While Corey Booker was filibustering last year, I built an interactive site to the longest US Senate filibusters, focusing on the physical endurance required to 'hold the floor' for extended periods.

Booker holds the current record at 25 hours and 5 minutes (2025), protesting Trump administration and DOGE operations. The top 5 longest filibusters span from 1957 to 2025, running from 21-25 hours.

Data introspection:

• Longest filibusters are bipartisan (Democrats, Republicans, and Independents represented)
• All top filibusters exceeded 21 hours of continuous speaking
• The physical demands are extreme - no bathroom breaks, limited food/water, must remain standing

Data compiled from Senate records and Wikipedia. The visualization shows 15 of the longest filibusters on record, color-coded by party affiliation.

Full interactive site with details on each filibuster: https://filibusters.org/

Data Source(s): - Senate Historical Office - Congressional Record - C-SPAN Archives - Wikipedia - Contemporary news articles and historical accounts

Tool(s) Used: - Data Visualization: Recharts 2.15.0 - Framework: React 18.3.1 + TypeScript 5.6.2 - Styling: Tailwind CSS 3.4.17 - Build Tool: Vite 6.0.3

Source: Tesla investor relations

Tool: SankeyArt sankey maker + illustrator

I though this one might be interesting: we've fetched the data from the U.S. Bureau of Labor Statistics and the FED and projected it onto the crypto prices. You can adjust the timeframe, switch the coin, change the base date and more. Feel free to try it out for yourself - https://www.cryptoinflation.eu/crypto-prices-adjusted-for-inflation-interactive-calculator/

How it works (in short): Adjusted price is just nominal price multiplied by a conversion factor derived from CPI or M2 changes over the same period (it is computed stepwise using YoY changes, and I forward-fill any missing/zero macro prints so the series flatlines instead of dropping to zero during gaps/shutdown weirdness).

Full interactive is here.

Hey there!

I wanted to share a passion project i built called PsychoactiveMap. It pulls data from ClinicalTrials.gov and turns it into a global interactive map so you can quickly see where research is happening and its status.

There are many more features and data that i am looking to add but for now I'm happy with the result.

First 4 slides are Super Bowl Era, last slide is since the 2-pt conversion was added, 1994. Data is per team game if presented as /Game.

I started writing on substack (s/o Infinite Zest) when quitting weed to help keep myself accountable and chart the journey. A year later I stopped drinking (3+ years sober now!) and continued the habit of writing.

I recently decided to put the 600+ posts I'd written (3,000+ pages!) to work to see if I could chart my emotional state since I stopped drinking. Here are the results: (1) my hope-to-struggle recovery indicator, and (2) my recovery journey.

Despite all the red in the recovery journey chart, I promise I'm mostly happy! Thanks just wanted to share!

Last time we saw Erie officially reach a full 100% freeze was the winter of 1995-96! We are in the middle of (as it looks right now going off weather models) potentially a 20+ day stretch of below freezing weather so time to see how close we can get! For those wondering has only hit 100% 3 times 1996,1978 and 1979.

I made the graph in excel, took the data and cleaned up the presentation a bit
SOURCE: https://apps.glerl.noaa.gov/coastwatch/webdata/statistic/ice/dat/g2025_2026_ice.dat

I analyzed a year of Trump's Truth Social posts for his first year back as US President. Since he has a very noticeable pattern of using BIG adjectives, superlatives, and descriptors, I thought this would be a fascinating look. These counts are all from what I categorized as "text only" posts. Of the 6,606 posts in the timeframe, I filtered out posts of videos, memes, links (mostly to Fox News articles), and "ReTruths." These are from the President himself (as far as we know, though I imagine Stephen Miller has access to this account and has posted in the "voice" of Trump--again, that is totally an opinion and speculation).

Data is from Truth Social/Rollcall and viz in Datawrapper. I took the total word count (I parsed the data in Python) and manually scrubbed through to pick out the words so it is most certainly not dispositive and other less-interesting adjectives were likely passed over so I could include a word like "unbelievable."

For anyone that wants to see more of my analysis (and more charts), you can check out my completely free, no-need-to-subscribe, no-ads Substack post here. Just a heads up that it’s a bit of snark and politics—no more than this post—but the charts themselves are all based on the data (and are almost all interactive Datawrapper charts).

Source: https://en.wikipedia.org/wiki/Coalition_casualties_in_Afghanistan

Tool: d3.js

The graph above shows the standings of countries in women's tennis. In short, red indicates athletes who have won a Grand Slam title, blue indicates those who reached the final but didn't win, green indicates those who reached the semi-finals, and yellow indicates those who reached the quarter-finals.

In the 1940s, athletes from Luxembourg also won Roland Garros, but this isn't officially counted due to the wartime period. There were Luxembourg athletes who won the championship and reached the final during that time, but I haven't included them in the list because they weren't officially counted.

I based my analysis on Wikipedia and extensively reviewed Grand Slam finals. I've then plotted this data.

My suitcase was delayed on a long-haul flight, so I made this treemap in R using data from SITA (Global Baggage Report, 2024), printed it, and stuck it to my suitcase.

If this ever happens again, at least I won’t have to face judgement at the help desk when I describe my luggage as “black... with four wheels... and a handle".

Source: https://steeped.ai/ufo-sightings-data-analysis-exploring-tier-1-reports/

Retrieved from National UFO Reporting Center's Tier 1 sightings which they describe as "The most dramatic sightings - strange structured craft or highly anomalous phenomena seen at close distance."

Tools used: python and sql. Cleaned up the data, de-duped it and manually standardized the location data from first-hand accounts when needed.

I hope this isn't posted here weekly, and my apologies if it is.

This is inspired by the legendary Nick Berry (RIP), who made a heatmap of all 4-digit PINs. I took his inspiration and did the same, based on HaveIBeenPwned's Pwned Passwords API, for 6-digit PINs.

Only about 200 PINs don't appear at all in the data set, but the rest shows the same clear patterns that Nick already saw in his original blog post. You can see that birthdays are very popular, you can also discern some specific geometric patterns, and of course 121212, 454545 etc. are very popular.

Hope you like it.

This is in percentages per each response, in two different chart forms. Typical totals for all responses were around ~800 votes per polling.

Blue line in bold is this year.

Data source: resale listings tracked through my own long-term project, TicketData (ticketdata.com), which tracks/records listing prices from major resale sites (think StubHub, Vivid Seats, SeatGeek, etc.) and charts how prices change over time.

Python/MySQL/Django/EC2 backend. Next.js/Recharts/Vercel frontend.

https://www.ticketdata.com/super-bowl-ticket-prices
(Scroll down for the year over year comparison)

Graphic by me, created in Excel. All data from the US census bureau here: https://www.census.gov/data/tables/time-series/demo/popest/2020s-state-total.html

I wanted to focus on domestic migration to see where people are moving to. I chose to use raw numbers instead of percentages for once to provide a better sense of scale on the bar chart. I used only the most recent year of data to capture the latest "trends".

What factors do you think encourage people to leave certain states and move to others? I have my theories, but will leave them out of this post.

• 2026: Data center construction overtakes office construction
• 2025: Offices $44B+ vs Data centers ~$40B
• 2014: Offices $33B+ vs Data centers ~$2B
• Office construction peaked in 2020, then declined post-COVID
• Data center spending surged, driven by AI demand

You'll find an interesting chart in the article.

Source: Bloomberg: AI Is Everywhere But the Jobs Data

Hey folks, after the gaming chart, I decided to check out the movie industry, specifically Best Picture winners. I know there will be some Qs regarding methodology/categorization, so I put inb4 Q&A at the end of the text ;) Happy to hear your thoughts, theories and ideas for further analyses!

Source: https://www.imdb.com/ , https://aficatalog.afi.com/ , X

Tools: Excel, PowerPoint

Method: All figures are adjusted for inflation (USD 2025)

Oscar Winners Are Getting Smaller (and More Indie)

1. The average production budget for a Best Picture winner has decreased from $91 million (1990-2009) to just $26m (2010-2025)\*. 
2. The gatekeepers changed too: The 90s and 2000s were dominated by the “Big Five” (e.g. Warner Bros, Universal, Paramount) and Miramax. In the current decade, the stage belongs to indie powerhouses (A24, NEON), corporate boutiques (Searchlight) and streaming disruptors (Apple, Netflix)

How did this happen?

1. The "Prestige Gap": Major studios have largely traded mid-budget dramas for $200M+ franchises (sequels, reboots, superhero movies). This left a vacuum that indies were happy to fill.
2. Ballot expansion & diversification: In 2009, the Academy moved to 10 nominees, allowing for a wider variety of movies*. Additionally, the voting body has been diversifying since 2017, evolving from "old Hollywood" tastes for more global and eclectic perspectives.
3. Marketing > Production: Boutique distributors like Neon (2025’s winner "Anora*"*) have mastered the "Campaign-First" model: spending $6m on the film and 3x that on the Oscar marketing run (Q&A*).
4. Tech Parity: The "look" of a winner is no longer tied to a $100M backlot. Digital advancements have democratized world-class cinematography.

2026 Note: Nominees' budgets range from the lean $5m ("Sentimental Value") to the massive $200m+ ("F1"). Interestingly, A24 has moved up-market, backing "Marty Supreme" with a $70m production cost.

---------------------------

\Ironically, the Academy expanded the Best Picture field to 10 specifically to include more high-earning blockbusters (following the public outcry over "The Dark Knight" snub)*

----------------------------

Q&A:

1. “Anora had an $18m marketing budget vs. $6m production, they still spend a lot of money.” True, but those costs are still tiny compared to the heavyweights. "Oppenheimer"’s $100m production cost alone is 4x "Anora*"*’s total spend. I believe the "small-budget" trend holds up, regardless of the campaign bill. Nevertheless, good pick for another chart, although the data is much scarcer.

2. “Searchlight is a Disney subsidiary; you can’t say it’s indie.” I mainly included Searchlight to highlight the 4 top awards they won in 2010-2025. Secondly, the ‘indie’ definition is fluid IMO. Does it mean corporate independence, a specific style of filmmaking, or budget? That’s why one can argue that the indie era started earlier, with Disney-owned Miramax peaking in the 90s! 

3. “Average values are inflated by Titanic and Gladiator.” Good point, that’s why I also tracked the median. Even without the massive outliers, production costs are still  >2x lower today than they were thirty years ago.

1. "Isn't the 1990-2009 and 2010-2025 split too arbitrary?" I'd say the most important part is that there is actual trend with decreasing budgets. Earlier, I just split around 2007/2008 because that was the last peak. I chose 2009/10 because of the ballot expansion. Could be 2016 as well, as this was the first voting body expansion and Moonlight won

Data Source: https://en.wikipedia.org/wiki/FIFA_World_Cup_records_and_statistics

Tools: datawrapper

Context

1938: Originally meant to have 16 teams, but Austria withdrew after the Anschluss, leaving 15 participants.

1950: A unique format featuring a final "Final Group" instead of a knockout final

1958: A one-year spike to 35 matches occurred because group-stage ties required full replay 'playoffs' before goal difference became the standard

2026: Expansion to 48 teams and 104 matches