- From: Alejandra Gonzalez-Beltran via GitHub <sysbot+gh@w3.org>
- Date: Sat, 02 Mar 2019 11:17:59 +0000
- To: public-dxwg-wg@w3.org
While we agreed that we shouldn't impose that distributions should be informationally equivalent, as in some cases we need more flexibility, we also discussed that the distinction between dataset and distributions is important. To ensure that distributions are informationally equivalent would potentially require automated transformations between distributions. So, we still leave it to a judgment call of the data providers to determine what can be distributions of a dataset, but we don't want to encourage a dataset having multiple distributions that are unrelated. Including at the distribution level properties that now belong to the dataset level (such as subject and temporal coverage) would blur the distinction between dataset and distribution. Thus, I think that if you need to specify the subject or temporal coverage (or similar properties) of a distribution, you should be considering if they are actually distributions of different datasets. -- GitHub Notification of comment by agbeltran Please view or discuss this issue at https://github.com/w3c/dxwg/issues/482#issuecomment-468911376 using your GitHub account
Received on Saturday, 2 March 2019 11:18:01 UTC