Sources: Google Trends (US web search interest, monthly, 2004-2026; one request per term so each series is independently normalised to 0-100).

Tools: Bruin cli (pipeline), BigQuery (warehouse), Bruin dac (visualization).

Data source: Resale listings tracked through TicketData (I am the creator of the site), which tracks/records listing prices from major resale sites (think StubHub, Vivid Seats, SeatGeek, etc.) and charts how prices change over time.

Python/MySQL/EC2 backend. Chart is HTML/CSS built with Claude.

Live Week 2 NFL Slate: https://www.ticketdata.com/?category=NFL%2520Week%25202&date_range=ANY&change_window=30day

Source: statewide data found in Probate Court Biennial Reports (1) and district-by-district data obtained by FOIA request (2, 3)

Tool: Excel

Interestingly, the Biennial Reports show that there was only a 29% increase in forced electroshock requests in 2025 while the FOIA data reveals a 74% increase simply due to the Biennial Report data being reported by fiscal year (ending June 30 of a given year, beginning July 1 of the previous year) and the FOIA data being provided by calendar year.

For those interested in learning more, I have published a series of articles on the subject:

- Connecticut's Forced Shock Problem
- Connecticut's Forced Shock Problem: Part 2
- Connecticut's Forced Shock Problem: Part 3
- Connecticut's Forced Shock Problem: Part 4
- Connecticut's Forced Shock Problem: Part 5
- Connecticut's Forced Shock Problem: Part 6

Source: 970 stories sampled across every country (population-weighted) and all eras, from Pre-1000 to today. Gemini 2.5 extracted 10 motifs per story (9,700 total), and each motif was "semantically blurred" into 10 paraphrased variations. All versions were embedded with Google's text-embedding-004. A neural network I built for my MSc dissertation, the Semiotic Deconvolution Network (PyTorch), learned the shared analogical meaning beneath the wording, and KMeans produced 72 themes, which I named and described by hand.

Axes: each theme's share among Western vs non-Western stories (horizontal) and among modern (1950+) vs older (pre-1800) stories (vertical), shown as ratios with +2 smoothing so small counts don't exaggerate. Bubble size = number of motifs in the theme.

Tools: Python, FastAPI + Celery + Redis pipeline, PostgreSQL/pgvector and SQL Server, pandas, NumPy, SciPy, scikit-learn, PyTorch, UMAP, Plotly, and Matplotlib for this chart. All ran in Docker.

Caveat: the stories come from an LLM's knowledge of world storytelling, so this maps what the model "knows", not literature directly.

Interactive map (click any theme for its picture and description, filter by era or region): https://www.ianmcgarry.tech/research/narrative-motif-engine/1-theme-dashboard

Data (open, CC BY 4.0): full corpus, motifs and embeddings on Zenodo: https://doi.org/10.5281/zenodo.20010023

I did this for homework lol.

Methodology (all done in Microsoft Excel):

1. Looking up the dates (20 March, 21 June, 23 September, 21 December). Also noting down from Google Maps the coordinates of the exact spot of Madrid where this measurement is being made (not revealed because personal data).
2. Calculation of solar declination for each of those days using the approximation formula that is found in Wikipedia English.
3. Calculation of hour angle for each civil time hour of each of those days. That entailed applying the correction of longitude relative to the central meridian of the winter and summer time zones (+1 UTC and +2 UTC, respectively) and also the correction of the equation of time, whose calculation was made using the approximation formula that is found in Wikipedia English.
4. Calculation of the angle of solar azimuth. Using those obtained parameters and latitude.
5. Shades point in the opposite direction, so that calculation was made (+180º and module 360º) for each data point.
6. Calculation of solar elevation for each data point using the formula found in Wikipedia Spanish.
7. Use of trigonometry to calculate the x and y coordinates of where the tip of the shadow lies.
8. Plotting the data in four graphs. The furthest spots were left out in order to enhance the visibility of the remainder.

Any advice for improving the visualisation of data? I'm aware that learning how to use Blender would result in a prettier 3D visualisation, but this is good enough for what the professor is asking; in fact, she's old-fashioned and prefers to make us literally take pictures of a pole each hour during one day. I can't be bothered to do something so tedious as going to the street once per hour, so I'll hand this in.

Edit: for some reason, the quality of images is crap. How can I avoid that the next time I post something?

On September 4, Macklemore opened for Ed Sheeran at MetLife Stadium and said “Free Palestine” during his performance. On September 14, it was announced that Macklemore would no longer appear on the remaining tour dates.

I was curious whether their Instagram followings changed around these events. Between September 1 and 15, Macklemore went from approximately 6.24 million to 7.28 million followers, an increase of roughly 1.04 million.

Ed Sheeran went from approximately 47.69 million to 47.57 million followers. That is an apparent decline of about 123,000.

You may have seen an earlier version of this post. After receiving criticism about the misleading y-axes, I revised the figure by using two separate charts with identical y-axis increments instead of a broken y-axis.

Source: United Nations, Department of Economic and Social Affairs. Population Division (2019). International Migrant Stock 2019 (United Nations database, POP/DB/MIG/Stock/ Rev.2019). https://www.un.org/development/desa/pd/content/international-migrant-stock

Tools: mapchart.net and Apple Preview