Replies: 4 comments 4 replies
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Interested in thoughts from @dyf, @saskiad, and @jtyoung84 |
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The three options from what I see are:
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These aren't fields that can be optional. If the examples (or real files) are missing this information, we need to get that information from the people who's data it is |
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Let me chime in here. I agree with Saskia: as we stabilize the schema, input data that do not conform to the updated schema will need to be updated accordingly. Maybe we could have an action that goes through all the data in the bucket and check that they conform to the schema? We could also run this kind of job manually, but having an action to do that (say weekly) will ensure that we spot any discrepancy in a timely manner. Thoughts? |
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How do we want to handle upgrades for data descriptions that lack relevant data which we now require?
I'm using
data_description_upgrade
and seeing that, in the new PydanticV2 implementation ofdata_description
, theinvestigators
andfunding_source
fields have a minimum size. When testing the upgrader though, multiple of the test examples on CO-test lack investigators/funders to be upgraded. This throws an error, which I think might also bin the entire upgrade, and is probably worse functionality than upgrading the description with a hole in the data.Just wanted to open some discussion on how we think this sort of thing should be handled. Here is a screencap of the error thrown by the upgrader at runtime:
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