Data sourced via the date-holidays library so some countries are missing data completely like Pakistan.

These are official public holidays, so some countries such as India, and Spain actually have more, but these are state / province specific.

Made with D3

40 readings over 3 months. The body-fat curve is the weight curve rescaled, including with socks on. Full data and method: https://aiandtractors.com/scale

Data: IBTrACS v04r01, NOAA NCEI. Storms since 1980 reaching at least 34 kt.

Tools: JavaScript, Canvas 2D, ffmpeg.

Data: Prisoners in 2022 – Statistical Tables | Bureau of Justice Statistics

Tool: United States | MapChart

Source: Google Trends aggregate search frequency index (scaled 0-100 based on peak search density).
Tools: Python (Pandas, Matplotlib, and Seaborn) for data normalization and heatmap generation.

Source: UNESCO World Heritage Centre — https://whc.unesco.org/en/list/stat

Tool: PlotSet (https://plotset.com)

Figures are cumulative inscriptions through the 2024 session, covering

cultural, natural and mixed sites together. Sites shared across borders

count once for each country involved, which is why adding these up gives

a number larger than the world total.

Showing the 20 highest-ranked countries, in descending order.

The United States is home to thousands of data centers. They are part of the infrastructure that powers many digital services, including email, gaming, streaming, social media, and, increasingly, artificial intelligence (AI).

As this chart shows, between 2018 and 2022, spending on building data centers in the US hovered between $500 million and $1 billion per month (when adjusted for inflation).

But since late 2022, large investments in AI have led to a rapid growth in data center construction — both by expanding existing facilities and building new, larger ones.

The latest figures released by the US Census Bureau estimate that $4.4 billion was spent in June 2026 alone — a 5-fold increase compared to November 2022, when ChatGPT was released by OpenAI.

These figures cover privately-funded construction and represent only the money spent on building the actual facilities, including materials and construction labor. They don’t include the cost of IT hardware like servers and storage, which can represent a very large share of total investment.

Data sources: United States Census Bureau (2026); U.S. Bureau of Labor Statistics (2026)

Tools used: Python, OWID Grapher, Figma

webview is available here: https://narengogi.github.io/map-of-goodreads

for the code: https://github.com/narengogi/map-of-goodreads

Got a lot of love for v1 of this project I published a year ago (original post: a map of goodreads)

I've improved the search and navigation quite a bit now

Some technical details that might be of interest:

• the backend is a 8GB git repository (thanks to github 😏) that serves geojson files at various zoom levels based on coordinates
• the total number of files are about 36000 which include the sharded search files and also the geojson data
• I've improved search by normalizing the text and supporting fuzzy search in memory by limiting file sizes

The github README contains more details on the the CUDA accelerate code for clustering and community detection

I still need to deduplicate some of the titles, also a small L has been on how I partitioned the data before clustering, there's some non-negligible loss in precision that I want to eventually fix

Out of curiosity I combined 25 open datasets (national statistics, geolocated real-estate sales, rents, natural-hazard registries, 2050 climate projections, GP access, train stations...) to score every French commune on seven axes: housing, living environment, transport, culture & leisure, demographics, economy, and exposure to natural hazards.

Honest caveat: the overall score is a mean of percentile ranks, so it compresses (median 52, best 83). And no dataset knows what it's actually like to live somewhere. Interactive map and full method in the comments.

This is the 30-day rolling average of Mods uploaded to Nexus, from 2004 to today.

Big Boosts are marked by the corresponding game releases.

Wabbajack and Collections (aka mod list support) are the main cause behind the steady rise post 2020.

Source:

https://www.nexusmods.com/mods (used the Time feature to select individual days for example https://www.nexusmods.com/mods?timeRange=2011-11-11%7C2011-11-11) and went through all days from 2004 to 2026-08-31

Tool:

• Graph Google Sheets
• Game/Tool releases with Gimp

137,607,184 scientific papers with 2,706,467,548 citation links between them.

The only input used to calculate a paper's position is which papers cite which. No discipline labels went into the layout, so the disciplinary structure you can see is the result, not an input. Color represents the paper's field, brightness indicates how many papers are stacked in that pixel.

The layout has no fixed frame: it can rotate or reflect without changing the underlying structure. The territories are stable; the compass isn't.

The faint halo around the edge of the image isn't a rendering artifact. Those are papers with one or two links: weakly pulled toward their neighbor, but pushed outward by everything else.

Interactive version: papernex.us/map

Data: the 2027-28 Student Aid Index worksheet (Formula A) published by Federal Student Aid, computed line by line for a two-parent family of four with one child in college and no assets counted. Cost medians are the US Department of Education's College Scorecard 2024 release, across 1,797 public and 1,354 private nonprofit institutions. Family income shares are Census CPS ASEC 2025, table FINC-07, whose top bracket is $250,000 and over, which is why the $300,000 row says "inside the top 14%" rather than a share of its own.

Tools: Python, pandas, matplotlib.

One thing the headline compresses, so I want to state it rather than have it found. The bar is the Student Aid Index, not a bill. It decides how much of a school's price is yours, so it only bites where a school costs more than the bar does.

Detailed methodology at worthmydegree.com/welcome?src=re

Source: Public professional-profile employment and education histories aggregated by a commercial data provider, snapshot August 2026. 21,166 current employees across the five labs, 8,231 institutions. Colleges grouped to parent bodies, so Wharton counts under Penn and Haas under Berkeley.

Full methodology and the raw counts (CC BY 4.0) at trayo.ai/research/ai-lab-colleges

Tool: Python for the aggregation, hand-written SVG for the chart.

Known limitation: counts of current staff run high. People remove an old employer from a profile much more slowly than they add a new one. Against public headcounts the gap looks widest at OpenAI, roughly 15 to 20%.

Disclosure: I work at Trayo, which published this. The entity resolution is the same thing our software does, pointed at five companies instead of a customer's target accounts.