Understanding Collaboration Using Social Network Analysis Diana Rhoten Office of Cyberinfrastructure National Science Foundation

Integration [collaboration] Diversity [Interdisciplinarity] HIGH LOW Breakthroughs From isolation to collaboration Source: Hollingsworth (2001). Research Organizations and Major Discoveries in Twentieth Century Science: A Case Study of Excellence in Biomedical Research. Research Paper 02–003. Berlin: Wissenschaftszentrum Berlin für Sozialforschung.

Rudi Ike Saul Ed Jun Axel Model the structure of research networks in different “interdisciplinary” research centers Assess the effect of individual, organizational, and relational factors on the structure of these research networks Analyze the dynamics and outcomes of the network’s interdisciplinary collaboration Interdisciplinary research center study

 Degree of Interdisciplinarity Across all research centers and labs, the networks tend to be more multidisciplinary than interdisciplinary and to demonstrate pockets of disciplinary collaborations connected by fewer cross-disciplinary ties A few key findings

Network Measures Density = 8% Cohesion = 2.6 Ave. Centrality = 5 = Hydro Engineering Discipline = Civil/Enviro Engineering = Mechanical Engineering = Ecology = Chemical Engineering = Applied Mathematics = Industrial Engineering = Eng Public Policy = Sustain/ Resource Mgt = Applied Anthropology = History of Science = Decision Science = Applied Physics = Epidemiology = Land Use Geography = Env Soc Sci Policy = Resource Economics = Behavioral Economics = Risk Analysis/Assess Multi- more than Inter- disciplinary Center 2 demonstrates “disciplinary pocket” pattern found in most centers, particularly at level of knowledge producing Shows all CLOSE connections by DISCIPLINE/FIELD based on responses to the following survey item: “Please indicate the strength of your relationship with other center affiliates.”

= Physical Sciences Science Field = Life Sciences = Social Sciences = Environmental Sci Eng = Engineering = Comp & Math Sciences = Environmental Soc Sci = Arts & Humanities Network Measures Density = 10% Cohesion = 2.6 Ave. Centrality = 6 Shows all CLOSE connections by SCIENCE based on responses to the following survey item: “Please indicate the strength of your relationship with other center affiliates.” Center 3 demonstrates the even more dramatic pattern of segregation of researchers by fields of science Multi- more than Inter- disciplinary

 Degree of Collaboration Across all centers, researchers average approximately 8 information sharing vs. 6 knowledge producing collaborations, and 5 interdisciplinary information sharing vs. 3 interdisciplinary knowledge producing collaborations  Degree of Interdisciplinarity Across all research centers and labs, the networks tend to be more multidisciplinary than interdisciplinary and to demonstrate pockets of disciplinary collaborations connected by fewer cross-disciplinary ties A few key findings

Network Measures Density = 47% Cohesion = 1.6 Ave. Centrality = 8 = Physical Sciences Science = Life Sciences = Social Sciences = Environmental Sci Eng = Engineering = Comp & Math Sciences = Environmental Soc Sci = Arts & Humanities Interdisciplinary information sharing and knowledge production Shows all CLOSE and COLLEGIAL INTERDISCIPLINARY connections by SCIENCE based on responses to the following survey item: “Please indicate the strength of your relationship with other center affiliates.” Center 1 networks illustrate the role of information sharing collaborations …

Network Measures Density = 16% Cohesion = 2.3 Ave. Centrality = 3 = Physical Sciences Science = Life Sciences = Social Sciences = Environmental Sci Eng = Engineering = Comp & Math Sciences = Environmental Soc Sci = Arts & Humanities … in the density of the interdisciplinary research networks in most centers Shows all CLOSE INTERDISCIPLINARY connections by SCIENCE based on responses to the following survey item: “Please indicate the strength of your relationship with other center affiliates.” Interdisciplinary knowledge production

 Organizational form and spatial distribution  Frequency and mode of interaction  Foci of collaboration  Personalities and positions Variation in Interdisciplinarity and Collaboration

Intraorganizational – concentrated

Interorganizational – distributed (F2F)

Transorganizational – distributed (F2F, V)

A mean density score of 50% compared to an approx. mean of 35% for other organizational types 1 SD above Mean Mean 1 SD below Mean Density metrics across all groups (close and collegial) Distance can enhance integration …

Time Same (synchronous) Different (asynchronous) Geographic Place Same Different 75.75%65.50% 58.75%56.50% … but collaboration still depends on F2F

 Foci of collaboration Collaboration practices and products benefit from a unifying vision, a common problem, a shared tool (methodological, technological) – “boundary object” – that could ground and guide the work  Personalities and positions Productive interdisciplinary collaborations require the “right” scientific and technical expertise as well as the “right” social and management skills to serve the project and evolve the process. A few key findings

 Network “Hubs” But it is not just about the leaders. While center or lab directors tend to be network “hubs”, research assistants are among the most central researchers in the networks -- particularly at the level of knowledge production A few key findings

= Associate Professor Position = Assistant Professor = Post Doc = Non-Tenure Researcher = Professor = Graduate Research Asst = Center Director Network Measures Density = 39% Cohesion = 1.6 Ave. Centrality = 15 Shows all CLOSE and COLLEGIAL connections by POSITION based on responses to the following survey item: “Please indicate the strength of your relationship with other center affiliates.” Center 4 demonstrates the common network pattern in which “hub” positions are occupied by center or lab directors and the central “core” is dominated by research assistants Network “hubs”

 Network “Bridges” Research assistants and non-tenure track scientists also tend to serve as the interdisciplinary “bridges” in the center networks. They often come from “hybrid” disciplines, have higher rates of previous interdisciplinary exposure, and/or are methodologists/ technicians versus content experts  Network “Hubs” But it is not just about the leaders. While center or lab directors tend to be network “hubs”, research assistants are among the most central researchers in the network -- particularly at the level of knowledge production A few key findings

Network Measures Density = 5% Cohesion = 3.4 Ave. Centrality = 3 = Physical Sciences Scientific Field = Life Sciences = Social Sciences = Environ Sci/Eng Pol = Engineering = Arts & Humanities s Shows all CLOSE INTERDISCIPLINARY ties by SCIENTIFIC FIELD based on responses to the following survey item: “Please indicate the strength of your relationship with other center affiliates.” = Environmental Soc Sci = Comp & Math Sciences Again, using center 4, it demonstrates the common network pattern in which “bridges” tend to be students more than faculty … Network “bridges”

Network Measures Density = 5% Cohesion = 3.4 Ave. Centrality = 3 = Physical Sciences Scientific Field = Life Sciences = Social Sciences = Environ Sci/Eng Pol = Engineering = Arts & Humanities s Shows all CLOSE INTERDISCIPLINARY ties by SCIENTIFIC FIELD based on responses to the following survey item: “Please indicate the strength of your relationship with other center affiliates.” = Environmental Soc Sci = Comp & Math Sciences Removing them demonstrates their importance to the overall connectivity of an interdisciplinary research network Network “bridges”

59% of respondents report collaboration “has enabled individual’s research in new ways” 62% report that it “has advanced individual’s thinking” Production & innovation Source: Rhoten and Parker (2006). Study commissioned by National Center for Atmospheric Research.

Integration [Collaboration] Diversity [Interdisciplinarity] HIGH LOW Breakthroughs Leveraging interdisciplinary collaboration Source: Hollingsworth (2001). Research Organizations and Major Discoveries in Twentieth Century Science: A Case Study of Excellence in Biomedical Research. Research Paper 02–003. Berlin: Wissenschaftszentrum Berlin für Sozialforschung. Engineered, concentrated, roster-driven, single generational Organic, distributed, problem-based, multi- generational

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Virtual Organizations

Virtual organizations offer a community of practice the opportunity to work together - sharing expertise, tools, information and facilities. Instances of Virtual Organizations (VOs) Computation Data, information management Sensing, observation, activation in the world Distributed, heterogeneous services for: Mechanisms for flexible secure, coordinated resource/services sharing among dynamic collections of individuals, institutions, and resources (the Grid or service layer problem) Interfaces for interaction, workflow, visualization and collaboration for individuals & distributed teams People* Alternate Names for VOs: Co-laboratory Collaboratory Grid (community) Network Portal Gateway Hub Virtual Research Environment * People engaged in discovery and learning as individuals and in teams Virtual Organizations