1 dataset. 100 visualizations.
The same small dataset visualised in a hundred different ways, with notes on the strengths and weaknesses of each one.
Link tags: data
360
Capt. Grace Hopper on Future Possibilities: Data, Hardware, Software, and People (Part One, 1982) - YouTube
Wow! Grace Hopper has always been a hero to me, but I had no idea she was such a fantastic presenter. She’s completely engaging, with the timing and deadpan delivery of a stand-up comedian at times.
DOC • The power of beauty in communicating complex ideas
As designers creating images to communicate complex ideas, we rationalize our processes, we bring objectivity to our craft, we want our clients to think that our decisions are based on reasoning. However, we should also defend our intuitions, our subjectivity. We should also defend pursuing beauty as it is one of our most powerful tools.
A microdata enhanced HTML Webcomponent for Leaflet | k-nut — Blog
Here’s a nice HTML web component that uses structured data in the markup to populate a Leaflet map.
Personally I’d probably use microformats rather than microdata, but the princple is the same: progressive enhancement from plain old HTML to an interactive map.
The global fight against polio — how far have we come? - Our World in Data
I think it’s always worth revisiting accomplishments like this—it’s absolutely astounding that we don’t even think about polio (or smallpox!) in our day-to-day lives, when just two generations ago it was something that directly affected everybody.
The annual number of people paralyzed by polio was reduced by over 99% in the last four decades.
Historical Trails
Maggie explores different ways of visualising journeys on the web, including browser histories:
Perhaps web browsing histories should look more like Git commit histories? Perhaps distinct branches could representing different topics and research avenues?
Design Systems Database: Surf among top‑notch Design Systems
A collection of collections, this is a directory of design systems, with the handy option to browse by component type. The blueprints section is still a bit thin on the ground, but likes the most useful bit—an in-depth dissection of individual compenent types.
scottjehl/PE: declarative data binding for HTML
This is an interesting idea from Scott—a templating language that doesn’t just replace variables with values, but keeps the original variable names in there too.
Not sure how I feel about using data- attributes for this though; as far as I know, they’re intended to be site-specific, not for cross-site solutions like this.
Apocalypse-Proof
Back in 2017 when I was in New York, I went on a self-guided infrastructure tour: 32 Avenue of the Americas, 60 Hudson Street, and the subject of this article, 33 Thomas Street. One of my pictures is used to illustrate its creepiness, both in real life and as an evil lair in fiction:
A windowless telecommunications hub, 33 Thomas Street in New York City embodies an architecture of surveillance and paranoia. That has made it an ideal set for conspiracy thrillers.
Counting Ghosts
Analytics serves as a proxy for understanding people, a crutch we lean into. Until eventually, instead of solving problems, we are just sitting at our computer counting ghosts.
This article is spot-on!
I don’t want your data – Manu
I don’t run analytics on this website. I don’t care which articles you read, I don’t care if you read them. I don’t care about which post is the most read or the most clicked. I don’t A/B test, I don’t try to overthink my content.
Same!
The ideal viewport doesn’t exist
Some lovely scroll-driven animations illustrate this great little microsite.
There’s something very pleasy about the chunky design that harkens back to the Zeldmanesque early web.
Fruit Of The Poisonous LLaMA? – Terence Eden’s Blog
I want to live in a future where Artificial Intelligences can relieve humans of the drudgery of labour. But I don’t want to live in a future which is built by ripping-off people against their will.
How to report better on artificial intelligence - Columbia Journalism Review
- Be skeptical of PR hype
- Question the training data
- Evaluate the model
- Consider downstream harms
Will GPT models choke on their own exhaust? | Light Blue Touchpaper
There’s a general consensus that large language models are going to get better and better. But what if this as good as it gets …before the snake eats its own tail?
The tails of the original content distribution disappear. Within a few generations, text becomes garbage, as Gaussian distributions converge and may even become delta functions. We call this effect model collapse.
Just as we’ve strewn the oceans with plastic trash and filled the atmosphere with carbon dioxide, so we’re about to fill the Internet with blah. This will make it harder to train newer models by scraping the web, giving an advantage to firms which already did that, or which control access to human interfaces at scale.
Space Elevator
Scroll up to the Kármán line.
Welcome to the Artificial Intelligence Incident Database
The AI Incident Database is dedicated to indexing the collective history of harms or near harms realized in the real world by the deployment of artificial intelligence systems.
GB Renewables Map
A lovely bit of real-time data visualisation from Robin:
It’s a personal project created at home in Wales with an aim to explore and visualise renewable energy systems. Specifically, it aims to visualise live generation from renewable energy systems around Great Britain and to show where that generation is physically coming from.
Adoption. — Ethan Marcotte
Ethan highlights a classic case of the McNamara Fallacy—measuring adoption of design system components.
Vibe Driven Development
This describes how I iterate on The Session:
It comes down to this annoying, upsetting, stupid fact: the only way to build a great product is to use it every day, to stare at it, to hold it in your hands to feel its lumps. The data and customers will lie to you but the product never will.
This whole post reminded of the episode of the Clearleft podcast on measuring design.
The problem underlying all this is that when it comes to building a product, all data is garbage, a lie, or measuring the wrong thing. Folks will be obsessed with clicks and charts and NPS scores—the NFTs of product management—and in this sea of noise they believe they can see the product clearly. There are courses and books and talks all about measuring happiness and growth—surveys! surveys! surveys!—with everyone in the field believing that they’ve built a science when they’ve really built a cult.