#427: quantum of sollazzo – 22 June 2021
The data newsletter by @puntofisso.
Other than my month notes, I don’t blog that often anymore. Last week, I published a blog post and, unexpectedly, it received quite a bit of love on social media. The article, The top 9 lessons I’ve learned at work, was about 10 years in the making: it’s the result of dozens of notes I’ve gathered in a text file with lessons I had learned about difficult situations at work. Feedback and comments are very welcome.
Among a few things I really liked this week: the University of Sheffield’s researcher Colin Angus, one to watch for this newsletter’s reader base, explains why he always releases his R source code alongside his charts, making a cogent argument for openness and transparency as a way of learning more and transferring knowledge.
Take a look at We All Count, a project to further equity in data science.“This project has a place for you in it if you want to work towards a world where data science is good, and good for everyone.”.
I absolutely love their call to arms “Demystify. Democratize. Demonstrate.”, which strongly resonates with what I’m trying to do in my day job.
By the way, I’m resurrecting my Etsy OpenStreetMap-powered print shop. Each map is pretty much a work of love, with a lot of time spent making it look good, both on my side and the printers. More map styles and locations will be added soon, and I accept requests.
And… shhhh… don’t forget that newsletter readers have an exclusive 10% discount on all products by using this link (or writing “NEWSLETTER” in the coupon code while placing an order).
‘till next week,
Six questions to... Miriam Quick & Duncan Geere
Miriam Quick is a Freelance Data Journalist and Researcher; Duncan Geere is a Freelance Information Designer. Together, they are the creators of the data sonification podcast Loud Numbers. They are on Twitter as @duncangeere and @miriamquick.
What is your daily data work like and what tools do you use?
DG: To make sonifications for Loud Numbers, we use Google Sheets to wrangle data, Sonic Pi to turn it into music, and then Logic Pro to turn that music into a track. For the scripts, we use Google Docs to write, Zencastr to record remotely, Descript to edit, and Logic again to sequence the episodes. For general comms and file-sharing we use Slack, Google Drive and Dropbox.
MQ: We start with a dataset that tells a strong story, then we create code sketches in Sonic Pi where we trial different ways of mapping the data to sound. We then export the audio from these into Logic Pro, adding effects and other sonic layers until the track sounds like music. We’re faithful to the dataset, though: once we’ve set up a sonification system we stick with it.
Tell me about a data project that you're proud of...
MQ: My illustrated book with Stefanie Posavec, I am a book. I am a portal to the universe. It makes data tangible for an all-ages audience. The direct precursors to Loud Numbers were Oddityviz, a David Bowie dataviz collaboration with Valentina D’Efilippo, and Sleep Songs – my first attempt at turning data into music.
DG: Does Loud Numbers count? 😅 If not, I’m very proud of the work I did last year for the Drawdown Review - visualizing the landscape of solutions to climate change. I’m also proud of the work I’m doing on Car Free Cities for climate charity Possible.
...and a data project that someone else did and you're jealous of.
DG: I really love The Conditional Orchestra by Richard Bultitude, which generates procedural ambient music that reflects the weather conditions where you are, or at any other point on the Earth. It’s particularly impressive because it maps so many different variables but still creates something interesting and beautiful from it.
MQ: For the concept, Ekene Ijeoma’s Deconstructed Anthems, which sonifies US mass incarceration data by removing notes from the Star Spangled Banner in a live jazz performance. I love the idea that silence can tell a more powerful story than sound. For sheer gorgeousness, Nathan Ho’s Venn 7 – a tool that turns all the overlaps in seven-way Venn diagrams into lush harmonies. I play with it often. 😀
If I say "dataset", you think of...
DG: Tempo, amplitude, volume, pitch and distortion.
MQ: This artist. A mate of mine was really into them back in the 2000s.
Give someone new to data a tip or lesson you wish you'd learned earlier.
DG: When sonifying data, don’t be afraid to add extra elements that are purely musical and not attached to the data. A simple drum loop, for example, can provide a sense of time passing in the same way that an axis would in a visualization, as well as making your sonification more enjoyable to listen to.
MQ: Less is often more with data sonification. In our first attempt at our launch track, based on climate change data from Alaska, we tried to sonify as many data layers as possible at once – like, six or seven or something. It didn’t work. We remade the track with just three data layers and it worked much better. Even one can be enough.
Data is or data are...
MQ: Data is!
DQ: Data is. This is a hill I will gladly die on.
Young, Indian, Unvaccinated
“How the ‘world’s largest vaccination campaign’ faltered, leaving some 600 million people aged between 18 and 45 years scrambling for COVID-19 shots.“
An in-depth article by Reuters Graphics, with a worrying look at the class/income divide.
Do women who pose with their art on Reddit get more upvotes?
What a world we live in, if questions like these need to get asked. But they do need to get asked. Erin Davies looks at this phenomenon.
“Seemingly half the commenters claim her art is only getting popular because she posed with it, and the other half of commenters are defending her right to do so.“
Daily estimates of British voting intention from 1955 to the present day
A pre-print academic paper by Jack Bailey and colleagues at the British Election Survey. The TL;DR is in this twitter thread.
How Wales leads the world in the Covid-19 vaccine race
“Nearly 85 per cent of Welsh adults have received at least one vaccine dose.”.
An outstanding result, as shown in figures from the New Stateman’s Nick Ferris.
Non-white footballers played better when stadiums were empty during the pandemic
“White players fared worse, implying that racist heckling may have an impact”.
Who’d have thought, eh? But it’s good to see the topic of racial abuse finally in the mainstream, and excellently visualized by The Economist. This topic and analysis also makes me think of a book recommendation: the series “What a Carve Up! – The Rotters’ Club – The Closed Circle” by Jonathan Coe.
Tools & Tutorials
“Turn (almost) any Python 2 or 3 Console Program into a GUI application with one line”.
This is pretty clever.
Game of Life
“…: is it possible to use Datawrapper to create Game of Life? Turns out that it is, although it involves a bit of coding and using the Datawrapper API.“
When Graphs Are a Matter of Life and Death
“Pie charts and scatter plots seem like ordinary tools, but they revolutionized the way we solve problems. […]
Almost no one looks at that chart and asks to see the seventeen missing data points“
In case you missed it, this article on the New Yorker by Hanna Fry is a must read.
Bring Back the Weekly Newspaper
“In fact, I was really not fond of the election forecasts leading up to the election. They are model-heavy and data-light in comparison with real-time calculators like the needle.“
And a few more quotable excerpts in this issue of Zeynep Tufekci’s newsletter.
There was quite a bit of discussion about Gini’s Coefficent recently. I happened to come across these two bits of writing about it, one pro-Gini and one against-Gini. Both have their own merits.
Dataviz & Interactive
Seeing How Much We Ate Over the Years
“The United States Department of Agriculture keeps track of food availability for over 200 items, which can be used to estimate food consumption at the national level.“
FlowingData’s Nathan Yau uses the data to show how much the average American’s diet has changed since the 1970s.
All the passes
“A visualisation of 882,536 passes from 890 matches played in various major leagues/cups” made using Three.js and StatsBomb on Observable.
The data is available under a sort of permissible licence.
Grand Slams by age
Including a reference to that Djokovic incident.
Support this newsletter & spread the word
Become a GitHub Sponsor. It costs about the price of a coffee per month, and you’ll get an Open Data Rottweiler sticker (and other stuff). Or you can Buy Me A Coffee.
quantum of sollazzo is also supported by ProofRed’s excellent proofreading service. If you need high-quality copy editing or proofreading, head to http://proofred.co.uk. Oh, they also make really good explainer videos.