Hey Folks, I stumbled over this discussion having a similar use case as described in some comments above: A DataSet with a bunch of arrays called count_a, test_count_a, train_count_a, count_b, ... , controlled_test_mean, controlled_train_mean, ... controlled_test_sigma, ... Obviously a hierarchical structure would help to arrange this.

However, one point I didn't see in the discussion is the following:

Hierarchical structures often force a user to come up with some arbitrary order of hierarchy levels. The classical example is document filing: do you put your health insurance documents under /insurance/health/2021, 2021/health/insurance,....?

One solution to that is a tagging of documents instead of putting them into a hierarchy. This would give the full flexibility to retrieve any flat DataSet out of a TaggedDataSet by specifying the set of tags that the individual DataArrays must be listed under.

Back to the above example, one could think of stuff like:

```python

get a flat view (DataSet-like object) on all arrays of tagged that have the 'count' tag

ds: DataSet(View) = tagged.tag_select("count") bar1 = ds.mean(dim="foo")

get a flat view (DataSet-like object) on all arrays of tagged that have the "train and "controlled" tag

bar2 = tagged.tag_select("train", "controlled").mean(dim="foo") # order of arguments to tag_select is irrelevant! ``` I hope it is clear what I mean, I know that there is e.g. some awesome file system plugins (he has incredibly nice high level documentation on the topic) that use such a data model.

Just wanted to add that aspect to the discussion even if it might collide with the hierarchical approach!

One side note: If every array in the tagged container has exactly one tag, and tags do not repeat, then the whole thing should be semantically identical to a DataSet because every tag_select will yield a single DataArray - I.e. it might be possible to integrate such functionality directly into DataSet !?!

Regards,

Martin