Jan-Willem Tulp pointed out
this amazing tool to choose a color palette: https:// colorcurves.app
You can choose between either a continuous palette or a discrete palette, with groups that is.
Here’s an example of an exponential color curve for a continuous palette using
There are numerous functions you can use to make your “
gradient color curve“.
Similarly, you can specify the lightness of the different colors along your curve.
Here’s another example, of an color arc for a categorical / discrete palette using
My colleague prof. Jack van Wijk pointed me towards these great guidelines by Deloitte on how to design an effective dashboard.
Some of these rules are
more generally applicable to data visualization. Yet, the Deloitte 10 commandments form a good checklist when designing a dashboard.
interpretation of the 10 rules:
Know your message or goal Choose the chart that conveys your message best Use a grid to bring order to your dashboard Use color only to highlight and draw attention Remove unneccessary elements Avoid information overload Design for ease of use Text is as important as charts Design for multiple devices (desktop, tablet, mobile, …) Recycle good designs (by others)
In terms of recycling the good work by others operating in the data visualization field, check out:
I just love how Deloitte uses example visualizations to help convey what makes a good (dashboard) chart:
Screenshot from the Deloitte slidedeck
Screenshots from the Deloitte slidedeck
Screenshot from the Deloitte slidedeck
I came across another great set of curated resources by one of the teams at Google:
. People + AI Guidebook
The People + AI Guidebook was written to help user experience (UX) professionals and product managers follow a
human-centered approach to AI.
The Guidebook’s recommendations are based on data and insights from over a hundred individuals across Google product teams, industry experts, and academic research.
These six chapters follow the
product development flow, and each one has a related worksheet to help turn guidance into action.
The People & AI guidebook is one of the products of the major
(People & AI Research). PAIR project team
Here are the direct links to the six guidebook chapters:
Links to the related worksheets you can find
on Schwabisch recently proposed ten guidelines for better table design.
Next to the
academic paper, Jon shared his recommendations in a Twitter thread.
Let me summarize them for you:
Right-align your numbers Left-align your texts Use decimals appropriately ( one or two is often enough) Display units (e.g., $, %) sparsely (e.g., only on first row) Highlight outliers Highlight column headers Use subtle highlights and dividers Use white space between rows and columns Use white space (or dividers) to highlight groups Use visualizations for large tables
Highlights in a table. Via twitter.com/jschwabish/status/1290324966190338049/photo/2
Visualizations in a table. Via twitter.com/jschwabish/status/1290325409570197509/photo/3
Example of a well-organized table. Via twitter.com/jschwabish/status/1290325663543627784/photo/2
Every now and then, Twitter will offer these golden resources.
Ashley Willis recently asked people to name
and the results are a resource I don’t want to lose. the best tech talk they’ve ever seen
Hundreds of people responded, sharing their contenders for the title.
Below, I selected some of the top-rated talks and clustered them accordingly. Click a category to jump to the section.
Big Idea & Programming Meta-Talks
The Future of Programming
Growing a Language
The Mess We’re In
Making Users Awesome
Ethical Dilemmas in Software Engineering
Adding Eyes to Your Test Automation Framework
TATFT – Test All The F*cking Time
How we program multicores (erlang)
Y Not- Adventures in Functional Programming (Ruby)
Core Design Principles for Software Developers
Design Patterns vs Anti pattern in APL
Containers & Kubernetes
The Container Operator’s Manual
Write a Container in Go From Scratch
Container Hacks and Fun Images
Kubernetes and the Path to Serverless
Let’s Build Kubernetes, With a Spreadsheet and Volunteers
Cover image via:
I really like
generative art, or so-called algorithmic art. Basically, it means you take a pattern or a complex system of rules, and apply it to create something new following those patterns/rules.
When I finished my PhD, I got
a beautiful poster of where the k-nearest neighbors algorithms was used to generate a set of connected points.
Marcus Volz’ nearest neighbors graph, via https://marcusvolz.com/#nearest-neighbour-graph
My first piece of generative art.
As we recently moved into our new house, I decided I wanted to have a brother for the knn-poster. So I did some research in algorithms I wanted to use to generate a painting. I found some very cool ones, of which I unforunately can’t recollect the artists anymore:
Note: these are NOT mine
However, I preferred to make one myself. So we again turned to the work of the author that made the knn-poster:
He has written (in R) many
other algorithms. And we found that one specifically nicely matched the knn-poster. His metropolis – or generative city:
Marcus’ generative city, via https://marcusvolz.com/#generative-city
However, I wanted to make one myself, so I download
Marcus code, and tweaked it a bit. Most importantly, I made it start in the center, made it fill up the whole space, and I made it run more efficient so I could generate a couple dozen large cities quickly, and pick the one I liked most. Here’s the end result:
And in action, in my living room:
You can find my code here on