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    • Not your imagination at all! That project was especially fun because we were faced with a genetics problem (too much relatedness in my dataset to use statistical methods that assume everyone in an analysis is unrelated), and I got to reach back into my memory of a graph theory course from when I was an undergrad majoring in math: how do you solve the problem of finding the maximum unrelated set in genetics? Mathematicians had already solved the problem of finding maximum sets- all we had to do was know the solution existed and adapt it for genetics! This is why it is so often useful for scientists to be interdisciplinary. The best geneticists I know come from other fields- physicists, anthropologists, and computer scientists often make fabulous geneticists! At the same time, because the scale of data we work with has grown so rapidly, developing resources for the necessary tech has been essential in my field. This trend is becoming more and more widespread- digital imaging of brains and retinas, proteomics, the microbiome, metabolomics- all are driving their respective fields into a space where tech and data analysis are going to be essential. An old mentor of mine used to say that he had enough data on his computer already to spend the rest of his life analyzing... but of course that doesn't mean we won't keep generating more! My field has been developing strategies for this tidal wave of data for more than a decade, and we have made a lot of mistakes on the way! I hope other fields are able to take lessons from genetics as they too become inundated with data and the accompanying tech necessary to handle it.