Academic Collaboration via Resource Contributions: An Egocentric Dataset

Abstract

In order to understand scientists' incentives to form collaborative relations, we have conducted a study looking into academically relevant resources, which scientists contribute into collaborations with others. The data we describe in this paper are an egocentric dataset assembled by coding originally qualitative material. It is 40 multiplex ego networks containing data on individual attributes (such as gender, scientific degree), collaboration ties (including alter-alter ties), and resource flows. Resources are coded using a developed inventory of 25 types of academically relevant resources egos and alters contribute into their collaborations. We share the data with the research community with the hopes of enriching knowledge and tools for studying sociological and behavioral aspects of science as a social process.

Reaching for Unique Resources: Structural Holes and Specialization in Scientific Collaboration Networks

“…Respondents represented a broad range of disciplines: natural sciences, social sciences, life sciences, the humanities, engineering, and technology on different levels of career from PhD candidates to professors. The detailed description of the sample can be found in Appendix A2 and in Bojanowski, Czerniawska, and Fenrich (2020). The data set is available online 3 .…”