More and more research projects are now required to have a data management plan, sometimes already upon submission of the proposal, sometimes early into the then funded project.
These plans lay out the kinds and amounts of data to be generated by the project, as well as information about their storage, archiving, accessibility and related questions. There are also software management plans or more generic project management plans, which serve similar purposes.
A big issue with these plans — which so far are usually just text documents, and often not even public ones — is monitoring compliance, i.e. if statements made in the plan have been followed up by appropriate action.
I think this problem could be eased substantially if these plans were both public and machine readable, which would also allow them to be used for things other than monitoring compliance, e.g. as a discovery tool — imagine you could subscribe to the feed of all data of kind X acquired from sources of kind Y.
What I mean by machine readability is expressed quite well in the introductory part of the Wikipedia article "Machine-readable data":
"Machine-readable data is data (or metadata) which is in a format that can be understood by a computer. There are two types; human-readable data that is marked up so that it can also be read by machines (examples; microformats, RDFa) or data file formats intended principally for processing by machines (RDF, XML, JSON).
Machine readable is not synonymous with digitally accessible. A digitally accessible document may be online, making it easier for a human to access it via a computer, but unless the relevant data is available in a machine readable format, it will be much harder to use the computer to extract, transform and process that data."
Any advice on how best to go about achieving machine readability for data management plans?