In the process of designing my Snellen Eye Chart typographical posters, I came across the Snellen font by Andrew Howlett. I wasn't happy with all the letters, so I made attempts at giving the font an update.
Not being a font designer, I will likely get myself into trouble.
While making my Snellen chart series, I entered the rabbit hole of optotype fonts ... and I can't get out!
The charts don't necessarily use the latest version of my Snellen font design, which fluctuates as my mood about some of the letters changes.
The optotype requirement is that letters be designed on a 5 × 5 grid, and have constant stroke width. This means that both lower and upper case letters need to share the grid and stroke. To stay compatible with the eyechart paradigm, letters should be as obvious as possible.
Lorrie Frear's article What are Optotypes? Eye Charts in Focus is a great read about optotypes and eye charts.
The uppercase letter design uses Herman Snellen's original chart as inspiration.
I have modified the design by Andrew Howlett (see below) for some letters. All the changes are relatively minor: more serifs and consistent stroke width for bars on R and K.
The lowercase characters should be considered experimental.
The progress of my redesign is shown below. I would greatly appreciate feedback and suggestions!
The distribution contains both Andrew's version and my redesign.
v7.1 4-jun-2018 — Download Snellen optotype font
Tidied all letter forms with Fontlab 6.
Fixed g and e. Thanks to Makeesha Fisher for suggestions.
Adjusted serifs on f, j, l, o, t to extend the full width of the grid. Added a lot more symbols.
Added lowercase, digits and symbols.
I'm exploring the lowercase characters. I don't know what I want to do with them. Make this into a more standard font in which lowercase letters are smaller, so that letters can fit their roles clearly when text is set in sentence case, or fill out the full optotype grid.
Flushed out some inconsistencies in the uppercase characters. Added serifs to more letters.
Now all the letters occuppy the full 5 × 5 grid, including the I, whose serifs were widened to allow this. While this new uppercase I isn't as pretty as the old one, it makes the entire typeface more consistent to its optotype roots.
Still struggling with the G. In the original version, the descending stroke was cut off in the middle of a grid, which I didn't like.
The S has been fixed—thanks to Elanor Lutz for feedback.
I've color coded the characters slightly differently, drawing attention to ones that I feel need more thought.
The lowercase characters aren't color coded (yet) because ... most of them need help. Primarily, I'm vacillating between making them fill the full size of the 5 × 5 square, just like the uppercase characters, and keeping them confined to a 4 × 4 square, which incurs loss of legibility. If I make the letters the same size, it will be impossible to distinguish lowercase and uppercase characters some cases (e.g. c, i). Perhaps this is desired?
First attempt at lowercase characters.
Outliers can degrade the fit of linear regression models when the estimation is performed using the ordinary least squares. The impact of outliers can be mitigated with methods that provide robust inference and greater reliability in the presence of anomalous values.
We discuss MM-estimation and show how it can be used to keep your fitting sane and reliable.
Greco, L., Luta, G., Krzywinski, M. & Altman, N. (2019) Points of significance: Analyzing outliers: Robust methods to the rescue. Nature Methods 16:275–276.
Altman, N. & Krzywinski, M. (2016) Points of significance: Analyzing outliers: Influential or nuisance. Nature Methods 13:281–282.
Two-level factorial experiments, in which all combinations of multiple factor levels are used, efficiently estimate factor effects and detect interactions—desirable statistical qualities that can provide deep insight into a system.
They offer two benefits over the widely used one-factor-at-a-time (OFAT) experiments: efficiency and ability to detect interactions.
Since the number of factor combinations can quickly increase, one approach is to model only some of the factorial effects using empirically-validated assumptions of effect sparsity and effect hierarchy. Effect sparsity tells us that in factorial experiments most of the factorial terms are likely to be unimportant. Effect hierarchy tells us that low-order terms (e.g. main effects) tend to be larger than higher-order terms (e.g. two-factor or three-factor interactions).
Smucker, B., Krzywinski, M. & Altman, N. (2019) Points of significance: Two-level factorial experiments Nature Methods 16:211–212.
Krzywinski, M. & Altman, N. (2014) Points of significance: Designing comparative experiments.. Nature Methods 11:597–598.
Celebrate `\pi` Day (March 14th) and set out on an exploration explore accents unknown (to you)!
This year is purely typographical, with something for everyone. Hundreds of digits and hundreds of languages.
A special kids' edition merges math with color and fat fonts.
One moment you're
:) and the next you're
Make sense of it all with my Tree of Emotional life—a hierarchical account of how we feel.