Gender inequity in speaking opportunities at the American 3 Geophysical Union Fall Meeting

Biases—structural, implicit, and explicit—exclude many people from STEM education and employment and devalue their contributions1,2. Most studies focus on bias against women. Few datasets offer enough generalizability or statistical power to evaluate the representation of ethnic and racial minorities, or to examine intersectionality—the compound obstacles that block, for example, a woman of colour in the US. We offer just such a dataset here.