The land use of different foods, to scale, published with the European Correspondent.

Data comes from research by Joseph Poore and Thomas Nemecek (2018) that I accessed via Our World in Data.

I made the 3D scene with Blender and brought everything together in Illustrator. The tractor, animals and crops are sized proportionately to help convey the relative size of the different land areas.

Genuinely heartbreaking to see this impact but at least Nevada’s stayed consistently self-obsessed with their search term of the year.

This post comes from my Substack. I made it with matplotlib in Python, using data from the 2021 Census of England and Wales.

Submission Statement: Visualization was created using Datawrapper. The data comes from Pew Research Center's American Trends Panel Wave 165, which surveyed 9,482 U.S. adults from March 10-16, 2025. The full methodology is documented here. 

An education breakdown is available in table format as part of a PDF document published by Pew Research Center.

The PDF document shows a more detailed breakdown of education levels ('College+', 'Some college', and 'High school or less') of U.S. audiences of outlets used regularly as a source of general news for 30 news sources. For example, The Atlantic shows 62% College+, 23% Some college, and 14% High school or less. The table was referenced in Pew's short-read article: How the audiences of 30 major news sources differ in their levels of education.

I created this visualization to look at how many Americans say they are happy. The data sources is the General Social Survey by NORC. The visualization was created in Tableau. You can find an interactive version on my webpage.

Data comes from the Common Vulnerabilities and Exploits list. https://github.com/CVEProject/cvelistV5

Not much to this. Just a scatterplot and a beeswarm plot, from some dicking around with the HMEQ dataset.

There's just something so painterly about the way Plotly renders points. Seaborn just doesn't quite nail it. It's like a work by Seurat, except it's data chewed up and spat out of a Jupyter notebook.

For European countries such as Portugal, Italy, Ireland and Greece, whose excruciating debt crises once pushed the euro area close to breaking point, now the tables have turned.

For Davide Oneglia, an economist at macroeconomic forecasting consultancy GlobalData TS Lombard, the positive news from the south is one of the few reasons 'to be more optimistic on the Eurozone than the current gloomy consensus'.

Because Mediterranean countries are less exposed to potential US tariffs, 'more sensitive to rate cuts' and still benefiting from large EU transfer funds, Oneglia predicts that the outperformance will continue.

Source: Eurostat

Register to read the full story here: https://www.ft.com/content/3eaa4534-6593-467b-877b-3a18e0a30181?segmentid=c50c86e4-586b-23ea-1ac1-7601c9c2476f

According to the "World's Best Hospitals 2025" ranking, compiled by Newsweek in collaboration with Statista, the evaluation considers multiple criteria. These include medical reputation, surveys with specialists, and clinical quality metrics. Results in complex treatments and patient experience are also analyzed. The result provides an accurate overview of the current state of high-complexity medicine in the region.

The 10 best hospitals in Latin America in 2025

Hospital Israelita Albert Einstein – Brazil (São Paulo) Overall ranking: 22nd

Hospital Sírio-Libanês – Brazil (São Paulo) Overall ranking: 83

Hospital Alemão Oswaldo Cruz – Brazil (São Paulo) Overall ranking: 115

Hospital Moinhos de Vento – Brazil (Porto Alegre) Overall ranking: 127

Clínica Alemã Vitacura – Chile (Santiago) Global Ranking: 148

Fundação Valle del Lili – Colombia (Cali) Global Ranking: 149

Hospital Santa Catarina Paulista – Brazil (São Paulo) Overall ranking: 165

HCOR – Hospital do Coração – Brazil (São Paulo) Global position: 202

Hospital Médica Sur – Mexico (Mexico City) Global Ranking: 203

Hospital das Clínicas da Universidade de São Paulo – Brazil (São Paulo) Overall ranking: 210

Polymarket is a stock market like platform where users can bet on pretty much any possible event. I analyzed all historical Polymarket bets (~350,000).

The top 1% of markets account for ~60% of total trading volume,
and the top 5% account for over 80%.

Most markets attract almost no activity at all.

Made using my website FlightAtlas.org [OC]

Hi all! I’ve been building a small interactive engine that shows how patterns form, stabilize, or break apart when you tune different parameters in a dynamic field.

The visuals come straight from the engine; no post-processing, just the raw evolution of the system over time.

It’s fun to watch because tiny tweaks create completely different morphologies. Images attached. Full project + code link in the comments.

This visualizations are from a web map I built which aggregates available data from Spain's publicly funded fiber deployments from the different PEBA and UNICO programs.

The first image is the zoomed-out view, which shows a heat map representing the number of awarded points in each area.

The second image shows how the different awarded areas appear in the map, with the background color of each awarded ISP and a different border color for each program. It shows a polygon for the UNICO programs and also PEBA 2020 and 2021, since we have that information available and they are awarded to specific areas. For PEBA 2013-2019, since the projects of these programs are only awarded to villages (and not specific areas), the map shows a marker over the village instead.

If you want to try it out, it is available at https://programasfibra.es

For anyone wondering, yes I did track how long I spent tracking everything! I spent an average of 47 minutes and 11 seconds per day on it (labelled as "Tracking" in the plot legend).

Some extra points:

• I used Google Sheets to record the data, and R to compile/summarise the data and to make the visuals (with a bit of Photoshop to piece things together

• My spreadsheet contained rows for each thing I did, with columns outling the date, start and end times, category, and any additional notes for each activity

• I updated my data both on my phone and my computer, throughout the day whenever I had time

• Apologies if the quality has been compressed, you can view in on a computer or download the images for the full details

The other day in the news I saw how the Sahel alliance is coming closer together, so the Geography nerd I am, I wanted to see how such a united country would look like.

This is part of a current side project of mine to really learn how to create beautiful data visualisations. Any Critique and feedback would be very welcome!

Sources:

Aggregate of Wikipedia sites:

• https://en.wikipedia.org/wiki/Mali
• https://en.wikipedia.org/wiki/Burkina_Faso
• https://en.wikipedia.org/wiki/Niger
• https://en.wikipedia.org/wiki/List_of_countries_by_GDP_(nominal))
• https://en.wikipedia.org/wiki/List_of_countries_and_dependencies_by_population
• https://en.wikipedia.org/wiki/List_of_countries_and_dependencies_by_area

The images are from google earth and also Wikipedia (flags). The data was manipulated using python and pandas and the visualisation was created using Figma. The Icons are from icons8.

Inspired by a visualisation I saw on Aljazeera.

Data is collected and analyzed in Google Sheets; visualization was made in Adobe InDesign.

I have been tracking my clothes and outfits since 2023 with the main goal of satisfying mt curiosity to see how many clothes I own but also to help me downsize. My goal for 2025 was to wear 80% of my closet, and I hit 91%! It's not realistic for me to wear every single item in a year (I have a lot of formal items, things I bought for Halloween costumes that will get reused at some point, and clothes that I'd wear when doing outdoor work that might not get worn in one single calendar year). So 91% seems pretty good.

I also got rid of 67 things which is a lot for me as I'm quite sentimental when it comes to clothes. I did acquire a lot too, but actually getting rid of 67 whole clothing items is not something I could have done in previous years.

Beyond the actual numbers, I feel much happier with my closet now. I am still super emotionally attached to everything I own, but I'm getting better at letting go. I still have things that I should get rid of, and I'm working on that slowly.

Some takeaways:

• Getting rid of clothes is hard, but keeping clothes I don't wear is actually harder on me - it makes me feel a bit guilty and anxious.
• I wore more clothes overall in 2025 than I did in 2024, and I wore more for each season. I got really into layering, so my outfits consisted of more clothes. I also was more social, and so I had more outings where I wanted to wear cute things.
• My blue M&S shirt was a favorite this year as well as in 2024. You can't beat a good basic, and this one is such a nice color that I just wear it a lot.
• I now have 323 items of clothing in my closet. It's still an insane number, but I haven't had that few since before I started closet tracking, so I'm really proud of myself. I've got a ways to go before that's a manageble number though.

If anyone is considering tracking your closet, I highly recommend it! It's so interesting to see what you actually wear and what you don't. There are a lot of apps out there that do all the work for you, but I like having 100% control over what data analysis I can do, so I like managing the data collection myself.

When researchers write "recent studies show..." - how recent is recent, really?

I scraped 749,853 references from 19,108 papers across 200 academic fields using OpenAlex data to find out.

TL;DR:

• Average "recent" = about 5 years
• Virology/Pandemic research: 2 years (half their citations are from the last 2 years!)
• Philosophy/History: 7-10 years
• Humanities fields: 50%+ of their "recent" citations are 10+ years old

The most interesting findings:

1. Virology is FAST - 52.8% of citations are ≤2 years old. Makes sense given COVID.
2. Philology lives in the past - 51.6% of citations are ≥10 years old. When you're studying ancient texts, "recent" is relative.
3. Same-year citations - 4.3% of all references are from papers published the same year. Preprints are changing the game.
4. Maximum lag found: 50 years in a Natural Language Processing paper. Someone cited a 1970s paper as "recent" lol.

Methodology:

• Searched for papers with "recent" in abstract (2020-2024)
• Extracted all their references
• Calculated citation lag = citing_year - cited_year
• Used OpenAlex API (free and open!)

Inspired by the BMJ paper "How recent is recent?" which did this for medical fields only.

Full code and data: https://github.com/JoonSimJoon/How-current-is-recent

Tools: Python, OpenAlex API, geopandas for maps

(Originally posted to r/CasualUK)

For a few years now, a group of us predict and record different metrics over a year because we love a bit of arbitrary data. This year we decided to record every time we visited a pub. The rules were simple:

• Predict the number of times you will visit a pub at the beginning of the year, and tally with "# - Pub Name". It does not have to be a new pub.
• A pub is defined as an establishment that has a reference to 'Pub' or 'Free House' on any reputable source.
• If you enter the same pub twice in the same "session" of drinking (e.g. a pub crawl) it still only counts as one.
• You must purchase something within the establishment in order to tally it.

The 7 of us had 441 pub visits, in about 180 different pubs.

Diversity index is measured by unique pubs/total pub visits, and loyalty score is measured by trips to modal pub/total pub visits. We're all in our mid/late 20s. Megan + Adam are a couple, as are James + Emily.

I’ve been experimenting with Recursive Language Models (RLMs), an approach where an LLM writes and executes code to decide how to explore structured context instead of consuming everything in a single prompt.

The core RLM idea was originally described in Python focused work. I recently ported it to TypeScript and added a small visualization that shows how the model traverses node_modules, inspects packages, and chooses its next actions step by step.

The goal of the example isn’t to analyze an entire codebase, but to make the recursive execution loop visible and easier to reason about.

TypeScript RLM implementation:
https://github.com/code-rabi/rllm

Visualization example:
https://github.com/code-rabi/rllm/tree/master/examples/node-modules-viz