If you go to the Mastering XBRL-based Digital Financial Reporting web page you will see a section called "Profiles". If you click on that first link Profiles, you will get an RSS feed. You can see what that link does if you click on the second link Download Excel Spreadsheet with Reads Profile Information and download a ZIP archive that contains an Excel spreadsheet that reads the RSS, reads the profile files pointed to by the RSS, which has pointers to all the metadata of the examples from that mastering web page.
If you use an XBRL processor such as Arelle which is open source you can more easily work with all that XBRL-based information. If you also use a logical reasoner that understands the conceptual model of a financial report such as Pacioli, you can do even more. WAY, WAY more.
Heretofore, I have been focused on logic and rules based artificial intelligence (i.e. deductive reasoning). Now I am going to dive into the realm of machine learning (i.e. inductive reasoning). Read this if you don't understand the difference. Or, read this comparison of machine learning and machine understanding.
If you want to understand the information and take advantage of this opportunity, please read Essence of Accounting and Understanding Method (Abridged).