This is bad statistics and very weak evidence of anything. I feel sad that this kind of thing is promoted by the media.
The authors brag about the "largest study to date" in the abstract without recognizing that the effect size is very minor and the bias, which does not depend on sample size, could be enormous. There are many sources of possible biases: small % who could be assigned a gender, the reliability of the gender assignment itself, the representativity of GitHub, the p-hacking, lack of controlling for confounders, you name it.
The weaknesses are listed only in the appendix, but hey, the media folks only read the abstract. Morally, if you want to even publish such weak evidence you should make that clear at the beginning. But of course, who cares about disseminating bad information the popularity is worth it.
FYI, when you do causal analysis with observational data (that is, gender causes pull acceptance or discrimination), you need to do a good job controlling for confounders. Who knows, for example, if woman are not more educated? With more experience?
Showing only the top of the bar in the graph, without starting from zero is a good artifact for those who can only find very small, irrelevant effects, and need therefore to focus on them. Fortunately people are becoming more aware of that, but not the media, not those without statistical knowledge that will believe and spread any garbage posing as science.
The small confidence intervals, and statistically significant results, caused by large sample size, focus on the irrelevant sampling error and dismiss other errors and biases, already mentioned. If your data covers the population badly, is not reliable, sampling error becomes less important compared to other errors and small effect sizes may be just biases.
Please, don't make yourself more important than the science and the information that others rely on. If you want to report weak evidence, make sure that is clearly stated.