I don't know that there can be a good standard other than what I was taught when studying environmental computer models, which is to be clear about everything up-front. Obviously this has to be documented somewhere in the expected place (like a README or the like) but the following things should probably be addressed on some way:
Methodology of how the data ...
I think the key is to that the data gathering and usage document should be pretty heavily integrated. This really should be documented as part of intended use and use caveats. In other words, documenting data sampling methods and assumptions, and problems gathering or releasing the data is what is needed, and gaps in data are just a part of that picture.
I do not know of standards in this area, but I do know that many data owners document missing data or other known issues in a dataset. This is generally documented in either the site from which the dataset is linked or in the metadata of the dataset itself.
In the old data.gov metadata schema, statistical datasets were required to supply a bunch of additional metadata that disclosed the following items:
Data quality (variances, CVs, CIs, etc)
This would ...
In RDF, you can do the trick mixing these ingredients:
an observation ontology or vocabulary;
RDF blank nodes;
a provenance ontology.
Sensor and Observations Ontologies
There are many ontologies for measurements and observations, see e. g. this (outdated) list.
In the example below, I will use quick and dirty ad-hoc vocabulary, do not take it seriously.