What can interdisciplinary collaborations learn from the science of team science?

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Suzi Spitzer (biography)

How can we improve interdisciplinary collaborations? There are many lessons to be learned from the Science of Team Science. The following ten lessons summarize many of the ideas that were shared at the International Science of Team Science Conference in Galveston, Texas, in May 2018.

1. Team up with the right people
On the most basic level, scientists working on teams should be willing to integrate their thoughts with their teammates’ ideas. Participants should also possess a variety of social skills, such as negotiation and social perceptiveness. The most successful teams also encompass a moderate degree of deep-level diversity (values, perspectives, cognitive styles) and include women in leadership roles.

2. Start off on the right note
Take some time before beginning a team task or project to make sure everyone is on the same page. Consider using checklists to ensure that an activity starts (and ends) successfully. For new science teams, a basic checklist could make sure that everyone knows 1) each other, 2) the details of the project, and 3) their role on the team.

3. Practice self-awareness as a leader
You don’t need to be good at all aspects of leadership, but it is important for everyone on a team to understand their own leadership style. Be transparent with others and yourself about where your strengths and weaknesses lie, and surround yourself with teammates who excel in areas you do not.

4. Employ different styles of collaboration to balance efficiency and integration
Sports can help us conceptualize different forms of collaboration. Pooled collaboration involves teammates simultaneously, but separately, contributing to a team task (gymnastics). Sequential collaboration involves a specified order of contribution, where one person’s output becomes the next person’s input, until the team completes the task (football). Reciprocal collaboration involves teammates contributing and communicating back and forth to complete a task (basketball). Science teams should adopt whichever collaborative structure is most appropriate for their project.

5. Go beyond avoiding jargon to develop a shared understanding
Interdisciplinary translation is a process that promotes understanding between scientists who speak different “disciplinary languages.” When working on a team of scientists with different epistemological backgrounds, always bear in mind that each teammate possesses their own “thought world,” or set of perspectives and experiences. When working on interdisciplinary teams, of course scientists must clarify disciplinary terms that others might not know, but less obviously, scientists must also make sure that their shared words have shared meaning (eg., culture, diversity, bias, objective).

6. Use visualizations as translation tools
Science teams can create and discuss interactive visuals to facilitate analytical thinking, knowledge integration, and data exploration. Visualizations, such as conceptual diagrams, can function as boundary objects between teammates who possess different perspectives or expertise. A visualization can also serve as a “great equalizer” because teams can use it to collapse hierarchies and layer information in a way that creates a more egalitarian structure where all ideas are represented.

7. Do not avoid conflict—it’s inevitable… and it can be healthy!
Learn how to express and resolve conflicts effectively. Be specific about the subject of the disagreement and your position on the matter, and express conflict directly to the antagonist, rather than through a third party. Avoid high-intensity behaviors that are offensive (eg., undermining) or defensive, (eg., stonewalling). Healthy debate can actually energize a team because it can be encouraging to collaboratively move towards a solution.

8. Share knowledge and advice
Effective teams have more communication and more equal communication. Social network analyses of successful teams show teammates learning from each other and forming close relationships with several other teammates (high network density and centrality). Avoid the “star model,” which signifies an underlying cultural understanding that there is one lone genius leading the team. This top-down model causes teams to miss out on valuable questioning and input flowing from the bottom. Instead, develop collective cognitive responsibility, where success of the group effort is distributed among members and not concentrated in a single leader.

9. Build in “alone time” to maximize team creativity
The most creative team ideas often do not emerge within a single meeting. Ideation in team science should be longitudinal, and oscillate between convergent and divergent stages. Teammates should have time to converge and deliberate and generate transformative ideas as a group, and then also have an opportunity to reflect on the ideas and let them marinate before the team reconvenes. The interplay of these opportunities discourages teams from settling on “mean (average) ideas” that represent a snapshot agreement, and instead makes ideas and teams stronger and more creative.

10. Think about collaboration as a scientific virtue
Teamwork makes the dream work, but it is not always easy. When the going gets tough, remind yourself that collaboration makes you flourish as a scientist. Think about collaboration as virtuous “scientific friendship.” Virtuous friendship does not stem from utility (they have something we need) or pleasure (we like them), but instead from a drive to be a good person and support others’ greater achievements. Team scientists have an “interest in ‘science-ing’ with others because it contributes to science excellence” and should pride themselves on their determination to “work with other scientists because it makes everyone’s science more awesome.”

Do you have other lessons to share? Are there lessons that you disagree with?

The ideas in this blog post represent a synthesis of the presentations and discussions throughout the duration of the conference, and, in particular, draw from the work of the following individuals: Anita Williams Woolley, James Sallis, Kevin Wooten, Laurie Weingart, Andi Hess, Suresh Bhavnani, Jennifer Cross, Hannah Love, Marshall Poole, Samuel Wilson, and Stephen Crowley.

This blog post is based on a longer version published on the website of the University of Maryland Center for Environmental Science Integration and Application Network (http://ian.umces.edu/blog/2018/05/31/how-to-improve-interdisciplinary-collaborations-lessons-learned-from-scientists-studying-team-science/).

Biography: Suzi Spitzer is a PhD student in the Marine Estuarine Environmental Sciences Graduate Program at the University of Maryland Center for Environmental Science, USA. She works as a Graduate Research Assistant at the Integration & Application Network (IAN) studying science communication and citizen science. She is researching how effective community engagement and science communication can facilitate collaborative learning between scientists and the public within the context of citizen science.

Two types of interdisciplinary scholarship

Community member post by Andi Hess

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Andi Hess (biography)

Would it be helpful to identify two distinct forms of interdisciplinary scholarship ― 1) individual interdisciplinarity and 2) interdisciplinary dialogue and team science ― and to make this distinction explicit in the literature? What are the benefits and challenges of each? Are a different set of resources and methods required to achieve effective interdisciplinary scholarship?

As integration scientists are aware, there are many analyses of appropriate methods for conducting interdisciplinary work. Each has its own benefits and challenges, and each requires a different set of resources and methods for achieving effective interdisciplinary scholarship. Continue reading

Creating community around the Science of Team Science

Community member post by Stephen M. Fiore

Stephen M. Fiore (biography)

How can we create new academic communities? I provide lessons from building the Science of Team Science (SciTS), a rapidly growing cross-disciplinary field of study. SciTS works to build an evidence-base and to develop translational applications to maximize the efficiency and effectiveness of team-based research.

I particularly draw lessons from the recent 8th annual conference attended by approximately 200 people. The conference aimed to:

  • disseminate the current state of knowledge in the SciTS field along with applications for enhancing team science;
  • provide opportunities to discuss future directions for advancing SciTS to improve the global scientific enterprise; and,
  • provide opportunities for interaction amongst a diverse group of stakeholders, including thought leaders in the SciTS field, scientists engaged in team-based research, institutional leaders who promote collaborative research, policymakers, and federal agency representatives.

Continue reading

Bringing the Immunity-to-Change™ process to the scientific community

Community member post by Erica Lawlor and Cheryl Vaughan

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Erica Lawlor (biography)

How can scientists whose careers were formed in an incentive system that cultivates competitive and territorial behaviors be helped to meet the expectations of collaborative research frameworks? A team-based approach that transcends disciplinary boundaries may be a tall order for scientists who “grew up” in a system where funding and promotion are based upon a proven record of individual contributions to a field of research. But that is the direction in which much of science is heading. Continue reading

Team science glossary

Community member post by Sawsan Khuri and Stefan Wuchty

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Stefan Wuchty (biography)
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Sawsan Khuri (biography)

As team science gains momentum, we present this glossary to standardize definitions for the most frequently used terms and phrases in the science of team science literature, and to serve as a reference point for newcomers to the field. Source material is provided where possible. Continue reading

Research team performance

Community member post by Jennifer E. Cross and Hannah Love

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Jennifer E. Cross (biography)

How can we improve the creativity and performance of research teams?

Recent studies on team performance have pointed out that the performance and creativity of teams has more to do with the social processes of interaction on teams, than on individual personality traits. Research on creativity and innovation in teams has found that there are three key predictors of team success:

  1. group membership,
  2. rules of engagement, and
  3. patterns of interaction.

Each of these three predictors can be influenced in order to improve the performance of teams, as the following examples show. Continue reading