This webpage was built from a template that automates the process of creating visualizations that make it easier to understand the trends and connections between a group of GitHub code repositories documented in an Awesome List, or otherwise included in configuration files.
Within smaller communities it can be difficult to find applicable open source code for your problem. A lot of what code you use is based on word of mouth and stumbling upon someone else’s repository working on similar problems. This can be inefficient.
Awesome lists are a great way to source code specific to a domain, problem, or use case that others think is “awesome”.
Awesome Lists have limits in that they don’t show connections between projects or directly show you how popularity of projects might have changed over time. These are some of the things this project seeks to make a little easier!
More information can be found in the README for the template at https://github.com/JustinGOSSES/awsome-list-visual-explorer-template
The README for this repository, which may or may not be the template, is: https://github.com/JustinGOSSES/satellite-image-deep-learning-visual-explorer.
This project started with Lawerence Livermore National Laboratory’s Software Portal and hacked it into something more reusable as a template that could be rebuilt a script that found where to look by scrapping a markdown file for GitHub links. Greatful for all their work that I can build a tiny bit on in a slightly different direction!
All the data is sourced via the GitHub API based on repositories found programmatically on Cole, R. M. satellite-image-deep-learning. https://github.com/robmarkcole/satellite-image-deep-learning