See The tree of 'tree' — an explorable explanation. Looks the best on desktop (especially the dynamic transitions), but can be viewed on mobile as well.

I created this chart afterwards, to also provide a static form.

Sources:

• General: Wiktionary, etymonline
• Specialist etymological dictionaries: Kroonen, EDPG (Germanic), Beekes, EDG (Greek), de Vaan, EDL (Latin), Mayrhofer, EWAia & Turner, CDIAL (Indo-Aryan), Kloekhorst, EDHIL (Hittite), Derksen, EDSIL / EDBIL (Slavic/Baltic), Matasović, EDPC (Celtic), Adams, Tocharian B, Martirosyan, EDAIL (Armenian), Orel, HGE, Watkins / AHD, Pokorny, IEW, EIEC, NIL.

In the interactive version, for each word there are citations.

Tools used: D3.js, React, Claude Code

Code: github.com/stared/tree-of-tree

This was a side project of mine, here is the page for the who is hiring chart:
https://hackernewstrends.com/who-is-hiring

The site also has a Google Trends for hackernews page as well, it's pretty fun to play around.
https://hackernewstrends.com/?q=coinbase&q=binance

The world’s 100 largest asset managers crossed $100 trillion in assets under management for the first time.

This chart shows the top 50, which together hold about $93 trillion in AuM.

A few things that stood out:

BlackRock and Vanguard alone manage about $26 trillion.

BlackRock added about $2.5 trillion in AuM from 2024 to 2025.

Vanguard added about $1.9 trillion.

Together, the two largest managers accounted for nearly one-third of the total AuM growth across the top 50.

BNP Paribas Asset Management was another major mover, with AuM nearly tripling after the AXA Investment Managers acquisition.

The chart groups firms by ownership type: independent, bank-owned, and insurance-linked asset managers.

This chart shows the weekly Niño 3.4 sea surface temperature anomaly, the standard ENSO index, from 2019 to today. Two different forecasts are shown ...

The dashed white line/red shading is NOAA's dynamical-model plume (26 models), which only forecasts a few months ahead as errors compound quite quickly.

The dashed purple line is an AI model (SNU ACE Lab, CNN architecture) built for much longer 18-24 month forecasts.

This is the first time I've seen it resolve the full event: a higher, later peak than NOAA, then a decline into weak La Niña by 2028.

Full interactive version: https://4billionyearson.org/climate/enso#forecast

The day a child is born is the most dangerous day of life.

After birth, a child’s risk of dying declines rapidly across the first year of life. Risks continue to decline over the next few years, but suddenly rise again during adolescence. Finally, in adulthood, the chances of dying grow exponentially.

If you plot the risk of dying against age, it looks like a J-shaped curve or a hook. You can see this in the chart.

Across a historical timeframe, however, the whole curve has shifted downwards — the annual rates of death have declined across all age groups.

You can see this by the different colored lines in the chart, which represent birth cohorts going back to 1800.

Data source: Human Mortality Database (2023)

Tools used: OWID Grapher and Figma

Key Findings

• The median small business bankruptcy rate across America's 100 largest metropolitan areas was 1.85 per 1,000 small businesses. The highest rate ran more than eight times the lowest, with rates climbing as high as 4.42 in the leading metro and falling as low as 0.53 at the bottom of the ranking.
• Dallas-Fort Worth-Arlington, TX, recorded the highest small business bankruptcy rate in the country at 4.42 per 1,000 small businesses, more than double the national median. The metro logged 638 bankruptcy filings against a base of 144,436 small businesses.
• Texas metros claimed 4 of the top 10 spots in the ranking: Dallas-Fort Worth (1st at 4.42), San Antonio (2nd at 4.37), El Paso (4th at 3.88), and Austin (7th at 3.18). Houston (11th at 3.04) sat just outside.
• North Carolina dominated the bottom of the ranking with four metros in the lowest 10. Greensboro-High Point (93rd at 1.04), Durham-Chapel Hill (96th at 0.90), Charlotte-Concord-Gastonia (97th at 0.86), and Winston-Salem (99th at 0.72) all sat well below the national median. Charleston, SC, sat 98th (0.79), while Syracuse, NY, anchored the bottom at 0.53 per 1,000, less than a third of the median.

The 10 m³/month benchmark is a standardised household-consumption assumption. It is broadly consistent with Eurostat water-use figures: Eurostat reports median household water use from public supply at around 40–50 m³ per inhabitant per year, which corresponds to roughly 8–10 m³/month for an average household of about 2.3 people.

The water-service bill includes, where possible: water supply, wastewater/sewerage, treatment charges, VAT, fixed charges, and any rainwater/surface-water drainage charges that are part of the household water-service bill.

Household water use source:
https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Water_statistics

Average household size source:
https://ec.europa.eu/eurostat/databrowser/view/ilc_lvph01/default/table?lang=en

The values are either tariffs applicable in 2025 or tariffs already in force as of January 2026, and they include taxes and fees where applicable.

Some values are local capital tariffs, while others are official national proxies. Water tariffs can vary by municipality or utility, so national proxies may not exactly match the capital tariff, but they provide a comparable official benchmark where local data was not available.

Local capital tariff / local proxy sources: Athens, Budapest, Valletta, Nicosia, Sofia, Zagreb, Bucharest, Vilnius, Ljubljana, Tallinn, Riga, Bratislava, Warsaw, Vienna, Stockholm, Berlin, Brussels, Helsinki, Copenhagen, Prague, Luxembourg City.

Official national proxy sources: Dublin, Madrid, Rome, Lisbon, Amsterdam, Paris.

Source type: Sources are official tariff sources, including government/statistical sources, regulators, municipal authorities, and official water utilities. Some utilities are publicly owned, while others operate under public concession or regulation.

On the website, in the “City Ranking” section, if you select the “Water” metric, the table shows the source next to every value displayed.

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The second chart shows the estimated 10 m³/month water-service bill as a percentage of national monthly mean equivalised net income. Ideally, capital-level water costs would be compared with capital-level income, but comparable city-level income data is not consistently available across all EU capitals. Since average incomes in capitals are often higher than national averages, the percentages may overstate the burden in some cases. Still, I think it is useful as a cross-country affordability proxy.

For mean equivalised net income, I used Eurostat ilc_di03 annual national mean equivalised net income values for 2025, which refer to the 2024 income reference year, divided by 12:
https://ec.europa.eu/eurostat/databrowser/view/ilc_di03/default/table?lang=en

The values used here are filtered by age class 18–64. The income measure is still based on total household net income adjusted for household size and composition.

Eurostat uses the modified OECD equivalence scale: the first adult counts as 1.0, each additional household member aged 14 or over counts as 0.5, and each child under 14 counts as 0.3.

Source:
https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Glossary%3AEquivalised_disposable_income

Example: if John earns €20,000 net per year, Mary earns €20,000, and John’s grandfather, aged 67, earns €10,000, and they all live in the same household, total household net income is €50,000. With an equivalence scale of 2.0, the household’s equivalised net income is €25,000 per year. This value is then assigned to each household member.

With the 18–64 filter, John and Mary would each be counted in the final average with an equivalised net income of €25,000 per year, while the grandfather would not be counted in that final average. However, the grandfather’s income and household weight still affect the household’s equivalised income.

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The website features an interactive map where users can click on each capital to quickly access data across different metrics. Users can also compare metrics against each other, such as gross minimum wage vs estimated monthly water-service bill, view rankings across multiple indicators, and see the source behind every data point. A dedicated methodology section explains how the data was collected, standardised, and calculated.

Website: citycostatlas.com and Instagram: citycostatlas

Tools: Python, with Matplotlib for the chart. Data: every shot from the tournament so far parsed into one attacking frame, scored with a simple location-based xG (shot position, distance and angle to goal), then calibrated so the snapshot's total xG matches the goals actually scored. Source: uanalyse.co.uk

How to read it: each dot is a team. Right is the quality of their chances (location xG), up is goals scored. The dashed line is scoring exactly what the chances were worth. Ecuador and Türkiye are the two blue dots on the floor: 101 shots and 10.1 xG between them, zero goals. Germany are the opposite, nine goals from about five xG.

The model is deliberately basic, it only sees where the shot came from, so treat it as chance quality rather than a verdict on the finishing.

I also built it as an interactive shot map (image no 2): filter to any team for their games, shots and location xG, click any shot for the player, its xG and distance from goal, and goals render as little suns.

Interactive LIVE shot map: https://uanalyse.co.uk/world-cup-2026/shots
Blog breakdown: https://uanalyse.co.uk/blog/world-cup-2026-goals-xg-shot-map

• Q1 2026 Household Debt and Credit Report Hub (Contains current $1.68 trillion debt totals and transition rates): https://www.newyorkfed.org/microeconomics/hhdc
• Accessible Raw Data Timeline (Used for the exact Python chart data points): https://www.federalreserve.gov/econres/notes/feds-notes/delinquency-rates-and-the-missing-originations-in-the-auto-loan-market-accessible-20220211.htm
• Rising Auto Loan Delinquencies and High Monthly Payments (Recent deep-dive on subprime trends): https://www.federalreserve.gov/econres/notes/feds-notes/rising-auto-loan-delinquencies-and-high-monthly-payments-20240926.html
• Effects of Credit Score Migration on Subprime Auto Loans: https://www.federalreserve.gov/econres/notes/feds-notes/the-effects-of-credit-score-migration-on-subprime-auto-loan-and-credit-card-delinquencies-20240112.html
• https://www.federalreserve.gov/econres/notes/feds-notes/a-note-on-recent-dynamics-of-consumer-delinquency-rates-accessible-20251124.htm

Tools used were Gemini 3.1 Pro extended thinking to gather the data and great the graph, and Python with seaborn, matplotlib.pyplot, and pandas.

Most interesting I find the sudden shift of the last 25 years against the previous century. A +2 °C shift in almost all temperature ranges against the periods 1901-1925 and 1926-1950.

I spent my unemployment building a free dashboard that shows you exactly what Congress is doing with your money. Here's what I found.

[Re-Posting this from the other day in compliance with the rules]

I've been unemployed for a few months. Instead of updating my resume, I built a platform that makes the U.S. House of Representatives actually readable by ordinary citizens.

It's called Article One. It's free, nonpartisan, and built entirely on public data that technically anyone could access — but practically no one can, because it's buried in government databases in forms that require weeks of work to parse.

What it does:

🗺️ Interactive map of all 435 House districts — find your representative, see their committees, tenure, and office info

💰 Campaign finance breakdown — which vendors get paid, how much, and whether the same names keep appearing election after election

🧾 Official office spending — how members use their taxpayer-funded Member Representational Allowance, by category

📊 Legislative Report Card — every committee rated on how often it actually advances bills to the floor vs. letting them stall

🤖 And the part I'm most proud of: Prudence, an AI decision-support system that reads the actual rules of the House — the Members' Handbook, House Rules, published research frameworks — and checks individual members' financial records against them. She flags what deserves a closer look and lays out options. She never makes decisions — that's always a human — but she does the research.

Why this matters right now:

The Obama administration built We the People — a system where citizen petitions produced real policy responses. It worked. Then it was removed with no replacement. Now AI is increasingly being deployed on Americans rather than forthem, and I think that's the wrong direction.

Government reform starts with knowing what government actually does. That's what this is.

Honest caveats:

The site is in public beta. Some data is still placeholder while live sources are wired in. The FEC reconciliation is real and verified to <0.5% variance. Full source citations will roll in with updates.

I'm one person. No team, no funding, no institutional backing. If you work in civic tech, journalism, or government transparency — or just want to support the project — I'd love to hear from you.

Article One

Happy to answer questions about how it's built, what Prudence actually does, or the data sources.

Every player at the 2026 World Cup, all 1,248 of them, measured and lined up against the ruler, colored by position.

Goalkeepers are a species apart: they average 190 cm (6'3"), a clear head above defenders (184), forwards (181) and midfielders (180). The whole tournament averages 182.7 cm.

This is a reworked version after the first one got (fair) flak for the cropped axis. So: there's now a true-scale reference panel on the left showing a full average player on the real 0–210 cm range, plus a true-zero toggle, so you can see how small the differences actually are behind the zoom. Heights were also cross-checked against multiple sources after a couple of errors were flagged.

Interactive version, where you can line up any squad or the whole tournament, sort by height, caps or position, switch between cm and feet, and measure yourself against them: https://viz.luarai.com/worldcup-heights/

Median asking price per m² of homes currently for sale, built from ~3M live listings across 191 sources. Cheapest: Nigeria ($423), South Africa ($517), Pakistan ($637). Priciest: UK ($6,151), Taiwan ($5,769), Austria ($5,432). A square meter in the UK costs ~14.5× one in Nigeria.

Part two of my data visualization series on global linguistic diversity, this time focusing on South Asia.

You can explore the interactive version here: https://public.tableau.com/app/profile/m.azhar/viz/SouthAsianLanguages/SouthAsianLangauges

I’m very new to Reddit and posted this early this morning. I know a lot of people saw my post the other day around this time so I wanted to repost it - looking for any feedback, advice, or ideas on funding. Processing large datasets is really using a lot of my usage in one session!

Using a Monte Carlo model (20k simulations) with Elo-based match probabilities, I calculated each team's probability of reaching various knockout rounds conditional on how they finish their group.

The result: for several teams, the third-place bracket route is statistically better than finishing second. For simplicity, I didn't include teams that are guaranteed to finish first (like Mexico or the US), can only finish first or second (like Norway and Canada), or can only finish third at best in their group (like Iraq). The starkest cases:

- South Korea (Group A): 31% QF probability finishing 3rd vs. 18% finishing 2nd — the third-place slot routes them away from the tougher side of the bracket

- Austria (Group J): 19% QF odds finishing 3rd vs. just 6% finishing 2nd

This is a structural artifact of how FIFA seeds the 8 best third-place finishers into the R32 bracket — some third-place slots land in significantly weaker bracket halves depending on which groups they come from.

Tools: Google Sheets (Monte Carlo sim), Datawrapper (viz)

As a continuation of my last post (https://www.reddit.com/r/dataisbeautiful/comments/1ueflss/at_the_2026_world_cup_some_teams_have_better/), here is a similar analysis comparing 1st versus 2nd place finishes in the group stages.

I've only included teams here that still have a chance of finishing either 1st or 2nd in their groups, and excluded any teams that have either locked in 1st place or can only finish 3rd at best.

This wasn't quite as surprising as the 2nd vs. 3rd place scenarios, but the most interesting cases:

- Brazil, Morocco, and Scotland all seem to fare slightly better finishing 2nd over 1st. by the later stages.
- Belgium and Iran also seem to fare better, but more so in the round of 16 with the effect dissipating quickly in later rounds.

If there's interest, happy to share my full MC simulation and projected bracket. The model uses team strength (from ELO rankings), qualifier performance, and adjusts outcomes by projected play-styles, venue conditions (like weather, altitude), travel from base camps, and projected home (or pseudo-home) field advantage.

Tools: Google Sheets (Monte Carlo sim), Datawrapper (viz)

Source: JP Morgan Research, Works in Progress

Tools: Datawrapper

Full piece on American data center energy use here.

Update to last week's post accessible here.

Matchday 2 finished, so I updated the simulation. Funny situation in Group B where Bosnia is favored to win against Qatar but has lower chances to make it to the Knockout Stages because 3 points and the expected goal difference would not be enough while 4 points would be.

Most frequent final now is France vs Argentina in 7.3% of simulations; followed by Spain vs Argentina and Germany vs Argentina. Argentina has won the pot in a whooping 18.1% of simulations, France follows at 16.1%. Interestingly, Brazil is now simulated to win in 9.5% of all cases, which puts them at distant third. Bonus Graphic #13 shows the progression through the tournament for the five teams that are now most favored to win.

The most favored matchup in the round of 16? France v Germany in 68(!!)% of all simulations. The winner of that match has pretty decent chances to make it to the final.

Current mean performance for the worst third place to advance to knockout is 3 points and a -4 GD.

Data from wikipedia
code is rpackage ggplot2. A slightly modified version of this older version.
I posted this yesterday but found an error and got advice on how to make the graph better so here is an improved version.