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.

A new boundary object to promote researcher engagement with policy makers / Un nuevo objeto frontera para promover la colaboración de los investigadores con los tomadores de decisiones

Community member post by María D. López Rodríguez

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María D. López Rodríguez (biography)

A Spanish version of this post is available

Can boundary objects be designed to help researchers and decision makers to interact more effectively? How can the socio-political setting – which will affect decisions made – be reflected in the boundary objects?

Here I describe a new context-specific boundary object to promote decision making based on scientific evidence. But first I provide a brief introduction to boundary objects.

What is a ‘boundary object’?

In transdisciplinary research, employing a ‘boundary object’ is a widely used method to facilitate communication and understanding among stakeholder groups with different epistemologies. Boundary objects are abstract tools adaptable to different perspectives and across knowledge domains to serve as a means of symbolic communication. Continue reading

Productive multivocal analysis – Part 2: Achieving epistemological engagement

Community member post by Kristine Lund

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Kristine Lund (biography)

In a previous blog post I described multivocalityie., harnessing multiple voices – in interdisciplinary research and how research I was involved in (Suthers et al., 2013) highlighted pitfalls to be avoided. This blog post examines four ways in which epistemological engagement can be achieved. Two of these are positive and two may have both positive and negative aspects, depending on how the collaboration plays out.

Once a team begins analyzing a shared corpus from different perspectives — in our case, it was a corpus of people solving problems together — it’s the comparison of researchers’ respective analyses that can be a motor for productive epistemological encounters between the researchers. Continue reading

Methods for integration in transdisciplinary research

Community member post by Matthias Bergmann

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Matthias Bergmann (biography)

To make progress in contributing to the solution of complex real-world problems, transdisciplinary research has come to the forefront. By integrating multiple disciplines as well as the expertise of partners from societal practice, transdisciplinary researchers are able to look at a problem from many angles, with the goal of making both societal and scientific advances.

But how can these different types of expertise be integrated into both a better understanding of the problem and more effective ways of addressing it?

Colleagues and I have collected 43 methods from a number of transdisciplinary research projects dealing with a variety of research topics. We have grouped them into seven classes following an epistemological hierarchy. We start with methods in the narrower sense, progressing to integration instruments. Continue reading

Problem framing and co-creation

Community member post by Graeme Nicholas

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Graeme Nicholas (biography)

How can people with quite different ways of ‘seeing’ and thinking about a problem discover and negotiate these differences?

A key element of co-creation is joint problem definition. However, problem definition is likely to be a matter of perspective, or a matter of how each person involved ‘frames’ the problem. Differing frames are inevitable when participants bring their differing expertise and experience to a problem. Methods and processes to support co-creation, then, need to manage the coming together of people with differing ways of framing the problem, so participants can contribute to joint problem definition. Continue reading

Integration – Part 2: The “how”

Community member post by Julie Thompson Klein

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Julie Thompson Klein’s biography

The “how” of integration focuses on pragmatics of process, with emphasis on methods. Toward that end, following the part 1 blog post on the “what” of integration, this blog post presents insights from major resources, with emphasis on collaborative research by teams.

Some widely used methods are well-known theories, for example general systems. Others are practiced in particular domains, such as integrated environmental assessment. Some utilize technologies, for example computer synthesis of data. And others, such as dialogue methods, target communication processes. Continue reading