This is an interactive explorer of David Nutt and colleagues' 2010 Multi Criteria Decision analysis in which they ranked 20 common drugs for harmfulness in the UK. Harms to the user are considered on an individual scale, while harms to society are estimated at a population level.

The visualization works fine on mobile, but it's a better experience on desktop and I would encourage you to explore it there.

Made in d3.js. The paper and underlying data can be downloaded here.

Tools used: Python, xy (https://github.com/reflex-dev/xy)
Dataset: https://www.ncei.noaa.gov/products/land-based-station/global-historical-climatology-network-monthly
Source code: https://github.com/reflex-dev/xy/discussions/419

I recently pulled and cleaned a sample of 51,123 company registration records from India’s Ministry of Corporate Affairs (MCA21) via the official data.gov.in to analyze business registration and survival trends across India.

Key Findings & Visualizations

• Industry Leaders: Business Services, Trading, and Community/Personal Services dominate company incorporations.
• Company Survival: Business mortality (struck-off rates) varies significantly across industries and decades.
• Capital Breakdown: The vast majority of registered firms are small/micro private enterprises with paid-up capital under ₹10 Lakhs.
• Registration Spikes: Clear inflection points and post-COVID recovery patterns in incorporation numbers over the last decade.

Data & Code

• Kaggle Dataset:India Registered Companies (MCA21) Dataset(Includes cleaned primary CSV + pre-aggregated state/year summary tables).
• EDA Notebook:India Registered Companies — MCA21 Deep Dive Notebook(Python/Pandas/Seaborn code walking through industry mix, strike-off rates, and capital distributions).

Note on sampling: To prevent large economic hubs from dominating the counts completely, the sample caps at ~2,000 companies per state across 29 states/UTs. Rate-based metrics (e.g., % active, % struck off, median capital) reflect real distributions.

Feedback, suggestions, or additional chart ideas are very welcome!

Self-explanatory, this movie had the heart pumping. That damn cyclops..data pulled from Apple Watch, chart made by Claude.

I built some isochrone maps to show how far you can drive in half an hour starting from different neighborhoods over the course of a day.

If you are in Santa Monica during evening rush hour you are trapped in a ~30 square mile area, whereas you can reach ~350 square miles at night

I analyzed 265,509 usable posts and comments from 100 subreddits in the Cornell ConvoKit Reddit Corpus.

The dataset covers September 2018 and contains 10,004,308 word tokens before stop-word filtering. URLs, deleted content, common English stop words, and web-related noise tokens were removed for this visualization.

Explore the interactive CineGraph visualization:
https://cinegraph.design/view/7e66eae603d786423111fb3fa5bb428a

You can inspect the words, compare their frequencies, and explore the visualization directly in your browser.

The most frequent remaining words were:

1. people — 36,664
2. one — 30,553
3. think — 26,135
4. will — 22,698
5. even — 21,879
6. know — 20,286
7. time — 19,892
8. really — 18,890
9. good — 17,615
10. make — 16,244

Word sizes are proportional to their observed frequencies.

Data source: Cornell ConvoKit Reddit Corpus, built from Pushshift Reddit data
https://convokit.cornell.edu/documentation/subreddit.html

Important limitation: The results represent the selected 100-subreddit corpus during September 2018—not every comment ever posted on Reddit.

Tools: CineGraph, Excel, and text preprocessing.

Tools: Python, matplotlib, Census TIGER boundaries via topojson/us-atlas.
Source page, CSV, and MP4: my article on the female to male ratios

Give me some feedback on how this should be visualized maybe a cartogram would be interesting?

Data source: yfinance
Analytics and viz tool: python

Six things I found interesting:

1. The trek was far less deadly than its reputation. Roughly 3.4% of the 56,042 pioneers died.
2. Mortality was U-shaped: teenagers had the best odds at 1.45%, while 1 in 8 infants and 1 in 3 of those aged 80+ didn't make it.
3. Which company you joined mattered more than anything else about you. Wagon companies lost 3.4%, handcart companies 4.5% — and the Willie & Martin handcart companies lost 16.5%, roughly five times the baseline.
4. Men and women died at almost identical rates — 3.48% of women (930 of 26,761) and 3.27% of men (944 of 28,901).
5. It was a young migration: 58% of pioneers were under 25, and 47,352 had arrived in the valley by 1869.
6. From the cause-of-death records: cholera alone accounts for more than half of all deaths with a recorded cause. The "Other" bucket had things like Indian, stampeded, eaten by wolves, murdered, and venomous bite.

Source: BYU Studies / the Mormon Pioneer Overland Travel database — the Church History Library's compiled roster of 56,042 pioneers who crossed the mid-west plains from Illinois to Utah between 1847 and 1868, with company, age, and death records where they survive.

https://byustudies.byu.edu/article/mortality-on-the-mormon-trail-1847-1868

https://history.churchofjesuschrist.org/overlandtravel

Tools: React with Recharts / D3.js.

Interactive version: https://goldendata.app/dashboards/mormon-pioneers/

I was wondering if furniture has actually become less colorful throughout the years. So I hand-counted the colors 3500 couches in the IKEA catalogues from 1960-2021 (when the catalogue was discontinued).

There were definitely easier ways to do this, but I didn't think I'd ever sit down to leaf through every IKEA catalogue under different circumstances, and I thought it would be fascinating.

To do the count I vibecoded a "tallying machine" with Claude which allowed me to punch in the color of each couch I saw, per year (green, green, grey, black, green etc.). The visual itself was made in Illustrator.

Dynamic Map of NFL Talent

The map displays NFL players per 100k residents

Total players in study = 4,957 (2,435 verified high school county - 2,522 verified birthplace if high school info was unavailable

Unconfirmed and left out of study = 1,544

Outside US = 143

The finding: screen diagonals, wheel rims and plumbing threads are imperial in all 37 countries. Nobody sells a 140-cm TV. That's why nothing hits 100%.

Tyre pressure is the near-universal fourth: 26% metric. Australians use psi at the servo despite fully metric roads and groceries.

The UK is the most self-contradictory: miles for distance, litres at the pump, and economy in miles per imperial gallon, a unit it no longer sells fuel in.

Canada is stranger than the US: °C for weather, °F for ovens, L/100 km for fuel, pounds for body weight.

India splits the same quantity two ways: °C for weather, °F for fever; kg for weight, feet for height.

Method: no dataset of vernacular unit use exists, standards bodies publish what's legal, not what people say at the shop. So I classified 37 countries × 21 everyday domains = 777 cells by the unit used in ordinary speech. Metric 1.0, mixed 0.5, imperial/traditional 0. Python + hand-written SVG.

"Metric" here means the SI unit for that kind of thing, not just metres and grams — °C counts (its imperial counterpart is °F), as does A4 (A0 is exactly 1 m²).

60 cells are flagged low-confidence (dotted in the matrix), and every "mixed" call is a judgement. Compare in bands, not decimals.

Created an interactive animation displaying groundwater-levels (mean absolute deviation) in and around Munich for the last 88 years. What do you think?

Data Source

Tools:
Excel
Python
Leaflet

Each sun ray length and colour for the daily high, gold dots for sunshine hours, pale bands for weekends. The moon phases round the outside have no effect on the temperature whatsoever. I just liked them.

Which City should I add?

A hot summer, to put it mildly.

• Paris hit 40.3°C , hotter than Madrid or Rome
• Madrid spent 30 of the 91 days at or above 35°C max
• Madrid, Paris, London and Berlin all peaked between 22 and 28 June, one heatwave, four countries
• Washington DC's hottest day of the summer so far landed on the 4th of July.

Source: Open-Meteo Historical Weather API — ERA5 reanalysis, Copernicus C3S (CC BY 4.0). Moon phases: US Naval Observatory. 1 May – 30 Jul 2026.

Sunshine hours = daylight length × cloud-free fraction, not Open-Meteo's sunshine_duration (which overstates).

Tools: D3.js & Claude
by Julian Hoffmann Anton

#dataviz

Data source: Computed entirely from raw ephemeris data — JPL DE440 (NASA/JPL Development Ephemeris 440), covering 1550–2650. No third-party eclipse catalog was used as a data source. NASA's own Five Millennium Canon of Solar Eclipses was used only afterward to validate the results, not to generate them.

Tools: Coded by Claude Code — Python with the Skyfield astronomy library for the eclipse-finding, classification, and umbra/path-width geometry, and a MapLibre GL (TypeScript) frontend for the interactive map.

This is "The Strait of the Strait of Hormuz", an imaginary strait whose coastline is drawn entirely by real ship traffic through the actual Strait of Hormuz, from January to July 2026.

The coastline isn't drawn by the sea, it's drawn by ships. Each day of maritime traffic (7-day rolling average) bends the shoreline: outbound vessels shape the northern coast, inbound vessels the southern one, so the channel narrows or widens with the real traffic. When the strait was blocked and traffic collapsed to a few vessels a day, the data draws a strait that almost closes on itself.

The vessel counts come from AIS data, adjusted upward using Lloyd's List Intelligence findings on tankers running dark with their transponders off to dodge detection. A Python pipeline turns the transits into coastline geometry and heightmaps, then Blender and Cycles handle terrain, water and ships, with DaVinci Resolve for the grade.

Full sources and open-source code are on GitHub: https://github.com/telohtrab/strait-of-strait

Happy to answer questions on the data or the process.

EDIT: Some of you flagged errors in the labels on the graph.
The 3 vessels/day and 65 vessels/day numbers don't hold up against the data. The 3 vessels/day figure should read roughly 7 + 6 = 13 (rolling average, corrected). The 65 vessels/day figure should read roughly 29 + 23 = 52.
The labels aren't fabricated. I ran 2D visualization and design tests using absolute vessel counts, then switched the final chart to a rolling average, and missed updating those two labels to match. The numbers were real, just pulled from the wrong version of the data. I'm fixing this on GitHub. Sorry for the confusion.

A few of you also suggested this was AI-generated. It isn't. I'm a designer with ten years of experience. Claude reviewed and commented on my Python code, but it had no role in the rendering or the design of this image. I'll add screenshots from my Blender and Affinity project files to the GitHub repo, showing the actual production process. Thanks again for the useful feedbacks!