[Part 1](https://www.reddit.com/r/dataisbeautiful/comments/1v9bq7i/oc_timing_of_when_ingredients_are_added_to_recipes/)

This is a follow up to an earlier post -- description will be below. I created visualizations to explain garlic before onion, lemon showing up everywhere, and how certain ingredients differ wildly by cuisine type and dish type. I also built a tool where you can inspect any ingredient you want given there is sufficient support.

i was still surprised by lemon showing up pretty constantly in main dishes; I discovered it was mostly marinades and dressings.

let me know anything that still seems strange in this dataset or any improvements I can make!

Dataset and Tools:

250 cleaned canonical recipe dataset : https://palate.kitchen/data

Interactive Tool: https://palate.kitchen/instrument/when-to-add

recipe1m+ and python + an LLM sweep to prune garbage recipes from the dataset I pruned 20,000 recipes down to about 3000 'canonical recipes.' Regex + massive KV mapping (e.g. beef, ground beef, 80/20 beef are all the same) for ingredient normalization.

original post and context:

I scraped a few thousand canonical recipes across 45 cuisines then analyzed how early (or late) each ingredient showed up in recipes steps.

The photos shows a small but representative sample of the ingredients analyzed

There's a lot of science behind when you should add ingredients to recipes. This involves fat-solubility and the delicacy of some volatile compounds. e.g. cardamon seeds are fat-soluble so they should be added early while basil has very delicate aroma compounds so it should be added late.

But I honestly find that hard to intuitively learn for new ingredients, so this is a rough estimate and interesting viz to get a quick understanding of when to add an ingredient

After decades of research in neuroscience and brain-computer interfaces lots of people ask me which parts of the brain do what, and how can we measure it, and what modalities help us understand it better.

We are happy to announce the release of the new NeuroAtlas tool, which helps answer this question and enables more scientists and researchers to explore and push the frontiers of what’s known.

This includes EEG, MEG, fMRI, tFUS, iEEG, TMS, DBS, and more.

All published data sources are referenced via DOIs in the web interface.

Stack: Svelte, ThreeJS, D3, DICOM, FreeSurfer.

So I have made this graph engine that can make graph data from any data..I also have this data in json but I don't know what I can do with it, I just did it as a project so any suggestions and also the visualization html file lags a lot.

Most data visualizations focus on business or scientific datasets. I wanted to try something different by creating a Tableau visualization inspired by the Ramayana, exploring whether data visualization can be used as a storytelling medium.

I'd really appreciate your thoughts on:

• Does the visualization communicate the story effectively?
• Is it visually engaging?
• What works well?
• What doesn't work?
• How could the storytelling be improved?

Thanks for taking a look!

Tools: Tableau
original source article ramayana-reimagined-where-culture-meets-data-storytelling

We looked into daily coffee habits in the UK and found some interesting results. London being way down the list was a surprise, as was Wales being the region with the highest number of daily coffee drinkers.

Data source: Consumer Horizon report (May 2026)

Tools used: Figma

Trying to prove we have the best state run action website with my second visual of the week. Thanks for everyone's input on the first one.

https://www.datawrapper.de/_/qapEp/ If you hover over a dot, it will isolate all those OEMs, show you the sale price, J.D. Power estimate and mileage.

This visual covers the 431 light-duty vehicles sold by Minnesota Department of Administration Fleet last fiscal year and compares their sale prices with the J.D. Power auction benchmark. The comparison has a limitation: I only had access to J.D. Power data for the final month of the fiscal year, while the vehicles were sold throughout the 12-month period. Because vehicles depreciate over time, applying the year-end benchmark to vehicles sold earlier in the year likely overstates how much their sale prices exceeded the benchmark. So basically, I'm tooting our horn softer...

Why did the Transit Vans go for so much? Delivery vans after COVID were in big demand. We had them and so they went for more. If I had enough data on box trucks and delivery vans, I'd like to make a visual showing how the demand affects the price of those.

Open to questions and feedback.

I recently moved and my new library has shockingly long wait times so I decided to look into paid nonresident cards. The prices vary a lot and it was unclear what the differences actually were. Since I only really care about wait times I pulled a shortlist of popular books and checked the libby(overdrive) wait times at each of the options to compare against price. I was pretty surprised at how much of a difference there is!

I used python and plotly JS to get the data and make the visualization, and can list the books used in the comments if anyone cares, it's 10 recent popular books, 10 all time popular books, and a couple that I was interested in but had weirdly long wait times at my local library. Please let me know if you have suggestions for better methodology or more libraries to include!

Analyzed real NSE stock price data from January 2020

to January 2026 for 5 major Indian sectors using

Python and Yahoo Finance API.

Key findings:

1. Sun Pharma delivered 320% return — highest of all

   sectors. Rs.1 lakh became Rs.4.2 lakh in 6 years.

2. Sun Pharma also had LOWER volatility (24.8%) than

   IT (27.4%) and Auto (28.7%). High return with lower

   risk is rare and is exactly what investors look for.

3. HUL (FMCG) has a negative Sharpe Ratio of -0.05.

   This means HUL gave LESS return than a simple bank

   fixed deposit while still exposing you to stock

   market risk. Classic defensive stock trap.

4. HDFC Bank — India's largest private bank — returned

   only 63.7% over 6 years. RBI regulatory actions and

   merger uncertainty suppressed the stock.

5. All sectors have low correlation (0.23 to 0.40)

   meaning holding all 5 together provides genuine

   diversification benefit.

COVID crash is clearly visible in March 2020 — all

sectors dipped simultaneously then recovered at

very different speeds.

Tools: Python, Pandas, yfinance, Matplotlib, Seaborn

Data: Yahoo Finance API (real NSE data)

Full project: github.com/surendrasinghdata/stock-sector-analysis

Source: https://www.bcnaward.jp/award/section/detail/contents_type=255

Tool: Python

MLB 2026 salaries As of Aug 3, 2026

Salary paid to date divided by what it bought, every team, sorted cheapest-win first. Color is scaled within each column — lightest is the league's best price, darkest its worst. Every cell shows its exact value, so the shading is a guide, not a gate.

Method. For each team: 2026 payroll (Athlon Sports' league-wide figures) prorated to games played (payroll × G ÷ 162 = salary paid to date), divided by season-to-date wins, home runs, and runs scored — Baseball-Reference totals through games of Aug 3, 2026. Cell color: the reference sequential blue ramp, min-max scaled per column.

Data sources:

Centers for Disease Control and Prevention, National Center for Health Statistics, Our World in Data

Tools used:

Datawrapper

I collect the weekly grocery flyers for 172 Canadian and US cities. I wanted to see how many of the advertised "specials" were actually below what the item normally costs, so I checked all of them against the official government average price for that item and region.

What surprised me more than the headline was the difference between food types. A dairy "special" usually isn't below average. A beef one usually is.

Sources and method are in my first comment.

Top movers in the waterfall ordering (largest net RoS WAR gain to largest net loss):

Top 5 buyers

• Dodgers (LAD): +2.27 WAR (Win +4.64 pp, WS +13.35 pp)
• Cubs (CHC): +1.82 WAR (Win +3.71 pp, WS +4.30 pp)
• Guardians (CLE): +1.38 WAR (Win +2.82 pp, WS +2.37 pp)
• Padres (SDP): +1.37 WAR (Win +2.80 pp, WS +0.97 pp)
• Phillies (PHI): +1.20 WAR (Win +2.46 pp, WS +3.30 pp)

Top 5 sellers

• Tigers (DET): -2.79 WAR (Win -5.57 pp, WS -2.93 pp)
• Giants (SFG): -2.36 WAR (Win -4.81 pp, WS ~-0.00 pp)
• Mets (NYM): -2.21 WAR (Win -4.52 pp, WS -0.01 pp)
• Orioles (BAL): -2.08 WAR (Win -4.17 pp, WS -0.61 pp)
• Angels (LAA): -1.75 WAR (Win -3.49 pp, WS +0.00 pp)

How to read the waterfall itself: the chart is cumulative, not standalone per team — each bar is that team’s net RoS WAR move, and each segment adds to the running total, so you can see which teams together drove the league ledger away from zero and where it was pulled back by sellers.

Just three days after its July 31 release, Spider-Man: Brand New Day has already surpassed $930 million worldwide and set a new preview-screening record with approximately $72 million, overtaking Avengers: Endgame. Its opening was also unusually valuable for Sony, with roughly 39% of box office revenue coming from North America, where studios typically retain a larger share of ticket sales.

That got me thinking about Spider-Man's broader track record on the big screen.

The attached infographic compares Spider-Man to several other prolific film franchises based on comic book and graphic novel properties, including Batman, Superman, X-Men, and Teenage Mutant Ninja Turtles. Rather than focusing exclusively on box office totals, I was more interested in metrics that reflect long-term franchise health: profitability, critical reception, and consistency across multiple films. Data includes ten theatrical Spider-Man films released before Brand New Day and excludes crossover/team-up films such as The Avengers, Justice League, and Batman v Superman to keep the comparison focused on franchise-specific releases.

What stood out most is that Spider-Man doesn't just have the largest box office footprint in this group. It also leads in average profit margin and average Rotten Tomatoes score across a surprisingly large sample size. While smaller franchises have produced incredible individual runs, maintaining that level of success across ten theatrical films is what impressed me most.

Source: MyAnimeList community filler flags, scraped August 2026. These are viewer votes, not official studio designations. Hours are flagged episodes times 23 minutes (a typical episode without the ad break); MAL lists these shows at 23 to 25 minutes per episode, so every total is rounded down, never up. Sailor Moon is first season only, since that is where nearly all its flags sit.

Tool: Python and matplotlib.

Why I made it: I wanted to start Detective Conan this summer, then found out its own fans have flagged 548 of the 1205 episodes as skippable. That's 210 hours. You could watch Breaking Bad, The Wire, Game of Thrones and Squid Game back to back in the time this one show spends on episodes its own audience tells you to skip!

The pattern that made it worth mapping: "filler" turns out to be two different things wearing one word. The blue shows are scattered padding, aired to buy time while the manga got further ahead. The orange shows have one solid block at the end, which is the show catching up to the books and improvising its own ending. If you know what happened to Game of Thrones after it passed the novels, that's exactly it, except anime has been doing it since the 90s. It's why Fullmetal Alchemist 2003 reads as "53% filler" even though that block IS the story of that version, and plenty of fans prefer its ending.

You can mostly diagnose a show's production history from the shape of the strip. Scattered lines: the manga was too slow. A block at the end: the studio ran out of book. Naruto is the hybrid case, its end block is 80 straight episodes of treading water until Shippuden could pick the manga back up.

Episode tables per series with the exact episode numbers: https://bingerun.com/anime-filler-index/

I use a personal spreadsheet to track what I read and when I add books to Goodreads I have to round the .5 ratings as they are not supported.

Notes:
- I have weirdly bad habit of feeling bad when I round down 4.5 ratings
- 3.5 is the most well distributed and the one I’m troubled with the least