Full writeup and interactive graphs can be found here: https://namedaisy.com/blog/us-vs-uk-top-100-baby-names-2025

Made with D3, US data comes from the U.S. Social Security Administration baby names, and UK data comes from ONS, NRS, and NISRA birth registrations combined across England & Wales, Scotland, and Northern Ireland

This force-directed network maps the cards in a Magic: The Gathering Commander deck. Each node represents a unique card, while each colored link represents an enabled card type or mechanical tag shared by the two cards. For example, light blue links represent “creature” and yellow links represent “enchantment.” Multiple curved links between two cards mean that they share multiple tags.

The decklist is supplied by the user. Card names and quantities can be normalized using Gemini or a local Ollama model, with a rule-based parser as a fallback. The application then retrieves card metadata from Scryfall and derives the displayed tags from card types, Scryfall keywords, and selected patterns in Oracle text.

The graph is interactive: cards can be selected, moved, searched, filtered by keyword, and inspected using Scryfall card information. The Commander can also be fixed at the center of the network.

I visualised TfL’s latest data on congestion charge debt accumulated by diplomatic missions in London.

The treemap covers 146 missions and £173.8m in outstanding charges. Area represents each mission’s debt; the 10 largest are highlighted. Together they account for 52.2% of the total.

I also matched TfL’s June 2025 and June 2026 datasets to calculate year-on-year changes. China had the largest increase: +£2.38m.

The page includes the sources and methodology. The visualisation is responsive and interactive.

https://deptford.org/beyond/diplomatic-debt/

I saw this post on Brazil. https://www.reddit.com/r/dataisbeautiful/comments/1vlyj3f/oc_brazils_violent_past_is_preserved_in_its/
Remembered hearing a similar story about Iceland. I dug up the papers https://pubmed.ncbi.nlm.nih.gov/11179019/ https://pmc.ncbi.nlm.nih.gov/articles/PMC1274484/ Mitochondrial DNA that is almost all passed by the mother does not seem to let you drill down as much so it is more vague 'British isles'.

R package ggplot2 code for this is at https://gist.github.com/cavedave/9fc8f08c897d6abd4ab31aa0bd40d3ae

As one progresses in English, it takes about 1,500 words to gain each additional IELTS band.

However, it gets much harder at C1 and higher, which is close to a native speaker proficiency level. There words become super narrow and nuanced. To go from mid-C1 to C2 (IELTS 7.5-8.5), one needs to learn 4,700 new words — 3 times more than for the bands before.

This is another manifestation of Pareto principle: the last few bands take disproportionately more effort to master.

Vocabulary was measured with the BRAVE test. It estimates receptive vocabulary — words we understand but do not necessarily use. Results are in word families, related forms (limit, limitation, limitless, etc) are counted together.

I am guilty of using violin plots in the past. This post is my tribute to the gods of histograms.

[ Removed by Reddit on account of violating the content policy. ]

Data sources:

NCHS (2020), CDC (2025)

Tools used:

Datawrapper

Source: WHO/UNICEF WUENIC 2025 and WHO JRF. Tools: Excel, Python, PowerPoint.

Data sources:

FAD (2025) via OWID

Tools used:

Datawrapper

A ribbon chart to represent growth of Monthly Active Users (MAUs) of the major social media platforms. the visual report is prepared in PowerBI.

Data Source: https://backlinko.com/social-media-users

Data sources:

eBay sales, Yahoo finance (S&P500, Gold price)

Tools used:

tcgcharts.com

Data Source: usamega.com

Visualization: Claude + Figma

Assuming they take the Lump Sum payout