How to create a graphical abstract
Graphical abstracts are becoming more and more important. Journal publishers such as Elsevier encourage you to create a concise visual summary of the main findings of your research.
But where to start? What steps should you follow to create the perfect graphical abstract for your article? What tools can you use?
On this page, you’ll find an overview of tips, tools and inspiration to create your own graphical abstract for your next article. Let’s show the world what your research is all about!
Digital tools to create graphical abstracts
You can use almost any graphical tool you want to create a graphical abstract. You could even use just pen and paper, if you like – the story is the most important part of the visual, not the artistic quality. Use the tool you know best, or feel the most comfortable with!
Here is a list of tools to get you started:
- Adobe Illustrator: adobe.com
- Affinity Designer: affinity.serif.com
- BioRender (for life sciences): biorender.com
- Canva: canva.com
- Diagrams: diagrams.net
- Infogram: infogram.com
- Microsoft PowerPoint
- Paint.NET: getpaint.net
- Piktochart: piktochart.com
- Venngage Infographic maker
- Whimsical: whimsical.com
Illustrations and icons to use in infographics
- Figshare (plants and more, for biologists): figshare.com/authors/Plant_Illustrations
- Freepik: freepik.com
- SMART (life sciences): smart.servier.com
- The noun project (icons): thenounproject.com
Photos to use in graphical abstracts
- Burst: burst.shopify.com
- Cathopic (catholic pictures): cathopic.com
- Flickr: flickr.com/
- Picography: picography.co
- Pixabay: pixabay.com
- Reshot: reshot.com
- Unsplash: unsplash.com
- Visual Hunt: visualhunt.com
Color schemes for graphical abstracts
- Adobe Color CC: color.adobe.com
- Coolors.co: coolors.co
- Pictaculous: digitalinspiration.com/color-palette-generator
- Website color schemes: canva.com/learn/website-color-schemes
- 100 brilliant color combinations: canva.com/learn/100-color-combinations
Creating graphs and maps for graphical abstracts
- Choose and design the perfect chart: chart.guide
- Choosing the right chart: infogram.com/page/choose-the-right-chart-data-visualization
- D3.js: d3js.org
- Dataviz catalogue: datavizcatalogue.com
- Datawrapper: datawrapper.de
- Flourish: flourish.studio
- LocalFocus: localfocus.nl
- Mapbox: mapbox.com
- Mapme: mapme.com/stories
- Snazzy maps (Google maps styles): snazzymaps.com
Books that can help you create better visuals
- Infographic designers’ sketchbooks, Steven Heller & Rick Landers
- Storytelling with data, Cole Nussbaumer Knaflic
- Trees, maps and theorems, Jean-Luc Doumont
- Visual thinking, Willemien Brand
People on Twitter talking about information design
- Twitter list: Dataviz world leaders
Blogs about information design
- Chartable (Datawrapper)
- Cool Infographics (Randy Krum)
- Flowing Data (Nathan Yau)
- Infographics for the People (John Grimwade)
- The Information is Beautiful blog (David McCandless)
- Junk Charts (Kaiser Fung)
- Nightingale (on Medium, mostly paid articles)
- The Storytelling with Data blog (Cole Nussbaumer Knaflic)
- The Visme blog on infographics
- Visualising Data (Andy Kirk)
Wrapping up
Did we miss something? Which indispensable resource did we miss? Make sure to let us know at koen@baryon.be!
Also, feel free to check out our other resource guides:
Read more:
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Fonts evoke emotions: there are very sophisticated fonts, playful fonts, attention-grabbing fonts, and elegant handwritten fonts. Using the wrong type of font can have a lot of impact. In data visualization the implications of typography are mainly focused on readability. Labels and annotations can easily become so small they get hard to read. Above all else, we should choose a font which is readable at small sizes.
Three roles of colour in a data visual
Colour is one of the most crucial tools we have to turn a normal chart into a powerful chart with a clear message, a chart which tells a story rather than simply presenting the information.
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A crucial step in building a powerful chart is choosing the right type of chart. A lot of charts don’t work because they simply use the wrong type of chart. To avoid this trap, we must ask ourselves a basic question: what’s the ultimate goal of our data visual? What do we want to show with our data?
Making a data visual noise-free
Removing noise from a data visual is not only about taking things away such as gridlines, axes or legends. That’s just one part of it, which we could call removing physical noise. Improving the signal-to-noise ratio is often also about adding little things that help our audience better understand the visual. We are helping them by removing mental noise, or mental barriers.
Three tips to create powerful charts in Excel
Creating charts in Excel can be a very powerful tool for making sense of complex data sets, and for visualizing them. But the default options are not always the most pretty or effective ones. Here are our top three tips to create better Excel charts.
A powerful chart has a high signal-to-noise ratio
‘Less is more’. It’s a crucial principle in most of our communication, and in data visualization in particular. Because of my background as a physicist, I prefer to talk about the ‘signal-to-noise ratio’. The message - our signal - should be amplified as much as possible, giving it all of the attention. Everything that can distract from our message - the noise - should be removed.
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