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    <title language="eng">Transforming Materials Science with Concepts for a Semantically Accessible Data Space</title>
    <abstract language="eng">The digital transformation in materials science enables more efficient and sustainable processes. Through technological adaptations and a commitment to the FAIR principles, materials and processes are holistically addressed across entire value chains. The Platform MaterialDigital (PMD) and related initiatives are developing innovative solutions to the challenges of digitalization. The focus is on the interoperable integration of heterogeneous materials and processes data in semantically accessible data spaces. An ontological framework, based on the PMD Core Ontology and application-specific ontologies, promotes semantic interoperability of cross-domain and multi-scale (meta)data. This framework can be extended through natural language processing in a script-supported manner, as demonstrated with the Microscopy Ontology. The presentation further highlights how freely available mechanical and microstructural datasets of various aging stages of an aluminum alloy can be semantically integrated and flexibly searched. Graph-based operations enable links between processing and microstructural properties to be established, facilitating enhanced correlation analysis and pattern recognition.</abstract>
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    <author>Bernd Bayerlein</author>
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      <value>FAIR Data</value>
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      <language>eng</language>
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    <title language="eng">Data-Driven Materials Science Supported by Community, Reproducibility, and Standardization</title>
    <abstract language="eng">Reproducible research in materials science demands a strong community and shared semantic standards. The interdisciplinary nature of the field has led to fragmented data practices, hampering collaboration and integration. Digitalisation initatives such as the German MaterialDigital Initiative addresses this by fostering a community-driven approach to FAIR data management, using ontologies to build consensus and semantic interoperability. Through interactive events like the Ontology Playground, a unified data culture that accelerates innovation and ensures reproducibility across materials research is supported.</abstract>
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    <author>Bernd Bayerlein</author>
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      <value>FAIR Data</value>
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      <language>eng</language>
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  <doc>
    <id>63741</id>
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    <title language="eng">Common Strategies for Advancing the Digital Transformation in  Materials Science</title>
    <abstract language="eng">Materials science and engineering is inherently interdisciplinary, bridging the natural sciences and engineering domains. This diversity is reflected in its digitalisation efforts, where subdomains often define distinct data structures, taxonomies, and terminologies. As a result, semantic ambiguities and fragmented research data management remain major obstacles to data integration across the field.&#13;
&#13;
Recent advances in AI underscore the role of data as a strategic resource for innovation. However, the effectiveness of these methods depends on the availability, completeness, and quality of data. Well-structured and semantically annotated materials and process data that is processed and stored using semantic technologies, for example, can enable breakthroughs, particularly at the interface of processing, structure, properties, and performance. A systematic, coordinated approach grounded in the FAIR guiding principles and lifecycle-oriented data management is therefore essential.&#13;
&#13;
The German MaterialDigital Initiative, funded by the Federal Ministry of Education and Research, exemplifies such a collaborative framework. It brings together diverse project consortia, fostering community-driven consensus through exchange forums like the Ontology Playground, and supporting harmonized solutions for semantic interoperability.&#13;
&#13;
By strengthening data culture and semantic infrastructure, collaborative digitalisation initiatives offer a scalable path to accelerated materials development and reproducible scientific progress paving the way for more efficient, data-driven innovation in materials science and engineering.</abstract>
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    <author>Bernd Bayerlein</author>
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