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This is more in the realm of training and awareness but titles of some data set, even from seasoned IMs, can be just appalling. I wonder if there is any sort of check, even if never gets beyond 'info', that would encourage better data set titles.
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We have a title-length check, which is a warn. That check looks for a title that is > 5 words, and "length" was as complex as we could reasonably handle (there is a similar check for abstract, also a warn). A more "semantic" check would have to try to intuit anticipated terms from a title (eg, dates, organism names, place names, measurements) - which I think will be beyond reasonableness for quite a while.
I agree that this is more of a training/awareness issue, and the BP doc also talks about what makes a good title and abstract too. The simplest way would be to ramp up evangelizing, and maybe the fields that go into a citation would be a good place to start, as they are most visible. IMO what would elevate the whole community is some sort of dataset-review step, either as peer review or as a copy editor with a checklist. Peer-review is just that (another data mgr), but a copy editor is more likely to be associated with the repository.
Thanks @mobb. I could envision a check, for example, that evaluates the title against the geography of place names, or implements some aspects of text- or sentiment-analysis. But, yeah, something along those lines is likely beyond the scope at this point and could be wishful thinking (though I hope not).
In the meantime, would love to chat more about how to ramp up evangelizing, and your ideas of 'review' - possible ASM fodder?
This is more in the realm of training and awareness but titles of some data set, even from seasoned IMs, can be just appalling. I wonder if there is any sort of check, even if never gets beyond 'info', that would encourage better data set titles.
The text was updated successfully, but these errors were encountered: