TY - JOUR A1 - Bayerlein, Bernd A1 - Waitelonis, Jörg A1 - Birkholz, Henk A1 - Jung, Matthias A1 - Schilling, Markus A1 - v. Hartrott, Philipp A1 - Bruns, Marian A1 - Schaarschmidt, Jörg A1 - Beilke, Kristian A1 - Mutz, Marcel A1 - Nebel, Vincent A1 - Königer, Veit A1 - Beran, Lisa A1 - Kraus, Tobias A1 - Vyas, Akhilesh A1 - Vogt, Lars A1 - Blum, Moritz A1 - Ell, Basil A1 - Chen, Ya‐Fan A1 - Waurischk, Tina A1 - Thomas, Akhil A1 - Durmaz, Ali Riza A1 - Ben Hassine, Sahar A1 - Fresemann, Carina A1 - Dziwis, Gordian A1 - Beygi Nasrabadi, Hossein A1 - Hanke, Thomas A1 - Telong, Melissa A1 - Pirskawetz, Stephan A1 - Kamal, Mohamed A1 - Bjarsch, Thomas A1 - Pähler, Ursula A1 - Hofmann, Peter A1 - Leemhuis, Mena A1 - Özçep, Özgür L. A1 - Meyer, Lars‐Peter A1 - Skrotzki, Birgit A1 - Neugebauer, Jörg A1 - Wenzel, Wolfgang A1 - Sack, Harald A1 - Eberl, Chris A1 - Portella, Pedro Dolabella A1 - Hickel, Tilmann A1 - Mädler, Lutz A1 - Gumbsch, Peter T1 - Concepts for a Semantically Accessible Materials Data Space: Overview over Specific Implementations in Materials Science N2 - This article describes advancements in the ongoing digital transformation in materials science and engineering. It is driven by domain‐specific successes and the development of specialized digital data spaces. There is an evident and increasing need for standardization across various subdomains to support science data exchange across entities. The MaterialDigital Initiative, funded by the German Federal Ministry of Education and Research, takes on a key role in this context, fostering collaborative efforts to establish a unified materials data space. The implementation of digital workflows and Semantic Web technologies, such as ontologies and knowledge graphs, facilitates the semantic integration of heterogeneous data and tools at multiple scales. Central to this effort is the prototyping of a knowledge graph that employs application ontologies tailored to specific data domains, thereby enhancing semantic interoperability. The collaborative approach of the Initiative's community provides significant support infrastructure for understanding and implementing standardized data structures, enhancing the efficiency of data‐driven processes in materials development and discovery. Insights and methodologies developed via the MaterialDigital Initiative emphasize the transformative potential of ontology‐based approaches in materials science, paving the way toward simplified integration into a unified, consolidated data space of high value. KW - Semantic Interoperability KW - Data Spaces KW - Integration Workflows KW - MaterialDigital PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-622952 DO - https://doi.org/10.1002/adem.202401092 SN - 1527-2648 SP - 1 EP - 25 PB - Wiley-VCH CY - Weinheim AN - OPUS4-62295 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Eisenbart, Miriam A1 - Hanke, Thomas A1 - Bauer, Felix A1 - Beygi Nasrabadi, Hossein A1 - Junghanns, Kurt A1 - Dziwis, Gordian A1 - Tikana, Ladji A1 - Parvez, Ashak Mahmud A1 - van den Boogaart, Karl Gerald A1 - Sajjad, Mohsin A1 - Friedmann, Valerie A1 - Preußner, Johannes A1 - Ramakrishnan, Anantha Narayanan A1 - Klengel, Sandy A1 - Meyer, Lars‐Peter A1 - Martin, Michael A1 - Klotz, Ulrich Ernst A1 - Skrotzki, Birgit A1 - Weber, Matthias T1 - KupferDigital: Ontology‐Based Digital Representation for the Copper Life Cycle N2 - The copper life cycle comprises numerous stages from the alloy production to the manufacturing and usage of engineered parts until recycling. At each step, valuable data are generated and stored; some are transferred to the subsequent stations. A thorough understanding of the materials’ behavior during manufacturing processes or throughout their product lifetime is highly dependent on a reliable data transfer. If, for example, a failure occurs during the service life, information about the manufacturing route can be of decisive importance for detecting the root cause of the failure. Additionally, the life cycle assessment hinges on the availability of data. Recording and storing interoperable structured data is, therefore, a thriving research field with huge implications for the economic strength of the manufacturing industry. In the KupferDigital project, it is demonstrated how an ontology‐based data space can be utilized not only as an innovative method for storing and providing interoperable life cycle data but also as a means to enable automated data analysis and evaluation, leading to new insights and the creation of new knowledge using semantic data and technologies. This work illustrates how data recorded at different research facilities can be integrated into one single data space, allowing queries across heterogeneous sources. KW - Copper Alloy KW - Ontology KW - Digitalization KW - Data Space KW - Semantic Representation PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-630213 SN - 1527-2648 DO - https://doi.org/10.1002/adem.202401735 VL - 27 IS - 8 SP - 1 EP - 29 PB - Wiley AN - OPUS4-63021 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Beygi Nasrabadi, Hossein A1 - Hanke, Thomas A1 - Skrotzki, Birgit T1 - Semantic Representation of Low‐Cycle‐Fatigue Testing Data Using a Fatigue Test Ontology and ckan.kupferdigital Data Management System N2 - Addressing a strategy for publishing open and digital research data, this article presents the approach for streamlining and automating the process of storage and conversion of research data to those of semantically queryable data on the web. As the use case for demonstrating and evaluating the digitalization process, the primary datasets from low‐cycle‐fatigue testing of several copper alloys are prepared. The fatigue test ontology (FTO) and ckan.kupferdigital data management system are developed as two main prerequisites of the data digitalization process. FTO has been modeled according to the content of the fatigue testing standard and by reusing the basic formal ontology, industrial ontology foundry core ontology, and material science and engineering ontology. The ckan.kupferdigital data management system is also constructed in such a way that enables the users to prepare the protocols for mapping the datasets into the knowledge graph and automatically convert all the primary datasets to those machine‐readable data which are represented by the web ontology language. The retrievability of the converted digital data is also evaluated by querying the example competency questions, confirming that ckan.kupferdigital enables publishing open data that can be highly reused in the semantic web. KW - Accessible KW - CKAN KW - Interoperable KW - Digitalizations KW - Ontologies KW - Reusable data KW - Fatigue testing PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-604991 UR - https://onlinelibrary.wiley.com/doi/10.1002/adem.202400675 DO - https://doi.org/10.1002/adem.202400675 SN - 1527-2648 SP - 1 EP - 11 PB - Wiley AN - OPUS4-60499 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Zia, Ghezal Ahmad Jan A1 - Hanke, Thomas A1 - Skrotzki, Birgit A1 - Völker, Christoph A1 - Bayerlein, Bernd T1 - Enhancing Reproducibility in Precipitate Analysis: A FAIR Approach with Automated Dark-Field Transmission Electron Microscope Image Processing N2 - AbstractHigh-strength aluminum alloys used in aerospace and automotive applications obtain their strength through precipitation hardening. Achieving the desired mechanical properties requires precise control over the nanometer-sized precipitates. However, the microstructure of these alloys changes over time due to aging, leading to a deterioration in strength. Typically, the size, number, and distribution of precipitates for a quantitative assessment of microstructural changes are determined by manual analysis, which is subjective and time-consuming. In our work, we introduce a progressive and automatable approach that enables a more efficient, objective, and reproducible analysis of precipitates. The method involves several sequential steps using an image repository containing dark-field transmission electron microscopy (DF-TEM) images depicting various aging states of an aluminum alloy. During the process, precipitation contours are generated and quantitatively evaluated, and the results are comprehensibly transferred into semantic data structures. The use and deployment of Jupyter Notebooks, along with the beneficial implementation of Semantic Web technologies, significantly enhances the reproducibility and comparability of the findings. This work serves as an exemplar of FAIR image and research data management. KW - Industrial and Manufacturing Engineering KW - General Materials Science KW - Automated image analysis KW - FAIR research data management KW - Reproducibility KW - microstructural changes PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-593905 DO - https://doi.org/10.1007/s40192-023-00331-5 SN - 2193-9772 SP - 1 EP - 15 PB - Springer Science and Business Media LLC CY - Heidelberg AN - OPUS4-59390 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -