Abstract
Scientists are increasingly charged with solving complex societal, health, and environmental problems. These systemic problems require teams of expert scientists to tackle research questions through collaboration, coordination, creation of shared terminology, and complex social and intellectual processes. Despite the essential need for such interdisciplinary interactions, little research has examined the impact of scientific team support measures like training, facilitation, team building, and expertise. The literature is clear that solving complex problems requires more than contributory expertise, expertise required to contribute to a field or discipline. It also requires interactional expertise, socialised knowledge that includes socialisation into the practices of an expert group. These forms of expertise are often tacit and therefore difficult to access, and studies about how they are intertwined are nearly non-existent. Most of the published work in this area utilises archival data analysis, not individual team behaviour and assessment. This study addresses the call of numerous studies to use mixed-methods and social network analysis to investigate scientific team formation and success. This longitudinal case-based study evaluates the following question: How are scientific productivity, advice, and mentoring networks intertwined on a successful interdisciplinary scientific team? This study used applied social network surveys, participant observation, focus groups, interviews, and historical social network data to assess this specific team and assessed processes and practices to train new scientists over a 15-year period. Four major implications arose from our analysis: (1) interactional expertise and contributory expertise are intertwined in the process of scientific discovery; (2) team size and interdisciplinary knowledge effectively and efficiently train early career scientists; (3) integration of teaching/training, research/discovery, and extension/engagement enhances outcomes; and, (4) interdisciplinary scientific progress benefits significantly when interpersonal relationships among scientists from diverse disciplines are formed. This case-based study increases understanding of the development and processes of an exemplary team and provides valuable insights about interactions that enhance scientific expertise to train interdisciplinary scientists.
Highlights
Scientists are increasingly charged with solving complex and large-scale societal, health, and environmental challenges (Read et al, 2016; Stokols et al, 2008)
To date, the literature examining successful interdisciplinary scientific team skills that result in successful outcomes is sparse (Fiore, 2008; Hall et al, 2018; Wooten et al, 2014)
This study answers the call of numerous researchers to use mixed-methods and social network analysis (SNA) to investigate scientific teams (Bennett, 2011; Borner et al, 2010; Hall et al, 2018; Woolley et al, 2010; Wooten et al, 2015)
Summary
Scientists are increasingly charged with solving complex and large-scale societal, health, and environmental challenges (Read et al, 2016; Stokols et al, 2008) These systemic problems require interdisciplinary teams to tackle research questions through collaboration, coordination, creation of shared terminology, and complex social and intellectual processes (Barge and Shockley-Zalabak, 2008; De Montjoye et al, 2014; Fiore, 2008). The earliest studies of collaboration in science used bibliometric data to search for predictors of team success such as team diversity, size, geographical proximity, inter-university collaboration, and repeat collaborations (Borner et al, 2010; Cummings and Kiesler, 2008; Wuchty et al, 2007) Building from these studies, current research focuses on team processes. Still more research is needed, and Hall et al (2018) called for team science studies that use longitudinal designs and mixed-methods to examine project teams as they develop in order to move beyond bibliometric measures of success and to explore the complex, interacting features in real-world teams
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