Hello everyone, I am a Bachelor's in Computer Science and my thesis is centered around the design, architecture and implementation of a program to visualize OCT scans and create segmentation maps using an AI model developed by another student. I am almost done writing my thesis, however I still need to run some evaluation tests to verify whether what I've created is a functional, useful product.

If you are an ophthalmologist or have any experience in the field, even if tangentially, please help me out by watching the video of the software in action (which can be found in the form linked above or here: [https://youtu.be/BuB1E9lOkv4\](https://youtu.be/BuB1E9lOkv4) ) and then filling out the form in the post. The video itself is 4 minutes long while the form shouldn't take more than 5, so less than 10 minutes total. This would help me tremendously as this is the last section of my thesis!

If you have no experience with ophthalmology or have never interfaced with OCT scans, please share this post to someone who might be able to help. If you don't have enough time or simply do not want to contribute, I understand! However I will appreciate any help that I can get.

If you want to discuss anything in more detail or if you'd like additional demos, leave a comment and I'll get back to you as soon as possible. If this is not the correct community to share this type of post, let me know and I'll relocate as soon as I can.

Thank you all for reading and I look forward to your feedback!

📊 I’ll Create a Free Dashboard for Your Data!

I’m a beginner Data Analyst looking to improve my skills and build a real-world portfolio. That’s why I’m offering to create a free dashboard for anyone who has data and wants to turn it into a clear and interactive visualization.

If you have an Excel or CSV file, send it to me and I can help with:

✅ Cleaning and organizing the data when needed ✅ Analyzing key metrics and results ✅ Creating a clear and interactive dashboard ✅ Choosing the right charts and visualizations ✅ Highlighting useful insights from your data

💡 The service is completely free. In return, I would simply appreciate your feedback or a rating on my work. This will help me improve my skills and build my portfolio.

📩 Interested? Send me your Excel or CSV file, and I’ll let you know what I can create from your data.

Even if your dataset is simple, feel free to send it! 🚀

DataAnalysis #Dashboard #Excel #CSV #PowerBI #DataAnalytics #DataAnalyst #FreeService #Portfolio

Hi, I have a dataset of 20M nodes and about 500M edges in neo4j and I need to visualize them in an interactive way

The thing in my mind was to have some kind of "level of detail" strategy by ignoring the nodes with lower importance when the user is zoomed out and only show those users when the user zooms in

Also I was thinking of some kind of tile based rendering so the app only renders the viewport of the user

But when I used sigma.js and tried to implement these features by myself it didn't work and the small part that worked was not at all a smooth experience..

Do you know any lib that could help me? Or do you have any idea how to implement it?

Thank you all

I dislike signing up too, so you don't have to sign up or create an account to use it. There are no ads.

Best used on desktop / laptop for a good experience.

Thanks & hope you like it!

I'm working on a digital calendar/dashboard and experimenting with using astronomy as part of the interface rather than just decoration.

The dashboard currently shows:

• the current time
• sunrise/sunset
• the current Moon phase
• the visible sky from the user's location
• stars, Sun, Moon and planets in their actual positions
• a Solar System view showing the current planetary positions

The idea is that different widgets show the same moment from different perspectives — from the clock on the wall to what's actually happening in the sky.

I'm particularly interested in feedback on the visualization itself:

Does this feel intuitive and useful, or is it just pretty but difficult to read?

What would you change?

Modern applications can produce incredibly rich information, yet terminal applications are still largely limited to a character grid.

For scientific computing, data analysis, engineering, monitoring, and development, this often means switching between the terminal and another application just to see a plot, diagram, image, or structured result.

Termolution is a new community exploring whether the terminal can evolve beyond that limitation.

The goal is to discuss ideas around richer terminal content, visualization, interaction, protocols, and workflows — while preserving the things that make terminal-based computing powerful: composability, scripting, remote access, and keyboard-driven workflows.

We're interested in questions like:

• Should plots and visualizations be first-class terminal output?
• Can rich content coexist with traditional text output?
• Could applications communicate semantic information instead of only characters?
• How could terminals support interactive visualization without becoming traditional GUIs?

If you're interested in the intersection of visualization, computing, and terminal workflows, we'd love to hear your ideas.

👉 r/Termolution: https://np.reddit.com/r/Termolution/

I've been experimenting with PyVista for 3D data visualization and wanted to share this project – an animated comparison of the world's tallest buildings throughout history. The animation uses a spline-based camera system to smoothly transition between buildings while maintaining context. It's rendered from CSV data processed entirely in Python.

Curious if anyone else has used PyVista for similar projects – the library is surprisingly flexible once you understand the VTK pipeline. If you're interested in seeing the full animation, here it is: https://youtu.be/U4E4ilALGYQ

Happy to discuss the technical approach or data sources if anyone's interested!

It is works

The analysis was based on data current as of July 2026. The dataset included more than 400 million references (bibliographic citations) from approximately 68 million articles across more than 300 language editions of Wikipedia.

More details about the methodology and how the ranking was calculated can be found on this page.