The trees along this city street,
Save for the traffic and the trains,
Would make a sound as thin and sweet
As trees in country lanes.
And people standing in their shade
Out of a shower, undoubtedly
Would hear such music as is made
Upon a country tree.
Oh, little leaves that are so dumb
Against the shrieking city air,
I watch you when the wind has come,—
I know what sound is there.
— Edna St. Vincent Millay
Nature Biotechnology Cover
11 April 2022, Issue 40, Volume 4
contents
During the design process, there's usually an early sketch phase — you try out various ideas and see which one sticks. For this design, I was lucky to skip this step entirely — I knew exactly what I wanted to do. It was just a matter of translating the vague vision in my head onto the page.
First thing I did was to assign each top-level cluster a number. I knew that eventually I wanted to be able to know what went where.
Next, I had to figure out a way to arrange the clusters on the branches of a tree in a way that would (a) fill the page, (b) have smaller clusters at tips of branches, and (c) minimize or eliminate overlap.
But I had to draw the tree first. Below are a couple of sketches. Branches are positioned to have the tree wrap around the title of the journal.
Once the clusters were placed in the tree, more adjustments needed to be made. Moving the clusters around so that everything looked “just so” took quite a bit of time.
The week of nudge.
I probably spent half a week 3 days moving the clusters around on the page.
By this point, I was pretty happy with the layout, but the branches seemed a bit sparse and much much too straight.
So, I had to get quite friendly with Illustrator's width tool, adding bends to the branches and variation to their width.
I also experimented with another color scheme that felt more summery.
You'll also notice that the boundaries of the clusters are no longer round — a littee smooth roughening grows a long way.
I created to sets of images. One had a black branch and wound up having a nighttime feeling — the clusters looking like lanterns.
And the other set having a light and airy (and almost underwater) feeling with white branches. I particularly like the light outlines around the clusters
These 8 candidates were submitted to the journal.
The editor selected the bottom left option from the light series.
A few final tweaks to the editor's selection saw a little more branch detail and contrast.
Below, I walk you through all the elements of the final cover image.
Nasa to send our human genome discs to the Moon
We'd like to say a ‘cosmic hello’: mathematics, culture, palaeontology, art and science, and ... human genomes.
Comparing classifier performance with baselines
All animals are equal, but some animals are more equal than others. —George Orwell
This month, we will illustrate the importance of establishing a baseline performance level.
Baselines are typically generated independently for each dataset using very simple models. Their role is to set the minimum level of acceptable performance and help with comparing relative improvements in performance of other models.
Unfortunately, baselines are often overlooked and, in the presence of a class imbalance5, must be established with care.
Megahed, F.M, Chen, Y-J., Jones-Farmer, A., Rigdon, S.E., Krzywinski, M. & Altman, N. (2024) Points of significance: Comparing classifier performance with baselines. Nat. Methods 20.
Happy 2024 π Day—
sunflowers ho!
Celebrate π Day (March 14th) and dig into the digit garden. Let's grow something.
How Analyzing Cosmic Nothing Might Explain Everything
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.
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.”
voids
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
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).
constellation figures
stars
cosmology
Error in predictor variables
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.
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.