Metacat

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)

redirect "short form" metacat read URIs to the the new Metacat UI using the configured UI context. This translates the docid -> pid to use the correct identifier for the correct service. https://projects.ecoinformatics.org/ecoinfo/issues/6546

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.

Add admin service to update DOI registrations by specifying a list of formatIds or DOIs, or update all.

use new method to override the CN URL when constructing a CNode instance. see https://redmine.dataone.org/issues/5142

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.

Store the cn url in the backup.

switch to use FIleUpload instead of O'Reilly COS library for handling chunked file uploads. https://projects.ecoinformatics.org/ecoinfo/issues/6517