I attended the ASM Microbe 2016 meeting in Boston this past weekend. It was my first big, national convention, and as a first-time attendee, the experience was overwhelming at times. It's a perpetual buffet of new material, new connections, new approaches, and free pens.
Though I genuinely enjoyed seeing hundreds of research posters and talking with the researchers behind them, the event I found most relevant to long-term career choices was a session on "Unique Perspectives on Science Communication". Speakers included genomics researcher and Canadian TV host Jennifer Gardy, journalist Maryn McKenna, and data artist Jer Thorp. I don't usually feel inspired by short talks - at least a couple of these speakers have given TED talks of various types before - but this session helped me realize something I'm passionate about: extracting something visually meaningful from otherwise impenetrable data sets.
I believe any field of science can benefit from more effective communication. Too often, researchers - and I'm counting myself in this group - assume that our results must be relevant because our data sets are large, or barring that, we simply expect our results to be self-explanatory. I don't think these expectations are helpful: novel results and conclusions require novel approaches to communication. I'm not saying that every scientific paper needs to have its own Snapchat account but I am saying that incomprehensible results can ruin even the most carefully-designed project.
So, with that in mind, my posts here will likely take on more of a data visualization flavor. I'll provide some simple tutorials and examples of approaches I've found helpful. I hope someone else finds it helpful, too.