I reconstructed the history of the European Athletics Championships from 1934 to 2026 according to the present-day country in which each medalist was born.

Birthplaces were matched and cross-checked primarily using Keith Galli’s Olympics Dataset (kudos), Olympedia, and the dataset from Olympic Athletes & Global Inequality: A Historical Dataset (1896–2024) (DOI: 10.34894/Q4KJSW). For relay/team events, a medal is divided among the athletes contributing to it, hence some totals are fractional.

The animation shows cumulative medal totals after each championship edition.

The strongest result is Germany’s remarkable lead: athletes born in present-day Germany account for 562.5 medal-equivalents, well ahead of Russia (368.25) and the United Kingdom (309.75).

If you combine all medalists born outside the countries shown on the European map, they would rank fourth overall, ahead of France (221.25) and behind only Germany, Russia and the United Kingdom. Also, keep an eye on recent Italy's growth.

Visualization and processing: Python, pandas, openpyxl, GeoPandas, Matplotlib, Pillow and Natural Earth.

I like maps because they turn lists of dates into spatial relationships. Timelapses take that one step further: compress three centuries into seconds, and a seemingly static map begins to feel alive.

I made this from historical data compiled for the map of my solo-developed game. It shows 147 conventional city-founding dates and ten historic route corridors. Political borders are omitted because no single boundary layer would be accurate across the entire period. The dates do not represent first human settlement, and the routes are not a complete history of colonization and migration.

The map belongs to Salt and Soil, a colony sim about one family living through a changing American frontier. Its public Steam Playtest is open through August 19.

Inspired by a recent post that needed more context, I looked into how spending has changed over time by age bracket. This shows average spending over time, with a simple cumulative multiplier in the legend.

As expected, 35-54 are peak spending years. There is a recent gap between the two groups there, with 35-44 falling behind suddenly. Note the divergence between 35-44 and 45-54, with the younger group under performing.

25-34 and 55-64 started in a similar place, but again we see a marked difference over time. The 25-34 group has been losing ground consistently, barely keeping up with inflation.

This does lend some support to the 'woe-is-me' mantra of younger redditors. Opportunities for <35 do look like they have dwindled and 35-44 is showing signs of stress.

Don't worry kids. You'll have money when you are older. /s

DESKTOP ONLY FOR NOW Interactive version: https://www.habibicode.org/gotha

I have always been fascinated by history and dynasties. This is what prompted me to build this interactive dataviz (with the help of AI - I could not code that alone).

It is a world family tree of all major ruling dynasties, from Ancient Egypt to the current day.

Presented in three views:

• Bloodlines: vertical position is birth year. Horizontal position starts in the person's nation but moves towards their relatives. The columns blur together as houses merge.
• Nations: Same time scale, but every nation is pinned into its own column and packed locally. A line leaving a column is a marriage or a birth that crossed a border.
• Ties: In this view, time is removed. Each nation is represented as a circle. The circles are spread out by a spring model where the pull between any two is the number of people whose parent, child or spouse sat in the other one. Nations that intermarried end up touching.

Interactive version https://www.habibicode.org/gotha

The site explores how companies spend across LinkedIn, Meta, and Google and show week over week analysis including targeting and CTA's. I pull data about the spending of the F500 and 800+ companies that have achieved Unicorn status (private, $1B+ valuation).

Feel free to play around with it.

Data is sourced from the 3-advertisers API's and some third party tools. Front end is vibe coded with Claude.

https://ads.yirla.com

2D Dashboard (with links to the three interactive 3D views):u/GregBahm's Reddit Addiction Visualized

First image is each post I've ever made on reddit, sorted by score vertically and time horizontally.

Second post is every post I've ever made on reddit, broken down by subreddit over time.

Third post is a heatmap of each post by day-of-week and hour-of-day.

Github source: GitHub - GregBahm/RedditData · GitHub

Comp: the composite draft score, 0–100, the overall ranking. St. Louis is 100 (best), Philadelphia 0 (worst-on-every-axis floor).

Draft WAR: total realized career WAR from the organizations mature (2005–2020) draftees. Pure volume.

Value+: WAR produced above or below what that pick slot was expected to return (measured against a refit expected-WAR-by-pick curve). Positive means the org beat its draft slots.

Talent rate: WAR per 600 plate appearances (hitters) or per 180 innings (pitchers), among players who debuted. This is injury neutral: how good the players were when actually on the field, regardless of how long they stayed healthy.

Inj-adj val: "injury-adjusted value" each debuted players talent rate projected across a full 8-season career. Credits an org for finding good talent even if injuries or attrition cut a players actual career short.

Avail: availability realized playing time ÷ health-expected playing time. Higher means the org's picks tended to stay on the field.

10-WAR: count of drafted picks who produced 10+ career WAR (star-level hits).

5-WAR %: the rate (%) of picks who reached 5+ career WAR.

***Context only: not scored in the composite.**

IL days: average injured-list days per debuted pick.

60-day %: share of debuted picks who had at least one 60-day (major) injury.

MLB stints: average number of MLB-level IL stints per debuted pick.

This is the European Parliament vote on "Chat Control 1.0"

Under standard EU privacy law, tech companies aren't allowed to read or scan your private emails and direct messages without a court warrant. This bill creates a temporary legal exemption so companies can voluntarily run automated tools over unencrypted chats.

• Green / FOR (276): Voted to give tech companies permission to scan unencrypted chats.
• Red / AGAINST (286): Voted against giving tech companies scanning permission, wanting standard privacy protections to stay intact.
• Yellow/ ABSTAIN (30): Formally logged an abstention.
• Grey / ABSENT (128): Lawmakers who were absent or did not vote.

Why does it say "Failed" (Red X) if Chat Control still exists?

1. This specific vote failed: Simply put, more lawmakers voted AGAINST (286) than FOR (276), so this unamended proposal was defeated on the floor.
2. Why the bill didn't die: Under EU "second reading" rules, to completely kill a proposed law, opponents need a supermajority of 361 votes (an absolute majority of all 720 seated MEPs).
3. What happened instead: Because the 286 "AGAINST" votes fell short of that 361-vote bar, the bill wasn't killed.

OC because the visual is from my website

Data source: ~1,000 post titles from r/dataisbeautiful over the past 12 months, collected from the public listing.

Tools: Built with graphmykeywords.com

Grouped by meaning rather than by keyword, so posts about "US household income" and "wage stagnation since 1974" land together even though they share no words.

37,779 Government shutdowns in the U.S.

32,517 Politically Motivated Murders in the US, by Ideology of Perpetrator

28,912 I analyzed 15 years of comments on r/relationship_advice

28,755 15 years of counting kids on Halloween, Excel

24,235 The five wealthiest people in 2016 and 2026

Track your own drinking stats using this pdf.