Chapter 1
Introduction
1.1 Digitalization and Data Management
Digitalization is one of the driving forces of technological and social progress today.
In the engineering sciences, in combination with a great variety of quantitatively reliable modelling and simulation approaches, digitalization supports the development
of what has become known as Industry 4.0 by contributing to virtual manufacturing
through cyber-physical systems. To predict thermodynamic, mechanical and other
physical properties of materials and processes, data-driven and physics-based models
are combined [1], supported by massively parallel simulation methods that continue
to become more scalable and performant [2]; model databases are developed [3, 4],
and data with a heterogeneous provenance (i.e. origin), based on different methods
and coming from different sources [5, 6], are integrated into shared data infrastructures [7]. A multitude of names have been proposed for the related lines of work in
academic and industrial research and development, including Integrated Computational Materials Engineering (ICME), with a focus on solids [8, 9], Computational
Molecular Engineering (CME), with a focus on fluids [4, 6] and process data technology or computer-aided process engineering (CAPE), with an orientation towards
process technology and CAPE-OPEN-based simulation technology [10–12]. This
book discusses data management in materials modelling, which is here understood
to encompass all these fields.
Digitalization is achieved in two steps: First, data must be available in digital
form. The process of making data available digitally is referred to as digitization;
in the engineering sciences, with certain exceptions (e.g. data published in old volumes of journals that have not yet been digitized by scanning), this can usually
be presupposed. However, the possible use of raw unannotated digital data, also
known as dark data [13, 14], is very limited. Beyond digitization, a second step
is therefore required for digitalization to ensure that the data are and remain findable, accessible, interoperable and reusable (FAIR): These are the FAIR principles
of data management or data stewardship [15–17]. For some applications, such as
mediation systems [18, 19] for Ontology-Based Data Access (OBDA) to distributed
© 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_1
1
Introduction
1.1 Digitalization and Data Management
Digitalization is one of the driving forces of technological and social progress today.
In the engineering sciences, in combination with a great variety of quantitatively reliable modelling and simulation approaches, digitalization supports the development
of what has become known as Industry 4.0 by contributing to virtual manufacturing
through cyber-physical systems. To predict thermodynamic, mechanical and other
physical properties of materials and processes, data-driven and physics-based models
are combined [1], supported by massively parallel simulation methods that continue
to become more scalable and performant [2]; model databases are developed [3, 4],
and data with a heterogeneous provenance (i.e. origin), based on different methods
and coming from different sources [5, 6], are integrated into shared data infrastructures [7]. A multitude of names have been proposed for the related lines of work in
academic and industrial research and development, including Integrated Computational Materials Engineering (ICME), with a focus on solids [8, 9], Computational
Molecular Engineering (CME), with a focus on fluids [4, 6] and process data technology or computer-aided process engineering (CAPE), with an orientation towards
process technology and CAPE-OPEN-based simulation technology [10–12]. This
book discusses data management in materials modelling, which is here understood
to encompass all these fields.
Digitalization is achieved in two steps: First, data must be available in digital
form. The process of making data available digitally is referred to as digitization;
in the engineering sciences, with certain exceptions (e.g. data published in old volumes of journals that have not yet been digitized by scanning), this can usually
be presupposed. However, the possible use of raw unannotated digital data, also
known as dark data [13, 14], is very limited. Beyond digitization, a second step
is therefore required for digitalization to ensure that the data are and remain findable, accessible, interoperable and reusable (FAIR): These are the FAIR principles
of data management or data stewardship [15–17]. For some applications, such as
mediation systems [18, 19] for Ontology-Based Data Access (OBDA) to distributed
© 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_1
1