Isn’t it always the way? You have amazing results, but you can see your colleagues’ eyes glaze over when you try to explain it to them. Why not try to present your data in a visually appealing way, and make sure all eyes are on your work? 

 1.     Make the data speak for itself

When you start to think about visualising your data, try to make them as standalone as possible. If you are presenting the work – for example, on a poster at a conference – make sure the visualisation is clear and comprehensible, so that people can grasp the concept without you needing to stand there and explain it.  

2.     Ain’t nobody got time for that!

One thing you have to realise – people want information, and they want it fast! They’re not going to read the captions, they’re not going to read all the beautiful text you’ve written, so the more you can put directly on the visualisation to help people understand it, the better.

3.     Drama, darling!

When you start talking about creating illustrations for more broad communication other factors come into play – use dramatic elements, make it eye-catching, appeal to human emotion, make it relatable and appealing, or possibly even controversial! It needs to stir emotions!

4.     Determine your target audience

Obviously if you’re going to publish in a scientific journal it’s really important to be accurate, because you’re trying to communicate with peers who have a similar level of knowledge to you. If you’re on the front page of the New York Times it’s probably more important to engage people and get people interested.

5.     Understand the concept

If you’re looking at complex multivariable relationship start by looking at the individual variables, and make sure that you understand what’s going on at a low level before you try and do something more complex.

6.     Don’t skip the planning phase

Decide on the concept. Sketch your plan. Draw a storyboard. Record narration if required. Once these processes are done you can move onto the design, and then we go into the design, modelling and animation process – depending on which medium you’ve chosen for your visualisation.

7.     Find patterns
By visualising biological data, scientists can see patterns. Find these patterns and make them stand out, and in doing so you’ll be able to better communicate your ideas to others and get them excited about your science.

8.     Filter, map and render
There are 3 main steps to getting your work visualised:

• First you filter the data to find exactly what you need
• Then you map – this might be working out how the data corresponds to the spatial layout of the visualisation
• Then it’s time to render – this is how you then encode the change or the signal on that map you have created.

9.     Keep it simple
Don’t try to put too much information in. Think about what needs to be removed to keep the message as concise and impactful as possible. It’s more important to get people excited about what you’re trying to show them than to convey every last detail 100% correctly.

10.  Determine your software
There are a number of tools out there that you can use to look at different types of data. Having visualisations that are done in Keynote or PowerPoint can be just as good as long as you know they’re useful.

Graphics programs such as the Adobe Illustrator Suite enable us to create a wide range of things. An excellent tool for scientists to create visualisations is a software program called R. It’s a programming language and an environment for interactive data science and data

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 Get inspired!

Check out these pages for great visualisation!

https://vizbi.org/Posters/
https://beatascienceart.com/

Original video with Janet Iwasa, Hadley Wickham, Seán O’Donoghue and James Proctor