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science + communication
Complaining about ineffective graphics is easy — fixing them takes time and love. After receiving a migrane-inducing graphic, I set out on a mission to tell the story of the data — obesity patterns around the world.
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The Nature Methods Points of View column column offers practical advice in design and data presentation for the busy scientist.

Effective poster design for science communication

Guidelines to get you started and keep you going

Martin Krzywinski / Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Guidelines for telling your research story—sober design, typography and data visualization tips all in one place with a minimum of fuss (v1.4 14 Jul 2020). (Download PDF template)

1 · Gimmicks and cheap tricks

There are no shortcuts—good explanations take effort. There are plenty of gimmicks and cheap tricks that masquerade as solutions—reject them.

Instead, seek out strategies that adopt the categorical imperative. If everyone used this strategy, would the (poster) world be a better place? A conference hall full of 300 QR-code posters? No thank you.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
There are many gimmicks and cheap tricks that masquerade as solutions—reject them.

2 · Poster design guidelines

The poster design guidelines are flexible. They are built on the concept that less is more. However, sometimes a little bit more is actually more. Think before you draw and adjust the design to the themes of your story.

See how the guidelines can be applied to posters in the wild by visiting the poster hospital.

# 1
Poster child of science.

A poster is your first opportunity to organize and communicate your reasearch to members outside of your lab. It will help you to practise telling and “drawing” your science story and its design should be based on its concepts, themes and transitions.

The poster is a prop—not a paper. In most settings, you will be there to present it. Match its content to the story you will tell.

Most posters are bad not because they are ugly (they are) but because they fail to present concisely what was done and, more importantly, why it was done.

Most posters have too much content, presented too flatly. Less is more: get to the point, then stop.


Detail on demand, not by default.


Curate your content, narrate and explain—don't dump.


Control your volume of speech and restrain exuberance, folly and whimsy.

2.1 · Storytelling

# 2
All science deserves excellent explanations—explain quickly and clearly.

Motivate why the work was done. What is the cost of not doing it?

State your hypothesis and conclusions clearly and early. Connect them with the fewest steps required for a first explanation.

# 3
Establish a story path and stick to it.

The reader won’t know what is important, so tell them.

Saliently and intuitively code key contrasts (e.g. healthy/disease, wildtype/mutant).

Good explanations are ones conveniently placed. Embed simple diagrams next to relevant text.

2.2 · Design

# 4
Only you can stop poster dumpster fires.

Clean and consistent design allows for subtle cues to call out important observations and other points of interest.

Clip art, pie charts, bullet points, boxes around text, background fills, textures and gradients. Only you can stop it.

# 5
Maintain good Gestalt grouping.

Create groups to encode real-world relationships and be on the lookout for unintended accidental groupings.


Similar shapes and colors will form groups.


Objects and shapes that are close to one another will form groups.

2.3 · Layout

# 6
Align aggressively.

Alignment of similar quantities subtly suggests where to look next.

Create alignment guides and use them consistently—the eye will find even small misalignments.

# 7
Let content inform layout.

Do not let a template bully you into using a specific column width. Change proportions to suit content.

Be prepared to rewrite. There are many ways to say something and some ways are easier to typeset.

# 8
Separate and organize elements with space.

Space makes groups.

Dividing lines can be effective but more can make the poster look congested.

Hollow boxes are jails and do not distinguish foreground from background.

# 9
Make room for negative space.

Don’t say everything you know. Your most valuable resource is the reader’s time.

Regions of unbalanced negative space are good candidates for annotations, credits, quotes, and other garnish that adds value to the poster. Don’t overdo it—most quotes rehash old tropes. If you must, find something that is passionate and slightly mysterious.

2.4 · Typography

# 10
Use classic fonts and match them based on historical period, family or creator.

Sans-serif is clearer than serif at small sizes and suitable for modest amounts of copy.

Match Helvetica/Minion, Frutiger/Apollo, Gill Sans/Perpetua, Gotham/Mercury, Legancy/Jensen, Syntax/Sabon, Univers/Meridien.

Italicize text with care and look for unintended italics in subscripts.

# 11
Maintain and control proportions.

This poster is 16” × 12” (1152 × 864 pt), uses Helvetica Neue with a 5, 8, 13, 21, 34, 55 pt scale ladder, and is legible on most screens.


A point is a unit of size used in typography. Without a physical size they lose their meaning, but can provide a helpful scale.


Select type sizes from one or a union of two modular scales built on the Golden Ratio (e.g., 55/34 ≈ φ = 1.618...).

Keep line length short and hyphenate instead of fully justifying.

# 12
Use a lead to announce an observation or insightful comment.

Establish subordinate content with italics.

Reserve small text for tangents and detail beyond the first explanation.

Bold caps for panel subtitles

Use typographical garnish sparingly—be creative, but in small steps. A well-placed symbol or label can connect themes or indicate the purpose of text (e.g. triangles suggest a legend).

Align symbols independently of subscripts and superscripts.

# 13
Force line breaks to improve readability.

Split a sentence into noun phrases or offer a natural pause, such as at a comma or a period.

Balance layout by shortening sentences—there are many ways to say something and some ways are easier to typeset.

2.5 · Writing copy

# 14
Everything is important, but some things are more important than others.

Establish a visual hierarchy by emphasizing your hypothesis, conclusion and the key points that connect them.

Relegate protocols, technical methods, and other minutiae to the bottom of the poster.

Always be mindful of what the reader needs to know to understand enough to ask insightful questions and frontload this information.

# 15
Best titles are short conclusions, not long introductions.

Avoid long addressess—no postal codes, no zip codes.

Your work is a “study” and explores a “relationship” to look for an “effect”. Treat that as a given and say what is important.

# 16
Avoid obvious headings such as “references” or "acknowledgements".

Citations can be set in a block of text, with bold numbers like this 1. R. Bringhurst, Elements of Typographical Style. 4th ed (2012) and 2. W. Strunk Jr., Elements of Style (1918). Unless a specific citation style is required, use a compact style that also includes the title.

# 17
Write with a sober and unaffected tone.

Don’t try to be snarky, cheeky or witty—most attempts do not succeed. Don’t trigger the jokers, cynics, cranks and curmudgeons.

Heed Strunk's Elements of Style:


“Make definite assertions. Avoid tame, colorless, hesitating, non-committal language. Use the word not as a means of denial or in antithesis, never as a means of evasion.”


“Use definite, specific, concrete language.”


“Do not overwrite. Rich, ornate prose is hard to digest, generally unwholesome, and sometimes nauseating.”


Be “compact, informative, unpretentious.” and avoid “a breezy style is often the work of an egocentric, the person who imagines that everything that comes to mind is of general interest.”


“Avoid a succession of loose sentences.”

2.6 · Color

# 18
Use color strategically.

Color powerfully classifies content. It is impossible to achieve this if everything is in color or if the poster is agrresively colourfully branded.

A ramp of colors of the same hue (e.g. green, blue) is useful to communicate continuous quantity. Use a single salient color (e.g. orange, magenta) to underscore a key theme, observation or conclusion.

Design for accessibility by colorblind readers.


Do not reuse the same color for a different meaning.


Work in CMYK space and avoid 100% saturated colors.

# 19
Use color for themes or data encoding and not as garnish.

The first color to appear should begin the core story.

Map salience to pertinence. When used in moderation, colors like orange or magenta say “look here”. You cannot look everywhere.

Avoid unintentional emphasis by equalizing for perceptual luminance.


Colored text may help emphasize a theme but use it sparsely.


Round corners slightly for eye comfort.


Extend beyond the frame to imply a crop or continuity.

2.7 · Data visualization

# 20
Use ink sparingly to make compact figures legible.

Dense is not necessarily crowded.

Explain an encoding once and reuse it.

Create a visual key for complex encodings and choose graphical explanations over text.

Use Brewer palettes, even for greys.

# 21
Avoid visual complications that are not relevant

Color blending can create distracting intersections of color.

Superimpose white outlines to emphasize shapes with an opaque fill.

Use multiply blend mode to layer dense data. Hollow points make excellent outliers.

# 22
Use small multiples.

Tabulate plots and text seamlessly with a column or row for explanations.

# 23
Arrows imply a relationship or change.

Do not use them to guide the eye, which can be achieved with spacing and alignment.

2.8 · Figures

# 24
Use figure titles to explain trends, not merely to specify the axes.

Don’t tell the reader what they're seeing: “a linear fit to a scatter plot” is redundant. Explain and interpret the figure instead.

# 25
Establish a visual hierarchy.

Trends and their explanations should be the most salient.

Do not use excess ink on axes and navigation elements.


Use 0.5pt lines for axis lines and ticks.


Avoid dense ticks and tick labels.


Place grids on top with a multiply blend.

Highlight regions of interests with a solid color (or grey), not outlines.

Cue important observations and intervals with arrows or outlines.

# 26
Forego legends in favour of inline explanations.

Embed text and attach labels to data to avoid legends.

Italicize variables in fit diagnostics and use shaded bands for confidence intervals.

Callout lines should be rectilinear or at 45° if the graphic already has such elements.

# 27
Share axes or align panels to clarify variables or emphasize changing scale.

Categorical variables in bar charts do not need an explicit axis. Specify sample sizes and what error bars represent (e.g. standard error of mean, `n = 5`). Report `P`-values with effect sizes or confidence intervals. A statistically significant observation isn’t necessarily of biological interest.

Establish continuity using figures that share an axis. Thresholds that span across panels (dashed lines, not dotted) lead the eye naturally to help tell the data story.

Use grids sparingly. Do not divide the plot more finely than precision allows.

Use grey for baseline, control or reference conditions. Dark grey is easier on the eyes than pure black. Avoid dark bar outlines.

# 28
Reveal qualitative trends in small multiples with order, cutoffs and cues.

Look for opportunities to include key observations and explanations in the figure—don’t leave it to the main text, where it may be far from the graphic. Emphasize what quantities are important—anticipate the reader’s questions and answer them.


Express trends without words and draw attention to important data subsets.


Distribute based on points of interest.


Axis breaks tell a story.

2.9 · Logos

# 29
Balance visual weight and size the logos equally.

If acknowledging institutional support, place it next to the logo.

Use vector-based logos, not low-resolution bitmaps. Do not change logos’ aspect ratio or crowd it with other elements—both are likely against its branding style.

news + thoughts

How Analyzing Cosmic Nothing Might Explain Everything

Thu 18-01-2024

Huge empty areas of the universe called voids could help solve the greatest mysteries in the cosmos.

My graphic accompanying How Analyzing Cosmic Nothing Might Explain Everything in the January 2024 issue of Scientific American depicts the entire Universe in a two-page spread — full of nothing.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
How Analyzing Cosmic Nothing Might Explain Everything. Text by Michael Lemonick (editor), art direction by Jen Christiansen (Senior Graphics Editor), source: SDSS

The graphic uses the latest data from SDSS 12 and is an update to my Superclusters and Voids poster.

Michael Lemonick (editor) explains on the graphic:

“Regions of relatively empty space called cosmic voids are everywhere in the universe, and scientists believe studying their size, shape and spread across the cosmos could help them understand dark matter, dark energy and other big mysteries.

To use voids in this way, astronomers must map these regions in detail—a project that is just beginning.

Shown here are voids discovered by the Sloan Digital Sky Survey (SDSS), along with a selection of 16 previously named voids. Scientists expect voids to be evenly distributed throughout space—the lack of voids in some regions on the globe simply reflects SDSS’s sky coverage.”


Sofia Contarini, Alice Pisani, Nico Hamaus, Federico Marulli Lauro Moscardini & Marco Baldi (2023) Cosmological Constraints from the BOSS DR12 Void Size Function Astrophysical Journal 953:46.

Nico Hamaus, Alice Pisani, Jin-Ah Choi, Guilhem Lavaux, Benjamin D. Wandelt & Jochen Weller (2020) Journal of Cosmology and Astroparticle Physics 2020:023.

Sloan Digital Sky Survey Data Release 12

constellation figures

Alan MacRobert (Sky & Telescope), Paulina Rowicka/Martin Krzywinski (revisions & Microscopium)


Hoffleit & Warren Jr. (1991) The Bright Star Catalog, 5th Revised Edition (Preliminary Version).


H0 = 67.4 km/(Mpc·s), Ωm = 0.315, Ωv = 0.685. Planck collaboration Planck 2018 results. VI. Cosmological parameters (2018).

Error in predictor variables

Tue 02-01-2024

It is the mark of an educated mind to rest satisfied with the degree of precision that the nature of the subject admits and not to seek exactness where only an approximation is possible. —Aristotle

In regression, the predictors are (typically) assumed to have known values that are measured without error.

Practically, however, predictors are often measured with error. This has a profound (but predictable) effect on the estimates of relationships among variables – the so-called “error in variables” problem.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Nature Methods Points of Significance column: Error in predictor variables. (read)

Error in measuring the predictors is often ignored. In this column, we discuss when ignoring this error is harmless and when it can lead to large bias that can leads us to miss important effects.

Altman, N. & Krzywinski, M. (2024) Points of significance: Error in predictor variables. Nat. Methods 20.

Background reading

Altman, N. & Krzywinski, M. (2015) Points of significance: Simple linear regression. Nat. Methods 12:999–1000.

Lever, J., Krzywinski, M. & Altman, N. (2016) Points of significance: Logistic regression. Nat. Methods 13:541–542 (2016).

Das, K., Krzywinski, M. & Altman, N. (2019) Points of significance: Quantile regression. Nat. Methods 16:451–452.

Convolutional neural networks

Tue 02-01-2024

Nature uses only the longest threads to weave her patterns, so that each small piece of her fabric reveals the organization of the entire tapestry. – Richard Feynman

Following up on our Neural network primer column, this month we explore a different kind of network architecture: a convolutional network.

The convolutional network replaces the hidden layer of a fully connected network (FCN) with one or more filters (a kind of neuron that looks at the input within a narrow window).

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Nature Methods Points of Significance column: Convolutional neural networks. (read)

Even through convolutional networks have far fewer neurons that an FCN, they can perform substantially better for certain kinds of problems, such as sequence motif detection.

Derry, A., Krzywinski, M & Altman, N. (2023) Points of significance: Convolutional neural networks. Nature Methods 20:1269–1270.

Background reading

Derry, A., Krzywinski, M. & Altman, N. (2023) Points of significance: Neural network primer. Nature Methods 20:165–167.

Lever, J., Krzywinski, M. & Altman, N. (2016) Points of significance: Logistic regression. Nature Methods 13:541–542.

Neural network primer

Tue 10-01-2023

Nature is often hidden, sometimes overcome, seldom extinguished. —Francis Bacon

In the first of a series of columns about neural networks, we introduce them with an intuitive approach that draws from our discussion about logistic regression.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Nature Methods Points of Significance column: Neural network primer. (read)

Simple neural networks are just a chain of linear regressions. And, although neural network models can get very complicated, their essence can be understood in terms of relatively basic principles.

We show how neural network components (neurons) can be arranged in the network and discuss the ideas of hidden layers. Using a simple data set we show how even a 3-neuron neural network can already model relatively complicated data patterns.

Derry, A., Krzywinski, M & Altman, N. (2023) Points of significance: Neural network primer. Nature Methods 20:165–167.

Background reading

Lever, J., Krzywinski, M. & Altman, N. (2016) Points of significance: Logistic regression. Nature Methods 13:541–542.

Cell Genomics cover

Mon 16-01-2023

Our cover on the 11 January 2023 Cell Genomics issue depicts the process of determining the parent-of-origin using differential methylation of alleles at imprinted regions (iDMRs) is imagined as a circuit.

Designed in collaboration with with Carlos Urzua.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Our Cell Genomics cover depicts parent-of-origin assignment as a circuit (volume 3, issue 1, 11 January 2023). (more)

Akbari, V. et al. Parent-of-origin detection and chromosome-scale haplotyping using long-read DNA methylation sequencing and Strand-seq (2023) Cell Genomics 3(1).

Browse my gallery of cover designs.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
A catalogue of my journal and magazine cover designs. (more)
Martin Krzywinski | contact | Canada's Michael Smith Genome Sciences CentreBC Cancer Research CenterBC CancerPHSA
Google whack “vicissitudinal corporealization”
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