Visualizing scientific data in a clear and compelling way can be hard, as the underlying matter is complex and time often is short. The “SPACE AHEAD” acronym helps conceptualise the desired chart and gives pointers on where/how to optimise the design and accessibility. This first part of the series can be used as a go-to checklist, when you’re not happy with the default settings of your software and don’t know where to start. I’ll dive into more details in the following posts.

space ahead

November 14, 2023

“Space ahead”. AI generated image using Midjourney with the prompt: ‘An adventurous astronaut in a space suit, seen from the back with a light angle. He is pointing his finger, as if he was pointing to a distant aim, where he aspires to go. In the background there are stars, planets and a few distant galaxies. The style should be a retrowave, synthwave album cover.’

Visualizing Scientific Data

I recently got the opportunity to host a workshop on datavisualization for my peers/colleagues at the 5th annual conference of the “junge Humangentik”. For this opportunity I put together an acronym that served as framework for the workshop. The letters also serve as a checklist applicable to most situations where you need to visualise complex data to convey a clear message: SPACE AHEAD.

During the workshop we covered all of the points below with many examples from “real world” sicientific publications. Discussing good/bad examples and sharing knowledge was a great experience.


While I think that ‘SPACE AHEAD’ is applicable to most occasions of data visualization, I will focus on the context of scientific communication, e.g. in papers, conference posters or talks.


SPACE AHEAD stands for…

General structure and concept


Adjust the level of detail and complexity to the occasion
Primary Message
Focus on the most important aspect
Adapt to the knowledge and experience of the target audience
Use colors deliberately, e. g. to highlight the primary message or to link data/groups across different charts. Avoid color if it’s not needed.
Consider combining sub-plots instead of overloading one single complex visual.
Use safe color palettes & direct labels, avoid distortion, and more
Emphasize the primary message or other relevant aspects
Eye movement
Reduce unneccessary eye movements, put data/information where readers expect them to be
Arrange axes to best use the space, arrange data to support the primary message
Remove redundant information and visual distractions

Read more on each aspect in this series of follow-up posts.

Choosing a Chart

The available chart types definitely depend on the type of data, you are dealing with. In many cases there is, however, more than one way to visualise the same data.1 So, before choosing a chart type, I recommend thinking about “Setting”, “Primary Message” and “Audience” at least. If you find yourself stuck meeting all requirements and struggle to include all relevant data, consider splitting the chart into complementary sublplots: an “Ensemble”.
Once, you’ve made up your mind about these points, go ahead and select the best-fitting chart(s):

  • If you’re looking for pointers or want to go beyond “the usual boxplots”, take a look at Data to Viz by Yan Holtz and Conor Healy. Their flow-chart helps finding a fitting chart type. Furthermore, they showcase and discuss possible alternatives for given data types.
  • Another great source is the visual vocabulary by the Financial Times team. They focus on what you want to say / show2 and suggest possible charts for this need.

Credits and Citation

The above mentioned ideas and concepts to improve data visualizations are well known and were not invented by me. You can find them in many places around the internet and in many good books on the matter. Hence, I often cannot cite specific sources on single recommendations above3, but I’ll leave a list of resources below, that proved helpful in preparing the workshop and that might be of interest to you, to “drill down” on specific items.

Building on that knowledge, I developed the acronym to help improve data visualizations in a structured approach. If you consider ‘SPACE AHEAD’ a useful tool or checklist, feel free to use it for your scientific dataviz projects or to educate others. I’d be happy if you gave credits and linked to my website or this post.

And, of course, let me know in the comments below if you have any feedback or suggestions, or you can send me a message e.g. here. Thanks!

Further Reading and Inspiration

To read more about each of the points above, have a look at the follow up posts in this blog.

I can also highly recommend reading these resources, if you want to dig even deeper into some of the aspects:

  • “Best Practice for Data Visualisation” by the Royal Statistical Society, written by Andreas Krause, Nicola Rennie and Brian Tarran July 2023.
  • “The Science of Visual Data Communication: What Works” by Franconeri et. al. 2021
  • Paper worth reading about color vision / perception, save color palettes and common problems of color use in scientific publications, see Crameri et. al. 2020
  • another interesting paper on psychological aspects of perception and cognitive processing of visual information in scientific charts, by Harold et. al. 2016
  • Book “Powerful Charts - The Art of Creating Clear, Correct and Beautiful Data Visuals” by Koen Van den Eeckhout (ISBN 978-9463937290)
  • Great collection of articles by Lisa Charlotte Muth on different dataviz topics. Regarding color use, I can especially recommend this and this
  • A great presentation / slide deck on colors and annotations by Cara Thompson
  • You can try to “consume” data visualizations more consciously in your every day life: If you intuitively like or dislike a visualization e.g. in a paper, pause for a second and try to figure out what made you feel that way. This can inform your future choices in the charts you will create yourself.
    A simple but great “tool” is to “X-RAY” data visualizations, as described by Alli Torban in this ~7 minute Episode of Data Viz Today
  • If your dataviz-tool is PowerPoint or Excel4, you might want to look into RAWGraphs
    • It’s a web app in your browser with an intuitive interface, easy to use and open source / free
    • It has a large selection of professional chart types, customizations and options.
    • Although it runs in the browser, all data is being processed locally on your computer, so they are not sent to a server. For details about this see the RAWGraphs authors’ publication: Mauri, M., et. al. (2017)


  1. These guys went all-in and visualized 1 dataset with merely 6 datapoints in 100 different ways↩︎

  2. aka the “Primary Message”↩︎

  3. I did wherever possible↩︎

  4. which is totally fine, use what you feel comfortable with!↩︎



BibTeX citation:
  author = {Gebhard, Christian},
  title = {Mind Your {SPACE} {AHEAD}},
  date = {2023-11-14},
  url = {https://christiangebhard.com/posts/2023-10-space-ahead},
  langid = {en}
For attribution, please cite this work as:
Gebhard, Christian. 2023. “Mind Your SPACE AHEAD.” November 14, 2023. https://christiangebhard.com/posts/2023-10-space-ahead.