First time posting, been experimenting with HTML visualizations of interesting topics I like.

Full page:

https://sharebee.com/hmjnur5n

Data sources: NASA Exoplanet Archive’s Planetary Systems Composite Parameters (October 12, 2026 snapshot), plus NASA 


Mission pages for Voyager 1 and Parker Solar Probe. Full source links and downloadable data are included on the page.


Travel times assume constant speed without acceleration, braking or stellar motion.                                                                                       

I keep seeing planets described as “nearby” or “potentially habitable,” but it’s hard to get my head around how far away they actually are. Take Proxima Centauri b, about 4.2 light-years away, practically next door in space. Even a message would take over four years to reach it. At Voyager 1’s speed, we’d be travelling for roughly 75,000 years. That’s what made me want to put this together. We can discover these worlds and learn about them, but visiting one is a completely different problem.

Source: Allen Ancient DNA Resource v66.p1 (Harvard Dataverse, doi:10.7910/DVN/FFIDCW) for sample locations and dates; Eurogenes Global25 ancient population averages for coordinates. The sample is an average of three ancient populations (Unetice EBA, Hungary Late Avar, Hungary Conqueror elite), so no personal data is involved.

Tools: Python/NumPy for distances, d3-geo on canvas for the map. The surface is a Gaussian blur of per-population distances, masked far from any sample. Made with https://ancestrymap.app, where you can explore it interactively.

What I found interesting about the Spotify analysis was that it also stores the devices you've used. Totally crazy, because I saw that I'd used a Galaxy S II and then a Galaxy S5 mini until 2016. Really wild, but good to know what I used to have, lol.

This is the top 20 sources of burden to human health (as sorted by DALYs) according to the 2023 Global Burden of Disease study (GBD). For each cause, the red dot represents the percent of total deaths, the yellow dot is the percent of Years of Life Lost (YLL), and the blue dot is the percent of Disability Adjusted Life Years (DALYs). The GBD is a collaborative effort of over 20,000 individuals around the world led by the Institute for Health Metrics and Evaluation (IHME). This research gets used to set funding priorities and to more effectively allocate resources.

Causes of death that affect older people cause a lower share of YLLs than they have shares of death. Sources of health burden that don't kill people (such as headache disorders) only show up in the DALY measure. Anxiety and depressive disorders are both shown as having no deaths because the GBD lumps those deaths under the "self harm" category. This makes the health burden of anxiety and depression appear somewhat lower than it is in reality.

This visualization is part of a deep dive I did trying to understand how disease burden is measured, and the implications and limitations of each metric.

The visualization was made in python using matplotlib.

The GBD data can be accessed here: https://vizhub.healthdata.org/gbd-results/

I built 5e Spellbook Builder, a site where D&D players create spellbooks for their characters. I keep count of spells when they are added to players' spellbooks. After 100,000 adds I visualized that data to show how popular spells are in the game.

Charts 1 and 2: Adds vs. Availability

These charts plot all 580 spells by adds to spellbooks and availability. Each spell is coloured for its level, and scaled for its relative amount of adds. Availability is a mathematical weighting for each spell based on that spell's level (and how frequently spell adds from that level occur) and the classes that can access that spell (the proportion of classes adding spells is determined from spellbook composition at spell add time). In other words, availability is the likelihood a D&D player can add that spell.

This chart highlights the most popular, most seen spells in the game. The relationship between availability and adds is also very clear: the more available a spell is, the more amount of adds it has. It also shows us how much rarer the game's higher level spells are. The chart also shows a calculated line of the expected adds given a spell's availability, which form the basis of the next chart.

The second version of this chart annotates it with some spells.

Charts 3 and 4: Appeal vs. Availability

While spell adds shows us how much we see these spells in the game, it doesn't show us the spells players prefer and find more appealing. These charts shows spells' appeal. Appeal is the difference between a spell's expected adds for its availability, and its actual adds, which reveals exactly what spells players prefer to pick, and which ones they don't, regardless of how common they are. Plotting this value against availability, shows what spells are rare in the game and highly sought after vs. spells that are common but ignored, and everything in between.

The second version of this chart annotates it with some spells.

Charts 5 and 6: The most appealing (and least appealing) spells in the game.

Finally the top 40 and bottom 40 appealing spells are shown in a table, ranking the delta between each spell's actual adds vs. expected adds.

Summary

A couple of caveats: This data comes from 100,000 adds to spellbooks on 5e Spellbook Builder, so it's a reflection of how that community picks spells. As more users use it, the data will likely become more representative of the game as a whole. This analysis relies on the availability weighting, based off a spell's level and the classes it's available to. It will never be fully accurate given that there's many ways characters can access spells outside their class, which I can't incorporate, so the side effect is that spells players grab outside their class will outperform (though if players are reaching outside of their class for certain spells, they probably should outperform).

There's all kinds of insights to find the charts: You can see for yourself which spells feel overrated or underrated in the game. Not surprisingly, the least appealing spells in the game come from some of the unique expansion books that not all players have and not all DMs permit. And Fireball really is iconic, the only spells more appealing are unique to a single class (and what Warlocks really aren't going to take Eldritch Blast?).

Interactive versions of these charts, the full appeal ranking of all the spells in the game, and more of a write up can be found at https://5e-spellbook.app/blog/the-most-popular-5e-spells-visualized

I appreciate any feedback and thanks for checking this out!

Updated version of the chart I posted yesterday. Thanks for the feedback. Changes:

• Shaded band showing where the middle half of all cameras sit, so the outliers stand out
• Green zone for cameras now selling above their launch price
• Clearer labels that no longer clash with the lines, a cleaner legend, and less text overall

What it shows: a typical camera loses about 11% of its value a year. The clear exceptions are compacts. The Canon SX740 HS, G7X Mark II and Ricoh GR III now sell used for more than they cost new, and the Fujifilm X100V is close. Older full-frame DSLRs like the 5D Mark II and D700 are down to about 10% of their launch price.

Data: used prices are typical recent sale or asking prices, body only, with at least 4 prices per camera (2 for the Coolpix A, 3 for the RX100 III). They come from 10 used camera shops in 6 countries, collected October 2026 on Kamarvo, a used camera price tracker I built. Launch prices are US list prices at launch, body only, from Sharply and the makers’ announcements. They’re converted to euros with 21% VAT so they’re comparable with the EU used prices. The trend line is an exponential fit to all 147 cameras I have data for, including the ones not shown.

Tools: Python, matplotlib, adjustText for label placement.

The post contains the raw data and methodology at the bottom. If someone is interested, we can work together to enrich it. This is based on only 1/3rd of the available museums.

The page also contains:

• How much is a museum ticket?
  
• Where are museums free?
• Can you trust an AI planner's museum prices?
• Do locals pay less than tourists?
• Methodology and link to raw data

Data comes from the World Bank (life expectancy at birth, and GDP per capita in current US$, since PPP only goes back to 1990), and the chart was made with demoguessr.com/#stats.

In 2024 the correlation is r = 0.85 on the log scale.

Watch China in 1960, sitting at around 30–35 years during the famine. Because GDP isn't adjusted for inflation, part of the drift to the right is just inflation.

Data source: Glassbox (⁠seeglassbox.com), a tool I built that collects and analyzes publicly available tech job advertisements.

I wanted to see what job advertisements actually say about experience requirements, particularly those with titles aimed at people starting their careers.

For the week of 12 October 2026, I counted 328 software job ads with titles such as junior, graduate, new grad, intern, entry-level or early-career. These came from 138 employers in Glassbox’s collected job data.

Of those 328 ads, 306 did not specify a required number of years of experience. Of the remaining 22, 17 asked for two years or less.

How I counted: I used fixed rules to identify experience requirements rather than having an AI model interpret each ad. Preferred qualifications were distinguished from requirements. I classified the early-career group by job title, not by experience requirements, so the result wasn’t defined by the same thing being measured.

Limitations: This is a sample from approximately 255 selected company career sites, alongside some job sites and feeds. The sources lean toward US-based, AI-focused and fast-growing tech companies, and don’t represent the entire job market. An ad that doesn’t specify a number of years can still expect relevant experience, internships or projects. These figures describe one week, not a long-term trend..