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

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.

Interdisciplinarity and evil – Understanding incommensurability

Community member post by J. Britt Holbrook

J. Britt Holbrook (biography)

Incommensurability is a recognized problem in interdisciplinary research. What is it? How can we understand it? And what can we do about it?

What is it?

Incommensurability is best illustrated by a real example. I once co-taught a class with a colleague from another discipline. Her discipline depends on empirical analysis of data sets, literally on counting things. I, on the other hand, am a philosopher. We don’t count. One day she said to our students, “If you don’t have an empirical element in what you’re doing, it’s not research.” I watched the students start nodding, paused for half a beat, and volunteered, “So, I’ve never done any research in my entire career.” “That’s right!” she replied, immediately, yet hesitating somewhere between a discovery and a joke. Continue reading

Epistemological obstacles to interdisciplinary research

Community member post by Evelyn Brister

Evelyn Brister (biography)

What causes interdisciplinary collaborations to default to the standard frameworks and methods of a single discipline, leaving collaborators feeling like they aren’t being taken seriously, or that what they’ve brought to the project has been left on the table, ignored and underappreciated?

Sometimes it is miscommunication, but sometimes it is that collaborators disagree. And sometimes disagreements are both fundamental and intractable.

Often, these disagreements can be traced back to different epistemological frameworks. Epistemological frameworks are beliefs about how particular disciplines conceive of what it is they investigate, how to investigate it, what counts as sufficient evidence, and why the knowledge they produce matters. Continue reading

Three tasks for transdisciplinary bridge builders

Community member post by Roderick J. Lawrence

Roderick J. Lawrence (biography)

Human groups and societies have built many kinds of bridges for centuries. Since the 19th century, engineers have designed complex physical structures that were intended to serve one or more purposes in precise situations. In essence, the construction of a bridge is meant to join two places together. What may appear as a mundane functional structure is built only after numerous decisions have been made about its appearance, cost, functions, location and structure. Will a bridge serve only as a link and passage, or will it serve other functions?

In discussing three things the transdisciplinary research community can do to build bridges, I use “building bridges” as a metaphor. I discuss a bridge as a human-made artefact that is attributed meaningful form. It is created intentionally for one or more purposes. Continue reading

Sharing mental models is critical for interdisciplinary collaboration

Community member post by Jen Badham and Gabriele Bammer

Jen Badham (biography)

What is a mental model? How do mental models influence interdisciplinary collaboration? What processes can help tease out differences in mental models?

Mental models

Let’s start with mental models. What does the word ‘chair’ mean to you? Do you have an image of a chair, perhaps a wooden chair with four legs and a back, an office chair with wheels, or possibly a comfortable lounge chair from which you watch television? Continue reading

Productive multivocal analysis – Part 2: Achieving epistemological engagement

Community member post by Kristine Lund

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