Data Sources: Market Capitalization: Sourced from MarketCapWatch.

We selected only publicly listed Japanese companies (Market Cap>USD $10 Billion) with significant operations in the white goods sector (large household appliances such as refrigerators, washing machines, and air conditioners). Market cap data as of Aug 22, 2025 (converted to USD where appropriate).

FAQ — Understanding This Chart

Q1: Why isn’t Sony included? Sony’s core business is in consumer electronics, gaming, entertainment, and financial services. It does not operate at scale in the white goods category (large household appliances like fridges, washing machines, ovens, and air conditioners), so it falls outside the scope of this chart.

Q2: Why isn’t Toshiba included? Toshiba was delisted from the Tokyo Stock Exchange in December 2023 after a buyout and is now privately held. Because our dataset only covers publicly listed companies, Toshiba is excluded.

Q3: What exactly counts as “white goods” in this analysis? We define white goods as large household appliances for cooling, cleaning, and cooking — e.g., refrigerators, washing machines, ovens, dishwashers, and air conditioners. Companies must have significant sales in these product categories to qualify.

Q4: Why are companies mainly HVAC specialists (e.g., Daikin) included? HVAC products — particularly large air conditioning systems — fall under the “white goods” umbrella in many industry classifications. Companies with significant domestic appliance presence in HVAC are included if they are publicly listed in Japan.

Q5: Why does the market cap vary so much between companies? Some giants (like Hitachi or Mitsubishi Electric) are diversified conglomerates with revenue streams far beyond appliances.

I tracked my body composition before a 7-day water fast, right after, and then after 7 days of refeeding.

• Total weight dropped from 162.1 → 150.4 lbs, then came back up to 157.2 lbs.
• Fat mass went down 21.4 → 16.8 lbs, then only partially returned (17.3 lbs).
• Lean tissue dipped during the fast but mostly came back after refeed.
• Bone mass stayed stable.

One picture shows just how extreme - and fascinating - the changes were 😊

My sheets document includes the sources, but the ranking uses 13 different sources. Sadly, not every country is included in every source so you will see blank spaces for countries that are left out in the data. I've also created a correlation index to see how different metrics matched up with each other and you can see the data I used for each ranking.

https://docs.google.com/spreadsheets/d/1YbfVevxEthNgDtK69P48Xm39bXLHi8eqfeFwxTTYEJE/edit?usp=sharing

Hope you like it, lemme know if you have any questions.

When using the file visualization graph view, the files from this subset naturally form a two-arm galaxy. Data source shown in following images. Tools used: obsidian MD

A dataset of PERM applications from the US Dept of Labor & AI chat to allow you to explore the data

I don’t know if this is appropriate for this community (but I assume a bot will inform me if not).

These are simply screen shots, laid against as blank a background as I can find on the Gaia GPS app, that show all of my human powered travel around Mt. Hood, Oregon in the last maybe 10-12 years. Ok, also a couple ski lifts (straight lines). I am lightly, but only lightly, considering turning this into a tattoo. (A potentially living document, as I add lines through more outdoor activity.)

I wonder if anyone is able to make this sort of data a bit more beautiful. The first shot is all of the routes, and this would comprise the main image. The second shot is zoomed in a little closer, just to give an idea how many lines there are. The last two shots are zoomed in even more on two of my heavily traveled areas, to show all the overlapping lines. I love the specificity, and some of these lines are even specifically evocative for me, but I don’t know if this would be tattooable, or look good if it is. But maybe there’s a way to do a sort of “heat map” - I dunno, maybe not, that’s why I’m throwing this out there!

I guess if someone wanted to go crazy I could figure out a way to send ACTUAL data, but that would be asking a lot…

Anyway, thanks, maybe!

Data source: Collected from my web app Daylettr, where users anonymously write one daily note for the next user and receive a random one from the previous one. This captures raw human thoughts under guaranteed anonymity (no logins, no tracking). Full dataset: 1548 messages

Tools: Python (pandas for processing, seaborn/matplotlib for visualization). Emotions classified via keyword matching (e.g., 'hope' for words like 'hope', 'better'; expandable for nuance).

Insights: Anonymity seems to encourage positivity (even if it seems that it might do the opposite), over 60% of messages fall into uplifting categories like kindness, gratitude, and hope. But there's depth: reflection dominates when people ponder life, with rare but raw sadness or humor peeking through. It shows humanity's spectrum: supportive yet vulnerable.

Methodology & scope:

• Universe: Publicly traded companies with ≥25% of revenue from defense‑related products/services.
• Source: Market capitalization (USD) as of Aug 2025, sourced from MarketCapWatch, Nasdaq’s 2025 defense stock review, and Forbes’ 2025 defense picks, cross‑checked with recent filings.
• Inclusions: Dual‑sector aerospace & defense firms (e.g., Boeing, Safran) where defense is a major revenue driver.
• Exclusions: Fully private/state‑owned entities (e.g., Rostec, NORINCO) without a listed arm.

Reposting with updated data , the 2012 composite used a different method and partial coverage, which made some regions (like Thailand) appear darker. This version uses average annual masked VIIRS data for a fairer 2014–2024 comparison.

Hi everyone!

I’m currently working on a university project. As part of the project, I created a visualization framework for League of Legends players and data analysts, and I’ve prepared a short survey to gather feedback. The goal is to see whether this tool can be scalable, useful, and even extended to other games.

The challenge is that I don’t have many friends so I’d really appreciate it if you could take a few minutes to complete the survey. Your input would mean a lot and help me improve the project.

Here's the link: https://forms.gle/pV9H7RYWNh2u2Y8P6

Thank you so much! 🙏

This animation shows water levels on the upper Guadalupe River from midnight July 4, 2025, to 6 p.m. July 5 (local time). The flood killed 119 people in Kerr County, including 25 girls and two teenage counselors at Camp Mystic.

Data sources

• Raw stream gauge data from the USGS was downloaded and processed to be consistent 5-minute data; it was then normalized to the average July water level at each station to get "height above normal."
• The basemap was created using data from Natural Earth, the National Hydrography Dataset, and the U.S. Census Bureau's TIGER database

Tools:

• Python for data harvesting, processing, and basemap generation
• Svelte 5, D3, and custom JavaScript for visualization

Interactive version with contextual information: https://www.willkoeppen.com/datavis/guadalupe-floods/

To assess how draft position affected league performance, I looked into over 400 12-man leagues (all snake drafts) and plotted win ratio, normalized points earned (normalized within a given league to account for various scoring and roster settings), and final league ranking for each draft position.

Surprisingly, 1st pick performed worst on average across all metrics.

League data collected from Sleeper API.

TL;DR: Comprehensive statistical analysis of Samsung 980 Pro thermal performance with/without passive cooling. Includes confidence intervals, effect size analysis, and thermal zone distribution visualization.

Data Source: AIDA64 CSV thermal logging during controlled CrystalDiskMark benchmarking Tools: Python (pandas, matplotlib, scipy.stats, seaborn) Sample Size: 2,266 pre-installation measurements, 3,089 post-installation measurements

Methodology:

• Automated test phase detection using temperature gradient analysis
• Thermal zone classification (Safe: <50°C, Warm: 50-65°C, Hot: 65-75°C, Critical: >75°C)
• Statistical significance testing with bootstrap confidence intervals
• Effect size calculation using Cohen's d

Key Visualizations:

1. Thermal Zone Distribution: Pie charts showing dramatic shift from 53.5% time in dangerous zones to 100% time in safe/warm zones
2. Statistical Confidence Analysis: Box plots with 95% confidence intervals demonstrating highly significant improvement (p<0.000001)
3. Before/After Timeline Comparison: Direct overlay showing consistent 20+ degree temperature reduction
4. Effect Size Visualization: Cohen's d = 1.813 indicates large practical significance beyond statistical significance

Notable Technical Details:

• Thermal recovery analysis reveals different cooling characteristics due to heatsink thermal mass
• Bootstrap distribution analysis confirms robust improvement across all measured parameters
• Automated cycle detection identified individual benchmark phases for granular analysis

Data Quality: All measurements taken under identical conditions with 1-second resolution. Raw CSV data and analysis scripts available on GitHub.

The visualization demonstrates how a $15 hardware modification can produce measurable, statistically significant performance improvements with proper data collection and analysis methodology.

Notes:

I only included countries with >0.830 HDI >5 Millions population.

Net migration rates are a cumulative average for the last 5-10 years.

🌎 💻 Mexico City locals are marching in the streets against digital nomads driving up their rent - but it's not the only LatAm hotspot facing this dilemma ↓

In case you missed it, hundreds of locals marched across Mexico’s capital and largest city in protest of a spike in mass tourism and digital nomads which began a few years back with the start of the COVID-19 pandemic.

The chilangos carried signs raging against the rising cost of living and gentrification across their city, in particular in sought-after neighborhoods like Condesa, Polanco, and Roma. They demanded stricter housing laws and regulation.

While Mexico City may be seeing the most virulent response to the influx of digital nomads since 2020, it’s far from the only metropolis attracting these remote workers. Across Latin America, teleworkers have traded in high US or European prices for sun, good food, and cheaper rents.

Let’s take a look at which places in our region they like the most, according to nomads.com, a site that helps them connect.

In fact, the world-famous carioca beaches of Leblon and Ipanema, classical architecture of Buenos Aires, and sensational food scene of São Paulo have all attracted international workers to come and establish their residency.

Governments across the region – including Mexico – have rolled out the red carpet for these foreigners (with their dollar- and euro-denominated salaries), with Brazil, Costa Rica, Ecuador, and Panama among the countries which have established a digital nomad visa in recent years. Colombia has one in the pipeline.

story continues... 💌

Source: Nomads.com

Tools: Figma, Rawgraphs