In my eyes this is still a great definition. And this week something important happened that is very relevant for Scholarly Markdown. A small group of people deeply involved in Markdown announced Standard Markdown:
We propose a standard, unambiguous syntax specification for Markdown, along with a suite of comprehensive tests to validate Markdown implementations against this specification. We believe this is necessary, even essential, for the future of Markdown.
Markdown is in widespread use, but a lack of standard syntax and set of comprehensive tests has hindered the adoption for more complex use cases, the development of cross-platform tools, and the use of markdown as a document interchange format. I am therefore 100% behind this initiative. In particular since this is not just an initiative by large commercial organizations heavily using Markdown such as Stack Exchange, Github or Reddit, but that the entire spec and both reference implementations have been written by John MacFarlane, the author of Pandoc, the universal document converter. Not only does Pandoc already support many of the features required by Scholarly Markdown (e.g. math and citations), but John is the Chair of the Department of Philosophy at UC Berkeley.
Markdown was developed in 2004 by John Gruber, and he holds the rights to the name Markdown. He didn’t want this initiative to use the name Standard Markdown, so the implementation was renamed to CommonMark.
The consequences of all this for Scholarly Markdown?
Introducing the PID Graph
Persistent identifiers (PIDs) are not only important to uniquely identify a publication, dataset, or person, but the metadata for these persistent identifiers can provide unambiguous linking between persistent identifiers of the same type, e.g. ...
The DataCite Technology Stack
DataCite is a DOI registration agency that enables the registration of scholarly content with a persistent identifier (DOI) and metadata. This content can then be searched for, reused, and connected to other scholarly resources. ...
Making the most out of available Metadata
Metadata are essential for finding, accessing, and reusing scholarly content, i.e. to increase the FAIRness [Wilkinson et al. (2016)] of datasets and other scholarly resources. A rich and standardized metadata schema that is widely used is the first step, ...