Metacat

adjust tests for production service -- more "real" information shows additional return values from the query.

switch to the production ORCID server for looking up orcid matches for our creators.
add test to summarize how many creator matches we can actually find. https://projects.ecoinformatics.org/ecoinfo/issues/6423

cache the imported models to avoid timeouts from remote hosts (or being locked out for too many requests in a given time period).

process all the returned annotation suggestions until we find one that is appropriately located in the subclass hierarchy for the given superclass.

use in-memory TDB dataset for querying annotations for indexing -- this comes with the same reasoning capabilities as the directory-based one, but has the benefit of not filling the directory with triples that will not be used again. prepping for d1 AHM

when indexing annotations directly, just use an in-memory triple store rather than TDB since we remove each graph as it is processed (and my TDB instance would get into the multi-GB range with a few runs, even if I removed the old models)

simplify lookup for classes and orcid. remove the "random" annotation code branches -- just too confusing to look at those bogus classes especially now that we have "real" generated annotations.

first pass at direct EML->semantic index method. Still produces an RDF model, but does not persist it in Metacat, only in the triplestore. Allows us to re-run without adding stale RDF to the MN store.

include BioPortal lookup for Entity matches using the data table description. TODO: only associate measurements to the entity observation if they apply.

check for null entities and/or attributes (typically when otherEntity is being used in EML).