2
1 Introduction
heterogeneous data sources [20, 21], these four principles are jointly fundamental
and cannot be separated from each other. In other typical cases, e.g. for complex
simulation workflows, interoperability is the main concern [22, 23]; however, even
in these cases, it is reasonable to follow good practices concerning all the aspects
of FAIR data management. Findability and accessibility are supported by systems
of persistent identifiers, with Digital Object Identifiers (DOIs) now covering almost
all scientific publications, as well as platforms and legal solutions for open-access
publishing.
The single aspect of greatest importance to the findability, interoperability and
reusability of data is semantically characterized data annotation, i.e. the provision of
metadata in a way that is widely agreed and understood on the basis of communitygoverned metadata standardization. This is the main topic of this book, where the
focus will be on the interoperability aspects of FAIR data management and its practical realization by digital platforms and data infrastructures for materials modelling.
1.2 Semantic Interoperability
Interoperability is generally understood as being constituted by an agreement of
multiple parties (platforms, code developers or similar) on a common standard, so
that certain issues can be dealt with by all of them in the same way or, at least, in a
sufficiently similar way. Ideally, this is the case when a whole community coherently
adopts a single approach. This is often also called compatibility; in the strict sense,
however, more recent use of the term compatibility restricts itself to the capability
of exchanging data bilaterally, in the absence of a community standard. Theoretically, compatibility would then be more immediate than interoperability, since an
intermediate third-party standard would not be required. However, it can be doubted
whether this is a particularly useful distinction. Virtually every work on compatibility eventually aims at the widespread acceptance of a standard, protocol or file
format. In this sense, interoperability is simply another, more modern word for all
efforts at ensuring that heterogeneous software architectures, in the broadest sense,
can function correctly.
Kerber and Schweitzer summarize that “interoperability has become a buzzword
in European policy debates on the future of the digital economy” where “one of the
difficulties of the interoperability discussion is the absence of a clear definition of
interoperability” [24]. This is certainly not coincidental. A research and development
landscape dominated by project-based funding from calls with priorities driven by
political or cultural trends is a sure recipe for rendering the associated terminology
vague to the point of complete dilution. “All stakeholders,” to use another buzzword,
aim at securing their share. This is evidenced by the multitude of researchers who have
only recently detected that their traditional line of work is actually a subdiscipline of
artificial intelligence, Industry 4.0 or data science (or, of course, all the three). In the
case of interoperability, this is particularly ironic given that one of its core elements
1 Introduction
heterogeneous data sources [20, 21], these four principles are jointly fundamental
and cannot be separated from each other. In other typical cases, e.g. for complex
simulation workflows, interoperability is the main concern [22, 23]; however, even
in these cases, it is reasonable to follow good practices concerning all the aspects
of FAIR data management. Findability and accessibility are supported by systems
of persistent identifiers, with Digital Object Identifiers (DOIs) now covering almost
all scientific publications, as well as platforms and legal solutions for open-access
publishing.
The single aspect of greatest importance to the findability, interoperability and
reusability of data is semantically characterized data annotation, i.e. the provision of
metadata in a way that is widely agreed and understood on the basis of communitygoverned metadata standardization. This is the main topic of this book, where the
focus will be on the interoperability aspects of FAIR data management and its practical realization by digital platforms and data infrastructures for materials modelling.
1.2 Semantic Interoperability
Interoperability is generally understood as being constituted by an agreement of
multiple parties (platforms, code developers or similar) on a common standard, so
that certain issues can be dealt with by all of them in the same way or, at least, in a
sufficiently similar way. Ideally, this is the case when a whole community coherently
adopts a single approach. This is often also called compatibility; in the strict sense,
however, more recent use of the term compatibility restricts itself to the capability
of exchanging data bilaterally, in the absence of a community standard. Theoretically, compatibility would then be more immediate than interoperability, since an
intermediate third-party standard would not be required. However, it can be doubted
whether this is a particularly useful distinction. Virtually every work on compatibility eventually aims at the widespread acceptance of a standard, protocol or file
format. In this sense, interoperability is simply another, more modern word for all
efforts at ensuring that heterogeneous software architectures, in the broadest sense,
can function correctly.
Kerber and Schweitzer summarize that “interoperability has become a buzzword
in European policy debates on the future of the digital economy” where “one of the
difficulties of the interoperability discussion is the absence of a clear definition of
interoperability” [24]. This is certainly not coincidental. A research and development
landscape dominated by project-based funding from calls with priorities driven by
political or cultural trends is a sure recipe for rendering the associated terminology
vague to the point of complete dilution. “All stakeholders,” to use another buzzword,
aim at securing their share. This is evidenced by the multitude of researchers who have
only recently detected that their traditional line of work is actually a subdiscipline of
artificial intelligence, Industry 4.0 or data science (or, of course, all the three). In the
case of interoperability, this is particularly ironic given that one of its core elements