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I presented at the ISPIM Conference in 2020 (ISPIM: International Society of Professional Innovation Management) on how co-production can bridge the gap between academic research and industry use-cases creating shared, collective intelligence. Co-production in this context is people with different skills and areas of expertise working on a common goal. By sitting at the intersection of these divergent perspectives, co-product marries aspects of each viewpoint, bringing ideas born in research to life in industry.

Cleaning data is often the primary job of the data scientist, and not necessarily the one they signed up for.  While getting the "words" right with computers is important, it is not fraught with the nuance that getting words right with humans is. Across disciplines the same word can have entirely different meanings and this miscommunication is only one aspect of team building that can go awry.

Innovation requires collaboration, but due to the current state of our filter bubbles, collaboration is stuck in a rut. Data science utilizing knowledge graphs and team and portfolio optimization software can help us climb out. It can increase the scale, the intentionality, and the nuance of how we collaborate. With the right data and algorithms, we can use software to optimize our teams and facilitate innovation.

How do you pick who works together, who reports to whom, and who exchanges information with whom? Usually it gets done within a department, within a project team, or based on some other common ground. It turns out we should be focusing on our differences a bit more.

The goal: investigate huge amounts of research data in new ways. The pool for teams: neuroscientists, data scientists, and software developers. The result: answering questions we didn’t even know we had.

Ever work in a place where you looked up to everyone there because they are experts at whatever they do and can pretty much solve any problem that arises? That’s what working at Exaptive is like. It’s a mix of expertise across various computer/data fields that works very well. Ever felt like you didn’t belong in a place like this?

Within a group, a team, a network, or organization that relies on members being connected to one another, connections can be based on a number of factors but almost always rely on the availability, awareness, and mobility of knowledge or information essential to the group. How does information move within a group or across groups? We are interested in identifying catalyzing actions that occur in group interactions to facilitate the ease of information and knowledge exchange and the establishment of new connections of members in the group. Research suggests that ideas have value to the extent that they can be shared with a new or different audience (Burt, 2004). This research also suggests that individuals who can establish new connections within a group bring competitive advantage to the development of new ideas within that group. In our experience, the purposeful translation of ideas to new audiences reduces serendipitous connections and takes advantage of certain individuals’ natural tendencies to broker these connections.

In Stanley Kubrick’s famous film based on Arthur C. Clark’s book, 2001: A Space Odyssey, a mysterious black monolith appears on Earth millions of years before modern humans. It’s the classic “black box.” We don’t know who made it, what’s in it, or how it works, but it’s miraculous and powerful and somehow results in jumpstarting the entire evolution of humankind.

James Verdier: Hi, I'm James Verdier and welcome to the American Institute of Biological Sciences’ BioScience Talks which is a forum for integrating the life sciences. On the second Wednesday of each month we discuss the latest bioscience publications. And as a reminder if you'd like to read more point your browser to academic.oup.com/bioscience.

“Good ideas are getting harder to find,” Exaptive CEO Dave King quotes from a recent paper by MIT and Stanford researchers. He points to the skyrocketing number of researchers employed in the U.S. and contrasts it with the inverse slope on a chart monitoring efficiency of researchers along the same timeline. “Those growing number of researchers are failing to produce value that outpaces what we’re spending to innovate.”