Dr. Raviv Murciano-Goroff
Raviv Murciano-Goroff, an assistant professor of Strategy and Innovation at Boston University’s Questrom School of Business, co-authored a piece in Nature last year on gender inequities in scientific publications. That article got a lot of attention but it wasn’t the first time Dr. Murciano-Goroff worked with the UMETRICS dataset. In fact, he did a postdoc with IRIS co-founder Julia Lane, which familiarized him with the dataset and opened up possible other research avenues.
Murciano-Goroff holds a B.A. in History from Harvard; a M.Sc. in the History of Science, Medicine, and Technology from Oxford; and a Ph.D. in Economics from Stanford. He recently shared his thoughts on the UMETRICS dataset and its potential uses.
How long have you been working with UMETRICS data, and how did they first come to your attention?
I’ve been working with UMETRICS data for six years, and I am still discovering new features of the data that are useful for research.
In the fall of 2018, Professor Julia Lane invited me to spend a year as a postdoc at New York University developing and executing projects leveraging UMETRICS data. During that year, I learned about the data, how the different elements of the dataset connected with each other, and what types of research questions the data could enable us to answer.
Many projects came out of the work that I conducted with Professor Lane. Along with our collaborators, we published an article in Nature that documented a gender gap in the rate that men and women scientists working in university labs receive attribution when scientific publications come out of those labs.
What have you been able to study using IRIS data that you’d otherwise have trouble with?
Understanding the production function of science is important for a variety of reasons. We may want to know how elastic scientific research is to changes in input prices, for example, or how various policies related to university research have impacted the productivity of university labs. Because UMETRICS provides detailed administrative data from inside thousands of labs across the country, for the first time, we are able to provide rigorous empirical analysis of the impact of these changes and policies.
You were an author of an article in Nature on the gender inequities in scientific publishing – what is the potential for further study in that direction with the IRIS dataset?
Our article in Nature examined the rate that individuals are named on scientific publications. Scientists typically work in teams and labs, however. Along with Britta Glennon, Russell Funk, and Matt B. Ross, we are therefore examining how the network topology of collaborations within labs are associated with the attribution rate of men and women scientists on scientific publications.
Tell us about your latest paper, i.e., the topic, findings, future directions, etc.?
Along with Ina Ganguli, we examined the affect of increases in the cost of hiring staff and students for university labs on employment levels in those labs. Our research will hopefully enable funding agencies and university administrators to carefully evaluate how policies that impact the cost of scientific work may also impact who is involved in scientific work.
What are some directions of study you might use the IRIS dataset for going forward?
I am very interested in understanding how incorporating new technologies into scientific labs impacts both the rate and direction of scientific work by those labs. Combining the accounting data in UMETRICS with the textual data from scientific papers has provided a plethora of potential new avenues to examine.