Tag: team science

Why do we need the “driver’s license”?

When I was a trainee in neuroscience, there were plenty of examples of physicists who decided that figuring out the brain was to be their next big idea. They did just fine. You’ve probably heard of them.

Growing up with two parents who had both received their doctoral degrees in philosophy (!) but were, in actuality, running a neurophysiology lab, further inured me to the notion of being scientifically siloed by our degree title.

These days I lead two teams that aren’t doing neuroscience at all. One works on the future of AI, the other at the intersection of ecology and microbiology. At the Institute I led for sixteen years, my closest colleague was a biophysicist whose scientific passion was astrobiology. At the end of his life he was deeply involved in thinking about d-orbital catalysis in chemistry.

So increasingly, I’m coming to the conclusion that excellent science can be done by anyone with sufficient creativity and a willingness to team up with like-minded collaborators. But: with the caveat that the team as a whole has the requisite methodological skills.

This I think has implications for the future of team science–which I’ve spoken about at length.

The Role of Single PI Science….

I often get the question, especially when speaking to biologists, about whether there is still a role for the single principal investigator (PI) style of hypothesis-based research that was mainly the norm for the life sciences in the last century. My parents’ neuroscience lab at Cal Tech was certainly emblematic of that approach in the 1970’s.  In such research groups, a single senior person was the PI for a grant (or grants). There were well-defined hypotheses for each research project executed by a junior person and generally cross-laboratory collaborations were rare.

I’ve written about team science, fairly recently. And the advent of Internet-enabled open collaboration technologies such as Jupyter Notebooksalong with large-scale major research facilities that produce open data are certainly driving a trend. But I do think there is still a critical role for the small-scale single PI approach.

Why? First, from the standpoint of training new scientists, junior folks tend to get more intense mentorship is such groups. At least for some trainees, that’s clearly desirable. Second, there is a lot to be said for the classic Popperian experimental design. With a falsifiable hypothesis and with proper statistical analysis, I think it’s less susceptible to p-hacking. Finally, I would argue that massive scientific teams might tend to be more conservative, missing out on truly transformative research findings that might arise in the laboratory of a courageous single-PI.