Where am I supposed to go? Where was I supposed to know?get lost in questionsmore quotes

vizbi: fun

In Silico Flurries: Computing a world of snow. Scientific American. 23 December 2017

Visual Design Principles—Communicating Effectively

This talk happened on Thursday, Mar 21st 2013 at VIZBI 2013 at the Broad Institute in Boston.

How often people speak of art and science as though they were two entirely different things, with no interconnection. An artist is emotional, they think, and uses only his intuition; he sees all at once and has no need of reason. A scientist is cold, they think, and uses only his reason; he argues carefully step by step, and needs no imagination. That is all wrong. The true artist is quite rational as well as imaginative and knows what he is doing; if he does not, his art suffers. The true scientist is quite imaginative as well as rational, and sometimes leaps to solutions where reason can follow only slowly; if he does not, his science suffers. —Isaac Asimov (The Roving Mind)

For more visualization and design resources, see my VIZBI 2012 tutorials, Nature Methods Points of View column, and rant about colors.

Do not allow encoding or other design choices to hijaack your message. Each element on the page must meaningfully contribute to your figure.

presentation video

The video will be posted at vizbi.org.

presentation slides

Slides are available as PDF and keynote (zipped).

1/144

A poet is, after all, a sort of scientist, but engaged in a qualitative science in which nothing is measurable. He lives with data that cannot be numbered, and his experiments can be done only once. The information in a poem is, by definition, not reproducible. He becomes an equivalent of scientist, in the act of examining and sorting the things popping in [to his head], finding the marks of remote similarity, points of distant relationship, tiny irregularities that indicate that this one is really the same as that one over there only more important. Gauging the fit, he can meticulously place pieces of the universe together, in geometric configurations that are as beautiful and balanced as crystals. —Lewis Thomas (The Medusa and the Snail: More Notes of a Biology Watcher)

breakout session—making good figures

Sketch notes by the inimitable Francis Rowland from our breakout group. The question was: what do you need to make good figures? (PDF)

small, medium and big data visualization

If you're asking how to visualize big data, first make sure you're doing a good job on small and medium data. Each scale requires good design.

Do not expect to use one way
to tell many stories

Also consider that there is a very large number of combinations of data sets, hypotheses and possible patterns. Because of this, you cannot expect to use one way to tell many stories. There is no Holy Grail of big data visualization. But there are many good questions to ask and practices to follow that make up a process which can help you get there.

Medium data visualization. This is what happens when you show the data (a strategy that works for small data), instead of explaining it. Yup, we need to work on this too. (A) Qi X et al. J Biotech 144:43 (2012) (Saturation-Mutagenesis in Two Positions Distant from Active Site of a Klebsiella pneumoniae Glycerol Dehydratase Identifies Some Highly Active Mutants) (B) Alekseyev, M.A. et al. Genome Res 19:943 (2009) (Breakpoint graphs and ancestral genome reconstructions)
Big data visualization. Yes, data sets are growing but are visual and cognitive abilities are not. There are many data sets, each requiring its own approach. You cannot use one way to tell many stories. Lewis SN et al. PLoS ONE 6:e27175 (2011) (Prediction of Disease and Phenotype Associations from Genome-Wide Association Studies)
VIEW ALL

Find and snap to colors in an image

Sat 29-12-2018

One of my color tools, the $colorsnap$ application snaps colors in an image to a set of reference colors and reports their proportion.

Below is Times Square rendered using the colors of the MTA subway lines.

Colors used by the New York MTA subway lines.

Times Square in New York City.
Times Square in New York City rendered using colors of the MTA subway lines.
Granger rainbow snapped to subway lines colors from four cities. (zoom)

Wed 19-12-2018

Drugs could be more effective if taken when the genetic proteins they target are most active.

Design tip: rediscover CMYK primaries.

More of my American Scientific Graphic Science designs

Ruben et al. A database of tissue-specific rhythmically expressed human genes has potential applications in circadian medicine Science Translational Medicine 10 Issue 458, eaat8806.

Predicting with confidence and tolerance

Wed 07-11-2018
I abhor averages. I like the individual case. —J.D. Brandeis.

We focus on the important distinction between confidence intervals, typically used to express uncertainty of a sampling statistic such as the mean and, prediction and tolerance intervals, used to make statements about the next value to be drawn from the population.

Confidence intervals provide coverage of a single point—the population mean—with the assurance that the probability of non-coverage is some acceptable value (e.g. 0.05). On the other hand, prediction and tolerance intervals both give information about typical values from the population and the percentage of the population expected to be in the interval. For example, a tolerance interval can be configured to tell us what fraction of sampled values (e.g. 95%) will fall into an interval some fraction of the time (e.g. 95%).

Nature Methods Points of Significance column: Predicting with confidence and tolerance. (read)

Altman, N. & Krzywinski, M. (2018) Points of significance: Predicting with confidence and tolerance Nature Methods 15:843–844.