Collaboration

My career has been a series of collaborations, some brief, but others lasting for decades. Examples of these can be found in my academic CV, while others are in my book, Practical Data Analysis for Designed Experiments.

Beyond my direct collaborations, I have strived to foster collaboration in multiple ways. For most of my faculty life, I was a member of the Biometry Program, a collaboration between the Statistics Department and the College of Agricultural & Life Sciences (CALS), directing that program for my last six years. This involved coordinating a team of academic staff and graduate students who met periodically with CALS and other researchers to review and improve experimental design and data analysis. Many of these collaborations led to research publications or federal grant support.

I continued this spirit of collaboration into the creation of the Data Science Hub (co-creator) and the Data Science Institute (founding director).

I collaborate by catalyzing partnerships widely, bringing together people with common interests who might not otherwise find each other. Central to this process is team leadership, fostering leadership among team partners. I think this is a crucial way in which I contribute to environmental data science and Indigenous data science. This collaboration includes personal connections and data-rich story telling about how technology and data science will be used effectively to shorten knowledge turns.

Knowledge Turns

I first learned about “knowledge turns” from a talk by Josh Sommer, Chordoma Foundation at the 2010 Sage Congress. He talked about how scientists with labs next door might not hear about each others’ ideas until hearing about them from colleagues at some distance. That is, the knowledge turns with distant colleagues may be quick/short (due to meeting at conferences or otherwise collaborating) while the knowledge turns from immediate colleagues may be slow/long (due to primary focus on department mechanics, etc.). Josh showed the following quote:

“The dominant cause for [the] discrepancy [between progress in the healthcare and microchip industries] appears to lie in the disparate rates of knowledge turns between the 2 industries…. Knowledge turns are indicators of the time it takes for an experiment to proceed from hypothesis to results and then lead to a new hypothesis and a new result.” Grove AS. Efficiency in the Health Care Industries: A View From the Outside. JAMA. 2005;294(4):490–492. https://doi.org/10.1001/jama.294.4.490