we develop the scientific influence analysis algorithms over the research grants information network, which compute the overall scientific influence score by integrating self-influence score and multiple co-influence scores. The self-influence score reflects the grant-based research collaborations among institutions, and the co-influence scores reflect various types of cross-institution collaborations in terms of disciplines and keywords (subject areas). Third, we leverage the cluster analysis on the institution graph as an example application of scientific influence analysis service. By partitioning the institution graph into
$K$
clusters, with
$K$
as one of the service interface parameters, we show how different disciplines and different keywords are co-related through the grant-based influence analysis. We evaluate
GImpact
using a real grants dataset, consisting of 2512 institutions and their grants received over a period of 14 years. Our experimental results show that the
GImpact
influence analysis approach can effectively identify the grant-based research collaboration groups and provide valuable insight on an in-depth understanding of the scientific influence of research grants on research programs, institution leadership, and future collaboration opportunities.