- From: Alejandra Gonzalez-Beltran via GitHub <sysbot+gh@w3.org>
- Date: Tue, 05 Mar 2019 20:52:35 +0000
- To: public-dxwg-wg@w3.org
Further to @makxdekkers' comment, indeed by saying that distributions don't need to be strictly fully informationally equivalent, we don't mean that they can hold totally different data (and I am not talking about the data type as in @agreiner's example of log files, but about the data itself). So, in your use case @agreiner, those would be different datasets and not different distributions of the same dataset. The ED currently states: ```In some cases all distributions of a dataset will be fully informationally equivalent, in the sense that lossless transformations between the representations are possible. An example would be different serializations of an RDF graph using RDF/XML, Turtle, N3, JSON-LD. However, in other cases the distributions might have different levels of fidelity to the underlying data. For example, a graphical representation alongside a CSV file. The question of whether different representations can be understood to be distributions of the same dataset is use-case specific, so the judgement is the responsibility of the provider.``` In my opinion, that text clarifies the points we made a few times in this discussion. The example given about a CSV vile and a graphical representation shows that in terms of the information they convey, different representations may not be identical in information, but we are not implying that the can be totally different. @agreiner do you think we need to add further clarifications on this? If so, can you please suggest some text? Thanks -- GitHub Notification of comment by agbeltran Please view or discuss this issue at https://github.com/w3c/dxwg/issues/482#issuecomment-469852526 using your GitHub account
Received on Tuesday, 5 March 2019 20:52:42 UTC