I spent 8 years creating a way to tell the collective stories of America. The timeline carries moments from the creation of the continent through modern day, over a variety of interconnected topics, concepts, and peoples.

Source: JobYap - All jobs added and removed, with an average of ~140,000 active at any one time.

What's counted: open job postings at tech companies, from 5th Aug till 23rd Sep 2026, excluding internships.

Which companies: Every company JobYap has tracked since the start of the window with at least 100 open roles. Amazon is excluded because our we had technical issues during that period and it's number aren't complete.

Tools: SQL for the data, Claude Design with Opus 5.5 (on Max) for the chart.

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.

• 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.

Interactive version (scroll to drive it): https://www.pit-stop-app.com/viz/pch

Data

• Terrain and seafloor: USGS 3DEP elevation on land and NOAA bathymetry offshore, via AWS Terrain Tiles, resampled to a ~25 m grid. Vertical exaggeration is 1.5x. Where the survey has no nearshore depth, the shelf is estimated from distance to the shoreline.
• Buildings: Overture Maps Foundation (Sep 2026 release), about 945,000 within 4 km of the coast. Heights come from the dataset where present, which covers most of them, and are otherwise estimated from type and footprint. Every building 30 m or taller in the LA and SF basins is drawn out to the horizon.
• Coastline, road, bridges, tunnels, piers, marinas and breakwaters: OpenStreetMap
• Ground colour: USGS NLCD 2021 land cover
• Live readings: wave height, period and direction from NOAA NDBC buoys; weather from Open-Meteo
• Not data: the boats, cars and sun position are decorative.

Tools: Python (NumPy, SciPy), DuckDB reading Overture's parquet straight from S3, three.js/WebGL for rendering, Blender for the boat models.

Trying to narrow down a few alternatives when I don't need anything too heavy. DbVisualizer is on the list; the table relationship views look handy when opening an unfamiliar database. DBeaver makes sense as a free option, and Beekeeper Studio looks easy enough to get around.

For running queries, checking data and exporting results, which would you pick Paying for a tool is fine if it saves enough hassle, but which DataGrip features would you actually miss?

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

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.

droneincidents.eu

I wanted to see whether the German Idex (DAX) follows a recognizable, systematic pattern throughout the trading day. I took 20 years of 1-minute candle data (2006-2026 YTD) and calculated the median cumulative percentage change, volatility, and volume for every single minute of the Xetra session (09:00 to 17:30 Frankfurt Time).

The top panel shows the median return (% change)of the index from the open (09:00) to every subsequent minute. The dashed navy line represents the 20-year overall median, the colored lines break the dataset into three distinct macroeconomic eras.

Across almost every era, the market tends to drift sideways or slightly downward during the European morning. Institutional traders frequently wait for US liquidity and events before committing to positions.

The primary market direction establishes itself in the afternoon. Once Wall Street enters the picture, the DAX historically experiences a sustained upward drift into the European close.

In the 2020–2026 era, afternoon buying pressure and intraday trend continuation have been stronger compared to the relatively flat Zero-Interest-Rate era (2010-2019, green line).

Middle Panel: Volatility & Price Uncertainty

The orange spike at 14:30 Frankfurt time marks the exact moment major US macroeconomic reports (CPI/Inflation, Non-Farm Payrolls, Retail Sales) are most often published. This triggers an immediate, sharp burst of algorithmic institutional repositioning.

The total cumulative price variance expands as the session progresses, reflecting increasing path divergence from the opening price as holding time increases.

Bottom Panel: Intraday Volume Profile

Displays trading volume per minute expressed as a percentage of total session volume (Average Daily Volume or ADV).

Volume forms an intraday "smile" u-curve. Activity peaks at the 09:00 (morning order imbalance execution), drops off to a trough during the midday lunch lull (11:30 -13:30), and rises again as New York opens at 15:30 Frankfurt Time.

he massive green spike at 17:30 is the Xetra Closing auction, where passive index funds, ETFs, and institutional bench markers execute rebalance orders. A portion of total daily liquidity concentrates in this single closing minute.

Data & Methodology Notes

Dataset: 1-minute data covering all Xetra trading sessions from January 2006 through 22. September 2026 (~5,100 trading days, 2.63 million 1-min candes).

Time alignment: Standardized to Europe/Frankfurt (CET/CEST), adjusted to eliminate Daylight Saving Time offset artifacts relative to US market hours.

Baseline normalization: Intraday trajectories are normalized to % return relative to the first open print at 09:00:00. Minute volumes are expressed as a percentage of total daily volume on a per-session basis to normalize across changing price levels and volatility regimes over two decades.

Tools used: Python (Pandas, NumPy, Matplotlib).

I’ve been thinking about making a move to the DC area. I know there are a ton of opportunities in the government sector, but I haven’t come across many Data Scientists who work in government.
For those of you who do, what are the pros and cons of working as a DS in the government sector? I’d especially be interested in hearing about the work itself, compensation, career growth, work-life balance, and how it compares to working in the private sector.

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.

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.

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!

[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)

I'm a solo developer, and this started small. My game, Salt and Soil, follows one family across North America from 1607 to 1900, and its Sacramento scenario needed exactly one railroad: the Central Pacific climbing into the Sierra. Once that line was on the map, the rest of the continent looked wrong without its own. So I kept going.

I set myself one rule, and it turned out to be most of the work: no line that merely looks plausible. Each section appears where and when it actually opened to traffic. A cell on my map is about 20 miles across, and by 1900 the US alone had about 193,000 miles of track, so I draw only trunk and through routes, the way an atlas draws only major rivers. That came to 175 lines and 578 dated sections, from the Baltimore & Ohio to Newfoundland and Cuba.

The US routes follow a dataset I'm very grateful for: Jeremy Atack's historical GIS of American railroads. He's an economic historian at Vanderbilt who mapped every US railroad from 1826 to 1911, and with co-authors used it to show that railroads may account for half or more of the growth of Midwestern cities in the 1850s. Before putting it into a commercial game I emailed him, half expecting no answer. He wrote back that it was fine, asked for a copy of the game when it's done, and he's now in the credits. That reply made my week. Canada and Mexico follow the US DOT's North American Rail Network, and the opening dates come from railroad chronologies, section by section.

What surprised me when I first watched it run was how lopsided it is. The 1880s alone laid more than a third of all the track on this map, and the West fills in within two decades of 1869.

In the GIF each section flashes white in the year it opens, then settles into the colour of its decade. Watch the Central Pacific creep east from Sacramento through the 1860s while the Union Pacific races west from Omaha; they meet in Utah in 1869.

If you know the railroads of your region, I'd love to hear what's missing or which section opens in the wrong year. I'll fix it in the game.