TY - CONF A1 - Bayerlein, Bernd T1 - PMDco - Platform MaterialDigital Core Ontology N2 - The PMD Core Ontology (PMDco) is a comprehensive set of building blocks produced via consensus building. The ontological building blocks provide a framework representing knowledge about fundamental concepts used in Materials Science and Engineering (MSE) today. The PMDco is a mid-level ontology that establishes connections between narrower MSE application ontologies and domain neutral concepts used in already established broader (top-level) ontologies. The primary goal of the PMDco design is to enable interoperability between various other MSE-related ontologies and other common ontologies. PMDco’s class structure is both comprehensive and extensible, rendering it an efficient tool to structure MSE knowledge. The PMDco serves as a semantic middle-layer unifying common MSE concepts via semantic mapping to other semantic representations using well-known key terms used in the MSE domain. The PMDco enables straight-forward documentation and tracking of science data generation and in consequence enables high-quality FAIR data that allows for precise reproducibility of scientific experiments. The design of PMDco is based on the W3C Provenance Ontology (PROV-O), which provides a standard framework for capturing the production, derivation, and attribution of resources. Via this foundation, the PMDco enables the integration of data from various data origins and the representation of complex workflows. In summary, the PMDco is a valuable advancement for researchers and practitioners in MSE domains. It provides a common MSE vocabulary to represent and share knowledge, allowing for efficient collaboration and promoting interoperability between diverse domains. Its design allows for the systematic integration of data and metadata, enabling seamless tracing of science data. Overall, the PMDco is a crucial step towards a unified and comprehensive understanding of the MSE domain in general. T2 - FEMS EUROMAT 2023 CY - Frankfurt a. M., Germany DA - 03.09.2023 KW - Knowledge Representation KW - Ontology KW - Semantic Interoperability KW - FAIR KW - Automation PY - 2023 AN - OPUS4-58197 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Schilling, Markus T1 - PMDco - Platform MaterialDigital Core Ontology: Achieving High-Quality & Reliable FAIR Data N2 - Knowledge representation in the materials science and engineering (MSE) domain is a vast and multi-faceted challenge: Overlap, ambiguity, and inconsistency in terminology are common. Invariant and variant knowledge are difficult to align cross-domain. Generic top-level semantic terminology often is too abstract, while MSE domain terminology often is too specific. In this poster presentation, an approach how to maintain a comprehensive and intuitive MSE-centric terminology composing a mid-level ontology–the PMD core ontology (PMDco)–via MSE community-based curation procedures is shown. The PMDco is designed in direct support of the FAIR principles to address immediate needs of the global experts community and their requirements. The illustrated findings show how the PMDco bridges semantic gaps between high-level, MSE-specific, and other science domain semantics, how the PMDco lowers development and integration thresholds, and how to fuel it from real-world data sources ranging from manually conducted experiments and simulations as well as continuously automated industrial applications. T2 - DVM Arbeitskreis Betriebsfestigkeit - Potenziale der Betriebsfestigkeit in Zeiten des technologischen und gesellschaftlichen Wandels CY - Munich, Germany DA - 11.10.2023 KW - Digitalization KW - Semantic Web Technologies KW - FAIR KW - Data Interoperability KW - PMD Core Ontology PY - 2023 AN - OPUS4-58602 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Bayerlein, Bernd T1 - Digital Transformation in Materials Science: Insights From the Platform MaterialDigital (PMD) N2 - The digital era has led to a significant increase in innovation in scientific research across diverse fields and sectors. Evolution of data-driven methodologies lead to a number of paradigm shifts how data, information, and knowledge is produced, understood, and analyzed. High profile paradigm shifts in the field of materials science (MS) include exploitative usage of computational tools, machine learning algorithms, and high-performance computing, which unlock novel avenues for investigating materials. In these presentations, we highlight prototype solutions developed in the context of the Platform MaterialDigital (PMD) project that addresses digitalization challenges. As part of the Material Digital Initiative, the PMD supports the establishment of a virtual materials data space and a systematic handling of hierarchical processes and materials data using a developed ontological framework as high priority work items. In particular, the mid-level ontology PMDco and its augmentation through application-specific ontologies are illustrated. As part of the conclusion, a discussion encompasses the evolutionary path of the ontological framework, taking into account standardization efforts and the integration of modern AI methodologies such as natural language processing (NLP). Moreover, demonstrators illustrated in these presentations highlight: The integration and interconnection of tools, such as digital workflows and ontologies, Semantic integration of diverse data as proof of concept for semantic interoperability, Improved reproducibility in image processing and analysis, and Seamless data acquisition pipelines supported by an ontological framework. In this context, concepts regarding the application of modern research data management tools, such as electronic laboratory notebooks (ELN) and laboratory information management systems (LIMS), are presented and elaborated on. Furthermore, the growing relevance of a standardized adoption of such technologies in the future landscape of digital initiatives is addressed. This is supposed to provide an additional basis for discussion with respect to possible collaborations. T2 - NIST Seminar on Digital Transformation CY - Gaithersburg, MD, USA DA - 11.06.2024 KW - Digital Transformation KW - Research Data Management KW - Ontology KW - Reusability KW - FAIR PY - 2024 AN - OPUS4-60381 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Bayerlein, Bernd T1 - Adapting FAIR Practices in Materials Science: Digital Representation of Material-Specific Characterization Methods N2 - Age-hardenable aluminum alloys undergo precise heat treatments to yield nanometer-sized precipitates that increase their strength and durability by hindering the dislocation mobility. Tensile tests provide mechanical properties, while microstructure evaluation relies on transmission electron microscopy (TEM), specifically the use of dark-field TEM images for precise dimensional analysis of the precipitates. However, this manual process is time consuming, skill dependent, and prone to errors and reproducibility issues. Our primary goal is to digitally represent these processes while adhering to FAIR principles. Ontologies play a critical role in facilitating semantic annotation of (meta)data and form the basis for advanced data management. Publishing raw data, digital workflows, and ontologies ensures reproducibility. This work introduces innovative solutions to traditional bottlenecks and offers new perspectives on digitalization challenges in materials science. We support advanced data management by leveraging knowledge graphs and foster collaborative and open data ecosystems that potentially revolutionize materials research and discovery. T2 - TMS - Specialty Congress 2024 CY - Cleveland, Ohio, US DA - 16.06.2024 KW - FAIR KW - Research Data Management KW - Semantic Interoperability KW - Ontologies KW - Materials and Processes Data Reusability PY - 2024 AN - OPUS4-60375 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Chen, Yue T1 - Ontopanel: a diagrams.net plugin for graphical semantic modelling N2 - Ontologies that represent a map of the concepts and relationships between them, are becoming an effective solution for data standardization and integration of different resources in the field of materials science, as efficient data storage and management is the building block of material digitization. However, building a domain ontology is not a simple task. It requires not only a collaborative effort between ontologists and domain experts, but also the modeling approaches and tools play a key role in the process. Among all approaches, graphical representation of domain ontologies based on standard conceptual modeling languages is widely used because of its intuitiveness and simplicity. Various tools have been developed to realize this approach in an intuitive way, such as Protégé plugins and web visualization tools. The Materials-open-Lab (MatOLab) project, which aims to develop ontologies and workflows in accordance with testing standards for the materials science and engineering domains, adopted a UML (Unified Modeling Language) approach based on the diagrams.net. It is a powerful, popular, open-source graphical editor. In practical case studies, however, many users’ needs could not be met, such as reusing ontology, conversion, and data mapping. Users must switch between different tools to achieve a certain step, and thereby invariably increase learning cost. The lack of validation also leads to incorrect diagrams and results for users who are not familiar with the ontology rules. To address these issues, we designed Ontopanel, a diagrams.net-based plugin that includes a set of pipeline tools for semantic modeling: importing and displaying protégé-like ontologies, converting diagrams to OWL, validating diagrams by OWL rules, and mapping data. It uses diagrams.net as the front-end for method modeling and Django as the back-end for data processing. As a web-based tool, it is very easy to expand its functionality to meet changing practical needs. T2 - MSE 2022 CY - Darmstadt, Germany DA - 27.09.2022 KW - Ontology KW - Tools KW - Material digital KW - Mat-o-lab KW - Graphic design KW - Ontology development KW - Data mapping KW - FAIR KW - Materials testing PY - 2022 AN - OPUS4-55884 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Bayerlein, Bernd T1 - Towards interoperability: Digital representation of a material specific characterization method N2 - Certain metallic materials gain better mechanical properties through controlled heat treatments. In age-hardenable aluminum alloys, the strengthening mechanism is based on the controlled formation of nanometer sized precipitates, which hinder dislocation movement. Analysis of the microstructure and especially the precipitates by transmission electron microscopy allows identification of precipitate types and orientations. Dark-field imaging is often used to image the precipitates and quantify their relevant dimensions. The present work aims at the digital representation of this material-specific characterization method. Instead of a time-consuming, manual image analysis, a digital approach is demonstrated. The integration of an exemplary digital workflow for quantitative precipitation analysis into a data pipeline concept is presented. Here ontologies enable linking of contextual information to the resulting output data in a triplestore. Publishing digital workflow and ontologies ensures the reproducibility of the data. The semantic structure enables data sharing and reuse for other applications and purposes, demonstrating interoperability. T2 - TMS - 7th World Congress on Integrated Computational Materials Engineering (ICME) CY - Orlando, Florida, USA DA - 21.05.2023 KW - Ontology KW - Semantic Interoperability KW - Digtial Representation KW - Data Management KW - Reproducibility KW - FAIR PY - 2023 AN - OPUS4-57548 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Shakeel, Y. T1 - NFDI-MatWerk - Reference Datasets N2 - Within NFDI-MatWerk (“National Research Data Infrastructure for Material Sciences”/ “Nationale Forschungsdateninfrastruktur für Materialwissenschaften und Werkstofftechnik“), the Task Area Materials Data Infrastructure (TA-MDI) will provide tools and services to easily store, share, search, and analyze data and metadata. Such a digital materials environment will ensure data integrity, provenance, and authorship. The MatWerk consortium aims to develop specific solutions jointly with Participant Projects (PPs), which are scientific groups or institutes covering different domains, from theory and simulations to experiments. The Data Exploitation Methods group of the Karlsruhe Institute of Technology-Steinbuch Centre of Computing, as part of TA-MDI, is developing specific solutions in close collaboration with three PPs. PP07, together with the University of Stuttgart, aims at the image-based prediction of the material properties of stochastic microstructures using large-scale supercomputers. PP13, in cooperation with the University of Saarland, focuses on tomographic methods at various scales in materials research. PP18, together with the Federal Institute for Materials Research and Testing (“Bundesanstalt für Materialforschung und -prüfung”), aspires to define the criteria for materials reference datasets and usage analytics. The requirements and goals are comparable for each PP: their research outputs, which are scientific datasets, should conform to the FAIR (Findable, Accessible, Interoperable, Reusable) principles. We aim to shape them from a data management perspective making use of the FAIR Digital Object concept, including structured metadata and storage solutions. The results will be a blueprint which will act as a reference for future datasets. Even though the collaboration is in an early stage, the initial steps already show the added value of this approach. This research has been supported by the Federal Ministry of Education and Research (BMBF) – funding code M532701 / the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - project number NFDI 38/1, project no. 460247524. T2 - HMC Conference 2022 CY - Online meeting DA - 05.10.2022 KW - NFDI KW - NFDI-MatWerk KW - Reference Data KW - FAIR KW - Creep PY - 2022 AN - OPUS4-56611 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Schilling, Markus T1 - FAIR data in PMD: Development of MSE mid-level and standard-compliant application ontologies N2 - The efforts taken within the project ‘platform MaterialDigital’ (PMD, materialdigital.de) to store FAIR data in accordance with a standard-compliant ontological representation (‘application ontology’) of a tensile test of metals at room temperature (ISO 6892-1:2019-11) will be presented. This includes the path from developing an ontology in accordance with the respective standard, converting ordinary data obtained from standard tests into the interoperable RDF format, up to connecting the ontology and data. The semantic connection of the ontology and data leads to interoperability and an enhanced ability of querying. For further reusability of data and knowledge semantically stored, the PMD core ontology (PMDco) was developed, which is a mid-level ontology in the field of MSE. The semantic connection of the tensile test application ontology to the PMDco is also presented. Moreover, Ontopanel, a tool for domain experts facilitating visual ontology development and mapping for FAIR data sharing in MSE, is introduced briefly. T2 - World Congress on Integrated Computational Materials and Engineering (ICME) CY - Orlando, Florida, USA DA - 21.05.2023 KW - Digitalization KW - Semantic Web Technologies KW - FAIR KW - Data Interoperability KW - PMD Core Ontology KW - Tensile Test Ontology PY - 2023 AN - OPUS4-57549 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Schilling, Markus T1 - Crafting High-Quality, Reliable, and FAIR Data: From Metadata, Schema and Ontologies to Data Management and Knowledge Transfer N2 - Following the new paradigm of materials development, design and optimization, digitalization is the main goal in materials sciences (MS) which imposes a huge challenge. In this respect, the quality assurance of processes and output data as well as the interoperability between applications following FAIR (findability, accessibility, interoperability, reusability) principles are to be ensured. For storage, processing, and querying of data in contextualized form, Semantic Web Technologies (SWT) are used since they allow for machine-actionable and human-readable knowledge representations needed for data management, retrieval, and (re)use. In this respect, the motivation for digital transformation in materials sciences stemming from the need to handle the ever-increasing volume and complexity of data will be elaborated on. By embracing digital tools and methodologies, researchers can enhance the efficiency, accuracy, and reproducibility of their work. The benefits of digital transformation in materials sciences are manifold, including improved data management, enhanced collaboration, and accelerated innovation. Being a core component of this transformation, ensuring data reliability and reproducibility is critical for the advancement of the field, enabling researchers to build on each other's work with confidence. Implementing FAIR data principles facilitates this by making data more accessible and usable across different platforms and studies. Furthermore, Semantic Web technologies (SWT) and ontologies play a crucial role in achieving these goals. Ontologies, typically consisting of the T-Box (terminological component) and A-Box (assertional component), provide a structured framework for representing knowledge. This presentation will outline the path of ontology creation and the formal transformation procedure, highlighting the various ontology levels that organize data into meaningful hierarchies. Real-world use cases presented, such as the Tensile Test Ontology (TTO) and the Orowan Demonstrator, illustrate the practical applications of these technologies. These examples will demonstrate how ontologies can be leveraged to standardize data and facilitate interoperability between different systems and research groups. Finally, in this presentation, Ontopanel is introduced, a tool designed to aid in the creation and management of ontologies. Ontopanel simplifies the process of developing and maintaining ontologies, making it accessible to researchers and practitioners in the field. By integrating these technologies and principles, the materials science community can move towards a more digital, interconnected, and efficient future making the knowledge and education on these topics very valuable. T2 - MaRDA MaRCN FAIR Train Workshop CY - Washington, DC, USA DA - 29.07.2024 KW - FAIR KW - Metadata KW - Digitalization KW - Data Interoperability KW - Ontology KW - Education KW - Workshop PY - 2024 AN - OPUS4-60720 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Shakeel, Yusra A1 - Ávila Calderón, Luis T1 - Creating Exemplary RDM Reference Datasets: Technical Process Overview N2 - The aim of the task area Materials Data Infrastructure (TA-MDI) of the consortium Materials Science and Engineering (MatWerk) of National Research Data Infrastructure (NFDI) is to shape scientific datasets obtained through the Participant Projects (PPs) from a data management perspective conforming to the FAIR principles, making use of the FAIR Digital Object (FAIR DO) concept, including structured metadata and storage solutions. As an example, they apply PP18 (BAM) as a use case to demonstrate the proposed technical workflow. T2 - All-Hands-on-Deck congress from the NFDI-MatWerk CY - Siegburg, Germany DA - 08.03.2023 KW - NFDI KW - Reference Dataset KW - FAIR KW - Research Data Management PY - 2022 AN - OPUS4-57149 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Chen, Yue A1 - Schilling, Markus A1 - von Hartrott, P. A1 - Beygi Nasrabadi, Hossein A1 - Skrotzki, Birgit A1 - Olbricht, Jürgen T1 - Ontopanel: A Tool for Domain Experts Facilitating Visual Ontology Development and Mapping for FAIR Data Sharing in Materials Testing N2 - In recent years, the design and development of materials are strongly interconnected with the development of digital technologies. In this respect, efficient data management is the building block of material digitization and, in the field of materials science and engineering (MSE), effective solutions for data standardization and sharing of different digital resources are needed. Therefore, ontologies are applied that represent a map of MSE concepts and relationships between them. Among different ontology development approaches, graphical editing based on standard conceptual modeling languages is increasingly used due to its intuitiveness and simplicity. This approach is also adopted by the Materials-open-Laboratory project (Mat-o-Lab), which aims to develop domain ontologies and method graphs in accordance with testing standards in the field of MSE. To suit the actual demands of domain experts in the project, Ontopanel was created as a plugin for the popular open-source graphical editor diagrams.net to enable graphical ontology editing. It includes a set of pipeline tools to foster ontology development in diagrams.net, comprising imports and reusage of ontologies, converting diagrams to Web Ontology Language (OWL), verifying diagrams using OWL rules, and mapping data. It reduces learning costs by eliminating the need for domain experts to switch between various tools. Brinell hardness testing is chosen in this study as a use case to demonstrate the utilization of Ontopanel. KW - Materials Testing KW - Ontology KW - Visual ontology development KW - Data mapping KW - FAIR PY - 2022 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-560339 DO - https://doi.org/10.1007/s40192-022-00279-y SP - 1 EP - 12 PB - Springer AN - OPUS4-56033 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Beygi Nasrabadi, Hossein T1 - Platform MaterialDigital (PMD) approach for the integration and management of FAIR low cycle fatigue (LCF) testing data N2 - This study represents the generation and storage of findable, accessible, interoperable, and reusable (FAIR) fatigue testing data by utilizing the Platform MaterialDigital (PMD) core ontology (PMDco) as well as some containerized PMD-server applications. Based on the specifications of the ISO 12106:2017-03 standard [1] and the acquired test reports of the mechanical testing facility, a highly comprehensive process graph of the fatigue testing procedure was created. Consequently, the PROV Ontology (PROVO) and PMDco [2] were used as upper-level ontologies to model the fatigue testing ontology (FTO). A part of the FTO classes hierarchy is shown in Fig. 1, where all the concepts of test procedure, test apparatus, test piece, and test properties were respectively located in the appropriate hierarchies of pmd:Process (prov:Activity), pmd:ProcessingNode (prov:Agent), pmd:Object (prov:Entity), and pmd:ValueObject (prov:Entity) classes. FTO is publicly available via the GitLab repository [3]. Reusing these upper-level ontologies and materials testing standards not only improves FTO's compatibility with other ontologies but also ensures its acceptance and deployment in industry [4]. The fatigue testing process graph has also been designed in such a manner that it allows for the entire mapping of testing metadata. In this respect, low-cycle fatigue (LCF) experiments were carried out on several cast copper alloys at various strain ratios, and the resulting CSV test report files were stored in a public repository [5]. Eventually, the processes of mapping the experimental test data into the fatigue process graph, converting the RDF data, storage of the triples in a triple store, and SPARQL query from the obtained triples are evaluated by different PMD-based tools like PMD OntoDocker [6]. T2 - MSE2024 CY - Darmstadt, Germany DA - 24.09.2024 KW - Digitalization KW - FAIR KW - Low cycle fatigue PY - 2024 AN - OPUS4-61151 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Ávila Calderón, Luis T1 - Framework for Curation and Distribution of Reference Datasets on the Example of Creep Data of Ni- Based Superalloys N2 - This presentation introduces our current effort to develop a best practice framework for generating and distributing MSE reference datasets using creep data of Ni-based superalloys as an example, building on previous approaches. Such reference datasets can be used in the verification of testing equipment or to compare experimental or simulation results among different laboratories or collaborative centers. Our framework concept, for reference data, includes the generation of the data along with an agreement on the necessary metadata, considering the intended usage, and the integration into an easily accessible digital infrastructure to annotate and discover/distribute the datasets using a metadata schema accordingly aligned to a domain ontology. T2 - International Materials Science and Engineering Congress - MSE 2024 CY - Darmstadt, Germany DA - 24.09.2024 KW - Referenzdaten KW - Digitalisierung KW - Data schema KW - NFDI-MatWerk KW - Datenschema KW - FAIR PY - 2024 AN - OPUS4-61175 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Schilling, Markus T1 - Seamless Science: Navigating Daily Lab Life through Semantic Data Integration, Electronic Lab Notebooks, and Knowledge Graphs in the Era of MSE Digitalization N2 - The field of materials science and engineering (MSE) is currently experiencing a significant paradigm shift towards enhanced digitalization which imposes a huge challenge on researchers, scientists, engineers, and technicians. In this respect, the quality assurance of processes and output data as well as the interoperability between applications following FAIR principles are to be ensured. For storage, processing, and querying of data in contextualized form, Semantic Web technologies (SWT) are used as they allow for machine-actionable and human-readable knowledge representations needed for data management, retrieval, and (re)use. The collaborative project ‘platform MaterialDigital’ (PMD) aims to solve digitalization tasks and implement digital solutions in the field of MSE in a prototypical manner. Therefore, the establishment of a virtual material data space and the systematization of the handling of hierarchical, process-dependent material data are focused. In this respect, selected MSE methods are semantically represented which are supposed to serve as best practice examples with respect to knowledge representation and the creation of knowledge graphs used for material data. Accordingly, this presentation shows the efforts taken within PMD to store data in accordance with a testing standard compliant semantic representation of a tensile test of metals at room temperature (ISO 6892-1:2019-11). A semantic link of the corresponding tensile test ontology (TTO) and data leads to enhanced data useability and interoperability. The PMD core ontology (PMDco), developed in PMD and used as mid-level ontology in TTO, is also presented briefly. Moreover, as a best practice example, the acquisition of tensile test data with subsequent generation of knowledge graph data by semantic interconnection using TTO was realized by applying an electronic laboratory notebook (ELN). Corresponding tensile tests were performed by undergraduate MSE students at the Technical University of Darmstadt. The resulting data pipeline, also illustrated in the presentation, enabled a fully-fledged digitally integrated experimental procedure, the approach of which may be transferred to other test series and experiments. In addition to facilitating the acquisition, analysis, processing, and (re)usability of data, this also raises the awareness of students with respect to data structuring and semantic technologies in terms of education and training. T2 - Materials Science and Engineering Congress 2024 CY - Darmstadt, Germany DA - 24.09.2024 KW - FAIR KW - Plattform Material Digital KW - Digitalization KW - Data Interoperability KW - Electronic Lab Notebook KW - Education PY - 2024 AN - OPUS4-61138 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -