The use of different data sources in the analysis of co-authorship networks and scientific performance

• We analyse collaboration style and scientific performance of Italian statisticians.
• We use three data sources to construct co-authorship networks.
• We assess network structures of the whole community and of the Statistics subfields.
• We model the effect of actor network position on scientific performance.
• We find distinct collaboration patterns and effects on scientific performance.

Scientific collaboration is usually derived from archival co-authorship data. Several data sources may be examined, but they all have advantages and disadvantages, especially when a specific discipline or community is of interest. The aim of this paper is to explore the effect of the use of three data sources – Web of Science, Current Index to Statistics and nationally funded research projects – on the analysis of co-authorship networks among Italian academic statisticians. Results provide evidence of our hypotheses on distinct collaboration patterns among statisticians, as well as distinct effects of scientist network positions on scientific performance, by both Statistics subfield and data source.