TY - JOUR
T1 - How Team Interlock Ecosystems Shape the Assembly of Scientific Teams
T2 - A Hypergraph Approach
AU - Lungeanu, Alina
AU - Carter, Dorothy R.
AU - DeChurch, Leslie A.
AU - Contractor, Noshir S.
N1 - Funding Information:
We deploy a novel hypergraph methodology to test our hypotheses using bibliographic data about teams submitting research proposals to a Clinical and Translational Science Award (CTSA) competition hosted at a large Midwestern University and funded by the National Institutes of Health (NIH). A total of 101 research teams, consisting of 147 participants, submitted proposals in two rounds of the grant competition. Given that we are examining the team assembly process, we excluded 47 proposals that were solo-authored. Additionally, eight proposals were excluded because either the exact same proposal team submitted proposals in both rounds of the competition (three teams) or because of data collection issues (five teams). The final dataset contains 46 proposals coauthored by 98 scientists, out of which only four proposals were awarded.
Funding Information:
The preparation of this manuscript was supported by funding from the Army Research Office [ARO W911NF-14-10686] and the National Institutes of Health [R01GM112938, U01GM112623, UL1RR025741].
PY - 2018/4/3
Y1 - 2018/4/3
N2 - Today’s most pressing scientific problems necessitate scientific teamwork; the increasing complexity and specialization of knowledge render “lone geniuses” ill-equipped to make high-impact scientific breakthroughs. Social network research has begun to explore the factors that promote the assembly of scientific teams. However, this work has been limited by network approaches centered conceptually and analytically on “nodes as people,” or “nodes as teams.” In this article, we develop a “team-interlock ecosystem” conceptualization of collaborative environments within which new scientific teams, or other creative team-based enterprises, assemble. Team interlock ecosystems comprise teams linked to one another through overlapping memberships and/or overlapping knowledge domains. They depict teams, people, and knowledge sets as nodes, and thus, present both conceptual advantages as well as methodological challenges. Conceptually, team interlock ecosystems invite novel questions about how the structural characteristics of embedding ecosystems serve as the primordial soup from which new teams assemble. Methodologically, however, studying ecosystems requires the use of more advanced analytics that correspond to the inherently multilevel phenomenon of scientists nested within multiple teams. To address these methodological challenges, we advance the use of hypergraph methodologies combined with bibliometric data and simulation-based approaches to test hypotheses related to the ecosystem drivers of team assembly.
AB - Today’s most pressing scientific problems necessitate scientific teamwork; the increasing complexity and specialization of knowledge render “lone geniuses” ill-equipped to make high-impact scientific breakthroughs. Social network research has begun to explore the factors that promote the assembly of scientific teams. However, this work has been limited by network approaches centered conceptually and analytically on “nodes as people,” or “nodes as teams.” In this article, we develop a “team-interlock ecosystem” conceptualization of collaborative environments within which new scientific teams, or other creative team-based enterprises, assemble. Team interlock ecosystems comprise teams linked to one another through overlapping memberships and/or overlapping knowledge domains. They depict teams, people, and knowledge sets as nodes, and thus, present both conceptual advantages as well as methodological challenges. Conceptually, team interlock ecosystems invite novel questions about how the structural characteristics of embedding ecosystems serve as the primordial soup from which new teams assemble. Methodologically, however, studying ecosystems requires the use of more advanced analytics that correspond to the inherently multilevel phenomenon of scientists nested within multiple teams. To address these methodological challenges, we advance the use of hypergraph methodologies combined with bibliometric data and simulation-based approaches to test hypotheses related to the ecosystem drivers of team assembly.
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U2 - 10.1080/19312458.2018.1430756
DO - 10.1080/19312458.2018.1430756
M3 - Article
C2 - 30906493
AN - SCOPUS:85042207896
VL - 12
SP - 174
EP - 198
JO - Communication Methods and Measures
JF - Communication Methods and Measures
SN - 1931-2458
IS - 2-3
ER -