2.1 Engineering Metadata
17
2.1.1.3 The Metadata Model and Its Implementation
When the object model is converted to a formal language, special care has to be
taken if parts of the object model already exist in some standard. With respect to the
categorization taken in Sect. 1.3, the probability to find existing, fitting standards for
technical or descriptive metadata is high, whereas for process- and domain-specific
standards they are not likely to be found. Some of the relevant standards are described
in quoted section; however, an excessive amount of standards exist.
Another consideration when implementing the model is choosing the right formal language for representing the metadata model. Most likely, this will be XSD
2 or
JSON Schema.
3 Both offer a strict structural definition of the entities, attributes and
relations, and the decision is more or less based on setting of the metadata model:
What are the skills available, what are the technical requirements for the implementation? For example, the question, which standard the database or repository supports,
where the metadata later will stored, is crucial in deciding for an implementation
language.
2.1.1.4 Metadata Processes
A metadata model alone is not sufficient. As Edwards puts it, metadata products such
as models have to be accomplished by metadata processes:
Metadata products can be powerful resources, but very often—perhaps even usually—they
work only when metadata processes are also available. [3, p. 668]
Otherwise, if processes are not available, something called “metadata friction” would
occur and the semantic assets would become worthless. This phenomenon would
indicate the additional effort of (manual) metadata annotation and management,
which has to be reduced by corresponding processes. This view is backed by the FAIR
principles [6] and the additional guidance from an EU report [7]. The FAIR principles
state metadata description as the main concept, and the study [7] accomplished this
rather technical approach by processes surrounding these principles. In the case
of materials modelling and computational engineering, in general, these processes
would include, but are not limited to, the following:
• Automated metadata extraction. One finding of [8] states that manual metadata annotation is a barrier for good research data management especially in the
engineering science. Hence, automated metadata extraction is a major supporting
process.
• Data and metadata stewardship. Data and metadata need clear responsibilities
and roles that define stewardship. This means that such a role has the responsibility
of supporting metadata annotation, building metadata models and checking the
2 https://www.w3.org/TR/xmlschema11-1/.
3 https://json-schema.org/.
17
2.1.1.3 The Metadata Model and Its Implementation
When the object model is converted to a formal language, special care has to be
taken if parts of the object model already exist in some standard. With respect to the
categorization taken in Sect. 1.3, the probability to find existing, fitting standards for
technical or descriptive metadata is high, whereas for process- and domain-specific
standards they are not likely to be found. Some of the relevant standards are described
in quoted section; however, an excessive amount of standards exist.
Another consideration when implementing the model is choosing the right formal language for representing the metadata model. Most likely, this will be XSD
2 or
JSON Schema.
3 Both offer a strict structural definition of the entities, attributes and
relations, and the decision is more or less based on setting of the metadata model:
What are the skills available, what are the technical requirements for the implementation? For example, the question, which standard the database or repository supports,
where the metadata later will stored, is crucial in deciding for an implementation
language.
2.1.1.4 Metadata Processes
A metadata model alone is not sufficient. As Edwards puts it, metadata products such
as models have to be accomplished by metadata processes:
Metadata products can be powerful resources, but very often—perhaps even usually—they
work only when metadata processes are also available. [3, p. 668]
Otherwise, if processes are not available, something called “metadata friction” would
occur and the semantic assets would become worthless. This phenomenon would
indicate the additional effort of (manual) metadata annotation and management,
which has to be reduced by corresponding processes. This view is backed by the FAIR
principles [6] and the additional guidance from an EU report [7]. The FAIR principles
state metadata description as the main concept, and the study [7] accomplished this
rather technical approach by processes surrounding these principles. In the case
of materials modelling and computational engineering, in general, these processes
would include, but are not limited to, the following:
• Automated metadata extraction. One finding of [8] states that manual metadata annotation is a barrier for good research data management especially in the
engineering science. Hence, automated metadata extraction is a major supporting
process.
• Data and metadata stewardship. Data and metadata need clear responsibilities
and roles that define stewardship. This means that such a role has the responsibility
of supporting metadata annotation, building metadata models and checking the
2 https://www.w3.org/TR/xmlschema11-1/.
3 https://json-schema.org/.