This paper intends to illuminate the relationship between science funding and citation impact in seven STEMM disciplines (science, technology, engineering, mathematics, and medicine). Using a regression model with Heckman bias correction, we find that funding has a positive, significant association with a paper’s citations in STEMM fields. Further analyses show that this association is magnified by the factors of multiple authorship and multiple institutions. For funded papers in STEM, multi-author and multi-institution papers tend to receive even more citations than single-authored and single-institution papers; however, funded papers in Medicine received less gain in citation impact when either factor is considered. Based on the finding that funding support has a stronger association with citation impact when it is treated as a binary variable than as a count variable, this paper recommends the allocation of funding to researchers without active funding support, instead of giving awards to those with multiple funding supports at hand.
Bibliographical noteFunding Information:
Table 1 includes papers both with and without funding. It shows that disciplines vary with respect to citation impact per paper, number of funding, sources per paper, and percentage of papers that received funding support. While the citation impact per paper in Medicine approaches 90, the average citation impact for three other disciplines is below 10: Engineering (8.39), Mathematics (5.20), and Computer Science (2.52). In terms of funding support, Medicine and Astrophysics are the disciplines with the highest funding intensity: each paper on average was supported by at least four different grants; meanwhile,
These studies have revealed the long term impact of science investments as new knowledge gained, jobs created, and new economic activity encouraged (Cragin et al. 2012; Lane 2009; Lane and Bertuzzi 2011; Lane et al. 2015; Sarli and Carpenter 2014; Van Noorden 2015; Weinberg et al. 2014). The present study focuses on the short-term impact of funding, as measured by its association with research outputs. Prior efforts in this area have largely focused on data specific to an individual funding organization, or to a particular research domain. Studies of the former type have examined the funding impact of the National Institute on Aging (Boyack and Börner 2003), Engineering and Physical Sciences Research Council (Ma et al. 2015), Natural Sciences and Engineering Research Council of Canada (Fortin and Currie 2013), National Natural Science Foundation of China (Wang et al. 2011), Transdisciplinary Tobacco Use Research Center of the National Cancer Institute (Trochim et al. 2008), and National Cancer Institute of Canada (Campbell et al. 2010). A domain-specific approach has been applied to library and information science (Cronin and Shaw 1999; Zhao 2010), nanotechnology (Wang and Shapira 2011), and biological chemistry (Rigby 2013), among other fields.
The funding information can be gathered through the funding agency field (FU) in the output file in the Web of Science database for papers published after 2008 (Costas and Leeuwen 2012; Paul-Hus et al. 2016). The information in this field contains funders and grant IDs in brackets, with multiple funding sources delimited through semi-colons: for instance, ‘‘National Science Foundation [CBET-1403871, DMR-1121107, CMMI-1150682, DGE-0946818]; National Science Foundation Graduate Research Fellowship [DGE-0946818]’’. A few pilot studies on the use of Web of Science funding acknowledgement data have suggested that the funding agency field provides high precision and recall (both above 0.9) (Grassano et al. 2017). The limitation of the data set and the use of funding acknowledgment data is discussed in the Limitations section.
All Science Journal Classification (ASJC) codes
- Social Sciences(all)
- Computer Science Applications
- Library and Information Sciences