How to visualise biological data

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

 Get inspired!

Check out these pages for great visualisation!


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


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7 ways to illustrate your work to broaden its impact

The effective visualisation of your results and ideas improves the discoverability, accessibility and impact of your work.

As visual culture and science historian Geoffrey Belknap concluded in an essay for Nature last year: “The visual continues to work as a foundation for making sense of data. The tools, as we have seen, have radically changed. The power of images has not.” (

Condensing your key findings into simple, visually appealing illustrations and infographics allows you to share your work on more diverse platforms and with more diverse audiences, increasing the reach and impact of your science.

Whether you work in collaboration with a graphic designer, or whether you apply key principles of design to your communication yourself, the following aspects are important for the effective and compelling visual presentation of data and ideas.

1. Tell a story through design

Humans seem almost programmed to connect with stories on both an intellectual and emotional level. As such, stories are powerful tools for communication. When creating an illustration or infographic, think about what story you want to tell and how it will engage your audience: What ‘characters’ (in this context, usually: genes, cells, pathways, diseases etc.) should be the focus of the story? Who is the story for and what interests them? Can you connect your story to their interests? What emotions do you want them to feel? The answers to these questions will help you focus on the aspects of your discovery that need to be prioritised and how to visualise them.

2. As complex as necessary, as simple as possible

If you are creating an infographic for specialists in your own field, then you can use specialist language and make assumptions about the prior knowledge of your audience. The further away your audience is in terms of expertise or experience from your peers, the more background and context you will need to provide, and the simpler your language and the concepts you illustrate will need to be.

3. Conceptualising is exploring, so draw sketches first

Just as you might conduct exploratory experiments before committing to a research approach, explore your storyline from different angles to see what works for you, your message and your audience. This is done most effectively by sketching ideas by hand in black and white. Experience shows that using software for this exploration can be distracting, either because the tools are not intuitive, or because the colours and options available in software steal focus from the goal: to find the compelling visual idea.

4. Loop the loop to refine your ideas

Think of the design process as moving forward in loops, rather than as a straight line. When you feel like you have a good idea, revisit it and ask yourself: Are all the elements shown key to the story, or can I leave some out? Simplifying means that you will communicate more clearly and that your audience will more quickly understand what is presented.

5. Design tools: use them effectively by using them sparingly

Once you are happy with your concept sketch, it is time to draw the final artwork. In your concept sketch, you laid out all the elements and probably already made some decisions about sizes and composition. You will now make additional choices about fonts and colours. As tempting as the numerous options might be, try to be restrained in your choices to ensure the graphic is clear and legible: One font with four font faces (regular, italic, bold, bold italic) and two or three colours initially are often sufficient to distinguish elements. You can always add more colour later if necessary, and starting out simple helps you to not clutter your illustration. Revisiting your artwork frequently helps you to keep it as simple as possible and as complex as necessary.

6. Plan your media strategy ahead

A lot of time will go in developing the idea for your graphic and drawing the actual artwork. Spend some time early on to think about how you can best use the same artwork across multiple communication channels. Different media require different sizes and file formats. To cater for this variety, draw you artwork using vector-based software like Adobe Illustrator or Inkscape. Vector-based illustrations, unlike pixel-based illustrations, can be scaled up or down without any quality-loss.

7. Attention is a limited resource

Thousands of articles, ideas and information are communicated daily, so people browse content quickly. If your graphic is eye-catching and easy to understand at a glance, it will both draw your reader’s interest to know more, and give them the key message about your findings in only a few seconds.

Sandra Krahl runs a course for EMBO Solutions on Applying Design Principles to Schematic Figures for scientists – for more information and to register, visit

Original video with Tabea Rauscher, Design Team Lead at EMBL, and Sandra Krahl, EMBO Alumna and Senior Graphic Designer and Illustrator

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Generating meaningful images – a report from Seeing is Believing 2019

By event reporters Liz Haynes @actin_crazy and Stephan Daetwyler @Daetwyler_St

Seeing is Believing event reporters Liz Haynes & Stephan Daetwyler, PHOTO: Liz Haynes/Stephan Daetwyler

The field of biology owes some of its most compelling discoveries to careful visual observation. From Van Leeuwenhoek’s use of new microscopes to describe microscopic “animalcules” in the late 1600s, to Ramon y Cajal’s pioneering 19th century work illustrating beautiful and complex neuronal architecture. Images inspire us, help us generate new hypotheses, and shed light into the tiny worlds yet unexplored. Indeed, these observations uniquely help us understand the structures and dynamics of life, something that would not be achievable with approaches like biochemistry alone.

The images are only as valuable as the amount of information that we can deduce from it.

Generating meaningful images, however, is not an easy task. There have always been limits to what we can observe, due to the properties of the sample or the techniques that we can apply to it. These are the boundaries that microscopists seek to push. A successful imaging experiment requires an amenable sample, a contrast agent to reveal the structures of interest, and a microscope that is capable of capturing an image at a relevant scale. Moreover, the images are only as valuable as the amount of information that we can deduce from it. Therefore, image storage, accessibility and analysis are crucial. Each one of these steps offers opportunities for optimisation and new technologies.

Co-organiser Jan Ellenberg opens the Seeing is Believing symposium, PHOTO: Liz Haynes & Stephan Daetwyler

The EMBO | EMBL Symposium “Seeing is Believing: Imaging the Molecular Processes of Life” (9-12 October 2019) presented us with exciting new developments in all of these fields, coupled with a drive to make new progress available as quickly as possible to the community through preprints, open-source initiatives, and resource sharing.

Advances in sample preparation

At the heart of every imaging approach is the sample. Even the best microscope is ineffective with dim or improperly prepared samples. At Seeing is Believing, we saw an emphasis on using expansion of samples to help overcome the resolution limits of microscopy and solve some traditionally difficult problems. In particular, we were impressed with expansion-based approaches to study centriole structure (Paul Guichard, Ultrastructural Expansion Microscopy) and resolve microtubules tightly packed within axons (Lukas C. Kapitein). By far, the biggest emphasis in sample improvement was on the development of new fluorescent probes and biosensors. Kai Johnsson presented design strategies for the improvement of live cell dyes, and introduced new MaP dyes that are SNAP and HALO compatible, and importantly require no wash to clear unbound probe. Periklis Pantazis presented a mechanosensor based on the Piezo1 stretch activated ion channel, allowing users to visualise mechanical stress within a live cell. Atsushi Miyawaki wowed the audience by meeting the challenge to “be better than a firefly” with a new variant of luciferase named AkaBLI, which his lab generated through targeted evolution. This improved luciferase allowed them to visualise neuronal activity within freely behaving mice and marmosets.

Advances in microscopy

New imaging methods on show at Seeing is Believing, PHOTO: EMBL Events

The features of our microscopes directly determine which questions we can address. Seeing is Believing highlighted exciting new development in building cutting-edge microscopy tools. Reto Fiolka presented a novel single-objective light-sheet microscope enabling imaging of live cells in microfluidics devices or 3D environments with 200 nm lateral resolution. Kevin Dean complemented novel light-sheet development by presenting an axially swept light-sheet microscope ideally suited for all clearing techniques that provides an unprecedented field of view enabling whole tissue imaging with sub-micron resolution. With her imaging approach, Alexandra Pacureanu surprised the audience with how X-ray holographic nano-tomography is capable of resolving the fine, dense and complex neuronal circuitry in large tissues or even organism providing a new route to understand how the nervous system processes information.

Nobel Prize winner Stefan Hell spoke on how to attain 1 nm resolution with super-resolution microscopy, PHOTO: Liz Haynes & Stephan Daetwyler

Further impressive advances were presented in fast volumetric imaging (Lars Hufnagel, light field imaging) and high-resolution imaging, e.g. MINFLUX by Stefan Hell, correlative EM imaging by Harald Hess and Lucy Collinson, GI-SIM/LLS-SIM by Dong Li, and 3D-STED deep in a tissue by Joerg Bewersdorf.

Advances in data analysis

All acquired data is meaningless if we cannot extract information from it. At Seeing is Believing, it became obvious how artificial neuronal networks have become important for image analysis. Applications range from segmentation to denoising an image (BGnet, W.E. Moerner and Noise2Void, A. Krull/Florian Jug). Particularly, the convolutional network architecture U-Net has become an important tool. To provide a user-friendly environment to apply those state-of-the art image analysis tools, Anna Kreshuk presented the iLastik platform as an easy to use tool. A new fundamental approach to handle, visualise and process the large amount of data coming from the microscopes was presented by Ivo Sbalzarini. Instead of using pixels to save an image, adaptive particles approximate the image content. Furthermore, Gaudenz Danuser gave a thought-provoking talk on how current perturbation-based approaches in cell biology can mislead us in our analysis. Danuser emphasised that the observed phenotype from a perturbation of a system (e.g. loss of a protein’s function) is not equal to the real function of the gene. For example, cutting a wire from the battery to the electronic board of radio would lead to the “phenotype” loss of sound. However, the function of the wire was simply to provide power to the radio, not to produce sound! As a better perturbation-free alternative, Danuser introduced a concept used in econometrics known as Granger causality.

Advances in biology

All of these new developments culminated in impressive new insights into biological processes. There were many talks on mitochondria and endoplasmic reticulum dynamics revealed by novel live-cell super-resolution techniques. Suliana Manley gave one of the most intriguing of those talks, on modes of asymmetric and symmetric mitochondrial division.

Co-organiser Jennifer Lippincott-Schwartz presents how RNA moves around the cell and is translated at different locations, PHOTO: Liz Haynes & Stephan Daetwyler

Jennifer Lippincott-Schwartz also gave a stunning presentation on how RNA granules can hitch a ride through an ANXA-11 mediated connection to lysosomes, and how ALS associated mutations in ANXA-11 break this connection. Furthermore, an intriguing new mRNA reading frame sensor (Moon and Sun tags) was presented by Sanne Boersma of the Tanenbaum lab to understand stochasticity of mRNA translation.

To conclude, the field of microscopy has grown so much that some may feel we have solved all the theoretical problems, and only engineering challenges are left – hardware improvements, new materials, new engineering solutions. At the closing dinner of the conference, however, Atsushi Miyawaki from RIKEN beautifully summarised how he felt about the future of microscopy, and of Seeing is Believing. Standing in the banquet hall of the Heidelberg Castle, he told us that castles in Japan remain unfinished. This state of incompletion is not due to any fault of the architects, but a feature of beauty, as it was believed that things that were incomplete had room to grow, and that growth is valuable. No matter how high our achievements are in the field of microscopy and image analysis, there will always be unforeseen avenues of growth. Attending Seeing is Believing has hopefully prepared us to follow those avenues, and to share what we find so we may all grow together.

For a more comprehensive summary of all talks presented at Seeing is Believing, and to get links to preprints, publications, and resources, visit our blog at

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