This project aimed to assess how the formation of the Hotchkiss Brain Institute (HBI; www. ucalgary.ca/hbi) at the University of Calgary (UCalgary) in October 2004 affected professional interactions among neuroscience researchers. Prior to the formation of the HBI, there was no single administrative structure linking neuroscientists who work in different academic departments at UCalgary. Since the formation of the HBI, essentially all neuroscientists working at UCalgary (including new hires) are encouraged to become members of the HBI. Thus, the establishment of the HBI provides a case study through which to examine whether the formation of an explicitly interdisciplinary administrative unit affects professional interactions among scientists at one institution. We conducted a whole network survey of the members of the HBI in November, 2010. We asked all current HBI members (N = 95) to fill out an online survey reporting on their professional interactions with each of the other members since the foundation of the HBI (2005-2010). In addition, for those members who joined the HBI in 2005, we asked about their interactions with other members before the foundation of the Institute. Eighty-one scientists (a response rate of 85%) filled out the survey, indicating their working relationships with other HBI members.
We analyzed the data using social network analytic techniques, described below, as well as descriptive statistics. We also examined whether individual-level characteristics of the scientists such as gender, rank, department, office location, research theme, and research pillar affected their relationships with other scientists. Research pillar is a term used in Canada to classify all health researchers into one of four categories--biomedical, clinical, health services or population health (Canadian Institutes of Health Research, 2009). Finally, for each of the professional interaction networks, we examined the positions of those who hold leadership roles in the HBI. We use our results to discuss the effect that the establishment of the HBI has had on professional relationships among neuroscientists at UCalgary. We conclude by reflecting on the usefulness of social network analysis as an evaluation method for interdisciplinary research institutes.
The need for collaboration in science is well accepted (Adams, Black, Clemmons, and Stephan, 2005), and many academic fields now encourage interdisciplinary work (Hackett, 2005). Funding agencies are increasingly mandating interdisciplinary teams on grant applications (Bammer, 2008; Mattson, Lager, Vindefjard, and Sundberg, 2010). However, there remains much debate in the literature over how to measure, classify and evaluate interdisciplinary research and collaboration (see Huutoniemi, Klein, Bruun and Hukkinen, 2010 for a recent overview).
The first large-scale interdisciplinary research centres were set up in the United States in the 1980's, in an attempt to encourage both cross-disciplinary and cross-sector (university and industry) research collaboration. Most evaluations of such centres have focused on productivity as measured by publications and patents (Geiger, 1990; Ponomariov and Boardman, 2010). Typically, co-authorship is used as a measure of collaboration, and journal subject categories are frequently considered as a measure of interdisciplinarity.
Collaboration involves more than simply co-authorship, though, and evaluations of interdisciplinary research teams need to capture the achievements of such teams beyond publications and patents (Katerndahl, 2012; Katz and Martin, 1997). Over the past twenty years, interdisciplinary institutes, centers and groups have been established at many institutions, yet methods for assessing and evaluating interdisciplinary scientific endeavors are still in their infancy (Harris, 2010; Yang, Park & Heo, 2010; Levitt & Thelwall, 2008).
Fundamentally, interdisciplinary scientific collaboration materializes through relationships and interactions among people from different academic disciplines. These relationships may involve people from different administrative units within one institution, or from many institutions. In order to evaluate interdisciplinary collaboration properly, we first need to understand how such relationships and interactions emerge and change. Second, we need to assess the factors that both encourage and hinder interactions among scientists (Katerndahl, 2012). In this paper, we utilize the case study method to examine how the professional interactions among scientists at one university change with the establishment of an interdisciplinary research institute. We draw upon the theories and techniques developed in the field of social network analysis to guide our study.
Social network analysis, which prioritizes the structure of social relationships over the attributes of individuals, would suggest that, fundamentally, scientific output is the product of social relations (Emirbayer, 1997). While this claim may be too broad, as it neglects additional factors that influence scientific productivity (such as discipline and funding) (Quinlan, Kane, and Trochim, 2008), social network analysis does provide data collection methods and data analytic techniques that have been developed specifically for relational data (Scott, 2000; Wasserman and Faust, 1994). Thus, we employ social network analytic techniques to examine and assess the professional interactions among the members of the HBI.
Social network analysis has been used in a variety of ways to examine collaboration in academic research. Most commonly, scholars have applied whole network analytic techniques to bibliographic databases to investigate how networks of co-authorship and citation change over time (Barabasi, Jeong, Neda, Ravasz, Schubert, and Vicsek Borgatti, 2002). Findings from these co-authorship studies highlight several factors which facilitate collaboration among scientists. Mattson et al. (2010), examining European research collaboration networks in the life sciences, found that while co-authorship is affected by geographical proximity and language, funding mechanisms also have a large impact on scientific collaboration. Wagner and Leydesdorff (2005), on the other hand, concluded that the recent growth in international research collaboration in six scientific fields was not driven by funding, but rather by the scientific interest of researchers.
Johnson, Christian, Brunt, Hickman, and Waide (2010) examined the US Long Term Ecological Research (LTER) Program using intersite publications as a measure of collaboration. They found the most important predictors of collaboration were common research theme and communication at meetings and conferences. Sun and Manson (2011) examined the growth of co-authorship in geographic information science from 1992 to 2007. They found language and country of practice to be important in predicting co-authorship. Braun, Glanzel, and Schubert. (2001) examined co-authorship in the field of neurosciences. They found that scientists in the middle of their career are most likely to work collaboratively than both newcomers and senior authors. Thus, findings from co-authorship studies suggest that while geography, language and funding all affect collaboration, shared research interests, communication channels, and career stage are also important.
Other researchers have used network survey data to highlight barriers and facilitators to interdisciplinary research both within and across universities (Aboelela, Merrill, Carley and Larson, 2007; Haines et al., 2010; Katerndahl, 2012; Godley, Barron & Sharma, 2011). These studies demonstrate that although researchers are still most likely to collaborate with those in their own discipline, established collaborations of researchers organized into institutes or research groups can increase interdisciplinary work. Thus, the results from both network analyses of co-authorship and network surveys of researchers attempting to engage in interdisciplinary research would seem to suggest that scientific collaboration should be enhanced by the establishment of an interdisciplinary research centre.
Most previous evaluations of interdisciplinary research centres have not used social network analytic techniques, but rather have relied on survey data and measures of productivity such as funding received and peer-reviewed publications to assess the success of the centres (Bozeman et al., 2010; Ponomaniov and Boardman, 2010; Wagner, Roessner, Bobb, Klein, Boyack, et al., 2011). Discussing methodological issues that arise in studying large research initiatives, Quinlan et al. argue that ideally evaluators should utilize multiple methods (including bibliometric analysis, surveys, and interviews) to gage the success of interdisciplinary research centers (Quinlan et al., 2008). Findings from these studies highlight several additional factors which appear to be important for the success of an interdisciplinary research centre, including infrastructure, shared vision, communication, and leadership.
Meyer, Fabor, and Hesselbrock (1988), in a study of an interdisciplinary research centre for alcohol addiction, found that success was determined by four factors: the strength of the infrastructure (including resources, facilities and personnel); the articulation of a shared vision; efficient management; and clear communication networks. Hagen et al. (2011) studied a multicenter clinical research network and also argue that the following are key to success: shared vision; governance; infrastructural support; and communication. Studying several university research centers in science and engineering, Boardman and Corley (2008) argue that while multidisciplinarity within a centre is necessary for research collaboration, funding had the biggest effect on the amount of time scientists allocate to collaborative work.
However, Bammer (2008) argues that...