- Updated titles and descriptions in TimeSlot, TimeSpan, TimeSpanType, and TimespanBlock for improved readability and understanding.
- Enhanced multilingual support with refined alt_descriptions and structured_aliases across various classes.
- Changed mapping types from broad_mappings to exact_mappings in WebClaimsBlock, WebCollection, WebPage, WebPlatform, WebSource, WorkExperience, and various YouTube-related classes for better alignment with schema definitions.
- Improved comments and modeling notes in VariantTypes to clarify usage and examples.
- General cleanup of unnecessary comments and formatting adjustments for consistency across YAML files.
- Implement `normalize_linkml_alt_descriptions.py` to convert structured alt_descriptions to the expected scalar form.
- Implement `normalize_linkml_structured_aliases.py` to flatten language-keyed structured_aliases into a standard list-of-objects format.
- Implement `validate_linkml_schema_integrity.py` to validate the integrity of LinkML schema bundles, checking for import resolution, YAML parsing, and reference existence.
- Removed obsolete slots: `has_or_had_custodian_observation`, `provider`, and `specificity_annotation`.
- Updated `has_or_had_score` slot to use `SpecificityScore` class and modified its description and examples.
- Added new slots: `end_seconds`, `end_time`, `has_archive_path`, `has_or_had_custodian_name`, `protocol_name`, and `protocol_version`.
- Introduced a script `check_annotation_types.py` to validate the presence and structure of `custodian_types` in YAML files.
- Added a script `update_specificity.py` to automate updates related to `SpecificityAnnotation` to `SpecificityScore`.
- Introduced VerificationStatus, Verifier, VersionNumber, ViabilityStatus, VideoCategoryIdentifier, VideoIdentifier, WhatsAppProfile, WordCount, WorkRevision, and WorldCatIdentifier classes to capture various aspects of data verification, categorization, and identification.
- Created corresponding slots such as analyzes_or_analyzed, unit_type, years_restricted, benefits_provided, consumes_or_consumed, has_or_had_contact_details, has_or_had_investment, has_or_had_liability, has_or_had_likelihood_score, has_or_had_location, has_or_had_net_asset, is_or_was_affiliated_with, is_or_was_allocated_to, is_or_was_alternative_form_of, is_or_was_categorized_as, is_or_was_used_by, and was_last_updated_at to facilitate detailed tracking and categorization of entities and their attributes.
- Each class and slot includes detailed descriptions, usage examples, and mappings to relevant ontologies to ensure interoperability and clarity in data representation.
