Diversity of individual research disciplines in scientific funding

Interdisciplinarity research based on NSFC-sponsored projects: A case study of mathematics in Chinese universities

We investigate the interdisciplinarity of mathematics based on an analysis of projects sponsored by the NSFC (National Natural Science Foundation of China). The motivation of this study lies in obtaining an efficient method to quantify the research interdisciplinarities, revealing the research interdisciplinarity patterns of mathematics discipline, giving insights for mathematics scholars to improve their research, and providing empirical supports for policy making. Our data set includes 6147 NSFC-sponsored projects implemented by 3225 mathematics professors in 177 Chinese universities with established mathematics departments. We propose the weighted-mean DIRD (diversity of individual research disciplines) to quantify interdisciplinarity. In addition, we introduce the matrix computation method, discover several properties of such a matrix, and make the computation cost significantly lower than the bitwise computation method. Finally, we develop an automatic DIRD computing system. The results indicate that mathematics professors at top normal universities in China exhibit strong interdisciplinarity; mathematics professors are most likely to conduct interdisciplinary research involving information science (research department), computer science (research area), computer application technology (research field), and power system bifurcation and chaos (research direction).

Embedding funding consultation in library services

Purpose

Although interdisciplinary research is an increasing trend in scientific funding projects, they are suffering from a lower probability of being funded. The purpose of this paper is to analyze the current situation on successful case of funding application and provides suggestions on how libraries can expand services to help scientific funding application.

Design/methodology/approach

This paper utilizes the co-occurrences of disciplinary application codes to construct an interdisciplinary knowledge flow network. Based on 193517 sponsored projects of the National Natural Science Foundation of China, the authors study the interdisciplinary flow of knowledge and investigate the evolution of network structure using social network analysis.

Findings

Results show that the interdisciplinary knowledge flow network is not only a small-world network but also a scale-free network. Two main knowledge flow paths across scientific departments exist, showing the heterogeneity of knowledge distributions across scientific disciplines. The authors also find that if two disciplines in the same scientific department both have a wide influence to other disciplines, they are more prone to link together and create a knowledge chain.

Originality/value

Funding consultation currently has not occupied an advisory role either in library services or in the research team. This paper conducts a co-occurrences network analysis of interdisciplinary knowledge flow in scientific funding projects. Considering the complexity of funding application and the advantage of traditional library services on information collection, integration, and utilization, the authors conclude the possibility and necessity of embedding funding consultation in traditional library services.

How Does National Scientific Funding Support Emerging Interdisciplinary Research: A Comparison Study of Big Data Research in the US and China

How do funding agencies ramp-up their capabilities to support research in a rapidly emerging area? This paper addresses this question through a comparison of research proposals awarded by the US National Science Foundation (NSF) and the National Natural Science Foundation of China (NSFC) in the field of Big Data. Big data is characterized by its size and difficulties in capturing, curating, managing and processing it in reasonable periods of time. Although Big Data has its legacy in longstanding information technology research, the field grew very rapidly over a short period. We find that the extent of interdisciplinarity is a key aspect in how these funding agencies address the rise of Big Data. Our results show that both agencies have been able to marshal funding to support Big Data research in multiple areas, but the NSF relies to a greater extent on multi-program funding from different fields. We discuss how these interdisciplinary approaches reflect the research hot-spots and innovation pathways in these two countries.