Gender bias in science is persistent and pervasive. Data sugest that male scientists are more likely to be hired, to be invited to talk, to act as reviewers, and to be highly cited. We are interested in quantifying this bias and understanding its causes and consequences. There are a number of potential projects, including but not limited to:
1. Gender bias at major scientific conferences
2. Gender bias in scientific citation networks
Data analysis and scripting in R will be required for both projects, so you must be willing to learn the basics (we can teach you!). The second project will require more programming experience, specifically to deal with complex APIs and extract data from a variety of poorly formatted XML files. For the second project, you should have a proficiency in or a willingness to learn Python.