Table 1. Representative multi-level insights about team science.


Insights from macro-level researchSpace/geography matters—even in the Internet age. Citation patterns show that over time, major research institutions cite more locally (18, 19).
Teamwork in science increasingly spans university boundaries, but the increasing social stratification in multi-university collaborations suggests a concentration of productivity in fewer rather than more centers of high-impact science (1).
Creating larger collaborative organizational structures is difficult because of traditions of scientific independence, difficulties of sharing implicit knowledge, and formal organizational barriers (20, 21).
Team characteristics can be used to identify those scientific and engineering teams and projects that will most benefit from adopting cyberinfrastructures (22, 23).
Structural elements of collaboration (among them the team formation, size and duration, organization, technological practices, and participant experiences) are interrelated and connected to a complex external environment (including the sector, organizational, and funding contexts) (24).
Today’s science is not driven by prolific single experts but by high-impact co-authorship teams (2, 25, 26).
Seven generic principles provide a coherent framework for thinking about evaluation of inter- and transdisciplinary team-based research (27).
Insights from meso-level researchMixed-methods approaches support evaluating the effectiveness of complex team science initiatives, the centrality of research on groups and teams to the field of SciTS, and the role of face-to-face communication in remote SciTS collaborations (28, 29).
Studies of coordination mechanisms in multi-university collaborations reveal that face-to-face coordination is especially important for training outcomes and that direct supervision is the most effective coordination mechanism (30).
Studies on “superstar extinction”—the retirement or death of a star scientist—reveal the boundaries of the scientific field to which the star contributes: the “invisible college” (31).
Scientists benefit from knowledge of the importance of network ties and how to locate prime collaborators (25).
Increased understanding about how high-impact collaborative networks are assembled (32) and the widespread availability (via digital sources) of research networking data aid the development of “social network”–based recommender systems that help scholars find expertise or resources and enable more effective team science (33).
The bulk of collaborative communication occurs within teams; this is where relationships among individuals and organizations emerge and affect team effectiveness (12).
Interdisciplinary research is team research. Thus, we should consider implementing principles from organizational science and the socio-cognitive psychology of teamwork and team training to improve interdisciplinary research and the practice of team science (8, 14).
Insights from micro-level researchPerceived interpersonal collaboration processes (such as greater trust, cohesion, and communication) are correlated to increased productivity (34).
Intrapersonal characteristics, such as the propensity to endorse multidisciplinary values and behaviors, are predictive for research productivity (34).
Although many young scientists are drawn to the intellectual rewards of interdisciplinary research as graduate students, they may also be deterred by the professional risks as early-career tenure-track scientists (35).
Social scientists’ observations of scientists can be more informative than scientists’ own experience. The ingredients of a successful collaboration include good leadership, trust among the participants, face-to-face meetings, and strong communication skills (36).