For this to happen, start with:

1. Open an article in the web browser (Google Chrome or Firefox) that can support pdf.js or epub.js or html if that is available
2. Open hypothesis app, and start annotating at passages (as in this annotation)
3. Add a consistent tag (see the other how_to tagged articles)
4. Then search all related hypothesis annotations and notes on pages using those tags and organise knowledge that way.

This is step one. Set up a standard set of tags. These tags will then classify resources. Let's say I want to classify all studies on polygenic risk scores on the web. These documents come in the form of HTML, or PDF. If on the top of that, you'd also like to tag something like tutorial, or software to do the job, then write those tags as well when you come across a resource and tag it. So, a list of tags to learn about polygenic risk scores could be something like:

• polygenic risk score
• polygenic_risk_score
• tutorial
• utility
• why_how So, all resources that pertain to polygenic risk scores can now be divided into a set of main document types: some that discuss tutorials and how tos, others discuss the utilisation values and debates. Later, these could be reassembled.