Collaboration allows researchers to combine the strength of different disciplines to undertake research that neither could do individually. Scientific collaboration can be examined by analysing patterns of co-authorship of papers in publication databases (e.g. Web of Science) using methods from Social Network Analysis. In this project, I describe three networks consisting of researchers in the Biology and Chemistry Departments at the University of York to investigate degree, degree distribution, key brokers and preference of researchers for collaborating within or outside their own research field. Clustering (or transitivity) was used to describe whether collaboration is more likely if two researchers have a collaborator in common. To introduce a control and realize the significance of the results produced, a network consisting of 98 researchers from the Chemistry and Biology departments was produced and compared with a distribution of 1000 ER random graphs for degree, transitivity and betweenness. We find that researchers in the Department of Biology (50 researchers) have fewer collaborations with their departmental colleagues than those in the Department of Chemistry (45 researchers): the average number of links each researcher had with others in the Biology collaboration network was 2.6, the corresponding values for Chemistry were 4.8 links per researcher. We also find that researchers within the Chemistry department were more likely than their colleagues in Biology to collaborate with another researcher if they had a collaborator in common. One aim of the study was to characterize the extent of interdisciplinary research within the Department of Biology. Staff in the Biology department were categorized into distinct research foci, indicating the discipline of the researcher. There were many links from the Bioinformatics and Mathematics, and Biophysics and Biochemistry foci, to other foci, implying that staff within these foci were interdisciplinary in their research—indicative of their role in providing techniques or tools that are applicable across discipline boundaries. This sort of analysis provides quantitative evidence to understand the social patterns of scientific collaboration and may be a useful tool in the development of strategies to promote interdisciplinary research within research institutions.
