A scientific partnership

In late June, the Proceedings of the National Academy of Sciences ( PNAS ) published an article showing that elite male scientists hire fewer women (as postdocs and graduate students) than other male scientists or elite women do. Almost as striking as the article's main result is the makeup of its authorship team. They are a couple: Jason Sheltzer, a graduate student studying cancer biology at the Massachusetts Institute of Technology (MIT) in Cambridge, and Joan C. Smith, a software engineer in Twitter's Cambridge office. This interview has been edited for brevity and clarity. ![Figure][1] “Elite male faculty in the life sciences hire particularly few women.” ILLUSTRATOR: ROBERT NEUBECKER > Q: Tell us about your backgrounds and how you met. > A: Sheltzer : I majored in molecular biology at Princeton University and then went to grad school in biology at MIT. I'm studying how changes in chromosome number affect cell physiology and cancer progression. Joan and I met on OKCupid. We started chatting about our mutual love of Richard Feynman and hydrogen atoms. We met up at a local cafe and haven't looked back since! > > Smith : I have a Bachelor of Science in physics from MIT, and I'm a software engineer at Twitter. From just a few dates, it was obvious that we valued the same things (work, science, feminism), had complementary personality quirks, and got along exceptionally well. > Q: How did you start working together? > A: Sheltzer : I was analyzing some microarray data, and I reached the limit of what I knew how to do in terms of data analysis. So I described the scientific question to Joan, and in about 30 minutes, she had set up a Python script to answer it. Collaborating with Joan really expands the range of questions that I'm able to address. > Q: Where did the data for your PNAS article come from? > A: Sheltzer : Joan and I started counting the grad students and postdocs from the websites of a few biology labs. We soon found a striking pattern—elite male faculty in the life sciences hire particularly few women—but we also found that it would be difficult to get a large enough sample size to make the results robust and representative of the life sciences as a whole. I had recently sold my car, so we ended up spending the money I had made to hire freelance data scrapers to collect more lab information than we could on our own. > Q: Are there any other important points buried in the article? > A: Sheltzer : It's quite striking how a small number of “elite” labs function as gateways to the professoriate. We found that about 10% of all faculty members are members of the National Academy of Sciences, but about 60% of new faculty members did a postdoc with a member of the National Academy. I think that this says something about the insular nature of academic science. It probably limits the scope of scientific questions that new investigators study. They're mostly coming from established labs working on established topics. > Q: What skills are needed to do data-intensive research? > A: Smith : Since the data is effectively limitless, the primary bounds need to be imposed by your scientific question. Figuring out which pieces of data need to go next to each other in a table is a good chunk of the hard part of data analysis. Beyond that, you have to know at least the basics of programming, and you have to have enough of a background in math that you have the confidence to figure out some new statistical method or tool that you haven't seen before. But in the end, the only things you really need are a computer, Google, time, and the confidence it takes to figure stuff out. > Q: How do you envision the future of your partnership? > A: Sheltzer : Professionally, we're working on a paper together using Joan's data-analysis ability to parse through gene-expression data from more than 20,000 cancer patients. Personally, I'm hoping that we get a cat soon. [1]: pending:yes