Creating community around the Science of Team Science

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 [Moderator update – In April 2026, an embedded link to the 8th annual conference in this sentence was no longer available and so the link structure has been left in place but the active link deleted: scienceofteamscience.org/2017-scits-conference]. 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.

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What can action research and transdisciplinarity learn from each other?

By Danilo R. Streck

danilo-streck
Danilo R. Streck (biography)

A man raises his hand and brings up the following issue: “Our community is constantly affected by terrible floods that not only destroy our houses, but are the cause of sicknesses of our children.” This statement—in the midst of a participatory budget meeting in South Brazil—raised issues concerning the deforestation of riverbanks, the deficient sewage system, contested land ownership and occupation, among others.

Our research group is primarily interested in citizenship education and in supporting it through studying what makes learning possible (pedagogical mediation) within discussions about the allocation of resources for the public budget. Stories like this one remind us of the limits of a simplistic approach to understanding citizenship. In this case, citizenship and citizenship education was clearly related to health, to ecology, to urban planning, to farming, among other fields of acting and knowing.

Action research, broadly understood as collective (self) reflection in action within situations that one wants to change, is intrinsically an exercise of disciplinary transgressions.

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Synthesis centers as critical research infrastructure

By Andrew Campbell

andrew-campbell
Andrew Campbell (biography)

When we think of research infrastructure, it is easy to associate astronomers with telescopes, oceanographers with research vessels and physicists with particle accelerators.

But what sort of research infrastructure (if any) do we need in order to do more effective multidisciplinary, interdisciplinary and transdisciplinary research on big, complex, ‘wicked’ challenges like climate change or food security?

Some eminent colleagues and I argue in a new paper (Baron et al., 2017) that the answers include:

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Four best practices for scaling up effective innovations

By Amanda Fixsen, Karen Blase and Dean Fixsen

authors_amanda-fixsen_karen-blase_dean-fixsen
1. Amanda Fixsen (biography)
2. Karen Blase (biography)
3. Dean Fixsen (biography)

What is involved in effective scaling up of innovations in order to achieve social impact? Here are four best practices, drawn from our experience in scaling up human services innovations and programs for children and families. We also provide definitions of the key terms used.

1. Understand the target audiences

Effectively scaling innovations first requires attention to defining the denominator, or population of interest for the scale-up effort, as well as the numerator, or the number of children and families who are receiving the innovation with fidelity and good outcomes.

2. Purposeful design leads to high-fidelity use

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Productive multivocal analysis – Part 2: Achieving epistemological engagement

By Kristine Lund

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.

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Productive multivocal analysis – Part 1: Avoiding the pitfalls of interdisciplinarity

By Kristine Lund

kristine-lund
Kristine Lund (biography)

Many voices are expressed when researchers from different backgrounds come together to work on a new project and it may sound like cacophony. All those voices are competing to be heard. In addition, researchers make different assumptions about people and data and if these assumptions are not brought to light, the project can reach an impasse later on and much time can be wasted.

So how can such multivocality be positive and productive, while avoiding trouble? How can multiple voices be harnessed to not only achieve the project’s goals, but also to make scientific progress?

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Good practices in system dynamics modelling

By Sondoss Elsawah and Serena Hamilton

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1. Sondoss Elsawah (biography)
2. Serena Hamilton (biography)

Too often, lessons about modelling practices are left out of papers, including the ad-hoc decisions, serendipities, and failures incurred through the modelling process. The lack of attention to these details can lead to misperceptions about how the modelling process unfolds.

We are part of a small team that examined five case studies where system dynamics was used to model socio-ecological systems. We had direct and intimate knowledge of the modelling process and outcomes in each case. Based on the lessons from the case studies as well as the collective experience of the team, we compiled the following set of good practices for systems dynamics modelling of complex systems.

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Responsive research – simple, right? The AskFuse case study

By Rosemary Rushmer

rosemary-rushmer
Rosemary Rushmer (biography)

Researchers are constantly being challenged to demonstrate that their research can make a difference and has impact. Practice and policy partners are similarly challenged to demonstrate that their decisions and activity are informed by the evidence base. It sounds like all we need to do is join the two groups together – simple, right?

In Fuse (the Centre for Translational Research in Public Health, www.fuse.ac.uk) we wanted to do exactly that. We wanted to supply the evidence that end-users said they wanted (supply and demand), and make it easy for them to access and use research evidence.

Yet, we knew that current approaches to supplying evidence (briefs, guidelines, publications) do not work as well as we once thought they did. It needed a re-think…

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Scaling up amidst complexity

By Ann Larson

ann-larson
Ann Larson (biography)

How can new or under-utilized healthcare practices be expanded and institutionalized to achieve audacious and diverse global health outcomes, ranging from eliminating polio to reversing the rise in non-communicable diseases? How can complex adaptive systems with diverse components and actors interacting in multiple ways with each other and the external environment best be dealt with? What makes for an effective scale-up effort?

Four in-depth case studies of scale-up efforts were used to explore if there were different pathways to positively change a complex adaptive system.

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Keys to transformation: Interactions of values, rules and knowledge

By Russell Gorddard, Matthew Colloff, Russell Wise and Michael Dunlop

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1. Russell Gorddard (biography)
2. Matthew Colloff (biography)
3. Russell Wise (biography)
4. Michael Dunlop (biography)

Adapting to climate change can require profound alterations in environmental management and policy. However the social context of a decision process limits options and resists change, often dooming attempts to adapt to climate change even before they begin. How can decision makers in policy and management more effectively see the institutional and social water they swim in, in order to better drive change?

Values, rules and knowledge (vrk) provide a useful heuristic to help decision makers analyze how the social system shapes their decision context. Put simply, decisions require:

  • knowledge of options and their implications
  • values to assess the options
  • rules that enable implementation.

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Improving mutual consultation among key stakeholders to optimize the use of research evidence

By Allison Metz

Alison Metz
Allison Metz (biography)

Processes to support the uptake of research evidence call for each of the key stakeholders to consider the challenges faced by other key stakeholders in making good use of research evidence. When stakeholders have the opportunity to consider perspectives other than their own, they will generally have a broader understanding of the problem space, and, in turn a greater commitment to co-creating prototypes for improving research translation.

Let’s consider a real world example in New York City’s public child welfare system.

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What’s in a name? The role of storytelling in participatory modeling

By Alison Singer

singer
Alison Singer (biography)

That which we call a rose,
by any other name would smell as sweet.

That Shakespeare guy really knew what he was talking about. A rose is what it is, no matter what we call it. A word is simply a cultural agreement about what we call something. And because language is a common thread that binds cultures together, participatory modeling – as a pursuit that strives to incorporate knowledge and perspectives from diverse stakeholders – is prime for integrating stories into its practice.

To an extent, that’s what every modeling activity does, whether it’s through translating an individual’s story into a fuzzy cognitive map, or into an agent-based model. But I would argue that the drive to quantify everything can sometimes make us lose the richness that a story can provide.

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