Chapter 2
Research Data Infrastructures and
Engineering Metadata
The two core elements of data technology in every field of science, in general, and in
materials modelling, in particular, are metadata or ontologies and data infrastructures.
Even though they can work independently, they are strongly connected. Whereas
metadata describes the data, the task of the research data infrastructure is to store
and to preserve the data and to connect it with its metadata description. So, mere data
becomes semantically interoperable and therefore a valuable piece of information
respecting the FAIR principles.
The chapter introduces metadata models as a semantic technology for knowledge
representation to describe selected aspects of a research asset in Sect. 2.1. The process
of building a hierarchical metadata model is re-enacted in this chapter and highlighted
by the example of EngMeta [1]. Moreover, this chapter gives an overview on data
infrastructures in Sect. 2.2. In this section, the general architecture and functions are
discussed and multiple examples of data infrastructures in materials modelling are
given.
2.1 Engineering Metadata
This section examines engineering metadata. The term is ambiguous on purpose.
First, engineering metadata names metadata which is used for engineering applications, such as materials modelling. Second, engineering metadata conceptualizes the
art of designing metadata in a more general way.
This section is organized as follows. First, it is described how an ontology-based
metadata model is created in a general way in Sect. 2.1.1. Second, this process is
explained along EngMeta, a metadata model for engineering in Sect. 2.1.2.
© The Author(s) 2021
M. Horsch et al., Data Technology in Materials Modelling,
SpringerBriefs in Applied Sciences and Technology,
https://doi.org/10.1007/978-3-030-68597-3_2
13
Research Data Infrastructures and
Engineering Metadata
The two core elements of data technology in every field of science, in general, and in
materials modelling, in particular, are metadata or ontologies and data infrastructures.
Even though they can work independently, they are strongly connected. Whereas
metadata describes the data, the task of the research data infrastructure is to store
and to preserve the data and to connect it with its metadata description. So, mere data
becomes semantically interoperable and therefore a valuable piece of information
respecting the FAIR principles.
The chapter introduces metadata models as a semantic technology for knowledge
representation to describe selected aspects of a research asset in Sect. 2.1. The process
of building a hierarchical metadata model is re-enacted in this chapter and highlighted
by the example of EngMeta [1]. Moreover, this chapter gives an overview on data
infrastructures in Sect. 2.2. In this section, the general architecture and functions are
discussed and multiple examples of data infrastructures in materials modelling are
given.
2.1 Engineering Metadata
This section examines engineering metadata. The term is ambiguous on purpose.
First, engineering metadata names metadata which is used for engineering applications, such as materials modelling. Second, engineering metadata conceptualizes the
art of designing metadata in a more general way.
This section is organized as follows. First, it is described how an ontology-based
metadata model is created in a general way in Sect. 2.1.1. Second, this process is
explained along EngMeta, a metadata model for engineering in Sect. 2.1.2.
© The Author(s) 2021
M. Horsch et al., Data Technology in Materials Modelling,
SpringerBriefs in Applied Sciences and Technology,
https://doi.org/10.1007/978-3-030-68597-3_2
13