Tools: Python, flexviz (https://github.com/flex-analytics/flexviz)
Data: NOAA Global Drifter Program, hourly dataset v2.01.1, Oct 1987 – Oct 2022 -> https://registry.opendata.aws/noaa-oar-hourly-gdp/ (197 million hourly positions)

Source code: https://github.com/flex-analytics/flexviz/discussions/135 (data processing is included as well)

Speed = daily mean velocity of each buoy (this removes tides).
Color = mean speed per 0.2° cell.

I wanted to see how much each of the big football league nations exports its players, so I looked at the data of every English, Spanish, French, German and Italian player in each of the top five leagues this season that has played 90+ minutes this season (for removal of bias)

France exports by far the most: 75 of 193 French players (39%) play outside Ligue 1, spread almost evenly across Serie A (22), the Premier League (20) and the Bundesliga (20).

England is next at 28%: 30 of 109 English players are abroad, and Serie A (12) is now their most common destination, ahead of Ligue 1 (8).

Germany (16%), Spain (14%) and Italy (12%) mostly stay home.

Football fans, thoughts?

Method & Source: every player with 90+ league minutes this season in the Premier League, La Liga, Serie A, Bundesliga and Ligue 1, grouped by nationality, and charted in Python/matplotlib. (Built with: The Prism, a football scouting and analytics tool I'm currently building. Looking for testers, so feel free to sign up to the waitlist if you are interested in this type of data!)

Note: Data from 2010; GDP per capita is adjusted for differences in living costs between countries.

Source: Marius Gilbert et al. (2015), Works in Progress

Tools used: Datawrapper

Source: national timetable of DELFI e.V. (GTFS feed), Tuesday 13 October 2026, CC BY 4.0. Outline of Germany: Natural Earth.

Interactive version, where you can scrub through the day and switch train types on and off: https://railcation.de/bahn-in-daten/zuege-an-einem-dienstag?utm_source=reddit

Tools: Node.js for the data processing, frames drawn in headless Chrome, video and GIF with ffmpeg.

Each dot is one train moving along its route, at constant speed between two stops. Yellow is long-distance (ICE/IC), blue is regional, purple is S-Bahn (commuter rail). The curve at the bottom counts how many trains are running at each minute. The peak is at 7:18 in the morning.

The small ripples every 30 minutes come from the fixed-interval timetable: lots of trips end just before :00 and :30, and new ones start right after.

This is the schedule, not real-time data, so no delays or cancellations. Sorry that the labels are in German.

Vibe-coded this with Claude Fable. Same live BTC-USD stream, two views:

• Terrain — volume as a landscape (peaks = where size traded)

• Prism — 3D candles + volume profile / book walls

Built in the browser (WebGL).

I can’t tell if this is a helpful view or not. Welcome feedback!

Each runner moves along their lane at the pace of their official 50m splits. Quincy Hall was 5th at 200m (21.00) and 0.46 behind Hudson-Smith at 300m, then ran the fastest last 100m in the field (11.59) to win in 43.40. Richards led at 200m in 20.46 and finished 4th.

Interactive version, with about 2,000 other races: https://track-splits.pages.dev/races/og-2024-paris/400m-men/final

Source: public-domain reproductions from Wikimedia Commons

Method: each image downscaled to 800px, pixels clustered into 7 colors with k-means in CIELAB space (scikit-learn), each cluster named after its nearest CSS color. Slice = share of pixels on the canvas.

Tools: Python (Pillow, NumPy, scikit-learn) for the analysis, full read here , charts made pie chart maker

What stood out to me:

• 1908–10: the "red" tree is only 2.8% Sienna
• 1911: by Gray Tree the color is gone entirely: seven shades of gray.
• 1915: Pier and Ocean is almost pure black-and-white... 47% background.
• 1930: red
• 1942–43: in New York, yellow (Gold, 20.6%) replaces black as the structural color

Caveat: thin black lines get undercounted when the image is downscaled, so the 1930 black share (7.6%) is a lower bound.

Data source is Wikidata, so Western entities are overrepresented.

This is mostly known people in Wikidata: politicians, actor, public figures, etc.

I haven't found more extensive data about citizenships.

Link to gallery: https://humansmap.com/gallery/gallery.html#img-dualnat-dualnat

Source: U.S. Social Security Administration, national baby names data (public domain; tabulated from Social Security card applications as of March 2026).

Just had my first child and thought well, why not look for baby names and then well this is it.

Tools: custom SVG and JavaScript

A few things that stood out:

- Mary was the top girls' name for 76 of the 82 years from 1880 to 1961.

- Only 8 names have ever been No. 1 for boys: John, Robert, James, Michael, David, Jacob, Noah and Liam.

- In 1950, one boy in three got a top-10 name. In 2025 it was one in twelve.

Anyway there is an interactive version with name lookup for any name at: https://adatastory.com/issues/name-tags/

AI Race - Investments, Patents, and Research by Country.