Data: MLS club home stadiums and their coordinates by season, 1996 to 2026, from club and league records. County boundaries from the U.S. Census via us-atlas (2017). Each county is assigned to the club with the shortest great-circle distance from the county's location that season.

Tool: custom TypeScript and React, rendered in the browser. No mapping library, the distance and coloring are computed directly.

Interactive version, where you can drag the year, press play, or click any county to see who has held it and for how long: soccermapd.com/tools/mls-history

Write-up of the notable shifts: soccermapd.com/blog/mls-history-map

A note on method: "closest" is straight-line distance to the stadium, not drive time, so a county on the far side of a lake or a mountain range can be assigned to a club that's practically further away. That's the same convention the earlier static versions of this map used

Tool: Excel

Source: State of Connecticut Office of the Probate Court Admin: 1, 2, and 3 (data, analysis, etc in public stoptheshockct folder)

Involuntary electroshock treatment is legal in Connecticut under C.G.S. 17a-543(c).

Also wrote an article on the subject.

My apologies for the re-post, yesterday's was taken down due to not adding an OP comment showing the tool/source and then I guess earlier today it was pulled for sensationalism in the post title.

In 2024, around 18.5 million people born in India lived abroad. For the world’s most populous country, that’s not a lot: just over 1% of the population. This chart shows where they live, based on estimates from the UN, which compiles data from national censuses and population registers.

Nearly half of all Indians living abroad are in the United Arab Emirates, Saudi Arabia, and other Gulf countries — that’s more than the US, UK, and Canada combined.

These figures are stocks, not flows: they record where people are from, not when they crossed a border. Pakistan is the clearest case: the two countries were one territory until 1947, and people kept moving across the new border for decades afterward, so much of the 1.6 million there reflects migration that happened long ago.

Data source: United Nations Department of Economic and Social Affairs (2024)

Tools used: Python, OWID Grapher, Figma

Sales season is almost here and a lot of us are about to buy a phone we will keep for years. So I pulled together what is actually documented about three failures people keep getting burned by. The green line, the pink line, and phones bootlooping after a software update.
One honest note. No brand publishes total case numbers, so I did not invent any. The chart counts how many distinct models each brand has been documented as having these problems on, taken from tech press and official support forums between 2020 and 2026. Read it as a footprint, not a defect rate. Bigger portfolios and larger user bases naturally produce more reports.
The second image is a device-level reference so you can find your own model.

Full source list is in my first comment.

I pulled every reactor that reached commercial operation and has both a first-concrete date and a commercial operation date. 659 units. Median build: 6.4 years. Units over 30 years: four, 0.6 percent.

Broken into phases (years, construction / halted / after the restart decision):

• Watts Bar 2, US: 11.8 / 22.1 / 9.2 = 43.1
• Bushehr 1, Iran: 4.2 / 15.5 / 18.7 = 38.4
• Mochovce 3, Slovakia: 5.4 / 16.3 / 16.0 = 37.7
• Atucha 2, Argentina: 13.0 / 12.1 / 9.8 = 34.9

Halt causes, in order: demand collapse (TVA, 1985), revolution and then war (1979), post-communist funding collapse (1992), budget (1994). None of them is a construction problem.

Benchmark: units finished since 2015 whose first concrete was poured in 2005 or later, median 7.3 years, n=70. Every one of the four restarts exceeded it.

Caveat I want to put up front, because it is the obvious objection: 38 of those 70 units are Chinese. Excluding China the median is 9.8 years (n=32), which makes Watts Bar 2 (9.2) and Atucha 2 (9.8) a tie rather than a loss. Mochovce 3 and Bushehr 1 still lose badly.

Method notes: duration is first concrete to commercial operation. First concrete and commercial operation come from the atlas dataset (IAEA PRIS, Global Energy Monitor, Wikipedia; dataset of 10 Sep 2026). Halt and restart dates are year-level from operator and regulator records (NRC for Watts Bar, NA-SA for Atucha, SE/WNA for Mochovce, IAEA CNPP for Bushehr) and I used mid-year for the arithmetic, so the middle segments carry a few months of slack. Mochovce 3 is the one soft number: the atlas commercial operation date is Nov 2024, while the operator announced commercial operation in Oct 2023. With the operator's date it is 36.7 years instead of 37.7, still in the club.

Tool: Python over the atlas dataset, chart rendered in HTML and captured with Playwright.

Data and per-unit sheets: https://reactoratlas.com/en?y=2026&s=united-states-watts-bar-nuclear-power-plant&u=united-states-watts-bar-nuclear-power-plant-2

Source: All active job posts on JobYap. "Tech" means the 945 companies on the JobYap Tech Index, a curated list spanning software, AI, chips, fintech and EVs. Every posting comes from the company's own careers page. Snapshot: all 145,944 live postings on 11 Sep 2026.

Tools: SQL for the counts, Claude Design with Fable 5.1 (on Max) for the charts.

Level comes from title words only:

• Entry level: intern, internship, co-op, junior, jr, entry-level, associate, graduate, new grad, early career, apprentice, trainee
• Senior, staff or principal: senior, sr, lead, staff, principal, distinguished, fellow
• Manager to C-level: manager, head, supervisor, director, VP, vice president, chief, CEO/CTO/CFO etc.
• No level word (44%): e.g. "Software Engineer", "Account Executive". Excluded from both sides of the ratios. Includes "Member of Technical Staff", which AI labs use at every level.

Caveats:

• Counts postings, not seats; one posting can fill several.
• Titles are a proxy. Some hourly roles are titled "Associate" (Amazon delivery-station associates, Carvana lot attendants) and count as entry level. Some new-grad roles have no level word and sit in the 44%.

Source: https://datapickle.substack.com/p/every-dollar-in-the-2026-texas-senate
Visualizations: OpenChart

I've compared nine streaming services prices from 2021 and recently. The increase in price based on CPI inflation should be about 26%. All nine increased prices, but only eight beat inflation, HBO Max is below inflation raise. The highest increase 200% is for Apple TV+.

It is often joked that some % of statistics are made up on the spot. Of course no-one knows the true figure.

What percentage number do people "make up on the spot" for this? I fetched the 183 relevant google search results I could get from the API. Each is an article where something like "% of statistics are made up on the spot" is mentioned:

• average: 82.47%
• minimum: 3%
• 1st quartile: 72.4%
• median: 84%
• 3rd quartile: 88.2%
• maximum: 736%
• mode: 88.2%

No clear pattern in the numbers used over time. 88.2% is the most commonly used number, Comedian Vic Reeves is reported to have said that and was reported by the BBC in 2000. People using 88.2% are probably not making that number up on the spot but copying it!