Tag: team science

education & learningevaluationinnovationresearchscience & technology

Openness and The Problem With Collaboration

Openness & Collaboration

Collaboration is everywhere. It’s fast becoming one of the highest virtues to strive for in media, health sciences, business. Whether it is crowdsourcing, groundswells, public engagement, participatory research, or e-democracy, collaboration is hot.

Why? One of the main reasons has to do with the mere fact that we are facing an increasing array of complex problems that have multiple sources, where no one person/group has the all the answers, and where large-scale social action is required if there is any hope of addressing them. The proposed solution is collaboration.

Collaboration is defined as:

collaboration |kəˌlabəˈrā sh ən|
1 the action of working with someone to produce or create something : he wrote on art and architecture in collaboration with John Betjeman.
• something produced or created in this way : his recent opera was a collaboration with Lessing.
2 traitorous cooperation with an enemy : he faces charges of collaboration.
collaborationist |-nist| noun & adjective (sense 2).
ORIGIN mid 19th cent.: from Latin collaboratio(n-), from collaborare ‘work together.’

At the root of the term is (from the Latin): co-labour — working together. That sounds great in theory and indeed, if we are working in a social environment (physical or electronic) we are very likely collaborating in some manner. Social media for instance is built upon collaboration. The picture posted along with this blog was courtesy of psd on Flickr and used under a Creative Commons Licence (thank you!), which encourages collaboration and remixing. Knowledge translation is a another concept that has collaboration at its very foundation. It’s commonplace to see it and, in the world of academic heath sciences, it is considered to be an important part of the work we do.

On the surface of things, my colleagues and I collaborate a lot. But a second glance suggests that this might be overstating things — a lot. The reason has to do with collaboration’s precondition: openness.

Openness is defined (selectively) as:

open |ˈōpən|
1 allowing access, passage, or a view through an empty space; not closed or blocked up : it was a warm evening and the window was open | the door was wide open.
• free from obstructions : the pass is kept open all year by snowplows.

2 [ attrib. ] exposed to the air or to view; not covered : an open fire burned in the grate.

3 [ predic. ] (of a store, place of entertainment, etc.) officially admitting customers or visitors; available for business : the store stays open until 9 p.m.

4 (of a person) frank and communicative; not given to deception or concealment : she was open and naive | I was quite open about my views.
• not concealed; manifest : his eyes showed open admiration.

Let’s consider these definitions for a moment within the context of health and social services, the area I’m most familiar with.

Allowing access refers to having the ability to gain entry to something — physical or otherwise. That might be simple if collaboration is with members of the same team — but what about when you have people from other teams? Other disciplines? Having worked on a project that focuses on interdisciplinary collaboration between teams of researchers I can vouch that it is not something to be taken for granted. Developing a collaborative approach to research, particularly in teams, is something that takes a long time to foster. Then there is confidentiality, rules and regulations about whom has access to what. Even in teams that are open to true collaboration, sometimes the rules that govern institutions don’t allow researchers to engage across settings to access data.

Having something “not blocked up” sounds good, but anyone looking for collaboration knows that there are a lot of preconceived ideas about what that means in practice. For example, are certain people expected to get credit even if they don’t offer anything substantive ? There are conventions for authorship that often grant those who lead the lab a prime authorship position with little attention to the amount of effort on a paper.

What about being “exposed to the air or to view; not covered”? This could mean open to new ideas or ways of working. Sure, it sounds nice to say that you’re open to ideas and suggestions, but what about real practice? Resistance to new ideas is how innovation is thwarted, but it also protects interests within an organization and with individuals. As the saying goes:

The only people who welcome change are wet babies

Lastly, frank and communicative action is a part of openness and if there is anything that represents the converse of that it is academic publishing. It probably should strike people as surprising how often scientists report positive results in the academic literature, but it doesn’t. Why? There is a well-known publication bias — whether real in terms of editorial bias or in terms of self-selection away from publishing negative trials. Another issue is that collaboration is hard, it’s not well funded (that is, the collaboration part — the science itself sometimes is), and it takes a long time to produce something of value. The reason is that it is based on normal human relationships and they don’t fit a timeline that’s particularly ‘efficient’.  It’s also hard to be frank when your reputation and funding is on the line.

So collaboration will continue to soar as an idea, yet until we acknowledge the challenges in an open, frank manner (as the term suggests) we are going to see a marginal benefit for science, health and innovation.

public healthsocial systemssystems thinking

Coordination, Teams and Knowledge Translation

In their column in this month’s Fast Company magazine, Dan and Chip Heath write about the importance of coordination and how it is often neglected in environments where there are multiple actors working together. They are writing primarily of business, but they might as well be writing about health care and public health. In their article, they point to the experience of the performance of the U.S.A. 4×100 men’s (and women’s) relay team in the 2008 Beijing Olympics. Both teams dropped the baton at the same point in the race.

In discussing the blown baton pass between Darvis Patton and Tyson Gay, the Heaths write:

“Team U.S.A.’s track coach, Bubba Thornton, told the media his runners had practiced baton passes ‘a million times.’ But not with their Olympic teammates. Some reporters noted that Patton and Gay’s practice together had been minimal.

Thornton’s apparent over-confidence was understandable. If you have four world-class experienced runners on your team, shouldn’t that be enough? Unfortunately, not it isn’t. The baton pass cannot be taken for granted — not on the track and not in your organization.”

It got me to thinking about how many times we speak of coordination in knowledge translation and public health practice more widely, yet how little we pay attention to it as a focus of our work. A search of Google Scholar using the terms “knowledge translation”, “team”, and “coordination” found 1400 articles that mention the terms. Yet, when we restrict this to terms in the title — presumably indicating importance and focus — we get exactly zero. Take out team and you also get zero. Take out coordination and you get two.

In all cases, the numbers are small. If putting a term in a title is a sign of importance and focus, then we have a long way to go.

Coordination is a systems problem and, as many scholars including myself have written about, we humans don’t deal with those particularly well. We don’t think about them in a manner relative to their weight, particularly when an estimated 80 to 90 per cent of change can be attributed to systems-level variables (see the work of Russell Ackoff, W. Edwards Deming and others for examples of this).

To take the U.S.A. relay team case study: here were elite athletes training for the biggest team event in their careers, yet not doing so as a team. This is a perfect example of where conventional thinking that assumes the parts equal the whole falls apart. We’ve seen this with other sports where ‘all-stars’ are put together on to a ‘team’ and yet fail to deliver. Time and again we’ve witnessed collections of athletes with superior individual talent fail when brought together to supposedly lesser teams. (Anyone watching the 2010 World Cup and the collapse of teams like France, Italy and England will find ample evidence of this – particularly with France).

If we are to take knowledge translation — undoubtedly a team process at many levels — and truly make it work, we need to make coordination and team part of the focus of much of the research and scholarship out there.

Just as there is no ‘i’ in team, there perhaps should be a silent ‘k’ and ‘t’ .

complexityeducation & learningsystems sciencesystems thinking

Can We Reduce Complexity?

A sketch of social complexity (by Phil Hawksworth)

Is it possible to make the complex simple? That was the subject of a conference call that I was a part of last week involving researchers and organizational change practitioners from around the world. The purpose of the call was to explore the potential for creating a conference that would address this very issue.

The call convenors were Eugen Oetringer and Dave Snowden . Like any topic worth spending time on, there was much debate on the very topic itself (and wide agreement that a conference framing the debate was a good idea).

The question of reducing or simplifying complexity is an important one for those trying to use complexity science methods to address wicked problems. The reasons are many. As a teacher (and always a learner) of complexity science and systems thinking methods and theories I can attest to the difficulty that people have with the subject matter. The reasons are also many.

First, in cognitive terms, the brain has a difficult time with processing multiple things at the same time. Research on cognitive complexity points to “chunking” as a promising means of supporting the necessary parallel processing of information necessary to make sense of complex information. The aforementioned citation by Halford, Wilson and Phillips (1998) nicely points to ways in which cognitive scientists define complexity:

Complexity is defined as the number of related dimensions or sources of variation. A unary relation has one argument and one source of variation; its argument can be instantiated in only one way at a time. A binary relation has two arguments, two sources of variation, and two instantiations, and so on.

In many complex problems, there are multiple arguments in play and no clear sense of what argument (if any) explains the problem in full. Much like Buddhist concepts of skillful and unskillful actions, complexity science deals with arguments that are more or less appropriate, not good or bad.

A second reason is that complexity is inherently mutli-disciplinary in its orientation. That is, the knowledge required to address problems of a complex nature cross many boundaries and it is rare if not impossible that one party will have a complete understanding of the situation. This requires that we problem-solve using not only multiple actors with different backgrounds, but multiple means as well. As the quote attributed to Albert Einstein illustrates:

We can’t solve problems by using the same kind of thinking we used when we created them.
Requiring different perspectives, and a diversity of tools, necessitates that there be some manner of engaging this diversity in a meaningful way. This is where we get social complexity. It is here that things often break down. The means of putting together individuals, ideas, and strategies from different backgrounds with different mental models of the way the problem is structured and about the landscape in which the problem occurs is probably the biggest challenge facing the task of complexity reduction.To reduce complexity, there is some need to get on the same page about what makes a problem complex, what elements exist within it, and how those elements are related before one can reasonably hope to make sense of what those patterns of relations actually mean, let alone devising a strategy for intervening.
Terms like “collaboration” are as commonly used as “innovation” and “networking” without much attention to what they mean at a fundamental level. Who among us in the academy, scientific, business or non-profit community would claim not to be innovative, networked or collaborative? My guess is few. Yet, the nature of what these terms means is critical for understanding the potential for creating strategies for addressing complex problems — let alone implementing and evaluating that strategy.
So: can we reduce complexity? The answer will depend on whether we can hope to organize ourselves in a manner that allows us to answer that question in the first place.
researchscience & technologysystems thinking

Reconceptualizing Team in Team Science


Team science could easily be viewed as an oxymoron by those who work in the scientific enterprise. Scientists are trained from the outset to be independent thinkers and the culture of academia is one based on the individual as the centre of knowledge activities. For us professors, nothing reminds us more of this than completing the annual PTR (progress through the ranks) report that comes this time of year. In this report we are asked to justify why we should be eligible for a raise, a promotion, a bonus, or simply be allowed to keep our job by highlighting all of the things that we did the past year. However, by “we” I am referring, in my case, to “me” because what goes on that CV are things that have my name on it and, preferably, my name at the front of the list.

Not all things are equal in this report. For example, on a manuscript with five authors it pays to be the first author much more than the second and far more than the fourth. In some settings, the fifth is a good place to be, but not everywhere.

There is no i in team

This well-worn aphorism used in management courses points to the notion that teams are, by the their very nature, collaborations and oriented towards promoting the good of the group, not just the individuals in it. To this end, it is worth referring to the work of Patricia Rosenfield on multi- inter- and transdisciplinary collaboration.

Multidisciplinary Collaboration

But going through this, it might be worth considering what kind of team you want to create. Sports provide us with the most obvious examples of teams and points of comparison. If you are playing some type of pick-up team game (basketball, football, hockey…) you are generally coach-less and probably each thinking about your immediate neighbours as team members more than you are the team as a whole. The product (that is, your goals/baskets etc..) is determined largely by a contribution of you and your most immediate neighbour on the pitch and that’s about it. The focus of everyone is on the problem, not so much on the team. In terms familiar to team science, this might be considered multidisciplinary collaboration (see Figure).

Another way to conceive of this is that you have a coach and bring together a team that is organized, and for whom some might have worked together or can relate as individuals, but a little less as a whole. For example, the Olympic hockey or basketball teams are now made up of professional all-stars who come together from other teams to compete as a unit for two-three weeks every four years. Depending on how they are coached, this might be considered akin to an interdisciplinary team.

Interdisciplinary Collaboration

The whole is greater than the sum of its parts

The last example is one more akin to a systems approach whereby the “whole is greater than the sum of its parts” and the product comes from the interaction between individuals in a manner that transcends individual expertise into some new, novel form. Using our sports analogy, this is more like the team that was formed by drafting players who had the right mix, worked together to create a unit where there might be few superstars, but a strong connection between players.

Transdisciplinary Collaboration

An example in the past has been the New England Patriots football team or the New Jersey Devils hockey team in their championship years. On these teams, the players themselves function as a unit so well that it transcends the talent of the individual players. All one has to do is look at the 2004 Olympics to see how the USA Basketball team, with arguably one of the best lineups in history, wound up with a bronze medal behind Argentina and Italy, two teams that may have had lesser talent on a player-by-player basis, but were better as teams.

Whether one views an ‘i’ in team might depend on whether the team is viewed through the lens of a multidisciplinary framework where individuals make their own unique contributions into a whole, an interdisciplinary framework where there is collaboration, or a transdisciplinary framework where there is integration.

Special thanks to my longtime collaborator and friend, Dr. Tim Huerta who originally developed these diagrams.

design thinkingeducation & learningresearchscience & technology

Structure of Team Science: Opportunities for Design

A space for creativity: Stanford's New D-School Building :

Last week’s conference on the Science of Team Science at Northwestern University provided two and a half days of thought-provoking presentations and discussion (for examples, see here, here, here and here) on the challenges and opportunities of team science and how it has the potential to (and indeed, already is) transform research.

One word that was nearly absent from the conference was design. While much attention was paid to the who (scientists, practitioners, policy makers, interdisciplinary interactions), the why (more productivity, better able to tackle wicked problems), a little on the what (what is the what of team of science), some on the how, and only partly on the where (with places like Northwestern and UBC leading the way). It is the last place, the where, that might be the most important.

As the Science of Team Science conference unfolded, another event was taking place that could be equally as important — if not more so — than what was being discussed at Northwestern: Stanford prepared to open its new d-school (design school) building. The picture above, from Fast Company’s story on the new school’s home, illustrates the look and feel of the place. It’s safe to say this is not something that would be seen at most places of research such as universities and laboratories (at least, not during office hours when the professors are around and the grad students aren’t left alone) .

The Institute of Design at Stanford University is set up to succeed in creating new ideas and transforming them into innovation. Sounds a lot like what universities and scientific laboratories are supposed to do isn’t it? Yet, how many institutions are set up like this? This is not about money — not entirely — it is about vision. Stanford’s dschool’s mission and vision fits on a napkin.  They see themselves as a place to bring together multidisciplinary groups to tackle hard (maybe wicked?) problems and provide space for interactions to take place and interact.

A quote from one of their team members (note, this isn’t “staff”, “faculty” or “students” — its team member)

We couldn’t be more different, except for our shared values. And that makes working together enjoyable

The new dschool building is designed to be “homey” for people who want to create, sketch, collaborate and be what I call artists in the service of innovation. They have designed their space and their program to be in the service of ideas and useful products, not just themselves. Look at the modern university, discussed recently by Seth Godin as an institution ready for a meltdown, and ask yourself if that is a venue for innovation? Are we creating the space for innovation and the structure of buildings and organizations to really promote the kind of creative process that Stanford’s dschool does or that the attendees at the Science of Team Science aspire towards?

It’s time to bring design into that conversation.

complexityeducation & learningresearchscience & technology

Science of Team Science 2

Day two of the Science of Team Science Conference wrapped up yesterday with a lot of energy and enthusiasm (plus some anticipation at today’s 1/2 day workshop on social network analysis). The tell-tale sign that the conference was a hit was the observation that nearly 4/5 of the room was full to hear the convener provide general closing remarks on a Friday afternoon (this after 20 hours of sitting in a hotel ballroom for two days). That speaks volumes about the conference and how much interest there is in the topic.

It is perhaps because of this interest that there is genuine hope that something will come from this beyond just another conference. The question I asked myself is: Why did this conference and this topic yield such interest and a positive response?

What is it about teams that makes this such a compelling issue?

I see three primary reasons:

1. Teams fit our basic need for human relatedness. As the barrier between work and the rest of life (ROL) dissolves further due to changing job structures, information technology, and human mobility the potential to become isolated is high. The gap between connection and community is enormous. We have ‘friends’ on Facebook, ‘followers’ on Twitter, and ‘connections’ on LinkedIn, yet of these many dozens or hundreds only a few really count. Of those, even fewer are ones that we can comfortably relate to. Yet, this appearance of hyperconnectedness provides a false sense of relationships and transmits into a remarkable leveling off of human experience (see Jaron Lanier‘s You Are Not a Gadget, discussed here).

David Whyte’s Crossing the Unknown Sea , Parker Palmer’s A Hidden Wholeness, or Meg Wheatley’s Turning to One Another are works that do a wonderful job of pointing to this problem of disconnection in work and argue for greater integration between one’s personal and spiritual life and their vocation. Seth Godin’s Linchpin (discussed in previous posts) is another book that illustrates the power of bringing one’s “art” to work with others. Science has traditionally been the domain of individual effort, working in small groups at best, but generally alone. This is isolating in itself, but add to the myriad other factors that foster isolation in modern scientific work it is not surprising that any avenue to build connections to others, while continuing to do the work that scientists love, has been embraced.

2. Teams confer genuine advantages in terms of productivity and outcomes. The conference offered a blend of theory, research and strategy, which is probably why it had such broad appeal to an audience that comprised people interested in all three of those things. When the focus was on evidence, it became clear that there is an emergent literature on team science impact. Team science is not a panacea, but it is effective for certain types of problems and provides an alternative option for those wishing to do research, stay social, and tackle complex, wicked problems. Some of the data presented in panels or posters points to teams being more successful at getting large grants, and that, for some, team science can boost productivity. Much more research is needed, but the early results are promising.

Conceptually, this makes sense. Diverse teams of individuals will see problems differently and, particularly with complex problems, complex responses are necessary and diversity provides this complexity. Teams are an ideal structure to addressing a problem that requires new ways of working, knowledge from many areas, and a method of coordinating that knowledge in order to mobilize it.

3. Team science is becoming “hot”. This is the more cynical perspective, but it nonetheless describes reasons why people pursue fields of inquiry. In recent years the creation of funding structures from the National Institutes of Health and National Science Foundation in the U.S. has led a lot of people to consider team science simply as a mechanism to raise research funding. This conference is a byproduct of those decisions. This is not to say that those who pursue team science funding are doing it just because of the money, but it is a powerful incentive. Research flourishes where there are resources to sustain it. It draws in researchers, attracts graduate students and post-docs, and shapes the way many create proposals.

Last night over dinner, a group of us discussed the role that financing plays and whether teams that come together because they want to work together and are looking for funding to support that function differently than those that come together to get funding and then do research based on the details of that grant. Like the conference as a whole, the responses were diverse and no agreement on what would work and why was made. Nor was one expected.

The conference organizers have proclaimed that this is the first annual event, which will mean that we have an opportunity to see where this goes and what a year will do to shaping this field. The conference website is going to be transformed into a community website, enabling researchers, practitioners and policy makers to interact and even create teams. Whether they form based on personal interest, whether we need a ‘coach’ or two, or whether there will be funding to draw people in remains to be seen.
For readers looking for another take on the conference and some insightful reflections on what was discussed, I’d encourage you to visit Stephanie Jo Kent’s Reflexivity blog and read the play-by-play comments on Twitter by searching the hashtag #teamsci10.

education & learninginnovationresearchscience & technology

Science of Team Science

For the last two days I’ve been attending the Science of Team Science conference at Northwestern University in Chicago. It is what I can only imagine is the closest thing to the Super Bowl or World Cup of team science (minus the colourful jerseys, rampant commercialism, and hooligans — although that would have made quite an impact as academic conferences go).

The presentations over the first day and a half have illustrated how far we have come in just a few years. In 2008 a similar conference was held near the NIH campus in Bethesda, MD. That event, sponsored by the US National Cancer Institute, was an attempt to raise the profile of team science by highlighting the theories and rationale underlying why the idea of collaboration, networks and multi-investigator applied research might be a good idea. The conference was aimed at sparking interest in the phenomenon of collaborative team research for health and resulted in a special issue of the American Journal of Preventive Medicine highlighting some of the central ideas.

Although there are many of the same people attending this conference as there was two years ago, the content and tenor of the conversation is markedly different. The biggest difference is that the idea of team science no longer needs to be sold (at least, to the audience in the room). There is wide agreement by attendees that team science is a good thing for a certain set of problems (particularly wicked ones) and that it will not replace normal science, rather complement it or fill in gaps that standard research models leave.

There is also much contention. Although, unlike other conferences, this contention is less about a clash between established bodies of knowledge, rather it is based on uncertainty over the direction that team science is going and the best routes to get there, wherever “there” is. Stephanie Jo Kent, a communications researcher from UMass, has been live blogging at the event (and encouraging the audience to join in — follow #teamsci10 on Twitter or Stephanie @stephjoke) and wrote a thoughtful summary of the first day on her blog. Here she points to one of the biggest challenges that the emergent field of team science and the conference attendees will need to address: Getting beyond “the what” of team science.

She writes:

Because everyone has their own thing that they’re into, whether its research or administration or whatever, we would have to come up with “a meta-thing” as a goal or aim that everyone – or at least a solid cadre of us – could get behind. What if we decided to answer the process question? Instead of focusing on, “What is ‘the what’ of team science?” which takes as its mission connecting the science; we propose an examination of self-reflective case studies in order to identify “what works” and thus be able to explain and train people in the skills and techniques of effective team science.

This issue of training is an important one. My own research with the Research on Academic Research (RoAR) project has found that many scientists working in team science settings don’t know how to do it when they start out. We scientists are rarely trained in collaboration and teamwork, and those that are, are not in science.

It will be interesting to see where things go from here. I suggest following us all on Twitter to see.