  In the field of materials science and manufacturing, a vast amount of
heterogeneous data exists, encompassing measurement and simulation data,
machine data, publications, and more. This data serves as the bedrock of
valuable knowledge that can be leveraged for various engineering applications.
However, efficiently storing and handling such diverse data remain
significantly challenging, often due to the lack of standardization and
integration across different organizational units. Addressing these issues is
crucial for fully utilizing the potential of data-driven approaches in these
fields. In this paper, we present a novel technology stack named Dataspace
Management System (DSMS) for powering dataspace solutions. The core of DSMS
lies on its distinctive knowledge management approach tuned to meet the
specific demands of the materials science and manufacturing domain, all while
adhering to the FAIR principles. This includes data integration, linkage,
exploration, visualization, processing, and enrichment, in order to support
engineers in decision-making and in solving design and optimization problems.
We provide an architectural overview and describe the core components of DSMS.
Additionally, we demonstrate the applicability of DSMS to typical data
processing tasks in materials science through use cases from two research
projects, namely StahlDigital and KupferDigital, both part of the German
MaterialDigital initiative.
