A collaborative blog series about collaborative research: a data scientist and a cognitive psychologist combine perspectives.

co-authored by Dr. Alicia Knoedler and Dave King

Dr. Alicia Knoedler: For the past 18 years, I have sought opportunities and means to advocate for researchers working to develop and accelerate their research programs. I had the very fortunate opportunity to meet Dave King in 2014 when he relocated his company to Oklahoma City. At that time I was the Associate Vice President of Research and leading a team called CRPDE. One of the core missions of this team was to assist researchers in their efforts to form teams and seeking resources and funding for those teams. Although it is challenging to form teams that work together well and pursue innovative work together, it was equally if not more challenging to demonstrate why a newly assembled team would be innovative.

When I found out that Dave had designed his company, Exaptive, to apply data analytics and data visualization tools to matters of team dynamics and innovation, I knew we had much to discuss and ideas to explore! We’ve been working together ever since, and this blog post is the first in a set of collaborative blog posts between the two of us.

Dave King and Dr. Alicia Knoedler discuss transdicplinary research teams.

In each post, we aim to take issues that I have experienced “in the trenches” of facilitating collaborative research and consider them through the technological lens that Dave brings. We hope you enjoy this series, and we encourage you to let us know if there’s a particular collaborative research challenge that you’d like us to tackle.

The way that innovative research gets funded is changing.

Teams that choose to pursue grant opportunities need to keep up with the trends if they want to secure dollars supporting their projects. Open accessFAIR data, and interdisciplinary collaboration are a few topics that have become more and more important to funders, whose priorities often reflect progressive social change and broad issues like inclusivity and accessibility.

As an example, considering various funding opportunities at the National Science Foundation (NSF), a federal funding agency with a long history of funding research teams and striving to advance groups of researchers to exceed their potential, here is a sampling of program expectations regarding teams:

  • Clearly establish the means of developing a coordinated, collaborative approach involving investigators across different institutions and jurisdictions. Describe interactions with other groups and organizations among the jurisdictions, and at the national and international levels, as appropriate (NSF EPSCoR Track 2 solicitation)
  • Support will be provided for groups of investigators to communicate and coordinate their efforts across disciplinary, organizational, institutional, geographical and/or international boundaries. The objectives are to facilitate open communication and exchange of information and resources, to integrate research, education, and/or cyber infrastructure activities of scientists, educators, and engineers working independently on topics of common interest; to nurture a sense of community among young scientists, educators, and engineers; and to minimize isolation and maximize cooperation so as to eliminate unnecessary duplication of efforts. (NSF Research Coordination Network (RCN) solicitation)
  • Team Formation: The process by which all necessary disciplines, skills, perspectives, and capabilities are brought together. Successful teams are interdependent, multidisciplinary, and diverse and can work and communicate effectively even when geographically dispersed. Team formation includes strategies to overcome barriers to effective, collaborative teaming, including the integration of members with different areas of expertise, different vocabularies and core values and ways of approaching problems, different understanding of the problems to be addressed, different values, and different working styles. (NSF Gen-4 Engineering Research Centers (ERC) solicitation)

An org chart, really?

I have worked with many teams. In the course of catalyzing and developing nascent teams, the processes to form teams, identify and include members, and establish team culture can be onerous. Creating representative ways to describe and visualize the teams as connected, integrated and cohesive groups of individuals working together can also be challenging. Unfortunately, in efforts to seek funding and resources for teams, the decision is often made to conservatively approach team visualization, using an organizational chart or similar hierarchical rendering to communicate roles and some relational pathways.

The exemplar solicitations above are challenging convention and attempting to expand the capabilities and expectations for research teams. But some of the requirements in these solicitations remain conventional. It is time for a change; not only in imagining how to visually represent teams but moving beyond visual relational images to technology platforms that serve to support the teams’ behaviors, dynamics, decision-making, collaborative work, and their potential to innovate together.

Let’s explore the NSF ERC solicitation in a bit more detail. The solicitation specifies that a successful proposal will delineate:

  • The overall management and reporting structure of the ERC.
  • Which personnel or groups will be responsible for Research, Engineering Workforce Development, Diversity and Culture of Inclusion, and the Innovation Ecosystem. Please explain the relevant experience and expertise of these individuals and how they fit their assigned roles. These individuals should be included in the leadership team.
  • An organizational chart, including advisory boards and the reporting/feedback loops involved.

The accompanying narrative for the organization chart should define the functional roles and responsibilities of each leadership position, and how these positions support the integrated strategic plan described earlier. It should also define the functional purpose of any additional advisory bodies that are deemed necessary to support the four foundational components, accomplish the proposed ERC vision, and achieve the desired long-term societal impact. Note that the functional roles of the two mandated ERC Advisory Bodies, the Council of Deans and the Student Leadership Council, are defined earlier in the section on Community Feedback. Since the quality of team member interaction is critical to team effectiveness, describe the managerial processes overlaying the organization chart that will be used to integrate the team. Please provide sufficient detail to allow critical evaluation.

The NSF ERC solicitation suggests that an organizational chart is the way to go. It details what the organizational chart should demonstrate: functionality, relationships, and integration within the team. These are important features of the ERC teams but will a two-dimensional, non-interactive, hierarchical representation really align with what the Gen-4 ERC teams are expected to do?

The Gen-4 ERC program has been re-envisioned. So, let me invite Dave to share his perspective on how the representations of teams can be re-envisioned too.

Ideas are networks. Your team should be, too.

Dave King: I don’t know nearly as much as Alicia when it comes to assembling academic researchers or applying for grants, but as a software architect and data scientist, I do know one thing for sure — a tree is not the same thing as a network. If you’re not sure what I mean by that, just check out this Dilbert cartoon:

The idea of printing out a website is laughable because a website is, well, a web. It’s a set of interconnected content that can be navigated in a multitude of different ways from a multitude of different directions. It’s not a tree. Sure, you can try to make it look like a tree by creating a “site map” like the image below:

There is some utility in being able to look at a website like that, because it helps you understand how a website is built. But it doesn’t really capture at all how a website is used. If you visualize the traffic through the different pages of a website, you don’t get a hierarchical tree, you get something like this:

The difference between those two visualizations and what they represent, how something is built vs. how it behaves, encapsulates exactly the problem with trying to convey the innovative trans-disciplinary nature of your research team through an org chart. The org chart might show how the team was built, but it doesn’t provide any insight into how the team will behave.

Like with a website, where it’s critical for a person to be able to jump around from page to page regardless of hierarchy, in research teams it’s critical for members to similarly jump from perspective to perspective and from expertise to expertise, because that sort of jumping around is exactly the process of ideation that makes trans-disciplinary teams so powerful, and different from multi-disciplinary teams. Just because you assemble a team that includes different expertise doesn’t guarantee that that expertise will be integrated together in synergistic ways. Said another way, it’s easy to build teams that have siloed experts within them (trees) but much harder to demonstrate teams that are true collaborations (networks), which is ultimately what organizations like the NSF want to fund.

Technology can help!

The good news is that when it comes to building networks, and visualizing them, technology can really help. There are an increasing number of tools for network visualization. Many of these tools are for technical developers, but the barrier to entry is getting lower and lower as it is becoming increasingly recognized that non-programmers have reasons to visualize networks too. It is now fairly easy to take an excel file and transform it into a network. For example, I made a spreadsheet of the two-person team comprised of me and Alicia, and some of our expertise and focus areas:

Then I used an Exaptive xap (pronounced zap) to turn it into this network:

(If you’d like to use this application yourself to experiment with visualizing your own team, just let us know here.)

Stephen Johnson, author of the book Where Good Ideas Come From gave afantastic TED talk about how ideas are networks. My hope is that as technology makes it easier for more people to define networks, visualize networks, and quantify the dynamics of networks, they will be able to start providing organizations like the NSF not org charts that ignore the inter-relationships within a team, but network charts that illuminate those overlaps. It’s only through that sort of visualization that a team’s true potential for innovation can be conveyed.

Not all team networks are structured the same way.

Once you start looking at teams from a network perspective, you open up new opportunities for characterizing teams. In the Harvard Business Review article “Better People Analytics”, Paul Leonardi and Noshir Contractor look at how different team network structures may offer different advantages for different situations, developing a set of team “signatures”. The figure on the left shows two signatures covered in the article, one better for innovation, one better for getting projects done on time!

These signatures are important because they open the door for us to be able to do more than just visualize teams — they allow us to start to quantify some metrics of team performance.

I’m particularly interested in a team’s capacity for cognitive exaptation — the application of an idea in a different context than the one in which it originally emerged — so I’ve focused my company on creating a platform that can help find teams more likely to exapt tools and techniques across fields. The screenshot below shows our analysis of the possible 3-person scientific teams that could be assembled from 143 scientists associated with a large pharmaceutical company’s research program. There were over half a millionpossible 3-person teams that could be assembled from just that small pool of researchers, but by modeling each possibility as a network, and then applying some analysis on each network’s signature, we could organize all those possibilities along a meaningful axis and allow for the interactive inspection of the different structure along that axis:

The goal is to augment human intelligence, not automate it.

Once we start talking about applying algorithms to “quantify” things, we start getting into the dangerous territory of thinking that we might be able to use algorithms to replace human decision making. Even though I’m a technologist and data-scientist, I’m not a fan of that approach. Don’t get me wrong, I think there are plenty of areas where computers can probably do a better job than humans making certain types of decisions, but I don’t think that designing innovative teams is one of them.

There are so many subtle parameters involved in high-performance teams and their interpersonal dynamics that a computer has no hope of having the complete picture. The computer, therefore, has to be used as just a tool in a human’s process. It’s important for that process to be a collaborative back-and-forth between what the computer can analyze and what the human can intuit. Computers can help separate some of the signal from the noise, but humans have to be able to iterate based on what the computer is helping them see, adding their own knowledge and experience into the mix, steering the computer in the most fruitful directions. Steve Jobs used to say that a computer is like a bicycle for our minds. In the case of team-building, I think a computer is like a flashlight for team-leads and facilitators.

That’s where people like Alicia come in. Research facilitators know where to shine that flashlight and how to take what the computer is telling them and combine it with all the things the computer doesn’t know. They know how to take the visualizations that a computer can produce and place them into the broader context of a proposal for innovative research. I’m excited about giving those people better tools to do their job. There is an increasing proliferation of research profiling systems that help organizations capture and harvest all the information they have about their researchers, publications, grants, focus areas, etc. In the effort to build those catalogs, it’s important not to forget what all those data are for — being able to assemble teams that can do innovative research, and being able to convey to sponsors why those teams are worth funding.

Where do we go from here?

Dr. Alicia Knoedler: We started this conversation by suggesting that providing more insightful visualizations for research teams is among the many opportunities for innovation in research programs. The technology is readily available and becoming easier to utilize. Dave and I are both excited by the prospect provided by technology to team facilitators to enhance their powers of observing, noticing, and catalyzing behaviors within research teams. But we also want to promote these ideas within funding agencies, research team sponsors, and to the reviewers who evaluate proposals. There is opportunity to open up requirements within funding programs for new ways to show team compositions, illuminating team structures and functions, the dynamics and roles of team members, and the proposed behaviors that will lead to competitive research outcomes.

We know that investigators submitting proposals for research funding know to follow the solicitation instructions to the letter. But if the solicitations are requiring conventional, traditional information for team descriptions, it is difficult for research teams to propose something really new. We think the accessibility of these new tools and technologies and will inspire others to push beyond the conventional approaches to suggest that visualizing research teams can be just as innovative as the research they are proposing.

Leave a Reply

This site uses Akismet to reduce spam. Learn how your comment data is processed.