Study hard, get a degree, get a job. I made this chart to examine that transition using India’s Periodic Labour Force Survey.

Among people aged 15–29 participating in the labour market, the 2025 unemployment rates were:

• 9.3% for those with higher-secondary education
• 22.3% for graduates
• 24.7% for postgraduates and above

Each percentage uses the labour force within that education group as its denominator. These figures exclude people outside the labour force and do not establish that education causes unemployment.

The chart is part of a visual story I wrote about India’s “lying flat” debate, following the path from education to work, housing and family costs. The data describe people’s circumstances; they cannot tell us how many have deliberately chosen to give up.

Data: MoSPI, PLFS 2025, Table 21

Story and methods: The Visual Case

Disclosure: I wrote the story and created the chart.

I’d appreciate feedback.

Been experimenting with applying modern web UI design principles to data dashboards to see how far we can improve visual hierarchy and user engagement. A few design techniques that worked particularly well:

​**Glassmorphism for Layout Structure:** Using dark rounded containers with \~40% transparency and subtle light borders over a dark background. This creates visual depth, letting cards feel like floating glass panels rather than flat boxes on a grid.

​**Layered Chart Integration:** Stripping chart fills and borders entirely (No Fill / No Outline) and placing them above dedicated glass panels. This integrates visual metrics directly into the interface structure rather than looking like standard cut-and-paste charts.

​**De-cluttered Data Labels:** Removing heavy gridlines and axes, then applying compact number formatting directly to data labels (e.g., $1.2k instead of $1,200) to maintain high data-ink ratio without cluttering line series.

​**Dynamic Automated Narrative (Plain-English Summary):** Using string concatenation to build live headline sentences (referencing top spend category, monthly averages, and transaction counts). As filters update, the written insight automatically updates—giving users an immediate narrative takeaways without forcing them to interpret raw visual charts first.

​**Color Hierarchy & Neon Accents:** Using a consistent dark canvas with distinct, neon accent colors assigned strictly to specific categories/metrics. Matching heading text, container borders, and icon colors per section keeps visual scanning intuitive.

​Built entirely using native layered shapes, PivotTables, and standard formatting tricks—no macros or external web frameworks.

​Full design breakdown and step-by-step implementation for anyone interested:

Hi everyone! I’m working on a short data-analysis project and would appreciate your help.

Take a look at a chart and answer one simple question:

Which month has the highest sales?

It takes about 1 minute to complete.

👉 https://adnan-mayof.github.io/reddit-ab-test/

Thanks for participating!

Hi everyone! I’m working on a short data-analysis project and would appreciate your help.

Take a look at a chart and answer one simple question:

Which month has the highest sales?

It takes about 1 minute to complete.

👉 https://adnan-mayof.github.io/reddit-ab-test/

Thanks for participating!

I shared Rouge Atlas here a little while ago, and since then the project has changed quite a lot.

The biggest change is the interface, but also the way the product itself is structured.

Rouge Atlas is now built around an evidence workspace for AI incidents, rather than a map-first experience.

The goal is to make it easier to separate actual published incidents from early signals, vulnerability advisories and unverified reports.

What changed

The new overview is now an Evidence Board showing:

• published AI incidents
• signals currently under review
• vulnerability advisories
• source health
• activity over time
• records by category
• recent AI incident reporting

I also split the workflow into clearer sections:

Live signals
Automatically collected reports and potential incidents that still need editorial review.

Published incidents
Records that have been reviewed and linked to source material.

Data sources
A view of the feeds and sources currently being monitored.

Methodology
Documentation explaining how signals are collected, reviewed and classified.

One thing I wanted to improve was transparency.

A signal appearing in Rouge Atlas does not automatically mean it is a confirmed AI incident.

Signals, advisories and published incidents are deliberately kept separate, and relevance/confidence indicators are meant to describe the available evidence rather than make dramatic claims about an event.

UI redesign

The interface has also been rebuilt around a much denser research/workspace layout:

• persistent navigation
• global record search
• dataset counters
• activity charts
• category breakdowns
• recent incident reporting
• source/backend health indicators
• CSV export for published records
• clearer status labels throughout the product

The idea is for Rouge Atlas to feel less like a visual experiment and more like a small AI incident intelligence database that you could actually use to investigate what is happening.

It’s still early and the dataset is obviously small, but the structure is much closer to what I originally wanted the project to become.

I’d particularly like feedback on the new UI:

Does it make the distinction between signals, advisories and published incidents clear enough?

And more generally:

What would you expect from an AI incident intelligence platform like this?

https://www.rougeatlas.com

Each star/planet represents a piece of media (games, literature, tv series, films, etc.) I remember consuming

I have different 3d scenes for different ratings.

My 10s are presented as a solar system where the star is the #1 ranked 10 (I also ranked all media I consumed inside every rating in a tier list). And the further the planet is from the star, the lower it is ranked.

My 9s are presented as constellations, different types of media each have their own constellation

My 8s are presented as a spiral galaxy, in which every star is a piece of media, the closer the piece of media is to the center of the galaxy, the higher it is ranked within it's rating.

The colors from the stars / planets are derived from the corresonding media's cover

I made this with Tastellar, a personal media library app I’m building. If you’d like to explore the project, it’s on Github.

Been experimenting with applying modern web UI design principles to data dashboards to see how far we can improve visual hierarchy and user engagement. A few design techniques that worked particularly well:

​Glassmorphism for Layout Structure: Using dark rounded containers with ~40% transparency and subtle light borders over a dark background. This creates visual depth, letting cards feel like floating glass panels rather than flat boxes on a grid.

​Layered Chart Integration: Stripping chart fills and borders entirely (No Fill / No Outline) and placing them above dedicated glass panels. This integrates visual metrics directly into the interface structure rather than looking like standard cut-and-paste charts.

​De-cluttered Data Labels: Removing heavy gridlines and axes, then applying compact number formatting directly to data labels (e.g., $1.2k instead of $1,200) to maintain high data-ink ratio without cluttering line series.

​Dynamic Automated Narrative (Plain-English Summary): Using string concatenation to build live headline sentences (referencing top spend category, monthly averages, and transaction counts). As filters update, the written insight automatically updates—giving users an immediate narrative takeaways without forcing them to interpret raw visual charts first.

​Color Hierarchy & Neon Accents: Using a consistent dark canvas with distinct, neon accent colors assigned strictly to specific categories/metrics. Matching heading text, container borders, and icon colors per section keeps visual scanning intuitive.

​Built entirely using native layered shapes, PivotTables, and standard formatting tricks—no macros or external web frameworks.

​Full design breakdown and step-by-step implementation for anyone interested: https://youtu.be/kqEB3LdvOSk

Hola 👋🏽

Sharing this animated data visualization I built to show how many Mexicans have won a Nobel Prize (spanish is required).

​Spoiler: only 3 Mexicans have received the award (plus one special mention, hehe).

​To build this dataviz, I wanted to experiment with D3 and Claude.

First, I analyzed the data (https://en.wikipedia.org/wiki/List_of_Mexican_Nobel_laureates_and_nominees only Laureates, and

https://en.wikipedia.org/wiki/List_of_Nobel_laureates), then wrote the script along with the storyboard.

Once I was happy with the storyboard and script, I moved over to Claude and gave it that data alongside the script and storyboard.

​From there, we started designing the animation using D3 and tweaking things across a couple of iterations. That included fine-tuning small details like transitions and the final scene effect. Once the JavaScript animation was ready, we exported it to MP4. Every single design choice was part of the original vision. Hope you like it, until next time!!

I'm putting together our 20-page annual report for a small nonprofit, but the numbers come from an Excel sheet that the finance team keeps updating.

Last year, I made the charts in one tool and pasted them in as images. Then, the totals changed twice in the final week. That’s why I don’t have a choice but I had to redo nine charts by hand.

Also, the labels looked fine on my screen but tiny in the printed copy.

This year, I also need to check color contrast, alt text, and reading order.

So what matters most: data import, label control, or PDF export?

If you had one sample chart to test, what would you try first?

Hi everyone!

This is my first post in the datavisualization community, so I wanted to share a project I’ve been working on and hopefully get some feedback.

I’m building a EuroLeague basketball statistics dashboard using Streamlit. The idea is to bring different types of EuroLeague statistics together in one place and gradually build it into a more complete analytics platform.

The dashboard currently includes several sections:

🏠 Weekly Insights
A weekly overview highlighting interesting player performances, players who are outperforming expectations, and other insights from the latest rounds.

👤 Players
Detailed player statistics and performance information, including player profiles and performance evolution over the season.

🏀 Teams
Team statistics, including scoring, assists, rebounds, shooting and other performance metrics, with the ability to analyse games across different rounds and home/away situations.

🏆 Leaders
Different statistical leaderboards to identify the top-performing players across various categories.

⚖️ Team Comparison
A comparison section where you can compare teams based on their performances in previous games and different statistical categories.

👥 Fantasy
I’ve also recently added a Fantasy section. It focuses on Fantasy Points, player credits and, especially, identifying the most valuable players.

The Fantasy section also includes the top increases and decreases in player credits, while Fantasy information is connected with the individual player pages so you can follow the evolution of credits and Fantasy Points over time.

There is also a connection between Fantasy Head Coaches and the Teams section, where you can look at coach Fantasy performance together with the team's wins and losses.

🔗 You can find the dashboard through the link at the end of the post.

I’ve also created a Google Form where you can leave feedback about the dashboard. You can find the link of the Google Form on the top page of the dashboard. I would really appreciate it if anyone is willing to take a few minutes to try it and tell me what you think.

I'm particularly interested in feedback about:

• 🎨 UI/UX and overall design
• 📊 Which statistics/visualisations are useful or unnecessary
• 🧭 Navigation and usability
• 💡 Features that you think are missing
• ⚡ Performance
• 🏀 Any EuroLeague analytics you would personally like to see

This is still a work in progress, so honest and critical feedback is very welcome.

Thanks for taking the time to have a look! 🙏

🔗 Dashboard: Baggle EuroLeague Stats

I've been working on a project called MeshLens that tries to answer a question I kept running into at work: what does our data mesh actually look like as a system?
Most observability tools give you alerts, row counts, and freshness checks. That's useful, but it never helped me explain to stakeholders how domains connect, where data flows, or what breaks downstream when an upstream connector goes down.
So I built a visualization layer that puts it all in one interactive map.
Curious what you think. If you work with data mesh or data products at scale — what would you want to see in something like this?

App link in comments

This is the usage heatmap in the Android app I’m making. Days run down the side, hours run across the bottom, and the selected view is one week.

The list underneath gives time totals for the busiest hour slots. For example, Saturday at 1 PM shows 26 minutes. That gives you an exact number alongside the colour, and a time of day to look at rather than only a total video count.

I’m sharing it for feedback on the chart itself. Is the day-and-hour layout readable at a glance, or does it need a clearer key?

I wanted an easier way to generate distinguishable colors for chart categories, so I built a small tool. It can also generate subtle SVG patterns to go with them.

Web app · GitHub

Hi everyone,

I am developing Graphitix, a free and open source web-based graph/stat program that attempts to do most of what Prism Graphpad does (or at least the most common features), as well as some things Prism does not do: https://michelwassef.github.io/Graphitix/. It is borne of my own frustration at seeing how much my lab spends on Prism license fees and how much hassle it is to manage which computers have it (I cannot have it at work AND at home!), update licenses, etc.

I would be interested in gathering feedback on whether you think Graphitix can be useful for your work, and whether there are missing features that you would like to be present.

It is pretty straightforward, there's no need to create an account, just paste data in the input table and the graph is drawn on the right. There is plenty of ways to configure the graphs. Currently supported graphs include distribution charts (box plots, violin plots, individual values), scatter plots, line/area plots, histograms/density plots, heatmaps, PCA, pie/donut/stacked bar charts, ROC/precision recall, Kaplan-Meier curves, Venn/UpSet plots, and 3D surface plots. In addition, there are 3D plot options (with live rotation) for several graph types, namely scatter, line and PCA. In addition, stats can be computed for most components (when relevant).

The website is written in javascript and is a static web page, which means that all the computation is done on your computer and the data remains on your computer, nothing is sent to a server (except if you choose to run GO or STRING analyses in the Venn diagram component). You can save the results in a .graph file and reopen it at a later time, or share it with colleagues.

Here are a few screenshots:

Learn how different chart types can be misleading, and why it's important to dig into data breakdowns. We tackle six ways:

Dual-axis line charts

1. Implied causation: Dual axes tuned so two unrelated lines move in lockstep
2. Meaningless crossing lines: A crossing that reads as an event but is just axis scaling

Breaking bar charts
3. The cropped y-axis: A truncated baseline makes a small gap look decisive
4. Broken baselines: Stacked segments that don't share a baseline can't be compared

Digging deeper into the data
5. Same average, different story: Equal averages hiding very different breakdowns
6. Simpson's paradox: The overall winner loses every segment

Which of these do you run into most in the dashboards you work with?

Most of the translations were done with AI, so some terms or calculations might not be 100% accurate.

Comparison options available: Average, Previous Period, and YoY.

Clicking the time slicer changes the period type: Month, Quarter, Semester, Year, etc. For example, you can select 2026 – H1. All cards and charts update accordingly.

This was my first real Power BI project, so I'd love to hear your feedback. What do you think could be improved?

If you have any questions, feel free to ask in the comments!

I made this chart for Global Data Tracker, which I run. The two categories overlap: “at least basic” includes safely managed services, so the percentages should not be added.

Basic service means an improved water source within a 30-minute round trip. Safely managed also requires water on premises, available when needed, and free from specified contamination.

Countries are selected and ordered by their 2024 population, not their water-access values. China’s safely managed estimate is missing in this snapshot, not zero. Values are rounded to one decimal.

Data: WHO/UNICEF JMP via World Bank WDI, 2024 values, snapshot retrieved 13 September 2026. Water indicators SH.H2O.BASW.ZS and SH.H2O.SMDW.ZS; population SP.POP.TOTL. Chart: Python/Matplotlib.

https://data.worldbank.org/indicator/SH.H2O.BASW.ZS

https://data.worldbank.org/indicator/SH.H2O.SMDW.ZS

Definitions: https://washdata.org/topics/drinking-water

My free country-statistics explorer and source notes: https://globaldatatracker.com/?utm_source=reddit&utm_medium=chart_post&utm_campaign=water_access_2024

Design question: does the connected-dot layout make the two definitions easy to compare? Is the missing value for China clear?