How to… Graphical Abstracts

In my visual communication classes students increasingly ask about graphical abstracts. Below I summarized a few key points I taught recently:

What are Graphical abstracts?

Graphical abstracts are increasingly common to explain biomedical concepts and research results. “Summary slides” have been for long been used in talks or lectures. Today, graphical abstracts are omnipresent a thumbnail previews in online publications, and are also used in posters, on lab websites, and in research grant applications.

The key element of every graphical abstract are pictograms-like visualizations or icons. With text and arrows, the pictograms are then arranged into a sequential narrative or ‘story’. A consistent color scheme and clear layout help to orienting the audiences. Below are a few quick suggestions to help you design a graphical abstract quickly.

One main message

Before starting the design process, spend a good amount of time brainstorming the key message to get across. I personally do this by doodling on paper and discussing with peers. Without a clear main message, it will be impossible to design a good graphical abstract.

Pictograms as visual elements

Pictograms have long been used in science and in the early 20th century, Otto Neurath and Gerd Arntz started to systemically designed icons for communicating data to broad audiences. The past decade saw an explosion of new pictograms such as emoticons in social media.

A new resource for pictograms is fontawesome, a unicode-based icon library that can be installed locally as a font. The font can then be used in e.g. PowerPoint or Illustrator to directly “write” pictograms. Alternatively, pictograms can be accessed online and downloaded (svg, png). A larger collection is available at the Nounproject. Here, designers can upload icons for re-use with attribution. Scientific pictograms for free re-use are collected at the EBI reactome icon library. This site allows upload of user-designed pictograms for sharing with the scientific community.

In a graphical abstract, the pictograms used should have a similar overall appearance. Ensure that colors, line widths, and level of detail are comparable in all used icons. Best practice would be to use pictograms from one designer or one source only. And: start your own personal collection, chances are you might need them again!

Pictogram examples
All pictograms used have similar overall appearance (color, size, design)
Pictograms in use
Bad combination of pictogram, all pictograms have different appearance

Layout: Dimensions

Layout describes the organization of visual elements on the page. First, consider the dimensions of your page: a graphical abstract for a journal website most often is square, while rectangle stretching across the entire page might be a better use of space for a graphical abstract in a grant application with limited word number.

Dimensions for Graphical Abstracts
Dimensions for Graphical Abstracts: square is often required by journals and works well online. Rectangle is easier for slides, posters etc. Adapt dimension when including Graphical Abstract in text.

Layout: Reading direction

The layout should provide a clear entry into the graphical abstract and a clear end. Typically, we read from left to right, and top to bottom. The visual elements should be arranged along the chosen reading direction.

For depiction of linear processes, which have a clear beginning and end, organization from left to right is most suitable: time is usually shown as the independent variable on the x-axis f graphs. Linea processes may be procedures, such as a methodical pipeline, or cellular events such as cell division, embryo development, or disease progression. For depiction of cyclic events, for example daily, annual or metabolic processes, consider a circular layout; for static events, e.g. contrasting two scenarios or providing two levels of details for one scenario, consider two parallel or nested organization.

Layout Options for Graphical Abstracts
Different layouts for Graphical Abstracts that have a clear start and end.


Arrows (and lines) have several roles in graphical abstracts. First, arrows reinforce a reading direction that is already visually defined by a layout, or point out an exception from the reading direction. Second, arrows often indicate motion: a molecule passes a membrane, a cell migrates into a tissue, animals flock to food source. And third, arrows are used for labeling structures or regions of interest. Here, the arrow may be replaced by a simple line. Depending on the arrow head, the meaning can also change to showing an inhibition or forking etc.

It is important to clearly signal to the audience the intention of an arrow and, if two types of arrows are used in parallel, to contrast them visually. Note how changing the context of an arrow can also change its perceived meaning.

Different arrow types and arrow usage in Graphical Abstracts
Different arrow types and arrow usage in Graphical Abstracts


Understanding of complex scenarios is easiest when text and visual are used in synergy (Mayer RE 2002; Hegarty, 1993). First, text is used to substitute for pictograms where these are no available (e.g. specific molecules: ‘acetylcholine’). Second, text also serves to label pictograms that are otherwise ambiguous (e.g. a circle for ‘cell’, ‘bacteria’, ‘nucleus’), Third, text also enforces the meaning of an arrow: an upward arrow could indicate ‘move up’, ‘increase’ or ‘good’, or a circular arrow could be day, year or life cycle. Last, text often provides further explanations. Here it is critical that the it remains short and without jargon and sparse abbreviations.


As in all visualizations, colors are used in graphical abstracts to highlight and contrast, to encode numerical data, or to show the natural appearance of a visualized object. It is key to use colors consistently. A change in color is perceived as a change in meaning. Also use color sparsely as color always draws attention of the audiences, and might eclipse the key take home message of the graphical abstract.

For picking harmonious colors schemes use e.g. Colors schemes can be based on adjacent colors to appear harmonious or on complementary colors to contrast scenarios.

Color usage in Graphical Abstracts
Color can highlight, encode numbers, or show natural appearance in Graphical Abstracts. Careful with color choice when using a background color!

Making of… Tools!

Graphical abstracts can, like a poster, be prepared with vector-design software (Illustrator, Inkjet, CorelDraw) or software for preparing slides (Powerpoint, Keynote). In both cases, pictograms can be included as images (png, tiff) or .svg files. allows a web-based, drag-and-drop design of slides with a harmonious overall layout and biomedically relevant pictograms. For an annual fee, users can export graphical abstracts/figures in publication quality resolution.

Finish by…

Design is an iterative process of adjusting and assessing. A common problem in graphical abstracts is an unclear reading directions (Hullman and Bach): assess if your graphical abstract support a visual hierarchy with text, lines, and arrows. Often, elements are not connected to the rest of the graphical abstract, which forces readers to guess. Confusing also arises from inconsistent visual style: are your pictograms similar in detail? Do arrow with the same meaning have same appearance? Are colors used sparsely and consistently?

As always, source feedback from colleagues, ask them to tell you back what they see!


Inspiration: a lovely hand-drawn visual abstract:

Examples are also provided in the author guidelines by Elsevier: Graphical abstracts (2016).


Tversky B. Lines, Blobs, Crosses and Arrows: Diagrammatic Communication with Schematic Figures. In: M. Anderson, P. Cheng, and V. Haarslev (Eds.): Diagrams 2000, LNAI 1889, pp. 221-230, 2000. Springer-Verlag Berlin Heidelberg.

Hullman J and Bach B. Picturing Science: Design Patterns in Graphical Abstracts. In: P. Chapman et al. (Eds.): Diagrams 2018, LNAI 10871, pp. 183–200, 2018.

Hegarty, M., Just, M.A.: Constructing mental models of machines from text and diagrams. J. Mem. Lang. 32, 717–742 (1993)

Mayer, R.E.: Multimedia learning. Psychol. Learn. Motiv. 41, 85–139 (2002)

20 Toes, 4 Feet, 2 Artists, 1 Passion: Life in numbers

English translation of 20 Zehen, 4 Füße, 2 Künstler, 1 Passion: Life in numbers

Our life can be described with numbers and visualized in diagrams. However, every mean and every chart are simplifications. Useful and fascinating, but also obstructive and restrictive.

Dresden premiere

Yesterday we experienced a wonderful visualization of two “lives in numbers” in the Festspielhaus Hellerau. The two choreographers and dancers Katia Manjate and Anna Till meet in the Dresden premiere of “Life in numbers”. Four years ago, the artists began to compare their lives on the basis of statistics and through these got to know each other. This gave the impulse to “Life in numbers”, and so the piece begins. With a comparison of life in Dresden, where Till lives, and Maputo, where Manjate lives. In the course of the 8 parts, we get to know key data, which are translated into dance on a Cartesian coordinate system, with movements along the invisible x- and y-axes. The dancers themselves form the data points, always in relationship with each other, but changing, converging, and diverging in the course of time.

Differences and similarities

In the middle part of the piece we turn away from the mean values and towards individual data points. No longer: “How long does a woman live in Germany and one in Mozambique” is discussed, but Till and Manjate ask themselves directly: “How old are you?“. The confrontational, curious questions are staged like a duel and bathed in bright light. The shadows of the bodies are projected into rectangles formed by the spotlights, thus visualizing our thinking in boxes. The audience waits with great anticipation for the next answer.

In the third part the tension is released, the light becomes warm and the dance playful. The artists now begin a joyful confrontation with their differences and celebrate their similarities. This is supported by music and rhythms from both countries.

Numbers are beautiful

In the end, Till asks, can we live without numbers? “Life in numbers” answers “no”, but also shows the life within numbers. And it demonstrates that numbers are more than economic values. Numbers may describe how often we laugh per day, how long we see the sun, and what rhythms feet can dance.

As a scientist, I avoid 3D diagrams because they distort data. With “Life in numbers” you can experience how dance, a three- or even four-dimensional visualization, can make numbers directly experienceable and fascinating.

The performance can be seen today and tomorrow in Dresden-Hellerau

19th/20th of October at tanzhausnrd ( and then in Maputo!


20 Zehen, 4 Füße, 2 Künstler, 1 Passion: Life in numbers

Tanz als Visualisierung

Unser Leben kann mit Zahlen beschrieben und in Diagrammen visualisiert werden. Jeder Mittelwert und jede Visualisierung sind jedoch immer Vereinfachungen. Nützlich und faszinierend, aber auch behindernd und einengend.

Premiere in Hellerau

Gestern konnten wir im Festspielhaus Hellerau eine wunderbare Visualisierung von zwei „Leben in Zahlen“ erleben. In der Dresden Premiere von „Life in numbers“ treffen sich die beiden Choreografinnen und Tänzerinnen Katia Manjate und Anna Till. Vor 4 Jahren begannen die Künstlerinnen ihre Leben anhand von Zahlen zu vergleichen und sich so kennenzulernen. Dies gab den Impuls zu „Life in numbers“, und so beginnt auch das Stück. Mit einem Vergleich der Leben in Dresden, wo Till lebt, und Maputo, wo Manjate lebt. Im Laufe der 8 Teile lernen wir so zunächst Eckdaten kennen, die tänzerisch auf einem kartesischen Koordinatensystem umgesetzt werden, mit Bewegungen entlang der unsichtbaren x- und y-Achsen. Die Tänzerinnen bilden hier selber die Datenpunkte, immer in einem Verhältnis zueinander, aber im Laufe der Zeit sich verändernd, konvergierend, divergierend.

Unterschiede und Gemeinsamkeiten

Im mittleren Teil des Stücks wenden wir uns von den Mittelwerten ab und den individuellen Datenpunkten zu. Nicht mehr: „Wie lange lebt eine Frau in Deutschland und eine in Mosambik?“ wird erörtert, sondern Till und Manjate fragen sich direkt: „Wie alt bist Du?“. Die konfrontativ-neugierigen Fragen werden wie ein Zweikampf inszeniert und in grelles Licht getaucht. Die Schatten der Körper werden in Rechtecke projiziert, die von den Scheinwerfern gebildet werden und visualisieren damit unser Denken in Kästen. Das Publikum wartet mit der Fragenden höchstgespannt auf die nächste Antwort.

Im dritten Teil löst sich die Spannung, das Licht wird warm und der Tanz spielerisch. Die Künstlerlinnen beginnen nun eine freudige Auseinandersetzung mit ihren Unterschieden und feiern ihre Gemeinsamkeiten. Dies wird von Musik und Rhythmen aus beiden Ländern unterstützt.

Zahlen sind auch schön

Am Ende fragt Till, können wir ohne Zahlen leben? „Life in numbers“ antwortet „nein“, zeigt aber auch das Leben in den Zahlen. Und es zeigt, dass Zahlen mehr sind als ökonomische Werte. Zahlen können auch beschreiben, wie oft lache ich pro Tag, wie lange sehe ich die Sonne, und welche Rhythmen können meine Füße tanzen.

Als Wissenschaftler vermeide ich 3D-Diagramme, weil sie Daten verzerrt zeigen. Bei „Life in numbers“ kann man erleben wie Tanz, eine drei- oder sogar vierdimensionale Visualisierung, Zahlen ganz unmittelbar erlebbar und faszinierend machen kann.


Die Vorstellung ist heute und morgen noch in Dresden-Hellerau zu sehen (

Am 19. Und 20. Oktober im tanzhausnrd ( und danach in Maputo!

Viel Spass!


Publikumsgespräch mit der Programmleitung und den Künstern

Misleading bar charts #3

We keep discussing axis layouts and the problematic cases of non-zero baselines (in bar charts). Here is another example from the city of Dresden. Dresden is a really pretty place and it is always worth coming for a visit. With the below chart, the city wanted to showcase that each year new record tourist numbers are recorded.

Dresden tourist numbers from 2010-2014.

Truthful bar chart

Now, since this isn’t the first time we discuss baselines, you should immediately spot that a rise from around 200,000 to 300,000 isn’t even close to a tripling of number as the bar length visually suggests. And overall, the bar-length does not actually even represent the increase at all. It rather seems that their lengths were chosen to fit an imaginary linear increase. I re-plotted the bar chart with a zero-baseline. Lo-and-behold, the rising number of tourist is still visible, but clearly not nearly as record-worthy.

Truthful bar chart reporting Dresden tourist data.

Really truthful bar charts

For each chart, we should not only think about baselines but ask: where do the numbers come from? And, do we see the complete dataset? In this case, the data was collected by the city of Dresden, which should be a reasonably good source for basic statistical data (all data is here:

The image however circulated in 2017 – three years after the last data-point shown! Now, if you know that since the end of 2014 Dresden is plagued by very prominent weekly demonstrations of right-wing activists, having no data after 2014 is alarming. In the local science and business community the problems are very evident: we have a clear drop in international scientists applying and accepting jobs in the city! I therefore went to the Dresden city website to get the data for the subsequent years and this confirmed what I suspected: tourist numbers no longer rose, instead, they even dropped!

Dresden tourists 2010-2018. Show all the relevant data – leaving out years might be misleading.

Line or bar chart?

Time trends are usually more  visible in line charts. Indeed, the drop of tourist numbers since 2014 is very apparent in a line chart, and even more so when we leave out the zero-baseline, which somewhat flattens the data (note: in line charts leaving out zero-baseline is ok and sometimes even necessary!).

Fun with Excel

And, did you know you can use a picture as the background for your chart in Excel!?!








Misleading bar charts #2

Bar charts encode categorical values by length. By comparing bar lengths, we can visually compare the category sizes.

When a bar is truncated due to a missing zero-baseline or an interrupted y-axis, the relative size difference between the bars changes. Now, the bars no longer visually encodes the actual category value. (Read more from a previous blog)

Misleading bar chart

A now almost classic example in DataViz for a misleading bar chart

The above is a DataViz classic. FOX NEWS  reported an (to them: alarming) increase in Obamacare enrollments over a few days with a bar chart. They apparently feared imminent bankruptcy of the USA and therfore save the nation by overemphasizing the increase with truncated bar. Instead of the moderate ~20% rise (6 to 7 milllion), their bar showed a 300% increase in length!

Non-misleading representation

I quickly re-designed the chart in Excel. The increase is still clearly visible, even with a zero-baseline.

Bar_v2 Same data with zero-baseline. Lower: adjusted axis labeling.

Line or bar chart?

Alternatively, FOX NEWS could have simply used a line chart. Indeed, temporal changes are easier to understand in line charts: our eyes can now follow an upward (increase), or downward (decrease) line. Note: a line chart for 2 time points only is known as a slope chart! 

Even better for FOX NEWS: omitting the zero-baseline is possible in line charts as they only communicate relative changes and focus on the trend and not absolute numbers (more here).

Alternative for temporal data: line chart/slope chart

Slopechart focusing on the increase from March 27 to March 31. (H/T Holger Brandl.)


Never trust the default!

On a side-note: the default bar chart generated by Excel for this data IS A BAR WITHOUT A ZERO-BASELINE! I am in shock (and obviously don’t use Excel very much)!

So maybe, just maybe FOX NEWS simply used Excel default settings??? Reader, beware: never ever trust a default setting!

Excel defaults sometimes are misleading!