Semantic Orchestration and Exploitation of Material Data: A Dataspace Solution Demonstrated on Steel and Copper Applications

In materials science and manufacturing, vast amounts of heterogeneous data (e.g., measurement and simulation logs, process data, publications) serve as the bedrock of valuable knowledge for various engineering applications. However, efficiently storing and managing this diverse data pose challenges due to limited standardization and integration across different organizational units. Addressing these challenges is essential to fully unlock the potential of data‐driven approaches. This article introduces a novel, comprehensive semantic methodology tailored to materials engineering and realized as a technology stack named Dataspace Management System (DSMS), which powers dataspace solutions that leverage the knowledge encoded in heterogeneous data sources to support data‐driven insights and to derive new knowledge. At its core, DSMS offers a distinctive knowledge management approach tuned to meet the specific requirements of the materials science and manufacturing domain, all while adhering to the FAIR (findable, accessible, interoperable, reusable) principles. DSMS provides functionalities for data integration, linkage, exploration, visualization, processing, data sharing, and services (e.g., consulting) to support engineers in decision‐making, design, and optimization. An architectural overview is presented, outlining core concepts and their technological implementation. In addition, the applicability of these concepts to common data processing tasks is demonstrated through use cases from the StahlDigital and KupferDigital research projects within Germany's MaterialDigital initiative.