[dxwg] Model Series of Data as Distributions of a single Dataset (#1429)

sabinem has just created a new issue for https://github.com/w3c/dxwg:

== Model Series of Data as Distributions of a single Dataset ==
In the Swiss DCAT profile we are modeling Dataseries such as yearly elections in a single dataset, where each distribution contains the election data of one year. I know that is considered an antipattern by DCAT. And I also know that all properties of Distributions are designed in such a way, that it is assumed that the all Distributions of a Dataset have comparable content. 

But there is also a sentence at https://www.w3.org/TR/vocab-dcat-3/#Class:Distribution:

> Nevertheless, the question of whether different representations can be understood to be distributions of the same dataset, or distributions of different datasets, is application specific. Judgement about how to describe them is the responsibility of the provider, taking into account their understanding of the expectations of users, and practices in the relevant community. 

I was recently surprised that we are not the only profile modeling a series of data with Distributions, and that there is a certain resistance to give this up this pattern, since the overall impression is, that this would make for too many datasets and the data portals would get harder to mangage with too many datasets.

On the other hand if DCAT would approve of that pattern or antipattern, then properties would be needed to describe the content in each Distribution. In the Swiss profile we added the attribute `dct:coverage` to Distributions, with a domain of either spatial or temporal values. 

I am just curious what DCAT's opinion on this topic is and I want of launch a discussion about this: Can't it be that sometimes Distributions differ in their content and shouldn't DCAT alos support these use cases with appropriate properties?

Please view or discuss this issue at https://github.com/w3c/dxwg/issues/1429 using your GitHub account


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Received on Sunday, 21 November 2021 21:51:10 UTC