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.

翻译：在材料科学与制造领域，存在大量异构数据，涵盖测量与仿真数据、机器数据、文献资料等。这些数据构成了可用于各类工程应用的宝贵知识基石。然而，由于跨组织单元缺乏标准化与集成，高效存储和处理此类多样化数据仍面临重大挑战。解决这些问题对于充分发挥数据驱动方法在这些领域的潜力至关重要。本文提出了一种名为数据空间管理系统（DSMS）的新型技术栈，用于支撑数据空间解决方案。DSMS的核心在于其独特的知识管理方法，该方法针对材料科学与制造领域的具体需求进行定制，同时遵循FAIR原则。这包括数据集成、关联、探索、可视化、处理与增强，以支持工程师进行决策制定及解决设计与优化问题。我们提供了DSMS的架构概览并描述了其核心组件。此外，通过德国MaterialDigital计划中StahlDigital与KupferDigital两个研究项目的用例，我们展示了DSMS在材料科学典型数据处理任务中的适用性。