Hey Blopig Readers,
I had the privilege to go down to Heidelberg last week to go and see some stunning posters and artwork. I really recommend that you check some of the posters out. In particular, the “Green Fluorescent Protein” poster stuck out as my favourite. Also, if you’re a real Twitter geek, check out #Vizbi for some more tweets throughout the week.
So what did the conference entail? As a very blunt summary, it was really an eclectic collection of researchers around the globe who showcased their research with very neat visual media. While I was hoping for a conference that gave an overview of some of the principles that dictate how to visualise proteins, genes, etc., it wasn’t like that at all! Although I was initially a bit disappointed, it turned out to be better – one of the key themes that were re-iterated throughout the conference is that visualisations are dependent on the application!
From the week, these are the top 5 lessons I walked away with, and I hope you can integrate this into your own visualisation:
- There is no pre-defined, accepted way of visualising data. Basically, every visualisation is tailored, has a specific purpose, so don’t try to fit your graph into something pretty that you’ve seen in another paper. We’re encouraged to get insight from others, but not necessarily replicate a graph.
- KISS (Keep it simple, stupid!) Occam’s razor, KISS, whatever you want to call it – keep things simple. Making an overly complicated visualisation may backfire.
- Remember your colours. Colour is probably one of the most powerful tools in our arsenal for making the most of a visualisation. Don’t ignore them, and make sure that they’re clean, separate, and interpretable — even to those who are colour-blind!
- Visualisation is a means of exploration and explanation. Make lots, and lots of prototypes of data visuals. It will not only help you explore the underlying patterns in your data, but help you to develop the skills in explaining your data.
- Don’t forget the people. Basically, a visualisation is really for a specific target audience, not for a machine. What you’re doing is to encourage connections, share knowledge, and create an experience so that people can learn your data.
I’ll come back in a few weeks’ time after reviewing some tools, stay tuned!
