Gender & Scientific Communities

The project

For DATUM in 2018, I used JSTOR article metadata to explore patterns of collaboration across academic fields. The network visualisations make relationships between authors visible at a scale that is difficult to read in a table. Patterns of association can raise questions about representation and collaboration, but do not by themselves establish the causes of a disparity.

Original project notes

Hidden in plain sight

I’ve often heard people in academia say “They won’t support my research article, they’re a boys club” or similar statements. By looking at patterns of how men and women collaborate within academic fields (globally), I was able to highlight not only clear gender biases among certain research networks at universities, but also the level of inter-connectedness.

I would absolutely love to do more on this topic.

gigantic and tricky datasets

These JSTOR journal meta-datasets were probably the largest and most convoluted datasets that I’ve worked with to date. Each area of research or faculty consisted of hundreds of thousands of journal articles. What was great was that it taught me a lot about how to use SQL queries more efficiently.