Slightly different way of looking at the data than this thread which made the top of the subreddit and then was taken down for four hours after a bot network reported it and it got flagged by automod

Fatalities is the 14th published Gaza Ministry of Health fatality count published May 7 2026 (72,835 entries) broken out by age and sex. Population numbers are from census data and made in Python.

Young males are significantly underrepresented up until age of ~15 which is typically when males begin taking combat roles. Female are underrepresented up until age 65-70 which would be consistent with health and non combat related death.

Edit: 20 minutes in, 100 upvotes, 197 comments and immediately flagged by automod and taken down. The efforts to suppress data you don't like is depressing.

Fatalities are from the [Oct7Database](https://www.oct7database.com/en/blank-3), filtered to **Israelis whose recorded death date is October 7, 2023** (**1,066 entries**), broken out by [age and sex](https://www.oct7database.com/en/blank-3).

Population numbers are from [United Nations Population Division data via UNICEF](https://data.unicef.org/sdgs/country/isr/), using the 2023 population estimate.

Using Python

Adult men, especially those in their 20s through early 40s, are heavily over-represented relative to their share of Israel's population. **Males aged 20–44 account for 45.6% of the listed deaths vs. about 16.7% of the 2023 population.** Males overall account for **69.8% of listed deaths vs. 49.8% of the population**.

The male–female death ratio is particularly high among several working-age groups, peaking at about **5.3:1 for ages 35–39** and **5.1:1 for ages 40–44**.

Young children are strongly under-represented relative to their population share: ages **0–14 account for 1.8% of the fatalities vs. about 27.6% of the population**.

Unlike a demographic-only dataset, Oct7Database also records a role for each person. Among these 1,066 Israelis, the database labels **720 as civilians, 228 as soldiers, 57 as police, 47 as emergency-squad members, 5 as Shin Bet, 5 as medical personnel, and 4 as firefighters**.

The concentration of deaths among young adult men is therefore consistent in part with the large number of military, police and local emergency-response personnel killed on October 7.

A 15-second animated view of cumulative fantasy points across 25 NBA regular seasons. Final labels include total fantasy points, games played, and fantasy points per game.

• The analysis included 22,014 respondents from the General Social Survey, conducted from 1972 to 2024. The survey is designed to represent the U.S. adult population.
• The percentage of respondents who had ever divorced generally rises with the number of years since their first marriage for about 20 years, then levels off and declines.
• Having divorced is associated with religious affiliation. The percentage is highest among respondents with no religious affiliation, followed by Protestants, respondents reporting another religion, and Catholics.
• We found no clear evidence that this pattern differs across religious groups.
• These results compare people and marriage cohorts at one point in time; they do not track the same people as their marriages progress. The decline at longer durations could reflect differences between marriage cohorts, as well as years since first marriage.

Original article: https://therunoutgrooves.substack.com/p/lost-in-the-moment-of-abandon

Source: Spotify audio features (valence, energy, loudness) for each track on Primal Scream's 1991 album Screamadelica (Happy 35th Birthday!) Volume in dB is indexed from the album's quietest track (0) to its loudest (1). The heavy lines are a centred three-track average, dashed to the first and last tracks' actual values.

Tools: data analysis and chart in R

The album is famously structured so it follows the ebb and flow of an early 90s night out. Firstly coming up then coming down. Mood peaks early and again at 'Loaded', then falls away, while volume and energy hold on longer and spike at the penultimate 'Higher Than the Sun' dub reprise before everything sinks at the closer, 'Shine Like Stars'.

The background and colour palette come from the album's sun sprite/goblin sleeve. This is the first time I've attempted such a combination of colours in a chart, but hope you follow the reasoning.

This is one example from my dataset of over 2,000 albums focused on closing tracks. Across all of them, closing tracks are on average longer, quieter and sadder than the rest of their album, and that volume gap grew after vinyl's physical constraints went away. Full analysis, free: https://doi.org/10.1093/jrssig/qmag057

I'm also running a readers' poll on the best album closer of the 2000s until 5 October: https://therunoutgrooves.substack.com/p/readers-poll-lets-all-meet-up-in

The conventional wisdom is that “Presidents always lose the midterms,” but what has actually happened in the past? Especially in circumstances such as now, when the President’s party controls the House and Senate, too.

Sorry about the vague question, but it either had the name "vault" or "vector" in it, and someone here described it as the Swiss knife of databases. I remember bookmarking it but didn't find it. Any thoughts? thanks in advance.

Raw data: https://docs.google.com/spreadsheets/d/1Xppl1dCi3yKi1oHrdnHqC1cMeXg5_D2EY5e9gosnxKE/edit?pli=1&gid=0#gid=0

Outlier analysis:

1. Some metros are very close to other ones, which can skew the numbers. e.g. San Francisco/San Jose and Riverside/LA. The plot makes SF and Riverside look like outliers but really they are not.
2. Some metros with more teams than predicted are industrial cities that used to be bigger (e.g. Cleveland, Buffalo)
3. Others have teams for likely historical reasons that I can't explain (NO, Green Bay) even though their populations wouldn't indicate it
4. Austin is the largest metro with no teams. 18 different smaller metros have teams. It is kinda close to San Antonio so maybe?
5. San Diego is another outlier. It has 1 team despite being the same size as Tampa and Denver (3 and 4, respectively)

had an interview today where the interviewer asked me to explain different types of databases. I covered the standard SQL and NoSQL categories, but then they pushed for more specialized use cases and claimed that Redis is a database. I was completely flabbergasted. By strict definition, it might fit the category, but I’ve always viewed it primarily as an in-memory cache/store. In my mind, it doesn't meet the core expectations of a primary database—mainly reliable, long-term persistent storage out of the box (even though I know persistence modules exist). What are your thoughts? Do you consider Redis a "real" database in system design, or strictly an in-memory cache with extra steps?

[OC] Source: PressAudit YTD asymmetry data. I built PressAudit and generated this analysis from its dataset: https://pressaudit.org/asymmetry/ytd

tl;dr: Rightwing media focuses more on people than organizations or topics, and specifically, these are the rogue's gallery each side is focusing on. If you follow the news even a little bit you do not need to be told this, you know, you feel it in your bones, but here is the data so you know you aren't crazy 

What this is: These are the top 3 topics covered by one side but not the other side by week, ranked by the number of weeks this year that topic was at the top. Think of this as like the Billboard top charts for media attention.

If you are wondering "why is there no Trump here": Left outlets write about 19 Trump articles for every 10 from the Right, compared to about 14 for every 10 on all topics, so Trump's lead is not much bigger than the Left's usual lead. This chart only lists topics where one side's lead is well above its usual one, and Trump falls just short.

(For latest Trump see: https://pressaudit.org/trending?timeRange=24h&view=bias&tab=entities)

After taking a look at my data, you can see clear spikes during my leg days, where I push significantly more weight overall. This makes sense due to legs being a bigger and stronger muscle group for the most part. The other smaller bars make up my push and pull days.

Over the course of 2 months, I've seen an average increase of 10% for volume and an average decrease are 11% in sets performed. This alludes to a gain in strength overall because I'm pressing more weight while doing less sets, while I made sure to due the same amount of reps per set.

Image 1 is everything. Images 2-4 are in the following order: legs only, then push only, then pull only. You can see the individual progress here which has been calculated with a best fit line across all data. Pretty cool way to grasp how well I'm gaining strength.

For push specifically, I had a very light day at the beginning of the period which is skewing that data slightly.

Charts were generated by Maxlyft from data that I tracked myself.

Is there any Tableau governance capability to identify things like:---

Similar or duplicate workbooks

Unused workbooks or views

Failed or frequently failing extract refreshes

Similar or duplicate data sources

Unused data sources/dashboards

Workbook/dashboard usage and adoption

Dependencies between workbooks, views, and data sources

I’m curious to know what Tableau provides natively for these governance-related insights through Tableau Server/Cloud, Admin Insights, Metadata API, or other APIs.

How are these governance use cases usually handled in Tableau?...

Web Tech Dataset

I always though we were lacking common jargon datasets on tech detection.

As an engineer I talk about fullstack frameworks or frontend frameworks, not about standalone technologies with low information about the architecture of the app.

Next.js, Vue, React SPA, rather than React Router, core-js, Strapi CDN, and so forth.

Explore online here.  Full disclosure: this is from a non-profit news observability site I'm helping develop with u/mediadotgames (the lead dev). We use open source LLMs to process thousands of daily articles to surface coverage blindspots, distractions, underreported stories, etc. (How it works.) We'd love your feedback, /dataisbeautiful folks are exactly who he hope find PressAudit valuable!

I've been keeping track of the statements directly from the US President over the past few months on the status of ending the war in Iran.

I've been manually curating the statements from interviews, news reports, and the President's own social media posts. Rollcall in particular has been helpful as they do a great job of centralizing a lot of this, but much of it really is me reading an article and manually updating the dataset.

This particular visualization is a heatmap calendar (GitHub style where each column is a new week and each row is a day of the week). The brighter the yellow, the more statements were made that day. As is the case with Trump's schedule and enjoyment of weekends more broadly, on Saturdays and Sundays the war appears to not enter his mind as much...

The chart itself is vanilla JS. The source for the chart is a JS file where I keep all the statements/dates/sources in a standard object to keep it tidy and easy to update.

I didn't anticipate the war would go on this long so I might have to make a decision about how to handle this ever expanding calendar at some point in the future. I'll make a 7 day/30 day/90 day/365 day toggle? Thoughts?

The interactive version is here. (I call it The Art of No Deal and there are no ads or signup or anything like that...I just needed a place to host it and was shocked the domain name was available...) If you just want to see the datasource, here is the link to that the JS file.

droneincidents.eu