Noshir conducted a workshop at the 2017 SciTS Conference

Noshir Contractor was a lead facilitator of a workshop at the Science of Team Science (SciTS) Conference in Clearwater Beach, FL, held on June 12-14, 2017.

Network Perspectives to Understand and Enable Team Science 

Description: In this workshop, attendees will be introduced to the basics of social network theories, methods, and tools.   They will come away with an improved understanding of the various forms of networks necessary for effective scientific collaborations.  This workshop is organized into three distinct parts.  (1) The first part provides an historical overview of the motivations to view team science from a social networks perspective. This first part will conclude with a brief introduction to the concepts of social networks, cognitive social networks, knowledge networks, cognitive knowledge networks and their relevance to team science. (2) The second part focuses on using network metrics to describe team science.  This part begins by defining various concepts used in network analysis: actors and attributes of actors, relations and properties of relations as well as two-mode networks. Next it describes various how these concepts influence strategies for the collection of network data. The session then defines and describes how various common network metrics are computed and interpreted at the actor, dyadic, triadic, sub-group, and component level. (3) The third part of the workshop addresses using network models to understand and enable team science. Here, a multi-theoretical multilevel (MTML) model is outlined to help stakeholders understand the dynamics for creating, maintaining, dissolving, and reconstituting social and knowledge networks in scientific communities. The session will provide a high level overview of statistical techniques to test MTML models of team science. Research exemplars are presented to illustrate the potential of the MTML framework to understand and enable team science. The session concludes with a demonstration of how these insights are being used to develop recommender systems for assembling effective scientific teams.